Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
listlengths 1
91
| keywords
listlengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
The p53 family activates many of the same genes in response to DNA damage . Because p63 and p73 have structural differences from p53 and play distinct biological functions in development and metastasis , it is likely that they activate a unique transcriptional network . Therefore , we performed a genome-wide analysis using cells lacking the p53 family members after treatment with DNA damage . We identified over 100 genes involved in multiple pathways that were uniquely regulated by p63 or p73 , and not p53 . Further validation indicated that BRCA2 , Rad51 , and mre11 are direct transcriptional targets of p63 and p73 . Additionally , cells deficient for p63 and p73 are impaired in DNA repair and p63+/−;p73+/− mice develop mammary tumors suggesting a novel mechanism whereby p63 and p73 suppress tumorigenesis .
p53 acts as a tumor suppressor gene by transcriptionally regulating a multitude of target genes in response to DNA damage [1] . Induction of these genes results in multiple cellular fates including apoptosis and cell cycle arrest . p63 and p73 share some of the same functions as p53; however , p63 and p73 are structurally more complex containing multiple isoforms [2] , [3] , [4] . The TA isoforms are structurally more like p53 and contain a transactivation domain while the ΔN isoforms lack this domain and are transcribed from an internal promoter unique to these isoforms [3] , [5] . Based on the fact that the TA isoforms are more similar structurally to p53 , the TA isoforms were hypothesized and shown to be the major isoforms that induce transcription and are thought to have tumor suppressive functions [3] , [5] , [6] , [7] , [8] . In contrast , the ΔN isoforms have been shown to act as dominant negatives against the TA isoforms of p63 and p73 and also against p53 . Because of the ability of the ΔN isoforms to act as dominant negatives and their overexpression patterns in human tumors [2] , [6] , [9] , [10] , [11] , these isoforms have been hypothesized to act as oncogenes [3] , [5] , [6] . Interestingly , recent data have revealed that the ΔN isoforms of p73 can induce apoptosis , cell cycle arrest and transactivate target genes , such as p21 , 14-3-3σ , and GADD45 [12] . Additional studies have demonstrated that expression of ΔNp73β at physiological levels can result in the suppression of cell growth in the presence or absence of p53 indicating that this isoform of p73 may act as a tumor suppressor gene [12] . Similarly , the ΔN isoforms of p63 have also been shown to have the ability to transactivate target genes [13] . In the case of p63 , the ΔN isoforms are more highly expressed in epithelial tissues [14] , and thus it is not be surprising that the ΔN isoforms transcriptionally regulate genes involved in the morphogenesis and differentiation of the epithelium . Given the structural complexity and expression of p63 , p73 , and their isoforms , the transcriptional targets of these genes are an area of growing research . We and others have shown previously that p63 and p73 can induce apoptosis in response to DNA damage [2] , [8] , [15] . Many of the target genes induced by p63 and p73 are shared with p53 [2] , [8] . Additionally , we have shown that the p53 family of genes is interdependent on each other in the apoptotic response and in the suppression of tumorigenesis . p53+/−;p63+/− and p53+/−;p73+/− develop some of the same tumor types as p53+/− mice , but the phenotype of the tumors in the compound mutant mice is highly aggressive and metastatic indicative of cooperativity between family members [7] , [15] . Mice heterozygous for combinations of the p53 family members develop a novel tumor spectrum compared to p53+/− mice indicative of functions of p63 and p73 independent of p53 [7] . These independent functions suggest that p63 and p73 may have unique transcriptional programs . To understand the transcriptional program of p63 and p73 , we made use of MEFs deficient for each of the p53 family members individually and in combination and performed a genome wide analysis using cDNA microarray analysis to determine whether p63 and p73 transcriptionally regulate genes independently of p53 in response to DNA damage . Interestingly , we found that p63 and/or p73 transactivate sets of genes independent of p53 . Among these sets of genes are those involved in homologous DNA repair , including Rad51 , BRCA2 , mre11 and Rad50 . p63 and p73 were found to bind to these gene promoters by ChIP assay and to transactivate them as demonstrated by luciferase assay . Surprisingly , the ΔN isoforms of p63 and p73 , which have been shown to be weak transactivators , transactivate the Rad51 and BRCA2 genes to high levels . In addition , p63−/− , p73−/− and p63−/−;p73−/− MEFs exhibited an impaired ability to repair their DNA and to survive in a clonogenic survival assay . Additionally , in vivo evidence from p63/p73 mutant mice supports this finding; p63+/−;p73+/− mice develop mammary adenocarcinomas at a high frequency [7] . Here , we show that these mammary tumors lose expression of p63 , p73 , BRCA2 , and Rad51 . Our findings indicate that p63 and p73 may suppress tumorigenesis by transcriptionally regulating critical genes in the DNA repair pathway .
The p53 family members , p63 and p73 , have previously been shown to share many of the same target genes as p53 [2] , [8] . Additionally , both p63 and p73 have the ability to bind to the p53 consensus binding site . p63 and p73 also have biological activities independent of p53 . Consequently , we were interested in determining whether p63 and p73 had unique transcriptional target genes . A cDNA microarray analysis was performed using E1A expressing MEFs deficient for each p53 family member individually ( p53−/− , p63−/− , p73−/− ) and in combination ( p63−/−;p73−/− ) . These cells were treated with doxorubicin , a DNA damaging agent , to induce apoptosis in wild-type E1A MEFs . p53−/− and p63−/−;p73−/− E1A MEFs have previously been shown to be resistant to this treatment while the p63−/− and p73−/− E1A MEFS are partially resistant to apoptosis [15] . Microarray analysis revealed a large number of genes differentially expressed in the MEFs deficient for each p53 family member . Because we were interested in identifying genes that are transactivated by the p53 family members in response to DNA damage , genes that are down regulated in the absence of the p53 family members were further analyzed . After filtering and statistical analysis using SAM [16] , 620 out of 15 , 488 genes were found to be down regulated in at least one of the single knockout E1A MEF lines compared to wild-type E1A MEFs in response to DNA damage . Eight-six of the 620 genes were down regulated in the p53−/− , p63−/− , and p73−/− E1A MEFs as illustrated by the Venn diagram ( Figure 1 and Figure S1 ) . There were also sets of genes that were uniquely regulated by each p53 family member; the p53−/− , p63−/− , and p73−/− MEFs each had 109 , 148 , and 131 genes down regulated respectively . Lastly , there were sets of genes that were regulated by two family members only; forty-seven were down regulated in the absence of p53 and p63 , 41 in the absence of p53 and p73 , and 58 in the absence of p63 and p73 . The final list of differentially regulated genes was processed through multiple bioinformatic pipelines to identify biological pathways regulated by the p53 family members . Pathway analysis using the web-based KEGG , BioCarta , and GenMAPP databases indicated that the p53 family members regulate numerous pathways including: cell cycle , DNA-damage , p53 signaling , apoptosis , ribosomal proteins , metabolic pathways , and growth factor signaling ( Table S1 ) . The putative target genes identified by microarray analysis were analyzed for the presence of a p53 or p63 consensus binding sites using a computer based genome wide search and HMMER1 software [17] . The promoter sequences ( defined as 5 kb upstream and downstream of the transcription start site excluding exons ) from the 724 down regulated genes were queried , and 700 of these genes were found to have p53 family member motifs . Of these , 669 genes contained p53 family member motif sites with the ideal p53 spacer of 6 nucleotides between the two half sites . Scores were then given to each identified binding site corresponding to how well they matched with previously published p53 or p63 matrices ( Table S2 ) . Hierarchical clustering was then performed in the specified knock out MEFs relative to wild-type after DNA damage to highlight patterns between the downregulated genes in the p53−/− , p63−/− and p73−/− E1A MEFs ( Figure 2 and Figure S1 ) . Interestingly , many genes were differentially regulated in the various MEF lines ( Figure 2 ) indicating that p63 and p73 have unique target genes . Also , many genes were found to be down-regulated in all mutant cell types supporting the hypothesis that all three transcription factors can transactivate some of the same gene targets ( Figure 2 ) . DNA damage triggers numerous cellular responses including an extensive DNA repair pathway involving numerous genes [18] . Microarray analysis revealed that the p53 family members regulate numerous genes involved in the DNA repair pathway . Many of these genes seemed to be uniquely regulated by p63 and/or p73 . After DNA damage , loss of p63 or p73 prevents induction of Brca2 ( Figure 2 , cluster 4 ) , an essential co-factor in Rad51-dependent DNA repair of double-stranded breaks , and Rad51 itself ( Figure 2 , cluster 3 ) [18] . Sequence analysis also indicates that p53/p63 response elements exist in both the promoter and intronic region of Brca2 and Rad51 ( Table 1 and Table S2 ) . Clustered with Rad51 are Dbf4 , a regulator of Cdc7 and a prognostic determinant for melanoma development , and Gas6 , which cooperates with the tyrosine receptor kinase Axl in tumor proliferation and cell survival ( Figure 2 , cluster 3 ) . We also found additional genes that were uniquely down-regulated in p63−/− , p73−/− , and p63−/−;p73−/− MEFs . These hits indicate that p63 and p73 have roles independent of p53 in the DNA-damage response pathway . For example , expression of Rad50 , which forms a complex with mre11 and Nebrin , is found to be down regulated in p63−/−;p73−/− MEFs relative to wild-type MEFs treated with doxorubicin . There are also p53 family response elements upstream of the transcription start site of Rad50 ( Table S2 ) . In addition to genes that are uniquely regulated by p63 and/or p73 , genes controlled by all three p53 family members were identified . Mre11 , a gene that functions in the repair of DNA double strand breaks , was found to be down-regulated in p53 , p63 , and p73 deficient E1A expressing MEFs ( Figure 2 , cluster 1 ) . In addition , sequence analysis revealed multiple p53/p63 response elements ( Table S2 ) . Genes with similar expression profiles as mre11 include the growth factor signaling components Ghr and Sos1 as well as the apoptotic components Traf1 and Cathepsin D all of which contain p53 family member binding sites ( Figure 2 , cluster 1 & 6 & Table S2 ) . Multiple genes involved in other biological processes , including tumor progression , metastasis and development were found to be differentially regulated in the various E1A MEF cells . For example , Mmp2 , a gene shown to play a role in embryonic development and tumor metastasis , is also down regulated in the absence of p73 after doxorubicin treatment . Clustered with Mmp2 are many signaling components such as Grb2 , Stat1 , Map3k14 , and Mapk8ip3- all of which have at least one p53 family member binding motif present near its promoter ( Figure 2 , cluster 5 and Table S2 ) . Interestingly , brachyury , the developmental transcription factor , was identified as a putative p63 target gene ( Figure 2 , cluster 2 ) . Given the identified roles of brachyury in limb development , cancer , and hematopoetic stem cells and the development phenotype of the p63−/− mouse , this putative target has important biological significance [19] , [20] , [21] , [22] . We found brachyury to contain multiple p53 family response elements both upstream of its transcriptional start site and within the first intron ( Table S2 ) . Other p63 dependent genes that cluster with brachyury include Abr , the GAP for the small GTPase Rac , Socs3 , involved in cytokine and apoptotic signaling , and the zinc-finger transcription factor Klf9 which is implicated in control of cell proliferation , cell differentiation , and cell fate ( Figure 2 , cluster 2 ) . Strikingly , the results from the cDNA microarray indicate that genes in the DNA repair pathway are differentially regulated in MEFs lacking p63 and/or p73 after treatment with DNA damaging agents . To verify these putative transcriptional targets of p63 and p73 , quantitative real time PCR was performed . The expression of mre11 , BRCA2 , Rad51 , and Rad50 was examined in wild-type , p53−/− , p63−/− , p73−/− and p63−/−;p73−/− E1A MEFs before and after treatment with doxorubicin for 12 hours and 5 Gy of gamma radiation . Interestingly , mre11 , BRCA2 , Rad51 , and Rad50 are all induced in wild-type E1A MEFs after these treatments ( Figure 3 ) . We measured the baseline levels of mRNA of mre11 , BRCA2 , Rad51 , and Rad50 to determine levels of these transcripts prior to DNA damage ( Figure S2 ) . After treatment with doxorubicin or gamma radiation , levels of mre11 mRNA are not induced to wild-type levels in p63−/−and p63−/−;p73−/− E1A indicating that p63 may transcriptionally regulate this gene ( Figure 3 ) . Similarly , the levels of BRCA2 are significantly lower in p73−/− and p63−/−;p73−/− E1A MEFs than in wild-type or p53−/− E1A MEFs ( Figure 3 ) after treatment with doxorubicin and gamma radiation . Likewise , the Rad51 gene is not induced to wild-type levels in p63−/− , p73−/− , and p63−/−;p73−/− E1A MEFs after treatment with DNA damaging agents ( Figure 3 ) , indicating again that p63 and p73 may be critical transcriptional activators of Rad51 after DNA damage . Lastly , Rad50 also showed a pattern indicative of transcriptional regulation by both p63 and p73 . The mRNA levels of Rad50 are approximately 4-fold lower in p63−/−;p73−/− E1A MEFs than in wild-type E1A MEFs ( Figure 3 ) after treatment with doxorubicin and gamma radiation . Taken together , these data indicate that mre11 , BRCA2 , Rad51 , and Rad50 may be transcriptional targets of p63 and p73 in response to DNA damage . As previously reported , twenty percent of mice heterozygous for p63 and p73 ( p63+/−;p73+/− ) develop mammary adenocarcinomas [7] ( Figure 4 ) , and ninety percent of these tumors lose the wild-type allele of p63 and p73 [7] . Given that BRCA2 plays an important role in the pathogenesis of mammary adenocarcinoma , this made it a relevant biological target for p63 and p73 in mammary tumors . The protein levels of Rad51 was first examined by Western blot analysis using wild-type and p63−/−;p73−/− MEFs . Interestingly , the basal level of Rad51 is lower in p63−/−;p73−/− MEFs compared to wild-type MEFs ( Figure 4A ) . The levels of Rad51 in p63−/−;p73−/− MEFs are not induced in response to gamma irradiation; however , a 2-fold increase in expression of Rad51 was detected in the wild-type MEFs after DNA damage . To determine whether this change in expression pattern of Rad51 was cell-type specific , we performed immunohistochemistry on mammary adenocarcinomas from p63+/−;p73+/− mice where LOH of p63 and p73 had occurred ( n = 10 ) ( Figure 4F–4I ) . Indeed , Rad51 as well as BRCA expression is detected in normal mammary glands ( n = 10 ) of p63+/−;p73+/− mice ( Figure 4B and 4D ) and is lost in hyperplastic mammary glands ( n = 4 ) and mammary adenocarcinomas ( n = 6 ) in these mice ( Figure 4C and 4E ) . Both the cDNA microarray and real-time RT-PCR data provide evidence that BRCA2 , Rad51 , and mre11 are transcriptionally regulated by p63 and p73 after DNA damage ( Figure 3 ) . Consequently , chromatin immunoprecipitation ( ChIP ) assay was performed to determine whether p63 and/or p73 could directly bind to the promoter region of these two genes . A subset of putative binding sites identified and summarized in Table 1 were assayed using ChIP . Sites chosen included those with the best scores for p53 and p63 . Four putative binding sites were assayed for RAD51 ( Table 1 ) . RAD51-1 and 2 are located in intron 1 , upstream of the start site , while RAD51-3 and 4 are found in intron 2 , downstream of the start site . One putative element was assayed for BRCA2 in intron 2 , 133 nucleotides downstream of the start site ( Table 1 ) . Lastly , three putative p53 family response elements were queried for mre11: MRE11-1 , 2 , and 3 , located in intron 1 , upstream of the start site ( Table 1 ) . ChIP analysis was performed using an antibody for p53 , p63 or p73 in wild-type , p53−/− , p63−/− , and p73−/− E1A MEFs treated with doxorubicin for 12 hours ( Figure 5 ) . Interestingly , p73 was the only p53 family member that binds to the RAD51 promoter after DNA damage treatment . p73 was found to bind to RAD51-2 and 3 in intron 1 and intron 2 respectively . The primers used for this PCR reaction did not distinguish between the two sites; therefore , it is possible that p73 only binds to one of these sites . p63 and p73 , but not p53 , were found to bind to the response element in BRCA2 after DNA damage ( Figure 5 ) . Lastly , p63 was the only family member found bound to the mre-11 promoter at site mre11-3 within intron 1 , 171 nucleotides upstream of the start site . The same binding pattern in the ChIP assay was obtained with other DNA damaging agents , such as gamma radiation ( data not shown ) . The ChIP results clearly demonstrate that p63 and/or p73 can bind to the promoters of these genes; however to gain a clear indication of which isoforms of p63 and p73 transactivate Rad51 , BRCA2 , and mre11 , luciferase assays were performed with TA and ΔN isoforms of p63 and p73 . Regions shown to bind by ChIP assay were used to construct firefly luciferase reporters . pGL3-Rad51-1 was designed by cloning intron 1 containing RAD-51-1 and 2 ( Table 1 ) in to the pGL3 basic vector and pGL3-Rad51-2 containing the elements , RAD51-3 and 4 , was cloned in to the pGL3 basic vector . These constructs were transfected in to p63−/−;p73−/− MEFs along with a renilla luciferase gene and one of the following isoforms of p63 or p73: TAp63α , TAp63γ , TAp73α , TAp73β , ΔNp63γ , ΔNp73α , and ΔNp73β . Interestingly , both ΔNp63α and ΔNp73β are the isoforms that transactivate the Rad51 reporter gene to appreciable levels . ΔNp63α transactivates pGL3-Rad51-1 11 fold and ΔNp73β transactivates this reporter 6 fold ( Figure 6A ) . These isoforms more modestly transactivate the pGL3-Rad51-2 reporter indicating that the p63/p73 element resides in intron 1 ( Figure 6A and 6B ) . Surprisingly , the TA isoforms did not transactivate the reporter gene . The p63/p73 family members also transactivate this reporter gene . ΔNp63α and ΔNp73β together can transactivate the Rad51-1 reporter 19 fold ( Figure 6A and 6B ) . Additionally , the other ΔN isoforms that modestly transactivate this reporter alone can transactivate this reporter to higher levels . For example , ΔNp63α along with ΔNp73α can transactivate this reporter gene 9 . 8 fold , demonstrating additive effects between these family members . Similar to the experiments for RAD51 , the BRCA2 region within intron 1 found to be bound by both p63 and p73 was cloned in to the pGL3 basic vector . Dual-luciferase reporter assay was performed in p63−/−;p73−/− MEFs as described above . Strikingly , the isoform with the highest ability to transactivate this reporter was ΔNp73β with a 4 fold induction . Additionally , ΔNp63α and ΔNp73β can transactivate the reporter 6 fold and other combinations of ΔN isoforms also show increases in transactivation of this reporter ( Figure 6C ) . The ability of p63 and p73 to transactivate the mre11 gene was also tested by luciferase assay . The region shown to bind to p63 by ChIP analysis was cloned in to the pGL3 basic vector to generate pGL3-Mre11 . This reporter was induced 3 . 8 fold by ΔNp63α and ΔNp73β together ( Figure 6D ) . pPERP-luc , which has previously been shown to be responsive to TAp63γ was used as a positive control for these experiments [23] , [24] . To determine whether p53 could transactivate these reporters , p53 was transfected with each reporter and luciferase activity was measured . p53 did not induce any of the reporters assayed ( Figure 6A–6D ) . In addition , we performed luciferase assays using the Rad51-1 and BRCA2 reporters in MEFs lacking p53 , p53−/−;p73−/− ( Figure 6E and 6F ) and p53−/−;p63−/− ( data not shown ) . These experiments yielded similar results as those shown in Figure 6A and 6C . Taken together , these data indicate that the trasactivation of Rad51 , BRCA2 , and mre11 is p53-independent . Rad51 and BRCA2 are both involved in homologous recombination ( HR ) DNA repair , one of the major pathways for repair of double strand breaks ( DSBs ) . Cells lacking genes involved in HR , like BRCA2 and Rad51 , have been shown to have an impaired ability to repair their DNA [18] , [25] , [26] , [27] . Consequently , we hypothesized that cells lacking p63 and/or p73 , which have low levels of these two proteins , may have a defect in repairing DSBs in damaged DNA . To test this hypothesis , wild-type , p53−/− , p63−/− , p73−/− , and p63−/−;p73−/− primary and E1A MEFs were treated with 5 Gy gamma-radiation or doxorubicin to generate DSBs . A comet assay was then performed to determine the DSB repair capacity in these cells . Comet assay , or single cell gel electrophoresis , is a commonly applied approach for detecting DNA damage in a single cell . The unwound , relaxed DNA migrates out of the cell during electrophoresis and forms a “tail” [28] . Therefore , cells that have damaged DNA appear as comets with tails containing fragmented and nicked DNA , while normal cells do not . The degree of DNA damage is represented using the parameter known as tail moment defined as the product of the tail length and the portion of total DNA in the tail . MEFs lacking the p53 family members were treated with DNA damage and incubated for a total of 16 hours allowing the homologous recombination repair to take place . Cells were and harvested at 0 ( untreated ) , 1 , and 16 hours for the Comet assay . In all cases , p63−/− , p73−/− , and p63−/−;p73−/− MEFs were found to have the largest tail moment after DNA damage ( Figure 7A–7D ) . The tail moment after DNA damage was significantly higher for p63−/− , p73−/− , and p63−/−;p73−/− primary and E1A MEFs ( 18 . 8 ) compared wild-type samples ( p <0 . 0001 ) . This result indicates that p63 and p73 play a critical role in DNA repair . Because loss of p63 and p73 impair DSB repair by regulating Rad51 , BRCA2 , and mre11 , it is likely that loss of p63 and p73 results in poor cell survival due to the inability to repair damaged chromosomal DNA . To determine whether loss of p63 and p73 results in a decrease in cell survival , a clonogenic survival assay was performed using both primary MEFs and E1A expressing MEFs after treatment with 1 , 2 and 3 Gy of gamma radiation and 0 . 34 , 0 . 5 , and 1 . 0 µM doxorubicin . After 12 hours , cells were replated and assayed for the ability to form colonies . p63−/−;p73−/− E1A MEFs and primary MEFs have an impaired ability to form colonies after gamma radiation indicative of defects in DNA repair ( Figure 7E and 7F ) . A similar result was seen after treatment with doxorubicin in these cells ( Figure 7G and 7H ) .
p53 transactivates a vast network of genes in response to DNA damage [1] . While p63 and p73 can also transactivate known p53 target genes to varying degrees , they play roles in distinct biological functions including development and metastasis and likely have unique transcriptional targets . The advantage of the system employed here is the use of isogenic primary cells with the deletion of a single p53 family member . Here , we used early passage MEFs lacking the p53 family members individually or both p63 and p73 in combination and expressing E1A , which sensitizes them to undergo apoptosis after DNA damage to identify changes in gene expression in this process . We identified sets of genes that are regulated by individual and multiple p53 family members indicating unique and overlapping functions for this family of genes in response to DNA damage . Six hundred twenty out of 15 , 488 genes queried were regulated by a p53 family member . Genes identified played a role in multiple processes including apoptosis and DNA repair . In addition to engaging pathways predicted to be induced by DNA damage , genes involved in other processes like development and metastasis were also induced . These are biologically significant given the reported developmental , tumor , and metastatic phenotypes of the p63/p73 mutant mice [7] , [20] , [22] , [29] . Lastly , the majority of the targets identified had binding sites that closely fit the p53 and p63 consensus binding site [14] , [30] , [31] indicating that they may be bona fide direct transcriptional targets of these family members . Indeed , we verified that Rad51 , BRCA2 , and mre11 , genes involved in DNA repair , are direct transcriptional targets of p63 and p73 . Given the high prevalence of mammary adenocarcinoma in mice mutant for p63 and p73 ( p63+/−;p73+/− ) , a group of genes of interest are those involved in DNA repair . These genes were induced in wild-type cells and down regulated in the absence of p63 or p73 . The mechanism for the tumor suppressive activity of p63 and p73 is not completely understood [6] , [7] , [32] . Regulation of DNA repair genes by p63 and p73 has not been demonstrated previously and could be a pathway employed by these genes in tumor suppression . Both Rad51 and BRCA2 were found to be direct transcriptional targets of p63 and p73 indicating that these mechanisms may be triggered during tumorigenesis . Interestingly , Rad51 has been shown previously to be repressed by p53 through a site found upstream of the start site [33] . Here , we show that ΔNp63 and ΔNp73 transactivate Rad51 through a distinct element in intron 1 indicating that there is an intricate and complex regulation of this gene by the p53 family and is likely a critical target in tumor suppression by this family . We also showed that transcriptional regulation of Rad51 , BRCA2 , and Rad51 by p63 and p73 is p53-independent/ It was surprising that the ΔN isoforms of p63 and p73 were more potent transactivators of Rad51 , BRCA2 , and mre11 than the TA isoforms . The TA isoforms have an acidic N-terminal domain necessary for transactivation [2] , [3] , and many studies have shown previously that the TA isoforms are more potent transactivators than the ΔN isoforms [2] , [8] . Furthermore , the ΔN isoforms are better known for the dominant negative activities that they impose on the TA isoforms of p63 and p73 and p53 . Interestingly , a number of recent studies have shown that the ΔN isoforms are capable of transactivating target genes due to a proline-rich transactivation domain that exists in these isoforms [12] , [13] . In addition , the ΔN isoforms of p63 are more highly expressed than TAp63 in certain tissues including the skin [14] making the ΔNp63 isoforms likely candidates for gene regulation in these tissues . Taken together , our results indicate that the roles of the ΔN isoforms are more complex than previously appreciated . We have shown previously that E1A expressing MEFs deficient for p63 and p73 are resistant to apoptosis [15] . Paradoxically , we found that p63−/−;p73−/− primary and E1A MEFs are radiosensitive in long-term clonogenic assays . This finding coupled with the inability of p63/p73 deficient cells to repair DNA as shown by Comet assay indicate that p63 and p73 play a critical role in DNA repair . This new finding does not preclude that p63/p73 deficient cells are resistant to apoptosis after acute exposure to DNA damage . These data demonstrate that surviving p63−/−;p73−/− cells are unable to proliferate and establish a colony after DNA damage . This is likely due to defects in the DNA repair mechanisms . Using a genome wide analysis , these studies have revealed novel transcriptional targets of the p53 family members . We have also identified a novel mechanism of the regulation of the DNA repair pathway by p63 and p73 . Given the high incidence of mammary adenocarcinoma in p63/p73 mutant mice , these studies have unveiled a potential mechanism for p63 and p73 as tumor suppressor genes . In addition , our studies have revealed further complexity by indicating that the primary transactivators of these DNA repair genes are the ΔN isoforms of p63 and p73 . These isoforms have previously been thought to act as oncogenes . More recent data have challenged this notion as these isoforms can also transactivate genes involved in apoptosis and the expression of these isoforms does not provide a growth advantage [12] . These studies provide further evidence that the ΔN isoforms may have some anti-tumor functions such as the ability to engage DNA repair pathways . Future studies using isoform specific knock out mice should yield important insights in to how each of these isoforms contributes to tumor suppression and shed light on the interactions of the complex p53 family .
The Laboratory of Genetics at The National Institute on Aging ( NIA ) cloned approximately 15 , 000 unique cDNAs into the NotI/SalI site of Ampicillin-resistant pSPORT1 vector ( Life Technologies ) . Average insert size of the clones is 1 . 5 kb ( 0 . 5–3 kb ) . Inserts were amplified for microarray printing following a modified version of the protocol described previously [34] . In 96 well format , bacterial stocks were grown overnight in 2X YT medium ( 100 µg/ml ampicillin ) with agitation . Ten microliters of the overnight bacterial culture was added to 90 µl ddH2O in PCR plates ( MJ Research ) and denatured at 95° C for 10 minutes . Following denaturation , plates were centrifuged for 10 minutes . To perform PCR , 5 µl of supernatant from each well was used as template in a 100 µl reaction with 3 . 5 units of AmpliTaq DNA polymerase ( Applied Biosystems ) , forward primer ( 5′–CCAGTCACGACGTTGTAAAACGAC-3′ ) reverse primer ( 5′-GTGTGGAATTGTGAGCGGATAACAA-3′ ) , and deoxynucleotide triphosphates ( dNTPs ) . Amplification was carried out in thermocyclers with a program that contained an initial denaturation step at 95°C for 2 minutes followed by 38 cycles of 30 s at 94°C , 45 s at 65°C , and 3 minutes at 72°C , and a final extension of 5 minutes at 72°C . The amplified inserts were then purified using Montage PCR96 cleanup Filter Plates ( Millipore ) on a BIO-TEK Precision 2000 Automated Microplate Pipetting System to a purified volume of 100 µl . Thirty-five microliters of each purified PCR product was added to a 384-well plate , and desiccated using a large Savant Speed-vac apparatus , then reconstituted in 7 µl of 3X SSC/1 . 5 M betaine to a mean concentration of 600 ng/µl . The microarrays were fabricated at the MIT BioMicro Center using Corning GAPS II Gamma Amino Propyl Silane slides . cDNA clones were printed using a BioRobotics Microgrid 600 TAS Arrayer with a 32-pin print head and quill pin microfluidic liquid transfer technology . All procedures involving mice were approved by the IACUC at U . T . M . D . Anderson Cancer Center and M . I . T . E1A-expressing mouse embryonic fibroblasts ( MEFs ) ( wild-type , p53−/− , p63−/− , p73−/− , and p63−/−;p73−/− ) were generated as described previously [15] from passage 1 primary MEFs . 3×106 E1A MEFs were plated on each of 6–15 cm dishes . Twenty-four hours after plating , the cells were treated with 0 . 34 µM doxorubicin . Twelve hours after treatment , total RNA ( 150–300 µg ) was extracted from treated and untreated E1A MEFs using the RNAeasy Midi Kit ( Qiagen ) . For each microarray hybridization , 100 µg of total RNA prepared from the reference or experimental cells were labeled by incorporating Cy3- or Cy5-labeled dUTP ( NEN ) using oligo d ( T ) ( MWG ) and Superscript II reverse transcriptase ( Invitrogen ) . The resulting probes were purified using the Qiaquick PCR purification Kit ( Qiagen ) and recovered in a volume of 30 µl ddH20 . The printed slides were rehydrated , UV cross-linked , and blocked to reduce background using succinic anhydride ( Sigma ) , 1-methyl 2-pyrrolidinone and sodium borate . Each slide was incubated in 60 µl total volume of hybridization solution containing Cy3- and Cy5-labeled target ( one probe is the reference invariant target and the other is the experimental target ) , 1 µg of Mouse Cot-1 DNA ( Invitrogen ) , 0 . 1 units of poly-A40–60 ( Amersham Pharmacia ) , and 10 . 1 µg of Salmon Testes DNA ( Sigma ) , 25% Formamide , 5X SSC , 0 . 1% SDS under a 22×40-mm lifterslip ( Erie Scientific Company ) at 42°C for 16 hours exactly . The slide was placed in a sealed hybridization chamber ( Corning ) containing two side wells with a total of 20 µl 3X SSC for humidification in a light-sealed water bath . After exactly 16 hours of hybridization , the slide was washed in 500 ml of 1X SSC , 0 . 03% SDS for 5 minutes after the lifterslips are gently removed in the wash solution . Then , the slides were washed for 5 minutes in 0 . 1X SSC , 0 . 01% SDS followed by 0 . 1X SSC . Slides were centrifuged in a speed-vac to dry . Each slide was scanned using an arrayWoRx Auto Biochip Reader that employs white light , polychromatic filter-wheel/CCD camera ( Applied Precision ) at wavelengths corresponding to each analog's emmision wavelength ( 595 and 685 nm for Cy3 and Cy5 , respectively ) . RNA from each sample was hybridized to four independent cDNA microarrays . For 2 replicates , the invariant target was labeled with Cy3 and the experimental target was labeled with Cy5 . For the other 2 replicates for each sample , the invariant target was labeled with Cy5 and the experimental target was labeled with Cy3 . The invariant reference target RNA used was extracted from untreated wild type- E1A MEFs . These cells were chosen as a source of reference target RNA because this species of RNA robustly hybridized to a large percentage of genes , and it is relevant to the experimental design . Total RNA was extracted from the E1A MEFs of the genotypes described above using the RNeasy Midi and Rnase-free Dnase kits ( Qiagen ) . RNA was quantified and tested for quality on the Agilent 2100 Bioanalyzer ( Agilent Technologies ) . To generate cDNA , RNA ( 2 µg ) from each E1A MEF line treated with 0 . 34 µM doxorubicin was used for random hexanucleotide- primed cDNA synthesis . Each 40 µl reaction contained 1X buffer , 10 µM DTT , 1 µg random hexamer , 2 µl of Superscript II ( Invitrogen ) , 0 . 5 mM each of all four dNTPs , and 80 units of RNase inhibitor ( Promega ) . Using heating blocks , reactions were incubated at 42°C for 1 hour , 70°C for 15 minutes , 37°C for 20 minutes , and 95°C for 2 minutes . RNase H ( 2 units ) ( Invitrogen ) was added to each reaction following the 70°C incubation . Afterwards , each reaction was diluted with ddH2O to a final working volume of 200 µl . cDNAs ( 2 µl ) were added to 25-µl reaction mixtures containing 12 . 5 µl of 2X SYBR Green master mix ( Applied Biosystems ) , and 40 nm of gene-specific primers . Primers were designed using Primer Express software ( Applied Biosystems ) . Assays were performed in triplicate with an ABI Prism 7000 Sequence Detector ( Applied Biosystems ) . All data were normalized to an internal standard ( 18 S ribosomal RNA; TaqMan Ribosomal RNA Control Reagents VIC Probe: Protocol: Rev C , Applied Biosystems ) or GAPDH . ChIP Assay was performed as described previously , E1A MEFs ( wild-type , p53−/− , p63−/− , p73−/− , and p63−/−;p73−/− ) were untreated or treated with 0 . 34 µM doxorubicin for 12 hours , which are the same conditions used for the array and real time PCR . Cellular proteins were crosslinked to chromatin with 1% formaldehyde . p53-DNA , p63-DNA or p73-DNA complexes were immunoprecipitated using the following antibodies: pan-p63 ( 4A4 , Santa Cruz ) , pan-p73 ( IMG-259a , Imgenex ) or p53 ( Ab-3 , Oncogene Research Products ) . Immunprecipitated complexes were recovered by Staphylococcus A cells , treated with proteinase K , and DNA was purified . PCR was performed for putative p53 family binding elements . Putative p53 family member binding sites were identified by scanning 1000 bp of the 5′ UTR , exon 1 , intron 1 , exon 2 and intron 2 for the consensus p53 binding site [31] . These sites are summarized in Table 1 . Sequences for primers used are available upon request . To generate the pGL3-Rad51 luciferase reporter , DNA was amplified from a BAC clone containing the Rad51 gene ( RP23-15121 , CHORI BACPAC resources ) using primers designed containing the p73 binding site shown by ChIP and 5′ NheI and 3′ XhoI cloning restriction enzyme sites: forward primer ( 5′- ACTAGCTAGCAGCAGGGCGACCAACCGAC-3′ ) and reverse primer ( 5′-CCGCTCGAGTGGCCCTCCCTATCCACAGG-3′ ) . To construct the pGL3-BRCA2 luciferase reporter , the DNA fragment containing the p63/p73 binding site shown by ChIP was amplified from C57/B6 genomic DNA by PCR using the following primers with 5′ XhoI and 3′ BglII cloning restriction enzyme sites: forward primer ( 5′-CCGCTCGAGAGAGGGATCCGGCGCGTC-3′ ) and reverse primer ( 5′-GGAAGATCTGGTCTAAGCTCTGTTGCTCCTG-3′ . To generate the pGL3-Mre11 luciferase reporter , DNA was amplified from a BAC clone containing the mre11a gene ( RP23-149D5 , CHORI BACPAC resources ) using primers designed containing the p63 binding site shown by ChIP and 5′ XhoI and 3′ BglII cloning restriction enzyme sites: forward primer ( 5′- CCGCTCGAGACAGAGAGAACCTCACCGAGAAC -3′ ) and reverse primer ( 5′-GGAAGATCTCTGTACCAGGTTCCTCTCCAAG-3′ ) . The resulting amplified DNA fragments were gel-purified ( Wizard Prep Kit , Promega ) after restriction enzyme digestion and then ligated to pGL3-basic vector ( Promega ) between the respective cloning sites . 6×105 wild-type and p63−/−;p73−/− MEFs were plated on 6 cm dishes . Twelve hours after plating , the MEFs were irradiated with 5 Gy of gamma-irradiation and then harvested at 10 minutes , 30 minutes , 1 , 2 , and 4 hours . The MEFs were lysed on ice in lysis buffer ( 100 mM Tris , 100 mM NaCl , 1% Nonidet P40 , protease inhibitor cocktail ( Roche ) ) . Thirty micrograms of each lysate was subjected to electrophoresis on a 10% SDS PAGE for Rad51 and transferred to PVDF membrane . Rad51 was detected using the anti-Rad51 antibody ( clone 51RAD01 , Neomarkers ) , and BRCA2 was detected using the anti-BRCA2 antibody ( clone H-300 , Santa Cruz ) . Slides were dewaxed in xylene and rehydrated in a graded series of ethanol following standard protocols [7] . Slides were incubated with primary antibodies for p63 ( 4A4 , Santa Cruz ) , p73 ( IMG-259A , Imgenex ) , Rad51 ( clone 51RAD01 , Neomarkers ) , or BRCA2 ( clone H-300 ) , Santa Cruz ) . at a dilution of 1∶100 for 18 hours at 4 deg C . Detection was performed using the Vectastain kit ( Vector Labs ) followed by the VIP kit or DAB kit ( Vector Labs ) and counterstained with methyl green ( Vector Labs ) . Ten normal mammary glands and ten mammary adenocarcinomas were stained with each antibody . p63−/−;p73−/− , p53−/−;p73−/− or p53−/−;p63−/− MEFs were plated on 6-well plates ( 3 . 5×105 cells per well ) . Twelve hours after plating , the MEFs were transiently transfected using Fugene HD ( Roche ) with 2 . 5 µg of the following Firefly luciferase reporter plasmids ( pGL3-Rad51-1 , pGL3-Rad51-2 , pGL3-BRCA2 ) or pPERP-luc [24] , 1 µg of Renilla luciferase plasmid ( transfection control ) , and 2 . 5 µg of empty vector ( pcDNA3 ) or plasmids encoding the p63/p73 isoforms ( TAp63α , TAp63γ , ΔNp63γ , TAp73α , TAp73β , ΔNp73α and ΔNp73β ) or p53/ In experiments where 2 isoforms of p63 and p73 were assayed simultaneously , 1 . 25 µg of each isoform was used . After 24 hr , cells were harvested and luciferase activity was measured using the Dual-Luciferase Reporter Assay system ( Promega ) and a Veritas microplate luminometer ( Turner BioSystems ) . The relative luciferase activity was determined by dividing the Firefly luciferase value with the Renilla luciferase value and the fold increase in relative luciferase activity was determined by dividing the relative luciferase value induced by p63 and p73 isoforms with that induced by the pcDNA3 control vector . Each experiment was performed in triplicate . E1A MEFs or primary MEFs were plated in 6-well plates ( 1×106 cells per well ) of the following genotypes ( wild-type , p53−/− , p63−/− , p73−/− , and p63−/−;p73−/− ) [15] . Twelve hours later , MEFs were irradiated with 1 , 2 , and 3 Gy of gamma radiation or 0 . 34 , 0 . 5 , and 1 µM doxorubicin . After 12 hr , 1200 cells were plated on 10 cm dishes . After 12 days of incubation , the cells were stained with clonogenic reagent ( 0 . 25% of 1 , 9-dimethyl-methylene blue in 50% ethanol ) . Surviving colonies were counted , and the survival rate was calculated as the ratio of the surviving colonies after DNA damage treatment over the number of colonies for each genotype before treatment . Each experiment was performed in triplicate on three independent MEF lines for each indicated genotype . Wild-type , p53−/− , p63−/− , p73−/− , and p63−/−;p73−/− primary and E1A MEFs were plated on 6-well dishes ( 1 . 6×105 cells per well ) . Twelve hours after plating , MEFs were irradiated with 5 Gy of gamma radiation . Cells were harvested 0 , 1 , and 16 hours later for Comet Assay ( Trevigen ) according to the manufacturer's protocol specific for DSB detection . Briefly , cells were suspended in PBS at a density of 3×105 cell/mL . Twenty microliters of each cell suspension was mixed with 200 µL of melted low melting point agarose ( LMA ) and 75 µL of this mixture was placed onto the Trevigen CometSlide for electrophoresis . Subsequent to electrophoresis , samples were visualized with SYBR Green I and fluorescence microscopy . Twenty pictures were taken for each sample and at least 135 cells per experiment were examined for comet tails using CometScore software ( TriTek Corporation ) . Three independent MEF lines for each genotype were assayed in triplicate . Student's t test was used for statistical analysis . All experiments were performed at least in triplicate . Data are represented as the mean ± SEM . Statistics for qRT-PCR , luciferase , clonogenic , and comet assays was performed using Student's t test for comparison between two groups . A p value of 0 . 05 was considered significant . | p63 and p73 have been identified as important suppressors of tumorigenesis and metastasis . Although they are structurally similar to p53 , they have many functions that are unique including roles in development and metastasis . Here we show , using a genome-wide analysis of cells lacking p63 and p73 individually and in combination , that p63 and p73 regulate many unique target genes involved in multiple cellular processes . Interestingly , one of these pathways is DNA repair . Further validation of differentially expressed target genes in this pathway , revealed that p63 and p73 transcriptionally regulate BRCA2 , Rad51 , and mre11 providing a novel mechanism for the action of p63 and p73 in tumor suppression . These findings have important therapeutic implications for cancer patients with alterations in the p63/p73 pathway . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"genetics",
"and",
"genomics/animal",
"genetics",
"genetics",
"and",
"genomics/cancer",
"genetics",
"genetics",
"and",
"genomics/bioinformatics"
]
| 2009 | p63 and p73 Transcriptionally Regulate Genes Involved in DNA Repair |
Accurate chromosome segregation during meiosis requires that homologous chromosomes pair and become physically connected so that they can orient properly on the meiosis I spindle . These connections are formed by homologous recombination closely integrated with the development of meiosis-specific , higher-order chromosome structures . The yeast Pch2 protein has emerged as an important factor with roles in both recombination and chromosome structure formation , but recent analysis suggested that TRIP13 , the mouse Pch2 ortholog , is not required for the same processes . Using distinct Trip13 alleles with moderate and severe impairment of TRIP13 function , we report here that TRIP13 is required for proper synaptonemal complex formation , such that autosomal bivalents in Trip13-deficient meiocytes frequently displayed pericentric synaptic forks and other defects . In males , TRIP13 is required for efficient synapsis of the sex chromosomes and for sex body formation . Furthermore , the numbers of crossovers and chiasmata are reduced in the absence of TRIP13 , and their distribution along the chromosomes is altered , suggesting a role for TRIP13 in aspects of crossover formation and/or control . Recombination defects are evident very early in meiotic prophase , soon after DSB formation . These findings provide evidence for evolutionarily conserved functions for TRIP13/Pch2 in both recombination and formation of higher order chromosome structures , and they support the hypothesis that TRIP13/Pch2 participates in coordinating these key aspects of meiotic chromosome behavior .
Meiosis generates haploid gametes from a diploid progenitor in order for proper ploidy to be restored after fertilization . Meiocytes accomplish haploidization by performing two rounds of chromosome segregation after a single round of replication . During prophase of the first meiotic division in most organisms , DNA double-strand breaks ( DSBs ) are produced by the Spo11 protein [1] and repair of these breaks promotes recombination , pairing , and synapsis of homologous chromosomes [2] . Meiotic recombination can lead to a crossover ( CO ) , involving reciprocal exchange of chromosome arms flanking the break , or to a non-crossover ( NCO ) . COs , in conjunction with sister chromatid cohesion , provide physical connections between homologous chromosomes that ensure their faithful segregation in the first meiotic division . As recombination progresses , chromosomes form higher order structures , most prominently the synaptonemal complex ( SC ) , which comprises two lateral elements ( one for each homologous chromosome ) and transverse filaments linking them together [3] , [4] . In many species , mutations affecting chromosome structure components perturb meiotic recombination , and , conversely , mutants defective for recombination proteins have chromosome structure defects ( reviewed in [5] , [6] ) . These and other observations demonstrate the interrelatedness of recombination and higher order chromosome structures [6] . In budding yeast , Pch2 ( pachytene checkpoint 2 ) is a meiosis-specific AAA+ ATPase family member that is required for checkpoint arrest in response to certain meiotic defects , including those caused by absence of the SC transverse filament protein Zip1 [7] , [8] . This checkpoint role depends specifically on a sub-population of Pch2 protein that localizes within the nucleolus [7] , [9] . Checkpoint-related roles have also been observed in C . elegans , where pch-2 is required for apoptosis of oocytes in mutants deficient for SC components [10] and in D . melanogaster , where pch2 is required for a delay in oocyte selection that occurs in mutants defective for certain crossover-promoting factors [11] . More recently , a chromosomally localized fraction of yeast Pch2 has been shown to play important roles in normal ( unperturbed ) meiosis . First , Pch2 is required for timely and efficient recombination: DSBs persist longer in pch2 mutants than in wild type [12]; pch2 mutants show a slight delay in meiotic divisions that is dependent on Rad17 , a checkpoint factor that responds to unrepaired DSBs [13]; and pch2 mutants are delayed for formation of both COs and NCOs [9] , [13] . Second , Pch2 is important for CO control: pch2 mutants are defective in maintaining normal separation of adjacent COs ( “CO interference” ) , maintaining wild-type numbers of COs when meiotic DSBs are reduced by hypomorphic spo11 mutations ( “CO homeostasis” ) , and ensuring formation of at least one CO per chromosome pair ( the “obligate CO” ) [14] , [15] . Third , Pch2 is required for proper formation and/or maintenance of SC or other higher order chromosome structures: pch2 mutants show abnormal chromosomal localization of the SC central element protein Zip1 and the axis-associated protein Hop1 [9] , [14] . Because Pch2 is needed for both recombination and chromosome structure formation , Pch2 has been hypothesized to coordinate these two features of meiotic chromosome dynamics [9] , [14] , [15] . In mouse , a hypomorphic mutation of the PCH2 ortholog , Trip13 ( thyroid hormone receptor interacting protein ) , supports apparently normal apoptosis of recombination- or synapsis-defective mutants , suggesting that checkpoint functions of TRIP13 are not conserved in mammals [16] . However , TRIP13 is essential for completion of otherwise wild-type meiosis in both male and female mice . Interestingly , mutant spermatocytes were defective for completing meiotic DSB repair but were competent to complete homologous synapsis and appeared to form normal numbers of COs . These observations led to the suggestion that , unlike Pch2 in yeast , TRIP13 is involved specifically in a recombination pathway ( s ) that leads to NCOs , but is dispensable for COs [16] . These findings thus suggested that Pch2/TRIP13 plays different roles in mouse than in other organisms . Here we present characterization of a more severe Trip13 mutant allele along with more detailed analysis of the previously described hypomorph . These studies reveal for the first time that TRIP13 is needed for efficient completion of homologous synapsis . Moreover , we provide evidence that TRIP13 promotes early steps of the DSB repair process upstream of the assembly of RAD51 complexes , and is required for normal number and distribution of COs , thus affecting both CO and NCO pathways . The TRIP13 functions revealed in this study are reminiscent of many of the functions observed for the chromosome-bound Pch2 protein in budding yeast , implying evolutionarily conserved roles .
Trip13 is widely expressed in adult tissues , including testis [16] ( Figure 1A ) , where it is expressed in spermatogonia , spermatocytes and spermatids ( Figure 1A–1D ) . A splice variant lacking exon 8 ( which includes the Walker B ATPase motif ) was detected specifically in spermatocytes and spermatids ( Figure 1A , 1E , 1G ) , although the functional significance of this form of the Trip13 mRNA is not yet clear . We created Trip13 mutant mice using ES cell lines containing a gene trap in either intron 2 ( clone CH0621 ) or intron 3 ( clone RRB047 ) ( Figure 1F ) . These alleles yielded different phenotypes . As described by Li and Schimenti [16] , Trip13RRB047 is a hypomorphic mutation that significantly reduces Trip13 expression , such that transcripts are detected by RT-PCR at reduced levels in Trip13RRB047/RRB047 mice ( Figure 1G ) . Trip13 expression was even more substantially reduced in testes from Trip13CH0621/CH0621 mice ( Figure 1G ) . Trip13CH0621 is thus significantly tighter than Trip13RRB047 . For clarity , we refer below to Trip13RRB047 as Trip13mod ( for “moderate” ) , and to Trip13CH0621 as Trip13sev ( for “severe” ) . Unless otherwise noted , control mice were littermates homozygous for the wild-type allele , and all analyses were conducted on at least two mutant/control pairs to ensure reproducibility . Both Trip13 alleles segregated in a sub-Mendelian ratio: only 11 . 5% of pups from Trip13+/mod mice were homozygous mutants ( 32 . 5% wild type and 56 . 0% heterozygotes , N = 416 ) , in agreement with prior results [16] . Similarly , only 10 . 9% Trip13sev/sev homozygotes were obtained from Trip13+/sev crosses ( 30 . 7% wild type and 58 . 4% heterozygotes , N = 202 ) . Segregation of the mutant alleles is not significantly different ( p = 0 . 956 , G test ) . The sub-Mendelian inheritance suggests that TRIP13 has another function apart from its role in meiosis . Trip13mod/mod animals in certain strain backgrounds were found to have reduced body size and abnormal tails [16] , although we observed no obvious somatic phenotypes for either allele in our colony ( data not shown ) . This difference between the two studies is likely attributable to effects of strain background on expressivity and penetrance of somatic phenotypes . As previously reported [16] , we found that TRIP13 is needed for the completion of meiosis , since homozygous mutant animals were sterile because of severe blocks to spermatogenesis or oogenesis . For both alleles , mutant males had significantly smaller testes than wild-type littermates ( 0 . 59±0 . 16% of total body mass in wild type ( avg . ± sd , N = 8 ) ; 0 . 28±0 . 22% in Trip13mod/mod ( N = 10 ) ; and 0 . 14±0 . 01% for Trip13sev/sev ( N = 4 ) ; p ≤ 0 . 05 for either mutant vs . wild type , t test; difference between mutants was not significant ( p = 0 . 225 ) ) . Testes contain seminiferous tubules , within which germ cells undergo spermatogenesis . Tubule cross sections can be classified in stages , from I–XII , based on the array of germ cell developmental steps present [17] . Histological analysis revealed that testes from both mutants had less populated tubules compared to wild type ( Figure 2A–2C ) . In Trip13mod/mod mice , spermatogenesis was mostly arrested at spermatocyte stages of epithelial stage IV , corresponding to pachynema ( red arrowheads , Figure 2B ) . However , spermatocytes occasionally escaped this arrest as judged by presence of some post-meiotic cells ( green arrowheads , Figure 2B ) . However , we did not observe any late-stage spermatids , implying that cells that escape pachytene arrest could not complete spermiogenesis . TUNEL staining revealed that most of the cells arrested at epithelial stage IV undergo apoptosis ( Figure 2E ) . Trip13sev/sev males also arrested spermatogenesis at epithelial stage IV ( Figure 2C and 2F ) , but no post-meiotic cells were observed , indicating that pachytene arrest is tighter . Oogenesis was also severely affected by both mutations . Female germ cells complete early stages of meiotic prophase I during fetal development and undergo cell cycle arrest at around birth . Soon after birth , each surviving oocyte becomes surrounded by somatic ( granulosa ) cells , forming a primordial follicle that remains arrested until recruited for further development during the reproductive life of the animal [18] . At this time , some follicles undergo an initial synchronous wave of development , in which the oocyte increases in size and the somatic cells change shape and proliferate ( Figure 2G ) . Ovaries from 21-day-old Trip13mod/mod animals had no detectable primordial follicles and had four-fold fewer developing follicles than wild type ( Figure 2H ) . By two months of age , no follicles were detected in this mutant ( data not shown ) . These results indicate that a large majority of Trip13mod/mod oocytes are unable to complete folliculogenesis; the small number of escapers that generate follicles are immediately recruited to develop , causing rapid depletion of the oocyte pool . Ovaries from Trip13sev/sev females showed an even greater defect , as they were devoid of follicles at 21 dpp ( Figure 2I ) . A block to folliculogenesis is typical of mutants unable to repair meiotic DSBs [19] , suggesting that TRIP13 is required for completion of meiotic recombination ( see below ) . During meiotic prophase , homologous chromosome pairing is stabilized by the SC . Synapsis can be monitored by following deposition of proteins of the lateral elements ( e . g . , SYCP3 ) and the central element ( e . g . , SYCP1 ) by immunofluorescence on spread chromosomes . In wild-type meiosis , stretches of SYCP3 first appear at leptonema ( Figure 3A ) . Later , at zygonema , homologs begin to synapse , marked by the presence of SYCP1 ( Figure 3B ) . At pachynema , homologous autosomes are completely synapsed along their lengths such that SYCP3 and SYCP1 completely co-localize ( Figure 3C ) . At diplonema , homolog axes separate but remain joined at sites where COs have occurred , called chiasmata ( Figure 3D ) . In Trip13mod/mod spermatocytes , progression of autosomal synapsis appeared similar to wild type [16] ( Figure 3E–3G ) , and at diplonema , homologous axes separated from one another normally , with most remaining joined at one or a few sites , consistent with chiasmata ( Figure 3H ) . However , a more detailed analysis revealed that SC formation was not fully normal . Specifically , total autosomal SC length at pachynema was on average 11% shorter than in wild type ( 152 . 2±8 . 5 µm in Trip13mod/mod ( N = 20 cells ) vs . 171 . 6±15 . 7 µm in wild type ( N = 32 cells ) ; p = 0 . 0001 , t test ) . To test if shorter SCs affected only chromosomes of a particular size range , we measured the length of all bivalents , ranked them by size , and then divided them into five groups of similarly sized chromosomes ( Table 1 ) . For each chromosome group , the average length of an SC in the mutant was significantly smaller than in wild type . Thus , all autosomal SCs were reduced in length to a similar extent . These findings indicate that wild-type levels of TRIP13 are needed for formation of structurally normal SC , but the defects in Trip13mod/mod mutants were relatively subtle . If these modest defects reflect a bona fide requirement for TRIP13 , then more substantial defects should be encountered in Trip13sev/sev cells . This was in fact the case . Synapsis initiation appeared normal in Trip13sev/sev spermatocytes , as judged by staining patterns for SYCP3 and SYCP1 in leptonema and zygonema ( Figure 3I–3J ) , but proper pachynema with fully synapsed autosomal bivalents was never observed . Instead , the most advanced cells showed a late zygotene-like morphology—given that full-length axes had developed but remained incompletely synapsed—but with characteristics of pachytene cells such as acquisition of the characteristic knob-like accumulation of SYCP3 at telomeres ( Figure 3K and Table 2 ) . Because there does not appear to be a stage when synapsis is complete , it is likely that the unsynapsed regions are asynaptic ( i . e . , where SC did not form at all ) rather than desynaptic ( i . e . , where SC had formed but subsequently disassembled ) . Interestingly , most of the incompletely synapsed bivalents had one end unsynapsed ( i . e . , a forked structure ) , and this was nearly always ( 93% ) the centromeric end ( Figure 3L and Table 2 ) . A minority of bivalents had other synaptic anomalies such as having both ends unsynapsed or only interstitial asynapsis ( a “bubble” ) ( Figure 3L and Table 2 ) . The unsynapsed regions accounted for 28 . 2% of total axis length in a representative population of pachytene-like cells ( N = 7 cells ) . Similar synaptic anomalies were also observed in Trip13sev/sev oocytes . Most oocyte bivalents at the pachytene-like stage presented some degree of asynapsis , with most showing unsynapsed centromeric ends ( Figure 4 and Table 2 ) . Recent studies have demonstrated that Trip13 is also required for changes in the protein composition of chromosome axes that accompany SC formation [20] . HORMAD1 and HORMAD2 are mammalian members of an evolutionarily conserved family of meiotic axis proteins , related to Hop1 in yeast [20] , [21] . In mouse , HORMAD1 and -2 become substantially depleted from chromosome axes soon after synapsis in wild type , but not in Trip13mod/mod cells [20] . Trip13sev/sev mutants have a similar defect , with substantial levels of HORMAD1 and -2 staining retained on the axes of nearly fully synapsed chromosomes in pachytene-like cells ( Figure 5 ) . We also evaluated the effect of the Trip13 mutations on the sex chromosomes . In pachytene spermatocytes , the X–Y pair forms only a short stretch of SC encompassing the small region of homology they share with one another , termed the pseudoautosomal region ( PAR ) ( Figure 3C and 3G ) . Because the PAR is small ( only ∼700 kb in at least one mouse strain [22] ) , this chromosome pair is very sensitive to perturbations of the synaptic process , and thus can report on subtle defects that might not be seen on the autosomes [23] . In Trip13mod/mod animals , the X and Y were separated at pachynema in 6 . 5% of spermatocytes ( N = 154 ) , a 7 . 2-fold increase over the frequency in wild type ( 0 . 9% , N = 116 , p = 0 . 01 Fisher's exact test ) . Moreover , 54 . 2% of pachytene-like Trip13sev/sev spermatocytes displayed unsynapsed X and Y chromosomes ( N = 24 ) ( Figure 3K ) , an 8-fold increase over the frequency in Trip13mod/mod and a 60-fold increase over wild type . The stronger phenotype in Trip13sev/sev cells reinforces the significance of the modest XY synapsis defect found in Trip13mod/mod mutants . During prophase in spermatocytes , the X and Y form a sub-nuclear domain ( the sex body ) that is transcriptionally silenced ( meiotic sex chromosome inactivation , MSCI ) ( reviewed in [24] ) . MSCI entails localization of BRCA1 protein to unsynapsed X and Y axes , subsequent recruitment of the kinase ATR to the axes and peripheral chromatin loops , and phosphorylation of the histone variant H2AX to form γH2AX , which appears on the X and Y chromatin from zygonema onwards ( Figure 6A–6C ) . All pachytene Trip13mod/mod spermatocytes analyzed ( N≥42 cells for each MSCI marker ) showed apparently normal accumulation of BRCA1 and ATR on sex chromosomes and sex body formation as judged by staining for γH2AX ( Figure 6D–6F ) , which indicates that MSCI takes place in Trip13mod/mod mutants . Normal sex body formation was also inferred from γH2AX patterns in earlier studies [16] . In contrast , only 32 . 0% of pachytene-like Trip13sev/sev spermatocytes ( N = 100 ) showed X and Y chromosome axes coated with BRCA1 , and only 0 . 7% of mutant cells ( N = 141 ) had the normal pattern of ATR covering the X and Y axes and chromatin . Instead , most cells showed numerous BRCA1 or ATR patches or foci distributed on many chromosomes , with no clear preference for the X and Y ( Figure 6G and 6H ) . These findings indicate that BRCA1 enrichment on the X and Y is not sufficient to ensure ATR enrichment . Only 9 . 8% of pachytene-like Trip13sev/sev spermatocytes formed a sex body-like accumulation of γH2AX ( N = 61 ) , with most cells displaying prominent patches of γH2AX that did not co-localize with the sex chromosomes ( Figure 6I ) . From analysis of other mouse mutants , it has been hypothesized that unsynapsed autosomal regions cause sex body failure by sequestering proteins required for MSCI [25] . It is thus likely that MSCI failure in Trip13sev/sev spermatocytes is an indirect consequence of autosomal asynapsis . MSCI failure is sufficient to trigger apoptosis of pachytene spermatocytes [24] , [26] , so this defect likely explains spermatogenic arrest in Trip13sev/sev mutants ( but not in Trip13mod/mod mutants , since these are proficient for MSCI; see next section for further discussion ) . As described in the Introduction , yeast Pch2 is needed for normal progression of homologous recombination toward both CO and NCO outcomes , and previous work provided evidence that mouse TRIP13 is also required for completion of recombination [16] . The availability of two Trip13 mutant alleles with different severity provided an opportunity to examine this issue in greater detail . DSB repair can be followed indirectly by staining chromosomes for γH2AX , which forms rapidly on chromatin near DSBs [27] , and for strand-exchange proteins RAD51 and DMC1 , which form cytological complexes at sites of ongoing recombination [28] , [29] . During zygonema , the number of RAD51/DMC1 foci peaks and γH2AX progressively disappears where synapsis has occurred . In normal pachynema , most of these early DSB markers have disappeared from autosomes and only a handful of RAD51/DMC1 foci remain on the unsynapsed regions of sex chromosomes ( Figure 7A ) , along with the DSB-independent γH2AX in the sex body ( Figure 6C ) . During pachynema , as previously described [16] , Trip13mod/mod mice showed persistent γH2AX and RAD51/DMC1 foci on synapsed autosomes , an indicator of delayed and/or inefficient DSB repair ( Figure 6F and Figure 7B ) . In our analysis , Trip13mod/mod spermatocytes at this stage displayed 7 . 2-fold more RAD51/DMC1 foci than wild type ( Figure 7J and Table 3 ) . Similarly , pachytene-like Trip13sev/sev spermatocytes showed persistent γH2AX on unsynapsed autosomes ( Figure 6I ) , and RAD51/DMC1 foci were also substantially elevated , although lower on average and more variable than in the weaker mutant ( 5 . 4-fold higher than wild type; Figure 7C and 7J and Table 3 ) . The few Trip13mod/mod cells that successfully reached diplonema had normal ( i . e . , very low ) levels of RAD51/DMC1 foci ( Figure 7J and Table 3 ) , so it is likely that these represent a small subset of cells that successfully repaired most of their DSBs due to residual expression of TRIP13 . No diplotene cells were recovered from Trip13sev/sev testes . The anti-RAD51 antibody used for this analysis shows weak cross-reaction with DMC1 on western blots ( data not shown ) , so we also examined wild-type and Trip13sev/sev nuclei immunostained with a DMC1-specific antibody . In wild-type pachynema , essentially all DMC1 foci have disappeared , but pachytene-like Trip13sev/sev spermatocytes showed large numbers of persistent DMC1 foci ( >300-fold more than wild-type littermates; Figure 7K and Table 3 ) . Analyses of other cytological markers extend these findings . Specifically , 8% of Trip13mod/mod pachytene spermatocytes displayed discrete BRCA1 foci on synapsed autosomes ( N = 50 ) , which was never observed in wild type ( N = 58 ) ( data not shown ) . In contrast , such foci were observed in 73% of pachytene-like Trip13sev/sev spermatocytes ( N = 100 cells ) ( Figure 6G ) . Pachytene-like Trip13sev/sev spermatocytes also displayed ATR foci on synapsed autosomes ( Figure 6H ) , which were not observed in wild type or Trip13mod/mod ( data not shown ) . These patterns indicate that Trip13sev/sev spermatocytes have a more severe block to recombination . Previous studies of other mouse recombination mutants demonstrated that the presence of unrepaired DSBs in late zygotene and early pachytene spermatocytes provokes a regulatory response that is separable from MSCI failure alone [30] . Thus , it seems likely that Trip13mod/mod spermatocytes undergo apoptosis as a direct consequence of defects in repair of SPO11-induced DSBs ( see also ref . [24] ) . In oocytes , meiotic DSB repair defects cause early and severe meiotic arrest that is partially suppressed by eliminating DSB formation [19] . Trip13 mutant ovaries resemble those of DSB repair-defective mutants such as Dmc1–/– and Msh5–/– ( Figure 2H and ref . [19] ) , and mutation of Spo11 substantially suppresses the early oocyte loss phenotype of Trip13mod/mod animals [16] , strongly suggesting that unrepaired breaks lead to oocyte loss in Trip13 mutants . The above findings support the conclusion that TRIP13 is needed for timely and efficient completion of meiotic recombination . However , further analysis revealed informative defects even earlier in meiotic prophase . From early leptonema to early zygonema , Trip13mod/mod spermatocytes had significantly fewer RAD51/DMC1 foci than wild-type cells of equivalent stage ( 63–86% of wild type on average , depending on stage; Figure 7J and Table 3 ) . Trip13sev/sev spermatocytes displayed even lower numbers ( 25–66% of wild type; Figure 7J and Table 3 ) . Staining with the DMC1-specific antibody revealed a very different result , however: strikingly , there was no detectable difference in numbers of DMC1 foci between wild type and Trip13sev/sev in early or late leptonema ( Figure 7K and Table 3 ) . These findings strongly suggest that TRIP13 is required specifically for assembly of normal numbers of RAD51 complexes . RAD51 and DMC1 colocalize extensively , such that most recombination-associated foci stain positive for both proteins [31] . In wild-type , there are fewer DMC1 foci than RAD51 foci ( Figure 7J and 7K ) ; assuming equivalent detection efficiencies , this finding suggests that there are a subset of foci that contain predominantly RAD51 . These could be a distinct subset of DSB sites that never accumulate DMC1 or , alternatively , a reflection of changes in protein composition over time at recombination sites . In any case , we note that numbers of DMC1 foci throughout prophase in Trip13sev/sev are sufficient to account for all of the foci observed with the less specific anti-RAD51 antibody ( Table 3 ) . ( The lower numbers of anti-RAD51 foci may reflect lower efficiency for detection of DMC1 by this antibody , as suggested by weaker anti-DMC1 western blot signal ( data not shown ) . ) It is possible that little or no RAD51 is ever loaded at DSB sites in this mutant , or that the mutant is specifically defective for forming a subpopulation of RAD51-only foci . More highly specific anti-RAD51 reagents will be necessary to evaluate this issue fully . Observing fewer RAD51 foci at early stages was unexpected , but the greater severity of Trip13sev/sev mutants reinforces the significance of this finding . The normal numbers of DMC1 foci in leptonema in Trip13sev/sev indicate that DSB formation is neither reduced nor delayed , but to address this question more thoroughly , we examined γH2AX formation during leptonema , which is provoked by SPO11-generated DSBs in a largely ATM-dependent fashion [23] , [27] , [32] . Both Trip13 mutants showed γH2AX distributed widely across the chromatin in leptonema , with timing and spatial patterns comparable to those seen in wild type ( Figure 7D–7F ) . To compare amounts of SPO11-induced γH2AX , we carried out western blotting on extracts from testes of juvenile mice , in which the only spermatocytes present are in the first , semi-synchronous wave of meiosis . At 11 . 5 dpp , most spermatocytes are in leptonema or zygonema , and few , if any , cells have reached later stages when MSCI-associated γH2AX formation begins ( data not shown ) . Thus , the majority of the γH2AX signal at this age is SPO11-dependent ( compare wild type with Spo11–/– littermates in Figure 7L , right panel ) . In this analysis , Trip13mod/mod and Trip13sev/sev mutants displayed similar γH2AX levels as wild-type littermates ( Figure 7L ) . These findings strongly indicate that DSBs form with normal timing and in roughly normal amounts in both Trip13 mutants . We also examined chromatin-bound complexes of the ssDNA binding protein RPA , which acts early in recombination to enhance formation of RAD51 and DMC1 nucleoprotein filaments on ssDNA [33] . Normal leptotene spermatocytes display relatively few RPA foci despite the presence of numerous DSBs with ssDNA tails , presumably because RPA is rapidly displaced by RAD51 and DMC1 [34] . ( Abundant , prominent foci of RPA are observed later on synapsed chromosome axes after RAD51/DMC1 complexes have disappeared [35] , but these RPA foci most likely mark D-loops or other later recombination intermediates . ) If TRIP13 promotes the assembly of RAD51 onto resected DSB ends , we reasoned that RPA foci might accumulate to higher numbers in leptotene spermatocytes in the Trip13 mutants . Indeed , RPA foci were increased 1 . 9-fold ( Trip13mod/mod ) and 2 . 5-fold ( Trip13sev/sev ) relative to wild type ( Figure 7G–7I and 7M ) ( p≤0 . 0012 for all pair wise comparisons , t test ) . Taken together , our findings place TRIP13 function earlier in recombination than previously anticipated , before RAD51 focus formation but after formation and resection of SPO11-induced DSBs . In most organisms , the presence of a meiotic CO in one genomic region makes it less likely that another CO will be found nearby [37] . This phenomenon , called CO interference , results in a wider and more even spacing of COs than expected if they were distributed randomly relative to one another [38] , [39] . Direct measurement of CO interference is difficult in mice , and is currently impossible in sterile mouse mutants . However , interference can be measured cytologically by analyzing the distances separating pairs of adjacent MLH1 foci on pachytene chromosomes ( Figure 9A ) [38] , [40] . When many such distances are examined , the strong interference typical of mammalian meiosis is readily seen: the relatively even spacing of MLH1 foci manifests as tight clustering of interfocus distances in frequency distribution plots ( Figure 9B–9F , blue lines ) and the steeply sigmoidal shape of cumulative frequency plots of the same data ( Figure 9G–9K ) . Wide spacing is seen from the rarity of MLH1 focus pairs that are separated by short distances ( e . g . , only 0 . 3% of focus pairs were separated by ≤25% of the length of the bivalent ) ( Figure 9B and 9G ) . Substantial interference was also observed in Trip13mod/mod spermatocytes , as revealed by a relatively narrow distribution of interfocus distances and by the rarity of closely spaced foci ( 0 . 4% of focus pairs were separated by ≤25% of the bivalent length ) ( red in Figure 9B and 9G ) . A routinely used approach to quantifying cytological interference is to model MLH1 inter-focus distances to a gamma distribution [23] , [38] . The gamma distribution that best fits the observed frequency distribution is characterized by a shape parameter ( abbreviated “υ” ) , which can be regarded as a measure of the strength of interference between MLH1 foci: a value of υ = 1 implies no interference , whereas higher υ values indicate more regular spacing between foci , and thus stronger interference ( Figure 9A ) . There appears to be a trend toward lower υ values in the Trip13mod/mod mutant ( Table 4 ) , which may indicate that MLH1 foci are less evenly spaced than in normal meiosis . However , this analysis confirms that substantial cytological interference is retained in the mutant . Nonetheless , closer inspection revealed an intriguing difference , namely , that there was a significant decrease in the average separation between MLH1 foci , from 60 . 8±13 . 4% of bivalent length in wild type to 56 . 8±13 . 8% in the mutant ( mean ± sd; p<0 . 0007 , two-sided t-test ) ( Figure 9B and 9G ) . Mean MLH1 focus separation in Trip13+/mod heterozygotes was indistinguishable from wild type ( 60 . 9±13 . 4%; data not shown ) . Expressing these measurements as percent of bivalent length corrects for the slightly shorter SCs in Trip13mod/mod cells ( see above ) ; the shift is much more pronounced if data are not normalized ( Figure S1 ) . Because different chromosomes show reproducible differences in CO distributions [e . g . , [38] , [41]] , we further analyzed these data after dividing into groups of similarly sized chromosomes . Strikingly , the shift toward shorter interfocus distances was specific for the largest chromosomes ( size ranks 1–5; Figure 9C , 9D , 9H , 9I ) . Thus , the altered interfocus distances are not a trivial consequence of the shorter SCs , because chromosomes of all size classes were affected equally by the decrease in SC length ( Table 1 ) . COs show biased distributions along chromosomes ( reviewed in [42] ) , which at least in part reflects position-specific differences in the likelihood that a given recombination intermediate will become a CO [38] . Few mouse mutants have been found that alter the CO distribution without blocking the recombination process outright , but as detailed below , Trip13mod/mod spermatocytes show pronounced changes in the distribution of MLH1 foci , implicating TRIP13 in the processes that regulate CO position . Distributions of MLH1 focus positions on pachytene spermatocyte autosomes are shown in Figure 10 . Distances , measured along the SC from the centromere , are expressed as percent of SC length to normalize for the decreased length in Trip13mod/mod cells and to facilitate comparisons of different sized chromosomes . For all patterns discussed below , results were highly reproducible between wild type and Trip13+/mod heterozygotes ( Figure S2; p = 0 . 51 for all chromosomes , Anderson-Darling two-sample test ) , so data for these genotypes were pooled for clarity . Chromosomes show distinct MLH1 localization patterns depending on their size and whether a bivalent has one or two foci [38] , [41] , so similar-sized chromosomes were grouped for comparison . Normal cells show a number of characteristic features , as documented previously ( e . g . , refs . [40] , [41] ) ( Figure 10 , dark blue curves ) . First , MLH1 foci are rare in the centromere-proximal ∼15% of each bivalent . Second , chromosomes with two MLH1 foci tend to have those foci clustered into a proximal region and a distal region , with very few in between ( Figure 10Ai , 10Bi , 10Ci ) , reflecting the wide spacing between COs caused by interference . Third , there is a greater chance that a focus will be centrally located when a chromosome has only a single MLH1 focus , although singleton foci can also occur in more proximal or more distal regions as well ( Figure 10Aiii , 10Biii , 10Ciii , 10Diii ) . Finally , singleton MLH1 foci are more likely to be located distally on smaller chromosomes than on larger ones ( e . g . , compare Figure 10Aiii with Figure 10Ciii ) . Distal MLH1 localization appears to reflect a position-specific tendency for recombination events to favor ( or not ) a CO outcome [38] and , unlike patterns for chromosomes with two foci , cannot be accounted for simply as a consequence of wide spacing imposed by interference . In Trip13mod/mod spermatocytes , CO repression near centromeres was intact , as inferred from the rarity of MLH1 foci within the first 15% of each chromosome ( 43 of 1021 foci in Trip13mod/mod ( 4 . 2% ) , compared with 99/2311 in wild type and Trip13+/mod ( 4 . 3% ) ) However , other patterns were altered such that the normal biases appeared relaxed ( Figure 10 , red curves , p = 0 . 0006 , Anderson-Darling two-sample test ) . Larger chromosomes ( size ranks 1–5 ) that had two foci showed less extreme biphasic clustering in the mutant , with foci occurring 2 . 3-fold more frequently than wild type in the central regions of the chromosomes ( Figure 10Aii , p = 1 . 4×10−5 , Fisher's exact test ) . Mid-size chromosomes ( ranks 6–11 and 12–16 ) showed the same tendency , but differences were not statistically significant ( Figure 10Bii and 10Cii , p≥0 . 23 ) . Importantly , the fact that this alteration is seen on chromosomes with two foci argues against the possibility that distributions in the mutant are solely a consequence of a shift toward more chromosomes in the population having only one focus . In addition , chromosomes in size ranks 12–16 with a single focus were more likely to have that focus in the central and proximal regions in Trip13mod/mod cells , at the expense of the normal preference for distally positioned foci ( Figure 10Ciii and 10Civ , p = 0 . 0006 ) . Chromosomes in size ranks 6–11 showed a similar pattern , but this difference was not statistically significant ( Figure 10Biii and 10Biv , p = 0 . 07 ) . The net effect of these alterations is that MLH1 foci , and presumably COs , are more evenly ( i . e . , randomly ) distributed . Importantly , all size classes and both one-focus and two-foci chromosomes showed similar overall tendencies , albeit manifesting more markedly on some chromosomes than others .
Previous studies [16] and those reported here demonstrate that Trip13mod/mod meiocytes inefficiently repair meiotic DSBs , as evidenced by abnormal persistence of several cytological markers ( e . g . , DMC1 ( and possibly RAD51 ) , γH2AX , BRCA1 , and ATR ) into late meiotic prophase . Intriguingly , however , the Trip13 mutants also display recombination defects much earlier in prophase , manifested as reduced numbers of foci that stain with an anti-RAD51 antibody during early leptonema through early zygonema . These findings are the first to situate TRIP13 function upstream of RAD51 . In principle , the reduced focus numbers could mean that DSBs are reduced or delayed . However , in both Trip13 mutants , DMC1 foci , RPA foci , and γH2AX appeared in early leptonema , i . e . , with similar timing with respect to axial element formation as wild type . Moreover , γH2AX levels were similar between wild type and TRIP13-deficient cells , and RPA foci were more numerous in the mutants at leptonema . We therefore favor the interpretation that DSBs are generated and resected in relatively normal numbers and in a timely fashion in the mutants , and that TRIP13 is required ( directly or indirectly ) for efficient execution of recombination steps at or prior to loading of RAD51 onto resected DSB ends . DMC1 loading is not affected , however . Interestingly , Brca1 mutant spermatocytes have also been reported to be specifically defective for assembly of RAD51 but not DMC1 foci [43] , suggesting that TRIP13 function may be related to that of BRCA1 . These results are in contrast to those with Brca2 or Tex15 mutant spermatocytes , which are defective for both RAD51 and DMC1 foci [34] , [44] , thus defining two classes of proteins that can be differentiated based on recombinase loading . The numbers of DMC1 ( and possibly RAD51 ) foci that persist in pachynema in the Trip13 mutants ( ≥100 per cell ) are in large excess over the number of recombination events that become COs in normal or Trip13mod/mod meiosis ( ∼20–25 per cell ) . The simplest interpretation is that most of the persistent foci represent sites of recombination intermediates that would have been resolved as NCOs ( or intersister events ) if they had been able to go to completion . Thus , a major function of TRIP13 is efficient execution of a NCO recombination pathway ( s ) [16] . However , close analysis revealed that Trip13mod/mod spermatocytes displayed fewer MLH1 foci at pachynema and more achiasmate autosome pairs at diplonema . The achiasmate frequency matched the frequency of pachytene autosomes that lacked an MLH1 focus , suggesting that this problem occurs even in those cells that escaped pachytene arrest because they had succeeded in repairing all of their DSBs . Trip13sev/sev pachytene oocytes also had fewer MLH1 foci . These findings suggest that TRIP13 is not absolutely required for CO or chiasma formation , but is needed for formation of wild-type numbers of COs and/or chiasmata as well as efficient formation of the obligate CO/chiasma . Importantly , these results reveal that the function of TRIP13 is not restricted solely to recombination pathways that lead to NCOs . Cytological interference between MLH1 foci remains strong in Trip13mod/mod pachytene spermatocytes , but we observed a significant decrease in the average distance between pairs of adjacent MLH1 foci . This decrease was seen even when normalizing for decreased SC length , and was more pronounced for longer chromosomes even though all chromosomes showed equivalent reductions in SC length . Thus , it appears that the interference alteration is not readily explained as a trivial consequence of shortened SCs . It is thought that chromosome axes serve as the conduit for signals that mediate interference [45] , [46] . Thus , one interpretation of our findings is that the strength of interference decays more rapidly with distance along chromosomes in TRIP13-defective cells as a consequence of altered properties of axes and/or other higher order chromosome structures . Strikingly , the distribution of MLH1 foci along chromosomes is altered in Trip13mod/mod mutant spermatocytes , with the general effect that positional biases seen in wild type were less pronounced in the mutant . This is an unusual phenotype—to our knowledge , the only other mouse mutations reported to cause large-scale alterations in focus positions are hypomorphic alleles of Mre11 or Nbs1 [47] . The tendency of larger chromosomes to have interstitial regions “filled in” when there were two MLH1 foci on a bivalent could reflect the decreased average interference distance discussed above . However , interfocus distances are not relevant when considering chromosomes with single foci , and such chromosomes also showed a tendency toward more even distributions . We thus infer that the altered MLH1 focus distributions are not simply a consequence of altered interference . Instead , we speculate that the shortened SCs , altered interference distances , and altered CO distributions are all separate outcomes of a common underlying defect , i . e . , altered axis structure . Recent studies of chromosome structure mutants in C . elegans provide precedent for such linked defects [45] , [46] , [48] . TRIP13 is needed for formation of complete , structurally normal SC , as indicated by three defects in Trip13 mutants . First , and most strikingly , three quarters of the autosomal bivalents from Trip13sev/sev mutant spermatocytes and oocytes showed substantial synaptic anomalies , especially pericentric forks . In human spermatocytes , pericentric regions often show delayed synapsis , indicating that SC elongation across these regions is slow or inefficient [49] . TRIP13 may be especially important for SC formation in such “difficult” regions . Possible molecular functions for TRIP13 in this process could include promoting efficient assembly of SC components , remodeling chromosome axes to a form permissive for SC elongation , or indirectly promoting SC formation via effects on recombination . A second defect is that SCs were shorter on average in Trip13mod/mod spermatocytes than in wild type . The Trip13mod allele thus joins a growing list of mouse mutations that alter the lengths of SC and/or chromosome axes , including Smc1β–/– ( shorter SCs ) [50] , and Sycp3–/– and Spo11+/–Atm–/– ( longer SCs ) [23] , [51] . The cause ( s ) of altered lengths in these mutants is not known , but could involve changes in number and length of chromatin loops that emanate from chromosome axes , or changes in the longitudinal compaction of bases of chromatin loops [52] . Third , TRIP13 is needed for depletion of HORMADs from chromosome axes soon after synapsis [20] ( Figure 5 ) . Effects of TRIP13 on the structure of chromosome axes is a possible unifying theme for the SC defects in the mutants , which , as discussed above , may also connect other aspects of TRIP13 function . Thus far , budding yeast and mouse are the only organisms in which Pch2/TRIP13 is known to be required during normal meiosis , and there are a number of similarities—and some interesting differences—between the meiotic phenotypes of mutants in the two species . ( Although currently available data do not indicate roles for PCH2 orthologs in unperturbed meiosis in C . elegans or D . melanogaster , it is possible that such roles may emerge upon further study . ) Importantly , Pch2/TRIP13 is required in both yeast and mouse for normal meiotic recombination . In yeast , CO interference is weakened and closely spaced double crossovers occur more frequently than normal [14] , [15] , and in mouse , we find that inter-CO distances tend to be shorter . Both organisms show defects in forming an obligate CO/chiasma [15] . Additionally , pch2 mutant yeast show defects at multiple steps in recombination , including early steps at or prior to DSB resection , although possible roles of Pch2 in efficient assembly of Rad51 or Dmc1 complexes have not been examined in yeast [9] , [12]–[15] . Comparisons between the two species are more complicated when considering effects of Pch2/TRIP13 deficiency on CO numbers . By tetrad analysis , some genetic intervals in yeast show increased crossing over in pch2 mutants [14] , [15] . On its face , this pattern appears different from that in mouse , where we observed globally reduced CO numbers ( MLH1 foci ) . However , the following points are noteworthy . First , not all intervals in yeast had increased crossing over , as some were unchanged in a pch2 mutant [14] . Second , pch2 mutation caused decreased CO numbers in some ( but not all ) intervals in a strain background that experiences fewer DSBs globally because of a hypomorphic spo11 mutation [15] . Third , CO formation was reduced and delayed when assayed by direct physical analysis of DNA at the HIS4LEU2 recombination hotspot [9] . Finally , our analysis of MLH1 focus distributions suggests that many genomic regions in mouse experience an increase in CO frequency in Trip13 mutants , even though global numbers are decreased ( Figure 10 ) . Thus , although it is not yet clear whether pch2/Trip13 mutations cause entirely congruent defects in crossing over in yeast and mouse , both organisms nonetheless are seen to have regionally variable alterations in CO number and placement . Pch2/TRIP13 is also required in both organisms for normal dynamics of higher order chromosome structures , especially depletion of Hop1/HORMADs from all ( mouse ) or a subset ( yeast ) of axes where synapsis has occurred [9] , [20] . Both organisms also show altered SC length , albeit with opposite directions for the net change ( SCs are longer in pch2 mutant yeast [14] ) . The available data thus strongly support the conclusion that the mammalian and yeast proteins have roles in multiple aspects of both meiotic recombination and the development of higher order chromosome structures that are themselves closely integrated with recombination . These findings suggest that many specific roles of Pch2/TRIP13 are more widely conserved than previously appreciated .
Experiments conformed to relevant regulatory standards and were approved by the MSKCC Institutional Animal Care and Use Committee . Mouse embryonic stem cell lines RRB047 ( Baygenomics , USA ) and CH0621 ( Sanger Institute , UK ) ( both cell lines from strain 129/Ola ) were used to generate the Trip13 mutant strains in this study . These cell lines contain a gene trap located in the second ( CH0621 ) or third ( RRB047 ) intron of the Trip13 gene , both of which create an in-frame fusion of TRIP13 and a β-geo reporter . The exact locations of the gene traps were determined by PCR and subsequent sequencing of the Trip13 locus . The ES cell lines were injected into blastocysts and transferred to pseudo-pregnant mice . Colonies derived from the resulting chimeric mice were maintained with 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 . Genotyping was performed by PCR of tail tip DNA as previously described [19] , with the following conditions: 2 min at 94°C , then 40 cycles of 20 s at 94°C , 30 s at 59°C , and 35 s at 72°C . The Trip13mod allele was detected with primers TRIP13 GeNo F4 ( 5′-CGGGCCCTATCTTTTCAGTTCC ) , TRIP13 GeNo R5 ( 5′-CTATAGTGCCCTTAGGCTCAGG ) , and RRB047 End F2 ( 5′-TAACCCACTCGTGCACCCAACT ) , which amplify fragments of 1115 bp for the wild-type allele and 1253 bp for the mutant . The Trip13sev allele was scored using primers SIGTR621 F2 ( 5′-CTCAGAGAAGACCCAAAGCACAGT ) , SIGTR621 R2 ( 5′-CCAGGGCACATCAGTAGGAAGC ) , and RRB047 End F4 ( 5′-GCGGTTAGCTCCTTCGGTCCTC ) , yielding amplification products of 319 bp for wild type and 642 bp for mutant . RNA from adult and juvenile mouse testes was obtained using an RNeasy Mini kit ( Qiagen ) and cDNA was produced using SuperScript III First-Strand Synthesis System ( Invitrogen ) using oligo dT as primers . Trip13 transcripts were amplified using the primers TRIP13 2A ( 5′-TGCAGCGCAGCGGAAGCACTGC ) and TRIP13 1B ( 5′-CCACTGAGGCCCTCACTCTTCCTT ) , which amplify most of the full-length transcript . β-actin was amplified as a control using primers β-actin1 ( 5′-AGGTCTTTACGGATGTCAACG ) and β-actin2 ( 5′-ATCTACGAGGGCTATGCTCTC ) . RNA in situ hybridization was performed on testis sections as described [53] , using a 416-bp fragment of the Trip13 transcript amplified using primers TRIP13 1A ( 5′-GTTTGTTCTGATTGATGAGGTGGA ) and TRIP13 1B and labeled with [α32P]-UTP . To separate different cell types using FACS , testes from adult mice were processed as described elsewhere [54] . Briefly , cells were liberated from testes by enzymatic treatment with collagenase , trypsin and DNase , then the resulting cell suspension was stained with Hoechst 33342 ( Sigma ) and cells were sorted using a MoFlo cytometer ( Dako ) with a 350 nm argon laser . Different cell types were separated based on DNA content and chromatin complexity [54] . Cellular composition was confirmed by spreading and immunofluorescence ( data not shown ) . For cytology , testes from 2–4 month-old mice were prepared for surface spreading and sectioning as previously described [23] , [55] . Prenatal ovaries were collected from 18 . 5–20 . 5 days post-coitum embryos and processed to obtain oocyte spreads as described [55] . Ovaries from 21-day-old mice were also recovered for histological analysis as described [19] . Immunofluorescence was performed using previously described methods [56] and antibodies [20] , [23] . Additional primary antibodies used were goat anti-ATR ( Santa Cruz ) , 1∶130 dilution; rabbit anti-BRCA1 ( generously provided by C . Deng , NIH ) [57] , 1∶200 dilution; rabbit anti-RPA ( generously provided by P . Cohen , Cornell University ) , 1∶100 dilution; and a rabbit anti-DMC1 ( Santa Cruz , H-100 ) , 1∶300 dilution . For RAD51 , DMC1 , RPA , and MLH1 focus counts , nuclei were staged using the extent of SYCP3 staining and synapsis as markers for meiotic prophase progression . Only foci that co-localized with the chromosome axes were counted . SC length and distances between the MLH1 foci on the SCs were evaluated as previously described [23] . Statistical tests were as described in the text . Mann-Whitney tests were applied to MLH1 focus numbers to avoid assuming normal distribution , but similar conclusions about statistical significance were obtained if Student t-tests were performed . To evaluate differences between wild type and mutants for MLH1 focus distributions along chromosomes without prior assumptions about the shape of those distributions , we used the Anderson-Darling k-sample test ( from the “adk” package in R , version 2 . 9 . 2 ) [58] , [59] . ( Note that the goal was not to compare means of these distributions , but to compare their overall shape ) . Similar conclusions about significance of the difference between wild type and Trip13mod/mod homozygotes , and about the lack of significance of difference between wild type and Trip13+/mod heterozygotes , were obtained if the Kolmogorov-Smirnov test was used instead ( data not shown ) . For histological analysis , testis and ovarian sections were stained with periodic acid-Schiff ( PAS ) using hematoxylin as a counterstain . TUNEL staining was performed as previously described [60] . Staging of the PAS-stained testis sections was performed as described elsewhere [61] . | 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 . Recombination is coordinated with the development of specialized , meiosis-specific chromosome structures that stabilize pairing interactions between homologous maternal and paternal chromosomes . We show here that the mouse TRIP13 protein is required for normal execution of many aspects of meiotic recombination and chromosome structure development that it was not previously known to influence . Intriguingly , many of these new roles appear to parallel known functions of a homologous protein from budding yeast , called Pch2 . These findings thus indicate that TRIP13/Pch2 functions are more widely conserved throughout evolution than thought before . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"developmental",
"biology/germ",
"cells",
"genetics",
"and",
"genomics/nuclear",
"structure",
"and",
"function",
"genetics",
"and",
"genomics/chromosome",
"biology",
"biochemistry/replication",
"and",
"repair",
"genetics",
"and",
"genomics/gene",
"function"
]
| 2010 | Mouse TRIP13/PCH2 Is Required for Recombination and Normal Higher-Order Chromosome Structure during Meiosis |
The parameters that modulate the functional capacity of secondary Th1 effector cells are poorly understood . In this study , we employ a serial adoptive transfer model system to show that the functional differentiation and secondary memory potential of secondary CD4+ effector T cells are dependent on the inflammatory environment of the secondary challenge . Adoptive transfer of TCR transgenic lymphocytic choriomeningitis virus ( LCMV ) Glycoprotein-specific SMARTA memory cells into LCMV-immune hosts , followed by secondary challenge with Listeria monocytogenes recombinantly expressing a portion of the LCMV Glycoprotein ( Lm-gp61 ) , resulted in the rapid emergence of SMARTA secondary effector cells with heightened functional avidity ( as measured by their ability to make IFNγ in response to ex vivo restimulation with decreasing concentrations of peptide ) , limited contraction after pathogen clearance and stable maintenance secondary memory T cell populations . In contrast , transfer of SMARTA memory cells into naïve hosts prior to secondary Lm-gp61 challenge , which resulted in a more extended infectious period , resulted in poor functional avidity , increased death during the contraction phase and poor maintenance of secondary memory T cell populations . The modulation of functional avidity during the secondary Th1 response was independent of differences in antigen load or persistence . Instead , the inflammatory environment strongly influenced the function of the secondary Th1 response , as inhibition of IL-12 or IFN-I activity respectively reduced or increased the functional avidity of secondary SMARTA effector cells following rechallenge in a naïve secondary hosts . Our findings demonstrate that secondary effector T cells exhibit inflammation-dependent differences in functional avidity and memory potential , and have direct bearing on the design of strategies aimed at boosting memory T cell responses .
During acute viral and bacterial infections , antigen-specific naïve T cells clonally expand and acquire effector functions that contribute to pathogen clearance . Upon elimination of the pathogen , a small proportion of effector T cells survive and differentiate into long-lived memory cells that provide rapid and enhanced protection against secondary challenge . Activated T cells have been shown to integrate numerous signals during the primary response that impact downstream effector and memory T cell differentiation [1] , [2] . Identification of signals that lead to the generation of functional memory T cells is a major goal for the design of vaccines and immunotherapies . The transition from the effector T cell phase to the formation of memory T cells is marked by the acquisition of heightened sensitivity to low levels of antigen , often referred to as functional avidity [3] . We have recently shown that sustained interactions between the T cell receptor ( TCR ) and peptide antigen presented by Class II MHC ( pMHCII ) promote the differentiation of long-lived CD4+ memory T cells [4] . TCR signals also influence the survival of activated CD4+ T cells and the differentiation of T helper effector and regulatory subsets [5]–[11] . However , T cell extrinsic differentiation cues , including inflammatory signals such as IL-12 and IFNγ , also play a long-appreciated and critical role in driving Th1 cell differentiation . The mechanisms by which external differentiation cues control memory Th1 cell continue to be a topic of intense study , although opposing roles for the cytokines IL-2 and IL-21 in promoting effector and central memory T cell differentiation , respectively , have been reported [12]–[16] . Recent evidence indicates that external differentiation cues can influence the functional avidity of Th1 effector cells ( defined as their ability to generate a functional response antigen stimulation ) . For example , we reported that the functional avidity of TCR transgenic Th1 effector T cells , with monoclonal antigen specificity , is not fixed , suggesting that the ability of individual T cell to translate TCR signals into a functional response can change in a TCR-independent manner [3] . Both CD8+ and CD4+ TCR transgenic T cells undergo changes to their functional avidity throughout the primary effector response to infection [17] , [18] , and we have previously reported that SMARTA TCR transgenic T cells , with monoclonal specificity to the LCMV-derived Class II-restricted epitope GP61-80 , increase their functional avidity as they transition from effector to memory [3] . One possibility is that TCR-independent signals influence memory T cell differentiation in part by modulating the ability of T cells to incorporate TCR signals in response to antigen . While many studies have focused on changes to T cell functional avidity during the primary effector and memory T cell response to infection , less is known about the mechanisms that control T cell function and secondary memory differentiation following secondary challenge . For CD8+ T cells , repetitive reactivation of memory T cells resulted in the acquisition of more effector-like phenotype [19] , a differentiation status associated with enhanced protection from some infections [20] but not others [21]–[23] . Additionally , infection-induced inflammatory signals such as IL-12 have also been shown to enhance the functional avidity ( defined as the ability to make functional responses to antigen stimulation ) of secondary effector CTL [24] , [25] . As compared to primary memory CD8+ T cells , secondary CD8+ memory T cells exhibit enhanced cytolytic capabilities and provide enhanced protection against certain infections such as Listeria monocytogenes , whereas they are more prone to functional exhaustion following chronic antigen exposure [26] , [27] . Therefore , one can conclude that the functional characteristics of secondary effector CTL depend at least in part on the nature of the secondary stimulus . Both naïve and memory CD4+ T cells show a similar delay in the onset of cell proliferation after exposure to antigen , despite the fact that memory T cells become activated and produce effector cytokines within several hours [28] . In the context of influenza A virus , secondary effector CD4+ T cells display distinct functional and phenotypic characteristics as compared to primary CD4+ effector T cells , including enrichment for producers of multiple cytokines , enhanced trafficking to tissue sites of infection and greater contribution to viral clearance [29] . The strength of pathogen rechallenge may also play a key role in mediating changes to the long-term fate and function of secondary Th1 cells [30] . We previously found that , unlike CD8+ memory T cells , a homologous secondary challenge failed to induce robust secondary expansion of CD4+ memory T cells [30] . A rapidly cleared homologous rechallenge of lymphocytic choriomeningitis virus ( LCMV ) - or Listeria monocytogenes ( Lm ) -immune mice , resulted in poor functional avidity of secondary Th1 effector function and decreased survival of secondary CD4+ memory T cells , Conversely , reciprocal heterologous rechallenge with a pathogen sharing single CD4+ T cell epitope resulted in Th1 secondary effector cells with high functional avidity and stable maintenance of secondary CD4+ memory T cells [31] . Certain aspects of secondary Th1 effector cell function are dependent on the pathogen used for the secondary challenge , providing additional evidence of a role for infectious environment in the differentiation of secondary Th1 effector and memory cells [31] , [32] . In an effort to further define the environmental differentiation cues that regulate the function of secondary Th1 effector cells , we employed a serial adoptive transfer system that allowed us to manipulate the stimulatory environment of the recall response . Initially , we injected naïve mice with small numbers of T cell receptor ( TCR ) transgenic SMARTA CD4+ T cells ( specific for LCMV Glycoprotein ) and infected with LCMV one day later . Following pathogen clearance and the establishment of memory ( >42 days post-infection ) , SMARTA memory cells were isolated and parked in either infection-matched LCMV-immune or naïve uninfected secondary hosts . Memory SMARTA cells were then induced to undergo a recall response following infection with Listeria monocytogenes expressing the MHC Class II-restricted LCMV GP61-80 epitope ( Lm-gp61 ) . As compared to SMARTA recall responses in LCMV-immune secondary hosts , SMARTA recall responses in naïve secondary hosts resulted in secondary effector cells with poor functional avidity ( as measured by IFNγ production following ex vivo restimulation with decreasing concentrations of GP61-80 peptide ) , increased death during T cell contraction following pathogen clearance and poor maintenance of the resulting secondary Th1 memory cells . The decrease in SMARTA cell functional following recall responses in naïve secondary hosts occurred in the later stages of the secondary response , and these hosts also exhibited a higher pathogen load . While transfer of higher numbers of SMARTA memory cells prior to rechallenge led to decreased expansion due to clonal competition for antigen , it did not prevent their loss of functional avidity , suggesting that environmental differences in inflammatory milieu induced by Lm-gp61 challenge of either naïve or LCMV-immune hosts , rather than differences in access to antigen , induced the acquisition or loss or functional avidity . In support of this , in vivo neutralization of IL-12 , exacerbated the loss of functional avidity by SMARTA recall responses in naïve secondary hosts , whereas disruption of IFN-I activity resulted in enhanced functional avidity . Loss of functional avidity corresponded to defects in TCR signaling events , leading us to conclude that TCR-independent inflammatory cues can regulate TCR-mediated activation and differentiation signals . Our findings define key parameters that regulate the acquisition of secondary CD4+ effector T cell function and the formation of stable secondary memory following rechallenge .
We previously observed that both LCMV GP61-80-specific polyconal and TCR transgenic SMARTA Th1 cells undergo functional avidity maturation , as measured by their ability to make IFNγ in response to ex vivo restimulation with decreasing concentrations of GP61-80 peptide , during the transition from the Th1 effector phase to the development of long-lived memory [3] . To confirm this , we transferred small numbers of naïve SMARTA T cells ( CD44lo , Thy1 . 1+ ) into naïve B6 hosts ( Thy1 . 2+ ) and infected with LCMV one day later . SMARTA cells analyzed in the spleen following the establishment of memory ( day 50 ) demonstrated higher functional avidity effector ( day 8 ) SMARTA cells ( Fig . 1A–B ) . Following heterologous rechallenge of LCMV-immune mice with recombinant Lm-gp61 , secondary effector SMARTA cells continued to demonstrate high functional avidity ( Fig . 1A–B ) . Because SMARTA cells , a TCR monoclonal population , demonstrated remarkable plasticity in their ability to make a functional response to TCR restimulation , we sought to establish a model system in which we could better define the TCR-independent factors controlling the ability of these cells to translate TCR stimulation into a functional response . We employed a serial adoptive transfer system in which LCMV-induced SMARTA memory cells ( Thy1 . 1+ ) , generated as described above , underwent a second adoptive transfer into a naïve B6 secondary host ( Thy1 . 1+ ) prior to Lm-gp61 rechallenge ( Fig . 1C ) . Unlike what we observed following heterologous rechallenge , in this setting secondary SMARTA effector cells exhibited decreased functional avidity as compared to SMARTA memory cells prior to rechallenge ( Fig . 1C–D ) . One possible interpretation of these results is that the functional avidity of secondary SMARTA effector cells was influenced by differences in the inflammatory environment following heterologous rechallenge of LCMV-immune mice versus rechallenge of SMARTA memory cells in naïve mice . To test this , we adoptively transferred LCMV-induced SMARTA memory cells ( Thy1 . 1+ ) into infection-matched LCMV-immune ( >42 days post-infection ) secondary recipients ( Thy1 . 2+ ) , followed by secondary stimulation with Lm-gp61 ( Fig . 1C ) . SMARTA Th1 effector cells generated in LMCV-immune hosts exhibited high functional avidity at the peak of the secondary response ( day 7 ) , comparable to that of the originating SMARTA memory population ( Fig . 1C-D ) . This observation was applicable to polyclonal T cell populations as well , as endogenous Th1 memory cells isolated from LCMV-immune mice and parked in naïve hosts also displayed a loss of functional avidity following secondary Lm-gp61 challenge , as compared to those parked in LCMV-immune secondary hosts ( Fig . 1E–F ) . We concluded that functional avidity of secondary SMARTA cells was sensitive to extrinsic factors , potentially including the inflammatory environment and antigen load of the secondary challenge . One potential caveat to these assays is that differences in antigen presentation in naïve or LCMV-immune secondary hosts might impact measurements of functional avidity . However , in all cases , MHC Class II expression was similar or higher following challenge of naïve hosts , as compared to LCMV-immune hosts , and we observed no differences in the distribution or frequency of dendritic cells , macrophages or B cells ( data not shown ) . In some settings , the generation of Th1 cells that can simultaneously produce multiple effector cytokines , particularly IFNγ , TNFα and IL-2 ( “triple producers” ) , correlates to protective immunity to subsequent infections [33] , [34] . Secondary SMARTA effector cells derived from challenge of naïve hosts showed a significant decline in the generation of triple cytokine producers at the peak of the secondary response , as compared to secondary SMARTA effector cells derived from challenge of LCMV-immune hosts , although these differences did not persist following the establishment of secondary memory ( Fig . 2A–B ) . Rechallenge of SMARTA memory cells in naïve secondary hosts resulted in greater secondary expansion but exacerbated contraction and led to poor survival as secondary memory cells , as compared to rechallenge of SMARTA memory cells in LCMV-immune secondary hosts ( Fig . 2C–D ) . The kinetics of secondary expansion , contraction and memory maintenance following rechallenge of SMARTA memory cells parked in LCMV-immune hosts replicated the kinetics of the secondary SMARTA response following heterologous Lm-gp61 rechallenge without a secondary transfer ( Fig . 2E ) . On a per cell basis , secondary SMARTA Th1 effector cells generated following rechallenge in naïve hosts showed far less secondary memory potential . While the number of secondary SMARTA cells declined 3 . 4-fold between day 8 and day 150 following secondary stimulation after transfer into a LCMV-immune secondary host , and 2 . 9-fold following heterologous rechallenge without transfer , they declined 179-fold following secondary stimulation after transfer into a naive secondary host ( Fig . 2C–E ) . Our findings confirm that the function , survival and memory potential of secondary Th1 effector cells are highly dependent on the environment induced by the secondary challenge . Secondary SMARTA memory cells exhibited high functional avidity , regardless of whether SMARTA cells were rechallenged in naïve or LCMV-immune secondary hosts ( Fig . S1 ) . Additionally , tertiary challenge of secondary SMARTA memory cells derived from either group resulted in tertiary SMARTA Th1 effector cells with high functional avidity ( Fig . S2 ) . Therefore , the primary impact of differences in the secondary activation environment appeared to be difference in the number of long-lived secondary memory cells , not long-term differences in their functional capacity . An additional possibility was that our observations could apply only to a single boosting agent ( Lm-gp61 ) . This was not the case , as LCMV rechallenge of SMARTA memory cells transferred into naïve or Lm-gp61 host resulted in similar differences in the functional avidity of secondary SMARTA effector cells . ( Fig . S3 ) . To better understand the infectious environment following re-activation of SMARTA memory cells parked in either naïve or LCMV-immune hosts , we investigated the kinetics of pathogen clearance and antigen presentation in each setting . During the course of the Lm-gp61 challenge , LCMV-immune mice exhibited more rapid clearance kinetics and significantly lower bacterial loads starting at day 3 , as compared to challenge of naïve mice ( Fig . 3A ) . Rapid clearance kinetics may have been due to the direct contribution of Th1-mediated secondary immunity or CTL-mediated immunity to a previously described Class I-restricted epitope within GP61-80 [35] . Therefore , we transferred 2×105 SMARTA memory cells into naïve mice prior to Lm-gp61 infection . Prior transfer of SMARTA memory cells led to a ∼4-fold decrease in colony forming units ( CFU ) in the spleen by day 3 post-challenge ( Fig . 3B ) , indicating a direct protective role for CD4+ memory T cells in this model . Antigen presentation was undetectable by day 5 after Lm-gp61 challenge of LCMV-immune mice , whereas antigen presentation was still readily detectable following challenge of naïve mice ( Fig . 3C ) . We further sought to determine whether changes in functional avidity were associated with functional plasticity in individual cells or merely the selective outgrowth of high or low functional avidity effector cells under distinct restimulation conditions . We assessed the kinetics of changes in functional avidity throughout the secondary response . At day 3 post-rechallenge , secondary SMARTA effector cells derived from rechallenge of both naïve and LCMV-immune hosts exhibited a massive increase in functional avidity , requiring ∼50-fold lower peptide concentration to induce a half-maximal response ( Fig . 3D–F ) . By day 5 post-rechallenge , the functional avidity of secondary SMARTA Th1 effector cells in both groups had declined , but only secondary SMARTA Th1 effector cells generated after rechallenge in naïve hosts underwent a continued loss in functional avidity , with a further 5-fold reduction in antigen sensitivity by day 7 post-infection correlating to the period of time in which the secondary challenge persists in these mice ( Fig . 3D–F ) . Our findings indicate that secondary SMARTA effector cells maintain the capacity for remarkable functional plasticity in a manner dependent on their rechallenge environment . Lastly , we determined whether extended antigen presentation in the later stages of the secondary response was sufficient to induce a loss of functional avidity by secondary CD4+ effector cell . We parked SMARTA memory cells in LCMV-immune hosts , challenged with Lm-gp61 as above and co-immunized with GP61-80 peptide-loaded DCs at days 2 , 4 and 6 . Secondary SMARTA effector cells maintained high functional avidity regardless of DC co-immunization , indicating that extending the period of antigen presentation alone had no impact on secondary SMARTA functional development ( Fig . 3G-H ) . Various factors may influence the functionality of secondary effector , including competition with other Th1 cells for antigen and resources , differences in priming , alterations in the inflammatory cytokine environment and the duration of the secondary challenge . We tested the hypothesis that functional avidity of secondary SMARTA effector cells might be influenced by access to antigen . CD4+ T cells are particularly sensitive to inter- and intraclonal competition for antigen [36] , [37] . To generate a system for limiting access to antigen in vivo , we transferred increasing numbers of SMARTA memory cells into naïve secondary hosts prior to Lm-gp61 challenge . As expected , while transfer of 1×104 SMARTA memory cells resulted in robust secondary expansion , transfer of 10-fold higher numbers of SMARTA memory cells ( 1×105 ) resulted in sharply decreased secondary expansion that was similar to the magnitude of secondary SMARTA cell expansion in LCMV-immune hosts ( Fig . 4A ) . However , both changes to functional avidity as well as the ability to produce cytokines following in vitro restimulation were independent of clonal expansion ( Fig . 4B-D ) . Therefore , we concluded that functional differentiation of secondary SMARTA Th1 effector cells was not dependent on increased access to antigen-dependent activation and differentiation signals . We considered two possibilities that might account for the role of pathogen-dependent inflammation in the control of T cell functional avidity . First , extended exposure to inflammation during Lm-gp61 stimulation of SMARTA cells in naïve hosts might lead to a decrease in T cell functional avidity . Second , qualitative or quantitative differences in in the inflammatory cytokine milieu following Lm-gp61 challenge of naïve or LCMV-immune hosts could account for differences in functional avidity . To test the possibility that extending the inflammatory environment alone could modulate the functional avidity of secondary Th1 cells , we co-challenged LCMV-immune mice containing SMARTA memory cells with Lm-gp61 and a recombinant Listeria expressing the irrelevant antigen OVA ( Lm-OVA ) , thus inducing longer lasting Listeria infection but without extending the time frame of GP61-80 antigen presentation . Due to the fact that Lm-Ova is erythromycin resistant , we were able to measure the colony forming units ( CFU ) in the spleen for both Lm-gp61 and Lm-Ova following co-challenge . Both Lm-gp61 and Lm-OVA reached similar bacterial loads by day 3 post-infection , but at day 5 , when Lm-gp61 was undetectable in the spleen , Lm-Ova persisted at levels similar to the bacterial burden observed in naïve mice infected with Lm-gp61 alone ( data not shown ) . Extending duration of infection-induced inflammation following heterologous challenge was not sufficient to induce a loss of functional avidity by secondary SMARTA effector cells ( Fig . 5A–B ) . Similarly , clearance of Lm-gp61 mediated by ampicillin treatment 24 or 48 hours after SMARTA rechallenge in naïve hosts had no impact on their functional avidity at the peak of the secondary response ( data not shown ) . Collectively , these findings demonstrate that the duration of the inflammatory response does not by itself significantly influence secondary effector T cell functional avidity . We further tested whether perturbations in the inflammatory cytokine milieu might influence the functional avidity of secondary CD4+ effector T cells . We targeted two key inflammatory pathways by treating mice with anti-IL-12 neutralizing antibodies or blocking antibodies to IFN α/β receptor 1 ( IFNAR1 ) following SMARTA cell rechallenge in either naïve or LCMV-immune hosts . Antibody treatment did not significantly influence Lm-gp61 bacterial load at day 3 post-rechallenge ( data not shown ) , a finding that could be due to the partially attenuated nature of Lm-gp61 . Following SMARTA rechallenge in naïve hosts , IL-12 neutralization resulted in poor secondary effector cell functional avidity ( Fig . 5C ) , whereas IFNAR1 blockade resulted in a significant increase in secondary effector cell functional avidity ( Fig 5D ) . Neither IL-12 neutralization nor IFNAR1 blockade had any effect on functional avidity following SMARTA rechallenge in immune hosts ( Fig . 5C–D ) . Further studies are necessary to determine whether antibody-mediated changes to the inflammatory milieu or the direct action of these cytokines accounted for differences in the acquisition of functional avidity by secondary SMARTA Th1 effector cells . However , these results overall indicate a clear role for the inflammatory environment in controlling the functional differentiation of secondary CD4+ effector T cells . To determine whether loss of functional avidity was associated with specific defects in the ability of secondary Th1 effector cells to initiate TCR-dependent signaling events , we analyzed differences in gene expression levels of TCR signaling molecules , survival factors , and signaling regulators . We observed enhanced gene expression of some ( Zap70 , Lck , and SLP76 ) but not all ( Fyn , PLCγ ) proximal TCR signaling molecules by SMARTA cells rechallenged in LCMV-immune hosts , as compared to those rechallenged in naïve hosts ( Fig . 6A ) . Conversely , SMARTA cells rechallenged in naïve hosts displayed increased expression of two molecules ( SHP-1 , DUSP-6 ) known to dampen TCR-dependent kinase activity ( Fig . 6B ) [38]–[42] . Again , this was not universally true , as SMARTA cells rechallenged in naïve hosts demonstrated decreased expression of Cbl-b ( Fig . 6B ) , an anergy-associated E3 ubiquitin ligase that regulates T cell activation by targeting proximal TCR signals [43] , [44] . Secondary SMARTA effector cells derived from challenge of LCMV-immune mice exhibited increased gene expression of Bcl-2 , a well-known CD4+ T cell survival factor , while demonstrating no difference in STAT5 expression , a transcription factor upstream of several pro-survival pathways ( Fig . 6C ) . Overall , the expression profile of SMARTA recall responses in LCMV-immune secondary hosts partially skewed towards pro-survival and pro-TCR signaling . To determine the effect on TCR signaling , we briefly restimulated secondary SMARTA effector cells with GP61-80 peptide in vitro . In accordance with the gene expression levels , secondary SMARTA recall responses in naive hosts exhibited a decreased ability to induce TCR signaling events , demonstrating reduced phosphorylation of Zap-70 and Erk1/2 following in vitro peptide restimulation for 30 or 60 minutes ( Fig . 6D ) . These findings suggest that differences in functional avidity , as determined by IFNγ production , are linked to differences in the ability to mediate TCR signaling events .
We employed two adoptive transfer systems to investigate the impact of the infectious environment in the expansion , function and survival of secondary effector and memory CD4+ T cell responses . LCMV-induced SMARTA memory cells were transferred into LCMV-immune or naïve secondary hosts and then rechallenged with Lm-gp61 . Due to differences in pathogen clearance between these two model systems , we explored the relative contributions of antigen and inflammation to secondary effector differentiation and the development of long-lived secondary memory . Based on our studies , we conclude that the inflammatory context of the rechallenge has profound consequences for secondary CD4+ effector and memory T cell differentiation . Rechallenge of SMARTA memory cells in LCMV-immune secondary hosts resulted in secondary responses with high functional avidity , limited secondary contraction and stable maintenance within the memory pool . The kinetics of the secondary response was similar to that seen following heterologous rechallenge . In contrast , rechallenge of SMARTA cells in naïve secondary hosts resulted in secondary responses with poor functional avidity , severe contraction and decline within the memory pool . A key biological consequence of rechallenge in these two settings is the number of resulting memory cells . While small numbers of SMARTA memory cells rechallenged in naïve secondary hosts enjoyed a proliferative advantage over those rechallenged in LCMV-immune secondary hosts , this advantage disappeared when SMARTA memory cells were present at more physiologically relevant levels . Furthermore , their numerical advantage during the secondary effector response was lost due to their severe contraction and poor survival within the memory pool . Rechallenge within the immune environment led to better boosting of memory cell numbers . Because we show a direct protective role for CD4+ memory T cell following Listeria challenge , the overall numbers of memory cells are likely to be a key measure of the efficacy of vaccination and immunotherapeutic strategies aimed at stimulating CD4+ T cell-mediated protection . Our report here corresponds to our previous studies linking functional avidity at the peak of the effector response to subsequent memory potential [3] , [31] . Therefore , we propose that identifying the factors that promote high functional avidity during the effector response will be a key step toward understanding memory differentiation . While the functional avidity of secondary effector T cells was independent of access to antigen , we found a key role for the inflammatory environment , as disruption of the IL-12 or IFN-I inflammatory pathways led to a decrease or increase , respectively , of secondary effector cell functional avidity . The role of inflammatory cytokines in controlling T cell function and protective capacity is complex , particularly with regard to the roles of IL-12 and IFN-I . While IFN-I has long been studied as a key factor in restricting viral replication , disruption of this inflammatory pathway enhances Listeria clearance [45] , [46] . A recent study also found that IFNAR1 blockade resulted in enhanced control of chronic viral infection in a CD4+ T cell-dependent manner [47] . In contrast , T cell-intrinsic IFN-I signaling promotes their expansion [48] , [49] . Recent findings have focused on the importance of inflammatory cytokines , including IFN-I , IL-12 , and IL-18 , in promoting the increased antigen sensitivity of local effector primary and secondary CD8+ T cells independent of antigen or clonal selection [24] , [25] . One possible hypothesis resulting from our studies is that IFN-I and IL-12 have opposing effects in controlling the functional avidity of CD4+ effector T cells . However , future studies are required to distinguish whether antibody-mediated disruption of IL-12 and IFN-I in the present report reflect a T cell intrinsic influence of IFN-I and/or IL-12-mediated signaling on secondary effector T cell function and subsequent memory potential or an indirect influence on secondary effector T cell function due to changes in the inflammatory milieu . While it is well established that the context of the primary infection is important for the differentiation , stability , and functional maturation of effector Th1 cells [2] , [50] , our findings show that the context of the secondary challenge can have profound consequences for the functional maturation of responding secondary Th1 effector cells and the long-term survival of subsequent Th1 memory populations . Functional attributes are not permanently imprinted on Th1 cells during primary activation . Instead , secondary Th1 differentiation demonstrates functional plasticity that is dependent on the context of the secondary challenge . Secondary SMARTA Th1 effector cells showed remarkable plasticity throughout secondary response . We have previously shown that in the context of a homologous rechallenge , where memory Th1 cells are weakly stimulated due to the limited persistence of the infection , secondary effector Th1 cells exhibit decreased functional avidity and diminished long-term survival [31] . A key conclusion of these studies is that secondary Th1 effector and memory differentiation are acutely sensitive to the context of the secondary challenge . We propose that more precise identification of environmental signals that enhance the function and memory potential of secondary Th1 effector cells will allow more effective design of boosting and immunotherapeutic strategies designed to optimize T cell function , protective capacity and long-term survival in high numbers . We recently reported an important role for sustained interactions between TCR and antigen in promoting CD4+ memory T cell differentiation [4] . One intriguing hypothesis is that non-specific differentiation cues , such as those delivered by inflammatory cytokines , could influence TCR signaling sensitivity and subsequent TCR-dependent memory differentiation . Other studies have shown that enhanced antigen sensitivity by T cells correlates to up-regulated expression of proximal TCR signaling molecules [51] , [52] . Their induction is TCR-dependent in other settings [39] , [40] , [42] , and while their up-regulation may represent , at least in part , antigen-driven feedback in regulating the ongoing secondary response , another likely conclusion is that their activity is regulated by environmental inflammatory cues . Our finding that differences in early TCR signaling events are associated with inflammation-dependent differences in functional avidity supports this idea . Future studies are required to understand the interplay between environmental and TCR-driven differentiation cues in the formation of primary and secondary memory T cells .
This study was carried out in accordance with the recommendations provided by the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . This study was approved by the University of Utah Animal Care and Use Committee ( PHS Assurance Registration Number A3031-01 , Protocol Number 12-10011 ) . 6-8 week old C57BL/6 ( B6 ) mice were purchased from The Jackson Laboratory . SMARTA TCR transgenic mice [53] were maintained at the University of Utah . Lymphocytic choriomeningitis virus ( LCMV ) Armstrong 53b was grown in BHK cells and titered in Vero cells [54] . Mice were infected i . p . with 2×105 plaque-forming units ( PFU ) . Listeria monocytogenes expressing the GP61-80 epitope of LCMV ( Lm-gp61 , M . Kaja-Krishna , University of Washington ) and Listeria monocytogenes expressing OVA ( Lm-OVA ) were propagated in BHI broth and agar plates . Prior to infection , the bacteria were grown to log phase and concentration was determined by measuring the O . D . at 600 nm ( O . D . of 1 = 1×109 CFU/ml ) . For primary infections or secondary rechallenge of LCMV-immune mice ( >42 days post-infection ) , mice were injected i . v . with 2×105 CFU Lm-gp61 . For Lm-OVA , mice were injected i . v . with 1×104 CFU . To generate primary SMARTA memory cells , untouched CD4+ T cells were isolated from the spleens of SMARTA mice ( Thy1 . 1+ ) using a MACS CD4+ T cell isolation kit ( Miltenyi Biotec ) . In addition , we added biotinylated anti-CD44 antibody ( eBiosciences , San Diego , CA ) to eliminate CD44hi “memory phenotype” SMARTA as previously [3] . Naïve SMARTA cells were re-suspended in PBS and injected i . v . into recipient mice ( Thy1 . 2+ ) 1 day prior to LCMV infection . For adoptive transfer of memory SMARTA cells , CD4+ T cells were isolated from the spleens of LCMV-immune B6 mice containing memory SMARTA cells ( >day 42 post-infection ) and then injected i . v . into secondary recipients that were subsequently infected 1 day later . Similarly , for adoptive transfer of endogenous GP61-80-specific Th1 memory cells , CD4+ T cells were enriched from the spleens of LCMV-immune B6 mice ( >d42 days post infection ) , and 5×106 CD4+ T cells were injected i . v . into secondary recipients prior to rechallenge . For anti-IL-12 antibody treatments , mice received a 0 . 5 mg injection of anti-IL-12 antibody ( clone C17 . 8 ) one day prior to challenge , as previously [55] , [56] . For IFN-I blockade , mice received a 1 . 25 mg injection of anti-IFNAR1 antibody ( clone MAR1-5A3 ) i . p . one day prior to infection as previously [47] , [57] . DCs were expanded in B6 mice with a Flt-3L-secreting B16 mouse melanoma cell line as previously described [30] , [58] . DCs were enriched to 70–80% purity from the spleens and lymph nodes by transient adherence overnight . They were then pulsed with 1 µM LCMV GP61-80 peptide for 2h in the presence of 1 µg/ml LPS . LCMV-immune mice ( >d42 days post infection ) were rechallenged with Lm-gp61 and subsequently injected with 1×106 DCs i . v . on days 2 , 4 , and 6 post-infection . Splenocytes were placed in single-cell suspension in DMEM containing 10% FBS and supplemented with antibiotics and L-glutamine . For CFSE experiments , naïve SMARTA splenocytes were labeled using the CellTrace CFSE Labeling Kit ( Invitrogen ) , according to the manufacturer's instructions , followed by i . v . transfer ( 1×106 SMARTA/mouse ) . For cell surface staining , cells were incubated with fluorescent dye-conjugated antibodies , with specificities as indicated ( eBiosciences , San Diego , CA , or BD Biosciences , Mountain View , CA ) , in PBS containing 1% FBS . Antibody-stained cells were detected on a FACSCanto II flow cytometer ( BD Biosciences ) and results were analyzed using FlowJo software ( TreeStar ) . Re-suspended cells were restimulated for 4 h with 10 µM GP61–80 peptide ( GLKGPDIYKGVYQFKSVEFD ) in the presence of brefeldin A ( GolgiPlug , 1 µl/ml ) . Cells were stained with cell surface Abs , permeabilized and stained with cytokine specific antibodies using a kit , per the manufacturer's instructions ( BD Biosciences ) . For functional avidity assays , cells were restimulated with a range of peptide concentrations ( 10 µM–0 . 1 nM ) prior to cytokine staining , with the percentage of maximal response determined by calculating the frequency of IFNγ–producing cells at any given concentration as a percentage of the frequency of IFNγ–producing cells at the highest peptide concentration . For intracellular staining of phosphorylated Erk1/2 ( T204/Y202 ) ( eBioscience ) and Zap70 ( Y319/Y352 ) ( BDBiosciences ) , mice were restimulated with 10 µM peptide for 30 or 60 minutes , followed by intracellular phospho-staining using a kit per the manufacturer's instructions ( BD Biosciences ) . Total RNA was isolated using the RNeasy kit ( Qiagen , Valencia , CA ) from FACS-sorted primary SMARTA effectors and secondary SMARTA effectors induced in either LCMV-immune or naïve hosts . cDNA was prepared from the RNA and real-time RT-PCR was performed on a Roche LightCycler 480 ( Roche , Indianapolis , IN ) using Superscript III Platinum Two-Step qRT-PCR Kit with SYBR Green ( Invitrogen , Carlsbad , CA ) , according to the manufacturer's instructions . Expression levels were normalized to GAPDH expression . Oligonucleotide primer sets used are as follows: Zap70: F-AGCGAATGCCCTGGTATCAC , R-CCAGAGCGTGTCAAACTTGGT; SLP76: F-AGAATGTCCCGTTTCGCTCAG , R-TGCTCCTTCTCTCTTCGTTCTT; Lck: F-TGGTCACCTATGAGGGATCTCT , R-CGAAGTTGAAGGGAATGAAGCC; Fyn: F-ACCTCCATCCCGAACTACAAC , R-CGCCACAAACAGTGTCACTC; PLCγ: F-ATCCAGCAGTCCTAGAGCCTG , R-GGATGGCGATCTGACAAGC; SHP-1: F-CCCGCTCAGGGTCACTCATA , R-CCCGAGTAGCGTAGTAAGGCT; DUSP-6: F-CCGTGGTGCTGTACGACGAG , R-GCAGTGCAGGGCGAACTCGGC; Cbl-b: F-GTCGCAGGACAGACGGAATC , R-GAGCTGATCTGATGGACCTCA; Bcl-2: F-GTGGTGGAGGAACTCTTCAGGGATG , R-GGTCTTCAGAGACAGCCAGGAGAAATC; STAT5A: F-CGCCAGATGCAAGTGTTGTAT , R-TCCTGGGGATTATCCAAGTCAAT; GAPDH: F-ATTGTCAGCAATGCATCCTG , R-ATGGACTGTGGTCATGAGCC . | A key to the development of strategies for manipulating immune responses is the identification of the factors that regulate the generation of memory T cells . Many vaccination strategies rely on multiple injections to boost memory cell numbers , yet the factors that regulate the function and survival of memory T cells following multiple challenges are not fully understood . Here , we define key parameters during boosting that regulate the functional capacity and longevity of memory T cells . We report that the boosting of highly functional and long-lived memory T cells is dependent on both the activation environment and duration of the secondary challenge . Our findings demonstrate that T cells have functional plasticity that depends on the inflammatory environment of the secondary T cell activation and have direct bearing on the design of strategies aimed at generating highly functional memory T cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"activation",
"immunity",
"to",
"infections",
"immunology",
"vaccination",
"and",
"immunization",
"white",
"blood",
"cells",
"inflammation",
"animal",
"cells",
"t",
"cells",
"immune",
"response",
"immune",
"system",
"cell",
"biology",
"clinical",
"immunology",
"immunity",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"vaccine",
"development",
"acquired",
"immune",
"system"
]
| 2014 | Dynamic Functional Modulation of CD4+ T Cell Recall Responses Is Dependent on the Inflammatory Environment of the Secondary Stimulus |
BICD2 is one of the two mammalian homologues of the Drosophila Bicaudal D , an evolutionarily conserved adaptor between microtubule motors and their cargo that was previously shown to link vesicles and mRNP complexes to the dynein motor . Here , we identified a G2-specific role for BICD2 in the relative positioning of the nucleus and centrosomes in dividing cells . By combining mass spectrometry , biochemical and cell biological approaches , we show that the nuclear pore complex ( NPC ) component RanBP2 directly binds to BICD2 and recruits it to NPCs specifically in G2 phase of the cell cycle . BICD2 , in turn , recruits dynein-dynactin to NPCs and as such is needed to keep centrosomes closely tethered to the nucleus prior to mitotic entry . When dynein function is suppressed by RNA interference-mediated depletion or antibody microinjection , centrosomes and nuclei are actively pushed apart in late G2 and we show that this is due to the action of kinesin-1 . Surprisingly , depletion of BICD2 inhibits both dynein and kinesin-1-dependent movements of the nucleus and cytoplasmic NPCs , demonstrating that BICD2 is needed not only for the dynein function at the nuclear pores but also for the antagonistic activity of kinesin-1 . Our study demonstrates that the nucleus is subject to opposing activities of dynein and kinesin-1 motors and that BICD2 contributes to nuclear and centrosomal positioning prior to mitotic entry through regulation of both dynein and kinesin-1 .
Spatial organization of eukaryotic cells requires active transport of proteins , macromolecular assemblies , and membrane organelles along cytoskeletal fibers . Transport is driven by motor proteins , which use actin and microtubules ( MTs ) as tracks for their movement . Cytoskeletal elements are polarized structures , and each particular motor can move along them only in one direction . For example , MT-based motors include kinesins , which with a few exceptions walk to MT plus ends , and dyneins , which drive minus end-directed transport [1] . Motor-dependent transport machineries display a high degree of complexity . First , the same motor can move multiple cargos . For example , cytoplasmic dynein is responsible for the movement of the majority of membrane organelles , mRNAs , and proteins to MT minus ends [1] , [2] . Second , the same cargo can simultaneously associate with multiple motors of opposite polarity and frequently switch the direction of movement [3] , [4] . Molecular mechanisms responsible for motor recruitment , activation , and switching of directions are still poorly understood . Motors are likely to be controlled by cargo-specific adaptor complexes , which often include structural components and small GTPases [5] , [6] . An example of a well-studied motor adaptor is Bicaudal D ( BICD ) , which is conserved throughout the animal kingdom [7] . BICD consists of several coiled coil segments separated by regions expected to be highly flexible . The N-terminal part of BICD binds to cytoplasmic dynein and its accessory factor dynactin; moreover , the BICD N-terminus is sufficient to recruit these complexes to organelles [8] , [9] . The C-terminal domain of BICD is the cargo-binding part of the molecule . In mammals and flies , it directly associates with the small GTPase Rab6 [10]–[12] . In mammalian cells , BICD participates in recruitment of dynein/dynactin to Rab6-positive exocytotic vesicles and promotes their MT minus end-directed transport [11] , [13] . The middle portion of BICD weakly binds to kinesin-1 [13] . The functional role of this link is not yet clear , but it is noteworthy that BICD-bound Rab6 vesicles move mostly towards the MT plus ends , suggesting that kinesin motor activity on Rab6 vesicles predominates over dynein-dependent transport [11] , [13] . In Drosophila , BicD participates in dynein-dependent mRNP transport [14] , [15] . This function depends on the association of BicD C terminus with the RNA-binding protein Egalitarian [15]–[17] . BicD is also involved in both dynein and kinesin-1-dependent movement of lipid droplets in fly embryos [18] . To investigate whether mammalian BICD is involved in other transport routes in addition to Rab6 vesicle trafficking , we searched for partners of the cargo-binding domain of BICD2 , one of the two mammalian homologues of the fly BicD [8] . We identified a component of the nuclear pore complex ( NPC ) , RanBP2 [19] , [20] , as the major interacting partner of BICD2 C terminus . RanBP2 ( also known as NUP358 ) is a large protein , which acts as docking factor in nucleocytoplasmic transport [21] and is an E3 ligase for posttranslational modification with the ubiquitin-like protein SUMO1 [22] . RanBP2 exists in a tight complex with the sumoylated form of RanGAP1 , the Ran GTPase-activation protein , and targets it to the NPCs [23] , [24] . RanBP2 forms extended fibers at the cytoplasmic side of the NPC and represents a good candidate for a link between the cytoskeleton and the nuclear envelope ( NE ) . Previous studies showed that cytoplasmic dynein is specifically recruited to the NE in late G2/mitotic prophase , where it participates in NE breakdown ( NEB ) during mitotic entry ( [25] , [26]; for review see [27] , [28] ) . Furthermore , NE-bound dynein is thought to pull centrosomes towards the NE , through its minus-end-directed motility , thereby contributing to proper attachment of centrosomes to the NE [29] , [30] . In yeast , a dynein light chain is a nucleoporin , but it likely acts at the NPC independently of the dynein motor [31] . In C . elegans , dynein is anchored to the NE by the nuclear membrane component SUN-1 and a hook protein ZYG-12 [32] . Also in mammals , SUN1/2 and Syne/Nesprin-1/2 complexes , together with associated MT motors , are important to maintain the connection between the centrosome and nucleus during neuronal migration [33] . However , the molecular mechanism of G2-specific dynein interaction with the NE in dividing mammalian cells has not yet been clarified . Here , we show that BICD2 specifically associates with the NPCs through RanBP2 in the G2 phase of the cell cycle and participates in the recruitment of the dynein/dynactin complexes to these structures . In addition , BICD2 associates with kinesin-1 [13] and we show that while dynein pulls centrosomes and the nucleus together during mitotic entry , kinesin-1 pushes them apart . During late G2 , cytoplasmic dynein activity predominates over kinesin-1 activity , and the centrosomes remain tightly associated with the NE . Furthermore , we show that BICD2 not only acts to recruit dynein to the NE but is also required for the oppositely directed kinesin-1 activity , explaining why loss of BICD2 results only in a mild defect in centrosome-nuclear attachment . These results suggest that similar to most other MT motor cargos in animal cells , the prophase cell nucleus is transported bi-directionally by a molecular complex combining MT motors of opposite polarity .
Our previous studies showed that the individual coiled coil segments of BICD2 display strong association with their binding partners , while the full-length molecule binds to the same proteins less efficiently , suggesting that it may be autoinhibited [8] , [9] , [11] . Therefore , we used the C-terminal coiled coil segment of BICD2 ( Figure 1A ) as a bait to search for new BICD2 cargos . We linked this BICD2 fragment to GFP and a biotinylation tag ( Bio ) , a short peptide sequence that can be modified by the addition of biotin when expressed together with the biotin ligase BirA [34] . The resulting Bio-GFP-BICD2-CT fusion was transiently expressed together with BirA in HeLa cells , which were used for pull-down assays with streptavidin beads ( Figure S1 ) . The resulting protein complexes were analyzed by mass spectrometry ( Table S1 ) . The most abundant newly identified potential BICD2 partner was the NPC component RanBP2 . RanGAP1 , the sumoylated form of which is known to form a tight complex with RanBP2 [23] , [24] , was also present among the isolated proteins in highly significant amounts ( Table S1 ) . The results of the pull-down assay were confirmed by co-immunoprecipitation ( co-IP ) of endogenous RanBP2 with endogenous BICD2; an abundant Golgi-associated protein GM130 served as a negative control ( Figure 1B ) . Further , we observed co-IP of endogenous BICD2 with RanBP2 from nocodazole-arrested HeLa cells , but neither BICD1 nor CLIP-170 , another cytosolic protein known to interact with dynactin [35] , were coprecipitated with RanBP2 in these conditions ( Figure 1C ) . Next , we investigated which domain of RanBP2 associated with BICD2 . RanBP2 is a protein of ∼350 kDa , which contains a leucine-rich region , four Ran-binding domains , eight zinc finger motifs , and a C-terminal cyclophilin A-homologous region ( Figure 1A ) . We generated expression constructs of five RanBP2 fragments , which covered most of the RanBP2 sequence , as fusions to CFP and the plasma membrane-targeting palmitoylation motif of GAP-43 ( Figure 1A ) . With the exception of the N-terminal fragment 1 , these fusions were expressed well in mammalian cells . Using co-IP from HEK293 cells , we found that the C-terminal domain of BICD2 specifically interacted with RanBP2 fragment 3 ( Figure 1D ) . This experiment also showed that BICD2-CT does not interact with the overexpressed GFP-tagged RanGAP1 ( Figure 1D ) , indicating that coprecipitation of RanBP2-RanGAP1 complex with BICD2-CT , observed by mass spectrometry , is due to BICD2 interaction with RanBP2 . The interaction between BICD2 and RanBP2 is direct , since BICD2-CT and RanBP2 segment 3 , purified from bacteria , specifically bind to each other in a glutathione S-transferase ( GST ) pull-down assay ( Figure 1E , F ) . Remarkably , the same RanBP2 fragment was previously shown to interact directly with kinesin-1 isoforms KIF5B and KIF5C [36]–[38] , supporting the notion that it is involved in MT motor recruitment . Next , we employed a yeast two-hybrid assay , which showed that RanBP2 fragment 3 binds exclusively to the C-terminal part of BICD2 and not to its N-terminal and middle segments ( Figure 1G ) . This is similar to the previously described interaction between BICD2 and Rab6 [8] , [11] and is in contrast to kinesin-1 KIF5A , which associates with the middle portion of BICD2 ( Figure 1G ) [13] . Finally , we tested if fragment 3 of RanBP2 was sufficient to recruit BICD2 C terminus to ectopic sites within the cell . For this , RanBP2 fragments were artificially targeted to the plasma membrane through addition of a palmitoylation motif , as described above . The palmitoylation motif fusions of RanBP2 fragments displayed a strong association with the plasma membrane , including filopodia , and also with the Golgi complex ( Figure 1H ) , but as expected , did not target to the NE . BICD2-CT expressed in mammalian cells associates with the Golgi and cytoplasmic vesicles , but not with the plasma membrane [8] , [11] . Interestingly , BICD2-CT was specifically recruited to the plasma membrane by RanBP2 fragment 3 but not by other RanBP2 fragments ( Figure 1H ) , suggesting that this domain of RanBP2 can serve as a recruitment factor for BICD2 . We next investigated whether endogenous BICD2 and RanBP2 co-localize in HeLa cells and found that BICD2 specifically associates with the NE in a subset of cells , where it largely overlaps with the RanBP2 staining ( Figure 2A ) . This localization pattern was visible not only in cells subjected to fixations with paraformaldehyde or a combination of cold methanol with paraformaldehyde ( which are optimal for preservation of the Golgi- and vesicle-bound fraction of BICD2 ) but also after fixation with cold methanol alone , which did not preserve the Golgi-bound or the cytoplasmic pool of the protein ( see below ) ; it was further enhanced by treating cells with the MT-destabilizing drug nocodazole ( Figure 2B–D ) . We hypothesized that the absence of BICD2 staining at the NE in a subset of cells was caused by cell cycle regulation . Indeed , all cells that showed BICD2 accumulation at the NE were positive for cyclin B1 , which is expressed exclusively in G2 and mitosis , and ∼75% cyclin B1-positive cells showed BICD2 localization at the NE , indicating that BICD2 associates with the NE in the G2 phase ( Figure 2A–D ) . G2-specific recruitment to the NE was also observed in another human cell line , U2OS cells ( Figure S2A , B ) . In addition to the NE , endogenous BICD2 also co-localized with RanBP2 in puncta in the cytoplasm ( Figure 2A , B ) . Comparison with previous studies suggested that these puncta are cytoplasmic stacks of NPCs known as annulate lamellae ( AL ) [39] . Indeed , these puncta were stained by 4 additional markers for NPCs: RanGAP1; the monoclonal antibody 414 ( MAB414 ) that reacts with several nucleoporins [40]; an antibody against NUP214 , an NPC component that binds to the cytoplasmic side of the nuclear pores independently of RanBP2 [41] , and YFP-tagged POM121 , a transmembrane NPC component that is also present in AL ( Figure S3 ) [42] . In line with the recruitment of BICD2 to NPCs in the NE in G2 cells , we observed the association of BICD2 with the AL in cyclin B1-positive but not in cyclin B1-negative cells ( Figure 2A , B ) . These results are important as they support the view that G2-specific recruitment of BICD2 to the NE is due to its interaction with the cytoplasmic part of the NPCs and not some other NE component . Since we found that BICD2 directly interacts with RanBP2 , we examined whether the NE localization of BICD2 was RanBP2-dependent . RanBP2 could be specifically depleted from HeLa and U2OS cells without affecting the expression of BICD2 ( Figure S4A , B ) . Indeed , depletion of RanBP2 blocked recruitment of BICD2 to the NE of G2 cells ( Figure 2C , E; Figure S2A , B ) . Based on the results described above , BICD2 is expected to specifically associate with individual NPCs on the NE . Indeed , both full-length BICD2 and GFP-tagged BICD2-CT co-localized with individual NPCs on the NE in cells that were pre-extracted with a Triton X-100-containing buffer to reduce the cytoplasmic pool of the GFP-BICD2 fusions ( Figure S5 ) . Similarly , colocalization of endogenous BICD2 with individual NPCs was also observed in methanol-fixed cells , in which cytosolic BICD2 signal has been removed ( Figure 3A , B ) . Fluorescent intensity profiles showed that most of the individual NPCs in the NE ( stained with MAB414 ) also showed a peak of BICD2 fluorescence ( Figure 3B , C ) . The significance of the overlap was confirmed by quantitative analysis: the coefficient of linear correlation between properly aligned MAB414 and BICD2 images was , on average , ∼0 . 5 , while it was close to zero when one of the two images was rotated , indicating that observed colocalization between the two markers was not due to fortuitous overlap between abundant dot-like patterns ( Figure 3D–G ) . Taken together , our results show that BICD2 binds RanBP2 both in vitro and in vivo , localizes to NPCs ( both in the NE and in AL ) in G2 cells , and that this recruitment to NPCs depends on RanBP2 . It is therefore likely that a direct interaction between RanBP2 and BICD2 links BICD2 to the NE in G2 cells . Although previous studies showed that BICD2 strongly co-localizes with Rab6 on the Golgi apparatus and cytoplasmic vesicles [10] , [11] , this was not the case in G2 cells where BICD2 accumulated at the NE ( Figure 4A ) . This conclusion was confirmed by staining nocodazole-treated cells , where the dispersion of the Golgi and nocodazole-induced enlargement of the AL permitted better distinction of protein localization in different cytoplasmic structures ( Figure 4B ) . In the cells where BICD2 associated with Rab6-bound membranes , it did not stain the NE or the AL . However , in the cells where BICD2 localized to the NE and AL , it displayed virtually no colocalization with Rab6 ( Figure 4B ) . Combined with the results described above , these observations indicate that BICD2 switches from Rab6-bound membranes to the NPCs in G2 phase cells . We next investigated whether the dynein-dynactin complex is recruited to the NE along with BICD2 . In untreated HeLa cells , both dynein and dynactin show diffuse cytosolic localization as well as an accumulation at the MT plus ends and the centrosomes ( unpublished data ) . However , in nocodazole-treated cells , colocalization of dynein and dynactin with BICD2 could be detected in specific cytoplasmic structures [8] . While in the majority of cells these structures coincided with Rab6-positive Golgi fragments , in all cells in which BICD2 co-localized with RanBP2 , both dynein and dynactin co-localized with BICD2 on RanBP2-positive membranes , suggesting that the association of BICD2 with dynein and dynactin is maintained when BICD2 switches from Rab6 to RanBP2 ( Figure 4C , D; Figure S6A ) . Because both BICD2 and RanBP2 were shown to bind to kinesin-1 , we also attempted to investigate the localization of its isoforms . Although we were able to specifically detect the predominant kinesin-1 isoform , KIF5B , in HeLa cells ( as confirmed by siRNA-mediated depletion ) , it displayed a largely diffuse distribution , and no clear accumulation of this protein could be detected at the NE or AL with or without nocodazole treatment ( unpublished data ) . Since BICD2 can directly bind both the NPCs and the dynein-dynactin complex , we next investigated whether BICD2 is required to recruit dynein and dynactin to the NPCs . Because we could not stain cells simultaneously for cyclin B1 and dynactin or dynein , we used an antibody against histone H3 phosphorylated at serine 10 ( phospho-H3 ) , which becomes highly phosphorylated in late G2/prophase cells [43] . All phospho-H3-positive cells displayed very strong recruitment of endogenous dynactin and dynein to both the NE and AL ( Figure 4E , F; Figure S6B , C ) . Strikingly , this recruitment was inhibited by depletion of BICD2 , but not BICD1 ( Figure 4E , F; Figure S6B , C; and unpublished data ) , demonstrating that BICD2 is required for the association of dynein and dynactin with the NPCs in prophase cells . In contrast , BICD2 was recruited normally to the NPCs after depletion of either the dynein heavy chain ( HC ) or dynactin large subunit p150Glued ( see below ) . These results suggest that BICD2 directly links the dynein/dynactin complex to the NE through its interaction with RanBP2 . Since dynein associates with the NE in G2 phase , we next examined how its depletion affected the relative position of the nucleus and centrosomes during mitotic entry . Because the perinuclear MT cytoskeleton is very dense and therefore difficult to analyze in HeLa cells , we used U2OS cells , in which MT arrays are more sparse and centrosome-centered . In control cells , the centrosomes were always located very closely to the NE in prophase ( Figure 5A , B ) . In contrast , in dynein-depleted cells the nucleus and the centrosomes were almost always found in opposite cell corners during prophase ( Figure 5A , B ) . Similarly , live cell imaging of U2OS cells stably expressing mCherry-α-tubulin showed that in control cells centrosomes migrate along the NE , to the opposite sides of the nucleus just before mitotic entry ( Figure S7A ) , allowing spindle assembly to initiate around the DNA . In contrast , in ∼90% of dynein-depleted cells , centrosomes and the nucleus had moved apart substantially at the time NEB occurred , and therefore the DNA was not positioned in between the centrosomes when spindle assembly initiated ( Figure S7A; Figure 5B ) ; similar results were obtained in HeLa cells after dynein or dynactin depletion ( Figure S7B , Videos S1–S3 ) . Importantly , not only the centrosomes lost their central position ( which could be explained by the loss of interactions between MT ends and the cell cortex upon dynein depletion [44] ) , but also the nucleus was rapidly pushed into one of the cell corners , towards MT plus ends ( Figure 5C–E , see Videos S4–S7 ) . Nuclear movement was initiated 54±4 min before NEB , and the average velocity of movement was ∼160 nm/min ( Figure 5E ) . The final distance between the two centrosomes at the time of NEB was the same in control and dynein-depleted cells ( 9 . 8±1 . 0 µm in control and 9 . 8±0 . 6 µm in dynein-depleted cells ( mean±SD ) ) , consistent with our previous findings [45] , indicating that centrosome separation does not require linkage to the NE or dynein function . To rule out that the detachment of centrosomes from the nucleus was due to defects arising after long-term dynein inhibition , we microinjected U2OS cells that were in late G2 with function blocking antibodies against dynein intermediate chain ( IC ) . As expected , dynein-inhibiting antibodies , but not the control antibodies , induced rapid separation of the nuclei and the centrosomes ( Figure 5F–H ) , demonstrating that dynein activity is required during late G2 to maintain the connection between centrosomes and the NE . Very similar results were obtained after microinjection of the first coiled coil fragment of the dynactin large subunit p150Glued ( CC1 ) , which is known to disrupt dynactin-dependent dynein mediated processes ( Figure S8 ) [46] . Pushing a relatively large nucleus into a flattened corner of a cultured cell would require substantial force , which is most likely generated by kinesin motors attached to the nucleus and moving to MT plus ends . We hypothesized that KIF5B might be involved in this process because it interacts with both BICD2 and RanBP2 . Indeed , co-depletion of KIF5B together with dynein fully restored centrosome and nuclear position at NEB ( Figure 5B , Figure S7A; for control of double knockdown efficiency , see Figure S4C ) , indicating that it is indeed driving the separation of nuclei and centrosomes in dynein-depleted cells . Taken together , these results suggest that the prophase cell nucleus is transported bi-directionally by the opposing activities of dynein and kinesin-1 , similar to many other cargoes . Since we showed that BICD2 is required for dynein and dynactin recruitment to the NPCs in G2 phase , we next investigated whether its depletion has an influence on the relative positioning of the centrosomes and the nuclei . Similar to HeLa , U2OS cells express both BICD2 and BICD1 , which can be depleted by a number of different siRNAs without affecting the expression of RanBP2 or MT motors ( Figure 6A; Figure S4C ) . Depletion of BICD2 induced the detachment of the centrosomes from the nucleus in prophase ( Figure 6B , C ( a–c ) ) . This effect was specific , since it was not observed after depletion of BICD1 or KIF5B , but it was much less severe than after dynein knockdown ( Figure 6B , C ( a–c ) ) . Cells with simultaneous knockdown of BICD1 and BICD2 displayed a phenotype similar to that of BICD2 depletion alone , confirming the view that BICD1 does not contribute much to centrosome positioning ( Figure 6C ( c ) ) . Centrosome detachment from the NE was also observed in cells overexpressing BICD2-CT , which is expected to uncouple dynein/dynactin from BICD2-containing cargos ( Figure 6B , C ( d ) ) [11] . Interestingly , RanBP2 depletion , which prevents BICD2 recruitment to the NE ( Figure 2C , E; Figure S2A , B ) also caused an increase in the distance between the nuclei and the centrosomes , while the depletion of another cytoplasmic nucleoporin , NUP214 , had no effect ( Figure 6C ( e ) ; Figure S4B ) . To further prove that BICD2 can exert a direct effect on centrosome positioning through its localization at the NE , we constructed a fusion protein in which we attached the N-terminal portion of BICD2 , including the dynein and kinesin-1 binding sites , to the C-terminal KASH ( Klarsicht , ANC-1 , Syne Homology ) domain-containing region of nesprin-3 , which is targeted to the NE by SUN proteins [47] . This fusion localized specifically to the NE and enhanced the accumulation of dynactin at the NE ( Figure 6D ) . Importantly , the expression of the BICD2-NT-nesprin-3 fusion completely suppressed centrosome detachment in RanBP2-depleted prophase cells ( in which endogenous BICD2 is no longer targeted to the NE , see Figure 2C , E ) , even at very low expression levels ( Figure 6C ( f ) ) . Taken together , these results support the view that BICD2 can recruit MT motors to the NE and regulate the relative localization of the nucleus and the centrosomes . Since the distance between the centrosomes and the nucleus in BICD2 or RanBP2 knockdown cells was much smaller than in the case of dynein knockdown , it appears that there is no severe imbalance between the activities of dynein and kinesin-1 at the NE under these conditions . These results suggest that BICD2 is not only involved in dynein function at the NE but might also be required for proper kinesin-1 function . If this idea is correct , BICD2 depletion should block the kinesin-1 driven separation of centrosomes from the nuclei observed after dynein depletion . Indeed , co-depletion of BICD2 with dynein strongly reduced the distance between the centrosomes and the nuclei observed after dynein depletion alone ( see Figure 5B ) . This rescue of centrosome-nuclear attachment was not due to a decreased efficiency of dynein depletion ( see Figure S4C ) ; moreover , the mitotic arrest in cells depleted of both BICD2 and dynein HC was similar to that observed for single dynein HC knockdown ( unpublished data ) , further confirming that dynein function was similarly perturbed in both cases and indicating that BICD2 depletion does not help to overcome later mitotic phenotypes associated with dynein loss . Why do centrosomes detach from the NE in prophase after BICD2 knockdown if both kinesin-1 and dynein activities are reduced ? We recently found that the plus-end directed kinesin-5 Eg5 , known to slide antiparallel MTs [48] , pushes centrosomes apart during prophase [45] . Thus , Eg5-dependent sliding forces might drive the centrosomes away from the nucleus when it becomes uncoupled from dynein and kinesin-1 due to BICD2 depletion . In line with this idea , inhibition of Eg5 with S-trityl-L-cysteine ( STLC ) significantly suppressed centrosome detachment caused by BICD2 depletion , indicating that Eg5 is at least in part responsible for pushing the centrosomes away from NE in BICD2-depleted prophase cells ( Figure 6C ( g ) ) . Taken together , our data show that centrosome separation and positioning at the opposite sides of the NE at the mitotic onset is driven by forces generated by dynein , kinesin-1 , and Eg5 . Do dynein and kinesin-1 indeed attach to the NE through NPCs ? If this were true , the positioning of AL , which contain cytoplasmic NPC components but are devoid of many other NE-specific proteins , such as the nuclear lamina and the proteins that are linked to it , should be affected by the depletion of MT motors . AL are relatively small structures that are normally located in the central cytoplasm; they can potentially serve as a sensitive readout for the forces exerted on them by cytoplasmic motors . In control cells , AL are predominantly located around the Golgi apparatus in G1 and S-phase; in G2 they shift towards the centrosome and gradually disappear ( [49] , Figure 7A , and see also Figures 2A , 4A , and Video S8 ) . The depletion of the dynein HC or dynactin subunit p150Glued induced relocalization of AL to the cell periphery of cyclin B1-positive cells ( towards MT plus ends; Figure 7A ) . Using a cell line stably expressing GFP-tagged RanGAP1 ( Figure S9 ) , we observed that the timing of movement of the AL to the cell periphery coincided almost exactly with the timing of peripheral displacement of the nucleus in cells lacking dynein activity ( ∼1 h before NEB ) ( Figure 7B , Video S9 ) . Furthermore , peripheral displacement of AL in dynein-depleted cells was completely dependent on kinesin-1 activity ( Figure 7C ) , similar to displacement of the nucleus in these cells ( see Figure 5B ) . Together , these results indicate that the forces that act to position the AL are mechanistically similar to those that position the nucleus . Consistent with this , knockdown of KIF5B caused a very strong accumulation of AL near centrosomes , where MT minus ends are located in G2 ( Figure 7A and Figure S10A , B , C ) , and centrosomal accumulation of AL was , in turn , dependent on dynein activity ( Figure 7C ) . The analysis of the timing of AL movement in kinesin-1-depleted cells showed that centrosomal accumulation started ∼3 h before NEB ( Figure 7B , Video S10 ) , which corresponds very well to the time at which BICD2 and dynein are recruited to NPCs , further suggesting that BICD2 and dynein recruitment to NPCs in G2 phase induces movement of these structures towards MT minus ends . Taken together , these results show that , similar to the nucleus , the position of AL in G2 phase is controlled by the antagonistic activities of dynein and kinesin-1 and further suggest that both dynein and kinesin-1 act to position the nucleus through their effect on NPCs , rather than through other NE-associated proteins . We also used AL displacement to strengthen our conclusions that BICD2 controls both dynein- and kinesin-1-dependent movement of NPCs . Importantly , BICD2 remained strongly enriched at the AL and the NE when dynein , dynactin , or KIF5B were depleted , indicating that BICD2 association with the NPCs is independent of MT motors ( Figure 7A ) . In control G2 phase cells , AL often displayed some perinuclear accumulation in the centrosome region ( see Figures 2A , 7C , Figure S11A , B ) . However , BICD2 depletion reduced perinuclear accumulation of AL in G2 phase ( Figure 7C , Figure S11A , B ) . These findings were confirmed by observing the behavior of AL by time-lapse microscopy after BICD2 knockdown , where AL remained randomly dispersed until the beginning of mitosis ( Figure S11C , Video S11 ) . These results further implicate BICD2 in the G2 specific activation of MT minus-end-directed force generation on NPCs . Furthermore , the shift of AL to the cell center or the cell periphery caused by kinesin-1 and dynein depletion , respectively , was strongly inhibited by co-depletion of BICD2 but not by control siRNAs or siRNAs against BICD1 or Rab6 ( Figure 7C , Figures S11C , S12; Videos S12 , S13 ) . These results demonstrate that BICD2 is required for the G2-specific movement of the AL by both kinesin-1 and dynein and strongly support the notion that both dynein and kinesin-1 control nuclear movement by acting through BICD2 on the NPCs . We have also investigated if other cytoskeletal systems , in addition to MTs , are directly involved in the G2-specific processes described above but found no evidence for direct involvement of the actin cytoskeleton or the intermediate filaments , keratin or vimentin , in the G2-specific nuclear-centrosome positioning pathway that relies on dynein and kinesin-1 ( Figures S13 , S14 ) .
During cell division the MT cytoskeleton and membrane organelles undergo a severe reorganization , which proceeds in a highly regulated manner . In many cell types , the two centrosomes move apart while maintaining their attachment to the NE . This helps to form the bipolar mitotic spindle around the chromosomes after NEB . In this study , we have obtained insight into molecular mechanisms that control the relative positioning of the nucleus and the centrosomes at mitotic onset . We show that the dynein/dynactin adaptor BICD2 is specifically recruited to the NPC in G2 phase through a direct interaction with the NPC component RanBP2 . In its turn , BICD2 is important for accumulation of dynein and dynactin at the nuclear pores in prophase cells . In line with previously published data ( reviewed by [27] , [28] ) , we find that cytoplasmic dynein is the major player responsible for the nucleus-centrosome attachment , but unexpectedly , we find that kinesin-1 also participates in this process by antagonizing dynein function . Since BICD2 and RanBP2 are likely involved in linking both MT motors to the NPCs , depletion of either protein causes only mild centrosome detachment from the nucleus . Our previous studies showed that BICD2 associates with MT motors through its N-terminus and the middle portion , while the C terminus is the cargo-binding site [9] , [11] , [13] . Here we identified a new cargo for BICD2 , the nucleoporin RanBP2 , which binds to the same domain of BICD2 as the small GTPase Rab6 . Our data suggest that the interaction of BICD2 with the two cargos is temporally regulated during the cell cycle: during G1 and S phase , BICD2 appears to associate predominantly with Rab6 , while in G2 it binds mostly to the NPCs ( Figure 8 ) . It is currently unclear how this switch is controlled , but it is likely that mitotic kinases are involved . Both during Rab6 vesicle trafficking and in nuclear positioning , BICD2 participates in transport processes that involve the opposing functions of cytoplasmic dynein and kinesin-1 . The predominating motor in the two processes is different: Rab6 vesicles are exocytotic carriers that preferentially move to MT plus ends , suggesting that kinesin-1 activity is dominant , while the nucleus and AL are mainly pulled by dynein ( Figure 8 ) . This indicates that BICD2 participation by itself is insufficient to determine direction of movement; therefore , additional factors or posttranslational modifications are likely to be involved . While it may appear strange that the two opposite polarity motors act together in processes that mostly depend on only one of them , this arrangement seems to represent a fundamental property of MT motor systems most likely required to allow flexibility and permit regulation of cargo distribution [3] , [4] . Our study shows that even the positioning of a very large cargo , such as the cell nucleus , is no exception to this rule . The mechanism underlying kinesin-1 recruitment to BICD2-bound NPCs is unlikely to be explained solely by the binding between BICD2 and kinesin-1 [13] , since RanBP2 can directly bind to kinesin-1 as well [36] , [37] . Intriguingly , both BICD2 and kinesin-1 interact with the same region of RanBP2; whether these interactions are competitive or cooperative and what consequences this has on the architecture of the motor complexes remains to be determined . It is clear that RanBP2 and BICD2 are not the only proteins participating in the motor recruitment and/or activation important for centrosome-nuclear attachment . First , other parts of the NPCs , additional dynein accessory factors , such as LIS1 and NUDE [50] , and cell cycle-dependent regulators of motor activation , like CDKs or Plk1 , are likely to be involved . This view is supported by the observed timing of binding and transport steps . BICD2 associates with the NPCs early in G2; this results in dynein activation that is sufficient to cause strong AL accumulation around the centrosome in the absence of kinesin-1 at ∼3 h before NEB . At a later stage ( ∼1 h before NEB ) , additional motor activation likely takes place; this is reflected by the peripheral displacement of the AL , the nucleus , and the centrosomes in dynein-depleted cells . Furthermore , Eg5 becomes active during prophase and pushes centrosomes apart . The forces induced by Eg5-dependent centrosome separation are kept in check by the complex of RanBP2-BICD2-dynein that prevents centrosome detachment from the nucleus while allowing centrosomes to separate . Second , KASH domain proteins such as nesprins are essential for attachment of nuclei to the cytoskeleton in different systems [33] , [51] , [52] . We observed that the displacement of endogenous nesprins from the NE indeed affected relative positioning of the nucleus and centrosomes in G2 phase , but the effect was much less severe than that of dynein depletion ( Tanenbaum , unpublished data ) . Moreover , nesprin displacement from the NE could not block kinesin-1-mediated nuclear displacement in dynein-depleted cells ( Splinter , unpublished data ) , indicating that in contrast to certain epithelial cells [53] , in cultured U2OS and HeLa cells nesprins are not essential for attachment of kinesin-1 to the NE . Nesprins bind to the NE through SUN proteins , which were shown to interact with nuclear pores [54] , and may therefore participate in the formation of the MT motor assemblies at the NE together with nucleoporins . It is likely that the relative importance of different molecular links between the NE and MT motors depends on the cell type and differentiation state . What is the function of the complex molecular events described in this study ? Clearly , positioning of the centrosomes at the opposite sides of the nucleus at NEB would decrease the chance that a kinetochore is captured by MTs emanating from both poles ( merotelic attachments ) , which can result in chromosome missegregation and aneuploidy , hallmarks of cancer [55] . Furthermore , spindle assembly in mammalian cells is controlled by both centrosome- and chromatin-dependent pathways , in which centrosomes are potent MT nucleation sites and chromatin can both nucleate and stabilize MTs . When centrosomes are positioned too far away from chromatin , newly nucleated MTs are unstable and as a consequence spindle assembly might be delayed . Indeed , loss of dynein results in a ∼15 min delay in bipolar spindle assembly , which can be rescued by restoring the relative positioning of centrosomes and the nucleus at mitotic entry through co-depletion of kinesin-1 ( Tanenbaum , unpublished data ) . A mechanism coupling centrosomes to the nucleus at mitotic onset could become even more important in very large cells like fertilized oocytes , in which the distance between centrosomes and chromosomes could become so extensive that centrosomes could no longer contribute to spindle assembly . In addition , the interaction of dynein with the NPCs through BICD2 could help to tear apart the NE [26] , [56] , a possibility that was not addressed here . Furthermore , dynein-mediated coupling between the nucleus , MTs , and the centrosome plays an important role during migration of differentiated cells [57] . In flies , BicD is involved in MT and dynein/dynactin-dependent positioning of the oocyte and photoreceptor nuclei [7] , and since BICD2 is ubiquitously expressed during mammalian development ( Akhmanova , unpublished data ) , it would be interesting to know if it plays a similar role in mammals .
We used the following previously described expression vectors: GFP-BICD2 [8] , HA-BICD2-CT [11] , myc-KIF5B [13] , BirA [58] ( a gift of D . Meijer , Erasmus MC , Rotterdam , The Netherlands ) , mCherry-α-tubulin [59] ( a gift of R . Tsien , UCSD , San Diego , CA , USA ) . Biotinylation and GFP-tagged BICD2 C terminus ( Bio-GFP-BICD2-CT , BICD2 amino acids 487–820 , accession number CAC51393 ) was generated in pEGFP-C2 ( Clontech ) by cloning at the NheI and AgeI sites in front of the GFP a linker encoding the amino acid sequence MASGLNDIFEAQKIEWHEGGG . CFP-tagged RanBP2 fragments with the N-terminal palmitoylation signal derived from GAP-43 were generated in a modified version of the pECFP-N1 vector ( Clontech ) by a PCR based strategy . GFP-BICD2-NT-nesprin-3 fusion was generated by attaching the amino acids 582–975 of nesprin-3 ( accession number NP_001036164 , [60]; a gift of A . Sonnenberg , Netherlands Cancer Institute , Amsterdam ) to the C terminus of GFP-BICD2-NT ( amino acids 1–594 of BICD2 [9] ) . GFP-RanGAP1 was generated in pEGFP-C1 by inserting into it the BglII-SmaI fragment of KIAA1835 ( accession number AB058738 , a gift of Kazusa DNA Research Institute , Japan ) . POM121 fused to a triple YFP tag [42] was a gift of Dr . E . Hallberg ( Södertörns University College , Huddinge , Sweden ) . We used the following siRNAs: KIF5B#1 , 5′-GCCUUAUGCAUUUGAUCGG ( siRNA 118426 , Ambion ) , KIF5B#2 , 5′-GCACAUCUCAAGAGCAAGU ( siRNA 118427 , Ambion ) , dynein HC DHC#1 5′-CGUACUCCCGUGAUUGAUG ( siRNA 118309 , Ambion ) , DHC#2 5′-GCCAAAAGUUACAGACUUU ( siRNA 118311 , Ambion ) , DHC#3 5′-GGAUCAAACAUGACGGAAU , RanBP2#1 5′-GGACAGUGGGAUUGUAGUG [61] , RanBP2#2 5′-CACAGACAAAGCCGUUGAA , RanBP2#3 Dharmacon SMARTpool , p150Glued 5′ GUAUUUGAAGAUGGAGCAG , BICD2#1 5′-GGAGCUGUCACACUACAUG , BICD2#2 5′-GGUGGACUAUGAGGCUAUC , BICD1#1 5′-CCUUAAUGCCAUAAUCCGG , BICD1#2 5′-GCAAAGAGCCAAUGAAUAU , BICD1#3 5′-GCAACUGUCUCGUCAAAGA , NUP214 5′ GUCACGGAAACAGUGAAAG [41] . As a control we used a previously described scrambled CLASP1 siRNA , the siRNA against luciferase [62] , or the siRNA to GAPD ( control Dharmacon SMARTpool ) . Bio-GFP-BICD2-CT and BirA were transiently co-expressed in HeLa cells; cells were lysed in a buffer containing 100 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 1% Triton X-100 , and protease inhibitors ( Complete , Roche ) . Streptavidin pull-down assays , mass spectrometry analysis , and IP from HEK293 cells overexpressing different protein fusions were performed as described by [13] . For the IP of endogenous proteins , HeLa cells were pelleted and lyzed in a buffer containing 20 mM Tris pH 8 . 0 , 150 mM KCl , 5% Triton X-100 , Protease inhibitors ( Complete Roche ) and phosphatase inhibitors ( Cocktail 1 and 2 , Sigma ) . For the IP of endogenous proteins from nocodazole-arrested cells , HeLa cells were treated with 75 ng/mL nocodazole for 18 h , washed with PBS , lyzed with digitonin in the buffer containing 20 mM HEPES pH 7 . 3 , 110 mM potassium acetate , 2 mM magnesium acetate , 1 mM EGTA , 1 mM DTT and protease and phosphatase inhibitors; lysates were centrifuged at 100000×g for 1 h . IP was carried out using standard procedures . 6XHIS-tagged BICD2-CT ( amino acids 630–820 ) was generated in pET28a . GST fusions of RanBP2 fragments 3 and 4 ( amino acids 2147–2287 and 2447–2887 , accession number NP_006258 ) were generated in pGEX-3X . Protein purification and GST pull-down assays were carried out as described by [63] . Binding reactions were performed in 50 mM Tris-HCl , pH 7 . 5 , 125 mM NaCl , 0 . 1% NP40 , and 5 mM EDTA , using ∼20 µg/ml of HIS-BICD2-CT and ∼70 µg/ml of GST fusions . For yeast two-hybrid assays , different bait constructs were prepared in pBHA ( lexA fusion vector ) and tested against various BICD2 fragments cloned into pGAD10 ( GAL4 activation domain vector , Clontech ) as described by [13] . HeLa , HEK293 , and U2OS cells were cultured as described previously [64] , [65] . PolyFect ( Qiagen ) , Lipofectamine 2000 ( Invitrogen ) , or FuGENE 6 ( Roche ) reagents were used for plasmid transfection . Stable HeLa clones expressing fluorescent proteins were selected using Fluorescence Activated Cell Sorting and cultured in the presence of 0 . 4 mg/ml G418 ( Roche ) . Synthetic siRNAs were transfected into HeLa cells plated at 20% confluence using HiPerFect ( Qiagen ) at the final concentration 5 nM; cells were analyzed by 3 d after transfection . U2OS cells were transfected with HiPerFect during plating at ∼20% confluence using 20 nM siRNAs; a second transfection with the same siRNA concentration was performed 1 or 2 d later , and the cells were analyzed 3 or 4 d after plating . We used affinity purified goat polyclonal antibodies against RanBP2 and RanGAP1 [22] , [66]; rabbit polyclonal antibodies against GFP ( Abcam ) , BICD1 and BICD2 [8] , [11] , HA tag , dynein HC , KIF5B and RanGAP1 ( Santa Cruz ) , NUP214 [41] ( a gift of Dr . R . Kehlenbach , University of Göttingen , Germany ) , and phosphorylated histone H3 ( Ser 10 ) ( Millipore ) ; mouse monoclonal antibodies against Rab6 ( which recognizes Rab6A and Rab6A' , a gift of A . Barnekow , University of Muenster , Germany ) , Arp1 ( a gift of Dr . T Schroer , Johns Hopkins University , USA ) , nucleoporins ( antibody 414 , Covance ) , α- , β- , and γ-tubulin ( Sigma ) , dynein IC ( Chemicon and Santa Cruz ) , cyclin B1 ( Santa Cruz ) , p150Glued and p50 ( BD Biosciences ) , pan-keratin ( clone C11 , Sigma ) , and vimentin ( Cymbus Biotechnology ) . For secondary antibodies we used Alexa 350 , Alexa 488 , and Alexa 594-conjugated goat antibodies against rabbit , rat , and mouse IgG , donkey antibodies against sheep IgG ( Molecular Probes ) , AMCA-labeled rat anti-mouse , FITC-labeled donkey anti-rabbit , and anti-mouse antibodies ( Jackson ImmunoResearch Laboratories ) . Actin was stained with Alexa-594-conjugated phalloidin ( Invitrogen ) . Cell fixation and staining procedures were described previously [8] . Briefly , we used the following fixations: 4% paraformaldehyde in PBS ( 15 min at room temperature ) , −20°C methanol ( 10 min ) , or −20°C methanol ( 10 min ) immediately followed by 4% paraformaldehyde in PBS ( 15 min at room temperature ) . For pre-extraction of live cells we used the following buffer: 60 mM PIPES , 25 mM HEPES , 10 mM EGTA , 0 . 5% Triton X-100 , 4 mM MgSO4 , and pH 7 . 5 . Western blotting was performed as described previously [64] . For preembedding immunoperoxidase electron microscopy , cells were fixed with 4% paraformaldehyde and stained with anti-RanGAP1 using the same conditions as for immunofluorescence with the exception that saponin ( 0 . 1% ) was used as a detergent in the preincubation step and no detergent was used in subsequent steps . Biotinylated horse anti-goat ( Vector ) was used as the secondary antibody , which was followed by incubation in avidin-biotin-peroxidase complex ( ABC , Vector Laboratories , USA ) and staining with diaminobenzidine ( DAB , 0 . 05% ) yielding a brown reaction product . Subsequently cells were fixed in 1% osmium , dehydrated , and embedded in Durcupan . Ultrathin ( 50–70 nm ) were contrasted with uranyl acetate and lead citrate and analyzed in a Phillips CM100 electron microscope with a bottom mounted TVIPS FastScan-F114 camera . Images of fixed cells with the exception of Figures 5A and 6B were collected with a Leica DMRBE microscope equipped with a PL Fluotar 100× 1 . 3 N . A . or 40× 1 . 00–0 . 50 N . A . oil objectives , FITC/EGFP filter 41012 ( Chroma ) and Texas Red filter 41004 ( Chroma ) , and an ORCA-ER-1394 CCD camera ( Hamamatsu ) . Images in Figures 5A and 6B were acquired on a confocal Zeiss LSM510 META ( CarlZeiss ) with a Plan Apochromat 63× 1 . 4 N . A . objective . Z-planes were acquired with 1 µm intervals . Images are maximum intensity projections of all Z-planes . Live cell imaging experiments with U2OS cells were performed on a Zeiss Axiovert 200 M microscope equipped with a Plan-Neofluar 40× 1 . 3 N . A . oil objective in a permanently heated chamber with 5% CO2 . Images were acquired every 3–5 min using a Photometrics Coolsnap HQ charged-coupled device camera ( Scientific , Tucson , AZ ) . Z-stacks were acquired with 2 µm intervals between Z-slices . Live cell imaging experiments with HeLa cells were performed on the inverted microscope Nikon Eclipse TE2000E ( Nikon ) with a CFI Plan Fluor 40× 1 . 30 N . A . oil objective , equipped with CoolSNAP-HQ2 CCD camera ( Roper Scientific ) controlled by MetaMorph 7 . 1 software ( Molecular Devices ) . For excitation we used HBO 103 W/2 Mercury Short Arc Lamp ( Osram ) and Chroma ET-GFP ( 49002 ) or Chroma ET-DsRed ( 49005 ) filter sets . Image analysis was performed by using MetaMorph software ( Universal Imaging , Downington , PA ) . Cells were kept at 37°C during observation . U2OS cells were microinjected in L-15 medium with dynein IC 70 . 1 ( Sigma ) or Myc ( Covance ) antibodies , diluted 1/10 from suppliers stock , or with purified CC1 fragment of p150Glued ( a gift of Dr . S . King , University of Missouri–Kansas City , USA ) using an Eppendorf Micromanipulator 5171 coupled to a Transjector 5246 , and cells were imaged as described above . Images were prepared for publication using Adobe Photoshop . The images of fixed cells were modified by adjustments of levels and contrast . Live images were modified by adjustments of levels and contrast and applying Unsharp Mask and Gaussian Blur filters . | Bidirectional microtubule-based transport is responsible for the positioning of a large variety of cellular organelles , but the molecular mechanisms underlying the recruitment of microtubule-based motors to their cargoes and their activation remain poorly understood . In particular , the molecular players involved in the important processes of nuclear and centrosomal positioning prior to the onset of cell division are not known . In this study we focus on the function of one of the mammalian homologues of Drosophila Bicaudal D , an adaptor for the microtubule minus-end-directed dynein-dynactin motor complex . Previously , Drosophila Bicaudal D and its mammalian homologues were shown to act as linkers between the dynein motor and mRNP complexes or secretory vesicles . Here , we identify a new cargo for mammalian Bicaudal D2 ( BICD2 ) –the nucleus . We show that BICD2 specifically binds to nuclear pore complexes in cells in G2 phase of the cell division cycle . We also show that this interaction is required for G2-specific recruitment of dynein to the nuclear envelope and thus for proper positioning of the nucleus relative to centrosomes prior to the onset of mitosis . Further , our findings demonstrate that the motor protein kinesin-1 opposes dynein's activity during this process and requires BICD2 for its activity . Our study therefore reveals BICD2 as the critical molecular adaptor that allows molecular motors to regulate nuclear and centrosomal positioning before cell division . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"cell",
"biology/cell",
"growth",
"and",
"division",
"cell",
"biology",
"cell",
"biology/cytoskeleton"
]
| 2010 | Bicaudal D2, Dynein, and Kinesin-1 Associate with Nuclear Pore Complexes and Regulate Centrosome and Nuclear Positioning during Mitotic Entry |
A diverse subset of pattern recognition receptors ( PRRs ) detects pathogen-associated nucleic acids to initiate crucial innate immune responses in host organisms . Reflecting their importance for host defense , pathogens encode various countermeasures to evade or inhibit these immune effectors . PRRs directly engaged by pathogen inhibitors often evolve under recurrent bouts of positive selection that have been described as molecular ‘arms races . ’ Cyclic GMP-AMP synthase ( cGAS ) was recently identified as a key PRR . Upon binding cytoplasmic double-stranded DNA ( dsDNA ) from various viruses , cGAS generates the small nucleotide secondary messenger cGAMP to signal activation of innate defenses . Here we report an evolutionary history of cGAS with recurrent positive selection in the primate lineage . Recent studies indicate a high degree of structural similarity between cGAS and 2’-5’-oligoadenylate synthase 1 ( OAS1 ) , a PRR that detects double-stranded RNA ( dsRNA ) , despite low sequence identity between the respective genes . We present comprehensive comparative evolutionary analysis of cGAS and OAS1 primate sequences and observe positive selection at nucleic acid binding interfaces and distributed throughout both genes . Our data revealed homologous regions with strong signatures of positive selection , suggesting common mechanisms employed by unknown pathogen encoded inhibitors and similar modes of evasion from antagonism . Our analysis of cGAS diversification also identified alternately spliced forms missing multiple sites under positive selection . Further analysis of selection on the OAS family in primates , which comprises OAS1 , OAS2 , OAS3 and OASL , suggests a hypothesis where gene duplications and domain fusion events result in paralogs that provide another means of escaping pathogen inhibitors . Together our comparative evolutionary analysis of cGAS and OAS provides new insights into distinct mechanisms by which key molecular sentinels of the innate immune system have adapted to circumvent viral-encoded inhibitors .
Pathogens constantly drive the evolution of populations they infect [1 , 2] . The burden of pathogens on host fitness results in selective pressure on both genes involved in immunity and host factors that are hijacked to promote infection . Therefore , alleles providing some measure of resistance to infection rapidly sweep through host populations . Evidence of past selective pressure can be observed at the molecular level by analyzing amino acid sequences for orthologous genes from a large number of related species [2 , 3] . Changes in the rate of nonsynonymous amino acid substitutions ( dN ) relative to the rate of synonymous changes ( dS ) —also referred to as ω—can indicate recurrent positive selection common to host-pathogen interfaces [2] . Other mechanisms of adaptation might be common at these interfaces as well . For example , evasion might proceed through alternate splicing events that result in isoforms missing surfaces recognized by pathogen inhibitors , but to date few studies have considered alternate mechanisms of adaptive evolution at host-pathogen interfaces . A set of host genes , termed pattern recognition receptors ( PRRs ) , initiate immune responses upon recognition of pathogen macromolecular structures ( Reviewed in [4 , 5] ) . Because such genes act as a “first line” of defense against pathogens , they have been subject to many genetic conflicts involving pathogen-encoded inhibitors that drive recurrent positive selection [2 , 6] . PRRs recognize pathogen-associated molecular patterns ( PAMPs ) , which include double-stranded RNA ( dsRNA ) and double-stranded DNA ( dsDNA ) produced by pathogens [4 , 5] . Multiple pathways have been described in mammals to detect microorganism-derived nucleic acids in the cell with most acting in the cytoplasm [4 , 5] . Two of these pathways involve the 2’-5’-oligoadenylate synthase ( OAS ) family of proteins [7] and the recently described cyclic GMP-AMP synthase ( cGAS ) [8] which appears to share a distant evolutionary relationship with OAS based on extensive overlap of protein structures [9–11] . Because PRRs like OAS and cGAS act as crucial sentinels of infection [7 , 12 , 13] , we set out to compare mechanisms by which they might adapt to pathogen-encoded inhibitors . OAS proteins are cytoplasmic dsRNA binding proteins that generate the second messenger 2’-5’ oligoadenylate ( 2-5An ) ( where n > = 2 and <20 ) upon RNA binding [7] . 2-5A leads to the dimerization and activation of the latent ribonuclease ( RNase L ) , which degrades host and viral mRNAs [13] . The core OAS unit consists of a nucleotidyltransferase ( NTase ) within the ClassI-CCase family and OAS1-C terminal domain [7 , 14 , 15] . The OAS family has a volatile evolutionary history across animals involving domain coupling and multiple gene duplication events [16 , 17] . In primates , the OAS family consists of OAS1 , OAS2 , OAS3 , and the catalytically inactive OASL , while rodent genomes contain 12 described OAS genes , eight of which are OAS1 paralogs [17] . OAS1 has one core OAS unit while OAS2 and OAS3 have two and three conserved core OAS units in tandem , respectively [7] . OASL encodes one OAS unit followed by a C-terminal domain consisting of two ubiquitin-like repeats and is enzymatically inactive [18 , 19] . Inhibition of RNA and DNA virus replication mediated by OAS proteins has been experimentally demonstrated [13 , 20 , 21] and a viral-encoded direct inhibitor of OAS1 has been described [22] . cGAS provides complementary surveillance as a cytoplasmic double-stranded DNA binding protein [12] that appears to dimerize upon binding of dsDNA [22 , 23 , 24] . DNA binding leads to the generation of the second messenger 2’-3’-cyclic GMP-AMP , also known as G ( 2’-5’ ) pA ( 3’-5’ ) p or cGAMP , from ATP and GTP by cGAS [11 , 25–28] . cGAMP activates the STimulator of Interferon Genes ( STING ) [25 , 29–31] , which in turn activates transcription of Type I Interferon genes through TBK1-IRF3 signals [8 , 29] . cGAS has been implicated in the control of DNA viruses [12 , 32 , 33] and retroviruses [34 , 35] , which is consistent with a strong preference for dsDNA substrates in vitro [12] . cGAS has also been linked to the detection of bacterial DNA [36 , 37] and even the inhibition of RNA viruses [32 , 38] . The initial characterization of cGAS highlighted several parallels with OAS mediated defenses ( Fig 1 ) : 1 ) nucleic-acid binding , 2 ) generation of a small nucleotide secondary messenger with a 2’-5’-phosphodiester bond , and 3 ) viral inhibition . Structural characterization of cGAS revealed that the three-dimensional x-ray crystal structures of OAS1 [14 , 15] and cGAS share extensive overlap [9–11 , 39] . In addition , recent structural characterization of the pathogenic protein DncV from Vibrio cholerae [40] , which also generates cGAMP , but differs in its phosphodiester linkage ( A ( 3’-5’ ) pG ( 3’-5’ ) p ) and the reaction order [40 , 41] , suggests a deep evolutionary history of the genes involving extensive sequence and functional divergence . Here , we focus on more recent evolution of cGAS and OAS to compare how these nucleic acid sensors have been influenced by selection from pathogens . Consistent with their vital role in immune surveillance [8 , 13 , 39] , we provide comprehensive evidence that cGAS and OAS1 have been under strong , recurrent positive selection in simian primates . We identified rapidly evolving amino acids sites at homologous positions of a common protein surface on cGAS and OAS1 proteins , supporting the surprising possibility of a shared recent evolutionary history of escape from antagonism by common pathogens . In addition , extensive evolutionary analyses of the primate OAS gene family revealed a novel model of adaptation through repeated gene fusion events . Furthermore , we identified multiple alternate spliced forms of cGAS , which maintain intact ORFs , including ones omitting an exon containing rapidly evolving residues . Together these results yield a wealth of insight into mechanisms of adaptive evolution for key nucleic acid sensors acting as a first line of host defenses against diverse pathogens .
Cyclic GMP-AMP synthase ( cGAS ) , previously referred to as C6ORF150 , provides a primary block against viruses [12 , 38] and intracellular bacteria [36 , 37] . Following binding of cytoplasmic dsDNA , cGAS generates cGAMP [12] ( Fig 1A ) , a secondary messenger that activates the interferon response via STING-TBK1-IRF3 signaling [12 , 25] . Although a study investigating the evolutionary origins of cGAS was recently reported [42] and a limited phylogenetic analysis was conducted [43] , little is known about the evolution of cGAS in primates , including humans . Given its crucial role as a DNA sensor triggering innate immunity , and related previous work , we hypothesized that cGAS has been subject to recurrent pathogen-driven evolution in primates . To determine if cGAS evolved under positive selection in primates , we cloned and sequenced cDNA of cGAS from 22 simian primates ( which includes several available primate cGAS sequences from public databases; see Methods and S1 Dataset ) to obtain a dataset representing approximately 40 million years of divergence ( Fig 2A ) . Next , we used a combination of maximum likelihood-based algorithms to assess ratios of non-synonymous to synonymous substitution rates ( dN/dS ) . The sites model implemented in Phylogenetic Analysis by Maximum Likelihood ( PAML ) [44] calculates dN/dS values per amino acid position and compares models that omit or accommodate elevated dN/dS to test for positive selection . Our alignment of primate cGAS orthologs revealed signatures of positive selection ( p-value <0 . 0001 ) ( S1 Table and S1 Fig ) . We further analyzed cGAS variants using the PARtitioning approach for Robust Inference of Selection ( PARRIS ) algorithm from the HyPhy package [45] , which also accounts for recombination events in the dataset , as well as BUSTED , a related measure to detect gene wide evidence of positive selection [46] . PARRIS and BUSTED revealed complementary evidence for positive selection on cGAS in the primate lineage ( p<0 . 017 and p<0 . 001 respectively ) ( S2 Table and S3 Table ) . To investigate whether cGAS has been subject to episodic positive selection during primate evolution , we calculated dN/dS values at each branch in our primate phylogeny using the free-ratio model in PAML . Consistent with a critical role as a host defense gene antagonized by specific viral inhibitors , cGAS exhibits dN/dS ratios exceeding one—a hallmark of positive selection—on various branches in hominoid , Old World , and New World monkey lineages ( Fig 2A ) . The branch separating ancestors of orangutans from humans , chimps , bonobos , and gorillas in the hominoid lineage was especially remarkable for its inferred episode of positive selection ( dN/dS = 8 . 01 , 22 inferred nonsynonymous ( N ) : 1 synonymous ( S ) amino acid changes ) . We carried out complementary analysis of episodic selection using the GA-Branch and aBSREL test in HyPhy [47] ( S2 Fig and S3 Fig ) , which also supports a history of episodic positive selection on cGAS in primates . Next we analyzed single amino acid sites in cGAS with evidence of positive selection . Amino acid positions with a dN/dS > 1 in innate immune factors have been experimentally demonstrated in several cases to be sites critical for protein-protein interactions between host and pathogen proteins [2 , 6] . Multiple amino acid sites in cGAS were inferred to have a dN/dS ratio significantly greater than 1 ( Fig 2B ) . The sites are distributed throughout the protein , a pattern common to other antiviral proteins [2] . Taking advantage of structural studies of cGAS , we mapped sites of selection to a solution of the crystal structure ( Fig 3A and S4 Fig ) . While the nucleic acid binding domains of other nucleic acid sensors appear under purifying selection [6] , we identified two sites under positive selection in cGAS that make contact with DNA ( S4 Fig ) . The remaining sites under positive selection are located at surface exposed residues on four distinct regions of the protein ( Fig 3A ) , consistent with previous observations of other nucleic acid sensors that adapt to evade pathogen-encoded inhibitors [2 , 6] . Biochemical and other experimental approaches have identified parallels between the OAS and cGAS pathways: 1 ) binding of viral nucleic acids , 2 ) generation of small nucleotide secondary messengers containing 2’-5’ phosphodiester bonds , and 3 ) use of these secondary messengers to activate an antiviral response [12 , 39] . In addition , crystallographic analyses of the cGAS protein [9–11 , 48] revealed extensive structural homology between OAS1 and cGAS despite limited overall sequence identity ( ~11% amino acid identity ) . Given these functional relationships , we hypothesized that cGAS and OAS1 might share similar modes of adaptation in response to viral antagonism . To test this idea , we carried out evolutionary analysis of OAS1 using cDNA sequences from the same panel of 22 primate species considered for our analysis of cGAS ( Fig 2C ) . Using PAML and PARRIS or BUSTED in HyPhy , we found that OAS1 is under positive selection in primates ( p<0 . 001 ) ( S4–S6 Tables and S5 Fig ) , consistent with previous reports with smaller datasets [49] . Branch specific analysis revealed multiple nodes across the primate phylogeny with elevated dN/dS values , similar to cGAS ( Fig 2C ) . We observed episodic positive selection of OAS1 in each primate lineage , including a notable bout leading to the chimpanzee lineage ( 12N:0S ) . Complementary analysis corroborated these findings ( S3 Fig and S6 Fig ) supporting a history of recurrent adaption of OAS1 in primates . Similar to cGAS , multiple amino acid positions are under selection in OAS1 ( Fig 2D ) . Phylogenetic analysis revealed roughly three times as many sites with statistically significant dN/dS ratios compared to our analysis of cGAS . The complementary MEME , and FUBAR tests ( HyPhy package ) identified multiple residues overlapping with PAML analysis under positive selection in OAS1 ( Fig 2D and S7 Table ) . These sites are distributed throughout the 364 amino acid protein , a pattern reminiscent of the antiviral Protein kinase R ( PKR ) [6] , and consistent with adaptation of OAS1 to many viral inhibitors . The arrangement of sites under positive selection can predict locations of binding interactions between host and pathogen proteins [2 , 6 , 50] . We mapped positively selected sites onto published x-ray crystal structures of human cGAS ( Protein Data Bank: 4KM5 ) [9] ( Fig 3A ) and human OAS1 ( Protein Data Bank: 4IG8 ) ( Fig 3B ) [14] solved in the apo-form , lacking nucleic acid activators and nucleoside triphosphate substrates . Consistent with the idea that rapidly evolving sites are involved in protein-protein interactions , sites with significantly elevated dN/dS mapped to protein surfaces of cGAS ( Fig 3A ) and OAS1 ( Fig 3B ) . For cGAS , the sites under selection localized to four distinct regions of the protein: 1 ) helix 1 and 2 , also referred to as the helical “spine” , 2 ) between helix 11 and 12 , 3 ) between β-sheet 4 and 5 , and 4 ) the unstructured N-terminus which was not crystallized [9] . For OAS1 most protein surfaces , including the helical “spine” , contain at least one rapidly evolving site . Because cGAS and OAS1 share extensive structural homology [9–11 , 48] , we examined an overlay of the structures to determine if any homologous amino acids or surfaces are rapidly evolving in both proteins . A merge of the two crystal structures highlighting sites under positive selection revealed analogous amino acid positions especially evident on the extended helical spine of the proteins . 4/11 sites in cGAS are located within the spine while 5/36 sites are located along the OAS1 spine as identified by PAML . Close examination of the structures ( Fig 3C ) suggests that three of these sites are analogous based upon the amino acid backbones and the directionality of the side chains: 1 ) Ser163/Ser11 , 2 ) Asp177/Cys25 , and 3 ) Thr181/Met28 ( human amino acid cGAS/amino acid OAS1 ) . Alignment of the cGAS and OAS1 amino acid sequences ( Fig 3D ) corresponding to the helices of the spine indicate that Ser163/Ser11 is an analogous position . Although the sequence alignment implies that Asp177/Cys25 and Thr181/Met28 may not be shared positions , the structure indicates otherwise . Permutation tests simulating co-occurrence of three analogous sites under positive selection in the helical spine suggest that such a pattern of overlap is unlikely to arise by chance ( p<0 . 001 ) ( see Methods and Materials , S1 Dataset ) . Therefore , comparing the location of sites under selection on the merged crystal structures identified distinct and overlapping surfaces under positive selection between cGAS and OAS1 . Similar to cGAS , some sites under positive selection in OAS1 ( Protein Data Bank: 4IG8 ) [14] contact dsRNA ( S7 Fig ) . There are two clusters of sites that contact the sugar phosphate backbone ( S7 Fig ) . The first cluster consisting of Arg47 and Cys54 resides at the C-terminus of the spine is in an unstructured loop between helix αN3 and β1 sheet . The second cluster of sites consists of Thr203 , Thr247 , and His248 with the latter two in an unstructured loop between helix αC5 and αC5 . Collectively , these sites are the first noted as being under positive selection at nucleic-acid binding surfaces for both cGAS and OAS1 . The overlap of positions under positive selection in cGAS and OAS1 prompted us to ask if these host defense genes might have a history of shared antagonism by pathogens during primate divergence . To investigate this idea , we took advantage of our datasets with 22 matching species to determine if there was a correlation between dN/dS values on matching branches of the primate lineage . This analysis uncovered evidence of a surprising correlation ( R = 0 . 57; S8 Fig ) between dN/dS values . We also tested the correlation of OAS1 and cGAS dN/dS values using the maximum likelihood method of Clark and Aquadro [51] . This method employs HyPhy to model a linear correlation between the branch dN/dS values of each gene and tests its significance by comparison to a null model with no relationship [52] . A likelihood ratio test between these models supported a correlation between OAS1 and cGAS ( P = 0 . 039 ) with the slope of in correlation model equal to 0 . 76 . Both this likelihood test and the linear regression of dN/dS estimates above support a positive correlation between OAS1 and cGAS . Together these results reveal unexpected parallels in the evolutionary history of OAS and cGAS . Given extensive positive selection on OAS1 , we set out to gain a more complete view of evolution of OAS genes . OAS1 belongs to a multimember gene family consisting of catalytically active OAS1 , OAS2 , OAS3 and the catalytically inactive OASL in primates [7] . The OAS genes are distinguished by the number of OAS units , which is the number of NTase and OAS1-C domains they contain through gene fusion events involving genomic tandem duplications ( OAS1-1 unit , OAS2-2 units , and OAS3-3 units ) ( Fig 4A ) [7] . Among the OAS family , the enzymatically inactive OASL gene uniquely encodes two ubiquitin repeats at its C-terminus [18 , 19] ( Fig 4A ) . All four members [7] have been implicated in virus inhibition with OAS1 , OAS2 , and OAS3 directly activating the 2-5A-RNaseL pathway [13] and OASL acting as an enhancer of RIG-I signaling in infected cells [53 , 54] . Because OAS1 has strong signatures of positive selection on protein surfaces , we were curious whether the other OAS family members also display signatures of positive selection , given the set of genomic fusion events that resulted in proteins that likely bury interacting surfaces . To determine the evolutionary history of the OAS family in primates , we carried out phylogenetic analysis on a matching panel of primates for all four genes from 11 primates with sequenced genomes and annotated OAS genes ( Fig 4B , S9 Fig , and S8–S9 Tables ) . Consistent with our observations of the more extensive dataset , OAS1 displayed strong evidence of positive selection across these 11 primates ( p<0 . 001 ) . OAS2 also displayed signatures of selection ( p<0 . 014 ) from analysis by PAML but not from complementary analysis with PARRIS ( p = 0 . 191 ) . A more thorough analysis of OAS2 consisting of 20 species further supports evidence for positive selection by all tests ( S10 Table and S11 Table ) . Moreover , the free-ratio model in PAML identified multiple lineages displaying dN/dS >1 across the 11 primates for both OAS1 and OAS2 ( Fig 4B ) . Notably in the 11 species analysis , 22 OAS1 sites were identified as having statistically significant dN/dS values as compared to only two sites for OAS2 using the PAML sites model ( Fig 4A ) . In contrast , a comparison of OASL sequences from primates did not exhibit significant signatures of positive selection ( p = 0 . 99 ) , while OAS3 was near the significance cut-off ( p = 0 . 08; S8 Table and S9 Table ) . A more comprehensive panel of OASL sequences , on par with our analysis of OAS1 and OAS2 , also failed to uncover signs of positive selection by all measures tested , including BUSTED ( S12 Table ) . Obtaining a larger panel of OAS3 orthologs was hindered by the large and repetitive nature of the three OAS units encoded by the gene . However , the BUSTED algorithm detected evidence of positive selection in OAS3 ( p = 0 . 024 , S13 Table ) . Analysis of sites under positive selection by PAML , MEME , and FUBAR in matching sets of 11 species for OAS1 , OAS2 , and OAS3 revealed reduced numbers of sites under selection in inverse correlation with the size of each protein ( Fig 4 and S7 Table ) . Therefore , in the divergence of the OAS family in primates , OAS1 revealed strong signatures of positive selection compared to OAS2 and OAS3 , consistent with the hypothesis that gene fusion events might obscure protein surfaces recognized by pathogen-encoded inhibitors . While gene fusions might provide adaptive escape through genetic addition , alternate splicing might provide escape through genetic subtraction . Alternate mRNA spliced variants ( spliceforms ) are well-documented for contributions to transcript diversity and regulation [55] . Alternative splicing is documented for antiviral proteins , including OAS genes [7] . However , OAS spliceforms have altered C-termini but maintain internal exon structures . By contrast , while cloning cGAS cDNAs , we identified multiple mRNA spliceforms lacking internal exons , some of which encoded intact ORFs . To assess the diversity of cGAS spliceforms across primates , we performed RT-PCR on cDNA extracted from interferon α-treated primary fibroblast cells ( Fig 5A and S10 Fig ) . We recovered several alternatively spliced cDNAs of cGAS in hominoid , Old World , and New World Monkey species ( Fig 5B and S10 Fig ) , consistent with a varied evolutionary history of transcript variation for cGAS . Sequencing confirmed a diverse set of cGAS mRNA spliceforms ( Fig 5C ) , many of which encode intact open-reading frames . Intriguingly , by comparing spliceform structures to a full-length cGAS gene structure we found cDNAs that lack exon 3 , which contains a set of sites under positive selection ( Fig 5C ) . Strikingly , all of the deletions we mapped remove entire helices or beta-strands at linker region boundaries , as opposed to within such domains , consistent with functional roles of the alternately spliced forms ( S11 Fig ) . These cGAS spliceform variants may represent a means to evade or inactivate counteract viral antagonism or perhaps even regulate cGAS .
OAS proteins are encoded by an ancient and dynamic gene family characterized by extensive duplications in some mammalian lineages [7 , 16 , 17] . It is hypothesized that the expansion of the OAS genes involved genomic duplications of the OAS core unit encoded by the first five exons from OAS1 [16] . Because each of these four proteins in primates ( OAS1 , 2 , 3 , and L ) detect dsRNA from a variety of viruses it is likely that these genes have been involved in genetic conflicts with several inhibitors from different viruses . Consistent with this hypothesis , we identified signatures of positive selection in OAS1 and OAS2 , but fewer sites under positive selection in OAS2 . Intriguingly , only a few sites appear under positive selection in OAS3 with even the more sensitive methods of detection ( Fig 4 and S7 Table ) , despite the fact that it synthesizes 2-5A upon dsRNA binding and can robustly block virus replication [7 , 13 , 57] . A potential explanation for these observations is that , despite antiviral functions , OAS2 and OAS3 have not been subject to as many pivotal genetic conflicts imposed by pathogen-encoded inhibitors , as is likely for OAS1 . Alternately , the domain duplications and gene fusion events that define OAS2 and OAS3 could themselves be adaptive steps in genetic conflicts over the divergence of primates . In this scenario , gene fusions of OAS2 and OAS3 bury protein surfaces via head-to-tail duplications and result in proteins resistant to viral inhibitors that target homotypic interactions ( Fig 6 ) . Consistent with this idea is the fact that OAS2 has roughly half as many sites under positive selection as OAS1 , and OAS3 half as many as OAS2 ( Fig 4 and S7 Table ) . Furthermore , while OAS1 appears active as a monomer , its activity might be enhanced or modulated by homotypic interactions or self-assembly [58] . As a consequence , some viral inhibitors might act to block OAS1 interactions . Future work will help determine whether , in addition to amino acid substitutions at individual sites under positive selection , gene fusions can provide single mutational steps that obscure protein surfaces from interactions with viral encoded inhibitors . As another potentially adaptive mechanism we identified multiple primate cGAS isoforms that encode intact ORFs . Intriguingly we found four isoforms that cleanly excise all of exon 3 from cGAS , which contains three sites under positive selection . Importantly , spliceforms that lack exon 3 but maintain exon 2 still contain the cGAMP catalytic residues . Based on published cGAS domain deletion data [12] and the presence of catalytic residues , it is possible that all identified cGAS spliceforms retain DNA binding activity owing to the presence of exon 1 . In addition , although spliceform 1 , 2 , and 4 ( Fig 5C and S11 Fig ) might synthesize cGAMP , it is possible that exon loss may disrupt protein folding . Indeed , it will be necessary to experimentally determine whether any cGAS spliceforms provide adaptive antiviral activity in future work . We posit that these isoforms may serve to remove surfaces antagonized by pathogens , consistent with the loss of several sites under positive selection or that the spliceforms may act as cGAS decoys that bind and sequester viral or bacterial inhibitors . Regardless of mechanism , alternative splicing has been noted in several cases for evasion of pathogens . Alternative splicing of human APOBEC3G , 3F , and 3H has been documented with varying impacts on antiviral activity and susceptibility to Vif antagonism [59 , 60] . Supporting the idea that removal of a protein surface may aid in evasion of viral antagonism , one APOBEC3F isoform was noted for resistance to Vif-mediated degradation [59] . On the other hand , another isoform is more susceptible to Vif-mediated degradation [59] . In addition , mutations leading to small deletions have been described for genes targeted by viruses . Of particular interest are a five amino acid deletion in the cytoplasmic tail of human tetherin , which lacks a site under positive selection , that disrupts the functional interaction with the lentivirus encoded antagonist Nef [61] , as well as alternately translated forms that resist HIV-1 [62] . Alternatively , it is possible that some of the cGAS spliceforms we identified may serve as antimorphic , negative regulators of cGAS signaling , in a manner analogous to the recently described mini-MAVS variants that modulate the activity of the innate defense factor MAVS [63] . Consistent with their critical role as PRRs [5 , 64] , our analysis indicates that both cGAS and OAS1 are rapidly evolving and reveals a potentially overlapping history of escape from antagonism by common viral inhibitors ( Fig 2 ) . Similar to other PRRs known to recognize nucleic acids as substrates [2 , 6] , both cGAS and OAS1 have sites distributed throughout the gene with signatures of positive selection ( Fig 2B and Fig 2D ) . A broad distribution of sites under positive selection is consistent with rapid evolution in response to interactions with inhibitors encoded by multiple pathogens as has been observed for several host defense genes , including the antiviral Protein kinase R [2 , 6] . That these signatures of adaptive evolution might reflect genetic conflicts with multiple inhibitors is consistent with the fact that OAS1 and cGAS detect multiple pathogens [15 , 32 , 33 , 35 , 38 , 65] . Furthermore , although cGAS exhibits only about a third the number of sites under selection compared to OAS1 , the robust signatures of selection we observed strongly predict the existence of multiple direct inhibitors of cGAS that have yet to be discovered . The localization of amino acid positions under positive selection can identify new interfaces involved in protein-protein interactions between host and pathogen factors [2] . Notably , although some protein domains may be dispensable for basal activity in the context of innate immunity , these domains may have as of yet undefined roles in regulation or may be targeted by pathogen factors to inactivate PRRs . For instance , the unstructured N-terminal 160 amino acids of cGAS are dispensable for cGAS activity in vitro and in vivo [12] . However , we identified several sites under positive selection within the cGAS N-terminus . Although the N-terminus is the least conserved domain of cGAS [12] , the statistically significant dN/dS ratios for these sites ( posterior probability >0 . 99 ) suggest that this domain may be a prime target for pathogen inhibitors of cGAS . In addition to identifying three structurally homologous rapidly evolving sites along the spine of both OAS1 and cGAS ( Fig 3 ) , we find evidence of an intriguing correlation between rates of evolution ( dN/dS values ) for matching branches in the primate tree ( Fig 2A , Fig 2C , and S8 Fig ) . This correlation of overall rates of evolution suggests that cGAS and OAS1 may have been subject to inhibition on the same primate branches—and perhaps even by the same pathogen or groups of pathogens—over the course of primate divergence . We hypothesize that double-stranded DNA viruses , such as poxviruses that replicate in the cytoplasm , represent strong candidates for encoding such inhibitors because they produce both double-stranded RNA and DNA and deploy inhibitors of immune functions . Consistent with this hypothesis is the observation that some viruses , such as poxviruses , are sensed by both cGAS [12 , 32 , 66] and OAS1 [21] . One known herpesvirus inhibitor of OAS1 is Us11 [22] , which in light of these data , is also an intriguing candidate that remains to be tested for inhibition of cGAS . The recent discovery of cGAS as the basis of a crucial nucleic acid sensing function has generated considerable interest in characterizing this newly described host defense [12 , 25] . Not only can cGAS sense and respond to a variety of pathogens , it has also been postulated to provide a means of spreading intercellular signals of infection via its generation of the secondary messenger cGAMP [67] . Our evolutionary analysis of cGAS over the divergence of primates is consistent with a vital function for cGAS in countering diverse pathogens . These data further predict the existence of at least several pathogen-encoded inhibitors of cGAS , which will be important to identify and characterize to gain a better understanding of the role of cGAS in countering infections . Another insight into cGAS evolution was the recent observation of extensive overlap in structure with the nucleic acid sensor , OAS1 [9–11 , 48] . These data suggest a deep evolutionary connection between the genes and also led us to discover a correlation of positive selection among cGAS and OAS1 during primate evolution as well as shared positions under positive selection . These data suggest a shared history of antagonism by inhibitors deployed by pathogens . Finally , both cGAS and OAS genes appear to adapt by additional mechanisms that drastically alter protein structure through alternate splicing or gene fusion events respectively . Taken together this study reveals central roles for cGAS and OAS genes as key sentinels of host defense in the descent of primates .
DNA Sequences from primates with sequenced genomes were retrieved from the NCBI database using BLAST searches or from the UCSC genome browser ( genome . ucsc . edu ) using BLAT searches . For other primates , sequences were obtained by Sanger sequencing of PCR amplicons using cDNA as a template or genomic DNA . Briefly , cDNA was synthesized using Superscript III mastermix ( Life Technologies ) or Maxima cDNA synthesis kit ( Thermo ) from total RNA extracted from fibroblast cell lines obtained from Coriell . Sequences of interest were PCR amplified from cDNA using Phusion High-Fidelity mastermix ( Thermo ) according to the manufacturer’s instructions and analyzed by 1–2% agarose gel electrophoresis . Amplicons of interest were excised , purified using Zymo gel extraction kit , and subject to Sanger sequencing or TOPO cloned ( Life Technologies ) followed by sequencing . For cGAS sequences from New World Monkeys , each exon was PCR amplified from genomic DNA . DNA sequences were analyzed using Geneious software . DNA sequence alignments were carried out using MUSCLE with default settings in Geneious . All sequences are available in S1 Dataset . Genbank accession numbers KR062003-KR062043 . DNA sequences were manually trimmed to remove indels and aligned using Geneious v6 . 1 . 7 ( Biomatters Ltd . ) using default settings . This alignment and a species trees representing currently accepted primate relationships [68] were used as input files for PAML analysis [44] and additional analyses using HyPhy software on Datamonkey . org [45] . We carried out permutation tests by generating two vectors representing cGAS and OAS1 of length 40 to represent 40 amino acids of the helical spine . Executing 1 , 000 , 000 trials we determined the probability of getting three sites overlapping between the two vectors ( the R script is included in S1 Dataset ) . Amino acids identified as being under positive selection using PAML and Datamonkey were mapped onto the three-dimensional crystal structures of the apoform of cGAS ( PDB: 4KM5 ) [9] and DNA co-crystal with mouse cGAS ( PDB:406A ) [24] and human OAS1 ( PDB: 4IG8 ) [14] using Chimera software ( http://www . cgl . ucsf . edu/chimera/ ) [69] . Total RNA from primate fibroblast cell lines treated with 1000 U of interferon/mL was extracted using the RNAeasy kit ( Qiagen ) . 1–2 μg of total RNA was reverse-transcribed using the Maxima cDNA synthesis kit ( Thermo ) . cDNA was diluted to a final volume of 50 μL of which 1 μl was used as a template for PCR . PCR was carried using Phusion according to the manufacturer’s protocol for 35 cycles using cGAS Fint 5’-accgggagctactatgagca-3’ and cGAS Rint 5’-tgtcctgaggcactgaagaa-3’primers . PCR amplicons were analyzed using 2% agarose gel electrophoresis . | A pathogen’s ability to infect new individuals within and across species is largely driven by its capacity to hijack cellular machinery and overcome the immune system . Pathogens have evolved multiple means to evade and shut down host immunity . Typically , mechanisms of inactivation involve direct interactions between host and pathogen factors . To escape inhibition over the course of generations , host factors frequently evolve in a manner that disrupts interactions at specific interfaces with pathogen factors . Likewise , pathogens adapt to restore such interactions , and these genetic tug-of-wars have been described as “molecular-arms races . ” Here we focus on the adaptation of two critical host immune factors , cGAS and OAS that share identity in protein structures despite very limited genetic similarity . Our analysis identifies a variety of ways , including amino acid changes on protein surfaces , by which these host factors appear to escape pathogen-mediated inhibition . Surprisingly , some amino acid substitutions are located at equivalent sites suggesting that cGAS and OAS may have adapted to evade common pathogen encoded inhibitors . These data also identify protein surfaces that are targeted by viruses to inhibit host immunity . Taken together our results indicate the existence of critical , yet-to-be identified viral antagonists of cGAS and OAS . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods",
"and",
"Materials"
]
| []
| 2015 | Overlapping Patterns of Rapid Evolution in the Nucleic Acid Sensors cGAS and OAS1 Suggest a Common Mechanism of Pathogen Antagonism and Escape |
Yaws and trachoma are targeted for eradication and elimination as public health problems . In trachoma-endemic populations mass administration of azithromycin can simultaneously treat yaws . We conducted a population-based prevalence survey in the five northernmost provinces of Vanuatu , where trachoma and yaws are suspected to be co-endemic . Clinical signs of trachoma were evaluated using the WHO simplified grading system , and skin examination with a serological rapid diagnostic test used to identify yaws . We enrolled 1004 households in 59 villages over 16 islands , and examined 3650 individuals of all ages for trachoma . The overall adjusted prevalence of trachomatous inflammation-follicular ( TF ) in 1–9 year-olds was 12 . 0% ( 95% Confidence Interval: 8 . 1–16 . 7% ) , and the overall adjusted prevalence of TT in those aged 15 years and greater was 0 . 04% ( 95% CI 0–0 . 14% ) . In multivariate analysis , the odds of children having TF was 2 . 6 ( 95% CI = 1 . 5–4 . 4 ) times higher in households with unimproved latrines , and independently associated with the number of children in the household ( OR 1 . 3 , 95% CI = 1 . 0–1 . 6 for each additional child ) . We examined the skin of 821 children aged 5–14 years . Two children had yaws , giving an estimated prevalence of active yaws in those aged 5–14 years of 0 . 2% ( 95% CI = 0 . 03–0 . 9% ) . Mass treatment with azithromycin is recommended in these provinces . Given the apparent low burden of yaws , integration of yaws and trachoma control programmes is likely to be useful and cost-effective to national programmes .
The World Health Assembly has targeted yaws and trachoma for global eradication and elimination-as-a-public-health-problem , respectively , by the year 2020[1] . Both are neglected tropical diseases found in rural areas of the world’s poorest countries . Trachoma is the world’s most common infectious cause of blindness , with an estimated 232 million people at risk of irreversible blindness in 51 countries[2] . Yaws is an infectious disease that causes disfiguring and often painful lesions of the skin and bones . Although data are limited , it is thought to be endemic in at least 13 countries , including three in the Pacific[3] . Trachoma is caused by ocular infection with the bacterium Chlamydia trachomatis . Infection is spread by direct or indirect contact[4][5] , and is linked to poor sanitation , crowded living conditions and inadequate access to water[6] . Infection in children is self-limiting and may present with lymphoid follicles on the upper tarsal conjunctivae , known as trachomatous inflammation-follicular ( TF ) , and/or intense inflammatory thickening of the conjunctivae , known as trachomatous inflammation-intense ( TI ) . Recurrent infection can lead to scarring of the tarsal conjunctivae and changes in the morphology of the eyelid , leading to trachomatous trichiasis ( TT ) , where the eyelashes turn inwards and rub on the globe . Over time , abrasion of the cornea by eyelashes can lead to an irreversible opacification of the normally clear cornea that may impair vision and eventually lead to blindness [7][8] . Yaws is caused by infection with the bacterium Treponema pallidum subsp . pertenue . It is transmitted from person to person through direct skin contact . It presents with papillomas or normally painless ulcers which , if left untreated , may be followed by multiple skin lesions and sometimes more severe tissue and bone disease . The majority of clinical cases are seen in children under 15 years old ( peak incidence 2–10 years ) , predominantly in isolated rural areas that have warm and humid climates [9] . The latest WHO clinical guidelines recommend that a confirmed case of yaws should lead to treatment of the entire community[10] . Implementation of these are challenging if health workers do not have a means to confirm suspected yaws cases . Traditional syphilis serology combines a highly specific treponemal antibody test ( TPPA/TPHA ) with a less-specific non-treponemal antibody test ( VDRL/RPR ) . The former test remains positive for life , whilst the latter varies over time , more accurately reflecting current disease activity . A reliable rapid diagnostic test ( RDT ) ( DPP-Syphilis Screen and Confirm Chembio , USA ) [11][12] has been developed with a sensitivity and specificity for treponemal antibodies of 88% and 95% respectively and for non-treponemal antibodies of 88% and 93% respectively . Mass treatment with azithromycin forms part of the WHO strategies for the elimination of trachoma and the eradication of yaws . In co-endemic regions , MDA with azithromycin reduces the community prevalence of both infections[13 , 14] , and therefore the integration of surveys for the two diseases is a logical step to provide baseline data for treatment planning . Vanuatu is a country comprising approximately 80 islands , situated approximately 1800km North-East of Australia ( Fig 1 ( Panel A ) ) . It has a total population of over 230 , 000 people and is composed of 6 provinces: Torba , Sanma , Penama , Malampa , Shefa and Tafea ( Fig 1 ( Panel B ) ) [15] . In 2009 , a trachoma rapid assessment was conducted in 17 sites in Tafea and Shefa province , indicating that trachoma was a likely to be significant public health burden [16] . A population-based prevalence survey was therefore required to determine the prevalence of TF in Vanuatu , to guide application of community-level interventions[17] . In 2011 and 2012 , the Vanuatu government reported 2 , 197 and 2 , 154 cases of yaws , respectively[18] , with the highest reported numbers of cases occurring in the Provinces of Tafea and Sanma . In 2013 , as part of the national yaws control programme , the government carried out mass drug administration ( MDA ) of azithromycin in Tafea province . We carried out a cross-sectional prevalence survey in order to estimate the baseline prevalence of trachoma and yaws in the 5 northern-most provinces of Vanuatu . The objectives were to estimate the prevalence of TF in children aged 1–9 years , the prevalence of TT in adults aged 15 years and greater , and the prevalence of yaws skin lesions in children aged 5–14 years . Due to the recent MDA with azithromycin , Tafea province was excluded from these surveys .
A two-stage cluster-randomised survey was conducted between 20th October to the 21st November 2014 , and followed the standard methodological principles of the Global Trachoma Mapping Project ( GTMP ) [19] . Excluding Tafea Province , the provinces of Vanuatu surveyed have a total estimated rural population of 144 , 276[20] and were therefore considered as one Evaluation Unit ( EU ) , in accordance with WHO guidelines for trachoma prevalence surveys [21] . Based on the anticipated number of children per household from the latest census data , 42 clusters were selected for survey . The islands were stratified in the sampling to ensure representation from each island group . Clusters were selected from island groups using probability proportional to size methodology . In each selected cluster , 30 households were selected randomly by drawing lots from a list of names of household heads compiled at the time of survey . If selected clusters contained fewer than 30 households , all households were invited to participate , and a number of randomly selected households from the next-nearest village sufficient to bring the total to 30 were randomly selected using the same methods . After obtaining informed consent , all individuals over the age of one year from selected households were examined for clinical signs of trachoma using the WHO simplified grading system[8][19] . Additionally , in those aged 5–14 years , graders performed an examination of the skin of the whole body , excluding the genitals and buttocks . Prior to the survey , graders were trained to perform a dermatology exam to recognise skin lesions consistent with active yaws , as classified in the WHO yaws pictorial guide [22] . In children with skin lesions consistent with active yaws , a finger-prick blood sample was collected and an RDT for yaws was performed . The RDT was considered positive if both the treponemal and non-treponemal component were positive in accordance with manufacturer’s instructions .
Data collectors were trained to collect Water , Sanitation and Hygiene ( WASH ) variables at household level[19] . Data were obtained by interviewing the head of the household or responsible adult . The questionnaire included items on access to and use of latrines , access to and use of water sources for drinking and washing , and the estimated time needed to retrieve water . The questionnaire was supported through use of observational data obtained by the data collectors on latrine type , handwashing facilities near to the latrine and the presence of soap and water at the handwashing facility at the time of survey .
Statistical analysis for risk factors was carried out using Stata 10 . 2 ( Stata Corp , TX , USA ) . Confidence intervals around prevalence estimates were constructed by bootstrapping the adjusted cluster proportions for TF and yaws in R 3 . 0 . 2 ( The R Foundation for Statistical Computing , 2013 ) . An exact binomial confidence interval was used to derive a TT upper confidence interval . Risk factor analysis was restricted to the binary outcome of the presence of TF in 1–9 year old children . Where present , TF is maximally found in this age-group , and control interventions are tiered by the prevalence in this age range . A mixed effects logistic regression model was used to evaluate the effect of each explanatory variable . Odds ratios were adjusted by household and cluster . Univariable associations were included in the multivariable model if p≤0 . 05 . Mantel-Haenszel Chi-square tests were used to assess collinearity between independent variables , with variables considered for exclusion if significant collinearity was demonstrated ( p≤0 . 05 ) . A multivariable model was developed using a step-wise inclusion approach , with the decision to retain a variable based on a likelihood ratio test between the included/excluded models using a significance level of p = 0 . 05 . Age and sex were included in the model a priori .
The overall study protocol was approved by the Executive Committee of the Ministry of Health of Vanuatu , and the ethics committee at the London School of Hygiene & Tropical Medicine . Both ethics committees considered the project to be a low-risk activity considered to be part of routine public health services , and therefore approved the use of verbal consent throughout the project . All adult subjects provided informed consent before examination . For all those examined under 18 years of age , consent was provided by their parent or guardian on their behalf . Consent was recorded electronically on a custom-made smartphone application used for all data collection . All individuals found to have TF or TI were offered treatment with oral azithromycin or topical tetracycline . All individuals found to have signs of TT were referred for further assessment at the nearest eyecare centre . All individuals found to have clinical evidence of active yaws were offered treatment with azithromycin . In addition , all villages where cases of yaws were found were offered total community treatment with azithromycin . All data were anonymised after collection for the purposes of analysis .
A total of 59 villages were surveyed over 16 islands . We invited 1004 households and 4095 individuals to participate , resulting 3650 ( 89 . 1% ) individuals consenting to examination ( S1 Table ) . 1869 ( 51 . 2% ) of those examined were female . 240 ( 5 . 8% ) household members were absent at the time of survey and 205 ( 5 . 0% ) did not give consent to examination . 135 ( 14 . 5% ) of 928 children aged 1–9 years had TF , with an age-adjusted TF prevalence of 12 . 0% ( 95%CI 8 . 1–16 . 7 ) Only two cases of TT were identified out of 2511 adults aged 15 years and greater . This represents an age- and sex-adjusted TT prevalence in adults of 0 . 04% ( 95%CI 0 . 0–0 . 14%; unadjusted prevalence in adults 0 . 08% ) . 3 additional TT cases were identified in children aged 5 , 6 and 14 years , which , after referral for ophthalmology opinion , were found to be false positives . The adjusted prevalence of TT in the whole population was 0 . 02% ( 95%CI 0–0 . 1%; unadjusted prevalence 0 . 04% )
1000 children aged 5–14 years were sampled for inclusion in the study . 821 ( 82 . 1% ) consented to examination . Twenty-two ( 2 . 7% ) had clinical signs consistent with yaws . Six children ( 0 . 7% ) had skin lesions consistent with yaws–three had papillomata , two had ulcers , one had squamous macules . A total of 18 children ( 2 . 2% ) had objective bony swelling on examination , with 16 having no associated skin lesions . Two children ( 0 . 2% ) met the criteria for clinically active yaws , having skin lesions consistent with yaws and objective bony swelling .
Five children with skin lesions consistent with yaws and had a subsequent RDT . Only 2 out of 5 children had a positive RDT . Of the two children with both skin lesions and bony swelling one had a positive RDT . Three children were reported to have had treatment for yaws with benzathine penicillin in the preceding 12 months . The estimated population-level prevalence of active yaws in 5–14 years was 0 . 2% ( 95%CI 0 . 03–0 . 9% ) .
Univariable analyses of associations of TF are shown in S2 Table . All children included in the study had access to a shared or private latrine . The final multivariable model ( S3 Table ) showed that TF was strongly and independently associated with a household’s use of an unimproved pit latrine ( OR 2 . 6 ( 95%CI 1 . 5–4 . 4 ) ) , the number of children in the household ( OR 1 . 3 –linear increase with each additional child ( 95%CI 1 . 0–1 . 6 ) ) , and a child’s age ( OR 1 . 1 –linear increase with each additional year ( 95%CI 1 . 0–1 . 2 ) ) .
Trachoma is endemic in Vanuatu . The prevalence of TF in children aged 1–9 years exceeds the threshold above which the WHO recommends MDA with azithromycin as well as facial cleanliness and environmental improvement initiatives . The age and sex standardised TT prevalence showed that TT was not a significant public health problem , and was below the WHO elimination threshold of 0 . 2% of the population aged 15 years and greater[23] . Consistent with other studies , we found that the number of children living in the household aged 1–9 years was strongly associated with TF[24 , 25] , the odds increasing with each additional child . Trachoma is thought to be spread through close contact , via exposure to infected ocular or nasal secretions . The reservoir of infection is predominantly in children[26–28] , and so it stands to reason that the more close contact opportunities children have with other potentially-infected children , the higher the chance that they will become infected themselves . This association was not maintained when considering the number of inhabitants of the household overall , consistent with contact with infected children preferentially facilitating spread . All households reported having access to some form of latrine and open air defaecation was not reportedly common . The WHO/UNICEF Joint Monitoring Programme ( JMP ) for Water Supply and Sanitation defines an unimproved form of sanitation to be that which fails to separate faecal material from human contact[29] . We found unimproved sanitation facilities in 385 households in which children aged 1–9 years were resident . After accounting for other possible confounders , children living in such households were 2 . 6 times more likely to have signs of TF in the survey . Trachoma has previously been associated with poor sanitation [6 , 30 , 31] . This is thought to be because areas of open defecation provide a preferential breeding ground for eye-seeking Musca sorbens flies which can mechanically transmit C . trachomatis between individuals as a passive vector . M . sorbens preferentially breeds on human faeces left lying on the soil , with faeces in pit latrines of any kind , including “unimproved” latrines , not constituting a site for oviposition [32 , 33] . In our data , poor latrine access may simply be a surrogate for deprivation overall , with its associated limitations in economic and educational opportunities . It was not possible to control for these variables in our analyses . In addition , any sanitation that is shared between households is considered to be unimproved . This is based on the belief that in shared facilities there are few incentives for individual users to keep the facility clean , and more vulnerable groups , including women and children , are less likely to use them[34 , 35] . In our survey the association between TF and the use of an unimproved latrine was not maintained in multivariable analyses if shared latrines alone were considered as a risk factor , suggesting that the latrine type available , rather than communal use conferred increased risk . We found an increasing odds of TF with increasing age in the range 1–9 years . Most studies report a higher prevalence and odds of TF in younger children[27 , 36] . Superficially this may be at odds with suggestions that young children are the main reservoir of infection in this population , although we are careful here to acknowledge the difference between ocular C . trachomatis infection and the clinical signs of active trachoma . In highly endemic areas , infection is likely to be first acquired in early infancy , with the age-of-first-acquisition on average increasing with decreasing prevalence . These differences in the age associations of infection and active trachoma are thought to be explained by a degree of immunity acquired in childhood , and socio-behavioural factors linked to hygiene practices and close contacts which may lessen the inflammatory response to , without changing the risk of , infection . It is possible that non-chlamydial bacteria such as Streptococcus pneumonia and Haemophilus influenzae also contribute to the follicular phenotype in individuals previously sensitised to C . trachomatis [37 , 38] . The phenotype seen may also be a response to other C . trachomatis serovars . Due to the small number of cases of active yaws in this study we did not conduct a risk-factor analysis for yaws , but poor sanitation and reduced access to hand-washing facilities was associated with an increased risk of yaws in a previous study in the Pacific [39] . This suggests that , in addition to MDA , the F&E components of a trachoma elimination programme may have additional synergistic benefits for yaws control . Although few in number , this survey identified cases of active yaws in Vanuatu . The aim for the WHO yaws control programme is eradication , therefore the identification and treatment of such cases is important . Yaws is seasonal , with higher numbers during the rainy season[40] . Our survey was conducted at the end of the dry season/beginning of the rainy season , and it is therefore possible that we have underestimated the true burden of disease . Additionally , the rapid diagnostic test was not performed on 16 individuals who had bony swellings but no skin lesions . The finding of bony swellings in isolation is consistent with active yaws , but this clinical sign was considered too non-specific to proceed to RDT in this survey . It was anticipated that the inclusion of those with this sign alone would lead to a prohibitively large number of RDTs being required , although in hindsight this would have only required a total of 22 RDTs ( children with either bony swellings or skin lesions or both ) —a finding that may guide future clinical/RDT surveys . Only 40% of children with skin lesions clinically consistent with yaws had a positive RDT , in keeping with a study conducted in Tafea province in 2013 , in which 35 . 5% of children with clinically suspected yaws had positive serology [41] . These findings suggest that relying only on clinical diagnosis of yaws is insufficient and efforts should be made to increase access to RDTs . Asymptomatic treponemal infection was not evaluated , so we do not have an estimate of the prevalence of latent yaws in this population . In survey data from other countries in the Pacific , there can be between 4–10 latent cases of yaws per active case found[3 , 39 , 42] . The routine clinical case notification rate ( which does not require serological confirmation ) from the five provinces included in this study is approximately 0 . 2 cases per 100 people , which is consistent with the results found in our survey ( personal communication- Fasihah Taleo ) Based on these findings , mass treatment with azithromycin and implementation of the F&E components of the SAFE strategy ( with the provision of improved latrines where needed ) are recommended in northern Vanuatu to eliminate trachoma and eradicate yaws as public health problems . Given the apparent low burden of yaws , integration of the two control programmes is likely to be useful and cost-effective to national programmes . | Yaws and trachoma are infectious diseases targeted by the World Health Organization ( WHO ) for eradication and elimination as a public health problem , respectively . Both diseases are found in the rural , isolated and underserved communities of the world’s poorest countries . The WHO strategy for trachoma elimination includes Mass Drug Administration ( MDA ) of the antibiotic azithromycin , with population-based prevalence surveys required to determine where interventions are needed . As yaws can also be treated with azithromycin , in co-endemic areas , azithromycin MDA will likely treat both infections . We conducted an integrated trachoma and yaws prevalence survey in establish the prevalence of both diseases in the 5 northern-most provinces of Vanuatu . The estimated prevalence of trachomatous inflammation—follicular ( TF , a sign of C . trachomatis infection ) in children aged 1–9 years was 12 . 0% , and the estimated prevalence of trachomatous trichiasis ( TT , advanced , potentially blinding disease ) in adults aged 15 years and above was 0 . 04% . Two children had yaws , giving an estimated prevalence of active yaws in those aged 5–14 years of 0 . 2% . Mass treatment with azithromycin is recommended in these provinces . Given the apparent low burden of yaws , integration of yaws and trachoma control programmes is likely to be useful and cost-effective to national programmes . | [
"Abstract",
"Introduction",
"Methods",
"Household",
"Risk",
"factors",
"Survey",
"Statistical",
"Analysis",
"Ethical",
"Considerations",
"Results",
"Clinical",
"Yaws",
"Treponemal",
"RDT",
"Results",
"Risk",
"Factors",
"Associated",
"with",
"TF",
"Discussion"
]
| [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"chlamydia",
"trachomatis",
"pathogens",
"immunology",
"tropical",
"diseases",
"geographical",
"locations",
"vanuatu",
"microbiology",
"health",
"care",
"treponematoses",
"bacterial",
"diseases",
"signs",
"and",
"symptoms",
"eye",
"diseases",
"chlamydia",
"neglected",
"tropical",
"diseases",
"sanitation",
"bacteria",
"bacterial",
"pathogens",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"inflammation",
"medical",
"microbiology",
"lesions",
"microbial",
"pathogens",
"yaws",
"immune",
"response",
"people",
"and",
"places",
"environmental",
"health",
"diagnostic",
"medicine",
"oceania",
"ophthalmology",
"biology",
"and",
"life",
"sciences",
"trachoma",
"organisms"
]
| 2017 | Integrated Mapping of Yaws and Trachoma in the Five Northern-Most Provinces of Vanuatu |
Snake bite is a major neglected public health issue within poor communities living in the rural areas of several countries throughout the world . An estimated 2 . 5 million people are bitten by snakes each year and the cost and lack of efficacy of current anti-venom therapy , together with the lack of detailed knowledge about toxic components of venom and their modes of action , and the unavailability of treatments in rural areas mean that annually there are around 125 , 000 deaths worldwide . In order to develop cheaper and more effective therapeutics , the toxic components of snake venom and their modes of action need to be clearly understood . One particularly poorly understood component of snake venom is aminopeptidases . These are exo-metalloproteases , which , in mammals , are involved in important physiological functions such as the maintenance of blood pressure and brain function . Although aminopeptidase activities have been reported in some snake venoms , no detailed analysis of any individual snake venom aminopeptidases has been performed so far . As is the case for mammals , snake venom aminopeptidases may also play important roles in altering the physiological functions of victims during envenomation . In order to further understand this important group of snake venom enzymes we have isolated , functionally characterised and analysed the sequence-structure relationships of an aminopeptidase from the venom of the large , highly venomous West African gaboon viper , Bitis gabonica rhinoceros . The venom of B . g . rhinoceros was fractionated by size exclusion chromatography and fractions with aminopeptidase activities were isolated . Fractions with aminopeptidase activities showed a pure protein with a molecular weight of 150 kDa on SDS-PAGE . In the absence of calcium , this purified protein had broad aminopeptidase activities against acidic , basic and neutral amino acids but in the presence of calcium , it had only acidic aminopeptidase activity ( APA ) . Together with the functional data , mass spectrometry analysis of the purified protein confirmed this as an aminopeptidase A and thus this has been named as rhiminopeptidase A . The complete gene sequence of rhiminopeptidase A was obtained by sequencing the PCR amplified aminopeptidase A gene from the venom gland cDNA of B . g . rhinoceros . The gene codes for a predicted protein of 955 amino acids ( 110 kDa ) , which contains the key amino acids necessary for functioning as an aminopeptidase A . A structural model of rhiminopeptidase A shows the structure to consist of 4 domains: an N-terminal saddle-shaped β domain , a mixed α and β catalytic domain , a β-sandwich domain and a C-terminal α helical domain . This study describes the discovery and characterisation of a novel aminopeptidase A from the venom of B . g . rhinoceros and highlights its potential biological importance . Similar to mammalian aminopeptidases , rhiminopeptidase A might be capable of playing roles in altering the blood pressure and brain function of victims . Furthermore , it could have additional effects on the biological functions of other host proteins by cleaving their N-terminal amino acids . This study points towards the importance of complete analysis of individual components of snake venom in order to develop effective therapies for snake bites .
A detailed understanding of the components of snake venom is important both for acquiring a more complete understanding of the pathology of envenoming and to aid in the development of improved treatments for snake bites , which are the cause of many deaths throughout the world each year . Snake venoms are complex mixtures of enzymatic and non enzymatic proteins , together with other components such as carbohydrates , lipids , nucleosides and metals . These function together to immobilize , kill and digest prey [1] . Some proteins such as hyaluronidase and L-amino acid oxidase are present in all 4 snake families ( Viperidae , Atractaspididae , Elapidae and Colubridae ) , while other proteins are restricted to certain families . For example viper venom has predominantly hemorrhagic , hypotensive and inflammatory effects , caused by the metalloproteases , serine proteases and C-type lectins present , while neurotoxins , which affect the central nervous system , are major constituents of elapid snake venoms . Despite extensive studies on individual proteins and many recent proteomic and transcriptomic analyses of snake venoms [2] there remains much to be learned about the components of snake venom and their functions . One of the least understood enzyme constituents of snake venoms is aminopeptidases . These enzymes remove one or more specific N-terminal residues from target proteins or peptides . For example aminopeptidase L ( APL ) removes an N-terminal leucine residue , aminopeptidase A ( APA ) removes an acidic N-terminal residue , aminopeptidase B ( APB ) removes a basic N-terminal residue , and aminopeptidase N ( APN ) removes a neutral N-terminal residue , typically alanine . There have been several reports of aminopeptidase activities present in venoms from elapids and vipers [3] , [4] , [5] , [6] , [7] , [8] , and a fraction exhibiting aminopeptidase A activity has been separated from the venom of Gloydius blomhoffi brevicaudus , a member of the Crotalinae ( pit viper ) subfamily of vipers [7] . A cDNA sequence from this snake represents the only determined sequence to date of a reptile venom aminopeptidase A . Interestingly , none of the complete snake venom proteomic studies done thus far has identified aminopeptidases [9] . Further study of such enzymes is important in order to understand their role within snake venom , and to help in the development of improved treatments for snake bite . Knowledge about this enzyme may also contribute to our knowledge of related mammalian enzymes such as mammalian APA , which is a candidate target for the treatment of hypertension . Here we demonstrate aminopeptidase activity in the venom of B . g . rhinoceros , a member of the Viperinae ( true viper ) subfamily of vipers and report for the first time the complete purification of a snake venom aminopeptidase which we have named rhiminopeptidase A . We have functionally characterised this enzyme and obtained cDNA and amino acid sequences . Since structural information is lacking both for snake venom aminopeptidases and for their mammalian homologues , we have created a structural model for rhiminopeptidase A . This , together with the sequence , is consistent with the ability of this enzyme to function as a calcium-modulated aminopeptidase A and could inform efforts in the future to develop improved treatments both for snake bites and for hypertension .
Lyophilized venom of B . g . rhinoceros was obtained from an existing collection of pooled venom labelled ‘Bitis gabonica Nigeria Box 13/Bot 10’ which was stored at the Liverpool School of Tropical Medicine , Liverpool , UK ( LSTM ) . Protein molecular weight markers and polyvinylidene fluoride ( PVDF ) membranes were from Bio-Rad . The low molecular weight column calibration kit , enhanced chemiluminescence ( ECL ) reagents and ECL glycoprotein detection module were from GE Healthcare . N Glycosidase F enzyme was from Roche Diagnostics Limited , and trypsin , thrombin and the GoTaq PCR Core System were from Promega . L-Glutamyl-7-amido-4-methylcoumarin ( Glu-AMC ) and L-aspartyl-7-amido-4-methylcoumarin ( Asp-AMC ) were obtained from Bachem . Macrosol and Stura crystallisation screening kits were from Molecular Dimensions Ltd and Wizard screening kits were obtained from Emerald BioSystems . L-Leucine-7-amido-4-methylcoumarin hydrochloride ( Leu-AMC ) , L-Arginine-7-amido-4-methylcoumarin hydrochloride ( Arg-AMC ) and L-Alanine 7-amido-4-methylcoumarin trifluoroacetate salt ( Ala-AMC ) were obtained from Sigma-Aldrich . All other chemicals used were analytical grade from Sigma Aldrich . Reducing SDS-PAGE , gel staining and immunoblotting on to PVDF membrane were all performed using standard techniques [10] . 50 mg of B . g . rhinoceros venom were dissolved in 2 ml of 0 . 02 M Tris-HCl pH 7 . 4 and loaded on to a Sephacryl HR 200 gel filtration column . 31ml fractions were collected using 0 . 02 M Tris-HCl pH 7 . 4 at a speed of 1 ml/minute after 40 ml of pre-elution . 100 µl of selected fractions were analysed by 10% reducing SDS-PAGE . The purified protein was quantified using the Bradford method [11] . This analysis was carried out at M-Scan Limited , Wokingham , UK . A band containing purified protein from a colloidal Coomassie stained 10% SDS-PAGE gel was sliced , reduced , alkylated and subjected to tryptic digestion . The resulting peptides were extracted and analysed by nano LC-ES-MS/MS using a Dionex Ultimate 3000 HPLC system coupled to a Q-TOF mass spectrometer . Data-dependent acquisition was utilised and peptides eluting from the nano LC column were automatically fragmented in the Q-TOF by recognition of their doubly or triply charged ion states . Preset charge and mass dependent collision voltages were applied by the software , which also allowed simultaneous MS/MS of up to 3 peptides . Processed spectral data were used to interrogate the mass spectrometry sequence database ( MSDB ) housed locally , using MASCOT software [12] . Several spectra were also checked manually in order to confirm automated peptide assignments . Glu-fibrinopeptide fragment ions in MS/MS mode were used to calibrate the instrument over the appropriate mass range . Aminopeptidase activities of venom and purified protein were measured using fluorescent substrates ( Leu-AMC to measure APL activity , Arg-AMC to measure APB activity , Ala-AMC to measure APN activity and Glu-AMC and Asp-AMC for APA activity ) as previously described [7] , [10] , [13] . Ten micrograms of venom or purified protein were mixed with various concentrations of substrate solutions and incubated at 37°C . Experiments were performed with and without 1 . 2 mM calcium chloride present . The amount of 7-amido-4-methylcoumarin ( AMC ) released was measured at different time intervals by spectrofluorimetry ( FLUOstar OPTIMA , Offenburg , Germany ) at an excitation wavelength of 366 nm and an emission wavelength of 460 nm . The kinetic parameters were calculated from Lineweaver-Burk plots . The results are represented by Km , kcat and kcat/Km values . All measurements were obtained from three separate experiments . To test the effect of various metal ions and protease inhibitors on aminopeptidase activity , the purified protein ( 10 µg ) was pre-incubated with various concentrations of metal ions or inhibitors at 37°C for 5 minutes . Then , 50 nM of Glu-AMC was added to each sample and incubated for 10 minutes at 37°C . The amount of AMC liberated was measured as mentioned above . Twenty micrograms of native protein were subjected to ( i ) 10% reducing SDS-PAGE followed by transfer to a PVDF membrane and ( ii ) glycosylation detection using the ECL glycoprotein detection module according to the manufacturer's protocol . Deglycosylation was achieved by mixing 100 µg of purified protein in 0 . 02 M Tris-HCl pH 7 . 4 with 5 units of N Glycosidase F in a total volume of 50 µl and incubating for 10 hours at 37°C . The cDNA of the B . g . rhinoceros venom gland was obtained from the cDNA library of B . g . rhinoceros ( LZ7 ) which had been created for another study and was maintained at LSTM , Liverpool . Specific primers were designed based on the untranslated regions of the aminopeptidase A gene sequence from G . b . brevicaudus ( NCBI accession number: AB262071 ) and synthesized by Sigma Aldrich , Poole , UK . The sequences of the primers are: forward primer - 5′CAAGCAAAAGCAGATGAGAAGGAA3′ and reverse primer - 5′TCAGAGTGGCGAATA TGTGGTTA3′ . These were used to amplify the aminopeptidase A gene by PCR ( 25 cycles ) using denaturation at 94°C for 30 seconds , annealing at 54°C for 30 seconds , extension at 72°C for 3 . 5 minutes and a final extension at 72°C for 10 minutes . The amplified product was analysed by 1% agarose gel electrophoresis and sequenced by Cogenics Limited , Essex , UK . The nucleotide sequence was translated and the molecular weight and estimated pI of the predicted protein were analysed using DNASTAR Lasergene software version 7 [14] . Similar sequences in the NCBI database were identified using BLAST . Multiple sequence alignments were performed with ClustalW2 [15] using default parameters of KTUP and gap opening and gap extension penalties . Transmembrane helices were predicted using PolyPhobius [16] . Interproscan [17] was used to identify functional domains within the sequence . Predicted N-Glycosylation sites were identified using the NetNGlyc 1 . 0 server ( http://www . cbs . dtu . dk/services/NetNGlyc/ R . Gupta , E . Jung , S . Brunak manuscript in preparation ) . Purified rhiminopeptidase A from B . g . rhinoceros venom ( in 0 . 02 M Tris-HCl pH17 . 4 ) was concentrated to 9 mg/ml using centrifugal membrane concentrators . Initial crystallisation screening was performed manually in 2 plus 2 µl drops using the hanging drop vapour-diffusion method in 24-well Linbro plates against the following commercial screens at 18°C: Macrosol I and II , Stura Footprint Screen I and II and Wizard I and II . From the 288 conditions screened , three hits ( one from each screen ) were found showing small rod-like crystals . Crystals typically appeared between 7 and 14 days . The most promising condition was Macrosol I No . 9 [8% ( w/v ) PEG-3500 , 0 . 1 M sodium acetate pH 4 . 5 , 0 . 2 M ammonium acetate] , which gave crystals with dimensions 50×20×10 µm . Micro-seeding was performed to increase the size and quality of the crystals obtained in screening . X-ray diffraction data were collected on an ADSC Q315 CCD detector at 100K on the macromolecular crystallography Beamline ID14-EH1 ( ESRF , France ) . Integration and scaling of the diffraction data were performed using MOSFLM and SCALA , respectively [18] , [19] . Secondary structure prediction was performed using PSI-PRED [20] . BLAST , genTHREADER [21] and Phyre [22] were used to identify the best template structure to use for creating a structural model . The template selected was the X-ray crystallographic structure of tricorn interacting factor F3 from the archaeon Thermoplasma acidophilum ( PDB code 1z5h ) [23] . The alignment of rhiminopeptidase A with tricorn interacting factor F3 was determined using alignments obtained from mgenTHREADER and Phyre . Three-dimensional structural models were constructed using the MODELLER comparative protein structure modelling program [24]; these were evaluated using Procheck [25] and ModFOLD [26] and the best quality model selected . Models were visualised using PyMOL ( DeLano Scientific ) .
SDS-PAGE of whole B . g . rhinoceros venom ( Fig . 1A ) shows a number of bands including a prominent well-resolved band at an approximate molecular mass of 150 kDa , which is approximately what one might expect for an aminopeptidase , consistent with previously characterised aminopeptidases ( 120–185 kDa [27] ) . This venom was fractionated using a 1 . 6 cm×95 cm Sephacryl HR 200 gel filtration column ( Fig . 1B ) and 14 fractions were analysed by SDS-PAGE ( Fig . 1C ) . A protein with molecular weight 150 kDa was found purified to apparent homogeneity on SDS-PAGE in fraction 1 ( Fig . 1C ) and partially purified in fraction 2 . Two sub-fractions between fractions 1 and 2 also contained pure 150 kDa protein ( data not shown ) . These 2 sub-fractions , together with fraction 1 , were pooled and concentrated by ultrafiltration in order to obtain the maximum amount of pure 150 kDa protein ( Fig . 1D ) . Using the Bradford assay [11] the estimated amount of protein obtained from 50 mg of whole venom was 1 . 3 mg . Sequence information was obtained by nano LC-ES-MS/MS of peptides derived by tryptic digestion of the 150 kDa gel band . Interrogation of the mass spectrometry database ( MSDB ) with the MS and MS/MS peak lists using MASCOT software identified a hypothetical protein from Pongo pygmaeus ( Bornean orang-utan; MSDB accession number Q5R7D5_PONPY ) ( P-value = 5×10−18 ) as the only non-contaminant protein . The sequence of this protein was 100% identical to that of a glutamyl aminopeptidase from Pongo abelii ( Sumatran orang-utan ) found in the NCBI sequence database ( accession number NP_001126365 . 1 ) . Manual sequencing of an individual MS/MS spectrum yielded the following sequence: GFI/LDDAFAI/LAR . Protein-Protein BLAST using the sequence GFIDDAFALAR showed that it was 100% identical to a fragment of aminopeptidase A from G . b . brevicaudus [7] . These results , together with the estimated molecular mass , suggest that the 150 kDa protein might be an aminopeptidase . To further investigate the function of the 150 kDa protein , functional assays for the main aminopeptidase activities ( APA , APB , APL and APN ) were performed on the whole B . g . rhinoceros venom and on the purified protein using fluorescent substrates as previously described [7] , [13] ( Fig . 2A ) . The venom showed significant levels of all the aminopeptidase activities tested , with APN>APA ( Glu-AMC ) >APL>APB>APA ( Asp-AMC ) , while the 150 kDa protein displayed relatively high APA ( Glu-AMC ) and APN activities , moderate APA activity towards Asp-AMC , very low APB and negligible APL activities . In the presence of calcium chloride the APA activities of both the venom and the pure protein towards both Glu-AMC and Asp-AMC increased substantially ( by at least 90% for Glu-AMC and more than 160% for Asp-AMC activity ) , while all other aminopeptidase activities of the protein were negligible , suggesting that in the presence of calcium the enzyme shows increased specificity towards acidic amino acids . A calcium titration showed that the highest aminopeptidase activity towards Glu-AMC was obtained using 1 . 2 mM calcium chloride ( data not shown ) . In the presence of calcium the APN activity of the venom was also negligible , but the APL activity was reduced by 25% and some APB activity remained . The observed activities of the protein were consistent with the partial sequence identification and provide further evidence that the 150 kDa protein is an aminopeptidase A . Thus we have named this protein ‘rhiminopeptidase A’ . The detection of APL and APB activities in the whole venom both with and without calcium present suggests the presence of one or more further aminopeptidases in the venom of B . g . rhinoceros . Table 1 shows the enzymatic parameters of rhiminopeptidase A measured in the presence and absence of 1 . 2 mM CaCl2 . In the presence of Ca2+ ions , the hydrolytic activity of rhiminopeptidase A was enhanced by increasing the kcat value and decreasing the Km value . However , these data confirm that the enzyme is more active against Glu-AMC than Asp-AMC . Fig . 2B shows the effects of various divalent cations on the activity of rhiminopeptidase A . When rhiminopeptidase A was incubated with Glu-AMC in the presence of Ca2+ ions , the hydrolytic activity increased . However , in the presence of Zn2+ ions the hydrolytic activity was strongly reduced , and eliminated completely at 0 . 5 mM . Co2+ and Cu2+ ions showed inhibition at higher concentrations and Mn2+ and Mg2+ ions showed no inhibitory effects on rhiminopeptidase A activity against Glu-AMC . These data suggest that Ca2+ is the only divalent cation to enhance the hydrolytic activity of rhiminopeptidase A towards Glu-AMC and that Zn2+ is the strongest inhibitor . To analyse the effects of various protease inhibitors , rhiminopeptidase A was treated with amastatin ( APL and APA inhibitor ) , bestatin ( APL inhibitor ) , leupeptin ( serine/cysteine protease inhibitor ) , pepstatin A ( aspartic acid protease inhibitor ) and PMSF ( serine protease inhibitor ) followed by incubating with Glu-AMC . As for known mammalian aminopeptidases , amastatin inhibited the Glu-AMC activity of rhiminopeptidase at a concentration of 50 µM ( Fig . 2C ) , however , the other inhibitors had negligible effect on this activity . This confirms that the Glu-AMC activity of the purified rhiminopeptidase A is due to the activity of an aminopeptidase A and is not caused by any other enzyme . Furthermore , 10 µM amastatin inhibited completely the Glu-AMC activity of 10 µg of venom , confirming that the Glu-AMC activity observed in the snake venom comes exclusively from aminopeptidase A . As many snake venom enzymes are known to be glycosylated [28] and the aminopeptidase A from G . b . brevicaudus venom was predicted to be glycosylated [7] , glycosylation detection was performed on rhiminopeptidase A using an ECL glycosylation detection module on a PVDF membrane . Rhiminopeptidase A was found to be substantially glycosylated . Thus deglycosylation was performed on the enzyme using N Glycosidase F and the resulting samples were run in 10% SDS-PAGE along with native rhiminopeptidase A . Fig . 3A shows that the estimated molecular mass of the deglycosylated protein was approximately 102 kDa , thus around 48 kDa ( 32% ) of the molecular mass of the native purified protein is due to glycosylation . Another replicate gel was transferred to a PVDF membrane and subjected to glycosylation detection using the ECL glycosylation detection module . The lack of signal on the deglycosylated protein confirms the deglycosylation , while a signal was detected in the lane corresponding to the native rhiminopeptidase A ( Fig . 3B ) . In order to obtain the complete sequence of rhiminopeptidase A cDNA was obtained from the stored venom gland cDNA library of a single B . g . rhinoceros snake ( LZ7 ) . Primers were designed based on the untranslated region of the G . b . brevicaudus aminopeptidase A gene [7] . PCR with these primers resulted in a product of approximately 3350 bp , which was consistent with the expected size of the aminopeptidase A gene . The nucleotide sequence of the amplified product contains 3232 nucleotides with an open reading frame between bases 66 and 2945 which encodes a polypeptide of 955 amino acids with an estimated molecular mass of 110 . 5 kDa and a predicted isoelectric point of 6 . 08 . The latter coincides with the isoelectric point ( 6 . 2 ) of the native protein in the venom as determined by liquid phase isoelectric focussing ( data not shown ) . Comparison of computer generated tryptic digested peptides derived from this amino acid sequence with the MS/MS data from the purified protein showed matches which covered 45% of the amino acid sequence , strongly suggesting that the sequence corresponds to the protein we have purified ( Figure S1 ) . Further , the partial sequence obtained from mass spectrometry is identical to the region of the sequence between amino acids 653 and 663 ( underlined in Fig . 4 ) . The absence of any other proteins with molecular weights around 150 kDa in the venom of B . g . rhinoceros or any other isoforms in the PCR amplified products provides further evidence that the gene we have sequenced corresponds to the rhiminopeptidase A protein which we had purified . The nucleotide sequence for the rhiminopeptidase A gene has been deposited in the EMBL database under Accession Number FN666431 . The protein sequence is 93% identical to that of aminopeptidase A from G . b . brevicaudus; 60–64% identical to aminopeptidase As from pig , cow , human , rat , mouse and orang-utan ( Fig . 4 ) ; and 63–64% identical to predicted aminopeptidase As from horse , chimpanzee , Rhesus macaque , dog , platypus , opossum , chicken and Zebra finch . Consistent with our experimental identification of glycosylated moieties attached to rhiminopeptidase A , ten potential N-glycosylation sites were predicted in the protein sequence . Nine of these are shared with the G . b . brevicaudus APA sequence and three are conserved in all the sequences in the alignment . There are 10 cysteine residues in the rhiminopeptidase A sequence , of which 8 are conserved in all the sequences compared . PolyPhobius [16] predicts a single transmembrane segment close to the start of the sequence , which is characteristic of a type II integral membrane protein . The recently cloned sequences of G b . brevicaudus APA and DPP IV were also predicted to be type II integral membrane proteins [7] , [29] and exosome-like vesicles containing these proteins were subsequently detected in the G . b . brevicaudus venom [30] . Analysis using InterProScan [17] suggests that the protein is a zinc metallopeptidase belonging to MEROPS peptidase clan MA ( E ) ( “gluzincins” ) family M1 . Gluzincin aminopeptidases are characterised by a consensus zinc binding motif HEXXHX18E [31] , of which the two histidines and the final glutamic acid have been shown to act as the zinc ligands [32] , [33] , and a conserved GAMEN motif [34] . These motifs are conserved in rhiminopeptidase A and its relatives ( Fig . 4 ) . The alignment also shows conservation of several key functional residues: Glu352 within the GAMEN motif and Glu215 , which have been shown to interact with the N-terminal amine of the substrate during catalysis [35] , [36]; Thr348 which is involved in substrate specificity [37]; and Tyr471 , which is involved in the stabilizing tetrahedral intermediate of the substrate during catalysis [38] . For consistency with other literature , the sequence numbering used here is for the mouse APA sequence ( NCBI accession number NP_031960 ) . Thus the sequence of rhiminopeptidase A contains the key amino acids required for it to function as a calcium-modulated aminopeptidase A . Crystallisation trials have so far yielded only small crystals of rhiminopeptidase A . However , the crystals obtained were cryoprotected in mother liquor containing 25% ( w/v ) glycerol by quick transfer directly from the hanging drop and X-ray diffraction data were collected . Data analysis revealed a final resolution of 7 . 5 Å ( Table 2 ) . The crystals were of sufficient quality to show that the protein crystallised in the monoclinic space group P21 with unit cell dimensions a = 97 . 6 , b = 67 . 3 , c = 186 . 6 Å , and β = 101 . 9° under the following conditions; 8% ( w/v ) PEG-3500 , 0 . 1 M sodium acetate pH 4 . 5 , 0 . 2 M ammonium acetate . Solvent content analysis using the programme MATTHEWS_COEF [39] suggested a solvent content of 30% with two molecules in the asymmetric unit . In the absence of an X-ray crystal structure we employed structure prediction tools to obtain a structural model for the rhiminopeptidase A protein . Secondary structure prediction using PsiPred 2 . 6 [20] and the secondary structure prediction tools used by Phyre [22] suggest that the protein has both α-helical and β-sheet regions , with the N-terminal regions being predominantly β-sheet and the C-terminal region being predominantly α-helical . Using BLAST the most similar protein with a known structure is tricorn interacting factor F3 from the archaeon Thermoplasma acidophilum ( PDB code 1z5h , [23] ) , which shares 31% sequence identity with rhiminopeptidase A . Tricorn interacting factor F3 is an 89 kDa zinc aminopeptidase with a strong preference for glutamate at the P1 position of the substrate [40] and is involved in the proteasomal degradation pathway of T . acidophilum . This structure was also confidently and consistently selected as the best template for modelling rhiminopeptidase A by several fold recognition servers [Phyre [22] ( e-value = 0 . 0 ) , mgenTHREADER [21] ( p<0 . 0001 ) ] . Structural models of rhiminopeptidase A were created using MODELLER software [24] and the best model was selected based on the scores obtained using ModFold [26] and Procheck [25] . The model ( Fig . 5A ) includes residues 95 to 944 of the amino acid sequence and the predicted structure is very similar to that of tricorn interacting factor F3 ( r . m . s . d . = 0 . 35 Å ) . Like F3 the predicted structure consists of 4 domains which together form a hook-like structure: an amino terminal saddle-shaped β-sheet domain , a mixed α and β catalytic domain , a β-sandwich domain and a large C-terminal α-helical domain . The sequences of the two proteins are most similar ( 46% identity ) in the catalytic domain . Within the catalytic domain the three proposed zinc-binding residues in rhiminopeptidase A align with identical residues in the F3 sequence; these are in identical positions and orientations in the modelled rhiminopeptidase A structure and that of F3 and thus positioned appropriately to bind zinc ( Fig . 5B ) . The residues proposed to be involved in substrate binding , calcium binding and substrate specificity are also in nearly identical positions in both structures . The final domain of F3 was found to be very flexible , and crystal structures were determined with the C-terminal portion of this in three different conformations which may relate to the structural changes which occur during substrate binding . Our structure was modelled based on the most open of these conformations but whether rhiminopeptidase A shares the flexibility of F3 in this region remains to be determined . The model co-ordinates have been deposited in the PMDB under accession number PM0076268 . Attempts were made to use the derived model ( in its entirety and by individual domain ) in a molecular replacement strategy to obtain a crystallographic structure solution using the current diffraction data . These have proved unsuccessful , likely owing to the low resolution limits of the current data and possibly due to the flexibility of the protein itself . Further efforts are underway to produce better diffraction quality crystals .
The data presented here demonstrate for the first time the presence of aminopeptidase activity in the venom of B . g . rhinoceros . We have purified an aminopeptidase A from this venom and shown that it has a relatively broad specificity ( APN , APB and APA activities ) in the absence of calcium , but a higher and very specific APA activity in the presence of calcium . This is consistent with the known calcium modulation of APAs [7] , [41] , [42] , [43] , [44] . As is the case for human aminopeptidase A , zinc ions act as an effective inhibitor of the APA activity of rhiminopeptidase A and copper and cobalt ions have a moderate inhibitory effect [44] . The aminopeptidase activity of the B . g . rhinoceros venom as a whole is different from that of the purified protein; the detection of APL and APB activity even in the presence of calcium suggests the presence of further aminopeptidases in this venom . APL and APB activities have been reported in the venoms of several other snakes [4] , [6] , [7] , [8] but to date no-one has identified the specific enzymes responsible . It is noteworthy that neither this protein nor any proteins which would have the APL or APB activities observed in the B . g . rhinoceros venom have been identified by proteomic studies of this venom [9] . There is also no reference to any aminopeptidases in the catalogue of transcripts encoded by the B . gabonica venom glands [45] . One possible reason for these discrepancies is that the approaches used for large scale identification of proteins or genes may make it difficult to detect low abundance , high molecular mass glycosylated proteins such as rhiminopeptidase A . An alternative reason for these differences could be variation of venom composition between individual snakes . Although we purified rhiminopeptidase A from pooled venom sourced from a number of snakes , we also ran gels on venom from seven individual snakes and showed that the protein profiles of the venoms from individual snakes were indistinguishable in terms of SDS-PAGE profiles both from each other and from the pooled venom . Thus the protein is likely to be present in at least the seven snakes which we analysed . It is clear that both large scale analyses and studies such as ours which focus on individual proteins remain important if we are to understand the complete spectrum of proteins present in snake venoms . The amino acid sequence of rhiminopeptidase A contains the key amino acids which are known to be involved in aminopeptidase enzymatic function . APAs are the only M1 aminopeptidases which are modulated by calcium [46] , and rhiminopeptidase A contains the two aspartic acid residues ( Asp216 and Asp221; corresponding to Asp213 and Asp218 in mouse APA ) which are thought to bind calcium . It also contains the amino acids which are thought to be important for the substrate specificity of APAs ( Glu218 , Glu355 and the recently established Thr351 [37] , corresponding to Glu215 , Glu352 and Thr348 in mouse APA ) . Interestingly , just prior to that study , the amino acid in that position had been suggested to be involved in the substrate specificity of aminopeptidases in general , and three subclasses of exopeptidases had been proposed: containing MGAMEN , AGAMEN and F/YGAMEN motifs [47] . The methionine has been proposed to exist in enzymes with broad specificities and to act as a cushion to accept substrates with different N-terminal sizes [48] , LTA4H , which contains the F/YGAMEN motif prefers basic or aromatic residues and AGAMEN is found in F3 , which prefers acidic residues . Rhiminopeptidase A and its homologues are also specific for acidic residues , but contain a TGAMEN motif , which may constitute an extension of the AGAMEN subclass . We have also found proteins with SGAMEN and PGAMEN sequences in the Uniprot database , although the correlation between the residue directly before the GAMEN motif and the sequence specificity becomes less clear when a wider range of sequences is considered . Given the importance of M1 peptidases in many organisms , it is important to obtain an understanding of their structures . Structural information is currently limited: to date only the structures of human LTA4H [49] , T . acidophilum F3 [23] and aminopeptidase Ns from Neisseria meningitidis [50] , Escherichia . coli [47] , [48] and Plasmodium falciparum [51]are known . These proteins have low sequence identities , although their structures are well conserved , particularly in the catalytic region . Information about the remaining domains is more variable . When the F3 structure was determined , the β-sandwich domain was thought to be unique to this protein , as it had not been found in the structure of LTA4H . However the aminopeptidase structures from N . meningitidis , E . coli and P . falciparum also have a β-sandwich domain , so this is no longer a unique feature of F3 . We have selected the F3 structure as the best template for creating a model of rhiminopeptidase A and our model structure also has this domain . This is consistent with the results of two secondary structure prediction programs which confidently predict this region of the protein to be exclusively β-sheet . This may have implications for the structures of other M1 peptidases . For example a model of mouse aminopeptidase [52] was created using the LTA4H structure as a template prior to the availability of the F3 structure and lacks this domain . The roles of the domains other than the catalytic domain are unclear , though their interaction with the catalytic domain clearly affects the substrates which can bind to the enzyme , and one study has suggested the role of other regions of the protein in interacting with other proteins [53] . The C-terminal region of mouse aminopeptidase A ( which corresponds to the final domain and around one third of the β-sheet domain ) has been shown to act as an intramolecular chaperone , being required for the correct folding of the enzyme but not for the enzymatic activity [54] . The potential roles of aminopeptidases in snake venom are far from clear . Indeed , although aminopeptidases are expressed in many mammalian tissues , even their roles are not completely understood . Generally , mammalian aminopeptidases have been found to cleave oligopeptides . For example mammalian APA cleaves brain angiotensin II to yield angiotensin III , and is thus implicated in the control of arterial blood pressure [55] . In vivo APA has also been shown to cleave cholecystokinin ( CCK-8 ) [56] , which is widely distributed in the mammalian central nervous system and could be involved in pain perception , feeding , anxiety and memory . Other possible natural substrates which have only been tested in vitro include neurokinin B , chromogranin and kallidin [44] . The latter lacks an acidic N-terminal amino acid , and is converted to bradykinin only in the absence of calcium . Together these results support the idea that mammalian APA is important for regulation of brain function , and blood pressure in particular , but further substrates may yet be found . Some studies suggest a role for APA in blood vessel formation , and these could reflect a more general effect of APA on angiogenic mechanisms such as a role in degrading the extracellular matrix [57] . Ogawa et al . [7] have shown that exosome-like vesicles isolated from G . b . brevicaudus venom contain APA and , like mammalian APA , degrade both angiotensin II and CCK-8 . It is therefore possible that a role of snake venom aminopeptidases is to cleave the N-termini of such oligopeptides in the victim and thus affect the corresponding physiological processes . Alternatively the aminopeptidases may simply assist the general degradation of the host tissue [3] , perhaps increasing its permeability to other venom components [8] . A further possible role for snake venom aminopeptidases could be to process other toxins within the venom [8] and it is entirely possible that the enzymes have more than one of these suggested roles . The diversity and relatively high levels of aminopeptidase in snake venoms offer a valuable source of protein for characterisation of this complex family of enzymes . As this is an important group of venom enzymes which may be involved in critical envenomation effects in victims of snake bite , these enzymes could be potential therapeutic targets for developing novel snake bite treatments . This study clearly points towards the importance of complete analysis of individual components of snake venom in order to develop effective therapies for snake bites . | Snake bite is a major neglected public health issue causing an estimated 125 , 000 deaths each year , predominantly within poor communities living in rural areas of countries in South East Asia and Africa . Current treatments for snake bites are costly and have limited effectiveness , thus there is a need to develop novel therapeutics . In order to do this the toxic components of snake venom need to be clearly understood . Enzymes called aminopeptidases have been noticed in several snake venoms , but their functions have not been characterised . Related enzymes are also present in mammals , where they are involved in the maintenance of blood pressure and brain function . To further understand this important group of enzymes within snake venom we have purified and analysed the function and structure of an aminopeptidase from the venom of the West African gaboon viper . Our results suggest that this enzyme could also affect the maintenance of blood pressure and brain function in victims of snake bites . Along with other snake venom components , aminopeptidases might be a potential therapeutic target for developing novel treatments for snake bites . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"biochemistry",
"public",
"health",
"and",
"epidemiology",
"chemical",
"biology/biocatalysis",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"biochemistry/protein",
"chemistry",
"biochemistry/macromolecular",
"chemistry",
"molecular",
"biology/bioinformatics",
"computational",
"biology/protein",
"structure",
"prediction",
"biophysics/protein",
"chemistry",
"and",
"proteomics",
"biophysics"
]
| 2010 | Purification and Functional Characterisation of Rhiminopeptidase A, a Novel Aminopeptidase from the Venom of Bitis gabonica rhinoceros |
Nervous systems are information processing networks that evolved by natural selection , whereas very large scale integrated ( VLSI ) computer circuits have evolved by commercially driven technology development . Here we follow historic intuition that all physical information processing systems will share key organizational properties , such as modularity , that generally confer adaptivity of function . It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections , known as Rent's rule , which is related to the dimensionality of the circuit's interconnect topology and its logical capacity . We show that human brain structural networks , and the nervous system of the nematode C . elegans , also obey Rent's rule , and exhibit some degree of hierarchical modularity . We further show that the estimated Rent exponent of human brain networks , derived from MRI data , can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species , again suggesting that these principles of nervous system design are highly conserved . For each of these fractal modular networks , the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded . This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs . Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity , resulting in the emergence of homologous principles of economical , fractal and modular design across many different kinds of nervous and computational networks .
Since the publication of Watts and Strogatz's seminal article , “Collective dynamics of ‘small-world’ networks” , network science , as it has now come to be called , has extensively pervaded the scientific community , transcending previously impermeable boundaries between disciplines at every turn [1] . The availability of integrative tools to quantify the emergent behavior of systems made up of many interacting parts , whether they be people in a social network , proteins in a protein-interaction network , or individual web pages in the WWW , allowed many disciplines to add an entirely new level of description and find common ground with traditionally unrelated fields . The beauty of the topological formalism stemmed from its simplicity: each connection between two parts of the system was indicated by a line of unitary length , collectively giving an understanding of connectivity structure in the abstract , e . g . in topological space . While this strong focus on interconnect topology has enabled seminal discoveries in a wide variety of networks in the past decade , it inevitably neglects a fundamental property of the majority of these systems: their existence in a physical space . Gene co-expression profiles have specific anatomical distributions throughout the body; proteins have spatial distributions within cells that may increase or decrease the probability of their interactions; humans have physical locations that may influence who they make friends with; countries have frontiers with each other that may affect their trade of goods . Each of these complex systems can be described by a network topology that is highly dependent on each node's physical location . Indeed , understanding the importance of physical node placement in network growth and resultant topologies is an active topic of research in network science [2] , [3] . At this timely juncture , we investigate the interdependence of topology and physicality in a “topophysical” analysis of information processing networks . High performance computer circuits have been empirically observed to exhibit a simple scaling relationship , known as Rent's rule , between the number of nodes or “gates” in any piece of the circuit and the number of connections ( inputs or outputs ) to that piece of circuit or “block of logic” , over a range of spatial scales [4] . First observed by Rent in the 1960s , this scaling relationship has held up remarkably well as circuits have evolved rapidly in terms of size and functional performance . Circuits with greater logical capacity have higher values of the Rent exponent , indicating more complex wiring or higher dimensionality of the interconnect topology of the circuit . Rentian scaling is one aspect of fractal or self-similar network design principles that are also reflected in the hierarchical modularity of VLSI circuits , which typically consist of “modules-within-modules” . Minimization of the cost of wiring VLSI circuits has been an important economic factor in their commercial evolution . Rentian scaling represents a cost-efficient solution to the challenge of embedding a high dimensional functional interconnect topology in a relatively low dimensional physical space with economical wiring costs [5] , [6] . Given the mounting evidence that many complex systems share important organizational properties in common [7] , we hypothesized that other informational systems , which have evolved by natural selection rather than technological development , would also be characterized by high dimensional fractal topologies mapped cost-efficiently into physical space . The hypothesis that biological and artificial information processing systems , in particular , might share network properties such as hierarchical modularity that confer adaptivity or evolvability of function [8] dates back to Simon's prescient analysis [9] but has not yet been extensively tested using contemporary datasets and network analysis tools . Here we study the only complete neuronal connectome currently available , that of the nematode worm Caenorhabditis elegans , as well as large-scale human brain structural networks recently derived from neuroimaging data ( using both magnetic resonance imaging , MRI , and diffusion spectrum imaging , DSI ) , and a benchmark VLSI circuit .
All three information processing networks demonstrated modularity of community structure , such that each network could be sub-divided into a number of sparsely interconnected modules each comprising a number of densely intra-connected nodes . Indeed , we found that there were often “modules within modules” , such that community structure was present on a hierarchy of topological scales . This property of hierarchical modularity can be discerned simply by inspection of the co-classification matrix of each network ( Figure 2 left ) but is more robustly demonstrated by the results of iterative modular decomposition using the Louvain algorithm [18] , [19] ( Figure 2 right ) . The C . elegans nervous system and the VLSI circuit both had significantly non-random modularity over 4 hierarchical levels . The human brain network derived from DSI data was significantly modular over 3 hierarchical levels and the network derived from conventional MRI data was modular over 2 levels; see supplementary Text S1 for additional results . It is important to note that such hierarchical modularity is consistent with a fractal or scale-invariant topology of connections between elements of the systems [5] , [6] . In the development of VLSI circuits , a simple power law , known as Rent's rule , has been discovered to define the scaling relationship between the number of external signal connections to a block of logic and the number of connected nodes in the block [4]: ( 1 ) where is the Rent exponent and is the Rent coefficient . Moreover , this scaling relationship can be measured in both physical space and topological space by defining a ‘block’ as either a physical box or a topological partition; see Figure 1 and Materials and Methods for details . We will refer to the Rent exponent estimated in physical space as the physical Rent exponent , denoted simply ; and we will refer to the Rent exponent estimated in topological space as the topological Rent exponent , denoted . As we show below , the topological Rent exponent can be used to estimate the fractal dimension of the network topology , and can be compared to the physical Rent exponent to assess the cost-efficiency with which the network has been embedded in a Euclidean dimensional space . In VLSIs , the self-similar nature of Rent's rule is used to predict the effect of scaling the system , by keeping the exponent and coefficient the same . Indeed , despite the lack of any explicit imperative for individual circuit designers to follow this law , Rent exponents can be a remarkably reliable predictor for emergent VLSI allometric scaling properties , over many orders of magnitude , such as can be seen in Figure 4A . We considered the related question of whether the Rentian scaling of connections between cortical regions in the human brain networks could be related to the allometric scaling of gray matter and white matter volumes previously described over a wide range of differently sized mammalian species , from mouse opposum to sea lion [25] , [26] ( Figure 4 ) . It can be shown that the strong power law relationship has an allometric exponent that is simply related to the physical Rent exponent of mammalian cortical networks , i . e . , ; see Materials and Methods and supplementary Text S1 for details . On this basis , we used the estimate of for human anatomical networks measured using MRI , , and using DSI , , to predict the allometric scaling relationship between cerebral gray and white matter ( Figure 4 ) . The 95% confidence interval on the slope of the true data leads to a range of while the interval estimated from the MRI network was and for the DSI networks ( the slight underestimation of from DSI data may be related to a measurement bias against long distance connections in DSI-based tractography; see supplementary Text S1 ) . The quality of prediction of mammalian allometric scaling from Rent exponents estimated in a single species is consistent with the idea that mammalian cortical networks are generally connected in accordance with the same Rent exponent , and this constrains the allometric scaling relations between gray and white matter volume which emerge over differently sized species . To make the same point a different way , we converted previously reported allometric scaling exponents for the cerebral cortex and cerebellum [26] to the corresponding Rent exponents and fractal dimensions . Prior estimates of the cortical allometric scaling exponent correspond to an interval of Rent exponents which includes the empirical estimates from human MRI data ( Table 1 ) ; whereas the prior cerebellar allometric scaling exponents correspond to an interval of Rent exponents . Although we were unable to verify the allometrically predicted Rent exponents for human cerebellum ( because the MRI datasets did not include cerebellar measurements ) , we note that the prediction of larger Rent exponents and fractal dimensions for human cortical compared to cerebellar systems is consistent with the arguably greater logical capacity of cortical systems and the observation from VLSI circuits that higher dimensional connectivity is associated with greater logical capacity; see Figure 4 . The hypothesis that cerebellar Rent exponents are indeed significantly reduced compared to cortical systems , and the more general idea that Rent exponents of neural systems are functionally related to their logical capacity , demand further direct investigation in future .
Modularity is a fundamental and protean word with many meanings in neuroscience . Psychological or functional modularity refers to separability or informational encapsulation of cognitive processes , which may be neurally represented by specialist , localised processing centres in the brain . It is one of the key ideas behind phrenology and faculty psychology [27] . There is also a well articulated neurodevelopmental aspect of modularity . For example , the embryonic development of chick hindbrain follows a strict chronological progression of cellular differentiation from caudal to rostral modules of nervous tissue , called rhombomeres , each of which comprises cells that share distinctive patterns of genetic co-expression compared to cells in neighbouring tissue modules [28] . Here we are concerned with topological modularity [18] , [29] - a more general and quantitative version of the concept - that we have applied to analysis of information processing network organization . Topological modularity is sometimes also referred to as the community structure of a network because it decomposes the global network into a set of modules or communities each comprising nodes that are densely intra-connected with each other and relatively sparsely inter-connected to nodes in other modules . This basic design of sub-systems within the global system is functionally advantageous in various ways . As Herbert Simon argued originally , the key advantage of such a design for an information processing network is that it confers rapid adaptivity or evolvability: the system can evolve or adapt to new information one module at a time , without risking loss of function in modules that are already well-adapted . For this reason , Simon predicted that all “physical symbol processing systems” would share a general architecture of complexity , including modularity ( or near-decomposability as he sometimes called it ) as a key principle [9] . Our results are compatible with Simon's prediction - all the information processing systems we considered could be decomposed into modules , or indeed “modules within modules” . For a classically modular decomposition , the system is decomposed into a single lower level of organization in terms of multiple sub-systems . For a hierarchically modular decomposition , the system is iteratively decomposed into multiple nested lower levels of organization in terms of sub-systems , sub-sub-systems , etc . When modularity is expressed consistently at several scales , we can describe the system as hierarchically modular . This property was clearly seen for the VLSI circuit , C . elegans and human connectomes , for each of which modularity was expressed consistently across several ( 4 or more ) topological scales , so the system as a whole could be described in terms of sub-sub-sub-systems , or even lower hierarchical levels in the case of C . elegans and the human DSI network ( see supplementary Text S1 ) . Even the smallest and least precisely estimated connectome , that derived from the human MRI datasets , also generated networks with some hierarchical modular properties ( across 2 topological scales ) and this observation is compatible with other data demonstrating hierarchical modularity in human brain functional networks derived from “resting state” functional MRI data [30] . The observation that nervous systems generally share a hierarchically modular topology is particularly relevant to the question of how they support function . As is perhaps intuitive , there is mounting evidence that the modular architecture of anatomical structure determines the possible emergent functions of the network under study [31] , [32] . Functional patterns on hierarchically modular architectures have specifically been shown to display computationally advantageous dynamics characterized by stability and diversity [33] , unlike simulated dynamics on either random or non-hierarchically small-world architectures [34] . Sporns provides a simple generative model for fractal hierarchical networks and shows further relationships between their structural and functional properties , suggesting that connectivity may strongly constrain dynamics [32] , [35] . Computational models of hierarchical modularity have shown that networks configured in this way have the distinctive advantage of being robustly stable under large scale reconnection of substructure [36] . Overall , we find there is strong empirical evidence , convergent with prior theoretical and computational results , for fractal modularity of information processing networks . We now consider some other aspects of the scale invariance of these systems . Fractal dimensions are most frequently encountered in analysis of the physical properties of some rough , irregular process in space or time . A famous example is the fractal dimension of the fjord-riven western coastline of Norway ( 1 . 52 ) which is considerably greater than the Euclidean dimension of a straight line ( 1 ) but less than the 2 dimensions of the Euclidean plane in which the coastline is embedded on the page of an atlas . This is a fractional or non-integer measure of the dimensionality of a geometric system , , and it will generally be less than or equal to the integer dimensional Euclidean space in which the process is embedded . Thus the irregular convolutions of sulco-gyral folding in the human brain are associated with fractal dimension of the cortical surface , greater than a smooth 2D plane but less than the 3-dimensional volume in which the brain is embedded . Although its estimation is a matter of ongoing methodological research [37] , [38] , the fractal dimension of the cortical surface has already been used to describe healthy and abnormal neurodevelopment [39] , [40] as well as aging [41] . However , here we have been concerned with a related but different measure: the fractional dimension of a topologically defined system , . The dimension of a topology is a non-integer measure of the complexity of the interconnections between nodes , regardless of their physical location , and is therefore not constrained by the dimensionality of the physical embedding space . The dimension of a topology can range from 0 to infinity , in the limiting case of a very large , perfectly random network . If the network is embedded in a physical space , its topological dimension may therefore be larger than its embedding dimension , which in real space is at most 3 . We found convergent evidence , by two independent estimators , for information processing network topologies having fractional dimensions greater than 3 . Thus it seems brains have both fractal geometry and fractal topology , although how these two aspects of brain organization are related to each other is a fascinating question we will leave unaddressed for the moment . In VLSIs , such high dimensional interconnect topology is related to logical capacity of the circuits , and we suggest that it is also likely to be functionally advantageous in nervous systems . However , greater than three-dimensional connection topology incurs an extra wiring cost , compared to the minimum cost of wiring the same set of physically located nodes interconnected by a nearest-neighbour , lattice-like topology with . Our results indicate that nervous systems and computational circuits are cost-efficiently but not cost-minimally embedded in physical space , meaning the wiring length of these networks is close to the minimum length it could be , given their high dimensional topology , but it is not absolutely minimized . Previous studies of brain and neuronal networks have shown that wiring costs are nearly if not absolutely minimized [15] , [42] , using a combination of component placement optimization and wiring placement optimization . Component placement optimization creates minimally wired networks by retaining the connectivity of the system ( edges ) while allowing components of the systems ( nodes ) to move in space . This approach maintains the inherent functionality of the system and asks whether , given such functionality , the components can be ordered in a different way to provide shorter average wiring . Conversely , in wiring placement optimization [15] , we retain the placement of the components of the system ( nodes ) and alter the connectivity of the system ( edges ) . As such , wiring placement optimization does not retain the inherent functionality of the systems but instead retains the inherent anatomical structure ( heterogeneous localization of brain regions or neurons ) . In this work , given the placement of nodes in space and preserving the number of edges , we ask: could these nodes be reconnected in a different configuration so as to yield a shorter average wiring length ? Our purpose in choosing wiring placement over component placement was to compare the topological and physical characteristics of a given brain network to those of a 3-dimensional lattice-like network in a realistic brain anatomy . By this approach we found that wiring costs in brain networks were nearly but not absolutely minimal . Given the high metabolic costs of the brain ( about 20% of the total energy budget for only 2% of body mass in the human ) , of which a large proportion is due to the costs of building and maintaining functional connections between anatomically distributed neurons [43] , [44] , it seems reasonable to ask: why have brain wiring costs not been more strictly minimized by natural selection ? Our answer to this question is that the selection of greater than 3-dimensional ( ) network topologies , which are associated with hierarchical modularity and greater logical capacity , has been prioritized despite the adverse impact on wiring cost that is entailed when any system that is topologically more complex than a lattice is embedded in physical space [15] , [42] . Absolute minimization of wiring cost in these nervous systems could only be achieved at the expense of reduced topological complexity . Moreover , the generalizability of this result to both C . elegans and Homo sapiens suggests that a trade-off between high dimensional connectivity and wiring cost has been negotiated in the evolution of nervous systems at microscopic ( cellular ) and macroscopic ( whole brain ) levels of description and in phylogenetically removed species . In comparing the results of these and other studies , it is important again to highlight the distinction between Rentian characteristics for partitioning ( topological Rentian scaling ) versus placement ( physical Rentian scaling ) [23] . Partitioning examines the un-embedded network topology , whereas placement examines the position of nodes embedded in a physical substrate . Therefore , the partition-based Rent exponent measures a characteristic more intrinsic to the VLSI circuit topology while the placement-based Rent exponent measures a characteristic of the extrinsic physical wiring properties [23] . In the construction of a VLSI , placement is the artificial process by which a given network topology is somehow embedded into a physical substrate by the manipulation of nodal placement . Minimization of wiring costs is an important economic factor in VLSI production and designers will seek to optimise the efficiency of network embedding . The optimal cost-efficient embedding will have a physical Rent exponent , based on placement , equivalent to the topological Rent exponent , based on partitioning . However , optimal placement is an NP-complete problem , as is optimal partitioning , and as such different placement algorithms can yield sub-optimal Rent exponents . While topological Rentian scaling of the C . elegans connectome has been previously reported [45]–[47] , the present work is the first , to the best of our knowledge , to report topological Rentian scaling in human anatomical networks derived from neuroimaging and also the first to explore physical Rentian scaling in neuronal networks . In the nervous systems studied here , the placement and topology have both been evolved by nature , and as such the physical Rent exponent is constrained by the ( sub ) optimality of natural selection rather than by the particular placement algorithm chosen ( as is the case for a VLSI ) . We found that for both human brains and the nematode connectome , the physical Rent exponent was close to its theoretical minimum , the topological exponent , indicating that natural selection has resulted in near-optimisation of cost-efficient network placement . This analysis has also provided the first direct evidence for a simple relationship between physical Rentian scaling of connectivity within the nervous system of a single species and allometric scaling of gray and white matter volumes across the differently sized brains of a range of mammalian species . This result needs to be considered in the context of a rich prior literature on allometric brain scaling and its possible theoretical relationship to isometric or fractal scaling of network connectivity . Early studies of allometric scaling in the brain showed , for example , that the number of neurons scaled with gray matter density while the number of synapses remained constant [48]–[50] . The study of the relationship between white and gray matter in mammalian cortex began with the work of Schlenska [51] and later Frahm [52] in the late 70s and early 80s . Both reported scaling relationships in independent mammalian datasets with exponents of 1 . 22 and 1 . 24 respectively . The strength and consistency of this finding , later underscored by a comparative MRI study [53] , prompted theoreticians to propose various geometric and mechanistic models which have been used to predict other scaling relationships between related neuronal variables [54] , [55] , serving as important guides for further experimental work . Beiu et al . suggested that the allometric scaling exponent for gray and white matter volumes between species was identical to the Rent exponent within a species [56] . The assumption that the scaling exponent between white and gray matter volumes is identical to Rent's exponent necessarily neglects the differences between a volumetric scaling and a network scaling ( e . g . , a scaling of nodes and connections ) . As we have shown in our derivation here and in the supplementary Text S1 , nodes and connections do not in fact scale directly with volume , and thus the exponents of volumetric scaling and Rentian scaling are not directly equivalent . Prothero developed a repeating units model [54] which suggests that all brains are made up of identical repeating units: larger brains simply have more of these units than smaller brains . Changizi developed a slightly more complicated , two-part model [55] partially based on the application of West's theory of branching to neuronal arbors [57] . While these two models sought to describe allometric relationships between a wide variety of neuronal variables , Zhang and Sejnowski elegantly propounded a model to explain only the allometric relationship between gray and white matter [25] . However , following the publication of these models , empirical developments have challenged one of the main assumptions underlying all three: namely , that there is a basic uniformity of the cerebral cortex as evidenced by a constant number of neurons in a unit area of cortical surface; this assumption now seems unrealistic [58] . Also , although the final two models produce an near-perfect 4/3 scaling exponent between white matter volume and cortical gray matter volume in mammals , they do not readily allow for distinct scaling exponents in non-cortical systems , e . g . , cerebellum , or in non-mammalian species . These challenges aside , prior studies have contributed seminal insights to our understanding of allometric scaling of brain properties which we hope we have been able to further refine . We suggest that the allometric scaling of white matter volume with gray matter volume is a direct consequence of the physical Rentian scaling of connectivity in a given brain . In contrast to the models previously described , our explanation allows an independent empirical validation or cross-check: we separately estimate the Rentian scaling within a single mammalian system and use it to predict the allometric scaling of white matter volume with gray matter volume across a range of mammalian species . In addition , our heuristic allows for differences in scaling relationships between distinct areas of cortex such as the neocortex and cerebellum or between different classes of animals such as vertebrates and invertebrates . This isometric generative mechanism for allometric scaling does not stand or fall by producing an ideal , e . g . , 4/3 , scaling relationship between white matter volume and gray matter volume , but allows for irrational or non-integer scaling exponents that may vary somewhat depending on the type of brain network and/or the phylogenetic class of species considered . However , like the other available models , our derivation does include some assumptions or approximations: 1 ) we have made the approximation of ignoring the effect of white matter dilation after determining that its contribution is small over the range of white matter volume values studied ( see supplementary Text S1 ) , and 2 ) we have assumed that the number of synapses in a cross-sectional area is constant as a function of gray matter volume based on the known invariance of synaptic density [50] . Empirically required alterations to these approximations and assumptions may induce small corrections to the estimation of the Rent exponent , . The formal and empirical connection we have made between fractal or self-similar connectivity of the nervous system of a single mammalian species and the allometric scaling of gray and white matter volumes over multiple mammalian species provides a novel mechanistic explanation for a long-established observation . We propose that allometric scaling of brain anatomy is constrained by fractal properties of the cortical network for information transfer in broadly the same way as the allometric scaling of mammalian metabolic rate with body mass is constrained by fractal properties of the respiratory network for gas exchange [57] , [59] . There are several methodological issues to be considered in evaluating the results of this study . The small size of both the human MRI and C . elegans networks limits the precision with which we have been able to estimate the fractal dimension of network topology , . We have tried to address this by reporting convergent results from two complementary estimators ( topological Rentian scaling and box counting ) and by using DSI data which have been parcellated into 1000 nodes . Nonetheless in future studies , it will be useful to apply finer grained parcellation templates to human neuroimaging data to improve estimation of fractal properties of network topology by analysing the systems over a larger range of scales . The use of covariation in gray matter volumetric variables as a measure of anatomical connectivity between brain regions [12] , [60]–[62] is indirect and entails some assumptions . For example , it has been assumed that reciprocal afferent connections have a mutually trophic effect on the growth and maintenance of both connected regions leading to positively correlated volumes in adult brains [13] , [62] . Recent studies have provided some experimental validation of this hypothesis by comparing pairs of regions with highly correlated volumetric properties to known fiber tracts established using diffusion tensor imaging [13] , [63]–[65] and tract tracing studies [66]–[68] . Nevertheless , the assertion of anatomical connectivity on the basis of regional covariation in gray matter volumes remains somewhat conjectural at this time . Moreover , the construction of a single group mean anatomical network from the MRI data means that the error in estimation of the Rent exponent , may be under-estimated by exclusion of any between-subject or between-network components of variability . The diffusion spectrum imaging network , on the other hand , contains an inherent distance bias [14] , meaning that long distance connections have a lower probability of being included in the network than short distance connections . While Hagmann and colleagues did use a distance bias correction in the preprocessing of these networks , the most complete correction method remains a matter of ongoing debate [14] . It is possible that some distance bias remains in the current dataset which may artefactually decrease the topological dimension , , the Rent exponent , , and the average wiring length , . However , it is not the purpose of this study to evaluate the available methods for distance bias correction and we have instead used this recently published dataset which represents one of the currently accepted methods . This previously published DSI dataset [14] includes data for 5 subjects with 1 subject scanned twice . As such , this dataset is not adequate to assess the inter-scan reliability or inter-subject reliability of the anatomical structural properties we are studying in this work . Recently , it has been shown that similar whole-brain networks derived from functional MEG data have reproducible topological properties [69] . However , a similar study in anatomical networks has not yet been published , and it will be important in future work to describe the reproducibility of network architecture in terms of both topology and physical embedding . In this work , the distance between any two network nodes was defined as the Euclidean distance between the center of mass of the brain regions ( in the human ) or neuronal cell bodies ( in C . elegans ) . While this definition is currently widely used [11] , [15] , [63] , [70] , it provides an indirect estimate and likely an under-estimate of the true length of white matter tracts and axons in these neural systems , which may take convoluted paths to connect a given pair of nodes . Future advances in diffusion imaging may provide us with better length estimates for major white matter tracts in the the human brain while advances in the microscopic characterization of neuronal tissue may provide us with better estimates of individual axonal pathways . The placement embedding for the VLSI circuit required the use of simulated annealing . The estimated physical Rent exponent based on placement , , was less optimal than a previously reported Rent's exponent based on partitioning [71] . It is important to be aware that Rent exponents based on placement and based on partitioning may not be identical; the partitioning method does not require simultaneous physical embedding of all gates in the entire system . We have chosen to use the placement embedding technique to make the results most comparable to the C . elegans and human brain network results . In a similar vein , it is important to note that we used the formalism of graphs and edges rather than hypergraphs and hyperedges . The latter are often used in the analysis of VLSI circuits but the concepts are not simply transferable to the biological networks studied here . Thus we have chosen to use simple edges in all reported analyzes to facilitate comparability across systems . The relationship between allometric scaling and Rentian scaling could be further supported by studying Rentian scaling in MRI or DSI/DTI datasets from a range of mammalian species rather than the human alone . In particular , it would be interesting to discern whether there is a difference in isometric Rentian scaling between mammalian and non-mammalian species as well as between marine and terrestrial mammals who arguably show distinct volumetric scaling relationships [72] , [73] . The construction of a comparable species-dependent MRI network would require structural scans from over 200 animals in that species . While no such data is currently available or likely to become available in the near future , the application of DTI specifically to the macaque monkey is a pressing line of current inquiry . The parallels we have identified between the properties of naturally selected nervous systems and commercially selected computational systems suggest that diverse information processing networks have convergently evolved to satisfy ubiquitous fitness criteria . Just as principles of natural selection were originally informed by Darwin's analysis of artificial selection pressures operating in the market for domestic animals , principles of nervous system evolution may be elucidated by comparative analysis of computational systems that have evolved in the market for logically advanced computers .
For the C . elegans nervous system , connection data and two-dimensional spatial coordinates for each neuron were taken from [15] , [16] . Each neuron was taken to be a node in the network and nodes were connected by edges where a chemical or electrical ( gap junction ) synapse between two neurons was known to exist . For the human nervous system , we used two sets of neuroimaging data from independent samples studied using complementary magnetic resonance imaging ( MRI ) methods; these include the most fine-grained view to date of whole brain white matter tract connectivity and the classical cytoarchitecturally constrained view of whole brain gray matter connectivity . It was hoped that in the combination of both complementary lines of inquiry , the discovery of consistent properties would underscore both replicability and robustness . In 259 healthy adults , regional gray matter volume measurements were made in 104 cortical and subcortical regions defined by an anatomically informed parcellation template applied to conventional MRI data [11] . The inter-regional partial correlation in gray matter volume was estimated for each pair of regions and thresholded to create an undirected graph where each node corresponded to a region and an edge indicated a suprathreshold correlation of volumes between regions [11] , [13] , [63] . In 5 healthy adults with 1 adult scanned twice , the probabilities of fiber tracts between any two regions of interest ( N = 1000 ) were determined from diffusion spectrum imaging ( DSI ) data using an altered path integration method [14] . The connectivity backbone of this probability matrix was determined by first calculating the minimum spanning tree and then adding connections with the highest probability weights until the average degree was 4 [14] . For the VLSI ( s953 ) circuit [17] , each node in an undirected graph represented one of 440 logic gates and an edge represented a wire between gates . The C . elegans data is freely downloadable from the Biological Networks website http://www . biological-networks . org/; the DSI data are freely downloadable from the Brain Connectivity Toolbox www . brain-connectivity-toolbox . net . To visually represent the hierarchical community structure of the networks , we used a co-classification algorithm which iteratively determines hierarchical nodal affinities based on topological overlap in the symmetrized matrix and uses this information to determine the relative relationships between nodes at all hierarchical levels [74]; see Figure 1 . Modularity of these matrices was estimated using the Louvain community detection algorithm [18] and compared to the modularity distribution ( N = 100 ) of two benchmark networks: 1 ) Pure random networks , i . e . , networks with the same number of nodes and edges as the original network , and 2 ) Functional random networks , i . e . , those with the same number of nodes and degree distribution as the original networks [75] such that each edge was rewired on average 15 times . A network was defined as being hierarchically modular if it contained first-level modules with significantly non-random modularity , i . e . , the presence of submodules was confirmed; see supplementary Text S1 for details . The fractal dimension of the network topology was estimated in two ways . First , we used a topological partitioning algorithm ( hMetis software , version 1 . 5 ) to compute the topological Rent's exponent , , which is then related to the topological dimension by [5] . The network was recursively partitioned into halves , quarters , and so on in topogical space . The slope in log-log space of the average number of nodes in a partition versus the average number of edges crossing the boundary of the topological partition was defined as the topological Rent's exponent , . In a complementary analysis , we estimated using the box-counting algorithm of Concas et . al [21] based on Song's renormalization algorithm [22] . This estimator counts the number of boxes required to cover all nodes in each network as box size is varied between 1 and . The gradient of a straight line fitted to versus using weighted linear regression is a consistent estimator of ; see Figure 1 . The expected wiring of a high dimensional topology which is embedded in a lower dimensional physical space is ( 4 ) for , where is the mean connection distance in terms of node-to-node spacing , is a constant of order unity , is the fractal dimension of the topology , is the Euclidean dimension of the embedding , and is the number of nodes [5] , [24] . The node-to-node spacing , , is given by ( 5 ) where is the Euclidean distance between any pair of nodes and . The mean connection distance in terms of node-to-node spacing , , is then given by ( 6 ) where is the Euclidean distance between any pair of connected nodes and . The Euclidean space of the networks was tiled with overlapping randomly sized boxes ( e . g . , two-dimensional squares or three-dimension cubes for the VLSI , C . elegans , and human networks . In each box we determined the number of nodes ( n ) and the number of connections ( e ) that cross the box boundaries; see Figure 1 . The gradient of a straight line fitted to versus using iteratively weighted least squares regression is an estimate of the Rent exponent ; see Figure 3 . To minimize ( Region II ) boundary effects , was estimated using the subset of boxes which contained less than half the total number of nodes , . Each network was minimally rewired by first computing a minimum spanning tree to ensure that all nodes were connected then iteratively adding the next shortest edge to the network until the connection density matched that of the observed networks [15]; see Table 1 . Randomly wired networks were pure random networks with the same number of nodes and edges as the observed network; see Table 1 , bottom panel . If we approximate the brain as a sphere , then the cross-sectional area , , of white matter volume , , is given by ( 7 ) which can be related to the number of connections , , according to ( 8 ) where denotes the number of connections per unit surface area of white matter which we assume to be constant given the independence of synaptic density and brain volume over the mammalian class of species [50] . We use to denote the number of constant-complexity processing elements , , per unit volume of gray matter , , which scales with synaptic density and is therefore a constant . On this basis , we can write ( 9 ) A system obeys Rent's rule if for some Rent coefficient and exponent ; inserting ( 8 ) and ( 9 ) we then have: ( 10 ) which simplifies to ( 11 ) Thus , ( 12 ) and multiplying the allometric scaling exponent by provides an estimate of the Rent exponent , . For a more detailed derivation , see supplementary Text S1 . | Brains are often compared to computers but , apart from the trivial fact that both process information using a complex physical pattern of connections , it has been unclear whether this is more than just a metaphor . In our work , we rigorously uncover novel quantitative organizational principles that underlie the network organization of the human brain , high performance computer circuits , and the nervous system of the nematode C . elegans . We show through a topological and physical analysis of connectivity data that each of these systems is cost-efficiently embedded in physical space; they are organized as economical modular networks , paying a modest premium in wiring cost for the functional advantages of high dimensional topology . We also show that the fractal properties of human brain network connectivity can be used to explain allometric scaling relations between grey and white matter volumes in the brains of a wide range of differently sized mammals—from mouse opossum to sea lion—further suggesting that these principles of nervous system design are highly conserved across species . We propose that market-driven human invention and natural selection have negotiated trade-offs between cost and complexity in design of information processing networks and convergently come to similar conclusions . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"physics/interdisciplinary",
"physics",
"neuroscience/theoretical",
"neuroscience",
"computational",
"biology/computational",
"neuroscience"
]
| 2010 | Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits |
RNA silencing , mediated by small RNAs including microRNAs ( miRNAs ) and small interfering RNAs ( siRNAs ) , is a potent antiviral or antibacterial mechanism , besides regulating normal cellular gene expression critical for development and physiology . To gain insights into host small RNA metabolism under infections by different viruses , we used Solexa/Illumina deep sequencing to characterize the small RNA profiles of rice plants infected by two distinct viruses , Rice dwarf virus ( RDV , dsRNA virus ) and Rice stripe virus ( RSV , a negative sense and ambisense RNA virus ) , respectively , as compared with those from non-infected plants . Our analyses showed that RSV infection enhanced the accumulation of some rice miRNA*s , but not their corresponding miRNAs , as well as accumulation of phased siRNAs from a particular precursor . Furthermore , RSV infection also induced the expression of novel miRNAs in a phased pattern from several conserved miRNA precursors . In comparison , no such changes in host small RNA expression was observed in RDV-infected rice plants . Significantly RSV infection elevated the expression levels of selective OsDCLs and OsAGOs , whereas RDV infection only affected the expression of certain OsRDRs . Our results provide a comparative analysis , via deep sequencing , of changes in the small RNA profiles and in the genes of RNA silencing machinery induced by different viruses in a natural and economically important crop host plant . They uncover new mechanisms and complexity of virus-host interactions that may have important implications for further studies on the evolution of cellular small RNA biogenesis that impact pathogen infection , pathogenesis , as well as organismal development .
RNA-mediated gene silencing is a widespread mechanism of host defense against viral [1]–[6] and bacterial [7] infections . The 21–24 nt small RNAs , produced from DICER processing of double-stranded RNAs ( dsRNAs ) or RNA transcripts with stem-loop structures , are broadly defined as small interfering RNAs ( siRNAs ) and microRNAs ( miRNAs ) , respectively [8]–[10] . They are incorporated into ARGONAUTES ( AGOs ) to form an RNA-INDUCED SILENCING COMPLEX ( RISC ) . The RISC then recognizes its target RNA/DNA sequences through specific base pairing , to activate RNA cleavage or translation repression or DNA methylation [9] , [11]–[14] . In plants , the miRNA precursors are processed into miRNA/miRNA* duplexes mostly by DICER-LIKE 1 ( DCL1 ) with 2-nt 3′overhangs [9] , [15] , [16] . After methylation at the 3′ end , the miRNA sequences are preferentially incorporated into RISC to regulate gene expression , whereas the miRNA* sequences are usually degraded [17] , [18] . Viruses encode dedicated proteins that function as viral suppressors of RNA silencing ( VSRs ) , or other multi-functional proteins , to defend against host RNA silencing by interfering with distinct steps of the silencing pathways [19] . Previous studies , based on RNA gel blots , showed that transgenic expression of VSRs from many different plant viruses often caused reduced accumulation of conserved miRNAs [20]–[26] . The Tobacco mosaic virus movement and coat protein interactions also alter accumulation of tobacco miRNAs [27] . The biological function of this down-regulated miRNA accumulation for viral infection or plant defense remains to be understood . The siRNAs are produced via processing of dsRNAs derived from distinct sources and are classified into three types: trans-acting siRNAs ( ta-siRNAs ) , natural antisense transcript-derived siRNAs ( nat-siRNAs ) and repeat-associated siRNAs ( ra-siRNAs ) . The ta-siRNAs are generated in a phased pattern through DCL4-processing of dsRNA substrates formed via the activity of RNA-DEPENDENT RNA POLYMERASE 6 ( RDR6 ) [28]–[31] . The nat-sRNAs are produced from dsRNAs formed by natural antisense cis-transcript pairs [32] , [33] . The ra-siRNAs are derived from transposons and repetitive elements [34] , [35] . Transgenic expression of VSRs from some plant viruses also often led to reduced accumulation of siRNAs , likely as a means of dampening host silencing against viral infection [20] , [23] , [24] . There is evidence that some cellular miRNAs play anti-viral roles against animal viruses , although a particular miRNA is exploited to support viral infection [4] , [5] . Many animal viruses cause generally down-regulated expression of host miRNAs as shown by microarrays [36]–[39] and quantitative real-time PCR [40] . Deep sequencing also revealed a similar pattern , and additionally identified a few new miRNAs induced only in virus-infected cells [41]–[43] . Some new miRNAs are induced in an organ-specific manner [41] . To gain further insights into viral interactions with the host RNA silencing pathways , we used deep sequencing to characterize the small RNA profiles of rice plants infected by two different RNA viruses together with microarray and quantitative RT-PCR analyses of the expression patterns of RNA silencing pathway genes . Rice is one of the most important crop plants and emerging models for RNA silencing studies [44]–[49] . Rice dwarf virus ( RDV ) , which causes millions of dollars crop losses each year , is a member of Phytoreovirus whose genome consists of 12 dsRNAs that encode 12 proteins . The RDV non-structural protein Pns10 has been identified as a VSR , which has siRNA-duplex binding activities [50] , [51] . Rice stripe virus ( RSV ) , another devastating rice pathogen , is a member of Tenuivirus whose genome consists of four negative sense and ambisense single-stranded RNAs that encode seven proteins . The nonstructural protein NS3 functions as a VSR that also has siRNA-duplex binding activities [52] . Both viruses are transmitted via insect vector in a persistent manner and the eggs from viruliferous female adults also carry viruses and spread diseases . RSV and RDV are transmitted to rice plants by the small brown plant hopper ( Laodelphax striatellus ) and leafhopper ( Nephotettix cincticeps or Resilia dorsalis ) , respectively . Our analyses showed that RSV and RDV infections differentially affected rice small RNA profiles . RSV infection enhanced the accumulation of some rice miRNA*s , but not their corresponding miRNAs , as well as accumulation of phased siRNAs from a particular precursor . Significantly , RSV infection also induced the expression of novel miRNAs in a phased pattern from several conserved miRNA precursors . In comparison , no such changes in host small RNA expression was observed in RDV-infected rice plants . RSV infection significantly elevated the expression levels of selective OsDCLs and OsAGOs , whereas RDV infection only affected the expression of certain OsRDRs . Our results provide a comparative analysis , via deep sequencing , of changes in the small RNA profiles and in the genes of RNA silencing machinery induced by different viruses in a natural and economically important crop host plant . They uncover new mechanisms and complexity of virus-host interactions that may have important implications for further studies on the evolution of cellular small RNA biogenesis that impact pathogen infection , pathogenesis organismal development , and crop-protection technology development .
We sequenced the small RNAs from rice plants infected with RDV and RSV , respectively , and from plants mock-inoculated controls by using the Solexa/Illumina deep sequencing method . The three-week-old rice seedlings were exposed , respectively , to viruliferous leafhopper , N . cincticeps ( RDV ) , virus-free N . cincticeps ( RDV , mock ) , viruliferous planthopper , L . Striatellus ( RSV ) and virus-free L . Striatellus ( RSV , mock ) . After three weeks , when virus-induced symptoms appeared in the systemic leaves of virus-infected plants , the plants from each treatment ( i . e . , RDV-infected , RDV mock , RSV-infected and RSV mock ) were pooled to prepare an RNA library for sequencing . We performed sequencing with three biological replicates for each treatment , with approximately 15 plants pooled in each replicate . ( All the sequencing data can be available from the website: http://www . cbi . pku . edu . cn/download/rdsv/rdsv . html . ) From each library , we obtained more than 50% of small RNA sequences that had at least one perfect match in the rice genome and no more than one mismatch in the virus genome ( Table 1 ) . The similar percentages of small RNA sequences matched to the rice and virus genomes in all replicates for a given treatment indicate similar quality of RNA preparation and sequencing . Because of the large volume of total sequence data and our primary focus in this study on learning about how the two viruses would affect small RNA profiles and the RNA silencing machinery in a common host , the virus-derived small RNA profiles and their biological implications will be analyzed and presented in a future report . For the rice small RNAs , we normalized the total sequence reads of each library to one million , and then used the average read value of unique sequences from all replicates in each treatment for further analysis . We compared the total reads of rice miRNAs among the four libraries , using pooled data from the three independent biological replicates . There were 4060 , 4852 , 8449 and 5014 unique sequences , with 89036 , 91409 , 118975 and 90214 reads , that match perfectly to miRNA precursors from RDV-infected , mock ( RDV ) -inoculated , RSV-infected , and mock ( RSV ) -inoculated rice plants , respectively ( Table 2 ) . Notably , the number of unique sequences mapped to miRNA precursors from the RSV-infected rice plants was nearly twice of those from each of the other three types of plants . Of all the sequence reads mapped to miRNA precursors , 94 . 7% ( 84344 ) , 93 . 1% ( 85112 ) , 68 . 9% ( 81975 ) and 91 . 0% ( 82086 ) were mature miRNA sequences , and 1 . 6% ( 1437 ) , 2 . 0% ( 1813 ) , 27 . 4% ( 32600 ) and 1 . 9% ( 1758 ) were miRNA* sequences ( Table 2 ) . Interestingly , the reads of miRNA* sequences in the RSV-infected rice small RNA library were 15 times higher than those from the other three libraries , and was nearly half of the miRNA reads from the same library . The sequence data from the three biological replicates are presented in Supplemental Table S1 , which reproducibly show more than about 10-times higher accumulation of miRNA* sequences in RSV-infected rice plants than in the other three groups of plants . For many miRNAs there were no obvious differences in expression levels between RSV-infected and mock-inoculated rice plants . However , a number of miRNAs showed significant changes in expression in RSV-infected plants . These were defined as those having reads of 100 or more and showing at least two-fold changes in reads between RSV-infected and mock-inoculated plants . As shown in Table 3 , expressions of miR156b , miR159a1 , miR164a , miR166 , miR167a , miR1884b , miR393b , miR396e and miR528 were down regulated , whereas miR535 , miR390 and miR171 were up regulated in the infected plants . Although data from the three independent biological replicates were reproducible ( Supplemental Table S1 and S2 ) , we nonetheless used RNA gel blots to verify the altered expressions of some miRNAs with higher reads . As shown in Figure 1A , miR156 , miR166 and miR167 were down regulated , whereas miR172 showed no obvious changes in accumulation . The expression of miR168 showed a two-fold increase on RNA gel blots , in near agreement with the sequence data ( Table 3 ) . By contrast , fewer miRNAs showed changes in accumulation levels in RDV-infected plant as compared to those in the mock-inoculated plants . Specifically , miR167a , miR171 and miR1863 were down regulated and only miR393 was induced in RDV-infected rice plants ( Table 3 ) . We confirmed the down-regulation of miR167 in RDV-infected rice in northern blots ( Figure 1A ) . Thus , different viruses can differentially affect the expression of some miRNAs in a common host . Using quantitative real-time RT-PCR , we analyzed the expression of one target gene for each miRNA shown in Figure 1A . As shown in Figure 1B , the expression of AGO1b , a target of miR168 , was induced during RSV infection ( Figure 1C ) . This mimics the situation in Arabidopsis thaliana where the expressions of miR168 and AGO1 are transcriptionally co-regulated [53] . Os03g43930 , a HD-Zip transcription factor and a target of miR166 [54] , was up-regulated in agreement with the down-regulation of miR166 expression during RSV infection ( Figure 1C ) . The expression of Os03g60430 ( target of miR172 ) [55] showed no changes , whereas the expression of Os04g57610 ( target of miR167 ) [54] increased in RSV infected rice ( Figure 1C ) . These patterns correlated well with unaltered expression miR172 and down-regulated expression of miR167 . Notably , none of these genes showed significant changes in expression levels , as did their cognitive miRNAs , in RDV-infected plants ( Figure 1B ) . Using four different pairs of primers , we were unable to obtain conclusive data about the expression of potential miR156 targets ( LOC_Os04g46580 and LOC_Os07g 32170 ) . In RSV-infected rice plants , many miRNA*s accumulated to high levels , whereas their corresponding miRNA sequences did not show any obvious changes compared to the mock control ( Table 3 ) . These include miRNA*s for some members of the miR160 family ( miR160a–f ) , miR166 family ( miR166a–e , g–l and n ) , miR167 family ( miR167a , c–e , h and i ) , miR171 family ( miR171c–f and miR171i ) , miR396 family ( miR396a–c , e and f ) and miRNA* of miR1318 , miR1425 , miR159a , miR168 , miR172d , miR390 , miR444b . 2 and miR528 . These data were not attributed to sequencing bias . First , the vast majority of miRNA* in RSV-infected plants were present at low levels as in mock control plants . Second , the levels of both miRNA and miRNA* in RDV-infected plants were not different from those in the mock controls . Third , northern blots confirmed that miR1425* , miR160* and miR171* were significantly up regulated , whereas their corresponding miRNAs were not ( Figure 2A ) . Thus , we concluded that RSV infection specifically enhanced the accumulation of certain miRNA* sequences , but not their corresponding miRNAs . We notice shorter sequences for miR160* and miR171* in RSV-infected rice ( Figure 2A ) , which maybe 20-nt variants of miRNA* through sequencing data ( Table 3; see also Table S2 for all sequencing data from three biological replicates ) . We predicted the putative targets for some of these miRNAs* . Os11g15060 , a SAM ( S-adenosyl-L-Met ) -dependent carboxyl methyltransferase , was predicted to be a target of miR1425* . Northern blots showed down-regulated expression of Os11g15060 in RSV-infected plants ( Figure 2B ) . A cleavage product was detected ( Figure 2B ) , and 5′-RACE ( Rapid Amplification of 5′ Complementary DNA Ends ) identified the cleavage site in the miR1425* binding region ( Figure 2C ) , providing direct evidence that a plant miRNA* could direct the cleavage of its target mRNA . We also identified a second cleavage site outside the miR1425* binding region ( Figure 2C ) . How this second site was derived is not clear , but many miRNA targets have more than one cleavage site as reported in other plants [56]–[58] and even in a green alga [59] . Whether this second cleavage results from the action of another yet-to-be identified small RNA induced by viral infection remains to be further investigated . Intriguingly , the expression of Os11g15060 was also down-regulated in RDV-infected plants ( Figure 2B ) . However , absence of a cleavage product and failed 5′ RACE to identify a cleavage site ( data not shown ) suggests that this down-regulation in the RDV-infected plants was not caused by RNA silencing , or caused by partial silencing as well as another mechanism that remains to be identified . We also analyzed the expression of some potential targets of miR160* and miR171* including Os11g38140 and Os02g49240 ( potential targets of miR160* ) , and Os03g38170 ( potential target of miR171* ) . The expression of the three genes decreased in RSV-infected rice as compared with that in RDV-infected rice ( Figure S1 ) . The small RNA libraries of RSV-infected rice contained many unique sequences , absent from the other three libraries , which are mapped to several conserved miRNA precursors ( Table 2 ) . Some of these belong to the miR159 family , whose precursors are approximately 200 nt in length with a stem structure of 80–90 nt ( Figure 3 ) . As shown in Figure 3A–D ( see also Supplemental Table S3 for all sequence data ) , besides the reported miRNA/miRNA* pair for each precursor of the family ( i . e . , miR159a . 1/miR159a . 1* , miR159a . 2/miR159a . 2* , miR159b . 1/miR159b . 1* , miR159c . 1/miR159c . 1* and miR159f . 1/miR159f . 1* , labeled red and blue respectively for each pair ) , new pairs of miRNA/miRNA* were produced from each precursor . These included miR159a . 3/miR159a . 3* , miR159b . 2/miR159b . 2* , miR159b . 3/miR159b . 3* , miR159c . 2/miR159c . 2* , miR159c . 3/miR159c . 3* , miR159f . 2/miR159f . 2* and miR159f . 3/miR159f . 3* . These pairs were generated in a phased pattern and often detected at higher levels than the reported pairs . The phased miRNA-miRNA*s have the characteristics of 2 nt overhangs at the 3′ end . These observations , together with the observation that such phased production of new miRNA/miRNA* were absent from RDV-infected or any mock-infected plants , ruled out the possibility that they were degradation products or sequencing errors . In addition to the above three-duplex phase forms of miRNA/miRNA*s , we also found two-duplex phase forms derived from some other precursors . The precursor of miR394 in the miRBase database ( http://microrna . sanger . ac . uk/sequences , version 12 . 0 ) is about 100 nt in length . However , we found that the actual precursor is longer and contains a 27-nt extension from the 5′ and 3′ ends of the reported precursor , respectively ( Figure 3E and Figure S2 ) . In this longer precursor , a new miR394 . 2/394 . 2* duplex appeared at the distal end of the stem structure , in phase with the reported 394 . 1/394 . 1* ( Figure 3E and Figure S2 ) . Northern blots confirmed the expression of miR159 . 2/miR159 . 2* , miR159 . 3 and miR394 . 2 ( Figure 3F ) . The resolution of northern blots did not permit distinction between family members , so that each band could contain multiple members of a family of miRNAs/miRNA*s . Using clustalW [60] , we analyzed the conservation of precursor sequences of miR159 , miR319 , and miR394 in different plants ( Figure S3 ) . We found that , compared with the reported mature miRNA/miRNA* sequences , the newly identified phased miRNA/miRNA* sequences are less well conserved . The target of miR159a . 2 is Os03g02240 , a GT2 transcription factor , which has been verified by 5′ RACE [61] . Quantitative real-time RT-PCR analysis showed that RSV infection down-regulated the expression of Os03g02240 ( Figure 3G ) . Os12g03530 and Os02g47000 , the predicted targets of miR159a . 3 and miR394 . 2 , respectively , were down-regulated in RSV-infected rice plants ( Figure 3G ) . However , in RDV-infected rice plants , the expression of these genes was unchanged ( data not shown ) . The transcripts encoded by the AK120922 locus of rice genome can fold into long inverted repeat structures , producing 12 21-nt phased small RNA duplexes [45] , [62] , [63] . From the stem region proximal to the terminal loop to the distal end of the RNA secondary structure , the 12 small RNA duplexes produced are named P1-12_5′ on the 5′ arm and P1-12_3′ on the 3′ arm ( Figure 4A ) . One of these duplexes was initially characterized as miR436/miR436* duplex [62] , but further studies demonstrated all small RNAs , including the so-called miR436 , are DCL4-dependent siRNAs [45] . Analysis of our sequencing data showed that the reads of some AK120922-derived siRNAs in the RSV-infected rice plants were much higher than those in the mock-inoculated plants . In particular , the reads of P5_3′ increased by at least 100-fold . The higher expressions of these siRNAs were confirmed by northern blots ( Figure 4B ) . No such changes were observed in RDV-infected plants ( data not shown ) . Using realtime PCR , we analyzed the expression of the potential targets of P5_3′ and P10_3′ . Surprisingly , we found that both of them were up-regulated in RSV-infected plants ( Figure S4 ) . To gain additional insights into the effects of RDV and RSV infection on the RNA silencing pathways/machinery , we characterized the expression profiles of various genes involved in the biogenesis/function of miRNAs/siRNAs by using the same plant materials as used for small RNA deep sequencing . These genes include 8 homologs of OsDCLs , five OsRDRs and 19 AGOs that have been identified in rice [44]–[49] . We first analyzed the expression profiles of OsDCLs , OsRDRs and OsAGOs , according to the annotations of Kapoor et al [48] . Figure 5A presents microarray data showing that RDV and RSV infections affected the expression of different members of OsDCL , OsRDR and OsAGO families . The microarray data were further verified by quantitative real-time RT-PCR measurements ( Figure 5B and C ) . Of the 8 DCL homologs in the rice genome [48] , OsDCL3a ( LOC_Os01g68120 ) and OsDCL3b ( LOC_Os10g34430 ) were significantly down-regulated , and OsDCL2 ( LOC_Os03g38740 ) significantly up-regulated , in RSV-infected plants ( Figure 5C ) . In contrast , RDV infection had almost no influence on the expression of OsDCLs in rice plants ( Figure 5B ) . In rice , there are four AGO1 homologs: OsAGO1a ( LOC_Os02g45070 ) , OsAGO1b ( LOC_Os04g47870 ) , OsAGO1c ( LOC_Os02g58490 ) and OsAGO1d ( LOC_Os06g51310 ) [32] , [33] . The expression of OsAGO1a-c as well as OsAGO2 ( LOC_Os04g52540 ) and OsAGO18 ( LOC_Os07g28850 ) increased significantly in RSV-infected rice plants ( Figure 5C ) . In RDV-infected plants , only the expression of OsAGO11 increased significantly ( Figure 5B ) . The expression of OsRDR1 ( LOC_Os02g50330 ) and OsRDR4 ( LOC_Os01g10140 ) was significantly enhanced in RDV-infected plants ( Figure 5B ) , but not in RSV-infected plants ( Figure 5C ) . OsRDR2 did not change expression levels in plants infected by either virus . These data demonstrate that the two viruses have distinct effects on the expression of different genes of the RNA silencing pathways in the common host rice .
Recent studies used deep sequencing to obtain profiles of viral siRNAs [64]–[68] and viroid small RNAs [69] , [70] in infected plants . Such data laid a foundation for further investigations on the biogenesis mechanisms and functions of viral and viroid small RNAs . Our present work provides the first deep sequencing analysis of plant small RNA profiles under viral infection conditions . This analysis not only filled a critical knowledge gap in RNA silencing-based virus-host interactions , but provided novel insights into the impact of viral infection on host small RNA biogenesis . Our results showed down-regulated as well as up-regulated accumulation of certain rice miRNAs , with up-regulation being more extensive . Most significantly , RSV infection , but not RDV infection , induced the expression of novel phased miRNAs from several families of conserved cellular miRNA precursors . Real-time RT-PCR experiments showed reduced accumulations of the predicted target mRNAs for these phased miRNAs ( i . e . , miR159 . 2 families , miR159 . 3 and miR394 . 2 ) , indicating that the induced phased miRNAs have regulatory functions . How the reduced accumulation of these target mRNAs contributes to the establishment of viral infection and/or host defense will be an important focus of future research . As compared to the short animal miRNA precursors , which are usually 70–80 nt , plant miRNA precursors are generally much longer with most known precursors to be 200–300 nt [15] . These long plant precursors make it possible to produce multiple miRNAs . Indeed , multiple , phased miRNAs are produced from some miRNA precursors in the single-cell alga , Chlamydomonas reinhardtii [59] , [71] and in higher plants [72] . Some of these miRNAs are differentially regulated by bacterial infection in A . thaliana [72] . Our finding that new phased miRNAs are induced during the infection of a plant virus significantly broadens the landscape of phased miRNA biogenesis during pathogen infection . Further analyses of the host small RNA profiles involving different pathogens and hosts may lead to additional examples , and an understanding of the broad biological significance , of pathogen infection-induced expression of phased and other novel miRNAs . The enhanced production of miRNA*s during RSV infection may be attributed to inhibition of RISC activity and miRNA/miRNA* unwinding by the RSV VSR , via direct or indirect interaction with the miRNA/miRNA* duplexes , as have been shown for VSRs of other viruses [22] , [24] . If this were the case , one would expect elevated accumulations of miRNAs and miRNA*s at the same time . However , our deep sequencing showed that only the accumulations of miRNA*s , not the corresponding miRNAs from many miRNA precursors were enhanced . Such special accumulation of miRNA* , but not miRNAs , may be explained if RSV enhanced the activities of certain RISCs specially associated with miRNA*s or interfered with loading of some miRNAs into RISCs . Since we do not have data yet to support this postulation , we need to leave other alternative possibilities open for exploration . Schnettler et al . ( 2010 ) reported stabilization of miR171c/miR171c* complex by several tospoviruses in infected N . benthamiana , with elevated accumulation of miR171c* in the infected plants . This stabilization appears to be due to the activity of the viral NSs proteins [73] . The specific accumulation of miRNA*s of several miRNA families and the conservation of the miRNA* sequences within the families suggest that there may be certain base preferences among the miRNA*s accumulated during RSV infection . Using WebLogo [74] , we found that there was an ‘A’ bias in the 19th nucleotide from the 5′ terminus ( Figure S5 ) . Considering that the A . thaliana AGO2 preferentially produces miRNA* sequences [75] and the up-regulation of OsAGO2 and OsAGO18 during RSV infection of rice , we propose that the ‘A’ bias may direct certain miRNA* sequences into these OsAGOs . Still we cannot exclude the potential influence of OsAGO1 , as the ‘A’ bias in the 19th nucleotide from the 5′ terminus of miRNA* corresponds to the 5′ terminal ‘U’ of miRNA . In Drosophila melanogaster , miRNA*s were reported to have regulatory functions [76] . Here , by northern blots and 5′ RACE , we demonstrated that Os11g15060 , a predicted target of miR1425* , was specifically down regulated by cleavage during RSV infection ( Figure 2C ) . This is the first direct experimental demonstration , to the best of our knowledge , that a plant miRNA* can regulate the stability of its predicted target mRNA . Although many siRNAs were produced in rice [77] , our current analysis showed that RSV infection enhanced mainly the accumulation of some phased siRNAs derived from the AK120922 transcripts . The specificity of this enhancement was supported by the observation that RDV infection did not have such an effect . Previous studies showed that accumulation of siRNAs decreased in transgenic plants expressing the VSRs of several plant viruses [20] , [23] , [24] . The inhibitory effect of VSRs on siRNA accumulation , via binding of VSRs with siRNAs , is one of the mechanisms of viral counter-defense [1] , [2] , [19] . The enhanced accumulation of phased siRNAs from AK120922 during RSV infection , in contrast to the generally observed decrease in siRNA accumulations , may be biologically significant . Whether the VSR or other proteins encoded by RSV play a role in this enhancement is an outstanding mechanistic question to be answered in the future . The induced expression of new phased miRNAs and the enhanced production of selective miRNA*s and phased siRNAs in rice plants infected by RSV , but not by RDV , indicate that such changes were not a general response to viral infection . Rather , they were caused by distinct virus-host interactions . One of the primary consequences of such interactions was conceivably altered expression and/or function of the RNA silencing machinery , which further leads to altered small RNA profiles . Indeed , our microarray and quantitative real-time RT-PCR analyses demonstrated that the expression files of OsDCLs , OsAGOs and OsRDRs were differentially altered in rice infected with the two viruses . During RDV infection , with the exception of OsRDR1 being enhanced , there were no significant changes in the expression of RNA silencing pathway genes . In contrast , during RSV infection , the expression levels of OsDCL3a and OsDCL3b were down-regulated , whereas the expression of OsDCL2 was enhanced . Three of the four OsAGO1 homologs , OsAGO1a , OsAGO1b and OsAGO1c , were up-regulated in RSV-infected plants . Whether the altered expression patterns of the OsDCLs , OsRDRs and OsAGOs are responsible for the altered miRNA/siRNA biogenesis/accumulations in the RSV- and RDV-infected rice plants remains to be determined . Current data indicate that OsDCL1 participates in the production of 21-nt miRNAs and DCL4 mainly produces some siRNAs [28] , [29] . DCL3 was recently reported to produce 24-nt miRNAs [14] . The four small RNA libraries we generated comprised mainly 24-nt sequences . However , in the RSV-infected rice small RNA libraries , the number of 24-nt small RNAs was reduced to about three quarters of the other three libraries ( Figure S6 ) . This correlated with a decreased level of DCL3 expression in RSV-infected rice . These results suggested that OsDCL3 may be primarily responsible for producing 24 nt small RNAs , just as the A . thaliana homolog does ( Figure 5 ) [78] , [79] . The production of 21 nt and 24 nt phased miRNAs suggests involvement of DCL1 and potentially also DCL3 . In summary , our data indicate that at least some viruses may have evolved mechanisms to induce expression of phased miRNAs from well-conserved cellular miRNA precursors . Whether such new miRNAs play a role in host defense or in viral infection will be an important question to be investigated in the future . While this manuscript was under review , Hu et al . ( 2011 ) reported that Oilseed rape mosaic tobamovirus infection of A . thaliana also led to elevated accumulation of host siRNAs and some miRNA-like small RNAs ( ml-sRNAs ) . These ml-sRNAs are derived in phase with known miRNAs from miRNA-precursors [80] . They are different from the sequences we report here . Altogether , as for many previous discoveries , virus-induced production of phased miRNAs and ml-sRNAs may provide yet another useful model system to study the molecular mechanisms underlying the evolution and biogenesis of miRNAs . It remains to be seen whether virus-induced biogenesis of phased miRNAs is more widespread in plants and other organisms . Another important question is how different viruses affect the production of different small RNAs and how such differences impact specific mechanisms of viral infection and host responses .
RDV Fujian isolate and RSV Jiangsu isolate , China , were maintained in “Oryza sativa spp . japonica” rice plants grown in greenhouses at 25±3°C , 55±5% RH and under natural sunlight . Insects ( Nephotettix cincticeps and Laodelphax striatellus ) were maintained in cages that contained rice seedlings in greenhouses at 25±3°C . To obtain high viruliferous insects , nymphs were reared on virus-infected rice plants for 1 week , and insects were maintained up to the adult stage with occasional replacement of seedlings by healthy rice seedlings . Rice seedlings were grown in a greenhouse at 25–28°C and 70±5% relative humidity under natural sunlight . Three-week-old seedlings were placed individually in single tubes of 4-cm in diameter and 25-cm in height that each contained 15–20 ml of liquid nutrient medium at the bottom . The viruliferous insects of N . cincticeps ( carrying RDV ) , L . Striatellus ( carrying RSV ) as well as virus-free N . cincticeps ( mock for RDV ) and L . Striatellus ( mock for RSV ) were added to each tube . Each tube was then sealed with a nylon net . After 2 days in growth chambers with a 14-h/10-h light/dark cycle , 70±5% relative humidity and a temperature regime of 28°C ( day ) /25°C ( night ) , the insects were removed and the rice seedlings were returned to the greenhouse to grow under the above greenhouse conditions . After approximately three weeks of growth , when the newly developed leaves started to show viral symptoms , the whole seedlings were harvested . With each sample , at least 15 rice seedlings were pooled for RNA extractions . Total RNAs were extracted using Trizol reagent ( Invitrogen , Carlsbad , CA , USA ) for RT-PCR , small RNA sequencing , microarray analysis , and northern blotting . RT-PCR was used to test individual rice seedlings for infection with RDV or RSV . All PCR primers are listed in Table S4 . The small RNA library construction and Illumina 1G sequencing were carried out at BGI-Shenzhen ( Shenzhen , Guangdong , China ) using standard Solexa/Illumina protocols . Briefly , the total RNA was separated through 15% TBE urea denaturing PAGE gels and small RNAs of 15–30 nt were recovered . Then , 5′ and 3′ RNA adaptors were ligated to these small RNAs followed by reverse transcription into cDNAs . These cDNAs were finally amplified by PCR and subjected to Solexa/Illumina sequencing . After removing the adaptor sequences , small RNA sequences with 18–28 nt in length were used for further analysis . BOAT provided by CBI ( http://boat . cbi . pku . edu . cn/ ) was used for mapping small RNAs to the O . sativa genome sequences ( TIGR Rice Annotation Release 5 . 0 , ftp://ftp . plantbiology . msu . edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/ ) as well as RDV ( ftp://ftp . ncbi . nih . gov/genomes/Viruses/Rice_dwarf_virus_uid14797/ ) and RSV ( ftp://ftp . ncbi . nih . gov/genomes/Viruses/Rice_stripe_virus_uid14795/ ) Rice stripe virus ) genome sequences . Small RNAs with perfect genomic matches were used for further analysis . The small RNAs were annotated with reference to the following databases: miRBase ( http://microrna . sanger . ac . uk/sequences , version 12 . 0 ) for miRNA sequences , Rfam ( http://www . sanger . ac . uk/Software/Rfam/ ) for noncoding RNA ( rRNAs , tRNAs , snoRNAs , and snRNAs ) sequences and Repbase ( http://www . girinst . org ) for transposons and repeats . WebLogo [74] was used for analyzing of relative frequencies of nucleotides at each position of small the sRNAs , and the mfold program [81] was used for predicting the stem-loop structures . Target genes were predicted by using the miRU web server ( http://bioinfo3 . noble . org/miRNA/miRU . htm ) and our own Perl script [59] . For submission to the web server , we chose the default parameters ( score for each 20 nt≤3 , G: U pairs≤6 , indels≤1 and other mismatches ≤3 ) and the TIGR Rice Genome mRNA dataset ( OSA1 release 5 , 01/23/2007 ) . For our own Perl script , based on the score standard mentioned before [59] , targets with score ≤5 were chosen . Identical search results from both methods were considered potential targets of newly identified miRNAs or phased miRNAs . For miRNA*s induced during RSV infection , their targets were validated based on the microarray data . ClustalW was used for the alignment of miRNA precursors and miRNA* sequences . Hybridization of GeneChip rice genome array ( Affymetrix ) , scanning and analysis were performed by the Affymetrix custom service ( CapitalBio , Beijing , China ) following standard protocols ( http://www . affymetrix . com/support/technical/manual/expression_manual . affx ) . Three biological replicates were conducted for RNA from each type of plant samples . Data analysis and comparison of the samples was finished using the standard Affymetrix protocol ( CapitalBio ) . Cluster3 . 0 software was used for cluster analysis . The expression profile of some mentioned gene through microarray analysis was shown in Table S5 . Total RNA was used for mRNA and small RNA northern blot hybridization . For small RNA blots , 10–60 µg of total RNA ( depending on the relative expression levels from deep sequencing ) of each sample was separated on 15% polyacrylamide denaturing gels and then transferred to Hybond-N+ membranes ( Amersham BioScience , Piscataway , NJ , USA ) . The membranes were cross-linked by UV transillumination and dried at 120°C for 30 min . DNA oligonucleotides complementary to small RNAs , which were labeled with γ-32P-ATP by T4 polynucleotide kinase ( New England Biolabs , Beverly , MA , USA ) , were used as probes for hybridization . Membranes were prehybridized with buffer for 2 h followed by hybridization with the DNA probes overnight at 40°C in 5X SSC , 20 mM Na2HPO4 ( PH 7 . 2 ) , 7% SDS , 2X Denhardt's Solution , 100 µg/ml salmon sperm DNA . After washing twice at 40°C with 2X SSC and 0 . 1% SDS , the blots were imaged with a PhosphorImager ( PerkinElmer , Shelton , CT , USA ) . The membranes were stripped with 0 . 1X SSC and 1% SDS at 100°C and reprobed . Probes complementary to U6 sequences were used as a loading control . For mRNA blots , 15 µg of total RNA was separated by 1% formaldehyde agarose gel and transferred to Hybond-N+ membranes that were then cross-linked and dried as described above . Prehybridization and hybridization solution was 5X SSC , 1% SDS , 5X Denhardt's Solution , 100 µg/ml salmon sperm DNA and 50% formamide . The probes were labeled with α-32P-dCTP by DNA polymerase 1 large ( Klenow ) fragment ( Promega , Madison , Wisconsin , USA ) and were complementary to a 500-bp fragment corresponding to the 5′ partial sequence of Os11g15060 ( target of miR1425* ) . Total RNA was treated with RNase-free DNase I ( TAKARA Biotechnology , Dalian , China ) at 37°C for 30 min . After phenol/chloroform extraction and isopropanol precipitation , the RNA was quantified with a UV/ visible spectrophotometer ( Amersham ) . Two µ\g of total RNA was reverse-transcribed using poly ( T ) adapter with SuperScript Reverse Transcriptase ( Invitrogen ) . qPCR was performed using SYBR Green Realtime PCR Master Mix ( Toyobo ) OsEF-1a gene was used as an internal control , with primers CX1597 ( 59-GCACGCTCTTCTTGCTTTCACTCT-39 ) and CX1598 ( 59-AAAGGTCACCACCATACCAGGCTT-39 ) [45] . Three independent biological replicates were conducted . These data were further normalized with the normalized expression values obtained from qRT-PCRs and bar charts plotted by using Microsoft Excel and SPSS ( Statistical Product and Service Solutions ) software ( IBM; Version . 10 . 0 ) . All the other primers used are listed in Supplemental Table S4 . Validation of target genes by mapping the cleavage sites was conducted with 5′ RACE by following the GeneRacer Kit ( Invitrogen ) protocols described previously [57] . Total RNA of RSV-infected rice was directly ligated to GeneRacer RNA Oligo adaptor without any modifications . RT-PCR was used to synthesize cDNAs using GeneRacer Oligo dT primer . GeneRacer 5′ Primer ( 5′CGACTGGAGCACGAGGACACTGA3′ ) and the target gene-specific outer primers ( Table S4 ) were used for the first round of amplification . Then the GeneRacer 5′ Nested Primer ( 5′ GGACACTGACATGGACTGAAGGAGTA ) and gene-specific inner primers ( Table S4 ) were used for the second round of amplification . The PCR products were cloned and sequenced to identify the cleavage sites . | Small RNA-mediated RNA silencing is a widespread antiviral or antibacterial mechanism in different organisms . Although the host and pathogen factors involved in this mode of host defense and pathogen counter-defense have been extensively investigated , much less is known about how a pathogen alters the small RNA metabolism in a host . To help fill this knowledge gap , we first used deep sequencing to characterize the miRNA and siRNA profiles of rice plants infected by two distinct viruses , Rice dwarf virus ( RDV ) and Rice stripe virus ( RSV ) , respectively . Our analyses showed that these two viruses had distinct impacts on rice small RNA metabolism . More significantly , RSV infection , but not RDV infection , enhanced the accumulation of some rice miRNA*s from conserved miRNA precursors and accumulation of phased siRNAs from a particular precursor . Furthermore , RSV infection also induced the expression of novel phased miRNAs from several conserved miRNA precursors . While RSV infection significantly elevated the expression of certain OsDCLs and OsAGOs , RDV infection only affected the expression of certain OsRDRs . These data uncover new mechanisms of virus-host interactions that affect host small RNA metabolism . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"mechanisms",
"of",
"resistance",
"and",
"susceptibility",
"plant",
"science",
"virology",
"plant",
"pathogens",
"plant",
"biology",
"plant",
"pathology",
"biology",
"microbiology"
]
| 2011 | Viral Infection Induces Expression of Novel Phased MicroRNAs from Conserved Cellular MicroRNA Precursors |
Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation . However , the few models available today have been based on simplistic assumptions about the sequences being modeled , or heuristic approximations of the underlying regulatory mechanisms . We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence , as a function of transcription factor concentrations and their DNA-binding specificities . It uses statistical thermodynamics theory to model not only protein-DNA interaction , but also the effect of DNA-bound activators and repressors on gene expression . In addition , the model incorporates mechanistic features such as synergistic effect of multiple activators , short range repression , and cooperativity in transcription factor-DNA binding , allowing us to systematically evaluate the significance of these features in the context of available expression data . Using this model on segmentation-related enhancers in Drosophila , we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone . We find clear support for the phenomenon of short-range repression , where repressors do not directly interact with the basal transcriptional machinery . We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution , and a noticeable fraction of these undergo lineage-specific changes . Our implementation of the model , called GEMSTAT , is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences .
We began by examining whether our models agree with existing data on transcriptional gene regulation during Drosophila embryonic development ( anterior–posterior axis specification ) . This involved training our model on 37–44 experimentally characterized CRMs and 6–8 transcription factors . The overall quality of fit as well as predictive ability of our models was remarkably high . Next , we applied different model variants to investigate mechanistic questions . We found that the transcriptional synergy arising from simultaneous contact of activators with the BTM contributes significantly to the accurate specification of expression patterns , and this contribution extends beyond the contribution from mutual interactions ( DNA-binding cooperativity ) between activators . Shifting attention to repressors , we then found that competition between repressors and activators for binding sites is an insufficient mechanism of repression [29] . We found evidence in favor of a short range repression mechanism for two of the TFs , consolidating experimental evidence that exists for this mechanism . However , our results also raised the possibility that long-range mechanisms ( such as direct interaction with the BTM ) may also contribute to the repressors' function . We also studied the importance of cooperative DNA-binding ( of both activators and repressors ) in this system . Our results provide clear evidence of cooperative effects of some TFs but give mixed signals with respect to other TFs . We also used our model to examine a contentious evolutionary issue . Several studies [30]–[32] have reported that TF binding sites undergo rapid turnover ( loss and/or gain ) during evolution . However , due to the difficulty of establishing true functionality of binding sites in practice ( e . g . , binding to a TF does not necessary lead to regulatory function [33] ) , it is not clear whether such turnover is largely limited to non-functional sites . We investigated this issue using our model in conjunction with evolutionary sequence comparison , and found that lineage–specific losses affect functional sites to a noticeable extent . As mentioned above , a few thermodynamics-based models have been proposed in the past , which we now review briefly . The approach of Reinitz and colleagues exploits physicochemical principles , and includes important mechanistic aspects such as short range repression through quenching [13] , [34] . However , the Reinitz model does not consider all possible molecular configurations , a fundamental tenet of the statistical thermodynamic treatment . Also , cooperative DNA-binding by TFs is not included in the model . Segal et al . [6] presented a model based on enumeration of all configurations of bound and unbound TFs . This model uses statistical thermodynamics to model TF-DNA interactions and to compute relative probabilities of configurations , but models the mapping from these configurations to their transcriptional output in a heuristic manner . Also , the Segal model ignores important mechanistic issues such as transcriptional synergy ( discussed above ) and short range repression . Furthermore , the formulation of transcriptional output in this model makes the computational task intractable . ( The authors adopted sampling methods to deal with this issue , thereby sacrificing exactness of the model computation . ) The models developed by other researchers make various simplifying assumptions , e . g . , binding of a single activator is strong enough to activate transcription [14] , and their implementations are often limited in their generality , e . g . , only sequences with a small number of binding sites are considered [12] , or all sites are assumed to have identical binding affinities [14] . See Table 1 for a summary of the strengths and weaknesses of the models discussed above . We have not undertaken a rigorous comparison of our approach versus the above-mentioned approaches , for three reasons . First , none of the previous models have a publicly available implementation that we could use in our setting . Bauer & Bailey's implementation [11] of the Reinitz model comes closest , but cannot be applied to more than one CRM at a time . Second , while Segal et al . [6] make their data set ( and their predictions for this set ) available , their method uses a much larger number of free parameters ( the position weight matrices of the TFs were estimated from data ) , precluding a fair comparison . Third , and most importantly , our main goal in this study was to search for mechanistic insights that are revealed by the data , rather than engineering a model with the best possible fit to the data . For the same reason , we have not attempted here to position our work in comparison to machine learning-based models of gene expression [35] , [36] . To facilitate future studies by other researchers , we make the source code of our implementation freely available online .
We begin with an overview of the statistical thermodynamic theory of gene expression , following Buchler et al . [7] . This theory has two components , one dealing with the occupancy of TFs at DNA sequences , and the other with the interactions of occupied TFs with the BTM . We first describe the model of TF occupancy . Consider a CRM with n binding sites ( e . g . , n = 2 in Figure 1A ) . A molecular configuration , denoted by σ , specifies which sites are bound and which are free . Thus there are 2n possible configurations . The statistical weight of configuration σ , denoted by W ( σ ) , and which we shall endeavor to compute , gives us the relative probability , P ( σ ) , of the configuration when the system is in equilibrium . In other words , we have P ( σ ) = W ( σ ) /Z , where Z is a normalization constant , defined as ∑σ W ( σ ) , and known as the partition function . Calculation of P ( σ ) would allow us to answer questions like: “What is the relative probability of site S being in the bound state ? ” This may be computed by summing P ( σ ) over all σ in which S is bound , and is also called the fractional occupancy of the site S . The statistical weight W ( σ ) depends on the number and affinities of the occupied binding sites in the configuration σ , and on interactions between bound TF molecules . We will present details of W ( σ ) when discussing specific models below . We next describe , at a high level , how the above molecular configurations ( σ ) affect gene expression . We assume that the gene expression level ( on a scale of 0 to 1 ) is equal to the fractional occupancy of the promoter by the BTM . Each of the configurations σ considered above ( specifying bound or unbound TFs ) may now correspond to two states , depending on whether BTM is bound or not . The statistical weight of these two states will be given by W ( σ ) Q ( σ ) and W ( σ ) respectively , where W ( σ ) is the contribution from TF–DNA interactions as explained above , and Q ( σ ) is the contribution from TF–BTM interactions , present only if the BTM is bound . Q ( σ ) depends on the bound TFs in the configuration σ , and may be construed as the transcriptional output from the configuration . We now calculate the relative probability of bound BTM as ZON = Σσ W ( σ ) Q ( σ ) , and that of unbound BTM as ZOFF = Σσ W ( σ ) , to obtain the gene expression level as follows ( note that “ON” and “OFF” represent the state of BTM occupancy , which is separate from the occupancy states of binding sites in the CRM sequence , as indicated by σ ) : ( 1 ) Here , we present details of how the W ( σ ) and Q ( σ ) terms are specified by the first of our two models . Under this model , DNA-bound transcription factors interact favorably ( activators ) or unfavorably ( repressors ) with the BTM , thus affecting the probability of the BTM being bound at the promoter . We call this the Direct Interaction ( “DirectInt” ) model . For a configuration σ , the statistical weight W ( σ ) has terms reflecting binding of TFs to their binding sites , and those reflecting interactions between TFs . Let q ( S ) denote the contribution of a single occupied site S to W ( σ ) . This depends on the concentration of the TF and the strength of the site , and is given by: ( 2 ) where ( See Text S1 for how Equation ( 2 ) is derived . ) Note that two unknown constants , one related to TF-DNA binding ( K ( Smax ) ) , and the other ( ν ) a constant of proportionality for TF concentration , appear together as a product , which can be treated as a single free parameter to be estimated from data . The above equation makes the implicit assumption that the binding energy of each position of a site is additive . This assumption has been questioned in several studies [37] , [38] , but is necessary in our case because there is not enough TF-DNA interaction data to construct accurate models incorporating higher-order interactions . Furthermore , the additivity assumption seems to be a reasonably good approximation for the TFs in the segmentation system [6] , [13] . The statistical weight of a configuration σ , in the absence of cooperative binding , is then given by , where σi is an indicator variable ( values 0 or 1 ) for Si being occupied by its TF in the configuration ( Figure 1A ) [7] . If two bound TFs interact ( protein–protein interaction ) , they make an additional contribution to the statistical weight of the configuration . We denote this contribution by ω ( d ) , where d is the distance between their binding sites ( Figure 1B ) . The dependence of this cooperativity term on the distance is discussed in Text S1 . The statistical weight of a configuration , accounting for cooperative binding , is the product of contributions of all occupied sites and all TF-TF interactions implied by that configuration [7]: ( 3 ) where ωij ( dij ) denotes the statistical weight contribution due to interaction between the TFs bound to sites Si and Sj , and dij is the distance between these sites . We assume that cooperative binding is possible only if the bound sites are adjacent in the configuration , i . e . , there is no other bound site in between . We also assume that it is predetermined whether any given pair of transcription factors exhibit cooperative binding or not . The model allows interactions between adjacent binding sites that may be either homotypic ( of the same TF ) or heterotypic ( of different TFs ) . Next , we describe how we model Q ( σ ) , the statistical weight contribution from TF-BTM interactions . We assume that each TF is either an activator or repressor . A bound activator A interacts with the bound BTM with statistical weight αA>1 , while a repressor R interacts with weight αR<1 ( Figure 1C ) . Q ( σ ) is the product of the α terms corresponding to each bound TF in the configuration . This corresponds to the intuition that a bound activator makes the configuration more energetically favorable ( thus , a greater weight ) while a bound repressor makes it less favorable . We also assume that each bound TF interacts independently with the BTM , with energy contributions that add up , which is reflected in the statistical weights being multiplicative . Computation of Equation ( 1 ) involves summation of an exponential number of configurations . We developed an efficient algorithm based on dynamic programming to carry out the computation ( see below and Text S1 ) . We note that Gertz et al . made the same model assumptions [12] , but , unlike their method , we provide a general and efficient implementation that is applicable to arbitrary sequences . The DirectInt model presented here largely follows Buchler et al . [7] , with the treatment of sequence-specific DNA binding ( Equation 2 ) being borrowed from Berg & von Hippel [9] . However , the approach of Buchler et al . [7] , designed for prokaryotic systems , assumed repressors to work by competition with the polymerase , and does not extend to distally located binding sites . In the DirectInt model above , repressor action is independent of the location of binding sites for repressors or activators . However , experimental work has shown that certain repressors act on activators only if they are bound within a “short range” , e . g . , less than 150 bp , of the activator binding site [17] . Such short range repression , also called “quenching” [17] , may work by repressors inhibiting DNA-binding of activators [39] , possibly by modifying chromatin accessibility . We model this mechanism by assuming that a bound repressor does not directly interact with the BTM , instead , it creates a new possible configuration , one where DNA in its “neighborhood” ( defined by a range parameter dR ) is inaccessible to binding by any other TF , for example by localized chromatin modification ( Figure 1D ) . A configuration where the neighboring chromatin is inaccessible ( Figure 1D , bottom ) competes with the configurations where the chromatin is accessible to activators , thus effectively reducing the occupancy of activators . We call this model the short-range repression , or SRR , model . Note that there are more configurations under this model than in the DirectInt model . In any configuration , an activator site may exist in one of two states ( bound or unbound ) as in DirectInt . In contrast , each repressor site may now exist in one of three states: unbound , “bound-only” , and “bound-effective” ( the bound-only state has the repressor bound but not interacting with either the BTM or the neighboring DNA , while in the bound-effective state the bound repressor makes the neighboring DNA inaccessible ) . Not all possible configurations are allowed , however . We assume that within a certain range of a bound-effective repressor , an activator site is not allowed to be bound ( thus implementing the idea of short-range repression ) . For a legitimate configuration σ , W ( σ ) in the SRR model is given by Equation ( 3 ) , multiplied by a repressor-specific constant βR for each bound-effective site of the repressor R ( Figure 1D , bottom ) . The parameter βR may be interpreted as the equilibrium constant of the reaction that changes the chromatin state from accessible to inaccessible . The value of βR controls the strength of the repressor . When it is close to 0 , there is no repression effect; when it approaches +∞ , the repressor completely shuts down all activator sites in the neighborhood . Thus , in this alternative to the DirectInt model , repression is modeled by augmenting the calculation of W ( σ ) , instead of direct interaction terms ( αR ) for the repressor in Q ( σ ) . Q ( σ ) is now a product of the direct interaction terms ( αA ) for activators alone . We show that even with this new model , it is possible to perform efficient computation of Equation ( 1 ) using dynamic programming ( see Text S1 ) . We consider the following question: how are the effects of multiple bound activators combined ? In both models described above ( DirectInt , SRR ) , their individual statistical weights ( αA ) were multiplied , in calculating the overall contribution of activator-BTM interactions . This is the “multiplicative effect” model of combined action by multiple activators . It reflects a scenario where the bound activators interact with different parts of the BTM ( or different steps of transcription initiation ) , and the energy terms are added . Veitia [26] shows that this multiplicative effect model results in “transcriptional synergy” , where the activating effect of two binding sites is greater than the sum of their individual effects , even in the absence of cooperative DNA-binding . We next consider an alternative scenario where in any given configuration , at most one activator molecule may interact with the BTM . This is plausible if for example the bound activators must interact with the same subunit of the BTM . In this case , the TF-BTM interaction term is written as Q ( σ ) = Σαi , where the sum is over bound activators in the configuration . This is the called the “additive effect” model ( Figure 1E ) . In this case , there will be no synergistic activation due to TF-BTM interaction , though some level of synergy may still arise from cooperative DNA-binding by activators . In Text S1 , we compare the two mechanisms that may lead to transcriptional synergy: multiplicative effect model , and additive effect model in combination with cooperative DNA binding . The basic insight is that synergistic effect will disappear at high activator concentration under the cooperative binding model ( activator binding has already been saturated under this condition , thus cooperative interactions will not be further helpful ) , but not under the multiplicative model . This difference in model behavior suggests that it is theoretically possible to distinguish two models from the data . To investigate the mechanism of synergistic activation , we implement both “multiplicative effect” and “additive effect” models as special cases of a more general model for combined activator action: a user-defined parameter NMA ( positive integer ) sets the limit on the maximum number of bound activators that can simultaneously interact with the BTM . We call this the “limited contact” model of activator action ( see Text S1 for details ) . The cases NMA = 1 and NMA = ∞ correspond to the additive and multiplicative effect models respectively . This general model can be combined with cooperative binding of TF molecules , thus allowing us to study the relative contribution of multiplicative activation and cooperative binding . As discussed earlier , the computation of Equation ( 1 ) involves summation of an exponential number of configurations . In this section , we describe an efficient algorithm for computing the DirectInt model with multiplicative effect of activation . ( The algorithms for other models are based on similar dynamic programming techniques and are presented in Text S1 . ) Let ZOFF ( i ) denote the total statistical weight of all configurations of sites up to the site i , with site i being occupied . We obtain the following recurrence , by summing over the position of the occupied site j nearest to site i: ( 4 ) where q ( i ) is the statistical weight of the site i , as defined in Equation ( 2 ) , ω ( i , j ) is the interaction between the occupied sites i and j , and Φ ( i ) is the set of sites to the left of i that do not overlap with i . This recurrence equation is similar to that in [40] , [41] . The constant term , +1 , corresponds to the case where no site to the left of i is occupied . Under this model , Q ( σ ) is the product of the transcriptional effects ( α terms , as described before ) of all occupied TF molecules in the configuration σ . Let f ( i ) be the factor bound at the site i , and αf ( i ) be the transcriptional effect of f ( i ) , then we have the following recurrence for ZON: ( 5 ) To calculate the values required for Equation ( 1 ) , we simply take the sum over all possible values of i: and . The time complexity of the algorithm is O ( n2 ) , where n is the number of sites in the sequence . However , if cooperative interaction between adjacent sites is not modeled , or the interaction only occurs within a constant range , the time complexity is linear in n . We started with the Drosophila segmentation data set from Segal et al . [6] . This set includes 44 bona fide CRMs with their A/P expression profiles , eight TFs ( Bcd , Cad , TorRE , Hb , Gt , Kni , Kr , and Tll ) with their concentration profiles and PWM motifs . Each expression profile ( or concentration profile ) consists of 100 real numbers between 0 and 1 representing the relative expression level of the CRM ( or relative concentration of a TF ) in positions along the A/P axis , divided into 100 bins from anterior to posterior . One problem with this data set is that not all relevant TFs in the terminal regions ( e . g . , Slp1 ) are included or known [42] . Also , the TorRE ( Torso Response Element ) motif included in this data set is assumed to correspond to a ( yet unknown ) TF that has activating role in the terminal regions of the embryo . Recent evidence suggests that on the contrary TorRE may correspond to the Capicua transcription factor , which is a repressor expressed in the trunk region of the embryo and post-transcriptionally degraded at the termini in response to Torso signaling [43] . This casts doubts over the inclusion of TorRE and in general the terminal regions of the expression profiles as part of the data set , especially for evaluating models that distinguish between activator and repressor mechanisms . We thus limited the CRM expression profiles to their portions lying between 20% and 80% egg length . The number of CRMs came down to 37 , after excluding those without patterned expression in this spatial range . This final data set included six motifs ( Tll and TorRE were excluded ) , of which five ( Cad , Gt , Hb , Kr , Kni ) were taken from Noyes et al . [44] and one ( Bcd ) was obtained from FlyREG [45] . Binding sites were annotated as those with log likelihood ratio ( LLR ) scores greater than 0 . 4 times the LLR score of the optimal site [46] . This threshold is weak enough to include a large number of putative sites for each TF , while keeping the running time low . Parameter training was performed using the Nelder-Mead simplex method and the quasi-Newton method , and restarts were used to deal with potential local optima . Optimization of the correlation coefficient between predicted and known expression values was alternated with optimization of the sum of squared errors ( See Text S1 for details ) . Note that model training is performed separately for each model ( DirectInt or SRR , with or without cooperative interaction , etc . ) . Thus , even though two models may share certain parameters , e . g . , ( K ( Smax ) ν ) , their values may be different under the two models after training . The running time of the program scales linearly with the number of TFs , and the total length of sequences ( for all models except the “limited contact model” , see Text S1 ) . In our dataset , with 6–8 TFs and about 40 CRMs of average length 1450 bp , the parameter training phase took about 3–4 hours of running time on a desktop computer with 2 . 2GHz CPU and 2GB memory .
GEMSTAT offers the following choices between various model features: The above choices are accompanied by parameters that may be set manually , and some of which may be left as free parameters to be trained from the data . All model parameters are described in Table S1 . The program takes as input the sequence and expression profiles of a set of CRMs , and the PWMs and concentration profiles of a set of TFs . Expression profiles and concentration profiles are specified as vectors of a fixed dimension , allowing it to be easily used to model any regulatory system . ( In our application , vector components correspond to positions along the A/P axis of the embryo , but in other applications these could be distinct anatomical domains or temporal points . ) The source code is available at http://veda . cs . uiuc . edu/Seq2Expr/ . The data set consists of 37 experimentally characterized CRMs driving patterned expression along the anterior-posterior axis of the blastoderm stage Drosophila embryo ( see Methods ) . We used several different approaches to objectively evaluate a model and compare models . Our first metric of model performance is the correlation between the model predictions and the observations . For each CRM , we calculated the Pearson correlation coefficient ( CC ) between the predicted and the observed expression profiles ( over 60 bins ) , and computed the average CC over all CRMs . We also recorded the number of CRMs with CC>0 . 65 . Additionally , we estimated for each CRM the significance of improvement ( in CC ) due to one model versus another , and combined these estimates into a p-value of improvement over the entire data set , as described in Text S1 . We also calculated the average CC under 10-fold cross validation ( denoted by “CVCC” ) , as a test of predictive ability , and for fair performance comparison between models with different numbers of parameters . For any given choice of model , an identical model that uses randomly permuted PWMs was evaluated as negative control . Any observation about model comparison based on correlation coefficients was also confirmed by visual inspection of the predicted expression patterns on all 37 CRMs . We note that there is no consensus yet on the most reasonable way to evaluate predictions of expression models for data sets such as that used here . We chose the correlation coefficient because of its ability to capture the salient pattern along the A/P axis , and we based all of our claims on this measure to keep our analysis objective and unbiased . We began by exploring the effect of cooperative DNA-binding by molecules of the same TF , i . e . , homotypic interactions . ( Modeling heterotypic interactions would involve many more free parameters and was not pursued in this study ) . Segal et al . [6] also studied this effect , but since their model lacks mechanistic details of activation , the effect of cooperative binding may not be distinguishable from simultaneous interaction of TFs with the BTM ( the “multiplicative effect” , also called “transcriptional synergy” [8] ) . As a baseline , we evaluated the DirectInt model that excludes any cooperative binding terms , but allows for transcriptional synergy . The average correlation coefficient ( CC ) of this model ( of 13 free parameters ) is 0 . 547 , with accurate predicted readout ( CC>0 . 65 ) on 16 of the 37 CRMs ( Table 2 ) . In contrast , 25 independent negative controls yielded a mean average CC of 0 . 211 ( standard deviation of this mean across the 25 trials was 0 . 075 ) . The cross validation correlation coefficient ( CVCC ) supports the high predictive ability of the model ( average CVCC of 0 . 4 , compared to 0 . 02±0 . 083 from negative controls ) . We then included self-cooperativity of each TF separately ( only one additional parameter at a time ) , and computed the average CC and CVCC as before . Each of the TFs showed an improved CVCC over the baseline of no cooperativity across almost every replicate of the cross validation exercise ( Table 2 , Table S2 ) , while Bcd and Kni showed the most pronounced effects of cooperativity in terms of average CC . When both Bcd and Kni were included as cooperative factors , the average CC improved further over the model with each factor alone . The improvement in going from no cooperativity ( average CC = 0 . 547 ) to self-cooperativity for Bcd and Kni ( average CC = 0 . 587 ) was highly significant ( p-value 1 . 3E-6 ) . Examination of the expression predictions on individual CRMs identified 12 CRMs where the cooperativity model was better and two where it was worse . ( Two cases are shown in Figure 2 ( A , B ) , and the complete list is in Figure S2 . ) Our results are consistent with Segal et al . [6] , who found self-cooperativity to improve prediction . Moreover , we find this to be the case even in the presence of transcriptional synergy , which if not accounted for could have confounded the effects of cooperative DNA-binding by activators . As a visual aid for interpreting the quantitative evaluations reported above , we present in Figure 3 all of the expression predictions from the above model ( with Bcd and Kni self-cooperativity ) , alongside their respective known expression patterns . A detailed summary of the model's performance is given in Table 2 , along with results from an appropriate negative control . This model was also fit to the entire data set of Segal et al . ( 44 CRMs , inclusive of terminal bins ) and found to have slightly ( but not significantly ) higher average CC than the published predictions of the Segal model [6] , although our model uses fewer free parameters ( see Figure S3 for details . ) GEMSTAT implements two alternative approaches to combining the effects of multiple activator sites , using the parameter NMA described in Table S1: the additive effect ( NMA = 1 ) and the multiplicative effect ( NMA = ∞ ) , as well as approaches that are in between these two extremes . The “multiplicative effect” model allows any number of activator molecules to simultaneously interact with the BTM , which as discussed in Methods , leads to transcriptional synergy , a source of synergistic activation that is distinct from cooperative DNA-binding [8] , [26] . We used the two extreme values of NMA to test whether this phenomenon leads to improved agreement with the data , while keeping other aspects of the model fixed ( Table 3 ) . The baseline model here was one with NMA = 1 ( no synergy ) and with no self-cooperative DNA-binding . The average CC from this model ( 0 . 516 ) improved significantly ( to 0 . 547; p-value 3 . 7E-4 ) when we introduced synergy due to the multiplicative effect of multiple activators ( NMA = ∞ ) . ( This change does not involve any additional free parameters . ) This was further confirmed by a greatly improved CVCC ( 0 . 295 to 0 . 40 , see Table 3 and Table S3 ) , as well as by examination of predictions for individual CRMs ( Figure 2 ( C , D ) , and for the complete results see Figure S4 ) : the multiplicative effect model showed clear improvements on 6 CRMs and was worse on 3 CRMs . These observations suggest that simultaneous interaction of multiple activators with the BTM is a plausible source of synergistic activation . Cooperative binding was kept out of the model in the above test . We next introduced cooperative binding ( only for the two activators ) into the model , and examined the contribution of the multiplicative effect . We found that the model with both sources of synergistic activation shows better quality of fits compared to the model with cooperative binding alone , in terms of average CC ( from 0 . 558 to 0 . 581 , p-value 7 . 3E-11 , see Table 3 ) as well as CVCC ( 0 . 292 to 0 . 396 ) . We also confirmed this improvement by examination of individual CRMs: the model using multiplicative effect along with cooperative binding led to better fits for 8 CRMs compared to the model with cooperative binding alone ( Figure S5 ) and was worse in no case . This result suggests that synergistic activation due to multiplicative effect of activators may be over and beyond that due to cooperative binding [26] . In all of the above tests , we had used a “Direct Interaction” model of repressor function , where a bound repressor is assumed to interact directly with the BTM , destabilizing the configuration , and thus curbing the roles of activator sites in the entire CRM . GEMSTAT also allows us to deploy a more “localized” form of repressor action , in the form of the short range repression ( SRR ) model , where a bound repressor makes the neighboring chromatin ( within some range dR ) inaccessible . Prior experimental work [20] suggests that the four repressors in our data set – Kr , Hb , Kni , and Gt – act over short distances ( ∼100–150 bp [14] ) , and in two of these cases ( Kr and Kni ) repression depends on the histone deacetylase dCtBP , which suggests a possible mechanistic basis for the short range action [20] . In our tests , we sought to examine if the SRR model implemented in GEMSTAT is realistic enough to capture the repressors' contributions to expression patterns . Starting with a baseline where every repressor was modeled by “Direct Interaction” , we introduced the SRR model for one repressor at a time ( with dR = 250bp ) , and compared the resulting model with the baseline . Although none of the four resulting models ( Kr- , Hb- , Kni- , Gt-SRR ) showed clear improvement over the baseline , we found strong evidence that for Kr and Hb , the SRR model implemented by GEMSTAT was able to capture the repressive effects of the factors almost to the same extent as the Direct Interaction model , as described next . We first extended our evaluation metric , the average CC , in the following way: we considered the best K CRMs for a model ( in terms of CC ) , and plotted the average CC over these K CRMs , for all values of K ( 1 … 37 ) . We found the Kr-SRR model to be highly similar ( in terms of average CC ) to the baseline model throughout the range ( Figure 4A ) . Additionally , for each model and each value of K , we plotted the average CC of the same model under a Kr “knock down” condition , i . e . , where the Kr concentration was set to 0 across the A/P axis . Such a “knock down” plot allows us to visualize the contribution of a TF ( Kr in this case ) to the model . We found Kr to contribute significantly to both models , although the contribution to the SRR model was not as strong as to the DirectInt model . This may reflect certain limitations of the SRR implementation in GEMSTAT , but the results strongly suggest that the short range nature of Kr action [20] is largely captured by our model . We also examined the performance differences between the models on individual CRMs . We found seven CRMs where the SRR model was as effective as or better than the Direct Interaction model in predicting readout , with a significant contribution from Kr ( Figure S7 ) . In five other cases , the Direct Interaction model yielded superior fits ( plots not shown ) . Similar evidence for the effectiveness of the Hb-SRR model is shown in Figures 4B and Figure S8 . However , the Gt-SRR model does not seem to elicit significant contribution from Gt , even though this repressor is found to be effective within the DirectInt model ( Figure S6A ) . A similar lack of evidence was encountered for the Kni-SRR model ( Figure S6B ) . Thus , we find that for Kr and Hb , quenching of activator sites within a distance is sufficient to capture the repressive effect of the TFs , supporting the hypothesis that these two TFs act mainly as short-range repressors , confirming what has been reported in earlier studies , which admittedly relied on a small number of CRMs and synthetic enhancers . On the other hand , we did not find strong evidence of short-range repression for Gt and Kni , and even for Kr and Hb the SRR model's performance was only as good as and not better than the DirectInt model . This is somewhat unexpected; it may be in part due to limitations of our SRR model , but may also be hinting that these TFs use long-range repression mechanisms as well ( see Discussion ) . Repression by competitive binding , as proposed in the literature [7] , [29] , involves the binding of repressors to sites overlapping activator sites , thereby suppressing their occupancy by activators . This mechanism may be thought of as a special case of the SRR model in GEMSTAT , with the repression range parameter ( dR ) set to ∼10 bp . At such a small value of dR , a repressor can only make its immediate neighborhood inaccessible , equivalent to inactivation of overlapping activator sites . Having observed above that the Kr and Hb repressors are effectively modeled in the SRR mode , we compared the Kr-SRR and Hb-SRR models at dR = 250 to their respective versions at dR = 10 . As shown in Figure 4 ( C , D ) , in both cases the competitive binding model ( dR = 10 ) was significantly worse than the SRR model , both in terms of average CC and in terms of the repressor's contribution . Finally , we sought to use the GEMSTAT program to probe an important question regarding the function and evolution of transcription factor binding sites . A number of recent studies have reported the “turnover” ( evolutionary gain and loss ) of binding sites , based on sequence comparison [30] , [31] , [47] or from ChIP-based experiments [48] . However , it is possible that such lineage-specific loss and gain are largely limited to non-functional sites , i . e . , the false positive matches to PWMs , or sites that are bound by TFs but do not regulate expression [33] , [49] , [50] . Here , we explored this possibility by asking if sites that change in lineage-specific ways are functional in contributing to the expression patterns . We note that lineage-specific losses may in part be artifacts of alignment errors ( i . e . , sites were completely conserved but not deemed so , due to misalignment ) . However , in practice , the true gain/loss of sites may be hard to distinguish from alignment errors , so we will call both cases as lineage-specific changes here . We predicted sites by demanding that any predicted site be conserved ( in the sense of being above threshold ) in all species analyzed , and examined how the quality of fit varies as this evolutionary filter was made more stringent by including more species . We found that more conservative evolutionary filters lead to greatly reduced average CC ( Figure 5 , red ) . This shows that a noticeable part of the CRMs' functionality is carried by sites ( in D . melanogaster ) that are not found to be conserved across all phyla . Those sites could , broadly speaking ( a ) be deeply conserved in the examined phylogeny , but with some lineage specific losses or ( b ) have arisen specifically in D . melanogaster or a recent ancestor . Next , we modified the evolutionary filter to demand deep ( but not necessarily complete ) conservation across the phylogeny ( see Text S1 ) and found that above-mentioned loss in quality of fits disappears ( Figure 5 , blue , compare number of species = 2 vs . 6 ) . Since the new evolutionary filter discards sites of type ( b ) mentioned above , we inferred that a noticeable part of the CRMs' functionality is carried by sites that are largely conserved but also undergo lineage-specific losses .
Three different mechanisms have been previously hypothesized for repressor action: ( i ) competition with activators for access to binding sites , ( ii ) direct interference with BTM recruitment and assembly , and ( iii ) local interference ( “quenching” ) with the function of nearby activator sites . The last hypothesis seems to be most likely in the context of the regulatory system we analyze , as suggested by the following observations: first , repressor and activator sites are often found to be close to each other [19]; second , CRMs of the same gene often work independently , i . e . , a repressor site within one module does not stop the function of another module for the same gene [54]; third , some repressors are found to depend on a co-repressor , CtBP , a histone deacetylase that presumably increases the association of nucleosomes to DNA , making it less accessible [20] . However , direct evidence of this so-called short-range repression ( SRR ) phenomenon is limited to a few CRMs and synthetic enhancers [20] . We implemented models that could investigate all three mechanisms with respect to their agreement with data on a moderate number of CRMs . Note that even though the short range mechanism has been implemented ( in other forms ) previously [13] , [14] , it has never been tested within a framework that also implements alternative mechanisms . We report the first direct data-based comparison between alternative hypotheses regarding repression . Our results clearly exclude the hypothesis of competitive binding being the main mechanism of repression , and are consistent with the SRR hypothesis for two of the four repressors studied ( Kr and Hb ) . It is somewhat unexpected that the SRR model does not explain the data as well as the DirectInt model for Gt and Kni . We note that while Gt is believed to be a short range repressor , Nibu et al . [20] leave open the possibility of this protein having long range mechanisms of action as well , in light of the fact that it does not require dCtBP to mediate repression . Similarly , Kr has been found to have long range mechanisms as well [21] , [55] . It is also likely that to some extent the inability of the SRR model to match ( for Gt and Kni ) or exceed ( for Kr and Hb ) the effectiveness of the DirectInt model arises from shortcomings of our model and evaluation procedure . Our model assumes that once a repressor molecule is bound , it will make its entire neighborhood inaccessible , defined by a range parameter . We would intuitively expect that the repression effect is stronger for closer chromatin regions , and this is not modeled due to our lack of understanding of the exact mechanism by which repressors may change the chromatin structure . Similarly , we do not know exactly how the effects of two repressor molecules are combined in the regions that may be affected by both , and this part is treated in a simplistic manner under our SRR model . The dataset may also limit our ability to study detailed mechanisms: the resolution of expression patterns is low and the dataset lacks informative negative controls ( all sequences are wild type CRMs ) . Finally , our tests are likely to have been weakened by the fact that models are compared on individual CRMs and not entire control regions . It is generally assumed that the short range mode of repressor action is necessary for the functional modularity of CRMs . For example , Kr is a key input to the eve stripe 2 enhancer , but it can adversely affect the expression readout of the adjacent eve stripe 3 enhancer; this interference is avoided presumably because of its short range of action [56] , [57] . Thus , the effect of SRR is already manifested in the compactness of CRMs , and if it were possible to compare SRR with the direct interaction model on entire gene control regions , we would likely observe a clear advantage to the former . Despite these limitations , the SRR model along with a detailed activation model allows to ask questions that cannot be addressed with simple non-mechanistic models of CRM function . Another important issue we explored is how multiple activator sites contribute to expression . It is likely that this multiplicity is important for the synergistic activation , where the total effect of multiple sites is larger than the sum of their individual effects . That such synergy is real and important has been shown through in vitro experiments on the effect of the number of sites [51] , as well as in vivo experiments on the typically sharp boundaries of gap gene expression domains [2] . Mechanistically , synergy may result either from cooperative DNA binding of multiple activator molecules or from simultaneous interaction of multiple activators with the BTM ( Text S1 ) [26] . Our model implements both mechanisms , and is thus able to examine the effect of each mechanism on readout , both in the absence and in presence of the other mechanism . We found that both mechanisms are involved in setting the precise expression profile; the effect of transcriptional synergy is evident , and complementary to that of cooperative binding . We have not explored in this study some important details on how synergistic interactions with BTM may occur , and these may worth further investigation . For example , we did not make any distinction between different activators . It is plausible that two different activators may interact with BTM simultaneously , contacting different subunits [52] , while the two molecules of the same TF may act in an additive fashion , contacting the same subunit . Other possibilities remain to be explored with regard to cooperative DNA-binding as well . One possibility stems from our assumption that only two adjacent bound molecules may interact with each other . Although this assumption has been commonly made in other studies dealing with cooperativity [6] , it is based partly on computational considerations and partly on our lack of understanding of the mechanistic details of interactions among TF molecules . On the topic of mechanistic limitations of our models , we note also that in equating gene expression to the fractional occupancy by the BTM , we are ignoring the internal dynamics of transcription initiation and elongation [58]–[60] . We found that for a number of CRMs , the model ( mis- ) predicts expression outside the CRM's primary expression domain ( s ) . For instance , the CRM “kni_ ( -5 ) ” drives anterior expression only , but the model additionally predicts modest expression in the central and posterior regions of the embryo ( Figure 3 ) . We noted that kni_ ( -5 ) has many binding sites for Cad , which is an activator present in the posterior half of the embryo . Presumably , the model fails to find strong evidence of appropriate repressive influence , and predicts kni_ ( -5 ) to drive expression in the posterior regions , mediated by the putative Cad sites . A similar observation was made with respect to the CRM “eve_stripe5” , which drives expression in the posterior half ( in a stripe between bins 60 and 70 , see Figure 3 ) . This CRM harbors several high quality putative sites for Bcd , which is an anterior activator , and this is presumably the reason why the model predicts modest anterior expression as well . That such incongruous predicted expression is often seen under multiple models suggests that the errors may not be due to the specifics of the model that we have been varying . Rather , it is possible that we are missing some additional repression mechanism , e . g . , from chromatin modifications , from unknown repressor sites , or mischaracterization of binding affinity . A relevant fact worth noting here is that there is some ambiguity about the appropriate binding profile to use for the important repressor Gt . In the current study , we used the profile estimated from in vitro Bacterial-one-hybrid ( B1H ) experiments [44] , which happens to be quite different from the profile estimated from verified Gt binding sites in DNA footprinting experiments [45] . However , because relatively few sites were verified , the footprinting-based Gt profile is too un-specific to be used for prediction of new sites . We observed that the total number of Gt sites in all CRMs is considerably smaller than most other factors . This may have led to underestimation of the repressive influence of Gt , and a consequent lack of repression ( as per the model's predictions ) in the region where Gt is expressed . An important area of future improvements to our approach will be the quality and amount of data . The spatial expression profiles used here were obtained from manual parsing of stained ( in situ hybridization ) images , and are essentially qualitative . This is one of the reasons why our evaluations were based on correlation between expression patterns rather than more absolute measures of prediction accuracy . More accurate quantifications that are under way [61] should lead to improved analysis . Our approach assumes that the expression profiles of TFs and CRMs were synchronized ( from the same developmental time ) , although this is not entirely true: the temporal resolution of the data set is not high enough to ensure such synchronization , and this is another direction where future , higher resolution data sets will be needed . Moreover , since we do not have data characterizing the dynamic state of chromatin ( nucleosome distributions and their chemical modifications ) , we did not explicitly model the changes of chromatin structure that may be induced by TF association . With more high-quality expression data and ideally more epigenetic data as well , it should be possible to extend our models with additional details and to incorporate theoretical models of chromatin structure [62] , [63] . The models presented here are intended to be usable in a variety of regulatory systems in different species . It is true however that a regulatory system would need to be very well understood at a qualitative level and characterized by quantitative measurements at multiple levels , before we can apply such models . We would need the following information to train the models: ( 1 ) the expression readouts of a set of promoters or CRMs , ( 2 ) a reasonably complete set of TFs involved in the regulatory network , ( 3 ) quantification of their concentration profiles , and ( 4 ) their binding specificities . At this time , such a data set is often not available , making it difficult to evaluate the generalizability of the models . A promising application of the proposed quantitative models lies in the prediction and characterization of novel CRMs . Once a sequence-expression model is trained , it may be applied genome-wide to predict segments that have the potential to direct the expression patterns of neighboring genes . The model may also be used to predict the effect of individual transcription factor perturbations , leading us to individual TF-CRM interactions . This paradigm requires quantitative measurements of TF levels , a requirement that may be mitigated to some extent by using mRNA levels of TF genes , but ideally by direct protein level measurements . Recent developments in proteomics and in high-throughput assays of post-translational modifications offer great hope in providing the necessary TF activity data [64] . The models offer new ways to approach the study of regulatory sequence evolution . Transcription factor binding sites have been reported to undergo frequent loss and gain , but it is not clear what the functional consequences of these changes are . We saw an example of how the functional context provided by the model may be combined with cross-species sequence comparison to provide new insights into binding site turnover . In general , sequence-expression models allow us to predict the changes in expression pattern that result from any evolutionary change at the sequence level . This interpretative power may be harnessed to investigate how regulatory sequences evolve under different schemes of selection , and begin to answer questions such as “With gene expression under purifying selection , how tolerant is a sequence to the gain and loss of binding sites ? ” or “How feasible is it to evolve a novel expression pattern using only simple nucleotide level changes , i . e . , substitutions , insertions and deletions ? ” [65] . Quantitative models have a natural relevance in the field of synthetic biology . In order to design gene networks with a well-defined input/output characterization , we need the ability to engineer gene promoters or enhancers that direct specific expression patterns ( outputs ) in response to the specific levels of the regulators ( inputs ) . This ability in turn requires a tool to predict the expression pattern corresponding to any given sequence . Moreover , to search in a very large sequence space , an efficient sequence-to-expression mapping will be crucial . This will be a place where our dynamic programming-based algorithms make a large difference . In the long run , we expect quantitative models to be able to consider for example the entire intergenic region next to a gene ( and not only individual CRMs ) and predict the gene's spatial-temporal expression pattern . The GEMSTAT models are an important preliminary step towards this grand goal . | The development of complex multicellular organisms requires genes to be expressed at specific stages and in specific tissues . Regulatory DNA sequences , often called cis-regulatory modules , drive the desired gene expression patterns by integrating information about the environment in the form of the activities of transcription factors . The rules by which regulatory sequences read this type of information , however , are unclear . In this work , we developed quantitative models based on physicochemical principles that directly map regulatory sequences to the expression profiles they generate . We evaluated these models on the segmentation network of the model organism Drosophila melanogaster . Our models incorporate mechanistic features that attempt to capture how activating and repressing transcription factors work in the segmentation system . By evaluating the importance of these features , we were able to gain insights on the quantitative regulatory rules . We found that two different mechanisms may contribute to cooperative gene activation and that repressors often have a short range of influence in DNA sequences . Combining the quantitative modeling with comparative sequence analysis , we also found that even functional sequences may be lost during evolution . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"genetics",
"and",
"genomics/gene",
"expression",
"computational",
"biology/transcriptional",
"regulation",
"biophysics/theory",
"and",
"simulation",
"computational",
"biology/macromolecular",
"sequence",
"analysis",
"biophysics/transcription",
"and",
"translation"
]
| 2010 | Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression |
The N-methyl-D-aspartate receptor ( NMDAR ) , a major excitatory ligand-gated ion channel in the central nervous system ( CNS ) , is a principal mediator of synaptic plasticity . Here we report that neuropilin tolloid-like 1 ( Neto1 ) , a complement C1r/C1s , Uegf , Bmp1 ( CUB ) domain-containing transmembrane protein , is a novel component of the NMDAR complex critical for maintaining the abundance of NR2A-containing NMDARs in the postsynaptic density . Neto1-null mice have depressed long-term potentiation ( LTP ) at Schaffer collateral-CA1 synapses , with the subunit dependency of LTP induction switching from the normal predominance of NR2A- to NR2B-NMDARs . NMDAR-dependent spatial learning and memory is depressed in Neto1-null mice , indicating that Neto1 regulates NMDA receptor-dependent synaptic plasticity and cognition . Remarkably , we also found that the deficits in LTP , learning , and memory in Neto1-null mice were rescued by the ampakine CX546 at doses without effect in wild-type . Together , our results establish the principle that auxiliary proteins are required for the normal abundance of NMDAR subunits at synapses , and demonstrate that an inherited learning defect can be rescued pharmacologically , a finding with therapeutic implications for humans .
In the mammalian central nervous system , excitatory transmission at synapses is mediated primarily by the amino acid glutamate , acting through the postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors ( AMPARs ) and N-methyl-D-aspartic acid receptors ( NMDARs ) [1] . Basal synaptic transmission is principally mediated by AMPARs , which are rapidly activated by glutamate , while the more slowly activated NMDAR primarily mediates various forms of synaptic plasticity . A large body of evidence indicates the NMDAR is essential for a prominent form of synaptic plasticity , long-term potentiation ( LTP ) at Schaffer collateral-CA1 synapses , and for hippocampal-dependent spatial learning and memory [2 , 3] . The core NMDAR is a heterotetramer comprised of two obligate NR1 subunits and two NR2 ( A-D ) subunits [1] . These core subunits are embedded in a multiprotein complex that includes more than 70 NMDAR-associated proteins [4] . An emerging theme in NMDAR biology is that proteins associated with the core NMDAR may have important roles in the trafficking , stability , subunit composition , or function of NMDARs and may therefore be critical for synaptic plasticity , learning , and memory [5] . However , proteins that function to specifically maintain synaptic NMDARs , which are well-known for AMPARs , have been elusive for NMDARs . We investigated the complement C1r/C1s , Uegf , Bmp1 ( CUB ) domain protein neuropilin tolloid-like 1 ( Neto1 ) [6 , 7] , which we have discovered to be an NMDAR-associated protein [8] . The CUB domain is an extracellular motif of approximately 110 amino acids originally identified in the complement subunits Clr/Cls , sea urchin epidermal growth factor , and bone morphogenetic protein 1 ( BMP1 ) . Comprised of 10 β-strands forming a “jellyroll” topology [9] , CUB domains mediate protein-protein interactions [10] . Notably , the CUB domain protein SOL-1 in Caenorhabditis elegans has been shown to be a component of the GLR-1 glutamate receptor [11] , required for its gating [12] , and another C . elegans CUB-domain protein LEV-10 has been found to regulate the clustering of acetylcholine receptors at the neuromuscular junction [13] . Whether CUB domain proteins are significant components or regulators of neurotransmitter receptor complexes at vertebrate synapses is unknown despite the presence of ∼100 identified or predicted CUB domain proteins in the vertebrate genome [14] . To investigate the role of Neto1 in the biology of mammalian excitatory synapses , we determined the molecular basis of the Neto1:NMDAR interaction and defined the nonredundant functions of Neto1 in synaptic plasticity , learning , and memory using Neto1 protein null mice . We found that Neto1 interacts with the core NMDAR subunits , NR2A and NR2B , and with the scaffolding protein postsynaptic density-95 ( PSD-95 ) . The complete loss of Neto1 reduced the abundance of NR2A but not NR2B subunits in the PSD of the hippocampus , leading to a decrease in the amplitude of synaptic NMDAR currents and a switch from the normal predominance of NR2A- to NR2B-containing NMDARs at Schaffer collateral-CA1 synapses . In Neto1-null mice , LTP at these synapses was reduced and spatial learning and memory was impaired . By indirectly enhancing NMDAR synaptic currents in the Neto1-null mice using the ampakine CX546 [15] , we rescued the deficits in both LTP and spatial learning and memory .
Neto1 encodes a 533 amino acid polypeptide with an N-terminal ER signal sequence , two CUB domains , one low-density lipoprotein receptor domain class A ( LDLa ) motif , a transmembrane domain , and a cytoplasmic tail terminating in a class I PDZ binding tripeptide ligand ( TRV-COOH ) ( Figure 1A ) . We designated this protein neuropilin tolloid-like 1 ( Neto1 ) [8] , because the first CUB domain is most similar ( ∼40% identity ) to the CUB domains of neuropilins [16 , 17] and tolloid [18] . To elucidate the role of Neto1 in the brain , we first examined the expression pattern of its mRNA . In adult mice , Neto1 mRNA was present throughout the central nervous system ( Figure 1B–1D and Figure S1 ) , with strong expression in cerebral cortex , hippocampus , olfactory bulb , olfactory tubercle , and caudate putamen . To identify the subcellular compartments in which Neto1 is localized , we performed subcellular fractionation and immunoblotting experiments of whole mouse brain lysates . Because the C-terminal sequence of Neto1 suggested that it localized to the PSD ( see below ) , we employed a cell fractionation strategy that separated synaptic subcompartments [19] . Neto1 was prominently expressed in the crude synaptosomal ( Figure 2A , lane S2 ) and PSD fractions , but was absent from the synaptic vesicle fraction ( Figure 2A , lane LP2 ) . To visualize the cellular distribution of Neto1 , we examined immunostained hippocampal sections by confocal microscopy . We found that Neto1 immunostaining decorated MAP2 positive dendritic arbors and co-localized with that of PSD-95 ( Figure 2B ) and NR1 ( Figure 2C ) . We also found that Neto1 co-localized with actin ( Figure 2D ) , which is highly enriched in dendritic spines in the hippocampus [20] . The immunostaining for Neto1 was not detected in hippocampus from Neto1-null ( Neto1tlz/tlz , see below ) mice ( Figure 2D , right ) , indicating that the staining was not nonspecific . Together , these findings demonstrate that Neto1 is a component of the PSD of excitatory synapses . The sequence of the C-terminal tripeptide of Neto1 , TRV , suggested that it is a PDZ ligand , predicted to bind preferentially to the third PDZ domain ( PDZ3 ) of PSD-95 [21 , 22] . Using the yeast two-hybrid system , we established that the cytoplasmic domain of Neto1 ( Neto1-cd ) associated with the full-length PDZ proteins PSD-95 ( Figure S2 ) , PSD-93 , and SAP102 , but not with SAP-97 or NIP [23 , 24] ( unpublished data ) . Furthermore , using crude synaptosomal fractions , we determined that anti-PSD-95 antibodies co-immunoprecipitated Neto1 from wild-type ( Neto1+/+ ) mouse brain ( Figure 2E ) . Conversely , anti-Neto1 antibody co-immunoprecipitated PSD-95 from Neto1+/+ ( Figure 3A , lane 1 ) but not from Neto1-null crude synaptosomal fractions ( Figure 3A , lane 2 ) . Negative control antibodies did not immunoprecipitate either Neto1 or PSD-95 ( Figures 2E and 3A ) . The Neto1 cytoplasmic domain bound most strongly to PDZ3 of PSD-95 , binding that was completely dependent on the C-terminal TRV of Neto1 , in both the two-hybrid system ( Figure S2 ) and in HEK293 cells ( Figure 2F , lane 2 ) . Moreover , the Neto1 cytoplasmic domain bound to a truncated PSD-95 polypeptide ( PDZ1–3 ) containing only the three PDZ domains ( Figure 2F , lane 3 ) . Altogether , these findings indicate that Neto1 associates with PSD-95 in brain synapses through the binding of its C-terminal tripeptide with the PDZ domains of PSD-95 . Because PSD-95 is a prominent NMDAR scaffold protein [25 , 26] , we asked whether Neto1 associates with NMDARs . We found that anti-Neto1 antibodies co-immunoprecipitated the NR1 , NR2A , and NR2B NMDAR subunits from crude synaptosomal fractions of wild-type but not Neto1-null mice ( Figure 3A and 3B , lanes 1 and 2 ) , whereas pre-immune antibodies did not ( Figure 3A and 3B , lane 3 ) . Reciprocally , anti-NR1 , anti-NR2A , and anti-NR2B antibodies co-immunoprecipitated Neto1 from wild-type synaptosomal fractions ( Figure 3C , lanes 2 , 4 , and 5 , respectively ) . In contrast , we were unable to co-immunoprecipitate Neto1 and GluR2 ( Figure 3A , lane 1 and Figure 3C , lane 3 ) , a major subunit of the AMPAR [27] . We therefore conclude that Neto1 is a component of the NMDAR complex but is not a general component of ionotropic glutamate receptor complexes . To determine whether the association of Neto1 with NMDARs was entirely dependent upon the binding of its C-terminal PDZ ligand to PSD-95 , we examined the binding of PSD-95 to an hemagglutinin ( HA ) -tagged Neto1 protein lacking the C-terminal 20 amino acids ( Neto1-Δ20HA ) . As predicted both by the interaction between PSD-95 and the NR2 subunits of the NMDAR [28] , and by the binding of Neto1 to PSD-95 described above , we found that Neto1 was co-immunoprecipitated by anti-NR1 antibodies from lysates of cells co-expressing Neto1 , PSD-95 , NR1 , and NR2B ( Figure 4A , lane 1 ) . Unexpectedly , however , anti-NR1 antibodies co-immunoprecipitated Neto1-Δ20HA ( Figure 4A , lane 3 ) . Moreover , Neto1 or Neto1-Δ20HA co-immunoprecipitated with both NR1 and NR2B , even in the absence of PSD-95 ( Figure 4A , lane 2 and 3 , respectively ) . These results indicate that the binding of Neto1 to PSD-95 was not required for Neto1 to interact with the NMDAR , and that Neto1 interacts with NMDARs through a PSD-95-independent mechanism . To identify the region of Neto1 that mediates the PSD-95-independent association between Neto1 and NMDARs , we examined the ability of a series of C-terminally deleted Neto1 proteins to co-immunoprecipitate with NMDARs from HEK293 cell lysates . Removal of the cytoplasmic tail and transmembrane domain of Neto1 did not abolish the Neto1:NMDAR interaction ( Figure 4B , lanes 2 and 3 ) , suggesting that it was mediated by the ectodomain of Neto1 . Moreover , a construct expressing only the signal sequence and N-terminal CUB domain of Neto1 was sufficient to mediate the NMDAR association ( Figure 4B , lane 5 ) . In contrast , no binding was observed between NMDARs and the ectodomain of CSF-1 ( Figure 4B , lane 6 ) , or between NMDARs and the CUB domains of neuropilin-1 ( Figure 4B , lane 7 ) . These results indicate that the Neto1:NMDAR extracellular interaction is dependent on the first CUB domain of Neto1 . We next asked which NMDAR subunit mediates the Neto1:NMDAR interaction , using heterologously expressed proteins in HEK293 cells . Full-length Neto1 or Neto1-Δ20HA co-immunoprecipitated with both NR2A ( Figure 5A , lanes 1 and 2 , and Figure 5C , lane 4 ) and NR2B ( Figure 5B , lanes 1 and 2 , and Figure 5C , lane 5 ) expressed in the absence of NR1 and PSD-95 . In contrast , in the absence of NR2 , no association was observed between Neto1-Δ20HA and NR1 ( Figure 5C , lane 3; Figure 5D , lane 2 ) . Consequently , we conclude that the PSD-95-independent Neto1:NMDAR interaction is mediated through NR2 subunits , and that the first extracellular CUB domain of Neto1 is sufficient for this binding . The simplest model consistent with our findings is that Neto1 interacts with the NMDAR bivalently , with one Neto1:NMDAR interaction mediated through the binding of the C-terminal tripeptide of Neto1 to PSD-95 , and the second through the extracellular domains of Neto1 and NR2 subunits . To determine whether Neto1 is required for normal brain function in the mouse , we disrupted the Neto1 locus by homologous recombination in mouse embryonic stem ( ES ) cells . We generated a protein null allele by simultaneously introducing a tau-lacZ ( tlz ) reporter gene [29] in-frame into the initiation codon of the Neto1 gene ( Figure 6A–6C ) . Both Neto1+/tlz and Neto1-null animals were normal in overall appearance with no gross morphological abnormalities in the brain . Hematoxylin and eosin staining revealed no histological abnormalities in any brain region examined in Neto1-null mice ( unpublished data ) , and Nissl ( Figure 6D and 6E ) , MAP2 immunostaining ( Figure 6F and 6G ) , and Golgi staining of the hippocampus showed no morphological defects in Neto1-null mice ( Figure 6H–6K ) . The absence of Neto1 had no effect on the overall abundance of NR1 , NR2A , NR2B , PSD-95 , GluR2 , VAMP2 , or GABAAR1 proteins ( Figure 6L ) in whole brain extracts , or of NR1 , NR2A , NR2B , or PSD-95 in crude synaptosomes ( Figure 6M ) . Moreover , the amount of NR2A , NR2B , and PSD-95 that co-immunoprecipitated with NR1 from crude synaptosomes was normal in Neto1-null mice , indicating that the lack of Neto1 did not alter the overall abundance of the NMDAR:PSD-95 holocomplex ( Figure 6N ) . Having shown that Neto1 is a component of the NMDAR complex , we asked whether glutamatergic synaptic transmission and plasticity are altered in the absence of Neto1 . Given that Neto1 is expressed in the CA1 region of the hippocampus ( Figure 1C ) , we studied synaptic transmission and plasticity at Schaffer collateral-CA1 synapses , which are widely used to investigate glutamatergic synaptic physiology [30] . We recorded field excitatory postsynaptic potentials ( fEPSPs ) in acute hippocampal slices from adult animals and used theta-burst pattern stimulation to induce long-term potentiation ( tbLTP ) , a robust form of NMDAR-dependent synaptic plasticity [31] . Basal fEPSPs , afferent fiber volley , and paired-pulse facilitation in slices from Neto1-null mice were not different from those of wild-type littermate controls ( Figure 7A–7C ) . In contrast , we found that tbLTP was reduced in Neto1-null mice ( Figure 7D ) : the magnitude of the potentiation in the mutant animals was approximately 50% of that in wild-type controls 60 min and longer after theta-burst stimulation . Because paired-pulse facilitation , a measurement of presynaptic function [32] , was not different in Neto1-null mice versus wild-type controls , the reduction in tbLTP is not the result of a deficit in presynaptic function . We therefore conclude that basal synaptic transmission at Schaffer collateral-CA1 synapses appears intact , whereas LTP is significantly impaired in Neto1-null mice . tbLTP at Schaffer collateral-CA1 synapses is NMDAR-dependent [31] . We investigated NMDAR excitatory postsynaptic currents ( EPSCs ) evoked by Schaffer collateral stimulation , by using whole-cell recordings from CA1 pyramidal neurons ( Figure 8 ) . In order to examine NMDAR EPSCs in relationship to synaptic activation , we recorded both NMDAR and AMPAR EPSCs in the same neurons in wild-type and Neto1-null slices . We found that the NMDAR:AMPAR EPSC ratio was significantly less in Neto1-null neurons ( Figure 8A ) regardless of the size of AMPAR EPSCs examined ( Figure 8B ) . Because basal synaptic transmission ( Figure 7 ) and AMPAR-EPSCs ( Figure S5 ) in Neto1-null neurons were not different from wild-type , we interpret the decrease in NMDAR:AMPAR EPSC ratio as indicating that synaptic NMDAR currents were reduced in Neto1-null neurons . The current-voltage relationship for NMDAR EPSCs in Neto1-null mice was comparable to that of wild-type animals , demonstrating that the Mg2+ blockade of the NMDARs was not altered by the lack of Neto1 ( Figure 8C ) . Furthermore , we observed no abnormalities in the current-voltage relationship for AMPARs in Neto1-null mice ( Figure 8D ) . Thus , basal NMDAR-mediated , but not AMPAR-mediated , synaptic responses are impaired in CA1 pyramidal neurons in the absence of Neto1 . These findings suggest that the impairment in NMDAR EPSCs may account for the reduced tbLTP in Neto1-null mice . The reduction in tbLTP and NMDAR EPSCs at Schaffer collateral-CA1 synapses suggested that there might be a decrease in the abundance or function of synaptic NMDARs . We found that the abundance of NR2A in the PSD fraction from whole hippocampal lysates from Neto1-null mice was reduced by approximately one-third compared with that of wild-type littermates ( Figure 9A and 9B ) . Consistent with this reduction , the number of NR2A puncta in stratum radiatum of the CA1 region was also reduced , by approximately 60% , in Neto1-null mice ( Figure 9C and 9D ) . In contrast , no significant differences were observed in the abundance of PSD-95 , NR1 , NR2B , or GluR2 between Neto-1 null versus wild-type mice ( Figure 9A and 9B ) . Similarly , there were no differences in the number of NR2B or PSD-95 puncta in CA1 stratum radiatum of Neto1-null mice ( Figure 9D and Figure S3A and S3B ) . These findings indicate that Neto1 is required to establish or maintain the normal abundance of NR2A-containing NMDARs in the PSD . To determine whether there was an overall decrease in cell surface expression of NR2A-containing NMDARs in Neto1-null mice , we quantified the abundance of biotinylated cell surface proteins in wild-type and Neto1-null hippocampal slices . No differences in the level of biotinylated NR1 , NR2A , or NR2B were found in Neto1-null compared with wild-type mice ( Figure S4A ) , indicating that the overall cell surface expression of NMDARs is normal in the hippocampus in the absence of Neto1 . Similarly , total NMDA-evoked current density and the fractional current carried by NR2A-receptors were also normal in acutely isolated CA1 pyramidal neurons from Neto1-null mice ( Figure S4B and S4C ) . Collectively , these findings indicate that lack of Neto1 does not alter the total surface expression of NMDARs , but rather decreases the targeting or stability of NR2A-containing NMDARs at synapses . To determine whether the decreased synaptic abundance of NR2A subunits leads to a reduction in NR2A-mediated synaptic currents we examined the relative contribution of NR2A versus NR2B to NMDAR EPSCs at CA1 synapses . In the adult hippocampus , NR2A-containing NMDARs make a larger contribution to basal NMDAR-mediated synaptic transmission than those containing NR2B subunits [33] . Consequently , if the decrease in NMDAR EPSCs was due to the reduced level of NR2A-NMDARs , the relative contribution of NR2B-NMDARs to synaptic NMDAR currents would be predicted to be increased in Neto1-null mice . We therefore compared the effect of blocking NR2B-NMDARs using the NR2B-selective antagonist , Ro25–6981 [34] , in wild-type and Neto1-null mice . Because Ro25–6981 is a use-dependent NMDAR blocker , we continued the regular synaptic activation ( 0 . 1 Hz ) during Ro25–6981 application and calculated its effect only after NMDAR EPSCs had stabilized , 20–30 min after the start of Ro25–6981 administration . In wild-type synapses , Ro25–6981 ( 2 μM ) reduced NMDAR EPSCs by ∼30% ( Figure 9E and Figure S6 ) . In contrast , in Neto1-null synapses the reduction was ∼70% ( p < 0 . 001 ) ( Figure 9E and Figure S6 ) , indicating that basal NMDAR EPSCs in Neto1-null synapses are mediated primarily by NR2B-containing NMDARs . Moreover , in Neto1-null synapses , but not in those of wild-type mice , the component of the NMDAR EPSC resistant to Ro25–6981 ( 2 μM ) decayed more rapidly than did the component sensitive to Ro25–6981 ( Figure S6 ) . Thus , the absence of Neto1 decreases the relative contribution of NR2A-containing receptors to NMDAR EPSCs at Schaffer collateral-CA1 synapses . We investigated the impact of the decrease of synaptic NR2A-mediated currents on tbLTP at Schaffer collateral synapses . Because basal NMDAR EPSCs in Neto1-null mice were mediated primarily by NR2B-containing NMDARs , we examined the effect of blocking NR2B-NMDARs on the induction of tbLTP in wild-type and Neto1-null mice using Ro25–6981 . In wild-type slices , Ro25–6981 ( 2 μM ) had no effect on tbLTP ( Figure 9F ) . In contrast , in Neto1-null slices Ro25–6981 led to a ∼60% reduction in tbLTP ( Figure 9F and 9G ) . These findings indicate that tbLTP in Schaffer collateral-CA1 synapses of adult Neto1-null mice is mediated primarily by NR2B-containing NMDARs . Taken together , these findings demonstrate that Neto1 is required for the normal abundance of synaptic NR2A-containing NMDARs and , as a result , for the normal contribution of NR2A-NMDARs to synaptic transmission and plasticity in CA1 hippocampus . We reasoned that the decrease in NMDAR abundance and function in the hippocampus of Neto1-null mice might disrupt NMDAR-dependent learning and memory [3] , and therefore tested wild-type and Neto1-null littermate mice in the Morris water maze task , with two acquisition phases [35] . We found no difference between wild-type and Neto1-null mice in latency to find a platform marked with a visible cue ( Figure 10A , pretraining ) , indicating that the lack of Neto1 had no detectable adverse effects on the visual and motor functions required for this task . Moreover , there were no differences between groups in the first acquisition phase ( Figure 10A , days 1–6 ) , nor in the first probe trial ( Figure 10B and Figure S7A ) . In contrast , when the platform was relocated in the second acquisition phase , Neto1-null mice failed to reduce their escape latency during training and were impaired in the second probe trial as compared with the wild-type controls ( Figure 10A , days 7–9 ) . The differences could not be explained by a deficit in motor performance because swim speed , measured in every trial , was not different between the two genotypes ( Figure S7B ) . In the second probe trial [35] , wild-type mice showed a strong preference for the new target quadrant whereas Neto1-null mice showed no preference for this quadrant ( Figure 10C ) . In addition , the mutant mice crossed the new platform location less frequently than their wild-type littermates ( Figure S7C ) and did not persevere in crossing the original platform location ( Figure S7C ) . Neto1-null mice predominantly used nonspatial search strategies , such as scanning and chaining , as compared with the spatial strategies such as focal searching and direct swims [36] used by wild-type mice ( Figure S7D and S7E ) . Altogether , the above findings establish that Neto1-null mice are impaired in hippocampal-dependent spatial learning . To further characterize the hippocampal-dependent learning abnormalities in Neto1-null mice , we used two other spatial learning tests—the delayed matching-to-place version of the Morris water maze task [37] and the displaced object ( DO ) task [38]—and a nonspatial test , the novel object recognition task [39] . Neto1-null mice were impaired in both the delayed matching-to-place task ( Figure 11A–11C and Figure S8 ) and the DO task ( Figure 11D ) . In contrast , the performance of Neto1-null mice was the same as wild-type littermates in the novel-object recognition task ( Figure 11E , Figure S9A , and Table S1 ) . Taken together , our findings from the behavioural studies indicate that Neto1-null mice have broad deficiencies in spatial learning whereas the nonspatial task examined did not require Neto1 . We considered that the deficits in LTP and learning might be restored by enhancing the residual NMDAR function in Neto1-null mice . Our strategy was to increase NMDAR-mediated currents preferentially at active synapses using the ampakine CX546 . CX546 decreases the desensitization of AMPARs [15] , thereby prolonging AMPAR EPSPs and secondarily increasing current through NMDARs by reducing the Mg2+ blockade . We found that at a concentration of 25 μM , CX546 had no effect on tbLTP in wild-type slices but restored tbLTP in Neto1-null slices to the wild-type level ( Figure 12A and 12B ) . At this concentration , CX546 prolonged AMPAR-mediated EPSCs ( Figure 12C ) and this effect was similar in both wild-type and Neto1-null neurons ( wild-type 160 ± 16%; Neto1-null 154 ± 21% ) . In contrast , CX546 ( 25 μM ) had no effect on the amplitude , decay , or voltage-dependence of pharmacologically isolated NMDAR EPSCs ( Figure 12D and 12E ) . CX546 ( 25 μM ) also had no effect on paired-pulse facilitation ( Figure S10A ) , indicating that presynaptic function was not altered by CX546 . Corresponding to the prolongation of AMPAR EPSCs CX546 caused an increase in the duration of the fEPSPs ( Figure S10B ) and CX546-prolonged fEPSPs showed an NMDAR-component ( Figure S10C ) . Moreover , we found that the fully rescued LTP in Neto1-null hippocampal slices was suppressed by more than 65% by Ro25–6981 , at a dose that was without effect on LTP in wild-type slices ( GMP , DN , RRM , MWS , unpublished data ) . These findings indicate that by prolonging AMPAR EPSCs , CX546 secondarily increases current through NMDARs in CA1 hippocampus in Neto1-null mice , thereby restoring tbLTP to wild-type levels . Finally , we asked whether the strategy of using CX546 to indirectly enhance NMDAR function restores learning and memory in Neto1-null mice . In the Morris water maze task we used a dose of CX546 ( 15 mg/kg ) that had no effect on learning in wild-type mice but that restored the escape latency and probe trial impairments in Neto1-null mice to normal ( Figure 13A–13C and Figure S11 ) . Moreover , in the DO task , Neto1-null mice treated with the same dose of CX546 spent the same amount of time investigating the DO as wild-type mice ( Figure 13D ) . All test groups had a similar habituation profile ( Figure S9B ) . In summary , tbLTP and spatial learning in Neto1-null mice were pharmacologically rescued by CX546 , at doses that were without effect in wild-type animals .
We have established that Neto1 is a critical component of the NMDAR complex , and that loss of Neto1 leads to impaired hippocampal LTP and hippocampal-dependent learning and memory . We have shown that Neto1 interacts with NMDARs through the extracellular domain of their NR2 subunits , as well as intracellularly through PSD-95 . Although Neto1 binds to both NR2A and NR2B , the loss of Neto1 leads to a reduction in the abundance of NR2A , but not NR2B , in the PSD fraction from hippocampus and a reduction in NR2A puncta in the CA1 region . Consistent with the reduction in NR2A protein in the PSDs of Neto1-null mice , which had no change in total NR2A abundance in whole brain , we identified a decrease in NMDAR EPSCs at Schaffer collateral-CA1 synapses , which are normally dominated by NR2A-containing receptors [40] . Blockade of NR2B-containing NMDARs in Neto1-null neurons caused a dramatic decrease in NMDAR-mediated EPSCs , indicating that the majority of NMDAR-mediated EPSCs in Neto1-null hippocampal neurons are contributed by NR2B-containing NMDARs and not NR2A-NMDARs . These findings indicate that Neto1 plays a critical role in maintaining the delivery or stability of NR2A-containing NMDARs at CA1 synapses . The preferential effect of the loss of Neto1 on the abundance of synaptic , but not total , NR2A-containing NMDARs would not have been predicted from studies on the basis of the disruption of other NMDAR-interacting proteins . Rather than having a specific regulatory role on synaptic targeting of NMDARs like Neto1 , loss of function of the other NMDAR-interacting proteins studied to date affects the overall cellular trafficking , function , or downstream signaling of NMDARs [41–43] . In its role in targeting NR2A-NMDARs to the synapse , Neto1 may be comparable to the TARPs , which control targeting of AMPARs to synapses [44 , 45] . Our identification of Neto1 as a critical auxiliary protein for NR2A-NMDARs raises the possibility that other proteins , perhaps other CUB domain proteins , may be required , like Neto1 , to maintain non-NR2A-NMDARs at synapses . Thus , Neto1 represents a new protein that functions to specifically maintain synaptic NMDARs , a protein that has been elusive for NMDARs . The loss of synaptic NR2A-containing receptors in the Neto1-null mice implies that the molecular events regulating the delivery or stability of NR2A-NMDARs at the synapse differ from those regulating NR2B-NMDARs . Despite the ability of Neto1 to bind to both NR2A and NR2B subunits in vitro , the differential effect of Neto1 on NR2A- versus NR2B-containing NMDARs in vivo , might be mediated by the extracellular , membrane or cytoplasmic domains of these NR2 subunits . The membrane domains of NR2A and NR2B , however , are over 95% identical and are therefore unlikely to be responsible for the differential effect of loss of Neto1 . The extracellular domains of NR2A and NR2B are 54% identical , being dominated by the S1 ligand-binding region and the amino terminal domain , with the extreme N-terminal sequence being the most divergent . The cytoplasmic domains of NR2A and NR2B are the most divergent , having only 29% sequence identity . Differences in motifs within the extracellular or cytoplasmic domains may thus be responsible for the differential effect on synaptic NR2A NMDARs in the Neto1-null mice . The functional consequences of the differences between NR2A and NR2B have been most clearly delineated for their cytoplasmic domains . For example , the endocytic motifs in the distal C termini of NR2A and NR2B , LL and YEKL , respectively , have been demonstrated to interact with clathrin adaptor complexes with different affinities [46] . After endocytosis , NR2A and NR2B sort into different intracellular pathways , with NR2B preferentially trafficking to recycling endosomes . Other studies indicate that the cytoplasmic domains of NR2A and NR2B preferentially associate with unique sets of proteins . For example , NR2B but not NR2A interacts with Ras-guanine nucleotide-releasing factor 1 ( Ras-GRF1 ) , which is critical for NMDAR-mediated activation of ERK [47] . NR2B also binds preferentially to CaMKII [48–51] allowing CaMKII to remain active after the dissociation of Ca2+/calmodulin . NR2 subunit-specific signalling mechanisms can therefore be dictated , in part , by the properties and context conferred by the different associated proteins . Thus , the Neto1-dependent subunit-specific regulation may reflect differences in NR2-NMDAR associated proteins . The loss of Neto1 , while having no effect on basal AMPAR-mediated synaptic transmission , suppresses LTP to a degree comparable to that observed in mice lacking NR2A [52] or its C-terminal tail [53] . In NR2A-null mutant mice , as in Neto1-null mice , LTP at Schaffer collateral-CA1 synapses is mediated by NR2B-NMDARs [54] . Moreover , the spatial memory deficit of Neto1-null mice in the Morris water maze task is comparable to that of NR2A-null mice: the initial acquisition is normal , but other tests of spatial memory are impaired including , for example , the “spontaneous spatial novelty preference test” [55] . Similarly , in mice lacking the C terminus of NR2A , the initial acquisition in the Morris water maze is normal but , like the NR2A-null , these mice also have impaired spatial working memory [55] . The deficits in the Neto1-null mice indicate that Neto1 may have specific roles in the acquisition of spatial memory . The deficit in the delayed matching-to-place indicates that Neto1 is crucial for rapid spatial learning as described by Nakazawa and colleagues [37] . Our discovery that Neto1 in vertebrates is a component of the NMDAR complex , together with the previous identification of SOL-1 [11] and LEV-10 [13] in C . elegans as CUB domain-containing proteins associated with the GLR-1 and ACh receptors , respectively , suggests that the CUB domain may be an evolutionarily conserved molecular signature of a significant subset of the proteins associated with neurotransmitter receptors . Loss of function of these three CUB domain proteins has no impact on the overall abundance of the associated receptor complexes . Rather , loss of Neto1 and LEV-10 each leads to a reduction in synaptic localization of the cognate receptors , whereas loss of SOL-1 leads to a loss of function of normally distributed GLR-1 . Both Neto1 and SOL-1 interact with ionotropic subunits by an extracellular CUB domain . Binding of a soluble CUB domain of SOL-1 partially rescues the function of GLR-1 ionotropic receptors [12] . It is not yet known whether soluble Neto1 CUB domains can rescue the impaired LTP or the reduced number of NR2A-containing receptors at hippocampal excitatory synapses in Neto1-null mice . Because Neto1 , SOL-1 , and LEV-10 are associated with neurotransmitter receptors of different classes , our work suggests that a critical interaction with a CUB domain-containing protein may be a general characteristic of ligand-gated ion channels throughout nature . In Neto1-null mice , the impairments in LTP and spatial learning were rescued by the ampakine CX546 , administered acutely by bathing hippocampal slices in the drug prior to LTP-inducing stimulation , or by administering it systemically prior to each training session , respectively . Importantly , CX546 was used at doses that we demonstrated to have no effect on synaptic plasticity or learning in wild-type mice . This is the first report of a pharmacological rescue of an NMDAR impairment , and consequently , our results extend the principle that in vertebrates , an inherited defect in synaptic plasticity and spatial learning can be corrected in the adult [56] . We showed that CX546 prolongs AMPAR-mediated EPSCs and that the prolongation is the same in wild-type and Neto1-null mice , but that it does not affect NMDAR-mediated EPSCs or paired pulse facilitation . Consequently , the most parsimonious explanation of the CX546-mediated rescue ( Figure 14 ) is that it indirectly facilitates NMDAR-mediated synaptic responses by prolonging AMPAR EPSCs , extending the temporary relief of the Mg2+ blockade and thereby increasing Ca2+ influx through NMDARs to the wild-type level required for full expression of the LTP signaling cascade [43 , 57] . A comparable strategy of modulating non-NMDARs to secondarily facilitate NMDAR currents has also been used , but with a genetic approach , in C . elegans: the disruption of foraging behaviour by mutant NMDARs was restored by a slowly desensitizing variant of the non-NMDARs [58] . Thus , we expect that a slowly desensitizing AMPAR variant would rescue LTP in the Neto1-null mice . The recovery of LTP or learning by CX546 could be explained by facilitation of either NR2A- or NR2B-NMDAR mediated responses . However , we found that the fully rescued LTP is suppressed by more than 65% in Neto1-null hippocampal slices by Ro25–6981 , at a dose that is without effect on LTP in wild- type slices indicating that NR2B-NMDARs , and not only NR2A-NMDARs , are required for the rescue of LTP . Hence , the rescue of spatial learning observed in Neto1-null mice may also be dependent on NR2B-NMDARs . In summary , in addition to the rescue of synaptic plasticity mediated by CX546 , we have discovered that the CUB domain protein Neto1 is a component of the NMDAR complex and that it plays a central role in the normal function of NMDARs at hippocampal excitatory synapses . Mice lacking Neto1 have a normal abundance of NR2B-containing NMDAR receptors but a reduction of NR2A-containing receptors at hippocampal excitatory synapses . The reduction of NMDAR-mediated synaptic currents , impaired synaptic plasticity at hippocampal Schaffer collateral-CA1 synapses , and impaired spatial learning observed in the Neto1-null animals can be attributed to the decreased levels of NR2A-containing receptors at hippocampal excitatory synapses . Altogether , our findings establish that Neto1 is an important regulator of the NMDAR complex required for normal NMDAR-mediated synaptic plasticity and learning . Our results , together with the identification of the CUB domain proteins SOL-1 and LEV-10 as regulators of ionotropic receptors in nematode , suggest that a critical interaction with a CUB domain protein may be a common feature of different types of ligand-gated ion channels across species . Moreover , our studies establish the principle that inherited abnormalities of synaptic plasticity and spatial cognition due to NMDAR dysfunction can be pharmacologically corrected .
Human UniGene clusters were analyzed using the BLAST algorithm [59] to identify proteins with motifs suggestive of a neurodevelopmental function . One retinal UniGene cluster , Hs . 60563 , a partial cDNA predicting a CUB-domain ORF related to neuropilins and tolloids , which we designated NETO1 [8] , was selected for further study . Full-length mouse Neto1 cDNAs were obtained by reverse transcription ( RT ) -PCR from adult mouse brain cDNA . To disrupt the Neto1 gene by homologous recombination , we generated a targeting construct with a tau-lacZ-loxP-pgk-neo-loxP cassette cloned in-frame with the Neto1 start codon ( Figure 6A ) . Mouse R1 embryonic stem ( ES ) cells were electroporated , and positive clones were identified by Southern blotting . Two independent mouse lines were generated by blastocyst injection , and transmitting male chimeras were mated with C57BL/6J mice . A proportion of F2 Neto1tlz/tlz mice were observed to have infrequent myoclonic seizures commencing at the age of weaning [8] . However , no F3 Neto1tlz/tlz mice or subsequent generations exhibited seizure activity either by behavioural observation or by EEG recording . Therefore , we used only F3 and later generation Neto1+/tlz and Neto1tlz/tlz mice in the present study . The generation of guinea pig anti-Neto1 antibodies is described elsewhere [60] . Rabbit antibodies to Neto1 were raised to the C-terminal 86 amino acids of Neto1 and prepared as described by Chow and colleagues [60] , except that the antigen was further purified by electroelution from a SDS-polyacrylamide gel . Other antibodies were purchased from commercial sources . See Table S2 for details . Immunostaining was adapted from Schneider Gasser et al . [61] . Briefly , fresh 300-μm vibratome-cut hippocampal slices , trimmed from sagittal brain slices , were fixed in 2% PFA/PBS on ice for 20 min , washed three times in PBS , and incubated “free-floating” in blocking solution ( 10% goat serum , 0 . 1% triton-X , PBS ) for 1 h . Primary antibodies ( see Table S2 ) in blocking solution were incubated with slices for 48 h under gentle agitation at 4 °C . Slices were washed three times in PBS , and incubated with appropriate secondary antibodies for 24 h under gentle agitation at 4 °C . Following incubation , slices were washed three times with PBS , transferred , and mounted on to glass slides with Immun-Mount ( Thermo Scientific ) . Images were acquired using a Zeiss LSM 510 confocal microscope . For quantitative studies , three age-matched ( 2-mo-old ) pairs of wild-type and Neto1-null littermates were examined . In each littermate pair , brain slices from each genotype were combined into the same well , and subsequently processed together under identical conditions , as described above . Slices were double-labeled with antibodies against Neto1 and either NR2A , NR2B , or PSD-95 . All slices from the same well were mounted onto the same glass slide , and images were acquired with fixed exposure settings . Puncta from stratum radiatum in CA1 of Neto1-null and control slices were quantified using ImageJ software with identical parameters . The yeast two-hybrid system was initially used to determine whether the cytoplasmic tail of Neto1 could interact with PSD-95 and the related proteins PSD-93 , SAP-102 , and SAP-97 . Fragments encoding the cytoplasmic region of Neto1 ( amino acids 345–533 ) and the C-terminal mutant ΔTRV ( comprising amino acids 345–530 ) were amplified by PCR from mouse whole brain cDNA and subcloned into the yeast vector pBD-GAL4 ( Stratagene ) containing the GAL4 DNA-binding domain . Full-length PSD-95 , PSD-93 , SAP-102 , and SAP-97 cDNAs , and cDNAs encoding different parts of PSD-95 were derived from mouse brain by RT-PCR using primers designed from published DNA sequences . The cDNAs were subcloned into the yeast vector pAD-GAL4 ( Stratagene ) . The controls used were the cytoplasmic domain of mouse neuropilin-1 [16] cloned into the pBD-GAL4 vector , and full-length NIP [24] cloned into the pAD-GAL4 vector . The yeast vectors were sequentially transformed into the Saccharomyces cerevisiae strain YRG-2 ( Stratagene ) and the interactions scored by growth in the absence of leucine , tryptophan , and histidine , and using a β-galactosidase filter assay . Full-length Neto1 cDNA ( encoding amino acids 1–533 ) and deletion mutants Neto1-ΔTRV ( 1–530 ) , Neto1-Δ20HA ( 1–513 ) , Neto1-Δ20-eGFP ( 1–513 ) , Neto1-Δcyto-eGFP ( 1–363 ) , Neto1-ΔcytoTM-eGFP ( 1–340 ) , Neto1 CUB12-eGFP ( 1–290 ) , Neto1 CUB1-eGFP ( 1–162 ) , Nrpn1 CUB12-eGFP ( 1–270 ) ( from neuropilin-1 ) [16] , and CSF-1 EC-eGFP ( 1–294 ) ( from macrophage colony-stimulating factor 1 receptor ) [62] were generated by PCR and subcloned into a variant of pcDNA3 . 1mycHisA ( + ) ( Invitrogen ) containing two copies of the influenza hemagglutinin ( HA ) epitope tag or the eGFP coding sequence , and sequence verified . GW1-PSD-95 ( full-length human PSD-95 ) and pM18S-PDZ1–3 ( containing PDZ domains 1 , 2 , and 3 of human PSD-95 ) have been described [63] . The NR1 construct used expresses the NR1-1a isoform , which lacks the PDZ binding motif [64] . For co-immunoprecipitation experiments , HEK293 cells were transfected using SuperFect ( Qiagen ) . Cells transfected with NR1 and NR2 subunits of the NMDA receptors were grown in the presence of 300 μM DL-2-amino-5-phosphonovaleric acid ( Sigma ) . 48 h after transfection , cells were washed with PBS and lysed in RIPA buffer ( 1 ml/100-mm plate ) , containing 50 mM Tris/HCl ( pH 7 . 4 ) , 150 mM NaCl , 1 mM EDTA , 1% Nonidet P-40 , 0 . 1% SDS , 0 . 5% deoxycholate ( DOC ) supplemented with protease inhibitors . Lysed cells were incubated on ice for 30 min , and centrifuged at 14 , 000g for 15 min at 4 °C . Cell lysates ( ∼1 . 5 mg of protein ) were incubated directly in the presence or absence of antibodies ( 2 μg ) for periods ranging from 1 h to overnight at 4 °C on a rotating platform . Lysates were subsequently incubated with either 30 μl protein A-agarose beads ( GE Healthcare ) or 30 μl anti-mouse IgG beads ( Sigma ) for 1–5 h at 4 °C on a rotating platform . After centrifugation , beads were washed three times with RIPA buffer . Bound proteins were eluted with SDS sample buffer and subjected to SDS-PAGE and immunoblotting . For immunoprecipitation from crude synaptosomal fractions , prepared as previously described [65] , 1 mg of synaptosomal protein was incubated in the presence or absence of antibodies ( 2 μg ) or pre-immune IgGs overnight with rotation at 4 °C , and further incubated with either 30 μl protein A-agarose beads or 30 μl anti-mouse IgG beads for 3–5 h with rotation at 4 °C . After centrifugation , beads were washed three times with RIPA buffer . Bound proteins were eluted with SDS sample buffer and subjected to SDS-PAGE and immunoblotting . Subcellular fractionation of mouse brains was performed as described [66 , 67] . All buffers contained a cocktail of protease inhibitors ( Roche ) . The PSD fraction was prepared from whole brains or pooled hippocampi from 2–4-mo-old mice as described previously [19] , except that PSDs were extracted only once with Triton X-100 . Crude synaptosomal fractions were prepared as previously described [65] from wild-type or Neto1-null brains . For protein quantification , proteins were solubilized by boiling in 1% SDS and quantitated using a detergent-compatible assay ( Bio-Rad ) . Biotinylation studies were performed as previously described with modifications [68] . Briefly , 200-μm hippocampal slices from age-matched wild-type and Neto1-null littermate mice were incubated in ACSF saturated in 95% O2 5% CO2 at room temperature for at least 1 h . Ten slices from each genotype were incubated in 2 ml of ACSF containing 500 μg/ml biotin ( Pierce ) , on ice , bubbled in 95% O2 5% CO2 , with gentle agitation for 1 h . Slices were washed three times in ACSF and homogenized with 1 ml of RIPA buffer with a protease inhibitor cocktail ( Roche ) and incubated on ice for 30 min . The homogenate was centrifuged and supernatant was collected , and quantified using the BioRad Dc protein quantification kit . 50 μg of total protein in a total volume of 300 μl was mixed with 200 μl of a 50% slurry of Neutravidin beads ( Pierce ) and rotated for 1 h at 4 °C . The beads ( first bound fraction ) were harvested by centrifugation and washed three times in RIPA buffer . The remaining supernatant was subjected to a second binding of 200 μl of 50% slurry of Neutravidin beads and rotated for 1 h at 4 °C . The beads ( second bound fraction ) were then centrifuged and washed three times with RIPA buffer . Samples were resolved by SDS PAGE and blotted with appropriate primary antibodies . A DNA fragment corresponding to the first CUB domain ( CUB1 ) of mouse Neto1 was used to hybridize RNA blots using standard procedures . For in situ hybridizations , mouse embryos and mature tissues were fixed in PBS/4% paraformaldehyde ( PFA ) overnight , rinsed in PBS , and equilibrated in PBS/30% sucrose at 4 °C . In situ hybridization was adapted from an established protocol [69] . Two-month-old animals were perfused with 4% PFA in PBS and brains were sectioned and stained using hematoxylin and eosin or cresyl violet using standard methods . Brains used for Golgi staining were processed according to manufacturer's directions ( FD Neurotechnologies , Inc ) . Serial coronal and saggital brain sections were examined . Hippocampal slices prepared from 8–12-wk-old littermate mice were placed in a holding chamber for at least 1 h prior to recording . A single slice ( 300 μm ) was then transferred to a recording chamber and superfused with artificial cerebrospinal fluid ( ACSF ) at 2 ml/min composed of 132 mM NaCl , 3 mM KCl , 1 . 25 mM NaH2PO4 , 2 mM MgCl2 , 11 mM D-glucose , 24 mM NaHCO3 , and 2 mM CaCl2 saturated with 95% O2 ( balance 5% CO2 ) at 28 ± 2 °C ( pH 7 . 40; 315–325 mOsm ) . fEPSPs were evoked using bipolar tungsten electrodes located approximately 50 μm from the cell body layer in CA1 and were recorded using glass micropipettes filled with ACSF placed in the stratum radiatum 60–80 μm from the cell body layer . Stimulation of Schaffer collateral afferents consisted of single pulses ( 0 . 08-ms duration ) delivered at 0 . 1 Hz . In LTP experiments , theta-burst stimulation ( TBS ) consisted of 15 bursts of four pulses at 100 Hz , delivered at an interstimulus interval of 200 ms . Stimulus intensity was set to 30%–35% of that which produced maximum synaptic responses . fEPSP slope was calculated as the slope of the rising phase between 10% and 60% of the peak of the response . Whole-cell EPSC recordings were done using the visualized method ( Zeiss Axioskop 2FS microscope ) with patch pipettes ( 3–5 MΩ ) containing intracellular solution composed of: 132 . 5 mM Cs-gluconate , 17 . 5 mM CsCl , 10 mM HEPES , 10 mM BAPTA , 2 mM Mg-ATP , 0 . 3 mM GTP , 5 mM QX-314 , ( pH 7 . 25; 290 mOsm ) placed in the cell body layer in the CA1 . Synaptic responses were evoked with a bipolar tungsten electrode placed approximately 50 μm from the CA1 cell body layer . ACSF was supplemented with bicuculline methiodide ( 10 μM ) . AMPAR EPSCs were recorded with cells held at −70 mV . Stimulation to evoke AMPAR EPSCs consisted of single pulses ( 0 . 08-ms duration ) delivered to Schaffer collateral-CA1 synapses at 0 . 1 Hz with increasing strength ( Figure 8 and Figure S5 ) . For each cell at each stimulus intensity tested , six consecutive EPSCs were recorded and the peak amplitudes averaged . NMDAR EPSCs were recorded from the same CA1 pyramidal neurons ( Figure 8 ) but held at +60 mV in order to remove the NMDAR-voltage-dependent Mg2+ block and perfused with ACSF containing DNQX ( 5 μM ) or CNQX ( 10 μM ) . The same stimulation protocol used to evoke AMPAR EPSCs was used to evoke NMDAR EPSCs . Current-voltage relationships for AMPAR and NMDAR EPSCs were also performed . Raw data were amplified using a MultiClamp 700A amplifier and a Digidata 1322A acquisition system sampled at 10 KHz , and analyzed with Clampfit 9 . 2 ( Axon Instruments ) and Sigmaplot 7 software . Recordings were performed with the experimenter blind to the genotype . ACSF was supplemented as indicated with Ro25–6981 ( 2 μM; Tocris ) , which was made fresh immediately before the experiment . ACSF was also supplemented as indicated with CX546 ( 25 μM; dissolved in H2O; Cortex Pharmaceuticals ) , which was made fresh immediately before the experiment . CX546 caused no change in the initial slope of the fEPSP but prolonged the decay phase . Data are presented as mean ( ±SEM ) . Student's t-test or two-way ANOVA with the Tukey test were used for statistical comparison . Acutely dissociated hippocampal CA1 neurons were obtained from Neto1+/+ and Neto1tlz/tlz mice as previously described [70] . At 20–22 °C , pyramidal CA1 neurons were voltage-clamped at −60 mV in the whole cell configuration using borosilicate micropipettes ( series resistance 3–8 MΩ ) filled with intracellular solution that contained ( in mM ) : CsF 140 , HEPES 10 , MgCl2 2 , ethylene glycol-O-O'-bis ( 2-aminoethyl ) -N , N , N′ , N′-tetraacetic acid ( EGTA ) 10 , magnesium adenosine 5”-triphosphate ( MgATP ) 4 , buffered to a pH of 7 . 4 using CsOH and adjusted to an osmolality of 290–300 mOsm . The CA1 neurons were then lifted into the stream of extracellular perfusion solution containing ( in mM ) : NaCl 140 , CaCl2 1 . 3 , KCl 5 . 4 , N-2-hydroxyethylpiperazine-N′-2-ethanesulphonic acid ( HEPES ) 25 , glucose 33 , tetrodotoxin 0 . 0003 , and glycine 0 . 01 , buffered to a pH of 7 . 4 with NaOH and adjusted to an osmolality of 320–325 mOsm . Rapid solution exchanges were accomplished by a motor-stepped fast perfusion system . NMDA-evoked current were recorded using the Multiclamp 700A amplifier with data filtered at 2 kHz , digitized using the Digidata 1322A , and acquired on-line at a sampling frequency of 10 kHz using the pCLAMP8 program . Prior to agonist exposure , a capacitance transient resulting from a 10-mV hyperpolarizing step was also recorded and used to estimate neuron size and current density in response to NMDA 1 mM . The concentration of NMDA that produced 50% of the maximal peak responses ( EC50 ) and the respective Hill coefficient ( nH ) were determined according to the equations: where Imax is the maximal response observed at a saturating concentration ( 1 mM ) of NMDA ( using Graphpad Prism version 4 ) . In experiments using ifenprodil 10 μM to inhibit NR2B-containing NMDA receptors , the ifenprodil was preperfused for 2 min before its co-application with NMDA 1 mM . Data are represented as mean ±SEM . For the Morris water maze task , mice tested were the 12–16-wk-old Neto1-null and wild-type F3 progeny of intercrossed Neto1+/tlz heterozygotes having a mixed genetic background averaging 50% C57BL/6J , 25% 129S1/SvImJ , and 25% 129X1/SvJ . Pink-eyed mice were excluded from behavioural testing to minimize variation in visual acuity . The water maze consisted of a 185-cm diameter cylindrical tank that contained a 15-cm circular platform and water ( 26 ± 1 °C ) rendered opaque by the addition of white nontoxic paint . The training regime consisted of three phases: pretraining to a visible ( V ) platform in the northeast quadrant ( NE ) for 1 d ( four trials; maximum duration , 90 s; inter-trial interval [ITI] , 30 min ) ; acquisition training to a hidden platform in the southeast ( SE ) quadrant for 6 d ( day 1–6; six trials per day; maximum duration , 90 s; ITI , 40 min ) ; second acquisition training to a hidden platform in the northwest ( NW ) quadrant for 3 d ( day 7–9; six trials per day; maximum duration , 90 s; ITI , 30 min ) . Probe trials ( 90 s duration ) were administered 18 h after the last acquisition and reversal trials , respectively . The same cohort of mice was further trained in a delayed matching-to-place task , in which mice had to repeatedly learn a new spatial location of a hidden platform within six training trials of a daily session [71] . In this test , each mouse was given six 90 s training trials ( ITI = 40 min ) every day for 12 d , with the hidden platform placed in a novel location at the start of each day . The scores of each trial were averaged across the last 4 d of the 12-day training period . Behavioural data for escape latency were analysed using a two-way ANOVA . For the probe trials , statistical comparisons between genotypes for the number of crossings over the former platform location were done using one-way ANOVA with the critical α level set to 0 . 05 for all statistical analyses . Swim paths of Neto1-null and wild-type mice in each trial of the second acquisition phase ( Figure S7D and S7E ) and delayed matching-to-place version of the Morris water maze task ( Delayed Matching-to-Place [DMP] days 9–12 , Figure S8A and S8B ) were categorized according to their swim search strategies , as described [71 , 72] . Thigmotaxis: swimming along the edge of the wall or wall-hugging . Random search: randomly swimming over the entire area of the pool . Scanning: adopting a more systematic and efficient way of swimming in the central area of the pool . Chaining: memorizing a specific distance between the platform and the wall and swimming in wide circles to all possible platform locations at that distance . Focal search: restricted swimming to a specific area of the pool . Focal search signifies the beginning of spatial navigation and it could be separated into focal search in the correct target quadrant and focal search in the incorrect quadrants . The highest level of precision in spatial navigation is reached when the animal employs direct swims to the platform , independent of its release point . Swim strategies were characterized according to the predominant swim strategy used during the entire length of each trial and overall swim strategies were presented as the percentage of time spent on the strategy of choice . The experimenter classifying the swim search strategies was blind to the genotype or trial sequence within the experiment . The chaining parameter in the Wintrack computer software [36] was used to statistically verify qualitative swim search strategies of Neto1-null and wild-type mice during the second acquisition phase of the Morris water maze task and days 9–12 of the DMP task . The chaining score comparisons between genotypes were analyzed using ANOVA . The modified open field procedure was performed as described [73] , with slight modifications , using a second cohort of Neto1-null and wild-type littermate mice . The open field apparatus consisted of a cubical box ( 41 × 41 × 33 cm ) made of clear Perspex ( Ugo Basile ) that was connected to horizontal and vertical infrared sensors . All behavioural events were video recorded and analyzed using Observer 5 . 0 software ( Noldus Information Technology ) . The test consisted of four sessions with intertrial intervals of 2 min during which mice were returned to their home cage . During the open field session , each mouse was placed into the center of the empty , brightly lit open field for 5 min and the baseline level of locomotion ( horizontal and vertical activity ) and other behavioural parameters were recorded . The behavioural parameters were latency to escape the center; time of freezing ( remaining in one place with only slight movement of the head ) ; time of self-grooming; number of risk assessments ( behaviour involving the mouse stretching its body from the corners/wall towards the center ) . Exploratory activity and walking were recorded separately for the central and peripheral field of the open arena , and the ratio between duration of central and peripheral activity was calculated . During the habituation session , four different plastic objects were presented in the open field: cube ( 5 × 5 × 5 cm ) ; hollow cylinder ( 6 cm height and 4 cm diameter ) ; solid cylinder ( 3 cm height × 6 cm diameter ) ; and prism ( 3 . 5 × 4 . 5 × 6 cm ) . Exploration of the four different plastic objects in the open field were measured every 5 min for 15 min under dim lighting ( habituation profile ) . In the spatial object recognition session , the four objects , initially placed in a square arrangement , were reconfigured into a polygon-shaped pattern by moving two DOs . The remaining two objects were left at the same location ( nondisplaced objects [NDOs] ) . Times of exploration of the DO and NDO were recorded for 5 min and expressed as a percentage of the total time of objects investigated . In the novel object recognition session , one of the familiar NDOs was replaced with a new object ( NO ) at the same location and the two familiar DOs were removed . The time examining a NO or a familiar object ( FO ) was recorded for 5 min and was expressed as a percentage of the total time of objects investigated . Data were analyzed with ANOVA with genotype as a between-subjects factor , and object rearrangement or object replacement as a repeated measures factor . The Tukey test was used for post hoc comparisons when ANOVA yielded statistically significant main effects or interactions . To examine the effects of CX546 in spatial learning , new cohorts of Neto1-null and wild-type littermate mice were used for the water maze and displaced-object tasks . In the water maze task , a single daily intraperitoneal injection of CX546 ( 15 mg/kg , dissolved in 25% cylcodextran ) or vehicle ( 25% cyclodextran ) was administered 30 min prior to training . No injection was given on probe trial days . For the displaced-object task , a single intraperitoneal injection of CX546 ( 15 mg/kg ) or vehicle was administered 30 min prior to displaced-object recognition testing . All animal procedures were conducted in accordance with the requirements of the Province of Ontario Animals for Research Act , 1971 and the Canadian Council on Animal Care ( CCAC 1984 , 1995 ) . GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers discussed in this paper are: PSD-95 ( D50621 ) ; PSD-93 ( AF388675 ) ; SAP-102 ( D87117 ) ; and SAP-97 ( NM_007862 ) . | The fundamental unit for information processing in the brain is the synapse , a highly specialized site of communication between the brain's multitude of individual neurons . The strength of the communication at each synapse changes in response to neuronal activity—a process called synaptic plasticity—allowing networks of neurons to adapt and learn . How synaptic plasticity occurs is a major question in neurobiology . A central player in synaptic plasticity is an assembly of synaptic proteins called the NMDA receptor complex . Here , we discovered that the protein Neto1 is a component of the NMDA receptor complex . Neto1-deficient mice had a dramatic decrease in the number of NMDA receptors at synapses and consequently , synaptic plasticity and learning were impaired . By indirectly enhancing the function of the residual NMDA receptors in Neto1-deficient mice with a small molecule , we restored synaptic plasticity and learning to normal levels . Our findings establish the principle that inherited abnormalities of synaptic plasticity and learning due to NMDA receptor dysfunction can be pharmacologically corrected . Our discoveries also suggest that synaptic proteins that share a molecular signature , called the CUB domain , with Neto1 may be important components of synaptic receptors across species , because several CUB-domain proteins in worms have also been found to regulate synaptic receptors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"neurological",
"disorders",
"neuroscience",
"genetics",
"and",
"genomics"
]
| 2009 | Neto1 Is a Novel CUB-Domain NMDA Receptor–Interacting Protein Required for Synaptic Plasticity and Learning |
The dose response curve is the gold standard for measuring the effect of a drug treatment , but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis . One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns , but provide no quantitative pharmacological information . We developed analytical methods that identify transcripts responsive to dose , calculate classical pharmacological parameters such as the EC50 , and enable an in-depth analysis of coordinated dose-dependent treatment effects . The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors ( imatinib , nilotinib , dasatinib and PD0325901 ) across a six-logarithm dose range , using 12 arrays per compound . The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets , dose-dependent effects on cellular processes were identified using automated pathway analysis , and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s . Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology . Moreover , these methods are automated , robust to non-optimized assays , and could be applied to other sources of quantitative data .
The necessity of dose information in interpreting drug effects has been recognized since the 16th century , when Paracelsus observed: “All things are poison , and nothing is without poison: the dose alone makes a thing not poison” [1] . Today , dose-response models are routinely used to evaluate drug effects in biochemical and cell-based assays . Pharmacological parameters such as the widely used EC50 value ( half-maximal Effective Concentration ) are central to any discussion of drug activities . In contrast , transcription profiling experiments are typically performed using replicate treatments at one dose , and effects are identified by analysis of variance [2] . Single-dose experiments cannot distinguish effects that have different potencies , and they limit the utility of expression data relative to other bioassays . This is regrettable given the many applications of transcriptional profiling in drug discovery [3]–[8] . There is no inherent reason for transcription profiling not to use the dose-response designs seen in every other area of chemical biology [9] . Transcript levels are known to exhibit dose-responsive behavior in response to ligands , toxins and pharmacological agents [10]–[12] . Compound:target interaction at a single site that follows the law of mass action is reflected by the sigmoidal dose response seen in many bioassays [13] . Although the algorithms used to quantify such dose responses in optimized bioassays are not ideal for microarray data , they have been used successfully to identify dose-responsive transcripts in two studies [11] , [14] , [15] . While transcriptional responses are typically controlled through second messengers , it can be shown mathematically [16] and empirically [12] that when intermediate steps have the same characteristics , the sigmoidal response is preserved . An important corollary of these properties is that if a compound binds with distinct potencies to multiple targets , multiple biological responses will occur , with EC50 values corresponding to the target-binding EC50 . Transcriptional profiling provides an informative genome-wide view of biological responses [17] , thus obtaining quantitative dose-response information for transcript responses has obvious application in characterizing compounds that have high potential to interact with multiple targets . For example , establishing selectivity of kinase inhibitors across the human kinome continues to be difficult [18] . We describe analysis of transcription profiling studies of the dose responses to four kinase inhibitors: imatinib , nilotinib , dasatinib and PD0325901 . Imatinib is a relatively selective [18] clinical ABL inhibitor; nilotinib is a similar but more potent second-generation compound [19] . Dasatinib is a highly potent clinical ABL inhibitor that has additional activities on Src family [20] and receptor tyrosine kinases [21] . PD0325901 is a non-ATP competitive inhibitor of MEK , a threonine/tyrosine kinase [22] . Like most pharmaceutical agents , these compounds bind a single site on their target and elicit sigmoidal dose responses in biochemical [23] , [24] and cellular [25]–[27] assays . We developed novel methods to efficiently identify the transcripts that exhibit a sigmoidal dose response , and to visualize and further characterize groups of coordinated transcriptional responses . These analyses allow comparisons of potency , establish connection to other cellular assays , and provide insight into the mechanism and selectivity of compounds .
Experimental data were obtained from the human lung cell line A549 , treated for four hours with imatinib , nilotinib , dasatinib and PD0325901 . This timeframe allows both down- and up-regulated transcripts to be identified , since there is detectable mRNA turnover for the majority of transcripts in four hours [28] . For the ABL inhibitors , a 20 hour treatment was also performed . Treatments used 12 concentrations from 170 pM to 30 µM ( a three-fold dilution series covering a six logarithmic range ) . This dose regimen is modeled on other dose-response bioassays , and is amenable to identifying responses with EC50s in the interval between 0 . 54 nM and 10 µM . Each of the 22 , 215 probesets on an Affymetrix HG-133A array was treated as an assay for the response of its corresponding transcript; the 12 intensity values for each treatment constituted the assay data . Only sigmoidal dose responses were evident in a hierarchical cluster analysis of the data ( Text S1 ) . A typical sigmoidal dose response curve is defined by an equation with four unknowns corresponding to minimal response ( A ) , maximal response ( B ) , EC50 ( C ) , and slope ( D ) [13] . To identify dose-responsive transcripts , we developed a grid search-based algorithm named Sigmoidal Dose Response Search ( SDRS ) . For each probeset , the SDRS algorithm tests a series of candidate EC50 values ( C ) across the dose range . The goodness of fit at every grid search dose is measured by an F-statistic , calculated as the ratio between mean square of regression and mean square of error . When the data for a given probeset fits a sigmoidal dose response , its F-statistic plot has an inverted ‘V’ shape ( Figure 1A ) , and the values of A , B , C and D that generate the maximal F are the best fitting model ( Figure 1B ) , and C approximates the true EC50 for the assay data . Given the normality of residuals ( Text S1 and Table S9 ) , the statistic follows an F-distribution , and F-tables can be used to establish significance . A probeset is designated as a ‘response transcript’ if its maximal F-statistic is larger than the critical value for P<0 . 05 ( see Methods ) In this work , this criterion for a response transcript is used for benchmarking to other algorithms , and to retrieve gene sets for study after their significance has been established with methods that employ multiple test corrections . The performance of SDRS compared favorably to XLfit ( Text S1 and Table S1 ) , software that implements the Levenberg-Marquardt algorithm [29] . Using SDRS , response transcripts were identified for the four kinase inhibitors in the seven treatments described ( Table S2 ) . As diagrammed in Figure 1C , one output of SDRS is qualitatively similar to that of an iterative algorithm: each probeset has a predicted EC50 , P value and fold-change ( Table S2 ) . However , SDRS also generates an F-statistic for every probeset at each grid search dose level . This output , which is unique to the grid search method , allowed us to characterize and compare the coordinated transcriptional dose responses using the novel approaches described below . SDRS identifies the subset of transcripts that exhibit sigmoidal dose response behavior , and estimates the potency of the effect . While each probeset is an independent assay , transcript levels respond to the treatment's effect on a limited number of biological targets . At doses where the treatment impacts a target , one might identify coordinated sets of transcription responses . Identifying more than one coordinated set of responses that occur at distinct doses within a treatment series would point to multi-target pharmacology of a drug . However , if one has only a single EC50 value per probeset , it is impossible to identify such coordinated responses without imposing binning criteria , which are inherently arbitrary and not amenable to statistical evaluation . The SDRS output allows an alternative , statistically rigorous means to identify coordinated transcriptional responses . We exploited the fact that SDRS provides a list of the F-statistic for every probeset at each grid search dose . ( To simplify presentation , we use only the F-statistic lists from ‘Summary Doses’: a log-evenly distributed subset of the SDRS search doses ) . A false discovery rate ( FDR ) correction was applied to each Summary Dose list; this multiple test correction effectively removes spurious response transcripts ( Text S1 and Table S8 ) . A bar chart of the results for a given FDR revealed ‘peaks’ at regions of the dose range , indicative of coordinated transcriptional responses ( Figure 2A ) . Since a single FDR criterion also represents an arbitrary limitation of analysis , we computed the numbers of response transcripts corresponding to FDRs from 1% to 35% . We devised a novel and effective visualization for this data: a heat map where columns correspond to the Summary Doses , each row is a histogram of results at a given FDR , and intensity reflects the number of response transcripts passing the FDR criterion . Thus , the data presented in Figure 2A is the tenth row of the heat map in Figure 2B . As with a histogram , ‘peaks’ were evident at distinct regions of the dose range ( Figure 2B , 2C and Text S1 ) . These peaks reflect the likelihood that a coordinated transcriptional response is occurring around a given dose . For example , the MEK-specific inhibitor PD0325901 induced two separate peaks of transcriptional responses at four hours ( Figure 2B ) . The first peak centered at 0 . 5 nM and contained 80 response transcripts at 35% FDR . A second peak centered at 6 µM contained seven response transcripts . Examination of loci with multiple response transcripts suggested that the width of these peaks reflects the precision with which an EC50 for a single target can be determined ( Table S3 ) . Of the three ABL inhibitors , dasatinib induced the most potent and populous transcriptional response at both time points , consistent with its multi-kinase activity [21] , [23] . Drug treatments are typically compared by evaluating the overlap between the two lists of regulated transcripts . The SDRS output allows a similar but more granular evaluation , since one can see whether transcripts are regulated with the same potency in each treatment . At each Summary Dose there is a list of probesets that may be sorted based on F-score and truncated based on FDR criteria . To compare the data from two treatments , we used a Fisher's exact test on each possible pair of Summary Doses , incrementing the response transcript lists from 1% to 35% FDR ( see Methods ) . The resulting grid of P values is effectively visualized as a heat map . Comparison of a treatment to itself produced low P values at points on the diagonal ( Text S1 ) . If two distinct treatments generate the same response transcripts but with different EC50s , low P values occur at points off the diagonal . For example , 4 hour treatment with PD0325901 gave a peak of transcriptional responses centered around 6 µM ( Figure 2B ) . Imatinib affected an overlapping set of response transcripts with EC50s around 500 nM . Conversely , a set of transcripts with EC50s around 1 nM for PD0325901 had EC50s over 1 µM for imatinib ( Figure 2C , D and Text S1 ) . The heat maps in Figure 2B , C and Text S1 provide an overview of transcriptional responses to a compound . To understand the underlying mechanism , the responding genes need to be mapped to cellular pathways . This typically involves evaluating the overlap between the regulated genes and lists of genes representing biological processes [30] . Results can be prioritized by P-value , usefulness of the process grouping and relevance to possible targets . The SDRS output allows a similar but more granular evaluation of pathway impact , since one can see the dose at which the cellular process is implicated . For each treatment , the FDR-corrected response transcript list at each Summary Dose was tested for overlap with lists of genes connected to processes in the KEGG [31] and Gene Ontology ( GO , [32] ) databases ( see Methods and Table S4 ) . For processes meeting a P<0 . 001 criterion , impact on a discrete pathway at a distinct region of the dose range was used to prioritize the following examples . For PD0325901 , plotting the results of this analysis ( Table S4 , Figure 3A ) indicated that the two peaks of transcriptional responses observed in Figure 2B represented distinct biological effects . PD0325901 specifically inhibits cellular MEK activity with an IC50 of around 1 nM in both Ras/Raf-mutant and wild-type cell lines [33] . The response transcripts with EC50s around this dose were enriched for components of the MAP kinase pathway , which contains MEK ( Figure 3B , upper panel , Table S5 ) and the Jak-STAT pathway ( Figure 3B , middle panel ) , which is reported to be indirectly affected by MEK function [34]–[36] . These response transcripts included MAP kinase phosphatases ( DUSP1 , DUSP4 , DUSP5 , and DUSP6 ) [37] and STAT regulators ( IFNGR1 , OSMR ) and targets ( BCL2L1 , CCND1 , MYC , LIF , IL15 , SOCS5 , SOCS6 ) [38]–[40] ( Figure 3B , C ) . In contrast , the response transcripts with EC50s around 6 µM comprised 5 genes ( CYP1A1 , CYP1B1 , ALDH1A3 , ALDH3A2 , and DHCR24; Figure 3B lower panel , Figure 3C ) , all mapped to the P450-mediated xenobiotic metabolism and tryptophan metabolism pathways . This transcriptional response was also provoked by treatment with imatinib at around 500 nM ( Figure 2D , and Table S4 ) , and could reflect a xenobiotic response to these compounds , or another shared target activity . Dasatinib had a complex transcriptional response across the dose range ( Figure 4A ) , reflecting its potent impact on numerous kinases [21] . Combining dose information with pathway analysis allows scientists to make realistic connections between these target kinases and responses . For example , at Summary Doses from 34–128 nM there is an enrichment of transcripts for genes in the TGF-β signaling pathway , including the ID repressor family and SMADs ( Figure 4B , C ) . However since dasatinib's cellular activity on TGF-β family kinases is in the micromolar range ( [41] , shown in Figure 4A ) they are not the relevant target . Instead , hypotheses should include the kinases that dasatinib does inhibit in this dose region: some are known to impact the TGF-β pathway , for example Src [42] . Another example is the impact on MAP kinase signaling at higher concentrations of dasatinib ( Figure 4B , D ) . Dasatinib's impact was distinct from that of PD0325901 by both automated comparison ( Text S1 ) , and by direct analysis of the response transcript lists ( Table S5 ) . PD0325901 predominantly affected loci assigned to the ‘classical’ MAP kinase pathway containing its target , MEK . Dasatinib regulated transcripts for a different set of loci in the ‘classical’ pathway , where it targets EGFR and Raf kinases . Dasatinib also regulated transcripts for loci in the KEGG p38/Jnk pathway , where it targets p38α , MLTK ( ZAK ) , and TGFβR2 . The potency of the transcript responses was consistent with known potencies for these targets ( [21] , [41] , see Figure 4A and Table S5 ) . For imatinib , dose-dependent effects have been invoked to explain discrepancies between pre-clinical studies [43] . In examining imatinib's transcriptional responses at 20 hours , we used the efficacious clinical plasma concentration of 3 µM [44] as a reference point . Of 245 response transcripts for imatinib ( 10% FDR , >1 . 5-fold change , Table S2 ) , only 44 have EC50s<3 µM ( Figure 5A , C ) . The remaining 201 response transcripts have EC50s>3 µM ( Figure 5B ) . The 201 response transcripts include 35 for endoplasmic reticulum-localized proteins such as XBP1 ( Figure 5C ) , supporting observations that in vitro treatments with imatinib at 5 µM affect the function of this compartment [45] . Pathway analysis identified enrichment of transcripts for the seven KEGG pathways ( P<0 . 0001 ) shown in Figure 5D . Transcripts with EC50s>3 µM had enrichment for several pathways affecting lipid metabolism , indicating distinct biological impacts as the dose increases beyond the required clinical range . Submicromolar doses of imatinib , dasatinib and nilotinib did not produce shared effects on transcription ( Text S1 ) , indicating that their shared potent inhibition of cellular targets such as ABL and PDGFR [23] , [41] does not provoke transcriptional responses in the A549 cell line . In the micromolar dose range , comparison of transcriptional responses indicated shared effects at 20 hours ( Text S1 ) . Pathway analysis indicated both shared and compound-specific effects in this dose range ( Text S1 ) . Pathway analysis found that a significant number of genes involved in the cell cycle ( KEGG pathway hsa04110 ) were represented in the transcriptional responses to dasatinib and nilotinib but not imatinib at 20 hours ( Figure 6A and Table S4 ) . The broad peak for dasatinib reflects impact at two distinct dose regions , based on the distribution of EC50s for individual response transcripts ( Table S6 ) . The first group of 44 response transcripts has EC50s around 10 nM and includes the DNA helicase complex , cyclins and CDKs ( Figure 6B , C ) . The second group of 35 response transcripts has EC50s in the micromolar range and includes known transcriptional targets of p53: GADD45 ( Figure 6C ) , CDKN1A and Stratifin [46] , PCNA and MDM2 . Pathway analysis also confirmed dasatinib's impact on DNA replication ( KEGG:hsa03030 ) and p53 signaling ( KEGG:hsa04115 ) ; Table S4 ) . By contrast , nilotinib impacts the cell cycle pathway in one dose region: 62/63 of the response transcripts have EC50s in the micromolar range ( Table S6 ) . While nilotinib also regulates transcripts for the DNA helicase complex , cyclins and CDKs , it does not have a significant impact on the p53 signaling pathway ( Figure 6B , C ) . To compare these findings from transcriptional profiling with typical cell-based assays for proliferation , we treated our A549 cell line with the 12-point dose range of dasatinib , imatinib , and nilotinib and assayed the ATP content of samples ( a surrogate for cell number ) at 96 hours , and the DNA content of dasatinib and nilotinib samples ( a surrogate for cell-cycle stage ) at 23 hours . The results reflected our findings from pathway analysis: dasatinib and nilotinib inhibited proliferation and decreased the S phase population , whereas imatinib did not ( Figure 6D , E ) . Importantly , the potency in these conventional measures of cell cycle impact agreed with the EC50s of transcripts for the DNA helicase complex , cyclins and CDKs ( EC50s around 10 nM for dasatinib and 6 to 9 µM for nilotinib ) .
This paper is the first description of a systematic application of genome-wide transcriptional profiling as a traditional dose-response assay . Since most compounds act on multiple targets with different potencies , target-specific effects of a compound may not be distinguished by the limited dose selection of a typical transcriptional profiling experiment . We show that with a dose-response study design that uses just 12 arrays per compound , one can use the existing technology in a more informative way , and establish connection to other cellular dose-response assays . We present new algorithms and visualization methods that allow one to identify , compare , and characterize transcriptional responses . Sigmoidal dose responses are usually identified by iterative nonlinear regression methods [29] . SDRS applies nonlinear regression in a grid search , and performs equally well . It is robust against natural variability , and will be amenable to identifying dose responses in other sources of quantitative data . The most important benefit of SDRS over iterative regression or clustering methods is obtaining the fitting statistic ( F ) across the dose range for each transcript . This provides a moving window to evaluate the transcriptome's coordinated responses across the dose range . The full set of F-statistics permitted the further methods we present , which easily characterize and compare the overall transcriptional dose response with statistical rigor . Our results demonstrate that transcription profiling has many of the properties of traditional dose-responsive bioassays that have been used for decades [47] . The ability to combine dose information from diverse bioassays with dose-dependent pathway analysis proves valuable in connecting transcriptional responses with targets . For example , the MEK-specific inhibitor PD0325901 produced a significant transcriptional response at the known cellular potency for MEK inhibition [33] , and had no further effect on transcription until micromolar doses . In contrast , the transcriptional response to dasatinib had multiple EC50s , consistent with its known activities on multiple targets [21] . Nonetheless pathway analysis allowed us to map cellular processes to distinct regions of the dose range , and connect them to likely kinase targets identified in other dose-response cellular assays [41] . Connection to discrete kinase inhibition events can be refined by numerous methods: kinases not expressed in the experimental cell line can be excluded from lists of hits from biochemical assays , and comparisons can be made with transcript response profiles for compounds that have overlapping target spectra , or with profiles generated following siRNA ablation of kinase targets . The power of a dose response study design stems from the ability to rigorously compare pharmacological parameters across assays [48] . For example , we show that PD0325901 affects STAT-regulated transcripts with an EC50 of 1 nM , the same potency as its cellular activity on MEK [33] , and this connectivity provides compelling support for earlier reports that the ERKs are STAT kinases [34] . We also show connectivity between the EC50s for transcriptional effects on cell cycle genes by dasatinib and nilotinib , and their EC50s in conventional cell cycle and proliferation assays . Such connectivity should be applicable across diverse cell lines: while it is always possible for drug potency to be modulated by extraneous factors , studies of these kinase inhibitors across multiple cell lines ( e . g . [33] , [42] ) support the biochemical prediction that a single site-binder has a defined potency for its target . Transcriptional dose responses can separate the biological effects of a multi-target compound . Whereas a microarray experiment at a single micromolar dose should identify dasatinib's impact on both DNA replication and p53 signaling , only a dose-response design revealed that the impact on the p53 pathway occurs at a micromolar dose , and thus is irrelevant to dasatinib's nanomolar anti-proliferative potency . Transcriptional dose responses also allow a more meaningful comparison to clinical parameters such as plasma concentrations observed in treated patients ( 200 nM for dasatinib [49]; up to 3 µM for imatinib [44]; 3 . 6 µM for nilotinib [50] ) . With regard to imatinib , our observation of numerous additional transcriptional responses as dosing increases through the micromolar range supports the assertion [43] that dosing level is critical in evaluating the relevance of in vitro assays or pre-clinical models to imatinib's clinical effects . There are some limitations to the current study . First , it is impossible to measure all possible dose-responsive treatment effects in a single cell line , as not all targets are functional . In the non-ABL dependent cell line A549 , we cannot evaluate and compare the on-target activity of the three clinical ABL-inhibitors . Second , the pathway analysis is limited by the quality of annotation [30] . Third , not all dose responses fit a single sigmoidal model [51] . The clustering analysis we apply for quality control occasionally reveals treatments in which groups of transcripts show sigmoidal induction at low doses but have sigmoidal down-regulation at high doses or vice versa ( Text S1 ) , presumably due to action on a second , counteractive target or process . Such behavior could be routinely identified and quantified by substituting the data model in the SDRS algorithm , permitting the subsequent analytical approaches described in this work . In summary , we have developed new methods that enable interpretation of transcriptome behavior , including dissection of dose-dependent activities , fine differentiation between compounds , and connection with other biochemical and cellular assays . This analytical method has application at all the points of drug development where transcription profiling is currently used . In early discovery , we have compared transcriptional effects of target knockdown to the dose responses for lead compounds . As lead compounds are developed , we routinely compare the dose-dependent effects of diverse chemotypes to identify the on-target biology . Development candidates can be more clearly differentiated from a first-in-class compound , and the relative potency of an off-target activity is valuable information when assessing its importance . On transition to the clinic , transcriptional biomarkers have added validity when they are connected to target biology by a clear dose response . Investigations of approved drugs have successfully used transcriptional profiling to clarify biology ( e . g . [4] ) ; such studies can only be facilitated by a more precise linkage between dose and effects . Ultimately , dose response strategies could be combined with a compendium database of response profiles [17] , [52] , enabling rapid cellular categorization of new compounds .
Imatinib Cat . # PKI-IMTB-010 was purchased from Biaffin GmbH & Co KG ( Cat . # PKI-IMTB-010 ) . Nilotinib , PD-0325901 and dasatinib were provided in pure form ( > = 98% ) by Bristol-Myers Squibb Chemistry Division . Compounds stocks in DMSO ( 10 mM ) were stored at −20°C and diluted in culture media before addition to cells . Assays were performed in triplicate . A549 cells were seeded in the interior 48 wells of 96-well plates at a density of 1×104/cm2 in 180 µl of media , 4 hours prior to treatment with DMSO vehicle or a 11-point dose range of compound ( 30 µM with 3-fold dilutions down to 0 . 51 nM; final concentration of DMSO vehicle was 0 . 5% for all treatments ) for 96 hours . ATP content was assayed using the CellTiter-Glo Assay ( Promega , Madison , WI ) with a Victor plate luminometer ( Wallac , Turku , Finland ) . A549 cells were seeded in 6-well cell plates at a density of 1×105/ml in 2 ml of media , 16 hours prior to treatment with DMSO vehicle or a 12-point dose range of compound ( 30 µM with 3-fold dilutions down to 0 . 17 nM; final concentration of DMSO vehicle was 0 . 5% for all treatments ) for 23 hours . Both attached and detached cells were recovered , fixed with 0 . 25% ultrapure formaldehyde ( Polysciences #04018 ) in dPBS ( Ca2+ Mg2+-free; Invitrogen #14190 ) followed by 80% methanol . Cells were stained with dPBS/1%BSA containing propidium iodide ( 5 µg/ml; Sigma #P4864 ) and RNAse ( 1 µg/ml ) for 30 minutes at RT in the dark . The samples were run on the FACSCanto with Diva 6 . 1 . 1software ( Becton Dickinson ) , and data was analyzed using FlowJo 8 . 5 . 3 . The experiment was performed twice ( Table S7 ) . All handling was performed in 96-well format . Positions of the 12 levels of each treatment were randomized using an experimental design that prevented row or column effects being confounded with dose effect . A549 cells were cultured at 37°C in RPMI1640 media containing 10% heat-inactivated Fetal Bovine Serum ( Mediatech , Manassas , VA ) . Cells were seeded at 1 . 7×105/cm2 16 hours prior to treatment with vehicle or a 12-point dose range of compound ( 30 µM with 3-fold dilutions down to 0 . 17 nM; final concentration of DMSO vehicle was 0 . 5% for all treatments ) . Cells were lysed with 1× Nucleic Acid Purification Lysis Solution ( Applied Biosystems , Foster City , CA ) at 4 hours or 20 hours . Total RNA was extracted using the Prism 6100 ( Applied Biosystems , Foster City , CA ) , purified by RNAClean Kit ( Agencourt Bioscience Corporation; Beverly , MA ) , and evaluated on a 2100 Bioanalyzer ( Agilent Technologies , Santa Clara , CA ) . cRNA preparation and hybridization on HT-U133A 96-array plates followed manufacturer's protocols ( Affymetrix , Santa Clara , CA ) . The CEL files were analyzed with the robust multi-array analysis ( RMA ) algorithm [53] , obtained from www . bioconductor . org . Following quality control and removal of non-expressed probesets ( Text S1 ) , RMA values were reverse-logged ( base 2 ) for use in sigmoidal dose response curve fitting . See also Supplementary Methods ( Text S1 ) . A standard four parameter dose response model was used to model gene expression changes in response to varying compound concentration: Y = A+ ( B−A ) / ( 1+ ( X/C ) D ) , where Y is the signal intensity value , X is compound concentration , C is the EC50 , D is the slope factor , and A and B correspond to the signal intensity at low and high plateau of the curve , respectively . The approach can be viewed as a grid search , where a series of 542 values for C , distributed across the experimental dose range , are tested for every probeset on the array . For each probeset , ranges for A and B are based on signal levels in the treatment dataset . At each value of C tested , the algorithm evaluates 10 , 240 models against the experimental data . Goodness of fit is measured by an F-statistic: F = MSR/MSE where MSR is the mean square of the variance explained by the model and MSE is the mean square of error ) . For every probeset , at every C tested , the highest F-statistic and the corresponding A , B , D parameters are recorded . Given the normal distribution of residuals , the F-statistic follows an F-distribution , F ( p-1 , n-p ) , where n is the number of experimental dose points and p is the number of parameters in the model ( i . e . 4: A , B , C , D ) . Note that the number of dose points is the most important influence on the degrees of freedom . A probeset was designated as fitted to a sigmoidal curve and corresponded to a ‘response transcript’ if its global maximal F-statistic ( i . e . best fit ) was larger than the critical F ( 95% significance level , i . e . the F-distribution table was consulted for 95 percentile with numerator degree of freedom of p-1 and denominator degree of freedom of n-p ) . For each response transcript , the values of A , B , C and D that gave rise to the maximal F-statistic define the optimal model and the predicted EC50 . Thus the estimated EC50 presented in the SDRS report is selected from 542 possible values for C . After SDRS , each probeset is associated with an F-statistic at each of the 542 test values of C . For use in further analytical methods including data visualization , the results at a subset of 79 log-evenly distributed values for C were selected as ‘Summary Doses’ ( i . e . data reduction from 542 lists to 79 lists ) . Each F-statistic was converted to the associated P-value . For each Summary Dose list the number of response transcripts ( i . e . probesets ) whose P value passed an FDR cutoff was calculated , using 1% increments from FDR = 1% to FDR = 35% , resulting in a 35×79 matrix . This FDR correction used the Simes procedure , which employs a series of linearly increasing critical values [54] and has been shown to control the FDR at pre-specified levels for independent test statistics [55] . All comparisons of lists were based on Fisher's exact test ( FET ) using the right test , which evaluates the significance of the intersection between two lists for positive association i . e . an enrichment of elements of list A in list B or vice versa [56] . Comparisons between two compounds were performed at each possible pair of Summary Doses ( one from each compound ) , using the lists generated by the FDR procedure described above . ( Note that there are as many as 35 distinct probeset lists for each compound at each of the 79 Summary Dose values ) . For each of the Summary Dose pairs , the lowest P value from the ( maximally ) 35×35 FETs was retained . The resulting 79×79 matrix is visualized as a heat map , where the depth of the color is proportional to the negative logarithm ( base 10 ) of the P value . Pathway analysis was performed using the lists generated by the FDR procedure described above . Probesets were consolidated to single gene loci to eliminate redundancy . ( Note there are as many as 35 distinct gene sublists at each of the 79 Summary Doses ) . Each such gene list was evaluated by FET against a pathway gene list , and the lowest P value from the ( maximally ) 35 comparisons at each of the 79 Summary Dose values was retained . For comparisons that met a P<0 . 001 criterion , the resulting 79-point dataset for each pathway of interest was plotted to examine significant enrichment for pathway genes as a function of the dose range . | Transcriptional profiling is arguably the most powerful hypothesis-free method for investigating biological effects of drugs—so why do the experiments typically use outmoded single-dose designs ? Such single-dose experiments will co-mingle effects that can occur with different potency ( e . g . , effects on the known target versus effects on additional undesired targets ) . Single-dose experiments have little comparability to the dose-response bioassays , which are now used throughout the drug discovery processes . One reason for the disparity between experimental approaches is that existing analytical methods for dose-response bioassays can't cope with the dimensionality of microarray data: a typical bioassay is optimized for one response , then used to run a screen against thousands of compounds; whereas transcriptional profiling measures thousands of non-optimized responses to a single compound . Conversely , existing methods for microarray data analysis can identify patterns , but provide no quantitative dose-response information . To overcome these problems , we developed novel algorithms and visualization methods that allow anyone to apply transcriptional profiling as a conventional dose-response assay . The approach provides far more information than limited-dose designs , yet is economical ( 12 arrays/compound ) . With this new analytical framework , it is now possible to identify distinct transcriptional responses at distinct regions of the dose range , to link these impacts to biological pathways , and to make realistic connections to drug targets and to other bioassays . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"biochemistry/drug",
"discovery",
"genetics",
"and",
"genomics/gene",
"expression",
"biotechnology/chemical",
"biology",
"of",
"the",
"cell",
"pharmacology/drug",
"development",
"molecular",
"biology/bioinformatics",
"computational",
"biology/systems",
"biology"
]
| 2009 | Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities |
The epithelium efficiently attracts immune cells upon infection despite the low number of pathogenic microbes and moderate levels of secreted chemokines per cell . Here we examined whether horizontal intercellular communication between cells may contribute to a coordinated response of the epithelium . Listeria monocytogenes infection , transfection , and microinjection of individual cells within a polarized intestinal epithelial cell layer were performed and activation was determined at the single cell level by fluorescence microscopy and flow cytometry . Surprisingly , chemokine production after L . monocytogenes infection was primarily observed in non-infected epithelial cells despite invasion-dependent cell activation . Whereas horizontal communication was independent of gap junction formation , cytokine secretion , ion fluxes , or nitric oxide synthesis , NADPH oxidase ( Nox ) 4-dependent oxygen radical formation was required and sufficient to induce indirect epithelial cell activation . This is the first report to describe epithelial cell-cell communication in response to innate immune activation . Epithelial communication facilitates a coordinated infectious host defence at the very early stage of microbial infection .
Intestinal epithelial cells line the enteric mucosal surface and provide a physical barrier to maintain the integrity of this vulnerable body surface and prevent invasive infection by luminal microorganisms . Like professional immune cells , intestinal epithelial cells express receptors of the innate immune system such as Toll-like receptors ( TLR ) or nuclear oligomerization domain ( NOD ) -like receptors ( NLR ) [1] , [2] . Recognition of microbial structures leads to epithelial production of antimicrobial effector molecules and proinflammatory chemoattractive mediators . Thus , it facilitates an active role in the initiation of the mucosal host response [3] , [4] , [5] . The recruitment of professional immune cells to the site of infection occurs within hours and provides a highly efficient dynamic mechanism of the epithelial host defence . It remains unclear , however , how low number of pathogenic microorganisms as well as the limited spectrum and only moderate amount of chemokine secretion per epithelial cell facilitates stimulation of an effective host defence . We therefore hypothesized that a horizontal intercellular communication between intestinal epithelial cells might help to induce a coordinated epithelial response towards infectious challenge and thereby to amplify the epithelial innate host defence . Listeria monocytogenes is an important human pathogen that causes meningitis , sepsis , and abortion in susceptible individuals . It is acquired with food such as unpasteurized milk and cheese and enters the body following penetration through the intestinal epithelial barrier . The microbial pathogenesis and the bacteria-host cell interaction of this facultative intracellular bacterium has been studied for many years [6] . L . monocytogenes induces its own internalization and subsequently lyses the endosomal membrane of its host cell by the secretion of listeriolysin O ( LLO ) and phospholipases , thus gaining access to the cytosolic space . Here , Listeria upregulates polar expression of ActA that recruits and polymerizes host actin filaments resulting in propulsive locomotion . Together with LLO and the phospholipases this allows to enter neighbouring cells and to spread within the epithelial cell layer . Importantly , recognition of Listeria by the epithelial innate immune system only occurs after internalization and lysis of the endosomal membrane through cytosolic innate immune receptors [7] , [8] , [9] , [10] . Since infection of individual cells can be traced using reporter gene technology , L . monocytogenes provides an excellent model to study cellular responses in respect to immune recognition at the single cell level . In the present study , we analyzed innate immune recognition and epithelial responses at the single cell level using the model of Listeria infection of polarized intestinal epithelial cells in addition to transfection and microinjection . We present the surprising finding that non-infected epithelial cells were the main source of chemokine secretion in response to bacterial challenge . We identify oxygen radical species produced by NADPH oxidase ( Nox ) 4 in response to cytosolic bacteria to facilitate horizontal intercellular communication and chemokine production by non-infected cells . These results provide the first experimental evidence for a yet unknown mechanism of intercellular communication between epithelial cells in response to innate immune stimulation and thus significantly broaden our understanding of mucosal innate host defence .
Infection of a confluent monolayer of intestinal epithelial m-ICcl2 cells with wild-type ( wt ) L . monocytogenes induced rapid cellular activation illustrated by secretion of the proinflammatory chemokine Cxcl-2 ( Fig . 1A ) . Strong epithelial activation was only observed using wt Listeria able to reach the cytosolic space ( Fig . 1B ) facilitating recognition by cytoplasmic innate immune receptor molecules ( Fig . 1C ) [7] , [8] , [9] , [10] . Bacterial mutants unable to lyse the endosomal membrane such as isogenic hly or hly/plcA/plcB triple mutants as well as heat inactivated bacteria exhibited a significantly reduced or even absent epithelial activation ( Fig . 1B and D ) . Of note , lack of hly or hly , plcA , and plcB expression did not affect bacterial invasion or intracellular viability ( Fig . S1A ) . Endosomal lysis-dependent stimulation of L . monocytogenes infected epithelial cells was also observed using flow cytometry . A time-dependent increase of the number of Cxcl-2+ and Cxcl-5+ epithelial cells was detected after infection with wt Listeria ( Fig . 1E ) . In contrast , a strongly reduced number of epithelial cells stained positive for Cxcl-2 after infection with hly mutant Listeria ( Fig . 1F ) . In accordance with the published literature , internalization-dependent activation was observed in epithelial cells , but not in macrophages ( Fig . S1B ) . These results suggested that activation of epithelial cells occurred primarily in directly Listeria-infected cells . To monitor Listeria infection and cellular activation simultaneously at the single cell level , bacteria transformed with a vector expressing green fluorescence protein ( GFP ) either under control of the inducible actA promoter [11] or the constitutive sod promoter [12] were used for subsequent experiments ( for details see Table 1 ) . Surprisingly , flow cytometry revealed that the vast majority of Cxcl-2+ epithelial cells ( 95% ) were Listeria-negative . In addition , only a minor fraction of GFP-positive , Listeria-infected epithelial cells exhibited MIP-2 synthesis ( Fig . 2A ) . Similar results were obtained using biotinylated Listeria ( Fig . S2A ) . These results were confirmed by immunohistological staining . Cxcl-2 and Cxcl-5 synthesis was not restricted to GFP+ Listeria-infected cells but , in fact , predominantly detected in neighbouring non-infected epithelial cells ( Fig . 2B and Fig S2B ) . Also flow cytometric cell sorting and quantitative RT-PCR analysis strongly supported this unexpected result . A marked upregulation of Cxcl-2 and Cxcl-5 mRNA expression was detected in GFPlow expressing ( Listeria-negative , Fig . S2C ) epithelial cells despite the absence of detectable Listeria DNA ( Fig . 2C ) . Thus , although epithelial activation requires lysis of the endosomal membrane and contact with cytosolic innate immune receptors , a transcriptional cellular response was mainly observed in non-infected cells . Several mechanisms might account for the observed activation of Listeria-negative epithelial cells . Activated epithelial cells might only appear to be Listeria-negative due to secondary bacterial escape facilitated by propulsion through ActA-induced actin polymerization in the cytosol and subsequent invasion of the neighbouring cell . Cells primarily infected but secondarily left by lateral spread might thereby appear Listeria-negative but in fact would have been previously in contact with cytosolic bacteria ( and thus were , in fact , directly activated ) . To avoid lateral cell-to-cell spread and restrict intraepithelial bacteria to apically infected cells , a Listeria actA mutant strain was employed . ActA-deficient Listeria exhibited a moderately reduced epithelial invasion ( Fig . S3A ) , a lower percentage of infected epithelial cells , and an enhanced number of bacteria per cell ( Fig . 3A and 3B ) . Nevertheless , high numbers of Cxcl-2 producing epithelial cells ( Fig . 3B ) and a strong chemokine secretion ( Fig . 3C ) was observed . Also , the number of activated , Cxcl-2 producing epithelial cells remained significantly higher than the number of Listeria-infected cells reaching approximately 10-fold excess of Cxcl-2+ cells ( Fig . 3B and Fig . S3B ) . Thus , indirect activation of epithelial cells was not due to escape from previously infected cells by ActA-driven secondary lateral spread . Epithelial cell stimulation could also be induced by bacteria either attached to the plasma membrane or remaining intraendosomal and membrane enclosed . To exclude a significant role of attached or intraendosomal Listeria , Cxcl-2+ and GFP+ epithelial cells were quantified after infection with Listeria expressing GFP either constitutively under control of the superoxide dismutase promoter ( Psod-gfp ) or inducible under the control of the actA promoter ( PactA-gfp ) ( Table 1 ) . Whereas Psod-gfp carrying Listeria exhibited strong reporter expression after growth in bacterial culture medium , only a moderate fluorescence was detected in PactA-gfp –positive bacteria ( Fig . S3C ) . In contrast , strong GFP expression was noted in PactA-gfp Listeria isolated from infected epithelial cells ( Fig . S3D ) . Flow cytometric detection of infected epithelial cells was observed after wt , but not hly mutant PactA-gfp Listeria illustrating the endosomal lysis-dependent induction of the actA promoter-driven GFP reporter gene expression ( Fig . S3E ) . Infection with Psod-gfp or PactA-gfp carrying wt Listeria resulted in a significant number of Listeria-infected epithelial cells . Importantly , a higher number of Cxcl-2+ activated cells as compared to Listeria-infected cells was observed by flow cytometry after infection with both reporter constructs and epithelial Cxcl-2 synthesis was similarly noted in Listeria-negative epithelial cells ( Fig . 3D ) . These results suggest that indirect epithelial activation was not a result of attached or intraendosomal bacteria [13] . Finally , the activation of Listeria-negative epithelial cells might be due to the stimulatory effect of secreted bacterial molecules , such as the cytolytic listeriolysin O ( LLO ) [14] , [15] . Therefore , the membrane damaging as well as the stimulatory effect of recombinant listeriolysin ( rLLO ) on red blood cells ( RBC ) and epithelial m-ICcl2 cells was analysed . High concentrations of rLLO induced significant hemoglobin and detectable lactate dehydrogenase ( LDH ) release by RBCs and epithelial m-ICcl2 cells , respectively ( Fig . S3F and Fig . S3G ) . Quantitation of epithelial cell activation in response to rLLO , however , revealed an only minor response as compared to epithelial Cxcl-2 secretion after viable wt L . monocytogenes infection ( Fig . 3E ) . Similarly , no significant Cxcl-2 secretion by epithelial cells was noted in response to bacteria-free culture supernatant derived from Listeria cultures with bacterial counts precisely corresponding to the infection model described above ( Fig . 3F ) . Yet , culture supernatants derived from wild-type or ActA-deficient bacteria exhibited significant hemolytic activity , in contrast to supernatant from hly-deficient Listeria ( Fig . S3H ) . No significant membrane damage was noted after infection of intestinal epithelial cells with wt , actA , or hly mutant Listeria ( Fig . S3I ) . Although a supportive effect of released bacterial factors cannot be excluded , these results suggest that bacterial mediators do not play a major role in the observed indirect epithelial activation . Thus , neither basolateral cell-to-cell spread nor membrane attachment , or the secretion of LLO in the cell culture supernatant appear to be responsible for indirect epithelial cell activation after L . monocytogenes infection . This suggests the presence of a previously unrecognized mechanism of epithelial intercellular communication in response to bacterial infection . To examine whether indirect epithelial stimulation by horizontal cell-to-cell communication might be a general effect of transcriptional activation of intestinal epithelial cells , a bicistronic expression vector encoding the NF-κB subunit RelA/p65 together with GFP under the control of a constitutive cytomegalovirus ( CMV ) promoter was employed ( Fig . S4A ) . Transient overexpression of RelA/p65 alone or bicistronic expression of RelA/p65 and GFP readily induced epithelial activation as illustrated by NF-κB reporter gene upregulation ( Fig . S4B ) and enhanced chemokine secretion ( Fig . S4C ) . Although RelA/p65-mediated Cxcl-2 production exhibited a slower kinetic as compared to following Listeria infection , a significant number of Cxcl-2+ cells was detected . Of note , RelA/p65-mediated cellular activation was restricted to GFP+ , i . e . directly activated epithelial cells ( Fig . S4D ) . Cxcl-2 production by GFP+ cells increased strongly ( 0 . 1 versus 2 . 4% ) , whereas the number of Cxcl-2+ cells in the GFP- population remained virtually unchanged ( 0 . 8% versus 1 . 2% ) . In addition , the number of Cxcl-2+ epithelial cells did not exceed the number of transfected GFP+ cells at any time ( Fig . S4E ) . Thus , epithelial activation per se does not induce indirect cell activation by horizontal intercellular communication . Indirect epithelial activation appears rather to be induced by innate immune signal transduction upstream of transcription factor activation . Next we investigated the mechanism underlying horizontal cell-to-cell communication and coordinated epithelial chemokine upregulation in response to Listeria infection . Functional gap junctional transport was examined by microinjection of transferable Lucifer Yellow together with non-transferable high molecular weight dextran . Fluorescence imaging visualized transport of Lucifer Yellow from the microinjected cell to the surrounding neighbouring cells . Addition of inhibitors of gap junctional transport , effectively reduced lateral diffusion of Lucifer Yellow after microinjection ( Fig . 4A and B ) . Inhibition of gap junctional intercellular communication , however , did not decrease the number of activated epithelial cells after Listeria infection as illustrated by the unaltered high ratio of activated ( Cxcl-2+ ) to infected ( GFP+ ) epithelial cells measured by flow cytometry ( Fig . 4C ) . Although these results do not completely rule out transfer of very small signaling molecules by gap junctional transport channels , they do not support a major role in the process of horizontal communication . Similarly , the potential role of a secreted protein messenger was examined . Intestinal epithelial m-ICcl2 cells were exposed to brefeldin A ( BFA ) , an effective inhibitor of the secretion of newly synthesized proteins ( Fig . S4F ) , prior and after infection with actA mutant L . monocytogenes . The number of Listeria-induced Cxcl-2+ cells , however , was not altered irrespective whether BFA was administered 30 min prior or 60 min after infection ( Fig . 4D ) . Second , cell culture medium was obtained 10 , 20 , 30 , 40 , or 60 min after Listeria infection , centrifuged to remove bacteria , and immediately transferred to naïve uninfected epithelial cells . Yet no epithelial activation was observed after exposure to conditioned culture supernatant despite significant Cxcl-2 synthesis detected in the Listeria infected cell population ( Fig . S4G ) . Of note , factors released by Listeria-infected cells might be unstable or immediately bound to neighbouring cells preventing their efficient release in the conditioned cell culture supernatant . Finally , widely used pharmacological inhibitors of prostaglandin synthesis and known intestinal epithelial ion channels were employed . Indomethacin , an inhibitor of cyclooxygenase isoenzymes ( COX1 , COX2 ) involved in prostaglandin synthesis , thapsigargin , an inhibitor of the endoplasmatic Ca2+ATPase , CFTR II , a selective apical Cl− ion channel inhibitor , and bumetanide , an inhibitor of a basolateral epithelial Na+K+Cl− cotransporter had no significant influence on the number of activated epithelial cells after Listeria infection illustrated as ratio of activated ( Cxcl-2+ ) to infected ( GFP+ ) cells ( Fig . S4H ) . These results do not identify a significant role of gap junctional transport , secreted protein or prostaglandin mediators , or ion fluxes in the observed indirect activation of epithelial cells after L . monocytogenes infection . Since unstable and highly reactive host-derived factors were not excluded by the previous experiments , a possible involvement of oxygen or nitrogen radicals in horizontal epithelial cell-cell communication was subsequently evaluated . Expression of members of two enzyme families , NADPH oxidases and nitric oxide synthase ( NOS ) , has been described in epithelial cells [16] . Indeed , addition of the NADPH oxidase inhibitor diphenylene iodonium ( DPI ) resulted in a significant reduction of Listeria-induced epithelial activation ( Fig . 5A ) . DPI did not reduce Listeria survival in epithelial cells ( Fig . S5B ) and had no effect on LPS or PMA-induced epithelial activation ( Fig . S5A ) . In contrast to DPI , the NOS inhibitor N ( G ) -nitro-L- arginine methyl ester ( L-NAME ) did not influence the number of activated epithelial cells ( Fig . 5B ) . In accordance with an inhibitory effect of DPI , synthesis of reactive oxygen intermediates ( ROI ) after Listeria infection was observed ( Fig . 5C ) . ROI was detected in focal areas of confluent epithelial cells surrounding Listeria-positive , infected cells in accordance with local production and lateral spread of ROI as early as 10 min after infection ( Fig . 5D ) . Innate immune receptor stimulation by Listeria infection of epithelial cells resulted in rapid activation of the mitogen-activated protein ( MAP ) kinase Erk in a ROI-dependent manner ( Fig . 5E ) . Whereas impairment of the MAP kinase Erk had no significant effect on ROI production ( Fig . 5F ) , Listeria-induced Cxcl-2 synthesis by intestinal epithelial cells was completely abrogated by Erk inhibition and partially also dependent on the MAP kinases p38 and JNK ( Fig . 5G ) . Of note , Erk inhibition did not affect bacterial invasion and the viability of intracellular Listeria ( Fig . S5B ) . Finally , exposure of epithelial cells to cumene hydroperoxide , a ROI liberating organic agent within the cell culture medium or by microinjection induced Cxcl-2 synthesis in neighbouring cells similar to L . monocytogenes infection ( Fig . 5H ) . Thus , Listeria-infection induces significant epithelial ROI synthesis , which in turn mediates MAP kinase Erk activation and downstream Cxcl-2 production . Oxygen radical synthesis is performed by an oligomeric protein complex involving a cell type-specific NADPH-oxidase ( Nox ) protein . Only significant expression of the Nox4 isoform was detected in primary small intestinal epithelial cells ( Fig . 6A ) . Nox4 synthesis was restricted to intestinal epithelial cells as demonstrated by immunostaining with a paranuclear expression pattern in accordance with a previous report ( Fig . 6B ) [16] . Importantly , downregulation of Nox4 expression in epithelial cells by siRNA interference significantly reduced Cxcl-2 secretion ( Fig . 6C ) and ROI production upon Listeria-infection ( Fig . 6D ) . In contrast , downregulation of Nox4 expression did not alter LPS- or PMA-induced chemokine secretion ( Fig . S6 ) . Thus , ROI production by Nox4 appears to be both necessary and sufficient to induce horizontal cell-cell communication in intestinal epithelial cells leading to chemokine secretion in neighbouring cells in response to Listeria infection .
Communication between individual cells is a fundamental feature of multicellular organisms . For instance , it mediates a coordinated reaction of muscle cell contraction , and allows neuronal signal transmission or endocrinological regulatory circuits . Cell-cell communication is also characteristic for the complex regulatory networks of the adaptive immune system . Cytokines bridge anatomical distances to coordinate and amplify the host response against pathogens . In the present study we investigated whether cell-cell communication between neighbouring cells might also contribute to innate immune activation within a confluent epithelial cell layer to coordinate the antimicrobial host defence at an early stage of the infection . Although inhibition of the overall epithelial responses by interference with the production of soluble mediators and gap junction integrity had previously been noted , the process of immune stimulation and cellular response upon bacterial infection has not been studied at the single cell level [17] , [18] , [19] , [20] , [21] . The present study therefore represents the first report to demonstrate epithelial horizontal cell-cell communication upon bacterial innate immune stimulation . For three reasons , Listeria infection of confluent intestinal epithelial cells represents an ideal model to study epithelial cell-cell communication downstream of innate immune stimulation . First , similar to other pathogenic bacteria Listeria monocytogenes escapes from the endosomal vacuole and proliferates within the host cell cytosol [6] . Endosomal escape is associated with a dramatic change in bacterial gene expression . Although expressed at low levels also during in vitro culture , a very strong upregulation of the actin polymerizing protein ActA provides an excellent reporter for detection of cytosolic entry [22] . Second , bacteria lacking hly , plcA , or plcB mediating endosomal lysis only induce an only minor activation which might result from intraendosomal recognition or a so far unidentified minor mechanism of endosomal escape . Thus , in contrast to macrophages that recognize Listeria also at the plasma membrane , epithelial cell stimulation is mainly observed when bacteria reach the cytosol , facilitating contact with cytosolic innate immune receptors such as Ipaf , Nalp3 , and Nod2 [9] , [10] . This finding excludes innate immune recognition and receptor-mediated initiation of signal transduction in non-infected , Listeria-negative cells . Third , one amino acid exchange between the mouse and human E-cadherin causes a strongly reduced infection rate in murine epithelial cells [23] , leaving most cells of a confluent cell layer uninfected and accessible to the analysis of indirect cellular activation . Using reporter gene technology , intracellular chemokine staining and flow cytometric analysis , we were able to demonstrate that the chemokine secretion in response to Listeria infection is mainly derived from uninfected , indirectly activated epithelial cells . Of note , the commonly used quantification of cytokine secretion in the cell culture supernatant or immunoblotting of total cell lysate proteins would not have disclosed this surprising finding . Epithelial stimulation on the transcriptional level by p65/RelA overexpression did not result in detectable indirect cell activation . Several possible mechanisms of horizontal cell-cell communication downstream of innate immune receptor signaling were therefore considered . In response to microbial stimulation , epithelial cells produce chemokines , prostaglandins , and cause local alterations of ion concentrations by regulating transmembrane ion channel activity . Also , gap junctional intercellular communication ( GJIC ) represents a direct cytosolic connection and might be used to forward the information of innate immune recognition within the epithelial cell layer [17] , [21] . Ca2+ fluxes via intercellular gap junctions have been shown to promote lung epithelial chemokine secretion [18] and intact gap junction formation has also been linked to innate immune stimulation and maintenance of the epithelial barrier [19] . On the other hand , connexin-26 hemichannel-mediated Ca2+ signaling has also been proposed to promote bacterial invasion and lateral spread [24] . Yet , neither protein secretion , nor ion channel activity or gap junction formation appeared to be involved in Listeria-induced indirect epithelial cell activation . Instead , our results indicate an important role of reactive oxygen intermediates ( ROI ) in horizontal epithelial cell-cell communication . ROI represent reduction products of molecular oxygen such as the radical superoxide ( •O2− ) and hydroxyl ( •OH ) , and the non-radical hydrogen-peroxide ( H2O2 ) . ROI production by professional phagocytes during oxidative burst provides significant bactericidal activity but synthesis is also observed in non-phagocytic cells [25] . ROI at subtoxic doses has been recognized as an important intracellular signal transducing molecule during the recent years [26] , [27] , [28] , [29] . In accordance with our results ROI-induced activation of MAP kinase activity has been reported [30] , [31] , [32] , [33] . In addition , an involvement of ROI in the cellular signaling leading to NF-κB activation [34] , apoptosis [31] , [35] , epidermal growth factor receptor signaling [36] , regulation of cellular proliferation [37] , and antimicrobial peptide production [38] , [39] has been described . ROI was also shown to prime Drosophila melanogaster hematogenic progenitor cells for differentiation [40] and to play an important role in the fruit fly's intestinal immunity [41] . Whereas the half-life of oxygen radical hydroxyl ( •OH ) is extremely short ( 10−9s ) and the superoxide •O2− is membrane impermeable [40] , H2O2 is able to diffuse to neighbouring cells and induce cellular activation . Indeed , a tissue gradient of H2O2 was shown to induce rapid recruitment of leukocytes into the wound margin following endothelial hypoxia [42] , [43] . NADPH oxidase activation has previously been linked to innate immune mediated antimicrobial killing [25] as well as receptor signal transduction [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] . Here we for the first time report Nox4 expression by intestinal epithelial cells and demonstrate Nox4-mediated ROI production in response to bacterial infection . Although enhanced Nox4 mRNA expression was shown to result in increased ROI production [52] , the initiation of Nox4-dependent ROI production upon Listeria infection was noted as early as 5–10 minutes after bacterial challenge . This excludes a significant role of transcriptional regulation of Nox4 in our model . Whereas the prototypical NADPH oxidase of phagocytes , gp91phox ( Nox2 ) , requires cytosolic proteins such as p47phox to form a functional NADPH oxidase complex , Nox4 functions independent of cytosolic accessory proteins . Interestingly , Nox expression has previously been linked to innate immune receptor signaling: Nox4 activation was shown to be involved in TLR4-mediated NF-κB activation in human epithelial kidney cells and monocytes [47] , [53] . In contrast , our results revealed activation of intestinal epithelial cells by Listeria infection in a Nod2- , Ipaf- , and Nalp3-dependent fashion which was followed by ROI production and subsequent MAP kinase signaling . Our data are therefore in accordance with previous reports on MAP kinase activation after Nox4-mediated ROI production [54] , [55] . A future analysis of the local paracellular concentration of the different species of oxygen radicals might help to improve our understanding of the regulatory role of Nox4-mediated ROI production for epithelial cell-cell communication . Interestingly , reduced chemokine synthesis was noted in directly infected , Listeria-positive cells . These cells were also impaired to respond to secondary innate immune stimulation illustrating the immune evasive behaviour of L . monocytogenes ( data not shown ) . Although the underlying mechanism is currently not resolved , high concentrations of ROI were previously associated with reduced susceptibility to immunostimulatory agents [56] . Yet other bacterial or host factors such as antioxidant enzymes might reduce local ROI concentrations and interfere with cellular activation and chemokine production in infected epithelial cells . In conclusion , our data for the first time analyzed intestinal epithelial activation in response to bacterial infection at a single cell level . We could detect Nox4 expression by intestinal epithelial cells which facilitated rapid ROI production upon infection and paracrine activation of neighbouring cells ( Fig . 7 ) . Our findings thus identify horizontal cell-cell communication to allow a coordinated innate immune activation of the intestinal epithelium . The present work significantly broadens our knowledge on the complex processes that underlie mucosal innate immune stimulation and illustrates the specific role of epithelial cells for an efficient activation of the antimicrobial host defence .
Intracellular Cxcl-2 ( MIP-2 ) and Cxcl-5 was detected using rabbit antibodies from Nordic Biosite ( Täby , Sweden ) . The rabbit polyclonal anti-actin antiserum was from Sigma-Aldrich ( Taufenkirchen , Germany ) . The rabbit-anti-mouse Nox4 antiserum was obtained by immunization with recombinant peptide . The rabbit anti-p-p44/42 ( phospho-Erk ) and the mouse anti-p44/42 ( total-Erk ) was from Cell Signaling Technology ( Beverly , MA , USA ) . The rabbit anti-Listeria antibody was from Dunn Labortechnik GmbH ( Asbach , Germany ) . Cy5- , Cy3- , HRPO-conjugated secondary antibodies were from Jackson ImmunoResearch ( West Grove , PA , USA ) and the Alexa Fluor ( AF ) 488- , and AF 555-conjugated donkey anti-rabbit IgG ( H+L ) was from Invitrogen ( Molecular Probes ) . The MFP590- and MFP488-labelled phalloidin were purchased from MoBiTec GmbH ( Goettingen , Germany ) . The Sulfo NHS-LC-Biotin was obtained from Pierce , Thermo Scientific ( Rockford , IL , USA ) . Escherichia coli K12 D31m4 LPS was ordered from List Biological Laboratories ( Campbell , CA , USA ) . Recombinant LLO ( rLLO ) was expressed and purified exactly as described before [57] . rLLO was applied to cells in a serial dilution with 0 . 05 µg/mL as highest concentration . Cxcl-2 was quantified using an ELISA from Nordic Biosite or R&D Systems ( Quantikine , R&D Systems GmbH , Wiesbaden , Germany ) . The NF-κB reporter construct pBIIX-luciferase carrying two copies ( 2× NF-κB ) of the κB sequences from the Igκ enhancer was provided by S . Ghosh ( Yale University Medical School , New Haven , CT , USA ) . Luciferase activity was quantified with luciferin substrate ( PJK GmbH , Kleinblittersdorf , Germany ) . The bicistronic RelA/p65 expression plasmid was cloned by removing the nef gene from a pCG-nef-IRES-GFP expression plasmid ( provided by J . Muench , Institute of Virology , University Clinic of Ulm , Germany ) by digestion with the restriction enzymes XbaI and MluI ( Fermentas , St Leon-Rot , Germany ) and replacing it in frame with the p65 encoding gene amplified from a p65 expression plasmid ( obtained from by U . Pahl , University Clinic , Freiburg , Germany ) using the forward: 5′-ACC TCT AGA CCA TGG ACG ATC TGT TTC C-3′ and reverse: 5′-ACG ACG CGT GCA CCT TAG GAG CTG ATC TGA-3′ primers and digested with XbaI/MluI prior to ligation . Plasmid DNA for transfection was prepared using the endotoxin-free plasmid kit from Qiagen ( Hilden , Germany ) . Targeted siRNA probes ( Tlr2 , Rip2 , Nox4 , Card12 , Cias1 , control siRNA , ) were from Qiagen ( Hilden , Germany ) , the Card15 siRNA was from Santa Cruz ( Heidelberg , Germany ) . Lipofectamin 2000 ( Invitrogen , Carlsbad , CA , USA ) and INTERFERin ( Polyplus Transfection , New York , NY , USA ) were used for plasmid and siRNA transfection , respectively . The pharmacological inhibitors and radical donors oleamide , carbonoxolone , α-glycerrhetinic acid , brefeldin A , thapsigargin , CFTR inhibitor II ( CFTR II . ) , indomethacin , bumetanide , N- ( G ) -nitro-L- arginine methyl ester ( L-NAME ) , UO-126 , hydrogen peroxide ( H2O2 ) and cumene hydroperoxide were purchased from Sigma Aldrich . The p38 inhibitor 3-O-Acetyl-beta-boswellic acid and the L-stereoisomer JNK inhibitor 1 were from Enzo Life Sciences ( Lörrach , Germany ) , and diphenylene iodonium ( DPI ) from Cayman Chemical ( Hamburg , Germany ) . Defibrinated sheep red blood cells ( SRBC ) were purchased from Oxoid ( Basingstoke , UK ) . The LDH Cytotoxicity Assay Kit was from Cayman Chemical ( Hamburg , Germany ) . Colorimetric ( ELISA , LDH ) , luminescent ( luciferase ) and fluorescent ( ROI ) measurements were carried out using a Victor3 Multilabel Plate Reader ( Perkin Elmer , Waltham , MA , USA ) . Cell culture reagents were purchased from Invitrogen . All other reagents were obtained from Sigma Aldrich ( Taufkirchen , Germany ) if not stated otherwise . The m-ICcl2 small intestinal epithelial cell line has previously been described [58] . Cells were cultured in a modified , hormonally defined medium with DMEM and F12 ( vol 1∶1 ) supplemented with 5% FCS , 2% glucose , 20 mM Hepes , 2 mM glutamin , 5 µg/mL insulin , 50 nM dexamethasone , 60 nM sodium selenite , 10 ng/mL epithelial growth factor , 5 µg/mL transferrin , and 1 nM 3 , 3′ , 5-triiodo-L-thyronine sodium salt . Cell passages 42–70 were used . Cells were grown at 37°C in a 5% CO2 atmosphere on collagen-coated cell culture plates or chambers to reach a polarized , confluent monolayer . Rat tail collagen was ordered from Institut Jacques Boy ( Reims , France ) . Specific targeted or control siRNA was transfected at a final concentration of 10 nM 36 hours prior to functional analysis . Stimulation with lipopolysaccharide ( LPS ) was performed at a final concentration of 10 ng/mL . Listeria monocytogenes EGD wild-type ( wt ) , actA , hly deletion mutant strains and the hly/plcA/plcB triple mutant strain are described in Table 1 . Fluorescent bacteria were generated by transformation [59] with GFP expression vectors under the control of the actA or sod promoter ( PactA-gfp , Psod-gfp; Table 1 ) Bacteria were routinely grown in Brain Heart Infusion ( BHI ) broth , supplemented with antibiotics when required . Overnight cultures were diluted 1∶50 , grown to middle logaritmic phase ( OD600 ) with mild agitation at 37°C , washed , and added in cell culture medium at the multiplicity of infection ( m . o . i . ) of 100∶1 ( if not stated otherwise ) followed by centrifugation ( 1500 rpm , 5 min , 4°C ) . 60 minutes after addition of bacteria , epithelial monolayers were washed three times with PBS , and fresh medium containing 50 µg/mL gentamycin was added to the culture medium to restrict extracellular bacterial growth . Unless indicated otherwise , infections were completed after 4 h post infection . To quantify bacterial invasion , co-culture of 20 , 40 or 60 min was followed by 1 h incubation in fresh cell culture medium supplemented with 50 µg/mL gentamycin . For 4 h and 6 h infection , gentamycin was supplemented 60 minutes after addition of bacteria , and incubation was carried out for additional 3 h or 5 h . After washing , cells were lysed in 0 . 1% Triton/H2O and the number of intracellular bacteria was determined ( CFU ) by serial dilution and plating . Bacteria free conditioned medium were prepared by centrifugation or filtering of cell culture medium , and immediately applied on naïve , uninfected m-ICcl2 cells . The rabbit anti-Listeria antibody was used for immunolabelling of bacteria ( 1∶500 ) . For alternative intracellular detection of Listeria , bacteria were biotinylated prior to infection according to the manufacturers protocol . Pharmacological inhibitors were added 30 min prior to infection if not stated otherwise . For intracellular Cxcl-2 or Cxcl-5 visualization , brefeldin A ( 0 . 5 µg/mL ) was added to the cell culture medium 1 h after stimulation . Cells were fixed in 3% PFA and incubated with anti-Cxcl-2 or Cxcl-5 antiserum ( 1∶100 ) . Nox4 was detected in formalin-fixed sections of mouse small intestine by incubation with a rabbit anti-Nox4 antiserum ( 1∶100 ) for 1 h at room temperature , followed after washing by a TR-conjugated secondary antibody . Cells were mounted in Vectashield Mounting Medium with Dapi ( Vector Laboratories , Eching , Germany ) and visualized using a Leica DM IRB Inverted Research Microscope with a TCS SP2 AOBS scan head ( Leica Microsystems GmbH , Wetzlar , Germany ) . For fluorescent detection , immunolabelled Listeria was additionally stained with AF 555-conjugated secondary antibody prior to infection , or biotinylated bacteria were labelled by streptavidin-conjugated Cy3 . For flow cytometry cells were trypsinized and fixed in Cytofix ( BD Biosciences ) . Cxcl-2 or Cxcl-5 was stained following permeabilization in 0 . 5% saponin/1% FCS/PBS buffer . Analysis was performed on a FACS Calibur apparatus ( BD Biosciences ) . The data acquisition on GFP+ ( recorded in channel Fl-1 ) and Cxcl-2+ or Cxcl-5+ cells ( Cy5-conjugated , Fl-4 ) was restricted to a total number of 10 . 000 events . The data acquisition on GFP+ ( recorded in channel Fl-1 ) bacteria was restricted to a total number of 100 . 000 events . Flow cytometry cell sorting was performed using a MoFlo ( XDP Upgrade , Beckman-Coulter ) at the Cell Sorting Facility , Medical School , Hanover . Cell were lysed in 3∶1 WB/SB vol/vol ( WB: 50 mM Tris , pH 7 . 4 , 120 mM NaCl; SB: 250 mM Tris , pH 6 . 5 , 8% SDS , 40% glycerol; supplemented with a proteinase inhibitor cocktail [Complete Mini , Roche Diagnostics] ) . Samples were sonified and the protein concentration was determined ( DC Protein Assay; Bio-Rad Laboratories ) . Protein was separated on 11% acrylamide gels and blotted on nitrocellulose . Membranes were incubated overnight at 4°C with the primary antibody . Detection was performed using peroxidase-labelled goat anti–rabbit or goat anti-mouse secondary antibodies in combination with the ECL kit ( GE Healthcare ) . Before restaining , membranes were stripped for 45 min at 50°C in 62 . 5 mM Tris HCl , pH 6 . 7 , 100 mM ß-mercaptoethanol and 2% SDS , followed by three 15-min washing steps . Cells were divided after cell sorting . DNA extraction was performed following incubation in lysosyme ( 10 mg/mL ) , proteinase K ( 10 mg/mL ) , and 5% SDS using TRIzol ( Invitrogen ) according to the manufacturer's instruction . DNA was washed in sodium citrate ( 0 . 1 mM ) and precipitated in 75% ethanol . Listeria genomic DNA was detected by PCR ( Taq DNA polymerase from Invitrogen ) using primers specific for the listerial hly gene ( forward: 5′-ATG TAA ACT TCG GCG CAA CT-3′ , reverse: 5′-TCG TGT GTG TTA AGC GGT TT-3′ , annealing 57°C , cycles 35 ) . A fragment encoding eukaryotic hypoxanthine phosphoribosyltransferase ( Hprt ) was amplified using oligonucleotides 5′-TGC TGA CCT GCT GGA TTA CA-3′ and 5′-GCT TAA CCA GGG AAA GCA AA-3′ ( annealing temperature 59°C , cycles 32 ) as control . Amplification products were analysed on a 2% agarose gel and visualized with SYBR Safe ( Invitrogen ) . Total RNA was extracted using the RNeasy Protect Cell Mini Kit ( Qiagen ) and first-strand cDNAs was synthesized using oligo-dT primers . Real-time PCR was prepared with absolute QPCR ROX mix ( Thermoscientific ) , sample cDNA , intron-spanning forward and reverse primers , as well as the 6-carboxy-fluorescein-conjugated target probe provided in the commercial TaqMan gene expression assay for murine Hprt1 and Cxcl-2 or Cxcl-5 ( Applied Biosystems ) . Analysis were performed using an ABI PRISM Sequence Detection System 7000 ( Applied Biosystems ) . Samples were normalized to the endogenous control . Results were calculated by use of the Δ2-CT method and are presented as fold induction of target gene transcripts in stimulated relative to unstimulated controls . The fluorogenic probe 5- ( and-6 ) -carboxy-2′ , 7′-dichlorofluorescein diacetate ( DCF-DA , Invitrogen ) was used for reactive oxygen intermediates ( ROI ) visualization . Prior to stimulation , cells were incubated with DCF-DA ( 10 µg/mL ) for 30 min at 37°C . After stimulation or infection for 20 min cells were rapidly rinsed with PBS , fixed in 3% PFA , washed twice with PBS and analyzed by fluorescence microscopy . For quantitative analysis of oxygen radical production , cells were rinsed with PBS to remove the free probe , and lysed in 200µl of 1% Triton/H2O . The lysate was transferred into microcentrifuge tubes , sedimented at 8 , 000×g for 5 min at 4°C , and 100 µl aliquots were dispensed in 96-well plates in triplicate . The index of oxidation ( DCF ) was calculated as the ratio of fluorescence intensity as compared to an untreated control . Cells were grown in collagen-coated 8-well chamber slides ( Nunc , Rochester , NY ) continuously bathed in cell culture medium . The 70 kDa high molecular weight gap junction impermeant fluorescent compound Texas Red Dextran ( Molecular Probes , 10 mg/mL ) was mixed with either the <1 kDa low molecular weight Lucifer Yellow ( Molecular Probes , 10 mg/mL ) or 0 . 5 mM cumene hydroperoxide in injection buffer ( 25 mM HEPES , 125 mM K-acetate , 5 mM Mg-acetate , pH 7 . 1 ) . Fluorescent mixtures were loaded into individual Femtotips II injection capillars ( Eppendorf , Hamburg , Germany ) . Cells were transferred to a LSM 510 META laser scanning confocal microscope equipped with an inverted Axiovert 200M stand ( Carl Zeiss , Germany ) and single cell microinjection was performed by using an InjectMan NI2/Femtojet injector system at pi: 180 hPa , ti: 0 . 2s , pc: 25 hPa . A minimum of 10 microinjected cells were analyzed per experiment . To study gap junctional intercellular communication , cells were analysed by live imaging microscopy after 5 min incubation . For ROI donor cumene hydroperoxide stimulation , cells were incubated 1 h , washed , and incubated in prewarmed fresh cell culture medium for an additional 3 h in the presence of 0 . 5 µg/mL brefeldin A . Cells were fixed in 3% PFA and further analyzed by intracellular chemokine staining and fluorescence microscopy . All experiments were performed at least three times and results are given as the mean ± standard deviation ( SD ) of one representative experiment . Statistical analyses were performed using the Student's t test . A p value<0 . 05 ( * ) or <0 . 01 ( ** ) was considered significant . | All body surfaces are covered by a single layer of epithelial cells . Epithelial cells form a physical barrier to separate the underlying sterile tissue from the environment . In addition , epithelial cells actively sense bacterial and viral infection . The recognition of pathogenic microorganisms results in cell stimulation and the secretion of soluble mediators that attract professional immune cells to the site of infection . This first line host defence works very efficiently despite the often low number of pathogens and the limited amount of mediators secreted per epithelial cell . We therefore investigated whether infection of one individual epithelial cell would result in activation of other , non-infected cells within a confluent epithelial monolayer resulting in a more substantial host response . Indeed , using the model of the gut pathogen Listeria monocytogenes and monitoring infection and epithelial activation at a single cell level , we can clearly show that the epithelial response is mainly mediated by non-infected cells . Also , we identify oxygen radicals as potential mediators to facilitate horizontal epithelial communication upon immune stimulation . Our results thus provide a novel concept of a coordinated epithelial host response upon microbial infection facilitated by horizontal epithelial communication . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"immunology/innate",
"immunity"
]
| 2010 | Potentiation of Epithelial Innate Host Responses by Intercellular Communication |
Temporal lobe epilepsy strongly affects hippocampal dentate gyrus granule cells morphology . These cells exhibit seizure-induced anatomical alterations including mossy fiber sprouting , changes in the apical and basal dendritic tree and suffer substantial dendritic spine loss . The effect of some of these changes on the hyperexcitability of the dentate gyrus has been widely studied . For example , mossy fiber sprouting increases the excitability of the circuit while dendritic spine loss may have the opposite effect . However , the effect of the interplay of these different morphological alterations on the hyperexcitability of the dentate gyrus is still unknown . Here we adapted an existing computational model of the dentate gyrus by replacing the reduced granule cell models with morphologically detailed models coming from three-dimensional reconstructions of mature cells . The model simulates a network with 10% of the mossy fiber sprouting observed in the pilocarpine ( PILO ) model of epilepsy . Different fractions of the mature granule cell models were replaced by morphologically reconstructed models of newborn dentate granule cells from animals with PILO-induced Status Epilepticus , which have apical dendritic alterations and spine loss , and control animals , which do not have these alterations . This complex arrangement of cells and processes allowed us to study the combined effect of mossy fiber sprouting , altered apical dendritic tree and dendritic spine loss in newborn granule cells on the excitability of the dentate gyrus model . Our simulations suggest that alterations in the apical dendritic tree and dendritic spine loss in newborn granule cells have opposing effects on the excitability of the dentate gyrus after Status Epilepticus . Apical dendritic alterations potentiate the increase of excitability provoked by mossy fiber sprouting while spine loss curtails this increase .
Several evidences have shown that prolonged seizures such as those in the animal models of Status Epilepticus ( SE ) induced by kainic acid or pilocarpine [1] act as a strong insult with consequent anatomical and functional sequelae . The neuroplastic changes associated to these alterations include cell loss , dentate gyrus ( DG ) granule cells ( GCs ) dispersion , mossy fiber sprouting ( MFS ) , dendritic spine loss , neurogenesis with dendritic branching pattern alterations and the presence of newly generated cells in ectopic places [2]–[8] . Although there is still some controversy on how much each one of these alterations contributes to epileptogenesis , it is clear that multivariate interactions are needed for the full appearance of the chronic state with spontaneous recurrent seizures ( SRS ) [9] . Basically these models replicate cellular and molecular plastic alterations , besides epileptiform EEG features observed in clinical TLE [10]–[12] . In this scenario , some of the most studied phenomena are the plastic alteration of the circuits of the DG GCs . In fact , DG GCs present a series of morphological and anatomical alterations induced by temporal lobe epilepsy ( TLE ) [6] , [7] , [13]–[17] . Their axons sprout collaterals directed towards the DG molecular layer and make recurrent excitatory synapses with other GCs ( this phenomenon is called MFS ) [2] , [6] . The size of their soma and the shape of their apical dendritic trees are altered , exhibiting shorter and narrower dendrites [16] . Furthermore , those newly born neurons display a significant reduction in the number of dendritic spines [7] , [18] , [19] . In some cases , there is a basal dendrite that sprouts towards the DG hilus [7] , [17] . There are also GCs that migrate to ectopic places such as the hilus and granular cells layer [6] , [13] . In parallel with these morphological alterations , TLE also induces a higher rate of neurogenesis of DG GCs [20] , [21] . Mossy fiber sprouting is the most widely studied of the above mentioned alterations using different approaches including animal models [22] , [23] , cell cultures [24] , [25] , knockout genes [26] , [27] and computational models [28]–[30] . These studies showed that mossy fiber sprouting is an important factor contributing to the hyperexcitability of the hippocampal circuit [22] , [23] , [25] , [28] , [31] , [32] . On the other hand , the effects of the other alterations on the excitability of the DG remain unclear , in special the effects of alterations in dendritic morphology and the combined effects of the interacting factors . Neuronal morphology is an important factor in the determination of the electrophysiological behavior of a cell [33] , [34] . However , due to its inherent complexity , it is difficult to assess experimentally the effect of neuronal morphological alterations on the behavior of a cell or of the circuit in which it is embedded . Computational modeling enters here as a valuable tool to assess this effect and many studies have been concerned with the evaluation of the coupling between neuronal morphology and electrophysiological behavior [34]–[38] . In a previous research [39] , [40] we studied the effect of morphological alterations in the apical dendritic trees of isolated GCs on their excitability using computational models built from three-dimensional reconstructions of GCs of animals that had Status Epilepticus ( SE ) induced by pilocarpine ( PILO ) and control animals . The models in the two groups ( PILO and control ) have the same distributions of ionic channels and the same maximal conductance densities over their dendritic areas measured according to their distance from soma ( proximal , medial and distal ) . With this approach we aimed to evaluate the effect of morphological alterations alone on single-cell excitability . We found that GCs with altered morphology are less excitable when stimulated in a simulation of a patch clamp protocol [39] . In the present work , we extended our single-cell study to a network context ( a collection of neurons ) to obtain information on the effect on DG hyperexcitability of seizure-induced apical dendritic morphological alterations in GCs in addition to dendritic spine loss . We use a previously described network DG model , which incorporates detailed structural and biophysical information on DG network and cells [28] . In their work , Santhakumar et al . [28] studied the effect of mossy fiber sprouting on the spread of activity through the DG network after simulated focal stimulation of the perforant path . They showed that even weak mossy fiber sprouting , e . g . 10% of the one observed in the PILO model of TLE , caused the activity to spread from the directly stimulated GCs to the entire network in a seizure-like fashion . The model of Santhakumar et al . [28] uses reduced compartmental models of DG granule , mossy , basket and hilar cells , as well as entorhinal perforant path-associated stimulation signals . Their GC model is highly simplistic and has only two dendrites , whereas ours replaces these GC models with morphologically realistic compartmental models based on data from three-dimensional cell reconstructions ( downloaded from neuromorpho . org ) . To do so we had to recreate the connections between GCs and the other cell types ( which were kept as in the original model ) . Our model has the same network structure of the model of Santhakumar et al . [28] with 10% mossy fiber sprouting . We used morphologically reconstructed models of mature and newborn GCs . The latter came from control animals that did not receive drug injections and from PILO-treated animals that exhibited SE . The models from animals with SE have morphological alterations in their apical dendrites in comparison to models of the control group [16] . We submitted networks with different percentages of newborn cells , either exclusively from the control group or exclusively from the PILO-treated group , to the same focal stimulation protocol used by Santhakumar et al . [28] . Thus , the objective of our study was to obtain mechanistic insights on the integrated action of three types of GC seizure-induced morphological alterations , namely mossy fiber sprouting , apical dendritic tree alterations and dendritic spine loss , on DG hyperexcitability .
We used a 1∶2000 scaled-down model of the dentate gyrus [28] with the reduced GC models replaced with morphologically realistic models to evaluate the impact of morphological variations on the excitability behavior of the DG . The network model contains 500 granule cells ( GC ) , 6 basket cells ( BC ) , 15 mossy cells ( MC ) and 6 hilar perforant-path associated cells ( HC ) , arranged in a ring structure with topographic connections between cells ( Figure 1 ) . Each MC contacts 200 GCs , 1 BC , 2 HCs and 3 other MCs . Each HC connects with 160 GCs , 4 BCs and 4 MCs . Each BC contacts 100 GCs , 3 MCs and 2 other BCs . In normal conditions each GC contacts 1 MC , 1 BC and 2 HCs , but when there is MFS , the GCs make contacts with other GCs . The MFS is quantified by the number of connections from each GC to other GCs ( Figure 1C ) . This DG model has been used to evaluate the propagation , across the 500 GCs , of a stimulation pattern from the entorhinal cortex which reaches only the first 100 GCs ( Figure 1 ) . Our model of DG was identical to the model by Santhakumar et al . [28] available on ModelDB ( http://senselab . med . yale . edu/ModelDb/ShowModel . asp ? model=51781 ) for the condition with 10% of MFS . The only change we made was to replace the two dendrites model of GC by another with realistic morphology from a sample of 74 normal GCs three-dimensional reconstructions available in neuromorpho . org . The sample consists of neurons reconstructed by the groups of Brenda J . Claiborne [41] , Dennis A . Turner [42] , Joseph P . Pierce [43] , Vijayalakshmi Santhakumar [44] and Giorgio Ascoli [45] . All of them were chosen because they come from studies of morphological characterization in which drugs were not used to alter the cell morphology , and also because these cells present the typical cone shape and size observed in adult GCs . From the total of 93 available rat GC reconstructions , we only used 74 ( see Table 1 with the name of the used models ) , discarding some of the biggest and smallest models and the ones that in a visual inspection did not look like a typical granular cell , trying to get a homogeneous set of models that can be used as a comparison group to the cell with altered morphology . The GC computational models were constructed following the methodology described in Tejada et al . [39] , in which we consider the same ion channel conductances , densities and distribution and synapses in all of the models of the GCs , using the pruned-distance 1 criterion , described by Tejada et al . [39] , for the classification of the dendritic tree . This criterion divided the dendritic tree into four segments: granule cell layer dendrites , ( GCLD ) proximal dendrite ( PD ) , medial dendrite ( MD ) and distal dendrite ( DD ) ( Figures 1B and 2 ) , and classified the dendrites according to the average maximum values of the distance measure from soma to the branching point of each dendrite . None of the morphological reconstructions have axons , with exception of the models from Ascoli's group [45] . For this reason axons were not explicitly simulated and were replaced by conductions delays following the original model of Santhakumar et al . [28] For the location of the synapses it was considered a virtual molecular layer with thickness equal to the average maximum dendritic length observed in the three-dimensional models plus and minus the standard error ( ) . This virtual molecular layer was divided into three regions following Murphy et al . [7]: the first 17% nearest to the granular cell layer ( Figure 2B ) was denominated inner molecular layer , the remaining 83% was subdivided into two equal size layers: middle and outer molecular layers . The synapses were located at the middle points of the sections of the apical dendrites within each of the three molecular layers . It is important to note that the criterion to classify the dendritic tree into proximal , medial and distal do not necessarily matches the criterion used to locate the synapses ( Figure 2 ) and , in some cases , it depends on the dendritic length . The same dendrite may extended from the inner up to the outer molecular layer , so consequently this dendrite may present different synapses along its length . In this sense , for instance , the synapses that receive the signal from the perforant path and which are generally located in the outer molecular layer were located in each one of the segments of the dendrites that reached this layer , independently of these dendrites being classified as distal or not . The same was made for the synapses located in the inner molecular layer ( Figure 2 ) . Some of the three-dimensional GC reconstructions used by us have basal dendrites and we kept them in our models . However , since our network model does not include the hilus we did not consider synapses located on the basal dendrites in the present study . Despite the increase in the number of synapses due to the increase in the number of dendrites , the connection between cells was maintained similar to the original model of Santhakumar et al . [28] with only one connection between the different kind of cells with the exception of mossy cells that may make more than one connection with the same GC , and also in the case of MFS in which a GC may make more than one connection with some neighboring GC . Following the original model of Santhakumar et al . [28] , the connections were randomly raffled choosing at random pre- and post-synaptic cells and also the specific dendrite which receives the presynaptic signal . Once the model with the three-dimensional cells was established ( we called it the mature model ) , we generated two big families of models with different proportions of seizure-induced neurogenesis ( from 0 up to 100% ) in which the mature GC models were replaced by three-dimensional reconstructions of newborn ( 30 days old ) GCs . In each of these models the mature GCs that were replaced by newborn cells were chosen at random from the entire population of mature GCs in the network . The sample of newborn cells consists of 40 reconstructions of doublecortin-positive DG GCs ( for details see [16] ) , 20 of them from rats that underwent SE after treatment with PILO and 20 from control rats , all of them reconstructed at the Neurophysiology and Experimental Neuroethology Laboratory ( LNNE ) of the Physiology Department at the University of São Paulo at Ribeirão Preto , Brazil . The computational models of these cells were also constructed following Tejada et al . [39] , [40] using the same pruned-distance 1 criterion , but in this case we used the average maximum values of the distance measure from soma to the branching point of each dendrite of the GC from control animals , by considering that a newborn cell has the same kinds of ion channels located in the same proportional places in which they are found in mature cells . We also used the same average maximum values found for the GCs from control animals in the dendritic tree classification of the GCs from PILO animals , because the use of this value would mean that the dendrites of the cells which were born after the seizure had their ends pruned ( see details about this nomenclature in [39] , [40] ) . On the other hand , the synapses placement followed a similar criterion to the used for the mature models but in this case we used the value of the thickness of the molecular layer measured for each one of the newborn models ( values ranged from up to ) to determine the thickness of the IML , MML and OML , and located the synapses in the middle of the section of each one of the dendrites that extends along these layers . We also included spine loss in the family of mature networks with newborn GCs from PILO animals . None of the GC reconstructions used in our study ( both of mature and newborn cells ) have spines implemented explicitly , so we had to represent spine loss in PILO GCs in an indirect way . We chose two ways of doing so here . The default way , present in all spine loss simulations , was done to represent spine loss by a reduction in the probability of connections of the PILO GCs inserted in the model to account for the reduction in dendritic synaptic sites . Three values of probability reduction were considered: 25% , 50% and 75% . The second way , considered together with the reduction in connection probability in some of our simulations , was done to introduce corrections in the membrane resistivity and capacitance of the PILO GC models to account for the reduction in membrane area [46] , [47] . These corrections consisted in making the values of dendritic membrane resistivity and capacitance of the cells equal to their somatic values ( these values are given in Table 3 of [39] ) . To implement the change in the probability of connections of the newborn PILO GCs with spine loss , when the network was built a newborn PILO GC had a reduction of 25% , 50% or 75% of receiving a connection from other cell types in the network . However , a change in the probability of connections can affect the convergence and divergence parameters of the network . Because of this , we simulated two possible scenarios in which spine loss is associated ( co-exists ) with MFS and dendritic alterations . In the first scenario , which we called spine loss 1 ( SL1 ) , we maintained the divergence and convergence parameters of the network by compensating the loss of connections by connecting the cells that would be connected with the newborn GCs that lost spines to other mature cells . In the other scenario ( SL2 ) , we did not create new connections with mature cells to compensate for the smaller amount of connections with newborn GCs with spine loss . Table 2 summarizes the different families of the DG model that were used in the present study . Finally , it is important to mention that for every DG family model the connection pattern among network cells was created anew before any new simulation , and each condition was simulated 20 times for the calculation of averages . The network activity was measured with raster plots and histograms of spike frequency , grouped for differentiating control versus PILO GCs activity and making comparisons using a two-way ANOVA with alpha of 0 . 01 in all cases . We used NEURON ( [48] , http://www . neuron . yale . edu ) to ran the simulations , with a time-step of 0 . 1 ms and custom-made Matlab ( The Mathworks , Inc . , Natick , MA ) scripts for data analysis and graphs .
Our first study was done to compare the intrinsic excitability of the mature and newborn GC models from control ( YOUNG ) and PILO samples . We compared with the mature GC models only the newborn GC models which have dendrites that reach the outer molecular layer and , consequently , can receive perforant path stimulation ( 6 YOUNG and 2 PILO GC models ) . The excitability was assessed via two protocols . The first was used to estimate the minimum depolarizing current pulse of 500 ms duration applied at a proximal dendrite required to evoke a single action potential ( rheobase current ) . The second was used to measure the number of spikes evoked by one depolarizing synaptic-like pulse applied at intervals of 100 ms during 1000 ms at a single distal dendrite ( to simulate synapses at the outer molecular layer ) . The results are shown in Figure 3 . The average rheobase current ( Figure 3A ) was much higher for mature cells than for newborn cells and the average number of spikes evoked by the train of synaptic-like pulses ( Figure 3B ) was much lower for mature cells than for newborn cells . We also compared the cases with spine loss and without spine loss , representing spine loss by the corrections in membrane resistivity and capacitance mentioned in the methods section . The results of Figure 3 show that the newborn GC models were more excitable than the mature GC models and the effect of spine loss did not change significantly the cell excitability in any of the studied cases . The simulations of the DG mature cells networks showed that the insertion of GCs with realistic morphology did not modify the expected response of the DG network model . Our simulations are consistent with the original model of Santhakumatar et al . [28] with and without MFS ( Figures 4 A–C ) . The major difference was in the the speed of propagation of the perforant path stimulation over the DG with realistic models was faster than the one shown by the original DG model . Another difference was the nonuniform activity propagation pattern for 50% of MFS seen in Figure 4C . However , in general terms , the behavior exhibited by the mature DG model matched the expected behavior of the original DG model . To perform our studies on the effect of the introduction of newborn GCs on the mature DG network , we chose the mature network with 10% of MFS . This case was chosen because this level of sprouting was almost sufficient to produce activity that spread to the entire network ( Figure 4B ) , so we could assess whether the introduction of newborn GCs would cause the network activity to go beyond this underexcited point or not . The results showed that the introduction of newborn cells , both of control ( YOUNG ) and PILO types , produced an overall increase in DG network activity , but only in combination with MFS ( Figures 4 D–I ) . This effect can be clearly seen by comparing Figures 4D with 4F and 4G with 4I . All raster plots in these four figures were obtained for networks with 50% of newborn GC models , but in the raster plots of Figures 4D and 4G there was no MFS while in the raster plots of Figures 4F and 4I there was 10% of MFS . One can see that in the cases without MFS the activity was restricted to the cells that received perforant path stimulation and died out shortly after that , but in the cases with MFS the activity spread to the whole network and reached a sustained , epileptic-like state . For 10% of MFS , Figures 4E and 4H show that a small amount of newborn GCs was already enough to produce activity that spread to the whole network . The addition of spine loss to the PILO GC models dramatically reduced the increase of activity produced by the newborn cells in the case with MFS ( Figures 4 K–L ) . In the case with 10% of newborn PILO GCs the activity pattern in the network returned to an underexcited state and in the case with 50% of newborn PILO GCs the activity spread to the entire network but was no longer epileptiform as in the case without spine loss . To quantify the effect of the introduction of newborn GCs on the activity of the network for the case with 10% of MFS we generated the graphs shown in Figure 5 . They give the total number of spikes emitted by the cells of a given network configuration during the simulation time divided by this simulation time , which we will call the overall frequency of the network , as a function of the fraction of newborn GCs introduced in the mature network model . Most of the graphs exhibited the same generic behavior , showing an increase in the overall frequency of the network with the increase in the fraction of newborn cells inserted . Despite the similarities observed among the graphs for the different families of models , the ANOVA test showed significant differences comparing the overall frequency along the different proportions ( ) and types ( ) of newborn GCs inserted and their interaction ( ) . The curves in Figure 5A for the networks with newborn GCs without spine loss ( MY and MP ) show that for small fractions ( up to 20% ) of inserted newborn GCs the overall frequency of the MY network was slightly smaller than the total activity of the MP network ( as can be seen in Figures 4E and 4H as well ) but for proportions of newborn GCs above 30% the total activity of the MY network became larger than the total activity of the MP network ( an effect that is more difficult to see in Figures 4F and 4I ) . The steeper increases in the overall frequency of the network for these two cases occur for proportions of newborn GCs between 30% and 50% with lower growth rates for proportions of newborn GCs beyond this range . Incidentally , it is interesting to mention that for proportions of inserted newborn GCs ( both YOUNG and PILO ) around and above 50% the overall frequency of the network with 10% of MFS became higher than the overall frequency of the mature network with 50% of MFS ( data not shown ) . Corroborating what was observed in Figures 4K and 4L , Figure 5A shows that the effect of spine loss was to reduce the overall frequency of the network for almost all proportions of inserted newborn GCs . The effect of spine loss was much more sensitive to the maintenance or not of the original convergence and divergence factors of the mature cells in the network than to the way in which spine loss was represented . In the cases with spine loss in which the convergence and divergence factors of the network were maintained ( MPSL1 and MPSL1c ) , Figure 5A shows that the overall frequency of these network types remained below the overall frequency of the networks without spine loss up to the point in which the proportion of inserted newborn GCs was close to 60% . For proportions of inserted newborn GCs between 60% and 80% the overall frequency of the networks with spine loss became similar to the overall frequency of the network with PILO GCs without spine loss but still below the overall frequency of the network with YOUNG GCs without spine loss . It was not possible to consider proportions of newborn GCs beyond 80% for these cases because it became impossible to maintain the convergence and divergence factors of the mature cells for networks with such large fractions of newborn cells . The reduction in the overall frequency of the network due to spine loss was much stronger in the cases in which the convergence and divergence factors of network were not maintained ( MPSL2 and MPSL2c ) . In these cases , the overall frequency of the network always remained at or below 50% of the overall frequency of the networks without spine loss . Moreover , in the cases with spine loss and altered divergence and convergence factors of the mature cells the growth of the overall frequency of the network with the proportion of inserted newborn GCs was much slower than in the cases with either no spine loss or spine loss with unaltered divergence and convergence factors of the mature cells . This indicates a strong limiting effect of spine loss over the network excitability in situations in which the rearrangement of connections do not preserve the original convergence and divergence factors of the mature cells that remain in the network . The effect of altering the probability of connections of newborn PILO GCs with spine loss can be seen in Figure 5B . It shows again the curves of the overall frequency of the network when the probability of connections was reduced by 50% ( indicated by MPSL1c 50% and MPSL2c 50% ) , which are compared to the corresponding curves for a smaller ( 25% ) reduction in the probability of connections and a higher ( 75% ) reduction in the probability of connections . Even when spine loss was not so effective in reducing the number of connections of newborn GCs ( the cases with 25% in Figure 5B ) the overall frequency of the network remained below the overall frequency of the network for the case without spine loss for most of the proportions of inserted newborn GCs . On the other hand , when spine loss was very effective in reducing the number of connections of newborn GCs ( the case with 75% in Figure 5B ) the overall frequency of the network remained unchanged at a small value for all proportions of newborn GCs inserted in the network .
The substitution of the simplified GC models by morphologically realistic GC models into the DG model originally proposed by Santhakumar et al . [28] did not cause significant alterations in the DG activity pattern . The networks constituted by mature GC models showed , qualitatively , the same behavior of the original model in both situations with and without MFS . The most important change was in the temporal spike propagation dynamics , which in our adaptation of the model was faster . This change may be due to the way in which we divided the molecular layer , which affected the distribution of recurrent MFS synapses and perforant path synapses from entorhinal cortex . In our model the IML corresponds to the first 17% of the molecular layer measured from the granular cell layer , and is therefore narrower than in the original model of Santhakumar et al . [28] . This means that in our model the MFS synapses , which are placed in the IML , are , on the average , closer to the soma than in the original model , and the perforant path synapses , which are located in the OML , are also , on the average , closer to the soma than in the original model . This makes the GCs to fire faster . Another change in comparison with the original model of Santhakumar et al . [28] was the nonuniform activity propagation pattern for 50% MFS seen in Figure 4C . This is probably due to the use of GC models with more realistic morphologies in comparison with the simple GC models of Santhakumar et al . [28] . The more complex dendritic trees of our GC models introduce nonlinearities in signal propagation through GC dendrites , which have stronger impact when the number of recurrent GC synapses is high and may be responsible for the observed activity breaks . The fact that the insertion of newborn GCs without MFS did not provoke an increase in the network activity highlights the importance of MFS in the induction of epileptiform behavior in the network . Otherwise , seizure-induced neurogenesis per se is not able to provoke significant changes in the network behavior despite these cells being smaller and therefore more excitable ( as shown in Figure 4 ) . Nevertheless , the amount of changes provoked by the simulated neurogenesis may be in the range of the subtle alterations expected from other kinds of processes , such as learning and memory [49] , which alter the DG not too much to provoke a seizure but enough to help in the process of codification of new information . Our simulations showed that the insertion of newborn GCs in a network with MFS provoked a higher increase in the network activity than the one produced by MFS alone . The insertion of the newborn GCs was based on the assumption that these newborn cells preserve the same conditions of the mature cells , with the same number of synapses and the same probability of making connections with other cells . The only difference was in the size of the newborn GC dendrites , which made some cells to not have dendrites extending through the medium or outer molecular layers . In these conditions the interaction between MFS and seizure-induced neurogenesis may produce a more excitable network . Indeed , our results showed that the effect of the newborn GCs can be comparable to the effect of MFS , which is the type of morphological alteration that mostly affects the excitability of the DG circuit , at least in models [28]–[30] . In the previous models , high levels of excitation ( epileptic-like ) were obtained only for high levels of MFS , viz . , the case with 50% of MFS in Figure 4C , but here we showed that similar levels of excitation can be obtained with low levels of MFS ( 10% ) as long as the proportion of newborn GCs in the network is high ( >50% ) . This suggests that newborn GCs have an important role in potentiating the effects of MFS . However , the insertion of newborn GC models derived from PILO treated animals generated a lower increase of activity compared with the one induced by the insertion of newborn GCs from control animals , in special for cases in which the fraction of inserted newborn GCs was larger than 40% of the total number of GCs . This finding is in agreement with the prediction of the modeling study of Tejada et al . [39] , [40] , in which it was reported that newborn PILO GCs are less excitable than newborn GCs from control animals . The latter are less excitable than the former when stimulated with a current clamp protocol , and this may be reflected in the slightly smaller increase in the firing rate of the DG network when the newborn GCs inserted on it are of the PILO group in comparison with newborn GCs from the control group . It is possible that the morphological alterations are followed by changes in the ion channels and their maximum conductance distributions , which were not considered in the present study . These ion channels alterations may lead to different firing behaviors than the ones observed in the GCs simulated here with consequences on the excitability of the network but the lack of evidence on the possible changes in the ion channel parameters of newborn GCs from rats that had SE after PILO treatment like the ones considered here leaves this question open . On the other hand , the inclusion of spine loss in the newborn PILO models provoked a significant reduction in the activity of the circuit for any of the percentages of newborn GCs inserted , even in the case in which the convergence and divergence of the GC models ( SL1 models ) were maintained . This reduction is consistent with the idea that spine loss acts to maintain homeostasis [19] in the sense that reduction in the number of spines ( alone ) reduces the number of connections and consequently might decrease the activity of the network . Nevertheless , it is important to point out that our model was specially designed to evaluate the effect of MFS , and in this context , the reduction of spines acts in the opposite direction than the MFS . But it is possible that when other kinds of features are included into the model , such as various types of inhibitory neuronal cells or simulated inputs from medial entorhinal cortex , a pattern in which spine loss does not necessarily reduces the excitability may emerge . Besides the reduction in the number of connections , spine loss also reduces the membrane surface area of a cell with consequent changes in the membrane resistivity and capacitance [46] , [47] . In our simulations , these changes in membrane properties produced small but not significant changes in the excitability , both at the single-cell and the network levels . The factor which had the strongest effect upon the excitability of the dentate network in our simulations was the preservation or not of the network convergence and divergence factors due to spine loss . In network models in which the convergence and divergence factors due to spine loss were not maintained ( SL2 models ) the excitability was strongly reduced in comparison with the cases in which these factors were maintained ( SL1 models ) . There is no obvious reason for the maintenance of the divergence and convergence factors of the network after the rearrangement of connections following the introduction of newborn GCs with spine loss . Therefore , it is reasonable to assume that the scenario without maintenance of the divergence and convergence factors ( SL2 models ) is more realistic than the scenario with maintenance of the divergence and convergence factors ( SL1 models ) . We can predict from this that spine loss has a strong protective effect in curtailing the increase of DG activity provoked by the insertion of newborn GCs . Our computational modeling study has shown that the different neuronal morphological alterations and seizure-induced neurogenesis considered here act much more in combination than as specific features of epileptogenic network activity . On the one hand , there are alterations that increase the number of recurrent connections , such as MFS , having as outcome an increase in the activity of the circuit . On the other hand , alterations such as spine loss can reduce the number of connections and consequently decrease the activity of the network . Along with the above , the DG is constantly exposed to the generation of new cells , which are usually shorter , much more branched [16] and expectedly more excitable , which as consequence potentiate the effect of MFS when inserted into the model . The potentiation provoked by MFS plus neurogenesis may be modulated by seizure-induced dendritic morphological alterations and spine loss , which both reduce the activity of the network . This suggests that the combination of seizure-induced morphological alterations in the apical dendritic tree of newborn GCs and spine loss may have a protective effect on the dentate network against the increase in the activity provoked by MFS and neurogenesis . Our simulation suggests that the changes in the morphology of GCs provoke diverse effects in the network activity . Some of these changes could be responsible for the increased activity observed in TLE but other could be acting in the opposite direction , decreasing the excitability of the circuit . The balance between these two drives may depend on other factors that were not included in our model , in special , ion channel alterations which may provoke more complex interactions between all of the factors present in an altered circuit . In relation to the adaptation made in the original model of Santhakumar et al . [28] , the changes observed in the activity of the DG model are due solely to the insertion of the different alterations in the GC models , namely altered dendritic tree and spine loss . Therefore , our version respects the topological characteristics of the original model and offers the possibility of studying the interaction of neurogenesis and three of the main morphological alterations usually found in the DG after SE: MFS , changes in the apical dendritic tree of GCs and GC spine loss . Future studies will obviously add new features to the current modeling , in order to approximate it even more to the complexity and emergent properties of the actual DG and its associated plastic substrates , for example: presence of synapses on basal dendrites [7] , [15] , [17] , DG GC dispersion [3] and ectopic neurogenesis [13] . In conclusion , our findings strongly suggest that the combined presence of morphological features such as MFS , altered apical dendritic tree and spine loss , in a computational model of the DG network , can explain better the inherent complexity of the circuits associated to temporal lobe epileptogenicity . The current network perspective reliably mimics dysfunctional characteristics not necessarily present when simplified or isolated parameters are considered . | Neurogenesis is currently a well known phenomenon in the adult brain , in special in some areas such as the subventricular zone and the dentate gyrus in the hippocampus , in which different endogenous and exogenous factors provoke cell proliferation . In the specific case of the dentate gyrus , granule cells proliferate exhibiting altered morphology after the induction of Status Epilepticus ( SE ) by pilocarpine ( PILO ) . Several days after the injury the new cells show different morphological alterations , for example , in dendritic spines and branching patterns , as well as with the formation of axonal sprouting . The way in which these new cells are integrated into the hippocampus is still unknown with conflicting data in the literature . Here we used computer simulation to test if the activity of the dentate gyrus is affected by the presence of different proportions of new cells after PILO-induced SE . Our results show that the specific morphological alterations present in the granule cells in rats with PILO-induced SE may be responsible for increasing ( mossy fiber sprouting ) or decreasing ( spine loss ) the activity in the network . The imbalance between these effects may be manifest as an epileptiform network behavior . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"circuit",
"models",
"neurobiology",
"of",
"disease",
"and",
"regeneration",
"medicine",
"and",
"health",
"sciences",
"computational",
"neuroscience",
"neurology",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"neuroscience"
]
| 2014 | Combined Role of Seizure-Induced Dendritic Morphology Alterations and Spine Loss in Newborn Granule Cells with Mossy Fiber Sprouting on the Hyperexcitability of a Computer Model of the Dentate Gyrus |
Plant infection by pathogenic fungi involves the differentiation of appressoria , specialized infection structures , initiated by fungal sensing and responding to plant surface signals . How plant fungal pathogens control infection-related morphogenesis in response to plant-derived signals has been unclear . Here we showed that the morphogenesis-related NDR kinase pathway ( MOR ) of the cucumber anthracnose fungus Colletotrichum orbiculare is crucial for appressorium development following perception of plant-derived signals . By screening of random insertional mutants , we identified that the MOR element CoPag1 ( Perish-in-the-absence-of-GYP1 ) is a key component of the plant-derived signaling pathway involved in appressorium morphogenesis . Constitutive activation of the NDR kinase CoCbk1 ( Cell-wall-biosynthesis-kinase-1 ) complemented copag1 defects . Furthermore , copag1 deletion impaired CoCbk1 phosphorylation , suggesting that CoPag1 functions via CoCbk1 activation . Searching for the plant signals that contribute to appressorium induction via MOR , we found that the cutin monomer n-octadecanal , degraded from the host cuticle by conidial esterases , functions as a signal molecule for appressorium development . Genome-wide transcriptional profiling during appressorium development revealed that MOR is responsible for the expression of a subset of the plant-signal-induced genes with potential roles in pathogenicity . Thus , MOR of C . orbiculare has crucial roles in regulating appressorium development and pathogenesis by communicating with plant-derived signals .
Colletotrichum orbiculare ( syn . C . lagenarium ) is a plant pathogenic fungus causing anthracnose disease in cucumber ( Cucumis sativus ) . Like other Colletotrichum species , C . orbiculare infects host plants hemibiotrophically: first , C . orbiculare forms melanized appressoria that mediate the direct penetration of host epidermal cells using a combination of mechanical force and enzymatic degradation , then it develops biotrophic hyphae inside living epidermal cells , and finally forms necrotrophic hyphae that kill and destroy host tissues [1 , 2] . In many plant pathogenic fungi including Colletotrichum species , adhesion to the plant surface is the first step to initiate the infection process [3] . An extracellular matrix that surrounds spores contributes to their attachment and creates a host surface environment for successful penetration . The matrix of the rust fungus Uromyces viciae-fabae and the powdery mildew fungus Blumeria graminis , also contains esterases and assists in initiating the infection process as well as in adhesion to the host cuticle [3] . After adhesion of the spores , appressorium differentiation is triggered by fungal perception of various physical and chemical signals from the host surface [3 , 4] . Previous studies have revealed that physical signals including hardness and hydrophobicity stimulate conidium germination and appressorium formation . Besides such physical stimuli , chemical signals such as leaf waxes and cutin monomers also induce appressorium formation in several plant pathogenic fungi . Analysis of the genome-wide expression profiles of C . higginsianum revealed that appressoria formed in vitro are morphologically indistinguishable from those in planta , their transcriptomes are substantially different . This indicates that these specialized cells are highly responsive to plant-derived signals that are perceived before penetration [2] . During infection by C . orbiculare , cell polarity is established and maintained in each developmental stage: conidial germ tube emergence , germ tube elongation , penetration peg formation from the appressorium , and hyphal tip extension inside the host plant [1 , 5] . Thus , establishment of cell polarity is essential for morphogenesis of infection structures and pathogenicity . In our previous study , we elucidated that CoKEL2 , a homolog of Schizosaccharomyces pombe tea1 , a landmark protein of microtubule plus ends [6] , localized at the apex of vegetative hyphae and germ tubes [7] . CoKel2 is required for proper morphogenesis of appressoria on artificial surfaces , but is dispensable on the host plant surface , suggesting a bypass pathway was activated by plant-derived signals independent of CoKel2 function [7] . Regulatory processes necessary for appressorium formation such as the cAMP/PKA and MAP kinase signaling cascades responding to plant-surface signals including physical and chemical signals have been described for C . orbiculare [1] , however , precise dissection about the signal cascades that discriminate perception of those signals has largely been obscure in fungal plant pathogens . Networks of protein kinase-based signaling pathways regulate a wide variety of key morphological processes . Members of the conserved NDR ( nuclear Dbf2-related ) kinases are important for controlling cell polarity and differentiation in various organisms [8] . Previously , studies in fungi and higher eukaryotes on NDR kinases have discovered the morphogenesis-related NDR kinase network ( MOR ) [9] , which is also called RAM ( regulation of Ace2 and morphogenesis ) . The central component of the system is an NDR kinase , Cbk1 in Saccharomyces cerevisiae , Orb6 in S pombe , and COT1 in Neurospora crassa , that associates with a regulatory subunit Mob2 [10–13] . This complex is activated by a Kic1/Nak1-related germinal center ( GC ) kinase and coordinated by Hym1/Pmo25-related and Tao3 ( also known as Pag1 ) /Mor2/FRY-related scaffolding proteins [9 , 14] . Although the MOR elements except Ace2 are highly conserved among eukaryotes , a deficiency in their functions can result in diverse cellular responses . While mutants in ascomycete yeasts S . cerevisiae [10] and S . pombe [11] display defects in cell polarity , mutations in the basidiomycetes Cryptococcus neoformans [15] and Ustilago maydis [16] result in hyperpolarized growth . In the filamentous ascomycetes including N . crassa [17] and Aspergillus nidulans [18] , mutants are blocked in hyphal tip extension and display hyperbranched growth . Thus , despite the MOR components being conserved among various fungi , the inputs and outputs to and from the MOR central core are most likely species-specific . Although the possible link between NDR and MAP kinases pathway was reported in N . crassa [19] , the upstream activators and downstream targets of MOR have been poorly studied [9] , and further comparative analyses of cellular signaling context are still required . Here , we demonstrate that the MOR of C . orbiculare plays a crucial role in the signal transduction pathway for appressorium development that is specifically induced by plant-derived cues . We also show that the signal molecule for appressorium induction via MOR is the cutin monomer n-octadecanal degraded from the host plant cuticle by conidial surface esterases . Furthermore , based on genome-wide gene expression analysis , we reveal that the MOR of C . orbiculare contributes to the regulation of a subset of the plant-signal-induced genes with potential roles in pathogenicity .
To determine the specific components of the plant-derived signaling pathway for appressorium differentiation , we screened 10 , 021 insertional mutants in the cokel2Δ background and obtained 38 mutants that formed abnormal appressoria on the host plant and were reduced in pathogenicity compared with cokel2Δ . We next examined whether the reintroduction of CoKEL2 into those mutants restored normal appressorium formation on artificial substrates to isolate mutants that have defects in the CoKEL2-independent and the plant-derived signaling pathways for appressorium formation . Eventually , six insertional mutants named as pathogenesis deficient mutants ( PDM ) potentially defective in the plant-derived signaling pathway were obtained ( S1 Fig ) . Microscopic analysis showed that the PDMs formed abnormal appressoria with lateral germination both on artificial surface and host plant surface ( S1A–S1D Fig ) . Consequently , PDMs were reduced in pathogenicity compared with cokel2Δ ( S1E Fig ) . The candidate mutated gene was determined by whole genome sequencing of those mutants . Of the identified candidates , the mutated gene of PDM-4 showed high homology to TAO3 ( Transcriptional-Activator-of-OCH1 ) , also known as PAG1 ( Perish-in-the-Absence-of-GYP1 ) in Saccharomyces cerevisiae , and we named this gene CoPAG1 . In S . cerevisiae , Tao3 is one of the components of RAM , a signaling cascade that is involved in the maintenance of cell polarity , separation and cellular morphogenesis [9 , 14 , 20] . CoPAG1 putatively encodes a 2419-amino-acid protein with MOR2-PAG1 domains , which are conserved in Pag1 homologous proteins ( S2A Fig ) . CoPag1 homologs are conserved among other fungi and eukaryotes [9 , 20] . All of its homologs are large proteins with more than 2000 amino acids and MOR2-PAG1 domains . In a phylogenetic tree generated on the basis of the derived amino acid sequences showed that the proteins of filamentous ascomycetes form a single clade , different from yeasts , basidiomcetes and other eukaryotes ( S2B Fig ) . Furthermore , CoPAG1 was confirmed as an ortholog of S . cerevisiae TAO3 by complementation of S . cerevisiae strain FLY1004 , carrying a deletion of TAO3 ( S2C and S2D Fig ) . Deletion of any of the RAM component genes including TAO3 leads to two distinct phenotypes: ( 1 ) the accumulation of large aggregates of cells that are unable to degrade the septum between the mother and daughter cells , ( 2 ) a loss of polarity [10] . In the tao3Δ mutant , cells are aggregated and round ( mean axial ratio 1 . 03 ) . In contrast , the yeast transformants expressing the CoPAG1 cDNA formed no aggregates and had a mean axial ratio 1 . 17 comparable with those of the wild type strain W303-1A ( mean axial ratio 1 . 18 ) and the tao3Δ strain transformed with TAO3 ( mean axial ratio 1 . 17 ) . Thus , the yeast complementation experiments showed that PAG1 of C . orbiculare is a functional ortholog of S . cerevisiae TAO3 . To define the function of CoPag1 , we isolated copag1 deletion mutants for the wild type and cokel2Δ strain . The gene replacements of CoPAG1 were verified by DNA gel blot analysis ( S3A and S3B Fig ) . To elucidate the function of CoPag1 on artificial substrates , we examined the copag1 deletion mutants for the formation of appressoria and penetration hyphae on cellulose membranes ( Fig 1A and 1B ) . Whereas cokel2Δ and copag1Δ cokel2Δ formed abnormal appressoria with lateral germination , copag1Δ produced normal appressoria similar to the wild type on artificial substrates and , moreover , retained its penetration ability on cellulose membranes ( Fig 1A and 1B ) . These results demonstrated that CoPag1 is not involved in the CoKel2-dependent physical signaling pathway for appressorium morphogenesis . To define the involvement of CoPag1 in appressorium formation induced by plant-derived signals , we analyzed the phenotypes of copag1 deletion mutants on the host plant . The copag1Δ and cokel2Δ mutants formed normal appressoria similar to the wild type on the plant surface ( Fig 1C and 1D ) . By contrast , appressoria of copag1Δ cokel2Δ showed abnormal lateral germination on the host plant and did not form penetration hyphae ( Fig 1C and 1D ) . Interestingly , abnormal appressoria formed by copag1Δ cokel2Δ strains developed from conidia that germinated bilaterally in contrast to cokel2Δ ( Fig 1A and 1C ) , suggesting that copag1 deletion leads to a defect in the determination of the germination site on the conidium . As expected , copag1Δ cokel2Δ was reduced in pathogenicity compared to single mutants ( Fig 1E ) . These results clearly showed that copag1Δ cokel2Δ has defects both in the physical and plant-derived/chemical signal transduction pathway and that CoPag1 is a key component of the plant-derived signaling pathway for appressorium morphogenesis . To test the involvement of CoPag1 in appressorium formation on the other host leaf surface , conidial suspensions of the deletion strains were inoculated onto leaves of Nicotiana benthamiana . Whereas the copag1Δ and cokel2Δ mutants formed appressoria that were morphologically indistinguishable from those of the wild type , copag1Δ cokel2Δ produced abnormal appressoria ( S4A and S4B Fig ) . Accordingly , leaves inoculated with copag1Δ cokel2Δ did not show disease symptoms in contrast to single mutants ( S4C Fig ) , suggesting that the involvement of CoPag1 in the plant-derived signaling pathway was not specific to the cucumber leaf surface . MOR components , which include TAO3/MOR2/FRY-related scaffolding proteins , are highly conserved among fungi and higher eukaryotes [9 , 14] . Therefore , we hypothesized that MOR plays a crucial role in appressorium development triggered by plant-derived signals in C . orbiculare . To test this hypothesis , we identified the NDR kinase CoCBK1 ( Cell-wall-Biosynthesis-Kinase-1 ) , the homologous gene of S . cerevisiae CBK1 , by searching the C . orbiculare genome database . The size of the predicted protein of CoCbk1 is 658 amino acids , and the catalytic domain of the fungal nuclear Dbf2-related kinase-like protein is conserved in CoCbk1 ( S5 Fig ) . We examined the effects of cocbk1 targeted gene deletion on cellular morphogenesis . However , we were unable to generate cocbk1Δ deletion strains , due to apparent growth defects . To elucidate the function of CoCbk1 on cellular morphogenesis during the infection process of C . orbiculare , we generated an analog-sensitive CoCbk1M352A variant ( CoCbk1-AS ) by site-directed mutagenesis ( Fig 2A ) , which was fully active in vivo and was specifically inhibited by the ATP-analog 1NA-PP1 [21 , 22] . On the basis of sequence alignments of CoCbk1 with S . cerevisiae Cbk1 , we identified an M352 residue in the catalytic domain as the putative ATP-analog binding pocket ( Fig 2A ) [21] . CoCbk1-AS-replaced strains of the wild type were obtained and incubated on PDA media containing 1NA-PP1 for 7 days to test whether CoCbk1-AS strains are sensitive to 1NA-PP1 . At 0 . 5 μM 1NA-PP1 , mycelial growth of CoCbk1-AS strains was significantly inhibited ( Fig 2B ) . By contrast , the inhibitor had no effect on the growth rate of the wild type . Therefore , strains expressing the CoCbk1-AS can be used to mimic the phenotype of CoCbk1 loss of function mutants . To investigate the role of CoCbk1 and functional relationship with CoPag1 in infection-related morphogenesis , we analyzed the development of appressoria and penetration hyphae on cellulose membranes and the host plant . Upon addition of 0 . 5 μM 1NA-PP1 during appressorium formation , the CoCbk1-AS strain was severely impaired in appressorium morphology , though conidial germination was not affected ( Fig 2C , 2D , 2F and 2G ) . Intriguingly , addition of 1NA-PP1 at 3 h after inoculation , when conidia germinate , resulted in conidia that germinated bilaterally and formed abnormal appressoria ( Fig 2D and 2F ) . This defect is similar to that observed in copag1Δ cokel2Δ mutants ( Fig 1A and 1C ) , suggesting that CoCbk1 and CoPag1 have related functions during appressorium formation of C . orbiculare . To investigate the role of CoCbk1 in plant infection , we inoculated intact cucumber cotyledons with conidia of the wild type or the CoCbk1-AS strain and evaluated virulence 7 days after inoculation . The wild type caused disease symptoms in the presence of 0 . 5 μM 1NA-PP1 , but the CoCbk1-AS strain did not ( Fig 2E ) . Taken together , these results indicate that although CoCbk1 activity is essential for morphological differentiation of infection structures and pathogenesis , CoCbk1 has related function with CoPag1 in the determination of the germination site on the conidium . In S . cerevisiae , Tao3 facilitates activation of the NDR kinase Cbk1 , the central module in the RAM network [10] . Therefore , we wondered whether Pag1 functions via activation of Cbk1 in C . orbiculare ( Fig 3A ) . To investigate this , we generated the constitutively active CoCbk1T649E mutation ( CoCbk1-CA ) by site-directed mutagenesis ( Fig 3B ) and obtained the CoCbk1-CA strain in the copag1 deletion background . S . cerevisiae Cbk1 works with five other RAM network proteins [10 , 21] . In cells lacking any other RAM component , the C-terminal phosphorylation site Cbk1T743 residue located in a hydrophobic motif is not phosphorylated , and this phosphorylation is necessary for both cell separation and the maintenance of polarized growth [23] . Replacement of T743 with glutamic acid , which is known to mimic phosphorylation , bypassed the absence of a functional RAM network [24] . Expectedly , introducing CoCbk1-CA into copag1Δ cokel2Δ resulted in normal appressoria on the host surface in contrast to copag1Δ cokel2Δ ( Fig 3C and 3D ) . Furthermore , pathogenicity of the copag1Δ cokel2Δ/ CoCbk1-CA strain was partially restored on the host plant compared with copag1Δ cokel2Δ ( Fig 3E ) . These results suggest that CoPag1 functions via activation of CoCbk1 in C . orbiculare . In addition , normal appressorium formation of copag1Δ cokel2Δ/ CoCbk1-CA was restored on artificial substrates ( Fig 3F and 3G ) , suggesting that constitutive activation of CoCbk1 provides normal morphogenesis of appressorium development in copag1Δ cokel2Δ without plant-derived signals . CoCbk1-CA introduction into the wild type resulted in normal appressoria but reduced virulence compared with the wild type ( Fig 3C–3E ) . This result indicates that appropriate control of CoCbk1 activity is important for host infection in C . orbiculare . Moreover , we found that deletion of CoHYM1 ( Hypha-like-Metulae-1 ) , the homologous gene of RAM component HYM1 in S . cerevisiae ( S3C , S3D and S6A Figs ) , showed the defect in the determination of the germination site on the conidium similar to the CoCbk1-AS mutant ( S6B and S6C Fig ) . By introducing CoCbk1-CA into cohym1Δ , normal appressorium formation was restored compared with the cohym1Δ ( S6B and S6C Fig ) , indicating that CoHym1 is also involved in regulation of CoCbk1 activity in C . orbiculare . To determine whether CoPag1 and CoHym1 regulate the phosphorylation of CoCbk1 , we detected kinase activity of CoCbk1-GFP purified from the copag1Δ and cohym1Δ by western blotting using a phosphospecific antibody of S . cerevisiae Cbk1 [23] . Phosphorylation level of CoCbk1 in the copag1Δ and cohym1Δ was lower than that of the wild type ( Fig 3H ) . In addition , in yeast two-hybrid assays , CoCbk1 interacted with CoPag1 and CoHym1 while no interaction was observed with CoKel2 ( S7 Fig ) . These results indicate that CoPag1 and CoHym1 directly regulate the activity of CoCbk1 in C . orbiculare . To test the specificity of the phenotype suppression by CoCbk1-CA , we analyzed the effect of CoCbk1-CA introduction into cocac1 and comekk1- cocmk1 mutants defective in of cAMP and MAP kinase signaling cascades , respectively in C . orbiculare . Conidia of the cocac1 , comekk1 and cocmk1 mutants are defective in germination and development of appressoria on the surface of either the host plant or glass . In addition , these mutants are nonpathogenic to cucumber [1 , 25–27] . Introduction of CoCbk1-CA into these mutants had no effect on these phenotypes ( S8 Fig ) , suggesting that CoCbk1-CA specifically restores the morphological defect of mutants of MOR components , thus no direct link of MOR to cAMP and MAP kinase signaling cascade was recognized . To characterize the constitutively active CoCbk1T649E mutation on the transcript level , genome-wide transcriptional profiling of the CoCbk1-CA strain in the copag1Δ mutant background and the wild type strain 104-T during appressorium development in vitro was performed using custom microarrays with probes designed against the 13 , 479 C . orbiculare genes . We collected samples from immature appressoria of each strain incubated on polystyrene petri dishes ( in vitro ) for 4 h . By 4 h , most conidia have germinated and started to develop appressoria . A total of 635 genes were differentially regulated ( fold change >2 , P < 0 . 05 ) in strain CoCbk1-CA compared with the wild type ( S1 Data Set ) , suggesting that the constitutively active CoCbk1T649E mutation causes transcriptional changes in C . orbiculare and that CoCBK1 contributes to regulation of these genes during appressorium morphogenesis . To identify plant-derived signals responsible for induction of appressorium development mediated by Pag1 in C . orbiculare , we initially tested appressorium formation in the presence of cucumber exudate on a polystyrene petri dish . Normal appressorium formation of cokel2Δ was restored in the presence of cucumber exudate . By contrast , copag1Δ cokel2Δ formed abnormal appressoria when incubated in the presence of cucumber exudate similarly in distilled water ( Fig 4A and 4B ) , suggesting that Pag1 of C . orbiculare is involved in controlling appressorium development upon recognition of chemical cue ( s ) present in the host . Because cutin monomers , a group of plant surface molecules , are known to trigger appressorium formation in other plant pathogenic fungi [28–30] , we analyzed the effects of two cutin monomers on appressorium development through CoPag1 . n-Octadecanal was isolated from cucumber exudate and 1 , 16-hexadecanediol was used as a commonly used cutin monomer . While cokel2Δ developed normal appressoria in the presence of n-octadecanal or 1 , 16-hexadecanediol on the petri dish , copag1Δ cokel2Δ formed abnormal appressoria either in the presence or absence of cutin monomers ( Fig 4C and 4D ) . This result suggested that these cutin monomers functioned as a signal molecule for appressorium development via Pag1 in C . orbiculare . To determine whether CoCbk1 is activated by cutin monomers , we tested the effect of n-octadecanal on the CoCbk1 phosphorylation . CoCbk1-GFP purified from mycelia of the wild type , incubated in the presence of n-octadecanal , exhibited the CoCbk1 phosphorylation 26% higher than that observed in the absence of n-octadecanal . By contrast , CoCbk1 phosphorylation of copag1Δ was not affected by n-octadecanal ( Fig 4E and 4F ) . Thus , it indicated that n-octadecanal activates CoCbk1 dependent on CoPag1 . In rust fungus Uromyces viciae-fabae and the powdery mildew fungus Blumeria graminis , extracellular cutinase and esterase from the surface of spores assist in the adhesion to the host cuticle and initiation of the infection process [3] . Therefore , we hypothesized that cutinase and esterase released from conidia of C . orbiculare hydrolyze the plant surface cuticle to form cutin monomers , and these cutin monomers induce appressorium development via MOR . To test whether hydrolytic enzymes are released from the conidia , conidia were placed on glass slides coated with an assay medium containing indoxyl acetate as the esterase substrate . Indeed , indigo blue crystals formed on the adhesion site of unwashed conidia ( Fig 5A ) . By contrast , no crystals appeared on the adhesion surface of washed conidia that had surface enzymes removed , and autoclaved conidia failed to accumulate crystals either intracellularly or extracellularly ( Fig 5A ) . These results indicate that enzyme activity is localized on the conidial surface in C . orbiculare . Additionally , esterase activity released from conidia was evaluated with p-nitrophenyl butyrate as the substrate . Conidia were repeatedly washed using a vortex , and each washing supernatant was assayed for esterase activity . The first washing supernatant had the highest activity , and subsequent washings had rapidly less enzyme activity than each previous wash ( Fig 5B ) , suggesting that the release of esterase occurred rapidly on contact with an aqueous environment . The localization of esterase on the conidial surface was also shown by native polyacrylamide gel electrophoresis ( Fig 5C ) . Whereas esterases were not detected in the intracellular protein preparation , one band of the extracellular esterase was visualized in the indoxyl acetate assay . To investigate that conidial surface esterases are involved in the release of cutin monomers from the leaf surface , we measured the quantity of n-octadecanal in the cucumber exudate by GC-MS ( Fig 5D ) . Droplets of either conidial suspension or distilled water were placed on the surface of cotyledons for 1 h at 24°C , then collected . Intriguingly , n-octadecanal was detected only in the cucumber exudate collected with the conidial suspension ( Fig 5D ) , indicating that conidial surface esterases hydrolyze plant surface cuticle to cutin monomers; the concentration of n-octadecanal in the cucumber exudate collected with the conidial suspension , 1 . 8 μg/mL ( 6 . 7 μM ) , is a concentration sufficient for inducing normal appressorium development of cokel2Δ ( Fig 4C and 4D ) . Taken together , we conclude that appressorium development via enhanced activation of CoCbk1 is induced by cutin monomers derived from hydrolysis of the host plant cuticle by conidial surface esterases . To examine the involvement of CoPag1 and CoCbk1 in regulation of genes that are specifically expressed during appressorium development in planta , we analyzed the transcriptome at the stage of appressorium formation in vitro and in planta using custom microarrays . We collected samples from immature appressoria of the wild type strain 104-T after incubating for 4 h on petri dishes ( in vitro ) and from the wild type , copag1Δ and CoCbk1-AS treated with 1NA-PP1 after 4 h on cucumber cotyledons ( in planta ) . In the wild type strain , 4069 genes ( 30% ) were upregulated in planta compared with in vitro ( fold change >2 , P < 0 . 05 ) , indicating that the transcriptome of C . orbiculare was highly responsive to plant-derived signals during appressorium development ( Fig 6A ) . In a hierarchical clustering of the 550 genes that are differentially regulated in copag1Δ compared with the wild type incubated in planta , 278 genes were downregulated in copag1Δ compared with the wild type in planta ( Fig 6B ) . Furthermore , the 278 genes were downregulated in CoCbk1-AS mutant treated with 0 . 5 μM 1NA-PP1 ( Fig 6B ) , indicating that CoPAG1-dependently expressed genes were under control of CoCBK1 in C . orbiculare . Of these 278 genes , 263 ( 95% ) were plant-signal-induced genes , suggesting that CoPAG1 specifically contributes to the expression of the plant-signal-induced genes ( Fig 6A ) . Interestingly , among the 263 genes , 30 encode secreted proteins including 16 small secreted proteins ( SSPs; predicted length <300 amino acids ) , 24 encode carbohydrate-active enzymes ( CAzymes ) including 12 enzymes associated with the degradation of plant cell wall constituents , 21 encode transporters and 9 transcription factors ( Fig 6C ) . CAZymes [31] that potentially degrade the plant cell wall [32] and remodel the fungal cell wall are thus important for establishment of infection . In addition , fungal-secreted proteins including effectors that facilitate infection by manipulation of host metabolism and evasion of host immunity [33] . Concomitantly , plasma membrane transporters function in the secretion of protein effectors and secondary metabolites including toxins to the plant cell or protection against plant defense compounds or disease control agents [34 , 35] . Therefore , the MOR components seemed to be linked to expression of a subset of the infection-related genes .
Appressorium development is driven by the perception of one or several host-derived signals . Previous work on U . maydis demonstrated that hydrophobicity and hydroxy fatty acids/cutin monomers stimulate the differentiation of appressoria [30] . Furthermore , two plasma membrane proteins , Sho1 and Msb2 , that act upstream of MAP kinases Kpp2 and Kpp6 , have been suggested to be involved in sensing of the hydrophobic surface for appressorium formation in U . maydis [36] . In M . oryzae , MoSho1 and MoMsb2 regulate surface recognition for appressorium formation via MAP kinase signaling [37] . In addition , Pth11 , a putative G protein-coupled receptor ( GPCR ) is involved in host surface recognition acting upstream of the cAMP pathway [38] . Previously , we elucidated that CoKEL2 , a S . pombe tea1 homolog is required for appressorium development on artificial surfaces [7] . The cokel2Δ mutant displayed abnormal appressoria , germinated laterally , and was defective in penetration into cellulose membranes ( Fig 1A and 1B ) . Surprisingly however , the cokel2Δ mutant was normal in appressorium formation and penetration on the host plant surface ( Fig 1C and 1D ) . Therefore , we hypothesized that CoKel2 is important for recognizing physical signals but not chemical signals , and a bypass mechanism was induced by chemical signals from the host plant , independent of CoKel2 function . Whereas plant pathogenic fungi including C . orbiculare have developed different mechanisms regulating appressorial development such as cAMP/PKA and MAP kinase signaling pathways , the signal transduction pathway specifically activated by plant-derived signals is unknown . In this study , we identified CoPag1 as a key component of the signaling cascade for appressorium formation activated by cutin monomers from the plant surface ( Figs 1 and 4 , S1 Table ) . Genome-wide transcriptional profiling showed that 95% of the CoPag1-dependently expressed genes were plant-signal-induced genes ( Fig 6A ) , suggesting that CoPag1 plays a crucial role in the signal transduction pathway for sensing and responding to plant-derived cues in C . orbiculare . On the other hand , the normal appressorium formation of copag1Δ on artificial surfaces suggests that CoPag1 is not involved in the CoKel2-dependent physical signal transduction pathway ( Fig 1A and 1B , S1 Table ) . If CoPag1 is not specific to chemical signals but involved in physical signals , the copag1Δ mutant would not form normal appressoria on artificial surfaces . Therefore , we propose that two separate cascades , Kel2- and Pag1-dependent , are induced by the physical and chemical signals , respectively , and function redundantly in appressorium development of C . orbiculare ( Fig 7 ) . Consistent with this hypothesis , the transcriptome of C . orbiculare in planta differed from that in vitro at the stage of appressorium development , although the appressoria on the two surfaces are morphologically indistinguishable ( Fig 6A ) . In addition , introduction of the phospho-mimetic point mutation did neither affect cAMP nor MAP kinase signaling pathway mutant phenotype ( S8 Fig ) , indicating the independency of the MOR from those signaling pathways . In this study , out of six insertional mutants , we identified a tagged gene besides CoPAG1 . The mutated gene of PDM-7 showed significant homology to S . cerevisiae SNF1 ( Sucrose-Non-Fermenting-1 ) kinase gene . Because of the other four mutants were not tagged by T-DNA insertion , we tested whether these phenotypes were caused by disruption of CoPAG1 . Whereas introduction of CoPAG1 to PDM-4 restored normal appressorium formation and pathogenicity , phenotypes of CoPAG1 complemented strains in the other PDMs were not affected ( S1A–S1E Fig ) , indicating that the disrupted genes of these PDMs were not CoPAG1 . In addition , we examined whether these mutations are under control of CoCbk1 by introducing CoCbk1-CA to PDMs and evaluation of their phenotype ( S1C–S1E Fig ) . This provided a possibility that these mutations lie up- or down-stream of CoCbk1 shown in S1F Fig . NDR kinases are regulated by phosphorylation at two conserved sites [8] . A serine residue within the kinase domain is autophosphorylated for the basal kinase activity [9 , 14] . Activation further requires a second phosphorylation in the C-terminal hydrophobic motif of the kinase [9 , 14] . In C . orbiculare , the introduction of phospho-mimetic point mutations in the hydrophobic motif of CoCbk1 ( T649E ) suppressed the defective phenotype of copag1 and cohym1 mutants ( Fig 3 and S6 Fig ) . Moreover , the level of CoCbk1 phosphorylation at the autophosphorylated serine residue ( S477 ) of copag1Δ and cohym1Δ was lower than that of the wild type ( Fig 3H ) . Yeast two-hybrid assays revealed that CoCbk1 interacted with CoPag1 and CoHym1 ( S7 Fig ) . These results indicate that CoPag1 and CoHym1 directly regulate the CoCbk1 activity through its phosphorylation at the hydrophobic motif and at the autophosphorylated serine residue of the kinase domain . Moreover , CoCbk1 is further activated by the cutin monomer dependent on CoPag1 ( Fig 4E and 4F ) . The copag1Δ cokel2Δ formed abnormal appressoria in the presence of cutin monomers compared with cokel2Δ ( Fig 4C and 4D ) . Therefore , we propose that enhanced activation of CoCbk1 dependent on CoPag1 required for appressorium development responding to cutin monomers in C . orbiculare . The MOR regulates cell polarity and morphogenesis in fungi . Despite the high conservation of its components , deletion phenotypes of MOR components in various fungi are diverse , ranging from loss of polarity to hyperpolarization and excessive branch formation . In C . orbiculare , gene deletion mutants of CoCBK1 and the uncharacterized MOR components ( CoMOB2 , CoKIC1 , CoSOG2 ) could not be obtained due to mycelial growth defect , whereas copag1 and cohym1 deleted mutants were obtained and did not show growth defectiveness . In western blotting with phosphospecific antibody , copag1 and cohym1 mutants retained partial CoCbk1 activity ( Fig 3H ) . Thus , the difference in the deletion phenotypes seems in consistent with the CoCbk1 phosphorylation level . To evaluate the role of CoCbk1 in appressorium development , we used the CoCbk1-AS mutant . The conditional inactivation of CoCbk1 and cohym1 deletion resulted in abnormal appressoria from bilateral germination of the conidia ( Fig 2D and 2E , S6B Fig ) . Similarly , copag1Δ cokel2Δ is defective in the determination of conidial germination site , although copag1Δ is normal in appressorium development because the physical signal pathway mediated by CoKel2 functions redundantly ( Fig 1A and 1C ) . These results indicate that the lower CoCbk1 activity leads to a defect in polarized morphogenesis during conidial germination in C . orbiculare . On the other hand , constitutively active CoCbk1 mutation in the wild type resulted in morphologically normal appressorium formation , but its virulence was slightly lower than that of the wild type ( Fig 3E ) . Therefore , proper regulation of the CoCbk1 activity is required for appressorium-mediated infection of C . orbiculare . One of the open questions in research on MOR concerns the identification of upstream activators and downstream targets of the cascade . In C . orbiculare , the MOR components CoPag1 and CoCbk1 is involved in appressorium development induced by cutin monomers , suggesting that MOR of C . orbiculare at least indirectly links to an upstream receptor for cutin monomers . There is no report about a sensor for cutin monomers in fungi , but G-protein-coupled receptors ( GPCRs ) activated by free fatty acids have been identified in mammals [39] . Thus , GPCRs have the potential to sense cutin monomers . In M . oryzae , 76 GPCR-like receptors were found , including 61 Pth11-like proteins [40] . Further research on GPCRs could help us understand the relation between MOR and extracellular signal sensing in fungi . On the other hand , from our genome-wide gene expression analysis , we found that CoPAG1 contributes to regulation of a subset of the plant-signal-induced genes predicted to encode secreted proteins , CAzymes , transporters and transcription factors ( Fig 6 ) . These genes were under control of CoCBK1 ( Fig 6B ) . Although a virulence function of these genes has not yet been demonstrated , it is tempting to suggest that these genes contribute to infection of C . orbiculare . Consistent with this hypothesis , the copag1Δ formed morphologically normal appressoria , but virulence was slightly lower than that of the wild type ( Fig 1 ) . Therefore , the MOR components are responsible for the expression of a subset of the plant-signal-induced genes with potential roles in pathogenicity of C . orbiculare . Cutin monomers are well-known surface signals recognized by several fungal plant pathogens [28–30] . n-Octadecanal , identified in the exudate from cucumber leaves , and 1 , 16-hexadecanediol induced normal appressorium formation of cokel2Δ but not of copag1Δ cokel2Δ ( Fig 4C and 4D ) . Using biochemical approaches , we showed that n-octadecanal promotes CoCbk1 activation in the wild type but not in copag1Δ ( Fig 4E and 4F ) , implying that the CoCbk1 is activated by the cutin monomer mediated by CoPag1 in C . orbiculare . These results indicate that cutin monomers function as key signal molecules for appressorium formation through MOR components in C . orbiculare . In addition , appressorium morphogenesis of copag1 and cokel2 mutants on N . benthamiana , a susceptible host plant for C . orbiculare showed no distinctive difference to that on cucumber leaves ( S4 Fig ) , suggesting that appressorium formation is mainly responding to cutin monomers common in host plants . The extracellular matrix that surrounds spores plays a role in attachment of the host surface and the pre-penetration development of many plant pathogens [3] . Thus , we hypothesized that conidial surface esterases may be involved in the release of cutin monomers from the leaf surface . Previous studies revealed that the extracellular matrix of Colletotrichum species contains high molecular weight mannose-rich glycoproteins , germination inhibitors and a variety of enzymes , including cutinase [41] . Consistent with this , esterase activity is localized on the conidial surface in C . orbiculare ( Fig 5A–5C ) . The release of esterase occurred rapidly on contact with an aqueous environment ( Fig 5B ) . Most notably , n-octadecanal was detected in the cucumber exudate collected with the conidial suspension , but not in the exudate collected with distilled water ( Fig 5D ) , indicating that conidial surface esterases hydrolyze plant surface cuticle to cutin monomers . The amount of n-octadecanal in the exudate with the conidial suspension was reasonable for inducing normal appressorium formation by cokel2Δ ( Figs 4C , 4D and 5D ) . Taken together , these data suggest that conidial surface esterases hydrolyze the cuticle to generate the signal molecule for appressorium development via the MOR components of C . orbiculare ( Fig 7 ) .
Strain 104-T ( MAFF240422 ) of C . orbiculare ( Berk . & Mont . ) Arx [syn . C . lagenarium ( Pass . ) Ellis & Halst . ] was used as the wild type strain [42] . All C . orbiculare strains were maintained at 24°C in the dark on 3 . 9% ( w/v ) PDA ( Difco ) or SD medium ( 0 . 67% w/v yeast nitrogen base without amino acids , 2% w/v glucose , 2% w/v agar ) . E . coli DH5α competent cells were used as the host for gene manipulation and maintained on Luria-Bertani medium [43] at 37°C . Agrobacterium tumefaciens strain C58C1 was used as the T-DNA donor for fungal transformation and maintained on LB medium at 28°C . The inoculation assays on detached cucumber leaves ( Cucumis sativus L . ‘Suyo’ ) were performed as described previously [44] . Conidia of C . orbiculare were obtained from 7-day-old PDA cultures , and six drops of 10 μL of a conidial suspension ( 5 × 105 conidia/mL ) were placed on the surface of cucumber leaves . The leaves were incubated in a humid box at 24°C with a 16 h photoperiod for 7 days . For appressorium formation and penetration assays in vitro , 20 μL droplets or 1 mL of a conidial suspension ( 5 × 105 conidia/mL distilled water ) was placed on a polystyrene petri dish or a cellophane membrane ( Wako Chemicals ) , respectively , and incubated in a humid environment at 24°C in the dark . For penetration assays in planta , 10 μL of a conidial suspension ( 5 × 105 conidia/mL distilled water ) was spotted onto the abaxial surface of cucumber cotyledons and incubated in a humid box at 24°C . After 3 days , the lower epidermis of the cotyledons were peeled off and stained with 0 . 1% lactophenol aniline blue solution [45] . The Agrobacterium tumefaciens-mediated transformation ( AtMT ) protocol was applied with slight modifications of a previously described method [44] . Hygromycin-resistant transformants were selected on PDA containing 100 μg/mL hygromycin B ( Wako Chemicals ) , 25 μg/mL meropenem hydrate ( Sumitomo Dainippon Pharma ) . Bialaphos-resistant transformants were selected on SD medium containing 4 μg/mL bialaphos ( Meiji Seika Kaisha ) and 25 μg/mL meropenem hydrate . Sulfonylurea-resistant transformants were selected on SD medium containing 4 μg/mL chlorimuron ethyl ( Maruwa Biochemical ) and 25 μg/mL meropenem hydrate . All C . orbiculare strains generated in this study are listed in S2 Table . The 10 , 021 insertional mutants in the cokel2Δ background were screened using the AtMT protocol as described previously [25] . Whole genome sequences of six insertional mutants ( PDM-2 , PDM-3 , PDM-4 , PDM-5 , PDM-6 and PDM-7 ) were investigated using Illumina HiSeq 2000 . T-DNA insertion sites were detected by comparison with the wild type strain 104-T genome sequence . Multiple sequence alignments were performed using the Clustal W program [46] . Phylogenetic trees were constructed using MEGA 6 ( www . megasoftware . net ) [47] with the minimum-evolution method , based on 1000 replicates . The amino acid sequences of Pag1 homologs were obtained from the database of the National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov/ ) . For synthesizing CoPAG1 cDNA , total RNA was extracted from vegetative mycelium of C . orbiculare by using RNeasy Plant Mini Kit ( Qiagen ) . CoPAG1 cDNA was amplified by RT-PCR using primer pairs CoPAG1orf_F3/CoPAG1orf_R2 and CoPAG1orf_F2/CoPAG1orf_R1 . The amplified PCR products were introduced into the yeast cDNA expression vector pYES2 ( Invitrogen ) using the In-Fusion HD cloning kit ( Clontech ) . These primers are listed in S4 Table . As a positive control , a plasmid expressing S . cerevisiae TAO3 was also constructed . The genomic DNA of TAO3 was amplified by PCR using primer pair ScTAO3_F1/ScTAO3_R1 . Yeast cells were transformed using the lithium acetate method [48] . Yeast cells were then grown on yeast SC minimal medium agar lacking uracil ( U ) . For observation of yeast cell morphogenesis , cells were cultured overnight either in SC or SC-U containing 20% galactose and 10% raffinose at 30°C . The axial ratio ( length/width ) of cells was calculated from images analyzed with the software ImageJ ( National Institutes of Health , http://rsb . info . nih . gov/ij/ ) . All plasmids were derived from a binary vector pBIG4MRBrev carrying the bialaphos-resistance gene cassette and pBIG4MRSrev carrying the sulfonylurea-resistance gene cassette . For plasmid constructions , the In-Fusion HD Cloning Kit ( Clontech ) or GENEART Seamless Cloning and Assembly Kit ( Thermo Fisher Scientific ) were used . All primers used in this study are listed in S4 Table . For generating the copag1 disruption vector , 1 . 0-kb upstream and downstream flanking sequences were amplified from C . orbiculare genomic DNA , and a 1 . 4-kb fragment of the hygromycin-resistance gene was amplified from pBIG4MRHrev with the respective primers ( S4 Table ) . These three fragments were inserted into a linearized pBIG4MRSrev . The same procedure was applied to generate the cohym1 disruption vector . For generating an analog-sensitive CoCbk1M352A variant ( ATG > GCG ) , a 2 . 3-kb fragment containing the 5′-region of CoCBK1 and a 2 . 0-kb fragment containing the 3′-region of CoCBK1 were amplified from C . orbiculare genomic DNA with the respective primers to generate a point mutation ( S4 Table ) . These two fragments were inserted into pBIG4MRBrev . The hygromycin-resistance gene or the sulfonylurea-resistance gene were amplified with the respective primers ( S4 Table ) and inserted downstream of the 3′ untranslated region of CoCBK1 in the CoCbk1M352A point mutation plasmid pBI-CoCbk1-asB . For generating a constitutively active CoCbk1T649E variant ( ACA > GAG ) , a 3 . 2-kb fragment containing the 5′-region of CoCBK1 and a 3 . 8-kb fragment containing the 3′-region of CoCBK1 and the sulfonylurea-resistance gene inserted downstream of the 3′ flanking region of CoCBK1 were amplified from the CoCbk1M352A point mutation plasmid pBI-CoCbk1-asBS with the respective primers ( S4 Table ) . These two fragments were inserted into pBIG4MRBrev . For constructing the CoCBK1-GFP fusion gene vector regulated by the CoCBK1 native promoter , a CoCBK1 fragment containing its 1 . 2-kb upstream and 900-bp downstream flanking sequences were amplified from C . orbiculare genomic DNA . The fragment was inserted into a linearized pBIG4MRBrev . The GFP fragment was amplified from pBI-glyGFP and inserted at the C-terminal end of CoCBK1 in the CoCBK1 complementation plasmid pBI-CoCBK1-B . The yeast two-hybrid screen was performed using the instructions of the Matchmaker Gold Yeast Two-Hybrid System ( Clontech ) . Full-length cDNAs of putative interaction partners were generated from C . orbiculare mycelia . The genes encoding the proteins tested for interaction were cloned into the pGBKT7 or pGADT7 vectors ( Clontech ) to express fusion proteins with the yeast GAL4 binding ( BD ) and activation domain ( AD ) , respectively . All BD or AD constructs were used to transform the Gold or Y187 yeast strain , respectively ( Clontech ) . After mating , diploid yeast was plated on double dropout synthetic selective medium lacking Trp and Leu ( DDO ) for mating control and on stringent medium supplemented with 20 mg/mL X-α-Gal and 100 ng/mL aureobasidin A ( DDO/X/A ) and incubated at 30°C for 5 d . Protein interactions were assessed by growth of diploid yeast on DDO , DDO/X/A , and stringent quadruple dropout synthetic selective medium lacking Trp , Leu , Ade and His ( QDO ) compared with corresponding controls . All yeast strains generated in this study are listed in S3 Table . Genomic DNA of C . orbiculare was isolated from mycelia , and DNA blot analysis was done by standard methods . DNA probes were labeled with DIG-dUTP using the BcaBEST DIG Labeling Kit ( Takara Bio ) . Hybridized DNA was detected using Anti-Digoxygenin-AP Fab fragments ( Roche Diagnostics ) , and light emission generated by enzymatic dephosphorylation of CDP-Star Detection Reagent ( GE Healthcare ) by alkaline phosphatase was detected using the FUJIFILM LAS1000 plus gel documentation system ( Fujifilm ) . The CoCbk1 activity of an analog-sensitive CoCbk1M352A variant ( CoCbk1-AS ) was inhibited by adding 0 . 5 μM 1NA-PP1 ( Carbosynth Ltd . ) . For pathogenicity tests , conidia were suspended in 0 . 5 μM 1NA-PP1 at 5 × 105 conidia/mL . For penetration assays in vitro , 0 . 5 μM 1NA-PP1 was applied to cells on a cellophane membrane at the appropriate time . For collecting exudates from cucumber cotyledons , 10 μL droplets of the wild type conidial suspension ( 5 × 105 conidia/mL distilled water ) were placed on the surface of cotyledons for 1 h at 24°C , then collected by filtering through a 0 . 2-μm-pore filter ( Sartorius ) . For testing the effects of cutin monomers on appressorium development , stock solutions of 1 mM n-octadecanal and 1mM 1 , 16-hexadecanediol were prepared in 100% ethanol , then diluted with distilled water to 10 μM . Conidia were suspended to 5 × 105 conidia/mL in the cucumber exudate , 10 μM n-octadecanal , 10 μM 1 , 16-hexadecanediol , distilled water or 1% ethanol as the control experiments . Drops of water applied to the leaf surface were collected ( total 3~5 mL ) and lyophilized . The dry residue was dissolved in methanol ( 100 μL ) and directly analyzed by GC-MS ( Shimadzu GCMS-QP5050A ) using a TC-FFAP capillary column ( 30 m × 0 . 25 mm ) . The GC was temperature programmed with an initial 1 min at 200°C , then a rise of 10°C/min to final isothermal period at 280°C . Detector temperature was set at 250°C . For quantitative determination , standard n-octadecanal was synthesized from commercially available n-octadecanoic acid ( Wako Chemicals ) by reduction with diisobutylaluminum hydride . The structure was confirmed by H-NMR ( Bruker AVANCE III 400 ) and ESI-MS ( Shimadzu LCMS-8040 ) . All mycelial samples were prepared from 5-day culture at 24°C in SD broth . For the cutin monomer induction assay , 100 μM n-octadecanal was applied at 4-day culture and incubated further 24 h . The protein lysates for immunoprecipitation were prepared from mycelia by grinding in liquid nitrogen . The tissue powder was dissolved in the lysis buffer contained 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 10% glycerol , 0 . 1% v/v Triton X-100 , 1mM DTT , 60 mM β-glycerophosphate , 10 mM NaF , 1 mM sodium molybdate , 0 . 1% cantharidin and a protease inhibitor cocktail tablet ( Roche Diagnostics ) , and total extracts were clarified by centrifugation at 16 , 900 × g , 4°C , 20 min . The CoCbk1-GFP proteins were immunoprecipitated from total protein extracts using anti-GFP ( abcam ) coupled to recombinant Protein G Sepharose beads ( Thermo Fisher Scientific ) as described [49] . Western blotting with phosphospecific antibodies [22] was modified from [49] . Proteins were separated by SDS-PAGE using a Novex 3–8% Tris-Acetate gel ( Thermo Fisher Scientific ) and transferred to a PVDF membrane ( Thermo Fisher Scientific ) . Membranes were blocked for 1 h at 24°C with 5% skim milk in Tris-buffered saline with 0 . 1% Tween ( TBST ) . The primary antibodies were diluted 1:1 , 000 ( anti-pS570 ) or 1:2 , 500 ( anti-GFP ) in TBST and incubated 1 h at 24°C . After washing three times for 5 min with TBST , membranes were then incubated with HRP-conjugated anti-rabbit secondary ( Vector Laboratories ) at 1:1 , 000 in TBST with 5% skim milk for 1 h at 24°C . Membranes were washed three times for 5 min in TBST , and were imaged and quantified using the FUSION SOLO 7S SYSTEM and the Fusion-Capt Advance 17 . 01 software ( Vilber-Lourmat ) . The qualitative esterase assay was modified from [50] . The assay medium contained 3 . 4 mM indoxyl acetate ( Wako Chemicals ) dissolved in 20 mM Tris-HCl , pH 8 . 0 , containing 0 . 99 M NaCl , 44 . 6 mM CaCl2 , and 17 . 5% gelatin . Hydrolysis of the substrate for esterase results in the accumulation of pigmented crystals of indigo blue at the site of hydrolysis . Conidia of C . orbiculare were placed on glass slides coated with a thin layer of assay medium; after a 2-h incubation in a humid box at 24°C under continuous light , conidia were examined . Cellular localization of esterase activity was determined by comparing unwashed spores and spores that had been washed 5 times for 1 min by vortex in 1 mL of a 0 . 01% Tween 20 solution in 20 mM Tris-HCl , pH 8 . 0 . Autoclaved spores were included as a negative control of hydrolysis . For collecting the extracellular matrix that surrounds spores , 100 mg of spores were repeatedly washed by vortexing for 1 min in 20 mM Tris-HCl , pH 8 . 0 , containing 0 . 01% v/v Tween 20 . The spore suspensions were then centrifuged , and collected supernatants were filtered through a 0 . 2-μm-pore filter . Esterase activity was determined by measuring the hydrolysis of p-nitrophenyl butyrate ( Sigma ) at 400 nm as described previously [50] . Assays were run at 37°C for 10 min . Proteins washed from 220 mg of spores were freeze-dried , then dissolved in 500 μL of 50 mM Tris-HCl , pH 7 . 4 , containing 150 mM NaCl , 1 mM EDTA , 1% v/v Triton X-100 , 0 . 1 M PMSF , and a protease inhibitor cocktail tablet ( Roche Diagnostics ) . Intracellular proteins were isolated from 220 mg of washed spores by grinding in liquid nitrogen . The tissue powder was dissolved in 500 μL of the same buffer as the extracellular proteins . The lysate was clarified by centrifugation ( 16 , 900 × g , 4°C , 20 min ) , and the supernatant was collected . Protein concentration was determined using the Bradford protein assay . Esterase from porcine liver ( Sigma ) was used as a positive control of hydrolysis . Proteins were separated using a Novex Tris-Glycine gel ( Thermo Fisher Scientific ) and Tris-Glycine Native Sample Buffer ( Thermo Fisher Scientific ) . Preparations containing 1 . 0 , 0 . 1 , or 0 . 05 μg proteins were loaded onto the gel . SeeBlue Pre-stained Protein Standard ( Thermo Fisher Scientific ) was used as the molecular weight marker . Electrophoresis was conducted at constant 125 V for 3 h at 4°C in 1× Tris-glycine native running buffer ( Thermo Fisher Scientific ) . The gel was then washed twice for 10 min each in 100 mM Tris-HCI , pH 8 . 0 . Esterase activity was detected using the indoxyl acetate assay as described [50] . Bright-field microscopy was performed using a Nikon ECLIPSE E600 microscope equipped with a Keyence VB-7010 charge-coupled device ( CCD ) color camera system to acquire images with 20× and 40× water immersion lenses or a 100× oil immersion lens ( Plan Fluor ) . Differential interference contrast ( DIC ) optics and a Zeiss Axio Imager M2 Upright microscope equipped with an AxioCam MRm digital camera were used to acquire images with a 100× oil immersion lens ( Plan Apochromat ) and Axiovision 4 . 8 software . For sampling of appressoria in vitro , 10 mL of a conidial suspension ( 5 × 105 conidia/mL 0 . 1% w/v yeast extract solution ) was placed on polystyrene petri dishes and incubated at 24°C in the dark for 1 h . The yeast extract solution was then removed and replaced with distilled water . After 3 h , the distilled water was removed , and cells were harvested using a scraper in 10 mL of RNAlater ( Thermo Fisher Scientific ) containing 1 mL of 0 . 01% ( v/v ) Tween 20 . The conidia were collected by centrifugation at 15 , 000 rpm at 4°C for 10 min , then subjected to RNA isolation using an Agilent Plant RNA Isolation MiniKit ( Agilent Technologies ) . For sampling appressoria in planta , 10 μL of a conidial suspension ( 5 × 105 conidia/mL distilled water ) was spotted onto the abaxial surface of detached cucumber cotyledons and incubated in a humid box at 24°C in the dark . After 4 h , the lower epidermises of the cotyledons were peeled off and ground in liquid nitrogen . Then total RNA was prepared using the Agilent Plant RNA Isolation MiniKit . Microarray analyses were performed using the Colletotrichum orbiculare ( 8 × 60k , 13352 independent probes , Design ID: 060762 ) Oligo Microarray , according to the Agilent 60-mer Oligo Microarray Processing Protocol ( Agilent Technologies ) . The quality of total RNA was monitored with the Agilent 2200 Tapestation . Total RNA samples ( 200 ng ) were used to prepare Cy3-labeled cRNA using a Low RNA Input Fluorescent Linear Amplification Kit ( Agilent ) . Fluorescence-labeled cRNAs were purified using an RNeasy RNA Purification Kit ( Qiagen ) . Three independent RNA samples were used to confirm the reproducibility of the microarray analyses . The images were analyzed using Feature Extraction Software ( Ver . 10 . 7 . 3 . 1 ) and GeneSpring GX 12 . 1 software ( Agilent ) . Normalization was performed as follows: ( i ) intensity-dependent Lowess normalization; ( ii ) data transformation , measurements less than 0 . 01 were set to 0 . 01; ( iii ) per-chip 75th-percentile normalization of each array; and ( iv ) per gene ( data were normalized to the median value of 12 measurements ) . After normalization , statistically significant ( t-test ) gene sets were defined as those showing P values less than 0 . 05 . In addition , the differentially expressed genes ( >2-fold change ) were identified . Thus , a combination of statistical analysis and FC method was used . The differentially regulated genes ( FC > 2 and P < 0 . 05 ) were selected and used for further analysis . The raw and processed data were deposited in the Gene Expression Omnibus ( GEO ) database ( access ID: GSE 7556 ) . Sequence data from this article can be found in the GenBank databases under the following accession numbers: CoPag1 ( ENH85423 ) , CoCbk1 ( ENH84264 ) , CoHym1 ( LC147073 ) , CoKel2 ( AB259753 ) , CoCac1 ( AB127957 ) , CoCmk1 ( AF174649 ) , CoMekk1 ( AB546841 ) , S . cerevisiae Tao3 ( NP_012137 ) , Cbk1 ( NP_014238 ) and Hym1 ( NP_012732 ) . Accession numbers of Pag1 homologs of other fungi and eukaryotes are as follows: C . higginsianum ( CCF34414 ) ; C . graminicola ( EFQ30591 ) ; Neurospora crassa ( XP_964442 ) ; Magnaporthe oryzae ( XP_003715305 ) ; Aspergillus fumigatus ( XP_750987 ) ; A . nidulans ( CBF89162 ) ; Candida albicans Tao3 ( XP_721647 ) ; Schizosaccharomyces pombe Mor2 ( NP_596172 ) ; Cryptococcus neoformans Tao3 ( ADY38376 ) ; Ustilago maydis Tao3 ( XP_011386655 ) ; Caenorhabditis elegans SAX-2 ( NP_741131 ) ; Drosophila melanogaster Fry ( AAG41424 ) ; Homo sapiens FRY ( NP_075463 ) . | Phytopathogenic fungi cause many of the most serious crop diseases . Many fungal pathogens form specialized infection structures , called appressoria in response to plant surface signals . Networks of protein kinase-based signaling pathways regulate a wide variety of key morphological processes including appressorium development in several fungal pathogens . However , the precise link between plant signals and fungal intracellular transduction is poorly understood . Here , we report on the identification of a native molecule and a cognate signal transduction pathway involved in appressorium morphogenesis of the cucumber anthracnose fungus Colletotrichum orbiculare . We demonstrate that the widely conserved morphogenesis-related NDR kinase pathway ( MOR ) of C . orbiculare regulates infection structure development triggered by plant-derived signals and involves in pathogenesis . The cutin monomer n-octadecanal released from the host cuticle by conidial esterases functions as the signal molecule for appressorium development via MOR . Inactivating MOR resulted in downregulation of the plant-signal-induced genes including fungal secreted proteins that potentially facilitate infection . Thus , MOR is the crucial coordinator connecting plant surface signals with infection-related morphogenesis and pathogenesis . While previous reports have revealed that MOR is crucial for controlling cell polarity and differentiation in other fungi , our study provides its new role in the interaction of fungal pathogens with host plant . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
]
| [
"plant",
"anatomy",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"plant",
"growth",
"and",
"development",
"enzymes",
"distillation",
"plant",
"embryo",
"anatomy",
"enzymology",
"fungal",
"structure",
"developmental",
"biology",
"fungi",
"plant",
"science",
"model",
"organisms",
"experimental",
"organism",
"systems",
"crops",
"vines",
"appressoria",
"plants",
"cotyledons",
"(botany)",
"morphogenesis",
"research",
"and",
"analysis",
"methods",
"saccharomyces",
"separation",
"processes",
"mycology",
"vegetables",
"crop",
"science",
"plant",
"embryogenesis",
"plant",
"development",
"proteins",
"pathogenesis",
"agriculture",
"yeast",
"cucumber",
"biochemistry",
"hydrolases",
"esterases",
"embryogenesis",
"biology",
"and",
"life",
"sciences",
"yeast",
"and",
"fungal",
"models",
"saccharomyces",
"cerevisiae",
"organisms",
"fruit",
"and",
"seed",
"anatomy"
]
| 2017 | The morphogenesis-related NDR kinase pathway of Colletotrichum orbiculare is required for translating plant surface signals into infection-related morphogenesis and pathogenesis |
Biogenesis of ribosomes is an essential cellular process conserved across all eukaryotes and is known to require >170 genes for the assembly , modification , and trafficking of ribosome components through multiple cellular compartments . Despite intensive study , this pathway likely involves many additional genes . Here , we employ network-guided genetics—an approach for associating candidate genes with biological processes that capitalizes on recent advances in functional genomic and proteomic studies—to computationally identify additional ribosomal biogenesis genes . We experimentally evaluated >100 candidate yeast genes in a battery of assays , confirming involvement of at least 15 new genes , including previously uncharacterized genes ( YDL063C , YIL091C , YOR287C , YOR006C/TSR3 , YOL022C/TSR4 ) . We associate the new genes with specific aspects of ribosomal subunit maturation , ribosomal particle association , and ribosomal subunit nuclear export , and we identify genes specifically required for the processing of 5S , 7S , 20S , 27S , and 35S rRNAs . These results reveal new connections between ribosome biogenesis and mRNA splicing and add >10% new genes—most with human orthologs—to the biogenesis pathway , significantly extending our understanding of a universally conserved eukaryotic process .
In eukaryotic cells , the synthesis of ribosomes is a complex process involving several hundred genes whose functions span transcription of precursor ribosomal ribonucleic acids ( pre-rRNAs ) , processing of pre-rRNAs , assembly of ribosomal proteins ( r-proteins ) with pre-rRNAs , and nuclear export of the ribosomal particles [1]–[6] . Ribosome biogenesis is an essential process , with mutations of ribosome biogenesis genes either causing lethality or increasing susceptibility to cancer—e . g . , bone marrow failure and leukemia [7] or breast cancer [8] . This pathway has been extensively studied over the past 30–40 y , and a broad picture of the major events is known for the yeast Saccharomyces cerevisiae . First , 35S polycistronic pre-rRNA is transcribed from the ribosomal deoxyribonucleic acid ( rDNA ) repeat by RNA polymerase I in the nucleolus . During transcription , the small-subunit processome and some small-subunit r-proteins assemble onto the 35S pre-rRNA to form a 90S particle . The 35S pre-rRNA is cleaved to release the pre-40S particle , which contains a 20S pre-rRNA . The pre-60S complex assembles on the rest of the transcript , and both subunits are further processed in the nucleus and independently exported through the nuclear pore complex ( NPC ) to the cytoplasm , where they undergo further maturation—e . g . , cleavage of 20S pre-rRNA to 18S rRNA . The mature small subunit contains 32 proteins and 18S rRNA , while the large subunit contains 46 proteins and three rRNAs: 5 . 8S , 25S , both derived from the 35S precursor , and 5S , which is transcribed separately by RNA polymerase III . Ribosome biogenesis is a temporally and spatially dynamic process requiring coordination of many trans-acting factors at different stages along the pathway , including at least 170 protein factors that act to modify and cleave pre-rRNAs and help to assemble and export ribosomal particles [5] , [9] . Many of these protein factors were first identified by yeast genetics . Later , biochemical purifications coupled with mass spectrometric analysis greatly expanded the number of known factors [10]–[16] . In addition , a large-scale effort using oligonucleotide microarrays identified 115 mutants that exhibited pre-rRNA processing defects , and 10 new genes were confirmed to affect pre-rRNA processing [17] . Despite these intensive studies , new ribosome biogenesis genes are still emerging , and recent computational analysis suggests that over 200 genes constitute the ribosome biogenesis regulon [18] , indicating that the genes in this fundamental cellular pathway have not been completely identified . We asked if recent functional genomic and proteomic studies could be applied in a predictive fashion to identify additional ribosomal biogenesis genes . In particular , functional networks of genes have been reconstructed , incorporating literally millions of experimental observations into probabilistic networks indicating genes likely to work together in cells . The emerging technique of network-guided genetics ( e . g . , [19] , [20] ) leverages such networks to computationally associate candidate genes with a biological process of interest , much as a genetic screen might do . We used such a probabilistic gene network [21] to predict the genes most likely to participate in yeast ribosome biogenesis based on connectivity to known ribosomal biogenesis genes , and we present here experimental confirmation of at least 15 new genes affecting ribosome biogenesis . Beyond providing new insights into ribosome biogenesis , this study therefore also represents one of the most extensive experimental studies to date of the principle of network-guided genetics , which we demonstrate to be a powerful approach for rational discovery of candidate genes , applicable to diverse biological processes .
In general , we expect genes of ribosome biogenesis to be coordinately expressed , to physically or genetically interact with each other , to show common subcellular localization , and so on . Many such associations have been observed in high-throughput experiments in yeast , but these data suffer from false-positive and false-negative observations . Nonetheless , the appropriate analyses of such data should rationally prioritize candidate ribosome biogenesis genes . We therefore constructed a computational predictor of ribosome biogenesis genes based on analysis of functional genomics , proteomics , and comparative genomics datasets that had been combined into a probabilistic gene network [21] covering about 95% of yeast proteome ( Figure 1A ) . This network employs a probabilistic scoring scheme to quantitatively integrate heterogeneous functional genomic and proteomic datasets , including mRNA-expression data across different conditions , protein-protein interaction datasets derived from literature curation , high-throughput yeast two-hybrid assay , affinity purification coupled with mass spectrometry , genetic interaction data , and in silico interaction datasets [21] . We calculated the naïve Bayesian probability that each yeast gene will belong to the ribosome biogenesis pathway based on gene connectivity information in the gene network—i . e . , “guilt-by-association” [22] , [23] with known ribosome biogenesis genes . Ribosome biogenesis genes were highly connected and predictable in this gene network , as shown by a plot of cross-validated true-positive versus false-positive prediction rates ( ROC plot; Figure 1B ) . From the top-scoring genes , 212 candidates were manually selected based on expert knowledge for experimental validation ( Table S1 ) . The synthesis of ribosomes is essential for cell growth and survival , and most genes involved in ribosome biogenesis are either essential or required for normal growth rates . In our list of candidate ribosome biogenesis genes , 50 genes are essential , and 162 genes are nonessential under standard laboratory culture conditions [24] . We thus performed growth assays for each strain with a deletion of one of the 162 nonessential genes under three temperature conditions: 20°C , 30°C , and 37°C ( Figure S1 ) . Of these , 51 mutants with constitutive or conditional slow-growth phenotypes were identified . These mutants and 50 mutants carrying conditional essential alleles were investigated further ( Figure 1A ) . For each of the selected 101 mutants , we tested for gross ribosome biogenesis defects by measuring the proportions of free 40S , 60S , and 80S subunits , as well as polysomes , in the mutant strains . After cleavage of the pre-40S particle from the 35S transcript , the syntheses of 40S and 60S subunits are largely independent [6] . Depletion of the factors required for the synthesis of one subunit usually does not significantly affect synthesis of the other subunit [25] , resulting in a change in the ratio of 40S to 60S , which is most evident in the free subunit pools in the cell . In addition , a reduction in the amount of 60S subunits can lead to a translation initiation defect , with 40S subunits awaiting 60S subunits to form 80S ribosomes . These stalled 40S subunits are observable as halfmer polysomes in a polysome profile [26] . Polysome profiles are generated by separating the ribosomal subunits and different-sized polysomes through a continuous sucrose density gradient and monitoring the absorbance of nucleic acids along the sucrose gradient [27] . We analyzed polysome profiles for the 50 mutants carrying conditional alleles controlled by either a tetracycline-regulatable ( tetO7 ) promoter [28] or a GAL1 promoter and for the 51 nonessential gene deletion mutants with conditional growth defects . Including controls , over 150 polysome profiles were generated . In order to compare different profiles and perform multivariate analyses such as clustering , we computationally aligned each profile to a reference wild-type profile by using a correlation-optimized warping ( COW ) algorithm [29] , which corrects for peak shifts of ribosome subunits and polysomes due to minor variations in sucrose density gradients . Similar polysome profiles were grouped together using hierarchical clustering [30] . From the clustergram , the signals corresponding to the ribosomal subunits , monosomes , polysomes , and halfmer polysomes were clearly identifiable ( Figure 2A ) . Importantly , nearly half of the tested mutants showed clear ribosome biogenesis defects by this analysis . This is a much higher ratio than the ∼1/30 expected by chance , indicating the strong enrichment for true ribosome biogenesis genes provided by the network-guided genetics . Several sets of mutants exhibited grossly similar biogenesis defects , detectable as coherent groups in the clustergram . Most of the profiles with high 40S to 60S ratios and halfmer peaks were in clusters 1 and 2 , which represent 60S biogenesis defects ( Figure 2C ) . Cluster 3 represents profiles from mutants showing protein translation defects ( Figure 2D ) , some of which also affected the ratio of 40S to 60S ribosomal subunits when compared to wild-type strains ( Figure 2B ) . It is noteworthy that the translation-initiation factor mutants , including fun12Δ , tetO7-TIF35 , tetO7-TIF34 , tetO7-RPG1 , and tetO7-DED1 , did not display the same defects , indicating that the observed ribosome biogenesis defects are not simply a general effect of inhibition of translation . The profiles with low 40S to 60S ratios were in cluster 4 , which suggests 40S biogenesis defects ( Figure 2E ) . The polysome profiles from three mutants ( ypr044cΔ , tif4631Δ , and snu66Δ ) were not clustered with 60S biogenesis clusters 1 and 2 , although they showed halfmer polysomes ( Figure 2A , 2C ) . Some mutants showed only subtle defects , and their profiles were interspersed among wild-type-like profiles during clustering ( Figure S2 ) . The polysome profiles provided initial suggestions about the function of these genes in ribosome biogenesis and translation . We further investigated 43 mutants that exhibited altered 40S to 60S ratios compared to wild-type strains ( Table S1 and Figure 1A ) . Most ribosome biogenesis factors associate with pre-ribosomal particles [3] . In order to distinguish factors associated with pre-40S particles from factors associated with pre-60S particles , we applied both a classical immunoblot approach and a novel mass-spectrometry-based approach in order to assess sedimentation patterns of potential ribosome biogenesis factors in sucrose density gradients ( Figure 1A ) . Most mutants defective for ribosome assembly display altered pre-rRNA processing [9] . The effects on pre-rRNA processing can be a direct consequence of a mutation in an enzymatic processing activity , or they can be indirect . Regardless of whether the effect is direct or indirect , the observed pre-rRNA processing defects provide valuable diagnostics for characterizing the ribosome biogenesis defects and thus the putative activity of a ribosome biogenesis candidate gene; we therefore examined pre-rRNA processing defects in each of the 43 candidate genes confirmed by polysome profiling to affect ribosome biogenesis . Several specific pre-rRNA processing events are critical to biogenesis: The 35S pre-rRNA undergoes extensive modification as well as sequential multiple endo- and exo-nuclease cleavages to give rise to the mature 18S , 5 . 8S , and 25S rRNAs [2] . The 35S pre-rRNA is first cleaved at sites A0 , A1 , and A2 to yield 20S and 27SA2 species ( Figure 5B ) , and the 20S pre-rRNA is further processed in the cytoplasm to form the mature 18S rRNA after cleavage at the D position . The 27SA2 pre-rRNA is processed by two different routes . The majority is cleaved at site A3 , followed by exonuclease digestion to site B1S to form 27SBS , while a small amount of 27SA2 undergoes endonucleolytic cleavage at B1L to generate 27SBL . Both 27SB species are further processed at sites C1 and C2 to yield the mature 25S species and 7S species , which mature to 5 . 8S by 3′-exonuclease digestion to E ( Figure 5B ) . To examine the detailed effects of the candidate ribosome biogenesis genes on pre-rRNA processing , we used Northern blotting with oligonucleotide probes ( Figure 5A ) to monitor the levels of 9 different pre-rRNA and rRNA species in each of the 43 mutant strains . In order to quantitatively analyze the change of each RNA species in a mutant relative to the wild-type strain under corresponding conditions , Northern blots ( Figure 5C–5E ) were quantified , and the logarithm of the intensity ratio of each RNA species from a mutant strain relative to that from its corresponding wild-type strain was calculated and used for hierarchical clustering analysis ( Figure 5F ) . We observed a dramatic increase ( red signal in Figure 5F ) or decrease ( green signal in Figure 5F ) of at least one pre-rRNA species for all of the mutants except eap1Δ and trf5Δ ( Figure 5F ) . The mutants with 60S biogenesis defects in polysome profile analyses clustered into two groups in Northern blotting analyses ( Figure 5F , green labels ) , and many 40S mutants in polysome profile analyses also clustered together ( Figure 5F , red labels ) , showing general correlation between polysome profile defects and pre-rRNA processing defects . In conjunction with the polysome profile and co-sedimentation data , these defects strongly suggest function for the candidate genes in or upstream of the implicated processing steps . We thus employed the observed defects to classify the candidate genes according to their potential general roles . As a last major characterization of the candidate ribosome biogenesis genes , we investigated their possible roles in ribosome nuclear export . Nuclear export of the ribosomal subunits through NPCs depends upon the RanGTPase cycle and receptor proteins that mediate the interaction between the ribosomal subunit and the NPC . The receptors can bind to adapter proteins or to the subunits directly . In the case of the 60S subunit in yeast , export depends upon the adapter protein Nmd3p and its receptor Crm1p ( Xpo1 in human ) , as well as the heterodimer of Mex67p/Mtr2p [63] and the specialized receptor Arx1p [64] , [65] . Export of the 40S subunit also requires Crm1p , and although it has been suggested that Ltv1p acts as a Crm1p-dependent adapter , Ltv1p is not essential , indicating that additional adapters and/or receptors remain to be identified [5] , [66] . To test whether the ribosome biogenesis candidates affect ribosome transport , we assayed ribosome export in the mutants by using Rps2-GFP and Rpl25-GFP as reporters for the small and large ribosomal subunits , respectively [10] , [67] , while monitoring the nucleolus with Sik1-mRFP [34] . In wild-type control strains cultured under various conditions , both small and large ribosomal subunits localized primarily in the cytoplasm ( Figure 7A–7B , first row , and Figure S3 ) . Upon depletion of Yrb2p , a known factor involved in small-subunit export [45] , ribosomal small subunits accumulated in the nucleus ( Figure 7A ) , while the large subunits were unaffected ( Figure S4 ) . In mutants defective in the synthesis of small subunits , including tetO7-BFR2 , bud22Δ , bud23Δ , tetO7-YDR339C , ygr081cΔ , GAL1-ENP2 , GAL1-NOP9 , GAL1-SGD1 , and GAL1-KRE33 , we observed significant accumulation of the small subunit reporter in the nucleus and/or nucleolus ( Figure 7A ) , whereas the large subunits were unaffected ( Figure S4 ) . The defective nuclear export of 40S subunits upon depletion of Kre33p is consistent with previous observation of a temperature sensitive mutant kre33-1 [10] . Because the pre-40S contains the 20S pre-rRNA as it is exported to the cytoplasm , a bona fide block in subunit export is expected to result in increased levels of 20S rRNA . This was in fact observed for the bud23Δ and ygr081cΔ mutants ( Figure 5F ) , which suggests that they act late in the biogenesis and export pathway , whereas the other genes are involved in early ribosome biogenesis . Recently , Bud23p has also been shown to methylate G1575 of 18S rRNA [68] . We note , however , that defective pre-rRNA processing and/or ribosome assembly may also lead to the inefficient transport of ribosomes to the cytoplasm [69] or accumulation of reporter proteins in the nucleus . In mutants with defective synthesis of large ribosomal subunits , including tetO7-AFG2 , tetO7-BCP1 , puf6Δ , tetO7-YDR412W , and tif4631Δ , strong accumulation of the large ribosomal subunits in the nucleolus and nucleus was observed ( Figure 7B ) , but not of the small subunits ( Figure S5 ) . Surprisingly , deletion of LSM6 or LSM7 inhibited the transport of pre-60S subunits to the cytoplasm ( Figure 7B ) but not the small subunits ( Figure S5 ) . Therefore , the accumulation of 20S upon deletion of LSM6 or LSM7 suggests that they act in 20S processing in the cytoplasm . In total , we identified 17 genes that affected export of either the ribosomal small or large subunits .
As expected , many genes for ribosome biogenesis are essential . However , a large number of nonessential genes are clearly involved in ribosome biogenesis , some of which show strong constitutive or conditional phenotypes ( Figure S6 ) . For example , deletion of PUF6 , SAC3 , or SNU66 resulted in strong defects at 20°C but only minor defects at the optimal growth temperature of 30°C . In contrast , the polysome profile of yor006cΔ showed 40S biogenesis defects at 30°C but no defects at 20°C . Several nonessential genes , including YIL096C , YCR016W , YJL122W , YNL022C , BUD20 , and NOP13 , form a tight cluster with known ribosome biogenesis genes in the gene network , and their encoded proteins co-sedimented with either 40S or 60S fractions , supporting them as being components of pre-ribosomes ( unpublished data ) . However , deletion mutants for those genes did not show growth defects at 20°C , 30°C , or 37°C ( Figure S1 ) , nor were polysome profiles of the deletion mutants different from wild-type cells ( unpublished data ) . However , lack of a mutant phenotype does not imply that these candidate genes are not part of the ribosome biogenesis pathway . In fact , Yjl122wp ( Alb1p ) was recently confirmed to interact directly with the known ribosome biogenesis factor Arx1p , although the deletion mutant had no observable phenotype [70] . It is therefore still likely that the remaining candidate genes participate in ribosome biogenesis but that we failed to identify a conditional phenotype or that these genes are functionally redundant with other genes . In the latter case , synthetic interaction assays might prove a useful strategy for deciphering the genes' functions . Indeed , we observed one such example: mutants with either deletion of TRF5 or depletion of Pap2p did not exhibit defects in polysome profile analyses at 30°C , but depletion of Pap2p in the trf5Δ mutant caused strong 60S biogenesis defects evident in polysome profile analysis ( Figure 8 ) , which suggests that TRF5 and its paralog PAP2 are required for efficient ribosome biogenesis , presumably by facilitating the removal of aberrant pre-rRNA molecules [71] . Thus , many of the remaining nonessential mutants without conditional phenotypes may still be involved in ribosome biogenesis . Gene network-based predictions based on binary associations between genes intrinsically help to identify genes that participate in multiple cellular processes . Correspondingly , several genes we identified have been reported to have other functions . For example , BCP1 is required for the export of Mss4p [72] , Sgd1p interacts with Plc1p and is involved in osmoregulation [73] , and a recent study showed that Mtr2p , known as an mRNA export receptor [74] , is directly involved in ribosomal large-subunit export [63] . Similarly , we identified Sac3p , which localized to the NPC and is involved in mRNA export [75] as a ribosome biogenesis factor based on polysome profile and Northern blot analyses of the deletion mutant ( Figures 2E , 5E ) . In addition , Sac3p co-sedimented with 40S fractions , suggesting its possible association with ribosomes ( Figure 3A ) . It is known that Sac3p can mediate protein export [76] , but we did not observe export defects for either ribosomal subunit in the sac3Δ mutant ( unpublished data ) . Thus , SAC3 joins MTR2 and MEX67 as genes participating in both the ribosome biogenesis and mRNA export pathways . Recently , the splicing factor Prp43p was confirmed to be a ribosome biogenesis factor by several groups , which suggests coordination of ribosome biogenesis and mRNA splicing [77]–[79] . We observed that four genes associated with mRNA splicing—LSM6 , LSM7 , PRP4 , and SNU66—also play roles in ribosome biogenesis . Although we do not exclude the possibility of indirect roles of PRP4 in ribosome biogenesis , deletion of SNU66 ( a component of the tri-snRNP ) not only delays 35S processing but also affects processing of the 5S rRNA precursor ( Figure 5E ) . Thus , these data provide further evidence for shared components between these processes , which supports a general connection between ribosome biogenesis and mRNA splicing . Whether this connection is direct or indirect generally remains to be established , although the specificity of the rRNA processing defect and the observed genetic interactions ( Figure 5G ) suggest a direct role for SNU66 in 5S processing . In conclusion , we applied the emerging technique of network-guided genetics to computationally predict and experimentally validate at least 15 previously unreported ribosome biogenesis genes ( TIF4631 , SNU66 , YDL063C , JIP5 , TOP1 , SGD1 , BCP1 , YOR287C , BUD22 , YIL091C , YOR006C/TSR3 , YOL022C/TSR4 , SAC3 , NEW1 , FUN12 ) ( Table 1 ) , most of which have human orthologs and thus represent evolutionarily conserved components of this essential core cellular process . Selecting candidates with a network-guided genetics approach therefore proved to be a powerful approach for identifying new genes in a pathway , even in such a well-studied cellular process as ribosome biogenesis , with ∼40% of the tested genes in the polysome profile analyses being shown to participate in this pathway . Although considerable effort has been spent predicting and validating gene functions from diverse functional genomics and proteomics data [17] , [80] , to our knowledge this is one of the most extensive experimental tests of predictions from network-guided genetics . These results add >10% new genes to the ribosome biogenesis pathway , significantly extending our understanding of a universally conserved eukaryotic process .
Haploid MATa deletion mutants [81] were obtained from Research Genetics . TetO7-promoter mutants [28] and TAP-tagged strains [31] were acquired from Open Biosystems . All commercial strains in this paper were verified by PCR , and four strains found to be incorrect in commercial collections ( ypr045cΔ , tetO7-SGD1 , Kre33-TAP , and tetO7-KRE33 ) were recreated . GAL1-promoter mutants were constructed in strain BY4741 ( Text S1 ) . Haploid deletion mutants were cultured to OD600 0 . 3–0 . 5 in YPD ( 1% yeast extract , 2% peptone , 2% dextrose ) at the conditional temperature ( 20°C , 30°C , or 37°C ) . TetO7-promoter mutants were cultured in YPD and then diluted into YPD with 10 ug/ml doxycycline ( Fisher Scientific ) for 9–20 h to OD600 0 . 3–0 . 5 . GAL1-promoter mutants were cultured in YPGal ( 1% yeast extract , 2% peptone , 2% galactose ) and then diluted into YPD for 12–20 h to OD600 0 . 3–0 . 5 . Strains carrying pRS416 and pRS413 derived plasmids were cultured in synthetic complete media minus uracil and histidine supplemented with 2% dextrose to OD600 0 . 3–0 . 5 . Detailed culture information for each individual strain is described in Table S1 . Yeast cells were cultured at various conditions to OD600 0 . 3–0 . 5 . Two hundred µg/ml cycloheximide ( Sigma ) was added to each culture . Cell lysate preparation and sucrose density gradient sedimentation were performed as previously described ( Text S1 ) [65] . Each mutant's polysome profile was aligned to the wild-type reference polysome profile using COW implemented in MATLAB [29] . Aligned polysome profiles were hierarchically clustered using Cluster and Treeview software [30] . TAP-tagged strains were cultured in YPD at 30°C to OD600 0 . 3–0 . 5 , and subsequent steps were performed in the same manner as for the polysome profile analyses . Fractions from the sucrose density gradient were collected , and 25 µl of each fraction was deposited onto a nitrocellulose membrane using a 96-well dot-blot system ( Schleicher & Schuell ) . The membrane was probed for the TAP-tagged proteins with the rabbit peroxidase anti-peroxidase soluble complex ( Rockland Immunochemicals ) , using Luminol ( Santa Cruz Biotechnology ) as the substrate for detection . The total intensity of each dot was quantified with Quantity One 1-D Analysis software ( Bio-Rad ) . The wild-type strain BY4741 was cultured in YPD at 30°C to OD600 0 . 3–0 . 5 and then lysed and fractionated on a sucrose density gradient in the same manner as for the polysome profile analyses . Proteins from each fraction were precipitated with 10% cold trichloroacetic acid , washed with cold 100% acetone , resuspended in 100 mM Tris buffer ( pH 8 . 0 ) , and digested with proteomic-grade trypsin ( Sigma ) for 24 h at 37°C . Each digested peptide mixture was separated by a strong cation-exchange column , followed by a reverse-phase C18 column . Peptides were analyzed online with an electrospray ionization ion-trap mass spectrometer ( ThermoFinnigan DecaXPplus ) , and proteins were identified at a 5% false-detection rate by using PeptideProphet and ProteinProphet software [82] . For each sucrose gradient fraction , the number of MS/MS spectra associated with a given protein was divided by the sum of the spectral counts across all proteins in that fraction to estimate the relative abundance of each protein within each fraction . The resulting relative abundance profiles were subjected to hierarchical clustering using the Cluster and Treeview programs . Raw mass-spectrometry data are deposited in the Open Proteomics Database as accession opd00106_YEAST . RNA was extracted by the hot acidic phenol method . The high- and low-molecular-weight RNA species were separated by 1% agarose-formaldehyde gel ( NorthernMax , Ambion ) and 8% polyacrylamide-TBE-urea gel , respectively . RNAs were transferred onto Zeta-Probe GT membrane ( Bio-Rad ) by capillary transfer for agarose gel or semi-dry electroblotting for polyacrylamide gel . After UV cross-linking of the RNAs to the membrane , 5′-P32-labeled oligonucleotide probes were sequentially hybridized , and the hybridization signals were detected by phosphorimaging and quantified using Quantity One ( Bio-Rad ) . The logarithm ratio of total intensity of each RNA species from a mutant to that from the corresponding wild-type was calculated and used for hierarchical clustering . Wild-type strains or mutants were transformed with either pAJ907 ( RPL25-GFP CEN LEU2 ) or pAJ1486 ( RPS2-GFP CEN LEU2 ) , and each strain was also transformed with pRS411-SIK1-mRFP ( SIK1-mRFP CEN MET15 ) . Strains were cultured in synthetic complete media minus leucine and methionine , supplemented with 2% dextrose or 2% galactose . Essential gene expression was inactivated in the same way as for the polysome profile analyses . Cells were fixed with 4% formaldehyde ( Pierce ) for 30 min and then washed twice with PBS ( pH 7 . 2 ) . DAPI ( Vector Laboratories ) was used to stain DNA , and images were acquired using a Nikon E800 microscope and a Photometrics CoolSNAP ES CCD camera . The GFP median intensities within the three different compartments ( cytoplasm , nucleus , and nucleolus ) for each cell were determined by custom image-processing software implemented in MATLAB ( Text S1 ) . Then the relative ratio of GFP median intensity in the nucleus or nucleolus to that in the cytoplasm for each cell was calculated . For each strain , the median of this ratio for a population of cells was used as an index for the enrichment of ribosomal subunits in either the nucleus or nucleolus . To compare this enrichment in mutants to that in their corresponding wild-type strains , the index of each strain was normalized to the index of the corresponding wild-type strain . | Ribosomes are the extremely complex cellular machines responsible for constructing new proteins . In eukaryotic cells , such as yeast , each ribosome contains more than 80 protein or RNA components . These complex machines must themselves be assembled by an even more complex machinery spanning multiple cellular compartments and involving perhaps 200 components in an ordered series of processing events , resulting in delivery of the two halves of the mature ribosome , the 40S and 60S components , to the cytoplasm . The ribosome biogenesis machinery has been only partially characterized , and many lines of evidence suggest that there are additional components that are still unknown . We employed an emerging computational technique called network-guided genetics to identify new candidate genes for this pathway . We then tested the candidates in a battery of experimental assays to determine what roles the genes might play in the biogenesis of ribosomes . This approach proved an efficient route to the discovery of new genes involved in ribosome biogenesis , significantly extending our understanding of a universally conserved eukaryotic process . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"molecular",
"biology/rna-protein",
"interactions",
"genetics",
"and",
"genomics/functional",
"genomics",
"molecular",
"biology/nucleolus",
"and",
"nuclear",
"bodies",
"molecular",
"biology/translation",
"mechanisms",
"computational",
"biology/molecular",
"genetics",
"genetics",
"and",
"genomics/bioinformatics",
"cell",
"biology/microbial",
"growth",
"and",
"development",
"molecular",
"biology/bioinformatics",
"genetics",
"and",
"genomics/gene",
"function",
"biochemistry/bioinformatics",
"biochemistry/macromolecular",
"assemblies",
"and",
"machines",
"computational",
"biology/genomics",
"biochemistry/transcription",
"and",
"translation",
"computational",
"biology/systems",
"biology"
]
| 2009 | Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics |
In host and cancer tissues , drug metabolism and susceptibility to drugs vary in a circadian ( 24 h ) manner . In particular , the efficacy of a cell cycle specific ( CCS ) cytotoxic agent is affected by the daily modulation of cell cycle activity in the target tissues . Anti-cancer chronotherapy , in which treatments are administered at a particular time each day , aims at exploiting these biological rhythms to reduce toxicity and improve efficacy of the treatment . The circadian status , which is the timing of physiological and behavioral activity relative to daily environmental cues , largely determines the best timing of treatments . However , the influence of variations in tumor kinetics has not been considered in determining appropriate treatment schedules . We used a simple model for cell populations under chronomodulated treatment to identify which biological parameters are important for the successful design of a chronotherapy strategy . We show that the duration of the phase of the cell cycle targeted by the treatment and the cell proliferation rate are crucial in determining the best times to administer CCS drugs . Thus , optimal treatment times depend not only on the circadian status of the patient but also on the cell cycle kinetics of the tumor . Then , we developed a theoretical analysis of treatment outcome ( TATO ) to relate the circadian status and cell cycle kinetic parameters to the treatment outcomes . We show that the best and the worst CCS drug administration schedules are those with 24 h intervals , implying that 24 h chronomodulated treatments can be ineffective or even harmful if administered at wrong circadian times . We show that for certain tumors , administration times at intervals different from 24 h may reduce these risks without compromising overall efficacy .
Neurons located in the suprachiasmatic nuclei ( SCN ) of the hypothalamus form a dominant circadian pacemaker that controls timing of many physiological processes , including cell cycle . The pacemaker integrates environmental cues and communicates timing information to peripheral organs , which respond appropriately to optimize their functions [1] . In host and cancer tissues , drug metabolism and susceptibility to the drug vary throughout the day . The characterization of daily rhythms in drug toxicity and efficacy was a foundation for the chronotherapy of cancer [2] . The main aim of anti-cancer chronomodulated treatment is to achieve an optimal balance between chronotolerance and chronoefficacy ( drug tolerance and efficacy as a function of time of administration ) . However , because many circadian-dependent factors influence the outcome of a treatment , determining the optimal schedule has been difficult to implement in clinics [3] . Cytotoxic chemotherapy suppresses the hematopoietic system , and neutropenia is a major limitation to the doses of drug that can be tolerated . Therapeutic advantages of chronomodulated treatments are seen mainly in the tolerance to higher drug doses , along with a decreased severity of side-effects , rather than in the prolonged survival of the patients [4] , [5] . The efficacy of a cytotoxic drug , at a given concentration , is given by the product between the fraction of cells sensitive to the drug and the fraction of sensitive cells killed by the drug . For cell cycle phase specific ( CCS ) drugs used in chronotherapy , the fraction of sensitive cells is defined by their cell cycle status ( e . g . fraction of cells in S or M phase ) [6] . The entry to S phase is induced by c-MYC and cyclin D1 , and the entry to M phase is gated ( blocked ) by WEE1 [7] , [8] . Since those genes are controlled by the circadian clock , the cell cycle status is determined by the time of the day as well . Thus , drugs like cisplatin or 5-fluorouracil ( 5-FU ) ( S phase specific ) , docetaxel ( M phase specific ) and selicilib ( G1 phase specific ) would each be expected to have maximal efficacy and minimal toxicity at different times of the day . Synchronization properties of the cell cycle to signals from the circadian pacemaker , namely phases and amplitudes , are tissue-specific . Blood cell progenitors [9] , tongue epithelium [10] , and cancer tissues [11] show tissue-specific daily variation in their DNA synthesis activity . In tumors , the response is perturbed and advanced-stage cancer cells can escape or even disrupt circadian control [12] , [13] . Therefore , we would expect that the development of a cell cycle phase specific cancer chronotherapy strategy would depend on at least three circadian-dependent factors . Here , we use a simple model of cell populations under circadian clock control and chronomodulated treatment to identify which biological parameters are important for the successful design of a chronotherapy strategy . We show that optimal CCS drug administration schedules , which minimize the sensitive fraction of the host cells and maximize the sensitive fraction of the tumor cells , are separated by 24 h intervals . However , if timing is wrong , a daily chronomodulated treatment schedule can lead to the worst therapeutic outcome as well . Using a theoretical analysis of treatment outcome ( TATO ) , we show that clinically measurable cell cycle kinetics parameters are crucial in determining the response to CCS drugs . We show that chronomodulated treatments can be beneficial if tailored for individual patients , but can also be ineffective or even harmful if administered at wrong circadian times . We show that for fast growing tumors , administration times at intervals longer than 24 h may reduce these risks while maintaining a good overall efficacy .
Renewing tissues have daily peaks in the fraction of cells in S phase [9]–[11] . To explore the influence of daily modulations of cell cycle kinetics on cell proliferation , we used a simple cell population model [17]–[19] ( Figure 1 ) . The cell population is divided into four phases: G0/G1 , S , G2 and M . G1 phase has a variable duration controlled by the transition rate and S , G2 and M phases have a fixed duration . The circadian clock controls the G1-S phase transition and the G2 phase duration: the G1-S phase transition rate and the G2 phase duration are 24 h periodic functions ( see Methods for a more detailed description ) . We simulated time courses over 48 h for cell populations with different cell cycle phenotypes: host cells , tumor cells with a short S phase duration ( fast growing tumors ) , and tumor cells with a long S phase duration ( slow growing tumors ) . Because G1 phase has a variable duration ( represented by an exponential distribution of times with parameter ) , cells tend to desynchronize when there are no synchronization factors present . Even when cells are initially synchronized , once the clock control is off ( ) , the fractions in each phase of the cell cycle reach a steady state within a few division cycles ( asynchronous cell growth ) . While the clock control is on ( ) , all populations , irrespective of their cell cycle length , show a circadian variation in the fraction of cells G1 , S , G2 and M phases ( Figure 2 ) . The fraction of host cells in S phase varies from 20% to 30% , and peaks around 12:00 every day ( Figure 2A , solid line ) . The fractions of tumor cells in S phase vary between 15% and 30% for fast growing tumors and between 42% and 47% for slow growing tumors , and they peak at different times ( Figure 2A , dashed and dashed-dotted lines respectively ) . The fractions of cells in G1 and G2/M phases also peak at different times of the day and their amplitudes are different for each phase ( Figure 2B , C ) . These results indicate that the fractions in each cell cycle phase match the circadian period but the time at which they peak is influenced by the cell cycle status ( tumor and host cells respond with different strength to the external cues ) . S phase fractions in the host and tumor populations peak at different times , a feature that could be exploited by a well-timed administration of an S phase specific drug . We simulated the effect of one course of treatment based on a standard protocol ( see Methods ) . We compared two tumor cell phenotypes: a fast growing tumor ( Figure 3A , B ) and a slow growing tumor ( Figure 3C , D ) . Cell cycle kinetic parameters for the host and tumor cells were estimated from experimental data in patients when available; otherwise , data from mice were used . We assumed that the circadian clock acts at the same time of the day in the host and tumor cells , albeit more strongly on the host cells . To determine the optimal treatment time , we defined an outcome function that measures the trade-off between anti-tumor efficacy and toxicity . We calculated the outcome of treatments given at different circadian times . The optimal treatment time for the fast and slow growing tumors is during night . However , the worst times of treatments are different: 17:30 for the fast growing tumor and 5:00 for the slow growing tumor ( Figure 3B , D ) . This shows that the S phase duration alone can strongly affect the outcome of a chronomodulated treatment . The fraction of cells in each cell cycle phase determines how sensitive to treatment tissues are . Therefore , it would be useful to predict the best time of treatment based on kinetic data without having to run full simulations . We developed a theoretical method , TATO , to predict the influence of cell kinetics on CCS drug toxicity and efficacy . If the G1-S phase transition rate ( due to circadian entrainment ) and the surviving fraction ( due to the treatment ) are 24 h-periodic , we can solve the periodic treatment problem by calculating the average host and tumor population growth rates under 24 h period perturbations . The contribution of the rhythmic entrainment of the cell cycle to the growth rate can be approximated by ( 1 ) where the subscript denotes the tumor and , the host . ( See Methods for a mathematical analysis ) . The value is the periodic component of the survival fraction of the cells that divide at time , when treated at time . The value is the periodic component of the G1-S transition rate at time . The integral , which is the average of the product between the two terms , is the net contribution of the periodic component to the rate of viable newborn cells over 24 h . As a function of , the sign of the integral determines the effect ( positive or negative ) of the clock and the treatment on the growth rate . We found that the integrals and are good approximations of the response values and computed by numerical simulation ( Figure 4 ) . The functions and , as approximations of response functions and , are useful to study the dependence of the treatment outcomes on the cell cycle kinetic parameters . For drugs targeting the S phase , three cell cycle parameters affect the periodic part of the growth rate: ( 1 ) the duration of the S phase , ( 2 ) the timing of the peak of the G1-S phase transition rate , and ( 3 ) the timing of the cell death rate , given by the timing of the drug administration . These parameters appear , explicitly or implicitly , in Eq . 1 . The extrema of Eq . 1 , which represent the largest and the smallest growth rates of the cell population , can be located when and are known . As a first approximation , when the death rate and the G1-S phase transition rate are sinusoidal and are largest at times and , the location of the extrema can be calculated explicitly . The maximum of occurs whenand the minimum of occurs when ( Figure 4A , white lines ) . Therefore , to kill the largest fraction of cells , i . e . to minimize , treatments should be applied halfway the S phase duration after the daily peak in G1-S phase transition . To spare the largest fraction of cells , the treatment should be applied 12 h later ( detailed analysis in Methods ) . Based only on and , TATO predicts that the extrema of are 12 h apart . This approximation is good for durations between 7 h and 24 h ( Figure 4B ) . When is larger than 24 h , the extrema are shifted by 12 h ( Figure 4 ) . When h , the timing of the treatment has no effect . Anticancer drugs interfering with DNA synthesis ( S phase ) are widely used , but other phases of the cell cycle can be targeted as well . Therefore , in addition to the simulations for drug specific to S phase , we ran full model simulations for drugs acting on G1 or G2/M phase and compared the outcome to prediction from TATO ( Table 1 ) . The treatment protocol was the same as for the S phase drug , which is also included in Table 1 . Optimal times of treatment in G1 , S and G2/M phases vary by as much as 9 h between fast and slow growing tumors ( formulas for optimal times are given in Methods ) . The worst times of treatment also show large differences between fast and slow growing tumors . Despite this , TATO predicts the optimal time within 2 . 5 h . Taken together , these results indicate that TATO , using only a reduced set of kinetic parameters , can reliably predict the outcome of full simulations . Previous computational studies have found that the fraction of cells killed with a constant drug infusion is higher ( more toxic ) than that killed with a chronomodulated infusion , for the same average killing rate [20]–[23] . Our model is consistent with these findings , and indicates that higher total doses of chronomodulated drug can be tolerated and are needed to achieve the same anti-tumor efficacy . These theoretical results are in agreement with clinical trials that showed consistent higher tolerance for chronomodulated compared to constant infusion [4] , even when given at non-optimal times [24] . Lesser toxicity is independent from the circadian rhythms , i . e . chronomodulated treatments are less toxic even in absence of circadian rhythms . Thus , clinical and theoretical evidence shows that the shape of the infusion profile alone affects the treatment outcome significantly . For that reason , a direct comparison between constant and chronomodulated treatment is not really possible . Instead , we asked whether the same drug concentration profile administered at intervals different from 24 h could improve efficacy . We simulated the chronomodulated administration protocol with intervals ranging from to h , starting on the first day at a time between 0:00 and 24:00 . The total quantity and the infusion profile of the drug administered was the same for all intervals tested . Therefore , the resulting difference between outcomes depends only on the initial timing and the period . As expected , the largest amplitude of outcomes as a function of , and the best outcomes globally , are at intervals h ( Figure 5A–D , solid lines ) . Likewise , the worst treatment outcomes also occur at intervals of 24 h . To avoid the worst outcomes , it may be safer to seek treatment intervals that minimize outcome amplitudes , while optimizing the average outcome ( maximizing ) . When is close to 24 h , the treatment times can be averaged over the treatment course and TATO predicts an outcome given by ( 2 ) where is the number of drug administrations during one course of treatment , and is the phase of a 24 h interval treatment . If is larger than 24 h , the starting time of treatment needs to be advanced to produce an outcome equivalent to the one obtained at . Here , using , each hour increment in leads to a 2 h-advance in the starting treatment time . When is much different from 24 h , i . e . , , the average treatment phase is undefined , and TATO predicts an outcome independent from . In both fast and slow growing tumors , at these values h and h , the outcome depends little on . These two intervals offer circadian-independent treatment controls for the chronomodulated treatment ( Figure 5B , D dashed and dotted lines ) . For a 24 h interval treatment to be safe to use , the time window during which the treatment is better than control should be large . TATO predicts that the outcome at h and h depends significantly on the duration of the sensitive phase ( Eq . 24 in Methods ) . Treatment intervals longer than 24 h are predicted to spare the most host and slow growing tumor cells while shorter intervals are expected to spare the most fast tumor cells . Numerical simulations confirmed that the outcomes depend on the intervals in a way that is specific to the tumor . Fast growing tumors showed the best response at intervals h except for a small time window around midnight ( Figure 5B ) , while the slow growing tumors showed a better response at h ( Figure 5D ) . Differences in the cell cycle lengths between the tumor and host cells could be exploited by adapting the interval between drug administrations [25] , [26] . Cell cycle length effects were also observed in the model in the presence of the circadian clock . Overall , a long interval tended to improve anti-tumor efficacy in fast growing tumors , while a short interval was detrimental ( Figure 5A ) . The opposite was observed for slow growing tumors , where shorter treatment intervals had a better outcome ( Figure 5C ) . This indicates that the cell cycle kinetics interacts with the timing of the drug administration to modulate outcomes , even in the presence of a circadian clock .
We have developed an analytic method , TATO , that allows us to identify the optimal treatment time based on the circadian status and on the cell cycle kinetics of the host and tumor tissues . TATO measures the average differential growth rate of host and tumor cells that is caused by the circadian modulation of the cell cycle . Three parameters are essential to calculate the differential growth rate: the G1/S phase transition rate , the duration of the drug susceptibility phase , and the death rate . Our model indicates that the cell cycle length , which can vary from 18 h to over 100 h in colorectal cancers [40] , is important to determine the best treatment times and intervals . 24 h interval treatments at the right time provided the best efficacy . Yet , the worse time of treatment can be as near as few hours from the optimal time [41] , making it risky to treat at 24 h intervals . A previous study has found a significant correlation between S phase duration and 5-FU sensitivity [36] . Here we showed that for fast growing tumor ( short S phase duration ) , administering a drug that targets the S phase of the cell cycle at 28 . 8 h intervals may be safer than treating at 24 h intervals . However , we found that for slow growing tumor ( long S phase duration ) , treating at 24 h intervals was indeed the best option , even when deviating from the optimal time . So far , schedules different from 24 h have not been tested in the context of circadian chronotherapy , but in this paper , we show that for fast growing tumors they might be a safer strategy . Drugs and the active drug metabolites used in chronotherapy are rapidly eliminated after delivery , which causes large modulations in their concentrations during the day . For that reason , patients with decreased 5-FU clearance rate due to a partial or complete loss of DPD activity might not benefit from chronomodulated treatments . An observed lower mean and amplitude of DPD activity in women is a possible explanation for the lower survival time with chronotherapy [5] . Here , we suggest how to individualize chronomodulated treatment schedules . First , patients with no overt circadian rhythm perturbations need to be selected , and their tumor kinetics assesed by measuring the S phase duration ( ) and potential doubling time ( ) . If the S phase duration of the tumor cells is short , a non-24 h schedule may be preferable . If the S phase duration of the tumor cells is long , a 24 h schedule could be more effective . Second , the best treatment time could be determined using TATO . Constant infusion is not the best control for 24 h schedules since the shape of the infusion profile is likely to have a significant effect on outcomes [3] . Chronomodulated treatments with intervals spanning the whole day equally allows minimizing circadian effects , thus they could make suitable controls . Unlike for 24 h schedules , a constant infusion control group could be used to assess the efficacy of non 24 h interval treatments . Third , once the optimal treatment time is determined , reverse pharmacokinetics could be used to retrieve the corresponding dose delivery schedule . Given a fixed dose delivered to a tissue at time , the fraction of surviving cells depends on the fraction of sensitive cells and the killing rate . If the killing rate varies in a predictable way during the day due to metabolism or elimination , it is possible to find a normalization dosage profile to make the killing rate time-independent . Thus , by knowing the quantity of drug needed to achieve a given killing rate , the fraction of surviving cells can be determined by the fraction of sensitive cells given by the model presented here . The accepted administration time for 5-FU , 4:00 , is based on the observation that in mice , the maximal tolerance is reached 5 h after light onset , corresponding to 5 h after beginning sleeping at 23:00 in humans [4] . In a recent study [28] , 8 groups of patients received chronomodulated 5-FU-LV with peak times staggered every 3 h . Toxicity showed a marked circadian dependency of timing of chronomodulated 5-FU with leucovorin and oxaliplatin or carboplatin in cancer patients , with optimal time of 5-FU in cancer patients near 4:00 with 90% confidence limits . This study also showed more toxicity and large variability in women . Chronomodulated drug infusion differs in two respects from constant rate infusion: modulated concentration profile and timing . Chronotherapy is based on adapting the timing of treatment regimens to the circadian rhythms [27] . Thus , for the chronotherapy principle to work once the effect of concentration profile is discounted , there should be a 12 h time window during which the therapeutic outcome improves . This means that only 6 h would separate the optimal treatment time and a no-effect treatment time . We conclude that for chronotherapy clinical trials , patients need to be grouped according to the chronotype , tumor growth kinetics and pharmacokinetics/pharmacodynamics characteristics .
The cell population is divided into four phases: G0/G1 , S , G2 and M . The G0/G1 phase includes cells that are actively dividing , but are in the pre-DNA synthesis or growth phase ( G1 ) and cells that are quiescent but can be recruited to the cell cycle ( G0 ) . The S phase includes cells in DNA synthesis . The G2 and M phases include cells that have synthesized DNA and are progressing through mitosis . We used a population model of cell proliferation [17]–[19] in which we introduced a circadian control ( Figure 1 ) . Each stage of the cell cycle and its relationship to the circadian clock is modeled . The input to the model is a treatment course and the output is the population size in each cell cycle phase at any given time of the day . We consider two cell types , host and tumor cells . Cell kinetic parameters for the host correspond to blood cell progenitors and for the tumor , to colorectal cancer cells . The model tracks the total cell number and fraction of cells in each phase for host and tumor during a course of chemotherapy , allowing estimates of efficacy and toxicity . The equations for the cell populations are ( 3 ) ( 4 ) ( 5 ) ( 6 ) Each equation represents the balance between fluxes of cells ( cells/hours ) entering ( terms ) and leaving ( terms ) a cell cycle phase ( see Figure 1 for details about the model ) . ( Eq . 3 ) is the G0/G1 phase cell number , ( Eq . 4 ) the S phase cell number , ( Eq . 5 ) the G2 phase cell number , and ( Eq . 6 ) is the M phase cell number . The total cell number is denoted . The term , , is the fraction of cells surviving the cell cycle ( S/G2/M phases ) at time . It is the product of phase specific survival rates , ( 7 ) Time delays ( ) account for the finite time required for cells to progress through each phase . The survival rates for the S , G2 and M phases are determined by integrating the phase-specific death rates over the duration of each phase , ( 8 ) where is one of , , . The duration is the total length of S , G2 , and M phases of cell dividing at time , ( 9 ) The phase and amplitude of are given by and . Similarly , the phase and amplitude of are given by and ( and are relative to and ) . A sinusoidal circadian input with a specific phase and amplitude is assumed for and , ( 10 ) ( 11 ) where the circadian function is ( 12 ) The coefficient and phase-shift are set for all simulations to 0 . 2 and 14 h respectively . The function mimics the typical expression profile of circadian genes in many tissues , for a given individual . Note that circadian rhythm variability among individuals affect these parameters . Kinetic parameters for bone marrow ( host ) and colorectal cancer ( tumor ) are derived from experimental data or were adjusted using this model . For the bone marrow [25] , [25] , [25] , [40] , , , h [25] , h , h , [9] , [8] , h [9] , h [8] . For the tumors , parameters are identical except , ( fast ) , ( slow ) , ( fast ) , ( slow ) , . The population model is linear and simulations of host and tumor cell growth show that their cell numbers grow exponentially with a circadian modulation . Here we neglect nonlinear terms that would eventually cause the cell number to stabilize . We assume that with the treatment , the cell number is far from equilibrium . For a small-size tumor , this is a reasonable assumption . We also neglect the systemic feedback mechanisms of normal tissue homeostasis , which are more relevant to study between courses of chemotherapy when patients are recovering . Therefore , a linear model is also considered for the host tissues under cytotoxic stress . We simulate a colorectal cancer treatment with 5-FU [42] , [43] . 5-FU is an S phase specific drug that inhibits thymidylate synthase activity required for DNA synthesis , and consequently induces cell death . Chemotherapy schedules used clinically are either chronomodulated at 24 h intervals , or a constant infusion of 5-FU for a few consecutive days . The treatment is repeated every two to three weeks [4] . For simplicity , we simulate only one course of chemotherapy . We consider three different schedules: chronomodulated with 24 h intervals , flat infusion , and chronomodulated with intervals different from 24 h . One course of treatment lasts 5 days or 5 chronomodulated administrations . To isolate the effect of chronomodulation of treatment , we ignore the pharmacodynamics/pharmacokinetics aspects and we assume that chemotherapy acts on tumor and host cells in the same way . Because cytotoxic chemotherapy affects the hematopoietic system , and neutropenia is a major limitation to drug tolerance , we simulate the effect of 5-FU with blood cells as the host tissue . The effect of 5-FU is simulated by adding a drug-induced death rate to the basal apoptosis rate of S-phase cells , ( 13 ) The chronomodulated drug-induced death rate , , takes the form of a truncated Gaussian function centered at circadian time , the treatment time ( between 0 and 24 h ) , ( 14 ) Drug administration is repeated at intervals of hours . The duration of drug infusion is h [4] . The coefficient is the maximal drug-induced cell death rate . The equivalent flat rate infusion ( normalized so that it kills the same fraction of cells than the chronomodulated infusion , in one day ) is the constant ( 15 ) The normalization factor is . For all simulations , the initial conditions were set to , , , and ( total number initialized to ) . With the parameters chosen , the relativepopulation is quickly synchronized by the circadian rhythm . Numerical simulations were performed with the Volterra solver of the package XPPAUT . Analysis was done with Matlab 7 . 0 . Codes ( XPPAUT and Matlab ) are available as supplementary text ( Texts S1 , S2 , S3 , S4 ) . The treatment outcome measure is defined as ( 16 ) where the functions and measure the cytotoxicity in tumor ( C ) and host ( H ) cells . The parameter is the circadian time of drug administration in case of a 24 h treatment interval . For non-24 h intervals , it is the time of administration on the first day of treatment . and , obtained from numerical simulations , are the normalized cell numbers 7 days after the first day of treatment , where is the total cell number as a function of . The outcome function E must increase with ( high tolerance ) and decreases with ( high killing rate ) . For the flat infusions , E is constant . Close to zero , a Taylor expansion gives ( 17 ) The outcome measures the difference between responses and , and penalizes both excessive toxicity and poor anti-tumor efficacy . An optimal treatment maximizing tumor cell kill and minimizing host cell loss is found by maximizing the outcome function . Equation 3 does not depend on other dynamical variables , so its stability analysis is simplified . Assuming a exponential growth , , where is a = 24 h-periodic function and is the growth rate , we have from Eq . 3 , ( 18 ) Taking the average over a period , we obtain ( 19 ) For cell death occurring in the S , G2 or M phase , the death rate is chronomodulated . By making the simplifying assumption that the function , ( 20 ) The angle brackets denote the average over a period and the tildes the remaining , oscillatory part with a zero average . Thus , periodic parameters act only on through the integral term , ( 21 ) The integral can be either positive or negative , modulating the growth rate accordingly . As a consequence , the growth rate ( tolerance ) is maximal when the integral is maximal and the death rate ( toxicity ) maximal when the integral is minimal . We consider and a drug specific to the S phase . Then , The values and are shifted 12 h when 24 h . If the drug acts on the G2/M phases , with then For cell death occurring in the G1 phase , the death rate is chronomodulated . We assume that is constant and therefore , the integral term becomes ( 22 ) If peaks at , meaning many cells in G1 are lost , the periodic solution will reach a minimum value at . Thus the ratio will have a maximum at and a minimum at . Assuming that peaks at and is minimum at , When treatment intervals are different from 24 h , the outcome will depend on the administration times over the whole course of treatment . If is the time of the -th administration , the effect on the growth rate isThe average effect of successive administrations at times , is ( 23 ) When , it is justified to replace the term with , whereTherefore , the outcomes will be equivalent when , with the phase of the 24 h interval treatment . The starting treatment time must then be ( 24 ) When , with , administration times are distributed equally around the circadian period and has little effect on the outcome . Neglecting the circadian clock allows computing the treatment intervals that minimize the growth rate of the equation , with a -periodic survival fraction if and 0 otherwise . This means that all cells in the sensitive phase are killed at intervals . The minimal growth rates occurs at values , since not a single cell would come out of the sensitive phase alive . The maximal growth rate occurs when and the fraction of cells in the sensitive phase is minimal . Let be the cell number in sensitive phase , given by . Right after administration , . The sensitive fraction reaches a minimum when . This occurs a time after the last administration , where ( 25 ) is the Lambert W function , and satisfies . | Chronotherapy of cancers aims at exploiting daily physiological rhythms to improve anti-cancer efficacy and tolerance to drugs by administering treatments at a specific time of the day . Recent clinical trials have shown that chronotherapy can be beneficial in improving quality of life and median life span in patients , but that it can also have negative effects if the timing is wrong . A theoretical basis for the rational development of individualized therapy schedules is still lacking . Here , we use a simple cell population model to show how biological rhythms and the cell cycle interact to modulate the response to cancer therapy . In particular , we show that the proliferation rate of cancer cells determines when treatments are most effective . We provide a simple formulation of the problem that can be used to compute an objective response function based on the drug sensitivity and the proliferation rate of tumor cells . Finally , we show that in some cases , treating at a different time every day may be more appropriate than standard daily chronotherapy . These results constitute an important step in designing individualized chronotherapy treatments , and point out to ways to design better clinical trials . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"mathematics/statistics",
"cell",
"biology/cell",
"growth",
"and",
"division",
"computational",
"biology/systems",
"biology",
"oncology/gastrointestinal",
"cancers"
]
| 2010 | Tumor Growth Rate Determines the Timing of Optimal Chronomodulated Treatment Schedules |
Mosquito-borne diseases are increasingly being recognized as global threats , with increased air travel accelerating their occurrence in travelers and their spread to new locations . Since the early days of aviation , concern over the possible transportation of infected mosquitoes has led to recommendations to disinsect aircraft . Despite rare reports of mosquitoes , most likely transported on aircraft , infecting people far from endemics areas , it is unclear how important the role of incidentally transported mosquitoes is compared to the role of traveling humans . We used data for Plasmodium falciparum and dengue viruses to estimate the probability of introduction of these pathogens by mosquitoes and by humans via aircraft under ideal conditions . The probability of introduction of either pathogen by mosquitoes is low due to few mosquitoes being found on aircraft , low infection prevalence among mosquitoes , and high mortality . Even without disinsection , introduction via infected human travelers was far more likely than introduction by infected mosquitoes; more than 1000 times more likely for P . falciparum and more than 200 times more likely for dengue viruses . Even in the absence of disinsection and under the most favorable conditions , introduction of mosquito-borne pathogens via air travel is far more likely to occur as a result of an infected human travelling rather than the incidental transportation of infected mosquitoes . Thus , while disinsection may serve a role in preventing the spread of vector species and other invasive insects , it is unlikely to impact the spread of mosquito-borne pathogens .
Mosquito-borne diseases such as malaria [1] and dengue [2] are major causes of morbidity and mortality globally . While these pathogens are endemic only in tropical and subtropical environments , modern air travel has broken down traditional geographic barriers to the extent that infected humans and mosquitoes may quickly travel anywhere in the world [3 , 4] . Infected travelers of either variety pose a risk of introducing pathogens to areas where the environment is suitable but the pathogen is not present , leading to local outbreaks . Furthermore , in areas where the pathogen does exist , introductions may result in the replacement of a local strain with a newly introduced one , perhaps with different pathogenicity or drug resistance profiles . Despite the recognized and realized risk that travelling humans and mosquitoes accidentally transported on aircraft present for the spread of vector-borne diseases [3] , there remains a great deal of uncertainty about how best to minimize the spread of pathogens via these two hosts . One option is disinsection , “the procedure whereby health measures are taken to control or kill the insect vectors of human diseases present in baggage , cargo , containers , conveyances , goods and postal parcels . ”[5] The broad implementation of such measures has been in place for many decades [6] and is specifically spelled out in the International Health Regulations [5] , which state that , “Every conveyance leaving a point of entry situated in an area where vector control is recommended should be disinsected and kept free of vectors . ” Effective disinsection could reduce the risk of spread of both specific mosquito species and pathogens [3] . However , specifically for pathogen introduction , it is unclear how important the role of incidentally transported mosquitoes is compared to the role of traveling humans . Introduction of a pathogen by either a human or a mosquito depends on a sequence of stochastic events . Starting in the source location , there is a probability that a human or mosquito is infected by the pathogen and a probability that a human or mosquito travels to another location . Upon arrival , there is some probability of transmission . For the infected human , this depends on being found by a competent mosquito , being fed upon during the infectious period , having that mosquito first survive the extrinsic incubation period and then transmit the pathogen to another person . For a transported infected mosquito , only the latter part of this sequence is required; the mosquito must survive the extrinsic incubation period and transmit the pathogen to a person before dying . Here we developed branching process models to compare the risk associated with traveling mosquitoes and traveling humans . Branching process models can be used to explicitly formulate chains of discrete random events such as those described above [7 , 8] . In this framework , each stochastic step is first characterized individually and then the sequential series of events is analyzed using well-developed mathematical methods . Here , we are interested in a single outcome , introduced autochthonous transmission , which could result from two alternative pathways , introduction by an infected human or by an infected mosquito . We focused on two mosquito-borne pathogens of global importance , the malaria parasite P . falciparum and dengue viruses . These pathogens differ in important ways , they are transmitted by different vectors ( Anopheles and Aedes mosquitoes , respectively ) and have different human infection dynamics ( multi-stage , long-term infection or single stage acute infection , respectively ) . To assess the worst-case scenario for potential introduction , we focused on highly endemic areas where transmission of each pathogen is near optimal and formulated a stochastic description of each step of the composite process using published literature to estimate the key parameters . We then estimated probabilities of introduction via each pathway and assessed the sensitivity of those outcomes to assumptions incorporated into the model .
The average number of human passengers on flights globally was 104 in 2015 according to the International Civil Aviation Organization , which we assumed to be Poisson distributed ( Fig 1A ) ( http://www . icao . int/annual-report-2015/Documents/Appendix_1_en . pdf ) . Approximately 0 . 91 mosquitoes ( 95%CI: 0 . 00009–5 . 3 ) were found per aircraft on average across 17 studies of 559 , 579 aircraft from 1931 to 1999 [9–25] ( Fig 1B ) . Because of the variety of aircraft , time periods , locations , objectives , methods , and data reported in these studies , we made the conservative assumption that all mosquitoes were competent vectors , female , and alive , and therefore capable of being infected and transmitting each pathogen . This scenario should therefore overestimate vector mosquito transportation by aircraft . Estimates of the prevalence in humans were based on over 3 , 000 surveys of 2–10 year olds [26] . The prevalence of P . falciparum reported in these surveys ranged from 0% to almost 100% , with a mean of 23% ( Fig 2A ) . Fewer data were available for mosquito populations , but the estimated prevalence of sporozoites in mosquitoes across 41 studies ranged from 0 . 01% to 7 . 6% with a mean of 2 . 3% [27] ( Fig 2B ) . Because our focus was the relative probability of introduction by each pathway , we reduced extreme variability by sampling only from the interquartile range of the distributions of pIH and pIM . Finally , to account for correlation between human and mosquito prevalence when comparing the two pathways , the samples were split into deciles and randomly paired within each decile , such that samples with relatively high human infection prevalence also had relatively high vector infection prevalence . R0HM was calculated as the product of the mosquito density r ( mean: 31 mosquitoes per person [27] ) , mosquito biting rate b ( above ) , the human-to-mosquito transmissibility pHM ( mean: 0 . 16 [1] ) , and the duration of infectiousness D ( mean: 205 days [28] ) ( Fig 2E ) : R0HM=rbpHMD . R0MH was calculated using the classic formulation [29]: R0MH=bpMHμe−μ⋅EIP , accounting for the mosquito biting rate b ( mean: 0 . 4 bites per day [27] ) , the mosquito-to-human transmissibility pMH ( mean: 0 . 55 [1] , the mosquito mortality rate μ ( mean: 0 . 13 per day [27] ) , and the length of the extrinsic incubation period , EIP ( mean: 10 . 9 days [27] ) ( Fig 2F ) . Estimates of the prevalence of DENV infection in humans were based on twenty seroprevalence studies ( primarily in children ) in hyperendemic locations across the globe [30–49] . The mean yearly infection rate ranged from 2% to 90% , averaging approximately 23% . To estimate the average daily prevalence of incubating and infectious humans , we sampled the duration of infection as the sum of the intrinsic incubation period ( IIP , mean: 5 . 9 days [50] ) and the adjusted infectious period ( D , mean: 5 . 0 days , see below ) discounted by the overlap ( O , approximately 1 day [51] ) , and multiplied yearly incidence by ( IIP + D–O ) /365 . The mean prevalence of DENV infection in humans was approximately 0 . 08% ( Fig 2C ) . Mosquito infection rates were estimated from 13 studies in areas with ongoing dengue outbreaks that provided either direct measurements of infection rates , minimum infection rates through pooled samples or indirect measurements of infection rates via maximum likelihood estimates of pooled samples [52–64] . The mean prevalence of DENV infection in mosquitoes was 3% ( Fig 2D ) . As for malaria , we sampled from the interquartile ranges of each distribution and stratified sampled human and vector prevalence by deciles . R0MH and R0HM for DENV ( Fig 2G and 2H ) were estimated as for malaria ( above ) under the assumption that temperature was approximately 30°C , i . e . conducive to efficient dengue transmission [65] . The mean mosquito biting rate b was 0 . 7 bites per day [66] , the mean mosquito-to-human transmissibility pMH was 0 . 5 , the mean mosquito mortality rate μ was 0 . 21 per day [67] , and the mean length of the extrinsic incubation period , EIP was 6 . 5 days [50] , and the mean mosquito density r was 2 mosquitoes per person [68] . To obtain the dengue parameter R0HM , we do not estimate the parameters pHM and D independently , as was done for malaria . Rather , we obtain them in aggregate as the ‘Human Total Infectiousness’ ( HTI ) , by integrating a logistic function of the human-to-mosquito transmissibility over the course of infection [69] .
The stepwise probabilities of P . falciparum or dengue virus introduction were quantitatively nearly identical for the mosquito pathway ( Fig 3A ) . The probability of a mosquito being on an aircraft was generally low but variable ( median: 0 . 25 , 95% Credible Interval ( CI ) : 9×10−5–0 . 995 ) . The median probability of at least one mosquito on board being infected was much lower , less than 0 . 005 for both pathogens and the median probability for an infected mosquito traveling and subsequently transmitting to a human was less than 0 . 002 . Humans , in contrast , were always present on aircraft and had high probabilities of completing each subsequent step of the introduction pathway: at least one traveler being infected , at least one instance of transmission to a mosquito , and at least one instance of transmission to a human ( Fig 3B ) . For traveling humans , the median probability of a local transmission event was approximately 1 . 0 and 0 . 4 for P . falciparum and dengue viruses , respectively . Overall , the median probability that a single aircraft traveling from an endemic area to another highly suitable area would lead to autochthonous human transmission of P . falciparum due to incidental transportation of mosquitoes was 0 . 001 ( 95% CI: 0 . 000–0 . 041 ) . In contrast , the median probability of introduction by infected human travelers was 1 . 00 ( 95% CI: 0 . 84–1 . 00 ) . For dengue viruses , the probabilities were 0 . 002 ( 95% CI: 0 . 00–0 . 04 ) for mosquitoes and 0 . 41 ( 95% CI: 0 . 17–0 . 69 ) for humans ( Fig 4 ) . The average odds of introduction by humans versus mosquitoes was 1 , 000:1 ( 95% CI: 500:1–1 , 800:1 ) for malaria and 240:1 ( 95% CI: 150:1–370:1 ) for dengue . For details on how these were obtained , see S1 Text . The difference in introduction probabilities between the mosquito and human pathways starts with different probabilities of travel; multiple humans are found on most aircraft , while mosquitoes are relatively rare . For malaria , infection was more prevalent in humans ( mean: 0 . 15 ) than in mosquitoes ( mean: 0 . 02 ) , so that the median estimated numbers of malaria infected humans and mosquitoes on an aircraft were 12 ( 95% CI: 2–40 ) and 0 . 005 ( 95% CI: ~1 . 4×10−6–0 . 1 ) , respectively . The estimated probability of having an infected human onboard was approximately 200 ( 95% CI: 10–1×106 ) times higher than the probability of an infected mosquito being onboard . For dengue , the probability of being infected was higher for mosquitoes in the midst of an outbreak ( mean: 0 . 03 ) compared to average human prevalence over time periods encompassing outbreaks ( mean: 0 . 007 ) . Nevertheless , the difference in frequency of travel still translates to the probability of infected human travelers being approximately 93 ( 95% CI: 5–4 . 0×105 ) times higher than the probability of infected mosquitoes being on an aircraft . We assessed the importance of each pathway-specific sub-component by estimating sensitivity coefficients ( Fig 5 ) . Sensitivity related to the prevalence of infection is intrinsically linked to the number of mosquitoes or humans on an aircraft because both contribute to the mean number of infected individuals onboard , λITM and λITH , respectively . The mosquito pathway was highly sensitive to λITM for both pathogens , a 1% change in either the number of mosquitoes or the prevalence of infection in mosquitoes resulted in a change of approximately 1% in the probability of introduction . The importance of R0MH was lower , with a mean change of approximately 0 . 8% in introduction probability per 1% change in R0MH . Sensitivity for the human introduction pathway varied between malaria and dengue: P . falciparum introduction was mostly insensitive to parameter changes as introduction was highly probable across the parameter space while dengue showed some sensitivity , especially to the number of humans on an aircraft and the prevalence of infection ( combined in λITH ) .
Mosquito-borne diseases such as malaria , dengue , yellow fever , chikungunya , and Zika are all endemic in tropical or subtropical areas . These diseases have also been documented among travelers and pose a transmission risk in areas where the pathogen is absent but the relevant mosquito vector is present , as has seen with chikungunya and Zika viruses in recent years . This risk is also a serious concern for areas where interventions are being targeted to prevent the invasion of drug resistant pathogens or to eliminate a pathogen altogether , such as current efforts to curb malaria [70 , 71] . We assessed the relative risk of vector-borne pathogen introduction by infected humans and infected mosquitoes aboard aircraft , specifically estimating the probability of introduction of P . falciparum and dengue viruses . To clarify the relative risks of the two alternative pathways we focused on a situation that would favor introduction , in which the origin of air travel was assumed to be a highly endemic location and the destination was assumed to be equally suitable for transmission but nevertheless free of the pathogen . Introduction via infected human travelers was far more likely than introduction via infected mosquitoes; more than 1000 times more likely for P . falciparum and more than 200 times more likely for dengue viruses . The low probability of introduction by mosquitoes stems from three key components . First , mosquitoes are rarely found on aircraft; the majority of aircraft from the 17 surveys had no mosquitoes on them and the highest number of vector species reported on a single aircraft was 17 Anopheles gambiae mosquitoes ( possibly including both males and females ) [13] . Second , if mosquitoes do make it onto aircraft , they are unlikely to be infected; the estimated infection prevalence of infection in mosquitoes was generally well below 5% even under the optimal conditions assessed here . The importation of P . falciparum or dengue virus via mosquitoes only becomes likely in the event that both of these rare conditions occur together . Finally , for a human to become infected at the destination , an imported , infected mosquito must survive long enough to complete the incubation period and feed on another human . Even in the highly endemic settings considered here , most infected mosquitoes do not survive long enough to transmit the pathogen . The introduction probabilities differed between the two pathogens primarily because of the difference in infectious period ( approximately 205 days for P . falciparum versus approximately 5 days for dengue viruses ) , which imparts increased human infection prevalence and increased human to mosquito transmission . Despite this difference and an estimate of higher prevalence in mosquitoes than humans , the relative risk for human introduction was still 200 times higher than for introduction by mosquitoes . This supports the generalizability of the finding that the frequency of travel and the differential transmissibility ( human-to-mosquito being higher than mosquito-to-human ) drive the difference in introduction risk . The estimates provided here reflect a worst-case scenario in which prevalence is high in the source location and transmission is highly likely in the destination location . This choice was made to explicitly focus on the relative probability of introduction via human or mosquitoes as opposed to the absolute probability . In more realistic situations , the risk of introduction by either humans or mosquitoes is likely to be much smaller due to a number of factors influencing the association between infection risk and travel likelihood . For example , infection risk may be lower among traveling humans compared to the general population because they may have different exposure risk ( e . g . if staying in an air-conditioned hotel ) or different infection risk ( e . g . adults are more likely to be immune to DENV infection ) . To assess how this might change the relative probability of introduction , we simulated a 90% reduction in human infection prevalence . Even under this condition , human travelers were more likely to introduce both pathogens than accidentally transported mosquitoes ( S1 Text ) . Moreover , airports are often far from rural areas with high P . falciparum prevalence , so the prevalence of infection in both humans and mosquitoes on aircraft is likely much lower than estimated here . And efforts to limit transmission further reduce this risk [72] . The frequency of vector mosquitoes being accidentally transported on aircraft was also likely overestimated as we used average numbers of all mosquitoes regardless of species , sex , and viability ( many were reported dead in the studies ) . These factors all suggest that we have overestimated the risk of introduction , especially by mosquitoes . However , the differences in transmissibility and human travel likelihood would change little , so the risk posed by travelling humans remains substantially higher than the risk posed by the accidental transportation of mosquitoes . Recommendations for disinsection targeted at mosquitoes of public health importance focus on safety , effectiveness of the disinsection process , and the prevention of the introduction of invasive mosquito species and invasive pathogens via infected mosquitoes [3 , 73] . The safety of passengers , crew , and the physical aircraft are key challenges not addressed here [73] . Effectiveness of the disinsection process itself is an additional concern; methods and implementation of disinsection vary , few insecticides are approved for use on aircraft , and many mosquitoes are resistant to some or all of those insecticides [73] . Safe and effective disinsection may help reduce the threat of vector mosquito invasion via aircraft to areas where those mosquitoes do not already exist [73] , though some key vector species ( e . g . Ae . aegypti ) are already widely distributed and introduction has most often been attributed to shipping [74–76] . The importation of vector mosquito species was not addressed further here as this work was limited to the risk of mosquito-borne pathogen introduction . This introduction can manifest as a single transmission event ( e . g . airport malaria or airport dengue ) or the more problematic initiation of local transmission . Our results show that even in the absence of disinsection and under the most favorable conditions , the probability of any transmission resulting from the introduction of an infected mosquito by aircraft is very low . Moreover , the risk of introduced transmission via human travelers is 2–3 orders of magnitude higher . Cases of reported airport malaria or dengue number in the single digits per year [3 , 77 , 78] , supporting the assertion that while infected mosquitoes on aircraft may transmit pathogens , it is extremely rare . Meanwhile hundreds to thousands of infections in human travelers are reported each year [79–84] each of which provides a higher likelihood of initiating transmission where vectors are present . Concern about the spread of vector-borne pathogens via mosquitoes on aircraft has existed almost as long as aircraft themselves [9] . That concern continues to grow with the drastic increase in air travel and the arbovirus pandemics of recent years . Vector-borne pathogens are named as such for their dependence on insect vectors to complete the transmission cycle . However , on an international scale , clearly vector-borne diseases spread via humans travelling on aircraft , rather than insects . Even with perfect disinsection , which is far from guaranteed , the likely impact of disinsection on the spread of vector borne disease is negligible as that spread is many times more likely to occur due to human travel . | Vector-borne diseases such as malaria , dengue , yellow fever , and Zika are global challenges to public health . International policies , such as the International Health Regulations , call for controlling mosquitoes on aircraft to prevent the introduction of mosquito-borne pathogens and invasive mosquito species . The research presented here used malaria and dengue data to estimate the likelihood of introduction of both pathogens via humans and mosquitoes travelling on airplanes . We found that the probability of introduction of either pathogen by mosquitoes is low due to few mosquitoes being found on aircraft , low infection prevalence among mosquitoes , and a short lifespan . Humans were hundreds of times more likely to spread pathogens via air travel , even in the absence of any mosquito control . Therefore , policies designed to prevent the transportation of infected mosquitoes on airplanes are unlikely to prevent the spread of vector-borne diseases . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
]
| [
"invertebrates",
"dengue",
"virus",
"medicine",
"and",
"health",
"sciences",
"parasite",
"groups",
"pathology",
"and",
"laboratory",
"medicine",
"plasmodium",
"engineering",
"and",
"technology",
"transportation",
"pathogens",
"vector-borne",
"diseases",
"microbiology",
"tropical",
"diseases",
"parasitic",
"diseases",
"animals",
"parasitology",
"viruses",
"apicomplexa",
"rna",
"viruses",
"insect",
"vectors",
"aircraft",
"infectious",
"diseases",
"medical",
"microbiology",
"microbial",
"pathogens",
"disease",
"vectors",
"insects",
"arthropoda",
"mosquitoes",
"flaviviruses",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"malaria",
"organisms"
]
| 2017 | Mosquitoes on a plane: Disinsection will not stop the spread of vector-borne pathogens, a simulation study |
The main objective of the study was to determine the degree of sensitization to Anisakis spp . antigens in healthy coastal population of Dalmatia given the high thermally unprocessed fish intake rate present in this area , suggested as a significant risk factor for anisakiasis . We performed a monocenter , cross-sectional pilot study stratified by geographic area of residence , conducted at the County secondary healthcare provider Medicine-biochemical Laboratory in Split ( Croatia ) , from November 2010 till December 2011 , on 500 unpaid volunteer subjects undergoing routine blood analysis and belonging to the south coast of the Adriatic Sea . We studied the IgE seroprevalence to Anisakis spp . Ani s l and Ani s 7 allergens by indirect ELISA in healthy subjects , which were selected at random in the region of Dalmatia ( Southern Croatia ) , among islands , coastal urban and inland rural populations . In order to detect possible cross-reactivity to other human helminthes , serum samples were tested also for the presence of IgG antibodies to Ascaris lumbricoides and Toxocara canis . The overall and coastal Anisakis seroprevalences for the sampled population were 2% and 2 . 5% , respectively . The logistic univariate regression analysis confirmed that regarding anti-Anisakis IgE seroprevalence , raw fish intake , daily fish intake , homemade origin of fish dish and occupational contact ( professional , artisanal or hobby contact with fishery or fish industry ) were risk factors associated to Anisakis spp . sensitization , but neither of the variables was exclusive for a particular seropositive population . Also , a significant difference was observed between seropositive and seronegative subjects that had stated allergy or symptoms associated with allergy ( atopic dermatitis , asthma or rhinitis ) in their previous history . Being the first in Croatia , our study underlines the necessity of incorporating Anisakis spp . allergens in routine hypersensitivity testing of coastal population .
Anisakidosis is a zoonotic disease caused by members of the nematode family Anisakidae , whereas anisakiasis ( = anisakiosis ) is caused by members of the genus Anisakis [1] . It is considered one of the most significant emerging food-borne diseases [2] , [3] , [4] because the more stringent measures regarding conservation of sea mammals , which are the final hosts , and the acquisition of new gastronomic habits throughout Europe [5] have led to an increase in Anisakis infection rate within paratenic fish host and human population . More medical consciousness of the disease and more detailed clinical examinations have enhanced the number of diagnosed cases in humans [6] , although it is still a misdiagnosed and underestimated entity in Mediterranean . Anisakis third-stage infective larvae are contracted through consumption of thermally unprocessed or lightly processed traditional seafood: sushi and sashimi in Japan [7] , tuna or sparid carpaccio , marinated , salted or pickled anchovy in Mediterranean [8] , [9] , [10] , smoked or fermented herrings ( maatjes ) in Netherlands [11] , dry cured salmon ( gravlax ) in Norway , raw salmon ( lomi lomi ) in Hawaii or ceviche in South America [12] . Depending on the site of infection , the parasitization by live Anisakis third-stage larvae can elicit gastric , intestinal or ectopic anisakiasis [13] . Gastric anisakiasis is characterized by epigastric pain , nausea and vomits after a short period of 1–12 h postingestion of live Anisakis larvae [1] . In the intestinal form , abdominal pain is also the predominant symptom , but the incubation period may be delayed until 48–72 h postingestion [14] . A relevant number of patients with gastric anisakiasis can present associated allergic symptoms ranging from urticaria to anaphylactic shock , and this clinical entity was named gastroallergic anisakiasis [15] , [16] . The allergic symptoms may predominate over gastrointestinal manifestations , which explains why many of these patients are attended by allergologists instead of digestive specialists . Furthermore , most Anisakis infections are subclinical [8] , [17] , and this condition can only be detected using immunological tests [18] . Anisakis infections were also related to the increased risk of upper gastrointestinal bleeding in patients consuming nonsteroidal anti-inflammatory drugs [17] and neoplastic and carcinogenic changes in human intestinal system [19] , [20] . The allergic aspects of Anisakis infections have been extensively studied in the past decade , mainly in Spain [6] , [16] , [21] , where hundreds of cases of allergy to Anisakis have been reported since 1995 [6] , [9] , [18] , [22] , [23] . These results have recommended to carry out serological studies in other Mediterranean populations , both healthy or with food allergies in anamnesis to understand the relevance of Anisakis infections in Europe [24] , [25] . In south coastal part of the Adriatic Sea , Croatian population has been traditionally engaged in preparation of home-made thermally unprocessed fish , mostly pickled , marinated , salted anchovy ( Engraulis encrasicolus ) and sardine ( Sardina pilchardus ) , or salted damselfish ( Chromis chromis ) , as a particular ethnically recognized dish , geographically limited to Island of Korčula . This is very important to consider in relation to Anisakis infection in humans because the elevated consumption of such dishes as national staple food correlates with the peak of tourist season in summer . The aim of this pilot study was to assess the seroprevalence of anti-Anisakis IgE antibodies in coastal healthy population , where infection is feasible given the high rate of undercooked anchovy consumption and anchovy's high infection rate with A . pegreffii [26] . Further , we aimed to pinpoint the extent to which thermally unprocessed fish intake , home-made or marketed seafood contribute to the risk of Anisakis sensitization applying a logistic regression analysis to data collected through an anonymous questionnaire .
This was a monocenter , cross-sectional pilot study stratified by geographic area of residence , and conducted at the County secondary healthcare provider Medicine-biochemical Laboratory in Split ( Croatia ) , from November 2010 till December 2011 . Split is the capital town of Split-Dalmatia County ( 455000 inhabitants ) , with a population of 200000 inhabitants ( second largest in Croatia ) , and a major administrative , commercial , touristic and transit junction . The sample size was a priori determined by use of the Epidat 4 . 0 software package ( http://dxsp . sergas . es ) assuming the Anisakis sensitization prevalence to be similar or less than 13 . 5% , which is the mean prevalence for various Spanish communities , as determined using the same procedure employed in the present study [9] , [17] . The estimated sample size for a population of 455000 inhabitants was 498 , considering a 3% absolute precision , and a 95% confidence level . The sample size was rounded up to 500 participants which were distributed by stratified random sampling in three groups of 200 , 200 and 100 participants according to residency in one of three geographic subareas considered , respectively: islands population ( 50000 inhabitants ) , assumed as high fish eaters ( group A ) ; coastal urban population ( 250000 inhabitants ) , assumed as medium fish eaters ( group B ) and inland rural population ( 70000 inhabitants ) , assumed as low or non-fish eaters ( group C ) . Northwest mountain population of Split-Dalmatia Country ( 85000 inhabitants ) was excluded from analysis because it does not belong to the historical Dalmatia region , a term informally used in practice nowadays . Initially , health providers recruited healthy subjects among individuals that underwent systematic medical examination for different purposes ( working permit , driving license , military training and routine cholesterol control ) over one year . All eligible participants were healthy adults , aged 25 or over , with residency in one of the three aforementioned geographic subareas . Subjects were excluded if they did not reside in the investigated area , were under 25 years of age or had acute or chronic infectious disease symptoms at the time of blood sampling . Approximately , 20% of eligible patients agreed to participate in the study . Signed consent and personal contact information were obtained from all subjects prior to the extraction of 5 ml of blood . Serum was separated by centrifugation at 3000 rpm for 10 minutes , and stored at −20°C . Those eligible participants were included in the software SimDis ( Monte Carlo simulation; http://www . izor . hr/web/guest/simdis ) , which generated a list of 500 random numbers per area of residence . Independent healthcare workers distributed 500 sealed , non-transparent envelopes to each group ( A , B , C ) at the healthcare provider , of which only a half contained an anonymous food frequency questionnaire regarding personal , occupational details and fish-eating habits ( see supporting Figures S1 , S2 and supporting Text S1 ) . The person that generated the random sampling was different from the healthcare providers that distributed the envelopes . The questionnaire included items related to: 1 ) personal , occupational and health details ( gender , age , contact with fishery or fish industry , food allergy or symptoms associated with allergy: atopic dermatitis , asthma or rhinitis ) ; 2 ) fish consumption preferences ( raw fish , frozen , grilled , cooked , canned ) ; 3 ) consumption of thermally unprocessed fish ( raw fish ) including method of preparation ( raw , salted , marinated , sushi ) ; 4 ) frequency of consumption ( daily , several times a week , once a week , rarely , never ) ; and 5 ) origin of fish for consumption ( home-made , retail , restaurant ) . Only eligible participants that had envelopes with the questionnaire were considered for inclusion . Finally , the eligible participants with coastal urban ( 200 subjects ) , island ( 200 subjects ) and inland rural ( 100 subjects ) residency that responded first ( of the 250 subjects in each group that received an envelope with the questionnaire ) were analyzed for Anisakis seropositivity . Outcome assessors and data analysts were kept blinded to the distribution of the participants in the three groups . IgE sensitization to Anisakis spp . was tested in indirect ELISA using recombinant Ani s 1 and Ani s 7 allergens as target , a method that has been reported to be highly specific and sensitive , and proposed as the gold standard for serodiagnosis of human Anisakis infections [18] , [27] . In this assay each serum ( 100 µl , undiluted ) was tested in three individual wells containing , respectively , Ani s 1 , Ani s 7 , or no antigen . The results , expressed as optical densities ( OD ) at 492 nm , were calculated by subtracting from the OD value given by each allergen , the OD value produced by the same serum in the absence of allergen . The cut-off OD values for Ani s 1 ( OD = 0 . 09 ) and for Ani s 7 ( OD = 0 . 05 ) were previously calculated using a collection of negative sera ( 200 sera for Ani s 1 and 561 sera for Ani s 7 ) from Spanish healthy blood donors aged 18 to 65 years [18] , [28] . For such calculations , the mean OD obtained with the negative sera plus 4 SD was considered . As previously reported [18] , a serum was classified as truly positive when it tested positive to Ani s 1 , Ani s 7 , or both allergens . Considered individually , there is agreement that both allergens are 100% specific [18] , [29] , [30] , but Ani s 1 proved to be less sensitive ( sensitivity = 61 . 1%; 95% confidence interval , CI 54 . 07–68 . 15% ) than Ani s 7 ( sensitivity = 93 . 94%; 95% CI 90 . 36–97 . 52% ) [18] . In order to confirm that the specificity of the Ani s 1/Ani s 7 serological test does not change when testing sera from non-Spanish populations , in parallel with anti-Anisakis IgE determinations we tested the Croatian sera for the presence of IgG antibodies to other two related ascarids , Toxocara canis and Ascaris lumbricoides . Such determinations were done using a commercial ELISA for detection of IgG antibodies to T . canis and A . lumbricoides ( Novatec Immunodiagnostica GmbH , Germany ) according to the manufacturer's recommendations . Three sets of serum samples were considered: i ) individual sera testing positive for anti-Anisakis IgE antibodies against the allergens indicated above ( n = 10 ) , ii ) pooled samples ( 19–20 sera pooled in each sample ) from islands ( n = 10 ) and coastal urban ( n = 10 ) populations , testing negative for Anisakis , and iii ) pooled sera ( 10 sera pooled in each sample ) from the inland rural population ( n = 10 ) . Briefly , horseradish peroxidase labeled protein A conjugate was added after incubation of sera in the 96-well microtiter plate coated with antigen from T . canis and A . lumbricoides and the reaction was visualized using 3 , 3′ , 5 , 5′-tetramethylbenzidine substrate . Absorbance at 450 nm was read using an ELISA microwell plate reader with the cut-off as the mean absorbance value of the cut–off control determinations . Assay results were presented in NovaTec-Units ( NTU ) as sera mean absorbance value ×10/cut-off = NTU ( cut-off value: positive >11 NTU ) [31] , [32] . The strength of association between dependent ( IgE seropositivity to Anisakis spp . ; yes/no ) and independent variables 1 ) raw fish consumption; 2 ) daily fish consumption; 3 ) several times per week consumption; 4 ) home-made origin of fish dish; 5 ) occupational contact; 6 ) age >50 ( yes/no ) ; was inferred by univariate logistic regression analysis using software package Stata/IC , version 11 . 2 . Both dependent and independent variables were dichotomous variables . Odds ratio ( OR ) values were considered statistically significant if the 95% CI did not include 1 . Anti-Anisakis seroprevalence and its Fisher's confidence intervals were calculated by WinPepi [33] . The correlation between antibody presence and subject age was performed using Phi coefficient ( rφ ) . Fisher's exact test ( two-tailed ) was used to determine the difference in prevalence of sensitization between the genders , as well as to detect any possible false positive result in the IgE determinations due to cross-reactivity with infections caused by other ascarid nematodes . p-values<0 . 05 were considered to be statistically significant in all the analyses . This research was approved by the Ethics Committee of the Croatian National Institute of Public Health No: 001- 41/1-11 . All patients included have given their written informed consent .
The age and gender distribution of the sample is shown in Table 1 . The mean age of the 500 subjects participating in the study was 58 . 1 years . The 51 . 6% of the population in the sample consisted of males ( n = 258 ) , whereas 48 . 4% were females ( n = 242 ) . The mean age of all the Anisakis spp . positive subjects ( n = 10 ) was 63 . 1 ( range 47–77 ) . No significant correlation was found between antibody presence and subject age ( Phi coefficient rφ = 0 . 1846; p = 0 . 3205 ) or any difference in the prevalence of sensitization between genders ( Fisher's exact two-tailed test p = 1 . 000 ) . The analysis of the questionnaire showed that in islands ( group A ) , predominated the answers “daily” and “several times a week fish consumption” ( 26 . 5% and 57 . 5% , respectively ) while in coastal urban population ( group B ) , the answers “once a week” and “several times a week fish consumption” were more frequent ( 61 . 5% and 25 . 5% , respectively ) . As expected , in the inland rural population ( group C ) , predominated the answers “rarely” and “never” ( 51% and 27% , respectively ) ( Supporting Figure S1 ) . The results ( Table 2 ) show that all Anisakis positive subjects were high fish consumers ( daily or several times a week ) , while most of them ( 9/10 ) reported eating raw and home-made thermally unprocessed fish prepared in the traditional manner . Also , most of seropositive subjects ( 8/10 ) reported professional , artisanal or hobby occupational contact with fishery or fish industry . Of the 500 sera tested for anti-Ani s 1 and Ani s 7 IgE antibodies by indirect ELISA , 10 tested positive ( 2% ) ( Table 2 ) . Seropositive subjects included 5 men and 5 women aged 57–77 and 47–75 years , respectively . Of the 10 seropositive subjects , 7 were from the islands population ( group A; prevalence = 3 . 5% , Fisher's exact 95% CI 1 . 42–6 . 45 ) and 3 from the coastal urban population ( group B; prevalence = 1 . 5% , Fisher's exact 95% CI 0 . 31–4 . 32 ) , which represents a mean seroprevalence of 2 . 5% ( 95% CI 1 . 1–3 . 9 ) for the whole sampled coastal population . Fisher's exact CI for rural population ( group C; n = 100 ) , where no seropositive subjects were detected , was 0 . 0–3 . 62% . Comparing the response to the two Anisakis allergens , 3 subjects were positive for Ani s 1 , while all were positive for Ani s 7 . Individual serum samples with IgE antibodies to Anisakis spp . ( n = 10; groups A and B ) , together with 20 pooled samples of the seronegative sera from coastal populations ( n = 390; groups A and B ) and 10 pooled sera from the inland rural population ( n = 100; group C ) were tested for IgG antibodies to A . lumbricoides and T . canis ( Figure 1 ) . Among the Anisakis-positive subjects , 4 individual sera were positive for Ascaris spp . and 2 individual sera were positive to Toxocara spp . ( 40% and 20% , respectively ) . Considering the Anisakis-negative subjects from coastal populations ( n = 390; groups A and B ) , 3/20 pooled sera were positive to Ascaris and another 3 pooled sera were positive to Toxocara ( 15% in both cases ) . Finally , in the inland rural population ( n = 100 ) , 5/10 pooled sera were positive to Ascaris and 1 pooled sera was positive to Toxocara ( 50% and 10% , respectively ) . No statistically significant NTU differences were found comparing Anisakis-positive and Anisakis-negative samples in these analyzed groups of sera considering either seropositivity to Ascaris ( Fisher exact two-tailed test; p = 0 . 1605 ) , or Toxocara ( Fisher exact two-tailed test; p = 0 . 6269 ) . In fact , only a single Anisakis-positive serum also tested positive on both Ascaris and Toxocara ELISA ( 29 and 14 NTU , respectively ) . Comparing the seropositive ( 10 subjects ) and seronegative populations ( 490 subjects ) , the univariate logistic regression analysis confirmed that raw fish intake ( OR = 10 . 95; p = 0 . 024 ) , daily fish intake ( OR = 10 . 54; p<0 . 0001 ) , home-made origin of fish dish ( OR = 16 . 34; p = 0 . 0008 ) and occupational contact ( OR = 8 . 89; p = 0 . 006 ) were risk factors associated with Anisakis spp . sensitization . Other details are shown in Table 3 . Regarding risk factors related to allergy , we have observed that 42/500 ( 8 . 4% ) of the subjects reported a history of food allergy or symptoms associated with allergy , while 3/10 ( 30% ) seropositive subjects reported allergy history . There was a significant difference between seropositive and seronegative subjects ( Fisher's exact two-tailed test p = 0 . 0437 ) reporting history of allergy symptoms with OR = 4 . 95 ( 95% CI 1 . 232–19 . 93 ) .
In this study we present the first epidemiological data on Anisakis infections in Croatia . Diagnosis of anisakiasis is difficult to suspect in countries where the illness was not previously reported , where it is infrequent , or in the cases of subclinical infections . These aspects point out the relevance of conducting epidemiological studies to assess the seroprevalence of anti-Anisakis IgE in the high-risk coastal population , where this zoonosis is more probable . Our data showing an anti-Anisakis IgE seroprevalence in healthy subjects of 1 . 5% in coastal urban population and 3 . 5% in islands population from Croatia indicated the existence of a relevant number of subclinical infections among general adult population . However , these numbers might be underestimated because patients from allergy services were not included in our study . In fact , a previous study reported an increase of seroprevalence from 11 . 7% to 16% when considering allergic patients [9] . In our study we have observed that Ani s 1 and Ani s 7 allergens were differentially recognized by positive subjects , which seems to be more tightly related to the immunodominance of the Ani s 7 allergen than to its life span in circulation . Whilst both belong to the major allergen category [27] , and antibodies to Ani s 1 are detectable in sera for a longer time than Ani s 7 [18] , there are several studies demonstrating that the number of patients recognizing Ani s 7 is higher than for Ani s 1 [18] , [27] , mainly in the group of patients having chronic urticaria [27] . This highlights its significance as the main target allergen for serodiagnosis of human anisakiasis . The analysis of the serological data presented in this work also seems to indicate that infections by the three antigenically related nematodes Anisakis , Ascaris and Toxocara [34] , [35] coexist in the Croatian population . This fact gave us the opportunity to investigate the specificity of the serological tests used in the study . From a statistical point of view it would be expected that if some antigens used in the different tests were cross-reactive , a significant proportion of seropositive subjects would test positive by more than one test . In addition , whether the Anisakis test was not totally specific , it would be also expected that positive cases to Anisakis were present in the population not consuming raw or undercooked fish , where infection by Ascaris/Toxocara was present . In the present study we have observed that 60% ( 6/10 cases ) of sera testing positive to Anisakis were also positive to Ascaris , Toxocara or both , which is greater than would be expected by chance , and therefore a possible indicator of cross-reactivity . This might be the case of a single serum that tested positive to Ascaris and Toxocara in the group of positive sera to Anisakis . However , since there were no positive cases to Anisakis in the inland rural population where cases of Ascaris/Toxocara were present , it seems that the release of cross-reactive antigens during Anisakis infections produces false positive results in the ELISA tests to Ascaris/Toxocara but not in the inverse way . This is logical taken into account that , unlike the Ascaris/Toxocara tests that use a pool of antigens and have only 95% specificity , the Anisakis test used in this study is based on the use of recombinant antigens that proved to be 100% specific for anti-Anisakis IgE determinations in previous studies carried out in Spain [9] , [18] , [30] , [36] . Ascaris and Toxocara infections are relatively frequent in Croatia; T . canis seroprevalence in the asymptomatic children with eosinophilia was reported to reach 31% [37] , while Ascaris spp . induced more acute non-allergic clinical manifestations [38] . Recent official data from the Croatian National Institute of Public Health reported prevalences of 29 . 45% for A . lumbricoides and 21 . 7% for Toxocara spp . in Croatian population during a two-year period ( 2010–2011; M . Sviben , personal communication ) . Through univariate logistic regression analysis we have confirmed that raw fish intake , daily fish intake , and home-made origin of fish dish , were the main risk factors associated to Anisakis spp . sensitization . Similarly , in Madrid population , very high consumption of boquerones ( 5 . 49 g/person/day ) and high infection rate in anchovy population contribute to the observed high anti-Anisakis seroprevalence [9] . The difference between the prevalence of 3 . 5% observed in islands and 1 . 5% in coastal urban populations seems to be due to differences in fish consumption between both areas . Likewise , home-prepared dishes ( undercooked or lightly grilled ) might also increase the risk of infection , as reported earlier [8] , [39] . However , given the overlap between consumption habits of the ten seropositive subjects in this study , where neither of the variables was discriminative for a specific group of subjects , it is unfeasible to pinpoint which of the risk factors is primarily associated with Anisakis seropositivity . Instead , it might be that all of them , together with other factors that affect larvae survival , as variations in the prevalence and intensity of infection in the fish , and the manner in which fish dishes are prepared , contribute to the probability of infection of consumers [8] . High variations in Anisakis seroprevalence are also frequently observed even among different regions of the same country [6] , [40] . In the case of Spain , a mean prevalence of 15 . 4% in adult population from Madrid was recently observed using the same technique as in the present study [17] . However , this percentage was only 0 . 4% [8] and 1 . 5% , respectively , in blood donors and general adult population ( unpublished results ) from Galicia ( NW Spain ) . Authors related these variations with a different tradition in consumption of pickled anchovies ( boquerones ) that is high in center [9] , south [39] and north of Spain [40] , but low in Galicia [8] , in spite of the fact that this latter region has one of the highest consumption of marine fish in Spain [8] , [41] . Similarly , a very low prevalence of Anisakis infections and high fish intake was recently reported by Lin et al . [42] in Bergen ( Norway ) , where consumption of raw or undercooked marine fish seems to be infrequent . In Croatia the mean fish consumption is relatively low ( 8 . 5 kg/year per capita ) , but it is extremely biased towards coastal and islands areas that have much higher consumption compared to inland rural area ( with <1 kg/year [43] ) . This very low fish consumption and no raw fish intake in the rural area explains why all cases of seropositive patients that occurred in our study were from coastal and islands populations , and reinforces previous studies showing that home-made raw fish is the main recognized risk factor for Anisakis infections/allergy [8] , [9] . Like in our study , the AAITO-IFIACI Anisakis Consortium [25] has reported that marinated home-prepared anchovies , frequently consumed , probably represent the most common food that causes Anisakis sensitization in Italy along the Adriatic west coasts . Along Croatian eastern side of the Adriatic Sea the traditional method of preparing raw fish varies according to regions and islands , but the methods adopted in fish manufacturing ensure killing of Anisakis spp . larvae [44] . This is important given the high frequency of Anisakis spp . prevalence in certain species of small pelagic fish [26] . Contrary to previous studies [39] , our logistic analysis revealed that the risk has no tendency to increase with age ( over 50 years ) , which may be due to the fact that there were no subjects in the range between 25 and 47 years in the islands population . Such underrepresentation of a “younger” age class might have introduced a bias in analysis of correlation towards underestimation of correlation between seroprevalence and age in our study . At the same time , it might have overestimated the overall seroprevalence in islands population , being enhanced by a larger number of older subjects that had a greater possibility of having more exposures to Anisakis allergens . This should be taken into consideration during interpretation of the results , as well as when designing similar studies in limited island areas . From all subjects engaged in the sampling , 8 . 4% reported history of food allergy or symptoms associated with allergy while this percentage increased to 30% ( 3/10 ) in the seropositive population to Anisakis ( Table 2 ) . The comparison between sensitized versus non-sensitized subjects showed significant difference in reporting food allergy or symptoms associated with allergy , with OR = 4 . 95 . This strongly suggests that Anisakis infections are responsible for some of the allergic reactions in the subjects of this study , confirming previous results [16] and supporting the need of introduction of routine Anisakis hypersensitivity tests in the risk population . Anisakis spp . was also related with occupational seafood allergy [40] , induced by the contact of parasite allergens with skin or respiratory epithelium [45] . In this sense , Italian authors reported that Anisakis larvae represent a potential occupational risk in fishermen and workers assigned to fish processing and sale , with specific anti-Anisakis IgE detected in 20 . 2% of the studied population [46] . Similarly , recent research in Croatia described that chronic respiratory symptoms associated with occupational asthma were significantly dominant in fish processing workers compared to controls [47] . To know whether Anisakis spp . is a risk factor in occupational allergy is important because many people in the Mediterranean , Croatia included , are engaged in fishery and fish processing or retail industry and as such need to be protected . In our study we have observed significant differences considering the variable “occupational contact” among seropositive and seronegative populations ( Table 3 ) that need to be taken into account for future allergologic studies as well as in routine testing . In summary , our study demonstrates that , like in other Mediterranean countries , in coastal Croatian populations there is a relevant prevalence of Anisakis infections , which were mainly related to the ingestion of home-made raw fish ( i . e . , anchovies ) . These data are of interest for allergologists and health authorities in order to carry out a correct diagnosis of Anisakis-induced allergy , introduce Anisakis in routine hypersensitivity testing and to prevent new Anisakis infections . | Anisakiasis is a zoonosis induced by infection with the Anisakis third-stage larvae , contracted through consumption of thermally unprocessed or lightly processed seafood . Its diagnosis is difficult to suspect in countries where the illness was not previously reported , where it is infrequent , or in the cases of subclinical infections . Therefore , it is of great relevance to conduct epidemiological studies to assess the seroprevalence of anti-Anisakis IgE in populations where this zoonosis is more probable . A cross-sectional pilot study was performed on 500 subjects undergoing routine blood analysis and belonging to the south coast of the Adriatic Sea . The results showed that IgE sensitization to Anisakis , tested by indirect ELISA using recombinant Ani s 1 or Ani s 7 allergens , reached 3 . 5% in the population of higher fish consumers ( islands ) . All Anisakis positive subjects were high fish consumers , mostly of raw and homemade thermally unprocessed fish prepared in the traditional manner . Most of them reported professional or hobby occupational contact with fishery or fish industry . We demonstrated that in coastal Croatian populations there is a relevant prevalence of Anisakis infections , mainly related to the ingestion of home-made raw fish , underlining the necessity to carry out a wider epidemiological study of Anisakis-induced allergy . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"medicine",
"infectious",
"diseases",
"veterinary",
"diseases",
"clinical",
"immunology",
"zoonotic",
"diseases",
"allergy",
"and",
"hypersensitivity",
"parasitic",
"diseases",
"veterinary",
"science",
"helminth",
"infection",
"foodborne",
"diseases"
]
| 2014 | Anti-Anisakis IgE Seroprevalence in the Healthy Croatian Coastal Population and Associated Risk Factors |
Robustness of organisms is widely observed although difficult to precisely characterize . Performance can remain nearly constant within some neighborhood of the normal operating regime , leading to homeostasis , but then abruptly break down with pathological consequences beyond this neighborhood . Currently , there is no generic approach to identifying boundaries where local performance deteriorates abruptly , and this has hampered understanding of the molecular basis of biological robustness . Here we introduce a generic approach for characterizing boundaries between operational regimes based on the piecewise power-law representation of the system's components . This conceptual framework allows us to define “global tolerance” as the ratio between the normal value of a parameter and the value at such a boundary . We illustrate the utility of this concept for a class of moiety-transfer cycles , which is a widespread module in biology . Our results show a region of “best” local performance surrounded by “poor” regions; also , selection for improved local performance often pushes the operating values away from regime boundaries , thus increasing global tolerance . These predictions agree with experimental data from the reduced nicotinamide adenine dinucleotide phosphate ( NADPH ) redox cycle of human erythrocytes .
Robustness , the notion that biological systems must be able to withstand a variety of perturbations is becoming a cornerstone of research in systems biology . Indeed , several approaches have been developed to understand this concept . These approaches tend to focus on the levels of genotype , intermediate network architectures , or phenotypic expression . None actually provides any relation between these levels because the fundamental mappings between levels have not been solved . At the level of the genotype , there are approaches dealing with neutral or near neutral mutations , which may be considered the result of a genetic code optimized by natural selection . These include nucleotide substitutions that leave the secondary structure of an RNA unchanged [1] , that result in a synonymous codon that leaves the protein sequence unchanged , or that lead to the substitution of an aminoacid with similar physical-chemical properties [2] . The fraction of mutations that fall into these classes provides a measure of the organism's “mutational robustness” . At the level of intermediate network architectures , there are approaches dealing with the number of redundant paths between points in the network . The number of such redundancies provides another measure of robustness . Perhaps the best example of such architectures is provided by networks at the metabolic level [3] . However , these approaches at the level of genotype and network architecture have little to say about any specific biological function . At the level of specific phenotypic function , the concept of robustness deals with the relationship between the physiological behavior and the underlying parameters of mechanistic models identified or hypothesized . Most approaches at this level have dealt with the local behavior as characterized by small ( infinitesimal ) changes . Robustness according to these approaches corresponds to parameter insensitivity–linear sensitivities [4] , logarithmic sensitivities [5] , [6] , or second-order sensitivities [7]–[9] . All of these approaches have shown what has been long known from experimental studies , that there is a spectrum of sensitivities with many parameters having very little influence and a smaller number having the major impact . There are other approaches that attempt to deal with local changes in parameter values analytically , but only in terms of preserving system stability . For systems with a stable steady state , parameter variations that lead to the loss of stability will first violate one of the last two Routh criteria . The magnitudes of these two conditions can be considered a measure of the “distance” from the boundaries of instability . This distance is often referred to as the margin of stability . The margin in the case of the penultimate condition is the more difficult to evaluate; it involves both kinetic order and rate constant parameters [10]–[12] . The margin in the case of the last Routh criterion is determined more simply by the determinant of the matrix of kinetic orders for the dependent variables [10] , [13] , alternatively by a method based on singular value decomposition of this matrix [14] . For many systems both conditions are critical and must be evaluated . However , these local approaches have little to say about a system's response to larger changes in parameter values . One approach to deal with large changes in parameter values involves random sampling of values to obtain an estimate for the volume of parameter space corresponding to physiological behavior [15] , although volume alone is not a sufficient measure . The shape of the volume is critical , as pointed out by Morohashi et al . [16] . Sengupta et al . [17] and Chaves et al . [18] have proposed a measure of robustness , based on a random walk in parameter space , that reflects the shape of the robust region . These methods are limited by the computational expense of dense sampling and random walks in high-dimensional parameter spaces . All of the existing methods have advantages as well as significant limitations . Thus , there is need of a generic approach for dealing with robustness to large changes in parameter values and identifying a variety of qualitatively distinct phenotypes , including but not limited to loss of stability . In this paper , we introduce such a method and illustrate its use in the context of a specific class of biochemical systems , moiety-transfer cycles . In such systems , the variables and parameters , which define its structure , must remain within a neighborhood of their nominal values so as to produce a physiological phenotype . When this neighborhood is exceeded the system exhibits a pathological phenotype . Our generic approach involves the precise characterization of boundaries between phenotypically distinct regimes and defines “global tolerance” as the ratio ( or its reciprocal , depending on which is greater ) between the normal value of a parameter and the value at such a boundary where there is an abrupt change in system performance . Thus , systems whose performance remains nearly constant for large deviations from the normal operating point are considered to be “globally tolerant” . This is in contrast to the conventional notion of “local robustness” , defined by small values for the system's parameter sensitivities [5] , which results in important aspects of system performance remaining almost constant near the normal operating point . As biochemical parameters might be subject to considerable variation , a small global tolerance might be disadvantageous even if system performance is locally robust . The notion that large global tolerances may evolve as “safety factors” against fluctuations in parameter values and/or in the loads placed by the environment has been proposed as a possible explanation for large mismatches found between actual biological capacities and apparent physiological needs [19]–[22] . For example , the measured capacity ( value ) of hexokinase exceeds the physiological flux in the cardiac muscle of exercising rainbow trout by over three orders of magnitude [21] . More recent studies [23] , [24] of concrete systems suggest that large tolerances of pathway fluxes to changes in the activity of the participating enzymes are the side-effect of fulfilling local performance criteria . However , we can envision a situation in which effective local performance will not necessarily lead to large tolerances , and therefore the possibility of performance breakdown due to normal variation in parameter values becomes a major consideration mediating natural selection . A similar point is highlighted by Morohashi M , et al . [11] , showing that various aspects of the design for a biochemical oscillator can be rationalized as attending to a requirement for both good local performance and large global tolerance . Therefore , local robustness and global tolerance are both important aspects for the evolutionary design of biochemical systems . In illustrating our generic approach , we also will address the question: does design for robust local performance necessarily improve global tolerance ? In moiety-transfer cycles , a moiety is transferred from a moiety-donor metabolite ( ) to an acceptor metabolite ( ) by way of a charged carrier ( ) ( Figure 1 ) . For our example , and under the conditions of interest , we will assume that the sum ( ) of the charged carrier ( ) and the uncharged carrier ( ) is held constant . This form of coupling between reactions is very prevalent in metabolism . Indeed , of all the enzyme-catalyzed reactions in the reconstructed metabolic networks of Escherichia coli [25] and Saccharomyces cerevisiae [26] , 836 ( 75% ) in the former organism and 561 ( 67% ) in the latter participate in moiety-transfer cycles . These calculations exclude cycles involving the ubiquitous metabolites H2O and H+ , and pairs of forward-reverse reactions . Redundant reactions catalyzed by distinct ( iso ) enzymes were counted as a single reaction . The large majority of these cycles mediate the transfer of moieties from catabolic ( i . e . , nutrient-disassembling and energy-producing ) to anabolic ( biosynthetic ) processes . In this context , they act as “moiety-supply” units , analogous to power-supply units in electric circuits: they must reliably supply a given moiety at the required rate ( analogous to current intensity ) while keeping the concentration of the charged carrier ( analogous to electric potential ) fairly constant . Here we address moiety-transfer cycles that play this specific role . Henceforth , when we use the term “moiety-transfer cycles” it should be understood that we are referring specifically to the class of moiety-transfer cycles that act as “moiety-supply” units . We also compare our analytical results to existing experimental results for the NADPH redox cycle of human erythrocytes .
We will assume that each enzyme involved in a moiety-transfer cycle ( Figure 1 ) has two substrates and that the reactions are irreversible . For our particular example , we will use Eqn ( 1 ) , which is valid for a wide range of two-substrate enzymatic mechanisms ( random-order equilibrium , compulsory-order , Theorell-Chance and ping-pong mechanisms ) [27]: ( 1 ) where: is the concentration of substrate ; is the concentration of substrate ; is the rate of catalysis by enzyme ; is the maximum rate of catalysis by enzyme ; is the Michaelis constant of enzyme with respect to substrate ; is the Michaelis constant of enzyme with respect to substrate ; is the equilibrium dissociation constant for the enzyme-substrate complex ; is 1 if the enzyme follows a random-order equilibrium or a compulsory-order mechanism in which binds first and is 0 if the enzyme follows a ping-pong mechanism . For purposes of illustration , we will assume that the charging enzyme follows a compulsory order mechanism in which binds first to the enzyme ( ) and the uncharging enzyme follows a ping-pong mechanism ( ) . For simplicity , and without ambiguity since we are only considering two different enzymes , we are going to discontinue using the subscript referring to the enzyme . Hence the terminology that we are going to use throughout the text is as follows ( see Figure 1 ) : The investigation of tolerance requires a mathematical framework that is able to address the effects of large perturbations while avoiding the mathematical complexities of unstructured nonlinear systems . The strategy for our analysis involves ( i ) decomposition of the system's design space into unique regions with boundaries precisely defined by the “breakpoints” in the piecewise power-law representation , ( ii ) determination of the system behavior in each region , ( iii ) evaluation of system behavior according to a set of quantitative criteria based on the function of the system , and ( iv ) determination of the global tolerance to changes in the values for the parameters and concentrations of the system . Our approach is based on the idea that performance differs when there is a change in the dominant flux or concentration terms . For instance ( Figure 2A ) , for enzymes that obey the Hill function , the characteristic concentration—typified by the —marks the breakpoint between two regimes in logarithmic space . One is characterized by most of the enzyme being in the free form ( slope equal to the Hill coefficient ) and the other by most of the enzyme being bound to the substrate ( slope equal to zero ) . More complicated enzyme mechanisms , will involve more than one breakpoint . For instance , some enzymes exhibit substrate inhibition at elevated substrate concentrations ( Figure 2B ) . For these enzymes , there will be three regimes separated by two breakpoints . At substrate concentrations much below the , most of the enzyme is in the free form ( slope equal to one ) ; at intermediate concentrations , above the and below the , the enzyme is mostly bound by a single molecule of substrate ( slope equal to zero ) ; at substrate concentrations much above the , the enzyme is mostly bound in an abortive or dead end complex between the substrate and one or several enzyme forms ( slope equal to −1 ) . The essential feature of a system , and that any mathematical framework for the analysis of tolerance has to capture , is thus the breakpoints between regimes . These ideas lead us to estimate tolerances within the framework of the piecewise power-law representation of enzyme kinetics , which is one of the four different representations within the power-law formalism of Biochemical Systems Theory [28] . This representation retains the mathematical tractability of the local power-law representation [5] , which provides a characterization of the system in terms of logarithmic gains , robustness ( as measured by parameter sensitivities ) and local stability , while extending the range of application to global considerations . Formulation of our piecewise power-law representation is analogous to the classical method of Bode [29] and involves three steps ( [10] , pp 335–341 ) : Using this method , we derive the piecewise power-law representation: ( 4 ) and ( 5 ) Although the asymptotes in this example are straight lines in both Cartesian and Logarithmic coordinates , this is not the general case . In the general case , the asymptotes are straight lines only in the Logarithmic coordinates . Under the condition ( Figure 3A ) there are three different regimes each with a different steady state . For very small values of , the steady state in Systemic Regime a is valid . In this steady state , the charging enzyme operates within its linear region and the uncharging enzyme operates on its plateau . As increases , there is a transition to the steady state in Systemic Regime c , in which both enzymes operate within their linear regions . Finally , as increases even further , there is a transition to the steady state in Systemic Regime b , in which the charging enzyme operates on its plateau and the uncharging enzyme functions within its linear region . Under the condition ( Figure 3B ) there are two different regimes each with a different steady state . For values of less than one , the steady state in Systemic Regime a is valid; when equals one the system experiences a discontinuity and transitions to the steady state in Systemic Regime b for values of greater than one . Through the analysis of these cases , and of the remaining ones ( see Text S1 ) , we are able to determine the design space available to the moiety-transfer cycle ( see Figure 4 ) . Each systemic regime is given by a specific and readily solvable steady-state equation for the dependent variable , and applies only to a particular region of the design space ( Table 1 ) . Given this partitioning of the design space into distinct regions , one can define global tolerance as the ratio between the value of a parameter at the operating point ( white point in Figure 4A ) and the value of that same parameter at the boundary to the next neighboring region ( black double headed arrows in Figure 4A ) . The system representation within each regime is a simple but nonlinear S-system for which determination of local behavior , after appropriate transformation , reduces to conventional linear analysis [10] . Thus , the local behavior is completely determined and readily characterized by the evaluation of the following quantitative indices . Logarithmic gains in concentration ( e . g . , the charged moiety ) or flux ( e . g . , the rate of charged-moiety supply ) in response to change in value for an independent variable ( e . g . , the concentration of the moiety-acceptor ) are defined by the relative derivative of the explicit steady-state solution . For example , ( 6 ) Parameter sensitivities of such state variables in response to change in the value for one of the parameters that define the structure of the system ( e . g . , Michaelis constants or maximal velocities ) are defined by the relative derivative of the explicit steady-state solution . For example , ( 7 ) Response time is given by the inverse of the eigenvalue , which is determined by analytical integration of the differential equation that applies for each systemic regime . What criteria must a moiety-transfer cycle fulfill in order to be considered a good one ? This is a question that only now is being posed by biologists . However , this question is analogous to one that engineers have long had to deal with , and the lessons they have learned can now be used to further our understanding of how biological systems are designed through natural selection . The performance of the moiety-transfer cycle , which is analogous to that of the power supply in an electrical circuit , can be evaluated in each systemic regime according to the following quantitative criteria: The concentration of charged carrier ( analogous to the voltage of the power supply ) should be well buffered against: The supply of charged carrier ( analogous to the electrical current ) should The sensitivity of the supply of charged carrier to changes in the concentration of moiety-acceptor should The response time should
In Table 2 , we summarize the results from the analysis of local performance in Systemic Regime a . ( Details of these results are presented in Text S2 ) It is apparent from these results that the performance in Systemic Regime a fulfills all of the criteria defined above . Furthermore , if Condition 1 , , is valid , the optimization of criteria 1 through 6 follows the same strategy: and should decrease while , and should increase . Note that there is one apparent conflict between optimizing Criterion 7 along with the previous criteria . In order to optimize criteria 1 , 2 and 6 , should tend to low values , whereas to optimize performance according to Criterion 7 , should tend to high values . This apparent conflict can be readily resolved with appropriate values for , or ( for which there are no trade-offs ) . Contrary to the results for Systemic Regime a , the performance in Systemic Regimes b and c cannot fulfill criteria 4 and 5 because there is no response to changes in moiety-acceptor ( detailed results in Text S2 ) . In addition , even though the performance in Systemic Regimes b and c can have a fast response time ( Criterion 6 ) , it will not be with respect to changes in . Therefore , the importance of this responsiveness becomes questionable . Finally , the optimum value of Criterion 1 in Systemic Regime c is 1 , whereas that in Systemic Regime b is 3 . Since Systemic Regimes b and c share the same optimum values for the remaining criteria , we conclude that overall local performance in Systemic Regime c is better than that in Systemic Regime b . From the analysis of local performance , it is clear that the only systems that can fulfill all criteria and do it efficiently operate in Systemic Regime a . Although systems that operate in systemic regimes b and c can fulfill some of the performance criteria , they fail in that their supply of charged carrier , , does not respond to changes in the concentration of moiety-acceptor . In analogy to electrical circuits , they resemble a power supply that will not provide additional current when there is an increased demand by the rest of the circuit . Hence , this is a poor design for a power supply unit . If there had been no regime capable of simultaneously fulfilling all the performance criteria then one would have to evaluate the relative impact on fitness of the failure to satisfy a specific criterion . Regimes that violate performance criteria with a weak effect on fitness would clearly be preferable to those that violate more important performance criteria . If the results showed that all regimes violated important performance criteria , then one may attribute this to an inappropriate model or to incomplete/inaccurate knowledge about the function of the system under analysis . In summary , we predict that in nature , under basal conditions , a moiety-transfer cycle should operate in Systemic Regime a . Moreover , natural selection should maintain the operating point far from the boundaries to the other regimes for the following two reasons . First , the circuit's local performance improves as the operating point moves away from the boundaries . Second , even where the intra-regime gradient in local performance is modest , excursions into neighboring regimes of poor performance are less likely when the operating point is farthest from the boundaries . Systemic Regime a holds in the region of design space ( Figure 4 ) defined by the following inequalities:Systems represented within these boundaries exhibit the best local performance and thus these boundaries provide the basis for a natural definition of global tolerance . Namely , By the use of this definition it is possible to determine analytically the global tolerance to change for each kinetic parameter and independent variable of the system operating in Systemic Regime a . In general , each parameter or independent variable can have a global tolerance with respect to its lower value as well as its upper value . These tolerance values will be denoted “[Tlow , Thigh]”; since one of these is often infinite , we also will use the notation “[Tlow” or “Thigh]” with the other infinite tolerance implied . There are two different boundaries for Systemic Regime a , and , so we present the tolerance expressions with respect to each in Text S3 . When considering each kinetic parameter and independent variable individually , its critical tolerance will be given by the lowest of its tolerance values given in Text S3 . Numerical values for these tolerances are given for a specific system in the following section . We have selected this moiety-transfer cycle to provide a numerical illustration of our results because the kinetic parameters of the enzymes and concentrations of the metabolites for this system have been well characterized experimentally [31]–[34] in view of this cycle's importance in malaria [35] . These values , which are in Text S4 , lead to the design space in Figure 5 depicting the steady-state concentration in the z-direction with a heat map . The physiological operating point for this system is found in Systemic Region a , as expected . The design space depicting the steady-state flux has a similar appearance ( data not shown ) . The local behavior of this system can be evaluated according to the seven criteria described earlier . In this case we have the numerical values for the various parameters and , thus , we can calculate the numerical values for the criteria and compare their values to the optimum values . As can be seen from the resulting data summarized in Table 3 , natural selection results in a design that has nearly optimal local performance according to the seven criteria . Given the numerical values that characterize the operating point for this system , and the boundaries surrounding Systemic Region a , we are able to determine the numerical value of global tolerance for each of the kinetic parameters and independent concentration variables . The values , summarized in Table 4 , are tolerances involving movement from Systemic Region a into Systemic Region c . They range from the smallest tolerance of 59 fold to the largest of 362 fold . The smallest values are associated with , , and , whereas the largest are associated with , , and . It should be emphasized that no change in the value of any single parameter or concentration is capable of moving the operating point of the system from Systemic Region a into Systemic Region b . In this sense , the largest tolerances ( essentially infinite ) are associated with the boundary between systemic regions a and b .
The organization of biochemical systems has traditionally been viewed as adhering to few general rules . Should it be real , this perceived lack of generally applicable organizing principles would reduce molecular biology to an accumulation of disparate facts with limited predictive value . However , research in molecular systems biology is revealing a number of design principles that associate function with design . For example , such design principles have been found in metabolic pathways [36]–[40] , signal transduction cascades [41]–[45] , mode of gene control [28] , [46]–[49] and coupling of gene circuits [50]–[54] . This research provides an understanding of why some designs are highly prevalent in biochemical systems while other feasible designs are rare . It also prompts predictive inferences of ( i ) what interactions among biochemical components should occur given the function of a network , or ( ii ) what is the likely function of a network given its component interactions . A high priority in the research program of biochemical systems theory is the characterization of design principles for the most common constituents of biochemical systems such as elementary gene circuits and simple metabolic networks . As noted in the Introduction , moiety-transfer cycles are among the most common functional units in metabolic networks . Hence , the material presented in this paper serves not only to introduce an important analytical framework within which to quantitatively characterize the design of biochemical systems , but also to provide insight regarding the design principles that govern one of the most common functional units in metabolic networks . It must be emphasized that the piecewise power-law representation described in this paper is not an arbitrary fit to the kinetic rate laws . It is not simply a convenient curve-fitting exercise that attempts to minimize the error in the representation by using a sufficiently large number of arbitrary pieces . The number of pieces , their slopes and the location of the breakpoints are all uniquely determined by the rational function in conventional Bode-type analysis ( [10] , pp 335–341 ) . Moreover , this representation is rigorously justified for the rational functions known to characterize the traditional rate laws of biochemical kinetics [55] . Thus , the method is highly constrained by the model and it produces a unique representation . The class of models can be quite general; for example , it includes generalized mass action models of chemical kinetics and rational function models of biochemical kinetics . Regardless of how one obtains a given model ( detailed kinetic analysis , an empirical fit to a model using limited data or a hypothetical model based on general considerations ) , as long as it falls within this very general class of functions then our approach can be applied . Differences between the steady-state solutions of the rational function and piecewise representations are greatest around the breakpoints , as is evident from Figure 5 . The lack of accuracy at these points may be considered a disadvantage of the piecewise power-law representation . Nevertheless , the piecewise power-law representation suggests the formulation of the design space , provides precise boundaries between regions , and gives a method for defining global tolerances in a quantitative manner . These are all major advantages that would be hard to derive directly from the rational-function representation . Thus , it must be emphasized that in our example the formulation of the design space and the boundaries were first derived from the piecewise representation ( depicted in Figure 5A ) and then used to display the results from the rational-function representation ( depicted in Figure 5B ) . The system design space that is defined by our approach provides an important framework to characterize the behavior of the system . Within each region , system behavior is readily solved , often analytically , as for the cases analyzed in this paper . The results presented in this paper can be generalized to other moiety-transfer cycles , as will be documented in a subsequent publication ( Coelho et al . , manuscript in preparation ) . The system design space also provides an important framework to represent and compare wild-type and mutant variants of these systems . The kinetic parameters of the systems can be measured and the resulting values plotted within the common design space . An example is provided in Figure 5 by making use of the data for the wild-type NADPH redox cycle in human erythrocytes [31]–[34] . The location of the operating point for mutants ( where such mutants and their kinetic data are available ) , in relation to that for the wild type and in relation to the boundaries between good and poor regions , will provide a method to quantitatively characterize the physiological significance of mutant phenotypes . There is a general theorem indicating that the robustness of feedback control systems is a conserved quantity , and thus increasing the robustness in one operating regime must cause it to decrease in another [56] . This suggests that trade-offs are inevitable in the design of a system . It is not yet clear how our results might be governed by this theorem . The differences may reside in the global dynamics of the system , since our analysis focuses on the steady-state behavior and only considers dynamics in the local sense . As we have seen , an important consideration affecting the location of the operating point for the wild type relative to regime boundaries is the interplay between global tolerance and local performance . Selection for improved local performance often pushes the operating point away from regime boundaries , thus increasing global tolerance . But in some cases modifying the value of a parameter in the direction that improves local performance may bring the operating point closer to regime boundaries , thus decreasing global tolerance . Our analysis identified two cases of potential trade-offs between specific criteria for local performance and global tolerance . Namely , increasing improves the buffering of the response time against fluctuations in the values of parameters and independent variables , but decreases global tolerances with respect to changes in the values of most parameters . Likewise , decreasing can in some conditions improve buffering against changes in the concentration of moiety-acceptor , but it can decrease global tolerances with respect to changes in the values of most parameters . However , because these same changes in or would also worsen several other important aspects of local performance they do not entail a real trade-off between overall local performance and global tolerances . Furthermore , none of the trade-offs mentioned above prevent the simultaneous improvement of both local performance and global tolerance by suitably changing the value of a second parameter . Therefore , the simple design of moiety-transfer cycles that we addressed here does not have any irresolvable trade-offs between global tolerance and local performance for the set of performance criteria we considered . This is a desirable property that facilitates the evolutionary adaptation of the cycle to changing environmental demands . | The ability of organisms to survive under a multitude of conditions is readily apparent . This robustness in performance is difficult to precisely characterize and quantify . At a biochemical level , it leads to physiological behavior when the parameters of the system remain within some neighborhood of their normal values . However , this behavior can change abruptly , often becoming pathological , as the boundary of the neighborhood is crossed . Currently , there is no generic approach to identifying and characterizing such boundaries . In this paper , we address the problem by introducing a method that involves quantitative concepts for boundaries between regions and “global tolerance” . To illustrate the power of these concepts , we analyzed a large class of biological modules called moiety-transfer cycles and characterized the specific case of the NADPH redox cycle in human erythrocytes , which is involved in conferring resistance to malaria . Our results show that the wild-type system operates well within a region of “best” local performance that is surrounded by “poor” regions . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"hematology/disorders",
"of",
"red",
"cell",
"metabolism",
"mathematics",
"computational",
"biology/metabolic",
"networks",
"biochemistry/theory",
"and",
"simulation",
"computational",
"biology/systems",
"biology"
]
| 2009 | Quantifying Global Tolerance of Biochemical Systems: Design Implications for Moiety-Transfer Cycles |
The cable equation is a proper framework for modeling electrical neural signalling that takes place at a timescale at which the ionic concentrations vary little . However , in neural tissue there are also key dynamic processes that occur at longer timescales . For example , endured periods of intense neural signaling may cause the local extracellular K+-concentration to increase by several millimolars . The clearance of this excess K+ depends partly on diffusion in the extracellular space , partly on local uptake by astrocytes , and partly on intracellular transport ( spatial buffering ) within astrocytes . These processes , that take place at the time scale of seconds , demand a mathematical description able to account for the spatiotemporal variations in ion concentrations as well as the subsequent effects of these variations on the membrane potential . Here , we present a general electrodiffusive formalism for modeling of ion concentration dynamics in a one-dimensional geometry , including both the intra- and extracellular domains . Based on the Nernst-Planck equations , this formalism ensures that the membrane potential and ion concentrations are in consistency , it ensures global particle/charge conservation and it accounts for diffusion and concentration dependent variations in resistivity . We apply the formalism to a model of astrocytes exchanging ions with the extracellular space . The simulations show that K+-removal from high-concentration regions is driven by a local depolarization of the astrocyte membrane , which concertedly ( i ) increases the local astrocytic uptake of K+ , ( ii ) suppresses extracellular transport of K+ , ( iii ) increases axial transport of K+ within astrocytes , and ( iv ) facilitates astrocytic relase of K+ in regions where the extracellular concentration is low . Together , these mechanisms seem to provide a robust regulatory scheme for shielding the extracellular space from excess K+ .
The interaction between neurons and glial cells has been the topic of many recent studies within the field of neuroscience ( see reviews in [1]–[3] ) . Astrocytes ( a species of glial cells ) play an important role in modulating excitatory and inhibitory synapses by removal , metabolism , and release of neurotransmitters [4] , homeostatic maintenance of extracellular K+ , H+ , and glutamate [5] , supply of energy substrates for neurons [6] , and neuronal pathfinding during development and regeneration [7] . Astrocytic cells seem to have key roles in many central nervous system disorders , ranging from neuropathic pain and epilepsy to neurodegenerative diseases such as Alzheimers , schizophrenia and depression [8] . Computational models of neuron-glia interactions is a prerequisite for understanding the dysfunctional situations , and for assessing glial cells as a potential therapeutic target [9] . To give a few examples , such models have been used to simulate glial regulation of extracellular K+-concentration [10]–[13] , and the relation between extracellular K+-dynamics and epileptic seizures [14]–[16] and spreading depression [17] , [18] . Regulation of the extracellular K+-concentration is considered one of the key cellular functions of astrocytes [2] . During normal conditions , the extracellular K+-concentration ( ) is typically maintained close to the baseline level ( ) . However , when neurons fire action potentials , they expel K+ into the extracellular space . During periods of intense neural activity , the local extracellular K+-concentration may increase by several millimolars , and may interfere with neural activity [10] , [19] , [20] . Concentrations between 8 and 12 mM are often considered a limit to pathological conditions [3] , [12] , [21] . Orkand ( 1966 ) [22] discovered that astrocytes can funnel out excess K+ from high concentration regions by a process coined spatial buffering [12] , [21] , [22] . According to this concept , K+ is taken up by the glial cell from high-concentration sites , evoking a local depolarization of the glial membrane . K+ is then transported longitudinally inside the glial cell ( and possibly through several glial cells connected by gap junctions into a glial syncytium [10] , [23] ) , and eventually expelled into the ECS at more distal cites where is lower . However , it has also been argued that astrocytes may reduce by local uptake and temporal storage , not necessarily including transport over distances [19] , [24] . Furthermore , diffusion through the ECS is also involved in transporting excess K+ out from high concentration regions . The relative importance of these different clearance mechanisms are under debate [25] . Electrical neural signalling is typically modeled using the cable equation , where dendrites and axons are represented as one-dimensional , possibly branching , electrical cables , and the transmembrane potential is the key dynamical variable [26] , [27] . With the possible exception of the signalling molecule Ca2+ ( see e . g . , [28] , [29] ) , ion concentrations are typically assumed to be constant . The effect of ionic diffusion ( due to concentration gradients ) on the net electrical currents is neglected in standard cable theory , and resistivities ( which in reality depend on ion concentrations ) are assumed to be constant . These are often good approximations , as concentrations of the main charge carriers ( K+ , Na+ and Cl− ) in the extracellular- ( ECS ) or intracellular space ( ICS ) typically vary little at the short time-scale relevant for electrical neural activity ( ) . Glial function typically involves processes that take place at a longer time-scale ( ) , at which significant variations in ionic concentrations may occur . For example , the process of spatial K+-buffering involves local uptake , a local depolarization of the astrocytic membrane , and longitudinal electrodiffusive transports through the intracellular- ( ICS ) and extracellular space ( ECS ) propelled both by voltage- and concentration gradients [30] . A mechanistic understanding of glial function thus requires a modelling scheme that in a consistent way can capture the intricate interplay between ion concentration dynamics and the dynamics of . Physically , is determined by the total electrical charge on the inside ( or outside ) of the membrane , which in turn is uniquely determined by the concentrations ( ) of all ionic species that are present there [31] . In some heart cell models , ion concentrations have been reported to drift to unrealistic values in long-term simulations , while maintain realistic values [32]–[34] . Whether the relationship between and is consistent , is a general concern with models that explicitly depend on both . If applied to general problems , and in particular in long-term simulations , models that do not ensure an internally consistent relationship may give erroneous predictions . Gardner-Medwin ( 1983 ) [10] proposed a pioneering computational model of the spatial buffering process , later re-analyzed by Chen and Nicholson ( 2000 ) [12] . In this model , spatial buffering was considered as an essentially one-dimensional transport process . The complex composition of the tissue ( Fig . 1A ) could then be simplified to a two-domain model as that illustrated in Fig . 1B [10] , [12] . There , the ICS of all cells participating in the transport process ( i . e . the astrocytes ) have been represented as an equivalent cable ( I-domain ) which is coated by ECS ( E-domain ) . The I-E system could be pictured phenomenologically as an representative single astrocyte , coated with the average proportion of available ECS per astrocyte . This geometrical simplification was motivated for one-dimensional transport phenomena through the glial syncytium [10] , [12] , but could in principle apply to any transport phenomena that justifies a geometrical simplification as that in Fig . 1 . A limitation with these modelling studies [10] , [12] , and related modelling studies by Newman and coworkers [11] , [21] , is that was derived from standard cable theory , which neglects effect from diffusive currents on . The concern regarding a consistent relationship between and the ionic concentrations thus also applies to these models . Qian and Sejnowski ( 1989 ) have previously developed a consistent , electrodiffusive scheme for modelling the dynamics on and ion concentrations [31] . Like the standard cable model , the electrodiffusive model assumes that transport phenomena are essentially one-dimensional . Unlike the standard cable model , the electrodiffusive model derived from the ion concentration dynamics , accounting for all ionic movements ( membrane fluxes , longitudinal diffusion , and longitudinal electrical migration ) , as well as for the concentration-dependent variation of the intracellular resistivities . An important limitation with this previous electrodiffusive model is that it only includes intracellular dynamics , whereas the ECS was assumed to be isopotential and with constant ion concentrations [31] . This was a useful simplification for simulating a small intracellular compartment , such as a dendritic spine [31] , but is not generally applicable to macroscopic transport mechanisms . In particular , it can not be applied for modelling the spatial buffering process , where ion concentration dynamics in the ECS plays a paramount role . In reality , the ECS comprises about 20% of the total neural tissue volume , while the remaining 80% is the ICS of various cells [12] . When a large number of cells participate in simultaneous ion exchange with the ECS , the impact on the ion concentrations in the ICS and ECS may be of the same order of magnitude . The aim of this work is twofold: First , we generalize the electrodiffusive formalim [31] to a explicitly include the ECS . The result is a general mathematical framework for consistently modelling the dynamics of the membrane potential ( ) , the intra- ( ) and extracellular ( ) ion concentrations for a set ( ) of ionic species . We believe that this framework will be of general value for the field of neuroscience , as it can be applied to any system that justifies a geometrical description as that in Fig . 1B . Next , we apply the electrodiffusive formalism in a spatially explicit model of astrocytes exchanging ions with the ECS . We run simulations to investigate the efficiency of the spatial K+-buffering process , relative to that of local uptake/storage by astrocytes , and that of diffusion in the ECS alone . Unlike the previous models [10]–[12] , [21] , our astrocyte model is based on the prevailing view that Na+/K+/ATPase-pump is the main uptake mechanism for K+ [3] . Furthermore , as our model was based on a physically consistent electrodiffusive formalism , we arrive at a full mechanistic description of the buffering process , which quantitatively describes the intricate interplay between and the dynamics of ion concentrations . This article is organized in the following way: The Model section contains two main parts . In the first part , we present the electrodiffusive formalism for computing the ion concentration dynamics in a system described by the geometry depicted in Fig . 1B . We consider this theoretical framework a key contribution of this work . However , the key concepts introduced in this part are summarized in Table 1 , and with this in hand , the reader who is mainly interested the biological process of spatial K+-buffering by astrocytes may therefore skip to second part of the Model-section . There , the model for astrocytes exchanging ions with the ECS is presented . The Results section is devoted to simulations on the astrocyte model , and provides an improved biophysical insight in the electrodiffusive mechanisms utilized by astrocytes to spatially buffer K+ . By comparing different versions of the model , we also assessed the importance of spatial buffering , relative to that of other clearance mechanisms such as local uptake/storage by astrocytes and diffusion through the ECS alone . Finally , in the Discussion section we address how our mathematical framework relates to previous electrodiffusive modeling frameworks . We also summarize the new insights that our simulations have given in the process of spatial K+-buffering by astrocytes .
In Fig . 1B , particles in I or E may move along the x-axis or across the membrane . In a segment of I , centered at x , and with volume , the particle concentration dynamics of an ion species is determined by: ( 1 ) where the transmembrane- ( ) , the intracellular- ( ) and the extracellular ( ) flux densities of particle species , have units mol/ ( m2s ) . The first term on the left represents the ionic flux that enter this segment through the piece of the membrane with area . The negative sign follows from ( by convention ) being defined as positive in the direction from I to E . The second and third terms represent the ionic fluxes that enter ( + ) /leave ( − ) the section through the left/right boundaries , with cross section areas . If the net flux into the segment is nonzero , the ion concentration will build up over time , according to the right hand side of Eq . 1 . We divide Eq . 1 by , and take the limit , to obtain the continuity equation on differential form: ( 2 ) ( 3 ) We have also written up the continuity equation for the extracellular domain . The axial flux densities are described by the generalized Nernst-Planck equation: ( 4 ) where is the valence of ion species , and the index n represents I or E . The first term on the right in Eq . 4 is the diffusive flux density ( ) , driven by the concentration gradients , and the last term is the field flux density ( ) , i . e . , the flux density due to ionic migration in the electrical field . The effective diffusion constant is composed of the diffusion constant in dilute solutions and the tortuosity factor , which summarizes the hindrance imposed by the cellular structures [12] , [35] . We use , where is the gas constant , the absolute temperature , and is Faraday's constant . The formalism is general to the form of , which may include contribution from multiple membrane mechanisms , such as ion pumps , co-transporters and ion channels . It is sufficient to require that is known at any point in time given the voltage across the membrane , the ionic concentrations on either side of the membrane , and possibly some additional local information ( ) reflecting the local state of the membrane: ( 5 ) As boundary conditions , we shall apply the sealed-end condition , i . e . , we assume that no fluxes enter or leave through the ends ( and ) of I or E: ( 6 ) Equations 2–3 , together with with Eqs . 4 , 5 and 6 , specify the system we want to solve . Before we derive the electrodiffusive formalism for this problem , we recall how the standard cable equation can be derived from the principles of particle conservation . We here present a model of astrocytes exchanging ions with the ECS , as sketched in Fig . 3 , and defined in further detail below . The astrocyte model was developed for macroscopic transport processes , involving a collection of astrocytes ( possibly connected via gap junction into a syncytium ) in a piece of tissue . For this problem , we used the geometrical simplification motivated in Fig . 1 , i . e . , we applied the geometry in Fig . 1B . We took the intracellular domain to represents a phenomenological “average” astrocyte ( the cable , ) , surrounded by a sheet of ECS ( the coating , ) . We used the empirical estimates that a fraction of neural tissue volume is ECS , while astrocytes take up a fraction of about of the total tissue volume [12] . The intracellular domain was therefore twice as voluminous as the intracellular . Table 1 contains a list of definitions that are necessary for the reader to follow the remainder of the paper . The dynamics in the system was due to fluxes of ions crossing the membrane , or axial fluxes in the ECS or ICS due to diffusion ( ) or migration in the electrical field ( ) . We assumed that only the three main charge carriers ( K+ , Na+ and Cl− ) contributed to electrodiffusive transport . For the diffusion constants ( ) , we used values valid for electrodiffusion in diluted media [36] , modified with the tortuosities ( ) estimated in [12] . The same values have also been used in earlier , related studies [31] , [37] . All relevant model parameters are listed in Table 2 . The system input , and the astrocytic membrane mechanisms are defined in further details below .
We investigated the ion concentration dynamics in the astrocyte model ( Fig . 3 ) in full detail . Fig . 4A–D shows the dynamics of selected variables in the input zone ( at ) . Fig . 4E–H shows how the same variables depend on at a time when the system was in SS . We explain this further below . The input was applied from to in the input zone ( ) . This is illustrated in Fig . 4A ( solid line ) , which shows the flux density of K+ ( ) entering the system in the input zone . We recall that the input was a cation exchange , so that there was an equal flux density of Na+ leaving the system ( ) . For simplicity , was not included in the figure , but we keep in mind that whenever K+ entered/left the system , an equal amount of Na+ left/entered . The cation-exchange input thus caused an increase in and a decrease in in the input zone . This can be seen in Fig . 4B . The notation represents the deviations from baseline concentration ( cf . Table 2 ) . As increased , the output from the system ( being proportional to ) increased . Also this is illustrated in Fig . 4A ( dashed line ) , which shows the flux density of K+ ( ) leaving the system from a point in the input zone . We recall that also the output was a cation exchange , so that the efflux of K+ implied a corresponding influx Na+ . The input was given in the input zone , while the output occurred over the full axis , depending on the local value of . During a transient period , the constant input changed the ion concentrations in the system . The system reached steady state ( SS ) when became sufficiently high . Then , the total amount of K+ entering the system per second , and the total amount of K+ leaving the system per second , coincided ( with the same being true for Na+ ) . This is illustrated in Fig . 4E , which shows how the and are distributed over the -axis at a time , when the system was in SS . The areas under the curves for and were then equal . In the input zone , however , the output rate was about 1/3 of the input rate ( Fig . 4A ) . This means that about 2/3 of the K+ that entered the system was transported in the positive -direction , and left the system from the decay zone . ( We recall from Fig . 3 that the decay zone is defined as any part of the -axis outside the input-zone ) . Fig . 4B–D shows how the local ( at ) intracellular ion concentrations , the extracellular ion concentrations and changed from the input had been turned on until the system reached SS . For the present example it took 49 s from the constant input had been turned on until the slowest variable ( ) reached 99% of its SS value . The other variables approached SS faster than this ( e . g . , 12 s for and 19 s for ) . During SS , was about 7 . 7 mM , corresponding to a concentration ( as the baseline concentration was ) . Although the input was applied to the ECS of the input zone , the local intracellular K+-concentration had increased even more ( ) . This reflects the astrocyte's propensity for local K+-uptake . The changes in ionic concentrations in the ECS and ICS coincided with a local depolarization of the astrocytic membrane , from the resting potential ( ) to about , reflecting concentration dependent changes in the reversal potentials of the involved ionic species . From here on , we focus on the SS-situation , i . e . , on the activity of astrocytes during periods of on-going intense neural activity . For all system variables , the devition from the baseline ( resting ) conditions were generally biggest at the point , i . e . , in the part of the input zone which is furthest away from the decay zone ( Fig . 4E–H ) . The average value of , taken over the input zone ( ) was approximately 10 mM ( about 6 . 9 mM above the resting concentration ) . During the model calibration , the constant input rate ( ) was tuned to obtain this value , which is on the threshold between functional and pathological conditions [3] , [12] , [21] . During SS , the gradients in ionic concentrations ( Fig . 4F–G ) and ( Fig . 4H ) were quite pronounced . We thus expect that both diffusive and electrical forces contribute to transporting ions through the system ( from entering to leaving ) . This is explored further in the following section . In addition to spatial buffering , K+ may also be buffered by diffusion through the ECS alone , or by local ( space independent ) storage by the glial cell , to be later released in the same region of the ECS [19] , [24] . To investigate the relative importance these clearance mechanisms , we compared the 6 six model versions depicted in Fig . 8A , including one group of three spatially extended models ( solid lines ) , and one group of three point models ( dashed lines ) . Both groups included one model version with an active astrocyte , one model version where the astrocyte had been replaced by a corresponding increase in the ECS volume ( the total ECS volume fraction increased to ) , and one version where the original ECS volume fraction ( ) was kept when the astrocyte was removed . The spatially extended model including the astrocyte , is the one we studied in the previous sections . The other models were reduced versions of this . All model versions were exposed to the input signal described by Eq . 30 , causing an increase in . The input was applied in the time window , which was sufficient for to reach its SS-value in all models . Fig . 8B shows the dynamics of the K+-concentration in the ECS at the point where the concentration was the highest ( ) . In the spatially extended models , this occurred at , i . e . in the part of the input zone furthest away from the decay zone . During SS , the net K+ efflux and influx from/to the system coincided . For the point models , having no spatial resolution , there was no distinction between the input zone and decay zone , as the input and output were injected to/subtracted from the same single compartment . The net output rate thus depended on in this single compartment . Therefore , all point models approached the same SS value ( ) . For the spatially extended models was lower , as parts of the K+ could leave the system also outside the input zone . For these models , depended on how efficient they were in longitudinally transporting K+ out from input zone before ( revisit Fig . 4 for more details ) . To gain insight in the importance of local K+-uptake by astrocytes , relative to diffusion in the ECS , we compared the performance of the point model including the astrocyte ( black , dashed line in Fig . 8B ) to that of the spatially extended model including only the ECS ( blue , full line ) . During the first few seconds after the stimulus had been turned on , the point model with the astrocyte ( representing local uptake ) was most efficient in terms of limiting . However , local uptake was limited by the storing capacitance of the astrocyte . After seconds with constant K+-influx to the system , the spatially extended model ( representing diffusion through the ECS ) performed better , as it could redistribute K+ over a larger spatial region . The astrocyte's ability to locally store excess K+ has been emphasized in previous investigations [19] , [24] . Our simulations predicted that the local storage mechanism is mainly important in relatively short time spans after potassium release ( a few seconds ) . A similar conclusion was also drawn from previous modelling studies [10] , [12] . We here add an additional point to this discussion: The performance of the point model with extended ECS ( dashed red lines ) more or less coincided with that of the point model including the astrocyte ( dashed black lines ) . In terms of local storage , the astrocyte ( with its membrane being highly permeable to K+ ) , essentially just acts to expand the local volume that the incoming flux of K+ enters into . It has been argued that because K+-transport is aided by transmembrane processes as well as internal processes in the glial cell , K+ can be cleared more effectively by glia than would be possible by a much enlarged extracellular space [40] . To investigate this claim , we compared the three spatially extended models ( solid lines ) . We found that the model including the astrocyte ( black , solid line ) was more successful in limiting than any of the other model versions . It was significantly more successful than diffusion in the ECS alone , even in the ( rather hypothetical ) system where the extracellular volume had been increased by a factor 3 . In conclusion: In terms of local storage , the astrocyte was not significantly more efficient than an increased enlarged extracellular space . In terms of spatial buffering , however , it was .
The astrocyte/ECS-model provided a mechanistic understanding of how astrocytes may remove K+ from high-concentration regions . In summary , the model astrocyte responded to a local extracellular increase in by a local depolarization of the membrane . At the same time , this depolarization ( i ) increased astrocytic K+ uptake in the input zone , ( ii ) increased astrocytic K+-release outside the input zone , ( iii ) decreased axial K+ transport in the ECS , and ( iv ) increased axial K+ transport inside the astrocyte . Furthermore , by comparing different versions of the model , we predicted that ( v ) local storage of K+ by astrocytes may play an important role at a short time scale , while ( vi ) at a longer time scale , the ability to distribute K+ spatially will be crucial for maintaining a low extracellular K+-concentration . In this regard , we found ( viii ) that the astrocyte was more efficient spatial buffering mechanism than diffusion in an enlarged extracellular space . The findings ( i–ii ) were due to well documented astrocytic membrane mechanisms that we implemented in the model . Uptake from high concentration regions was mediated by the K+/Na+-pump , while release into low-concentration regions of the ECS was mediated by the Kir-channel . This supports the dominant view of glial K+-buffering [2] , [3] , [25] , [39] , [41] . The findings ( iii–iv ) were due to an interplay between electrical and diffusive forces . When locally depolarized ( in the region with high extracellular ) , longitudinal voltage gradients arose , and ions in the ICS and ECS were exposed to an electrical force . In the ICS , the electrical force and diffusive force both drove K+-ions in the same direction ( out from the high concentration regions ) . In the ECS , the electrical force acted in the opposite direction from the diffusive force , and reduced the net longitudinal transport through the ECS . Hence , in addition to being an efficient transport route for K+ out from high-concentration regions , the astrocyte actively reduces the extracellular K+-transport . This represents a ( to our knowledge ) novel mechanism that astrocytes may utilize to shield the extracellular space from excess K+ . All these effects ( i–iv ) taken together turned the astrocyte into an efficient sluice for removing K+ from the input zone . The findings ( v–viii ) shed light on the relative efficiency of spatial buffering and other K+-clearance mechanisms , such as local storage by astrocytes , or diffusion in the ECS alone . An interesting prediction was that , in terms of local storage , the astrocyte did not have a stronger effect on than an enlarged extracellular space would . In terms of longitudinal transports , however , the astrocyte performed much better ( by spatial buffering ) than diffusion in an enlarged extracellular space ( Fig . 8 ) . We do , however , wish to comment that these mechanisms are not mutually exclusive . In fact , an ( initial ) local accumulation of intracellular K+ is required for the astrocyte to initiate the spatial buffering process . It is this local accumulation that evokes the intracellular voltage- and concentration gradients that the astrocyte utilizes for intracellular K+-transport . It is likely that the mechanisms responsible for spatial buffering vary between brain regions and between different species of glial cells [30] . Previous literature has suggested several mechanisms for spatial buffering apart from the ones that were included in our model . K+-uptake by Na+K/K+/Cl−-cotransporters and K+/Cl−-cotransporters are two candidate mechanisms that likely could affect the simulated results [38] . Furthermore , regions in the endfoot processes of astrocytes have been shown to have an extremely high K+-conductance compared to the membrane in general [42] . Such high concentration regions could improve the buffering process by transferring ( siphoning ) excess K+ into the vitreos humor or vasculature [43] . The buffering process may also be affected by water influx and swelling experienced by the astrocyte during the uptake process [13] , [30] , [38] . Rather than increasing the biological complexity , by e . g . , including multiple candidate buffering mechanisms , we have in this study strived towards elucidating the fundamental physical processes involved in spatial K+-buffering . As our simulations demonstrated , K+-buffering is a highly complex process . It involves an intricate and sensitive interplay between and ionic concentrations , and between electrical and diffusive transports . We therefore highlight the importance of applying an electrodiffusive , physically consistent , modelling scheme which ensures a complete book-keeping of ion concentration dynamics and its effects on . In previous models of spatial buffering , was derived from standard cable theory [10]–[12] , [21] , where diffusive currents are assumed to have a negligible impact on , and where the resistivity is assumed to be constant ( i . e . , not dependent on ion concentration variations ) . During our simulations , intra- and extracellular resistivities changed by as much as 10% and 20% , respectively , and the diffusive current was about 25–30% of the field current in the ECS . The assumptions underlying standard cable theory are therefore poorly justified if applied to the spatial K+-buffering process . The astrocyte/ECS-model was represented phenomenologically as a single astrocyte coated with the average proportion of available ECS per astrocyte ( see Figs . 1 and 3 ) . This geometrical representation is justified for macroscopic transport processes , when a large number of astrocytes perform the same function simultaneously [12] . For the current study , this was a reasonable assumption , as the input was a change in the ion-concentrations in the ECS , shared by all present astrocytes . If we instead wanted to study a cell specific signal , such as e . g . , the response of a single astrocyte to a transmembrane current injection , the geometrical representation in Fig . 1B would be less appropriate . Firstly , the notion of the ECS as a relatively thin coating following a single cell is only motivated at the macroscopic “average transport”-level . Secondly , if only a single cell was involved in a particular process , we would expect that . That is , a single active cell would have a significantly larger proportion of the ECS to its own disposal , compared to the macroscopic case , where all cells in a piece of tissue are active , and share the limited amount of available ECS . In single-cell models it is common to assume that conditions in E are constant , so that only I is modeled explicitly . In this limit , the electrodiffusive formalism reduces to the one-domain model presented previously by Qian and Sejnowski [31] . The framework presented here is essentially an expansion of the one-domain model by Qian and Sejnowski [31] to a two-domain model that includes both the ECS and ICS . Like the one-domain model , the framework ensures ( i ) a consistent relationship between and ionic concentrations . Unlike the one-domain model , the framework ensures ( ii ) global particle/charge conservation , and ( iii ) that the charges on either side of a piece of membrane must be equal in magnitude and opposite in sign ( ) . The latter constraint is implicit when the the membrane is assumed to be a parallel plate capacitor , an assumption made in most models of excitable cells ( see e . g . , [26] , [27] , [31] ) . It is also related to the topic of electroneutrality . Electroneutrality in electrodiffusive models of biological tissue has been the topic of many discussions [44]–[46] . It is relevant for how the electrical potential ( v ) , occurring in the Nernst-Planck equation , is derived . Generally ( at sufficiently course spatial resolutions so that the charge density can be assumed to be continuous ) , v obeys Poisson's equation: ( 43 ) where is the dielectric constant , and is the total charge density . In biological tissue , the charge relaxation time is very small in any region except in the thin Debye layer ( ) surrounding a bio-membrane . Any nonzero net charge density in the bulk solution will decay very rapidly ( ) to zero [36] . Several models have simulated electrodiffusion by solving the Nernst-Planck equations in one or more dimensions , with Poisson's equation for ( see e . g . , [45] , [47]–[50] ) . The advantage with such a procedure is that the Poisson-Nernst-Planck ( PNP ) equations can be implemented in a general way in three-dimensional space . The challenge is then to specify the appropriate boundary conditions for solving Eq . 43 in the vicinity of membranes . Generally , PNP-solvers apply a fine spatial resolution near the membrane , and simulation time steps smaller than the charge-relaxation time [48] . For these reasons , they tend to be extremely computationally demanding [51] . The formalism presented in this work belongs to a class of of one-dimensional models , including the cable equation and several electrodiffusive models [10] , [12] , [13] , [17] , [31] , [52] , [53] , which bypasses the computationally heavy PNP-scheme . The physical interpretation of these models is as follows: Any net charge in a volume is implicitly assumed to be located in the thin Debye-layer surrounding the capacitive membrane , and is identical to the charge that determines . The remainder of the space ( i . e . , the bulk ) will therefore be electroneural ( ) . Note that any finite volume , enclosing a piece of membrane , will also be electroneutral . This follows from the charge symmetry condition ( Eq . 18 ) , constraining the charge on either side of the membrane to be equal in magnitude and opposite in sign . The charge symmetry condition and the electroneutrality condition are in this way closely related . In these electroneutral models , charge relaxation is implicit . This is a plausible assumption at time scales relevant for most biophysical processes . Accordingly , simulations may be run with time-steps ranging from 1 ms to 1 s , depending on the time course of the included membrane mechanisms . To our knowledge , the formalism summarized in Fig . 2 is the first biodiffusive model where the intra- and extracellular voltage gradients have been derived from the charge symmetry condition . Eqs . 25 and 26 can be interpreted as summarizing all local and global electrical forces driving the system towards electroneutrality . A natural future ambition would be to generalize the electrodiffusive formalism to 2 or 3 spatial dimensions , so it can address the same 3-dimensional transport problems as PNP-solvers . The challenge will be to formulate the system as a grid of coupled constraints ( electroneutrality in the bulk and Eq . 12 for across the membrane ) for which the Nernst-Planck equations can be solved with time steps much longer than those involved in the charge relaxation process . | When neurons generate electrical signals they release potassium ions ( K+ ) into the extracellular space . During periods of intense neural activity , the local extracellular K+ may increase drastically . If it becomes too high , it can lead to neural dysfunction . Astrocytes ( a kind of glial cells ) are involved in preventing this from happening . Astrocytes can take up excess K+ , transport it intracellularly , and release it in regions where the concentration is lower . This process is called spatial buffering , and a full mechanistic understanding of it is currently lacking . The aim of this work is twofold: First , we develop a formalism for modeling ion concentration dynamics in the intra- and extracellular space . The formalism is general , and could be used to simulate many cellular processes . It accounts for ion transports due to diffusion ( along concentration gradients ) as well as electrical migration ( along voltage gradients ) . It extends previous , related formalisms , which have focused only on intracellular dynamics . Secondly , we apply the formalism to model how astrocytes exchange ions with the extracellular space . We conclude that the membrane mechanisms possessed by astrocytes seem optimal for shielding the extracellular space from excess K+ , and provide a full mechanistic description of the spatial ( K+ ) buffering process . | [
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
]
| []
| 2013 | Electrodiffusive Model for Astrocytic and Neuronal Ion Concentration Dynamics |
Schistosomiasis ( or bilharzia ) , a major parasitic disease , affects more than 260 million people worldwide . In chronic cases of intestinal schistosomiasis caused by trematodes of the Schistosoma genus , hepatic fibrosis develops as a host immune response to the helminth eggs , followed by potentially lethal portal hypertension . In this study , we characterized hepatic and splenic features of a murine model of intestinal schistosomiasis using in vivo magnetic resonance imaging ( MRI ) and evaluated the transverse relaxation time T2 as a non-invasive imaging biomarker for monitoring hepatic fibrogenesis . CBA/J mice were imaged at 11 . 75T two , six and ten weeks after percutaneous infection with Schistosoma mansoni . In vivo imaging studies were completed with histology at the last two time points . Anatomical MRI allowed detection of typical manifestations of the intestinal disease such as significant hepato- and splenomegaly , and dilation of the portal vein as early as six weeks , with further aggravation at 10 weeks after infection . Liver multifocal lesions observed by MRI in infected animals at 10 weeks post infection corresponded to granulomatous inflammation and intergranulomatous fibrosis with METAVIR scores up to A2F2 . While most healthy hepatic tissue showed T2 values below 14 ms , these lesions were characterized by a T2 greater than 16 ms . The area fraction of increased T2 correlated ( rS = 0 . 83 ) with the area fraction of Sirius Red stained collagen in histological sections . A continuous liver T2* decrease was also measured while brown pigments in macrophages were detected at histology . These findings suggest accumulation of hematin in infected livers . Our multiparametric MRI approach confirms that this murine model replicates hepatic and splenic manifestations of human intestinal schistosomiasis . Quantitative T2 mapping proved sensitive to assess liver fibrogenesis non-invasively and may therefore constitute an objective imaging biomarker for treatment monitoring in diseases involving hepatic fibrosis .
Present in many tropical and subtropical countries , schistosomiasis ( or bilharzia ) , the second most prevalent parasitic disease in the world after malaria , affects more than 260 million people and leads to 200 000 deaths per year [1] . This helminthic disease is caused by trematodes of the Schistosoma genus . S . haematobium , S . mansoni , and S . japonicum are the main species infecting humans . These parasites have aquatic gastropods as intermediate hosts and a final vertebrate host . S . mansoni is the principal agent of digestive forms of the human disease . The gastropod is infected with miracidia released from the S . mansoni eggs that transform into sporocysts . These sporocysts shed cercariae in the water that can penetrate the skin of the mammalian hosts . After maturation , male and female worms reproduce in the mesenteric venous plexus and produce eggs that are discharged with the stool into the environment [2 , 3 , 4] . However , many eggs are not eliminated and disseminate in the intestines and the liver where they obstruct presinusoidal capillary venules [2 , 4 , 5] . The host immune response elicited by these eggs leads to the formation of periovular granulomas and tissue damage . An imbalance between scarring and regeneration causes an accumulation of extracellular matrix rich in collagen ( particularly types I and III ) leading to hepatic fibrosis [6] . Hepatomegaly occurs early in the disease as a consequence of the granulomatous inflammation [2 , 3] . In ca 10% of patients , 5–20 years after infection , serious complications occur such as splenomegaly , pulmonary arterial hypertension accompanied by right heart failure , periportal fibrosis resulting in portal hypertension and esophageal varices , that can lead to ascites and gastrointestinal bleeding with high mortality [3 , 4 , 5 , 7 , 8] . Chronic schistosomiasis is also associated with an increased incidence of hepatocellular carcinoma [4 , 9] . The standard treatment is the trematodicide Praziquantel , which kills adult worms , but is ineffective on juvenile mammalian-stage schistosomes . The mechanism of action and molecular targets of Praziquantel are unknown . However many studies have reported vacuolation and blebbing of worm teguments and suggested a direct disruptive effect on Ca2+ channels , whereas a recent work has described distinct effects on male and female worms [10 , 11] . Although effective even at a single dose , reinfection is frequent and in 2013 only 13% of people necessitating treatment could benefit from it [1] . Despite extensive research , an anti-schistosomiasis vaccine is not yet available [12 , 13] . The mouse model of schistosomiasis is well suited for pharmaceutical and basic research purposes since mice are infected with the parasite species that are pathogenic for humans , and are therefore expected to accurately recapitulate the pathological features of the human disease . The objective of this study was to provide the first characterization of hepatic and splenic features of murine intestinal schistosomiasis using in vivo magnetic resonance imaging ( MRI ) . Another aim was to search for non-invasive biomarkers allowing future evaluation of new therapeutic strategies against the intestinal form of the disease in murine models . Ultrasound is the leading medical imaging examination for the diagnosis of human intestinal schistosomiasis , and allows the assessment of liver involvement and portal hypertension [14 , 15] . Liver fibrosis can be monitored using ultrasound transient elastography ( FibroScan ) , a technique allowing the assessment of liver stiffness [16] . Elastography is based on the measurement of tissue elasticity following the propagation of a mechanical shear wave through the liver . However , ultrasound transient elastography has the disadvantage of being highly operator dependent , less reliable for deep organs and not readily available for the exploration of rodent models [17] . Analysis of texture features from computed tomography ( CT ) images enables staging of fibrosis throughout the liver , but is less accurate in case of heterogeneous fibrosis distribution and is considered inferior to ultrasound transient elastography [18] . Whole abdominal coverage can also be achieved with MRI . In addition to the detection of morphological indicators of liver fibrosis such as splenomegaly and portal hypertension on conventional images [19] , various MRI methods can be used for the study of ( schistosomiasis-induced ) liver disease . Magnetic resonance elastography ( MRE ) measures viscoelastic properties of the liver tissue , namely its capacity to return with time to its original shape after the application of deforming forces , by quantifying the propagation of the shear waves . This technique can be implemented on standard MRI systems but requires a mechanical vibrator device for the generation of shear waves . Liver stiffness has been shown to correlate with fibrosis stage in patients [20] and animal models [21] . A recent study suggests that MRE more accurately discriminates between early fibrosis stages than ultrasound transient elastography [22] . However , increased liver stiffness is not specific for fibrosis [23] , and MRE can be hampered in subjects with severe iron overload [22] . Qualitative and quantitative scores of liver texture features on double contrast-enhanced MRI , an MRI technique based on the injection of two different types of contrast agents ( super paramagnetic iron oxides and gadolinium chelates ) have been shown to distinguish between mild and severe fibrosis [24] . In rodent models , MRI using a collagen type I targeted gadolinium-based contrast agent has shown differential uptake and washout in fibrotic livers with a good correlation to histological quantification of collagen [25 , 26] . Diffusion-weighted MRI , a technique sensitive to water diffusivity generally used to study tissue microstructure , has also been performed since fibrosis is expected to restrict water motion [27] . However , due to lack of standardization and confounding factors such as altered perfusion or accompanying inflammation this technique is not sensitive to mild stages of liver fibrosis [28 , 29] . Another MRI technique , known as intravoxel incoherent motion imaging [30] can distinguish between microscopic motion of water molecules in intra- and extracellular compartments and the microcirculation of blood . Consequently , the derived parameters correlate better with fibrosis stage than conventional diffusion-weighted imaging in patients [31] and animal models [32] . However , this technique has not proved superior to MRE in distinguishing mild to intermediate fibrosis stages in patients [33] . Relaxometry has been proposed for the staging of hepatic fibrosis and evaluated in a number of studies . Relaxometry studies magnetic relaxation , a process by which magnetization of magnetic nuclei returns to its equilibrium state ( parallel to the static magnetic field , B0 ) after it was disturbed from equilibrium and tipped into the plane orthogonal to B0 ( transverse plane ) by a radiofrequency pulse . Two relaxation time constants can be measured: the longitudinal relaxation time-constant T1 corresponding to the recovery of the magnetization along B0 , and the transverse relaxation time-constant T2 describing the decay of the magnetization in the transverse plane . Another relaxation time , called T2* , is a measure of T2 taking into account the effects of static magnetic field inhomogenities on relaxation . Relaxation time constants depend on tissue structure and composition , which may vary with physiological or pathological processes , and strongly influence contrast in MRI . Maps derived from MRI images representing a spatial distribution of quantitative values of a selected relaxation time-constant ( T2 , T2* or T1 ) can be generated . Although relaxation time-constant values may vary with magnetic field strength , the tendency to increase or decrease upon a physiological or pathological process is independent of the magnetic field strength . An increase of the longitudinal relaxation time T1 has been observed with increasing severity of hepatic fibrosis . However , alterations of the transverse relaxation time T2 during the process of hepatic fibrogenesis were generally more sensitive [34 , 35 , 36] . In particular , in pre-clinical MRI studies using MRI systems equipped with high or ultra high magnetic field strength ( 3T < B0 < 21T ) to increase the spatial resolution or the signal-to-noise ratio , T1 relaxation times tend to differ less between tissues than transverse relaxation times . In addition , T2 mapping is less challenging than T1 mapping in conjunction with respiratory gating ( synchronization of MRI sequences with respiration ) since all echoes required for T2 analysis are acquired within a few hundred milliseconds , while T1 mapping requires repeated sampling over a couple of seconds . The T2 and T2* relaxation times are also sensitive markers of iron deposition and hemorrhage [37 , 38] . In this study , multiparametric MRI including quantitative T2 and T2* mapping was performed in an attempt to establish a quantitative measure of the liver damage and related complications caused by schistosomiasis , and in particular to assess early stages of liver fibrosis . A mouse model of the intestinal form of the disease obtained with S . mansoni was explored . In vivo anatomical and relaxometry findings were compared to histological staging of liver disease and fibrosis progression .
Animal studies were in agreement with the French guidelines for animal care from the French Ministry for Agriculture ( Animal Rights Division ) , the directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 , and approved by our institutional committee on Ethics in animal research ( Comité d’Ethique de Marseille n°14 , project authorization n°: 02157 . 02 ) . Twenty-four female CBA/J mice ( 6-week old ) from Charles River Laboratories ( l’Arbresle , France ) were used . Mice were maintained at 22 . 5°C with a 12h light/12h dark cycle in an enriched environment with free access to food and water . Twelve mice were infected percutaneously at the age of seven weeks with 30 cercariae of the Venezuelan strain of S . mansoni under intraperitoneal anesthesia ( ketamine 100 mg/kg , xylazine 4 mg/kg ) . Cercariae , which are maintained in our laboratory by passage through Biomphalaria glabrata snails , were counted under binocular microscope , diluted in 500 μl of water , and placed for 60 minutes on the sheared abdomen of mice to replicate the natural route of infection . Mice were weighed before each MRI session . In vivo MRI was performed on two animal cohorts . The first cohort included a group of 6 infected mice and a group of 6 uninfected mice , which were both imaged at 2 weeks and 6 weeks post infection . MRI at 6 weeks was followed by histology . The second cohort consisting of a group of 6 infected mice and a group of 6 uninfected mice was explored only once at 10 weeks post infection and the animals were sacrificed after MRI for histology . MRI experiments were performed on a Bruker AVANCE 500 WB MR system ( Bruker , Ettlingen , Germany ) operating at very high magnetic field ( 11 . 75 T ) , equipped with actively shielded gradients ( 1 T/m maximum gradient strength and 9 kT/m/s maximum slew rate ) and a 30 mm-diameter transmitter/receiver volume birdcage coil . A catheter was inserted into the intraperitoneal cavity of the mice for contrast agent delivery . The animals were positioned in a cradle , and a pneumatic pressure probe was placed under their chest for respiration monitoring . Anesthesia was maintained with isoflurane in air using 1 . 3–1 . 8% via a face mask and body temperature was maintained using the water circulation of the gradient cooling system set to 42°C . All sequences were prospectively gated with respiration using an MRI compatible monitoring and gating system ( PC-SAM , Small Animal Instruments Inc . , Stony Brook , NY ) . Images were acquired in the transverse plane with a field of view of 24×24 mm2 and a slice thickness of 0 . 5 mm . Structural imaging at high in plane resolution ( matrix 240×240 , in-plane resolution 100 μm ) was performed using a 2D spin-echo sequence ( repetition time TR ≥ 448 ms; echo time TE = 14 ms ) with 20 contiguous slices and repeated on adjacent 20 slices to cover the liver and spleen entirely . Images were acquired before and 15 minutes after intraperitoneal injection of a paramagnetic contrast agent ( 50 μl of 0 . 5 M gadoteric acid , DOTAREM , Guerbet , Villepinte , France ) ( number of accumulations for each acquisition: 2 and 4 respectively ) . Prior to contrast agent injection , T2 and T2* maps ( TR ≥ 9 s; matrix 64×64 , 2 accumulations ) were acquired in a single slice positioned 0 . 5 mm caudal of the bifurcation of the portal vein , using a multi-spin echo sequence ( 12 equally spaced echoes at TE = 7 . 5 to 120 ms ) and a multi-gradient echo sequence ( 8 equally spaced echoes at TE = 1 . 6 to 13 . 5 ms ) , respectively . Using the bifurcation of the portal vein as landmark this axial slice position was reproducible between animals and covered sufficient liver tissue . Respiratory rate was kept between 60 and 70 breaths per minute by adjusting the isoflurane percentage leading to a total acquisition time of approximately 15 min for the anatomical imaging and 20 min for each map . For quality control , an external reference with a known T2 of ca 21 ms , consisting of a capillary filled with the paramagnetic contrast agent diluted in saline was placed in the image field of view . Relaxometry studies were performed at 11 . 75T on water and solutions of collagen at four different concentrations ( 1 . 66 g/L , 3 . 33 g/L , 6 . 25 g/L and 12 . 5 g/L ) . Collagen solutions were prepared with Type I collagen from rat tail ( Sigma-Aldrich , St Quentin Fallavier , France ) solubilized under magnetic stirring in 0 . 1 M acetic acid at 40°C . T1 and T2 relaxation times were measured using an inversion recovery gradient echo sequence with 7 inversion times ( Tinv 15 ms to 15 s ) and a multi-spin echo sequence with 80 TE ( 10 to 800 ms ) . TR was 20 s . Images were analyzed under ImageJ ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://imagej . nih . gov/ij/ , 1997–2014 , last access January 2015 ) for liver and spleen volumetry , portal vein diameter , and T2-map generation . Liver and spleen were delineated on each slice ( i ) to measure the cross-section ( Ai ) of the organ and estimate its volume in mm3 by V=0 . 5∑iAi . To assess portal hypertension , the portal vein cross-section was measured 0 . 5 mm caudal of the portal bifurcation , and its diameter was estimated as the Feret’s diameter . The Feret’s diameter is the maximum caliper length namely the longest distance between any two points along the section boundary . Relaxation time maps were computed by fitting the signal intensity S to S ( TE ) =S0exp ( TE/T2 ( * ) ) or S ( Tinv ) = S0|1 − 2E exp ( −Tinv/T1 ) | using the simplex algorithm in ImageJ . Here , S0 is the signal at thermal equilibrium , and E the efficiency of the inversion pulse . Histograms of quantitative T2 and T2* were obtained from a large region of interest ( ROI ) covering the liver but excluding the hepatic hilus and large vessels as well as the gall bladder , bowels or stomach when present in the slice . After euthanasia , livers were fixed in 10% buffered formalin for a minimum of 48 hours , sampled according to standardized procedures [39] , paraffin-embedded and routinely processed for histology . Three 3-μm paraffin serial sections per mouse were stained with Hematoxylin-Eosin ( HE ) , with Sirius Red for collagen , and Perls’ stain for iron . Grading of inflammatory activity and staging of fibrosis was performed according to the METAVIR scoring system , a histological scale used to quantify the degree of inflammation and fibrosis of a liver biopsy . “A” refers to the intensity of necrosis and inflammation and may vary from A0 to A3 ( A0 = no activity , A1 = mild activity , A2 = moderate activity , and A3 = severe activity ) . “F” refers to the extent of fibrosis and may vary from F0 to F4 ( F0 = no fibrosis , F1 = portal fibrosis without septa , F2 = portal fibrosis with rare septa , F3 = numerous septa without cirrhosis , and F4 = cirrhosis ) [40] . Sirius Red stained sections were examined by semiautomatic computer-based morphometry using the NIKON DS-Fi2 camera and NIS Elements imaging software ( Nikon , Japan ) . Morphometric analysis was made on one entire lobe section , measuring semi-automatically delineated Sirius Red stained zones ( S1A Fig ) and subtracting when present the egg area or unstained central granuloma area deprived of collagen deposition . All analyses were performed in a blinded manner . Statistical analyses were performed using either GraphPad Prism version 5 . 00 ( San Diego , CA ) or JMP 9 . 0 ( SAS , Cary , NC ) . Values are reported as means ± standard deviation . The non-parametric Mann-Whitney test was used to compare uninfected and infected mice at each time point . The Kruskal-Wallis test was used to compare the METAVIR score or the percentage of Sirius Red stained zones among controls and diseases mice at 6 and 10 weeks after infection . Correlations between T2 values and METAVIR score or T2 values and fibrosis quantification by Sirius Red were performed using the Spearman test . The Smirnov test was used to compare distributions from two independent samples [41] . Values of P<0 . 05 were considered significant .
Weight control performed before each MRI session showed a continuous increase without significant difference between infected and control groups at any time point . No signs of general health alteration , such as cachexia , reduced mobility or behavioral changes were observed , suggesting animals were in good health condition during the study . All the animals of the first cohort exposed to the cercariae of S . mansoni were successfully infected as confirmed by MRI and histology at 6 weeks after infection , whereas only 4 out of 6 mice of the second cohort were successfully infected and developed hepatosplenic signs as shown by MRI and histology at 10 weeks after infection . All the images acquired were of good quality , except one T2* map obtained from a control animal of the second cohort , which showed motion artefacts and therefore was not included in the analysis . The two mice of the second cohort in which the infection had failed were also discarded from the analysis . While no differences were detected between infected and control groups at 2 weeks post infection by anatomical MRI ( Fig 1A ) and relaxometry , anatomical MRI was able to detect signs related to murine schistosomiasis ( Fig 1B–1D ) as early as 6 weeks post infection . Indeed , hepatomegaly ( Fig 1B ) , splenomegaly ( Fig 1C ) , and portal hypertension ( Fig 1D ) as assessed by MRI were significant at 6 weeks: +19% ( P = 0 . 0043 ) , +52% ( P = 0 . 0087 ) and +60% ( P = 0 . 0087 ) and progressed to +72% ( P = 0 . 0095 ) , +170% ( P = 0 . 0095 ) and +139% ( P = 0 . 0139 ) 10 weeks post infection , respectively . The evolution of the portal vein diameter was similar to that of the cross-section ( control: 1 . 19±0 . 12 mm , infected: 1 . 25±0 . 13 mm , P = 0 . 393 at 2 weeks , control: 1 . 17±0 . 19 mm , infected: 1 . 43±0 . 16 mm , P = 0 . 027 at 6 weeks , and control: 1 . 16±0 . 10 mm , infected: 1 . 68±0 . 13 mm , P = 0 . 004 at 10 weeks ) . However , the most remarkable findings were multifocal hyperintensities ( disseminated “white spots” ) of the liver in 4 infected mice at 10 weeks post infection ( Fig 1A ) . These hyperintensities were visible before contrast agent injection ( Fig 2A ) , and were contrast-enhanced after injection . The average T2 value in hepatic ROIs ( Fig 2A ) at 10 weeks post infection was not significantly increased compared to the average T2 value in control livers ( control: 10 . 4 ± 1 . 3 ms , infected: 12 . 2 ± 1 . 5 ms , P = 0 . 1714 ) . However , the distributions of hepatic T2 values in infected and control mice were different ( Fig 2B ) . The area fraction with 16 ms < T2 < 26 ms was 13 . 4 ± 6 . 9% in infected animals at 10 weeks , while it was 1 . 1 ± 1 . 0% in control mice ( Kruskal-Wallis test P = 0 . 0048 ) ( Fig 2C ) . Liver T2 values > 16 ms on the T2 maps were co-localized with the hyperintensities on anatomical images ( Fig 2D ) . The distributions of T2* values in the livers of infected mice were shifted to lower values at 6 weeks ( average T2* = 5 . 4 ± 1 . 5 ms in infected mice versus T2* = 5 . 8 ± 1 . 6 ms in control mice ) and at 10 weeks ( average T2* = 4 . 6 ± 1 . 5 ms in infected mice versus T2* = 6 . 1 ± 1 . 5 ms in control mice ) post infection ( Fig 2E ) . Relaxometry studies performed on collagen solutions did not reveal any significant change of either T1 or T2 relaxation time with increasing collagen concentration ( Fig 3 ) . Histology showed that adult parasites ( Fig 4A ) and eggs began to lodge in the liver with minimal inflammation and occasionally with isolated periovular granulomas ( S1B Fig ) by 6 weeks post infection , corresponding to METAVIR scores ≤ A1F0 . All infected animals showed discrete periportal and portal inflammation at 6 weeks post infection but only one of them was considered A1F0 . The number of eggs trapped in liver tissue dramatically increased thereafter . By 10 weeks post infection , 4 mice presented with severe portal fibrosis with granulomatous chronic inflammation , corresponding to METAVIR scores of A1F1 to A2F2 ( Fig 4B–4H ) . Eggs were surrounded by a dense population of immune cells , with mild to marked extracellular matrix deposition leading to intergranulomatous fibrosis and fusion of several granulomas replacing some portal spaces ( Fig 4E–4H ) . In a large number of advanced stage granulomas , pigment accumulation in macrophages ( S1C Fig ) , which appeared negative on the Perls’ stain ( S1D Fig ) , was present . This is highly suggestive of hematin , a degradation product of hemoglobin excreted by the worm [42] . Histology confirmed the absence of lesions in the two remaining mice in which the infection had failed; consequently these mice were excluded from analysis . METAVIR score and area fraction of fibrosis stained with Sirius Red increased simultaneously from 6 weeks post infection on ( Fig 5A and 5B ) and were both significantly correlated with the area fraction of T2 values comprised between 16 and 26 ms ( Fig 5C and 5D ) at 10 weeks after infection . Since control values obtained at 6 and 10 weeks for the METAVIR score or the Sirius Red staining were identical ( A0F0 , or 0% respectively ) , they were pooled in the statistical analysis ( Fig 5A and 5B ) . The correlations obtained for the values measured at 6 and 10 weeks are shown in S2A and S2B Fig .
This exploratory study on a small number of animals aimed at evaluating a non-invasive MRI method for the assessment of liver fibrosis progression . A limitation of this study is that different animal groups were used at the last two imaging time points , due to the fact that a validation by histology was mandatory . A longitudinal study on the same group of animals up to a time point beyond 10 weeks post infection would be desirable . For disease monitoring and treatment evaluation in small animals T2 mapping is easy to implement and less challenging than transient elastography or MRE . Indeed , this technique does not require the use of additional equipment such as vibrators and sequences for relaxometry are readily available on standard pre-clinical MRI systems . Magnetic resonance relaxometry has the advantage of being quantitative without any contrast agent injection that could interfere with biological parameters and alter disease development . However , administration of a commercially available nonspecific extracellular contrast agent such as Gd-DOTA enhanced the lesions , demonstrating increased vessel permeability . The quantitative T2 and T2* maps performed in this study allow further optimization of image weighting parameters for future studies on schistosomiasis . T2 mapping is a quantitative and non-invasive marker of non-steatotic liver fibrosis with sensitivity close to that of histology even at early stages . This multiparametric and quantitative MRI approach can monitor hepatic and splenic disease progression and assess liver fibrosis non-invasively . In preclinical studies and in settings were MRI facilities are readily available , this objective quantitative imaging biomarker may be used to monitor response to therapy in diseases involving hepatic fibrosis . | Schistosomiasis ( or bilharzia ) , a major helminth disease , affects more than 260 million people worldwide . While the adult worms survive for years within veins of the gastrointestinal system , symptoms are due to inflammatory reactions to their eggs in several organs . Hepatic fibrosis may develop in chronic cases of infection with Schistosoma mansoni and lead to portal hypertension with potentially lethal complications . In this study , we aimed at establishing a non-invasive quantitative and readily available magnetic resonance imaging ( MRI ) technique to monitor in vivo the development of hepatic fibrosis and portal hypertension in Schistosoma mansoni infected mice . We evaluated the transverse relaxation time T2 , an easily measurable MRI parameter , as an early and quantitative imaging biomarker for hepatic fibrogenesis and validated it with histological techniques for fibrosis detection and quantification . In addition , we confirmed that this mouse model of schistosomiasis replicates the human pathology closely . The quantitative imaging biomarkers validated in this study will aid in the preclinical and clinical evaluation of new therapeutic strategies against hepatic fibrogenesis . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| []
| 2015 | In Vivo MRI Assessment of Hepatic and Splenic Disease in a Murine Model of Schistosmiasis |
Cerebellar Purkinje cells display complex intrinsic dynamics . They fire spontaneously , exhibit bistability , and via mutual network interactions are involved in the generation of high frequency oscillations and travelling waves of activity . To probe the dynamical properties of Purkinje cells we measured their phase response curves ( PRCs ) . PRCs quantify the change in spike phase caused by a stimulus as a function of its temporal position within the interspike interval , and are widely used to predict neuronal responses to more complex stimulus patterns . Significant variability in the interspike interval during spontaneous firing can lead to PRCs with a low signal-to-noise ratio , requiring averaging over thousands of trials . We show using electrophysiological experiments and simulations that the PRC calculated in the traditional way by sampling the interspike interval with brief current pulses is biased . We introduce a corrected approach for calculating PRCs which eliminates this bias . Using our new approach , we show that Purkinje cell PRCs change qualitatively depending on the firing frequency of the cell . At high firing rates , Purkinje cells exhibit single-peaked , or monophasic PRCs . Surprisingly , at low firing rates , Purkinje cell PRCs are largely independent of phase , resembling PRCs of ideal non-leaky integrate-and-fire neurons . These results indicate that Purkinje cells can act as perfect integrators at low firing rates , and that the integration mode of Purkinje cells depends on their firing rate .
Cerebellar Purkinje cells exhibit a wide range of dynamical phenomena . They are intrinsic neural oscillators , firing spontaneously and highly rhythmically in the absence of synaptic input , at a rate of 10–180 Hz [1]–[5] . They also exhibit intrinsic bistability [2] , [3] , which influences their responses to sensory stimulation [3] . In addition , interactions between spontaneously firing Purkinje cells can result in waves of activity travelling down the cerebellar folia [4] , or in high frequency oscillations [6] , which may contribute to the generation of precise temporal patterns in the cerebellar network [7] . Hence , the firing of Purkinje cells is highly time- and state-dependent , and thus they represent excellent targets for dynamical systems analysis . The phase response curve ( PRC; [8]–[12] ) is a powerful tool to study neuronal dynamics at the cellular level . The PRC describes the effect of a brief perturbation on the firing phase of a neuron , and can be used to predict the response of a neuron to more complex stimulation patterns [8]–[12] . The shape of the PRC is linked to the type of neuronal excitability [13] , [14] , to oscillatory stability [15] and to network synchronization properties [16]–[19] . Studying Purkinje cell PRCs is therefore an essential step to explore their dynamic repertoire , probe their biophysical mechanisms , and to construct models of Purkinje cells to determine their role in information processing at the network level . PRCs can be obtained by directly perturbing the membrane potential by short ( infinitesimal ) square current pulses [8]–[12] or synaptic conductance pulses [12] , [19]–[22] , and via indirect methods [23]–[25] . Using the direct method , infinitesimal PRCs are obtained by repeatedly injecting brief current pulses while neurons are firing action potentials ( APs ) . Phase and phase perturbation are measured by using the AP immediately preceding the current pulse as a reference , and we refer to these PRCs as “traditional” PRCs throughout this paper . We show using electrophysiological experiments and in simulations that the interspike interval variability present in Purkinje cells introduces a systematic bias in this traditional calculation of the PRC . The bias results from loss of causality caused by the jitter of the APs surrounding the current pulse , and gives rise to an empty triangular region in the PRC plot , which we call the “Bermuda Triangle” . We introduce a method for calculating the PRC which corrects for this bias by using all spikes in the spike train as a reference , one at a time . We refer to PRCs obtained by this method as “corrected” PRCs . Note that in our study both “traditional” and “corrected” PRCs are calculated using the same experimental data: perturbation of the firing of Purkinje cells with brief square current pulses . Using the corrected method we show that the shape of the Purkinje cell PRC changes fundamentally depending on the firing rate of the neuron .
Somatic whole-cell patch-clamp recordings were made in current-clamp mode from spontaneously firing Purkinje cells in mouse cerebellar slices . To construct PRCs , a single brief depolarizing current pulse ( amplitude , 0 . 05 nA; duration , 0 . 5 ms ) was injected after a 100–150 ms baseline period ( see Fig . 1A ) . Repeating this protocol many times should result in a homogenous sampling of phase space in spontaneously spiking neurons . The resulting change in interspike interval ( ISI ) relative to the mean ISI corresponds to the PRC value denoted by . Plotting , for each trial , as a function of the phase , , at which the pulse arrived shows the overall ISI shortening corresponding to a positive PRC ( Fig . 1B , neuron firing at 180 Hz; see Materials and Methods ) . Three observations can be made . First , at late phases there is a triangular region entirely void of data points ( outlined in green ) which we call the “Bermuda Triangle” . This causes a negative bias of the running average at late phases ( Fig . 1B , dashed red line ) . Second , the intrinsic variability in the ISI [1] of spontaneously firing Purkinje cells acts as a source of noise , giving rise to data points with . However , removing all points beyond 1 does not eliminate the negative bias ( Fig . 1B , solid red line ) . Finally , many trials ( typically more than 5000 ) were required to calculate the Purkinje cell PRC reliably , while PRCs in other cell types are normally obtained from 100–200 trials [8] , [15] . The ISI variability in Purkinje cells [1] results in PRCs with low signal-to-noise ratio , increasing the bias at late phases and leading to a miscalculation of the PRC when this traditional method is used . Thus , a robust and unbiased method for the calculation of Purkinje cell PRCs in the presence of noise is required . To better understand the negative deflection of the PRC at late phases , a control PRC ( cPRC; see Materials and Methods ) was calculated from the unperturbed voltage traces prior to the current pulse . The cPRC should be zero throughout all phases . However , the negative bias of the PRC at the late phases persisted in the cPRC ( Fig . 1C ) . We conclude that it is not the result of the brief current pulse injection . Rather , it results from the inhomogeneous sampling of the phase in the presence of noise . Indeed , the phase histogram ( Fig . 1C , lower panel ) indicates that late phases are sampled less frequently . To reproduce the effect of noise , PRCs were obtained from a Purkinje cell model [26] in which Gaussian current noise was added to reproduce the irregularity of real Purkinje cell spiking ( example model neuron firing at its spontaneous firing rate of 27 Hz; see Materials and Methods ) . The model PRC exhibited the same negative deflection at late phases as observed in the experimental PRC ( Fig . 1D , dashed red line ) . As before , removing all points for which the phase exceeds 1 did not eliminate the negative deflection ( Fig . 1D , solid red line ) . Similarly , the cPRC in the model exhibited the same negative bias and the same inhomogeneous phase distribution ( Fig . 1E ) as the experimental cPRC . Therefore , the negative bias at late phases is a general feature of the traditional method for calculating PRCs , and must be due to the intrinsic ISI variability . In order to explain how the ISI variability might affect the PRC calculation , we sketch twelve representative scenarios in which spike jitter due to noise causes misclassification of the phase and/or the PRC value . In these scenarios ( shown in Fig . 2A–L ) , we jittered either the first or the second AP ( Fig . 2A–L , black lines ) with respect to a perfectly periodic cycle of firing ( Fig . 2A–L , grey lines ) . We divided the sketches into three blocks depending on the phase of the current pulse within the cycle ( Fig . 2 A , D , G , J: early phase; B , E , H , K: middle phase; C , F , I , L: late phase ) . The misclassification of phase and/or PRC value ( arrows ) becomes clear when comparing them against their deterministic counterparts . The jitter of the spike preceding or following the brief current pulse can lead to a loss of causality and hence to a drastic miscalculation of the PRC . The most serious consequences of the ISI variability due to noise occur in the scenarios illustrated in Fig . 2C and 2J , where the jitter causes the current pulse to fall into a different cycle of firing , resulting in a significant bias at the late and early phases of the PRC , respectively . Specifically , the “Bermuda Triangle” effect present in both model and experiment can be explained by means of the sketch in Fig . 2C: when the pulse arrives at late phases , and the AP jitter results in the pulse falling into the subsequent ISI as compared to the deterministic case , the resulting phase is small according to the new ISI boundaries . Due to causality , it is impossible for a PRC point to fall into the green “Bermuda Triangle” in Fig . 1 , since for all points in the triangle the shortening of the ISI would be larger than the actual phase difference of the pulse to the end of the ISI . This explains the observation that phases are sampled less frequently in the late part of the ISI , and thus the PRC values are underestimated and the effect of ISI noise is not averaged out . To visualize the resulting phase and PRC misclassification , we translated each of these twelve sketches onto a corresponding phase plot ( Fig . 2M , N ) . This allows the resulting phase and PRC values of each of the twelve cases to be compared against their deterministic counterparts . More specifically , regularly spaced spike times were defined and jittered independently by noise taken from a Gaussian distribution . The known actual phase without noise was plotted against the sampled phase . The assumption that the process underlying spiking is perfectly periodic and that the presence of a spike does not reset this underlying process is made only for generating the data in Fig . 2M , N ( and subsequently Fig . 3B–E ) , and only for purposes of illustration . In a purely deterministic scenario , the sampled phase is linearly dependent on the actual phase ( Fig . 2M , points on the diagonal ) . This is also the case for occurrences in which the noise has no effect on the phase ( e . g . the scenarios in Fig . 2A or 2B; yellow points in Fig . 2M ) . For any deviations of the sampled phase from the actual phase due to noise , the points are scattered across the plot ( Fig . 2M , color coding as in A–L ) . Based on the same principles , the effect of noise in each of the twelve scenarios on the PRC plot is shown in Fig . 2N ( color coding as in A–L ) . To summarize , the bias at late phases of the PRC calculated using the traditional method is due to erroneous phase sampling , which results from the substantial ISI variability present in spontaneously firing Purkinje cells , and the loss of causality between the current pulse and the jitter in the times of either of its two surrounding APs . Our new method to correct for the bias in the traditional PRC and obtain a homogeneous phase histogram is illustrated in Fig . 3A . The red spike immediately preceding the pulse is the one used as a reference ( ) in the traditional method . In our new method , instead of using just the spike immediately preceding the current injection , each spike in the spike train is taken as a reference one at a time and the corresponding phase values ( indicated under the arrows in Fig . 3A ) are all taken into account ( see also Materials and Methods ) . In this case , the two spikes prior to the stimulation pulse ( red and black in Fig . 3A ) predominantly contribute to the phase interval [0 , 1] of the PRC ( Fig . 3B , red and black points ) . The impact of the pulse on the subsequent ISI , the PRC2 , is then determined by the two spikes following the current pulse ( Fig . 3A blue and cyan spikes; also compare [24] ) and so on . It is worth emphasizing that even though more than one spike is included in the PRC calculation , the presence of each reference spike resets the phase to zero ( ) . Our method restores periodicity in the spiking jitter as can be seen in Fig . 3B ( all points , in analogy to Fig . 2M ) . By taking only the points according to the traditional calculation of the PRC , a sharp boundary is drawn ( Fig . 3B , red ) resulting in an inhomogeneous distribution of sampled phases ( Fig . 3C , red ) . In contrast , by including the second spike prior to the pulse , spike jitter effects are averaged out ( Fig . 3D ) . The bias at the late phases of the PRC plot observed when taking points according to the traditional calculation of the PRC ( Fig . 3E , red ) is thereby eliminated ( Fig . 3E , all points ) , as is the bias in the cPRC ( not shown ) . In order to validate our new method , we applied it to neuronal models for which the PRC can be calculated analytically ( from the adjoint [27] ) . PRCs of the Morris-Lecar model ( parameters from [28] ) , in the presence and absence of noise , were compared with the analytically derived PRC ( Fig . S1A ) . The PRCs calculated using both the traditional and our corrected method overlap perfectly ( except near and , due to the finite time step and finite amplitude of the current pulse in the simulations ) , and match the analytically derived PRC . In the presence of noise , the PRC calculated by the traditional method is biased at late phases , as described above . Our new method eliminates most of this bias . However , it has been shown that noise can directly affect the dynamics of neurons underlying the PRC , leading to changes in the PRC which are not due to measurement errors ( e . g . in the Morris-Lecar model [29] ) . We therefore used an additional model , the non-leaky integrate-and-fire model , in which noise-dependent changes of dynamics can be excluded . When noise was introduced in this model , the traditional method resulted in a biased PRC , as compared to the analytically derived PRC and the PRC in the absence of noise . Again , our corrected method removed most of this bias ( Fig . S1B ) . The same analysis was repeated in a leaky integrate-and-fire model . This shows that the “Bermuda Triangle” and its consequences on the PRC are the result of the traditional calculation of PRCs , separate from the effect of noise on the dynamics of the system ( Fig . S1C ) . Next , using the Purkinje cell model [26] , we compare the result of our method ( Fig . 4A , black ) to the PRC obtained with the traditional method ( Fig . 4A , red ) and the deterministic PRC without noise ( Fig . 4A , green ) . When the noise is increased , reflected by an increased coefficient of variation ( CV ) of ISIs , the traditional PRC deviates from the deterministic one and the bias becomes more pronounced ( Fig . 4A , dashed red line ) . In contrast , our corrected method performs as well as with low CV ( Fig . 4A , dashed black line ) . The strong bias at late phases is eliminated . In order to evaluate the performance of our method in comparison to the traditional method , we calculated the integral of the differences between PRCs and their deterministic counterparts ( PRC error; Fig . 4B ) . As the CV increases , the PRC error shows larger increases using the traditional ( Fig . 4B , red line ) compared to our corrected method ( Fig . 4B , black line ) . In conclusion , the “Bermuda Triangle“ present in PRCs is due to shortcomings of the traditional method for calculating PRCs . The bias can for the most part be compensated for by taking the two spikes preceding the pulse as a reference , one at a time , instead of just the spike immediately preceding the pulse as in the traditional method . Spontaneous firing frequencies of Purkinje cells range from 10–180 Hz both in vitro [1] , [2] , [4] , [5] and in vivo [3] , [30] . To test how the dynamics of Purkinje cells change according to the firing frequency , we recorded from cells firing spontaneously at low ( 15–40 Hz , n = 10 ) and high ( 55–180 Hz , n = 6 ) rates and calculated their PRCs using our corrected method . A representative corrected PRC is shown in Fig . 5A ( the same example of a rapidly firing ( 180 Hz ) Purkinje neuron as in Fig . 1B ) . The PRC is positive , indicating that the brief current pulse causes an advance of the following spike ( shortening of the ISI relative to the mean ) with maximum displacement when the pulse arrives near the middle of the ISI . It is worth noting that the phase histogram is homogeneous ( Fig . 5A , lower panel ) , suggesting that , with the corrected method , the ISI is equally sampled throughout . In order to study the effects of the brief pulse on the subsequent intervals we plotted the PRC2–5 ( Fig . 5B; see Materials and Methods ) . PRC2 is negative , suggesting that the subsequent ISI is lengthened relative to the mean . A PRC2 with opposite sign to the PRC has been previously reported [24] and it is believed to be due to a compensatory effect on the current ISI length . Indeed , as seen from PRC2–5 , these curves are negative and the effect dies out after about 4 ISIs . In comparison , an example of a PRC of a slowly firing ( 30 Hz ) Purkinje neuron is shown in Fig . 5C . The brief current pulse causes the same positive displacement of the following spike independently of its position within the ISI , resulting in a square PRC . The phase histogram is homogeneous , indicating that there is an equal probability for the pulse to arrive at each phase within the ISI ( Fig . 5C , lower panel ) . In order to study the effects of the brief pulse on the subsequent intervals we calculated the PRC2–5 ( Fig . 5D; see Materials and Methods ) . They were negative , similar to those of cells firing at a high rate , but exhibited larger fluctuations . It is interesting to note that the PRC phase advances occur at a different scale in the slowly and rapidly firing Purkinje cells . However , when converted back into time units , the PRC values are of the same order of magnitude in both cases ( see below ) . The PRCs of Purkinje cells exhibiting slow ( 15–40 Hz; n = 10 ) and rapid ( 55–180 Hz; n = 6 ) spontaneous firing were calculated using our corrected method . The PRCs switched from square ( phase-independent ) for lower frequencies ( Fig . 6A ) to phase-dependent for higher frequencies ( Fig . 6B ) . The switch occurred at a frequency of approximately 50 Hz . The average PRC of all neurons firing at low rates ( Fig . 6A , thick line ) is phase-independent . To our knowledge , such a square PRC has not been previously reported . A square PRC can only be obtained if the cells act as perfect non-leaky integrators . In contrast , the average PRC of all Purkinje cells firing at high rates ( Fig . 6B , thick line ) exhibited a sharp peak . It is useful to compare these average PRCs ( Fig . S2 , thick black and red lines ) with the biased ones obtained with the traditional method ( Fig . S2 , thick green lines ) . To quantitatively assess the switch in dynamics we plotted the peak-to-baseline ratio of the PRCs in relation to the firing rate ( Fig . 6C; see Materials and Methods ) . This quantity essentially compares the extreme value in the first half of the PRC with the extreme value in the second half . The switch at a firing rate of approximately 50 Hz can be seen clearly in this representation . The switch becomes particularly apparent when both the phase and the phase shift of the PRC are plotted in units of time , and phases are aligned with respect to the second AP in the ISI ( Fig . 6D ) . Then , the peaks of the PRCs measured at high firing rates coincide ( red ) , indicating that an input signal causes an effect only in a 3 ms window prior to the output spike irrespectively of the precise firing rate of the cells in that group . This peak in the PRC is shown to give way to a larger phase-independent plateau ( black ) at low firing rates , in which incoming signals will affect the spiking of the cell regardless of the time at which they arrive . A transitory PRC ( thin solid red lines in Fig . 6B and Fig . 6D , indicated by arrows ) showing both a plateau at early phases and a peak at late phases was observed in a cell with intermediate firing frequency ( 55 Hz ) . To summarize , the PRCs of Purkinje cells largely depend on the intrinsic firing frequency of the cells: they are phase-independent at low firing rates ( 15–40 Hz ) and phase-dependent at high frequencies ( 55–180 Hz ) . The firing rate of a Purkinje cell changes depending on modulation of its inputs [31]–[35] . For example , during locomotion in cats the firing frequencies of Purkinje cells can increase from an average of about 40 Hz [34] to more than 100 Hz [35] . To test whether the switch in Purkinje cell dynamics can occur in the same cell , we recorded Purkinje cell PRCs while modulating their firing frequencies using injected current ( n = 3; Fig . 6C , points labeled with two colors ) . We first recorded at the spontaneous firing frequency , and if the spontaneous frequency was low , we next increased the firing rate by injecting a positive constant current . Alternatively a negative constant current was injected if the spontaneous frequency was high . The PRCs for both fast and slow states were calculated ( Fig . 7 , color coding as in Fig . 6C ) . When Purkinje cell spiking was changed from slow ( 33 Hz ) to fast ( 104 Hz ) , the originally square PRC ( Fig . 7A ) , exhibited a sharp peak ( Fig . 7B ) . This change in the PRC was reversible , as when the neuron was allowed to relax back to its intrinsic firing rate ( 40 Hz ) the PRC returned to a square shape ( Fig . 7C ) . Conversely , another neuron initially firing at a high rate ( 71 Hz ) exhibited a peaked PRC ( Fig . 7D ) , which was switched to a square shape by reducing its firing rate to 26 Hz via injection of hyperpolarizing current ( Fig . 7E ) . When the neuron was then allowed to fire at its intrinsic firing rate ( 84 Hz ) the sharp peak in the PRC reappeared ( Fig . 7F ) . Therefore , the switch in Purkinje cell dynamics reflected in the switch of the PRC can also occur in the same cell .
We have determined Purkinje cell PRCs by injecting brief current pulses and measuring the phase change in the subsequent neuronal spiking . Since at the typical spontaneous firing rates of Purkinje cells these phase changes were small compared to the spike jitter during spontaneous spiking [1] , many trials were required . This revealed a general bias of the traditional method at late phases of the PRC in the presence of noise ( Fig . 1 ) . We characterized the effect in a model with and without noise , and showed that the bias is related to inhomogeneous phase histograms caused by interspike interval jitter ( Fig . 1 and Fig . 3 ) . To correct for this , we developed a new method , which recovers periodicity in the spike jitter due to noise ( Fig . 3 ) . We showed that this method homogenizes the phase sampling in the experimental data and removes most of the bias observed in the PRCs calculated using the traditional method ( Fig . 4A ) . Our corrected approach can be directly applied to existing experimental data in order to measure PRCs under low signal-to-noise conditions . It should be applicable to a wide range of cell types , as neuronal noise and the resulting ISI variability are not restricted to Purkinje cells [36] . The use of indirect methods to obtain PRCs , for example from the spike triggered average [23] or the poststimulus time histogram ( PSTH ) [24] are possible alternatives to the traditional method . Here we have applied a correction to the traditional method , which resulted in reliable PRC measurements in Purkinje cells . Further alternative methods for calculating PRCs exist . For example , dynamic clamp was previously used to study hippocampal spike-timing-dependent plasticity in relation to PRCs [37] . In this special case , underlying subthreshold oscillations provide phase locking . Such a method is only applicable if phase information is accessible to the experimenter , independent of spiking . PRCs can also be calculated using Bayesian statistics [25] , or by injecting trains of rectangular current pulses [38] and noisy inputs [11] . These methods result in periodic PRCs , but only because periodicity is imposed as part of the optimization ( fitting ) techniques employed . In conclusion , our method can be applied to noisy experimental data to calculate PRCs while avoiding possible bias or overfitting problems present in some of the currently available methods . A wide , comparative study will be required in the future to find out which methods for calculating the PRC yield the best results under different conditions . Purkinje cells fire spontaneously and modulate their firing in response to synaptic input . The spontaneous firing rate of Purkinje cells varies from 10 to 180 Hz , but firing frequency can also be increased by the ∼150 , 000 parallel fiber synaptic inputs [39] or decreased by molecular layer interneurons during the execution of motor tasks such as smooth-pursuit eye movements [40] , maintenance of posture [41] and locomotion [35] , [42] . For example , the rate of Purkinje cell firing can exhibit a consistent temporal relationship with wrist movement [31] or be monotonically related to eye velocity during smooth-pursuit eye movements [40] . How is the integration of single inputs affected by the firing rate of the Purkinje cell ? We have addressed this question by measuring the PRC at different firing rates . Using our new approach , we determine experimentally the PRCs of cerebellar Purkinje cells and show that their shape changes significantly depending on the firing rate ( compare Fig . 5A and Fig . 5C ) . At high firing frequencies ( >50 Hz ) Purkinje cell PRCs are monophasic ( Fig . 6B ) . However , at low firing rates ( <50 Hz ) , Purkinje cell PRCs become phase-independent ( Fig . 6A ) . To the best of our knowledge , this is the first study to report a phase-independent PRC in a mammalian neuron . It was previously reported in a spike-frequency adaptation model of cortical neurons that an increase in firing frequency causes a shift of the PRC peak from rightward skew to the centre with a decrease in amplitude [24] , implying that the integrative properties of this model neuron change depending on the firing rate . Specifically , it was suggested that the model cell acts like a coincidence detector at low firing rates and more of an integrator at higher firing rates [24] . Purkinje cells appear to show the opposite behaviour , acting as perfect integrators at low firing rates . The shape of the PRC is thought to be linked to the type of excitability of the neuron . Neurons with type I excitability , whose f-I curves are continuous , are thought to display purely positive PRCs while neurons with type II excitability , characterized by a discontinuity in the f-I curve at the onset of firing , exhibit biphasic PRCs [11] , [13] , [14] . While biphasic PRCs intuitively result in resonator behavior , neurons with purely positive PRCs act as integrators of synaptic input [11] , [13] , [14] , [43] . Although Purkinje cells exhibit type II excitability [2] , [44] , [45] , their PRCs are positive at all firing rates , implying that they are integrators rather than resonators . These findings suggest that the type of excitability of a neuron is not strictly correlated with the PRC shape . Similarly , Tateno and Robinson [15] showed that low-threshold spiking , fast spiking and non-pyramidal regular spiking interneurons can exhibit both purely positive and biphasic PRCs which do not always strictly correspond to the type of excitability of the neuron . The shape of the PRC has functional implications for the integration of synaptic inputs . At high firing rates , Purkinje cells are most sensitive to inputs during the last 3 ms of their firing cycle ( Fig . 6D ) , imposing a strict relationship between the timing of the input and the timing of spike output , with direct consequences for network dynamics . It has been shown theoretically that oscillators which are described by type I PRCs and are coupled by excitatory synapses tend not to synchronize [16] . However , the opposite is true for inhibitory coupling between oscillators [16] , [46] , such as coupled Purkinje cells . Indeed , theoretical and experimental evidence indicates that Purkinje cells tend to synchronize via inhibitory inputs [4] , [6] , [7] . As the firing rate of Purkinje cells decreases , and the levels of synaptic and intrinsic conductances and currents are modified , the PRC switches from monophasic to phase-independent ( Fig . 6C ) . The phase-independent PRCs at low firing rates suggest that Purkinje cells integrate their synaptic inputs independently of their timing within the interspike interval ( Fig . 6A ) . Our results therefore support the idea that at low firing rates , Purkinje cells cannot read out the timing of their inputs , which would exclude the use of a temporal code . Instead , in this regime they are well suited for rate coding . What are the biophysical mechanisms responsible for the switch in PRC behaviour at different firing rates ? To generate an entirely flat PRC would require a neuron to effectively completely compensate for its leak conductance . This is illustrated by the example of the PRC of a simple leaky integrate-and-fire neuron in which the leak conductance was eliminated ( Fig . S1B and C ) . However , this absence of leak is unlikely to occur in real Purkinje cells , and the biophysical implementation remains unknown . PRCs qualitatively similar to those observed in our experiments at high firing rates can be generated by the Purkinje cell model of Khaliq and colleagues [26] ( Fig . S3A ) . However , when the firing rate is lowered in the model , no qualitative switch in the shape of the PRC can be observed . A hint to the mechanisms underlying the switch in the experiment is provided by using the model of Akemann and Knöpfel [47] ( a further development of the Khaliq et al . model ) : at low firing rates a ‘shoulder’ appears in the early phases of the PRC ( Fig . S3B ) . However , none of these models fully capture the experimentally determined switch in Purkinje cells , perhaps reflecting the fact that both of these models represent dissociated Purkinje cells . Thus , our experimental results could aid the refinement of existing models in order to capture the full dynamic behaviour of Purkinje cells . In conclusion , our experimental findings indicate that Purkinje cells display different dynamic behavior depending on their firing rate . At high firing rates these neurons act as coincidence detectors of synaptic inputs , with maximal sensitivity at the late phases of the interspike interval . In contrast , at low firing rates Purkinje cells are not suited for precise coincidence detection , but instead appear to perfectly integrate their inputs independently of their position within the interspike interval . Thus , at high firing rates Purkinje cells can transmit information via a temporal code whereas at low firing rates they are well-suited for rate coding .
All procedures were approved by the U . K . Home Office . Twelve- to fifteen-day-old L7-tau-gfp mice [48] were anaesthetised using isoflurane , decapitated and their brains were transferred to ice-cold low Ca2+ artificial cerebrospinal fluid ( ACSF ) containing ( in mM ) : 125 NaCl , 26 NaHCO3 , 25 glucose , 2 . 5 KCl , 26 NaH2PO4 , 0 . 5 CaCl2 and 3 MgCl2 , saturated with carbogen ( 95% oxygen and 5% carbon dioxide gas ) . 230-µm-thick sagittal brain slices from the cerebellar vermis were cut on a VT1200S microtome ( Leica Microsystems ) and were transferred to normal ACSF containing the following ( in mM ) : 125 NaCl , 26 NaHCO3 , 25 glucose , 2 . 5 KCl , 26 NaH2PO4 , 2 CaCl2 and 1 MgCl2 , again bubbled with carbogen . The slices were incubated for 30–40 minutes at 37°C and were then allowed to cool to room temperature . Thick-walled , filamented , borosilicate glass electrodes ( Harvard Apparatus Ltd . ) were pulled to a tip resistance of 4–5 MΩ ( PC-10 microelectrode puller , Narishige ) . Cells were visually identified with the aid of an upright infrared differential interference contrast ( IR-DIC ) microscope ( Axioskop , Carl Zeiss ) and a video camera ( C2400-07 , Hamamatsu ) . Purkinje cell somatic whole-cell patch-clamp recordings were obtained using an internal solution containing the following ( in mM ) : 130 methanesulfonic acid , 10 HEPES , 7 KCl , 0 . 05 EGTA , 2 Na2ATP , 2 MgATP , 0 . 5 Na2GTP and 0 . 4% biocytin , pH-adjusted to 7 . 3 with KOH . All recordings were performed at 34 . 5±1°C in the presence of carbogen-bubbled ACSF supplemented with GABAA receptor blocker SR95331 ( 10 µM ) . Recordings were made with an Axoclamp 2B amplifier ( Axon Instruments ) and were filtered at 3 kHz and sampled at 50 kHz using an ITC-18 DAC board ( Instrutech ) and Axograph 4 . 9 ( Axon Instruments ) . Series resistance and pipette capacitance were carefully monitored and compensated throughout the experiment . Methanesulfonic acid was obtained from Fluka , and other chemicals from Sigma-Aldrich and BDH Chemicals . Data were analyzed with MATLAB ( The MathWorks ) . To determine how spike timing during spontaneous firing is shifted by a brief perturbation , we injected rectangular current pulses of 0 . 5 ms duration and 50 pA amplitude , after a baseline of 150 ms ( 50 ms ) of spontaneous firing in subsequent trials of 350 ms ( 100 ms ) for a slowly ( rapidly ) firing cell . A control PRC ( cPRC ) was calculated using the unperturbed part of the voltage traces and assuming a current pulse injection ( 0 pA amplitude ) after 50 ms ( 25 ms ) of spontaneous firing in subsequent trials of 350 ms ( 100 ms ) for a slowly ( rapidly ) firing cell . The cPRC should be zero throughout all phases . The dynamics of a neuronal oscillator can be reduced to a single variable: the phase . is calculated by dividing the time from the previous spike by the period of the oscillation; it increases linearly from 0 to 1 between two spikes . Depending on the phase of the stimulus , a change in phase , , of subsequent spiking will occur . Traditional method: A brief current pulse is injected at a random time . The spikes before and after it are identified . is calculated by the difference between the unperturbed and the perturbed [8]–[12] . When the unperturbed is defined as the mean ISI ( ) , a point on the PRC plot becomes: ( 1 ) where denotes the ISI which contains the brief current pulse and is the PRC point calculated in reference to the spike just prior to the stimulus . is the time between the pulse and the preceding spike . The resulting curve is a plot of against . The curve is positive ( negative ) when the injected current advances ( delays ) the next spike . In the experimental and model ( with noise ) PRCs , we refer to raw data as the estimated measurements ( ‘points’ ) on the PRC plot . A moving average was calculated with a Gaussian kernel over the raw data . Corrected method: A major problem with the traditional method is the loss of periodicity of the sampling reference ( Fig . 3B ) , which results in an inhomogeneous sampling of phases in the presence of spike jittering . In order to restore periodicity , points unaffected by the stimulation pulse can be added to the ensemble of PRC points , which allows the spiking jitter to average out properly . These points can be obtained from the same data by adding PRC values when the preceding ISI is taken into account: ( 2 ) When preceding and subsequent ISIs are taken into account as in: ( 3 ) and ( 4 ) periodicity in the spiking jitter is restored , phases are sampled homogeneously and the cPRC becomes flat . In the resulting plot , the phase interval ranges from to and the PRC component affecting directly the interval corresponds to all points in the phase interval [0 , 1] , termed PRC1 . Successive PRC2–5 , correspond to phase intervals [−1 , 0] , [−2 , −1] , [−3 , −2] and [−4 , −3] , respectively and indicate how are affected by the pulse . Peak-to-baseline ratio: In order to distinguish the phase-independent PRCs from the phase-dependent ones , PRCs were classified according to the peak- to-baseline ratio . Inspired by Tateno and Robinson [15] , local extrema at the two halves of the PRC ( i . e . for and ) were calculated and were denoted as early ( ) and late ( ) respectively . The peak-to-baseline ratio is then defined as: Simulations were performed in NEURON [49] using a model of Purkinje cells consisting of a single compartment [26] , [47] . The model includes seven voltage-gated conductances ( a resurgent Na+ current , fast and slow K+ currents , P-type Ca2+ current , Ca2+-activated K+ current and the hyperpolarization-activated current Ih ) and one voltage-independent conductance ( Ileak ) , based on voltage clamp measurements from Purkinje cells [26] . The membrane surface area of the neuron was modified ( ×13 ) to reproduce input resistance values close to those observed in Purkinje cells ( 80 MΩ ) . In order to mimic the noise observed in Purkinje cells , noisy current input drawn from a normal distribution with ( mean ) and ( standard deviation ) was injected at each time step of the simulation ( every 25 µs ) into the soma . The noise injection resulted in a coefficient of variation of ISIs of 0 . 05 , which is comparable to the values measured in real Purkinje cells in the experiments presented here ( see also [1] ) . Current pulses of 0 . 5 ms duration and 250 pA amplitude were injected after 2500 ms , at a time at which spike jitter had randomized spiking phase . Data shown is taken from more than 15000 trials . Additional neuron models were used in the supplementary parts of the manuscript . For Fig . S1 , the Morris-Lecar model was directly implemented using parameters from [28] . The adjoint was calculated using XPPAUT [27] and the PRCs with noise were integrated in MATLAB ( The MathWorks ) . The parameters for the leaky integrate-and-fire model were: a membrane time constant of , a reset potential of , a threshold potential of , a membrane resistance of , and a steady driving current of ( to result in 50 Hz firing ) and was simulated at time steps of . For the non-leaky integrate-and-fire model the time constant was set to infinity and , otherwise the same parameters were used . An alternative model for Purkinje cell firing was used for Fig . S3 which also includes a resurgent Na+-current and modified voltage-gated K+-conductances [47] . In this model , current pulses of 0 . 5 ms duration were injected at amplitudes of 10 pA in the low firing rate ( 33 Hz ) case and 60 pA in the high firing rate ( 111 Hz ) case . Simulation results were analysed in the same way as the experimental data . | By observing how brief current pulses injected at different times between spikes change the phase of spiking of a neuron ( and thus obtaining the so-called phase response curve ) , it should be possible to predict a full spike train in response to more complex stimulation patterns . When we applied this traditional protocol to obtain phase response curves in cerebellar Purkinje cells in the presence of noise , we observed a triangular region devoid of data points near the end of the spiking cycle . This “Bermuda Triangle” revealed a flaw in the classical method for constructing phase response curves . We developed a new approach to eliminate this flaw and used it to construct phase response curves of Purkinje cells over a range of spiking rates . Surprisingly , at low firing rates , phase changes were independent of the phase of the injected current pulses , implying that the Purkinje cell is a perfect integrator under these conditions . This mechanism has not yet been described in other cell types and may be crucial for the information processing capabilities of these neurons . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"neuroscience/neuronal",
"signaling",
"mechanisms",
"neuroscience/theoretical",
"neuroscience"
]
| 2010 | A New Approach for Determining Phase Response Curves Reveals that Purkinje Cells Can Act as Perfect Integrators |
A significant current challenge in human genetics is the identification of interacting genetic loci mediating complex polygenic disorders . One of the best characterized polygenic diseases is Down syndrome ( DS ) , which results from an extra copy of part or all of chromosome 21 . A short interval near the distal tip of chromosome 21 contributes to congenital heart defects ( CHD ) , and a variety of indirect genetic evidence suggests that multiple candidate genes in this region may contribute to this phenotype . We devised a tiered genetic approach to identify interacting CHD candidate genes . We first used the well vetted Drosophila heart as an assay to identify interacting CHD candidate genes by expressing them alone and in all possible pairwise combinations and testing for effects on rhythmicity or heart failure following stress . This comprehensive analysis identified DSCAM and COL6A2 as the most strongly interacting pair of genes . We then over-expressed these two genes alone or in combination in the mouse heart . While over-expression of either gene alone did not affect viability and had little or no effect on heart physiology or morphology , co-expression of the two genes resulted in ≈50% mortality and severe physiological and morphological defects , including atrial septal defects and cardiac hypertrophy . Cooperative interactions between DSCAM and COL6A2 were also observed in the H9C2 cardiac cell line and transcriptional analysis of this interaction points to genes involved in adhesion and cardiac hypertrophy . Our success in defining a cooperative interaction between DSCAM and COL6A2 suggests that the multi-tiered genetic approach we have taken involving human mapping data , comprehensive combinatorial screening in Drosophila , and validation in vivo in mice and in mammalian cells lines should be applicable to identifying specific loci mediating a broad variety of other polygenic disorders .
The Online Inheritance in Man ( OMIM ) lists over 3500 loci which when mutated give rise to heritable human disease . Approximately one third of these disorders are dominant , most of these cases being due to dosage sensitive requirements for gene function ( i . e . , haploinsufficiency or elevated gene activity ) and a minority resulting from the production of an aberrant interfering protein product as has been extensively studied for peripheral neuropathies [1] , [2] . In addition to single locus disorders resulting from altered gene dose , a large fraction of the genome may be involved in multi-locus complex disorders resulting from alterations in gene dose due to heterozygosity for macroscopic deletions or duplications , smaller chromosomal lesions resulting in copy number variation ( CNV ) [3] , [4] , or interactions between two or more genes in separate genomic intervals ( e . g . , as identified by the HapMap initiative [5] ) . Given the large numbers of potentially interacting loci that could underlie polygenic disorders , systematic approaches to identify such loci are urgently needed . In the current study , we present a multi-tiered genetic approach that could be generalized to a broader range of disorders , to identify genes that interact to cause congenital heart defects ( CHD ) . Based on human genetic data delimiting a short interval on the distal tip of chromosome 21 containing a small set of candidate genes which may contribute to CHD in human DS patients [6]–[10] , we first employed Drosophila as a model to systematically examine the effect of over-expressing these candidate genes individually or in pairwise combinations in the pumping heart tube of adult flies as well as in a neurologically relevant tissue ( the eye ) . Although the fly heart is a much less complex structure than its vertebrate counterparts , it has several important basic properties common to all hearts including developmental genes involved in specifying the heart primordium [11] , proteins mediating periodic contractility , ion channels responsible for rhythmic beating , and morphological adaptations required for directional fluid pumping ( e . g . , valves ) [12] , as well as manifesting age-dependent deterioration [13] . In addition to being able to test many genetic combinations rapidly and to target gene over-expression to specific cell types such as the heart or eye , the fly provides a relatively stringent system for identifying genetic interactions since dominant phenotypes resulting from altering the dose of single genes are far less common than in humans . Our comprehensive screening of CHD candidate genes in Drosophila identified DSCAM and COL6A2 as the most strongly interacting pair of genes . The effect of modestly over-expressing these genes in the mouse heart was then examined and , as in flies , these two genes interacted synergistically to cause defects in heart morphology and physiology . Over-expression of DSCAM and COL6A2 also resulted in a transcriptional response in cardiac H9C2 cells . Consistent with the known cell biological roles of these two genes in mediating cell-matrix adhesion , we found clear transcriptional signals for genes involved in cell adhesion as well as genes involved in cardiac disease . We discuss the prospects of using similar multi-tiered genetic approaches to identifying genes involved in other polygenic disorders .
Congenital heart defects are observed in approximately 50% of DS patients and the genes responsible for this phenotype have been mapped to a small candidate region near the tip of chromosome 21 [9] , [10] . Several of the genes included in this interval are known to be expressed in the heart and among this group a subset encode extracellular proteins or proteins interacting with them: SH3BGR , DSCAM , COL6A1 , COL6A2 and COL18 . Homologs of all human DS CHD candidate genes that were chosen for study are present in Drosophila ( Table S1 ) . We assayed the effect of over-expressing mammalian candidate CHD genes and their Drosophila orthologs selectively in the fly heart using the UAS/GAL4 trans-activation system [14] . Several independent UAS transgenic lines were generated for each of the mammalian and fly candidate genes , and these genes were expressed individually and in all pair-wise combinations in the Drosophila myocardium using the heart-specific GMH5-GAL4 driver [15] ( Figure 1a ) . We assayed the effects of over-expressing CHD genes by measuring basal heart rate and by testing for heart failure following stress [15] . For the stress test , adult flies were subjected to a heart-pacing paradigm in which the heart rate was doubled ( i . e . , electrically stimulated to 6 Hz ) for a period of 30 seconds . Following pacing , we monitored the proportion of flies with consequent cardiac dysfunction ( termed here ‘heart failure’ ) and their recovery after 2 minutes [15] ( Figure 1b , 1c and Table S2 ) . In these performance tests , heart dysfunction was manifested by uncoordinated fibrillation or protracted periods of non-beating ( asystole ) . When expressed individually in the fly heart , several genes caused an increase in stress-induced cardiac dysfunction compared to controls for at least one of the three parameters tested , and three genes ( DSCAM , COL6A2 , and SH3BGR ) altered two indices of heart function ( Figure 1d and Table S2 ) . Using these effects as a baseline , we next tested for cooperative interactions among CHD candidate genes by co-expressing them in all possible pair-wise combinations . The criterion we used to define a genetic interaction between two candidate genes was that the effect of co-expressing single copies of two genes was significantly greater than that caused by expressing two copies of each gene separately . This analysis revealed that the strongest interacting gene combinations were DSCAM+COL6A2 and DSCAM+SH3BGR ( Figure 1c , 1d and Table S2 ) . For these two genetic combinations , all three indices of heart function , heart rate , failure , and recovery rate , were altered ( p<0 . 05 ) . As an example of a cooperative effect , expression of either DSCAM or COL6A2 alone resulted in ≈35% heart failure rate ( N = 200 for each genotype ) , but when these two genes were co-expressed , the failure rate nearly doubled to 60% ( N = 200; p<0 . 05 ) ( Figure 1c ) . Similarly , co-expression of DSCAM+COL6A1 or SH3BGR+dCOL18A1 resulted in significant perturbation of all 3 parameters tested ( Figure 1d ) . Notably , three of the interacting genes ( DSCAM , COL6A2 , and SH3BGR ) also had moderate effects when expressed individually . This comprehensive combinatorial study of CHD candidate genes in the fly heart revealed that over-expression of DSCAM caused the greatest disruption of heart function and that co-expression with COL6A2 most effectively potentiated this effect on all heart parameters scored . In parallel to testing for defects in heart performance , we examined the effect of expressing CHD candidate genes in the fly eye , which is another widely used assay system for genetic interactions and a well established model for defining mechanisms underlying neurological disorders . As in the case of the heart , we expressed each CHD candidate gene alone and in all pair wise combinations using the eye specific GMR-GAL4 driver and observed varying degrees of roughened eyes ( Figure 1e , 1f ) ranging from mild to moderately disorganized eyes . As in the case of the heart expression experiments , we used these single gene phenotypes to assess potential cooperative effects of co-expressing candidate genes in all pairwise combinations . Again , we found several instances in which DS CHD candidate genes interacted by producing stronger roughened eye phenotypes when co-expressed than when expressed individually or in two copies ( e . g . , GMR>COL6A1+SH3BGR have highly disorganized eyes with discoloration - Figure 1e ) . When we considered the aggregate data from over-expressing CHD genes in both the heart and eye , DSCAM and COL6A2 emerged as the most consistently and intensely interacting pair of genes ( Figure 1f ) . We therefore selected this particular combination of genes for further detailed analysis in the hearts of both flies and mice . In flies , we next examined the basis for the interaction between DSCAM and COL6A2 by taking movies of individual semi-intact fly heart preparations using high speed digital video imaging . We analyzed autonomous heart function using a semi-automated heartbeat analysis that provides several quantitative measures of the dynamic contractile properties of the beating heart [16] . Typically , hearts from young wild-type flies exhibit rhythmic beating patterns with narrow distributions of both diastolic and systolic intervals [13] . We examined dynamic indices of the heart beat in young ( one week old ) flies expressing both DSCAM and COL6A2 using the heart specific GMH5 driver ( GMH5>DSCAM+COL6A2 ) and found that they exhibited slower and less rhythmic beating than control flies ( e . g . , non-expressing flies: GMH5/+ , DSCAM/+ , and COL6A2/+ or flies expressing the transgenes individually ( GMH5>DSCAM and GMH5>COL6A2 ) ) ( Figure S1a-S1d ) . In addition , the distribution of the heart period in flies expressing both DSCAM and COL6A2 was substantially broadened ( Figure S1b ) . The increase in heart period in these flies could be attributed to the increase in diastolic interval ( Figure S1b-S1d ) . The alteration of basal heart rate and rhythmicity and disruption of heart function in response to pacing when co-over-expressing DSCAM and COL6A2 indicate that mis-regulation of this particular combination of genes greatly impairs heart function in the fly motivating an analysis of over-expressing these two genes in a mammalian system . Having demonstrated a cooperative interaction between DSCAM and COL6A2 in flies , we next asked whether over-expression of this same pair of genes would similarly result in a synergistic disruption of heart function and/or morphology in mice . We over-expressed each of these genes separately and in combination under the control of the murine heart specific alpha-MHC promoter , which is active in myocardial cells both during early development and in the adult [17] . Single transgenic lines expressing either DSCAM or COL6A2 were fully viable and fertile . Single transgenic lines expressing comparable levels of DSCAM and COL6A2 were chosen and crossed to each other to obtain double transgenic mice co-expressing the two genes in the developing heart . The levels of DSCAM and COL6A2 proteins in transgenic adults were only modestly elevated relative to wild-type ( Figure 2a , 2b ) . In contrast to the full viability of the single transgenic lines ( which pertained even to lines expressing considerably higher levels of the transgenes than those used to generate the double transgenic mice ) , double transgenic adult mice were recovered at only 58% of the expected frequency ( Table S3 ) . Since we targeted expression of the DSCAM and COL6A2 transgenes specifically to the heart , the reduced proportion of viable double transgenic mice suggested that some of these individuals may have succumbed to severe heart defects and that surviving adults might have observable abnormalities in heart morphology or function . Consistent with this possibility , dissected hearts from double transgenic 3-month-old adult mice weighed more than those from wild type controls ( Figure 2c ) . We searched for potential morphological defects using ( 10 µm ) Micro-CT analysis to generate high resolution 3D reconstructions of double transgenic and wild type hearts as well as by inspection of serial heart sections ( Figure S2b ) . The most obvious gross defect we observed in double transgenic hearts was an increased thickness of the left ventricle ( LV ) wall and interventricular septum ( IVS ) relative to control wild-type hearts ( Figure 2d–2f ) . In the most extreme double transgenic hearts , digital reconstruction of the heart chambers revealed that the walls of the LV were enlarged to the point of nearly occluding the lumen ( Figure 2f ) . These hearts also had thickened IVS and RV walls . Enlarged left ventricular walls in the hearts of double transgenic mice resemble left ventricular hypertrophic cardiomyopathy that occurs frequently as a result of pressure overload as can result from partial aortic occlusion [18] . Examination of myocyte morphology at the IVS and LV ( Figure 2g , 2h ) confirmed that the increased thickness of the ventricular walls in the double transgenic animals was a result of increased cell size , as has been observed in various hypertrophic cardiomyopathies [19] . None of these gross morphological defects were observed in wild-type ( judged by MicroCT and H&E staining ) or single transgenic ( by H&E staining ) littermates ( data not shown ) . We also examined hearts in greater detail using the full resolution of Micro-CT reconstructive imaging of fixed whole mount hearts as well as standard dissection procedures ( Figure 3a , 3b ) . Both types of fine morphological analysis identified the presence of atrial septal defects ( ASDs ) in double transgenic mice ( N = 7 ) , but not in control wild-type littermates ( N = 7 ) . In order to determine whether such frank holes had a physiological consequence and whether such defects were penetrant , we complemented the morphological studies with sensitive physiological measurements monitoring blood flow using two in vivo imaging methods . The first method , digital subtraction angiography ( DSA ) , can detect abnormal shunting of blood between the heart chambers by injecting a radio-opaque dye into the right jugular vein and following it through the heart cycle in real-time by radiography [20] . We used DSA analysis to compare the heart function of wild-type , single and double transgenic animals at 3 months of age . We found that 53% of the double transgenic mice tested ( N = 15 ) exhibited abnormal shunting of the dye from the left to the right atrium , indicative of a functional ASD ( Figure 3c , 3e ) . In contrast , none of the wild-type or single transgenic animals displayed any shunting using this assay ( Figure 3e , N = 9 for each genotype ) , demonstrating that this leakage phenotype is fully dependent on both genes being over-expressed . The second assay we employed , saline contrast echocardiography , provides a yet more sensitive test for shunting in which agitated saline infused with highly reflective micro-bubbles is injected into the right jugular vein and then followed by 2-dimensional echocardiography . During the period when the pressure in the right atrium exceeds that in the left atrium , even minute amounts of right-to-left blood flow shunts can be detected [21] , [22] . In this case , we observed a yet higher frequency of shunting from the right to left atrium in double transgenic mice ( 80% , N = 10 ) ( Figure 3d , 3e , and Videos S1 and S2 ) . We also observed shunting in one wild-type mouse ( 14% , N = 8 ) and in two single DSCAM transgenic mice , ( 22% , N = 9 ) , but not any of the single COL6A2 transgenic mice ( N = 7 ) . For several double transgenic mice that displayed atrial shunting by echocardiography ( N = 6 ) , we performed additional morphological analysis by serial sectioning of the hearts . These individuals all displayed multiple defects in the atrial septum as well as septal dysmorphologies ( e . g . , Figure S2 ) . We conclude both by functional and morphological criteria that double , but not single , transgenic mice have a high frequency of functional atrial septal defects . DSCAM is a cell adhesion molecule and its genetic interaction with COL6A2 , an extracellular matrix component , suggested a possible direct or indirect interaction involving cell-substrate adhesion . We investigated this possibility by transfecting the rat cardiac myoblast cell line H9C2 with DSCAM to generate a stable cell line constitutively expressing DSCAM ( H9C2-DSCAM ) . These cells moderately over-express DSCAM and exhibit a subcellular distribution that is similar to the endogenous protein ( Figure S3 ) . We plated these DSCAM-expressing cells or non-transfected H9C2 control cells on microtiter wells coated with COL6 or BSA to assay substrate adhesion . We observed that H9C2-DSCAM cells adhered more firmly to the COL6 substrate than non-transfected cells in a time dependent fashion ( Figure 4a ) . Since H9C2-DSCAM cells exhibited elevated adhesion in a COL6-dependent fashion and because adhesive interactions are known to induce transcriptional responses , we asked whether expression of DSCAM in H9C2 cells might alter the transcriptional profile of these cells . We purified RNA from H9C2-DSCAM and control H9C2 cells plated on COL6 and performed RNA deep sequencing ( RNA-Seq ) and compared the transcriptomes of the DSCAM-expressing and parental cell lines . Our analysis identified 1251 genes that were differentially expressed in the two cell lines ( Dataset S1 ) . Gene ontology ( GO ) analysis of genes that were changed more than 2-fold in DSCAM-expressing versus parental cells ( p<0 . 0005 ) identified genes involved in cellular adhesion ( GO terms: ECM-Receptor interaction and focal adhesion - Figure 4b ) and cardiomyopathies ( GO: hypertrophic cardiomyopathy , cardiac hypertrophy , fibrosis - Figure 4c and Table S5 ) , which is consistent with the prominent cardiac hypertrophy we observed in double transgenic animals expressing DSCAM and COL6A2 ( Figure 4b and Table S4 ) . In line with the bulk analysis of GO terms , we found that a number of genes that promote adhesion were up-regulated including collagens , cadherins and integrins ( e . g . COL6A1 , COL4A1 , COL18A1 , CDH3 ) , whereas genes associated with migration or metastatic tumor invasion were down-regulated ( e . g . TWIST1 , TIAM1 , SLIT3 , STAT1/STAT5A , BMP2 ) . In summary , the whole genome transcriptional analysis supports the notion that co-expression of the DSCAM and COL6A2 genes results in a transcriptional misregulation of genes involved in cell-cell adhesion and ECM-cell interaction , which may contribute to the observed increased adhesion of H9C2-DSCAM cells to collagen substrate , as well as genes that respond transcriptionally in patients with cardiomyopathies . The altered expression levels of genes mediating cell-substrate interactions or genes misregulated in cardiac myopathy and hypertrophy in H9C2-DSCAM/COL6 cardiomyocyte cells suggested that these genes might also be misregulated in vivo in double transgenic DSCAM+COL6A2 mice . We therefore examined the relative expression levels of a select set of genes in hearts dissected from wild-type and double transgenic embryos . mRNA was isolated from individual hearts and tested for quantitative changes in gene expression by qRT-PCR . This analysis confirmed that several gene transcripts altered in the cellular cardiomyocyte model were also affected in the hearts of the double transgenic mice by qRT-PCR ( Figure 4d ) , including those encoding focal adhesion protein tenascin N ( Tnn ) , hypertrophy associated cardiac troponin gene ( Tnnt2 ) , calcium binding protein S100A4 , which is involved in fibrosis and tissue remodeling in several diseases [23] , Cxcr7 , which is associated with various cardiac defects including septal defects [24] , [25] , and the transcription factor Gata2 , which is associated with familial early-onset coronary artery disease [26] . Taken together , the expression results from the cardiomyocyte and transgenic mice hearts suggest that increased DSCAM and COL6A2 expression induce a transcriptional response that could amplify excessive adhesion and contribute to heart malfunction .
In this study , we began with information provided by decades of mapping in human DS patients that delimited a small region of chromosome 21 responsible for causing CHD [6]-[10] . Expression data indicating which genes in this interval were expressed in the heart further restricted the set of potential candidate genes that might contribute to CHD . A contributory role of DSCAM has also been proposed [9] , but given the large numbers of subjects needed for fine genetic mapping it would have been very difficult to go much beyond this level of analysis using human genetic data alone . We therefore turned to the fly as a model system with a beating heart tube that shares many basic cell biological features of the mammalian heart to identify stringent forms of genetic interaction associated with over-expression of CHD candidate genes in all possible pairwise combinations . This comprehensive first-order analysis of basic heart function indices pointed to two genes , DSCAM and COL6A2 , as causing the most severe synergistic disruption of heart function in flies . Further in depth analysis using quantitative real-time imaging of fly hearts over-expressing DSCAM and/or COL6A2 revealed additional cooperative defects such as arrhythmicity . While the interaction between these two genes was the strongest , we note that there were also fairly strong interactions between DSCAM and two other genes , COL6A1 and SH3BGR . The interaction of DSCAM with COL6A1 is not too surprising given that its gene product and that of COL6A2 are subunits of a common tripartite helical collagen fiber . The interaction with SH3BGR warrants further scrutiny in future studies , however , as it may provide a link between the extracellular compartment and signal transmission into cardiac cells . Because DSCAM and COL6A2 cooperatively altered heart function in flies when expressed in myocardial cells , we selected this combination of genes to over-express at modest levels in the murine heart . We chose the myosin-alpha chain gene promoter to drive over-expression of these genes in the myocardium since it is heart specific and drives modest levels of expression both during heart development and in adult hearts . While this mode of expression obviously does not precisely recapitulate the endogenous pattern of DSCAM and COL6A2 expression , it does restrict expression of these genes to the heart throughout a protracted period . Since we were using this driver to express molecules targeted to the extracellular space , we reasoned that these proteins might similarly accumulate extracellularly whether they were expressed by cardiomyocytes or other cardiac cells such as fibroblasts that are known to produce these proteins as well ( note that since both DSCAM and COL6A2 are expressed in developing fetal myocytes , we recapitulated at least a subset of the endogenous expression pattern ) . Additional studies with endogenous promoters or promoters driving expression of these genes in other cell populations in the heart ( fibroblasts or neural crest derivatives ) are clearly warranted . The choice of gene expression vehicle notwithstanding , we observed a very strong synergistic interaction between DSCAM and COL6A2 when over-expressed in the mouse heart including , highly penetrant and prominent cardiac hypertrophy , atrial septal defects associated with physiological shunting , and a mortality rate of 50% . Most importantly , these dramatic phenotypes were not observed in transgenic mice expressing either transgene alone , but only in mice co-expressing both transgenes . An obvious possible mechanism by which DSCAM and COL6A2 might disrupt heart development and/or function is by altering cell-substrate adhesion given that DSCAM is a transmembrane adhesion molecule and COL6A2 is constituent of the ECM . In line with this possibility , a rat cardiomyocyte cell line expressing DSCAM exhibits a time-dependent increased adhesion to COL6 coated plates . Although it is known that adhesion of cells has transcriptional effects , we were surprised that much of this transcriptional response seems to be focused on regulation of genes involved in the adhesion network , suggesting that positive feedback mechanisms further stabilizing adhesive interactions may be a prominent element of this interaction . These pervasive changes in gene expression could be mediated either by DSCAM itself , which is known to transduce a variety of signaling events during axonal pathfinding and axon branching including self-adhesive signaling and a response to Netrins , or by other adhesion dependent effectors such as the focal adhesion pathway components of which are regulated in the transcriptional response of H9C2-DSCAM grown on COL6 . The reciprocal regulation of several components promoting adhesion versus cell migration may also contribute to the CHD phenotypes that are observed in DSCAM+COL6A2 double transgenic mice , particularly since a number of the responsive genes identified in the cell culture experiments were also misregulated in hearts of double transgenic mice . While further studies will be needed to assess the importance of altered cell-substrate adhesion and cell migratory processes in mediating the morphological and physiological effects of DSCAM+COL6A2 over-expression , we speculate that such defects could lead to a developmental delay in closing the atrial septum and may also contribute , either developmentally or as part of a physiological feedback loop , to hypertrophic phenotypes that we observed in affected mice . Since we set out to identify genes contributing to DS CHD , a natural question is whether the phenotypes we observe recapitulate those associated with DS in humans . It is certainly noteworthy that we observed ASDs with high penetrance in double transgenic mice , since this is one of several salient features of DS CHD . However , other typical DS CHD phenotypes were not observed such as atrial ventricular septal defects , perimembranous and muscular ventricular septal defects , Tetralogy of Fallot , or persistent ductus arteriosus . Also , since atrial septal defects can have many etiologies , in order to determine whether this phenotype is similar to that in human DS patients , it will be important to examine in greater detail the origin of these defects in mice ( e . g . , premium or secundum ASD ) and compare them to the occurrence of these defects in DS patients [27] . There are several possible reasons for DSCAM+COL6A2 double transgenic mice exhibit only a subset of DS CHD phenotypes . Perhaps most obviously , as noted above , endogenous promoters may drive expression of these genes at different levels or in distinct spatial and temporal patterns than we achieved using a heterologous promoter . With regard to expression level , however , we note that DSCAM+COL6A2 double transgenic mice express only modestly elevated levels of the transgenes . Whether these match precisely with the altered dose in DS patients is unknown . It is noteworthy in this context that the level of altered gene expression in DS patients is not always elevated by precisely 50% , and in some cases can be as much as 2-3 times the normal level [28] , [29] , as is the case for COL6A2 in human DS brains [29] . Another factor to consider is that in our studies we focused on phenotypes caused by over-expressing DSCAM and COL6A2 in myocardial cells . However , some of the AV septal complex formation relies on endocardium and the endocardial derived mesenchyme , which undergoes complex remodeling processes in the AV cushion region [30] , and thus would not be targeted by the transgenic promoter we utilized . Moreover , migratory neural crest derivatives of the heart as well as cardiac fibroblasts , which have been implicated in cardiac hypertrophy [31] , may also play important roles in DS CHD . In addition , there may be species specific differences in response to altered gene expression levels . For example , mice carrying a complete copy of human chromosome 21 ( Tc1 mice ) do not reproduce the full spectrum of DS CHD defects observed in humans [32] , [33] . Similarly , trisomy of mouse chromosome 16 , which includes most but not all murine orthologues of genes carried on human chromosome 21 , results in abnormal atrioventricular junction defects that are not present in human DS CHD [34] . Finally , DS CHD may involve the over-expression of other genes in addition to DSCAM and COL6A2 . Since DSCAM also interacted strongly with SH3BGR in flies , it would be of particular interest to examine the consequence of over-expressing this pair of genes in mice . The most prominent phenotype we observed in DSCAM+COL6A2 double transgenic mice was pronounced left ventricular hypertrophic cardiomyopathy , which is not typical of DS CHD [35] , [36] . The basis for this non-DS phenotype may be the same as those responsible for only a partial recapitulation of the DS phenotype such as level , timing , or pattern of transgene expression . The exact basis for this hypertrophic phenotype notwithstanding , it is highly penetrant in double transgenic mice . Since this phenotype , like ASD , is only observed in double transgenic mice and not in the single transgenic strains , this genetic interaction may be highly relevant to the etiology of various forms of cardiac hypertrophy such as those resulting from increased load [18] , [19] , since coordinate up-regulation of these genes that may occur spontaneously as a result of somatic mutation or epigenetic responses , may generate similar phenotypes in humans . Further analysis will be required to determine whether DSCAM and COL6A2 contribute to ASD typical of DS and whether levels of these two genes are jointly increased in patients with inherited or spontaneous forms of cardiac hypertrophy . Success in identifying DSCAM and COL6A2 as mediators of CHD phenotypes in these studies resulted from the combined use of three systems: comprehensive candidate testing in flies , validation of synergistic genetics interactions in mice and cell culture , and high resolution genetic mapping in humans . In addition to these genetic studies , we then employed cell based assays to investigate the underlying molecular mechanisms regulated by these gene products . For this initial study , we restricted our screen to genes that we suspected had a high chance of causing heart defects based on a priori assumptions including genetic analysis in human DS patients , expression in the developing heart , and the potential to physically interact in the extracellular environment . While these criteria naturally limit our analysis to a subset of possible contributing genes , they nonetheless lay the groundwork for future studies to analyze contributions of additional genes from the CHD candidate region . We suggest that application of this multi-tiered genetic strategy should be broadly applicable to other multigenic genetic diseases in which sorting though many genetic combinations is necessary to identify promising candidate loci underlying disease phenotypes . Examples of such multigenic disorders include the identification of interacting genes within loci defined by the HapMap initiative [5] , contiguous gene disorders associated with macroscopic duplication or deletion syndromes such as those underlying autism [37] , and potentially a large number of spontaneous as well as heritable conditions resulting from alterations in gene dose due to CNVs , which have been identified throughout the human genome [4] . These data also imply that extracellular proteins can exert potent effects on gene transcription programs as a component of their phenotypic effects , raising intriguing mechanistic questions for future investigation .
All fly stocks were raised at 25 °C . Full-length cDNAs were mis expressed in the heart or developing eye using the GAL4-UAS transactivation system [14] . Full-length mammalian cDNAs of SH3BGR ( NM_007341 ) , DSCAM ( NM_031174 ) , COL6A1 ( NM_001848 ) and COL6A2 ( NM_001849 ) have been described previously [10] . Drosophila full-length cDNA of SH3β ( CG8582 ) and COL18A1 ( CG33171 ) were obtained from Open Biosystems . The mammalian and Drosophila cDNAs were cloned into pUAS vector and injected to w1118 embryos . Several independent transgenic lines were generated by P-element-mediated germ line transformation technique for each pUAS construct . GMR-GAL4 Stock was obtained from Bloomington , Indiana . The GMH5-GAL4 line [15] was used to drive the expression of DS CHD candidate genes specifically to the myocardial cells in the heart . Heart rate , electrical pacing , semi intact heart preparation and movie analysis were conducted as described previously [15] , [16] . 250 µL of nonionic contrast was injected into the jugular vein over a period of 1 to 2 seconds and video images were acquired on half-inch super-VHS videotape at 30 frames per second under constant fluoroscopy with the XiScan 1000 C-arm x-ray system ( XiTec , Inc; 3-inch field of view , anterior-posterior , lateral and left anterior oblique projections ) . Later , the interlaced video images were edited and digitally processed off-line ( Silicon Graphics R10000 system , Motif 6 . 5 operating system ) with a resolution matrix of 512x512 pixels , 256 shades of gray , 60 fields per second [20] . Mice were sedated with isoflurane . The jugular vein was dissected and cannulated for the intravenous saline administration . Imaging of the heart was performed using an Echo ultrasound system . Image acquisition in the axial four-chamber views was begun just before injection of contrast and continued until contrast effect in the myocardium had dissipated [21] , [22] . Detection of contrast in the LA and LV within 1-2 heart beats following injection of contrast saline was an indication of an abnormal shunting . Occasionally contrast was detected in the LV after 6 or more heart beats due to residual contrast recirculation through the lungs , and was not considered an indication of shunting . High-resolution volumetric Computed Tomography ( CT ) of hearts was performed by Numira Biosciences ( Irvine , CA ) at 10-µm3 isometric voxel resolution using an eXplore Locus SP MicroCT specimen scanner ( GE Healthcare , London , Ontario , Canada ) . Visualization of sections was performed with MicroView Software ( GE healthcare ) and volume rendering was performed with OsiriX Medical Image software . Hearts were cut at the horizontal short-axis plane , fixed in 4% paraformaldehyde , embedded in OCT and sectioned . Frozen cryosections wild-type and double transgenic mice were stained with anti DSCAM ( N-16 ) ( Santa Cruz biotechnology ) or anti COL6A2 ( D20 ) ( Santa Cruz biotechnology ) , and anti Rabbit Alexa594 secondary ( Jackson immunochemicals ) , counter stained with Hoechst 33342 ( Invitrogen ) imaged with a Perkin Elmer UltraView Vox spinning disk confocal microscope . For myocytes size determination , 14 micron frozen cryosections were stained with Alexa 594 –conjugated wheat germ agglutinin ( Invitrogen ) , and the myocyte cross-sectional area was measured for assessment of cell size using NIH image J software . For staining of H9C2 and H9C2-DSCAM cells , cells were grown on 35 mm plates , fixed in 4% paraformaldehyde , permeabilized in PBS/Triton X100 0 . 1% , and stained with rabbit anti DSCAM ( a kind gift from Dr . Elke Stein , Yale University , CT ) , or mouse anti Myc ( 9E10 , Santa Cruz biotechnology ) primary antibodies , Alexa594 secondary antibodies ( Jackson immunochemicals ) , counter stained with Hoechst 33342 ( Invitrogen ) and imaged with a Zeiss Axioplan 2 fluorescence microscope . DSCAM was cloned in to an expression vector expressing a C-terminal myc tag and a puromycin resistance gene . The promoter used was a modified chicken beta actin promoter which can be expressed in a wide variety of tissues and cell types ( pCAGGS promoter ) . H9C2 cells were transfected and selection resistant clones were isolated . We chose a cell line that over-expressed DSCAM at the lowest level that we could still detect tagged DSCAM by western blotting . Cell adhesion was performed as described [38] . Briefly , 96 well plates were coated with 10 µg/ml COL6 ( Meridian life sciences , ME ) in PBS ( -Ca++ , -Mg++ ) overnight , washed twice with PBS ( -Ca++ , -Mg++ ) , and then incubated for 2 hours with 1% BSA/PBS ( -Ca++ , -Mg++ ) to block non-specific binding . In control ( BSA ) plates addition of collagen was omitted and plates were processed in parallel . 2 . 5×105 cells/ml H9C2 or H9C2-DSCAM cells were plated in serum free media on blocked 96 well plates in triplicate . At the indicated time points , wells were washed twice gently in serum free media , and attached cells were fixed in 10% neutral buffered formalin for 5 minutes . Cell number was determined by crystal violet staining , read at OD540 . The percent of cells bound was calculated relative to cells plated in non-blocked wells which displayed 100% adherence at the end point of the experiment . | A large fraction of human genes may contribute to polygenic disorders , yet few experimental methods for identifying such genes are currently available . For example , with regard to congenital heart defects ( CHD ) caused by extra copies of genes on chromosome 21 in Down syndrome patients , it is not known which genes contribute to this complex phenotype . In this paper , we identify two genes , DSCAM and COL6A2 that interact strongly to produce CHD when over-expressed at modest levels in the mouse heart . These two genes were identified as the most strongly interacting pair of CHD candidate genes when over-expressed in the Drosophila heart , where they disrupted several indices of heart function . We then over-expressed these genes in the mouse heart alone or in combination and found that while expression of either gene alone had little or no effect , co-expression of the genes , as in flies , lead to severe cooperative defects in heart physiology and morphology . The strategy we have followed in this study is broadly applicable to identifying genes involved in other polygenic disorders , such as obesity , autism , and schizophrenia , which have been linked to altered copy number of multiple genes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"medicine",
"genome",
"expression",
"analysis",
"cardiovascular",
"animal",
"models",
"model",
"organisms",
"chromosomal",
"disorders",
"biology",
"down",
"syndrome",
"congenital",
"heart",
"disease",
"clinical",
"genetics",
"genetic",
"screens",
"genetics",
"genomics",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics",
"human",
"genetics"
]
| 2011 | Over-Expression of DSCAM and COL6A2 Cooperatively Generates Congenital Heart Defects |
Infectious disease treatments , both pharmaceutical and vaccine , face three universal challenges: the difficulty of targeting treatments to high-risk ‘superspreader’ populations who drive the great majority of disease spread , behavioral barriers in the host population ( such as poor compliance and risk disinhibition ) , and the evolution of pathogen resistance . Here , we describe a proposed intervention that would overcome these challenges by capitalizing upon Therapeutic Interfering Particles ( TIPs ) that are engineered to replicate conditionally in the presence of the pathogen and spread between individuals — analogous to ‘transmissible immunization’ that occurs with live-attenuated vaccines ( but without the potential for reversion to virulence ) . Building on analyses of HIV field data from sub-Saharan Africa , we construct a multi-scale model , beginning at the single-cell level , to predict the effect of TIPs on individual patient viral loads and ultimately population-level disease prevalence . Our results show that a TIP , engineered with properties based on a recent HIV gene-therapy trial , could stably lower HIV/AIDS prevalence by ∼30-fold within 50 years and could complement current therapies . In contrast , optimistic antiretroviral therapy or vaccination campaigns alone could only lower HIV/AIDS prevalence by <2-fold over 50 years . The TIP's efficacy arises from its exploitation of the same risk factors as the pathogen , allowing it to autonomously penetrate superspreader populations , maintain efficacy despite behavioral disinhibition , and limit viral resistance . While demonstrated here for HIV , the TIP concept could apply broadly to many viral infectious diseases and would represent a new paradigm for disease control , away from pathogen eradication but toward robust disease suppression .
The TIP concept capitalizes upon and extends the phenomenon of interfering particles that occur naturally in many viruses , spread along with the viral pathogen [15] , and have demonstrated potential therapeutic efficacy against HIV [16] , [17] , [18] , [19] . TIPs are minimal versions of the pathogen engineered to lack the virulent replication and structural genes of the wild-type pathogen and instead encode therapeutic elements that target key host or viral processes . Since a TIP genome is significantly shorter than the wild-type virus genome , TIP genomes are synthesized at a faster rate , resulting in increased numbers of TIP genomes compared to wild-type virus genomes in the infected cell ( see Text S1 and [15] ) . Specifically , for HIV , the proposed TIP is a lentiviral gene-therapy vector that lacks all structural and envelope genes required to self-replicate , but retains HIV's genomic packaging signals . The TIP can mobilize out of the infected cell only by co-opting wild-type HIV capsid and envelope gene-products [16] . By parasitizing a pathogen's resources , TIPs mobilize from cell to cell [16] , [18] and , in a recent clinical trial , this mobilization of a gene-therapy vector against HIV did not appear to be detrimental to patient health [17] . Due to their ability to mobilize and reproduce within hosts , TIPs have the potential to decrease wild-type pathogen levels in vivo by many orders of magnitude [19] . By sharing all packaging elements with the wild-type pathogen , TIPs also have the potential to spread between individuals [20] , and would spread via the same transmission routes as the disease-causing pathogen . In this respect , combating an infectious disease using TIPs raises unique safety and ethical concerns but bears similarity to the use of live attenuated vaccines . In particular , a recognized advantage of Oral Polio Vaccine ( OPV ) is that it replicates in vivo and sheds , thereby transmitting among susceptible hosts and delivering additional protection via ‘transmissible immunization’ at the population scale [21] . There are , however , crucial differences between TIPs and live attenuated vaccines: ( i ) TIPs cannot replicate in uninfected hosts and , at most , the TIP will remain dormant until the host is coinfected by wildtype pathogen [22]; and ( ii ) replication elements are missing from the TIP , so , unlike OPV , TIP cannot revert to virulence in healthy individuals .
To test whether a TIP against HIV could autonomously target high-risk groups , and effectively reduce HIV prevalence , we build upon an established epidemiological model of HIV/AIDS transmission in sub-Saharan Africa that includes four classes of sexual risk behavior based on field data [12] . We develop a data-driven , three-scale model ( Figure 1 ) that translates molecular-level characteristics of the TIP to predict patient-level HIV viral load and ultimately predict HIV/AIDS incidence and prevalence at the population scale . At the single-cell level , the model considers the dynamics of competition between TIP genomic mRNA and HIV genomic mRNA for packaging components [23] . These molecular-level effects of the TIP are translated to viral loads using an established in vivo model of HIV dynamics [24] that includes TIP dynamics [19] . Measured relationships between viral load and transmission [25] are used to estimate TIP and HIV transmission rates between individuals , and the rate of disease progression is estimated based on field data of HIV viral load [26] ( see Text S1 ) . For ART , the model assumes an optimistic ‘test-and-treat’ deployment [27] where 75% of all infections in both high-risk and low-risk populations are treated with regimens that stop 99% of all HIV transmission [27] . Our test-and-treat model differs from some previous projections [27] , [28] by incorporating two additional behavioral factors described in real populations [29]: ( i ) ART failure or dropout rates that have been measured in sub-Saharan African populations [30] , [31] , [32]; ( ii ) population risk structure . While our model predicts smaller benefits from test-and-treat programs than some earlier work [27] , [28] , the results are consistent with previous ART projections that have incorporated risk structure [12] , [33] . For the vaccine , the model assumes optimistic immunization coverage ( 80% or 95% coverage ) of both high-risk and low-risk populations and considers a vaccine that is 30% protective , slightly higher than reported in the recent ‘Thai trial’ [34] , or a hypothetical 50% protective vaccine; life-long efficacy is assumed for both vaccines ( i . e . no HIV mutational escape ) but not for the TIP . For the TIPs , we analyze interventions that generate a 0 . 5-Log to 1 . 5-Log viral-load reduction in vivo , as reported in a recent HIV gene-therapy trial [17] . The model predicts the effects of vaccination or TIP intervention on HIV/AIDS prevalence in a resource-poor sub-Saharan setting . Strikingly , TIP intervention reduces disease prevalence and incidence more effectively than either widespread ART or a 30% or 50% protective vaccine against HIV/AIDS ( Figure 2a–b ) . The least effective TIP analyzed—which reduces HIV in vivo viral load by 0 . 5-Log ( from 105 to 104 . 5 copies/mL ) —leads to a reduction in HIV/AIDS prevalence from 29% to 6 . 5% in 50 years , despite initial deployment to only 1% of individuals while a TIP that generates a 1 . 5-Log decrease in HIV viral-load—as transiently achieved in a Phase-I clinical trial for an HIV gene-therapy [17]—would reduce HIV/AIDS prevalence from 29% to below 1% prevalence in 30 years ( Figure 2a ) . In comparison , a 30% protective vaccine deployed to 80% of the entire population ( including 8 out of 10 uninfected high-risk individuals ) reduces HIV/AIDS prevalence from 29% to 23 . 9% in 50 years and a 50% protective vaccine deployed to 95% of the entire population ( including virtually all uninfected high-risk individuals ) reduces HIV/AIDS prevalence from 29% to 18 . 7% in 50 years . ART to treat 75% of all new infections would reduce disease prevalence to a level between a 30% and 50% protective vaccine . A striking short-term impact of TIP intervention on HIV incidence , as compared to vaccines and ART , is also projected ( Figure 2b ) despite extremely rapid rollout of vaccines and ART ( Figure S1 in Text S1 ) . Similar results are obtained when comparing TIP intervention to vaccination and ART in terms of either the fraction-of-individuals-living-with-AIDS or AIDS incidence ( Figure S2 in Text S1 ) . Thus , TIPs constructed using parameters recently reported in Phase-I trials [17] , and given to a small fraction of the population ( 1% ) , have the potential to swiftly and substantially reduce disease burden at the population level . This efficacy and robustness of TIP intervention arises from the unique and defining ability of TIPs to transmit between hosts . Analysis of TIPs that generate a 0 . 5–1 . 5 Log decrease in viral load , but do not transmit between hosts , shows only a minimal decrease in population-level disease burden ( Figure S3 in Text S1 ) —in agreement with the projected impact of acyclovir treatment which also generates a ∼0 . 5 Log decrease in HIV viral load [33] . Accordingly , we have paid particular attention to ensuring that our results are robust with respect to changes in basic model assumptions about transmission biology and robust under parameter sensitivity analysis ( see Text S1 ) . We also consider two competing models of HIV transmission biology—infection by either a single ‘founder’ virus that enters the new host individual or ‘bottlenecking’ where multiple viruses enter and replicate locally but are then winnowed down by competition within the host [35] , [36] , [37]—and we provide arguments that our treatment of TIP transmission is consistent with either transmission mode and that TIPs could transmit efficiently in either case ( see Text S1 section entitled: “Considerations for TIP transmission to uninfected hosts” ) . To be completely sure that our model results are robust to changes in assumptions about TIP transmission , we repeated the simulations under the worst-case assumption that TIPs are completely unable to transmit in the absence of HIV , and found results that are qualitatively unchanged from Figure 2 ( see Text S1 section: “Sensitivity of model to removal of independent transmission of TIPs ( i . e . removal of ST individuals ) ” ) . This somewhat surprising result arises because TIPs autonomously target the highest-risk groups , which are highly likely to be already infected with HIV due to their high-risk status , and thus the majority of the TIP infection ‘flow’ occurs through the already infected individuals . In summary , while there is physiological basis to support that TIPs could transmit efficiently to HIV-uninfected persons , the efficacy of TIP intervention is largely independent of this assumption ( i . e . TIPs need not convert susceptible individuals into ‘TIP carriers’ for population-level efficacy to be retained ) . These results are not intended to argue that ART campaigns be abandoned or vaccine trials be halted . On the contrary , as we show below , the TIP's ability to target high-risk groups allows the TIP to complement ART ( or vaccine ) campaigns and significantly enhance the population-level efficacy of these approaches . Current prevention and treatment approaches also face the challenges of poor compliance and behavioral disinhibition , wherein successful disease control leads to a reduced sense of personal risk from the disease and can result in increases in risk behavior . Disinhibition is a significant concern for current HIV prevention and control [38] and has the potential to generate the perverse outcome that a successful therapeutic may actually increase HIV incidence [39] . The transmissibility and single-dose administration of TIPs effectively circumvent these problems , unlike current pharmaceutical approaches ( i . e . ART ) or vaccination . Indeed , the public health benefits of TIPs are uniquely robust to disinhibition , since the intervention spreads more effectively if contact rates increase ( Figure 3a ) . In contrast , the same degree of disinhibition in the presence of ART or a 30% or 50% protective vaccine could have the unfortunate effect of increasing HIV/AIDS prevalence and could increase the number of deaths due to AIDS ( Figure 3b ) , as highlighted by previous analyses [39] . Any intervention against HIV is likely to be administered in the context of the existing ‘standard of care’: ART . Since ART halts HIV transmission , ART would also halt TIP transmission from an individual , leading to the potential that the TIP intervention could be severely hampered . However , the TIP's ability to concentrate in highest-risk groups ( see next paragraph ) , where ART is at best the target coverage fraction ( e . g . 75% ) , would allow TIP intervention to maintain efficacy , reduce HIV/AIDS disease prevalence , and reduce AIDS deaths , more effectively than ART alone , even under optimistic coverage scenarios for ART campaigns ( Figure 3c–d ) . Thus , ART would not interfere with TIP intervention at the population scale , and TIPs could be used as a powerful complement to ART and pharmaceutical treatments in general . The increased efficacy of TIPs relative to vaccination is due to the TIP's transmissibility along the same transmission routes as the pathogen . Consequently , the TIP transmits to a specific risk group in proportion to that group's risk behavior , leading to more focused targeting of TIPs in more heterogeneous populations and resulting in TIPs concentrating in the highest-risk populations ( Figure 4a ) . In contrast , reaching high-risk classes with ART depends upon active and sustained targeting of these rare high-risk individuals , while partially-protective vaccines tend to concentrate in the lowest risk classes ( because higher-risk individuals still become HIV-infected , given partially protective vaccines ) and lack the ability to dynamically redistribute between risk classes ( Figure 4b ) . Vaccines and drug treatment strategies also face the challenge of mutation and the strong selective pressure for the pathogen to escape any successful control . For HIV , rapid mutation leads to resistance against anti-retroviral therapy and poses significant challenges for vaccine development [40] . However , unlike conventional therapies , TIPs replicate with the same speed and mutation rate as the pathogen , which sets up an evolutionary arms race between the TIP and the pathogen . To examine how HIV might respond in such an arms race resulting from TIP intervention , we consider the multi-scale dynamics across a range of parameter values for the molecular-level properties used to design a TIP . Specifically , we consider the interplay of HIV and TIP levels as a function of both the strength of TIP-encoded inhibition of HIV and the engineered TIP genomic abundance within a dually infected cell . For HIV , the TIP design encodes an inherent evolutionary tradeoff that generates conflicting selection pressures at different scales ( Figure 5 ) . On the one hand , inhibition of HIV replication by TIP-encoded therapy genes inevitably limits TIP production—since any TIP-encoded antiviral that inhibits HIV will compromise the TIP's ability to mobilize . However , due to the diploid nature of retroviral genomes , high concentrations of TIP genomic mRNA alone will inhibit HIV production by wasting the majority of HIV genomes in virions containing one HIV RNA and one TIP RNA , and these heterozygous-diploid virions are not viable [18] , [23] . Thus , the lowest TIP-mediated inhibition generates the highest production of TIPs from an infected cell ( Figure 5a ) . The increased numbers of TIP virions then compete more effectively against HIV for target cells which generates a greater reduction in HIV viral-load at the patient-level ( Figure 5b ) , and the lowest HIV/AIDS prevalence in the population ( Figure 5c ) . These results suggest a non-intuitive design criterion for a TIP against HIV: TIPs lacking an inhibitory factor for HIV will be most effective in reducing HIV levels , both in individual patients and at the population level . Similarly , the cellular-scale selective pressure for HIV to escape from TIP-encoded inhibition would point in the same direction ( toward zero TIP inhibitory effect ) and would lead to increased TIP production ( Figure 5a ) . So , counter-intuitively , HIV escape from TIP-mediated inhibition ( at the molecular scale within cells ) would reduce HIV viral load and HIV population prevalence to lower levels ( Figure 5b–c ) . The TIP approach carries unique safety concerns [41] and ethical concerns associated with introducing an intervention that transmits and evolves , even in the TIP's limited fashion , within the population . Importantly , clear ethical precedents for transmissible therapies exist in the use of live-attenuated vaccines . Regarding safety , one major concern is that the TIP may recombine with ( i . e . acquire ) an element that ‘upregulates’ pathogen production and in turn upregulates its own production from the cell . To explore this concern , we examine HIV viral load and population prevalence in the regime where TIP encodes HIV inhibition and in the regime where TIP encodes potential upregulation of HIV gene expression within a single cell . ( Figure 6a ) . As expected , at the single-cell level upregulation of HIV generates increased HIV and TIP production . However , at the individual patient level upregulation of HIV leads to increased TIP viral loads ( Figure 6a , inset ) which actually generate even lower HIV viral loads ( Figure 6a ) and HIV population prevalence ( Figure 6b ) . Interestingly , at the population level , there is an optimal value of TIP-encoded inhibition , which yields a maximum in TIP prevalence ( Figure 6b , inset ) . Thus , the TIP appears to be subject to competing selection pressures at multiple scales which may limit the potential for evolutionary breakdown of TIP therapies , echoing recent proposals for antivirals that resist viral escape [42] and ‘evolution-proof’ malaria insecticides [43] . Detailed experimental and theoretical study is required to predict the ultimate direction of TIP evolution , but the competing selection pressures may effectively constrain TIP phenotypes to a range that assures low HIV viral load and low HIV disease prevalence . TIP evolution is likely to be dominated by mutational processes , since recombination between TIPs and wild-type HIV appears to be severely limited by fundamental sequence-homology constraints on retroviral recombination [44] that render recombination between full-length 9 . 7 kb HIV genomes and shorter lentiviral genomes ( e . g . TIP ) non-competent for integration [18] . This molecular argument against recombination between HIV and TIP is also supported by data from murine models [45] , [46] and the recent human clinical trial data [17] , neither of which detected recombination between wild-type HIV-1 and shorter lentiviral therapy vectors . To fully address safety , there is obviously a need for cautious trials in vitro , and in vivo , before a TIP intervention could ever be considered for implementation . Importantly , TIPs for HIV would not specifically target , or require , stem cells since the TIP would target the same cells as HIV ( primarily CD4+ T lymphocytes ) and thus oncogenic concerns as a result of insertional mutagenesis [47] are minimized . This argument is supported by a recent Phase-I lentiviral gene-therapy clinical trial for HIV [17] and previous gene therapy in peripheral blood lymphocytes in patients followed since 1995 [48] , neither of which detected insertional mutagenesis or oncogenic transformation in patients . As with all models , our analysis is a relatively simple representation of a complex system and necessarily makes certain assumptions . Importantly , the TIP's robustness and efficacy stems from the unique and defining ability of TIPs to transmit between hosts and , as such , the general results presented for the TIP are qualitatively robust to changes in parameter values or in basic model assumptions about transmission biology ( see Text S1 ) . TIP efficacy also appears qualitatively robust to decreases in transmission efficiency as a result of widespread ART coverage ( Figure 3c–d ) or re-parameterization of transmission functions ( see Text S1 ) . Nevertheless , our analysis is intended as a first step towards motivating research into transmissible therapies , rather than a proof of efficacy . The molecular , epidemiological , and ethical bases of using TIP intervention against pathogens will require extensive study , but our results show that TIPs may offer a unique strategy for targeting both high-risk and hard-to-reach populations , overcoming behavioral barriers , and circumventing mutational escape to achieve indefinite disease suppression of HIV , and possibly other pathogens , in resource-limited settings . As an added benefit for intervention in resource-limited settings , TIPs may have the potential to be administered as a therapy requiring only a single dose , thereby allowing for increased treatment access and minimizing treatment compliance issues . Our results shows that deploying TIPs as a therapy to even a few individuals who are already infected can reduce the prevalence of a disease to very low levels . Due to the rapid and sustained transmission dynamics in high-risk groups , the impact of TIP intervention is robust even if the TIP is quickly cleared from TIP ‘carriers’ so that these individuals rapidly revert back to ‘susceptibles’ ( see Text S1 ) . With the ability to enter proviral latency , dormant TIPs could be complementary to ART on an individual scale , by reactivating during ART failure and acting to reduce viral load . While recent models argue that widespread ART campaigns alone could halt the HIV/AIDS pandemic [27] , [28] , there remains significant controversy as to whether ART can succeed in reducing overall HIV transmission [12] , [29] , especially in the presence of high-risk groups exhibiting treatment non-compliance . Significant challenges to achieving widespread ART coverage in resource-limited settings include: slower-than-hoped rollout , persistent logistical problems linked to insufficient health systems and weak infrastructure , the need for on-going high-level donor funding , and the social stigmas that prevent people from getting tested and hence starting treatment . These factors will likely produce long-term heterogeneity in coverage , with the most impoverished and disadvantaged groups receiving the least access to ART . Based on these challenges , it is prudent to consider alternative and complementary approaches .
The multi-scale analysis of TIPs and HIV-1 is built upon previous data-driven models [12] , [19] and is composed of three constituent ordinary differential equation models describing dynamics at different hierarchical scales: ( i ) among a population of host individuals ( ‘population level’ ) ( ii ) within host individuals ( ‘individual patient’ ) ( iii ) within infected host cells ( ‘intracellular’ ) . The multi-scale model specifies mechanistic links between each scale and the next scale of organizational complexity ( intracellular → in vivo → population level ) . The population-level TIP model is a simplified version of a risk-structured model constructed from UNAIDS field-data collected from antenatal clinics in Malawi [12] , which includes a risk-structure formulation with four distinct sexual-activity classes ( SACs ) and which we refer to as the ‘Baggaley model’ . Individuals are classified as susceptible ( S ) , HIV infected ( I ) , susceptible to HIV but infected with TIP ( St ) , dually infected with HIV and TIP ( Id ) , as an AIDS patient with wild-type virus ( Aw ) , or as a dually infected AIDS patient ( Ad ) . Individuals in all disease-states are divided into SACs in accordance with field data ( indicated by subscript i ) , except that all individuals in the Aw class are assumed ( as in [12] ) to have sexual contacts at the rate corresponding to the lowest risk group ( SAC 4 ) owing to their poor health . The model equations are as follows: Parameter definitions , values , and corresponding references are shown in Table S1 in Text S1 . The transmission probabilities per partnership are denoted where Y represents the disease state of the source of the infection , and X represents the viral strain ( which is wild-type HIV , denoted X = W , for the vaccine model ) . The per-partnership transmission probability ( describing transmission of wild-type HIV by individuals in the I disease state ) is set to agree with the weighted average of the Baggaley model [12] and is the per-partnership probability of wild-type HIV infection originating from an AIDS patient , and is set following the Baggaley model [12] . Consideration of alternative parameterizations of the viral load transmission curve did not qualitatively affect the results ( see Text S1 ) . The parameters and are static parameters that represent the transmission probability and the duration of the asymptomatic phase of individuals infected with only wild-type virus . In contrast , to describe quantities that depend on the specific design of the TIP , such as: ( i ) transmission probabilities , and ( ii ) the duration of the asymptomatic period , functions are used in place of parameters . These functions are calculated based on measured correlations between transmission , disease progression , and viral load [25] , [26] where viral load is predicted from the in vivo TIP model ( see Text S1 ) . For example , the transmission probabilities in the presence of TIP and the duration of the asymptomatic phase in dually-infected individuals in the TIP population models are represented by functions of steady-state viral load ( i . e . viral set point ) as predicted by the in vivo model ( see Table S3 in Text S1 for a description of the transmission-probability functions ) . The function is used to compute the duration of the asymptomatic phase in dually-infected individuals , and is also calculated in Text S1 . Contacts between individuals in the TIP population model are weighted by statistically independent transmission probabilities ( β ) which are calculated from steady-state HIV and TIP viral loads from the in vivo model ( see Text S1 section: ‘Calculation of Transmission-Rate Function’ ) . There are six distinct transitions between infection classes in the TIP population model ( see Table S3 in Text S1 for details ) . Briefly , contact between two individuals is represented by a contact function that considers asymmetric mixing of individuals among the four SACs: This contact function describes an individual in disease state X ( and SAC i ) becoming infected by an individual in disease state Y . The subscript j denotes SAC j , cj is the average number of sexual partners per year in SAC j , and Nj is the sum of all sexually active individuals in SAC j . In the contact function , is the degree of assortative mixing with corresponding to entirely assortative mixing and corresponding to entirely random mixing . The first term inside the brackets of the contact function describes assortative mixing in which infected individuals are encountered in proportion to their prevalence in SAC i . The second term describes random contacts in which infected individuals are encountered in proportion to their contribution to all of the sexual contacts being made in the entire population . We set the mixing parameter ε equal to 0 . 37 , as estimated in [12] . Simulation of the TIP population model is conducted as follows: the Baggaley model is allowed to reach steady-state and then a TIP is introduced to 1% of all individuals without any targeting to high-risk classes . Similar benefits were obtained using much more restrictive initial conditions ( e . g . utilizing TIP as a therapy and targeting TIP to <1% of only I and Aw individuals in the least active SACs—SAC 3 and SAC 4—generates similar results to Figure 2 ) . Behavioral disinhibition is simulated as in [49] by increasing the contact rates c for all SACs and number of AIDS deaths averted by the vaccination campaign is defined as: AIDS deaths averted = ( AIDS deaths during 100 years of epidemic without treatment ) – ( AIDS deaths during a 50 year epidemic followed by 50 years of treatment ) . Vaccine and ART models use the same risk structure as above and are presented in Text S1 . A complete list of model parameters and state variables are presented in Tables S1 , S2 , S3 , S4 , S5 , S6 , and S7 in Text S1 . All numerical simulations were performed in Mathematica 7 . 0 . | We introduce a proposed intervention against infectious diseases that extends and optimizes the recognized benefit of ‘transmissible immunization’ that occurs with live-attenuated vaccines such as Oral Polio Vaccine ( OPV ) , the vaccine chosen for the worldwide polio eradication campaign . The intervention proposed here is based upon Therapeutic Interfering Particles ( TIPs ) that are engineered to replicate only in the presence of the wildtype pathogen and act to inhibit the growth of the pathogen . Therefore TIPs ‘piggyback’ on the pathogen , leading to two important differences from live-attenuated vaccines: TIPs can only transmit from individuals already infected with wildtype pathogen , and TIPs could only revert to virulence in individuals already carrying the wild-type pathogen . Intriguingly , because TIPs spread between individuals using the same transmission routes as the pathogen , they automatically find their way to the populations at greatest risk of infection , thus circumventing the unsolved problem of how to identify superspreaders and target them for preventive measures . Based on clinical-trial data , we analyze the impact that TIP intervention would have on HIV/AIDS in sub-Saharan Africa and show that TIPs could lower HIV/AIDS prevalence more effectively than vaccines or drugs alone and , in fact , would effectively complement these other interventions . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
]
| [
"immunodeficiency",
"viruses",
"viruslike",
"particles",
"population",
"modeling",
"evolutionary",
"modeling",
"virology",
"synthetic",
"biology",
"biology",
"computational",
"biology",
"infectious",
"disease",
"modeling",
"microbiology",
"molecular",
"biology"
]
| 2011 | Autonomous Targeting of Infectious Superspreaders Using Engineered Transmissible Therapies |
The Sonic hedgehog ( Shh ) signaling pathway is crucial for pattern formation in early central nervous system development . By systematically analyzing high-throughput in situ hybridization data of E11 . 5 mouse brain , we found that Shh and its receptor Ptch1 define two adjacent mutually exclusive gene expression domains: Shh+Ptch1− and Shh−Ptch1+ . These two domains are associated respectively with Foxa2 and Gata3 , two transcription factors that play key roles in specifying them . Gata3 ChIP-seq experiments and RNA-seq assays on Gata3-knockdown cells revealed that Gata3 up-regulates the genes that are enriched in the Shh−Ptch1+ domain . Important Gata3 targets include Slit2 and Slit3 , which are involved in the process of axon guidance , as well as Slc18a1 , Th and Qdpr , which are associated with neurotransmitter synthesis and release . By contrast , Foxa2 both up-regulates the genes expressed in the Shh+Ptch1− domain and down-regulates the genes characteristic of the Shh−Ptch1+ domain . From these and other data , we were able to reconstruct a gene regulatory network governing both domains . Our work provides the first genome-wide characterization of the gene regulatory network involved in the Shh pathway that underlies pattern formation in the early mouse brain .
Pattern formation in early animal development is controlled by signal transduction cascades , in which transcription factors ( TFs ) play crucially important roles as downstream effectors . The signal transduction cascades together with the gene regulatory networks they activate determine the temporal and spatial expression of a wide range of genes for the specification of regions and differentiation of cells [1] . Sonic hedgehog ( Shh ) is a classical signal molecule required for pattern formation in many aspects of animal development , not least in neural development . In the central nervous system ( CNS ) , depending on the graded Shh concentration along the dorsal-ventral axis in the mouse ventral neural tube , particular TFs are activated in different regions , resulting in specification of these regions [2]–[5] . The Shh signaling pathway is itself activated when Shh binds to its receptor Ptch1 , which , without the ligand , inhibits the cell membrane protein Smo . Shh binding removes the inhibition on Smo and triggers the activation of three GLI family TFs , Gli1 , Gli2 and Gli3 , which further activate or inhibit specific TFs to determine regional cell fate . Identifying those downstream TFs and how they work is a central task in the elucidation of early CNS development . Several recent studies using high-throughput in situ hybridization ( ISH ) have provided a rich harvest of information on spatio-temporal gene expression in early mouse development . The data are available in databases such as GenePaint [6] , Eurexpress [7] and Allen Brain Atlas ( ABA ) [8] . GenePaint and Eurexpress have focused on whole mouse embryos at the E14 . 5 stage and covering almost the entire set of known mouse genes . In contrast , ABA ( http://developingmouse . brain-map . org ) recently offered manually annotated ISH data for the developing mouse brain from three developmental stages: E11 . 5 , E13 . 5 and E15 . 5 . It includes information about expression intensity , density and pattern for more than 2000 genes , many of which are TFs and key genes in early brain development . Because ISH data contain high-resolution spatial information on gene expression , they are invaluable for in-depth study of gene regulation in pattern formation during early development . For example , Visel et al . showed that it is possible to identify the probable targets of Pax6 , a key TF in early mouse brain , by the clustering of co-expressed genes using E14 . 5 ISH data [9] . However , co-expression of genes does not guarantee that they are directly co-regulated by the same TF . Furthermore , developmental genes in animals are often regulated by a combination of TFs acting through cis-regulatory modules [10] . Therefore , high-throughput ISH data has to be integrated with direct gene regulatory data such as genome-wide ChIP-seq data to delineate the specific regulatory mechanisms underlying particular developmental processes . Such an approach would be very useful in elucidating the genetic networks involved in early brain development . Gata3 and Foxa2 are two key TFs implicated in early animal development , including early brain development . Gata3 is a member of the GATA family , consisting of Gata1-6 , among which only Gata2 and Gata3 have been reported to be expressed in the CNS [11] . Mice homozygous for a Gata3 null mutation were found to have serious malformations of the embryonic brain , revealing its essentiality for that stage [12] . Furthermore , continuous expression of Gata3 in the brain from early embryo to adulthood suggests that it is important for the maintenance of brain functions beyond early development [13] . Recent genome-wide studies of Gata3 have mainly focused on the molecular mechanisms underlying its critical roles in T cells [14] and breast cancer [15] . In breast cancer , Gata3 has been shown to function as a “pioneer factor” to help open up condensed chromatin and recruit other TFs . However , no genome-wide study on Gata3 in the CNS has been conducted so far . Foxa family TFs including Foxa1 , Foxa2 and Foxa3 are involved in development , organogenesis [16] and metabolism [17] . Similarly to Gata3 , there is increasing evidence that they also play crucial roles as pioneer factors [18] . Unlike Gata3 , however , the role of Foxa2 in brain development has been better studied . Notochord-secreting Shh required for patterning of the neural tube fails to form when Foxa2 is mutated , hindering the entire subsequent developmental process [19] . Genome-wide ChIP-seq analysis of Foxa2 targets in midbrain dopaminergic neuron ( mDA ) progenitors revealed that Foxa2 directly regulates key genes in the Shh signaling pathway and that Foxa2 promotes gene expression in the floor plate while repressing the genes normally expressed and required in the ventro-lateral region of midbrain [20] . In this study , we investigated gene regulation in the Shh signal transduction cascade in early developing mouse brain , focusing on Gata3 and Foxa2 as two putative key TFs . We have found that they demarcate two mutually exclusive domains in the early mouse brain coinciding with two domains defined by the reciprocal expression patterns of Shh and its receptor Ptch1 . These will be designated as the Shh+Ptch1− and Shh−Ptch1+ domains . To understand the molecular functions of Gata3 in the early mouse brain , we used PC12 cell line established from rat adrenal medulla pheochromocytoma to mimic the Gata3-expressed domain in early mouse brain . We performed Gata3 ChIP-seq in PC12 cells and RNA-seq experiments in Gata3 siRNA knockdown cells . We found that Gata3 target genes that are down-regulated by Gata3 siRNA knockdown were enriched in the Gata3-expressed domain . By contrast , Foxa2 target genes were enriched in both Foxa2- and Gata3-expressed domains . These results suggested that the fates of the two domains were controlled by distinct regulatory mechanisms directed by Gata3 and Foxa2 . The interaction between these two domains was transmitted via the Shh signaling pathway . In addition , we identified , amongst Gata3 target genes , Slit2 and Slit3 , which are involved in axon guidance , as well as Slc18a1 , Th and Qdpr , which function in neurotransmitter synthesis and release . From these findings and ChIP-seq data , we were able to reconstruct a gene regulatory network for the genes in Shh+Ptch1− and Shh−Ptch1+ domains . Our study expands current knowledge of the Shh pathway and sheds new light on the gene regulatory mechanisms controlling cell fates in the early mouse brain .
We used ISH data of developing mouse brain at E11 . 5 stage from the ABA database . The data consists of more than 2000 genes manually annotated by experts for the ISH image series . Gene expression properties were characterized by utilizing three metrics: intensity ( Undetected , Low , Medium and High ) , density ( Undetected , Low , Medium and High ) and pattern ( Undetected , Full , Regional and Gradient ) . In our study , to convert the textual annotation to numerical data , we used intensity as the metric and treated “Undetected” as 0 and “Low , Medium and High” as 1 for our downstream analysis . Based on this gene expression data of E11 . 5 mouse brain , we observed that the expression patterns of Shh and its receptor Ptch1 were obviously stratified along the ventral-dorsal axis . From this observation , we defined the Shh+Ptch1− domain as containing 20 brain sub-regions in the anatomical map provided by ABA , and the Shh−Ptch1+ domain , which we found contains 30 brain sub-regions ( Figure S1 ) . Next , Fisher's exact test was used to identify genes expressed exclusively in the Shh+Ptch1− domain ( P value <0 . 0001 , odds ratio >1 , expressed in more than 10 Shh+Ptch1− sub-regions ) and those in the Shh−Ptch1+ domain ( P value <0 . 0001 , odds ratio <1 , expressed in more than 15 Shh−Ptch1+ sub-regions ) . These two sets were accordingly defined , respectively , as Shh+Ptch1−-pattern genes and Shh−Ptch1+-pattern genes . A heatmap containing these two types of genes was generated by the R program ( http://www . r-project . org ) and is shown in supplementary material Figure S2 . The gene annotations and repeat-masked genome sequences for six mammalian species including human , marmoset , mouse , rat , cow , pig were downloaded from ENSEMBL ( version 62 ) . Promoter sequences defined as the region upstream 1000 bp to downstream 200 bp from transcriptional start site ( TSS ) were extracted using Perl Script from each species . For each mouse gene , we obtained their orthologous gene information in the other five mammalian species using ENSEMBL homologs data ( version 62 ) . Promoter analysis was performed based on Pscan program [21] , by which we can obtain the enriched transcription factor ( TF ) binding motifs in each set of promoters of orthologous genes . The relationships between TF binding motifs and TFs were obtained from TRANSFAC [22] . In this study , TF motif-target relationships were determined by selecting TF motifs with the criteria that enrichment P value less than 0 . 005 and the rank is at least top 20 . Motif enrichment in the promoter sequences of Shh+Ptch1−- and Shh−Ptch1+-pattern genes were performed using Pscan solely on mouse genes . Enriched TF groups were selected with P values less than 0 . 005 . The functional analysis of gene sets based on gene ontology ( GO ) resources was performed using GOToolBox program ( http://genome . crg . es/GOToolBox ) with “Mouse Genome Informatics ( MGI ) ” and “Rat Genome Database ( RGD ) ” as the corresponding annotations respectively for distinct sets of genes . Results are supplied in Table S1 . The statistical significance of enrichment between gene group of interest and background gene group was calculated by applying the one-sided Fisher's exact test . PC12 cells were plated on a Poly-L-lysine-coated dishes ( Corning ) and maintained in DMEM/F12 ( Invitrogen ) with 5% FBS ( Biochrom ) , 5% horse serum ( Gibco ) and 1% penicillin/streptomycin at 37°C in 5% CO2 . ChIP assays were carried out using materials from PC12 cells and performed as described previously [23] . Briefly , cells were cross-linked with formaldehyde and sonicated to generate chromatin fragments size-enriched to between 200–600 bp . Antibody against GATA3 ( 558686 , BD Pharmingen™ ) was used . Chromatin from 20 million cells was used for each ChIP experiment , which yielded approximately 10 ng of DNA . As input , 2% of sonicated chromatin was treated with proteinase K at 50°C for 2 hr and purified using the QIAquick PCR Purification Kit ( Qiagen Cat # 28106 ) . Both input DNA and ChIP DNA fragments were blunt-ended , ligated to the Illumina adaptors , and sequenced with the Illumina Hiseq 2000 . Sequencing reads of ChIP-seq were mapped to the rat genome ( Baylor 3 . 4/rn4 ) using Bowtie ( version 1 . 0 . 0 ) [24] , with the setting that sequence alignments can have no more than 3 mismatches . Then MACS ( Model-based Analysis of ChIP-seq; version 1 . 4 . 2 ) [25] was used to identify Gata3 binding regions and peak summits which were further annotated by using CEAS [26] . Two tools in Cistrome were deployed to calculate the correlation coefficient for our biological replicates and the PhastCons scores [27] . De novo motif analysis was performed using MEME-ChIP version 4 . 9 . 0 [28] after masking query sequences using RepeatMasker ( http://www . repeatmasker . org/ ) . A gene was defined to be the target gene containing a binding site if this site is located between 10 kb upstream of transcription start site ( TSS ) and 3 kb downstream of transcription end site ( TES ) of this gene with the exception that the binding site on Th was found when we extended its promoter region to17 , 491 bp upstream of TSS . The MACS output file about binding sites , together with the associated target genes , is provided in Table S4 . We used two different custom-made siRNAs against Gata3 . siGATA3-1 ( sense 5′-GUACUACAAACUCCACAAUTT-3′ and antisense 5′-AUUGUGGAGUUU GUAGUACTT-3′ ) , siGATA3-2 ( sense 5′-CCGUAAGAUGUCUAGCAAATT-3′ and antisense 5′-UUUGCUAGACAUCUUACGGTT3′ ) and negative control ( sense 5′-UUCUCCGAACGUGUCACGUTT-3′ and antisense 5′-ACGUGAC ACGUUCGGAGAATT ) were obtained from GenePharma ( Shanghai ) . All siRNA experiments were conducted at a final concentration of 50 nM . Transfections were conducted using Lipofectamine RNAiMAX ( Invitrogen ) . Total RNA was isolated from cells using Trizol ( Invitrogen ) . Purified mRNA was used to prepare the cDNA library as per the manufacturer's instructions . The short cDNA fragments were ligated to the Illumina sequencing adaptors and sequenced with the Illumina Hiseq 2000 . Total RNA was isolated from cells to synthesize cDNA with SuperScript II Reverse Transcriptase ( Invitrogen ) . qRT-PCR amplification mixtures ( 20 µl ) contained 3 µl water , 1 µM forward and reverse primer , 10 µl LightCycler 480 DNA SYBR Green I Master Mix buffer and 5 µl template cDNA . All reactions were run on LightCycler 480 ( Roche ) . All sequencing reads of RNA-seq were mapped to the rat genome using TopHat with default settings ( http://tophat . cbcb . umd . edu/; version 2 . 0 . 7 ) [29] . The output data were analyzed by Cuffdiff to identify differentially expressed genes [30] . The results were filtered by the criteria: “status” = OK and “P value”<0 . 05 . Our ChIP-seq and RNA-seq data were submitted to ArrayExpress database with accession number: E-MTAB-2008 . The original CEL files of GSE42565 from the Shh stimulation experiment [31] and GSE15942 performed in PC12 cells [32] were downloaded from Gene Expression Omnibus ( GEO ) . The method “RMA” from R package “affy” was used to normalize the raw data . For GSE42565 , student's t-test was used to identify differentially expressed genes responding to the Shh stimulation . 1677 genes were selected as the downstream genes of Shh on the basis that they had a P value <0 . 05 .
In this study , the expression patterns of 2074 genes , manually annotated based on ISH images for 78 regions in E11 . 5 mouse brain , were downloaded from Allen Brain Atlas . We converted the textual annotation of ISH data to binary gene expression data of 0 and 1 ( Materials and Methods ) . We observed that the genes coding for key signaling molecules , such as Fgf8 expressed in 9 regions , Shh in 31regions , Notch2 in 24 regions , Bmp1 in 12 regions and Bmp4 in 3 regions as well as critical developmental genes including En1 in 25 regions , En2 in 12 regions , Hes3 in 3 regions and Otx2 in 34 regions , showed restricted expression patterns at the E11 . 5 stage . In particular , we found that the gene expression patterns of Shh and its receptor Ptch1 were clearly segregated along the ventral-dorsal axis , especially in the regions from midbrain to hindbrain . Shh was highly expressed in the ventral brain region while Ptch1 was expressed just above the Shh-expressed region . Shh occupied the entire floor plate while Ptch1 occupied the whole alar plate and most of the basal plate ( Figure 1A and 1B ) . We used the expression patterns of Shh and Ptch1 to define two adjacent non-overlapping brain domains: a Shh+Ptch1− domain where Shh is expressed but Ptch1 is not expressed and a Shh−Ptch1+ domain , defined by the reciprocal pattern , where Shh is not expressed but Ptch1 is expressed ( Figure S1 ) . Next , to identify the factors controlling the specification of these two domains , we searched for the genes specifically expressed in the Shh+Ptch1− and Shh−Ptch1+ domains respectively . We identified 45 Shh+Ptch1−-pattern genes and 337 Shh−Ptch1+-pattern genes using Fisher's exact test ( P<0 . 0001 ) ( Figure S2 ) . We then cross-compared these two groups of genes with the genes specifically expressed in midbrain floor plate ( FP ) and ventral-lateral region ( VL ) of neural tissues obtained from an independent microarray study [33] . We found that the Shh+Ptch1−-pattern genes were significantly enriched among the FP genes while the Shh−Ptch1+-pattern genes were enriched among the VL genes ( Figure S3A and S3B ) . The consistency between the two datasets supported our method of defining the Shh+Ptch1−-pattern and Shh−Ptch1+-pattern genes based on ISH data . Gene ontology ( GO ) enrichment analysis revealed that biological processes of system development , anatomical structure development and regulation of transcription were significantly enriched in both Shh+Ptch1−-pattern and Shh−Ptch1+-pattern genes , which highlighted their importance in early brain development ( Table S1 , Figure S3C and S3D ) . To discover the potential transcriptional regulators for Shh+Ptch1−- and Shh−Ptch1+-pattern genes , we conducted promoter analysis for these two groups of genes . Motif enrichment analysis showed that known TF binding motifs for GATA and GLI family TFs were significantly enriched in the promoters of Shh−Ptch1+-pattern genes ( p = 5 . 95e–05 for GATA motif , p = 6 . 82e–06 for GLI motif ) but not in Shh+Ptch1−-pattern genes ( Table S2 ) , indicating the importance of these two families of TFs in controlling the specification of the Shh−Ptch1+ domain . Interestingly , GLI family TFs Gli1 and Gli2 , the downstream transducers of the Shh signaling pathway , are expressed in the Shh−Ptch1+ domain but not in the Shh+Ptch1− domain . Furthermore , our promoter analysis predicted that both Gli1 and Gli2 directly target the GATA family member TF Gata3 , which is known to be a pioneer factor and strictly expressed in the Shh−Ptch1+ domain . Therefore , it is likely that Shh secreted in the Shh+Ptch1− domain diffuses to the neighboring Shh−Ptch1+domain to exert its influence via the transcriptional activation of Gata3 . In other words , Gata3 may determine the specification of the Shh−Ptch1+ domain via the Shh signaling pathway . In contrast , in the Shh+Ptch1− domain , among all of the eight Shh+Ptch1−-pattern TFs annotated by ABA , Foxa1 and Foxa2 have been shown to function as master regulators to specify the identity of ventral midbrain progenitor cells by regulating Shh signaling [34] . As shown in a published Foxa2 ChIP-seq dataset [20] , Twenty four out of the total of 45 identified Shh+Ptch1−-pattern genes , including Shh , were targeted by Foxa2 . There is evidence that Shh is activated by Foxa2 while its downstream effectors , Ptch1 , Gli1 , Gli2 and Gli3 are all repressed by Foxa2 [20] , [34] . This would explain the absence of Ptch1 , Gli1 , Gli2 and Gli3 expression in the Shh+Ptch1− domain and suggests that Foxa2 plays a key role in determining the fate of the ventral Shh+Ptch1− domain . These observations led us to propose that the Shh signaling pathway affects the pattern formation of the Shh+Ptch1− and the Shh−Ptch1+ domains in E11 . 5 mouse brain along the ventral-dorsal axis via the mutually exclusive expression of Foxa2 and Gata3 . In total , we found 8 and 147 TF genes in the Shh+Ptch1−-pattern and Shh−Ptch1+-pattern , respectively . Amongst the Shh−Ptch1+-pattern TFs , critical developmental genes such as Pax6 , Pax3 , Lhx1 , Irx3 , Isl , Ascl1 and Gata3 were found . For these two groups of TFs , we were able to predict their regulatory targets within the two domain patterns by promoter analysis . Some known regulatory relationships , such as Foxa1 targeting Foxa2 and Gli1/2 targeting Ptch1 , were correctly recapitulated by our promoter analysis [16] . Five out of eight predicted targets of Foxa2 including Nfib , Aff3 , Foxa2 , Foxq1 and Nfia were supported by the Foxa2 ChIP-seq data [20] . Notably , Foxa2 ChIP-seq data showed that Foxa2 targetsGata3 and our promoter analysis predicted that Gata3 targets Foxa2 . Together with the non-overlapping expression patterns of Foxa2 and Gata3 ( Figure 1C and 1D ) , it seems to suggest a potential mutual inhibitory relationship between Foxa2 and Gata3 . The role of Foxa2 in regulating the expression of Shh and other genes expressed in the Shh+Ptch1− domain has been previously characterized [20] . Here , however , we investigated the functional role of Gata3 in mediating the Shh signaling pathway in the Shh−Ptch1+domain . To this end , we sought a proper cell line that can mimic the gene expression pattern of this domain . We therefore analyzed the expression of Shh−Ptch1+-pattern genes in published microarray data available in Gene Expression Omnibus ( GEO ) for neuron-like cell lines , including PC12 , neuro2a and N1E cells . Shh−Ptch1+-pattern genes , when compared to other genes annotated by ABA , were only found to be significantly enriched among the highly expressed genes of the PC12 cells ( Fisher's exact test , P value = 0 . 00007 ) but not in neuro2a and N1E cells . In particular , according to the microarray data as well as our Real-time PCR assay , Gata3 has high expression in PC12 cells while Foxa2 is not expressed ( Table S3 ) . PC12 cells are able to synthesize noradrenaline [35] and have the properties of neurons in that their exposure to Neuron Growth Factor ( NGF ) causes them to stop dividing and begin to grow neurites similar to those of sympathetic neurons . This neuron-like character makes this cell line a versatile model system for researches in neurobiology and neurochemistry [35] . Therefore , we selected PC12 cells to perform ChIP-seq [36] analysis for Gata3 and specifically to identify its target genes . Our ChIP-seq experiments included two biological replicates for ChIP and input materials respectively . The high correlation ( Pearson's r = 0 . 97 ) between the two ChIP replicates suggested that our ChIP experiments were highly reproducible . After mapping all sequencing reads to the rat genome ( rn4 ) , we used the MACS program for peak calling , which yielded 1296 peaks with a default P value cutoff ( Table S4 ) . De novo motif analysis of these binding regions by MEME-ChIP revealed a significantly enriched Gata3 motif ( Figure 2A ) . The elevated average phastcon scores around the center of Gata3 binding sites suggested that Gata3 binding sites were more conserved compared with the neighboring regions , an indication of functional binding sites ( Figure 2B ) . We used the CEAS program to examine the distribution of Gata3 binding sites across the genome . We found that Gata3 binding sites were significantly enriched in the promoter regions with respect to the whole genome , i . e . 4 . 1% of ChIP regions fell within 1000 bp , 7 . 2% within 3000 bp and 13 . 6% within 10000 bp upstream of the transcription start site ( TSS ) of different genes . Furthermore , 32 . 7% of Gata3 binding sites were located in the gene bodies compared to 26 . 3% in the genome background . Among the binding sites in the gene bodies , 30 . 6% were within introns , 0 . 2% within the 3′UTRs and 0 . 7% within the 5′UTRs ( Figure 2C ) . Using the gene annotation data of the rat rn4 genome downloaded from UCSC , we obtained 683 Gata3 target genes in PC12 cells ( Table S4 ) . GO analysis showed that these Gata3 targets were involved in biological processes such as nervous system development , cell differentiation , and cell maturation ( Figure 2D ) . While Foxa2 targets were significantly enriched in genes in both the Shh+Ptch1−- and Shh−Ptch1+-patterns , Gata3 targets identified in our study were only enriched in Shh−Ptch1+-pattern genes but not in Shh+Ptch1−-pattern genes , indicating that Gata3 mainly influences the Shh−Ptch1+domain ( Figure 3A and 3B ) . The genes of eight Shh−Ptch1+-pattern TFs , including Abl1 , Cebpe , Gata2 , Isl2 , Myt1l , Nfib , Pou2f2 and Sox12 , were targeted by Gata3 , as shown by our ChIP-seq experiment . Among Gata3 targets identified by ChIP-seq , we found two known regulators of the Shh signaling pathway , Sufu and Gsk3b ( Figure 4A ) . Previous studies have shown that Sufu negatively regulates Shh signaling by direct interaction with Gli1 protein [37] and that Sufu is involved in Gli3 phosphorylation mediated by Gsk3b to induce the repression of Shh downstream genes [38] . To further investigate the involvement of Gata3 in the Shh signaling pathway , we systematically searched for Shh downstream genes by analyzing a published microarray dataset ( GSE42565 ) on Shh stimulation in in vitro neural progenitors [31] . We found 74 Gata3 ChIP-seq target genes among the downstream genes of Shh . Among them , 45 genes including Slit2 and Slit3 were up-regulated by Shh stimulation ( Figure 4B ) and 29 genes were down-regulated by Shh ( Table S5 ) . Sixteen out of the 45 Gata3 target genes up-regulated by Shh were annotated by ABA in E11 . 5 ISH data . Eight of them were Shh−Ptch1+-pattern genes including Cotl1 , Foxn3 , Klhl29 , Limk1 , Mapt , Myt1l , Nfasc and Scg3 ( Figure 4C ) . Nfasc is well-known as a cell adhesion molecule important for cell-cell communication and neurite outgrowth . Nfasc also influences cell differentiation and maintenance in the brain but the signaling pathways upstream of Nfasc in the nervous system are unclear [39] . Two of the genes in this set , Mapt and Limk1 , are known to be essential for brain development . A Mapt mutation is associated with neurodegenerative disorders such as Alzheimer disease [40] , while the brain-specific Limk1 is implicated in axonal elongation [41] . Our study indicated that the specification of these genes in the Shh−Ptch1+ domain is likely due to Gata3 regulation in the Shh signaling pathway . Gata3 is known to control the synthesis of noradrenaline and serotonin [42] . The ISH data for Th , Ddc , and Dbh , which are involved in noradrenaline synthesis , and Tph2 , which is involved in serotonin synthesis , support the idea that the Shh−Ptch1+domain includes brain regions that eventually develop into noradrenergic and serotonergic neurons . Previous work has shown that a mutation in Gata3 reduced Th expression [43] . In our ChIP-seq experiments , we found Gata3 binding sites located in the potential promoter region of Th ( Figure 4D ) . Furthermore , we found that Gata3 regulated two other neurotransmitter-associated genes , Qdpr and Slc18a1 ( Figure 4D ) . Qdpr is an enzyme involved in biosynthesis of tetrahydrobiopterin biosynthesis , which functions as a coenzyme in the reaction converting tyrosine to L-DOPA catalyzed by Th . L-DOPA can further lead to the formation of neurotransmitters including dopamine , noradrenaline , and adrenaline . Slc18a1 is a vesicular transporter that transports neurotransmitters including dopamine , noradrenaline , adrenaline and serotonin into synaptic vesicles and which thus plays an important role in neurotransmitter release . Functional disruption of Slc18a1 leads to neuropsychiatric diseases resulting from disorders of the corresponding neurotransmitter systems [44] . Our discovery that Gata3 targets the promoters of Qdpr and Slc18a1 further supports Gata3's important role in neurotransmitter synthesis and release . To further uncover the functional roles of Gata3 , we applied siRNAs to knockdown Gata3 in PC12 cells . RNA-seq was performed in Gata3 siRNA-knockdown PC12 cells . Comparing our RNA-seq data with a published microarray data in wild-type PC12 cells ( GSE accession: GSE15942 ) showed that they are highly correlated ( Spearman's Rho = 0 . 77 , P value <2 . 2e–16 ) . The gene expression values of two independent Gata3-knockdown samples with knockdown efficiencies of 50% and 51% respectively were also highly correlated ( Pearson's r = 0 . 997; P value <2 . 2e–16 ) . We integrated these results and obtained 1 , 121 differentially expressed genes compared to wild-type PC12 cells . Among them , 731 that were down-regulated by Gata3-knockdown , including Gata3 itself , were enriched in Shh−Ptch1+-pattern genes . By contrast , 390 up-regulated genes were not enriched in either the Shh−Ptch1+ or Shh+Ptch1− patterns ( Table S6 , Figure 3C and 3D ) . This result supports our hypothesis that Gata3 preferentially up-regulates Shh−Ptch1+-pattern genes . We then integrated the results of Gata3 ChIP-seq and Gata3 knockdown RNA-seq . Seventy seven ( 77 ) differentially expressed genes in RNA-seq assays were directly targeted by Gata3 . The RNA-seq analysis revealed that Slc18a1 was up-regulated after Gata3 knockdown . Notably , the expression of two Gata3-targeted genes from ChIP-seq , Slit2 and Slit3 ( Figure 4B ) , together with Robo1 , were all down-regulated after Gata3-knockdown . Other identified genes were further validated by our Real-time PCR analysis ( Table S3 ) . SLIT/ROBO , functioning as a ligand/receptor signaling system , is involved in axon guidance and neuronal migration in the CNS . Its special function in regulating axons to project across the midline has attracted a lot of attention [45] . Furthermore , recent studies have suggested that the Slit2/Robo1 signaling might be enlisted for treating glioma because it can inhibit glioma cell migration [46] . Earlier microarray analysis of Shh-induced expression also suggested that Slit2/3 were downstream genes of the Shh pathway . Altogether , our results demonstrate that the SLIT/ROBO system was activated by Shh through the direct regulation of Gata3 in the Shh−Ptch1+ domain . We next reconstructed a gene regulatory network downstream of the Shh signaling pathway in early mouse brain . We downloaded all suitable ChIP-seq data from Gene Expression Omnibus ( GEO ) database or published papers [47]–[50] for Shh+Ptch1−-pattern TFs , including Foxa2 , Foxp1 , Phf19 , and Shh−Ptch1+-pattern TFs including Gli1 , Gata2 , Pbx1 , Sox11 and Ctnnb1 ( Table S7 ) . Except for Foxp1 whose target genes were directly obtained from the original paper , as the raw data were not available , we downloaded the ChIP-seq data for all other TFs and annotated the target genes using the same procedure as our own Gata3 ChIP-seq data analysis . In this network , only Shh+Ptch1−- and Shh−Ptch1+-pattern genes , as identified from our ISH data analysis , were included as potential target genes of the TFs . The regulatory relationships between TFs and target genes were based on the result of ChIP-seq data . Considering that many TFs can have both positive and negative regulatory functions , the TFs in one domain may target genes in the other domain as well . The complete gene regulatory network is illustrated in Figure S4A . As shown in this network , Gata3 was targeted by TFs Foxa2 and Phf19 . Since the expression of Gata3 is mutually exclusive with Foxa2 and Phf19 , we propose that Gata3 is negatively regulated by Foxa2 and Phfl9 . Similarly to Foxa2 , Phf19 is expressed only in the floor plate of the entire hindbrain at the E11 . 5 stage . Studies showed that Phf19 , a subunit of the polycomb repressor complex 2 ( PRC2 ) , has essential functions in cellular differentiation and embryonic development , in binding to H3K36me3 and being associated with H3k36me3 histone demethylase NO66 , thereby mediating transcriptional silencing [49] , [51] . We also found that Gli2 , Ptch1 and Foxa2 are all targeted by Ctnnb1 while Foxa2 targets Ctnnb1 . Ctnnb1 encodes β-catenin , the signal transducer for the Wnt signaling pathway that is involved in early brain development [52] . Our analysis suggests that Gli2 , Ptch1 and Foxa2 are downstream of this signaling pathway , reflecting its crosstalk with the Shh signaling pathway [53] . Using the MCODE program , we identified two gene regulatory modules in our network ( Figure S4B and S4C ) . In the first module , Dnmt3a , Nfasc and Mytl1 are co-regulated by both Gata3 and Pbx1 ( Figure S4B ) . Pbx1 is expressed throughout the entire alar plate and basal plate in the E11 . 5 mouse brain . It has been reported that embryos died at day E15/16 when Pbx1 was deleted , with developmental defects in multiple organs [54] . In the second module , both Phf19 and Foxa2 target a total of 12 known genes that are enriched in the Shh−Ptch1+ domain , including Ptch1 , while Foxa2 is under the regulation of Phf19 and Foxa2 itself ( Figure S4C ) . Furthermore , Pax3 in the Shh−Ptch1+ domain is targeted by Foxa2 in the Shh+Ptch1− domain , consistent with an earlier study showing that Pax3 inhibits the differentiation of the floor plate while Foxa2 itself activates its specification [55] . Previous studies have shown that Foxa2 positively regulates Shh+Ptch1−- pattern genes including Shh , Foxa1 and Ferd3l while negatively regulating Shh−Ptch1+-pattern genes , including Ptch1 , Gli1 and Gli2 [20] . Our study found that the targets of Foxa2 were not only enriched in the Shh+Ptch1− domain but also in the Shh−Ptch1+ domain ( Figure 3A ) . In particular , Foxa2 and Gata3 shared nine common target genes in the Shh−Ptch1+domain: Abl1 , Cadm1 , Cotl1 , Enc1 , Foxn3 , Isl2 , Myt1l , Nfib and Sox12 . Therefore , Foxa2 not only up-regulates Shh+Ptch1−- pattern genes but also down-regulates Shh−Ptch1+-pattern genes and thereby antagonizes the effect of Gata3 . This model is illustrated in Figure 5 .
In this study , we integrated high-throughput ISH data , published microarray and ChIP-seq data , and our own experimental data , to investigate the gene regulatory circuit underlying the classical Shh signaling pathway in pattern formation of the E11 . 5 mouse brain . Based on our analysis , we propose that the fates of the adjacent Shh+Ptch1− and Shh−Ptch1+ domains are determined by distinct sets of TFs that are up- or down-stream of the Shh signaling pathway . Among them , two pioneer factors , Gata3 and Foxa2 , seem to play the key roles . Foxa2 up-regulates Shh+Ptch1−-pattern genes but down-regulates Shh−Ptch1+-pattern genes , while Gata3 up-regulates Shh−Ptch1+-pattern genes but has no obvious influence on Shh+Ptch1−-pattern genes . In our proposed model , the gene coding for Shh is activated by Foxa2 so that Shh is secreted in the Shh+Ptch1− domain . However , the downstream effectors of Shh such as Gli1/2 are not activated in that domain due to inhibition by Foxa2 . As Shh diffuses into the neighboring Shh−Ptch1+ domain , Gata3 is activated either directly by GLI family TFs or indirectly through Nkx2 . 2 , as previously suggested [56] ( Figure 5 ) . Lending support to this hypothesis , it has been shown that the expression of Gata3 is increased upon Shh stimulation in 3T3-L1 cells [57] . Here we have reported that Gata3 can then turn on the gene regulatory programs to specify cell fates in the Shh−Ptch1+domain . During early mouse development , Foxa2 is already expressed in the CNS at least from E8 . 0 [58] , one day earlier than the onset of Gata3 expression [11] . This temporal order is consistent with our model that Foxa2 is upstream of Gata3 in the Shh signaling cascade . Recently , Shu et al . proposed a “seesaw” model in which counter-acting specifiers of two different lineages balance each other to maintain an undifferentiated cell state [59] . Loss of that balance leads to differentiation into one of the lineages . For example , Gata3 , which is involved in mesendodermal specification , antagonizes Gmnn in ectodermal specification to induce pluripotency and facilitate reprogramming . In our study , we found an antagonizing relationship between Foxa2 and Gata3 and their competing roles in cell fate specification in their mutually exclusive domains: Shh+Ptch1− and Shh−Ptch1+ domains in early brain development . Therefore , the spatial patterning along dorsal-ventral axis in early mouse brain can be due to the symmetry breaking of the balance between two counteracting forces represented by Foxa2 and Gata3 . However , we think that Foxa2 and Gata3 are unlikely to be the only important specifiers for these two domains , as our reconstruction of the gene regulatory network implicated the involvement of a cohort of additional TFs . The detailed roles of these TFs and their relationships await future investigation , by both in vivo and in vitro experiments . Currently , high-throughput ChIP-seq experiments to obtain direct regulatory interactions of TFs and their targets have been most conveniently conducted in in vitro systems . Typically , a ChIP-seq experiment requires around 10 million cells . This poses a technical challenge for conducting such ChIP experiments directly on the spatially restricted brain regions in embryos . To mimic midbrain dopaminergic neurons in the floor plate of the early mouse brain , Metzakopian et al . used the progenitors of midbrain dopaminergic neurons , formed by in vitro differentiation of NestinLmx1a-transfected ES cells , in their Foxa2 ChIP-seq experiments [20] . In our study , we used neuron-like PC12 cells as a surrogate for the Shh−Ptch1+ domain because of their high expression of Gata3 and ability to synthesize noradrenaline [35] . Our ChIP-seq experiments in PC12 cells uncovered new Gata3 target genes involved in neurotransmitter synthesis and release as well as axon guidance . Gata3 is also known for participating in the specification of serotonergic and noradrenergic neurons originated from the Shh−Ptch1+ domain . Our result showing that Gata3 targets Slc18a1 and Qdpr adds new evidence for a central role of Gata3 in the development of serotonergic and noradrenergic neurons . Furthermore , we found that Gata3 regulates the SLIT/ROBO system . Interestingly , Metzakopian et al . have also found that Foxa2 regulates Slit2 and Slit3 in the floor plate region , analogous to the Shh+Ptch1− domain in our study [20] . Our study thus sheds new lights on the potential parallel function of Gata3 to that of Foxa2 in axon guidance and neuron migration in the Shh−Ptch1+ domain [60] . Nevertheless , future in vivo experiments on embryonic Shh−Ptch1+ domain are necessary to validate our result in PC12 cells . In addition to Shh , the genes coding for other signaling molecules such as Fgf and Bmp also show restricted spatial expression in ISH data . The combined expression patterns of these signaling molecules determine the specification of key neurons in the brain [42] . Using the same strategy as the one employed in this study , it should be possible to further delineate the gene regulatory networks controlled by these signaling pathways for distinct brain regions . Our study is the first example to systematically utilize high throughput ISH data to generate a new hypothesis of early brain development . This approach thus promises to be valuable in future work designed to unravel the molecular mechanisms that underlie spatial and temporal patterning during early animal development . | Recent large-scale projects of high-throughput in situ hybridization ( ISH ) have generated a wealth of spatiotemporal information on gene expression patterns in the early mouse brain . We have developed a computational approach that combines publicly available high-throughput ISH data with our own experimental data to investigate gene regulation , involving signal molecules and transcription factors ( TFs ) , during early brain development . The analysis indicates that two key TFs , Foxa2 and Gata3 , play antagonizing roles in the formation of two mutually exclusive domains established by the Sonic hedgehog signaling pathway in the developing mouse brain . Further ChIP-seq and RNA-seq experiments support this hypothesis and have identified novel target genes of Gata3 , including the axon guidance regulators Slit2 and Slit3 as well as three neurotransmitter-associated genes , Slc18a1 , Th and Qdpr . The findings have allowed us to reconstruct the gene regulatory network brought into play by the Sonic hedgehog pathway that mediates early mouse brain development . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"developmental",
"biology",
"biology",
"and",
"life",
"sciences",
"gene",
"regulatory",
"networks",
"computational",
"biology"
]
| 2014 | Reconstruction of the Gene Regulatory Network Involved in the Sonic Hedgehog Pathway with a Potential Role in Early Development of the Mouse Brain |
During mismatch repair ( MMR ) MSH proteins bind to mismatches that form as the result of DNA replication errors and recruit MLH factors such as Mlh1-Pms1 to initiate excision and repair steps . Previously , we identified a negative epistatic interaction involving naturally occurring polymorphisms in the MLH1 and PMS1 genes of baker’s yeast . Here we hypothesize that a mutagenic state resulting from this negative epistatic interaction increases the likelihood of obtaining beneficial mutations that can promote adaptation to stress conditions . We tested this by stressing yeast strains bearing mutagenic ( incompatible ) and non-mutagenic ( compatible ) mismatch repair genotypes . Our data show that incompatible populations adapted more rapidly and without an apparent fitness cost to high salt stress . The fitness advantage of incompatible populations was rapid but disappeared over time . The fitness gains in both compatible and incompatible strains were due primarily to mutations in PMR1 that appeared earlier in incompatible evolving populations . These data demonstrate a rapid and reversible role ( by mating ) for genetic incompatibilities in accelerating adaptation in eukaryotes . They also provide an approach to link experimental studies to observational population genomics .
DNA mismatch repair ( MMR ) acts primarily during DNA replication in prokaryotes and eukaryotes to correct DNA polymerase misincorporation errors that include base substitutions , frameshift mutations , and insertions/deletions [1–3] . In S . cerevisiae MutS homolog ( MSH ) heterodimers can track with the replication fork to recognize and bind to DNA mismatches [4 , 5] . MSH-marked repair sites are recognized primarily by the MutL homolog ( MLH ) heterodimer Mlh1-Pms1 . The resulting ternary complex interacts with downstream excision factors such as Exo1 to remove the newly replicated DNA strand where the misincorporation event had occurred . Defects in MMR result in the accumulation of deleterious mutations and an overall loss in fitness ( e . g . [6] ) . Interestingly , studies in microbes have shown that the mutation rate per base pair is inversely proportional to genome size , and that changes from the wild-type rate are selected against [7–10] . However , approximately 10% of natural E . coli isolates display a mutator phenotype with 1–3% displaying defects in the MMR pathway [11–12] . The finding that a high mutation rate is typically selected against , but that some bacterial isolates can be observed in populations that are mutators , suggests that mutators may play an important role in adaptive evolution [11 , 13–20] . One explanation for this observation is that mutators have an increased likelihood of acquiring the first adaptive mutations within a population . However , such a strategy is not sustainable due to the accumulation of deleterious mutations that ultimately outweigh beneficial mutations . Bacteria appear to solve this problem through horizontal transfer; mismatch repair genes are exchanged between genomes at higher than average rates , which is likely due to the hyper-recombination phenotypes exhibited by MMR-deficient strains [14] . Do eukaryotes also regulate MMR functions to adapt to new selective pressures ? Previously Thompson et al . [21] showed that diploid baker’s yeast lacking the MSH2 MMR gene display an adaptive advantage when competed against diploid non-mutators . However , this advantage was not seen in haploids . Previously we hypothesized that MMR function could be modulated in eukaryotes through negative epistatic interactions [22] . This hypothesis was based on experiments in which we mated two S . cerevisiae strains , S288C and SK1 , which show 0 . 7% sequence divergence , and identified one MLH genotype , S288c MLH1-SK1 PMS1 , that conferred mutation rates 100-fold higher than wild type in an assay in which mlh1 and pms1 null strains display a 10 , 000-fold higher rate [22] . The S288c MLH1-SK1 PMS1 combination was defined as ‘incompatible’ , while the other three combinations , which did not display a mutator phenotype , were labeled ‘compatible’ . A single nucleotide polymorphism ( SNP ) in PMS1 combined with a single SNP in MLH1 were primarily responsible for the incompatibility [22] . Dobzhansky and Muller proposed a model to explain how hybrid incompatibilities can arise without causing defects within parental strains or species [23–26] . As described previously [22 , 27] , the evolution of the S288c MLH1-SK1 PMS1 MMR incompatibility ( Fig 1 ) fits this model . Mating of S288C and SK1 , followed by sporulation and segregation of gene variants within progeny , creates an S288c MLH1-SK1 PMS1 genotype that shows negative epistasis ( Fig 1; [27] ) . Such negative epistasis is similar to the interactions thought to underlie hybrid incompatibility between established [28–32] or incipient species [33] . DNA sequence analyses of natural and laboratory yeast strains indicated that S288c and SK1 strains have mated naturally [22] . This finding suggests that incompatible combinations were likely to have been created in nature but were not maintained due to losses in fitness associated with defects in MMR ( e . g . [3 , 22 , 27] ) . We hypothesize that negative epistasis involving MMR gene variants provides a transient advantage critical for adaptive evolution . To test this idea we constructed isogenic compatible and incompatible MLH1-PMS1 strains and subjected them to adaptive evolution in high salt . We found that incompatible populations adapted more rapidly to high salt than compatible strains without displaying an apparent fitness cost . Furthermore , we show that mutations in PMR1 were causative for high salt resistance in incompatible populations . Interestingly , mutations in this same gene , PMR1 , subsequently arose in compatible populations though at a slower rate . Together these observations demonstrate an experimentally validated role for genetic incompatibilities in accelerating adaptation to environmental challenges in eukaryotes .
We tested if the negative epistasis phenotype seen in yeast bearing the S288c MLH1-SK1 PMS1 genotype confers an adaptive advantage during stress . This study was initiated by constructing isogenic compatible and incompatible MLH1-PMS1 strains that displayed , prior to adaptation , similar fitness levels in YPD and YPD + 1 . 2 M NaCl media as measured in growth and competition assays ( Materials and Methods; S1 Table and S1 Fig ) . We assessed the mutator phenotype of compatible and incompatible strains using the lys2-A14 reversion assay . Compared to SK1 MLH1-SK1 PMS1 compatible strains , S288c MLH1-SK1 PMS1 incompatible strains showed increased reversion rates similar to that seen in previously constructed incompatible strains ( 100 to 120-fold higher than S288c MLH1-S288c PMS1; S2 Table; [22] ) . Compatible and incompatible lines were analyzed for adaptation to high salt conditions by growing them in YPD media containing 1 . 2 M NaCl as described in the Materials and Methods . In YPD media both compatible and incompatible lines completed 8–9 generations per transfer . In YPD + 1 . 2 M NaCl media both compatible and incompatible lines completed ~5 . 5 generations after the first transfer . A steady rise in the number of generations completed , from ~6 . 0 to ~6 . 8 , was seen after Transfers 2 to 20 . Cultures obtained after 7 ( ~50 generations ) , 10 ( ~70 generations ) , and 16 ( ~120 generations ) transfers showed the maximal fitness advantage gained by incompatible lines . Growth rate was determined by measuring the OD600 of cultures every two hours following dilution into YPD + 1 . 2 M NaCl . Pair-wise competition experiments were performed by mixing equal amounts of cells obtained from randomly chosen incompatible and compatible cultures . The proportion of cells in each culture was determined at T = 0 and T = 24 hrs following mixing ( Materials and Methods ) . We assessed the growth of isogenic compatible and incompatible lines in YPD and YPD + 1 . 2 M NaCl . These lines could be distinguished from each other in experimental cultures because they contained different antibiotic resistance markers ( KANMX , resistance to G418 , and NATMX , resistance to nourseothricin ) linked to the MLH1 locus ( S1 Table ) . The markers could be switched between compatible and incompatible strains without an effect on fitness in growth and competition assays , indicating that the antibiotic markers did not confer a selective advantage . As shown in Fig 2 , incompatible lines displayed a faster growth rate in YPD + 1 . 2 M NaCl after Transfer 7 ( ~50 generations ) . This advantage was more apparent after Transfer 10 ( ~70 generations; p < 0 . 0001 , n = 25 ) , but was not seen after Transfer 16 ( ~120 generations; p >0 . 05; n = 8 ) . In addition to direct growth measurements , we measured fitness in competition assays in which cells from randomly chosen compatible and incompatible evolved lines were mixed together at an approximately 1:1 ratio . These competitions involved cells adapted in YPD + 1 . 2 M NaCl that had undergone the same number of transfers . After mixing , the lines were grown in YPD or YPD + 1 . 2 M NaCl for 24 hrs ( seven generations ) and the proportion of each type was determined ( Materials and Methods ) . As shown in Table 1 and S2 Fig , neither compatible nor incompatible lines showed a competitive advantage in YPD media . In YPD + 1 . 2 M NaCl media , neither of the two lines showed a competitive advantage after Transfer 7 . However , incompatible lines displayed a competitive advantage in this media after Transfer 10 ( p = 0 . 0031 ) , with an average fitness advantage of 16% over compatible lines ( Table 1 ) . This advantage was lost after Transfer 16 ( p = 0 . 39 ) . Together , these data indicate that a MMR incompatibility generated by recombination involving naturally occurring variants in PMS1 and MLH1 can lead to an elevated rate of occurrence of adaptive mutations , and thus accelerate adaptation in a eukaryote . How can we explain the temporal rise in fitness advantage seen in incompatible versus compatible lines ? The most straightforward explanation is that the supply of mutations in the incompatible lines is higher than in the compatible lines , thus providing a greater likelihood for obtaining beneficial mutations that reach a high enough frequency to be selected and maintained in a population ( e . g . [20 , 21 , 37] ) . The mutation supply available is a function of the mutation rate and the population size ( N ) . To test this idea , we lowered the mutation supply by reducing the number of cells ( and thus population size ) per transfer in YPD-1 . 2 M NaCl by ten-fold to ~2 x 106 cells per transfer . We then performed cell growth and competition assays . In this experiment we were unable to observe a statistically significant advantage ( p> 0 . 05 ) in fitness for the incompatible strains even though the number of generations completed , 70 to 75 after Transfer 10 , were similar . In this experiment w = 0 . 99 +/-0 . 04 ( SEM , n = 4 ) in YPD , and w = 0 . 96 +/- 0 . 02 ( SEM , n = 4 ) in YPD-1 . 2 M NaCl ( see also S3 Fig ) . These observations thus support the premise that mutation supply is critical to achieve the fitness advantage seen in incompatible strains . Why was a fitness advantage in high salt seen in incompatible strains at Transfer 10 but not at Transfer 16 ? One possibility is that compatible populations adapt more slowly due to a lower mutation supply , but eventually obtain beneficial mutations that are selected for and maintained in the population . Alternatively , and/or in conjunction , incompatible populations accumulate a greater number of deleterious mutations and lose fitness over time . As shown in Table 1 and S2 Fig , the fitness of compatible and incompatible cultures was similar in YPD media even after 16 transfers , when the fitness advantage for incompatible lines in YPD + 1 . 2 M NaCl media was no longer apparent . Together these observations and the mutation supply experiments presented above and S3 Fig indicate that the speed by which compatible and incompatible populations adapt is dependent on the mutation supply rate , which is higher in the incompatible strains . Are mutant alleles of the same genes responsible for salt resistance in incompatible and compatible populations ? We answered this question by isolating salt-resistant clones ( one per line ) from independent incompatible and compatible lines . NaCl resistance in evolved strains can be easily phenotyped on YPD + NaCl plates because they grow to larger colony sizes relative to unevolved strains ( Fig 3B , left panel ) . To determine the complexity of the NaCl resistant phenotype , we mated these clones ( primarily from transfer 10 ) to unevolved strains of the opposite mating type ( Fig 3 ) to form diploids . While most ( five of eight tested ) of the diploid strains were sensitive to NaCl , indicating recessive transmission , three of the eight displayed a semi-dominant phenotype ( example in Fig 3B , left panel ) . Four diploids created by mating evolved and unevolved strains were then sporulated and phenotyped for salt resistance . Interestingly , all four strains displayed primarily a 2 NaClr:2 NaCls segregation phenotype on YPD + NaCl plates , indicating that a single locus in the evolved strain was causative . At least 18 NaClr and 18 NaCls spore clones derived from each of the four matings were pooled separately and subsequently analyzed by whole-genome sequencing using a bulk segregation strategy ( Materials and Methods ) . As shown in Table 2 , only one to three mutations were identified in each of the four clones . Interestingly , in all four clones only one locus , PMR1 , displayed strong linkage , as measured in sequence read counts , to the NaClr phenotype ( p < 10−5 for all linkages to PMR1 ) . As described in further detail below , PMR1 encodes a membrane-bound P-type Ca2+ dependent ATPase involved in transporting Mn2+ and Ca2+ into the Golgi [38] . In subsequent paragraphs we describe a detailed analysis of pmr1 mutants identified in evolved cultures , with the goal of explaining the genetic basis of adaptation to a defined stress , in this case , high salt . We sequenced in total 37 clones obtained from independent compatible or incompatible lines grown in YPD +1 . 2 M NaCl . Twelve of these were subjected to whole genome sequencing . For 25 clones Sanger sequencing was performed on the PCR-amplified PMR1 locus . As shown in Table 3 and Fig 4 , 21 different mutations in PMR1 were identified in the 37 clones that mapped to the predicted cytoplasmic domain of Pmr1 . While most mutations resulted in amino-acid substitutions in the 950 amino acid Pmr1 protein sequence , two involved start codon disruptions , and one was a frameshift mutation predicted to disrupt the reading frame beginning at amino acid 220 . The following observations suggested that pmr1 mutations conferred strong adaptive advantages earlier in incompatible populations due to a higher mutation supply . 1 . Almost all of the evolved incompatible clones isolated from evolved lines that completed 10 ( twelve of fourteen ) or 16 transfers ( seven of eight ) contained pmr1 mutations . 2 . Only two of ten such clones from compatible lines after Transfer 10 contained a pmr1 mutation . 3 . For compatible lines at Transfer 16 , six of six such clones contained pmr1 mutations ( Table 3 ) . We also sequenced clones isolated from the same line , but at different transfers . For two compatible lines ( lines A , N ) and three incompatible lines ( B , F , I ) the same mutation was identified in clones isolated after Transfers 10 and 16 ( Table 3 ) . However , for an incompatible line D two different pmr1 mutations were identified in clones isolated after Transfer 10 ( pmr1-T2213C ) and 16 ( pmr1-G3T ) . Whole genome analysis of these clones showed that two indel mutations were present in both Transfer 10 and 16 , suggesting that these mutations reached fixation prior to Transfer 10 ( S3 Table ) . We then sequenced additional clones from line D incompatible line at Transfers 10 and 16 . For Transfer 10 , the PMR1 gene was sequenced in nine clones; eight of these contained the T2213C mutation and one contained the wild-type sequence . For Transfer 16 , all three clones that were sequenced contained the G3T mutation . Together these observations are consistent with the T2213C mutation being adaptive , but before it could fix the G3T mutation appeared in the population and swept to fixation . Clones obtained from the compatible and incompatible evolved populations showed mutation rates that were similar to those measured in the corresponding unevolved lines ( S2 Table ) . Thus it is not surprising that the total number of mutations detected in the evolved lines were consistent with the genotype of the line ( compatible vs incompatible ) . On average 2 . 4 mutations were identified per compatible line vs . 8 . 0 mutations per incompatible line ( p < 0 . 0002 ) . Interestingly , the vast majority of indel mutations ( ~90% ) detected in this study were in homopolymeric runs , with five times as many indels detected per line in incompatible compared to compatible lines . The latter comparison is consistent with the mutation spectra seen in mlh1ts strains grown at the non-permissive temperature [3 , 6] . PMR1 encodes a P-type Ca2+ dependent membrane ATPase involved in protein sorting and calcium homeostasis . It is primarily localized to the Golgi membrane and is involved in transporting Mn2+ and Ca2+ into the Golgi lumen [38] . An uncharacterized pmr1 mutation was identified by Park et al . [39] that conferred increased tolerance to NaCl . The authors proposed that such NaCl tolerance occurred because increased levels of cytosolic calcium in the pmr1 mutant activated calcineurin , a calcium/calmodulin-dependent phosphatase . This activation increased expression of ENA1 , a gene encoding a P-type ATPase pump that functions in sodium and lithium efflux to permit salt tolerance [39 , 40] . In such a model it is not surprising that pmr1 null and hypomorph strains are also both resistant to lithium ( Fig 5 ) . However if only this pathway is involved then the pmr1Δ mutation should also confer NaCl tolerance . In fact pmr1Δ confers NaCl hypersensitivity in isogenic cell lines as well as in the S288c yeast knockout collection ( Fig 5 ) . We do not have a clear explanation for why the pmr1 alleles identified in this study confer NaCl resistance while the pmr1 null confers hypersensitivity . One possibility , suggested by Park et al . [39] is that factors that act in calcium homeostasis are differentially regulated in the presence or absence of full or partial-length Pmr1 , and thus may differentially regulate pumps that function in sodium and lithium efflux . To further characterize the mutations in PMR1 and their phenotype related to salt resistance , we replaced the wild type PMR1 gene in unevolved strains with constructs containing pmr1 alleles identified in our studies ( Fig 5 ) . All assessed alleles ( pmr1-T412C , pmr1-T2G , pmr1-A557G , pmr1-C554T ) conferred NaClr and LiClr phenotypes in the unevolved strains that were identical to the phenotypes observed in the corresponding evolved strains ( Fig 5 ) . These results indicate that the phenotypes identified in evolved lines can be completely explained by mutations in PMR1 , supporting the data shown in the bulk segregation experiments ( Table 2 ) . To obtain a better mechanistic explanation for why pmr1 point , frameshift , and initiation codon mutations , but not pmr1Δ conferred NaClr , we transformed two reporter constructs , pKC201 and pMZ11 , into wild-type , pmr1Δ , and pmr1 allele strains . pKC201 is a pmr2/ena1::lacZ reporter plasmid used to measure expression levels of the Pmr2/Ena1 ion pump , a major P-type ATPase required for sodium ion flux . pmr2Δ/ena1Δ strains are sensitive to NaCl but strains containing increased copy number or expression of this locus display increased resistance [41–43] . pMZ11 is a UPRE::lacZ reporter used to monitor the unfolded protein response , a signaling pathway that improves endoplasmic reticulum ( ER ) function during ER stress [44] . As shown in S5 Fig , pmr1Δ and evolved pmr1 strains each displayed constitutive expression of Pmr2/Ena1 at levels that were higher in the absence of NaCl than seen in wild-type . Using the pMZ11 reporter , we found that pmr1 and pmr1Δ strains displayed similar phenotypes with respect to the unfolded protein response ( S6 Fig ) . Together these results suggest that ENA1 overexpression or the induction of the unfolded protein response cannot explain the different NaClr phenotypes seen in pmr1 and pmr1Δ strains . At present we favor the idea that factors acting in calcium homeostasis are differentially regulated in the presence or absence of regulatory sequences during translation of Pmr1 or in the Pmr1 polypeptide , and may differentially regulate pumps that function in sodium and lithium efflux . It is important to note that not all clones that showed salt resistance contained mutations in PMR1 . In fact most NaClr clones obtained from compatible lines that had undergone 10 transfers did not contain pmr1 mutations ( S3 Table; S4 Fig ) . Whole genome sequencing identified mutations in other candidate genes that may be causative . For example , clone C10B isolated from the compatible line at transfer 10 ( C10B ) contains a mutation in CNB1 . Cnb1 is a regulatory subunit of calcineurin that is linked to stress responses ( [45]; see below ) . While cnb1 null mutants show sensitivity to NaCl , mutations in CNB1 were previously identified in lines evolved in NaCl [46] . A clone isolated from a compatible line that had completed 10 transfers ( C10C ) contained a mutation in GCN2 and a clone isolated from a compatible line that had completed 16 transfers ( C16B ) contained a mutation in PTK1 . Gcn2 is a protein kinase that phosphorylates the alpha-subunit of translation initiation factor eIF2 in response to starvation and Ptk1 is a putative kinase that is involved in polyamine transport [47 , 48] . Gene replacement approaches will be required to test whether these or other mutations identified in these clones are causative .
The majority of mutations that conferred salt tolerance mapped to the PMR1 locus ( Table 2 and Fig 5 ) . Previously Anderson et al . [42] identified mutations in other loci that are linked to NaCl tolerance including PMA1 , which encodes a proton efflux pump , ENA1 , which encodes a sodium efflux pump , and CYC8 , which encodes a global transcriptional repressor that regulates ENA1 activity [42] . Possible reasons for why different loci were targeted in the two studies include: 1 . The strains used in the two studies were not identical and were likely to have different background mutations . 2 . We imposed a stronger selection for NaCl tolerance ( 1 . 2 M ) than Anderson et al . ( 1 . 0 M ) [42] . 3 . We screened for adaptive advantages at earlier generations ( 70–100 ) than Anderson et al . ( 100–500 ) [42] . This is of interest because of recent observations made by Lang et al . [60] , who studied the appearance of beneficial sterility mutations in haploid S . cerevisiae . In their system they estimated that roughly 100 generations of adaptation were required to generate a threshold level of genetic diversity upon which beneficial mutations could be selected . Thus different target genes might be identified depending on when adaptation is measured . 4 . The effective population size per transfer is different in the two studies; we used a 10-fold higher number of cells than Anderson et al . [42] . Such a difference would likely alter the frequency and likelihood that mutations in any one locus would emerge . It is important to note that despite the differences in genes identified between the two studies there is a nice commonality in that NaClr in both studies is likely to involve altered regulation of the Ena1 efflux pump ( S5 Fig ) . Epistatic effects involving interacting alleles have been detected for specific fitness measurements between individuals within a population ( e . g . [61] ) . One of the best demonstrations of such effects in yeast was obtained by Brem et al . [62] , who crossed two strains of baker’s yeast and then searched for genetic interactions by measuring the levels of all transcripts in a large number of spore progeny . In their analysis they identified statistically significant interactions between locus pairs for 225 transcripts . Based on a population survey of MLH1 and PMS1 alleles , we argued previously that the incompatibility that was identified between MMR genes is similar to epistatic interactions seen in hybrids formed from established or incipient species ( [28 , 29]; see examples in [22] ) . Support for such an idea is based on the fact that mild reproductive barriers have already been shown to exist between some S . cerevisiae strains [22] , and the MMR machinery has been shown to contribute to reproductive isolation when S . cerevisiae strains with sequence divergence are mated [63 , 64] . The experiments presented in this paper provide an interesting twist to this idea because the incompatibility involving MLH1 and PMS1 might also provide opportunities for adaptive evolution by moderately increasing mutation rates .
Yeast strains , all isogenic to the FY/S288c background , were grown in YPD ( yeast extract , peptone , dextrose ) , YPD + 1 . 2 M sodium chloride ( NaCl ) , or YPD + 0 . 4 M lithium chloride ( LiCl ) ( S1 Table; [65 , 66] ) . DNA fragments containing S288c or SK1 derived MLH1 and PMS1 genes ( MLH1:KANMX , MLH1::NATMX , and PMS1::HIS3 ) were introduced into S288c-background strains by gene replacement ( S1 Table; [22 , 67] ) . S288c derived pmr1::URA3 alleles ( URA3 is located 500 bp upstream of PMR1 ) were also introduced into S288c background strains by gene replacement ( S1 Table , pEAA602-606 digested with NotI and XhoI ) . Integrations were confirmed by PCR amplification of yeast chromosomal DNA , prepared as described by Holm et al . [68] using primers located outside of the ends of the DNA fragments used for integration . Allele integrations were confirmed by sequencing the relevant PCR products using the Sanger method . The sequences of the oligonucleotides used to perform PCR are available upon request . pMZ11 ( UPRE::lacZ , ARS-CEN , TRP1 , reporter to measure the unfolded protein response ) and pKC201 ( pmr2::lacZ , 2μ , URA3 reporter to measure PMR2 expression ) were generously provided by Jeff Brodsky and Kyle Cunningham , respectively . Single colonies of Saccharomyces cerevisiae were inoculated into 6 ml of YPD and grown for 24 hrs at 30°C in 20 ml glass tubes in a New Brunswick G25 shaker run at 250 RPM . Approximately 2 x 107 of each culture were then transferred into fresh 6 ml YPD or YPD + 1 . 2 M NaCl ( to achieve an initial OD600 of 0 . 1 , Shimadzu UV-1201 spectrophotometer ) and then grown for 24 hrs . This procedure was repeated for up to 20 transfers . The number cell generations completed per transfer was determined using the equation log2 ( Nt/No ) , where No = total cell count at 0 hrs and Nt = total cell count at 24 hrs post transfer . A Wilcoxon sign-ranked test was used to compare growth of independent cultures [69] . Incompatible and compatible MLH1-PMS1 strains were created in which the MLH1 gene was marked with KANMX or NATMX ( S1 Table ) . These markers were shown previously to not affect fitness [70 , 71] . After 7 , 10 , and 16 transfers in YPD or YPD + 1 . 2 M NaCl ( approximately 50 , 70 and 110 generations respectively ) , incompatible and compatible populations were mixed at a 1:1 ratio ( 1 x 107 cells each inoculated into 5 ml YPD or YPD + 1 . 2 M NaCl ) and grown for an additional 24 hours . The ratio of incompatible and compatible populations was assessed by replica plating YPD plates containing ~ 200 yeast colonies ( plated prior to , or after 24 hrs of growth ) onto YPD-G418 and YPD-nourseothricin plates [70] . Fitness values ( Table 1 ) were separately determined after competition experiments in which evolved cultures were randomly mixed at a 1:1 ratio and grown for an additional 24 hours ( estimated to be 7 generations ) in YPD or YPD + 1 . 2 M NaCl . Fitness ( w ) [34 , 35] of the incompatible cells relative to the compatible cells was calculated as w = ( ( pt/qt ) / ( po/qo ) ) 1/t , where po and qo are the number of incompatible and compatible cells , respectively at 0 hrs and pt and qt are the number of incompatible and compatible cells , respectively , at 24 hrs , with t = 7 generations of growth . Fitness differences were analyzed for significance using one-way ANOVA [36] . lys2-A14 strains ( S1 Table; ( A ) 14 inserted into the LYS2 gene ) were analyzed for reversion to Lys+ as described previously [27 , 72] . All strains were inoculated in YPD overnight and plated onto LYS drop out and synthetic complete plates . The 95% confidence intervals were determined as described by Dixon and Massey [73] . Pair-wise Kruskal–Wallis tests were performed between each pair of incompatible and compatible strains to determine the significance of the differences in median reversion rates . Individual NaClr clones isolated from incompatible and compatible strains grown for 10 transfers were phenotyped and then crossed to isogenic , unevolved strains . The resulting diploids were first struck onto YPD + 1 . 2 M NaCl plates to determine if the NaClr phenotype observed in the evolved haploid strain was dominant or recessive . The diploids were then sporulated using either liquid or solid media containing 1% potassium acetate . Tetrads were dissected and spores clones germinated on YPD were struck onto YPD + 1 . 2 M NaCl to assess NaCl resistance . The resulting NaClr and NaCls spore clones , at least 18 of each , were pooled in equal cell amounts to create resistant and sensitive bulk pools that were subjected to whole genome sequencing . Parental , NaClr evolved compatible and incompatible clones , and the bulk pools described above , were grown in 8 ml cultures . Chromosomal DNA was isolated using Affymetrix Prep-Ease kit and quantified using the Qubit dsDNA HS Assay Kit ( Life Technologies ) . This DNA was then barcoded using Illumina Nextera XT . High throughput sequencing of chromosomal DNA was performed on an Illumina HiSeq 2500 at the Cornell Biotechnology Resource Center . GATK was used as a platform-independent Java framework . The core system uses the standard sequence alignment program BWA against a reference sequence to create SAM format files [74] . We used the S288c reference sequence in this study ( SGD: http://www . yeastgenome . org/ ) because our strains are isogenic to this background . All differences between our starting unevolved strain and the reference were subtracted . A binary alignment version of the SAM format , called binary alignment/map ( BAM ) , was then compressed and indexed using picard ( http://picard . sourceforge . net ) . Finally , BAM files were analyzed by GATK to optimize the genotyping analysis ( http://www . broadinstitute . org/gsa/wiki/index . php/The_Genome_Analysis_Toolkit ) . SNPs with quality scores of less than 75 were removed from the analysis . Uniprot was used to predict the topology of Pmr1 ( http://www . uniprot . org/ ) . pKC201 ( pmr2::lacZ , 2μ , URA3; [41] ) was transformed into evolved strains to determine if mutations in PMR1 activated ENA1/PMR2 expression . Transformants were grown overnight in uracil dropout media in the presence or absence of 1 . 2 M NaCl and then analyzed in liquid assays for beta-galactosidase activity ( permeabilized yeast cell assay , [51] ) . pMZ11 ( UPRE::lacZ , ARS-CEN , TRP1; [44] ) was transformed into evolved strains to determine if mutations in PMR1 activated the unfolded protein response pathway . Transformants were grown for the indicated times in tryptophan dropout media with or without 1 . 2 M NaCl and then analyzed in liquid assays for beta-galactosidase activity . DTT was included at 5 mM to serve as a positive control for the unfolded protein response . | In nature , bacterial populations with high mutation rates can adapt faster to new environments by acquiring beneficial mutations . However , such populations also accumulate harmful mutations that reduce their fitness . We show that the model eukaryote baker’s yeast can use a similar mutator strategy to adapt to new environments . The mutator state that we observed resulted from an incompatibility involving two genes , MLH1 and PMS1 , that work together to remove DNA replication errors through a spellchecking mismatch repair mechanism . This incompatibility can occur through mating between baker’s yeast from different genetic backgrounds , yielding mutator offspring containing an MLH1-PMS1 combination not present in either parent . Interestingly , these offspring adapted more rapidly to stress , compared to the parental strains , and did so without an overall loss in fitness . DNA sequencing analyses of baker’s yeast strains from across the globe support the presence of incompatible hybrid yeast strains in nature . These observations provide a powerful model to understand how the segregation of defects in DNA mismatch repair can serve as an effective strategy to enable eukaryotes to adapt to changing environments . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| []
| 2015 | A Genetic Incompatibility Accelerates Adaptation in Yeast |
Studies on the Bin-Amphiphysin-Rvs ( BAR ) domain have advanced a fundamental understanding of how proteins deform membrane . We previously showed that a BAR domain in tandem with a Pleckstrin Homology ( PH domain ) underlies the assembly of ACAP1 ( Arfgap with Coil-coil , Ankryin repeat , and PH domain I ) into an unusual lattice structure that also uncovers a new paradigm for how a BAR protein deforms membrane . Here , we initially pursued computation-based refinement of the ACAP1 lattice to identify its critical protein contacts . Simulation studies then revealed how ACAP1 , which dimerizes into a symmetrical structure in solution , is recruited asymmetrically to the membrane through dynamic behavior . We also pursued electron microscopy ( EM ) -based structural studies , which shed further insight into the dynamic nature of the ACAP1 lattice assembly . As ACAP1 is an unconventional BAR protein , our findings broaden the understanding of the mechanistic spectrum by which proteins assemble into higher-ordered structures to achieve membrane deformation .
Membrane deformation is needed for a wide range of cellular processes , including intracellular transport , organelle biogenesis , cell division , and cell motility [1 , 2] . Some of the best characterized proteins that deform membrane possess a Bin-Amphiphysin-Rvs ( BAR ) domain [3–8] . Studies on how this domain structure induces membrane curvature have suggested two mechanisms . One way involves scaffolding [5 , 9 , 10] . The dimerization of the BAR domain produces a curved , banana-like structure , which can then impose curvature onto the underlying membrane through electrostatic interactions . A second way involves protein insertion into the membrane [4 , 11 , 12] . Some BAR domains possess an amphipathic helix , which can insert into one leaflet of the membrane to create asymmetry between the bilayers , resulting in curvature induction . In recent years , a more detailed understanding of how BAR proteins induce membrane curvature has come from high-resolution electron microscopy ( EM ) studies , which couples cryo-EM with protein crystallography , resulting in an atomic-level view of how BAR proteins are organized into higher-ordered structures on membrane for curvature induction [5 , 8 , 11 , 13 , 14] . Emerging from these studies has been a general paradigm for how BAR proteins deform membrane . Briefly , the dimeric BAR structure acts as the basic repeating unit , which propagates along its length through tip-to-tip interactions , and also laterally through side-to-side interactions , to assemble into lattice structures that appear as “criss-crossing” strands on membrane for curvature induction [15 , 16] . A number of recent work has revealed how BAR domains [17–19] or banana-shaped rods [20] cluster to form scaffolds on membranes . While the lattice assembly was shown to be dynamical in nature , the intermediary stages of the assembly process have been less clear . We have recently uncovered a different paradigm for how a BAR protein deforms membrane . A subset of BAR proteins possesses the BAR domain in tandem with a Pleckstrin Homology ( PH ) domain . Early studies found that the PH domain in these proteins is critical for membrane deformation , but the explanation had remained elusive [21–24] . We recently addressed this puzzle in studying a GTPase-activating protein ( GAP ) for ADP-Ribosylation Factor 6 ( ARF6 ) , known as ACAP1 ( Arfgap with Coil-coil , Ankryin repeat , PH domain 1 ) . Besides its traditional role as a regulator of the ARF6 , ACAP1 also acts as an ARF6 effector [25] . This involves ACAP1 functioning as a coat protein , which deforms endosomal membrane to generate transport carriers for recycling to the plasma membrane [26–28] . To achieve a better understanding of how ACAP1 acts in this capacity , we recently pursued structural studies on the BAR-PH tandem of ACAP1 ( ACAP1BAR-PH ) , which is the minimal portion of ACAP1 sufficient for membrane deformation . The solved structure suggested that , rather than the BAR domain engaging the membrane , the PH domain contacts the membrane . We then pursued functional studies to show that a loop in the PH domain likely inserts into the membrane to impart curvature . Thus , rather than its BAR domain , the main driver of membrane deformation for ACAP1 is its PH domain [8] . We also pursued high-resolution EM-based studies to gain insight into how ACAP1BAR-PH organizes into a higher-order lattice structure to achieve membrane deformation . The result revealed another key difference between how ACAP1 versus how a conventional BAR protein deforms membrane . Whereas conventional BAR domains contact the membrane along the entire length of their curved structure , through the concave side [29 , 30] , only one end of the ACAP1BAR-PH dimer contacts the membrane . Consequently , whereas the basic repeating unit of protein lattices formed by conventional BAR proteins is the dimer , the tetramer constitutes the basic repeating unit in the ACAP1 lattice , which is achieved by the end of an ACAP1BAR-PH dimer that does not contact the membrane interacting instead with the mid-portion ( arch ) of another dimer , resulting in an “end-to-arch” interaction between two dimers in forming a tetramer [8] . Here , we have pursued multiple complementary approaches to gain insight into how ACAP1 assembles into this unusual lattice structure for membrane deformation .
We had previously reconstructed an ACAP1BAR-PH protein lattice on tubulated portions of liposomes , which predicts the structural organization needed for membrane deformation [8] . Overall , the ACAP1BAR-PH dimer is predicted to oligomerize , both longitudinally and laterally , in forming helical strands that wrap around a tubular membrane with regular periodicity , resulting in an “criss-crossing” appearance by which ACAP1 coats the membrane ( Fig 1a ) . Initially , performing closer inspection of this arrangement , we could appreciate three major regions of protein contacts ( highlighted by green , blue , and magenta boxes in Fig 1a ) . The relative positions of these three interfaces can be further defined when considering a grouping of six contiguous ACAP1BAR-PH dimers within the lattice structure , which consist of two dimers in the upper helical strand ( defined as positions N-2 and N-1 ) , two dimers in the middle strand ( defined as positions N and N+1 ) , and two dimers in the lower strand ( defined as positions N+2 and N+3 ) ( Fig 1a ) . Interface I ( green box in Fig 1a ) is an inter-strand interaction , and involves the two dimers of the upper strand ( positions N-2 and N-1 ) contacting the two dimers of the middle strand ( positions N and N+1 ) . This interface is further highlighted in Fig 1b , in which the relative position of one dimer in the upper strand ( N-1 , colored orange ) is shown in relation to one dimer in the middle strand ( N , colored cyan ) . Interface II ( blue box in Fig 1a ) is an intra-strand interaction , and involves two adjacent dimers in the middle strand interacting with each other ( positions N and N+1 ) . This interaction is further highlighted in Fig 1c , with one dimer ( position N , colored in cyan ) interacting with the other dimer ( position N+1 , also colored in cyan ) . We had previously noted the unusual nature of this contact , as it involves an “end-to-arch” interaction between the PH domain ( at the end ) of one dimer with the BAR domain ( at the mid-section ) of the other dimer in generating a tetramer , which is also the basic repeating unit of the lattice structure [8] . Interface III ( magenta box in Fig 1a ) is another inter-strand interaction , and involves two dimers in the middle strand ( positions N and N+1 ) interacting with two dimers in the lower strand ( positions N+2 and N+3 ) . This interaction is further highlighted in Fig 1d , in which the relative position of a dimer in the middle strand ( position N , colored cyan ) to that of two dimers in the lower strand ( positions N+2 and N+3 , colored in orange ) is shown . Our previous reconstruction of the ACAP1BAR-PH lattice did not achieve sufficient resolution to identify specific residues that mediate these three major interfaces of contacts . Thus , we initially sought to refine the resolution by pursuing a type of molecular dynamics ( MD ) simulation , known as MD flexible fitting ( MDFF ) . This is a computational approach that employs MD simulations to fit atomic structures into cryo-EM density maps [31] and has been successfully applied to improving the structural details of multiple macromolecular assemblies [32–35] . A grouping of 12 contiguous ACAP1BAR-PH protein dimers were embedded onto the cryo-EM density map ( summarized in S1 Fig; map resolution 14 Å ) . This resulted in ~140 , 000 protein atoms being analyzed , or ~2 , 340 , 000 total atoms when the solvent and ions were also included . The crystal structure of ACAP1BAR-PH protein ( PDB: 4NSW ) was used for the fitting . Protein configurations before ( colored in green ) and after ( colored in magenta ) simulations are shown in Fig 1e , with further details of typical refined regions shown in Fig 1f and 1g . The Fourier Shell Correlation ( FSC ) profile between the map and the model validated the improvement of the structure after simulation ( Fig 1h ) . The result also revealed that a number of interacting residues would be missed by the rigid-body docking method ( S2 Fig ) , and many of these residues are predicted to reside in the three major interfaces ( S3 Fig and S1 Table ) . In the more refined structure , interface I is predicted to be composed of two contacting regions ( Fig 2a ) , with the upper of these two contacts highlighted in Fig 2b , and the lower contact highlighted in Fig 2c , 2d , and 2e . For the upper contact , multiple residues ( R236 , E239 , Q240 , Q247 and K248 ) in the α4 helix of the BAR domain in the dimer N are predicted to interact with the same group of residues in the dimer N-1 . This interaction occurs symmetrically and in an anti-parallel fashion , which would be analogous to two persons shaking their right hands . The simulation results further predicted that residue Q240 forms H-bonds with Q247 and K248 in this contact . For the lower contact , simulation results predicted that multiple residues ( D99 , H103 , Q107 , R118 , D122 , R125 , D126 , R129 , Q150 and E154 ) in the α2 helix of the BAR domain in the dimer N would interact symmetrically and in an anti-parallel fashion to the same group of residues in the dimer N-1 . This contact could be further sub-divided into three regions , denoted as left ( Fig 2c ) , central ( Fig 2d ) and right ( Fig 2e ) . The central region has multiple charged residues ( R118 , E122 , R125 , D126 , R129 ) , with the dimer N interacting symmetrically and anti-parallel with the dimer N-1 ( Fig 2c ) , while the left ( Fig 2d ) and right ( Fig 2e ) regions have several polar residues . Overall , Interface I is created by four salt-bridges , residue pairs E122-R125 , and E122-R129 , twice in each dimer pair , and six H-bonds , residues Q107-E154 , E122-R125 , E122-R129 , twice in each dimer pair due to anti-parallel symmetry ( summarized in Table 1 ) . Interface II is predicted to be created by a portion of the BAR domain in the dimer N+1 forming a binding pocket , and a portion of the PH domain in the dimer N forming a loop that inserts into the binding pocket ( Fig 2f ) . Specifically , a portion of the α0 helix ( residues 1 to 20 ) of the BAR domain in the dimer N+1 is predicted to form a binding pocket , while residues 276 to 282 of the PH domain in the dimer N is predicted to form the insertion loop ( designated as Loop1 and located between the β1 and β2 sheets of the PH domain ) . The refined model also suggested some specific interactions . These include salt bridges and H-bonds formed by residue D6 of the BAR domain in the dimer N+1 interacting with residue K281 of the PH domain in the dimer N . H-bonds are also formed between residues D6 of the BAR domain ( in the dimer N+1 ) and S277 of the PH domain ( in the dimer N ) , and between residues E9 of the BAR domain and N278 of the PH domain . In addition , the insertion loop ( Loop1 in the PH domain of the dimer N ) is found to interact with part of the α4 helix ( residues 234 to 245 ) of the BAR domain in the dimer N+1 . This interaction should further stabilize the main contact described above , created by the insertion of Loop1 in the dimer N into the binding pocket formed by the α0 helix in the dimer N+1 ( Fig 2f ) . An H-bond was also observed between R241 of the BAR domain in the dimer N and S277 of the PH domain in the dimer N+1 ( Fig 2g and Table 1 ) . Interface III is predicted to be generated by two α helices in the BAR domain of the dimer N interacting with the BAR domain in the dimer N+2 and the PH domain in the dimer N+3 ( Fig 2h ) . Specifically , D92 of α2 helix in the BAR domain of the dimer N forms a salt bridge and H-bond with R236 in the α4 helix of the BAR domain in the dimer N+2 ( Fig 2i and S1 Table ) . Another part of the α2 helix ( charged residue K82 ) in the dimer N contacts with the β4/β5 loop of the PH domain ( charged residues D310 and D311 ) in the dimer N+3 ( Fig 2i ) . Overall , as the electrostatic interactions are about one order of magnitude higher than the van der Waals interactions , additional analysis suggested that charged residues at the three interfaces should provide the main driving force for the protein contacts ( S1 Table ) . We next sought to confirm the above predictions through functional studies . For interface I , we generated three sets of mutations . One set targeted a cluster of clustered charged residues , R236 , E239 and K248 , which were predicted to participate in the upper contact in Interface I ( see Fig 2b ) . Mutation of these residues to alanines ( R236A/E239A/K248A ) resulted in mutant 1 ( Mut1 ) ( Fig 3a ) . A second set of mutations targeted another set of charged residues R118 , E122 , R125 and D126 , which were predicted to participate in the lower contact in Interface I ( see Fig 2d ) . Mutation of these residues to alanines ( R118A/E122A/R125A/D126A ) resulted in mutant 2 ( Mut2 ) ( Fig 3a ) . We also generated mutant 3 ( mut3 ) , which combined the mutations in Mut1 and Mut2 ( R118A/E122A/R125A/D126A/R236A/E239A/K248A ) ( Fig 3a ) . For Interface III , we targeted a predicted critical interaction between the D92 residue in the BAR domain of one dimer and the R236 residue in the BAR domain of another dimer ( see Fig 2i ) . Mutation of these residues to alanines ( D92A/R236A ) generated mutant 4 ( Mut4 ) ( Fig 3a ) . Interface II was more challenging to disrupt . This contact involves two ACAP1BAR-PH dimers interacting through an “end-to-arch” interaction in forming a tetramer ( see Fig 1c ) . Thus , disruption of this interaction required that we only target one of the two PH domains in the ACAP1BAR-PH dimer . We had previously overcome this hurdle by noting that ACAP1BAR-PH dimerizes in a symmetrical and anti-parallel fashion , and this orientation could be preserved by covalently linking two ACAP1BAR-PH monomers in a “head-to-tail” fashion [8] . Importantly , the resulting fusion protein ( referred as BARPH-BARPH ) was shown to be functional , retaining the ability to tubulate liposomes similar to that seen for wild-type ACAP1BAR-PH ( which dimerizes through non-covalent interaction ) [8] . Thus , to target Interface II , we generated a dimer fusion protein and then mutated residues S277 , N278 , F280 , and K281 in only one of the two PH domains in this fusion protein , resulting in mutant 5 ( Mut5 ) ( Fig 3a ) . We then pursued functional studies . Initially , we examined membrane binding by ACAP1 , and found that all five mutations impaired the recruitment of ACAP1 to membrane to some extent ( Fig 3b ) . Subsequently , we pursued additional approaches to assess membrane deformation by ACAP1 . First , we examined the ability of ACAP1 to induce liposome tubulation , as done previously [8] , and found that all mutations affected this ability of ACAP1 ( Fig 3c ) . We also pursued a second assay . ACAP1 acts as a coat protein in generating transport carriers for endocytic recycling , and we had previously established the reconstitution of ACAP1-dependent carrier formation from endosomal membrane [8] . Performing this reconstitution , we confirmed that all five mutants also reduced the ability of ACAP1 to support vesicle formation from endosomal membrane ( Fig 3e ) . We also performed MD simulations of Mut3 , Mut4 and Mut5 , which revealed disassociation of dimers from tetramer state , leading to disruption to the protein lattice ( S4 Fig ) . Thus , these results confirmed in complementary ways that the MDFF simulations had identified key protein-protein contact sites that enable dimeric ACAP1 to assemble into a higher-ordered lattice structure for membrane deformation . We then considered a potential clue . Whereas the mutations had modest effects on the membrane-binding assay , they exhibited more severe effects in the two assays of membrane deformation , i ) liposome tubulation and ii ) carrier formation from endosomal membrane . This disparity suggested that the initial stage of lattice assembly , which involves the recruitment of ACAP1 to membrane , may be dynamic , and thus could not be captured completely by a simple membrane-binding assay . Thus , to explore this possibility , we next embarked on simulation studies , which are better suited in capturing dynamic situations . We initially pursued an algorithm based on parallel tempering Monte Carlo ( PTMC ) simulation [36] , which investigates the orientation by which the ACAP1BAR-PH dimer is adsorbed onto a negatively charged membrane surface under different surface charge densities ( SCD ) and ionic strengths ( IS ) . Simulation parameters ( SCD and IS ) are listed in S2 Table . The parameters for modelling the membrane surface have been described previously [37] . Moreover , a coarse-grained united-residue model was employed , i . e . , each amino acid of the protein was reduced to an interaction site centered at the α-carbon of the residue , with parameters as previously described [37] . Since the inter-molecular interactions between the charged surface and the protein are important , and the intra-molecular interactions of the protein itself are less important , the protein structure was kept rigid . This modelling approach has been successfully applied previously to elucidate the adsorption orientation of lysozyme [36] and antibodies [37] on charged surfaces . For the ACAP1BAR-PH dimer ( PDB ID: 5H3D ) , which was predicted to be neutrally charged , there was no significant electrostatic repulsion with the negatively charge surface . When IS and SCD were set as 0 . 18 M and -0 . 127 C·m-2 , respectively , “one-end-on” orientation ( shown in Fig 4a ) became favored , with the potential energy ( -303 . 5 kJ/mol ) , which is lower than that of symmetrically binding ( -274 . 4kJ/mol ) . We also manually constructed a symmetric dimer structure , and similarly the asymmetric binding model is more favorable . These findings predicted that the ACAP1BAR-PH dimer would be approaching the membrane asymmetrically , and thereby explaining why its final configuration in the lattice structure shows only one of the two ends in the dimer contacting the membrane . We sought to confirm this prediction in two ways . First , we sought validation that PTMC simulation would accurately predict the recruitment behavior of BAR proteins that have been well characterized . For an F-BAR protein ( PDB ID: 2EFK [38] ) ( Fig 4b ) , which has -6e net charges , the electrostatic repulsion still existed , due to the relatively uniform distribution of charges on protein surface and a smaller dipole moment . In this case , adsorption strength was relatively weak . At a low surface charge density , electrostatic interactions were similar to van der Waals interactions . At a high surface charge density , electrostatic interactions became dominant , and this was caused by the shielding effect of solution ionic strength on the surface charge . We obtained almost the same optimal orientation with SCD of 0 . 007 and 0 . 127 , which was a “lying-on-side” orientation that involves the entire length of the protein interacting with the model surface . Notably , this mode of membrane interaction has been predicted previously as an intermediate stage of lattice assembly for this BAR protein [5] . We further noted that the key residues predicted to mediate membrane binding include K56 , E92 , K104 , Q107 , K114 , K122 , R125 , Q160 , A167 , Q170 , and K174 ( S3 Table ) , and a number of these residues have already been confirmed by previous functional studies [5 , 10 , 38] . For the N-BAR protein ( PDB ID: 1X03 [12] ) ( Fig 4c ) , which has -20e net charge , there was significant electrostatic repulsion with negatively charge surface , and consequently adsorption strength was significantly reduced . However , due to the uneven distribution of charged residues on the protein surface and the shielding effect of the strong solution ionic strength on the electrostatic repulsion , the negatively charged protein still adsorbed onto the negatively charge surface . With increasing surface charge density , adsorption became stronger . This resulted in almost the same optimal orientation with SCD = 0 . 007 and SCD = -0 . 127 . In these cases , the “two-end-on” orientation was the optimal orientation . Moreover , as shown in S3 Table , the key adsorption residues were predicted as R174 , Q175 , G176 , K177 , I178 , and E182 , with the positively charged R174 and K177 interacting electrostatically with the negatively charged surface being predicted to be the main driving force . Notably , these predictions have also been confirmed previously by functional studies [4 , 12] . We next pursued a second way of validating the results of the PTMC simulations on ACAP1 . Besides predicting an asymmetric approach to the membrane by ACAP1 , PTMC simulations also predicted specific residues that are critical for this behaviour . These residues could be sub-divided into two regions of ACAP1 , with one cluster ( F280 , K281 , D322 , and E325 ) located in the PH domain , and another cluster ( R147 , R148 , A149 , Q150 , and Q151 ) located in the BAR domain . A critical role for the clustered residues in the PH domain was expected , as our previous structural elucidation of the ACAP1 lattice on membrane revealed that this region provides the sole means by which the lattice contacts the underlying membrane [8] . We had also performed functional studies that confirmed this situation [8] . In contrast , a role for the clustered residues in the BAR domain was unexpected , as this region was not observed to contact the membrane in the previously elucidated structure of the ACAP1 lattice on membrane [8] . Thus , we next pursued functional studies to confirm this unexpected finding . We generated two types of mutations . When the R147 and R148 residues were mutated to glutamates ( R147E , and R148E; Mut 6; Fig 4d ) , we found that membrane binding of the ACAP1BAR-PH protein was reduced to some extent ( Fig 4e ) . When the entire cluster was converted to alanines ( R147A , R148A , A149 , Q150A , and Q151A; Mut 7; Fig 4d ) , membrane binding was affected similarly ( Fig 4e ) . In the liposome tubulation assay , we found that the mutations reduced membrane deformation by ACAP1 more dramatically ( Fig 4f ) . Similarly , we found that that mutations had a more dramatic effect in reducing the reconstitution of ACAP1-dependent endocytic recycling carrier formation ( Fig 4g ) . We further noted that these results on mutants 6 and 7 paralleled those seen above for the effects of mutants 1–5 . That is , the membrane-binding assay was only modestly affected by the mutations , while the assays of membrane deformation , liposome tubulation and carrier formation from endosomal membrane , were affected more drastically . Moreover , as the residues in the BAR domain were not seen to contact the membrane in the ACAP1 lattice structure that we had previously elucidated [8] , the collective considerations suggested that the BAR domain residues likely participated in ACAP1 contacting the membrane in a dynamic manner . The position of the residues in the BAR domain also suggested how this dynamic recruitment could occur . In the solved structure of the ACAP1 dimer , these residues are located in close proximity to the residues in the PH domain , which we had previously documented to participate in membrane contact [8] . Moreover , when considering that the residues in the BAR domain are positioned more laterally than these residues in the PH domain , we concluded that the participation of the BAR domain residues would result in the ACAP1 dimer initially contacting the membrane in a more “tilted” manner than that would have been predicted if only the residues in the PH domain were involved ( S4 Fig ) . Unlike other BAR proteins , such as PICK1 [39] , which form tetramer or octamer in the solution , ACAP1 exists as a dimer at the concentration from 10 to 50 μM [8] . We next addressed a fundamental question arising from the prediction that the ACAP1 dimer would initially contact the membrane through only one of its two ends . As this dimer is structurally symmetrical , how can it behave asymmetrically for membrane contact ? To gain further insight , we next pursued additional simulation studies . Initially , we performed multiple independent MD simulations of a single ACAP1BAR-PH dimer in solution ( Fig 5a ) . By examining the fluctuation of Cα atoms in the protein backbone , we uncovered that the ACAP1BAR-PH dimer was intrinsically asymmetric in its dynamics . B-factors computed from atomic displacement of the residues indicated the relatively active regions on the molecular surface of the PH domain ( Fig 5b ) . Remarkably , residue loops in the PH domain , which facilitated the interaction between the ACAP1BAR-PH dimer and the membrane surface were also found to be dynamic in simulations , even when no membrane was present . The two PH domains of the protein dimer showed different but consistent root-mean-square-fluctuation ( RMSF ) profiles in all three independent simulations ( Fig 5c ) . One of the PH domains ( PH1 ) always had relatively larger fluctuations in Loop 1 ( residue 276–282 ) ( >2 . 5 Å ) than the other PH domain ( PH2 ) . Also , the RMSF of Loop 4 ( residue 322–235 ) in one PH domain , serving as the linking residues between Loop 2 and Loop 3 , was higher than that in the other PH domain . These fluctuations were predicted to disrupt membrane binding by the PH domain on one end of the ACAP1BAR-PH dimer greater than that of the PH domain in the other end . Note that the initial protein structure of MD simulations is the crystal structure 4NSW , which consists of two chemically identical chains . In our MD simulations , either PH domain may possess a higher RMSD . This phenomenon may come from an allosteric effect [40] . The asymmetry of the RMSF are consistent with the “one-end-on” configuration , i . e . , it would be much easier for PH2 to bind to the membrane than PH1 . Principle component analysis ( PCA ) of the MD trajectories further supported the above conclusion . The two monomers within the ACAP1BAR-PH dimer were found to exert different but correlated dynamics even without the presence of the membrane ( Fig 5d ) . In the first few low-frequency modes ( sorted by the associated eigenvalues in descending order ) , introduction of a model membrane surface altered the residue fluctuation correlation matrix for both monomers . Generally , the two monomers had a stronger dynamic correlation without the lipid molecules . Integrating more normal modes ceased the difference between proteins , regardless of the presence of a model membrane . Projection of the trajectories onto the first two essential dynamics of the protein also revealed two distinct dynamical states ( S7 Fig ) . Further analysis employing the time-lagged independent component analysis ( TICA ) [41 , 42] , which finds a subspace with maximized autocorrelation , also show that the two dynamical states are distinguishable and independent of minor revision to molecular force-field ( S8 Fig ) . Thus , these findings suggested that the distal regions of the ACAP1 dimer exert unique internal dynamics even in solution , and this behavior diminishes after it comes toward the membrane . We then pursued another line of investigation that further reinforced the dynamic nature by which ACAP1 assembles into its protein lattice structure . Previous studies on the assembly of BAR proteins into lattice structures on membrane have sought insight into intermediary stages by examining the coating organization of these proteins on non-tubulated portion of liposomes , which is predicted to reveal a stage of lattice assembly toward its final functional form [5] . Taking a similar strategy , we initially also observed ACAP1BAR-PH to coat liposomes on both non-tubulated and tubulated regions ( Fig 6a ) . Thus , to gain insight into an intermediary stage of ACAP1 lattice assembly , we next sought to deduce how it is organized on the non-tubulated portions of liposomes . Sub-tomogram averaging suggested that ACAP1 on these liposomes formed curved densities , which are organized into particle units that are ~25 nm long and ~8 nm wide . These particles were flanked by adjacent similarly shaped particles that run roughly parallel ( Fig 6b ) . We fitted a structural model of ACAP1BAR-PH dimer ( PDB: 4CKG ) onto the curved density map and found that the coated layer is longer than the length ( ~16 nm ) of the ACAP1BAR-PH dimer . Thus , each particle unit was predicted to have more than one dimer . Further analysis revealed that the EM map could match closely with two ACAP1BAR-PH dimer molecules packed with each other via the interactions between the PH domain and BAR domain of the adjacent protein dimer in the same row ( Fig 6c ) . Notably , these tetramers exhibit a more elongated shape as compared to tetramers that we had previously observed on tubulated liposomes . Collectively , the above findings suggested that the ACAP1BAR-PH dimer packs onto planar membrane ( non-tubulated portions of liposomes ) with a different arrangement than that previously seen on curved membrane ( tubulated portions of liposomes ) . Thus , we concluded that we have detected an intermediary stage of ACAP1 lattice assembly on membrane . In this case , it involves tetramers of ACAP1 having already been formed on membrane , but not having reached its final configuration seen on tubulated portions of liposomes . Importantly , such a result further reinforced the dynamic nature of the ACAP1 lattice assembly .
We have pursued multiple , and complementary , approaches to gain insights into how ACAP1 assembles into an unusual lattice structure for membrane deformation . The unusual organization was originally revealed by pursuing high-resolution , structural , and EM-based studies [8] . However , due to the limited resolution previously achieved , we were unable to address a key question . What are the specific protein-protein contacts that mediate this unusual lattice organization ? In the current study , we initially pursued MDFF simulations to refine the model , which predicted specific residues to participate in forming the key contact points . The nature of the residues involved suggested that electrostatic interactions would be the main driver in assembly of the ACAP1BAR-PH lattice structure . Further notable was that a number of the interacting residues predicted by MDFF simulations would have been missed by the rigid-body docking method , and importantly many of these residues are predicted to participate in the major contact points within the lattice . We then pursued functional studies to confirm the predicted key contact points . One assay examined ACAP1 binding to membrane , while two other assays assessed membrane deformation by ACAP1 , these being liposome tubulation and carrier formation from endosomal membrane . Although all three assays were affected by mutations of the predicted residues , a notable difference was that the mutations affected the ability of ACAP1 to deform membrane more severely than its ability to bind membrane . Thus , when also considering that the two assays of membrane deformation are predicted to interrogate a more complex situation than the simpler membrane-binding assay , we concluded that ACAP1 is likely recruited to the membrane more dynamically than that could be detected by the membrane-binding assay , which monitored a more static situation . We then pursued simulation studies , as they are better suited for dynamic situations . ACAP1 exists in solution as a dimer that forms a symmetrical curved structure . However , unlike the conventional BAR proteins , which have been predicted to be recruited to the membrane symmetrically , having both ends of their dimeric curved structure contacting the membrane , PTMC simulations predicted that the ACAP1 dimeric structure would be recruited to the membrane asymmetrically , having only one of the two ends of the curved structure contacting the membrane . This prediction also suggested a key puzzle to address . How can a symmetrical structure bind to the membrane in an asymmetric fashion ? Pursuing further simulation studies , our results predicted that the PH domain in the ACAP1 dimer exhibits dynamic fluctuations in solution , which would explain why the ACAP1 dimer initially contacts the membrane asymmetrically . We had previously pursued a simpler simulation approach , which allowed us to model only how the concave side of the curved ACAP1 dimer approaches the membrane in a perpendicularly fashion . In the current study , pursuing a more comprehensive analysis , which queries all angles by which ACAP1 approaches the membrane , we have achieved a remarkable insight . The simpler simulation that we had previously pursued identified key residues in the PH domain to be critical for membrane binding . This was expected , as the previous structural elucidation of the ACAP1 lattice also shows these residues to be involved in membrane binding[8] . In contrast , the more comprehensive simulation analyses that we have pursued in the current study predict that a region in the BAR domain would also participate in ACAP1 contacting the membrane to initiate lattice assembly . This was unexpected , as this region of ACAP1 had not been observed to contact the membrane in our previous structural elucidation of the ACAP1 lattice . Importantly , the location of these BAR domain residues also suggests how they could be participating in membrane contact in a dynamic manner . Relative to the PH domain that contacts the membrane , the predicted residues in the BAR domain is located more proximal and lateral . Thus , the participation of these residues in membrane binding predicts that the curved dimeric ACAP1 structure would contact the membrane in a more “tilted” fashion . Notably , such a scenario is reminiscent of how a conventional BAR protein has been predicted to undergo assembly to form a functional protein lattice on membrane , as its curved dimer has also been proposed to contact the membrane initially through its side rather than perpendicularly through its concave surface . Thus , despite exhibiting notable differences in their final lattice organization on membrane , with the dimeric ACAP1 exhibiting asymmetric contact with the membrane and the conventional BAR domain exhibiting symmetric contact with the membrane , both types of lattices are predicted to transit through an intermediary stage of assembly that involve the more lateral surface of their curved dimeric structure coming into contact with the membrane . We also pursued another line of investigation that further reinforces the dynamic nature by which ACAP1 assembles into a lattice structure for membrane deformation . We had previously pursued structural and EM-based studies to elucidate a lattice structure formed by ACAP1 on tubulated liposomes , which is predicted to be the protein organization that deforms membrane . In the current study , we have further combined structural and EM-based studies with computational approaches to uncover how ACAP1 is organized on the non-tubulated portion of liposomes , which is predicted to gain insight into an intermediary stage of lattice assembly . As cryo-electron tomography and sub-tomogram averaging suggested a stage of lattice assembly in which tetramers of ACAP1 have been formed but have not reached their final configuration seen on tubulated liposomes , we conclude that the dynamic assembly of the ACAP1 lattice extends even to the point when tetramers have been formed . Thus , ACAP1 exhibits dynamic behavior not only in its initial stage of recruitment to membrane , but also subsequently during its assembly on membrane into a higher-ordered structure for membrane deformation .
ACAP1BAR-PH ( a . a . 1–377 ) and different mutants were cloned into the plasmid pGEX-6P-1 ( GE Healthcare ) , expressed as GST-tag fusion proteins in Escherichia coli BL21 ( DE3 ) cells . Cells were grown in Terrific Broth medium at 37°C until the OD at 600 nm reached 1 . 2~1 . 5 , and then induced at 16°C for 16 ~18 hours with 0 . 2 mM IPTG . Cells were harvested and re-suspended in PBS buffer ( 140 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 1 . 8 mM KH2PO4 , pH 7 . 4 ) and lysed by sonication . After centrifugation for 30 minutes at 15 , 000 rpm , the supernatant was collected and incubated with glutathione-Sepharose 4B at 4°C , and then was washed by PBS buffer . After cleavage using Precision Protease ( GE Healthcare ) to remove the GST tag , the eluted target proteins were changed to buffer A ( 50 mM Hepes , pH 7 . 4 , 50 mM NaCl ) and stored at -80°C . Site-directed mutations of select residues were performed by overlap PCR . To introduce mutations to only one subunit of dimeric BAR-PH , one copy of BAR-PH was inserted into plasmid pGEX-6P-1 with nonstop , and then another copy with Deletion ( S277-N278-F280-K281 ) was cloned into pGEX-6P-1-BAR-PH , to generate a tandem fusion version Mut5 ( BAR-PH-Deletion ) , with a sequence ( GGGSGGRLGSSNSG ) as a linker between BAR-PH and deletion . All lipids were purchased from Avanti Polar lipids . Lipid mixtures were similar to that previously described [8] , containing 40% phosphatidylcholine ( DOPC ) , 30% phosphatidylethanolamine ( DOPE ) , 20% phosphatidylserine and 10% of L-α-phosphatidylinositol-4 , 5-bisphosphate ( PI ( 4 , 5 ) P2 ) . They were dried under gas nitrogen and then kept under vacuum for at least three hours . Dry lipid mixtures were suspended in 50 mM HEPES , pH 7 . 4 , 50 mM NaCl for 30 minutes at 37 °C , frozen in liquid nitrogen , and thawed at 37 °C for 5 cycles , and extruded through membrane filters of 0 . 2 μm for the production of 200 nm liposomes . For the sedimentation assay , the 200 nm liposomes ( 1 mg/ml ) and ACAP1BAR-PH proteins ( 0 . 2 mg/ml ) were incubated for 60 minutes at room temperature before ultra-centrifugation at 250 , 000 g for 15 minutes . The supernatants and pellets were then subjected to SDS-PAGE analysis . The reconstitution system was performed essentially as previously described [8] . Briefly , to collect total membranes and cytosol , HeLa cells were incubated with biotin-conjugated transferrin ( Tf ) at 4°C for 1 hour and then at 37°C for 15 minutes , which allows a pool of Tf receptor ( TfR ) at the cell surface to accumulate at the early endosome in tracking endosomal membranes . Cells were then resuspended in buffer ( 20mM HEPES , pH 7 . 4 , 150 mM NaCl ) , followed by homogenization by passing through a 23-gauge needle 16 times on ice . The homogenate was then subjected to centrifugation to obtain total membranes ( with early endosomes labeled by biotin-conjugated Tf ) and cytosol . To reconstitute recycling vesicles from endosomal membrane , total membranes and cytosol , collected as described above , were incubated with 1mM GTP at room temperature for 30 minutes . To detect the level of recycling vesicles formed after this incubation , the sample was subjected to centrifugation at 13 , 000 x g for 20 minutes at 4°C to derive pellet fraction ( P ) , which contains organellar membranes , and supernatant fraction , which containing vesicles and cytosol ( S ) . Recycling vesicles were detected in the supernatant fraction by blotting for biotin-conjugated Tf using a horseradish peroxidase-conjugated streptavidin . To assess the effect of different mutations of ACAP1 on the formation of recycling vesicles , HeLa cells were treated with siRNA against ACAP1 , followed by the collection of total membranes and cytosol , as described above . Cytosol was also collected from HeLa cells that overexpressed different mutant forms of myc-tagged ACAP1 . Cytosol from the two sources of cells ( treated with siRNA against ACAP1 or overexpressing different mutant ACAP1 ) were mixed at 9:1 ratio , respectively , to obtain physiologic level of different mutant ACAP1 expressed in the cytosol . This resulting cytosol was then incubated with total membrane derived from HeLa cells treated with siRNA against ACAP1 . The level of recycling vesicles generated after this incubation was then assessed through the tracking of biotin-conjugated Tf , as described above . Simulations employed cryo-EM density maps from one of two classes of protein lattices associated with tubular membranes of various radius . The system contained ~140 , 000 protein atoms and ~2 , 340 , 000 total atoms , including solvent . Six protein tetramers served as the basic configuration unit for MDFF simulations . Initial structures of the protein were based on the crystal structure of ACAP1BAR-PH protein ( PDB: 4NSW ) . Proteins were solvated in a box of TIP3P water molecules [43] with 180 mM KCl using VMD [44] . Periodic boundary conditions were introduced to the system . The MDFF simulations were performed using NAMD 2 . 11 [45] with CHARMM36 force field [46] . A timestep of 1 fs has been used . Cut-off distance was 10 Å for the non-bonded interactions . Temperature was maintained at 310 K using a Langevin thermostat coupled to all heavy atoms with a damping coefficient of 5 ps-1 . Restraints for secondary structures were introduced to the system . Symmetrical restraints [47] were also introduced for simulating part of a helical structure . A total of 27 ns of trajectory was generated . Only data after reaching equilibrium were taken for further analysis . As a result , the final 15 ns of trajectories were used for analysis unless otherwise specified . All quantities shown are the averaged values over the simulation windows . A salt bridge was defined as a pair of acidic oxygen and basic nitrogen atoms separated by less than 4 Å of distance . A hydrogen bond ( H-bond ) is defined as a pair of polarized oxygen , nitrogen or sulphur atoms separated by less than 3 . 5 Å of distance and forming an angle less than 30° with the adjacent hydrogen atom . The occupancy of a salt bridge/H-bond is defined as the percentage of time that the salt bridge/H-bond existed throughout the simulations . We have also constructed molecular systems , in which a single ACAP1BAR-PH dimer was placed in solvent with ions . Initial structures of the protein were based on the crystal structure of ACAP1BAR-PH protein ( PDB: 4NSW ) . The systems were solvated and ionized with TIP3P water molecules [43] and 180 mM KCl ions using VMD [44] . Periodic boundary conditions were introduced . Three independent simulations have been performed . The MD simulations were performed using NAMD 2 . 11 package [45] . The molecular force-fields used were either CHARMM36 [46] or CHARMM36m [48] . A timestep of 2 fs has been used . Electrostatic interactions were calculated using the particle mesh Ewald sum method [49] with a cutoff of 12 Å . Before production runs , the system was minimized in energy , heated to 310 K , and pre-equilibrated by step-wisely releasing harmonically restrained protein backbone and water oxygen atoms . Simulations were then continued in the constant NPT ensemble with 310 K and 1 atm . Langevin thermostats with a damping coefficient of 0 . 5 ps-1 , and Langevin-piston barostats [50] with a piston period of 2 ps and a damping time of 2 ps were used . In total five independent 500 ns of trajectories were generated for an ACAP1 dimer in ionic solvent . Only data after reaching equilibrium were taken for further analysis . Unless otherwise specified , the last 200 ns of data were used for analysis . All quantities presented in this article are averaged values over the chosen windows . The RMSF profile of two PH domains were calculated from three of the trajectories ( using CHARMM36 force-fields ) . Convergence of the RMSF profiles have been confirmed by using a moving 50 ns window starting at time = 100 , to 500 ns and lack of large deviations along the principle components of motions ( S6 Fig ) . A coarse-grained united-residue model [37] was employed to explore the preferred orientation of BAR domain proteins on negatively charged surfaces under different SCD and IS . In the model , each amino acid was reduced to an interaction site centred at the α-carbon of the residue . As the objective of PTMC simulations is to obtain the favourable orientation of BAR proteins on model charged surfaces , and when also considering that the inter-molecular interactions between protein and surface are important , while the intra-molecular interactions within the protein itself are less important , the protein structures were kept rigid . The charged surfaces had both van der Waals’ ( VDW ) and electrostatic interactions with the protein . To mimic the membrane bilayer , surfaces were assigned net negative charges and the SCD were calculated according to membranes adopted in experiments . The simplified flat model considered the most essential two factors , surface charge density and ionic strength , which could make the flat model provide a reasonable prediction of protein orientation on membrane surfaces . The parameters for model surfaces were taken from our previous works [37] . Five replicas , each in the canonical ensemble , were simulated in parallel at different temperatures of 310 K , 500 K , 800 K , 1500 K and 2500 K , which ensured sufficient energy overlap between neighbouring replicas to allow for the acceptance of configuration swaps . The swaps were performed every 500 cycles . The adsorption and preferred orientation of five BAR domain proteins were studied at different IS and SCD . Orientation angle ( θ ) is used to quantitatively characterize the orientation of adsorbed proteins on surfaces , which is defined as the angle between the unit normal vector to the surface and the unit vector along the dipole of a protein . The cosine value of this angle ( cosθ ) was calculated for each possible orientation . The orientation and corresponding preferred configuration of each protein on different charged surfaces at different ionic strengths were then derived . The direction of dipole was also calculated . The total potential energy ( Utot ) , VDW potential energy ( UVDW ) , electrostatic potential energy ( Uele ) and the cosθ of each protein adsorbed on surfaces at different SCD and IS were also calculated . The Monte Carlo ( MC ) simulation in each replica was carried out in a box of 30 nm × 30 nm × 30 nm . Initially , the protein was put 10 nm above the surface with a random orientation . During simulations , the protein was translated and rotated around its centre of mass . The displacement of each move was adjusted to ensure an acceptance ratio of 0 . 5 . A total of 15×106 MC cycles were carried out , in which the first 5×106 cycles for equilibrium and another 10×106 cycles for production . Besides , 310K is the target temperature . The ACAP1BAR-PH and mutant proteins ( 1 mg/ml ) were incubated with liposomes ( 0 . 5 mg/ml ) for 60 minutes and then the mixture was applied onto a glow-discharged carbon-coated EM grid and stained with uranyl acetate . The EM grids were examined with a transmission electron microscope ( FEI Tecnai20 or Talos ) and the micrographs were recorded with a Gatan UltraScan1000 CCD camera under the nominate magnification of 9 , 600X or with a FEI Ceta camera under the nominate magnification of 13 , 500X . The sample preparation for Cryo-electron tomography was the same as previously described[8] . And tomographic data collection was also the same as described before[8] . Tilt images were aligned using Markerauto[51] . Tomograms were reconstructed using weighted back projection ( WBP ) 37 . To obtain average results , 976 individual particles were manually picked and cut out using IMOD36 , To avoid any kind of over-fitting , the initial model for sub-volume alignment was generated by averaging all particles in random orientations[52] and refined using RELION 1 . 4[53] . Then the model was low pass filtered to 60 Å before subjecting into PEET37 for further refinement . The final STA procedure was carried out using PEET , following an iterative angular refinement step . To be noted that the parameter flgWedgeWeight in PEET was set to 0 to include the information in the missing wedge . The final searching step of Euler angle is 1° . Cryo-EM maps were displayed , and fitted with atomic models using UCSF Chimera[54] . | Membrane remodeling is needed for a wide range of cellular events . Our current understanding of how proteins bend membrane to achieve such remodeling has come , in large part , from studies on proteins that contain the BAR domain . These studies have elucidated that the BAR domain dimerize to form a crescent-shaped structure . This structure induces membrane deformation through scaffolding and/or insertion of an amphipathic loop into the membrane . Some BAR-containing proteins also contain a PH domain in tandem with their BAR domain . We have found previously that one such protein , known as ACAP1 , relies primarily on the PH domain , rather than the BAR domain , for membrane deformation . To explain this phenomenon , we have utilized , in the current study , molecular dynamics simulations , Monte Carlo calculations , and together with Cryo-electron sub-tomography experiments , to gain insights into how the ACAP1BAR-PH protein assembles into an unusual protein lattice through multiple dynamics stages . The results are further verified by functional experiments . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"vesicles",
"classical",
"mechanics",
"protein",
"interactions",
"membrane",
"proteins",
"materials",
"science",
"damage",
"mechanics",
"protein",
"structure",
"oligomers",
"cellular",
"structures",
"and",
"organelles",
"polymer",
"chemistry",
"proteins",
"deformation",
"chemistry",
"liposomes",
"molecular",
"biology",
"cell",
"membranes",
"physics",
"biochemistry",
"biochemical",
"simulations",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"dimers",
"materials",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
]
| 2019 | ACAP1 assembles into an unusual protein lattice for membrane deformation through multiple stages |
Random Item Generation tasks ( RIG ) are commonly used to assess high cognitive abilities such as inhibition or sustained attention . They also draw upon our approximate sense of complexity . A detrimental effect of aging on pseudo-random productions has been demonstrated for some tasks , but little is as yet known about the developmental curve of cognitive complexity over the lifespan . We investigate the complexity trajectory across the lifespan of human responses to five common RIG tasks , using a large sample ( n = 3429 ) . Our main finding is that the developmental curve of the estimated algorithmic complexity of responses is similar to what may be expected of a measure of higher cognitive abilities , with a performance peak around 25 and a decline starting around 60 , suggesting that RIG tasks yield good estimates of such cognitive abilities . Our study illustrates that very short strings of , i . e . , 10 items , are sufficient to have their complexity reliably estimated and to allow the documentation of an age-dependent decline in the approximate sense of complexity .
Knowledge forming the content of several academic fields , including mathematics , follows from precocious core knowledge [1–3] , and then follows a specific developmental course along the lifespan [4] . Numerosity ( our approximate implicit sense of quantity ) has been a privileged target of recent research , because numbers form one of the main pillars of elementary mathematical knowledge [5] , but the study of randomness perception and statistical reasoning has also yielded striking results in the field of probability: adults with no formal education [6] as well as 8 to 12 month-old children [7 , 8] have the wherewithal for simple implicit probabilistic reasoning . One of the toughest problems when it comes to Bayesian reasoning , however , is the detection of randomness , i . e . , the ability to decide whether an observed sequence of events originates from a random source as opposed to produced by a deterministic origin [9] . Formally , the algorithmic ( Kolmogorov-Chaitin ) complexity of a string is the length of the shortest program that , running on a universal Turing machine ( an abstract general-purpose computer ) , produces the string and halts . The algorithmic complexity of a string is a measure of how likely it is to have been produced deterministically by a computer program rather than by chance . In this way , a random string is a string that cannot be compressed by any means , neither statistically or algorithmically , that is a string for which no computer program shorter than the string itself exists . Humans , adults and infants [10 , 11] , have a keen ability to detect structure , both of statistic and algorithmic nature ( e . g . 0101… and 1234… ) that only algorithmic complexity can intrinsically capture ( as opposed to e . g . entropy rate ) . Within the field of study devoted to our sense of complexity , the task of randomly arranging a set of alternatives is of special interest , as it poses almost insurmountable problems to any cognitive system . The complexity of a subject-produced pseudorandom sequence may serve as a direct measure of cognitive functioning , one that is surprisingly resistant to practice effects [12] and largely independent of the kind of alternatives to be randomized , e . g . , dots [13] , digits [14] , words [15] , tones [16] or heads-or-tails [17] . Although random item generation ( RIG ) tasks usually demand vocalization of selections , motor versions have comparable validity and reliability [18 , 19] . RIG tasks are believed to tap our approximate sense of complexity ( ASC ) , while also drawing heavily on focused attention , sustained attention , updating and inhibition [20 , 21] . Indeed to produce a random sequence of symbols , one has to avoid any routine and inhibit prepotent responses . The ability to inhibit such responses is a sign of efficient cognitive processing , notably a flexibility assumed to be mediated by the prefrontal cortex . Instructions may require responding at various speeds [22] , or else the generation of responses may be unpaced [27] . Participants are sometimes asked to guess a forthcoming symbol in a series ( “implicit randomization” , [51] ) , or vaguely instructed to “create a random-looking string” [23] . The consensus is that , beyond their diversity , all RIG tasks rely heavily on an ASC , akin to a probabilistic core knowledge [24 , 25] . Theoretical accounts of the reasons why RIG tasks are relevant tests of prefrontal functions are profuse , but pieces of experimental evidence are sparse . Sparse empirical factors indirectly validate the status of RIG tasks as measures of controlled processing , such as the detrimental effect of cognitive load or sleep deprivation [26] or the fact that they have proved useful in the monitoring of several neuropsychological disorders [27–31] . As a rule , the development of cognitive abilities across the lifespan follows an inverse U-shaped curve , with differences in the age at which the peak is reached [4 , 32] . The decrease rate following the peak also differs from one case to another , moving between two extremes . “Fluid” components tend to decrease at a steady pace until stabilization , while “crystalized” components tend to remain high after the peak , significantly decreasing only in or beyond the 60s [33] . Other evolutions may be thought of as a combination of these two extremes . Two studies have addressed the evolution of complexity in adulthood , but with limited age ranges and , more importantly , limited ‘complexity’ measures . The first [22] compared young and older adults’ responses and found a slight decrease in several indices of randomness . The second [15] found a detrimental effect of aging on inhibition processes , but also an increase of the cycling bias ( a tendency to postpone the re-use of an item until all possible items have been used once ) , which tends to make the participants’ productions more uniform . In both studies , authors used controversial indices of complexity that only capture particular statistical aspects , such as repetition rate or first-order entropy . Such measures have proved some usefulness in gauging the diversity and type of long sequences ( with e . g . , thousands of data points ) such as those appearing in the study of physiological complexity in [34–36] , but are inadequate when in comes to short strings ( e . g . , of less than a few tens of symbols ) , such as the strings typically examined in the study of behavioral complexity . Moreover , such indexes are only capable of detecting statistical properties . Authors have called upon algorithmic complexity to overcome these difficulties [37 , 38] . However , because algorithmic complexity is uncomputable , it was believed to have no practical interest or application . In the last years , however , methods were introduced related to algorithmic complexity that are particularly suitable for short strings [39 , 40] , and native n-dimensional data [41] . These methods are based on a massive computation to find short computer programs producing short strings and have been made publicly available [42] and have been successfully applied in a range of different applications [41 , 43 , 44] . The main objective of the present study is to provide the first fine-grained description of the evolution over the lifespan of the ( algorithmic ) complexity of human pseudo-random productions . Secondary objectives are to demonstrate that , across a variety of different tasks of random generation , the novel measure of behavioral complexity does not rely on the collection of tediously long response sequences as hitherto required . The playful instructions to produce brief response sequences by randomizing a given set of alternatives are suitable for children and elderly people alike , can be applied in work with various patient groups and are convenient for individual testing as well as Internet-based data collection . Participants with ages ranging from 4 to 91 performed a series of RIG tasks online . Completion time ( CT ) serves as an index of speed in a repeated multiple choice framework . An estimate of the algorithmic complexity of ( normalized ) responses was used to assess randomization performance ( e . g . , response quality ) . The testing hypothesis is that the different RIG tasks are correlated , since they all rely on similar core cognitive mechanisms , despite their differences . To ensure a broad range of RIG measurements , five different RIG tasks were selected from the most commonly used in psychology . The experiment is some sort of reversed Turing test where humans are asked to produce configurations of high algorithmic randomness that are then compared to the occurrence of what computers can produce by chance according to the theory of algorithmic probability [39 , 40] .
This study was approved by the University of Zurich Institutional Review Board ( Req00583 ) . The five tasks used , described in Table 1 , are purposely different in ways that may affect the precise cognitive ability that they estimate . For instance , some tasks draw on short-term memory because participants cannot see their previous choices ( e . g . , “pointing to circles” ) , whereas in other tasks memory requirements are negligible , because the participant’s previous choices remain visible ( “rolling a die” ) . Completion times across the various tasks showed a satisfactory correlation ( Cohen’s α = . 79 ) , suggesting that participants did not systematically differ in the cognitive effort they devoted to the different tasks . Any difference between task-related complexities is thus unlikely to be attributable to differences in time-on-task . Complexities were weakly to moderately positively correlated across the different tasks ( Cohen’s α = . 45 ) , mostly as a consequence of the “filling the grid” task being almost uncorrelated with the other tasks ( for more details , see SI Principal Component Analysis ) . Despite this moderate link , however , all trajectories showed a similar pattern across the lifespan , with a peak around 25 , a slow , steady decline between 25 and 60 , followed by accelerated decline after 60 as shown in Fig 1 . The hypothesis to test that the different tasks are positively related to each other was partially supported by the data , especially in view of the results obtained on the “filling the grid” task . At the same time , CTs showed correlations supporting the testing hypothesis together with the developmental complexity curves in agreement pointing in the same direction . This suggests that all tasks do tap into our ASC as well as into other cognitive components with similar developmental trajectories , but that different tasks actually require different supplementary abilities or else they weight the components of these abilities differently . The “filling the grid” task appeared unique in that it was loosely correlated with all the other tasks . The fact that it required binary responses cannot account for this lack of association , since the “tossing a coin” task yielded results uncorrelated with the “filling the grid” responses . Bi-dimensionality could possibly have had an effect , but the “pointing to circles” task was also unrelated to the grid task . On the other hand , one factor distinguished the grid task from all others in the set: the option offered to the participants to change their previous choices as many times as they wished . For that reason , the grid task may in fact have relied more on randomness perception , and less on inhibition and randomness production heuristics . Indeed , participants could change the grid until they felt it was random , relying more on their ASC than on any high order cognitive ability serving output structure . This hypothesis is supported by the fact that participants did indeed change their minds . There were only 9 cells ( that could turn white or black ) on the grids and participants’ end responses had a mean of 4 . 08 ( SD = 1 . 8 ) selected ( black ) cells thus generally favoring whiter configurations ( possibly as a result of the all-white initial configuration ) . However , the number of clicks used by participants during this task was far larger ( M = 10 . 16 , SD = 9 . 86 ) , with values ranging from 5 to 134 ( this latter trying almost a fifth of all possible configurations ) . Thus the option to change previous choices in a RIG task may have been an important factor , and should accordingly be considered a novel variable in future explorations of randomization tasks ( and balanced with an all-black configuration ) . In this view , the “filling the grid” task would reflect our ASC in a more reliable fashion than other tasks , while being less dependent on inhibition processes . The present findings are based on data collected online . One could argue that this might bias the sample toward participants more comfortable with technology . Although direct control over a participant’s behaviour online is certainly limited , as compared to the laboratory environment , there is an increasing number of studies demonstrating the convergence of laboratory-based and web-based data collection [4] . This is the case in very particular procedural situations , such as lateralized [46] or very brief [47] stimulation , presentation and the measurement of reaction times [48] , and it also holds for the assessment of cognitive changes across the lifespan [49] . Compared to these special situations , our research procedure was simple , the tasks were entertaining , and response times did not have to be collected with millisecond precision [50] . We thus think that any disadvantages of online-testing have been more than compensated for by the advantages of enrolling a large number of participants across a wide age range . To investigate possible modulating factors ( besides age ) , we used general linear models with complexity and CT as DV , and age ( squared ) , sex , education , field and paranormal beliefs as IV . The variable Sex was chosen in order to test in a large sample whether the absence of differences in laboratory RIG experiments could be replicated in an online test . Similarly , Education was important to test given previous claims in the RIG literature that human randomization quality may be independent of educational level [51] . Paradoxically , participants with a scientific background may perform worse at producing random sequences , thanks to a common belief among them that the occurrence of any string is as statistically likely as any other ( a bias deriving from their knowledge of classical probability theory ) , which further justifies controlling for Field of education , simplified as humanities v . science . Finally , the variable Paranormal Belief was included as it has been related to RIG performance in previous studies [52] . The variables field and paranormal belief were , however , only asked in a subset of the 3313 participants that were above the age of 15 and we ignored the responses of younger participants as they were not considered to have a differentiated education background nor a fixed belief concerning paranormality . The analysis was performed on a task-wise basis . As we report , neither field or education level had no significant effect on any of the complexity or CT scores . A sample of 3429 participants took part in the experiment ( age range: 4–91y , M = 37 . 72 , SD = 13 . 38 ) . Participants were recruited through social networks , radio broadcasts and journal calls during a 10 month period . Basic demographic characteristics of the sample are displayed in Table 2 , the experiment is to this date still available online for people to test ( URL available in the next section ) . Each of the five ( self-paced ) RIG tasks consisted in the production , as fast and accurately as possible , of a short ( with length range 9–12 ) pseudo-random series of symbols , with various numbers of possible symbols and variations among other instructional features ( Table 1 ) . A specific web application was designed to implement the experiment online . Participants freely logged on to the website ( http://complexitycalculator . com/hrng/ ) . The experiment was available in English , French , German , and Spanish ( all translated by native speakers for each language ) . In the case of young children as yet unable to read or use a computer , an adult was instructed to read the instructions out loud , make sure they were understood , and enter the child’s responses without giving any advice or feedback . Participants were informed that they would be taking part in an experiment on randomness . They then performed a series of tasks ( all participants performed the tasks in this order , see SI for screen shots ) before entering demographic information such as sex , age , mother tongue , educational level , and main field of education ( science , humanities , or other ) if relevant . Before each task , participants ( or a parent , in the case of youngsters ) read the specific instructions of the task and only press “start” key after full agreement that they have understood the requirements of the task . Only then , that action initiated the measurement of the completion time ( CT ) for each task , which was recorded alongside the responses . Practice trials were not allowed in order to minimize boredom effects leading to drop-out rates and bias and to maximize spontaneity . One last item served as an index of paranormal beliefs and was included since probabilistic reasoning is among the factors associated with the formation of such beliefs [53 , 54] . Participants had to rate on a 6-point Likert scale how well the following statement applied to them: “Some ‘coincidences’ I have experienced can best be explained by extrasensory perception or similar paranormal forces . ” For each task , CT ( in seconds ) was recorded . The sum of CTs ( total CT ) was also used in the analyses . An estimate of the algorithmic complexity of participants’ responses was computed using the acss function included in the freely publicly available acss R-package [42] that implements the complexity methods used in this project . Complexities were then normalized , using the mean and standard deviation of all possible strings with the given length and number of possible symbols , so that a complexity of 0 corresponds to the mean complexity of all possible strings . For each participant , the mean normalized complexity ( averaged over the five tasks ) was also computed , serving as a global measure of complexity .
Our main objective was to describe the evolution over the lifespan of mean complexity , which is achieved here using an approximation of algorithmic complexity for short strings ( but see SI Entropy for a discussion of entropy ( under ) performance ) . Following Craik and Bialystok’s [33] view , the developmental curve of complexity found in Fig 2 , suggests that RIG tasks measure a combination of fluid mechanics ( reflected in a dramatic performance drop with aging ) and more crystallized processes ( represented by a stable performance from 25 to 65 years of age ) . This trajectory indirectly confirms a previous hypothesis [15]: attention and inhibition decrease in adulthood , but an increased sense of complexity based on crystallized efficient heuristics counters the overall decline in performance . Plotting complexity and CT trends on a single two-dimensional diagram allowed a finer representation of these developmental changes ( Fig 3 ) . It confirmed the entanglement of complexity ( accuracy ) and CT ( speed ) . In the first period of life ( <25 ) , accuracy and speed increased together in a linear manner . The adult years were remarkable in that complexity remained at a high level for a protracted period , in spite of a slow decrease of speed during the same period . This suggest that during the adult period , people tend to invest more and more computational time to achieve a stable level of output complexity . Later in life ( >70 ) , however , speed stabilizes , while complexity drops in a dramatic way . These speed-accuracy trade-offs were evident in the adult years , including the turn toward old age . During childhood , however , no similar pattern is discernible . This suggests that aging cannot simply be considered a “regression” , and that CT and complexity provide different complementary information . This is again supported by the fact that in the 25–60 year range , where the effect of age is reduced , CT and complexity are uncorrelated ( r = − . 012 , p = . 53 ) . These findings add to a rapidly growing literature that views RIG tasks as good measures of complex cognitive abilities [21 , for a review] . We have gone further here in several respects than any previous literature . First , we present a set of data collected in RIG tasks with a broad variety of instructions as to what and how to randomize: our participants playfully solved binary randomization tasks along with multiple-alternative variants; they explicitly attempted to generate random sequences , but also distributed their responses in a guessing task , typically considered “implicit random generation” . The expected outcome was unidimensional in some tasks and two-dimensional in others; constraints imposed by working memory capacity were high in some tasks , but almost absent in others . In the cognitive science literature , such diverse tasks have never been compared directly . We do not deny that the various tasks we used may tap into slightly different subcategories of prefrontal functioning , with some relying more on working memory and others on inhibitory control . Yet , we set out to illustrate the commonalities among the different tasks leaving a more fine-grained analysis to future studies . Cross-sectional studies should try to relate behavioural complexity to the degree of maturation or degeneration of specific prefrontal cortex regions . Neuropsychological investigations could use the tasks and measures employed here with selected patient groups to perform lesion-symptom mappings , as has been done recently [57] , but preferably in patients with focal vascular lesions . In parallel with such investigations , Internet-based work such as the project presented here may still play a powerful role . They may complement RIG tasks with brief behavioural tasks having a known neurocognitive basis and well-studied developmental trajectories . Thus , laboratory testing and web-based approaches may conjointly help pinpoint the cognitive mechanisms underlying the age-related modulation of behavioural complexity . A second extension of the existing literature on subject-generated random sequences is the number of participants tested and their age-range . To date , only two studies have investigated age-related changes in RIG tasks with a range comparable to the one investigated here [15 , 22] . They both compared groups of young adults and older adults and were thus unable to describe the continuous evolution of complexity across the lifespan . Finally , one of the most exciting novel aspects of this research is that we have presented an estimate of algorithmic complexity that relies on sequences shorter than any that research on RIG reported in the psychological literature would have dared to use because of the limitations of other complexity indexes . RIG tasks require a sense of randomness or complexity , as well as cognitive functions such as attention , inhibition and working memory . The evolution of algorithmic complexity over the lifespan is compatible with the idea that RIG tasks , even in a computerized and shortened format , reflect such abilities . The developmental curve reveals an evolution compatible with the concept of a combination of fluid and crystallized components in cognition , with the latter predominating . Beyond the similarity of complexity trajectories , we found that the variety of RIG tasks offered different and probably complementary information about a participant’s cognitive abilities . The exact component of cognition that is assessed by RIG tasks , and which factors differentiate the tasks , are still open questions . Our findings shed light on the developmental change in ASC , on which inductive reasoning about randomness is built . They will hopefully further our understanding of human probabilistic core knowledge . Like other complex cognitive abilities , the trend in evidence here must not occlude important intra- and inter-subject variations . Age ( squared ) explains about 2% of the variance in mean complexity , and 4% of the variance in CT . Although age is the predominant variable , CT and complexity are also affected , in the case of some tasks , by sex , statistical intuition and paranormal belief . Future research should investigate the impact of other variables on RIG performance . Examples comprise a participant’s tendency to persevering which would have to be established in an independent task . Alternatively , use of computers and familiarity with an online environment might be considered . Anonymized data are available from https://github . com/algorithmicnaturelab/HumanBehavioralComplexity | It has been unclear how this ability evolves over a person’s lifetime and it had not been possible to be assessed with previous classical tools for statistical randomness . To better understand how age impacts behavior , we have assessed more than 3 , 400 people aged 4 to 91 years old . Each participant performed a series of online tasks that assessed their ability to behave randomly . The five tasks included listing the hypothetical results of a series of 12 coin flips so that they would “look random to somebody else , ” guessing which card would appear when selected from a randomly shuffled deck , and listing the hypothetical results of 10 rolls of a die . We analyzed the participants’ choices according to their algorithmic randomness , which is based on the idea that patterns that are more random are harder to encode in a short computer program . After controlling for characteristics such as gender , language , and education . We have found that age was the only factor that affected the ability to behave randomly . This ability peaked at age 25 , on average , and declined from then on . We also demonstrate that a relatively short list of choices , say 10 hypothetical coin flips , can be used to reliably gauge randomness of human behavior . A similar approach could be then used to study potential connections between the ability to behave randomly , cognitive decline , neurodegenerative diseases and abilities such as human creativity . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"discussion"
]
| [
"computer",
"applications",
"education",
"decision",
"making",
"sociology",
"social",
"sciences",
"neuroscience",
"age",
"groups",
"adults",
"cognitive",
"psychology",
"cognition",
"computer",
"and",
"information",
"sciences",
"behavior",
"educational",
"attainment",
"people",
"and",
"places",
"psychology",
"reasoning",
"biology",
"and",
"life",
"sciences",
"population",
"groupings",
"cognitive",
"science",
"attention"
]
| 2017 | Human behavioral complexity peaks at age 25 |
Transposon mutagenesis , in combination with parallel sequencing , is becoming a powerful tool for en-masse mutant analysis . A probability generating function was used to explain observed miniHimar transposon insertion patterns , and gene essentiality calls were made by transposon insertion frequency analysis ( TIFA ) . TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon . The gene essentiality calls were compared to: 1 ) previous genome-wide direct gene-essentiality assignments; and , 2 ) flux balance analysis ( FBA ) predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1 . A three-way comparison between FBA , TIFA , and the direct essentiality calls was made to validate the TIFA approach . The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential , and that genes that contain insertions are not always nonessential . The TIFA calls were in reasonable agreement with direct essentiality calls for S . oneidensis , but agreed more closely with E . coli essentiality calls for orthologs . The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions , and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions .
Transposon mutant analysis has been extensively used to generate genome-wide mutant libraries and to define gene essentiality [1] , [2] . With the introduction of parallel sequencing , transposon-based methods have developed into phenotype information gathering tools , rather than forward genetic screens with the aim to isolate individual mutant strains . Tn-seq , and closely related methods such as Bar-seq or DNA shearing [3] , investigate mutant fitness at a genomic scale by counting the abundance of a mutant-specific DNA sequence before and after a short competitive growth period [4] , [5] . In Bar-seq the unique piece of DNA is located between known flanking sequences and can be sequenced directly [6] . In Tn-seq , a type IIS restriction enzyme that cuts outside its recognition sequence is used to extract transposons from the mutant genomes , including a flanking sequence ( 17 bp for miniHimar ) that is used to map the location of the transposon insertion . Transposon insertion sequencing has been used to identify essential genes in an increasing number of microorganisms from a wide range of ecological niches [7] . Of particular significance is the application of Tn-seq to infectious agents in order to identify essential genes that could serve as targets for therapy [8]–[12] . However , fitness due to disruption of coding sequences is not the only type of data that has been obtained from this method . When transposon mutant libraries were generated to genome-saturating conditions , the essentiality related to disruption of non-coding regions was identified [13] , [14] , facilitating the identification of non-coding regulatory elements . In S . oneidensis , himar and Tn5 transposons have been used to identify a number of mutants and elucidate cellular physiology [15]–[17] . Barcoded genome-wide mutants of S . oneidensis have been created with the himar transposon and their individual fitness evaluated in a large number of growth conditions using microarrays [18] . The creation of tagged transposon mutant libraries has also enabled systems-level analyses of S . oneidensis , such as mass-spectrometry based metabolite profiling of mutants [19] and computational inference of gene regulatory networks based on fitness data [20] . Transposon mutagenesis-based gene essentiality measurements are exceptionally informative for the validation of genome-wide modeling techniques . The genome-wide scale and low-cost nature of disposable single gene knockout libraries provide powerful datasets to evaluate the performance of genome-scale network reconstructions [21] , as well as to enrich genomic information currently used for automated network reconstructions [22]–[24] . The presented transposon insertion frequency analysis ( TIFA ) improves on the more direct interpretation in which presence of an insertion in the gene core is interpreted as sufficient evidence of nonessentiality , and absence of insertions is interpreted as essentiality [18] , [25] . The need for a more sophisticated approach has been recognized by others , with recent estimations of gene essentiality using hidden Markov models ( HMM ) [26] , [27] . TIFA distinguishes itself from an HMM approach by considering insertional biases in sequence preference and genomic location for each insertion . The insertional biases of the miniHimar transposon in S . oneidensis were investigated using an existing dataset [28] . TIFA determines the likelihood of the number of experimentally observed insertions in each gene and utilizes a probability generating function that accounts for observed insertional biases of the miniHimar transposon . The thus determined gene essentiality for growth on Shewanella Basal Medium ( SBM ) under aerobic conditions was used to investigate the stoichiometric and thermodynamic constraints on the existing MR-1 metabolic model [29] , simulating aerobic growth on SBM .
The miniHimar transposon has a strong preference to insert inside a TA sequence [30] . The flanking sequences of the chromosomal transposon insertion library confirmed this preference , with just over 95% of the mutations located inside a TA sequence . We discarded the remaining ∼5% of insertions and investigated the TA inserted transposons in more detail ( Fig . 1 ) . A potential bias in the chromosomal insertion position was investigated by plotting the insertion frequency as a function of chromosomal location ( Fig . 1A ) . Because the exact same insertion may have occurred in several independent colonies , the number of insertions was estimated from the number of TA locations that had not been inserted ( Material and Methods ) . The locational bias was quantified by fitting an absolute value second order polynomial through the observed frequency data ( Fig . 1A ) . The insertion frequency showed an approximate 25% symmetrical bias towards the origin of replication compared to the midpoint of the chromosome . Presumably , this bias is the result of the presence of multiple partial copies of an actively replicating circular chromosome [31] near the replication fork , leading to a higher physical copy number of genes closer to the origin of replication . The effect of the two flanking nucleotides on either side of the target dinucleotide was investigated in detail using genes that contained no fewer insertions ( p>0 . 1 ) than expected from the binomial distribution prediction ( Fig . 1B ) . The dataset was subdivided into 136 sections , each section corresponding to a unique combination of the two flanking nucleotides prior and the two nucleotides following a TA location . The two complementary strands of the genome result in two possible sequence orientations , with each sequence on the plus strand matching a complementary sequence on the minus strand . Sixteen sequences are palindromic , resulting in a total of ( 256-16 ) /2+16 = 136 unique sequences . For each of the 136 unique sequence combinations , the occurrences and insertion events were determined as before . The insertion probability was sequence dependent , with 3% of the probabilities differing significantly from the mean probability ( Fig . 1B ) . To determine if the flanking nucleotides affected the insertion probability independently , we assumed that the number of insertions for each flanking sequence was the product of the independent contributions of the nucleotides . Thus , each of the 136 determined insertion frequencies was given by , where is the observed insertion probability for a given flanking nucleotide combination j , and pij are the contributions of the individual nucleotides in the combination . Using weights that were inversely proportional to the variance for each sequence insertion probability , nonlinear χ2-fitting was used to calculate the parameter pij . Assuming multinomial variance , the observed probabilities differed significantly from a linear model ( χ2 test , p<0 . 01 ) , indicating that the contribution of the flanking nucleotides on the insertion probability were not independent . Fig . 1C shows the linear approximation of the nucleotide contributions , and although the linear approximation indicated that the TATATG sequence had the highest insertion probability , the independent contributions underestimated the insertion probability for this sequence by 30% . In addition , there were three sequences for which an even higher experimental insertion probability than the experimental value for the TATATG was found . Visual inspection of the experimental sequence probabilities suggested that the preferred consensus sequence was TATAxA . Just TA enrichment alone was not sufficient , exemplified by the barely average insertion probability associated with AATATT . The highest experimental insertion probability was associated with TATATA ( p = 0 . 53 ) , and the smallest probability was associated with GTTAAC ( p = 0 . 057 ) , indicating an approximate tenfold spread in insertion preference . Transposon mutagenesis results in the random disruption of genes , often reducing or eliminating gene function . In principle , transposon mutagenesis therefore reports on gene essentiality . The essentiality of a gene can be investigated by comparing the number of observed mutations in a gene to the number of expected insertions . A transposon probability model was formulated that accounted for the observed sequence and location specific biases . For each gene , the expected number of insertions was estimated using a probability generating function with the sequence specific probabilities that were weighted by the genome locational specific bias . Using this probability model , the number of expected insertions was calculated for each gene and compared to the observed number of insertions ( Fig . 2 ) . Genes were called essential if the combined probability of finding as few as , or fewer insertions than observed , was less than one over the number of genes in the dataset i . e . we accepted one false positive in our essential gene selection . To establish the exact cutoff value , the marginal probabilities for each nonessential gene to be found essential by chance for a given cutoff value were summed , and the cutoff value was adjusted until the marginal probabilities summed to exactly one . Monte Carlo sampling was used to confirm this result , and the same cutoff value was retrieved ( Text S1 ) . The transposon insertion distribution was visualized as a histogram of the difference between the expected and observed number of insertions , scaled by the standard deviation calculated on the probability generating function . Monte Carlo sampling generated a very similar distribution ( Fig . 2 ) . The right hand side of this distribution is of particular interest , as an elongated tail in the observations could suggest genes with significantly more insertions than expected . Only three such genes were observed ( SO3264 , SO4100 , SO4785 ) , indicating that TIFA formed a good description of the observed transposon insertion behavior . The poorer fit on the left hand side of the distribution could be the result of mutants with a lower than wild type fitness . Clones with a reduced fitness are more likely to escape detection due to their lower absolute abundance , yielding fewer observed insertions in nonessential genes that show reduced fitness upon deletion . Some genes with slow growing mutant phenotypes could have been called essential as a result . For 50 of the 273 identified essential genes a fitness value larger than zero was observed . To investigate if mutants with insertions in genes that were called essential grew more slowly than other clones , the nonessentiality probability was plotted against fitness ( Fig . 3A ) . Although the average fitness of clones with insertions in essential genes was lower than fitness of clones with insertions in nonessential genes , the spread was very large , indicating that slow growth was an insufficient explanation for all detected insertions in essential genes . The insertion locations were plotted together with the positions of conserved domains and , to investigate the potential for reinitiation of transcription and translation downstream of the transposon , estimates of the strength of ribosomal binding sites associated with alternative start codons inside the genes ( Figure S1 ) . Following identification of essential genes with a nonessential gene insertion model , the nonessential genes were identified with an essential gene insertion model . The insertion frequency in essential genes was approximated by multiplying the TA location-specific insertion probabilities with the ratio of the observed insertions in essential genes by the expected number of insertions for nonessential genes . The expected number of insertions for each gene was calculated using this essential gene model , and each gene that contained significantly more insertions than expected , was called nonessential . Following the earlier logic , a cutoff value was used that allowed for a single false-positive nonessential gene identification . This method identified 2 , 216 genes as nonessential . No essentiality call could be made for 1 , 722 genes , and three genes ( SO2148 , SO3175 and SO3872 ) were identified as both essential and nonessential . Hence , the number of insertions in these three genes was significantly fewer than could be expected for nonessential genes , yet significantly more than could be expected for essential genes . For example , closer inspection of SO3872 revealed that insertions were concentrated in the second quartile and most showed a reduced fitness ( Fig . 3B ) . Two additional insertions in the latter half of the gene showed high fitness , and no other insertions were present . There was very good sequence support for the gene assignment based on the sequence alignment with the arylsulfate sulfo-transferase pfam PF05935 [32] . Conceivably , only the beginning and second half of the gene were essential for the production of a functional protein , and start codons around the midpoint of the gene with associated ribosomal binding sites of moderate projected strength [33] , suggested that translation may be reinitiated within the gene resulting in separate expression of the second half of the gene ( Fig . 3B ) . More generally , essential genes could have nonessential regions such as a regulatory site which may be highly inserted , and be adjacent to uninserted regions necessary for the essential gene role , which could cause a dual essential-nonessential identification . Gene essentiality calls that were performed in previous work on the bases of absence or presence of insertions [18] were compared to TIFA calls in detail ( Table 1 and Dataset S1 ) . Seventy eight essential gene calls and 1 , 958 nonessential gene calls were in agreement . Eighty one genes that were previously identified as nonessential ( including two orthologs to essential genes in E . coli ) were identified as essential by TIFA , 36 of which contained no insertions in our dataset . Fifty one of the genes previously identified as essential were identified as nonessential by TIFA , and contained an average of 10 insertions per gene . The number of TA sequences and the number of detected insertions were tallied for each gene percentile to investigate if the positions of insertions inside ( essential ) genes were biased ( Fig . 4 ) . No percentile showed a difference in relative insertion frequency prior to identification of essential genes ( Fig . 4A ) . Analysis of just the nonessential and essential genes revealed a substantial increase of insertions in the last 2–3% of essential genes . Essential genes were almost twice as likely to contain insertions in the last 2% nucleotides , but insertion frequencies were still only half of the frequencies observed in nonessential genes ( Fig . 4B ) . Nonetheless , the increased insertion frequency at the very end of essential genes provides quantitative support for the omission of insertion data from the last 2% of genes . Previously , much larger areas of genes were excluded from insertion analysis , arguing that insertions in the distal parts of genes may not be effective in eliminating gene function [4] . No relative increase in insertion frequency was observed at the beginning of genes . The ability to identify essential and nonessential genes with TIFA was utilized to validate essentiality predictions of an existing genome-scale metabolic model of S . oneidensis MR-1 that had been manually curated previously [29] . The MR-1 model includes 774 reactions , 783 gene and 634 unique metabolites . Flux balance analysis ( FBA ) was used to infer gene essentiality for genes that were present in the MR-1 . The transposon mutants were selected under aerobic conditions on SB media , and FBA predicted 209 essential genes and 574 nonessential genes in the MR-1 model for these conditions . The annotations of thirteen genes in the MR-1 model were obsolete , which reduced the comparison of FBA to TIFA predictions to 770 genes . TIFA was able to determine essentiality for 481 genes ( 62% ) of this set ( Table 2 ) providing a comprehensive evaluation of the network essentiality predictions . Of the 273 identified essential genes identified by TIFA , 75 were present in the MR-1 model . Fifty seven of the included 75 genes were correctly predicted essential , and 374 of the 406 FBA-nonessential genes that were present in the MR-1 model and were identified as nonessential by TIFA ( Fig . 5 ) . FBA essentiality predictions were insensitive to the 1% biomass production cutoff , with 1% growth resulting in the same knockout predictions as no growth ( Text S1 ) . The TIFA essentiality calls were fairly sensitive to the essentiality cutoff . If for instance the lower 2 . 5% likelihood of gene nonessentiality had been used as essential gene cutoff instead ( Fig . 5a ) , twice as many genes ( 623 ) would have been identified as essential suggesting that only half of the essential genes were called . However , the number of false positive would have been much larger ( 54 genes , calculated from marginal probabilities ) , and the agreement between FBA and TIFA prediction was indeed substantially better when using the stringent cutoff ( Fig . 5a ) . False nonessential FBA predictions could be caused by an array of pleiotropic effects , such as the built-up of a toxic metabolite resulting from the removal of a downstream reaction in a pathway . More directly , false nonessential predictions may be caused by a combination of incorrect gene-reaction association , lax thermodynamic constraints , and over-inclusion of metabolic reactions . Such an overly inclusive metabolic network could also arise as a result of the absence of , or an incomplete gene regulatory layer . Blocked reactions , which cannot carry flux under any circumstances , point to the most easily interpretable shortcomings of the network . Eleven of the 18 FBA-nonessential , TIFA essential genes were associated with such blocked reactions ( Text S1 ) . These reactions were evidently also not required for biomass formation by the model , indicating that the biomass equation was not sufficiently inclusive to test all essential genes , or in reality unused reactions provided an alternative route to essential biomass . In addition , because the reactions were blocked , TIFA suggested that the MR-1 model requires modifications to unblock these TIFA essential reactions . In the case of aconitase ( E . C . 4 . 2 . 1 . 3 ) , the discrepancy between the MR-1 model and the TIFA essentiality data highlighted the need for a comprehensive gene expression and protein activity regulation simulation in metabolic models . In the MR-1 model , acnB ( SO0432 ) and acnD ( SO0343 ) were independently assigned to the aconitase reaction ( OR relationship ) , resulting in a nonessential prediction for acnB . However , an acnB deletion strain was unable to grow in the presence of oxygen [28] , which was consistent with transposon insertion data . Genes that were falsely predicted essential by FBA could be interpreted as resulting from missing reaction in the network , insufficiently permissive thermodynamic constraints , errors in gene-to-reaction relationships , an overly inclusive ( essential ) biomass equation , or an incorrect regulatory layer . However , pleiotropic effect cannot explain these discrepancies . Of the 2216 TIFA identified nonessential genes , 374 genes were correctly predicted to be nonessential and only 32 were incorrectly predicted essential by FBA ( Fig . 5 ) . Of the incorrectly essential predictions , SO1498 and SO3745 would have been correctly predicted nonessential if the two biomass components lipopolysaccharide ( LPS ) and glycogen were not included in the biomass equation . LPS may indeed not be required for growth [34] . The transporter for ammonia , amtB ( SO0760 ) may only become essential at very low ammonia concentrations [35] . A detailed comparison between TIFA and FBA essentiality is shown in Text S1 . The observed discrepancies between TIFA and the model predictions were further investigated by: 1 ) allowing metabolites to freely leave the network , and 2 ) by removing the thermodynamic constrains from the model . Lifting of stoichiometric constraints on endpoint metabolites resulted in a marginal deterioration of essential gene predictions , and no change in nonessential gene predictions ( Table 2 ) , suggesting that blockage of reactions due to stoichiometric constraints on endpoint metabolites was not important in FBA gene essentiality predictions for the MR-1 model . Removal of the thermodynamic constraints resulted in a substantial deterioration of essential gene predictions and a very marginal improvement of nonessential gene predictions ( Table 2 ) , confirming the importance of correct thermodynamic constraints in gene essentiality predictions . In comparison to a model of central metabolism of S . oneidensis MR-1 [36] that was formulated for elementary mode analysis , the much larger scale MR-1 model improved gene essentiality predictions substantially . Only one gene ( SO3547 ) of the previously eight genes falsely predicted essential ( SO0424 , SO0323 , SO0538 , SO1926 , SO2629 , SO3547 , SO0274 , and SO3517 ) was still predicted incorrectly , demonstrating the previously observed enhanced predictive capabilities of more complete networks [37] A direct comparison between TIFA and the previously made direct essentiality calls ( DECs ) that were based on the presence/absence of insertions in the 80% core sequence of genes [18] is shown in Table 1 . Note that FBA gene essentiality predictions for the DEC dataset had to be computed for LB medium instead of SBM . Due to the richer composition of LB , 18 genes fewer were FBA essential , eleven of which could be explained by the presence of tryptophan and pyrimidine in LB ( Dataset S1 ) . One would expect that the FBA prediction agree more with the TIFA essentiality calls than with the original MR-1 essentiality calls , if 1 ) TIFA identifications are an improvement over the original direct method , and 2 ) FBA predicts gene essentiality correctly more often than not . A three-way comparison between the TIFA essentiality calls , DECs , and FBA predictions was used to investigate the relative agreement between the FBA predictions and the two essential gene identification methods . A gene-by-gene comparison is included as supplementary data ( Dataset S1 ) . Because all TIFA predictions that could be alternatively explained by polar operon effects had been removed , the number of comparisons between TIFA and FBA were substantially fewer than between the DEC essentiality calls and FBA ( Fig . 6 ) . The overall performance of TIFA , expressed as percent correct predictions ( combined true essential and true nonessential predictions divided by all essentiality predictions ) , was much higher than for DEC: 90% vs 79% ( Fig . 6 ) , indicating that TIFA calls were indeed better . To investigate the potential influence of polar effects on the essential gene predictions , the TIFA calls that had previously been removed from the dataset because their essentiality could be explained alternatively by polar effects , were compared to the essential gene TIFA calls . Note that polar effects are only a problem for essential , and not for nonessential gene calls . The correct prediction percentage of the discarded essential gene predictions was 69% ( Fig . 6 , 43/ ( 43+19 ) ) , which was only slightly lower than the 76% ( Fig . 6 , 57/ ( 57+18 ) ) for the retained TIFA comparisons . This suggested that polar effects may not result in many false essential gene assignments if these assignments had been used . This was not surprising given that for a polar effect to occur , a downstream neighboring gene in the same operon had been identified as essential . Genes that were exclusively identified as essential by DEC were wrong more often than not ( 40% correct essential gene predictions , ( Dataset S1 ) . Genes exclusively identified as essential using TIFA , had a correct prediction percentage of 68% , which was only slightly lower than the 76% for all TIFA essential gene predictions ( Dataset S1 ) . Genes exclusively identified as nonessential by TIFA were in equally good agreement with FBA predictions as the entire TIFA nonessential gene calls ( 92% compared to 91% agreement ) . DEC nonessential gene assignments were in 87% agreement with FBA predictions , and exclusive DEC nonessential gene assignments were only 74% in agreement with FBA predictions . In summary , the TIFA gene essentiality calls ( both essential and nonessential gene calls ) were in much closer agreement with the FBA model predictions than the DEC essentiality calls , providing strong support for TIFA as a better method for gene essentiality calls ( Dataset S1 ) .
Tn-seq is a powerful and readily available technology for the genome-wide evaluation of metabolic networks . The observed transposon insertion pattern suggested that 28 . 6% of essential genes were occasionally inserted , and that insertions in essential genes were not limited to the periphery of genes as was previously presumed [18] . Conversely , several genes that did not contain insertions were not identified as essential . Both TIFA , and the previously developed HMM models , are able to identify essential genes containing insertions , but unlike the current HMM models , TIFA explicitly corrects for the observed transposon insertional biases . Note that the confidence associated with an essentiality call was depended on the number of TA locations within a gene . For genes that contained only a handful of TA sites , essentiality could not be established , even if no insertions were found . Hence , to establish essentiality in very short or GC rich genes , a very large mutant library is required . Conversely , genes with many TA locations could be called essential , even if they contained a significant number of insertions . With the current library size , essentiality of 1725 ( 41% ) of the genes could not be called . The observed transposon insertion pattern was in close agreement with Monte Carlo insertion simulations that utilized location specific insertion probabilities . The absence of insertional “hotspots” in comparison to the Monte Carlo simulations was interpreted as validation for the essential gene assignments . The data used for this study was generated from a transposon that was transcriptionally terminated . As a consequence , essentiality could only be determined for a subset of the genes that were identified as essential by TIFA ( ∼70% , Table 1 , 273/ ( 273+120 ) ) . A substantial number of genes ( 120 ) contained sufficiently few insertions to be identified as essential ( Table 1 ) , but the lack of viability could alternatively be explained by the presence of an adjacent downstream essential gene in the same operon . Note that experimental operon predictions from RNA-seq data [38] could improve the here used computational operon projections . Alternatively , utilization of an unterminated read-through transposon would eliminate polar gene essentiality experimentally . The TIFA essential gene predictions agreed fairly well with direct essentiality calls for MR-1[18] , but were in closer agreement with essentiality expectations from E . coli orthologs ( Dataset S1 ) and FBA predictions of the MR-1 model . In addition , many genes that had been previously identified as essential based on the absence of insertions often contained many insertions in our dataset . TIFA was able to provide transposon insertion-based essentiality calls for 481 of the 770 ( 62% ) non-obsolete genes in the MR-1 model , and was thereby able to perform a comprehensive validation of the MR1 model . For example: the 32 genes incorrectly predicted essential by FBA , suggested that the current MR-1 model was incomplete . And , the 11 TIFA essential genes associated with blocked reactions suggested that some essential reactions in the MR-1 model could not be used , again indicating that the current MR-1 network was incomplete . In addition , FIFA data demonstrated that thermodynamic constraints on the reaction directionalities greatly improved FBA gene essentiality predictions .
A detailed description of the transposon experiment that generated the data used for this work was published previously [28] . Briefly , a single-mutant library of S . oneidensis was generated with the miniHimar transposon under kanamycin selection on Schewanella Basal Medium ( SBM , which is a well-defined rich medium ) , plates under aerobic conditions [39] . The fitnesses of the pooled clones were evaluated under aerobic conditions using SBM as previously described [4] . Samples of the pooled library were collected before and after a short growth period . The raw sequence data for each sample was mapped to the genome , was filtered to only retain sequences that occurred at least eight times , and that could be mapped uniquely . For TIFA , the data of all four samples were combined to assess the viability of insertional mutants under the condition that were used to generate the library . The fitness values used in the manuscript were calculated from the aerobic treatment data only . Gene fitness was calculated as the median fitness of all clones that were inserted into the same gene ( Text S1 ) . The probability of an insertion occurring r times at a given location was approximated by the Poisson distribution: , which reduces to for r = 0 , where λ is the insertion probability m/n with m the number of colonies , and n the number of TA sites in the genome . This yielded the probability of finding at least one insertion for a given location: , and the total number of observed insertions . The number of colonies in the library was estimated by substituting the number of unique sequences in the library , the number of TA sites ( n ) in the genome , and the mean insertion probability p . The same approach was followed for determining the sequence-specific insertion probabilities , limiting the analysis to the fraction of TA locations with a given flanking motif ( Text S1 ) . Assuming equal insertion probability for each TA site , the probability of observing at least the number of experimentally inserted locations in a gene is given by the cumulative probability of the binomial distribution , β ( s , pe ) which equates to: , where pe is the insertion probability for a TA site , s is the total number of TA sites in a gene , and t the number of observed mutations . A probability generating function was used if equal insertion probability could not be assumed . The general form of G ( x ) for each gene was written as: , where s is the number of TA locations in a gene , and pi the specific probability for the insertion location . In the power series expansion of G ( x ) , the coefficient of xt is the probability P ( X = t ) . The cumulative probability of observing up to t insertions in s possible TA locations was expressed as . The transposon insertion expectation for a gene was calculated as with a variance of . The normalized deviation of expectation ( NDE ) is given by: . Transposon gene deletion simulations were performed for two different media conditions: LB for the dataset from Deutschbauer [18] , and SBM for the dataset from Brutinel [28] . Because both mutant libraries were generated under aerobic conditions , FBA predictions were made for aerobic conditions . To prevent artifacts resulting from unrealisticly large redox exchanges with the media , nutrient uptake rates were limited to the concentrations in the media ( Text S1 for details ) . The low concentration of metals in both media were therefore unable to sustain dissimilatory metal-reducing growth . Genes were designated FBA essential if removal resulted in <1% biomass production relative to wild type [40] Using zero biomass production as alternative cutoff [41] , [42] resulted in identical predictions , suggesting that prediction were insensitive to the cutoff value ( Text S1 for details ) , which was consistent with previous observations for E . coli networks [40] , [42] . Computationally zero growth was assessed as a biomass production of <1e-6 to eliminate the influence of computational noise . The S . oneidensis MR-1 metabolic model was previously reconstructed from the original genome annotation [29] , which was used in this study for comparison ( NCBI , NC_004347 . 1 ) . Genes that were removed in later annotations ( NC 004347 . 2 ) were not used . In addition , all TA loci in areas where two genes overlapped were excluded from the dataset . The essentiality of 4 , 214 genes in the S . oneidensis genome was investigated by TIFA . The S . oneidensis essentiality and fitness predictions were performed in MATLAB ( MathWorks , Natick MA ) , by using the COBRA toolbox [43] in combination with the linear optimization routine ( simplex algorithm ) from the CPLEX software suite ( IBM , Armonk NY ) . Operon calls for S . oneidensis were taken from ProOpDB [44] and only the terminal genes on operons were used for fitness analysis . Essentiality calls were made for all terminal genes . In addition , upstream genes on the operon were used if they were evaluated as nonessential by using TIFA , or if the directly downstream gene was called nonessential by TIFA . TIFA , genome and Monte Carlo analyses were performed with custom MATLAB and Python ( http://www . python . org/ ) scripts . Unless otherwise indicated , significance was evaluated at p<0 . 05 . | Metabolic modeling techniques play a central role in rational design of industrial strains , personalized medicine , and automated network reconstruction . However , due to the large size of models , very few have been comprehensively tested using single gene knockout mutants for every gene in the model . Such a genetic test could evaluate whether genes that for a given condition are predicted to be essential by a model , are indeed essential in reality ( and vice versa ) . We developed a new probability-based technology that identifies the essentiality of genes from observed transposon insertion data . This data was acquired by pooling tens of thousands of transposon mutants , and localizing the insertion locations all at once by using massive parallel sequencing . We utilized this gene essentiality data for the genome-scale genetic validation of a metabolic model . For instance: our work identified nonessential genes that were predicted to be essential for growth by an existing metabolic model of Shewanella oneidensis , highlighting incomplete areas within this metabolic model . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"systems",
"biology",
"biology",
"and",
"life",
"sciences",
"microbiology",
"computational",
"biology"
]
| 2014 | Genome-Scale Metabolic Network Validation of Shewanella oneidensis Using Transposon Insertion Frequency Analysis |
Dengue has emerged as a significant public health problem in Sri Lanka . Historically surveillance was passive , with mandatory dengue notifications based on clinical diagnosis with only limited laboratory confirmation . To obtain more accurate data on the disease burden of dengue , we set up a laboratory-based enhanced sentinel surveillance system in Colombo District . Here we describe the study design and report our findings of enhanced surveillance in the years 2012–2014 . Three outpatient clinics and three government hospitals in Colombo District that covered most of the Colombo metropolitan area were selected for the sentinel surveillance system . Up to 60 patients per week presenting with an undifferentiated fever were enrolled . Acute blood samples from each patient were tested by dengue specific PCR , NS1 ELISA and IgM ELISA . A sub-set of samples was sent to Duke-NUS Singapore for quality assurance , virus isolation and serotyping . Trained medical research assistants used a standardized case report form to record clinical and epidemiological data . Clinical diagnoses by the clinicians-in-charge were recorded for hospitalized cases . Of 3 , 127 febrile cases , 43 . 6% were PCR and/or NS1 positive for dengue . A high proportion of lab confirmed dengue was observed from inpatients ( IPD ) ( 53 . 9% ) compared to outpatient ( clinics in hospitals and general practice ) ( 7 . 6% ) . Dengue hemorrhagic fever ( DHF ) was diagnosed in 11% of patients at the time of first contact , and the median day of illness at time of presentation to the sentinel sites was 4 . Dengue serotype 1 was responsible for 85% of the cases and serotype 4 for 15% . The sensitivity and specificity of the clinicians’ presumptive diagnosis of dengue was 84% and 34% , respectively . DENV-1 , and to a lesser degree DENV-4 , infection were responsible for a high proportion of febrile illnesses in Colombo in the years 2012 to 2014 . Clinicians’ diagnoses were associated with high sensitivity , but laboratory confirmation is required to enhance specificity .
Dengue is one of the most important vector-borne viral disease worldwide , transmitted by Aedes mosquitoes and caused by any of the four dengue virus serotypes [1] . The extent of dengue transmission and therefore the risk of outbreaks is determined by a combination of various factors; these include the virulence of the dominant viral genotype , the level of herd immunity , the abundance , species and vector competence of Aedes mosquitoes; weather and climate variables , human population density , and the distribution and movement of the viruses , vectors and humans [2] . The dramatic global spread and increased frequency and magnitude of epidemic dengue/dengue hemorrhagic fever ( DEN/DHF ) in the past 40 years underscores the critical need for more effective surveillance , prevention and control of this disease [3] . The main purpose of surveillance is to provide the information necessary for risk assessment , program evaluation , and to allow timely action to prevent or control epidemic dengue . Most endemic countries do not have adequate dengue surveillance and rely on passive syndromic surveillance , which is known to lack sensitivity and specificity [4] . Many experts have called for more active surveillance to provide better early warning signals to trigger effective dengue emergency response [4] . In 2011 , the European Commission funded three European consortia with a specific focus on surveillance and control of dengue [5] . One of these three dengue consortia , “DengueTools” ( www . denguetools . net ) was funded to set up a laboratory-enhanced sentinel surveillance in Sri Lanka [6] . In Sri Lanka , dengue increasingly poses a significant socio-economic and public health burden [7] . The geographic spread , incidence and severity of disease is of major concern ever since the first dengue hemorrhagic fever epidemic occurred in 1989 [8] . Periodic epidemics have become progressively larger , peaking with the 2009–2014 epidemic with 28 , 000 to more than 40 , 000 cases reported each year ( 44 , 461 cases in 2012 ) [9] , which may be partly due to increasing awareness and diagnosis , but is more likely a true reflection of dengue emergence in this country , as also observed in the Asia Pacific region . During that same period , the disease dramatically expanded to the whole island . Historically surveillance was passive with mandatory dengue notifications based on illness clinically compatible with dengue and only limited laboratory confirmation . To augment the existing passive surveillance in the country , we set up a laboratory-enhanced sentinel surveillance system in Colombo District , Sri Lanka . Here we describe the study sites , research design and the findings of the first two years .
Ethical approval for the study was obtained from the Ethics Review Committee , Faculty of Medicine , University of Colombo , Sri Lanka . All informed consent by participants were in written format . Parental consent was obtained for all participants up to the age of 18 years . For children below 12 years of age , the informed consent was written and signed by their parents/guardians while for those who were above 18 years of age , informed consent was written and signed by them . All data that were analyzed were anonymized . Colombo district reports the highest dengue caseload for any given year . Colombo is one of the 26 administrative districts in Sri Lanka , located in the Western part of the country . The national population is 20 . 4 million ( Census 2012 ) , of which approximately 12% live in Colombo District . The greater part of the area is urban ( 54 . 6% ) , with the country’s highest population density of 3 , 438 km-2 ( Dept . of Census and Statistics , Sri Lanka ) . The weather is wet ( average rainfall of 2 , 306 mm/year ) and tropical ( an average temperature of 27°C ) . Colombo is the commercial and administrative hub of the country with an average of one million people commuting in and out of the city on a daily basis . Free health-care service is available to the entire population . We selected three government hospitals ( Lady Ridgeway Hospital for Children ( LRH ) , Infectious Disease Hospital ( IDH ) , and District Base Hospital–Homagama ( BHH ) ) and three general practitioner ( GP ) clinics . LRH is the national Centre of Excellence for children on tertiary medical care with over 900 beds and an outpatient attendance over one million per year . IDH is the national Centre of Excellence on infectious disease medical care . The selection of sentinel sites was made ensuring wide geographical coverage in the District ( Fig 1 ) . Patients presenting to any of the 6 sentinel sites were recruited if they fulfilled the following inclusion criteria: undifferentiated febrile illness with a duration of less than 7 days ( with fever defined as temperature greater than 38 . 0°C ) and providing informed consent . Total recruitment per week was capped at 60 patients . Cases from LRH were enrolled on a daily basis ( maximum 10 per week ) , and from the other two hospitals once a week ( for patients presenting on one specified day of the week , maximum 10 on that particular day ) . In addition , the research team visited the three GP clinics once per week and enrolled up to 10 febrile patients that presented with an undifferentiated fever on that day . From all enrolled patients , a 2 . 5–5 . 0 ml venous blood sample was collected and transported to the Medical Research Institute ( MRI ) laboratory on the same day . Interviewer administered , pre-tested case report forms ( CRF ) were developed to collect the patient clinical and epidemiological data . The CRFs were administered and filled in by research assistants who were pre-intern medical officers trained in the WHO dengue case classification and in obtaining data in a standardized manner . Data were collected on age , sex , residency , presumptive clinical diagnosis , signs and symptoms with duration by systems , past medical , drug and vaccination history , laboratory test report findings on day of first presentation to the sentinel site , and treatment received . Day of illness was defined as the day since onset of fever . The medical research assistants recorded their presumptive diagnosis at the time of the interview based on the presenting symptoms , as well as the presumptive diagnosis that was recorded by the clinicians-in-charge in the patients’ source records ( not the CRFs ) at first contact . All hospitalized patient records were reviewed after their discharge and the discharge diagnosis was entered as recorded per the clinician-in-charge . The WHO classical dengue case classification from 1997 was used for the diagnosis of DHF and dengue shock syndrome ( DSS ) . All CRFs were crosschecked for completeness by the medical research assistants and then passed on to the data management team at the central epidemiology office at the Ministry of Health in Colombo . A coding key was used and data entered in duplicate into a Microsoft SQL Server 2008 database by trained data-entry staff . Duplicate databases were compared and discrepancies resolved by referring to the original documents . The database was sent to Umeå University in Sweden for further data cleaning and random quality control . All laboratory testing was carried out at the Medical Research Institute which is the national center for laboratory services . A new dengue diagnostic laboratory at MRI was set up to process the samples for this study . Genetech Research Institute ( GRI ) Colombo , a private sector non-profit research institute was selected as a supporting reference laboratory to the project and functioned as an interim testing laboratory in the first year of the project to ensure smooth transition of diagnostic testing to the newly established MRI dengue laboratory . Dengue screening RT-PCR was performed on all samples followed by serotyping by semi-nested PCR for all positive samples [10] . Dengue IgM capture ELISA ( Standard Diagnostics , Korea ) and Dengue NS1 antigen ELISA ( Standard Diagnostics , Korea ) were conducted on all samples according to manufacturer’s specifications . We defined current laboratory confirmed dengue diagnosis as PCR or NS1 positive . However , as a vast majority of patients were enrolled at day 4–5 of illness , when often PCR turns negative and dengue IgM positive , in order to capture all cases , we included dengue IgM positivity to the diagnosis of laboratory confirmed dengue . Results for PCR , NS1 and IgM positivity are reported separately and combined , as well as per day of illness A sub-sample of sera from dengue positive and negative patients was sent to the Duke-NUS Graduate Medical School in Singapore once every 3 months for quality control assessments , virus isolation , and serotyping . Virus isolation was done using C6/36 Aedes albopictus cell lines and mosquito inoculation of selected samples[11] . Serotype-specific detection of dengue viruses was done by fourplex real-time reverse transcriptase PCR assay developed by the CDC[12] . To determine dengue specific IgG and dengue specific IgM we used an in-house protocol according to standard methodologies . Statistical analysis was performed with STATA version 12 ( StataCorp , TX , USA ) . Sample proportions and means of demographic characteristics were presented in a descriptive table ( Table 1 ) . Categorical variables were compared by Fisher’s exact or Chi-square test , as appropriate , between groups . The Student’s t-test was used for continuous variables . Significance was assigned at P < 0 . 05 for all parameters and were two-sided unless otherwise indicated .
Between 1 April 2012 and 31 March 2014 , we enrolled 3 , 127 patients of which 1737 ( 55 . 6% ) were males . Of these 3 , 127 febrile cases , 90 . 1% ( n = 2 , 817 ) were enrolled from the three sentinel hospitals , where 2 , 150 ( 76 . 3% ) were from the inpatient departments ( IPD ) , 667 cases ( 23 . 7% ) from the hospitals outpatient departments ( OPD ) and; 310 patients ( 9 . 9% ) were enrolled from the GPs ( Table 1 ) . The mean age of the study population was 22 . 3 years ( SD 17 . 5; range 1 month to 90 years of age ) . The majority ( 80 . 6% ) of the subjects were recruited ( and hence blood samples collected ) on day 3–6 of illness with a mean of 4 . 2 days ( SD 1 . 6 ) . The median day of illness at time of presentation to any of the sentinel sites was 4 for all cases , with the mean day of illness being 4 . 6 days for hospitalized cases ( SD 1 . 40 ) and 3 . 1 ( SD 1 . 46 ) for non-hospitalized patients ( p<0 . 001 ) . DHF cases presented on day 4 . 83 of illness ( SD 1 . 33 ) ; lab confirmed dengue fever ( without DHF ) on day 4 . 69 ( SD 1 . 31 ) and other febrile illnesses ( OFI; all dengue assays negative ) on day 4 . 22 ( SD1 . 59 ) . The average duration of hospitalization was 4 . 13 days ( SD 1 . 85 ) . Table 1 summarizes the differences between in-and outpatients in terms of demographic variables and outcomes . There were 5 deaths ( 0 . 16% ) , all classified by the clinicians as due to DHF , and all were laboratory confirmed dengue . The case fatality was 5 out of 525 DHF ( 0 . 95% ) . Of the 3 , 127 febrile cases , 43 . 6% were PCR and/or NS1 confirmed dengue cases ( Table 2 ) . Overall , the proportion of NS1 positivity was higher compared to PCR ( Fig 2 ) . The majority of the laboratory confirmed cases were from patients recruited from IPD ( 53 . 9% ) compared to OPD ( hospitals and GPs ) ( 7 . 6% ) . Of patients presenting within 5 days of illness at GP settings , 36 out of 310 had a positive PCR or NS1 ( 11 . 6% ) . Table 2 shows the differences of the assay results between the inpatients and outpatients ( hospital outpatients and GPs ) , both separately as well as in combination . A subset of samples ( n = 536 ) was sent to the Emerging Infectious Diseases Program Laboratory at the Duke-NUS Graduate Medical School in Singapore for serotyping , quality assurance ( IgM and PCR ) and secondary infections ( dengue IgG ) . Serotyping identified DENV-1 in 85% and DENV-4 in 15% . The percentage agreement for both PCR and IgM tests between the Singapore and Sri Lanka was 80 . 7% and 77 . 5% , respectively ( Table 3 ) . Using positive virus isolation as gold standard , the sensitivity and specificity of the Sri Lanka performed PCR was similar to the Singapore performed PCR , confirming the quality of the Sri Lanka laboratory ( Table 4 ) . Based on IgG testing in the subset of samples at Duke-NUS , the overall IgG seroprevalence ( in patients with or without dengue ) was 74 . 4% with increasing percentages from 67 . 9% in those below the age of 18 to 90 . 3% in those above age 45 . In the absence of a paired sample , we used a positive dengue specific IgG < 7 days as proxy for a secondary dengue infection . The proportion of patients with PCR or NS1 confirmed dengue who had secondary dengue infections ( positive IgG ) was 57 . 1% . NS1 and PCR positivity was higher in patients with primary infections ( defined as IgG negative ) ( 96% and 98% ) compared to secondary infections ( IgG positive ) ( 81 . 2% and 83 . 7% ) while IgM positivity was higher in patients with secondary infections ( 60 . 2% ) in the selected sample set sent to Duke-NUS . As we only did dengue IgG in a sub-set of patients , we were not able to correlate secondary versus primary infections with disease outcome in the total set of patients . Medical research assistants reviewed the signs and symptoms of all cases and made a presumptive clinical diagnosis at first presentation to the sentinel sites . Among cases that were clinically classified as DF or DHF ( n = 2081 ) , 1653 ( 79 . 4% ) were laboratory confirmed dengue . The majority of the cases diagnosed at time of interview as DF and DHF cases were from IPD ( 92 . 9% , n = 1934 ) compared to OPD ( 7 . 02% , n = 146 ) . In other words , dengue was over-diagnosed in the in-patient setting and under-diagnosed in outpatient settings . Those who presented with a clinical diagnosis of DHF had a far higher proportion of being laboratory dengue confirmed than those with a presumptive diagnosis of dengue fever ( 93 . 6% versus 76 . 6% ) . Using the definition of any of the 3 diagnostics assay ( NS1 , PCR or IgM ) as positive for ‘laboratory confirmed’ dengue , the sensitivity and specificity of the trained medical research assistant in distinguishing dengue ( includes both DHF and DF cases ) from OFI at the time of interview was 87 . 3% and 64 . 3% respectively ( Table 5 ) . The sensitivity was higher for IPD patients ( 96 . 1% ) compared to OPD ( 25 . 9% ) . The positive and negative predictive values are presented in Table 5 . The presumptive clinical diagnosis of the clinicians-in-charge ( who were not part of the study ) was obtained from the source files ( clinical records ) . These diagnoses were only available for hospitalized cases ( n = 2 , 230 ) . Using laboratory confirmed dengue as the ‘gold standard’ , the sensitivity of the presumptive clinical diagnosis for dengue vs OFI at the time of first contact was 84 . 7% , versus 94 . 2% at the time of discharge ( Table 5 ) . However the specificity was low for both: 32 . 5% at admission and 37 . 1% at discharge . Fig 3 shows the changes of sensitivity and specificity per day of illness at first presentation and compares the sensitivity and specificity of the routine clinicians-in charge versus the trained research assistant . Sensitivity increases with the duration of illness , while specificity remains low throughout . The sensitivity of the trained research assistant was higher than that of the clinicians-in-charge , while the specificity was higher for the clinicians-in-charge .
Syndromic surveillance is the mainstay of many surveillance systems in dengue endemic countries including Sri Lanka , but is known to lack sensitivity and specificity[4] . A laboratory-enhanced sentinel surveillance system provides more precise information to public health authorities and policy-makers on virus serotype and disease severity ( as measured as proportion of DHF over all dengue cases ) , in addition to location and time . Here we report the results of 2 years of laboratory-enhanced sentinel surveillance in the Colombo District of Sri Lanka . Several important observations can be drawn . First , we showed that a large proportion of febrile illnesses ( 46 . 3% ) in the sentinel surveillance sites were in fact due to dengue infections , thus highlighting the burden of dengue in Sri Lanka . The proportion of dengue as a cause of febrile illness was–as expected -far higher in cases seen in the hospitals versus GPs . Because the majority of cases enrolled in this surveillance were from hospitals , the proportion of lab confirmed dengue in this study is higher than that reported from other surveillance systems which usually report between 5–15%[4] . The proportion of dengue seen at GP settings ( 11 . 6% ) is consistent with the latter . The high dengue burden that we documented was consistent with the peak of dengue cases reported to the Ministry of Health in the same time period[9] . The mean age in our study was 22 years highlighting that adults are also affected by dengue in Sri Lanka . The mean duration of hospitalization was 4 . 1 days; the frequent hospitalization of dengue cases and the length of around 4 days contribute appreciably to the economic burden of dengue in endemic countries . Second , we were able to document the specific serotypes responsible for dengue infections in 2012–2014 . All laboratory confirmed cases were due to either DENV-1 or DENV-4 , with DENV-1 being the predominant serotype ( 85% ) . DENV-1 replaced DENV-3 in 2009 triggering a wave of severe dengue epidemic in Sri Lanka , all associated with a higher incidence and mortality than with any previously recorded epidemic in the country [13] . Bayesian phylogeographic analyses suggest that the 2009 Sri Lankan epidemic DENV-1 strain may have been imported from Thailand , then spread within Sri Lankan , and from there it spread further to Pakistan and Singapore[13] . Sustained outbreaks over several years due to one serotype are unusual . Because of herd immunity , most epidemics usually only last for about one year and subsequent outbreaks are then triggered by the introduction of a new serotype[3 , 14 , 15] . However , genetic changes in the virus over time also within a country can occur independent of new introductions from other countries thereby triggering new outbreaks [16] . For example , in 1989 the existing genotype of DENV-3 in Sri Lanka was shown to have undergone a lineage change which led to the first dengue hemorrhagic fever outbreak despite the fact that this serotype had been in circulation since at least 1981[17] . Sequencing of the DENV-1 taken from different time periods is still ongoing , the results of which may help us understand why this serotype was associated with sustained epidemic transmission for 6 years . Third , we documented a high proportion of DHF . In Sri Lanka , the 1997 WHO dengue case classification is still used routinely by clinicians , hence we relied on the managing clinicians’ diagnosis using the classification they are familiar with rather than the 2009 TDR dengue case classification . Dengue hemorrhagic fever ( DHF ) was diagnosed in 11% of patients at the time of first contact . For hospitalized cases , 22% were discharged with the diagnosis of DHF ( with 5 fatal outcomes due to DHF ) . A DHF proportion of 22% is unusually high , as most studies report a proportion of 5–10% underscoring that the current epidemic is associated with more severe disease . Patients who had signs and symptoms of DHF already at first encounter to our sentinel system presented much later compared to dengue fever or OFI , which could be one explanation why such cases have a worse outcome . 0 . 9% of the DHF cases died . Fourth , to assess whether costly diagnostic assays are indeed worthwhile in a sentinel surveillance system , we compared the clinicians’ presumptive clinical diagnoses with the laboratory confirmation . The clinicians’ diagnosis for dengue at time of admission had a sensitivity of 84 . 7% and specificity of 32 . 5% . The positive predictive value for the clinicians was 82 . 3% and the negative predictive value 36 . 4% at time of admission . The sensitivity and specificity was higher at discharge , most likely because the clinicians by then had all laboratory tests at hand and were familiar with the clinical evolution over time , even without knowing the results of our assays . The trained research assistants had a higher sensitivity and specificity compared to the clinicians who saw the patients as part of their daily clinical routine on busy days . The higher sensitivity by the research assistants who were all medically trained can be explained by the fact that they spent more time reviewing all the symptoms and signs as documented by our standardized case report form . This underscores that training does indeed improve sensitivity . However , having said this , the sensitivity for the routine clinicians-in-charge was still high , as Sri Lankan doctors are very familiar with dengue . Furthermore , we were able to show that the sensitivity and specificity depends on the day of illness ( Fig 3 ) , with the highest sensitivity and specificity later in the course of illness . In other words , if patients presented later ( after day 3–4 of illness ) , sensitivity increased , while specificity remained low . Mild respiratory illnesses and non-specific viral fevers are usually of shorter duration and hence present a large proportion of febrile patients seen in the first 3 days of illness . Patients with DHF were much more often correctly diagnosed as a laboratory confirmed dengue case . This finding is consistent with previous reports from Thailand , where the authors concluded that clinicians had a 62% sensitivity and 92% specificity in identifying DHF according to the WHO’s definition , without the need for laboratory confirmation of dengue virus infection in endemic areas[18] . DHF presents clinically with a very characteristic constellation of clinical symptoms , signs and changes in leukocytes , platelets , and hematocrit; a constellation that is so pathognomonic for DHF that WHO in its 2009 revised dengue case classification does not require laboratory confirmation for severe disease but does require laboratory confirmation for dengue with or without warning signs ) [19] . Overall , our findings show that the clinicians’ suspicion of dengue was very high as seen in the high sensitivity , but specificity was very low . To enhance specificity it is important to add laboratory confirmation of dengue . Fifth , we were able to assess three diagnostic essays ( PCR , Dengue IgM ELISA and NS1 ELISA ) in a sentinel surveillance setting . For dengue , day of illness determines the choice of diagnostic assay . During the viremic phase ( up to day 4–5 of illness ) , molecular biological or virological approaches such as PCR or NS1 should be employed; after the viremic phase ( ≥5 days ) , serological assays are indicated , with dengue IgM being the most frequently used assay [20] . Our figures confirm the temporal changes in positive assays per day of illness , with PCR and NS1 being positive in the earlier phase , and IgM later . A combination of methods that target different time periods maximizes diagnostic sensitivity . Given the constraints of such a large sentinel surveillance as ours , it was programmatically not feasible to take a convalescent serum at 14–21 days after discharge which would have helped in confirming the diagnosis–hence we lack a definitive “gold standard” diagnosis in those patients where we only have a single IgM result . In the absence of convalescent sera , we defined “dengue IgM , NS1 or PCR positive” as laboratory confirmed dengue which may have overestimated the results , as some IgM positive ( if NS1 or PCR negative ) cases may have been recent dengue infections rather than current ( IgM can remain positive for 3 months ) . Hence , we also report our results for PCR , NS1 and IgM separately and in different combinations . The proportion of PCR and/or NS1 positive of all 3 , 127 febrile cases was 43 . 6% , but the proportion of PCR and/or NS1 and/or IgM was 61 . 6% . As NS1 based assays are increasingly used in endemic countries , we also particularly looked at the issues of NS1 as measured by ELISA . NS1 was positive for more days of illness compared with PCR . Indeed , NS1 can be found in the peripheral blood circulation for up to 9 days from illness onset , but can persist for up to 18 days for some cases[20 , 21] . Hence NS1 offers a larger window of opportunity for diagnosis of dengue compared with virus isolation and PCR[20] . Furthermore , the proportion of NS1 positive subjects in the first 5 days of illness was higher than that of PCR . Higher sensitivity of NS1 compared with PCR has been documented in some studies [22–24] but not in others[25] . It is important to note that we tested NS1 by ELISA and not with the cheaper rapid diagnostic ( RDT ) kits that are now widely available . Hunsperger et al showed that sensitivity of NS1 by ELISA is higher ( 60–75% ) compared with NS1 RDT ( 38–71% ) [24] . Hunsperger’s analysis also showed that NS1 was more sensitive in primary versus secondary infections , an observation that we can confirm with our findings . Six , with the EU funded laboratory-enhanced surveillance project a dedicated government laboratory was set up to perform routine molecular testing for dengue . As a result , the capacity to do PCR and serotyping is now well established in Colombo . Quality assurance with a subset of samples sent to the Duke-NUS Laboratory “Emerging Infectious Diseases Program” showed a high agreement . At Duke-NUS , we also did virus isolation . Although virus isolation is highly specific , the sensitivity is reported to be only approximately 40% [26] . Virus isolation has the advantage of providing a virus isolate that can be used for further genome sequencing , or virus neutralization and other in vitro studies[20] , but it requires highly trained operators , depends on a short viremia period , thus providing only a narrow window of opportunity from illness onset; in quintessence , it is not a diagnostic approach suitable for developing countries . Cost effectiveness studies are needed to evaluate the need for assays such as PCR on a routine basis in Sri Lanka compared to the more affordable NS1 only; and our sentinel surveillance can potentially serve as a basis for such studies . The advantage of laboratory enhanced sentinel surveillance is that it is less expensive ( being restricted to small areas ) and produces data of higher quality than nationwide passive syndromic surveillance . The disadvantage of sentinel surveillance however is the inability to ensure that the sample population is representative and the inability to calculate incidence rates compared to cohort studies . The age distribution in our cohort for example is largely representative of the hospitals selected , and the high proportion of laboratory confirmed dengue reflects the fact that the majority of subjects were recruited from hospitals . In conclusion , dengue poses a high burden in Sri Lanka as evidenced by a substantial proportion of laboratory confirmed dengue cases in our sentinel surveillance system . DENV-1 and to a lesser degree , DENV-4 infection were responsible for a high proportion of febrile illnesses in Colombo during the years 2012–2014 . Clinicians’ diagnoses were associated with high sensitivity , but laboratory confirmation is required to enhance specificity . Adding laboratory confirmation to syndromic surveillance will add costs , but laboratory confirmation will also enhance specificity , help monitor changes in serotype distribution , new serotype introduction , and help to better define trigger thresholds for intensified vector control measures . | The dramatic spread of epidemic dengue underscores the urgent need for better surveillance and control of this disease . The main purpose of surveillance is to provide timely and more accurate information to institute preventive or control measures for epidemic dengue . Dengue has emerged as a major public health problem in Sri Lanka . To obtain more data on the burden of dengue , a laboratory-based enhanced sentinel surveillance system was established in metropolitan Colombo . In this paper we describe how we set up the sentinel sites and report the results of the first two years of enhanced surveillance ( 2012–2014 ) . Of 3 , 127 patients presenting with acute onset of fever , 43 . 6% had laboratory confirmed dengue ( PCR of NS1 positive ) , mainly caused by dengue serotype 1 . Clinicians’ diagnoses were associated with high sensitivity , but our findings also show that laboratory confirmation is required to enhance specificity . There were 5 deaths , all due to dengue hemorrhagic fever . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"medicine",
"and",
"health",
"sciences",
"clinical",
"laboratory",
"sciences",
"geographical",
"locations",
"tropical",
"diseases",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"research",
"facilities",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"sri",
"lanka",
"dengue",
"fever",
"epidemiology",
"clinical",
"laboratories",
"research",
"laboratories",
"people",
"and",
"places",
"infectious",
"disease",
"surveillance",
"diagnostic",
"medicine",
"asia",
"disease",
"surveillance",
"government",
"laboratories",
"viral",
"diseases"
]
| 2016 | Laboratory-Enhanced Dengue Sentinel Surveillance in Colombo District, Sri Lanka: 2012-2014 |
The female Aedes aegypti salivary gland plays a pivotal role in bloodmeal acquisition and reproduction , and thereby dengue virus ( DENV ) transmission . It produces numerous immune factors , as well as immune-modulatory , vasodilatory , and anti-coagulant molecules that facilitate blood-feeding . To assess the impact of DENV infection on salivary gland physiology and function , we performed a comparative genome-wide microarray analysis of the naïve and DENV infection-responsive A . aegypti salivary gland transcriptomes . DENV infection resulted in the regulation of 147 transcripts that represented a variety of functional classes , including several that are essential for virus transmission , such as immunity , blood-feeding , and host-seeking . RNAi-mediated gene silencing of three DENV infection-responsive genes - a cathepsin B , a putative cystatin , and a hypothetical ankyrin repeat-containing protein - significantly modulated DENV replication in the salivary gland . Furthermore , silencing of two DENV infection-responsive odorant-binding protein genes ( OBPs ) resulted in an overall compromise in blood acquisition from a single host by increasing the time for initiation of probing and the probing time before a successful bloodmeal . We also show that DENV established an extensive infection in the mosquito's main olfactory organs , the antennae , which resulted in changes of the transcript abundance of key host-seeking genes . DENV infection , however , did not significantly impact probing initiation or probing times in our laboratory infection system . Here we show for the first time that the mosquito salivary gland mounts responses to suppress DENV which , in turn , modulates the expression of chemosensory-related genes that regulate feeding behavior . These reciprocal interactions may have the potential to affect DENV transmission between humans .
With 2 . 5 billion people now living in areas at risk for epidemic transmission , dengue has become the most important mosquito-borne viral disease affecting humans [1] . Dengue virus ( DENV ) is a positive-strand RNA virus of the family Flaviviridae , genus Flavivirus . It exists as four closely related but antigenically distinct serotypes ( DENV-1 , -2 , -3 , and -4 ) , all of which have Aedes aegypti mosquitoes as their primary vector , with A . albopictus as a secondary vector . The incidence and geographic range of dengue and dengue hemorrhagic fever have increased dramatically in recent decades , and since there is at present no licensed vaccine or drug treatment against DENV , vector control remains the best method for preventing transmission . Although vertical transmission of the virus has been reported [2] , [3] , mosquitoes mainly acquire DENV by feeding on the blood of an infected human . DENV first infects and replicates in the mosquito midgut epithelium . It subsequently spreads through the hemolymph to replicate in other organs such as the fat body and trachea , finally infecting the salivary gland at approximately 10–14 days post-bloodmeal [4] . Once in the saliva , DENV can be inoculated into a human host when the mosquito acquires a blood meal , thus spreading the disease . The mosquito salivary gland plays important roles in DENV transmission . Firstly , infection of the gland itself is an essential part of the transmission cycle . Secondly , the salivary gland produces numerous anti-coagulant , anti-inflammatory and vasodilatory molecules which facilitate probing and bloodmeal acquisition [5]–[10] , as well as immune factors that reduce microbial loads in ingested blood and nectar . Lastly , mosquito saliva can impair the immune response of the vertebrate host to arbovirus infection , resulting in increased viremia levels and increasing the risk of virus transmission ( reviewed in [11] ) . Despite its importance in pathogen transmission , the current knowledge on antiviral defense in the salivary gland is limited and is mainly represented by a recent study which identified a cecropin-like peptide with antibacterial and antiviral activities that was induced upon DENV infection of the gland [12] . Mosquitoes are exposed to a variety of microbes in their natural habitats , and possess an innate immune system capable of mounting a potent response against microbial challenge . In addition to RNA interference ( RNAi ) [13] , the Toll and Janus kinase signal transducer and activator of transcription ( JAK-STAT ) pathways have been found to be key players in A . aegypti anti-DENV defense [14] , [15] . To date , however , most studies of mosquito antiviral immunity have examined DENV replication in the midgut , but not in other biologically relevant compartments such as the salivary gland . In addition , despite the well-documented involvement of the Toll and JAK-STAT pathways in insect immunity , the specific molecular mechanisms by which these pathways act remain uncharacterized . Viral pathogen-associated molecular patterns ( PAMPs ) and their associated insect pattern recognition receptors ( PRRs ) have not yet been discovered , and only a few putative antiviral effector molecules have been identified [12] , [15]–[17] . To gain a better understanding of how the A . aegypti salivary gland experiences DENV infection at the global transcriptome level , we have used whole-genome microarray-based analyses to compare the naïve and DENV-infected salivary gland . These experiments revealed intriguing patterns of differential transcript abundance that suggested a broad impact of DENV infection on a variety of salivary gland functions , including those implicated in immunity , host-seeking , and blood acquisition . To confirm the functional relevance of DENV-modulated transcript abundance , we used an RNAi-mediated gene silencing approach to show that three DENV infection-induced salivary gland-enriched transcripts can modulate DENV replication in the salivary gland , corroborating the earlier finding [12] that this organ mounts an anti-viral response . In addition , we show for the first time that silencing of two DENV infection-induced odorant-binding protein ( OBP ) transcripts impaired the host-seeking and blood-feeding ability of mosquitoes , suggesting that the virus is capable of modifying mosquito behavior through the regulation of chemosensory genes . Finally , inspired by these findings , we extended our study to show that DENV is likely to exert a broader impact on mosquito chemosensation by infecting its main olfactory organs , the antennae .
To determine the A . aegypti salivary gland transcriptome in terms of genes whose transcripts are enriched in the uninfected mosquito salivary gland relative to the carcass , we used whole genome microarray analyses to compare transcript abundance in naïve salivary gland and naïve carcass samples . We reasoned that this analysis would yield information about potential gene function , since salivary gland-enriched transcripts would be more likely to perform functions specific to this organ . Of the total number of salivary gland-expressed transcripts , 2255 ( 13 . 2% ) were significantly enriched in the salivary gland relative to the carcass , 2565 ( 15 . 0% ) were significantly enriched in the carcass relative to the gland , while 8722 ( 51 . 1% ) had a similar level of transcript abundance in the two mosquito compartments . The transcripts of 3805 genes ( 22 . 3% ) were non-detectable or did not meet our signal-to-noise criteria ( Figure 1A ) . Differentially expressed transcripts are presented in Table S1 . A previous study by Ribeiro et al . ( 2007 ) [7] detected transcripts from 835 annotated genes through sequencing of an A . aegypti salivary gland expressed sequence tag ( EST ) library . Our study detected the vast majority ( 789 out of 835 ) of these transcripts , supporting the robustness and validity of our microarray-based approach . Since our microarray-based analyses only provide information on the ratio of differential transcript abundance between the compared samples , we also considered the absolute abundance levels of salivary gland transcripts . Based on the fluorescence intensity of their spots on the microarray , we categorized transcripts into high , medium , and low abundance ( Table S2 ) . 330 transcripts ( 2 . 4% of the total ) were classified as high abundance ( fluorescence values >5000 ) , 661 ( 4 . 9% ) were medium abundance ( fluorescence values of 1000–5000 ) , and 12551 transcripts ( 92 . 7% ) were low abundance ( fluorescence values<1000 ) ( Figure 1B ) . This distribution is comparable to what has been observed for the Anopheles gambiae salivary gland transcriptome under the same analysis [6] . Genes that displayed differential transcript abundance between the salivary gland and the carcass represented a range of functional classes ( Figure 1C ) . We next provide a brief description of several functional classes that we consider pertinent to salivary gland function . Since DENV replication in the salivary gland is a prerequisite for virus transmission , we next performed a microarray analysis to compare transcript abundance between DENV-infected and naïve salivary glands at 14 days post-bloodmeal ( dpbm ) to gain a better understanding of how this organ responds to infection . DENV infection stimulated a significant enrichment of 130 and depletion of 17 salivary gland transcripts ( Figure 1A , 1C , Table S3 ) . DENV altered the abundance of 38 transcripts with functions related to metabolic processes , transport and stress response . The majority of these transcripts ( 33 of 38; 87% ) were enriched in the infected salivary gland , perhaps indicating a shift in cellular metabolic state to support virus replication . Six transcripts with predicted cytoskeletal functions were enriched upon infection; this may reflect maintenance in structural integrity of the infected salivary gland , since cytopathology has been reported in this organ following arbovirus infection [28] , [29] . Also up-regulated were two tetraspanin transcripts , which encode transmembrane proteins that have roles in cell-cell interactions , adhesion , motility , and proliferation . Tetraspanins have been found to be induced upon DENV infection of Aedes albopictus C6/36 cells [30] , and are believed to facilitate cell-to-cell spread of virus . Twelve transcripts with immune-related functions were induced by DENV infection , and included two MD2-like gene family members , which code for secreted proteins containing Niemann-Pick lipid recognition domains . Mammalian MD2 is a co-receptor that is required for Toll-like receptor 4 ( TLR4 ) binding to lipopolysaccharide ( LPS ) [31] , [32] , and silencing of the A . gambiae MD2-like family member AgMDL1 significantly increases midgut Plasmodium falciparum infection levels [33] . These data suggest a potential role for A . aegypti MD2-like family members in immune defense against DENV . Transcripts encoding a transferrin and a fibrinogen-related protein were also up-regulated . Transferrins bind iron with high affinity and play roles in iron metabolism , immunity , and development . They are up-regulated upon parasite or bacterial infection , and may sequester iron from pathogens; alternatively , proteolytic fragments from these proteins have also been suggested to also act as anti-microbial peptides or inducers of the immune response [34] . Fibrinogen-related proteins bind bacteria and parasites in mosquitoes and may function as pattern recognition receptors [35] . Transcripts of three leucine-rich repeat ( LRR ) -containing proteins and one ankyrin repeat-containing protein were induced by DENV infection . The broader LRR-containing protein family includes the mosquito Tolls , and family members are commonly involved in protein-protein interactions and signal transduction pathways [36] . Ankyrin repeats mediate protein-protein interactions and are present in several immune-related proteins , such as the IkB inhibitory domain of the NFkB-like transcription factor Rel2 of the mosquito IMD immune pathway . Three cathepsin B transcripts and a putative cystatin transcript were induced upon DENV infection . Cathepsin Bs are lysosomal cysteine proteases known to be involved in the apoptosis of immune cells [37] . They can also play roles in TLR signaling , and are required to cleave the endolysosomal TLRs 7 and 9 before these molecules can signal [38]–[40] . Cystatins are cysteine protease inhibitors that may play roles in regulating apoptosis , since many enzymes ( such as the caspases and cathepsins ) involved in apoptotic pathways are cysteine proteases [41] , [42] . A cystatin has also been reported to induce autophagy in mammalian cells [43] , and DENV is known to induce autophagy as a means of regulating lipid metabolism in the host cell [44] , [45] . The transcripts of three peptides with sequence similarity to secreted salivary peptides from Culicine mosquito species were also up-regulated by DENV infection . The functions of these peptides remain unknown , but some may be involved in the production of allergic reactions to mosquito bites [7] . Finally , DENV also induced two OBP transcripts ( OBP10 and OBP22 ) , which had also been found to be enriched in the naïve salivary gland . To determine whether our observed salivary gland transcriptomic infection responses were specific for this organ , or if they also occurred in other tissues , we went on to characterize the DENV infection-responsive carcass transcriptome at 14 dpbm . DENV infection significantly up-regulated 61 transcripts and down-regulated 74 in the carcass compartment ( Figure 1C , Table S4 ) . Only 28 genes were similarly regulated between the salivary gland and the carcass upon infection ( Figure 1D ) , indicating that the transcriptomic responses in these two compartments are quite distinct . Our transcriptomic analyses suggested that at least some of the DENV infection-responsive transcripts may play roles in limiting infection , or reflect a virus-mediated modulation of salivary gland functions that could have implications for mosquito behavior . We were particularly interested in modulators of DENV replication in the salivary gland . Based on our transcriptomic analyses and literature searches , we selected seven candidate genes for functional analysis via RNAi-mediated gene silencing ( Table 1 ) . We have elaborated on the potential modes of action of these genes in the previous section . Mosquitoes were orally infected with DENV through a bloodmeal , and candidate genes were silenced in the salivary gland at 7 dpbm by the injection of 2 ug of dsRNA per mosquito [19] . At this time point , the midgut and carcass are fully infected , and the virus is initiating infection of the salivary gland [4] . Gene silencing efficiency ranged from 26–90% ( Figure S1 ) . Salivary glands were subsequently dissected at 7 days post-silencing ( 14 dpbm ) , and virus titers were determined by plaque assay . Silencing of the putative cystatin ( AAEL013287 ) and the conserved hypothetical protein with ankyrin repeats ( AAEL003728 ) genes significantly increased salivary gland DENV titers , while silencing of the cathepsin B ( AAEL007585 ) gene resulted in significantly reduced DENV titers ( Figure 2 ) . Since injection of dsRNA into the mosquito thorax results in non-compartment-specific silencing , it is possible that the altered virus titers observed in the salivary gland are a consequence of gene silencing in other parts of the mosquito carcass . However , we consider this less likely for several reasons: firstly , DENV infection induced these genes only in the salivary gland and not in the carcass ( Table 1 ) , suggesting that they play infection-related functions in the gland; secondly , dsRNA injections were carried out at 7 dpbm , when carcass DENV titers have already peaked , while salivary gland infection is just beginning [4]; and lastly , we found no significant differences in virus titers between the carcasses of DENV-infected gene-silenced and control GFP dsRNA-treated mosquitoes ( Figure S2 ) . Transcripts of OBPs 10 and 22 ( AAEL007603 and AAEL005772 ) displayed an elevated abundance in the salivary gland upon DENV infection , and were also enriched in the naïve gland . This finding was unexpected and intriguing to us , and we hypothesized that these genes could participate in chemosensory signaling during host-seeking or probing . To test this hypothesis , these genes were individually silenced by the injection of 2 ug dsRNA per mosquito , 4 days prior to a behavioral feeding assay . Mosquitoes were offered an anesthetized Swiss Webster mouse , and the following parameters were measured: a ) Probing propensity ( percentage of mosquitoes that probed within a fixed time period ) ; b ) Probing initiation time ( time from the introduction of the mouse to the time at which the mosquito initiated probing – a rough measure of host-seeking ability ) ; and c ) Probing time ( time from the initial insertion of the proboscis in the skin to the initial engorgement of blood [6] , [10] ) . Silencing of the OBP10 or OBP22 genes resulted in a reduced probing propensity , which was statistically significant for OBP22-silenced mosquitoes ( Figure 3A ) . Knockdown of either OBP was found to significantly increase the probing initiation time compared to GFP dsRNA-treated mosquitoes ( Figure 3B ) . Probing time was also increased in OBP gene-silenced mosquitoes , although this increase was not statistically significant ( Figure 3C ) . Since only mosquitoes that probed were considered for the probing time analysis , the lower number of mosquitoes that probed in OBP-silenced groups could have contributed to the lack of statistical significance for this parameter . Taken together , these data indicate that gene silencing of these OBPs impairs the efficiency of mosquito blood-feeding . The observed effect on feeding behavior could also be due to gene silencing in the chemosensory organs ( antennae and maxillary palps ) instead of or in addition to the salivary gland . To further investigate the molecular basis of this interesting phenotype , we determined OBP gene silencing efficiency in these two body compartments by quantitative RT-PCR . High silencing efficiencies for both OBP genes were consistently obtained in the salivary gland ( averages of 87 . 4% and 86 . 8% respectively ) , while efficiencies were lower and more variable in the antennae and palps ( average of 22 . 9% for OBP10; 0% , 73 . 4% , and 32 . 5% for three trials of OBP22 ( Figure 4A ) ) . While these data suggest that the impaired feeding behavior was at least in part due to OBP gene silencing in the salivary gland , we also considered the possibility that the altered host-seeking and feeding behavior was due to DENV infection of the antennae and its effect on OBP transcript abundance there . To test the hypothesis that DENV infects the antennae and as such can influence OBP transcript abundance , immunofluorescent staining was first performed on head squashes of orally-infected mosquitoes at 14 dpbm . DENV-infected cells were clearly present in the antennae of infected mosquitoes but not in mock-infected controls ( Figure 5 ) . Female A . aegypti antennae consist of 13 flagellar segments; DENV-specific labeling was detected throughout the antennae but was stronger in the proximal segments ( Figure 5D–I ) . DENV labeling was also detected in the maxillary palps and the proboscis ( Figure 5J–O ) . Additionally , we also detected DENV by quantitative RT-PCR in the antennae and palps at 10 and 14 dpbm . Relative DENV loads were significantly higher at 14 dpbm compared to 10 dpbm , indicating that virus actively replicates in the chemosensory organs ( Figure 4B ) . We next compared OBP transcript abundance in the chemosensory organs ( antennae , palps and proboscis ) of DENV- and mock-infected mosquitoes . While OBP22 transcript abundance was not affected by DENV infection , OBP10 transcripts were enriched by almost 2 . 5-fold at 14 dpbm ( Figure 4C ) . Insect ORs form heteromeric complexes consisting of a conventional OR and a highly conserved universal co-receptor , termed OR co-receptor ( Orco ) [46] . Orco is required for trafficking of OR/Orco complexes to the sensory cilia where signal transduction occurs , and is essential for OR-mediated chemosensation in vivo [47] . We found that A . aegypti Orco ( Aaeg\Orco ) transcripts were enriched by approximately two-fold in the chemosensory organs of DENV-infected mosquitoes as compared to mock-infected mosquitoes at 14 dpbm ( Figure 4C ) . The behavioral and gene expression data presented above suggest that DENV infection may heighten the chemosensory abilities of mosquitoes , rendering them more efficient at bloodmeal acquisition . To test this hypothesis , we compared the blood-feeding behavior of DENV- and mock-infected mosquitoes at 14 dpbm . Slightly shorter probing initiation and probing times were observed for DENV-infected mosquitoes compared to mock-infected mosquitoes , but these differences were not statistically significant ( Figure 6A , B ) . Furthermore , we did not observe changes in probing propensity upon DENV infection ( Figure 6C ) .
We have used genome-wide microarray analyses to characterize the naïve and DENV-infected A . aegypti salivary gland transcriptomes , and to identify candidate genes with potential roles in controlling DENV replication or mosquito feeding behavior . RNAi-mediated gene silencing in conjunction with infection assays revealed three genes that modulate DENV replication in the salivary gland , and two olfaction-related genes that modulate mosquito host-seeking and blood-feeding behavior . DENV induced 130 and repressed 17 transcripts in the salivary gland at 14 dpbm , indicating that significant molecular and biochemical changes are induced in this organ by infection . In contrast , DENV infection appeared to have an overall negative effect on transcript abundance in the mosquito carcass , repressing more than half of the 135 differentially represented transcripts in this compartment at the same time point . The carcass response to infection at this late stage of infection was also subdued compared to what we have previously observed at 10 dpbm [14] , suggesting that the transcriptional response is being negatively regulated after an initial induction phase . Furthermore , the salivary gland infection-responsive genes were largely distinct from those in the carcass at the same time point ( 14 dpbm ) , as well as from those regulated at 10 dpbm in both carcass and midgut [14] , suggesting unique host-pathogen interactions . It is of course possible that some of the many tissues and cell types of the carcass may have displayed greater changes in transcript abundance that were not detected because of a dilution with the transcripts of other compartments . As has been previously reported [6] , [7] , the naïve salivary gland was enriched for transcripts involved in the digestion of blood and sugar meals , and for those that play anti-hemostatic and anti-inflammatory roles during bloodmeal acquisition . In addition , the gland was also enriched for numerous transcripts with immunity-related functions , suggesting that this organ is capable of mounting an immune response against both vertebrate pathogens and microbes encountered during feeding . An unexpected finding was the large number of transcripts with putative chemosensory roles , many of which were found to be enriched in the naïve gland compared to the carcass tissue , suggesting an as yet uncharacterized function for these molecules in mosquito saliva . Despite their high abundance in the antennae and maxillary palps , mosquito OBPs are not exclusively expressed in olfactory tissue . OBPs have previously been detected in the salivary gland , as well as in other body compartments such as the proboscis , thoracic spiracles , midgut , and even A . aegypti semen [6] , [26] , [27] , [48]–[50] . Indeed , a screen of Culex quinquefasciatus OBPs revealed that only a minority are transcriptionally expressed solely in olfactory tissue [51] . In A . gambiae , a number of OBP transcripts are enriched in the bodies of males and females relative to olfactory tissue , without the coordinate expression of chemoreceptors [52] . Taken together , these studies suggest multiple ligand-binding roles for OBPs beyond olfaction . Insect OBPs and chemosensory proteins ( CSPs ) have been isolated in non-chemosensory organs complexed with endogenous ligands [53] , [54] , suggesting roles similar to those of vertebrate OBPs which deliver hydrophobic pheromones to the environment in urine or saliva . The intimate association of the salivary gland and the proboscis raises the possibility that salivary gland-expressed OBPs and ORs may function in gustatory-related roles in the proboscis during blood- and sugar-feeding . Indeed , OBPs and ORs have been detected in the mosquito and fly proboscis [48] , [55]–[57] , as well as in other gustatory tissues [58] , suggesting dual roles for these molecules in olfaction and taste . In addition , although the proboscis is primarily a gustatory organ , it also responds to olfactory stimuli , and OR neurons extend from the proboscis into the antennal lobes of the brain , suggesting that the proboscis may be involved in olfactory processes that are important at close proximity to the host , such as alighting , probing and blood-feeding [59] . We speculate that chemosensory molecules secreted in saliva could coat the proboscis and facilitate tasting , although there is as yet no evidence for this process . RNAi-mediated gene silencing assays were performed to identify genes that may modulate DENV replication in the salivary gland as a mosquito defense response . The silencing of a cathepsin B gene significantly reduced salivary gland DENV titers , while silencing of a putative cystatin significantly increased DENV titers in the salivary gland . These genes could potentially play roles in apoptosis: cathepsins are lysosomal cysteine proteases that trigger apoptosis through both caspase-dependent and –independent pathways , probably by leaking or translocating from the lysosome into the cytosol [37] , [60] , while cystatins are cysteine protease inhibitors that may regulate the activity of pro-apoptotic caspases and cathepsins [41] , [42] . While DENV-induced cytopathology has not been observed in A . aegypti [61] , [62] , West Nile virus ( WNV ) ( also a mosquito-borne flavivirus ) infections have been reported to cause cytopathology with features of apoptosis in both salivary glands and midguts of Culex mosquitoes [28] , [29] , [63] , although it should be noted that WNV tends to replicate to higher titers than DENV [29] , [64] . A possible hypothesis for our observations is that apoptosis of infected cells facilitates the cell-to-cell spread of DENV in the salivary gland . A separate but related possibility is that infected cells that produce dsRNA triggers of RNAi are removed from the population by apoptosis , thereby facilitating infection by the virus . Inhibition of apoptosis ( by cathepsin B silencing for example ) would preserve these cells , maintaining dsRNA production and impairing DENV replication through RNAi . Since silencing a cysteine protease ( cathepsin B ) and a cysteine protease inhibitor ( the putative cystatin ) resulted in opposite effects on virus titers , it is tempting to speculate that these two genes are involved in the same process , but further studies are obviously required to test this hypothesis . Silencing of a gene encoding a hypothetical protein containing ankyrin repeats resulted in significantly elevated salivary gland DENV titers . Ankyrin repeats mediate protein-protein interactions and are present in immune-related proteins such as the IkB inhibitory domain of Rel2 , the NFkB-like transcription factor of the mosquito IMD immune signaling pathway . This protein lacks a predicted signal peptide , and could act intracellularly to regulate immune signaling . DENV infection induced OBP10 and OBP22 transcripts in the salivary gland , a finding that surprised and intrigued us . OBPs facilitate the olfactory processes of host-seeking and probing , which mosquitoes rely on for bloodmeal acquisition . Since DENV transmission also relies on these same processes , we investigated the possibility that these OBPs influence feeding behavior . Indeed , silencing of these OBP genes reduced the percentage of mosquitoes that probed on mice , and also increased probing initiation and probing times , indicating less efficient feeding behavior . To our knowledge , this is the first observation of potential arbovirus modulation of mosquito feeding behavior through chemosensory-related molecules . We could not conclusively determine if OBP gene silencing in the salivary glands or in the chemosensory organs was responsible for this impaired feeding behavior . OBP silencing efficiency in the salivary gland was much higher and more consistent than in the antennae and maxillary palps , suggesting that silencing in the salivary glands might be responsible for at least part of the observed feeding impairment . More efficient gene silencing in A . aegypti antennae has been achieved through thoracic injection of dsRNA into pupae [65] , as well as with a double subgenomic Sindbis virus expression system [66]; these methods may be useful for elucidating the location of action of these OBPs . Since both probing initiation time ( a rough measure of host-seeking behavior , associated with antennal function ) and probing time ( associated with salivary proteins that inhibit hemostasis and inflammation ) were negatively affected by gene silencing , both compartments could potentially be involved . In consideration of this , we provide evidence that DENV successfully infects and replicates in the female A . aegypti antennae and maxillary palps , and that transcripts of one of the OBPs ( OBP10 ) also increases in the chemosensory organs upon DENV infection . In addition , increased transcript abundance of Aaeg\Orco , the universal OR co-receptor which is essential for OR-mediated chemosensation , was also observed in the chemosensory apparatus . The significance of this is unclear , but may indicate an overall increase in OR/Orco complex abundance and chemosensory-related signal transduction during DENV infection . Insect OBPs are quite diverse , and more than 60 members of this family have been found in the A . aegypti genome [24] . OBPs 10 and 22 are both “classic” OBPs containing a highly conserved pattern of six cysteine residues , and share 44% protein sequence identity but almost no nucleotide sequence similarity . Neither of these OBPs is exclusively expressed in olfactory tissue . OBP10 is male-enriched , increases with mosquito age , and ( in addition to the antennae and palps ) is expressed in the wings , legs , and proboscis [58] . Since gustatory sensilla are present on these tissues [67] , this OBP could be involved in taste perception mediated through these organs . OBP22 has been detected in A . aegypti semen , and is transferred to the spermathecae of mated females [48] , [49] , perhaps indicating roles in pheromone binding and delivery . OBP22 is also expressed in thoracic tracheal spiracles , suggesting roles in respiration , and in the proboscis [48] . The expression patterns of these OBPs make it difficult to pinpoint their mode and location of action , but suggest that they could fulfill multiple functions . Our data imply that viral induction of OBPs could facilitate mosquito host-seeking and/or probing behavior , and thus at least theoretically increase transmission efficiency . DENV readily infects the mosquito brain , nervous system [4] , [68] , and , as we show here , the chemosensory apparatus , making such behavioral modulation plausible . A number of groups have reported changes in locomotor activity and metabolism in A . aegypti infected with various pathogens or symbionts [69]–[71]; specifically , DENV-infected A . aegypti displayed an increase in locomotor activity [71] , perhaps suggesting an increased ability to seek out hosts . However , we found no significant differences between the feeding behavior of DENV-infected and mock-infected mosquitoes , although a small shift towards shorter probing initiation and probing times was observed in infected insects . As behavioral experiments are sensitive to numerous environmental variables that are difficult to control in a laboratory setting , this does not rule out the hypothesis that DENV modulates mosquito feeding behavior through the regulation of chemosensory transcripts . In the field , mosquitoes must be able to locate hosts over longer distances and this feature cannot be effectively replicated in the laboratory; small differences in feeding behavior may thus have greater consequences on host-seeking under such conditions . In addition , mosquito defense mechanisms against arboviral infections can carry fitness costs [72] . The high level of DENV infection achieved under our experimental conditions alters many physiological processes other than chemosensation , such as energy metabolism , immunity , and stress responses , any of which could also affect feeding behavior , thereby counteracting the direct effect exerted on the chemosensory system . Apart from our data , the effect of DENV infection on mosquito feeding behavior has been previously studied , with conflicting results: one study found no effect of infection status on feeding behavior [73] , while another observed longer probing times in DENV-infected mosquitoes [68] . In agreement with the latter study , infection has also been found to increase intradermal probing times for several other pathogen-vector combinations [74]–[76] , and it is thought that this allows more time for inoculation of the pathogen into the vertebrate host . In these scenarios , we speculate that an up-regulation of chemosensory-related transcripts may be a result of an attempt to compensate for this less efficient feeding behavior . Our transcriptomic analysis suggests novel and uncharacterized roles for many genes in salivary gland function and response to pathogens . In addition , DENV infection in the salivary gland not only regulates genes that modulate virus replication , but also genes that potentially affect bloodmeal acquisition ( and hence DENV transmission ) by modifying mosquito host-seeking or probing behavior . Further characterization of these genes will yield a clearer picture of these reciprocal host-pathogen interactions in this poorly-studied organ .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Animal Care and Use Committee of the Johns Hopkins University ( Permit Number: M006H300 ) . Commercial anonymous human blood was used for dengue virus infection assays in mosquitoes , and informed consent was therefore not applicable . The Johns Hopkins School of Public Health Ethics Committee has approved this protocol . A . aegypti mosquitoes ( Rockefeller/UGAL strain ) were maintained on 10% sucrose solution at 27°C and 95% humidity with a 12 h light/dark cycle . The C6/36 Aedes albopictus cell line was maintained in MEM ( Gibco ) supplemented with 10% heat-inactivated FBS , 1% L-glutamine , 1% non-essential amino acids , and 1% penicillin-streptomycin . BHK-21 ( clone 15 ) hamster kidney cells were maintained on DMEM ( Gibco ) supplemented with 10% FBS , 1% L-glutamine , 1% penicillin-streptomycin , and 5 ug/ml Plasmocin ( Invivogen ) . C6/36 cells were incubated at 32°C and 5% CO2 , while BHK-21 cells were incubated at 37°C and 5% CO2 . Mosquito infections with DENV were carried out as previously described [77] . The New Guinea C ( NGC ) DENV-2 strain was propagated in C6/36 cells: Cells seeded to 80% confluency in 75 cm2 flasks were infected with virus stock at a multiplicity of infection ( MOI ) of 3 . 5 , and incubated for 6 days at 32°C and 5% CO2 . Infected cells were scraped into solution and lysed to release virus particles by repeated freezing and thawing in dry ice and a 37°C water bath . Virus suspension was mixed 1∶1 with commercial human blood and supplemented with 10% human serum . For experiments involving an uninfected control , a flask of uninfected C6/36 cells was maintained under similar conditions and used to create a naïve bloodmeal . The bloodmeal was maintained at 37°C for 30 min and then offered to mosquitoes via an artificial membrane feeding system . DENV-infected and control mosquitoes were dissected at 14 days post-bloodmeal , and salivary glands and carcasses were collected and stored in Buffer RLT ( Qiagen ) with 1% β-mercaptoethanol . Three independent biological replicates were performed , with approximately 200 salivary glands and 20 carcasses per replicate . Total RNA was extracted from samples using the RNeasy Mini kit ( Qiagen ) . The Low Input Quick Amp Labeling kit ( Agilent Technologies ) was used to synthesize Cy-3- or Cy-5-labeled cRNA probes from total RNA ( 100 ng for salivary gland samples and 200 ng for carcass samples ) . In addition to three biological replicates , a pseudo-replicate containing an equal amount of Cy-5-labeled probe from each experimental biological replicate was also included . Hybridizations were carried out on an Agilent-based microarray platform using custom-designed whole genome 4×44K A . aegypti microarrays , and arrays were scanned with an Agilent Scanner . Expression data were processed and analyzed as previously described [14] , [15] , [78]; in brief , background-subtracted median fluorescent values were normalized with the LOWESS normalization method , and Cy5/Cy3 ratios from replicate assays were subjected to t-tests at a significance level of p<0 . 05 using TIGR MIDAS and MeV software . Expression data from replicate assays were averaged with the GEPAS microarray preprocessing software ( http://www . gepas . org ) and logarithm ( base 2 ) -transformed . Self-self hybridizations have been used to determine the cutoff value for the significance of gene regulation on these microarrays to 0 . 78 in log2 scale , which corresponds to 1 . 71-fold regulation [79] . Numeric microarray gene expression data are presented in Tables S1 , S2 , S3 , S4 . For classification of transcripts by abundance , microarray spot hybridization fluorescence intensities were used as an indicator of transcript abundance . Spot intensity values were averaged across replicate assays for each spot , and then for each gene using the GEPAS software . Transcripts were then categorized into three categories based on mean fluorescence value ( at 635 nm ) : high abundance ( fluorescence intensity ≥5000 ) , medium abundance ( 1000–5000 ) , and low abundance ( ≤1000 ) . Data are presented in Table S3 . RNA interference ( RNAi ) -mediated candidate gene silencing in mosquitoes was performed as previously described [14] , [15] , [80] . For gene silencing assays targeting the salivary gland , 5-day old female mosquitoes fed with DENV-supplemented blood were held until 7 dpbm , at which time they were cold-anesthetized and injected with 2 ug of dsRNA per mosquito [19] . Mosquitoes injected with dsRNA to GFP were used as controls . Salivary glands were dissected at 14 dpbm and individually stored in DMEM at −80°C until they were titrated by plaque assay . Data presented are a pool of three independent biological replicates , and p-values were determined with the Mann-Whitney U test . dsRNA was synthesized using the HiScribe T7 in vitro transcription kit ( New England Biolabs ) . The primer sequences used for dsRNA synthesis are presented in Table S5 , and primer sequences used to confirm gene silencing by real-time PCR are presented in Table S6 . DENV titers in midguts and salivary glands were determined by plaque assay on BHK-21 ( clone 15 ) cells . Individual midguts and salivary glands were homogenized in DMEM with a Bullet Blender ( NextAdvance ) , serially diluted , and then inoculated onto cells seeded to 80% confluency in 24-well plates ( 100 ul per well ) . Plates were rocked for 15 min at room temperature , and then incubated for 45 min at 37°C and 5% CO2 . Subsequently , 1 ml of DMEM containing 2% FBS and 0 . 8% methylcellulose was added to each well , and plates were incubated for 5 days at 37°C and 5% CO2 . Plates were fixed with a methanol/acetone mixture ( 1∶1 volume ) for >1 h at 4°C , and plaque-forming units were visualized by staining with 1% crystal violet solution for 10 min at room temperature . Candidate genes were silenced in 4-day old female mosquitoes by the injection of 2 ug of dsRNA per mosquito ( dsRNA to GFP was used as a control ) , and behavioral assays were carried out at 4 days post-injection . Mosquitoes were deprived of sucrose solution overnight prior to the assay . Mosquitoes were transferred in pairs to a small cage and allowed to rest for at least 10 min before being offered an anesthetized Swiss Webster mouse . The mosquitoes were observed for a maximum of 400 seconds , and the following parameters were measured for each mosquito: a ) Probing propensity ( percentage of mosquitoes that probed within the fixed time period of 400 seconds ) ; b ) Probing initiation time ( time from the introduction of the mouse to the time at which the mosquito begins to probe ) ; and c ) Probing time ( time from the initial insertion of the mouthparts in the skin to the initial engorgement of blood; if the mosquito makes multiple probing attempts , the subsequent probing times are added to the first until blood is ingested , and the interprobing times are not included [6] , [10] ) . Data presented are a pool of six independent biological replicates , and p-values were determined with the Mann-Whitney U test . Behavioral assays involving DENV-infected mosquitoes were carried out at 14 dpbm . A maximum of seven mosquitoes were placed in a single cage and deprived of sucrose solution overnight . A Swiss-Webster mouse was euthanized via the administration of an overdose of ketamine , and offered to the mosquitoes . To counteract the drop in body temperature , a heat pack was placed over the mouse for the duration of the assay . Because of the larger number of mosquitoes per cage , video recordings were made during the assay to allow the experimenter to keep track of individual mosquitoes . As above , probing propensity , probing initiation time and probing time were measured . Quantitative RT-PCR was used to measure relative transcript abundance of odorant-binding proteins in the mosquito chemosensory apparatus following DENV infection , as well as to measure relative DENV loads in these organs . Antennae and maxillary palps were dissected from DENV- and mock-infected mosquitoes at 10 and 14 days post-bloodmeal . Total RNA was extracted from samples using the RNeasy Mini Kit ( Qiagen ) , treated with Turbo DNase ( Ambion ) and reverse-transcribed with M-MLV Reverse Transcriptase ( Promega ) and oligo-dT20 . For detection and relative quantification of DENV transcripts , total RNA was reverse-transcribed with a DENV-specific reverse primer . Real-time quantification was performed using SYBR Green PCR Master Mix and the StepOne Plus Real-Time PCR system ( Applied Biosystems ) . Three independent biological replicates were analyzed , and technical duplicates were run for each sample . Expression values were normalized against the ribosomal gene S7 . p-values were determined with the student's t test . Primers used in this assay are listed in Table S6 . Heads were dissected from DENV- and mock-infected mosquitoes at 14 days post-bloodmeal , squashed on 3-aminopropyltriethoxysilane ( APES ) -treated glass slides , and fixed in acetone at 4°C overnight . Slides were incubated with mouse hyper-immune ascitic fluid specific for DENV2 ( diluted 1∶1000 in PBS with 0 . 1% Triton X-100 and 0 . 2% BSA ) at 4°C overnight , and washed three times in PBS . Slides were then incubated with AlexaFluor 568-conjugated goat anti-mouse IgG ( Molecular Probes ) for 1 hour at room temperature , and washed three times in PBS . Samples were covered with ProLong Gold Antifade with DAPI ( Invitrogen ) , and sealed with a cover-slip and nail varnish . Slides were visualized under a Leica fluorescence microscope . | Dengue virus ( DENV ) is transmitted between humans through the bite of infected Aedes aegypti mosquitoes . Since the virus is inoculated in saliva , infection of the mosquito salivary gland is an essential requirement for transmission . In addition , the gland also produces numerous biologically active compounds that facilitate blood-feeding . Despite the salivary gland's crucial role in DENV transmission , very little is known about the host-pathogen interactions , at the molecular level , in this organ . In this study , we characterized the A . aegypti salivary gland response to DENV infection at both the gene expression and functional levels . We found that DENV induced the expression of several gene transcripts whose products modulate virus replication in the salivary gland . Unexpectedly , the virus also induced transcripts of two odorant-binding proteins , which we demonstrate to be important for mosquito host-seeking and probing behavior . This is the first study to demonstrate that besides affecting cellular processes that modulate virus replication , DENV also has the potential to alter chemosensory processes in ways that may result in increased virus transmission . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"immunity",
"virology",
"biology",
"microbiology"
]
| 2012 | Dengue Virus Infection of the Aedes aegypti Salivary Gland and Chemosensory Apparatus Induces Genes that Modulate Infection and Blood-Feeding Behavior |
Enterocytozoon bieneusi is a widespread parasite with high genetic diversity among hosts . Its natural reservoir remains elusive and data on population structure are available only in isolates from primates . Here we describe a population genetic study of 101 E . bieneusi isolates from pigs using sequence analysis of the ribosomal internal transcribed spacer ( ITS ) and four mini- and microsatellite markers . The presence of strong linkage disequilibrium ( LD ) and limited genetic recombination indicated a clonal structure for the population . Bayesian inference of phylogeny , structural analysis , and principal coordinates analysis separated the overall population into three subpopulations ( SP3 to SP5 ) with genetic segregation of the isolates at some geographic level . Comparative analysis showed the differentiation of SP3 to SP5 from the two known E . bieneusi subpopulations ( SP1 and SP2 ) from primates . The placement of a human E . bieneusi isolate in pig subpopulation SP4 supported the zoonotic potential of some E . bieneusi isolates . Network analysis showed directed evolution of SP5 to SP3/SP4 and SP1 to SP2 . The high LD and low number of inferred recombination events are consistent with the possibility of host adaptation in SP2 , SP3 , and SP4 . In contrast , the reduced LD and high genetic diversity in SP1 and SP5 might be results of broad host range and adaptation to new host environment . The data provide evidence of the potential occurrence of host adaptation in some of E . bieneusi isolates that belong to the zoonotic ITS Group 1 .
Microsporidia are obligate intracellular eukaryotic parasites that infect a wide range of animals and are closely related to fungi [1 , 2] . Genome analyses conducted in several recent studies strongly suggest that some species of microsporidia could have a diploid or polyploid stage and a sexual cycle , and might be true Fungi [3–8] . Nevertheless , the ploidy level of Enterocytozoon bieneusi and whether it undergoes mating and a meiotic cycle are still unclear . E . bieneusi is the most common human microsporidian species and can colonize a variety of other mammals and birds [2 , 9] . This ubiquitous pathogen causes diarrhea of various severity and duration in relation to host immune status [2 , 10] . Genotyping of isolates has improved our understanding of the genetic characteristics and the potential transmission modes of E . bieneusi among hosts . E . bieneusi exhibits high genetic diversity among isolates from different hosts [11 , 12] . Over 200 E . bieneusi genotypes have been identified in humans , companion animals , livestock , horses , birds , and wildlife based on DNA sequence analysis of the ribosomal internal transcribed spacer ( ITS ) and the established naming convention [13 , 14] . The genotypes form several genetically isolated clusters ( Groups 1 to 8 ) in phylogenetic analysis , with some found in specific host groups [11 , 12 , 15] . Humans and pigs are mainly infected with the zoonotic Group 1 genotypes , ruminants with host-adapted Group 2 genotypes , and dogs with the genotypes in an outlier group [2 , 16–22] . The public health potential of E . bieneusi in animals has been assessed in numerous studies and pigs were recognized as the most significant reservoir [2 , 17 , 23] . However , this was based on results of sequence analysis of a single ITS marker ( 392 bp in length ) , which may not adequately represent the evolutionary history of the E . bieneusi genome with a length of about 6 Mb [24] . Several mini- and microsatellites with sufficient resolution have been available to infer subgroup-level phylogenies [25] . Coupled with the ITS locus , they have been used effectively in characterizations of population structures and substructures of E . bieneusi in primates [26–28] . In these studies , a clonal structure was found in human E . bieneusi populations from Peru , India , and Nigeria and no apparent geographic segregation of the isolates was observed . Nevertheless , two genetically isolated subpopulations ( SP1 and SP2 ) were identified within the overall population . Significant linkage disequilibrium ( LD ) and limited recombination in SP1 support a clonal population structure , while the rapid expansion of some specified multilocus genotypes ( MLGs ) in SP2 obscures the limited genetic exchange because of its epidemic population structure [26 , 27] . Although the isolates used in SP1 and SP2 mainly belong to ITS Group 1 genotypes D , IV , and A with zoonotic potential , some of them displayed host-specific features when mini- and microsatellites were considered in the analysis [26 , 27] . Population genetic traits of E . bieneusi in non-human primates from China were similar to those in humans [28] . These observations on the population genetics of E . bieneusi in primates need to be substantiated in other hosts . The objectives of this study were to characterize 101 pig E . bieneusi isolates that belong to nine genotypes in zoonotic Group 1 at the subtype level at five genetic loci , to assess the population structure and substructures , and to compare them with similar data from E . bieneusi subpopulations SP1 and SP2 in primates to examine the occurrence and extent of host segregation in different E . bieneusi subpopulations .
This study was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Ministry of Health , China . Prior to experiments , the protocol of the current study was reviewed and approved by the Institutional Animal Care and Use Committee of Northeast Agricultural University ( approved protocol number SRM-08 ) . For specimen collection , we obtained permission from animal owners . No specific permits were required for the described field studies , and the locations where we sampled are not privately owned or protected in any way . The field studies did not involve endangered or protected species . Isolates of E . bieneusi were obtained from pigs in cities Changchun , Daqing , Harbin , and Qiqihar in northeast China . All of them were previously genotyped by PCR and sequence analysis of the ITS locus [17 , 29] . A total of 101 isolates of ITS genotypes CHN7 , CS-4 , EbpA , EbpB , EbpC , Henan-I , Henan-IV , O , and PigEBITS3 in zoonotic Group 1 were selected for population genetic analysis in this study . The number of isolates and their ITS genotype designations by city are shown in Table 1 . For comparative purposes , population genetic data from 101 E . bieneusi isolates in humans from India , Peru , and Nigeria and 5 isolates in captive baboons from Kenya were included in this analysis ( Table 1 ) [26 , 27] . Genomic DNAs extracted from 101 pig fecal specimens were used for PCR amplification of a minisatellite marker MS4 and three microsatellite markers ( MS1 , MS3 , and MS7 ) as described [25] . The secondary PCR products with the expected sizes ( bp ) of approximately 676 for MS1 , 537 for MS3 , 885 for MS4 , and 471 for MS7 were sequenced in both directions at the Beijing Genomics Institute , China . Raw sequences were assembled and edited with the software Chromas Pro version 1 . 33 ( Technelysium Pty . Ltd . , Helensvale , Queensland , Australia ) . The sequences obtained were compared to the sequence data of each target available in GenBank [26 , 27] using the software MAFFT version 7 . 300 ( http://mafft . cbrc . jp/alignment/software/ ) [30] . The software DnaSP version 5 . 10 . 01 ( http://www . ub . edu/dnasp/ ) was used to determine E . bieneusi genotypes at each of the five markers and the MLGs with consideration of both single base nucleotide substitutions and short insertions and deletions ( indels ) polymorphisms [31] . Intragenic LD and recombination rates for individual locus and concatenated multilocus data set were estimated from the segregating sites without consideration of indels using DnaSP [31] . Recombination rates were further assessed using the methods GENECONV , MaxChi , and SiScan implemented in the software RDP version 3 . 44 ( http://darwin . uvigo . es/rdp/rdp . html ) [32] . Tests for genetic diversity and neutrality ( Fu’s Fs and Tajima’s D ) were run on the concatenated contigs using DnaSP ( based on segregating sites ) and the software Arlequin 3 . 5 . 1 . 2 ( http://cmpg . unibe . ch/software/arlequin35/; based on both segregating sites and indels ) [31 , 33] . The different nucleotide sequences ( considering both substitutions and indels ) were assigned as distinct alleles and the alleles at each of the five loci defined the allelic profile or sequence type . We measured the pairwise intergenic LD on annotated allelic profiles using the exact test and Markov chain parameters implemented in Arlequin [33] . Values of standardized index of association ( ISA ) were calculated with LIAN 3 . 5 ( http://guanine . evolbio . mpg . de/cgi-bin/lian/lian . cgi . pl/query ) on five-loci haplotypes [34] . We assessed population structure of E . bieneusi by analyzing the intragenic and intergenic LD , ISA , neutrality , and recombination events ( Rms ) . Wright’s fixation index ( FST ) calculated using Arlequin and gene flow ( Nm ) calculated using DnaSP were applied to evaluate the degree of genetic differentiation between E . bieneusi populations [31 , 33] . A Bayesian analysis implemented in the software MrBayes version 3 . 2 . 1 ( http://mrbayes . sourceforge . net/ ) was used in clustering nucleotide sequences using Markov chain Monte Carlo ( MCMC ) methods [35] . The general time reversible model ( GTR+G ) was determined to be the best-fit nucleotide substitution model with the program ModelTest 3 . 7 ( http://www . molecularevolution . org/software/phylogenetics/modeltest ) [36] . An MCMC-based analysis of phylogeny was conducted using the GTR+G model and the default parameters settings as described [26] . The maximum clade credibility tree generated by these analyses was visualized and edited using the software FigTree version 1 . 3 . 1 ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Pairwise distance matrices among nucleotide sequences of MLGs were calculated using eachgap calculating method with the dist . seqs command in the software MOTHUR version 1 . 24 . 1 ( http://www . mothur . org/wiki/Download_mothur ) [37] . A principal coordinates analysis ( PCoA ) via covariance matrix with data standardization was performed on the generated matrices with the software GENALEX version 6 . 501 ( http://biology-assets . anu . edu . au/GenAlEx ) [38] . A Bayesian cluster analysis was performed on the allelic profile data using the software STRUCTURE version 2 . 3 . 1 ( http://pritch . bsd . uchicago . edu/software . html ) to assess the presence of distinct subpopulations [39] . We also constructed haplotype networks using the median-joining method implemented in the software Network version 4 . 6 . 1 . 1 ( http://www . fluxus-engineering . com/sharenet_rn . htm ) to estimate the genetic segregation and evolutionary trend of E . bieneusi isolates [40] .
Sequence analysis of 101 pig E . bieneusi isolates identified 9 , 15 , 5 , 15 , and 11 genotypes at the loci ITS , MS1 , MS3 , MS4 , and MS7 , respectively . Single nucleotide polymorphisms ( SNPs ) were the only source of genetic diversity at the ITS locus , while genetic variation at the other four loci included the lengths of trinucleotide TAC and TAA repeats at MS1 , dinucleotide TA repeats at MS3 , tetranucleotide GGTA repeats at MS4 , and TAA repeats at MS7 and the SNPs outside the tandem repeat regions . Some MS4 fragments also carried isostructural GG to AA substitutions in the first tetranucleotide repeat . Gene diversity ( Hd ) at individual loci was calculated using DnaSP and is shown in Table 2 . The number of genotypes and Hd value of each locus were also measured for 106 primate E . bieneusi isolates ( Table 2 ) . Generally , markers MS1 and MS4 had higher resolution than the other ones ( Table 2 ) . The intragenic LD among segregating sites for each locus was calculated based on a linear regression analysis in DnaSP . The markers MS1 , MS3 , and MS7 had complete LD ( LD = 1 ) when only the pig E . bieneusi isolates were analyzed , whereas only MS1 and MS3 had complete LD in the analysis of all 207 isolates ( Table 3 ) . Table 3 displays the number of pairwise comparisons and the number of significant pairwise comparisons after Fisher’s exact test and Bonferroni correction . The occurrence of intragenic recombination was assessed using DnaSP . As shown in Table 3 , genetic recombination was only detected in markers with incomplete LD . To investigate the genetic diversity and population characteristics of E . bieneusi , the five loci were concatenated into a single multilocus contig of 2 , 128 bp in length . The contigs from 101 pig isolates include 159 polymorphic sites ( 41 segregating sites and 118 indel sites ) . Due to the difference in substitution rate between SNPs and indels , two methods were used to estimate genetic diversity . The finite population genetic variance estimates that consider both SNPs and indels allowed identification of a total of 44 MLGs with a Hd value of 0 . 95 and a nucleotide diversity ( Pi ) value of 0 . 0197 ( Table 4 ) . In contrast , the use of infinite population genetic variance estimates that consider only SNPs led to reduced genetic diversity ( MLGs = 37 , Hd = 0 . 93 , Pi = 0 . 0065 ) ( Table 4 ) . The genetic diversity was also estimated for each of the four cities in northeast China ( Table 4 ) . Tests for intragenic LD and recombination among segregating sites were performed on combined multilocus contigs . The overall population and individual populations of E . bieneusi in pigs from four geographic locations all had significant but incomplete LD ( Table 4 ) . The negative slope returned by LD score regression is indicative of LD index declines with increasing nucleotide distance , implying the potential occurrence of recombination . A varying number of Rms were detected in the overall and individual pig E . bieneusi populations from four cities using DnaSP ( Table 4 ) . The occurrence of Rms was also confirmed using the GENECOV , MaxChi , and SiScan methods in RDP4 ( S1 Table ) . The neutrality tests conducted with DnaSP ( using SNPs only ) and Arlequin ( using both SNPs and indels ) were both significant , rejecting the null hypothesis of a neutral population at mutation-drift equilibrium ( Table 4 ) . The negative Fs and D values ( highlighted in bold in the center of Table 4 ) obtained in the tests of the pig E . bieneusi populations implied an excess of low frequency polymorphisms , as would be expected from a recent population expansion . We also calculated ISA and compared the values of VD ( variance of pairwise differences ) and L ( the 95% critical value for VD relative to the null hypothesis of panmixia ) to assess the population structure of E . bieneusi using allelic profile data [34] . As presented in Table 5 , significant positive ISA values ( at least 0 . 2733 in Qiqihar , PMC < 0 . 001 ) were obtained for the overall and individual pig E . bieneusi populations . The value of VD was greater than that of L for each data set as well . Thus , the populations tested were all in strong LD . Calculation of ISA and comparison of VD and L were also applied for the analysis of MLGs to avoid the possibility that LD would result from a clonal expansion of one or more MLGs which might mask the underlying equilibrium . The analysis showed the overall pig E . bieneusi population retained LD ( ISA = 0 . 1441 , PMC < 0 . 001; VD > L ) when the isolates with the same MLG were treated as one individual ( Table 5 ) . Pairwise intergenic analysis of the five loci using the allelic profile data revealed strong LD as well ( S2 Table ) . We also estimated the effective migration rate ( Nm ) using the FST method . Pairwise analysis between geographic populations yielded FST values ranging from 0 . 327 to 0 . 506 and Nm values ranging from 0 . 24 to 0 . 48 ( Table 6 ) . Thus , geographic segregation of the isolates and limited gene flow occurred among the pig E . bieneusi populations from four cities . Population genetic analysis was also conducted when the MLST data from 106 E . bieneusi isolates from humans and baboons were included . Significant LD and limited recombination were obtained in the analysis of a total of 207 isolates ( Tables 4 and 5 ) . The test of selective neutrality revealed a nonneutral structure for the population ( Table 4 ) . The negative Fs and D statistics ( highlighted in bold at the bottom of Table 4 ) obtained from this test signified potential epidemic expansion of some MLGs and genetic subdivision . A Bayesian method was used to infer phylogenetic relationships among E . bieneusi isolates from pigs , humans , and baboons ( Fig 1A ) . The isolates were divided into two major phylogenetic clusters ( one includes 44 MLGs from pigs and 1 MLG from a Peruvian adult with HIV infection and the other one includes 56 MLGs from humans and 4 MLGs from baboons ) ( Fig 1A ) . Additional subdivision within the two clusters led to the formation of five genetically isolated subgroups ( Fig 1A ) . The same clustering patterns appeared in the 3D image of PCoA with two main clusters ( blue balls represent the isolates from humans and baboons and red balls from pigs ) and five genetic subdivisions generated ( Fig 1C ) . Considering the high concordance of the grouping patterns of MLGs formed in Bayesian inference and PCoA , we defined two known primate E . bieneusi subpopulations as SP1 and SP2 and three novel pig E . bieneusi subpopulations as SP3 to SP5 . Subpopulations SP1 to SP5 contained 51 , 9 , 18 , 14 , and 13 MLGs derived from 66 , 39 , 35 , 41 , and 26 E . bieneusi isolates , respectively ( Tables 2 and 4 , Fig 1D ) . In general , subpopulations SP2 to SP4 had higher frequency of MLGs than SP1 and SP5 ( Fig 1D ) . We also performed substructure analysis based on allelic profile data using STRUCTURE . The initial run with K = 2 identified two major subclusters ( Fig 1B ) . One subcluster in red included all isolates from pigs and one isolate from a human and the other subcluster in green contained the isolates from humans and baboons ( Fig 1B ) , corresponding to the two major substructures generated in Bayesian inference and PCoA . The following runs at K = 3 and 4 yielded various intermediate patterns of population subdivision . The run at K = 5 showed the presence of five clear and robust subpopulations ( Fig 1B ) . The isolates in each of the five subpopulations agreed with those in SP1 to SP5 except that several isolates from SP4 were clustered into SP5 ( Fig 1 ) . We also measured the alpha ( α ) values generated in the substructure analysis . When there are firm subdivisions in a population , the α values are held constant and commonly range from 0 to 0 . 2 in different runs . The runs at K = 2 to 5 in this study yielded consistent α values around 0 . 03 , which supported the robustness of the substructure formation . However , when the analysis was performed at K = 6 or more , a fairly mixed and confused scene of clustering was observed ( Fig 1B ) . Taken together , these results suggest that the run with K = 5 provided the best fit to our data . Based on the results from Bayesian phylogeny , PCoA , and substructure analysis , it was apparent that five genetically isolated subdivisions were present in the total population . Distribution preference of the pig E . bieneusi isolates from Changchun and Daqing in SP3 and SP5 and those from Harbin and Qiqihar in SP4 suggested the presence of genetic segregation at some geographic level ( S3 Table ) . Genetic diversity , intragenic LD , and Rms were measured based on multilocus sequences for each of the five subpopulations . Pairwise LD comparisons among the subpopulations showed that the clonality of E . bieneusi isolates in SP2 to SP4 was stronger than that in SP1 and SP5 ( Table 4 ) . In agreement with this , higher ISA values were generated in the analysis of SP2 to SP4 than in SP1 and SP5 ( Table 5 ) . The measurement of population divergence among SP1 to SP5 was performed by the analysis of FST and gene flow ( Table 6 ) . SP1 and SP5 were shown to have a close genetic relationship ( FST = 0 . 185 and Nm = 1 . 10 ) ( Table 6 ) . Nevertheless , in comparative analysis of SP1 or SP5 to any other three subpopulations , the FST values of at least 0 . 342 and Nm values of at most 0 . 48 indicated the presence of significant population differentiation and very limited gene flow . Median-joining networks were used to infer the relationships between MLGs ( Fig 2 ) . The analysis showed the existence of five clusters marked in blue , green , yellow , purple , and red , which corresponded to SP1 to SP5 , respectively ( Fig 2 ) . As central haplotypes are generally considered possible ancestors of the peripheral ones [26 , 41] , the MLGs in SP2 to SP4 might have derived from the central ones in SP1 and SP5 ( Fig 2 ) . In addition , high dimensional networks in SP1 suggested the presence of significant recombination ( Fig 2 ) .
Despite advances in defining E . bieneusi ITS genotypes from different hosts and geographical regions , the relationship between genotypes and phenotypic traits such as host specificity and zoonotic potential remains unclear [2 , 11] . Analysis of population structure can help us understand the epidemiology and evolution of parasites [42] . MLST analysis has provided substantial new insights into the population genetics of E . bieneusi in humans and non-human primates [26–28] . Herein , we evaluated intragenic and intergenic LD , ISA , neutrality , and Rms to determine the genetic structure and substructures in E . bieneusi populations from pigs using both sequence and allelic profile data from five genetic loci . The presence of strong LD and very limited recombination supported the existence of significant clonal structure in the overall and individual pig E . bieneusi populations from four cities , northeast China . In addition , as illustrated in Fig 2 , the results of several neutrality tests suggest that selection acting in pig E . bieneusi populations has led to the expansion of several dominant MLGs , which might play a role in enhancing their adaptation to specific hosts . Two recent studies described that two other microsporidian species known to infect honeybees ( Nosema apis and Nosema ceranae ) were also under selective pressure and experienced a population expansion [43 , 44] . The estimates of FST and Nm and the distribution of the pig E . bieneusi isolates revealed the existence of geographic segregation among the cities surveyed . Pigs are considered a potential reservoir for human microsporidiosis based on genotypic features of E . bieneusi isolates at the ITS locus [2 , 17 , 23] . The ITS genotypes of pig E . bieneusi isolates used for MLST analysis in this study all belong to phylogenetic Group 1 with zoonotic potential [2 , 17 , 23] . The ITS genotypes of primate E . bieneusi isolates used for comparative analysis are also Group 1 members [26 , 27] . These Group 1 isolates formed several genetically isolated subpopulations ( two existing primate SP1 and SP2 and three novel pig SP3 to SP5 ) in MLST analysis of five genetic loci . SP1 to SP5 probably have different phenotypic traits as reflected in the distribution of E . bieneusi isolates in these subpopulations . As observed in Fig 1A and S4 Table , SP1 and SP5 are comprised mainly of the isolates pertaining to zoonotic ITS genotypes D , IV , and EbpC that are found in a wide range of hosts and regions around the world . In contrast , SP2 to SP4 contain mainly isolates belonging to ITS genotypes A , EbpA , and EbpB that have narrow host and geographic ranges . The stronger LD and higher occurrence of specific MLGs observed in SP2 to SP4 than SP1 and SP5 suggest the presence of higher clonality of E . bieneusi isolates in the former three subpopulations . The high diversity of E . bieneusi isolates in SP1 and SP5 may enable responses to environmental challenges and adaptations to new hosts [45] . Thus , MLGs in SP1 and SP5 might be responsible for cross-species E . bieneusi infections and have zoonotic potential . This is supported by the broad host range of E . bieneusi ITS genotypes D , IV , and EbpC in the two subpopulations . Reduced gene flow between primate SP1 and SP2 and between pig SP5 and SP3 might promote the emergence of advantageous haplotypes in SP2 and SP3 and allow these haplotypes to remain intact despite the possibility of recombination [46] . These processes might enable adaptation to specific host niches and initiate allopatric speciation [46] . In particular , this may have led to the adaptation of ITS genotype A in SP2 to humans and genotypes EbpA and EbpB in SP3 to pigs . Thus , MLGs in SP2 and SP3 are probably involved in host-specific colonization . SP2 was previously reported to have an epidemic population structure [26 , 27] . Likewise , genetic structure of SP3 with host-adapted features can also be considered to be epidemic . SP4 consists of E . bieneusi isolates belonging to the ITS genotypes CS-4 , Henan-I , Henan-IV , PigEBITS3 , and EbpC . The former four genotypes were shown to infect a very limited number of host species as shown in S4 Table , while EbpC has a wide host range . Thus , the subpopulation probably represents an evolutionary intermediate between SP3 with host-adapted traits and SP5 with cross-species capability . The placement of one human E . bieneusi isolate with ITS genotype EbpC in SP4 supported the zoonotic nature of some E . bieneusi isolates . Population genetic analysis is an indirect but powerful way to assess reproductive modes that are often difficult to uncover in some microorganisms [42] . Sexual and asexual reproduction alternates in many protozoan species under environmental pressure [47] . It is generally accepted that microsporidia undergo sexual reproduction , whereas some species or genotypes possibly have switched to obligate asexuality [48] . This is supported by the existence of clonal population structure in SP1 and SP5 and epidemic population structure or host-adapted traits in SP3 to SP4 . Although a sexual phase might be rare or virtually absent in some microsporidian species , there are footprints of recombination in their genomes [47 , 48] . This could be responsible for the small number of recombination inferred in subpopulations SP1 through SP5 . Although limited recombination would not constitute sufficient evidence for the presence of sexuality in SP1 and SP5 , the weakened LD and high levels of MLG diversity might facilitate E . bieneusi to cope with host variations and environmental challenges . The haplotype network suggests that clusters SP3/SP4 may have been derived from SP5 and that SP2 may have arisen from SP1 . Thus , SP2 to SP4 with host-specific features might serve as recurrent transitions to asexuality from otherwise SP1 and SP5 with sexual potential and some of the isolates in SP2 to SP4 might have outcompeted or partially displaced their relatives in SP1 and SP5 under certain ecological conditions . This process would limit host range and result in the emergence of highly successful E . bieneusi genotypes . It has been suggested that some asexual microsporidian populations might originate independently several times from their sexual ancestors [48 , 49] . These results indicate that E . bieneusi might have a sexual phase in its life cycle , sex could be lost or cryptic , and the parasite could switch to obligate asexuality when the population structure becomes epidemic . In conclusion , we have shown an overall clonal population structure of E . bieneusi in pigs . Combined with the MLST data from primate E . bieneusi isolates , five distinct subpopulations have been defined . Among them , the very strong LD and low genetic recombination are indicative of the epidemic or host-adapted characteristics of SP2 to SP4 , whereas the weakened LD and higher genetic diversity in SP1 and SP5 may represent higher potential for cross-species transmission of E . bieneusi infections . These data demonstrate the existence of genetic structure within E . bieneusi ITS Group 1 and the evolutionary potential for adaptation to host species in some of the subpopulations . Nevertheless , additional MLST data from other hosts including humans in China are needed for in-depth assessment of the potential for zoonotic transmission , host adaptation , and population differentiation of E . bieneusi isolates in different hosts . | This study explored the genetic characteristics of the ITS and four mini- and microsatellite markers and assessed the population structure and substructures in 101 E . bieneusi isolates from pigs in China . The measures of LD and recombination events supported the occurrence of clonal evolution among the isolates from four study areas of China . Three subpopulations ( SP3 to SP5 ) with potentially varied host ranges were identified within the isolates , which were genetically differentiated from two existing primate subpopulations SP1 and SP2 . Population genetic analysis indicated that the isolates in SP1 and SP5 with clonal structure might be responsible for the cross-species transmission and thus have zoonotic potential , while the isolates in SP2 , SP3 , and SP4 with epidemic structure or host-adapted traits might colonize specific hosts . The data revealed the presence of clonality , potential host adaptation , and population differentiation of E . bieneusi in different hosts . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"biogeography",
"livestock",
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"population",
"genetics",
"vertebrates",
"animals",
"mammals",
"primates",
"population",
"biology",
"infectious",
"diseases",
"swine",
"geography",
"zoonoses",
"phylogeography",
"pathogenesis",
"evolutionary",
"genetics",
"genetic",
"loci",
"agriculture",
"host-pathogen",
"interactions",
"earth",
"sciences",
"genetics",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"amniotes",
"organisms"
]
| 2016 | Clonal Evolution of Enterocytozoon bieneusi Populations in Swine and Genetic Differentiation in Subpopulations between Isolates from Swine and Humans |
Recent advances in sleep neurobiology have allowed development of physiologically based mathematical models of sleep regulation that account for the neuronal dynamics responsible for the regulation of sleep-wake cycles and allow detailed examination of the underlying mechanisms . Neuronal systems in general , and those involved in sleep regulation in particular , are noisy and heterogeneous by their nature . It has been shown in various systems that certain levels of noise and diversity can significantly improve signal encoding . However , these phenomena , especially the effects of diversity , are rarely considered in the models of sleep regulation . The present paper is focused on a neuron-based physiologically motivated model of sleep-wake cycles that proposes a novel mechanism of the homeostatic regulation of sleep based on the dynamics of a wake-promoting neuropeptide orexin . Here this model is generalized by the introduction of intrinsic diversity and noise in the orexin-producing neurons , in order to study the effect of their presence on the sleep-wake cycle . A simple quantitative measure of the quality of a sleep-wake cycle is introduced and used to systematically study the generalized model for different levels of noise and diversity . The model is shown to exhibit a clear diversity-induced resonance: that is , the best wake-sleep cycle turns out to correspond to an intermediate level of diversity at the synapses of the orexin-producing neurons . On the other hand , only a mild evidence of stochastic resonance is found , when the level of noise is varied . These results show that disorder , especially in the form of quenched diversity , can be a key-element for an efficient or optimal functioning of the homeostatic regulation of the sleep-wake cycle . Furthermore , this study provides an example of a constructive role of diversity in a neuronal system that can be extended beyond the system studied here .
Disorder , which originates from both noise and diversity , is naturally present in all biological systems . In neuronal systems some examples are the random opening and closing of ion channels , the multitude of stochastic input currents in the neurons , and the diversity of shapes , sizes , and electrophysiological properties of the neurons [1] , [2] . Disorder is often considered to be harmful to the systems' functioning and to information encoding . However , it was likewise repeatedly demonstrated that a certain level of disorder can facilitate signal encoding by enhancing system's response to an external stimuli . For instance , quenched diversity clearly shows its constructive role in the phenomenon of diversity-induced resonance , in which an assembly of heterogeneous excitable units presents an optimal response to an external forcing for a suitable intermediate degree of heterogeneity [3] , [4] , [5] . Similar constructive effects can be observed in the presence of noise . For example , interplay of noise and nonlinear forces produces the directed motion of motor proteins [6] , order-disorder transitions , oscillations , and synchronization in assemblies of excitable units [7] , [8] , [9] , and an optimized system response in the ubiquitous phenomenon of stochastic resonance [10] , [11] , e . g . in ion-channels and neurons [12] , [13] , [14] , [15] , [16] , [17] . In the present study we examine the effects of noise and diversity ( heterogeneity ) in a physiologically based neuronal model of sleep-wake cycles [18] . This model introduces a novel mechanism of the homeostatic regulation of sleep based on the dynamics of a wake-promoting neuropeptide orexin ( also called hypocretin ) , assuming depression of orexinergic synapses during wakefulness and their recovery during sleep . This mechanism is based on the experimental findings of the essential role of orexin system in maintaining wakefulness and its ability to integrate the sleep-wake relevant information coming from many brain areas [19] , [20] and respond to changes in the body external and internal environments by encoding the body activity state , energy balance , sensory and emotional stimuli [21] , [22] . In the original model interaction between only two representative neurons is simulated: the orexin neuron and the local glutamate neuron that are reciprocally connected to each other according to the experimentally established physiological connections [23] . Both orexin and glutamate neurons are firing during wakefulness and are silent during sleep . The transitions between firing and silence are governed by the interplay between the circadian input and homeostatic mechanisms as initially proposed by Borbely [24] . For simplicity , in this model only a single type of orexin neurotransmitter ( instead of the two types actually known ) is considered , and it is assumed that the system can be either in the wake state or in a generic non-Rapid Eye Movement sleep state , without specifying ultradian structure of sleep . Also this model did not consider noise effects , and diversity could not be included since there are only two neurons present . In the present paper we extend the above described two-neuron model to a more realistic multi-unit model with heterogeneous neurons . The aim of the study is to first of all investigate how the presence of diversity in the neuronal population affects sleep-wake transitions , since it is well-known that neurons are highly heterogeneous by their nature . In particular , within the orexin neurons population significant intrinsic diversity can be found: different electrophysiological properties , sizes in the diameter range – , and various shapes such as a spherical , fusiform , or multipolar [19] , [25] , [22] . Secondly , also stochastic fluctuations , representing current noise , are added to the model and the response of the system is studied for different levels of noise . The question naturally arises , to what extent noise and diversity are essential ingredients for the functioning of assemblies of neurons and other complex systems , and what is the optimal level of noise and diversity required for the emergence of an optimal response to external stimuli . It is shown below that the model under study presents both diversity-induced resonance and stochastic resonance , but the former appears more clear and robust , since it is always associated with a regular almost-periodic spiking-silence activity , rather than to the irregular random transitions characterizing the stochastic resonance regime .
The original model of the homeostatic regulation of sleep has a minimal structure consisting of two representative interacting neurons A and B , as depicted in Fig . 1 . The neuron A simulates a representative neuron from the orexinergic neuronal population , while the neuron B represents a local glutamate interneuron ( for details see [18] ) . The state of wakefulness or sleep is determined by the firing regime of neurons A and B , since these neurons are known to fire during wakefulness and be almost silent during sleep ( see e . g . [22] ) . Interaction between the neurons A and B takes place through glutamate and orexin neurotransmitters , as detailed below . The neuron A is acted upon by a stimulus in pace with the circadian rhythm , here treated as a periodic external signal — a simplification justified by its independence from the homeostatic process [26] . The homeostatic process itself is described by an additional macroscopic variable simulating availability of orexin . Dynamics of the neurons A and B are based on a Hodgkin-Huxley-type model [27] . The membrane potentials of the neurons A ( ) and B ( ) are thus calculated as: ( 1 ) ( 2 ) where ( ) are the membrane capacitances per unit area of the respective neurons , ( ) are the ionic currents , are the maximum conductances , and are the equilibrium potentials . The capacitance values are taken as . The values of all the other model parameters are listed in Table 1 . In the following we give a detailed explanation of different parts of the model . • External forces . The current acting on the neuron A and the noise currents , , can be considered as external forces , in the sense that they do not depend on the system variables . The external current is assumed to simulate a stimulus associated with the circadian rhythm . For simplicity in the present study a periodic pulse input is used to introduce circadian activation of the system: , . Such current can be interpreted as an awakening effect of an alarm clock or some other disturbance coming with a period of 24 hours . In the following we employ a train of rectangular pulses with length ( ) and height , as depicted in Fig . 2-top , ( 3 ) where is an integer . This simple form is chosen because it is convenient for carrying out a systematic study of the neuron response at different parameters sets . However , it should be kept in mind that it represents a drastic simplification , and more realistic shapes of circadian currents can also be used [18] . The noise term represents fluctuating currents that are known to be always present in neurons . For simplicity , we assume zero-average Gaussian white-noise processes: ( 4 ) with being the noise intensity . • Internal dynamics . The leakage , sodium , and potassium currents ( ; ) in the equation of the neuron depend only on the variables of the same neuron and , thus , describe the neuronal internal dynamics . The leakage currents represent a flow of ions with a small conductance driving the membrane potential toward the negative value . The depolarizing Na-currents have a maximum conductance and a large positive equilibrium potential . The activation variables , with , represent the fraction of open ion-channels contributing to the Na current . Because of their fast activation relative to the other time scales , the Na-current is assumed to be activated instantaneously , according to its voltage-dependency: ( 5 ) where is the sigmoid function ( 6 ) is the steepness of the sigmoid function and is the half-activation potential . The repolarizing K-currents are characterized by a maximum conductance , a large negative equilibrium potential , and a longer activation time than the depolarizing Na-current , namely . Consequently , the dynamics of the K-currents activation variables are modelled as ( 7 ) where is defined in Eq . ( 6 ) . Couplings . The neurons A and B are mutually coupled by chemical synapses through the glutamate-induced ( ) and the orexin-induced ( ) currents . Unlike the Na and K currents , and depend on the activity of both presynaptic and postsynaptic neurons . The activation variables and depend on the appearance of a spike in the presynaptic neuron , i . e . on the presynaptic voltage . Additionally these currents depend on the voltage of the postsynaptic neuron , similarly to other ionic currents . Both glutamate and orexin are excitatory neurotransmitters , so they are assumed to open depolarizing ion channels , such as Na-channels . The activations of the glutamate-induced currents are modeled as: ( 8 ) This equation is similar to Eq . ( 7 ) but has the important difference that the equilibrium value for the activation variable depends on the membrane potential of the other neuron ( if , if ) . The time constant accounts for the delay coming from the activation of glutamate receptors , and the following activation of ion channels . The orexin-induced current represents the effect of orexin produced by the neuron A and acting on the neuron B . It is modeled in a form similar to the glutamate-induced current . This current provides a simplified description of the effects of orexin on the neuron B which appear after a complex series of processes , involving production of orexin in the soma of the neurons , its release in the synaptic cleft , and activation of G-protein coupled metabotropic receptors . The dynamics of the activation variable depend not only on the membrane potential , but are also related to the availability of orexin at time , described by the additional variable ( ) . The dynamics of the variables and are defined by the equations: ( 9 ) ( 10 ) The term in the Eq . ( 9 ) reflects activation of the synaptic current due to appearance of a spike in the presynaptic neuron A . At the same time it determines the rate of orexin availability reduction in Eq . ( 10 ) due to spiking of the neuron A with a time constant . The first term in Eq . ( 10 ) determines recovery rate of the orexin availability with time constant . The meaning of the product is that there is orexin-induced activity in neuron B if ( 1 ) there is enough orexin available above a critical threshold [] , and ( 2 ) the neuron A is in the firing state [] . The time constants accounting for the orexin dynamics are much longer than the time constants associated with ionic current terms . The time constant of the homeostatic regulation process is even longer , being of the order of magnitude of the daily period . For numerical convenience , simulations are made over rescaled daily and orexin time scales: the daily period was assumed to be , instead of , achieved through a suitable rescaling , which was applied to the orexin time scale and the production and reduction times . The other time parameters are left unchanged . Since such rescaled and are still much larger than any other time scale of the microscopic dynamics , the rescaling does not change the main results of the simulations . See [18] for a detailed validation of such rescaling procedure . All the parameter values for the currents are listed in Table 1 . It is assumed that the neurons A and B share the same parameter values , unless specified otherwise . Such an assumption is justified , because the major properties of these neurons required for the model are the tonic firing ( periodic single spike activity ) and silent states . Without any external input both neurons should be in a silent state , while they are brought to firing activity in response to depolarization . Therefore , change of parameters in a physiologically allowed range would primarily lead to the different amount of depolarization needed to excite neurons , and would not affect the major outcomes of the simulations . The system defined above is essentially an excitable feedback system , i . e . both the external input of sufficient strength and the AB coupling are essential elements for maintaining firing activity of the neurons . Orexin-related dynamics , with the associated long time scales , are expected to direct the homeostatic sleep process , which regulates the sleep-wake transitions . The healthy sleep-wake cycles in this system are realized as follows: Two examples of the two-neuron model dynamics without noise are illustrated in Fig . 2 . The left part of the figure represents the response obtained for a pulse length and height . In each period orexin is depleted during the neuronal activity and recovered while the neurons are silent . The stimulus parameters used in this example have been intentionally chosen close to the critical firing threshold , so that by slightly reducing the pulse height or length , the periodic appearance of a continuous time interval of spiking regime is lost . Such case is demonstrated in the right hand side of the figure , where the current pulse height is slightly lower , , while all the other parameters are kept the same . There the prolonged wake state is induced only every other day , because the input is insufficient to induce sustained spiking at the same levels of orexin availability . By reducing the pulse amplitude or duration even further it is possible to observe different behaviors such as triple or higher-order periodicities . As a step toward a more realistic model we generalize the two-neuron model into a heterogeneous multi-neuron model . For simplicity we first increase the number of orexin neurons only . To do this we replace the single neuron A by a set of neurons ( ) , while still maintaining only one neuron B . Also , in this paper we assume that the diversity is constant in time in order to consider the simplest case possible . In reality a certain level of heterogeneity is observed in all neuronal parameters . However , given that our model neurons are simple pacemaking neurons such diversification of different model parameters ( in a physiologically allowed range ) would simply lead to slightly different firing rates of the neurons . This , in turn , will result in diversity in activations of synaptic currents , which can be mimicked by simply diversifying their activation thresholds . Thus , in the following we can limit ourselves to studying the effects of diversity in activation thresholds of synaptic currents without loss of generality . Furthermore , as a first step , the heterogeneity is only introduced in the glutamate-induced currents to avoid having a too complicated system , which would become difficult to understand . With regard to the coupling topology among the orexin neurons , so far there is no detailed experimental data . Therefore , for simplicity , we chose an all-to-all coupling via gap junctions , but other variations can be tested in the future . The intensity of the coupling has been chosen large enough to ensure that the neurons respond in pace to the external current . The equations of the two-neuron model are modified accordingly . • Dynamics of the neurons . The membrane potentials of the neurons , are described by equations analogous to Eq . ( 1 ) : ( 11 ) The current terms are similar to those in the two-neuron model , apart from the additional coupling currents between two generic neurons and , , with , where is the gap junctions conductance that can be treated as coupling strength . The currents' activation variables and are modeled in accord with the equations of the two-neuron model . Note that the specific values of the activation variables will be different for different neurons since they depend on voltages of each particular neuron . For simplicity the same external current given by Eq . ( 3 ) is assumed to act on all neurons ( see Fig . 3 ) . The noise terms as well as the noise acting on the neuron B ( see below ) are also defined similarly and assumed to be statistically independent from each other . For convenience the properties of all stochastic forces are written together ( ) : ( 12 ) • Connections from the neuron B to the neurons . The neuron B has glutamatergic synaptic inputs to each of the neurons as depicted in Fig . 3 . Diversity is introduced in the activation thresholds of the glutamate-induced currents according to the following equation for the activation variables: ( 13 ) The thresholds adopt different values for each neuron that are independently extracted from a probability distribution defined later in the text . • Connections from the neurons to the neuron B . Each of the neurons has synaptic projections to the neuron B . This is translated in the model by replacing the single glutamate- and orexin-induced currents with their averages such that Eqs . ( 8 ) and ( 9 ) for the activation variables become: ( 14 ) ( 15 ) Note that diversity is again introduced in the activation thresholds of the glutamate-induced currents corresponding to heterogeneous synapses located at the neuron B . Due to the differences in the neurons , the orexin availability function is different for different neurons , although still following Eq . ( 10 ) . The above described set of equations constitutes the multi-neuron heterogeneous model of the homeostatic regulation of sleep . Numerical results were obtained using a variation of the Runge-Kutta 2nd-order method , which is suitable for equations with stochastic terms , namely the Heun method [28] . Identical initial conditions were assumed for all neurons , corresponding to a silent state . In this section a heuristic criterion is introduced in order to evaluate and compare the quality of the system responses obtained for different external signals or internal parameter values . For this purpose , the period is divided into a “day” wakefulness sub-period of length and a “night” sleep sub-period of length , with . The quantity is defined as a wake fraction . A typical sleep-wake cycle with an eight-hour sleep sub-period has . For the day corresponding to the -th period , the “day” is represented by the sub-interval , which covers the first fraction of the period , while the “night” extends in the complementary fraction of the period in the time interval . For each period , we compute wakefulness time intervals and spent by the system in the wake state during the day , , and night , . The wake/sleep state is identified with the spiking/silent regime . A simple quantitative estimate of the quality of the sleep-wake cycle can , thus , be done through the following linear function of the wakefulness time intervals , ( 16 ) where , , represent the average of the wakefulness time intervals during the day ( ) and during the night ( ) , with being the total number of periods of the simulation . The fractions ( ) can vary in the interval ; then the coefficient in Eq . ( 16 ) is limited in the interval . The maximum value corresponds to an optimal cycle with ( wakefulness during the entire day ) and ( sleep during the entire night ) ; any deviation from the optimal state ( ) comes either from values ( implying some sleep during the day ) or values ( meaning at least some wakefulness in the night ) . See Text S1 for further details on the definition of the time intervals , and the coefficient .
Here we investigate the effects of the noise currents in the equations for the membrane potentials . For clarity only the cases in which noise currents are present either in the neurons or in the neuron B are considered . The effects introduced by a heterogeneity in the neurons are dramatic compared to the effects of noise . The corresponding improvement of the system response for suitable intermediate amounts of diversity can be detected very clearly . This is the main result of this paper and it is illustrated in this section . Noiseless neurons are assumed for easier estimation of the heterogeneity effects ( ) . As in the study of noise described above , we carry out the study of diversity starting from the same configuration with a non-optimal double-periodic response to the external periodic stimulus , corresponding to a zero diversity ( homogeneous system ) . Heterogeneity is then introduced in the glutamate-induced currents , either in the thresholds regulating the response of the synapses at the neuron B or in the thresholds of the synapses at the neurons A . This is done by randomly extracting values from a probability density and assigning them to the threshold parameters ( ) . The probability density used here has a bell-shape , where , the quantity represents the average value , while measures the dispersion of the distribution around the average value and is related to the standard deviation by . For further details see Text S1 . The width is assumed in the following as the measure of neuronal diversity . In order to carry out meaningful comparisons with the homogeneous ( two-neuron ) model , the average values are set equal to the corresponding parameters of the homogeneous two-neuron model , ( 17 ) The other parameters are unchanged compared to the two-neuron model , see Table 1 .
In the present work we have introduced a heterogeneous multi-neuron version of the previously developed physiologically motivated model of the homeostatic regulation of sleep . The multi-neuron model is composed of a population of conductance-based orexin-producing neurons and a single representative glutamatergic neuron . In this model the glutamatergic and orexinergic neurons are undergoing transitions between firing and silence depending on the external circadian input and internal homeostatic mechanisms . These transitions correspond to the transitions between wake ( firing ) and sleep ( silence ) , with the homeostatic mechanism being dependent on the availability of orexin . The specific aim of this study was to explore the effects of noise and diversity in the regulation of sleep-wake cycles in such a model . It is clear that diversity and noise are integral parts of all biological systems , including the orexinergic neuronal population in the lateral hypothalamus . However , the role of disorder , and especially diversity , is rarely considered in the physiologically based mathematical models of sleep-related systems [29] , [30] , [31] , [32] , [33] , [34] , [35] . To our knowledge , diversity had so far been included only in one such model , i . e . the model of interacting circadian oscillators [5] , and here we present another example of the constructive role of diversity in regulation of sleep . We have demonstrated the existence of a diversity-induced resonance , leading to a clear and strong improvement of the quality of the sleep-wake cycles , at a physiologically justified intermediate level of diversity of the orexin-producing neurons . However , only a mild improvement was found with varying noise intensity ( stochastic resonance phenomena ) . We have considered the simplest system with only 20 heterogeneous orexin neurons and one local glutamate neuron . Also we have used a very simple all-to-all network topology for the connections among orexinergic neurons . However , it can be expected that constructive effects of diversity will be found also in other model configurations . In the future , more realistic modifications of the model with a larger population of glutamatergic neurons and more sophisticated inter-populations connections should be considered . Furthermore , in the future studies interplay between noise and diversity should likewise be investigated , since in nature both types of disorder are normally present . The validity of the result obtained within this model may be more general , since diversity-induced resonance is known to take place for suitable values of the parameters in general networks of interacting ( non-linear ) oscillators . A question then naturally arises: whether the phenomena encountered here could also characterize other systems where there is a coupling between two very different time scales or , in other words , if homeostatically regulated biological systems may take advantage from a suitable level of heterogeneity of their components . | All biological systems are inherently noisy and heterogeneous . Disorder is mostly expected to disturb proper functioning of a system , like it can be the case with noise in a radio signal . However , it has been demonstrated by numerous studies that noise can actually improve signal encoding – the so-called stochastic resonance phenomenon . Recently , it was discovered that quenched diversity ( heterogeneity ) can also enhance the response of a system to an external perturbation ( diversity-induced resonance ) . In this study we investigate the role of noise and diversity in a neuronal model of sleep-wake cycles based on the dynamics of the wake-promoting orexin neurons that is crucial for stability of wake and sleep states . We demonstrate that suitable levels of diversity introduced in the orexin neurons can significantly improve the quality of the sleep-wake cycle , and may be essential for proper sleep-wake periodicity . Noise , on the other hand , provides only a mild improvement . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"physics",
"computational",
"neuroscience",
"interdisciplinary",
"physics",
"biology",
"biophysics",
"neuroscience"
]
| 2012 | Diversity and Noise Effects in a Model of Homeostatic Regulation of the Sleep-Wake Cycle |
Movement interactions and the underlying social structure in groups have relevance across many social-living species . Collective motion of groups could be based on an “egalitarian” decision system , but in practice it is often influenced by underlying social network structures and by individual characteristics . We investigated whether dominance rank and personality traits are linked to leader and follower roles during joint motion of family dogs . We obtained high-resolution spatio-temporal GPS trajectory data ( 823 , 148 data points ) from six dogs belonging to the same household and their owner during 14 30–40 min unleashed walks . We identified several features of the dogs' paths ( e . g . , running speed or distance from the owner ) which are characteristic of a given dog . A directional correlation analysis quantifies interactions between pairs of dogs that run loops jointly . We found that dogs play the role of the leader about 50–85% of the time , i . e . the leader and follower roles in a given pair are dynamically interchangable . However , on a longer timescale tendencies to lead differ consistently . The network constructed from these loose leader–follower relations is hierarchical , and the dogs' positions in the network correlates with the age , dominance rank , trainability , controllability , and aggression measures derived from personality questionnaires . We demonstrated the possibility of determining dominance rank and personality traits of an individual based only on its logged movement data . The collective motion of dogs is influenced by underlying social network structures and by characteristics such as personality differences . Our findings could pave the way for automated animal personality and human social interaction measurements .
Groups that are not able to coordinate their actions and cannot reach a consensus on important events , such as where to go , will destabilise , and individuals will lose the benefits associated with being part of a group [1] , [2] . Decision-making usually involves some form of leadership , i . e . ‘the initiation of new directions of locomotion by one or more individuals , which are then readily followed by other group members’ ( [3] p83 ) . Several factors may give rise to the emergence of leadership . In some species or populations , leaders are socially dominant individuals ( consistent winners of agonistic interactions [4] ) and have more power to enforce their will [5] . For example , in rhesus macaques ( Macaca mulatta ) the decision to move is the result of the actions of dominant and old females [6] . Similarly , dominant beef cows ( Bos taurus ) have the most influence on where the herd moves . They go where they wish while subordinates either avoid or follow them [7] . Leaders could appear in species or populations without any dominant individuals , or independently from social dominance . Leaders may have the highest physiological need to impose their choice of action [1] , [3] , [8]–[10] , or they may possess special information or skill [11] , [12] . Finally , an individual of a personality type that is more inclined to lead or does not prefer following others may also initiate collective movements [13] , [14] . For example , leadership is associated with boldness in sticklebacks ( Gasterosteus aculeatus ) [15] , [16] . The investigation of the relationship between leadership and personality might reveal which personality types occupy particular positions in the leadership network , and conversely , network metrics could identify potential personality traits . With this study our aim was to reveal potential links between leadership in collective movements , motion patterns , social dominance , and personality traits in domestic dogs ( Canis familiaris ) . It is often assumed that domestic dogs inherited complex behaviours from their wolf ancestors ( Canis lupus ) . The typical wolf pack is a nuclear or extended family , where the dominant/breeding male initiates activities associated with foraging and travel [17] . However , family dog groups may consist of several unrelated individuals with multiple potential breeders . In large wolf packs with several breeders , leadership varies among packs , and dominance status has generally no direct bearing on leadership , but breeders tend to lead more often than non-breeders [18] . Similarly , leadership in Italian free-ranging dogs interchanged between a small number of old and high-ranking habitual leaders . Interestingly , affiliative relationships had more influence on leadership than agonistic interactions [19] . Family dogs are often kept in groups ( for instance , 33% of owners in Germany [20] and 26% of owners in Australia [21] have 2 or more dogs ) , however interactions within freely moving dog groups and their relationship with social dominance are still unexplored . The capacity of dogs to form robust dominance hierarchies is highly debated [22] , [23] . However , the reason for the inability to detect hierarchies might be due to methodological issues in certain cases , as instead of aggression patterns , submissive behaviours appear to be better indicators of dominance relationships in dogs [24] . To describe what characterises the collective movement of a group of dogs , and to investigate links between leadership , social dominance , personality [25] , and characteristics of individual motion trajectories , we collected high-resolution spatio-temporal ( 1–2 m , 0 . 2 s ) GPS trajectory data from a group of dogs and their owner during everyday walks . Directional choice dynamics and potential leading activity were assessed by quantitative methods inspired by statistical physics [26] , [27] . Personality and dominance rank of the dogs were measured by questionnaires completed by the owner . Because the capacity to form dominance hierarchies is likely to vary from breed to breed [28] , we chose a group that contains multiple individuals of the same breed , the Hungarian Vizsla . The studied group is composed of five Vizslas ( with two dam-offspring pairs ) and one small-sized , mixed-breed dog .
A general overview of the GPS-logged trajectories ( see Figure 1 and Video S1: our animation showing a 3-minute-long part of a walk ) shows that the dogs run away from the owner periodically , then turn back and return to her , in a loop . Figure S5 shows a typical trajectory of dog V1 . It can also be seen that they prefer running these loops or a part of them with one or more group members ( see details in the Data Analysis ) . Given that the dogs' speed was significantly higher than that of the owner ( 1 . 5–3 . 7 times ) , this motion pattern allows dogs to cover a greater distance than the owner while also keeping the group together . We calculated several simple characteristics of the trajectories and performed an analysis concerning the returning events ( Table 1 and Text S1 ) . The preferred running speeds of the dogs , the relative distances covered , and the distances from the owner were unique and consistent characteristics of an individual dog's path , while other characteristics ( e . g , distance from dogs ) were less consistent and/or distinctive ( for details see Text S1 ) . To extract information about the interactions between group members , we used a directional correlation analysis [26] with a time window to quantify the fast , joint direction changes for all possible pairings of the dogs ( Figure 2A; Table S1; for more details see Data Analysis and Text S1 ) . We detected frequent short-term interactions and leading tendency differences between dog pairs within the group . The leading and following roles between interacting pairs were often changed during walks and between walks . To check the robustness of the interactions , the directional delay times were calculated for the first 7 and the second 7 walks separately for all pairs . High correlation was found ( two-tailed Pearson correlation: r = 0 . 635 , n = 15 , p = 0 . 011 ) , i . e . significant differences in leading tendency were detected over longer timescales . Calculated from a Gaussian fit to the peak of the relevant distributions ( Figure S8 , Table S1 ) we found that dogs play the role of leader in a given pair about 50–85% of the time ( 57% to 85% when directed leader-follower relationships were found ) . Based on the directional delay time values , we created a summarised leadership network ( Figure 2B ) . In the network each directed link points from the individual , which played the role of the leader more often in that given relationship toward the follower . We used this network to calculate leading tendency , which is the number of followers that can be reached travelling through directed links . We also calculated ‘active connections’ , which shows the number of how many interactions a dog has ( with the number of edges a dog is connected with in the network ) . Correlations between trajectory-based variables , leading tendency , personality traits ( Jones , 2008 , Table 2 ) and dominance rank ( Pongrácz et al . , 2008 , Table 2 ) were calculated using two-tailed Pearson correlation for the Vizslas only ( n = 5 ) ( Figure 3 ) and also for all subjects ( n = 6 ) . We tested our data for normality using a Shapiro-Wilk test ( p<0 . 05 ) , and where a significant deviation from a normal distribution was found , we used Spearman correlations ( indicated as rS ) . Our main aim was to investigate whether the leadership we defined based on the motion patterns had any connection with the social dominance . We found that the leading tendencies calculated from the GPS data significantly correlated with the dominance ranks gained from the dominance questionnaire [29] ( r = 0 . 92 , n = 5 , p = 0 . 026 ) . To support this result , we performed a comparison with a randomisation using all possible permutations , and this correlation value proved to be significantly higher than it was for the randomised cases . For more details see Text S1 and Figures SI11–13 . To find more correlations in our dataset of trajectory variables and personality traits , all 300 possible pairings were analysed . Note that due to the large number of variable pairs and the small number of dogs involved in the study , none of the p-values remain significant after correction for multiple comparisons ( Bonferroni , Sidak or Benjamini–Hochberg procedure ) . But the correlations mentioned here were all significantly higher than the corresponding values of the randomly permuted cases . The distance from other dogs correlated with the fear of dogs facet ( rS = 0 . 92 , n = 5 , p = 0 . 028 ) and the excitability facet ( rS = 0 . 92 , n = 5 , p = 0 . 026 ) . Dogs that , according to the owner , avoid other dogs and seek constant activity maintained a longer distance from their group mates during the walks . The time period of the returns ( the average time duration between returning events ) was found to be inversely correlated with the controllability facet ( r = −0 . 82 , n = 6 , p = 0 . 046 ) , and the dominance rank measure ( r = −0 . 84 , n = 6 , p = 0 . 036 ) . Dominant dogs who were more responsive to training returned to the owner more often . The far-from-owner ratio ( the time ratio of being relatively far from the owner , for more details see Text S1 ) correlated negatively with companionability ( r = −0 . 87 , n = 6 , p = 0 . 024 ) . Dogs that , according to the owner , seek companionship from people also like staying in the owners' proximity . The preferred running speed correlated with the general aggression facet of the aggression toward people factor ( r = 0 . 95 , n = 5 , p = 0 . 015 ) . More aggressive dogs ran faster during the walks . In addition to being correlated with dominance rank ( mentioned earlier ) , leading tendency was positively correlated with: age ( r = 0 . 91 , n = 5 , p = 0 . 032 ) , responsiveness to training ( rS = 0 . 92 , n = 5 , p = 0 . 028 ) , controllability ( r = 0 . 98 , n = 5 , p = 0 . 003 ) , and aggression towards people ( r = 0 . 95 , n = 5 , p = 0 . 013 ) . These relations indicate that those dogs that have a tendency to take the leading role during walks are more aggressive and dominant , and they are also more controllable by the owner , based on the personality questionnaires ( Figure 3 ) .
By analyzing the GPS trajectories of freely moving dogs and their owner during walks , we found significant differences in simple path characteristics of the individual dogs . The preferred running speed of Vizslas ranged from 1 . 5 to 4 . 0 m/s ( 5 . 4–14 . 4 km/h ) , they covered a 1 . 8–3 . 7× longer distance than the owner during a walk , and the usual distances from the owner ranged from 16 to 20 m . These results might be useful for conservation managers in establishing areas where dog walking is prohibited [30] and may also help in designing parks , as dog-walking is a popular method for increasing human physical activity ( for a review , see [31] ) . A directional correlational analysis [26] , [27] revealed leader-follower interactions between the group members . We detected a loose but consistent hierarchical leadership structure . Due to the dynamic nature of the pairwise interactions , role reversals did occur during walks and an individual took the role of the leader in a given pair in about 73% ( ranging from 57% to 85% ) of their interactions , where directed leader-follower relationships were found . This ratio is of similar magnitude to the case of wild wolf packs with several breeding individuals , where leaders led for 78% of the recorded time , ranging from 58% to 90% [18] . The role of initiating common actions is also frequently interchanged between guide dogs and the owner [32] and between dogs during play [33] . But over a longer timescale , differences in leading tendency remained consistent; thus decision-making during the collective motion was not based on an egalitarian system in our sample . Although the existence of an overall dominance hierarchy in dogs is debated [23] , and the Vizsla is a “peaceful” breed , which , compared to other breeds , rarely fights with conspecifics [34] , we detected a dominance hierarchy via a questionnaire assessing agonistic and affiliative situations [29] . We found that dominance rank and leadership were strongly connected . Dogs who tend to win in everyday fighting situations , eat first , bark more or first , and receive more submissive displays from the others , and have more influence over the decisions made during collective motion . The correlation between leadership and dominance is consistent with a trend in ‘despotic’ social mammals [5] , but probably not characteristic in wolves with several breeding individuals [18] . In large wolf packs ( with 7–23 individuals ) , breeding individuals lead during travels , independently from dominance status . But this situation is relatively rare , as the typical wolf pack is a nuclear or extended family , where the only breeding male leads the pack during travel [17] . Unlike wolves , the dog is a promiscuous species , and in a group , there is usually no single pair of breeders [22] . In our family dog group , the highest ranking dog ( V2 ) was neutered , which may suggest that both leadership and dominance have little or no relationship with reproductive behaviour in family dogs , consistent with observations in feral dogs in India [35]–[37] . We also investigated the relationship between leadership and personality to reveal which personality types occupy particular positions in the leadership network . We found that leaders/dominants were more responsive to training , more controllable , and more aggressive than followers/subordinates . Other data also suggest that dominance cuts across different contexts and is correlated with boldness , extraversion , and exploratory tendencies in several taxa [38] , and assertiveness in wolves [18] , but reported links between personality and leadership are rare [14] . Age was a reliable indicator of leadership and dominance . Several studies have reported a positive correlation between age and dominance [39] . Age-related dominance might be due to greater fighting skills ( e . g . [40] ) or enhanced possibility of forming alliances with other individuals , among other factors [41] . If rank acquisition is learnt at an early age with regular reassessments of dominance , younger dogs may remain subordinate , long after initial body weight differences have disappeared . In our group , both dams were dominant over their adult offsprings , and each adult Vizsla dominated the juvenile Vizsla , which supports the hypothesis that the acceptance of subordinate status within a dog group is probably mediated by conditioning . Not only leadership and dominance , but movement characteristics were also related to personality . Fearful and excitable dogs maintained a longer distance from other dogs . More controllable and dogs returned to the owner more often , while less companionable dogs spent more time far from the owner . Surprisingly , more aggressive dogs ran faster during the walks . As male dogs harvest more game than females in preindustrial societies [42] , and experimental evidence on mice suggests that testosterone increases persistence of food searching in rodents [43] , higher speed might be related to testosterone levels . Note , however , that even the most “aggressive” score was relatively low in our sample ( 2 . 67 out of the maximum 8 ) . Social organization and social structure vary among populations [44] , and in the case of dogs , they vary among breeds and groups [45] , thus group decision-making processes are expected to vary accordingly [46] . The main limitation of our study is the low sample size . Observing other groups and breeds may provide different results . For example , the hierarchical network of sled dogs which work as a team with a lead dog [47] is more robust than that of our sample . It would also be interesting to investigate what happens with the leadership network if the owner runs or rides a bike , and her speed is comparable to the dogs' speed . To summarise , by using GPS devices we found that the leader and follower roles are dynamically interchanged during walks , but are consistent over a longer timescale . The leader-follower network was hierarchical , and the dogs' positions in the network correlated with dominance order derived from everyday life situations . Leadership also correlated with age and personality traits such as trainability and aggression . Our findings on the connection between variables extracted from GPS trajectory data , dominance rank , and personality traits could pave the way for automated animal personality and dominance measurements . As dogs are ideal models of human social behaviour [48] , [49] and social robots [50] , the present study may also be applied to measure social interactions in humans , as in the case of parents walking with their children , or humans interacting with robots .
Non-invasive studies on dogs are currently allowed to be done without any special permission in Hungary by the University Institutional Animal Care and Use Committee ( UIACUC , Eötvös Loránd University , Hungary ) . The currently operating Hungarian law “1998 . évi XXVIII . Törvény” – the Animal Protection Act – defines experiments on animals in the 9th point of its 3rd paragraph ( 3 . §/9 . ) . According to the corresponding definition by law , our non-invasive observational study is not considered as an animal experiment . The owners volunteered to participate and gave written consent to the publication of the photos . 6 dogs ( 5 Hungarian Vizslas and one mixed breed; labelled V1 to V5 and M , respectively ) and their owner took part in the experiments . Demographic characteristics are shown in Table 2 . Photos of the subjects are presented in Figure S2 , kinship is depicted in Figure S3 . GPS data were collected during 14 daily walking tours , each lasting about 30–40 minutes between 2 May 2010 and 25 November 2010 . We analysed 823 , 148 data points . The high-resolution GPS devices were attached to the dogs with ordinary harnesses ( Figures S1 , S2 ) , while the owner carried one device attached to her shoulder . The 5 Hz custom-designed GPS devices had a time resolution of 0 . 2 s and previous independent tests with the same devices showed a spatial accuracy of 1–2 m ( [4] – Text S1 ) . Weighing only 16 g , and with dimensions of 2 . 5 cm×4 . 5 cm , it is reasonable to suppose that the devices did not hinder the dogs' movements . The group always walked on the same open grassy field , with the approximate dimensions of 500×1000 m , near Budapest , Hungary ( located 47°25′17″N latitude , 19°8′45″E longitude ) . The task of the owner was walk continuously and with a constant speed as far as possible during the walks . The dogs were allowed to walk and run freely , and the owner called the dogs back to herself only when she noticed some kind of danger , which happened on just a few occasions . Graphical summary of the Procedure is presented in Figure S1 . The personality of the dogs was quantified using two questionnaires that were completed by the owner at the end of the GPS measurements . To extract information concerning the interactions between group members , we used a directional correlation analysis [26] with a time window to quantify the fast , joint direction changes of pairs . Highly correlated direction changes of pairs are usually found only when two dogs interact by running a part of a loop together . The timescale of the owner's direction changes was much larger than that of the dogs , and – due to the short time window and the typically small time delays – it was not covered in the calculations . Therefore interactions between the owner and the dogs were not detected with this method . However , we know that the owner was walking on a predetermined route , and clearly led the whole group on a longer time scale ( Figure 1 , Figure S5 and Video S1 ) . We calculated directional correlation values for all short trajectory segments that were in a 6 s time window ( twin; in other details the method was identical to [26] ) , thus isolating short-term effects . We used twin = 6 s in the study , but the exact choice for the time window size has no substantial effect on the results ( Figure S8 ) . A local interaction event was defined to exist when corresponding trajectory segments had a higher correlation value than Cmin = 0 . 95 ( Figure S7 ) . To extract leading tendency differences between members of pairs , the temporal directional correlation delay times ( τij ) were determined with the maximal correlation value . Positive τij values correspond to leading events when dog i leads dog j , as the direction of motion of i is ‘copied’ by j delayed in time . For each pair , leading-following events corresponding to different τij time delays were summed for each case in a walk , and for all 14 walks measured . For a detailed description of the applied method and a histogram of the found time delays between dog i and dog j , see Figure 2A and Figure S8 . If a clear maximum of the time delay histogram exists , it indicates frequent interaction between a dog pair at and near a well-defined time delay ( see detailed description in Text S1 and Figures S8 , S9 ) . In many cases it can be seen from the histograms of those dog pairs where interaction was found ( Figure 2A shows a typical example ) that the leading and following roles ( i . e . the sign of the time delay ) are dynamically changing during a walk and also between walks . Significant deviation from zero in the location of the maximum value indicates that the dogs in the current pair have different leading propensities , suggesting a directed leader-follower interaction . The full width at half maximum of the histogram ( see Text S1 ) characterises how stable the leader-follower relationship between a pair is . We constructed an interaction network based on the detected interactions and leading tendency differences ( Figure 2B , see also Figure S10 ) . An edge ( or link ) indicates detected interaction between a dog pair . In those pairs where there is a significant difference in leading tendency we defined a directed edge ( pointing from the dog who was found to lead more frequently to the one who more often assumes the role of follower ) . The result of the method using the directed edges of the leadership network to characterise active connections was confirmed in an independent way . From the positional data we determined whether members of a pair spend more time in the close vicinity of each other compared to a randomized case ( for more details see Text S1 ) . This vicinity method does not require synchronised movement from interacting pairs . The resulting “social” network of the directional correlation and the vicinity method are in high correlation ( two-tailed Pearson correlation , r = 0 . 600 , n = 15 ( number of possible pairs ) , p = 0 . 018 ) . | How does a group of family dogs decide the direction of their collective movements ? Is there a leader , or is decision-making based on an egalitarian system ? Is leadership related to social dominance status ? We collected GPS trajectory data from an owner and her six dogs during several walks . We found that dogs adjusted their trajectories to that of the owner , that they periodically run away , then turn back and return to her in a loop . Tracks have unique features characterising individual dogs . Leading roles among the dogs are frequently interchanged , but leadership is consistent on a long timescale . Decisions about running away and turning back to the owner are not based on an egalitarian system; instead , leader dogs exert a disproportionate influence on the movement of the group . Leadership during walks is related to the dominance rank assessed in everyday agonistic situations; thus , the collective motion of a dog group is influenced by the underlying hierarchical social network . Leader/dominant dogs have a unique personality: they are more trainable , controllable , and aggressive , additionally they are older than follower/subordinate dogs . Dogs are an ideal model for understanding human social behaviour . Therefore , we address the possibility of conducting similar studies in humans , e . g . walking with children and detecting interactions between individuals . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"mathematics",
"animal",
"behavior",
"statistics",
"biology",
"biostatistics",
"zoology"
]
| 2014 | Leadership and Path Characteristics during Walks Are Linked to Dominance Order and Individual Traits in Dogs |
Functional characterization of causal variants present on risk haplotypes identified through genome-wide association studies ( GWAS ) is a primary objective of human genetics . In this report , we evaluate the function of a pair of tandem polymorphic dinucleotides , 42 kb downstream of the promoter of TNFAIP3 , ( rs148314165 , rs200820567 , collectively referred to as TT>A ) recently nominated as causal variants responsible for genetic association of systemic lupus erythematosus ( SLE ) with tumor necrosis factor alpha inducible protein 3 ( TNFAIP3 ) . TNFAIP3 encodes the ubiquitin-editing enzyme , A20 , a key negative regulator of NF-κB signaling . A20 expression is reduced in subjects carrying the TT>A risk alleles; however , the underlying functional mechanism by which this occurs is unclear . We used a combination of electrophoretic mobility shift assays ( EMSA ) , mass spectrometry ( MS ) , reporter assays , chromatin immunoprecipitation-PCR ( ChIP-PCR ) and chromosome conformation capture ( 3C ) EBV transformed lymphoblastoid cell lines ( LCL ) from individuals carrying risk and non-risk TNFAIP3 haplotypes to characterize the effect of TT>A on A20 expression . Our results demonstrate that the TT>A variants reside in an enhancer element that binds NF-κB and SATB1 enabling physical interaction of the enhancer with the TNFAIP3 promoter through long-range DNA looping . Impaired binding of NF-κB to the TT>A risk alleles or knockdown of SATB1 expression by shRNA , inhibits the looping interaction resulting in reduced A20 expression . Together , these data reveal a novel mechanism of TNFAIP3 transcriptional regulation and establish the functional basis by which the TT>A risk variants attenuate A20 expression through inefficient delivery of NF-κB to the TNFAIP3 promoter . These results provide critical functional evidence supporting a direct causal role for TT>A in the genetic predisposition to SLE .
TNFAIP3 encodes A20 , an ubiquitin-editing enzyme with a key role in negatively regulating NF-κB pathway activity downstream of activating cell surface receptors [1]–[4] . Murine models have been illustrative in demonstrating the importance of A20 in limiting immune responses . For example , mice globally deficient for A20 experience widespread organ inflammation and perinatal death [2] . Mice with A20 deficiency localized to B lymphocytes demonstrate enhanced responses to toll-like receptor , B cell receptor and CD40 receptor stimulation , elevated numbers of plasma and germinal center B cells and immune complex deposition in the kidneys [5]–[7] . Mice with A20 deficient dendritic cells excrete high levels of proinflammatory cytokines and spontaneously activate lymphoid and myeloid cells resulting in lymphadenopathy and splenomegaly [8] . In humans , at least 8 GWAS in 5 autoimmune diseases have reported genome wide significant associations with variants in the vicinity of TNFAIP3 and others have reported suggestive association [9]–[18] . Lymphoid malignancies such as diffuse large B-cell lymphoma , marginal zone lymphoma , follicular lymphoma , MALT lymphoma and Hodgkin lymphoma , often carry deletions or inactivating point mutations in TNFAIP3 suggesting a role for TNFAIP3 as a tumor suppressor [19]–[23] . These observations , in both animal models and human subjects , highlight the need to clarify how SLE associated genetic variants in the TNFAIP3 locus may influence the maintenance of immune homeostasis toward the development of autoimmunity . SLE is a severe autoimmune disease characterized by immune complex mediated inflammation of target organs ( kidney , brain , skin ) , high titer autoantibody production and dysregulated interferon pathway activity . There is no curative therapy for SLE . Patients are most often treated with broad-spectrum immunosuppressive agents , the side effects of which contribute to the already considerable morbidity of the disease . Ongoing efforts to better understand the genetic , immunologic and environmental factors that contribute to SLE holds promise for future advances in the prognosis , diagnosis and therapy . To that end , genetic studies have convincingly identified over 30 loci associated with SLE [24] , [25] . However , for most loci , the variants responsible for association ( causal variants ) still await identification . Of the three known independent genetic effects reported in the TNFAIP3 locus , the most consistently replicated is a ∼100 kb risk haplotype that spans the TNFAIP3 gene body [9] , [15] , [17] , [26] . This risk haplotype has been observed in SLE subjects of both European and Asian ancestry but has not been convincingly detected in SLE subjects of African origin [27] . Genetic studies in other autoimmune diseases including systemic sclerosis , Sjogren's syndrome and rheumatoid arthritis indicate that they likely share this risk haplotype with SLE [28]–[31] . A coding variant , rs2230926 , which results in a phenylalanine to cysteine substitution at position 127 in exon 3 of TNFAIP3 , has been used as a marker of the TNFAIP3 risk haplotype in genetic studies . Even though the risk allele ( G ) of rs2230926 is associated with decreased potency for inhibiting NF-κB signaling compared to the nonrisk ( T ) allele using in-vitro transfection assays [26] , the evidence for this polymorphism as a causal variant is not convincing . The primary evidence supporting this conclusion comes from the observation that the G allele of rs2230926 has a minor allele frequency of 30–40% in African American SLE and yet no significant association with SLE is observed in this population [27] . Therefore , while this variant may alter A20 function , is not likely a causal variant . We recently proposed a pair of tandem polymorphic dinucleotides ( rs148314165 , rs200820567 ) located in the genomic DNA 30 kb telomeric of TNFAIP3 to be the most likely candidate variants responsible for association with SLE based on transpopulation differences in LD between in associated ( European and Asian ) and non-associated ( African American ) populations and bioinformatic annotation demonstrating that these variants are located in an evolutionarily conserved region of regulatory significance [27] . The TT>A risk alleles are carried on a risk haplotype that is associated with hypomorphic expression of TNFAIP3 transcripts and A20 protein [27] . The mechanism by which the TT>A risk variants might influence the hypomorphic expression of A20 is unknown and serves as key evidence for assigning causality . In this study , we demonstrate that the TT>A variants are located in a functional enhancer element that binds NF-κB and SATB1 and the risk alleles of TT>A directly lead to reduced expression of A20 by their inability to effectively bind and deliver NF-κB to the TNFAIP3 promoter through long-range DNA looping .
The variants rs148314165 ( -T ) and rs200820567 ( T>A ) , referred to as TT>A , are located in a conserved region of genomic DNA that exhibits open chromatin , epigenetic marks of active enhancers and interaction with several transcription factors including NF-κB ( Figure S1 ) . Since the TNFAIP3 gene product , A20 , functions to restrict NF-κB signaling , we focused on characterizing the binding of NF-κB subunits to the region . We used the UniProbe database [32] to evaluate the region defined by the ENCODE NF-κB binding signal ( chr6:138 , 229 , 889–138 , 230 , 230 , hg19 ) for the presence of NF-κB binding motifs . Three NF-κB sites were identified , with the first site incorporating the TT>A variant ( Figure S2A ) . Our previous work [27] used an EMSA probe that included both the TT>A site and the second NF-κB site , so we redesigned the probes to include only the TT>A site in order to isolate the contribution to the EMSA signal to this site . EMSA demonstrated stimulus enhanced binding of a nuclear protein complex to the 40 bp non-risk ( TT ) probe using nuclear extracts from EBV transformed B cells ( Figure 1A ) . Complex formation was reduced when the risk allele ( -A ) was introduced into the probe sequence , suggesting that the risk allele alters the binding affinity of this complex ( Figure 1A ) . Super shift experiments demonstrated NF-κB subunits NFKB1 ( p50 ) , cREL , RELA ( p65 ) in the nuclear protein complex ( Figure 1A ) . Similar results were also observed using nuclear extracts from the monocytoid cell line , THP1 ( Figure S3 ) . The specificity of our EMSA probes was confirmed by competition with unlabeled probe ( Figure S4 ) . To validate the EMSA results using an orthogonal approach , we performed chromatin-immunoprecipitation ( ChIP ) followed by quantitative PCR using EBV cell lines carrying all three genotype combinations ( TT/TT , TT/-A , -A/-A ) at the TT>A polymorphic site . We observed significantly lower enrichment as a percentage of input DNA from cell lines carrying the risk allele ( TT/-A or -A/-A ) for all three NF-κB proteins ( p<0 . 05 ) ( Figure 1B ) . Control experiments using antibodies to acetyl-histone H3 ( positive control ) or rabbit isotype control IgG ( negative control ) demonstrated no specific differences in enrichment as expected ( Figure S5 ) . Together with the EMSA data , these data confirm that NF-κB subunits bind to this regulatory element following cell stimulation and that this binding is impaired by the presence of the risk ( -A ) variant . Given the substantial distance of the TT>A polymorphism from the TNFAIP3 promoter , the stimulus dependent recruitment of NF-κB subunits to the site and ENCODE histone marks , we hypothesized that this element may function as an enhancer . To test this hypothesis , we cloned the non-risk ( TT ) or risk ( -A ) variants and approximately 168 bases of flanking sequence ( chr6:138 , 229 , 810–138 , 230 , 149; hg19 ) that included the two NF-κB sites downstream of TT>A ( Figure S2A ) into a minimal TK promoter construct . Plasmids were transfected into HEK293T or THP1 cells followed by stimulation with PMA/ionomycin ( PI ) ( HEK293T and THP1 ) or LPS ( THP1 only ) . Compared to the minimal TK promoter alone , we observed a significant increase in luciferase activity following stimulation with PI or LPS for both non-risk ( TT ) and risk ( -A ) plasmids suggesting that this regulatory element functions in a manner consistent with an enhancer ( Figure 2 ) . However , the risk ( -A ) construct produced significantly lower levels of luciferase activity compared with the non-risk ( TT ) construct ( Figure 2 ) . Similar differences were also observed using constructs lacking the two downstream NF-κB sites ( Figure S6 ) . These results demonstrate that the regulatory element containing the TT>A variant functions as an enhancer and the presence of the risk ( -A ) allele , which binds NF-κB with reduced affinity , impairs enhancer function . To identify other proteins that interact with the TT>A enhancer we affinity purified proteins bound to biotinylated probes used in our EMSA experiments followed by gel purification and mass spectroscopy ( MS ) analyses . We identified a band that migrated between 80 and 100 kD pulled down by both the risk ( -A ) and non-risk ( TT ) probes that was not observed using a control probe with a scrambled sequence ( Figure S7A ) . MS results from three separate experiments identified this protein as special AT-rich binding protein 1 ( SATB1 ) ( Figure S7B ) . Inspection of the probe sequences revealed a SATB1 binding motif ( AATAA ) adjacent to the NF-κB ( Figure S2B ) . We confirmed the presence of SATB1 by western blotting using eluted protein from affinity purification ( Figure S7C ) and EMSA supershift ( Figure S3 ) . No differences in affinity for the risk versus non-risk probes were observed suggesting that the TT>A polymorphism may not directly influence the binding affinity of SATB1 . A primary function of SATB1 is to facilitate long-range gene transcription through chromatin remodeling and DNA looping [33] , [34] . To determine if long-range DNA looping occurs between the TT>A enhancer and the TNFAIP3 promoter , we performed chromatin conformation capture ( 3C ) using a series of PCR primers ( Figure 3A ) distributed across key regulatory elements in the genomic sequence upstream of TNFAIP3 and in the TNFAIP3 promoter and gene body ( Figure 3A ) . We detected interaction from three regions of TNFAIP3 ( Figure 3B ) . The largest and most reproducible relative crosslinking frequency ( RCF ) was located in the TNFAIP3 promoter in a region enriched for transcription factor binding sites . Importantly , the peak RCF detected by primer 8 , is near a region of the promoter previously reported to bind NF-κB and stimulate transcription of TNFAIP3 [35] . The second highest RCF ( primer 16 ) was located in the second intron , again in a region enriched in transcription factor binding sites but was approximately 10 fold weaker than the promoter signal . The third and weakest RCF ( primer 24 ) was located in the 3′ untranslated region and was half the magnitude of the second signal . To verify these results we tested other cell lines derived from a variety of lineages . The RCF in the promoter of TNFAIP3 was reproducibly observed in all cell types evaluated ( Figure S8 ) . Stimulating THP1 cells with LPS for 2 hours produced a significant increase in the RCF detected between the TT>A enhancer and the TNFAIP3 promoter and was accompanied by a concomitant increase in A20 protein and phospho-IκBα expression ( Figure 3C ) . These results reveal a novel mechanism of transcriptional regulation whereby the TT>A polymorphic enhancer delivers an NF-κB payload to the TNFAIP3 promoter leading to increased expression of A20 . We next tested whether the RCF between the TT>A enhancer and TNFAIP3 promoter was dependent on expression of SATB1 ( Figure 4 ) . SATB1 expression was inhibited using a SATB1 specific shRNA construct transfected into HEK293T cells followed by 3C ( Figure 4B ) . Results from these experiments demonstrated a significant reduction in the RCF between the TT>A region and the TNFAIP3 promoter ( Figure 4A ) with inhibition of SATB1 expression , accompanied by a reduction in A20 protein expression ( Figure 4B ) . These data suggest that the TT>A polymorphism modulates TNFAIP3 transcription through a SATB1 mediated long range looping mechanism and that interfering with looping leads reduced TNFAIP3 transcription and A20 protein expression . Having established that the TT>A enhancer interacts with the TNFAIP3 promoter and that inhibition of looping results in reduced A20 expression , we wanted to determine if the autoimmunity associated risk allele ( -A ) influenced the interaction frequency . Evaluation of crosslinking frequencies in resting EBV transformed B cell lines demonstrated a significantly higher RCF in homozygous ( TT/TT ) non-risk cells compared to homozygous risk cells ( -A/-A ) ( Figure 5A ) . This was accompanied by reduced expression of A20 ( Figure 5B ) and increased basal NF-κB pathway activity as measured by IκBα phosphorylation in homozygous risk cell lines ( Figure 5C ) . To validate these results and to reduce potential bias in the detection of the RCF due to the multi-step 3C protocol , we developed a sequencing-based read-counting allele specific 3C assay that tallies the number of ligation products occurring from each allele in heterozygote ( TT/-A ) cell lines . Using this method , we again detected significantly fewer looping interactions produced from the risk allele ( -A ) compared with the non-risk ( TT ) allele thus confirming our results in homozygous cell lines ( Figure 5D ) . These results suggest that reduced binding of NF-κB to the TT>A risk allele results in less interaction with the TNFAIP3 promoter and lower expression of A20 protein .
In this report , we describe the functional characterization of the TT>A variants that are associated with human SLE in the region of TNFAIP3 on chromosome 6q23 for which previous genetic and bioinformatics analyses suggest they are likely to be causal variants . Identification and functional characterization of causal variants responsible for disease predisposition is a fundamental goal of human genetics . Even though GWAS have identified thousands of variants reproducibly associated with hundreds of complex genetic diseases [36] only a small fraction of these variants are presumed to be causal . This is due to the presence of linkage disequilibrium ( LD ) in the human genome , which streamlines GWAS discovery but renders causal variants statistically indistinguishable from noncausal variants on the same haplotype . Isolating causal from noncausal variants is a formidable task and most often involves a combination of genetic ( finemapping , resequencing , imputation ) and bioinformatic ( variant annotation , modeling building ) approaches . Typically , the end result of these studies is a prioritized list of variants that must be systematically evaluated for allelic differences in biological function . Our results provide a functional explanation for the genetic association between SLE and the minor alleles rs148314165 and rs200820567 and compelling evidence that these variants are causal variants for this risk haplotype . The TNFAIP3 locus exhibits complex genetic architecture with multiple variants demonstrating significant genetic associations across multiple autoimmune phenotypes . Our study focuses specifically on the ∼100 kb risk haplotype that spans the TNFAIP3 gene body first identified in SLE . These variants likely also explain the association signals detected in other autoimmune diseases testing variants in strong LD with rs148314165 and rs200820567 in subjects of European or Asian background including the coding variant rs2230296 . The TT>A risk haplotype is , however , distinct from a TNFAIP3 risk effect first reported in psoriasis and marked by SNP rs610604 [37] located in the sixth intron of TNFAIP3 . The correlation in European and Asian populations between rs610604 and rs7749323 , a perfect proxy for TT>A , is low ( r2<0 . 1 ) , indicating that psoriasis is likely associated with different causal variants . Our results also do not explain both risk and protective associations located ∼200 kb upstream of TNFAIP3 reported most robustly in rheumatoid arthritis [18] , [38] and celiac disease [39] but also in SLE and inflammatory bowel disease . It is possible that variants associated with these diseases will impact other uncharacterized enhancers in a manner similar to that described here for the TT>A enhancer . Alternatively , a long non-coding RNA encoded on the negative strand adjacent to TNFAIP3 ( AK124173 ) shares the same promoter region and may influence TNFAIP3 expression or translation through as yet to be defined mechanisms . Despite the uncertainties , further genetic and functional characterization in appropriate disease subjects will be required to clarify the causal variants responsible for these associations and the mechanisms of TNFAIP3 function that they govern . In summary , these results reveal a novel mechanism of TNFAIP3 transcriptional regulation whereby the TT>A enhancer element delivers NF-κB to the TNFAIP3 promoter through long-range DNA looping thus stimulating A20 protein expression . The SLE associate TT>A risk alleles , through their inability to effectively deliver NF-κB to the TNFAIP3 promoter , impair A20 expression leading to enhanced NF-κB pathway activity and predisposition to autoimmune disease . Clarifying the functional basis by which DNA sequence variants such as these perturb cellular pathways toward the disease state will be crucial in translating GWAS discoveries into knowledge that can improve human health .
Written informed consent was obtained from all study participants . The overall study was approved by the IRB of the Oklahoma Medical Research Foundation ( OMRF ) . THP-1 , U937 , Jurkat and Daudi were purchased from ATCC . EBV-transformed B cell lines were obtained from the Lupus Family Registry and Repository ( OMRF ) with IRB approval . EBV cell lines were selected using genotype data corresponding to the TT>A variant proxy marker rs7749323 . Cell lines were maintained in RPMI 1640 medium supplemented with 10% FBS , penicillin , streptomycin , L-glutamine and 55 µM β-mercaptoethanol . Lipopolysaccharide ( LPS ) , Phorbol myristate acetate and Ionomycin ( P/I ) were purchased from Sigma-Aldrich . The following antibodies were used in this study: Anti-phospho-IκBα , anti-SATB1 , anti-β-actin and anti-GAPDH ( Cell signaling Inc . , Danvers , MA ) , Anti-A20 antibody ( Ebioscience Inc . , San Diego , CA ) , anti-p50 , anti-p65 , and anti-cRel antibodies ( GeneTex Inc . , Atlanta , GA ) . 40 base pair ( non-risk ) or 39 bp ( risk ) DNA probes were synthesized and end-labeled with ( γ-32P ) adenosine triphosphate ( MP Biomedicals Int . ) using T4 polynucleotide kinase ( Invitrogen , Grand Island , NY ) . Nuclear protein extracts were prepared from cells stimulated with LPS ( 1 ug/mL ) or P/I ( 50 ng/ml , 500 ng/ml ) for 2 hours and incubated for 25 min at 37°C with labeled probes in binding buffer ( 1 ug poly dI-dC , 20 mM HEPES , 10% Glycerol , 100 mM KCl , and 0 . 2 mM EDTA , pH 7 . 9 ) . DNA-protein complexes were resolved on non-denaturing acrylamide gels . Supershift assays were performed by adding 80–100 ug of anti-p50 , p65 , c-Rel antibodies or Rabbit IgG isotype control antibody ( Alpha Diagnostic Int . Inc . ) to the mixture followed by incubation at room temperature for 15 min prior to adding labeled probe . ChIP assays were performed using the Magna ChIP A kit ( Millipore , Billerica , CA ) according to the manufacturer's recommendations . In brief , 1×107 EBV transformed B cells were treated with P/I ( 50 ug/ml , 500 ng/ml ) in 10 ml growth medium for 2 hours and were cross-linked with 1% formaldehyde . Nuclei were isolated and sonicated in 500 ul of lysis buffer with a Covaris S1 sonicator ( Woburn , MA ) . Fifty microliters of chromatin-protein complexes were immunoprecipitated overnight at 4°C by mild agitation with antibodies specific for p50 , p65 , cRel , acetyl-histone H3 ( positive control ) ( Millipore , Billerica , CA ) , or normal rabbit IgG ( negative control ) ( Millipore , Billerica , CA ) . DNA was eluted from the immunoprecipitated chromatin complexes , reverse-crosslinked , purified by Agencourt AMPure XP beads ( Beckman Coulter , Brea , CA ) and subjected to real-time PCR analysis using RT2 SYBR Green ( Qiagen , Germantown , MD ) and primers neighboring TT>A polymorphic region ( Table S1 ) . We cloned 340 bp ( non-risk ) or 339 bp ( risk ) of DNA sequence surrounding the TT>A polymorphism into a minimal promoter luciferase plasmid , pGLuc-mini-TK ( New England BioLab , Ipswich , MA ) . Each plasmid was transiently co-transfected using FuGene HD ( VWR , Radnor , PA ) with a pGL3-promoter control plasmid for calculation of transfection efficiency and normalization ( gift from Dr . Carol Webb , OMRF ) . Luciferase assays were performed in HEK293T and THP1 cells . Twenty fours hours post transfection , cells were treated with 1 ug/ml LPS for 24 hours or 50 ng/ml PMA/500 ng/ml ionomycin for 48 hours . To assay enhancer activity , Gaussia luciferase was analyzed from the cell culture media using BioLux GLuc assay kit ( New England BioLab ) . To measure transfection efficiency , cells were lysed and firefly luciferase activity was measured using the Luciferase Assay System ( Promega , Madison , WI ) . We screened for other proteins that bind to the EMSA probes by biotinylating the oligonucleotides used for EMSA and a scrambled oligonucleotide that served as a negative control ( Table S1 ) . Streptavidin magnetic beads ( 200 ug; Dynalbeads M-280 Streptavidin; Invitrogen ) were subjected to two rounds of blocking with 1% BSA in PBS for 15 min and washing with PBS containing 1M NaCl and TE buffer . Biotinlyated oligonucleotides were linked to half the amount of streptavidin beads by incubating for 30 min at room temperature in TE buffer followed by washing with TE buffer . To pre-clear the nuclear extracts of material that could bind non-specifically to the biotinylated oligonucleotides , we incubated the other half of the BSA-blocked beads with 100 ug of nuclear extract in binding buffer ( 250 mM NaCl , 50 mM Tris Cl , 50% glycerol , 2 . 5 mM DTT , 2 . 5 mM EDTA , pH 7 . 6 ) containing 15 ng/ul poly dI:dC ( Sigma-Aldrich ) , 0 . 5 ug/ml BSA , and 0 . 1% NP40 for 30 min on ice . We then incubated the pre-cleared nuclear extracts with the oligonucleotide-linked Streptavidin beads for 30 min in 37°C water bath with gentle shaking every 5 min , and subsequently washed the products with binding buffer containing 0 . 1% NP40 three times . The proteins were eluted in 50 ul of 0 . 2% SDS sample buffer by boiling for 5 min and were then resolved on a Nu-PAGE 4%–12% Bis-Tris gel followed by silver nitrate staining . Mass spectrometry analysis was performed using a ThermoScientific LTQ-XL mass spectrometer coupled to an Eksigent splitless nanoflow HPLC system . Bands of interest were excised from the silver nitrate stained Bis-tris gel and destained with Farmer's reducer ( 50 mM sodium thiosulfate , 15 mM potassium ferricyanide ) . The proteins were reduced with dithiothreitol , alkylated with iodoacetamide , and digested with trypsin . Samples were injected onto a 10 cm×75 mm inner diameter capillary column packed with Phenomenex Jupiter C18 reverse phase resin . The peptides were eluted into the mass spectrometer at a flow rate of 175 nL/min . The mass spectrometer was operated in a data-dependent mode acquiring one mass spectrum and four CID spectra per cycle . Data were analyzed by searching all spectra that were acquired against the human RefSeq databases using the program Mascot ( Matrix Science Inc . Boston , MA ) . Minimum identification criteria require two peptides with ion scores greater than 50 that are then verified by manual inspection . Western blots were performed to verify the identities of proteins . We performed the 3C-qPCR assays as described [40] with minor modifications . All cell lines were cultured and harvested in log phase growth . We incubated 1×107 cells in 10 ml of RPMI-1640 culture medium with 1% buffered formaldehyde at room temperature for 10 min . Crosslinking was stopped by adding 1 . 425 ml of ice cold 1 M glycine . Cells were lysed in 5 ml lysis buffer ( 10 mM Tris-HCl , pH 7 . 5; 10 mM NaCl; 5 mM MgCl2; 0 . 1 mM EGTA; Protease and Phosphatase Inhibitor Cocktail Tablets from Roche Applied Science ) for 10 min at 4°C . The nuclei were suspended in 500 µl 1 . 2× restriction buffer [1× Buffer 4; 1× bovine serum albumin ( BSA ) , New England BioLabs ( NEB ) Inc . , Ipswich , MA] containing 0 . 3% SDS and incubated at 37°C for 1 h with shaking at 900 rpm . The SDS was then sequestered by adding Triton X-100 to 2% and incubating at 37°C for another hour with shaking . One hundred units of the restriction enzyme NlaIII ( NEB ) were added for a 24 h digestion . The reaction was stopped by adding SDS to 1 . 6% and incubating at 65°C for 30 min . The digested chromatin was diluted in 6 . 125 ml of 1 . 15× ligation buffer ( NEB ) . Residual SDS was sequestered by adding Triton X-100 to 2% and incubating at 37°C for 1 h with shaking . The reaction was then cooled to 16°C and 2000 U of T4 DNA ligase ( NEB ) were added . After ligation overnight , the chromatin mixture was incubated with 100 mg/ml proteinase K at 65°C overnight to reverse crosslinks . RNA was removed by RNase A ( 0 . 5 mg/ml ) treatment for 60 min at 37°C . The 3C sample was purified by phenol-chloroform extraction and then amplified by PCR using specific primers listed in Table S1 . An enhancer constant primer was designed according to the negative strand of DNA 20 bp downstream of the TT>A polymorphism . A TaqMan probe was designed based on the positive strand DNA sequence located 10 bp downstream of the first NlaIII enzymatic digestion site and 10 bp upstream of the TT>A polymorphism , hybridizing to the opposite strand as compared to the enhancer constant PCR primer . Multiple primers were designed as close as possible to the NlaIII digestion sites in TNFAIP3 gene region . The primer/probe configurations guarantees that the probe only signals upon extension of the primer across the ligated junction . TaqMan quantitative real-time PCR was performed with TaqMan Universal PCR Master Mix according to the manufacturer's protocol using the following cycling conditions: 50°C for 2 min; 95°C for 10 min; and 45 cycles of 15 s at 95°C and 60 s at 60°C . PCR products were purified using a QIAGEN quick gel purification kit and the sequence of each chimeric DNA was determined by Sanger sequencing . To normalize primer efficiency , control PCR templates were generated by digestion and random ligation of bacterial artificial chromosomes containing TNFAIP3 gene and the TT>A enhancer ( clone RP11-76M10 , Empire Genomics , Inc , New York , USA ) [41] . A total of 5 µg of BAC clone was digested with NlaIII and then ligated with T4 ligase . The paired primers/probe designed for 3C-qPCR assay were tested on the random ligation product that contains all possible chimeric DNA ligation products in equal molar concentrations . The SATB1 shRNA and non-silencing shRNA constructs were purchased from SABiosciences , Valencia , CA . HEK293T cells were transiently transfected with SATB1 shRNA construct or control plasmids using the calcium phosphate method . The extent of shRNA-mediated inhibition of SATB1 and its effect on SATB1 expression were evaluated by western blot analysis with anti-SATB1 antibody . Protein expression of A20 was determined using Western blot with anti-A20 antibody . 3C was performed on EBV-transformed B cell lines heterozygous for the TT>A variant as previously described . Chimeric DNA generated by ligation was amplified by PCR and subject to gel purification with a DNA purification kit ( QIAGEN Inc . , Valencia , CA ) . Sequencing libraries were constructed using the Truseq DNA LT Sample Prep Kit v2 as per the manufacturer's protocol ( Illumina , San Diego , CA ) . Sequencing of indexed library pools was performed on an Illumina MiSeq instrument with 100 bp , paired-end reads . Reads were mapped to the human reference genome ( hg19 ) using the Burrows Wheeler Aligner ( BWA ) ( Li and Durbin , 2009 ) . The read-count from each allele of the 3C DNA was normalized to the read-count from each allele of genomic DNA ( Table S2 ) . A paired t-test was used to compare the difference in crosslinking frequencies occurring from two alleles within each individual . | A key objective of human genetics is the identification and characterization of variants responsible for association with complex diseases . A pair of single nucleotide polymorphisms ( rs148314165 , rs200820567 ) 42 kb downstream from the promoter of TNFAIP3 , have been proposed as the variants responsible for association with systemic lupus erythematosus based on comprehensive genetic and bioinformatic analyses . TNFAIP3 encodes for the ubiquitin-editing enzyme , A20 , which plays a central role in maintaining immune system homeostasis through restriction of NF-κB signaling . Cells that carry this risk haplotype express low levels of TNFAIP3 compared to cells carrying the nonrisk haplotype . How the risk alleles of rs148314165 and rs200820567 might influence low TNFAIP3 expression is unknown . In this paper , we demonstrate that these variants reside in an enhancer element that binds NF-κB and SATB1 enabling the interaction of the enhancer with the TNFAIP3 promoter through long-range DNA looping . Impaired binding of NF-κB directly to the risk alleles or shRNA-mediated knockdown of SATB1 inhibits interaction of the enhancer with the TNFAIP3 promoter resulting in reduced A20 expression . These results clarify the functional mechanism by which rs148314165 and rs200820567 attenuate A20 expression and support a causal role for these variants in the predisposition to autoimmune disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| []
| 2013 | An Enhancer Element Harboring Variants Associated with Systemic Lupus Erythematosus Engages the TNFAIP3 Promoter to Influence A20 Expression |
Leprosy is characterized by polar clinical , histologic and immunological presentations . Previous immunologic studies of leprosy polarity were limited by the repertoire of cytokines known at the time . We used a candidate gene approach to measure mRNA levels in skin biopsies from leprosy lesions . mRNA from 24 chemokines and cytokines , and 6 immune cell type markers were measured from 85 Nepalese leprosy subjects . Selected findings were confirmed with immunohistochemistry . Expression of three soluble mediators ( CCL18 , CCL17 and IL-10 ) and one macrophage cell type marker ( CD14 ) was significantly elevated in lepromatous ( CCL18 , IL-10 and CD14 ) or tuberculoid ( CCL17 ) lesions . Higher CCL18 protein expression by immunohistochemistry and a trend in increased serum CCL18 in lepromatous lesions was observed . No cytokines were associated with erythema nodosum leprosum or Type I reversal reaction following multiple comparison correction . Hierarchical clustering suggested that CCL18 was correlated with cell markers CD209 and CD14 , while neither CCL17 nor CCL18 were highly correlated with classical TH1 and TH2 cytokines . Our findings suggest that CCL17 and CCL18 dermal expression is associated with leprosy polarity .
Leprosy is a spectrum of illnesses involving the nerves , skin and upper airways of humans [1] . Depending on the host response to Mycobacterium leprae , the clinical presentation can vary widely between individuals [2] . Histologic features of these lesions can be quantitated on a 5 point classification system ( Tuberculoid tuberculoid ( TT ) , Borderline Tuberculoid ( BT ) , Borderline Borderline ( BB ) , Borderline lepromatous ( BL ) , and Lepromatous Lepromatous ( LL ) ) defined by Ridley and Jopling in 1966 which helps to define the polar spectrum found in clinical disease cause by M leprae [3] . In lepromatous leprosy ( LL and BL ) , the clinical course and histologic features are distinguished by uncontrolled replication of the bacilli in dermal foamy macrophages and poor granuloma formation . In tuberculoid leprosy ( BT and TT ) , the skin lesions are characterized by well-demarcated granulomatous inflammation within nerves and skin , CD4 and CD8 T cell dermal infiltration , and little evidence of bacilli within lesions . Early studies of leprosy intradermal cytokine expression determined that leprosy polarity is associated with TH2 cytokines IL4 , IL5 , IL10 in lepromatous lesions , and TH1 polar cytokines , IFNG , IL2 , in tuberculoid lesions [4] . In addition , CD4 T-cell frequency was increased in the tuberculoid lesions compared to the lepromatous lesions [5] . Since these early seminal studies , many additional cytokines and chemokines have been identified but not analyzed in leprosy lesions . Genome-wide transcriptional profiling has also been used to define cytokine mRNA levels within lesions [6] . The sample size of polar subgroups was low in this study and precluded a full comparison of the tuberculoid and lepromatous groups . To date , a detailed study of leprosy using larger sample sizes and surveying a broad , current repertoire of cytokines and chemokines has not been performed . Differentiation and polarization of T cell subsets is characterized by a complex network of cytokines and chemokines [7] , [8] . Initially , T-cell subsets were defined by their release of specific cytokines; TH1 associated with IFNG and IL2 and TH2 subset associated with release of IL4 , IL5 , and IL10 . Since these influential studies , the number of cytokines that defined T cell subsets have been expanded to include TH17 , TH22 , and Tregs [8] . CCL17 and CCL18 are chemokines important in T cell mediated reactions [9]–[15] . CCL17 is secreted by alternatively activated macrophages [16] and is markedly elevated in patients with allergic atopic dermatitis who have a TH2 dominance [15] . In murine studies , CCL17 has been implicated in both TH1 and TH2 responses [15]–[19] . CCL17 is increased in dendritic cells activated with M . tuberculosis [20] . CCL18 , is elevated in atopic dermatitis [21] and also is secreted by dendritic cells upon recognition of MTB by innate immune cells [22] . Additionally , CCL18 has been implicated in differentiation of macrophages into an alternatively activated phenotype [23] . In the following study , we measured the mRNA levels of 24 soluble cytokines and 6 cell specific markers in 82 individuals with biopsy-confirmed polar leprosy with the goal to better characterize and define the immune responses in individuals with leprosy phenotypes . Herein we show that two chemokines , CCL17 and CCL18 , more accurately define leprosy polarity than traditional TH1 and TH2 cytokines .
All human subjects provided informed consent to the procedures below . No children were enrolled in the study . Both written and oral informed consent was given due to the high rates of illiteracy . Subjects that were unable to read and write provided a thumbprint as proof of consent , while those that could read and write provided a signature . All informed consent documents and procedures were approved by the University of Washington Institutional Review Boards per the US Department of Health and Human Services Guidelines , The Medical Ethics Committee of Leiden University Medical Center , and Nepal Health Research Council ( NHRC ) . Dermal biopsies were obtained from patients at Anandaban Hospital in Kathmandu , Nepal . These cases comprised more than 8 different ethnic and religious groups included Vaishya , Chhetri , Brahmin , and Sudra ( Table 1 ) . Eighty five patients were enrolled based on clinical presentation with the leprosy diagnosis confirmed by skin slit smears and biopsy . One of the biopsy samples was used for histological diagnosis and the other for isolation of mRNA . Leprosy class was determined by Ridley Jopling ( RJ ) classification [3] after the biopsies were fixed , mounted in paraffin , and stained with both Fite stain and hematoxylin and eosin for viewing under conventional light microscopy by experienced leprosy pathologists at the Schieffelin Institute of Health – Research & Leprosy Centre , Karigiri , Tamil Nadu , India . mRNA levels from dermal biopsies were measured by RT-PCR with a BioMark Fluidigm platform which included 38 with tuberculoid ( borderline tuberculoid ( BT ) and tuberculoid ( TT ) ) , 3 with borderline borderline ( BB ) , and 44 with lepromatous leprosy ( borderline lepromatous ( BL ) and lepromatous ( LL ) ) ( Table 1 ) . Although Bacillus Calmette–Guérin ( BCG ) vaccination for the participants was not recorded , it is routine for individuals in Nepal to get vaccinated as an infant in Nepal . Routine population-wide vaccination began in 1966 and therefore individuals born before then may have not received the BCG vaccine as an infant [24] . The biopsies were obtained in a leprosy tertiary care center , where a significant number of people diagnosed with leprosy reactive states are referred . 48 patients out of 85 undergoing analysis for mRNA levels had reactions at the time of biopsy , including the three diagnosed with BB disease . 45 of these patients were included in the polarity analysis and shown in Table 1 . For ELISA studies 20 additional , newly diagnosed patients and 6 control individuals from the same region ( endemic controls: EC ) had serum collected . In addition , skin biopsies were performed as above to determine RJ classification . Half of each biopsy sample was macerated in RNAlater ( Invitrogen , Carlsbad , CA ) preservative and stored at −20°C for later processing . Samples were homogenized with a high shear homogenizer ( OMNI international , Kennesaw GA ) using disposable tips . Total RNA was isolated using RNeasy mini columns and cDNA was made using Applied Biosystems high capacity cDNA reverse transcriptase kits ( Foster City CA ) . RT-PCR was performed with PrimeTime primer probe sets from Integrated DNA Technologies ( Corallville , IA ) ( Table 1 ) and Taqman ( Life Technologies ) for genes CD1a ( catalog number Hs00233332_m1 ) using the Fluidigm 48×48 dynamic array platform . Briefly 2 . 5 ng of DNA was preamplified in a 5 ul reaction with 25 nM concentration of all primers and 12 . 5 nM concentration of all probes ( For list see Supplemental Table S1 ) and amplified for 15 cycles with a 30 sec denaturation step at 98°C and 4 minutes at 60°C . The pre-amplified reaction was added to the Fluidigm 48×48 dynamic array platform for each individual diluted in 2× mastermix buffer . 10× IDT PrimeTime gene expression assays were added to the assay portion of the Fluidigm chip and amplified for an additional 40× cycles in the Fluidigm assay chip . To verify that the Fluidigm assay was accurate , for a set of probes ( CCL17 , CCL18 , and CD1a ) we did single 20 ul RT PCR assays . Both singleplex assays and Fluidigm chip assays values were corrected by the GAPDH values to control for variability in biopsy size , mRNA yield , and cellularity within biopsy samples . Single assay RTPCR and Fluidigm values had R2 values of approximately 0 . 7–0 . 9 values ( Supplemental Figure S1 ) . 4 um paraffin sections were deparaffinized and rehydrated with heat-mediated antigen retrieval performed in citrate buffer ( pH 6 ) . Slides were blocked with 2 . 5% normal horse serum , incubated with anti-CCL18 primary antibody ( Peprotech , Catalog # 500-P108 ) and anti-CCL17 ( RND Systems , Catalog #AF364 ) overnight at 4°C followed by ImmPress rabbit HRP ( CCL18 ) and ImmPress goat HRP ( CCL17 ) ( Vector Laboratories , Burlingame CA ) . Slides were developed with QuantoDAB ( Fisher Scientific ) and counter-stained with hematoxylin . Stained tissue biopsies were scored by a dermatologist blinded to the subject's leprosy classification . Sections were surveyed on low power , and representative areas showing dermal or subcutaneous inflammation on each specimen were identified . Several representative 40× fields were assessed in each specimen and scored as 0 for <1% , 1+ for 1–10% , 2+ for 10–20% , or 3+ for >20% cells staining for CCL18 . ELISA . Six ( 6 ) control and 20 individuals with documented leprosy had sera collected and assayed for CCL17 and CCL18 protein levels by sandwich ELISA ( RND biosystems ) , per manufacturers protocol . The Mann-Whitney U-test was used to make comparisons of the cytokine production between groups , as small sample sizes precluded an assumption of normal distribution . Two-sided testing was used for all comparisons to evaluate statistical significance . A P value of ≤0 . 05 was considered significant in initial analysis . Bonferroni corrections for multiple comparisons were added as described . Statistics were calculated with Stata software ( version 11 . 2 ) . For dermal expression correlation the non-parametric Spearman's rank correlation test rho ( ρ ) statistic was used for correlation coefficient , and was generated using R program version 3 . 0 . 1 ( R: A Language and Environment for Statistical Computing , Vienna Austria ) . For iterative analysis , we used Stata 11 . 0 random number generator to randomly assign discovery and validation cohorts on 40 successive iterations and used Mann-Whitney U test for significance . For iterative analysis P values were not adjusted for multiple comparisons; P values of less than 0 . 05 were considered significant . Hierarchical clustering of the Spearman's ρ statistics was performed in R program using the complete-linkage method [25] which clusters individual tests based on the maximum distance between tests [26] . Graphical representation of clustering was displayed using corrplot package by Teiyun Wei in R program [27] , [28] . The following HGNC genes and corresponding refseq accession numbers were used in the paper: CCL1 , NM_002981; CCL17 , NM_002987; CCL18 , NM_002988; CCL2 , NM_002982; CD14 , NM_000591; CD1A , NM_001763; CD209 , NM_021155; CD22 , NM_001771; CD3D , NM_000732; FOXP3 , NM_014009; GAPDH , NM_002046; IFNA1 , NM_024013; IFNA8 , NM_002170; IFNB1 , NM_002176 , IFNG , NM_000619; IL10 , NM_000572; IL12A NM_000882; IL12B NM_002187; IL13 , NM_002188; IL17A , NM_002190; IL18 , NM_001562; IL1B , NM_000576; IL1RN , NM_000577; IL21 , NM_021803; IL22 , NM_020525; IL23A , NM_016584; IL27 , NM_145659; IL4 , NM_000589; IL6 , NM_000600; TNF , NM_000594; IFNL1 , NM_172140
We examined whether skin mRNA levels of candidate immune genes differed in individuals with tuberculoid ( TT/BT , n = 38 ) and lepromatous ( BL/LL , n = 44 ) leprosy . The cohort included Nepalese adults evaluated at a tertiary care center for leprosy ( Table 1 ) . Skin biopsies were analyzed for mRNA levels of TH1 and TH2 chemokines/cytokines , type I , II and III interferons , other T-cell associated cytokines , IL12 family members , IL1β family members , cellular markers , and house-keeping genes ( for complete list see Table S1 ) . Of the 24 soluble mediators of inflammation that were measured , 7 showed significant differences between lepromatous and tuberculoid leprosy ( CCL2 , CCL17 , CCL18 , IFNA1 , IL10 , IL22 , and TNF ) ( Table 2 ) . CCL2 , CCL18 , IL10 expression was higher in lepromatous lesions , while CCL17 , IFNA1 , TNF , and IL22 were more highly expressed in tuberculoid lesions . In addition , median values for the prototypic TH1 cytokine , IFNG , were higher in tuberculoid lesions compared to lepromatous , but it was not statistically significant ( median values = 0 . 212 vs 0 . 015 , P = 0 . 103 ) . Only CCL17 , CCL18 and IL-10 remained significant after adjustment for multiple comparisons using Bonferroni correction . Stratification of samples by type 1 reaction for leprosy reactive state did not alter the associations observed for CCL17 and CCL18 ( For P values see below ) . In order to adjust for age we analyzed CCL17 and CCL18 associations with leprosy phenotype in patients born before 1966 ( cutoff age of 45 ) . We found that the majority of the association of CCL17 and CCL18 expression was seen in individuals less than 45 ( age >45 n = 26: CCL17 , P = 0 . 09; CCL18 , P = 1 . 4×10−3 ) , ( age <45 , n = 56; CCL17 , P = 8 . 2×10−4; CCL18 , P = 5 . 7×10−5 ) . To further adjust for multiple comparisons , we randomly arranged our data into a discovery ( n≈20 ) and validation cohort ( n≈20 ) and ran the analyses on 40 separate sample iterations . Increased CCL18 levels were associated with lepromatous leprosy in randomly generated discovery and validation cohorts in 40/40 ( 100% ) of the iterations , while increased CCL17 levels were associated with tuberculoid lesions in 29/40 ( 73% ) . On the other hand , IL10 expression , only distinguished polarity in 18/40 ( 45% ) of the iterations . Both CCL17 ( Figure 1A ) and CCL18 ( Figure 1B ) expression were highly significant when analyzed in rank order based on Ridley-Jopling classification ( non-parametric trend test , P = 1 . 5×10−5 and 3 . 0×10−8 , respectively ) . Together , these data suggest that both CCL17 and CCL18 dermal mRNA levels are associated with tuberculoid and lepromatous leprosy , respectively and are more strongly associated with polarity than common TH1/TH2 markers typically used to characterize leprosy lesions . Next we analyzed the lesions for mRNA expression of markers known to distinguish innate immune cells from B and T cells ( CD14-macrophage , CD209-dendritic cell , CD1a-dendritic and Langerhans cells , CD22-B cell , CD3d-T cells , FoxP3-T-Regulatory cells ) ( Table 3 ) . We measured cell specific mRNA to both determine which cell types were predominant in the lesions and whether certain mediators were associated with a particular cell type . CD14 and CD209 were more highly expressed in lepromatous lesions , while CD1a had higher expression in tuberculoid lesions . CD14 remained significant after corrections for multiple comparisons with Bonferroni adjustment . These data confirm that macrophage infiltration is a characteristic of lepromatous lesions . We next examined whether cytokine and cellular markers were significantly different between clinical leprosy reactive states . We analyzed expression levels in BL/LL patients with type 2 ENL reactions ( n = 9 ) compared to those without type 2 ENL reactions ( n = 35 ) and determined that transcripts from mediators CCL18 , IL12b , and cell marker CD14 were significantly elevated in lesion samples with ENL ( Figure 2 ) . However , all of these failed to reach significance when adjusted for multiple comparisons . Next we examined mRNA levels in patients from whom biopsies were obtained with ongoing type 1 reversal reaction ( n = 36 , 42% of the tuberculoid patients and 45% of lepromatous patients ) versus those who had no reversal reaction ( n = 46 , 58% of tuberculoid patients and 55% of lepromatous patients ) . No marker distinguished these two clinical phenotypes . In addition we performed subgroup analysis of mRNA level differences between patients with type 1 reversal reaction and those without type 1 reversal reaction limited to individuals with either tuberculoid leprosy or lepromatous leprosy , and also saw no differences in the cytokines transcript levels examined . Cytokine levels of CCL17 ( Figure 3A ) and CCL18 ( Figure 3B ) were significantly different between lepromatous and tuberculoid lesions regardless of the type 1 reactive state ( CCL17 with T1R P = 0 . 0038 , without T1R P = 0 . 011 , CCL18 with T1R p = 3 . 6×10−4 , without T1R P = 2 . 6×10−4 ) . Next we examined CCL17 and CCL18 protein expression within dermal lesions ( n = 17 LL+BL and 17 TT+BT ) using antibodies directed to human CCL17 and CCL18 . Control staining using secondary antibodies showed no immunoreactivity . We were unable to visualize CCL17 staining above background levels despite using multiple antibody dilutions . CCL18 stained cells were detected , primarily in the cytoplasm of large polygonal cells within the dermis and subcutaneous tissues most consistent with the monocyte-derived histiocytes typically found in such inflammation . Lesions stained with CCL18 antibodies were scored semi-quantitatively for percent of immunoreactive cells ( Figure 4A ) . More CCL18 staining was seen in LL+BL lesions ( Figure 4B ) compared to TT+BT lesions ( Figure 4C ) ( non-parametric Mann-Whitney test P<0 . 01 ) . Next the serum levels of CCL17 and CCL18 were compared in 6 EC ( 5 from Nepal and one from non-endemic area ) patients without leprosy , versus 20 patients with leprosy ( 11 with BT , 2 with BL , and 7 with LL by RJ classification as determined by biopsy ) . Patients with lepromatous leprosy had a non-significant trend ( p = 0 . 16 ) of decreasing CCL17 levels ( Figure 5A ) and a significant trend ( p = 0 . 036 ) of increasing CCL18 ( Figure 5B ) levels in the serum . We next examined which cytokines and chemokines , correlated and clustered with cell marker levels ( Figure 6 ) . Our analyses revealed that CCL18 and IL10 expression were associated with CD209 and CD14 expression ( Spearman's rho = 0 . 13 to 0 . 87 ) , dendritic and macrophage markers respectively . In contrast , CCL17 expression was poorly correlated to all cell type markers except the dendritic and Langerhans marker , CD1a ( rho = 0 . 53 ) . Hierarchical clustering identified 5–7 main groups that had similar signatures ( Figure 6 ) : CCL18 group ( IL10 , CD209 , CD14 , and CCL2 ) , TNF group ( IL17a , CD22 , IL1β , IL6 , IL12b , CD3d , and IL23a ) , IFNG group ( CCL1 , FoxP3 , IL27 , IL21 , and IL12a ) , IL4 group ( IL13 , IL22 , and IL29 ( IFNλ1 ) ) and type I interferons ( IFNA1 , IFNA8 , and IFNB1 ) . CCL17 and CD1a also tended to cluster with type I interferons . These data suggest that within leprosy lesions CCL18 and CCL17 expression may be associated with specific innate immune cells rather than TH1 and TH2 T-cell cytokines traditionally thought to define leprosy ( IFNG and IL4 ) .
The primary finding of our study is that dermal mRNA levels of CCL17 and CCL18 , two chemokines important in the development of a TH2 T-cell response , are associated with tuberculoid and lepromatous leprosy respectively . We also found that classical markers for leprosy and T-cell subset polarity , IL4 and IFNG , were inferior in distinguishing polar leprosy compared to CCL17 and CCL18 . We also confirm an association of increased expression of IL10 within lepromatous lesions . Our findings highlight an association of increased dermal expression of CCL17 in the skin of patients with tuberculoid leprosy . The mechanism of how CCL17 is increased in the dermal lesions of patients , who have been traditionally thought to have TH1 polar disease , is largely unknown , but there may be many possible explanations . First , increases in CCL17 expression may arise by direct stimulation of resident cells by mycobacterial antigens . Previous data suggest that CCL17 secretion from DCs is associated with increased TH1 responses [17] , [18] . However , other papers suggest that CCL17 is associated with TH2 responses [13]–[15] , [19] . Direct stimulation of CCL17 mRNA expression by mycobacterial antigens would suggest that the response to M . leprae in patients with TT and BT disease may have more of a TH2 polarity than had been previously thought . To support this , there have been reports of increased CCL17 expression in dendritic cells stimulated with M . tuberculosis in mice [20] , but this has not been shown in humans . Second , elevated dermal expression of CCL17 could occur following recruitment of a cell type that constitutively expresses CCL17 in patients with tuberculoid leprosy . Third , chronic M . leprae exposure could promote differentiation of resident myeloid cells into CCL17-secreting cells that are more apt to control M . leprae replication . These second and third mechanisms would require there being significant differences in innate M . leprae detection between individuals with tuberculoid and lepromatous leprosy leading to either differences in cellular differentiation , or differences in the release of soluble mediators by resident cells that would influence differentiation or recruitment of new cells to lesions . Recent data suggest that NOD2 regulates M . leprae induced differentiation of myeloid precursors into cells associated with tuberculoid lesions [29] . Whether the immune cell associated with tuberculoid lesions ( thought to be CD1b ( + ) ) more actively produce CCL17 is currently unknown . Ultimately the mechanism that underlies the association of increased expression of CCL17 in tuberculoid lesions will need to be elucidated in future research . The mechanism behind the association of increased CCL18 expression in lepromatous lesions is similarly unclear . Our clustering data suggest that CCL18 expression is associated with the expression patterns of IL10 , CD209 and CD14 in lepromatous lesions , possibly implying that recruitment or differentiation of a specific cell type may be one mechanism to support the increased expression of CCL18 . In support of this hypothesis , studies have described CD14+ and CD209+ monocyte-derived skin antigen presenting cells that have an immature or tolerant phenotype in normal human skin [30] , [31] and have been associated with the production of CCL18 [32] . Whether these cells exist in lepromatous leprosy lesions , is not known . Mycobacterial antigens present in leprosy lesions could also stimulate the direct production of CCL18 , since this has been described in monocyte derived macrophages and primary alveolar macrophages [22] . Furthermore the release of CCL18 in lepromatous lesions may lead to the development of more tolerant cells through a positive feedback loop [23] , by promoting the differentiation of myeloid suppressor cells . These myeloid suppressor cells are thought to suppress the protective immune response in human T cells malignancies , and one could speculate that these cells could have a similar role in lepromatous leprosy . Furthermore , evidence that CCL18 may play a direct role in leprosy is suggested by linkage and genome wide association studies that show several CCL18 polymorphisms are associated with development of leprosy [33] , [34] . In the GWAS study , three of eight CCL18 polymorphisms were significantly associated with susceptibility to leprosy using a conventional significance threshold ( but not GWAS level significance ) [34] . Recent data suggest that CCL18 is a ligand for CCR8 and that it induces chemotaxis of TH2 polarized T cells [35] . Whether or not CCL18 has primary effects on lepromatous leprosy development and persistence is currently unknown . However , these data suggest that CCL18 may propagate lepromatous leprosy by recruiting TH2 T cells . Our data show trends in levels of serum CCL18 and CCL17 that match clinical findings observed with mRNA levels in skin lesions and immunohistochemical analysis ( for CCL18 ) . Our study may be underpowered to determine a significant association between CCL18 and CCL17 serum levels and leprosy phenotype . Confirmation in larger studies will be needed to determine whether there is a correlation with increasing CCL18 and declining CCL17 levels in patients with lepromatous disease . The association of CCL17 and CCL18 expression with younger individuals is an interesting result . While these data may suggest an influence of age on the dermal expression of these two cytokine , these data may also suggest an influence of the BCG vaccine that was uniformly administered by a nationwide program that began in Nepal in 1966 . Our study did not record the vaccine status of the tested individuals . Further studies will be needed to determine whether expression of CCL17 and CCL18 is modulated by age or BCG vaccination . There are several limitations to our study . First , a potential limitation is biopsy sampling error . We isolated RNA from biopsies that were adjacent to areas used to histologically classify the patient , and these two biopsies may have been significantly different . Although we cannot exclude that possibility , this limitation would apply to any leprosy biopsy study . Our patients were diagnosed comprehensively based upon slit skin smear , skin biopsy histopathology , clinical exam and neuropathy assessments; so there were unlikely to be large misclassifications due to biopsy sampling errors . Another possible limitation of this study is type II ( false positive ) error due to population bias . The population studied was recruited at a tertiary care center for leprosy , and contained a large percentage of individuals who were simultaneously undergoing Type I reversal and Type II ENL reactions . This bias may influence the types of cytokines and chemokines that are significantly different . Our analysis , however , showed that the main differences ( CCL17 , CCL18 , and CD14 ) were preserved despite the presence or absence of reactive states in individuals . Interestingly , our study demonstrated that CCL17 and CCL18 distinguished leprosy polarity with greater accuracy than the traditional TH1 and TH2 cytokines ( IL10 , IFNG ) . Although this difference may be due to a stronger biologic association of CCL17 and CCL18 with polarity , other explanations are also possible . First , many previous studies had small sample sizes and compared patients at the extreme end of the leprosy poles ( for example , LL versus BT and TT ) and did not include as many patients with the entire spectrum of leprosy as our study did . In addition , our samples did not proportionately represent the leprosy poles with many more LL ( n = 27 or 61% of the lepromatous pole patients ) than TT patients ( n = 3 , or 8% of the tuberculoid pole patients ) . These proportions could potentially skew the data in the favor of LL associations and weaken TT associations of traditional cytokines ( IFNG with TH1 and tuberculoid leprosy ) . | Leprosy presents with a polarized spectrum , with lepromatous leprosy having high bacillary numbers and TH2 dermal cytokines , versus tuberculoid leprosy showing very few bacilli and TH1 cytokines . The mechanism underlying this polarized presentation is largely unknown . In the following study , we isolated mRNA from skin biopsies from 85 individuals with leprosy and measured the expression of a panel of 24 cytokines and 6 cell markers . We found that three soluble mediators ( CCL17 , CCL18 and IL10 ) and one cell marker ( CD14 ) were differentially expressed in leprosy dermal lesions . CCL18 and IL10 were more highly expressed within lepromatous lesions , and CCL17 and CD14 were more highly expressed within tuberculoid lesions . In addition , CCL18 protein expression was confirmed by immunostaining . CCL17 and CCL18 , were more strongly associated with leprosy polarity than traditional TH1 and TH2 cytokines . These data suggest that newer soluble chemokines may be important in leprosy pathogenesis and uncover a molecular signature of the two polar phenotypes of leprosy , which may be useful in future diagnostics . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"bacterial",
"diseases",
"infectious",
"diseases",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immunity",
"neglected",
"tropical",
"diseases",
"biology",
"and",
"life",
"sciences",
"immunology",
"microbiology",
"tropical",
"diseases",
"leprosy",
"immune",
"system"
]
| 2014 | Differential Dermal Expression of CCL17 and CCL18 in Tuberculoid and Lepromatous Leprosy |
In oocytes , where centrosomes are absent , the chromosomes direct the assembly of a bipolar spindle . Interactions between chromosomes and microtubules are essential for both spindle formation and chromosome segregation , but the nature and function of these interactions is not clear . We have examined oocytes lacking two kinetochore proteins , NDC80 and SPC105R , and a centromere-associated motor protein , CENP-E , to characterize the impact of kinetochore-microtubule attachments on spindle assembly and chromosome segregation in Drosophila oocytes . We found that the initiation of spindle assembly results from chromosome-microtubule interactions that are kinetochore-independent . Stabilization of the spindle , however , depends on both central spindle and kinetochore components . This stabilization coincides with changes in kinetochore-microtubule attachments and bi-orientation of homologs . We propose that the bi-orientation process begins with the kinetochores moving laterally along central spindle microtubules towards their minus ends . This movement depends on SPC105R , can occur in the absence of NDC80 , and is antagonized by plus-end directed forces from the CENP-E motor . End-on kinetochore-microtubule attachments that depend on NDC80 are required to stabilize bi-orientation of homologs . A surprising finding was that SPC105R but not NDC80 is required for co-orientation of sister centromeres at meiosis I . Together , these results demonstrate that , in oocytes , kinetochore-dependent and -independent chromosome-microtubule attachments work together to promote the accurate segregation of chromosomes .
It is well established that oocyte spindle assembly in many organisms occurs in the absence of centrosomes [1–3] . Instead , chromatin-based mechanisms play an important role in spindle assembly . The interactions between chromosomes and microtubules are paramount in oocytes , necessary for both the assembly of the spindle and the forces required for chromosome segregation . Less well understood , however , is the nature of the functional connections between chromosomes and microtubules in these cells . The role of the kinetochores , the primary site of interaction between chromosomes and microtubules , is poorly understood in acentrosomal systems . For example , spindles will assemble and chromatin will move without kinetochores in both Caenorhabditis elegans and mouse oocytes [4 , 5] . In addition , both C . elegans and mouse oocytes experience a prolonged period during which chromosomes have aligned but end-on kinetochore-microtubule attachments have not formed [6–8] . We have previously shown that the central spindle , composed of antiparallel microtubules that assemble adjacent to the chromosomes , is important for spindle bipolarity and homolog bi-orientation [9 , 10] . These studies suggest that lateral interactions between the chromosomes and microtubules drive homolog bi-orientation , but whether these interactions are kinetochore-based is not clear . There have been few studies directly analyzing kinetochore function in oocyte spindle assembly and chromosome segregation [5 , 11 , 12] . Assembling a functional spindle requires the initiation of microtubule accumulation around the chromatin , the organization of microtubules into a bipolar structure , and the maturation of the spindle from promoting chromosome alignment to promoting segregation . Whether the kinetochores are required for spindle assembly or the series of regulated and directed movements chromosomes undergo to ensure their proper partitioning into daughter cells is not known . In Drosophila , the chromosomes begin the process within a single compact structure called the karyosome [13] . Within the karyosome , centromeres are clustered prior to nuclear envelope breakdown ( NEB ) [14] . This arrangement , which is established early in prophase and maintained throughout diplotene/diakinesis , is also found in many other cell types [15] . It is possible that the function of centromere clustering is to influence the orientation of the centromeres on the spindle independent of chiasmata [16 , 17] . Following NEB , the centromeres separate . In Drosophila oocytes , centromere separation depends on the chromosomal passenger complex ( CPC ) [10] . Whether this movement depends on interactions between chromosomes and microtubules remains to be established [10] . Following centromere separation , homologous centromeres move towards opposite spindle poles . During this time in Drosophila oocytes , the karyosome elongates and achiasmate chromosomes may approach the poles , separating from the main chromosome mass [18] . As prometaphase progresses , the chromosomes once again contract into a round karyosome . These chromosome movements appear analogous to the congression of chromosomes to the metaphase plate that ultimately results in the stable bi-orientation of chromosomes . In mitotic cells , congression depends on lateral interactions between kinetochores and microtubules [19 , 20] , and bi-orientation depends on the formation of end-on kinetochore-microtubule attachments [21 , 22] . In oocytes , lateral chromosome-microtubule interactions have been suggested to be especially important , but how lateral and end-on kinetochore-microtubule attachments are coordinated to generate homolog bi-orientation has not been studied [9 , 23] . To investigate the roles of lateral and end-on kinetochore-microtubule attachments in spindle assembly and prometaphase chromosome movements of acentrosomal oocytes , we characterized Drosophila oocytes lacking kinetochore components . The KNL1/Mis12/Ndc80 ( KMN ) complex is at the core of the kinetochore , providing a link between centromeric DNA and microtubules [24 , 25] . Both KNL1 and NDC80 bind to microtubules in vitro [26] , but NDC80 is required specifically for end-on kinetochore-microtubule attachments [24] . Therefore , we examined oocytes lacking either NDC80 to eliminate end-on attachments or the Drosophila homolog of KNL1 , SPC105R , to eliminate all kinetochore-microtubule interactions . We also examined Drosophila oocytes lacking the centromere-associated kinesin motor CENP-E because CENP-E promotes the movement of chromosomes along lateral kinetochore-microtubule attachments in a variety of cell types [19 , 20] . Our work has identified three distinct functions of kinetochores that lead to the correct orientation of homologs at meiosis I . First , SPC105R is required for the co-orientation of sister centromeres at meiosis I . This is a unique process that fuses sister centromeres , ensuring they attach to microtubules from the same pole at meiosis I . Second , lateral kinetochore-microtubule attachments are sufficient for prometaphase chromosome movements , which may be required for each pair of homologous centromeres to establish connections with microtubules from opposite poles . Third , end-on attachments are dispensable for prometaphase movement but are essential to stabilize homologous chromosome bi-orientation . Surprisingly , we found that although Drosophila oocytes do not undergo traditional congression of chromosomes to the metaphase plate , CENP-E is required to prevent chromosomes from becoming un-aligned and to promote the correct bi-orientation of homologous chromosomes . We also show that the initiation of acentrosomal chromatin-based spindle assembly does not depend on kinetochores , suggesting the presence of important additional interaction sites between chromosomes and microtubules . The stability of the oocyte spindle , however , becomes progressively more dependent on kinetochores as the spindle transitions from prometaphase to metaphase . Overall , this work shows that oocytes integrate several chromosome-microtubule connections to promote spindle formation and the different types of chromosome movements that ensure the proper segregation of homologous chromosomes during meiosis .
To study the role of kinetochores in oocyte spindle assembly and chromosome orientation , we sought to eliminate kinetochore function in Drosophila oocytes . Mutations in Drosophila kinetochore genes are lethal prior to the initiation of oogenesis [27–30] , and germline clones of kinetochore mutants failed to complete oogenesis ( S1 Table ) . Therefore , we used RNAi to deplete kinetochore proteins in Drosophila oocytes ( see Materials and Methods ) . Mitotic cells lacking NDC80 have persistent lateral kinetochore-microtubule attachments [31] , while loss of SPC105R ( the Drosophila homolog of KNL1 ) results in destabilization of all kinetochore-microtubule attachments [32] . Therefore , we decided to use Ndc80 and Spc105R depletion to examine the roles of kinetochores and discriminate between the roles of lateral and end-on kinetochore-microtubule attachments in oocytes . One Ndc80 ( GL00625 ) and two Spc105R ( GL00392 and HMS01548 ) RNAi constructs were obtained . In oocytes , expression of these constructs knocked down Ndc80 gene expression by 94% and Spc105R gene expression by 87% and 96% , respectively . No significant phenotypic differences were observed between the two Spc105R constructs; therefore , for simplicity all experiments shown used only the GL00392 hairpin except where noted . We found that localization of NDC80 and SPC105R to kinetochores was absent in Ndc80- or Spc105R-depleted oocytes , respectively ( S2 Table and Fig 1A and 1B ) , showing that the RNAi knockdown was effective . In addition , NDC80 and NSL1 ( a member of the Mis12 complex ) failed to localize to kinetochores in Spc105R-depleted oocytes ( S2 Table and Fig 1A and 1C ) , while both SPC105R and NSL1 localized to kinetochores in Ndc80-depleted oocytes ( S2 Table and Fig 1B and 1C ) . These results are consistent with results from mitotic cells in Drosophila embryos and cell culture [28 , 33]: localization of KMN complex proteins in Drosophila depends on SPC105R but not NDC80 . To determine if kinetochore-microtubule attachments are affected in oocytes depleted of kinetochore components , we examined microtubule localization relative to the centromere protein , CENP-C . The robust central spindle makes it difficult to directly observe kinetochore microtubules; therefore , we used conditions that depolymerize central spindle microtubules to directly observe kinetochore microtubules . Wild-type oocytes exposed to colchicine ( see Materials and Methods for details ) resulted in the loss of most spindle microtubules except for those that ended at the centromeres ( Fig 2B , 17/18 oocytes ) . In contrast , in colchicine-treated oocytes lacking NDC80 , the microtubules were weaker , and those remaining often appeared to be interacting laterally with the centromeres ( Fig 2D , 7/12 oocytes ) . In colchicine-treated oocytes lacking SPC105R , no end-on kinetochore-microtubule attachments were observed ( 0/12 oocytes ) , and we observed some oocytes in which all of the microtubules were eliminated ( Fig 2F , 3/12 oocytes ) . These results suggest that NCD80 is required for end-on kinetochore-microtubule attachments , while all kinetochore-microtubule interactions depend on SCP105R . To confirm that SPC105R , but not NDC80 , is required for kinetochore-microtubule interactions , we measured the distance between each centromere and the nearest microtubules in oocytes not treated with colchicine . In wild type , the majority of centromeres were within 0 . 2 μm of the microtubules ( Fig 3A and 3B ) . A similar frequency was found with loss of NDC80 , suggesting these defective kinetochores still interacted with the microtubules ( Fig 3A and 3B , P = 0 . 07 ) . Oocytes lacking SPC105R , however , had significantly fewer centromeres within 0 . 2 μm of the microtubules ( Fig 3A and 3B , P = <0 . 0001 ) . Based on the results with colchicine , it is likely that centromeres lacking SPC105R move within 0 . 2 μm of a microtubule by chance . These results suggest SPC105R , but not NDC80 , is required for the kinetochores to attach to the microtubules . To examine microtubule interactions using a functional readout for end-on attachments , we examined the localization of ROD . ROD is part of the RZZ complex , which localizes to kinetochores until the formation of end-on kinetochore-microtubule attachments when it leaves the kinetochore by streaming along the kinetochore microtubules [34] . In wild-type oocytes , ROD was present at kinetochores , and we observed streams of ROD along microtubules ( S1 and S2 Figs ) , suggesting that end-on kinetochore-microtubule attachments indeed form in Drosophila oocytes . In C . elegans , localization of the RZZ complex to the kinetochore depends on KNL-1 , the homolog of Drosophila SPC105R [35] . Similarly , we did not observe localization of ROD to kinetochores in Spc105R-depleted oocytes ( S1 Fig ) . In Ndc80-depleted oocytes , however , ROD was present at kinetochores , but in most oocytes we did not observe streaming along the microtubules ( S1 Fig ) . Therefore , the lack of ROD streaming demonstrates that end-on kinetochore-microtubule attachments do not form in the absence of NDC80 . To determine the role of kinetochore-microtubule attachments in oocyte chromosome movement , we examined prometaphase karyosome configurations after knockdown of Ndc80 or Spc105R . During prometaphase I , chromosomes undergo movements to facilitate contact with the spindle , a process that may be required for chromosome alignment [18] . In Drosophila oocytes , chromosomes are compacted into a karyosome [13] so congression to the metaphase plate is unnecessary . Prometaphase chromosome movements still occur and are visible through the elongation of the karyosome and the separation of achiasmate chromosomes from the karyosome ( S3 Fig ) . Collections of oocytes can be enriched for either prometaphase or metaphase depending on how the females are treated ( see Materials and Methods ) [18] . We found that prometaphase karyosome configurations in Ndc80-depleted oocytes were similar in frequency to wild type ( 28% vs . 26%; Table 1 ) . In contrast , in Spc105R-depleted oocytes , elongation of the karyosome and separation of achiasmate chromosomes was rarely observed ( 6% of oocytes; Table 1 ) . Therefore , prometaphase chromosome movements depend on SPC105R , but not NDC80 , suggesting that lateral kinetochore-microtubule attachments are sufficient to drive prometaphase chromosome movements in oocytes . Karyosome morphology does not reveal information about individual chromosomes . Therefore , to gain a more direct picture of chromosome behavior in the absence of kinetochore proteins , we examined the position of all centromeres using immunolocalization of CENP-C ( Fig 3A ) . Drosophila have four pairs of homologous chromosomes . Because each homologous chromosome is formed from four chromatids , 16 centromeres are present at meiosis I . During meiosis I , however , sister centromeres are fused to promote their co-orientation toward one pole . In agreement with this , we found an average of 7 . 0 CENP-C foci were visible per wild-type oocyte ( Fig 3C ) . The deviation from the expected value of eight is due to an inability to resolve centromeres of different chromosomes that are fortuitously close together . In Spc105R-depleted oocytes , the average CENP-C foci number was significantly elevated to 11 . 1 ( P = <0 . 0001; Fig 3C ) , suggesting a loss of co-orientation . This difference is in contrast to Ndc80-depleted oocytes that had an average of 7 . 7 CENP-C foci , which does not differ significantly from wild type ( P = 0 . 13; Fig 3C ) . One possibility is that kinetochore-microtubule attachments are required for co-orientation . An alternative , however , is that SPC105R is required for the kinetochore localization of proteins that do not depend on NDC80 [36] . In yeast , the monopolin complex promotes co-orientation [37] , and MEIKIN provides this function in vertebrates [38] . Perhaps SPC105R is required for the kinetochore localization of the as-yet-unidentified invertebrate functional equivalent of monopolin/MEIKIN . Although little is known about the molecular mechanism of co-orientation in metazoan oocytes , sister chromatid cohesion has been shown to be involved [22] . To determine whether loss of cohesion results in sister centromere separation in Drosophila oocytes , we examined ord mutants ( Fig 3A and 3C ) . ORD is required for sister chromatid cohesion during meiosis [39] . In ord mutants , we observed an average of 10 . 8 CENP-C foci , which is not significantly different from Spc105R-depleted oocytes ( P = 0 . 76; Fig 3C ) . We also examined sister centromere separation in mei-S332 mutants , which mutate the Drosophila homolog of Shugoshin [40] . With an average of 7 . 5 CENP-C foci , this is not significantly different than wild type ( P = 0 . 47; Fig 3C ) , but is significantly different from both Spc105R depletion ( P = 0 . 0005 ) and ord mutants ( P = 0 . 01 ) , consistent with the conclusion that MEI-S332 function is not required until anaphase I [22] . These results suggest that SPC105R is required for co-orientation in oocytes , perhaps through the protection of sister chromatid cohesion during meiosis I . While only Spc105R-depleted oocytes had a co-orientation defect , immunolocalization of CENP-C revealed a defect present in both Ndc80- and Spc105R-depleted oocytes . In wild-type Drosophila oocytes , centromere foci are clustered into two groups at the edge of the karyosome closest to each spindle pole ( Fig 3A ) . This represents when microtubule connections to the spindle poles pull homologous chromosomes in opposite directions . In Ndc80- and Spc105R-depleted oocytes , the centromere foci were not clustered into two groups oriented toward each spindle pole , but rather were scattered around the karyosome ( Fig 3A ) . This is a failure of the centromeres to orient towards a spindle pole . To quantify this phenotype , we measured the angle of displacement of each CENP-C focus with respect to the axis of the half spindle , defined by the line between a point at the spindle pole and the center point of the karyosome ( Fig 3D ) . Oriented centromeres have measurements as low as 0 degrees , while centromeres that fail to orient and are scattered around the karyosome result in angle measurements up to 90 degrees . In wild type , CENP-C foci angles had a median value of 17 degrees ( Fig 3D ) . CENP-C angles in Ndc80- or Spc105R-depleted oocytes were skewed significantly higher with median values of 28 degrees and 48 degrees , respectively ( P = <0 . 0001 for each; Fig 3D ) , demonstrating that kinetochore-microtubule attachments are required for chromosomes to orient towards a spindle pole . However , there was also a significant difference in CENP-C foci angles between Ndc80- and Spc105R-depleted oocytes ( P = <0 . 0001 ) . This is reflected in the data by the greater number of oocytes with angles close to 90 degrees in Spc105R-depleted oocytes . These data show that loss of NDC80 disturbs chromosome orientation , although not as dramatically as loss of SPC105R . To explain this difference , we suggest that lateral kinetochore-microtubule attachments are sufficient for some partial or unstable chromosome orientation , while end-on attachments cement orientation towards a spindle pole . Karyosome morphology and immunolocalization of CENP-C in Ndc80- or Spc105R-depleted oocytes suggested kinetochore-microtubule attachments allowed chromosomes to orient towards a spindle pole . To test whether each chromosome associated randomly with a pole , or if homologs oriented towards opposite poles ( “bi-orientation” ) , we used FISH to examine specific chromosomes . Chromosome bi-orientation at meiosis I depends on the establishment of connections between homologous chromosome pairs and opposite spindle poles . When this occurs , tension across the homologous chromosome pair generates an increase in the inter-homolog centromere distance . We used FISH probes to the repetitive sequences present at the centromeres of the second and third chromosomes to determine directly whether the separation of homologous centromeres away from each other depends on kinetochores and their end-on attachment to microtubules in oocytes ( Fig 4A ) . In wild type , we observed an average distance between homologous centromeres of 3 . 0 μm ( Fig 4B ) . In Ndc80-depleted oocytes , the average distance between homologous centromeres was not significantly reduced ( 2 . 7 μm , P = 0 . 4; Fig 4B ) , suggesting that end-on kinetochore-microtubule attachments are not required for homologous centromeres to move away from each other . In contrast , in Spc105R-depleted oocytes , the average distance was significantly reduced to 1 . 8 μm ( P = 0 . 0007; Fig 4B ) . These results suggest that lateral kinetochore-microtubule attachments are sufficient for homologous centromeres to orient towards a spindle pole and separate from each other in what may be the first step in the bi-orientation process . Because CENP-E is a kinesin motor involved in the lateral movement of chromosomes along microtubules , we hypothesized that CENP-E could mediate some of the kinetochore-dependent movements that depend on SPC105R but not NDC80 . Drosophila melanogaster is unusual because it has two Cenp-E genes , cana and cmet , arranged in inverse orientation on the chromosome ( S4 Fig ) . The proteins encoded by cana and cmet show considerable sequence similarity throughout their motor and stalk domains ( 42% identical overall ) , and only 6 out of the 12 sequenced Drosophila species have two copies of Cenp-E , suggesting a recent duplication event . It was previously shown that cmet mutants are inviable [41] . We generated cana mutants and cana cmet double mutants , which we refer to as Cenp-E mutants ( see Materials and Methods for details ) . Like cmet mutants , Cenp-E mutants are inviable; however , cana mutants are viable and fertile . To determine the function of CENP-E in chromosome movement in oocytes , we focused on cana hemizygous mutants , depletion of cmet by RNAi ( GL00404 from TRiP ) , and Cenp-E germline clones generated using the dominant female sterile technique [42] . In oocytes , expression of the GL00404 construct knocked down cmet gene expression by 75% . In cana mutant , cmet-depleted , or Cenp-E mutant oocytes , bipolar spindles formed ( Fig 5 ) . However , in cmet-depleted or Cenp-E mutant oocytes , the karyosome frequently split into multiple masses ( Fig 5 and Table 1 and S3 Table ) . This karyosome defect was more frequent in Cenp-E mutant oocytes than in cmet-depleted oocytes ( 43% vs 15% , P = 0 . 001 ) , demonstrating that both CENP-E homologs are required for proper karyosome organization . CANA and CMET are partially redundant because CMET is necessary for karyosome organization even when there is a functional copy of CANA . These results are the first evidence that the second Drosophila CENP-E homolog CANA is functional . Additionally , these results suggest that , although traditional chromosome congression does not occur in Drosophila oocytes , CENP-E is required to prevent chromosomes from becoming un-aligned and separated from the main karyosome mass . To further explore the role of CENP-E in chromosome movements in oocytes , we examined the orientation of centromeres using FISH . Because cana mutants are fertile and do not exhibit chromosome segregation errors , not surprisingly we found no defect in centromere orientation ( Fig 4C and Table 2 ) . In contrast , homologous centromeres were frequently mis-oriented in both cmet-depleted and Cenp-E mutant oocytes ( Fig 4C and Table 2 ) . The frequency of mis-orientation was similar between cmet-depleted and Cenp-E mutant oocytes ( 29% vs 24% , P = 0 . 7 ) , suggesting that CMET is the primary CENP-E homolog functioning in bi-orientation . Importantly , the cmet mis-orientation phenotype is distinct from either Ndc80 or Spc105R depletion in that centromeres are not scattered around the karyosome: they orient , but often towards the wrong spindle pole ( Fig 4C ) . This suggests that stable end-on kinetochore-microtubule attachments are not eliminated in the absence of CENP-E , but that CENP-E is required for establishing the correct kinetochore-microtubule attachments to direct homologs toward opposite spindle poles . To determine more directly the nature of microtubule attachments in the absence of CENP-E , we examined the localization of ROD . ROD accumulates at kinetochores until the formation of end-on kinetochore-microtubule attachments [34] . We found that ROD was present at kinetochores and streaming along microtubules in cmet-depleted oocytes , similar to wild type ( S1 Fig ) . This demonstrates that CENP-E is not required to form end-on kinetochore-microtubule attachments . However , one known role of CENP-E is in the regulation of end-on kinetochore-microtubule attachment stability [43 , 44] . Consistent with this , upon closer investigation using live imaging , we observed an increased frequency of ROD at the kinetochores in cmet-depleted oocytes ( S2 Fig ) . Kinetochores that are not streaming ROD should undergo re-orientation to achieve stable bi-orientation . This can be observed in live imaging of wild-type oocytes because kinetochores with accumulated ROD change position within the karyosome ( S5 Fig ) . The kinetochores that accumulated ROD in the absence of CMET , however , often failed to change position within the karyosome ( S5 Fig ) , suggesting that in the absence of CMET , the ability to re-orient following a failure to bi-orient is defective . Because the karyosome is maintained in the absence of the kinetochore components NDC80 or SPC105R ( Table 1 ) , active congression via kinetochore-microtubule attachments may not be required for chromosome organization in Drosophila oocytes . On the other hand , the karyosome splits apart in the absence of CENP-E , resulting in the un-alignment of chromosomes ( Table 1 ) . Because CENP-E is typically thought to move chromosomes via lateral kinetochore-microtubule attachments , we wanted to test whether the splitting apart of the karyosome in the absence of CENP-E depends on kinetochore-microtubule attachments . We examined karyosome configurations in two types of oocytes: those depleted of both cmet and Ndc80 or both cmet and Spc105R ( Fig 5 and Table 1 ) . We found that loss of CMET in the absence of SPC105R did not result in karyosome splitting ( Table 1 ) . This suggests that the movement of chromosomes that results in splitting of the karyosome depends on kinetochore-microtubule attachments . On the other hand , the karyosome split apart in Ndc80 cmet double-depleted oocytes ( Table 1 ) , suggesting that lateral kinetochore-microtubule attachments are sufficient for the splitting of the karyosome , and that CMET opposes this movement . Strikingly , the karyosome defect was enhanced in Ndc80 cmet double-depleted oocytes compared to cmet-depleted oocytes ( 40% vs . 15% , P = 0 . 004 ) . In fact , Ndc80 cmet double-depleted oocytes are not significantly different from Cenp-E oocytes ( 40% vs . 43% , P = 0 . 8 ) . One possibility is that NDC80 is required for CANA function such that loss of NDC80 and CMET together effectively recapitulates the complete loss of CENP-E . Alternatively , NDC80 ( via end-on kinetochore-microtubule attachments ) and CMET ( via lateral kinetochore-microtubule attachments ) may work together to oppose the forces driving chromosome un-alignment . In any case , these results demonstrate that CENP-E prevents chromosome un-alignment via lateral-kinetochore microtubule attachments . Our results thus far show that kinetochores participate in chromosome alignment , bi-orientation , and co-orientation in Drosophila oocytes . Since chromatin-mediated pathways direct spindle assembly in oocytes [45] , we investigated the contribution of the kinetochores to spindle assembly and stability at prometaphase I and metaphase I . Metaphase I-arrested spindles tend to be shorter than prometaphase spindles with less prominent central spindles ( Fig 6A and 6B ) . In fact , we observed that spindles were weak , that is very small , faint , or lacking microtubules entirely ( indicated below as “weak/absent” ) , more frequently in metaphase-enriched oocyte samples ( 33% , P = <0 . 0001 ) ( see Materials and Methods for details of how metaphase- or prometaphase-enriched samples are collected ) than in prometaphase-enriched oocyte samples ( 11% ) ( Fig 6D ) . This difference suggests that in Drosophila oocytes , spindle assembly proceeds via an elongation phase during which spindles are robust ( prometaphase ) , followed by a contraction phase in which microtubule density decreases ( metaphase ) . We found that Spc105R-depleted oocytes form bipolar spindles in prometaphase-enriched oocyte collections . In prometaphase-enriched collections , weak/absent spindles were increased in Spc105R-depleted oocytes compared to wild type ( 21% , P = 0 . 04; Fig 6D ) . This difference suggests that the prometaphase spindle is destabilized in the absence of kinetochore components . Weak/absent spindles were significantly increased compared to wild type in metaphase-enriched collections of Spc105R-depleted oocytes ( 60% , P = 0 . 003; Fig 6B and 6D ) . These results suggest that kinetochore microtubules contribute early to the organization of the prometaphase spindle , and then form the majority of microtubules in the metaphase-arrested spindle . These results make predictions about the microtubules that assemble in Spc105R-depleted oocytes . First , although these spindles are bipolar , they should lack kinetochore microtubules . Indeed , Spc105R-depleted oocyte spindles appear hollow , as if they are missing the microtubules that , in wild type , end at the chromosomes ( Figs 1 , 3A , 4A , 5 and 6A ) . Ndc80-depleted oocytes also form hollow spindles ( Figs 1 , 3A , 4A and 5 ) ; therefore , the stable kinetochore microtubules are most likely only those that form end-on attachments . The microtubules in these hollow spindles could depend on the prominent central spindle that forms in Drosophila oocytes and is required for bipolarity and chromosome bi-orientation [10 , 46] . To directly determine whether the central spindle forms properly in the absence of kinetochores , we examined localization of INCENP , a member of the CPC , in Spc105R-depleted oocytes . We found that INCENP localized normally in the hollow spindles from prometaphase-enriched collections ( Fig 6A ) . Indeed , most spindles in Spc105R-depleted prometaphase oocytes are bipolar , suggesting the central spindle is sufficient to organize the spindle poles . However , the central spindle was disorganized in metaphase-enriched Spc105R-depleted oocytes ( Fig 6B ) , indicating that kinetochores contribute to the stability of the central spindle at metaphase . If all microtubules present in Spc105R-depleted oocytes are associated with the central spindle , then the meiotic spindle is likely composed of two types of microtubules: kinetochore-dependent and central spindle-dependent . To test this hypothesis , we knocked down both subito , which is required for the formation of the central spindle [46] , and Spc105R in oocytes . We found that sub-depleted oocytes had polarity defects similar to sub null mutants [46 , 47] ( Fig 6C ) , but this depletion did not significantly increase weak/absent spindles in prometaphase-enriched collections ( 17% , P = 0 . 5; Fig 6D ) . In contrast , sub Spc105R double depletion resulted in a significant increase in weak/absent spindles in prometaphase-enriched collections ( 58% , P = <0 . 0001 ) , comparable to the spindle destabilization observed in metaphase-enriched collections from Spc105R-depleted oocytes ( Fig 6C and 6D ) . These results suggest that both the organization and the stability of the prometaphase oocyte spindle depend on kinetochores and the central spindle . The metaphase-arrested spindle , on the other hand , depends mostly on kinetochore microtubules . The CPC is required for the assembly of all microtubules around the karyosome in Drosophila oocytes ( Fig 1 ) [10] . The CPC is also required for localization of central spindle components such as SUB [10] . Because we have shown that kinetochores and the central spindle coordinately contribute to spindle stability , we wanted to determine whether kinetochore assembly also depends on the CPC . We found that the KMN complex did not localize after depletion of aurB , which encodes the CPC component Aurora B kinase ( Fig 1 ) . Interestingly , loss of the CPC in Drosophila oocytes results in a complete loss of microtubules around the karyosome ( Fig 1 ) [10] . This phenotype is more severe than the double knockdown of Spc105R and sub in which ~40% of oocytes showed significant spindle microtubules , albeit thin and disorganized ( Fig 6C and 6D ) . These data demonstrate that while the CPC controls oocyte spindle stability through its regulation of kinetochore assembly and SUB localization , the CPC also regulates additional spindle assembly factors that promote the initiation of spindle assembly .
While the spindle is assembling and becoming organized , our evidence suggests that the chromosomes undergo a series of movements that ultimately result in the bi-orientation of homologous chromosomes . We found that the separation of clustered centromeres is CPC-dependent [10] , but not kinetochore-dependent ( Figs 3A and 7A ) . One possibility is that the CPC-dependent interaction of microtubules with non-kinetochore chromatin drives centromere separation . An alternative is that CPC activity may result in a release of the factors that hold centromeres together in a cluster prior to NEB . A candidate for this factor is condensin , a known target of the CPC , that has been shown to promote the “unpairing” of chromosomes in the Drosophila germline [48] . Following separation of clustered centromeres , each pair of homologous centromeres bi-orients by separating from each other towards opposite poles . How bi-orientation is established in acentrosomal oocytes is poorly understood . Previous studies in C . elegans and mouse oocytes have suggested a combination of kinetochore-dependent and kinetochore-independent ( e . g . involving chromokinesins and chromosome arms ) microtubule interactions drive chromosome alignment and segregation [23 , 49] . We have found that kinetochores play multiple roles , and the process of chromosome bi-orientation can be broken down into a series of chromosome movements that depend mostly on the kinetochores . First , the centromeres make an attempt at bi-orientation ( Fig 7D ) . In Drosophila oocytes , this results in the directed poleward movement of centromeres toward the edge of the karyosome and is accompanied by a stretching of the karyosome . Lateral kinetochore-microtubule attachments mediated by SPC105R are sufficient for this initial attempt at bi-orientation . End-on kinetochore-microtubule attachments via NDC80 , however , are essential to maintain the bi-orientation of centromeres . Maintenance of centromere bi-orientation is associated with the stable positioning of the centromeres at the edges facing the poles ( Fig 7F′ ) . The lateral-based chromosome movements required for chromosome orientation are probably mediated by the meiotic central spindle , which we previously showed was essential for chromosome segregation [9 , 47] . In addition , recent reports in both mitotic and meiotic cells suggest that the initial orientation of chromosomes depends on the formation of a “prometaphase belt” that likely brings centromeres into the vicinity of the central spindle [7] . Therefore , we propose that the initial attempt at bi-orientation occurs during the period when both kinetochores and the central spindle are required for spindle stability ( Fig 7B ) . Then , as the oocyte progresses toward metaphase , and the central spindle decreases in importance , this reflects a trend toward the formation of stable end-on kinetochore-microtubule attachments that , in turn , stabilize the bipolar spindle ( Fig 7C ) . This model is also corroborated by evidence from mouse oocytes that stable end-on kinetochore-microtubule attachments form after a prolonged prometaphase [6] . Our data demonstrate that some chromosome movements , critical for bi-orientation , are dependent on lateral kinetochore-microtubule attachments . The kinetochore-associated kinesin motor CENP-E is thought to be responsible for chromosome movement along lateral kinetochore-microtubule attachments , resulting in chromosome alignment on the metaphase plate [20] . However , because Drosophila meiotic chromosomes are compacted into a karyosome prior to NEB , they do not need to migrate in a plus-end-directed manner to achieve congression and alignment . Instead , centromeres must move toward the poles , perhaps in a minus-end directed manner , to achieve bi-orientation ( Fig 7D ) . Interestingly , we found that CENP-E opposes this minus-end directed movement ( Fig 7E ) because in the absence of CENP-E , the karyosome split via lateral kinetochore-microtubule attachments ( Figs 5 and 7F″ ) . It is not yet clear what mediates the minus-end-directed movement , but the motors Dynein and NCD ( the Drosophila kinesin-14 homolog ) or microtubule flux [50 , 51] are prime candidates . We also observed that CMET is required for the correct bi-orientation of homologous chromosomes ( Fig 4C and Table 2 ) . The function proposed in opposing minus-end directed movement may be required for making the correct attachments . As the centromere moves to the edge of the karyosome , CENP-E may not only prevent its separation from the karyosome , but could also force it back towards the opposite pole in cases where the homologs are not bi-oriented ( Fig 7E ) . A similar idea has been proposed for CENP-E in mouse oocytes [52] . Alternatively , CENP-E has a second function in tracking microtubule plus-ends and regulating kinetochore-microtubule attachments [43 , 44 , 53] . In fact , we found that end-on kinetochore-microtubule stability is affected in the absence of CENP-E ( S2 and S5 Figs ) . Regulating the stability of microtubule plus-end attachments with kinetochores is critical for establishing correct bi-orientation of homologs [54] . Therefore , both functions of CENP-E could contribute to the correct bi-orientation of centromeres in Drosophila oocytes . Loss of SPC105R has a more severe phenotype than loss of either NDC80 or CENP-E , consistent with a role as a scaffold [36] . It recruits additional microtubule interacting proteins like NDC80 and CENP-E and also recruits checkpoint proteins such as ROD [36] . In analyzing oocytes lacking SPC105R , we discovered another class of factors it may recruit: proteins required for co-orientation of sister centromeres during meiosis I . Co-orientation is a process that fuses the core centromeres and is important to ensure that two sister kinetochores attach to microtubules that are attached to the same spindle pole [22] . Co-orientation could involve a direct linkage between sister kinetochores , as may be the case with budding yeast Monopolin [55] or in maize , where a MIS12-NDC80 linkage may bridge sister kinetochores at meiosis I [56] . In contrast , in fission yeast meiosis I , cohesins are required for co-orientation . Cohesion is stably maintained at the core centromeres during meiosis I but not mitosis , and this depends on the meiosis-specific proteins Moa1 and Rec8 [57] . There is also evidence that Rec8 is required for co-orientation in Arabidopsis [58] and we found that loss of ORD , which is required for meiotic cohesion , also results in a loss of centromere co-orientation ( Fig 3A and 3C ) . Further studies , however , are necessary to determine if cohesins are required for co-orientation in Drosophila . Indeed , the proteins and mechanism that mediate this process in animals has not been known . Recently , however , the vertebrate protein MEIKIN has been found to provide a similar function to Moa1 [38] . Interestingly , both Moa1 and MEIKIN depend on interaction with CENP-C , but do not show sequence homology . Thus , Drosophila may have a Moa1/MEIKIN ortholog that has not yet been identified . In the future , it will be important to identify the proteins recruited by SPC105R and their targets in maintaining centromere co-orientation and how these interact with proteins recruited by CENP-C . The mechanism may involve the known activity of SPC105R in recruiting PP1 , because PP1 has been shown to have a role in maintaining cohesion in meiosis I of C . elegans [59] . Our model for spindle assembly and chromosome orientation raises several important questions for future consideration . The CPC is required for spindle assembly in Drosophila oocytes [10] and our results highlight the importance of two CPC targets in homolog bi-orientation . One target is central spindle proteins , possibly through the CPC-dependent recruitment of spindle organization factors such as SUB [10] . The CPC is also required for kinetochore assembly ( Fig 1 ) , similar to what has been shown in yeast , human cells , and Xenopus [60] and consistent with the finding in human cells that Aurora B promotes recruitment of the KMN complex to CENP-C [61 , 62] . It will be important to identify targets of the CPC that drive the initiation of spindle assembly , centromere separation , and bi-orientation . In addition , while we have found that the CPC is required for kinetochore assembly , it is not known if the CPC promotes error correction by destabilizing kinetochore-microtubule attachments [63–65] . The CPC may not promote kinetochore-microtubule detachment during meiosis because of the different spatial arrangement of sister centromeres during meiosis I . Indeed , it is not known what is responsible for correcting incorrect attachments at meiosis I or how they are differentiated from correct attachments . In prometaphase , the central spindle and kinetochores contribute to spindle stability . Our data suggests that the kinetochores increase in importance as the oocyte progresses to metaphase ( Fig 7C ) , perhaps as a result of the stabilization of end-on kinetochore-microtubule attachments as homologous chromosomes become bi-oriented . However , lateral kinetochore-microtubule interactions demonstrated some resistance to colchicine and allow bivalents to stretch in mouse oocytes [65] . Thus , further studies are necessary to determine if lateral kinetochore-microtubule interactions also confer some stability . Our model also proposes that the transition from prometaphase to metaphase involves a switch from dynamic lateral kinetochore-microtubule interactions to stable end-on kinetochore-microtubule attachments . This transition involves the loss of central spindle microtubules , which occurs regardless of microtubule attachment status . Further studies will be necessary to determine if the prometaphase-to-metaphase transition is developmentally regulated rather than being controlled by the spindle assembly checkpoint . As proposed in mouse oocytes [65 , 66] , this may contribute to the propensity for chromosome segregation errors in acentrosomal oocytes by closing the window of opportunity for error correction after key developmental milestones have been passed . Finally , one of the most poorly understood features of meiosis is co-orientation of sister centromeres at meiosis I [22 , 55] . What SPC105R interacts with to mediate co-orientation will provide the first insights into the mechanism and regulation of this process in Drosophila .
Flies were crossed and maintained on standard media at 25°C . All loci not described in the text are described in FlyBase ( flybase . org ) [67] . Fly stocks from the Transgenic RNAi Project ( TRiP , flyrnai . org ) were obtained either directly or from the Bloomington Stock Center . The cmetΔ allele was a gift from Byron Williams and Michael Goldberg . The ord mutant was ord5/ord10 and the mei-S332 mutant was mei-S3321/Df ( 2R ) BSC597 . The GFP-tagged ROD transgenic line was a gift from Roger Karess . RNAi constructs generated by the Transgenic RNAi Project ( TRiP ) [68] were: aurB ( previously known as ial , GL00202 ) [10] , Ndc80 ( GL00625 ) , Spc105R ( GL00392 and HMS01548 ) , and cmet ( GL00404 ) . The sub RNAi construct ( GL00583 ) was moved to a 2nd chromosome location through standard P element transposition crosses . To deplete oocyte proteins by RNAi , the expression of a short hairpin RNA is under control of the UAS/GAL4 system [69] . To confine the expression to oocytes , we used matα4-GAL-VP16 , which is expressed throughout oogenesis after the initiation of meiosis [10 , 70] . The long duration of meiotic prophase allows knockdown of gene expression within one cell cycle , thereby eliminating the possibility of confounding effects from going through rounds of aberrant cell division with decreasing amounts of protein . Some cmet RNAi experiments used nanos-GAL4:VP16 [71] . When nanos-GAL4:VP16 was used to express short hairpins to aurB , Ndc80 , or Spc105R , no oocytes were produced , probably due to the failure of the germline mitotic divisions . In all cases , the females expressing these short hairpins were sterile . To quantify knockdown of gene expression , late-stage oocytes were collected from females carrying both driver and RNAi construct by mass disruption of abdomens in a blender filled with phospho-buffered saline ( PBS ) . Oocytes were filtered through meshes to remove large body parts and allowed to settle quickly in solution to remove smaller egg chambers . For reverse transcriptase quantitative PCR ( RT-qPCR ) , total RNA was extracted from late-stage oocytes using TRIzol Reagent ( Life Technologies ) . RNA was converted to cDNA using the High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) . The qPCR was performed in a StepOnePlus ( Life Technologies ) real-time PCR system using TaqMan Gene Expression Assays ( Life Technologies , Dm01843531_g1 for cmet , Dm01838612_g1 for Ndc80 , Dm01792082_g1 for Spc105R , and Dm02134593_g1 for Rpll140 as control ) . Previous work has shown that cmetΔ mutations cause lethality [41] , but no cana mutants have yet been reported . To generate mutations of either cana , cmet , or both genes , we screened for imprecise excision of a P element inserted between the cana and cmet genes ( P{GawB}NP5235 , S4 Fig ) . The insertion site is 217 bp away from cana and 832 bp away from cmet . Excisions were tested for viability by crossing to Df ( 2L ) BSC236 . DNA was prepared from adult flies for viable excisions [72] and screened for deletion by PCR and DNA sequencing . Lethal excisions were crossed to cmetΔ to check viability and to cana13 for PCR screening of cana deletion . For excisions that likely deleted both cana and cmet sequences , embryos homozygous for lethal excision chromosomes were selected over a GFP-tagged 2nd chromosome balancer [73] and DNA for PCR was prepared by the same method as adult flies . Three cana mutants were isolated , but the results reported here focus on cana13 in which most of the motor domain was deleted ( S4 Fig ) . Previous studies have shown that the motor domain of CENP-E is required for chromosome movement during mitosis [74] , suggesting that cana13 is a null allele . Interestingly , however , there is an alternative transcript predicted for cana , encoding a protein that does not contain the motor domain ( S4 Fig ) . Therefore , if there is a non-motor function for cana , this may not be affected by cana13 . Hemizygous cana mutants ( cana13/Df ( 2L ) BSC236 ) are viable and fertile , and the percentage of X chromosome non-disjunction was similar to wild-type controls ( 0 . 1% , n = 2266 ) . These results suggest that the motor domain of CANA does not play an essential role in mitosis or meiosis . We also isolated three P element excisions that were lethal when heterozygous with cmetΔ , and PCR analysis also showed deletion of cana sequence , showing that both cana and cmet were deleted . For our studies , we focused on Df ( 2L ) Cenp-E141 , which will be referred to as Cenp-E141 ( S4 Fig ) . This mutation deletes sequences encoding part of the CMET motor domain and the entire CANA motor domain , suggesting that it is null for CENP-E function . Although Cenp-E141 is lethal when heterozygous with either cmetΔ or Df ( 2L ) BSC236 , rare homozygotes can be found . These Cenp-E141 homozygotes , however , have developmental abnormalities , including no ovary development ( in 100% of dissected the females , only rudimentary ovaries composed of predominantly somatic cells were present ) , consistent with the idea that CENP-E is important for cell division . To generate oocytes lacking both Cenp-E genes , the Cenp-E141 allele was crossed onto a chromosome bearing an FLP recombination target ( FRT ) sequence inserted at 40A near the centromere on the left arm of chromosome 2 . Females with this recombinant chromosome were crossed in vials to males with a matching FRT chromosome carrying the dominant female sterile mutation ovoD1 and a heat-shock-inducible FLP recombinase . After 3–4 days , the parents were transferred to new vials and progeny were heat shocked for one hour in a 37°C water bath . Female progeny carrying both FRT chromosomes and the FLPase were selected for examination as germline clones . In a sample of late-stage Drosophila oocytes , three cell cycle stages are present: prophase , prometaphase , and metaphase . Oocytes in prophase are distinguished by the presence of the nuclear envelope . We skewed the proportion of prometaphase vs . metaphase oocytes in fixed samples by controlling the age of the females and speed of egg-laying [18] . Late-stage oocytes were collected either from two- to four-day-old females aged two days on yeast with males ( “prometaphase”-enriched ) or from three- to thirteen-day-old females aged three to five days on yeast without males ( “metaphase”-enriched ) . Oocytes were prepared for immunofluorescence ( 5% formaldehyde/heptane fixation ) and FISH ( 8% formaldehyde/100 mM cacodylate fixation ) essentially as described [75] . For colchicine experiments , oocytes were incubated for 10 min in 0 . 12% ethanol ( control ) or 0 . 12% ethanol plus 150 μM colchicine prior to fixation . Primary antibodies used for immunofluorescence were mouse anti-α-tubulin conjugated to FITC ( 1:50 dilution , clone DM1A , Sigma ) , rabbit anti-CENP-C ( 1:5000 ) [76] , rat anti-INCENP ( 1:400 ) [77] , chicken anti-NDC80 ( 1:500 , Tom Maresca ) , rabbit anti-NSL1 ( 1:500 ) [29] , rabbit anti-SPC105R ( 1:4000 ) [28] , rabbit anti-GFP ( 1:400 , Life Technologies ) , rat anti-α-tubulin ( 1:75 , clone YOL 1/34 , Millipore ) , and chicken anti-CID ( 1:250 ) [78] . Cy3- , Cy5- , AlexaFluor647- ( Jackson Immunoresearch ) , or AlexaFluor488- ( Molecular Probes ) conjugated secondary antibodies were used . DNA was labeled with Hoechst 33342 ( 1:1000 , Invitrogen ) or TO-PRO-3 ( 1:1000 , Invitrogen ) . FISH probes used were to the AACAC satellite ( 2nd chromosome ) and dodeca satellite ( 3rd chromosome ) as described [14 , 75] . Samples were mounted in SlowFade Gold ( Invitrogen ) . Images were collected on a Leica TCS SP5 or SP8 microscope with a 63x , 1 . 4 NA lens using LAS AF software . Images are shown as maximum projections . Image analysis was performed with Imaris image analysis software ( Bitplane ) . Spindles and CENP-C foci were identified and distances measured using the Distance Transformation Xtension . XYZ coordinates for CENP-C foci and spindle axes were determined using Imaris . This information was used to calculate angles with respect to the spindle axis using Microsoft Excel . For live imaging , females were matured at 18°C for 4 to 7 days and oocytes from mature females were manually separated in halocarbon oil ( Halocarbon ) . Oocyte stages 13 and 14 were determined by the morphology of the dorsal appendages [79] . pUASp-cmet shRNA ( TRiP . GL00404 ) attP2/UASp-RCC1:mCherry nos-GAL4:VP16 ( MVD1 ) ROD:GFP and a wild-type chromosome over UASp-RCC1-mCherry nos-GAL4:VP16 ( MVD1 ) ROD:GFP were used for a cmet RNAi and a control . Imaging was carried out using an Axiovert ( Zeiss ) microscope attached to a spinning disc confocal head ( Yokogawa ) controlled by Volocity ( PerkinElmer ) . Structures within oocytes were examined using the Plan Apochromat 63x , 1 . 4 NA lens ( Zeiss ) . Immersol 518F oil ( Zeiss ) was applied . Z sections , separated by 0 . 5 μm , covering the entire fluorescent structure were taken every 1 min . Statistical tests were performed using GraphPad Prism software . Prometaphase karyosome configurations and spindle stability were compared using Fisher’s exact test . Distances between CENP-C and the spindle , distances between homologous centromeres using FISH , and the number of CENP-C foci per oocyte were compared using a t test . CENP-C angles were compared using a Mann-Whitney test . | In acentrosomal oocytes , spindle assembly depends on the chromosomes . The nature of the chromosome-microtubule interactions in oocytes that organize spindle bipolarity and orientation of the homologs has been unclear . We have found that several types of functional chromosome-microtubule interactions exist in oocytes , and that each type participates in unique aspects of chromosome orientation and spindle assembly . We present here a model for chromosome-based spindle assembly and chromosome movements in oocytes that highlights the multiple and unappreciated roles played by the kinetochores and has implications for how homologous chromosomes bi-orient during meiosis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| []
| 2015 | Lateral and End-On Kinetochore Attachments Are Coordinated to Achieve Bi-orientation in Drosophila Oocytes |
Many algorithms for sequence analysis rely on word matching or word statistics . Often , these approaches can be improved if binary patterns representing match and don’t-care positions are used as a filter , such that only those positions of words are considered that correspond to the match positions of the patterns . The performance of these approaches , however , depends on the underlying patterns . Herein , we show that the overlap complexity of a pattern set that was introduced by Ilie and Ilie is closely related to the variance of the number of matches between two evolutionarily related sequences with respect to this pattern set . We propose a modified hill-climbing algorithm to optimize pattern sets for database searching , read mapping and alignment-free sequence comparison of nucleic-acid sequences; our implementation of this algorithm is called rasbhari . Depending on the application at hand , rasbhari can either minimize the overlap complexity of pattern sets , maximize their sensitivity in database searching or minimize the variance of the number of pattern-based matches in alignment-free sequence comparison . We show that , for database searching , rasbhari generates pattern sets with slightly higher sensitivity than existing approaches . In our Spaced Words approach to alignment-free sequence comparison , pattern sets calculated with rasbhari led to more accurate estimates of phylogenetic distances than the randomly generated pattern sets that we previously used . Finally , we used rasbhari to generate patterns for short read classification with CLARK-S . Here too , the sensitivity of the results could be improved , compared to the default patterns of the program . We integrated rasbhari into Spaced Words; the source code of rasbhari is freely available at http://rasbhari . gobics . de/
k-mers , i . e . words of length k , are used in many basic algorithms for biological sequence comparison . Word matches are used , for example , as seeds in the hit-and-extend approach to database searching and read mapping [1–3] . Here , fast algorithms are applied to find pairs of identical or similar words between two sequences . A slower but more sensitive alignment method is then used to extend these word pairs to both directions , to distinguish biologically relevant homologies from spurious word matches . In alignment-free sequence comparison , sequences are often represented by word-frequency vectors to estimate distances or similarities between them , e . g . as a basis for phylogeny reconstruction [4–8] , see [9] for a review . Similarly , word statistics are used to classify DNA or protein sequences [10–12] , for datamining [13] and for remote homology detection [14] . It is well known that many word-based approaches produce better results if spaced words or seeds are used instead of the previously used contiguous words or word matches . That is , for a pre-defined binary pattern P representing match and don’t-care positions , one considers only those positions in a word of the same length that correspond to the match positions of P . Pattern-based word matching has been proposed for hit-and-extend database searching by Ma et al . [15] , see also [16] . Spaced seeds are also routinely used in metagenome sequence clustering and classification [17 , 18] , protein classification [19] , read mapping [20 , 21] , to find anchor points for multiple sequence alignment [22 , 23] and for alignment-free phylogeny reconstruction [24] . Similarly , the average common substring approach to sequence comparison [25] could be improved by allowing for mismatches [26–30] . Brejova et al . extended the concept of spaced seeds to homologies among protein-coding regions [31] and introduced vector seeds [32] . In general , the advantage of pattern-based approaches is the fact that spaced-word occurrences at neighbouring sequence positions are statistically less dependent than occurrences of contiguous words [33 , 34] . Often sets of patterns are used , instead of single patterns; such multiple spaced seeds are now a standard filtering step in homology searching [35 , 36] . In pattern-based approaches , the underlying patterns of match and don’t-care positions are of crucial importance for the quality of the results . Generally , non-periodic patterns are preferred since they minimize redundancies between overlapping words or word matches and lead to a more even distribution of matches . This increases the probability of obtaining a hit between two homologous sequences in database searching and leads to more stable distance estimates in phylogeny reconstruction . Noé and Martin [37] defined a coverage criterion for multiple spaced seeds and showed that this criterion is related to the Hamming distance between two sequences . In the context of database searching , patterns or sets of patterns are often called seeds . ( Originally , the word seed denoted a match of—contiguous or spaced—words between a query and a database sequence that would be extended to the left and to the right . But now seed often denotes the underlying pattern in pattern-based approaches ) . In hit-and-extend database searching , the sensitivity of a pattern set is defined as the probability of finding at least one hit within a gap-free alignment of a given length L and probability p for a match between two residues . Since each hit is extended to a local alignment , the sensitivity is the proportion of homologies that will be found by a search program—under the above simple model of homology , and under the assumption that each extension of a hit in a homologous region will verify the homology . In database searching , the goal is thus to maximize the sensitivity of pattern sets . Calculating the sensitivity of a pattern set is NP-hard [33] . The sensitivity can be approximated by dynamic programming [15 , 38] , but the run time of this algorithm is still exponential in the length of the pattern . In PatternHunter II , a greedy algorithm is used to find suitable patterns . In 2007 , Ilie and Ilie introduced the overlap complexity of a pattern set and showed experimentally that—for a given number of patterns with a given length and number of match positions—minimizing the overlap complexity corresponds to maximizing the sensitivity in database searching [39] . In contrast to the sensitivity , however , the overlap complexity can be easily calculated . To find optimal pattern sets , Ilie and Ilie proposed a hill-climbing algorithm that minimizes the overlap complexity . They implemented their algorithm in a software tool called SpEED [40] , which is several orders of magnitude faster than competing approaches and is now considered the state-of-the-art in seed optimization . Recently , we proposed to use spaced-word frequencies instead of word frequencies for alignment-free sequence comparison [24 , 41] . We showed that phylogenetic trees calculated from spaced-word frequencies are more accurate than trees calculated from contiguous-word frequencies . As in database searching , our results could be improved by using multiple patterns . In our original study , we used randomly generated multiple patterns of match and don’t-care positions . In a follow-up paper , we studied the number N of spaced-word matches between two DNA sequences for a set of binary patterns [34] . Our data suggest that minimizing the variance of N for pattern sets improves alignment-free phylogeny reconstruction . In this paper , we first show that the variance of the number N of spaced-word matches is closely related to the overlap complexity of the underlying set of patterns . We propose a modified hill-climbing algorithm that can be used to generate pattern sets , either with minimal variance of N , or with minimal overlap complexity , or with maximal sensitivity in database searching , depending on the application at hand . While the algorithm proposed in [39] iterates over all patterns P in a set P of patterns and all pairs of positions in P to improve P , we calculate for each pattern P ∈ P how much P contributes to the variance or overlap complexity , respectively , of P . We then modify those patterns first that contribute most to the variance or complexity . The implementation of our approach is called rasbhari ( Rapid Approach for Seed optimization Based on a Hill-climbing Algorithm that is Repeated Iteratively ) . Experimental results show that pattern sets calculated with rasbhari have a slightly higher sensitivity in database searching than pattern sets calculated with SpEED , while the run time of both programs is comparable . In alignment-free sequence comparison , we obtain more accurate phylogenetic distances if we use rasbhari to minimize the variance of N for the underlying pattern sets , than we obtained with the randomly generated pattern sets that we previously used . In a third application , we used pattern sets generated with rasbhari in the program CLARK-S [18] for short read classification . The sensitivity of the classification could be improved in this way , while rasbhari is substantially faster than the method that is used by default for pattern generation in CLARK-S . A earlier version of this paper has been published at the preprint server arXiv [42] .
We consider sets P = { P 1 , … , P m } of binary patterns , where ℓr is the length of pattern Pr and ℓ = maxr ℓr . That is , each Pr is a word of length ℓr over the alphabet {1 , 0} . A ‘1’ in a pattern Pr represents a match position , a ‘0’ a don’t-care position . For a single pattern Pr , the number of match positions is called its weight w . For simplicity , we assume that all patterns in a set P have the same weight . In [34] , we considered for two patterns Pr , Pr′ and s ∈ Z the number n ( Pr , Pr′ , s ) of positions that are match positions of Pr or match positions of Pr′ ( or both ) , if Pr′ is shifted by s positions to the right , relative to Pr . If s is negative , Pr′ is shifted to the left . For Pr = 101011 , Pr′ = 111001 , for example , if Pr′ is shifted by 2 positions to the right , relative to Pr , then there are 6 positions ( marked by asterisks below ) that are match positions of Pr or Pr′ . Thus , for s = 2 , we have n ( P , Pr′ , 2 ) = 6: P r : 1 0 1 0 1 1 P r ′ : 1 1 1 0 0 1 * * * * * * $ $ For the same situation , Ilie and Ilie [39] defined σ[s] = σr , r′[s] as the number of positions where Pr and Pr′ have a match positions , such as the positions marked by ‘$’ above . In the above example one would therefore have σ[2] = 2 . The overlap complexity ( OC ) of a set of patterns P = { P 1 , … , P m } is then defined in [39] as ∑ r ≤ r ′ ∑ s = 1 - ℓ r ′ ℓ r - 1 2 σ r , r ′ [ s ] ( 1 ) Note that , since for any two patterns Pr , Pr′ and s ∈ Z , the equality σ r , r ′ [ s ] = 2 w - n ( P r , P r ′ , s ) holds , the overlap complexity of a set P can be written as ∑ r ≤ r ′ ∑ s = 1 - ℓ r ′ ℓ r - 1 2 σ r , r ′ [ s ] = 2 2 w · ∑ r ≤ r ′ ∑ s = 1 - ℓ r ′ ℓ r - 1 ( 1 / 2 ) n ( P r , P r ′ , s ) ( 2 ) Consequently , if we are looking at sets P of m patterns with fixed weight w and lengths ℓr , then minimizing the overlap complexity of P is equivalent to minimizing the sum ∑ r ≤ r ′ ∑ s = 1 - ℓ r ′ ℓ r - 1 ( 1 / 2 ) n ( P r , P r ′ , s ) ( 3 ) Ilie and Ilie showed experimentally that the OC is closely related to the sensitivity of a pattern set . More precisely , they showed that for pattern sets with a given number of patterns of given lengths and weight , minimizing the OC practically amounts to maximizing the sensitivity . Consequently , in order to find suitable pattern sets for hit-and-extend approaches in database searching , they proposed to search for pattern sets with minimal OC . The main advantage of this approach is the fact that the OC of a pattern set is much easier to calculate than its sensitivity . For a pattern P of length ℓ , we say that two sequences S1 and S2 have a spaced-word match with respect to P at ( i , j ) , if the ℓ-mers starting at i and j have identical characters at all match positions of P , i . e . if one has S1 ( i + π − 1 ) = S2 ( j + π − 1 ) for all match positions π in P . The sequences below , for example , have a spaced-word match at ( 2 , 4 ) with respect to the pattern P = 110101 . Indeed , the 6-mers starting at positions 2 and 4 of the sequences are identical at all positions corresponding to a match position ( ‘1’ ) in P , while positions at don’t-care positions ( ‘0’ ) may be matches or mismatches . S 1 : A A T C G A T C A S 2 : C G T A T T G A T T P : 1 1 0 1 0 1 In [34] , we considered spaced-word matches between two sequences S1 and S2 with respect to a set P = { P 1 , … , P m } of patterns , so-called P-matches . Note that there can be up to m P-matches at each pair ( i , j ) of positions of S1 and S2 , one P-match for each pattern Pr in P . We studied the number N = N ( S 1 , S 2 , P ) of P-matches between sequences S1 and S2 under a simplified model of evolution without insertions and deletions , with a match probability p for pairs of homologous positions and a background match probability of q . Thus , in our model we have P r ( S 1 [ i ] = S 2 [ j ] ) = p if i = j q if i ≠ j It is easy to see that , for a pattern set P , the expected number of P-matches depends only on the number m of patterns in P and on their lengths ℓi and their weight w , i . e . number of match positions , but not on the particular sequence of match and don’t-care positions in P . The variance of N , however , does depend on the sequence of match and don’t-care positions . As discussed in [34] , many alignment-free distance or similarity measures are—explicitly or implicitly—a function of the number N of ( spaced ) word matches . To obtain stable distance measures for phylogeny reconstruction , it is therefore desirable to use pattern sets with a low variance of N . For a given set P = { P 1 , … , P m } of patterns of lengths ℓ1 , … , ℓm and weight w , and with the above simple model of evolution , the variance of N can be approximated by V a r ( N ) ≈ ( L - ℓ + 1 ) · ∑ r ≤ r ′ ∑ s ∈ R ( r , r ′ ) p n ( P r , P r ′ , s ) - p 2 w + ( L - ℓ + 1 ) · ( L - ℓ ) · ∑ r ≤ r ′ ∑ s ∈ R ( r , r ′ ) q n ( P r , P r ′ , s ) - q 2 w ( 4 ) where L is the length of S1 and S2 , respectively , and R ( r , r ′ ) = { 1 - ℓ r ′ , … , ℓ r - 1 } if r < r ′ { 0 , … , ℓ r - 1 } if r = r ′ is the range in which Pr′ is to be shifted against Pr [34] . Note that for different patterns Pr′ ≠ Pr we have to consider all shifts between 1 − ℓr′ and ℓr − 1 of Pr′ against Pr , for example: P r : 1 0 1 1 1 0 1 1 P r ′ : 1 0 1 0 1 , ⋯ , 1 0 1 0 1 s : - 4 3 By contrast , if a pattern Pr is shifted against itself , only shifts between 0 and ℓr − 1 need to be considered , to avoid double counting of shifts , for example: P r : 1 0 1 1 1 0 1 1 P r : 1 0 1 1 , ⋯ , 1 0 1 1 s : 0 3 In [34] , we ignored this fact and gave a slightly different estimate for Var ( N ) . On the right-hand side of Eq ( 4 ) , the first summand is the variance of the ‘homologous’ spaced-word matches ( in a model without indels , these are spaced-word matches involving the same positions in both sequences ) , while the second summand comes from background matches . The relative weight of the background matches in Eq ( 4 ) depends on the match probability p and the sequence length L; for p >> q and small L , the Var ( N ) is dominated by the ‘homologous’ term , see Fig 1 . Obviously , for large L , the background spaced-word matches dominate the ‘homologous’ ones , since the number of background matches grows quadratically with L , while the ‘homologous’ matches grow only linearly . Note that , for L , ℓ and w fixed , minimizing the Var ( N ) amounts to minimizing ∑ r ≤ r ′ ∑ s ∈ R ( r , r ′ ) p n ( P r , P r ′ , s ) + ( L - ℓ ) · ∑ r ≤ r ′ ∑ s ∈ R ( r , r ′ ) q n ( P r , P r ′ , s ) ( 5 ) Comparison with Eq ( 2 ) shows that , in the special case of p = 1/2 , the first summand of Eq ( 5 ) that corresponds to the homologous matches is almost identical with the overlap complexity defined by Ilie and Ilie ( except for the range R ( r , r ) in which a pattern Pr is shifted against itself ) . For sequences of moderate length , the overlap complexity can therefore be seen as an approximation to the variance of the number of spaced-word matches . In any case , the overlap complexity and the Var ( N ) for a set of pattern P = { P 1 , … , P m } both have the form ∑ r ≤ r ′ α r , r ′ ( P ) ( 6 ) with α r , r ′ ( P ) = ∑ s = 1 - ℓ r ′ ℓ r - 1 2 σ r , r ′ [ s ] ( O C ) ( L - ℓ + 1 ) ∑ s ∈ R ( r , r ′ ) p n ( P r , P r ′ , s ) + ( L - ℓ ) · q n ( P r , P r ′ , s ) ( V a r ) ( 7 ) Our optimization problem is therefore: for integers m , ℓ1 , … ℓm , w , find a set P of m patterns of lengths ℓ1 , … , ℓm and weight w that minimizes the sum Eq ( 6 ) . Both SpEED and our new algorithm start with randomly generated pattern sets and use hill-climbing to gradually reduce the OC or Var ( N ) . If one wants to find a pattern set with maximal sensitivity , the sensitivity is calculated for the pattern set that is produced by this procedure ( this step is omitted , of course , if rasbhari is used to minimize Var ( N ) or OC ) . The whole procedure is repeated , and the pattern set with the overall highest sensitivity—or lowest Var ( N ) or OC , respectively—is returned .
To evaluate rasbhari , we first applied it to generate pattern sets , maximizing the sensitivity for database searching and read mapping . For the number m and weight w of the patterns and for the length H and match probability p of the homology regions , we used the parameter settings from SHRiMP2 [43] , PatternHunter II [38] and BFAST [44] . We and compared it to the sensitivity of pattern sets obtained with Iedera [45] , SpEED [40] , AcoSeeD [46] , FastHC and MuteHC [47] as published by the authors of these programs; the results of this comparison are shown in Table 1 . Here , the sensitivity values of AcoSeeD are average values over 10 program runs reported in [46] . If pattern sets with maximal sensitivity are to be found , and if the lengths ℓr of the patterns are small , the run time of rasbhari is comparable to SpEED . In this case , the most time-consuming step in both programs is to calculate the sensitivity of pattern sets which , by default , is done 5 , 000 times per program run in each of the two programs . For longer patterns , however , SpEED can be much slower since it carries out hill-climbing until a local minimum is reached and , as explained above , each single step in the hill-climbing procedure of SpEED takes O ( m2 ⋅ ℓ4 ) time . In contrast , rasbhari terminates this procedure after a given number of iteration steps , and it considers only a limited number of swaps of match and don’t-care positions in one iteration step . Next , we wanted to know how alignment-free phylogeny reconstruction can be improved with rasbhari . To this end , we simulated pairs of DNA sequences and estimated the distances between them using the Spaced Words approach described in [34] . We then measured the accuracy of the distance estimates for different underlying pattern sets . First , we used rasbhari to minimize the variance of the number N of spaced-word matches between two sequences . Since there is no other method to minimize Var ( N ) , we compared the pattern sets from rasbhari with the randomly generated pattern sets that we previously used . The phylogenetic distances estimated with both types of pattern sets were compared to the ‘real’ distances between the sequences , i . e . the average number of substitutions per position . As test data , we generated nine data sets with 2 , 500 pairs of DNA sequences of length 100 kb each . The distances d of the sequence pairs ranged between 0 . 1 and 0 . 9 substitutions per position . For each program run , we used a set of m = 3 patterns of length 20 with 16 match and 4 don’t-care positions . Fig 2 shows the root mean square error of the estimated distances , compared to the ‘real’ distances d . The pattern sets generated with rasbhari were superior to the randomly generated pattern sets , especially for large distances . As a third test case , we used different pattern sets for CLARK-S [18 , 48] , a recently developed tool for short read classification . We evaluated the accuracy of CLARK-S with three underlying pattern sets , namely ( A ) with the patterns used by default in the program , ( B ) with patterns from rasbhari minimizing overlap complexity and ( C ) with patterns from rasbhari maximizing sensitivity . CLARK-S uses sets of m = 3 patterns of length ℓ = 31 and with a weight of w = 22 . Since SpEED is too slow to generate pattern sets with long patterns , the authors of the program generated pattern sets for CLARK-S by exhaustively searching over all single patterns with ℓ = 31 and w = 22 . If the first and the last position in the patterns are required to be match positions , this approach has to evaluate ( 2920 ) ≈107 possible patterns . The sensitivity of each of these patterns was calculated , and the three patterns with the highest sensitivity were selected . Note however , that maximizing the sensitivity of single patterns is only an approximation to finding a set of patterns with maximal total sensitivity . Fig 3 shows the default pattern set from CLARK-S and the two pattern sets generated by rasbhari as described . The exhaustive procedure used by CLARK-S took 2 hours to generate the pattern set . rasbhari , by contrast , calculated pattern sets with the same parameters within 7 . 54 seconds with the slow version where the sensitivity is calculated , and within 0 . 068 seconds with the fast version where the overlap complexity is maximized without considering the sensitivity explicitly . The slow version of rasbhari is thus around 480 times faster than the exhaustive procedure in CLARK-S , while the fast version is around 52 , 000 times faster . The theoretical sensitivity of the three pattern sets is 0 . 999771 for the default patterns from CLARK-S , 0 . 999811 for the rasbhari patterns with minimized overlap complexity and 0 . 999822 for the rasbhari patterns with maximized sensitivity . To evaluate the classification accuracy of CLARK-S with these three pattern sets experimentally , we used five data sets from the literature , namely two sets , HC1 and HC2 , from the MetaPhlAn project [49] and three sets , simHC , simMC and simLC , from the FAMeS databases [50] . For each of these data sets , we calculated precision and sensitivity of the classification at the species level as defined in [11] . That is , for a classification task where objects are to be assigned to classes , precision is defined as the fraction of correct assignments among the total number of assignments , while sensitivity is the ratio between the number of correct assignments and the number of objects to be classified . The two values are not the same since not every object is necessarily assigned to one of the classes; precision is always larger than or equal to sensitivity since the denominator in the definition of precision is smaller or equal to the denominator in the definition of sensitivity . Since this definition of sensitivity refers to the ability of a program to correctly classify objects , it is not to be confused with the sensitivity in database searching as discussed above . Table 2 summarizes precision and sensitivity of CLARK-S with its default pattern set and with a pattern set generated by rasbhari . Fig 4 shows how the overlap complexity ( OC ) of pattern sets produced by rasbhari depends on the number of iteration steps carried out in the hill-climbing algorithm . For a set of m = 10 patterns of length ℓ = 14 and weight w = 8 , a single run of the hill-climbing procedure converges after around 3 , 000 steps; for m = 20 , ℓ = 44 , w = 14 , it converges after around 80 , 000 steps . The OC is further improved if the hill-climbing procedure is run multiple times and the best result of these runs is used . In the previous section , we mentioned that the OC is related to the variance of the number N of spaced word matches . Comparison of eqs ( 5 ) and ( 2 ) showed that , in the special case where p = 1/2 and the contribution of the ‘background’ spaced-word matches is small , minimizing the OC is equivalent to minimizing the variance of N . In general , however , this is not the case , as the following example shows . We applied rasbhari to generate two sets of m = 10 patterns each , with length ℓ = 20 and weight w = 8 , one set by minimizing the OC and the other one by minimizing Var ( N ) . When generating the second set , we used a match probability of p = 0 . 75 and a sequence length of L = 10 , 000 . The pattern set that we obtained when we minimized the OC had an OC of 11 , 116 , the set for which we minimized Var ( N ) had an OC of 11 , 195 . Conversely , when we minimized Var ( N ) , we obtained a pattern set with a variance of 156 , 061 , while the variance was 156 , 152 when we minimized the OC . It thus makes a difference which one of these two parameters is minimized .
We developed a program called rasbhari to calculate sets of binary patterns—or spaced seeds , as they are often called—for read mapping , database searching and alignment-free sequence comparison . For sequence-homology searching , rasbhari optimizes the sensitivity of pattern sets , i . e . the probability of obtaining at least one hit between a query and a database sequence sharing a gap-free homology of a given length and with a given match probability between nucleotides . Since the sensitivity of a pattern set is expensive to calculate , our algorithm optimizes the overlap complexity of the produced pattern sets which is closely related to its sensitivity . We use a hill-climbing algorithm , similar to the one used in SpEED , to minimize the overlap complexity . Unlike SpEED , however , our algorithm does not calculate the overlap complexity of all neighbours of a current pattern set , but modifies those patterns first that contribute most to the overlap complexity of the current pattern set . To maximize the sensitivity in database searching , we calculate the sensitivity of the current pattern set after a certain number of iterations . We repeat this procedure and , finally , we pick the pattern set with the highest sensitivity in all iterations . Since calculating the sensitivity is time consuming , rasbhari can alternatively minimize the overlap complexity alone , without calculating the sensitivity of pattern sets . This option is useful in situations where large pattern sets are needed for which it would take too long to calculate the sensitivity . As a third option , rasbhari can minimize the variance of the number N of spaced-word matches in alignment-free sequence comparison which is used by various methods to estimate phylogenetic distances between sequences . We could show that , mathematically , the variance of N has a similar form as the overlap complexity of a pattern set , so the same optimization algorithm can be used to minimize both of them . In both homology searching and read classification , pattern sets generated by rasbhari are more sensitive than alternative pattern sets , so more homologies can be detected and more reads can be correctly classified . At first glance , the increase in sensitivity that we obtained seems moderate; as shown in Table 1 , the improvement is usually in the first or second digit after the decimal mark . In database searching and read mapping , however , even small improvements in sensitivity can lead to a large number of additional hits . Moreover , as these additional hits will be mostly in the ‘twilight zone’ of low sequence similarity , they may be of particular interest to the user . In the context of read alignment , Ilie et al . pointed out that , with a 100-fold coverage of the human genome , a 1 percent improvement in pattern sensitivity would mean that 3 billion more nucleotides could be mapped [40] , so the improvement that we achieved with rasbhari would still lead to tens or hundreds of millions of additionally mapped nucleotides . In database searching , the situation is similar . If we consider , for example , homology regions of length H = 64 with a match probability of p = 0 . 8 at the nucleotide level , then with w = 11 , the sensitivity of rasbhari is improved by less than 0 . 01 percentage points compared to SpEED , see Table 1 . Note , however , that these sensitivity values are already close to 100% , so the fraction of homologies that are not detected can be considerably reduced with the slight improvement in sensitivity obtained with rasbhari . In our example , the number of homologies that are missed is reduced by >7% if rasbhari is used instead of SpEED . With the same parameters , but with p = 0 . 7 , the sensitivity of both programs is around 93% . Here , the number of missed homologies is still reduced by 3% with rasbhari , compared to SpEED . For alignment-free sequence comparison , pattern sets produced by rasbhari lead to more accurate phylogenetic distances than the random pattern sets that we previously used . While this result may not be surprising , rasbhari is , to our knowledge , the first program that has been designed for this purpose and that can minimize the variance of the number of spaced-word matches . We therefore integrated rasbhari into our web server for alignment-free sequence comparison [41] . In read classification , the sensitivity of CLARK-S could be increased by 0 . 08 and 0 , 07 percentage points , respectively , for the largest data sets that we used , HC1 and HC2 . Each of these data sets contains around one million reads , so the improvement in sensitivity that we achieved with rasbhari means that 800 more reads from HC1 and 700 more from HC2 could be correctly classified by CLARK-S . This is remarkable , since the classification accuracy of CLARK-S is already very high , so it is hard to further improve the program . An interesting question in the context of CLARK-S is how the length and weight of the patterns influence its accuracy . So far , it was difficult to investigate this question systematically , since the exhaustive method that the program uses by default , is too time consuming . With the massive improvement in runtime that we achieved with rasbhari , it is now possible to systematically investigate how the accuracy of CLARK-S depends on the parameters of the underlying pattern sets . In the hill-climbing procedure , our default of 25 , 000 iteration steps was sufficient to obtain stable results for the parameter settings that we used in our benchmark studies; we were unable to further improve these results by increasing the number of iterations . For different values of m , w , ℓ , p and H , however , it may be advisable to adapt the number of iteration steps . Fig 4 shows that , if the number of patterns or their length and weight are increased , a larger number of iteration steps can improve the results . The number of iterations within one round of hill climbing and the number of times the hill-climbing is carried out can be passed to rasbhari through the command line; the users can therefore adapt these parameter values for their particular applications if they do not want to use the default values of the program . | We propose a fast algorithm to generate spaced seeds for database searching , read mapping and alignment-free sequence comparison . Spaced seeds—i . e . patterns of match and don’t-care positions—are used by many algorithms for sequence analysis; designing optimal seeds is therefore an active field of research . In sequence-database searching , one wants to optimize sensitivity , i . e . the probability of finding a region of homology; this can be done by minimizing the so-called overlap complexity of pattern sets . In alignment-free DNA sequence comparison , the number N of pattern-based matches is used to estimate phylogenetic distances . Here , one wants to minimize the variance of N in order to obtain stable phylogenies . We show that for spaced seeds , the overlap complexity—and therefore the sensitivity in database searching—is closely related to the variance of N . Our algorithm can optimize the sensitivity , overlap complexity or the variance of N , depending on the application at hand . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"sequencing",
"techniques",
"taxonomy",
"applied",
"mathematics",
"split-decomposition",
"method",
"database",
"searching",
"simulation",
"and",
"modeling",
"algorithms",
"multiple",
"alignment",
"calculation",
"optimization",
"phylogenetics",
"data",
"management",
"mathematics",
"molecular",
"biology",
"techniques",
"sequence",
"similarity",
"searching",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"computer",
"and",
"information",
"sciences",
"sequence",
"alignment",
"biological",
"databases",
"evolutionary",
"systematics",
"molecular",
"biology",
"sequence",
"databases",
"computational",
"techniques",
"database",
"and",
"informatics",
"methods",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"evolutionary",
"biology"
]
| 2016 | rasbhari: Optimizing Spaced Seeds for Database Searching, Read Mapping and Alignment-Free Sequence Comparison |
Bacillus anthracis produces a binary toxin composed of protective antigen ( PA ) and one of two subunits , lethal factor ( LF ) or edema factor ( EF ) . Most studies have concentrated on induction of toxin-specific antibodies as the correlate of protective immunity , in contrast to which understanding of cellular immunity to these toxins and its impact on infection is limited . We characterized CD4+ T cell immunity to LF in a panel of humanized HLA-DR and DQ transgenic mice and in naturally exposed patients . As the variation in antigen presentation governed by HLA polymorphism has a major impact on protective immunity to specific epitopes , we examined relative binding affinities of LF peptides to purified HLA class II molecules , identifying those regions likely to be of broad applicability to human immune studies through their ability to bind multiple alleles . Transgenics differing only in their expression of human HLA class II alleles showed a marked hierarchy of immunity to LF . Immunogenicity in HLA transgenics was primarily restricted to epitopes from domains II and IV of LF and promiscuous , dominant epitopes , common to all HLA types , were identified in domain II . The relevance of this model was further demonstrated by the fact that a number of the immunodominant epitopes identified in mice were recognized by T cells from humans previously infected with cutaneous anthrax and from vaccinated individuals . The ability of the identified epitopes to confer protective immunity was demonstrated by lethal anthrax challenge of HLA transgenic mice immunized with a peptide subunit vaccine comprising the immunodominant epitopes that we identified .
Whether viewed as a threat to human health in anthrax endemic regions , as a bioweapon , or as a potentially devastating pathogen of livestock , there is pressing need to gain better insights into the immune response to Bacillus anthracis . The urgency has been underlined by recent clusters of fatal and and near-fatal anthrax infections among European intravenous drug users [1]–[4] . The pagA , lef and cya genes encode the three toxins associated with pathogenicity: protective antigen ( PA ) , lethal factor ( LF ) and edema factor ( EF ) . PA binds to the host cell surface receptors , tumor endothelial marker 8 ( TEM8 ) and capillary morphogenesis gene 2 protein ( CMG2 ) [5] , [6] , with recent work suggesting that α4β1- and α5β1-integrin complexes can also bind PA [7] . PA then complexes with LF to form Lethal toxin ( LT ) , which is translocated into the host cell cytoplasm . LT is implicated in several aspects of host immune subversion . It interferes with antigen presenting cell ( APC ) function in the priming of adaptive immunity: expression of the co-stimulatory molecules CD40 , CD80 and CD86 on dendritic cells , essential for the induction of adaptive immunity in CD4+ T cells , are down-regulated in the presence of LT [8] . Furthermore , LT can induce selective apoptosis of activated macrophages by disrupting the TLR dependant , p38 mediated , NF-κβ regulation and expression of pro-survival genes . LT also has a role in impairing B cell function , reducing proliferation in response to TLR2 , TLR4 , BCR , and CD40 [9] . Natural killer T ( NKT ) cells are shifted by LT from an activated to anergic state [10] , [11] . Vaccination strategies in anthrax infection have been largely dominated by PA [12] , [13] . For more than 40 years the major vaccines used to protect against anthrax have been the AVA ( Biothrax ) vaccine in the US , a filtered supernatant from the Sterne strain of B . anthracis , and AVP vaccine in the UK , an alum-precipitated , cell-free culture supernatant of the Sterne strain containing PA and a variable , minor , amount of LF . Both the AVA and AVP vaccines require extensive vaccination regimens , involving annual boosters . With concerns about the levels of immunity induced by these vaccines and the high rates of adverse effects [14] , [15] , there have been efforts to design effective next-generation vaccines with improved immunogenicity and low reactogenicity [12] . Strategies to develop recombinant protein vaccines have centered largely on PA [16] . PA based vaccines can elicit humoral immunity while avoiding the adverse reactions associated with older , filtrate based vaccines [17]–[19] . Recent vaccination programmes have investigated the impact of HLA polymorphisms , revealing considerable genetic variability in responses of human donors , notably , the very low response of HLA-DQB1*0602 individuals [20] , [21] . However , the rapid decrease in humoral immune responses against PA observed in both humans and rabbits following the cessation of boosting with either filtrate based or recombinant PA ( rPA ) vaccines suggests that anti-PA humoral immunity induced by these vaccines may not be long-lasting [22]–[24] . The development of PA antibodies has also been shown to vary greatly within infected human populations [25] , [26] . This in combination with evidence that PA-based vaccines may provide protection against lethal challenge with only select strains of B . anthracis [27] , indicates that the induction of anti-PA antibody responses should not be the sole strategy for anthrax vaccination . Previous research has also indicated that co-immunization with a range of B . anthracis antigens , such as the capsular poly-γ-D-glutamic acid , surface polysaccharides , or toxins may augment the development of protective immunity [28]–[30] . Analysis of naturally-infected humans in Zimbabwe showed that most individuals mounted a response to both LF and PA [31] . We recently studied the CD4+ T cell immune repertoire in patients from the Kayseri region of Turkey who had become infected with B . anthracis and had been hospitalised for cutaneous anthrax following contact with infected livestock [32] . The study encompassed individuals who had suffered severe sepsis and undergone protracted antibiotic therapy . Contrary to expectation from our knowledge of immune subversion by LT in experimental settings , we found robust immune memory to anthrax components , with particular focus on domain IV of LF . Importantly , we were able to quantify CD4+ T cell memory responses in naturally exposed cutaneous anthrax patients and in AVP vaccinees , concluding that the T cell response in the former group was equally strong in response to both PA and LF , while in the latter group the major response was to LF . This prompted us to reappraise CD4+ T cell immunity to anthrax LF in detail . For many microbial pathogens there is strong evidence for HLA polymorphisms as determinants of disease risk , through variable effects on the strength of immune response [33] , [34] . Different HLA class II sequences vary in the anchor residues of the peptide binding groove , presenting different peptides from a given antigen , which will have an effect on the responding T cell repertoire [35] . While such studies are clearly pertinent to pathogens such as B . anthracis which are variably lethal to infected humans , no such analysis has been prevoiously undertaken . In the present study , we characterize the CD4+ T cell immune response to LF in HLA class II transgenic mice and in infected and vaccinated humans . We observed that LF is highly immunogenic , and that specific domains and epitopes show variable immunodominance depending on HLA class II expression , with a hierarchy of response to the toxin determined by HLA class II polymorphism . This is the first time that such effects have been described in the context of anthrax . Importantly , we define highly immunodominant epitopes , common to all HLA types screened . The CD4+ T cell epitopes were incorporated into a peptide subunit vaccine and its protective immunity demonstrated in HLA transgenic mice following live anthrax challenge .
Different HLA class II molecules vary in their peptide binding specificity and so present different peptides of a given antigen , with consequences for the CD4+ T cell repertoire activated during the immune response . As a reductionist tool for dissecting the role of individual HLA heterodimers we used mice transgenic for each of the human HLA alleles , DRB1*0101 ( HLA-DR1 ) , DRB1*1501 ( HLA-DR15 ) , DRB1*0401 ( HLA-DR4 ) , DQB1*0302 ( HLA-DQ8 ) and DQB1*0602 ( HLA-DQ6 ) in the absence of endogenous MHC class II expression . Following immunization with recombinant LF , all HLA transgenic mice responded to LF protein , but responses to the four domains of which the protein is composed varied ( Figure 1 ) . Using mouse strains differing only in their expression of human HLA class II alleles , we found a pronounced hierarchy of response , with HLA-DR1 transgenics mounting a considerably larger response than HLA-DQ6 , DR15 or DR4 transgenics , and HLA-DR4 transgenics showing the weakest response ( Figure 1A ) . This was not a simple reflection of strain differences in HLA transgene expression or CD4+ positive selection , as the least responsive strain , HLA-DR4 , shows the highest level of HLA class II expression ( data not shown ) . Of particular interest with respect to diversity of outcomes during infection of outbred human populations , expression of different HLA class II alleles was associated with a focus on different domains of the LF molecule . LF immunized HLA-DR1 transgenics showed an elevated response specifically to restimulation with LF domains II and IV ( Figure 1B ) , while the HLA-DQ8 transgenics response to domain IV was significantly elevated relative to the domain I response ( Figure 1C ) . HLA-DR15 transgenic mice showed a significantly elevated response to domain II alone ( Figure 1E ) , while HLA-DQ6 transgenic mice demonstrated significant responses to domains II and IV ( Figure 1D ) . HLA-DR4 transgenics respond to all four domains ( Figure 1F ) . All of the HLA transgenics used in this study generated a memory recall to domain II of LF ( Figure 1B–F ) . These results confirm that HLA polymorphisms play a role in the differential response to the domains of LF , and contrast with the corresponding lack of response to LF domains in sham immunized HLA transgenic mice ( Supplementary Figure S1A ) . A peptide library of overlapping 20-mers representing the complete anthrax LF sequence was evaluated for binding to seven common HLA-DR alleles , DRB1*0101 ( DR1 ) , DRB1*0401 ( DR4 ) , DRB1*1101 ( DR11 ) , DRB1*0701 ( DR7 ) , DRB1*1501 ( DR15 ) , DRB1*0301 ( DR3 ) and DRB1*1301 ( DR13 ) ( Table 1 ) . The region of the LF sequence encompassing amino acids 457–486 contains at least 2 epitopes able to bind most or all HLA-DR alleles tested with exceptionally high affinity . A further twelve peptides , LF101–120 , LF171–190 , LF241–260 , LF251–270 , LF261–280 , LF457–476 , LF574–593 , LF594–613 , LF604–623 , LF644–663 , LF674–693 and LF694–713 , showed strong to moderate binding across all seven HLA-DR alleles . The immunodominant CD4+ T cell epitopes within LF were mapped by immunizing HLA transgenics with recombinant LF protein and restimulating draining lymph node cells with a peptide library spanning the LF sequence . The resulting epitope maps reveal a picture of HLA-restricted epitopes in LF indicating that the immunodominant epitopes were largely localized to domains II and IV ( Figure 2 ) . The immunological memory to the LF peptides contrasted with the lack of responses to the peptides in sham immunised HLA-DR4 mice ( Supplementary Figure S1B ) . The two epitopes shown to be exceptionally high affinity binders to diverse HLA-DR alleles , LF457–476 and LF467–486 , located in domain II , not only elicited very sizeable responses , but were both recognised by all LF immunized HLA transgenics , suggesting that these epitopes were both immunodominant and promiscuous in their HLA binding . Whilst promiscuous peptides have been previously identified which bind strongly to a number of distinct HLA-DR or HLA-DQ molecules [36]–[38] , the substantial differences between the binding grooves of HLA-DR and HLA-DQ isotypes [39]–[41] have resulted in the identification of a relatively low number of peptides that can be presented by such diverse isotypes [42] . These two LF epitopes , able to stimulate CD4+ T cells at very high frequency and across HLA class II differences are thus highly unusual and of considerable interest both for efforts to understand immunity to anthrax and to design universally stimulatory vaccines . A number of regions that had shown strong HLA binding affinity were indeed identified as functional , immunodominant epitopes , with domain IV especially rich in epitopes able to induce a strong in vivo response . CD4+ T cell responses to the domain IV peptide , LF547–567 , were identified in HLA-DR1 , HLA-DR4 and HLA-DR15 transgenic lines , indicating that this epitope was presented solely by HLA-DR alleles . Two more domain IV epitopes , LF724–743 and LF744–763 , were both HLA-DR4 and HLA-DR15 restricted . While domains II and IV contained a number of HLA-DR restricted epitopes , the majority of HLA-DQ8 restricted epitopes were found in domains I and II , and the HLA-DQ6 restricted epitopes were located only in domain II . The greatest number of epitopes identified were DRB1*0101 restricted , with the HLA-DR1 transgenic strain recognising 14 epitopes , this was followed by 13 DQB1*0302 restricted epitopes . Ten epitopes were DRB1*1501 restricted , and 7 DRB1*0401 restricted epitopes were identified , while only 2 DQB1*0602 restricted epitopes were identified . Some HLA-restricted peptide epitopes were identified which lay within regions of the LF protein not previously shown to elicit a response when provided as a whole protein antigen . LF immunized HLA-DR1 and HLA-DQ8 transgenics responded to peptides located within domain I , which as an intact domain did not elicit memory recall in the respective LF immunized transgenic mice ( Figure 1B and 1C ) ; similarly LF immunized HLA-DR4 and HLA-DR15 transgenics generated responses to peptide epitopes in domain IV , which also did not demonstrate a recall response following stimulation with the whole domain ( Figure 1E and 1F ) . The HLA specific epitopes identified in mice transgenic for DR1 , DR4 and DQB1*0302 are modeled on the LF crystal structure in Figure 3 . Despite the heterogeneity which can be observed in the range of LF peptides presented by the HLA transgenics , there were identifiable areas rich in allele specific immunodominant peptides , presumably indicative of structural accessibility to cleavage by antigen processing enzymes . The T cell responses to epitopes located in the catalytically active domain IV were overwhelmingly dominated by HLA-DR presentation , as only a single DQB1*0302 restricted epitope ( LF594–613 ) , and no DQB1*0602 restricted epitopes , were identified in this substrate recognition and binding domain . It is also possible to identify , within the VIP2-like domain II , the cluster of epitopes containing the immunodominant peptides LF457–476 and LF467–487 , which were presented by all the HLA transgenics . Domain III , which has marked structural similarity to domain II , possibly due to its origins as a duplication of this domain , displays none of the immunogenicity associated with domain II [43] , [44] . We observed no immunodominant T cell epitopes within this domain in any of the HLA transgenic strains utilised in the epitope mapping ( Figures 1 and 2 ) . While we had previously investigated responses of human donors to epitopes within domain IV [32] , it was important to obtain a comprehensive picture of the immune responses of human donors following either natural infection or vaccination . Having shown in the more reductionist context of HLA transgenics expressing single HLA class II heterodimers that LF is highly immunogenic and epitope rich , one would expect an even more complex picture in , heterozygote humans carrying multiple HLA class II isotypes . We found a heterogeneous response , spread across domains I–III of the entire protein , which was distinct according to the nature of exposure to anthrax: epitopes that were predominantly a feature of the response of vaccinees were rarely recognized by the majority of infected donors or healthy controls , and vice versa ( Table 2 ) . In the AVP vaccinated individuals the immunodominant response encompassed five epitopes . Of these peptides , LF41–60 , LF417–436 , and LF437–456 did not induce a response in any of the HLA transgenics ( LF337–356 was identified as a cryptic epitope which was identified in the HLA-DQ8 transgenics , data not shown ) . The T cell responses to the domain I peptide LF101–120 was confirmed as an HLA-DQ8 specific response in the transgenic mice . In the naturally infected donors from Kayseri , however , the T cell response was focused on two LF peptides . In parallel with the epitope hierarchy identified in the AVP vaccinees , a peptide epitope , LF281–300 , was also identified which did not induce a response in any of the HLA transgenics . The remaining domain II peptide , LF467–487 , had previously been identified as an immunodominant HLA-promiscuous epitope , capable of eliciting a T cell response from all the HLA transgenics . We have previously documented immune responses to domain IV in humans [32] , however it is interesting to note that , of the epitopes identified in that previous study , in AVP vaccinees , LF674–693 has been confirmed as an immunodominant epitope in both HLA-DR1 and HLA-DR15 transgenics , and the peptides LF574–593 , LF654–673 and LF694–713 were all identified as immunodominant epitopes in this study , which each elicited a T cell response in a single HLA transgenic strain . Furthermore , the domain IV epitopes previously reported in Turkish naturally infected anthrax patients , LF694–713 and LF714–733 have both been identified as immunodominant epitopes in HLA-DR15 transgenics . Although the domain IV peptide LF584–603 which was a feature of the AVP vaccinee's immune response , did not induce any response in any of the HLA transgenics in this study . There was very little overlap in responses of the infected and vaccinated human cohorts: the immunodominant , strongly binding epitope , LF467–486 , was recognized by a high proportion of naturally infected donors , but not vaccinated individuals . This suggests that the peptide is processed and strongly immunogenic during infection , but is not recognized in the response to the protein antigen during immunization . Could epitope differences between the cohorts be explained by the fact that the individuals come from different geographical regions and express different HLA class alleles ? We report the HLA-typing of the donors , and indeed the common HLA class II alleles present in the studied region of Turkey are not substantially different from the common alleles in the studied cohort regions of the UK . Ultimately , the number of individuals in this study , powered for functional rather than genetic association studies in its inception and design , is too low to draw conclusions about the possibility that different HLA allele frequencies may drive different preferences for immunodominant epitopes . The majority of HLA class II restricted epitopes characterised by this study were identified by more than one experimental system ( Figure 4A ) . The most notable epitope , LF467–486 showed strong or moderate HLA-DR binding affinity across a range of alleles , and was immunogenic in HLA-transgenics and infected humans . A comparison of HLA-DR restricted peptides , showed the overlapping subsets of allele specific and promiscuous epitopes identified by binding affinity ( Figure 4C ) and immunogenicity in HLA transgenic mice ( Figure 4B ) . Although the binding affinity assays suggest 21 peptides demonstrated strong or moderate promiscuous binding to HLA-DR1 , DR4 and DR15 ( Figure 4C ) , only three peptides , LF457–476 , LF467–486 and LF547–568 were immunogenic in all three HLA-DR transgenic strains analysed ( Figure 4B ) . It is interesting to note that , according to the binding affinity studies LF467–486 and LF547–568 , but not LF457–476 , were strong binders to all three HLA-DR alleles ( Table 3 ) , demonstrating the importance of validating the immunogenicity of T cell epitopes in vivo . It is important to recognise the limitations of this study; the strength of HLA binding is based exclusively on seven HLA-DR alleles , whilst all of the human cohorts presented the peptides through a diverse and heterogeneous mixture of HLA class II alleles . Nonetheless , it is striking that LF467–486 not only showed strong binding affinity across all HLA-DR alleles assayed , but all 5 HLA transgenic strains and in infected individuals , showed strong T cell responses to this peptide ( Tables 1 and 2 ) , demonstrating a truly promiscuous HLA class II binding and immunogenic nature . The primary importance of humoral immunity in mediating protection against anthrax has been brought into question by recent studies suggesting that IFNγ producing CD4+ T cells play an important role in long lasting immunity [32] , [45] . In addition , induction of memory CD4+ T cells may feedback not only to cellular immunity , but also aid in the production of toxin neutralising antibodies , Ig class switching and B cell affinity maturation . To determine whether the immunodominant T cell epitopes identified within LF could be incorporated into an epitope string vaccine capable of conferring protection against lethal anthrax challenge in a mouse model , the HLA-DQ8 transgenic mice were immunized with either a fusion protein comprising HLA-DQ8 restricted epitope moieties expressed contiguously after a tetanus toxin helper domain , or a cocktail of the same epitopes as synthetic peptides . HLA-DQ8 transgenics primed and boosted with 3 doses of an LF fusion construct containing HLA-restricted LF epitopes were fully protected against challenge with 106 cfu B . anthracis STI . The naïve , sham immunized group showed a significantly lower survival rate than either the group primed and boosted with 3 doses of the pooled peptides which were expressed in the fusion protein ( p>0 . 01 ) or the fusion protein ( p>0 . 01 ) immunized groups ( Figure 5A ) . Only 2/6 naïve mice survived to day 20 post-infection , with a median survival time of 6 days in this group . The bacterial loads recovered from the spleens of surviving mice showed that the immunized mice appeared to clear the infection more successfully than the naïve mice , ( naïve group ( 1883 . 4+/−317 cfu ) , peptide cocktail ( 801 . 2+/−469 cfu ) and LF fusion ( 153 +/−54 cfu ) ) , however it was not possible to detect a significant difference between groups in terms of bacterial burden ( Figure 5B ) . The high degree of protection against anthrax infection observed in both the immunized groups indicated , not only that the LF fusion protein was capable of conferring the same protective affect as the individual peptides , but also validated the immunoprotective effects of the epitopes identified within this study . Evaluation of peptide-specific responses on a second group of HLA-DQ8 transgenic mice immunized with either LF fusion protein or peptide cocktail showed that the strongest peptide recall in both groups was to LF467–486 ( data not shown ) . These data suggest that LF467–486 and the promiscous epitopes which were included in these immunisations prime a strong T cell response , playing a role in protection against anthrax .
While considerable attention has been devoted to the profound immune subversion mediated by anthrax toxins [46] , recent human studies , including this one , show that anthrax infection can be immunogenic [4] . The role of LT in the disruption of the MAPK signalling pathways , with its consequences for the apoptosis of antigen presenting cells , specifically the lysis of dendritic cells and macrophages , might be expected to subvert host immunity and promote systemic anthrax infection . However , investigation of the inverse relationship between sensitivity to LT and resistance to infection , indicates that mice which possess alleles encoding an LT-sensitive form of Nlrp1b promote a pro-inflammatory response predominantly driven by inflamasome-mediated cell lysis and release of IL-1β [47]–[50] . The associated cell infiltration and cytokine milieu seen in early inflammation may be crucial in driving antigen presentation and T cell priming . Recent studies ranging from asymptomatic seroconversion of wool-workers to our own recent work with near lethal anthrax infection in intravenous drug users , show common themes in terms of strong induction of adaptive immunity [4] , [25] . For an infection in which we believe there is a key role of host Th1 immunity , it would be assumed that IgG2a neutralizing antibodies would be an important correlate of protection . However , since the most relevant studies in which this can be analyzed in detail tend to be primate studies based on protection by alum-adjuvanted vaccine , it is the vaccine formulation itself that tends to be the main driver of protective IgG subclasses , both IgG1 and IgG2a being found in the protective response [51] . LF protein boosts PA-specific antibody responses following co-administration [30] , [52] , and the incorporation of a truncate containing the N-terminal region of LF into a PA plasmid expression vector enhances the PA-specific antibody response [52] , while LF truncated proteins are capable of conferring protection against B . anthracis aerosol challenge [53] , [54] . Thus LF-specific responses may be more important mediators of protective immunity than previously thought . Previous work by our lab has identified LF as a major target of T cell immunity in humans [32] , despite the amount of LF released by B . anthracis being one-sixth that of PA [55] . Antigen presentation through both HLA-DR and DQ is important in the induction of immunity , and the allelic diversity inherent in these class II molecules shapes the T cell repertoire and influences susceptibility to infection [56] . The reductionist approach of using transgenic models was deployed here as a means of defining HLA restricted T cell responses to immunogenic epitopes of LF . Across the transgenic lines , representing five HLA class II alleles , along with the expected allele specific epitopes , the T cell response showed a number of broad similarities . This was most evident in the response to LF domain II , which produced immunogenic responses in both HLA-DR and DQ transgenic mice following stimulation with either the whole domain , or the individual peptides LF457–476 and LF467–487 , which dominate the T cell response to this domain . These immunodominant epitopes , which were also found to have a high binding affinity for a wide range of HLA-DR molecules , therefore comprise ‘public specificities’ or promiscuous epitopes which are efficiently presented by APCs , to a peptide-MHC specific TCR repertoire , in all HLA transgenics ( Table 3 ) . The C-terminal domain II of LF shows structural homology with the ADP-ribosyltransferase found in the Bacillus cereus VIP2 toxin . In conjunction with domains III and IV , domain II forms the active site which is involved in substrate recognition and binding [57] . The amino terminus of the MAPK kinases substrates fit into the LF groove which contains several , conserved , long chain , aliphatic residues [58] . These residues occur in three distinct clusters; the first is composed of Ile298 , Ile300 , Ile485 , Leu494 , and Leu514 , the second cluster of residues contains Ile322 , Ile343 , Leu349 , Leu357 , and Val362 which lie at the end of the catalytic groove . The final cluster of aliphatic residues lies close to the domain IV groove; Leu450 , Ile467 , Leu677 , Leu725 , and Leu743 [58] . Both of the immunodominant epitopes LF457–467 and LF467–487 overlap two of the aliphatic residues , Ile467 and Ile485 which may have an effect upon the substrate binding of MAPK kinases . It is tantalising to note that the host response focuses on this active site , for which the evolutionary cost of mutation would be high for the pathogen; one must of course note , however , that anthrax is not an obligate human pathogen , is not commonly spread between people and can survive in spore form in soil . Thus , this is not an infection where there is likely to be an overt host-pathogen arms race . The T cell responses to the peptide LF547–567 , from domain IV , appeared to be HLA-DR restricted , as only T cells from the DR transgenics , HLA-DR15 , HLA-DR4 and HLA-DR1 , not the DQ transgenics HLA-DQ6 and HLA-DQ8 , responded to this peptide . Domain IV is the catalytically active center of the LF toxin [43] , and its protein folds contain a sequence which shares similarity with the zinc-dependant metalloproteases found in the toxin produced by C . tetani [59] . Previous work has indicated that this homologous region of the tetanus toxin contains a number of HLA-DR restricted T cell epitopes [60] . The ability of the LF domain IV to readily provoke a recall response in CD4+ T cells in the HLA-DR transgenics , suggests that the immune response to this particular domain of the LF protein is also dominated by HLA-DR restricted T cells . It has been observed that mutations in the sequence coding for domain IV disrupts the substrate binding groove created by domains II , III and IV , eliminating the peptidase activity of LF , and thereby abrogating its toxicity [61] . The putative zinc binding site [HEFGHAV] which occurs between the amino acid residues LF686 and LF690 [62] was only a feature of the HLA-DR1 transgenic response to LF674–693 A number of immunodominant epitopes identified within LF showed broad HLA binding characteristics , most notably the domain II epitopes LF457–476 and LF467–487 which showed strong binding across a range of HLA-DR molecules as well as the preponderance of epitopes from domain IV which were presented by HLA-DR . The strength of HLA binding does not however appear to predict the immunodominance of the peptide epitope . This contrasted with a number of studies , which have described a strong correlation between the affinity of binding and the ability of a peptide to be presented by a particular MHC molecule resulting in an immunodominant T cell response [63]–[66] . Of the five HLA strains challenged with domain I peptides , only the HLA-DR1 and HLA-DQ8 transgenics showed CD4+ T cell responses to peptide epitopes from this domain . Domain I binds with high affinity to the proteolytically active 63 kD PA heptamers which are responsible for the membrane-translocation of the anthrax toxins [67] . Over the first 250 residues , this domain shares significant sequence identity and similarity with domain I of EF [43] . The N-terminal sequence of both toxins contain a common domain for PA binding , which in LF has been shown to be sufficient to act alone as a carrier for delivery of heterologous proteins across membranes in the presence of PA [68] . The sequence homology between the two toxins within domain I was demonstrated by the use of LF induced antibodies which were cross-reactive with EF [69] . One of the cross-reactive epitopes LF265–274 ( which corresponds to EF257–268 ) , overlapped with the HLA-DR1 restricted T cell epitopes LF251–270 and LF261–280 , indicating that these epitopes have the potential to induce a neutralising antibody response to both LF and EF as well as the T cell response , making them interesting candidates for inclusion in a polyepitopic anthrax vaccine . In contrast to domain IV peptides , epitopes in this domain are presented in the context of both DR and DQ , although there appears to be minimal overlap in the specific peptides presented . None of the transgenics showed immunodominant CD4+ T lymphocyte responses against the individual peptides which make up domain III . The helix bundle which makes up domain III is inserted into domain II , and may have arisen from repeated duplications of a structural element of domain II [40] . Although these domains share elements of their structure and function , the CD4+ T cell response to each is very different . Domain III appears to be a hidden or infrequent target of the immune response . Most vaccine strategies against anthrax have concentrated on PA , although the UK AVP vaccine , which contains both PA , and lower levels of LF , stimulates LF specific antibodies [70]–[72] , while exposure to natural infection results in a faster , antibody response to LF than PA [73] . It was discovered that the magnitude of the CD4+ T cell response to LF antigens was greater in naturally infected individuals than in vaccinees [32] . The T cell immunity to LF , particularly domain IV , identified in naturally infected individuals is in contrast to the expected response to LF exposure , especially in the context of infection , which might be expected to impair the T cell memory of B . anthracis in survivors of natural infection . Taking into account all the HLA-DP , DQ and DR products , as well as inter and intra isotypic mixed pairs , a heterozygous human can present peptides for CD4+ T cell recognition on up to 12 different class II molecules . It is therefore interesting to note that despite the immunogenetic heterogeneity seen in human populations , which along with differences in exposure to the antigen , might be expected to complicate the pattern of epitopes recognised by the human cohorts studied , amongst the naturally infected individuals , the immunodominant promiscuous LF467–487 epitope was one of the main targets of a strong CD4+ T cell response . Some CD4 epitopes identified in human vaccinees were not seen in the naturally infected individuals; it might be expected that some epitopes present in the context of vaccination would be lost on infection . It is unclear whether such changes in antigen focus reflect differential antigen processing of pathogen proteins encountered in vaccination in contrast to infection , or if this represents an artefact of the repeated AVP vaccinations which may skew the cytokine environment during induction of the immune response , impacting upon the T cell epitope repertoire [74] . Humans exposed to LF following cutaneous anthrax infection generate robust long-term T cell memory to B . anthracis epitopes , in many cases several years after the initial infection event . The T cell response in these naturally infected individuals showed significantly elevated levels of the pro-inflammatory cytokines associated with Th1 , Th2 , Th9 and Th17 subsets compared to vaccinees and naïve controls [32] . The inhibitory effects of both LT and ET upon expression of the activation markers CD25 and CD69 and the secretion of the pro-inflammatory cytokines IL-2 , IL-5 , TNFα , and IFNγ by human T cells has been described in vitro [75] , [76] . Murine lymphocytes show impaired TCR-mediated activation and T cell dependent production of IL-3 , IL-4 , IL-5 , IL-6 , IL-10 , IL-17 , TNFα , IFNγ and GM-CSF following exposure to LT and ET [77] . However , the cellular immunity we have identified within the naturally infected humans indicates that , although in vitro exposure to ET has been implicated in immune deviation towards both the Th2 and Th17 pathways [78] , [79] , the human immune response against LF encompasses a strong IFNy response . It was suggested to the authors that , since the predominant mechanism of protective immunity to anthrax toxin is antibody neutralization , it is possible that T follicular helper cells , characterised by the co-production of IFNγ and IL-21 and vital for B cell help , may be important here . In response to the reviewer's suggestion , we have considered the notion that this is a TFH response by looking for IL-21 accompanying the IFNγ response in each of our donor responses , but , as we now report , detected none; we therefore consider it less likely that these are predominantly TFH cells . Despite the presence of many potential peptide epitopes within LF , the elicited T cell response indicates that immunodominant LF epitopes are concentrated in domains II and IV . The immunodominant epitopes identified within these domains appear to comprise essential residues of LF which are critical for efficient catalytic activities and the execution of substrate cleavage . We therefore suggest that a number of the immunodominant epitopes which we have identified represent regions of the LF protein in which the cost of mutation to B . anthracis would be too high , due to the resultant loss of function . The identification of the immunodominant epitope LF467–487 , which represents a rare truly promiscuous antigen , capable of binding strongly to multiple diverse HLA alleles , and which is also a feature of a robust T cell response in naturally infected individuals , presented us with a unique opportunity to develop a polyepitopic vaccine in which each epitope is promiscuous , or covers a number of HLA alleles . This increases the chance that each individual in a genetically heterogeneous population acquires immunity to multiple epitopes from a pathogen , thus offering increased protection to a population . We found that the 12 HLA-restricted LF epitopes , either incorporated as a fusion construct or as a peptide pool , conferred protection against lethal challenge with B . anthracis . In addition to their defined role in the T cell response to LF antigens in vitro , this suggests that the epitopes we have described here are capable of priming a strong , long-lasting T cell response that play a role in protection against anthrax . Further work to attribute this protection to a specific response , through both cellular and humoral markers , would be of merit in determining the potential of the LF fusion protein as a future anthrax vaccine candidate . The nature of anthrax infection and the need to evolve tractable strategies , notably in a biodefense setting , has necessarily led to a reliance on a program of PA vaccines tested in primate challenge studies . Study of immunity in naturally-exposed humans , who seem to be immune to reinfection , raises the possibility of learning from these immune repertoires , including the role of LF as a target .
Recombinant full-length LF ( rLF ) and individual domains were produced in an E . coli expression system as previously described [80] . In brief , the cysteine residue at position 687 was replaced with glutamic acid to produce a biologically inactive form of LF . The gene sequence of LF was codon optimized for expression in E . coli ( GenScript , USA ) to allow for the high AT nucleotide content of the protein . Using the pQE30 expression system ( Qiagen , Germany ) the full length LF and LF domain sequences were cloned and expressed from E . coli as recombinant N-terminal histidine-tagged proteins . Bacterial pellets were disrupted using a French press , and the target proteins recovered by centrifuging for 20 minutes at 45000×g at 4°C . These were then incubated with Talon metal affinity resin ( Clontech , USA ) to bind the N-terminal histidine tag . The proteins were eluted from this resin at 4°C by washing with protein elution buffer . Protein concentration was determined using a bicinchoninic acid ( BCA ) protein assay protocol ( Pierce , Thermo Scientific , USA ) and dialyzed against HEPES buffer , using a 10000 molecular weight cut-off dialysis cassette ( Pierce , Thermo Fisher Scientific , USA ) , to a final endotoxin level of <4 EU/mg . A synthetic peptide panel , HPLC purified to a purity of ≥98% purity , comprising of 20mer amino acids overlapping by 10 amino acids encompassing the full-length sequence of LF were obtained from a commercial supplier ( Abgent , USA ) . All peptides were resuspended in DMSO at 25 mg/ml . HLA class II transgenic mice carrying genomic constructs for HLA-DRA1*0101/HLA-DRB1*0101 ( HLA-DR1 ) , HLA-DRA1*0101/HLA-DRB1*0401 ( HLA-DR4 ) , HLA-DRA1*0101/HLA-DRB1*1501 ( HLA-DR15 ) , HLA-DQA1*0301-DQB1*0302 ( HLA-DQ8 ) and HLA-DQA1*0102/HLA-DQB1*0602 ( HLA-DQ6 ) , crossed for more than six generations to C57BL/6 H2-Aβ00 mice , were generated and described previously [81]–[86] . All experiments were performed in accordance with the Animals ( Scientific Procedures ) Act 1986 and were approved by local ethical review . All mouse experiments were performed under the control of UK Home Office legislation in accordance with the terms of the Project License granted for this work under the Animals ( Scientific Procedures ) Act 1986 having also received formal approval of the document through the Imperial College Ethical Review Process ( ERP ) Committee . Human blood samples for the Kayseri ( Turkey ) component of this study were obtained with full review and approval by The Ethics Committee of the Faculty of Medicine , Erciyes University; all participants were adults over 18 year old . Participants were given a full , verbal explanation of the project and written consent was obtained from all those who elected to participate . Human vaccinees based at DSTL , Porton Down , participated in the context of a study protocol approved by the CBD IEC ( Chemical and Biological Defence Independent Ethics Committee ) ; the subjects were all adults aged over 18 years and all provided written , informed consent . Healthy control blood samples were collected under the approval of Ethics REC reference number 08/H0707/173 . HLA-DQ8 transgenic mice were challenged intra-peritoneally with 106 colony forming units of B . anthracis STI strain . The animals were monitored daily for 20 days post-infection , and post-mortem spleens were homogenized in 1 ml of PBS prior to plating out at a range of dilutions onto L-agar plates . Colonies were counted after 24 hours culture at 37°C , and the mean bacterial count per spleen was determined . Leukocytes were isolated from human peripheral blood samples and stimulated as described previously [32] . In brief , sodium heparinised blood was collected with full informed consent from 9 Turkish patients treated for cutaneous anthrax infection within the last 8 years . ( Ericyes University Ethical Committee ) , 10 volunteers routinely vaccinated every 12 months for a minimum of 5 years with the UK Anthrax Vaccine Precipitated ( AVP ) vaccine ( UK Department of Health under approval by the Convention on Biological Diversity Independent Ethics Committee for the UK Ministry of Defence ) , and 10 age-matched healthy controls with no known exposure to anthrax antigens ( Ethics REC reference number 08/H0707/173 ) . PBMCs were prepared from the blood using Accuspin tubes ( Sigma , Dorset , UK ) and washed twice in AIM-V serum free medium ( Life Technologies , UK ) . Cells were counted for viability and resuspended at 2×106 cells/ml . Mice were immunized in the hind footpad with 50 µl of 12 . 5 µg rLF , LF peptides , individual LF domains or a control of PBS , emulsified in an equal volume of Titermax Gold adjuvant ( Sigma-Aldrich , USA ) . After 10 days , immunized local draining popliteal lymph nodes were removed and disaggregated into single cell suspensions . Lymph node cells ( 3 . 5×106/ml ) were challenged with 25 µg/ml of either recombinant full-length LF , the 4 domains which comprise the LF protein , or the overlapping 20mer peptides covering the full-length LF sequence . This generated a map of the entire LF protein sequence . To confirm the immunodominant epitopes identified by this large scale mapping , mice were then immunized subcutaneously with 12 . 5 µg of the individual LF peptides in Titremax adjuvant . After 10 days the lymph node cells were challenged in vitro with 25 µg/ml of the recombinant full-length LF and the immunising and two flanking LF peptides . In the human T cell assays , the peptide library was prepared in a matrix comprising 6 peptides per pool , so that each peptide occurred in 2 pools but no peptides occurred in the same two pools . This allowed the determination of responses to individual peptides . The in-well concentration of each peptide was 25 µg/ml and total peptide concentration per well was 150 µg/ml . Leukocytes were resuspended at 3 . 5×106 cells/ml in HL-1 media ( 1% L-Glutamine , 1% Penicillin Streptomycin , 2 . 5% β-Mercaptoethanol ) and 100 µl/well was plated out in triplicate on 96 well Costar tissue culture plates ( Corning Incorporated , USA ) . The cells were stimulated with 100 µl/well of , appropriate antigen , positive controls of 5 µg/ml Con A ( Sigma-Aldrich , USA ) or 25 ng/ml of SEB ( Sigma-Aldrich , USA ) or negative controls of medium alone . The plates were incubated at 37°C , 5% CO2 for 5 days . Eight hours prior to harvesting , 1 µCi/well of [3H]-Thymidine ( GE Healthcare , UK ) was added . The cells were harvested onto fiberglass filtermats ( PerkinElmer , USA ) using a Harvester 96 plate harvester ( Tomtec , USA ) and counted on a Wallac Betaplate scintillation counter ( EG&G Instruments , Netherlands ) . Results were expressed as stimulation index ( SI ) ( cpm of stimulated cells divided by cpm of negative control cells ) . An SI of ≥2 . 5 was considered to indicate a positive proliferation response . Quantification of murine antigen-specific IFNγ levels was carried out by ELISpot ( Diaclone ) analysis of T cell populations directly ex vivo . Hydrophobic polyvinyldene difluoride membrane-bottomed 96-well plates ( MAIP S 45; Millipore ) were pre-wetted with 70% ethanol , washed twice and then coated with anti-IFNγ monoclonal antibody at 4°C overnight . After blocking with 2% skimmed milk , plates were washed and 100 µl/well of antigen was added in triplicates . For each assay a medium only negative and a positive control of SEB ( 25 ng/ml ) were included . Wells were seeded with 100 µl of 2×106cells/ml in HL-1 medium ( 1% L-Glutamine , 1% Penicillin Streptomycin , 2 . 5% β-Mercaptoethanol ) and plates were incubated for 72 h at 37°C with 5% CO2 . Plates were washed twice with PBS Tween 20 ( 0 . 1% ) then incubated with biotinylated anti-INFγ monoclonal antibody . Plates were washed twice with PBS Tween 20 ( 0 . 1% ) , and then incubated with streptavidin-alkaline phosphatise conjugate , washed and then treated with 5-bromo-4-chloro-3-indolyl phosphate and nitroblue tetrazolium ( BCIP/NBT ) and spot formation monitored visually . The plate contents were then discarded and plates were washed with water , then air-dried and incubated overnight at 4°C to enhance spot clarity . Spots were counted using an automated ELISpot reader ( AID ) , and results were expressed as delta spot forming cells per 106 cells ( ΔSFC/106 ) ( SFC/106 of stimulated cells minus SFC/106 of negative control cells ) . The results were considered positive if the ΔSFC/106 was more than two standard deviations above the negative control . Human T cell INFγ levels were quantified by ELISpot ( Diaclone ) as previously described [32] . In brief , the plates were prepared in a similar manner to the murine ELISpots and following addition of antigen to the wells ( with each peptide represented in two separate triplicates ) they were frozen at −80°C until use . Wells were seeded with 100 µl of human PBMCs at 2×106 cells/ml ( range; 1 . 6×106–2 . 1×106 cells/well ) in AIM-V medium and plates were incubated for 72 hours at 37°C with 5% CO2 . 50 µl supernatant was removed from each well for further determination of cytokines , the remaining plate contents were then discarded and plates were washed with PBS Tween 20 ( 0 . 1% ) and incubated with biotinylated anti–INFγ , followed by a further wash and the addition of streptavidin-alkaline-phosphatase conjugate . Following a final wash , plates were developed by addition of BCIP/NBT . Spots were counted using an automated ELISpot reader ( AID ) , and results were expressed as delta spot forming cells per 106 cells ( ΔSFC/106 ) ( SFC/106 of stimulated cells minus SFC/106 of negative control cells ) . The results were considered positive if the ΔSFC/106 was more than two standard deviations above the negative control and ≥50 spots . IL-21 release from peptide-stimulated donor T cell cultures was determined by ELISA ( eBiosciences ) . Competitive ELISAs were used to determine the relative binding affinity of LF peptides to HLA-DR molecules , as previously described [87] . Briefly , the HLA-DR molecules were immunopurified from homozygous EBV-transformed lymphoblastoid B cell lines by affinity chromatography . The HLA-DR molecules were diluted in HLA binding buffer and incubated for 24 to 72 hours with an appropriate biotinylated reporter peptide , and a serial dilution of the competitor LF peptides . A control of unlabeled reporter peptides was used as a reference peptide to assess the validity of each experiment . 50 µl of HLA binding neutralisation buffer was added to each well and the resulting supernatants were incubated for 2 hours at room temperature in ELISA plates ( Nunc , Denmark ) previously coated with 10 µg/ml of the monoclonal antibody L243 . Bound biotinylated peptide was detected by addition of streptavidin-alkaline phosphatase conjugate ( GE Healthcare , Saclay , France ) and 4-methylumbelliferyl phosphate substrate ( Sigma-Aldrich , France ) . Emitted fluorescence was measured at 450 nm post-excitation at 365 nM on a Gemini Spectramax Fluorimeter ( Molecular Devices , St . Gregoire , France ) . LF peptide concentration that prevented binding of 50% of the labeled peptide ( IC50 ) was evaluated , and data expressed as relative binding affinity ( ratio of IC50 of the LF competitor peptide to the IC50 of the reference peptide which binds strongly to the HLA-DR molecule ) . Sequences of the reference peptide and their IC50 values were as follows: HA 306–318 ( PKYVKQNTLKLAT ) for DRB1*0101 ( 4 nM ) , DRB1*0401 ( 8 nM ) , and DRB1*1101 ( 7 nM ) , YKL ( AAYAAAKAAALAA ) for DRB1*0701 ( 3 nM ) , A3 152–166 ( EAEQLRAYLDGTGVE ) for DRB1*1501 ( 48 nM ) , MT 2–16 ( AKTIAYDEEARRGLE ) for DRB1*0301 ( 100 nM ) and B1 21–36 ( TERVRLVTRHIYNREE ) for DRB1*1301 ( 37 nM ) . Strong binding affinity was defined in this study as a relative activity <10 , and a moderate binding affinity was defined as a relative activity <100 . A fusion protein comprising HLA-restricted T cell epitopes from LF downstream of the universal T cell helper domain from the tetanus toxin C fragment ( aa 865–1120 ) was designed and codon optimized to reflect Salmonella enterica Typhi codon usage ( GenScript Corp ) . This construct was expressed as a recombinant N terminal histidine tagged protein on the commercially available expression system pQE30 in E . coli M15 ( Qiagen ) . The LF epitopes included in the fusion protein were: LF101–120 , LF151–170 , LF261–280 , LF467–486 , LF547–567 , LF574–593 , LF614–633 , LF654–673 , LF674–693 , LF714–733 , LF724–743 and LF744–763 . Briefly , cultures derived from a single colony were grown overnight at 37°C in LB broth with antibiotic selection . Overnight cultures were subcultured in fresh LB broth until they reached an OD600 of 0 . 550–0 . 600 . To induce protein expression , isopropyl β-D-thiogalactopyranoside ( IPTG ) was added to a final concentration of 1 mM . Cultures were then incubated at 25°C ( 200 rpm ) for 16 hours . Cells were harvested by centrifugation at 10 , 000 g at 4°C for 20 minutes . His-tagged fusion proteins were purified from bacterial pellets under denaturing conditions; all steps were conducted at 4°C unless otherwise stated . The bacterial pellet was resuspended in suspension buffer ( SB ) ( 50 mM NaH2PO4 , 300 mM NaCl , pH 7 ) by gentle pipetting until a homogenous suspension was obtained . Phenylmethanesulfonylfluoride ( PMSF ) and lysozyme ( Sigma- Aldrich , St . Louis , MO ) were added to final concentrations of 1 mM and 0 . 25 mg/mL respectively . The suspension was stirred for 20 minutes before the addition of deoxycholic acid ( Sigma- Aldrich , St . Louis , MO ) to a final concentration of 1 mg/mL . The lysate was incubated at 37°C , with occasional stirring , until viscous , and DNase I added to a concentration of 0 . 01 mg/mL . The lysate was stored at room temperature until no longer viscous before centrifugation at 10 , 000 g for 20 minutes . The resulting pellet was washed three times in SB containing 1% Triton X-100 , then washed in SB containing 2M urea before resuspension in SB containing 8M urea and centrifugation at 13 , 000 g for 15 minutes . The supernatant was collected and incubated with Talon metal affinity resin ( Clontech Laboratories ) to bind the N terminal histidine tag . Following washing of the resin with SB containing 6 M urea , the protein was recovered at 4°C in elution buffer ( 150 mM imidazole , 50 mM sodium phosphate and 300 mM NaCl , 6 M urea , pH 7 ) . Eluate was dialyzed using a 10 , 000 MW cut off dialysis cassette ( Pierce , Thermo Scientific ) in dialysis buffer ( DB ) ( 10 mM HEPES , 50 mM NaCl , 400 mM L-Arginine , pH 7 . 5 ) containing sequentially decreasing concentrations of urea for 1 hour periods . Finally , eluate was dialyzed against 4 L HEPES buffer ( 10 mM HEPES , 50 mM NaCl , pH 7 . 5 ) . Protein identity was confirmed by SDS-PAGE and Western Blot analysis ( Bio-Rad Laboratories ) . Protein bands were detected by staining with Coomassie Blue after electrophoretic transfer onto polyvinylidene difluoride membranes ( Millipore ) by Ni-NTA HRP Conjugate ( QIAgen Inc . ) . The protein was of expected size and was recognized by specific antibodies . The endotoxin content of the different protein preparations was determined by the Limulus amoebocyte lysate linetic-QCL assay according to the manufacturer's instructions ( Lonza ) . Protein concentrations were determined using a BCA protocol ( Pierce , Thermo Scientific ) [88] . The complete amino acid sequence of the fusion protein is: MKNLDCWVDNEEDIDVILKKSTILNLDINNDIISDISGFNSSVITYPDAQLVPGINGKAIHLVNNESSEVIVHKAMDIEYNDMFNNFTVSFWLRVPKVSASHLEQYGTNEYSIISSMKKHSLSIGSGWSVSLKGNNLIWTLKDSAGEVRQITFRDLPDKFNAYLANKWVFITITNDRLSSANLYINGVLMGSAEITGLGAIREDNNITLKLDRCNNNNQYVSIDKFRIFCKALNPKEIEKLYTSYLSITFLRDFWGSDVLEMetYKAIGGKIYIVDGDYVYAKEGYEPVLVIQSSEDYQHRDVLQLYAPEAFNYMetDKFKIYLYENMetNINNLTATLGADLENGKLILQRNIGLEIKDVQIEYIRIDAKVVPKSKIDTKIQKLITFNVHNRYASNIVESAYYLVDGNGRFVFTDITLPNIAEQYTHQDEIYEQVHSKGLYVAVDDYAGYLLDKNQSDLVTNSKKFIDIFKEEGSNLTSYGRSEGFIHEFGHAVDDYAGYLL . Mice transgenic for HLA-DQ8 were immunized with 25 µg of fusion protein , or alternatively with a peptide pool consisting of 25 µg of each peptide represented in the fusion protein ( total concentration 300 µg peptide ) , control mice were sham-immunized with PBS . All immunizations were adjuvanted 1∶1 in Titremax Gold and administered by the i . p . route ( 0 . 1 mL ) . Mice were immunized on days 0 , 14 and 35 prior to challenge with B . anthracis STI strain on day 77 . | Anthrax is of concern with respect to human exposure in endemic regions , concerns about bioterrorism and the considerable global burden of livestock infections . The immunology of this disease remains poorly understood . Vaccination has been based on B . anthracis filtrates or attenuated spore-based vaccines , with more recent trials of next-generation recombinant vaccines . Approaches generally require extensive vaccination regimens and there have been concerns about immunogenicity and adverse reactions . An ongoing need remains for rationally designed , effective and safe anthrax vaccines . The importance of T cell stimulating vaccines is inceasingly recognized . An essential step is an understanding of immunodominant epitopes and their relevance across the diverse HLA immune response genes of human populations . We characterized CD4 T cell immunity to anthrax Lethal Factor ( LF ) , using HLA transgenic mice , as well as testing candidate peptide epitopes for binding to a wide range of HLA alleles . We identified anthrax epitopes , noteworthy in that they elicit exceptionally strong immunity with promiscuous binding across multiple HLA alleles and isotypes . T cell responses in humans exposed to LF through either natural anthrax infection or vaccination were also examined . Epitopes identified as candidates were used to protect HLA transgenic mice from anthrax challenge . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"biology",
"and",
"life",
"sciences",
"medicine",
"and",
"health",
"sciences"
]
| 2014 | Anthrax Lethal Factor as an Immune Target in Humans and Transgenic Mice and the Impact of HLA Polymorphism on CD4+ T Cell Immunity |
Zoonotic pathogens such as Ebola and rabies pose a major health risk to humans . One proven approach to minimizing the impact of a pathogen relies on reducing its prevalence within animal reservoir populations using mass vaccination . However , two major challenges remain for vaccination programs that target free-ranging animal populations . First , limited or challenging access to wild hosts , and second , expenses associated with purchasing and distributing the vaccine . Together , these challenges constrain a campaign’s ability to maintain adequate levels of immunity in the host population for an extended period of time . Transmissible vaccines could lessen these constraints , improving our ability to both establish and maintain herd immunity in free-ranging animal populations . Because the extent to which vaccine transmission could augment current wildlife vaccination campaigns is unknown , we develop and parameterize a mathematical model that describes long-term mass vaccination campaigns in the US that target rabies in wildlife . The model is used to investigate the ability of a weakly transmissible vaccine to ( 1 ) increase vaccine coverage in campaigns that fail to immunize at levels required for herd immunity , and ( 2 ) decrease the expense of campaigns that achieve herd immunity . When parameterized to efforts that target rabies in raccoons using vaccine baits , our model indicates that , with current vaccination efforts , a vaccine that transmits to even one additional host per vaccinated individual could sufficiently augment US efforts to preempt the spread of the rabies virus . Higher levels of transmission are needed , however , when spatial heterogeneities associated with flight-line vaccination are incorporated into the model . In addition to augmenting deficient campaigns , our results show that weak vaccine transmission can reduce the costs of vaccination campaigns that are successful in attaining herd immunity .
Zoonotic pathogens represent a global threat to human welfare . Rabies circulating in domestic dogs in Asia and Africa , for example , results in 59 , 000 human deaths each year [1] . Ebola , a disease that circulates in non-human primates and bats , killed over 11 , 000 people during the 2014 outbreak [2] . In addition to the continual threat posed by zoonotic pathogens that occasionally spill over into human populations , zoonoses function as a major source of new infectious diseases in humans [3 , 4] . Over 60% of emerging infectious diseases in humans originated as zoonotic pathogens , and recent studies predict that new harmful zoonoses are most likely to originate in geographical hotspots where health infrastructure is poorest [3] . Given these global risks , the ability to vaccinate free-ranging animal populations against dangerous zoonotic pathogens remains an essential goal for safeguarding human populations against future infectious diseases . Free-ranging animal populations present challenges to mass vaccination . The ultimate goal of any vaccination campaign is to establish herd immunity against a targeted pathogen , that is , to vaccinate a proportion of the population that is sufficient to preclude the pathogen’s spread . In the US , various free-ranging mammalian populations , including coyotes , gray fox , and raccoons , still act as potential or active reservoirs for multiple variants of the rabies virus [5] . These wildlife pose a serious health risk to humans or domestic pets that come into contact with a rabid animal . However , achieving herd immunity in these populations requires that vaccine be distributed across thousands of square kilometers [5 , 6] . Because of the inaccessibility of wildlife hosts , Oral Rabies Vaccine ( ORV ) baits , distributed by aircraft , have been the primary means of vaccinating animal populations that are spread across large tracts of land [7 , 8] . ORV bait programs have been crucial in lowering the incidence of raccoon rabies in the US and Canada , and played a fundamental role in eliminating canine rabies from difficult-to-access populations such as coyotes and foxes [9 , 10] . Though proven effective in some cases , ORV programs highlight challenges that long-term wildlife vaccination campaigns must overcome . In North America , raccoons serve as the primary reservoir of the raccoon variant of the rabies virus . In order to mitigate the risk of transmission to humans , the US and Canadian governments have organized intense vaccination efforts since the 1990s , with the goal of preventing the westward spread of raccoon rabies across the Appalachian mountains , as well as the northward spread of the virus into Canada [11 , 12] . However , low rates of seroconversion in raccoons , and bait competition with non-targeted hosts , together prevent vaccine coverage from exceeding the herd immunity threshold [13–15] . In turn , despite decades of ongoing vaccination effort , the rabies virus still occasionally breaches vaccination barriers meant to contain it [5 , 16 , 17] . For other wildlife reservoirs , such as coyotes and gray fox , ORV programs in the US are successful at establishing and maintaining herd immunity [9] . However , to ensure that the rabies virus cannot re-invade , these programs may need to be maintained for decades before the risk of rabies re-infection has passed . These challenges highlight the need for cost-effective ways to immunize populations that are difficult to access . Transmissible vaccines are a promising new technology that , when paired with oral vaccine technology , could transform our ability to vaccinate wildlife populations . Transmissible viral vaccines are engineered to transmit between hosts , inoculating hosts they infect . Vaccine transmission supplements direct vaccination efforts and increases vaccine coverage . To date , transmissible vaccines have been explored for zoonotic pathogens such as Ebola in non-human primates [18] and Hantavirus in deer mice [19] , and have been suggested as a possibility for rabies [20] . Although transmissible vaccines that target human pathogens are still in the early stages of development , a transmissible vaccine targeting myxoma and rabbit hemorrhagic fever has been both developed and tested in European rabbits . Studies of the rabbit vaccine demonstrated relatively high levels of transmission in caged rabbit populations , and in field trials , the vaccine was shown to immunize a substantial portion of a rabbit population through horizontal transmission [21 , 22] . In addition to this promising empirical work , theoretical models of transmissible vaccines suggest that low levels of transmission can dramatically increase the level of vaccine coverage in a well-mixed host population [23–25] . However , little is known about the extent to which weak vaccine transmission might augment campaigns that target a geographically widespread , free-ranging animal population in which host interactions are spatially localized . The extent to which the vaccine transmits is encapsulated in the basic reproduction number , notated R0 , v , that describes the average number of secondary vaccine infections caused by one vaccine-infected individual in a susceptible population . Weakly transmissible vaccines , defined as vaccines with R0 , v < 1 , are particularly desirable as they have a reduced likelihood of vaccine evolution , which reduces the risk of vaccine reversion , as well as competition between the vector and vaccine [23 , 26] . We use a mathematical modeling framework , based on the SIR ( Susceptible-Infected-Recovered ) infection model , to quantify the benefits imparted by vaccine transmission on long-term ORV-style vaccination campaigns that target wildlife in the US . Our focal questions are: ( 1 ) can weak levels of vaccine transmission augment campaigns in the US that fail to establish herd immunity in raccoon populations ? ( 2 ) to what extent can vaccine transmission reduce the costs of maintaining herd immunity in ORV programs that are successful ? We address these questions using mathematical models parameterized with data from historical campaigns that targeted raccoons , coyotes and gray fox in the US .
We start with a model that describes a well-mixed host population . The model tracks the densities of hosts that are susceptible to rabies infection ( S ) , hosts that are currently infected with a transmissible vaccine ( Iv ) , and hosts that have recovered from vaccine infection ( V ) . In the model , new susceptible hosts are born at constant rate b , and all hosts die at per-capita rate d . Vaccination of susceptible hosts occurs in one of two ways . The first is through direct consumption of a vaccine bait containing a transmissible vaccine , which occurs with per-capita rate σ . Upon consumption of the bait , susceptible hosts become infected with the vaccine virus . We assume that , simultaneously , exposure to the rabies antigen that the vaccine carries prompts a host immune response that results in lifelong immunity to the rabies virus . Alternatively , susceptible hosts can become vaccinated through infectious contact with another host that is infected with the vaccine . The rate at which such contacts occur will depend on attributes of the vector virus from which the vaccine is made and the rate at which hosts experience infectious contact with each other . We assume that vaccine-infected hosts transmit the vaccine to susceptible hosts at frequency-dependent rate β v S ( t ) I v ( t ) S ( t ) + I v ( t ) + V ( t ) . Vaccine-infected hosts clear the infection at per-capita rate δv , and transition into a vaccine-recovered class ( V ) . After recovering from infection with the vaccine , the host is immune to subsequent vaccine infection , as well as infection with the rabies virus . These biological assumptions lead to the following system of differential equations: d S d t = b - σ S - d S - β v S I v S + I v + V d I v d t = σ S - ( d + δ v ) I v + β v S I v S + I v + V d V d t = δ v I v - d V ( 1 ) Many ongoing rabies campaigns utilize aircraft or cars to distribute vaccines into geographically widespread wildlife populations . In these scenarios , the vaccine is distributed along lines in the environment . In order to ensure an even distribution of vaccines , the flight-line spacing must be chosen with the home range of the host animal in mind [27] . Choosing a flight-line spacing that is too large relative the animal’s home range , for example , will cause gaps in seroprevalence between flight-lines . We modify System ( 1 ) to investigate how vaccine transmission addresses these unique spatial challenges associated with flight-line vaccination . The resulting model tracks the same classes as System ( 1 ) , however each state variable is a one-dimensional spatial density described by a partial differential equation . For each host class , we use a diffusion term with diffusion coefficient k to model the movement of a host throughout its lifetime ( S1 Appendix ) . In the model , flight-lines are spaced at intervals of width 2L , and the vaccination rate σ is normally distributed around flight-line positions according to 2Lf ( x ) σ . Here , f ( x ) is a normal distribution that is truncated to the interval [−L , L] with standard deviation ξ; the factor 2L ensures that the mean density of vaccine effort is independent of the flight-line spacing that is chosen ( more details in S2 Appendix ) . Now , vaccine infection is a spatially localized process , so that an infected host at location x can only infect susceptible hosts that are also at location x . The resulting system is ∂ S ∂ t = k ∂ 2 S ∂ x 2 + b - 2 L f ( x ) σ S - d S - β v S I v S + I v + V ∂ I v ∂ t = k ∂ 2 I v ∂ x 2 + 2 L f ( x ) σ S - ( d + δ v ) I v + β v S I v S + I v + V ∂ V ∂ t = k ∂ 2 V ∂ x 2 + δ v I v - d V ( 2 ) We also use variations of Systems ( 1 ) and ( 2 ) to model campaigns that use a nontransmissible vaccine . For these simulations , the Iv class is omitted , βv is set to 0 , and directly vaccinated susceptible hosts transition into the V class . We use data from the USDA to parameterize our models . Each year , the USDA compiles a “National Rabies Management Summary Report” that provides an overview of the previous year’s vaccination efforts , including where vaccination campaigns were carried out , types of wildlife that are vaccinated , and the number of vaccine baits used . In addition , these reports document the seroprevalence that was measured in follow-up population surveys . All data were retrieved from summary reports posted on the USDA website for the years 2006–2010 [28] . If campaigns occur only rarely , the effective vaccination rate σ is zero , and the host population relies on vaccine transmission to distribute the vaccine . In this case , our nonspatial model reduces to a classic SIR infection model . Local stability analysis of our model indicates that if a small number of vaccine-infected individuals are introduced into an otherwise susceptible population , the density of seropositive hosts will increase when R0 , v > 1 , and comprise a fraction ϕ = 1 - 1 R 0 , v ( 3 ) of the host population at steady state ( S3 Appendix ) . Here , R0 , v is the so-called basic reproduction number of the vaccine , defined as the number of secondary vaccine infections caused by one infected individual in an otherwise susceptible population ( R 0 , v = β v d + δ v , parameters defined in Table 1 ) . Eq ( 3 ) implies that , if the goal of a campaign is to maintain seroprevalence in the host population at a level ϕ , the vaccine used must transmit at a level R 0 , v = 1 1 - ϕ . ( 4 ) To understand the extent to which vaccine transmission can augment long-term campaigns when regular vaccination is possible , we find steady states of System ( 1 ) with σ > 0 . Stability analysis indicates that with constant vaccination , the seroprevalence of System ( 1 ) approaches a level ϕ described by the expression ϕ = d ( 1 - R 0 , v ) - σ + ( d R 0 , v + d + σ ) 2 - 4 d 2 R 0 , v 2 d R 0 , v ( 5 ) ( S3 Appendix ) . Eq ( 5 ) shows that the long-term effect of vaccine transmission on seroprevalence is again encapsulated in the vaccine’s R0 , v . Furthermore , for a fixed value of R0 , v , the steady state benefit from transmission does not depend on the length of time over which these secondary infections occur , which is given by 1 δ v . To find the level of vaccine transmission that is necessary to augment real-world campaigns , we parameterize σ in Eq ( 5 ) to a range of seroprevalence outcomes from USDA vaccination campaigns applied to raccoons . Between 2006–2010 , follow-up seroprevalence surveys reported average seroprevalence that varied from a minimum of 0 . 29 in 2006 , to a high of 0 . 37 in 2010 . Interpreted as steady state seroprevalence levels , and assuming that raccoons live for 2 . 5 years , these values of ϕ imply a range of vaccination rates 0 . 17 < σ < 0 . 24 yr−1 ( S1 Appendix ) . We use our spatial model to understand how heterogeneities in vaccine distribution affect the benefits of a transmissible vaccine . To this end , we numerically solve for steady state solutions of System ( 2 ) on the interval [−L , L] , with Neumann boundary conditions that describe the aggregate effects of many repeating flight-lines . We simulate high and low values of spatial heterogeneity in the distribution of vaccines by adjusting ξ , and we use values of the diffusion coefficient k to simulate small ( 1 km2 ) and large ( 10 km2 ) host home ranges . This variability in home range is chosen to reflect the variability that is found in raccoons in peri-urban and rural environments ( details in S1 Appendix ) . We nondimensionalize System ( 2 ) to better understand the potential for vaccine transmission to smooth spatial heterogeneities in population seroprevalence . Nondimensionalization is an analytical technique that summarizes the effects of a model’s parameters into unitless parameter combinations ( S2 Appendix ) . Our analyses show that spatial heterogeneities are encapsulated in two nondimensional parameters . ξ ^ describes the level of spatial heterogeneity in the distribution of vaccination effort around each flight-line location , scaled relative to one-half of the flight-line spacing . κ is referred to as scaled dispersal , and describes the capacity for spatial heterogeneities in seroprevalence to persist as a function of host home range , the duration of vaccine infection , and the spacing of flight-lines in the environment: κ = k ( d + δ v ) L 2 ξ ^ = ξ L ( 6 ) Motivated by transmissible vaccine designs with a long duration of infection , we investigate how vaccines with slow recovery rates ( i . e . small δv ) might augment the spatial lows that are predicted by our model . We parameterize our model to the yearly averaged seroprevalence levels that were realized in campaigns targeting raccoons . Next , we use a root-solving method to determine the minimal amount of vaccine transmission , R0 , v , that is necessary to achieve herd immunity . In these simulations , we consider a population protected from rabies when the minimum of the spatial seroprevalence is raised to the herd immunity threshold ϕ = 0 . 5 ( details in S2 Appendix ) . All numerical analysis is performed in the statistical language R [29] . In populations where a traditional oral vaccination campaign can achieve herd immunity ( e . g . , coyotes and gray fox ) , the use of a weakly transmissible vaccine could result in large reductions in program costs . To quantify the savings that might be realized by using a transmissible vaccine , we use the spatially homogeneous model , described by System ( 1 ) , to find the fractional reduction in the rate of vaccination that is required to sustain herd immunity at level ϕ in a host population . In doing so , we use the fact that a fractional reduction in the vaccination rate is equivalent to a fractional reduction in the rate at which vaccine baits must be deposited ( S2 Appendix ) . Furthermore , if bait depletion by other animals can be ignored , a continual vaccination rate σ relates to the number of vaccines distributed per year , ρ , by σ = ρ ( b d ) - 1 . ( 7 ) Here , b d is the steady state density of the host population . If a nontransmissible vaccine is used to maintain seroprevalence at level ϕ , the rate of vaccination must exceed σ N T * = d ϕ 1 - ϕ ( 8 ) By solving Eq ( 5 ) for σ , we find that a transmissible vaccine can achieve the same seroprevalence with σ T * = d ϕ 1 - ϕ ( 1 - R 0 , v ( 1 - ϕ ) ) ( 9 ) ( S3 Appendix ) . With Eqs ( 8 ) and ( 9 ) , we calculate the fractional reduction in the rate of vaccination that is required for sustained herd immunity , f σ = 1 - σ T * σ N T * = R 0 , v ( 1 - ϕ ) . ( 10 ) Note that the population density b d is not present in the fractional reduction calculation , and need not be estimated . We use Eq ( 10 ) to calculate the theoretical reduction in bait costs that would have been possible in past campaigns if a transmissible vaccine with R0 , v = 0 . 9 was used . To parameterize ϕ , we use seroprevalence outcomes in campaigns that targeted coyotes and gray fox between 2006–2010 . Next , we multiply the calculated reductions by the total number of vaccine baits that were used , and the cost per vaccine bait . For this calculation , we assume that the per-unit cost of the transmissible vaccine bait is the same as a nontransmissible bait , and later evaluate how the anticipated savings might differ if the transmissible vaccine is more expensive . Accounting for inflation , and using vaccine bait costs that were reported for similar campaigns [30] , we estimate a current value of $2 . 12 per bait ( details in S1 Appendix ) . In addition to the expenses associated with the number of vaccine baits that are required , campaigns must also acquire , maintain , and man aircraft that distribute baits . To better understand the cost reductions that are possible in such programs , we define a function that incorporates both the expenses from the use of aircraft ( e . g . wages , maintenance , fuel ) , and the purchase of vaccine baits . To this end , we assume the vaccinated region A is an ℓ × w km2 rectangle . Given that flight-lines are arranged along either the ℓ or w direction and spaced at intervals of 2L , the linear flight distance required to vaccinate the region A grows according to A 2 L km . Defining Cf as the cost per linear kilometer of flight , the total flight costs of vaccinating the area A scale with flight-line spacing as C f A 2 L . The expenses from the purchase of vaccine baits are given by the product C b σ ( b d ) A , where Cb is the cost per bait , and σ ( b d ) A is the number of vaccine baits required per year to achieve an effective vaccination rate σ when population density is b d ( S2 Appendix ) . Combining flight and bait costs , and dividing by the area of region A gives a per km2 cost of C = C f 1 2 L + C b b d σ . ( 11 ) To estimate the cost reduction that is possible in flight-line vaccination campaigns , we use a numerical solver to find the pairing of vaccination rate σ* , and flight-line spacing 2L* km , that minimizes Eq ( 11 ) while maintaining seroprevalence at level ϕ = 0 . 5 . To convert the optimal strategy into a dollar amount , we use the same baseline vaccine bait cost as before ( Cb = 2 . 12 ) , and a flight-line cost of Cf = 18 . 16 km−1 . We vary Cb to better understand how sensitive the cost reductions are to the cost markups that might apply to transmissible vaccines . The value of Cf is derived using averaged flight costs reported for campaigns in Ohio , and multiplying by the standard flight-line spacing ( 0 . 5 km ) to convert to cost per linear kilometer of flight ( S1 Appendix ) . We choose host densities of b d = 1 , 10 , 100 km−2 to simulate the wide range of densities found in raccoons . In order to gauge the sensitivity in the cost reductions that are predicted by our model , we also calculate the cost reductions that occur when assumptions of the model are changed . The Baseline model simulates a vaccine with a 1 month infectious period , R0 , v = 1 , and a desired seroprevalence of ϕ = 0 . 5 . The “Lagged Immunity” and “Temporary Immunity” variants are obtained by changing the equations of the Baseline model . In the Lagged Immunity variant , hosts are not immune to rabies until they have fully recovered from vaccine infection . In the Temporary Immunity variant , rabies-immunity wanes after a period of one year . All other variants are obtained by changing parameter values . More details can be found in the S2 Appendix .
In the absence of repeated vaccinations , a single campaign could in theory preempt the establishment of rabies if R0 , v is sufficiently large . Standard epidemiological theory implies that to achieve seroprevalence ϕ , the vaccine must transmit at level R 0 , v = 1 1 - ϕ . This expression implies that 1 . 7 < R0 , v < 2 . 5 is required to achieve the seroprevalence that successfully preempted the reinvasion of rabies into wild canines ( 0 . 4 < ϕ < 0 . 6 , [9] ) . Similarly , R0 , v ≈ 2 is required to achieve the recommended seroprevalence in raccoons ( ϕ ≈ 0 . 5 , [31 , 32] ) . Because it is currently unknown whether these levels of vaccine transmission are feasible or will ever be deemed safe to implement in free-ranging animal populations , we next evaluate the extent to which vaccine transmission can augment ongoing campaigns that regularly vaccinate the host population . If spatial heterogeneities are ignored , our model predicts that weak vaccine transmission could be effective at augmenting US campaigns that target raccoons but do not achieve the desired herd immunity threshold of ϕ = 0 . 5 . When parameterized to vaccination outcomes reported in National Rabies Management Summary Reports between 2006–2010 , our model suggests that a vaccine with 0 . 85 < R0 , v < 1 . 18 would augment the range of seroprevalence averages to that required for herd immunity ( Fig 1 ) . This implies that even weakly transmitting vaccines , i . e . those that do not transmit sufficiently to remain endemic in the population , might substantially benefit campaigns that seek to establish herd immunity in raccoon populations . When spatial heterogeneities are incorporated , elevating the minimal seroprevalence to the herd immunity threshold can require substantially higher levels of vaccine transmission . Both host movement and vaccine bait heterogeneity influence the amount of vaccine transmission that is necessary to raise seroprevalence levels above the 0 . 5 herd immunity threshold ( Fig 2 ) . Our model predicts that hosts with small home ranges ( ∼1 km2 ) are most likely to be affected by heterogeneities in vaccine coverage when the distribution of vaccine is spatially clustered along flight-lines . In these populations , seroprevalence falls below the herd immunity threshold even when vaccine transmission is relatively high , R0 , v = 1 . 5 . As a result , portions of the population remain unprotected from pathogen invasion ( Fig 2 ) . A nondimsionalization of our model reveals that the parameter combination κ = k ( d + δ v ) L 2 determines the extent to which spatial heterogeneities in seroprevalence persist at steady state . Small values of κ describe scenarios where flight-line spacing is too large , relative to host dispersal , to significantly smooth out heterogeneities in seroprevalence . One way to overcome these heterogeneities is to increase vaccine transmission via R0 , v . However , augmenting the spatial lows in seroprevalence requires relatively high levels of vaccine transmission ( R0 , v > 1 ) . Specifically , when scaled dispersal is small , κ ≈ 10−2 , and the steady state distribution of baits is relatively clustered around each flight-line , increasing vaccine transmission from no transmission , R0 , v = 0 , to modest transmission , R0 , v = 1 , fails to substantially augment the minimal seroprevalence in the spatially explicit model ( Fig 3 ) . This demonstrates that weak transmission has a limited effect on augmenting seroprevalence lows that result from a heterogeneous bait distribution . The expression for κ implies that vaccines with longer infectious periods might be beneficial for overcoming spatial heterogeneities in vaccine coverage . For fixed R0 , v , increasing the duration of vaccine infection increases the scaled dispersal parameter κ , which , in turn , smooths out spatial heterogeneities in the seroprevalence profile . In a host with a 1 km2 home range , our results indicate that establishing herd immunity requires R0 , v ≈ 2 when vaccine infection lasts 1 month , but only R0 , v ≈ 1 . 5 if the duration of infection is lifelong ( Fig 4 ) . However , our results also indicate that weak levels of vaccine transmission , even when paired with a longer duration of infection , will likely be ineffective at augmenting seroprevalence levels in raccoons with small home ranges . In contrast , for populations with larger home ranges , the required levels of vaccine transmission are similar to those predicted by the spatially homogeneous model , regardless of the duration of infection ( Fig 4 ) . Our model provides broad estimates of the cost-savings that might be possible in campaigns that use transmissible vaccines . Assuming a homogeneous host population , the fractional reduction in the rate at which vaccine baits need to be distributed , while maintaining herd immunity at level ϕ , is f σ = R 0 , v ( 1 - ϕ ) . The expression for fσ implies that , in campaigns that seek to maintain herd immunity at a level ϕ = 0 . 5 in wildlife , a weakly transmitting vaccine with R0 , v = 0 . 5 would reduce the number of vaccine baits required each year by 25% ( Fig 5 ) . Evaluating fσ with R0 , v = 1 shows that the maximal reduction in baits that is provided by weak transmission is 50% . The cost-savings that are predicted by fσ can be substantial . Between 2006 and 2010 , vaccination efforts of the US Wildlife Services that targeted coyote and gray fox populations distributed approximately 2 million baits every year . In gray fox populations , these efforts resulted in an average seroprevalence of 0 . 69 . fσ implies that the corresponding reduction due to a vaccine with R0 , v = 0 . 9 is 27 . 9% . Given that 1 . 53 million baits were distributed each year , we calculate that by using a transmissible vaccine , the same seroprevalence could be achieved with 430 , 000 fewer baits per year . In coyotes , the average seroprevalence was 0 . 55 , which implies that a 40 . 5% reduction in the number of baits is possible with R0 , v = 0 . 9 . This reduction would bring the number of baits required each year down from 571 , 000 to 340 , 000 . Using a cost per vaccine bait reported in other USDA campaigns , the total cost-savings on vaccine baits associated with transmission at R0 , v = 0 . 9 is $1 . 4 million each year ( S1 Appendix [30] ) . Here , the savings due to vaccine transmission account for 32% of the estimated $4 . 4 million total bait costs . When the costs of aerial bait delivery are incorporated into our model , our results suggest that a transmissible vaccine can reduce the costs of vaccination programs in two ways: by reducing the spatial density of flight-lines that are necessary to ensure even coverage , and by reducing the rate at which vaccine baits need to be distributed ( Fig 6 ) . Parameterized with the aircraft and vaccine bait costs of Ohio campaigns between 1997 and 2000 , our model finds the optimal flight-line spacing and vaccination rate that minimizes the costs of maintaining seroprevalence at 0 . 5 . Though we do not prove it , numerical explorations suggest that the optimal combination of vaccination rate and flight-line spacing is unique ( S1 Fig ) . Compared to the strategy using a vaccine that does not transmit , the effect of vaccine transmission on the optimal strategy is to reduce the vaccination rate , and to a lesser extent , decrease the total flight distance needed to distribute the vaccine by widening the flight-line spacing ( Fig 6 ) . Our results imply that the primary role of vaccine transmission is to reduce the quantity of vaccine that needs to be distributed along each flight-line , as opposed to changing how the vaccine baits are distributed spatially ( Fig 6 ) . Our results imply that the cost-savings associated with vaccine transmission will be greatest in high density populations ( Fig 7 ) . The fractional reduction in costs predicted by the spatial model is always less than the savings predicted by the homogeneous model . This is due to the limited effect that vaccine transmission has on easing the flight-costs of vaccination . However , in campaigns that target host populations at high densities , the cost reductions that result are similar to those predicted by the homogeneous model , where the sole cost is the purchase of vaccine . In campaigns that target hosts at low densities , the flight costs comprise a greater proportion of the total costs . In this case , a vaccine with a long duration of infection can provide a greater reduction in total costs . For a modestly transmissible vaccine with R0 , v = 1 , and moderate raccoon densities of 10 km−2 , the net reduction in cost is about 20% when home range is small , and between 20-30% for larger home ranges , depending on the duration of infection . Next , we investigate how our model’s prediction of the cost reduction due to vaccine transmission changes with different model assumptions . One important factor that will influence the anticipated cost savings is the price of vaccine baits that contain a transmissible vaccine virus , compared to the price of conventional , nontransmissible baits . Our model shows that , for a vaccine that transmits at a level R0 , v = 0 . 5 , up to a 30% increase in vaccine bait cost can be tolerated and still reduce the overall costs of the campaign . If the vaccine transmits at R0 , v = 1 , an increase of 89% is allowable ( Fig 8 ) . Table 2 summarizes how modifying other assumptions of the baseline model changes the cost-reduction that is provided by vaccine transmission . When parameterized to a vaccine with R0 , v = 1 and a host with a 10 km2 home range , our baseline model predicts a 22% reduction of the summed aircraft and vaccine unit costs . Similar reductions occur when hosts that are vaccinated with a transmissible vaccine do not gain rabies immunity until after they recover from vaccine infection ( “Lagged immunity” variant ) , or when the underlying distribution of vaccines is tightly clustered . Even greater reductions are predicted when limitations of the host cause rabies-immunity to wane after an average period of one year ( “Temporary immunity” variant ) , or if vaccine transmission occurs throughout a host’s lifespan ( “Lifelong vaccine infection” variant ) . However , this reduction is only 16% if high levels of seroprevalence are necessary for herd immunity , or if the transmissible vaccine is 25% more expensive than the nontransmissible vaccine .
Technology that engineers vaccine transmission may never be deemed safe for use in humans , but empirical studies have shown its efficacy and safety in non-human animals [20–22] . The need for better vaccine technology , particularly in the control of zoonotic pathogens in free-ranging animal populations , is apparent from ongoing campaigns in the US . A primary concern for the anticipated use of transmissible vaccines is the extent to which vaccine transmission can be engineered . Our results , combined with the capacity of oral vaccine campaigns to distribute vaccine to free-ranging host populations , demonstrate that weak vaccine transmission should be explored as a means of augmenting campaigns that do not achieve seroprevalence levels that are required for herd immunity . Namely , our results imply that weak vaccine transmission could bolster ongoing rabies campaigns that target raccoons , yet fail to establish herd immunity . More generally , though empirical research into the engineering of transmissible vaccines is still in its early stages , our results indicate that even weak levels of vaccine transmission could play a major role in the global control of infectious disease . | Zoonotic pathogens pose a significant health risk to humans . Mass vaccination programs have shown promise for controlling zoonoses in reservoir populations and , in turn , lessening the health burden posed to neighboring human populations . Despite some significant successes , major logistical challenges remain for programs that seek to establish and maintain herd immunity in free-ranging animal populations . Specifically , limited host access and costs associated with vaccine distribution may hinder efforts to vaccinate a host population and preempt spillover of a zoonotic pathogen . We use mathematical models , parameterized with data from campaigns in the US that target rabies in wildlife , to illustrate how transmissible vaccines can overcome these challenges . Specifically , we find levels of vaccine transmission necessary to boost vaccination efforts that seek to preempt the spread of rabies , and also predict the cost savings that could be realized with a transmissible vaccine . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
]
| [
"animal",
"types",
"medicine",
"and",
"health",
"sciences",
"zoonotic",
"pathogens",
"animal",
"pathogens",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"vertebrates",
"animals",
"mammals",
"vaccines",
"preventive",
"medicine",
"viruses",
"rabies",
"raccoons",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"vaccination",
"and",
"immunization",
"zoology",
"rabies",
"virus",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"zoonoses",
"medical",
"microbiology",
"microbial",
"pathogens",
"lyssavirus",
"eukaryota",
"wildlife",
"immunity",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"amniotes",
"organisms"
]
| 2019 | A little goes a long way: Weak vaccine transmission facilitates oral vaccination campaigns against zoonotic pathogens |
During development , signaling networks control the formation of multicellular patterns . To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear . Here , we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network . Applying this framework to vulval development in C . elegans , we demonstrate that quantitative changes in the regulatory network can render ∼500 multicellular phenotypes . This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes . Using metrics to gauge the robustness of these phenotypes to parameter perturbations , we identify a select subset of novel phenotypes that are the most promising for experimental validation . In addition , our model calculations provide a layout of these phenotypes in network parameter space . Analyzing this landscape of multicellular phenotypes yielded two significant insights . First , we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations . Second , we show that the predicted multicellular patterns include not only those observed in C . elegans , but also those occurring exclusively in other species of the Caenorhabditis genus . This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus . Using our computational framework , we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species . Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture . Furthermore , by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species , we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus .
During development , regulatory signaling networks instruct cell populations to form multicellular patterns and structures . To what extent perturbations in the quantitative performance of these networks may lead to phenotypic changes remains unclear . Experimental genetics studies typically uncover mutant phenotypes that emerge from extreme modes of perturbation ( e . g . , knockout or overexpression ) [1] , [2] . However , there is ample evidence that biological networks operate amidst quantitative fluctuations [3]–[6] . The sources of these quantitative perturbations include stochastic behavior , population heterogeneity , epigenetic effects and environmental changes . The fundamental question then is how much phenotypic variation is possible by quantitative perturbations in network performance without wholesale changes to network topology . On the one hand , we may expect that the wild-type multicellular phenotype may be highly robust to quantitative variations . Indeed , computational analysis of the Drosophila segment polarity network demonstrated the robustness of the wild-type multicellular pattern to significant parameter changes [7] . This robustness may be a more pervasive property of developmental regulatory networks that allows their modular utilization in different multicellular geometries and developmental contexts [8] . On the other hand , for a given multicellular system , some degree of fragility in the regulatory network is essential for evolutionary diversification . New multicellular phenotypes must be accessible through modifications to the underlying regulatory network , providing avenues for sampling new phenotypes that may be more beneficial under different selective pressures . The extent to which this phenotypic diversification must involve a topological overhaul of the regulatory network as opposed to quantitative changes to a fixed network topology remains unclear . Closely related species may have evolved by subtle , quantitative changes in network interactions rather than large-scale changes to network topology . Indeed , there is evidence for such “quantitative diversification” of phenotypes in the evolution of maize and finch beaks [9] , [10] . However , analyzing extant species identifies only quantitative changes that have withstood selection and conceals the complete phenotypic diversity that a regulatory network can render . Meanwhile , experimentally reconstructing that diversity faces the challenge of systematically imposing quantitative regulatory perturbations in vivo and scoring the numerous phenotypes that would be generated . Computational modeling has proven to be a useful tool for predicting multicellular patterns and morphology based on the underlying regulatory mechanisms [7] , [11]–[18] . Thus , such models may provide an effective framework to explore the full diversity of phenotypes that is accessible through quantitative changes to a particular developmental regulatory network . Here , we develop a computational approach to analyze quantitatively the phenotypic diversity of C . elegans vulval development . The C . elegans vulva develops from an array of six precursor cells that commit to a spatial pattern of distinct fates ( Figure 1 ) [19] , [20] . We have described previously a mathematical model of the regulatory network that controls C . elegans vulval development and elucidated potential quantitative advantages of the biochemical coupling in this signaling network [21] . In this work , we extend this mathematical model of the signaling network to make predictions about the range of phenotypes that this network can render . We probed whether this developmental network is so robust to parameter changes that only a narrow set of multicellular phenotypes is possible . Or , can quantitative variations give rise to a broader range of phenotypes ? In contrast to other recent models of C . elegans vulval development [12] , [13] , our model incorporates directly the underlying molecular mechanisms and the quantitative strength of these molecular regulatory pathways . Thus , it provides the necessary foundation for examining quantitative diversification of multicellular phenotype . Our computational analysis reveals that a significant amount of phenotypic diversity is achievable through quantitative changes to the regulatory network . Thus , this developmental regulatory network is not “wired” to generate robustly only the wild-type phenotype . Furthermore , the phenotypes predicted by the model include not only those observed in C . elegans , but also those found exclusively in several closely-related species [22] . Thus , our model predictions validate the hypothesis that quantitative changes to a common regulatory network have occurred during the diversification of several species within the Caenorhabditis genus . Furthermore , by applying our modeling framework to analyze published experimental phenotypic data , we extract the quantitative regulatory differences that may have accrued during the evolution of three major species of the Caenorhabditis genus .
We sought to better understand how much phenotypic diversity a developmental regulatory network can produce through quantitative changes without altering the network architecture . To conduct this analysis , we started with our previously reported mathematical model of the regulatory network that controls vulval development in C . elegans [21] . This model uses ordinary differential equations to track the activity of two key signals in each precursor cell: MAP kinase and the lateral Notch signal ( details are provided in Materials and Methods ) . The levels of these two signals are then used to predict the fate of each cell . The model consists of eight dimensionless parameters whose values influence the pattern of fate choices ( Figure 2A ) . To determine the phenotypes that are accessible through quantitative modulation of the network , we allowed each parameter to vary across a broad range of physiological values ( Materials and Methods ) . For each combination of parameter values , the multicellular phenotype was computed . In this manner , the multidimensional parameter space was divided into sub-regions associated with specific multicellular phenotypes ( Figure 2B ) . This phase diagram of phenotypes represents the predicted multicellular patterns that the vulval developmental network can produce . Extreme values along each parameter axis emulate the classical experimental scenario where specific molecular pathways are eliminated ( e . g . , knock out ) or overexpressed . Away from these extremes , the phase diagram represents phenotypes that are predicted to occur when regulatory mechanisms are tuned quantitatively without wholesale changes to network topology . Thus , by counting the number of unique phenotypes that exist in this multidimensional parameter space , we sought to quantify the “phenotypic capacity” of the C . elegans vulval signaling network . Our calculations show that the phenotypic capacity has an upper limit . That is , even as the parameter space is broadened , the number of distinct phenotypes saturates at approximately 560 multicellular patterns ( Figure 2C ) . This result reveals that the developmental network is not constrained to a few outcomes . The wild type and a handful of well-studied mutant phenotypes by no means represent the phenotypic capacity of this system . Furthermore , in this six-cell system there are four fates possible to each cell ( see Materials and Methods ) . Hence , the theoretical upper limit to the number of phenotypes is 4 , 096 . Our model predicts that the molecular network constrains the system from accessing ∼85% of the theoretically possible phenotypes . To better understand how the phenotypes are represented in parameter space , we determined the amount of parameter space associated with each phenotype ( see Materials and Methods ) . Phenotypes that occur only at a few points in parameter space may be inaccessible experimentally , while their counterparts occupying a large fraction of parameter space may represent the more tangible outcomes . The distribution of Parameter Space Occupancy ( PSO ) resembles a log-normal distribution ( μ = −19 . 60 , σ = 4 . 90 ) with a slight positive skew ( Figure 2D ) . On the low end of the distribution , our model predicts 19 phenotypes that are two standard deviations below the mean PSO ( Table S4 ) , and 9 of these phenotypes do not entail the mixed ‘m’ cell fate ( Table S1 ) . Consistent with this prediction , none of these predicted phenotypes are among the well-studied experimentally observed phenotypes . These highly unlikely outcomes reduce our evaluation of the overall phenotypic capacity of this system . Meanwhile , on the other end of the distribution , a small subset of phenotypes occupies a disproportionately large portion of the parameter space ( Figure 2D ) . Within the positive skew is the wild-type phenotype , consistent with a previous study that showed that the developmental segment polarity network robustly produces the wild-type multicellular pattern [7] . Extending beyond the wild-type phenotype , our model predicts an additional 33 phenotypes with PSO values that are two standard deviations above the mean ( see Table S5 for a list of these phenotypes ) , 25 of which do not entail the mixed ‘m’ cell fate . These phenotypes are highly represented in parameter space and suggest that significant phenotypic diversity may be sampled by tuning quantitatively a common underlying regulatory network . In fact , consistent with model predictions , several of these 25 phenotypes have been observed in C . elegans genetics experiments [23] , [24] . However , 10 of these 25 phenotypes have not been reported and are novel predicted phenotypes for future experimental validation . To further evaluate these 10 novel phenotypes , we developed two metrics that provide additional insights into how phenotypes are distributed in parameter space . While the PSO metric quantifies what fraction of points in parameter space are associated with a particular phenotype , it does not report how these points are distributed in parameter space . One extreme is that the parameter points associated with a phenotype are disjointed and scattered throughout parameter space ( Figure 3A ) . In this case , a perturbation in any parameter value would alter the phenotype , i . e . the phenotype would be highly fragile to parameter changes . The other extreme is that the parameter points are contiguous and clustered together into a subspace . In this scenario , the phenotype would be more robust to parameter variations . However , the level of robustness would depend on the shape of the phenotype subspace . A phenotype subspace that contains a high fraction of points at the “surface” ( i . e . , borders parameter points belonging to another phenotype ) would be less robust than a phenotype where all its parameter points are tightly packed into a subspace with minimal exposure to other phenotypes . To capture these aspects of how parameter points of a particular phenotype are distributed in parameter space , we developed a Connectivity and Shape ( CS ) metric ( Materials and Methods ) . The value of the CS metric is bounded between 0 and 1 and represents the average likelihood that for any point in phenotype subspace , a unit change in any single parameter value maintains the phenotype ( Figure 3A ) . Thus , a CS value of 0 would refer to a highly fragile phenotype whose points in parameter space are “isolated” or surrounded by other phenotypes . In contrast , a CS value of near 1 would refer to a highly robust phenotype for which most of the points in its parameter subspace are surrounded by other points associated with the same phenotype . As a complementary approach to gauge the robustness of a phenotype to parameter changes , we quantified the Mean Path Length ( MPL ) as the average number of unit changes or “jumps” in parameter values needed to start from any point within a phenotype subspace and land on a foreign phenotype ( Figure 3B , Materials and Methods ) [25] . Large values of MPL indicate that many changes in parameter values are needed to change phenotype , signifying a highly robust phenotype . We calculated the MPL and CS metrics for the 26 phenotypes with the highest PSO , including the wild-type phenotype ( Figure 3C ) . In addition , we computed these metrics for two phenotypes ( 1°2°2°1°2°1° and 2°1°2°1°2°1° ) that occupy less parameter space ( ranked 78th and 79th , respectively , in terms of PSO , Figure 2D ) but are well-established experimental outcomes . Among these 28 phenotypes , our calculations show a high correlation between MPL and CS , suggesting that these two metrics are equivalent ways to gauge the robustness of a phenotype to parameter variations . The model predicts seven phenotypes with CS and MPL values greater than that of wild type . All seven are experimentally observed in C . elegans , suggesting that robustness , as quantified by these metrics , may be an important determinant of experimental realizability . Meanwhile , there are 20 phenotypes with CS/MPL metrics lower than the wild type . Among these 20 , ten have been observed in C . elegans genetics experiments , while the remaining 10 are the aforementioned novel phenotypes that have not been observed in C . elegans . Notably , the CS and MPL values of some of these novel phenotypes ( e . g . , 2°2°2°3°2°2° , 3°2°2°3°2°2° , and 3°2°3°1°3°2° ) falls within the range of experimentally observed counterparts , suggesting that these novel phenotypes may be the most realizable experimentally upon performing the correct manipulations in the LIN-3/MAP kinase and the LIN-12 pathways . Having predicted novel phenotypes and the experimental realizability of these outcomes , a key question is how does one render such phenotypes experimentally ? The classical computational approach is to choose reference parameter values for the wild-type phenotype and then to test the effect of specific parameter perturbations . The choice of parameter perturbation is motivated typically by a corresponding mutation that has been performed experimentally with the goal of determining whether the predicted phenotype matches the experimental outcome . The pitfall , however , is that suitable reference parameter values for the in vivo biochemistry of signaling pathways in live worms are unknown . Furthermore , worms are not quantitative clones , and each worm is likely to differ in its parametric settings . Finally , the execution of a particular experimental perturbation is unlikely to be realized in the same quantitative manner in each worm in every trial . Based on these considerations , we take a different approach that is enabled by the phase diagram of phenotypes that we have computed for this system . Using this phase diagram , we determine all possible single-parameter changes ( i . e . , single mutations ) that successfully transition the wild-type phenotype into a mutant phenotype of interest . The fraction of these successful single-parameter changes that is associated with a particular parameter reveals the relative efficacy with which that parameter perturbation “transitions” the wild-type phenotype into the mutant outcome ( Figure 4A and Materials and Methods ) . In this manner , these computations yield a transition probability that an increase ( or decrease ) in each parameter will shift the phenotype from wild type to a mutant pattern . Parameter changes with a higher transition probability have a greater likelihood of generating the desired mutant phenotype . Thus , this approach is the computational equivalent of a random genetic screen that evaluates all possible mutations to determine the most effective ones that lead to the mutant phenotype of interest . To test initially this approach , we applied it to mutant phenotypes that have been well established by genetics experiments in C . elegans . We first predicted the best single-parameter changes needed to transform the wild-type organism into a vulvaless mutant . Vulvaless phenotypes have been observed in genetics experiments and occur when all vulval precursor cells acquire the 3° fate [2] , [24] , [26] . Our model predicts that the best way to render the 3°3°3°3°3°3° phenotype is by decreasing the level of inductive signaling ( Figure 4B ) . This prediction is consistent with experiments in which anchor cell ablation yields the uninduced all-3° fate pattern [27] . In the other extreme of phenotypes , mutant worms with multiple vulvae have been observed when the inductive signaling pathway is hyperactivated [28]–[30] . In these mutants , the vulval precursor cells acquire an intriguing alternating pattern of 2°1°2°1°2°1° where each 1° cell produces an invagination [31] . Consistent with this experimental observation , the model predicts an increase in inductive signal as one of the most prominent ways to yield this alternating phenotype ( Figure 4C ) . In addition , because all possible single mutations are evaluated , our model analysis predicts additional “equivalent mutations” that would render the same 2°1°2°1°2°1° phenotypic outcome ( Figure 4C ) . One of these equivalent mutations is to flatten the gradient in soluble inductive factor ( Figure 4C ) . This particular prediction is remarkably consistent with what has been recently uncovered about the most classical experimental mutation to yield this phenotype . The loss of lin-15 has been shown to cause the secretion of LIN-3 from the surrounding cells , an event that would ablate the gradient [32] . A second equivalent mutation predicted by the model is an increase in the threshold of lateral signaling needed to inhibit the MAP kinase pathway ( κL ) . This prediction for generating a well-established phenotype through a non-canonical perturbation is testable experimentally by decreasing the binding affinity of the lateral signaling transcription complex ( LAG-1:LIN-12-cyto ) to LBS elements in the cis-regulatory regions of the genes that negatively regulate inductive signaling ( ark-1 , lip-1 , lst-1 , 2 , 3 , 4 ) [33] . This mutation would require greater lateral signaling to inhibit the inductive MAP kinase pathway and would be an indirect way to inflate the inductive signaling activity , conceptually consistent with the direct hyperactivation of the inductive signaling pathway . An intriguing feature of mutants , such as lin-15 ( lf ) [24] , [31] and let-60 ( gf ) [34] , is that the observed multicellular pattern is variable . In addition to 2°1°2°1°2°1° , the other prominent outcome is 1°2°2°1°2°1° . There are several possible sources of variability [5] . The quantitative levels and interactions of signaling molecules may differ among wild-type organisms in which the mutation is performed; thus , their response to a specific perturbation may produce different outcomes . Alternatively , even if two organisms were “quantitative clones , ” the magnitude of a perturbation being introduced by the mutation may vary; for example , the amount of RNAi delivered may be different . Finally , even if the perturbation and the wild-type organisms were exactly the same , the execution of the molecular network may deviate due to stochastic effects . Regardless of the source of variability , the key question we focused on is why this variability would produce these two particular outcomes and not others . We hypothesized that in the parameter space , variable mutant phenotypes may lie in the same general direction from the wild-type phenotype . That is , because the starting point , the extent of perturbation and the execution of a perturbation may differ ( Figure 4A ) , the target points in parameter space on which these perturbations land will vary but lie within a common vicinity . To test this hypothesis , we determined what other phenotypes would be predicted by the model upon increasing the inductive signal ( Figure 5A ) or flattening the gradient ( Figure S3 ) . Indeed , the 1°2°2°1°2°1° phenotype is predicted to occur in response to both perturbations , revealing that the variable mutant phenotypes lie in the same direction in parameter space from the wild-type phenotype . Furthermore , our model predicts that converting the wild-type phenotype to either the 2°1°2°1°2°1° or 1°2°2°1°2°1° phenotypes would require approximately the same amount of increase in inductive signal ( Figure 5B ) . These predictions confirm the hypothesis that these two phenotypes may co-occur because these outcomes exist at similar positions relative to the wild-type phenotype in the multidimensional parameter space . An apparent conundrum in our model predictions is that when inductive signal is increased , the number of predicted phenotypes is far greater than that observed experimentally in C . elegans ( Figure 5A ) . In fact , similar calculations show that phenotypes other than 3°3°3°3°3°3° are possible when the level of inductive signal is decreased ( Figure 5A ) . Why then is the remarkably rich set of predicted phenotypes vastly under sampled in experiments with C . elegans ? One possibility is that our model predicts phenotypes that may occur when the inductive signal is tuned to intermediate levels; such phenotypes may not be sampled by classical genetics experiments that typically involve knock-out or strong overexpression strategies . Another hypothesis is that our model predicts phenotypes that arise not only in C . elegans , but also in several closely related species . Several members of the Caenorhabditis genus undertake a similar step in vulval development where precursor cells commit to a 3°3°2°1°2°3° wild type pattern [22] , [35] , [36] . Compelling recent experiments have revealed that tuning the level of inductive signal produces distinct species-specific mutants even though the starting wild-type phenotype is the same ( Figure 6A ) [22] . Since vulval development in all of these species involves the same regulatory “parts” ( EGF and Notch signaling ) , these experimental results have raised the intriguing hypothesis that a common regulatory network has quantitatively diversified , so that the network still produces the wild-type phenotype , but when quantitatively perturbed , each species has access to unique phenotypes ( Figure 6B ) . Experimentally testing this hypothesis of quantitative diversification would involve uncovering the regulatory network driving vulval development in each member of the Caenorhabditis genus and proving that the network architecture is indeed the same . This approach raises practical hurdles of performing numerous genetics experiments across multiple species . A deeper challenge is that it is difficult to prove unequivocally that the regulatory network is the same between two species , since one cannot rule out the existence of an undiscovered mechanism . On the other hand , a modeling framework can be particularly effective in testing the quantitative diversification hypothesis . A model can directly test whether the proposed vulval regulatory network that has been inferred from studies in C . elegans is capable of rendering the breadth of phenotypes observed across multiple species solely through quantitative changes in regulatory mechanisms . To conduct this analysis , we compared our predicted phenotypes ( Figure 5A ) to the experimentally observed phenotypes in the three major members of the Caenorhabditis genus , C . elegans , C . briggsae and C . remanei ( Figure 6A ) . We find that 8 of the 9 experimentally observed phenotypes across the three species are captured by our model predictions . These results demonstrate unequivocally that a common vulval developmental network is capable of producing a significant fraction of the phenotypic diversity observed in the three major members of the Caenorhabditis genus . Thus , the model provides new and strong support for quantitative diversification of a common vulval developmental network during the evolution of the Caenorhabditis genus . Where the model fails also provides intriguing insight . Our model does not predict the 3°2°2°2°3° phenotype that occurs when inductive signal is decreased moderately in C . briggsae . C . briggsae is phylogenetically closer to C . remanei than to C . elegans [22] , [36] , suggesting that quantitative diversification hypothesis may fail to explain fully how C . briggsae and C . elegans vulval regulatory networks have diverged . In addition , the model predicts several phenotypes that are not found in C . elegans , C . briggsae and C . remanei ( Figure 5A ) . These additional phenotypes may occur in other members of the Caenorhabditis genus . The seminal dataset collected by Felix in fact spans eight additional species . We are currently developing algorithms for systematically clustering and comparing model-predicted phenotypes to this larger experimental dataset . Meanwhile , an important feature of the experimental data gathered by Felix is that species-specific phenotypes emerge only when the inductive signal is tuned to a certain quantitative level ( Figure 6B ) . The 3°3°3°3°3° ( Class A ) and 1°1°1°1°1° ( Class D ) phenotypes are observed only when inductive signal is strongly decreased or increased , respectively; meanwhile Class B ( 3°2°3°2°3° , 3°2°2°2°3° and 3°3°1°3°3° ) and Class C ( 1°2°1°2°1° , 2°2°1°2°1° , 2°2°1°2°2° and 2°1°1°1°2° ) phenotypes occur upon moderate decrease and increase in inductive signal , respectively . Thus , a more rigorous test of quantitative diversification is not only to prove that a common regulatory network can render the breadth of experimentally-observed phenotypes , but also to demonstrate that the predicted phenotypes occur only when the network is tuned in the appropriate quantitative manner . To undertake this more rigorous test of quantitative diversification , we determined the amount of change in inductive signal needed to render the predicted phenotypes . The predicted quantitative hierarchy of phenotypes ( Figure 5B ) directly matches experimental observations ( Figure 6B ) , providing stronger evidence to support the quantitative diversification hypothesis . The model predictions directly validate the hypothesis that the parameter space associated with the wild-type phenotype actually contains several subspaces , each representing different species . A key question is which subspace of parameter values corresponds to each species ( Figure 6A ) . The answer to this question would reveal how the quantitative settings of this developmental network have evolved during the emergence of the Caenorhabditis genus . To address this question , we analyzed more closely the layout of phenotypes in the multidimensional parameter space . We know that each species produces different phenotypes when the level of inductive signal is changed ( Figure 6B ) [22] . For example , C . elegans transitions from the phenotype 3°2°1°2°3° ( WT ) to 3°3°1°3°3° when inductive signal is reduced moderately; meanwhile , C . remanei forms 3°2°3°2°3° upon intermediate reductions in inductive signal . In both species , a strong reduction in inductive signal produces 3°3°3°3°3° . Therefore , by identifying the subset of wild-type parameter values that produce a WT→3°3°1°3°3°→3°3°3°3°3° transition versus WT→3°2°3°2°3°→3°3°3°3°3°3° transition upon reducing inductive signal , we isolated the C . elegans and C . remanei parameter subspaces ( Materials and Methods ) . Similarly , C . briggsae forms patterns with adjacent 1° fates upon mild increase of inductive morphogen signal , while C . elegans requires a strong increase in morphogen activity to render such outcomes . Therefore , by distinguishing between WT→1°2°1°2°1°→1°1°1°1°1° transitions and WT→2°1°1°1°2°→1°1°1°1°1° transitions , we identified the subset of wild-type parameter values that correspond to C . elegans and C . briggsae subspaces . We find that C . elegans represents 41 . 01±7 . 90% , C . briggsae represents 3 . 71±1 . 95% , and C . remanei represents 41 . 31±8 . 20% of all wild-type space points . The remaining 13 . 97±2 . 25% of wild-type space points represents transition patterns that are inconsistent with experimental results for these three species . Having identified the sub-region of wild-type parameter space belonging to C . elegans , C . briggsae and C . remanei , we determined how the parameters differ among these species ( Figures 7A and 7B ) . The model identifies two potential groups of parameters . The values of the first group may be higher or lower in C . elegans relative to C . remanei ( ΔI , χ , λ , θ and κL ) or C . briggsae ( λ , φ and κM ) . In contrast , the model predicts that a second group of parameters has changed in a biased manner , either selectively increasing or decreasing , during the evolution of C . elegans , C . remanei , and C . briggsae . The value of φ is higher in C . remanei than in C . elegans , indicating that inductive signaling produces a stronger lateral signal in C . remanei ( Figure 7A ) . Furthermore , the threshold of inductive signaling ( κM ) needed to trigger the lateral signal is lower in C . remanei . Taken together , these predictions reveal that the ability to send out lateral signals is far stronger and more sensitive to inductive signaling in C . remanei than in C . elegans . On the other hand , C . briggsae deviates from C . elegans primarily in the ability to receive lateral signals ( θ ) and thereby inhibit inductive signaling ( χ , κL ) . Inductive signaling is predicted to be more sensitive to lateral inhibition in C . elegans than in C . briggsae ( lower χ and higher κL in Figure 7B ) . These results reveal that during evolution , the members of the Caenorhabditis genus have taken remarkably divergent paths in quantitatively modulating a common developmental signaling network . We demonstrate that the underlying quantitative molecular changes can in fact be inferred from experimental observations of phenotypic variability . This inference requires a computational approach , since the underlying molecular signaling network is highly interconnected and its relation to emergent multicellular phenotypes is non-intuitive . Our approach hinges on a mathematical framework for predicting multicellular phenotypes from the underlying signaling network and a broadly applicable computational approach to analyze the phenotypic landscape . With growing interest in quantitative mechanistic models of developmental systems [14] , [16] , [18] , the computational approach described here will likely find broad application in other developmental contexts and offers a systematic approach to mapping the quantitative regulatory changes that have given rise to divergent developmental phenotypes .
In order to explore phenotypes that would result from quantitative variations in network performance , we varied the value of each dimensionless parameter , starting from its central value and expanding in a step-wise fashion by increasing and decreasing its value by ∼3–4 fold . The central values of the dimensionless parameters were determined as described in Supporting Text S1 . In this manner , the parameter space hypervolume was expanded sequentially and contained 38 , 58 , 78 , 98 and ultimately 118 points . Therefore , at its maximum size , the parameter space contained 11 values per parameter ( equally spaced on a log scale ) , spanned 5–6 orders of magnitude for each parameter ( Tables S2 , S3 ) , and represented 118 parameter combinations in total . For each combination of 8 model parameter values , we computed the fate pattern . Importantly , the fate of each cell i is determined by whether the amounts of MAP kinase and lateral signals in that cell ( mpki and lati ) exceed threshold levels ( and latTh , respectively; see Table S1 ) . Because these threshold values are unknown , and in fact , may be a source of variation in an evolutionary context , we computed fate patterns across a broad range of threshold values . Specifically , and latTh were varied across the ranges and , respectively . The cumulative number of fates predicted across the 8-dimensional parameter space for every combination of threshold values is reported in Figure 2C . To quantify the PSO for each phenotype , we determined the number of parameter points associated with each phenotype at every combination of threshold values . This total level of occurrence of each phenotype was divided by the total number of parameter points to yield the fraction of parameter space occupied by that particular phenotype . Phenotypes were binned according to the fraction of parameter space occupied in unit log10 bins ( i . e . , 1 to 0 . 1 , 0 . 1 to 0 . 01 , etc ) . The number of distinct phenotypes in each bin is plotted on the y-axis in Figure 2D . The distribution of parameter space occupancy was then fit to a log-normal probability distribution . There are 19 phenotypes two standard deviations below the mean ( Table S4 ) and 34 phenotypes two standard deviations above the mean ( Table S5 ) . Each point in the 8-dimensional parameter space maps to a phenotype ( Figure 2B ) . We refer to the collection of points in the parameter space that are associated with a particular phenotype as the phenotype subspace . To quantify the CS value for each phenotype , we distinguished between isolated , edge , and interior points in the phenotype subspace . Isolated points are those points for which unit jumps along both ( increase and decrease ) directions of every parameter axis lead to points associated with another phenotype . In the other extreme , there are interior points for which unit jumps in both directions along every parameter axis reach points that still belongs to the same phenotype . Finally , between these possibilities are edge points: a unit jump in at least one direction along at least one parameter axis leads to another phenotype . To calculate the CS metric for a phenotype , we assign each point in the phenotype subspace a score equal to the number of neighboring points that belong to the same phenotype . This score ranges between 0 ( for isolated points ) and 16 ( for interior points ) . We add the scores of each point in the phenotype subspace and normalize this total by the maximum possible score for the phenotype space , accounting for edge effects due to finite parameter domains . This normalized score is the CS value plotted in Figure 3C . A complementary approach to gauge robustness is to quantify how easy it is to drift out of the phenotype subspace by computing the MPL of escape from the phenotype subspace . We choose randomly a point in the subspace and then make unit jumps along a randomly selected parameter axis and direction . We record the number of jumps taken before exiting the phenotype . This process is repeated until the running average number of jumps stabilizes . We conduct 10 such drift trial reseeding the random number generator between trials . The mean path length is the average over these 10 trials . Importantly , the 8-dimensional phenotype phase diagram will be sensitive to the threshold values of MAPK ( ) and lateral ( latTh ) signals . Recall that these thresholds determine how fates are assigned ( Table S1 ) . Hence , we computed the MPL and CS metrics across 25 different threshold combinations spanning the following ranges:Figure 3C reports the average and standard error across these 25 threshold combinations . Each phenotype , including the wild type , occupies a subspace within the 8-dimensional parameter space ( Figure 2B ) . This phase diagram of phenotypes was analyzed to address the following question: given a choice of 8 single mutations ( i . e . , 8 parameter perturbations ) , which single-parameter change ( i . e . , single mutation ) would be most likely to promote a transition from wild-type ( W ) to a mutant ( M ) phenotype ? To address this question , we rank ordered the parameters according to their relative transition probabilities ( Figures 4B and 4C ) , computed as described below . The same transition probability metric is computed to quantify the single-parameter differences that distinguish C . elegans from closely related species ( Figures 7A and 7B ) . For this analysis , “transitions” between parameter spaces associated with C . elegans and another species ( C . briggsae or C . remanei ) were considered . For the purpose of this discussion , let Pk denote each dimensionless parameter where k = 1 to 8 . Let i denote a point in the W parameter space , and j denote a point in the M-parameter space ( Figure 4A ) . By scanning through all ( i , j ) pairs , we determined the total number that differ only by a single parameter value . These pairs represent the cases where a single-parameter change can cause a W→M phenotype transition . Among this total number of single-mutation pairs , we determined the fraction of phenotype transitions that are attributable to an increase in a particular parameter Pk . This fraction is the transition probability of W→M phenotype transition by increasing Pk . The same calculation was conducted for quantifying the transition probability due to a decrease in Pk . To determine the robustness of the transition probability to variations in the fate-determining thresholds , we computed the transition probability for 25 different threshold combinations presented above . Hence , the y-axes of Figures 4B , 4C , 7A , and 7B report the mean transition probability computed over all these 25 threshold combinations , and the error bar denotes the standard deviation . Starting from the wild-type phenotype , we determined all the mutant phenotypes that may be rendered solely by increasing ( or decreasing ) the inductive signal . Since some mutant phenotypes are more prevalent than others , we quantified the likelihood that an increase ( or decrease ) in inductive signal would produce each mutant ( M ) . To quantify this likelihood of phenotype occurrence , we first tallied the total number of ways that a change in inductive signal ( I ) would abolish the wild-type ( W ) phenotype . Among this total , we quantified the fraction that shifted W to a specific mutant M upon an increase ( or decrease ) in I . This fraction represents the likelihood of producing M phenotype by an increase ( or decrease ) in inductive signal ( I ) . Phenotype assignments must be sensitive to fate-determining threshold values of MAPK and lateral signals ( Table S1 ) . To quantify the robustness of the likelihood of phenotype occurrence to threshold variations , we performed the calculation for 25 different threshold combinations ( as described above ) . The mean of the likelihood of phenotype occurrence is reported in Figure 5A and Figure S2A , and the error bars denote the standard deviation . Figure 5A shows the mutant phenotypes with the greatest likelihood of phenotype occurrence upon an increase ( empty ) or decrease ( filled ) in inductive signal . The more complete set of phenotypes , including the ones that occur less frequently , are shown in Figure S2A . Similar calculations were performed to determine the phenotype diversity due to changes in gradient steepness . Figure S3 shows the mutant phenotypes with greatest likelihood of phenotype occurrence upon an increase ( empty ) and decrease ( filled ) in gradient steepness . Note the occurrence of 1°2°2°1°2°1° and 2°1°2°1°2°1° phenotypes in both Figure 5A and Figure S3 . In addition to the likelihood of generating a particular mutant phenotype , it is also important to gauge the amount of change in inductive signal needed to render each mutant . Some mutant phenotypes may require only small changes , while others may require substantial perturbations . Therefore , we quantified the fold change in I needed to produce a specific mutant phenotype ( M ) . For every increase ( or decrease ) in I that produced phenotype M , we kept track of the associated magnitude of change in I . The geometric mean of these magnitudes was computed to give the fold change in I . As with other calculations , we examined the robustness of this quantity to variations in fate-determining thresholds . The mean fold change in I across a broad range of threshold settings is reported in Figure 5B and Figure S2B , and the error bars represent the standard deviation . A key experimental observation is that changes in inductive signal produce species-specific phenotypes [22] . Figure S4 highlights the progression of phenotypes observed in C . elegans , C . briggsae , and C . remanei along the inductive signal axis . We developed a computational approach to analyze how these experimental phenotypes are arranged in our predicted phase diagram of phenotypes with the goal of identifying the regions within the wild-type subspace that belongs to each species . First , we designated each phenotype with a letter code ( Figure S4 ) , so that a string of characters or a word may be used to represent the phenotype progression of each species . Phenotypes that are not described in Figure S4 were designated ‘O’ . For example , following the lines for C . elegans in Figure S4 , one word is APWRD . Using this nomenclature , we identified the words that are consistent with the fate progression observed experimentally in C . elegans , C . briggsae , and C . remanei ( Table S6 ) . Next , we determined the word associated with every predicted point in the wild-type subspace . To construct the word , we varied the value of I from its minimum to maximum while holding all other parameter values constant . As the I-axis was traversed , we recorded each phenotype with its character designation , thereby yielding a 11-character word ( 11 characters because of the discretization of the I-axis ) . The length of these words was then condensed by eliminating adjacent repeats of a character . For example , APPPOWOSSDD would become APOWOSD ( Figure S5 ) . Since ‘O’ phenotypes include cases where VPCs are designated as ‘m’ fate ( a fate whose experimental equivalent remains to be elucidated ) , we removed ‘O’ from the predicted words . In the example , APOWOSD would become APWSD . Thus , at the end of this step , every point in the wild-type parameter is associated with a word that characterizes how the phenotype would change when I is increased or decreased . Finally , we compared the predicted words associated with each point in wild-type parameter space with the experimentally observed phenotype progressions/words of C . elegans , C . briggsae , and C . remanei . In this manner , we identified the regions within the wild-type parameter space associated with each species . | The diversity of metazoan life forms that we experience today arose as multicellular systems continually sampled new phenotypes that withstood ever changing selective pressures . This phenotypic diversification is driven by variations in the underlying regulatory network that instructs cells to form multicellular patterns and structures . Here , we computationally construct the phenotypic diversity that may be accessible through quantitative tuning of the regulatory network that drives multicellular patterning during C . elegans vulval development . We show that significant phenotypic diversity may be sampled through quantitative variations without overhauling the core regulatory network architecture . Furthermore , by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species , we systematically deduce the quantitative molecular changes that may have transpired during the evolution of the Caenorhabditis genus . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"developmental",
"biology/developmental",
"evolution",
"computational",
"biology/signaling",
"networks",
"computational",
"biology/systems",
"biology"
]
| 2009 | Predicting Phenotypic Diversity and the Underlying Quantitative
Molecular Transitions |
Toxoplasma gondii is found worldwide , but distribution of its genotypes as well as clinical expression of human toxoplasmosis varies across the continents . Several studies in Europe , North America and South America argued for a role of genotypes in the clinical expression of human toxoplasmosis . Genetic data concerning T . gondii isolates from Africa are scarce and not sufficient to investigate the population structure , a fundamental analysis for a better understanding of distribution , circulation , and transmission . Seropositive animals originating from urban and rural areas in Gabon were analyzed for T . gondii isolation and genotyping . Sixty-eight isolates , including one mixed infection ( 69 strains ) , were obtained by bioassay in mice . Genotyping was performed using length polymorphism of 13 microsatellite markers located on 10 different chromosomes . Results were analyzed in terms of population structure by Bayesian statistical modeling , Neighbor-joining trees reconstruction based on genetic distances , FST and linkage disequilibrium . A moderate genetic diversity was detected . Three haplogroups and one single genotype clustered 27 genotypes . The majority of strains belonged to one haplogroup corresponding to the worldwide Type III . The remaining strains were distributed into two haplogroups ( Africa 1 and 3 ) and one single genotype . Mouse virulence at isolation was significantly different between haplogroups . Africa 1 haplogroup was the most virulent . Africa 1 and 3 haplogroups were proposed as being new major haplogroups of T . gondii circulating in Africa . A possible link with strains circulating in South and Central America is discussed . Analysis of population structure demonstrated a local spread within a rural area and strain circulation between the main cities of the country . This circulation , favored by human activity could lead to genetic exchanges . For the first time , key epidemiological questions were addressed for the West African T . gondii population , using the high discriminatory power of microsatellite markers , thus creating a basis for further epidemiological and clinical investigations .
Toxoplasma gondii is a worldwide haploid protozoan parasite , and distribution of its genotypes varies across the continents ( e . g . [1] ) . In Europe and the USA , T . gondii has a low genetic diversity with three main lineages , Type I , II and III , based on clonal population structure and virulence in mice [2] , [3] . In tropical regions of South America , T . gondii strains are highly divergent from those of Europe or North America and display a high degree of genetic diversity [4] , [5] , [6] , [7] . Although Type II isolates have been found in Chile and Brazil [8] , [9] , they seems very rare elsewhere in South America [2] , [10] . Genetically distinct isolates are found in different regions of South America [11] . Common clonal lineages , different from the three classical Types , may circulate on this continent [5] with some atypical genotypes highly pathogenic to humans [6] . For example , a high frequency of ocular toxoplasmosis in some areas of Brazil [12] , as well severe cases of acquired toxoplasmosis in otherwise healthy adults have been reported [4] , [13] . In contrast to Europe and the Americas , the genetic diversity and population structure of T . gondii from Africa , where limited data are available , are still controversial . Two recent genotyping studies based on T . gondii strains isolated from chickens from diverse African countries [14] , [15] have suggested that like in Europe and in the USA , the same three main lineages predominate in Africa with one strain considered to be a recombinant between Type II and III strains [16] . Nonetheless , non classical genotypes of the parasite , called Africa 1 and Africa 2 , have been isolated from immunocompromised patients with toxoplasmosis acquired in Western and Central Africa [17] . Because these genotypes were also repeatedly recovered in patients from different African countries they were proposed as common clonal lineages in Africa . It is clear that issue of the population structure of T . gondii in Africa is far from being resolved . As in many African countries , Gabon has a contrasted environment with remote rural areas and urban centers which permitted analysis of genotype circulation in different biotopes . Microsatellites , as rapidly evolving neutral markers , are excellent tools for differentiating among strains and analysing population structure . In the present paper , we genotyped 69 T . gondii strains from domestic animals in Gabon using for the first time 13 microsatellite ( MS ) markers [17] , [18] , [19] , [20] to precisely identify the strains , study the Gabonese population genetic structure and make comparison with reference strains and isolates from different continents . Haplogroups associated with Africa are described and correlated to mouse-virulence . Finally , we discuss the possible relationships between human pathogenicity , T . gondii genetic diversity , and population structure on the African continent .
All procedures carried out on animals were in agreement with ethical rules . All experimental procedures were conducted according to European guidelines for animal care ( “Journal Officiel des Communautés Européennes” , L358 , December 18 , 1986 ) after reviewed by the Ethics Committee Ile de France Sud ( Registration number: 07-004 ) . T . gondii isolated from animals originating from eight different areas in Gabon ( Figure S1 ) between February 2007 , and December 2007 , were analyzed . Samples were collected from the households of four main areas: Libreville ( latitude: 0° 23′ North , longitude: 9° 27′ East ) , Franceville ( latitude: 1° 37′ South , longitude: 13° 34′ East ) , Makokou ( latitude: 0° 33′ North , longitude: 12° 50′ East ) , and Dienga ( latitude: 1° 52′ South , longitude: 12° 43′ East ) , a small rural village with a high prevalence of T . gondii infection [21] . Contrary to Dienga , the three localities Libreville , Franceville and Makokou are considered as urban environments . Occasionally , samples were obtained from Bakoumba ( latitude: 1° 42′ South , longitude: 12° 53′ East ) , La Lopé ( latitude: 0° 05′South , longitude: 11° 36′ East ) , Léconi ( latitude: 1° 35′South , longitude: 14° 15′ East ) , and around Mougoundou , a Congolese village near the border with Gabon , 10 km south of Dienga , ( latitude: 1° 57′ South , longitude: 12° 39′ East ) . These areas were located 10 to 570 km apart from each other . For a seroprevalence study , 425 animals were screened for T . gondii antibodies at 1∶20 , 1∶40 , 1∶400 and 1∶800 dilutions using a modified agglutination test ( MAT ) technique . A total of 267 domestic animals ( >1-year old ) had positive T . gondii antibody titres >1∶20 . According to the availability of animals at each site and for homogeneity of sampling in different locations , 72 seropositive animals were selected for bioassay in mice . For Dienga , 19 samples were taken from free-range chickens , and 19 from other domestic animals to give an overview of the strains present in the village: 12 goats , six sheep , and one domestic cat . Thirty-four free-range chickens were obtained in the seven other geographically distant locations: 17 were collected in Libreville , six in Franceville , seven around Makokou , and one in Bakoumba , La Lopé , Léconi , and Mougoundou . The animals were purchased and brought alive to the International Medical Research Centre of Franceville ( CIRMF ) where they were bled and euthanized . 0 . 5–1 . 5 ml of serum was stored at +4°C until use . Adult Swiss mice ( Mus musculus ) ( Charles River France , L'Arbresle , France ) three to seven weeks of age were used in this study . They were individually housed in level two bio safety facilities at CIRMF . Brain and cardiac muscle tissue from seropositive domestic animals ( 4 . 2–50 g ) were homogenized in 125–250 ml 0 . 9% NaCl containing 0 . 4% trypsin and 40 µg/ml gentamycin and incubated for 90 min at 37°C . The suspensions were filtered through fine mesh gauze , washed three times by centrifugation for 10 min at 433 g . The pellets were then resuspended in 0 . 9% NaCl before inoculation ( 700 µl i . p . ) into mice ( 3–6 per group ) . A 200 µl aliquot from this suspension was also used for DNA extraction and quantification of T . gondii by real-time quantitative PCR assay targeting the 200- to 300-fold repetitive 529 bp DNA fragment ( GenBank accession number AF146527 ) [22] . Inoculated mice were monitored daily for clinical signs of acute toxoplasmosis; i . e . roughcast hairs , ascites , tottering gait , hunched appearance with evidence of early emaciation and dehydration . In case of clinical signs , the presence of tachyzoites was examined in peritoneal exudates by microscopy . Surviving mice were tested for T . gondii antibodies at four weeks by MAT starting at a 1∶20 dilution . All surviving mice were euthanized at four to six weeks post-inoculation . Microscopic examination was performed for the detection of cysts in brain . Depending on the virulence of the isolate , ascites with tachyzoite forms and/or brain tissue suspensions with cyst forms were collected and aliquots ( 200 µl ) for DNA extraction stored at −20°C . Live parasites were cryopreserved in liquid nitrogen with RPMI containing 10% FCS and 10% DMSO . All samples were sent to the T . gondii Biological Resource Centre ( BRC Toxoplasma ) laboratory of Limoges , for genotyping studies . Sixty-eight T . gondii isolates were obtained by bioassay in mice . DNA from ascitic fluids or brain tissue was extracted using the QIAamp DNA MiniKit ( Qiagen , Courtaboeuf , France ) . Reference strains , obtained from BRC Toxoplasma , were studied in parallel with the new isolates: reference strains for Type I ( GT1 , ENT , and B1 ) , Type II ( Me49 and PRU ) , and Type III ( CTG , VEG and NED ) and seven other reference strains originating from Africa [an Africa 2 strain ( CCH002-2004-NIA ) , and an Africa 1 strain ( DPHT ) ] , South America ( TgCkBr93 , TgCkBr59 , TgCkBr40 ) , Caribbean islands ( ENVL-2002-MAC ) , or France ( GPHT ) [4] , [17] , [18] , [23] , [24] , [25] , [26] . Genotyping was performed using the length polymorphism of 13 multilocus MS markers located on 10 different chromosomes ( Table S1 ) , in 2 multiplex PCR assays . The first multiplex assay included 7 MS markers , TUB2 , W35 , TgM-A , B18 , B17 , M33 [17] , [19] and M48 [20] . Six other MS markers were used in the second multiplex PCR assay: AA , N82 , N83 , N60 , N61 [18] , and M102 [20] . We also sequenced the W35 marker region as described elsewhere [4] . This was done because polymorphism of Type II and III strains does not differ by fragment length but by the nature of the tandem repeats ( TC ) 7 ( TG ) 2 for Type II and ( TC ) 6 ( TG ) 3 for Type III [19] . Primers for PCR ( sequences are shown Table S1 ) were synthesized by Applied Biosystems , France . For multiplex PCR assays we used the QIAGEN Multiplex PCR kits ( Qiagen , France ) with 2x QIAGEN Multiplex PCR Master Mix ( final concentration 1x ) , 0 . 04 µM of each primer , 5 . 5 µl distilled water and 4 µl DNA in a total volume of 25 µl . Amplifications were carried out in a GeneAmp PCRSystem 2700 thermalcycler ( Applied Biosystems , France ) : 15 min at 95°C , followed by 40 cycles consisting of 94°C for 30 s , 61°C for 3 min , and 72°C for 60 s . The last extension step was at 60°C for 30 min . Electrophoresis of PCR products was carried out on an ABI Prism 3130xl genetic analyzer ( Applied Biosystems , France ) and data were stored and analyzed with GeneMapper analysis software ( version 4 . 0 , Applied Biosystems , France ) .
Three MS markers showed low allelic polymorphism: one allele for B18 , M33 and N82 , two alleles for TUB2 , TgM-A , B17 and M102 , three for W35 and four for N60 . Higher allelic polymorphism was found for the other MS markers , particularly N61 and AA with nine alleles ( Table S2 ) . Compared to data obtained from other continents , there were no novel variant alleles on the 13 MS markers among these isolates . The mean genetic diversity ( HS ) for the whole Gabonese population was 0 . 29 . HS values were 0 . 12 , 0 . 27 , 0 . 26 and 0 . 27 for the populations of Dienga , Libreville , Franceville and Makokou , respectively . Overall , a total of 27 different genotypes based on 13 MS markers was found in the population of 69 Gabonese animal strains ( Table S2 ) . Fourteen of them differed from another genotype by only one MS marker . Twelve genotypes comprised two or more isolates , while 15 genotypes corresponded to a single isolate . Each genotype was confined to one geographic area , if we considered Dienga and Mougoundou ( located at 10 km apart ) , which shared the same genotype ( #12 ) , as one unique area . In Libreville or Dienga , eight genotypes were found , while four genotypes were found in Franceville or Makokou . The genotypic diversity was 0 . 39 ( 27/69 ) . A mixed infection found in one isolate ( GAB3-2007-GAL-DOM5 ) was identified by the presence of two alleles at six loci: W35 ( 248 and 242 ) , TgM-A ( 205 and 207 ) , M48 ( 227 and 229 ) , N60 ( 147 and 142 ) , N83 ( 131 and 135 ) , and N61 ( 128 and 134 ) ( Table S2 ) . Remarkably , two T . gondii strains , A and B , were identified based on differential virulence in the mouse bioassay . Mice infected with strain “A” produced ascites with tachyzoites , while strain “B” produced only brain tissue cysts . T . gondii genotyping of ascites identified a lone genotype #4 . Genotyping of brain tissue cysts detected mixed genotypes . Knowing the MS profile of genotype #4 , genotype #9 was deduced from the mixed genotypes . Regarding the geographical populations , all pairwise FST values from Dienga , Makokou , Libreville and Franceville , were significant ( p = 0 . 0083 ) with values ranging between 0 . 11 and 0 . 64 . In addition , pairwise FST values for the Dienga population vs the three urban populations ( Makokou , Libreville and Franceville ) were higher ( 0 . 56–0 . 64 ) than pairwise FST values between these three latter populations ( 0 . 11–0 . 19 ) ( Table 2 ) . The two main clusters ( K = 2 ) and subclusters ( K = 3 and K = 5 ) recognized by model-based and distance based analyses were also supported by F-statistics . All FST values were >0 . 22 and significant ( p≤0 . 05 ) indicating strong genetic differentiation between these clusters . Concerning the LD calculations for Libreville and Dienga populations , 9 out of the 45 pairs ( 20% , n = 10 polymorphic loci ) and 12 out of the 36 pairs ( 33% , n = 9 polymorphic loci ) remained in significant linkage disequilibrium , respectively , after sequential Bonferroni correction . This cannot be attributed to close physical linkage between loci , since the 13 loci were distributed among 10 different chromosomes , so that all but three pairs among the 75 involve loci located on different chromosomes ( Table S1 ) . These findings indicated strong linkage at a genome-wide scale . For Type III population from Dienga ( sympatric conditions ) , the LD was calculated . Five out of 10 pairs ( 50% , n = 5 polymorphic loci , all on different chromosomes ) remained in significant linkage disequilibrium , after sequential Bonferroni . The characteristics used to define virulence for each isolate are shown in Table S2 . Inoculum dose ranged from one to 44 600 parasites in the total volume inoculated . The majority of strains belonging to haplogroups Africa 1 and Africa 3 were isolated from tissue samples with higher dose classes ( ≥1000 parasites or between 100 and 1000 parasites ) , than Type III strains ( <100 parasites ) ( Table S2 and 1 ) . A Fisher exact test found that the three groups were significantly different according to dose classes ( p<0 . 0001 ) . Considering these results , all the further analyses were adjusted on dose effect . The single genotype and Type III-like genotypes appeared to be weakly pathogenic , the small sample size was not amenable to statistical analysis and , therefore , these strains were excluded from further analyses . Using the Cox proportional-hazards regression analyses , we assessed the relationships between survival time and the two factors: dose and haplogroup membership of the isolates . The dose factor was slightly significant ( p = 0 . 04 ) in relation with survival time compared to haplogroup membership factor ( p<0 , 001 ) ( Table 3 ) . The proportion of mice killed by Type III isolates ( 8 . 3% at four weeks ) indicated a weakly virulent phenotype , whereas Africa 1 and 3 isolates demonstrated a highly virulent phenotype ( mortality greater than 90% ) ( Table 1 ) . The survival curves showed that mice infected with strains harboring alleles I ( Africa 1 and 3 ) had a shorter median survival time ( respectively 8 days and 14 days ) compared with those infected with Type III ( median survival time not reached ) ( Figure 4 ) . The estimation of the hazard-ratio adjusted for quantity of parasites , showed that isolates from the Africa 1 haplogroup killed mice nearly two times faster than isolates from the Africa 3 haplogroup ( Figure 4 , Table 3 ) . According to logistic regression analysis , the presence of ascites adjusted for dose effect , was significantly associated with haplogroups ( p<0 . 001 ) . Ascites occurred in 60 ( 67 . 4% ) of 89 mice which died before 4 weeks and only for 2 ( 1 . 7% ) of the 116 surviving mice . No interaction between dose and haplogroup was found in both statistical models .
Compared to Europe and the Americas , the population structure of African T . gondii has been underexplored . Previous studies were based on a limited number of isolated African strains and markers used to characterize these strains defined the Type level [14] , [15] , [16] , [17] but not the population structure . The development of MS markers [18] , [20] , that discriminate within these Types , has for the first time enabled us , to reliably address or define important epidemiological issues such as i ) the diversity of strains and groups , ii ) the existence of geographical subpopulations , and iii ) the extent of gene flow throughout Toxoplasma circulation between these subpopulations . The impact of homoplasy with MS markers is well known [40] . In this work it has been minimized by the selection of variable and numerous ( n = 13 ) MS markers located , for most of them , on different chromosomes ( Table S1 ) . Moreover , this effect was shown as being not a matter of concern in population genetics [40] . This study represents the most comprehensive attempt to document within African diversity in T . gondii to date . Nonetheless , some sample sizes remain a limit in population genetic terms , although efforts were made to correct for any confounding effects . Similarly , caution is required given the deviation of T . gondii from the assumptions of most standard population genetic models due to clonality . Our results , with 27 genotypes out of 69 strains , suggest a diverse T . gondii population in the Gabon area . However , the mean genetic and genotypic diversities could be considered as moderate , compared to data sets from other continents which were studied with equivalent MS characterization [4] , [41] . A very high diversity in T . gondii strains was found in the wild environment of the Amazonian rainforest in French Guiana and Surinam [4] , [6] . The mean genetic diversity for Gabonese strains was similar to what has been found for 104 French animal isolates from three regions ( French BRC for T . gondii , personal data ) . This moderate and similar genetic diversity in the two environments ( French and Gabonese animals ) could be explained by a comparable degree of anthropization with domestication of cats ( definitive host ) and intermediate hosts for sampling areas [4] . Allelic polymorphism for each MS marker is comparable to polymorphism observed in other studies using the same markers [1] , [41] . The few differences observed for some loci , as the monomorphic version of N82 marker , may be explained by the diverse geographic areas studied . No novel alleles was found in this African sample population . The Bayesian model for predicting population structure , distance-based analysis methods , and F-statistics analysis resolved partitions among the total sample of 69 isolates . All these tests were concordant and clearly demonstrated the existence of three main haplogroups among strains sampled in Gabon , Type III , Africa 1 and 3 haplogroups . In addition to genetic factors , phenotypic factors provided by virulence analysis ( survival time and presence of ascites ) distinguished these three haplogroups ( see below ) . Except for Type III , the two other haplogroups did not correspond to classical Type I and II , This result contrasted with studies of isolates collected from chickens in several African countries showing a predominance of classical lineages in these areas: mainly Type III and some Type II [15] , and a majority of Types I and II , with just one Type III in Uganda [14] . The most remarkable difference with our study is the predominance of Type II strains in these studies . This may be explained by the geographic origins of samples . Type II was found predominantly in Uganda , a Central-East African country , whereas other studies [15] , [17] , and this one used mainly samples from Western and Middle Africa . The finding of Type III in our Gabonese isolates confirms the widespread distribution of this Type , already described in North and South America , Europe , Africa , and Asia [3] , [15] , [18] , [42] , [43] . But in Gabon , except for two strains from Libreville and Léconi , all the Type III strains came from the small village of Dienga . We might have expected the opposite phenomenon ( Type III in the large cities of the country ) if the global spread was due to recent migration [1] . An explanation may be the limited sampling in urban areas . The location of Type III in a remote rural area of Africa suggests either a recent introduction in this village from an imported animal ( the high LD argued for a local clonal spread of this lineage ) or that Type III is ancestral in Africa . This last hypothesis on the ancestral nature of Type III is in agreement with the hypothesis concerning ROP18 III previously proposed [44] . Velmurugan and colleagues [15] also found genotypes different from the classical Types II and III . Due to different genotyping markers , it is not possible to compare these non-classical genotypes with those described in the present study . In Lindstrom and colleagues [14] , incomplete genotyping for some isolates ( only two to four markers for defining Type I ) could have misidentified non classical isolates or recombinant genotypes . Among the non-classical haplogroups , the Africa 1 was also found in patients originating from other West and Central African countries [17] . It has been collected at different times in various and distant areas , from Senegal to Uganda through Gabon . Another haplogroup described for the first time in this study , Africa 3 was largely distributed in Gabon and represents another major haplogroup in Africa . The Africa 2 haplogroup , described by Ajzenberg and colleagues [17] , was not found in Gabon . The question of endemicity of the African haplogroups described in this study must be addressed . In different papers , other strains ( GPHT , FOU ) genotyped with microsatellite markers as Africa 1 [4] , [17] clustered with one of the Brazilian Type ( BrI ) defined by PCR-RFLP markers [5] or with other Brazilian strains in haplogroup 6 defined sequencing of introns [7] . We confirmed these observations by using these strains and other strains from South or Central America , or from Africa as reference strains in our genotypic divergence tree ( Figure 3 ) . These strains clustered either with Africa 1 or 3 haplogroups , or with one of the Type III-like strains . These findings show that these African and American strains share a common ancestor and support the hypothesis which suggested the possibility of T . gondii migration during the transatlantic slave trade during the 18th and 19th centuries [1] , [7] . The model for dissemination of T . gondii strains proposed by Khan and colleagues [7] does not take into account African continent . The widespread distribution of these major haplogroups , together with the propagation considered as predominantly clonal for T . gondii in a domestic environment [4] , strongly suggest that Africa 1 , and 3 strains may correspond to new major clonal lineages . More sampling across the world would be needed to confirm this hypothesis . Whether these additional haplogroups for Africa represent minor variations of Type I , II , and III , or recombinant strains of these three lineages remained to be determined . A deeper sequencing as performed by Lindström Bontell and colleagues [16] has demonstrated the existence of one natural Type II and III recombinant strain inside the previous Uganda isolates data set . Such a sequencing would be needed for strains of our haplogroups . However , even if sequencing demonstrated such recombination , it would be a successful recombinant , widespread over continents , representing a lineage with enhanced fitness , as shown for Types II , I and III [45] . Type I was described as one major lineage . However , considering the literature on multilocus typing of Toxoplasma strains , it was rarely encountered in nature [46] . Africa 1 strains clustered with Type I in divergence trees ( Figures 2A , 3 ) . Considering the rarity of Type I and the genotypic diversity within Africa 1 , one could evoke the possibility of Type I being a divergent strain from Africa 1 . Regarding the haplogroups Africa 3 as the Africa 2 , the question remains . In our study , we demonstrated a clear relationship between haplogroups and mouse-virulence: isolates of haplogroups with Type I alleles ( Africa 1 and 3 ) were significantly associated with presence of ascites and mortality in infected mice , while Type III isolates were associated with survival . Africa 1 haplogroup was associated with a shorter survival time than Africa 3 haplogroup ( Figure 4 ) . Even if this relationship between haplogroup and virulence at isolation , independent of a dose-effect , was clearly shown using statistical analysis , this should be confirmed by an experimental study in controlled conditions . The higher proportion of elevated doses in inoculums of Africa 1 and 3 haplogroups compared to Type III , independently of host species ( chicken , sheep , goats and cat ) , was found significant , indicating that parasite burden could be higher in Africa 1 and 3 naturally infected animals . Although these data demonstrate different intrinsic properties of the different strains , the expression of this virulence in a given host species is a more complex trait which depends on several host and parasite characteristics [2] . Pathogenicity in humans cannot be deduced from virulence in the mouse model . However , several studies argued for a role of genotypes in the clinical expression of human toxoplasmosis [47] , [48] , [49] , [50] , [51] . Notably , a higher proportion of ocular disease was found in South America , associated with certain non-classical genotypes [12] , [52] . In Africa , the prevalence of toxoplasmosis in uveitis may be high . It has been estimated up to 43% in Sierra Leone [53] . A 100-fold higher incidence of ocular toxoplasmosis was observed in patients born in West Africa compared to patients born in Britain [54] . The similarity demonstrated in this study between genotypes found in Africa and in South or Central America should encourage further studies in Africa associating clinical data and genotype analysis . MS analysis permits population structure study on a large as well as local scale . In our study , considering the large distances between areas ( from 105 to 590 km apart ) and the sampling method , geographic subdivision was expected . The significant genetic differentiation between the populations of Dienga , Libreville , Franceville and Makokou sustains this geographic isolation ( Wahlund effect ) . But isolation by distance cannot explain all genetic differences . Whereas distances between Franceville and Libreville , and Makokou were far more important than the distance between Dienga and Franceville ( Figure S1 ) , strains from Franceville are genetically closer to Libreville and Makokou strains than to Dienga strains ( Figure 5 ) . Some genotype circulations which would lead to gene flow between these urban populations throughout the history was suggested by i ) the higher FST values between Dienga vs urban populations than between the three urban populations , ii ) the clustering of urban populations ( K = 2 ) by STRUCTURE , iii ) their structure similarity ( K = 3 and K = 5 ) , and iv ) more generally , low bootstrap values in distance genetic analysis ( Table 2 , Figures 2 , 4 ) . It may be explained by economic and human exchanges between the three towns . The trade of animals , food and pets , together with rodents , could be a migration opportunity for T . gondii isolates favoring genetic exchanges between isolates of the large cities . Conversely , Dienga is a village with very few trade exchanges with the other locations , which may explain the divergence of this T . gondii population . Intensive anthropization and urbanization may have an impact on the circulation of T . gondii strains in Africa . This detailed population genetics study of T . gondii is an original process concerning T . gondii epidemiology . It demonstrated in an African country the existence of a genetic heterogeneity at a country scale with new major haplogroups and a substantial population structure at a microgeographic scale . The approach used here needs to be applied to strains of T . gondii from other African countries and other continents to ascertain these population observations and compare the possible differences of structuring . The geographical genetic structure inside a same country indicates that further epidemiological and clinical studies should integrate different scales ( country , districts… ) and environment ( urban or rural areas , anthropized or wild environment ) . | Prevalence of human toxoplasmosis in tropical African countries usually exceeds 50% . Its role as a major opportunistic infection of AIDS patients is regularly described . Due to the lack of investigation , congenital infection is certainly underestimated in Africa . Incidence of Toxoplasma ocular disease is higher in Africa and South America than in Europe . Severe cases in immunocompetent patients were described after infection acquired in Amazonia , but nothing is known about such cases in Africa . Several studies argued for a role of genotypes in the clinical expression of human toxoplasmosis , and for a geographical structuration of Toxoplasma across continents . Genetic data concerning isolates from Africa are scarce . Here , apart from the worldwide Type III , we described two main haplogroups , Africa 1 and 3 . We detected genetic exchanges between urban centers favored by trade exchange and transportation . It shows how important human influence is , even in shaping the genetic structure of a zoonotic disease agent . Finding of identical haplogroups in South America suggested that these African and American strains share a common ancestor . As a higher pathogenicity in human of South American genotypes has been described , this similarity of genotypes should encourage further clinical studies with genotype analysis in Africa . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"microbiology/parasitology",
"infectious",
"diseases/protozoal",
"infections",
"ecology/population",
"ecology",
"genetics",
"and",
"genomics/population",
"genetics"
]
| 2010 | Additional Haplogroups of Toxoplasma gondii out of Africa: Population Structure and Mouse-Virulence of Strains from Gabon |
Respiratory syncytial virus ( RSV ) is the major cause of viral lower respiratory tract illness in children . In contrast to the RSV prototypic strain A2 , clinical isolate RSV 2–20 induces airway mucin expression in mice , a clinically relevant phenotype dependent on the fusion ( F ) protein of the RSV strain . Epidermal growth factor receptor ( EGFR ) plays a role in airway mucin expression in other systems; therefore , we hypothesized that the RSV 2–20 F protein stimulates EGFR signaling . Infection of cells with chimeric strains RSV A2-2-20F and A2-2-20GF or over-expression of 2–20 F protein resulted in greater phosphorylation of EGFR than infection with RSV A2 or over-expression of A2 F , respectively . Chemical inhibition of EGFR signaling or knockdown of EGFR resulted in diminished infectivity of RSV A2-2-20F but not RSV A2 . Over-expression of EGFR enhanced the fusion activity of 2–20 F protein in trans . EGFR co-immunoprecipitated most efficiently with RSV F proteins derived from “mucogenic” strains . RSV 2–20 F and EGFR co-localized in H292 cells , and A2-2-20GF-induced MUC5AC expression was ablated by EGFR inhibitors in these cells . Treatment of BALB/c mice with the EGFR inhibitor erlotinib significantly reduced the amount of RSV A2-2-20F-induced airway mucin expression . Our results demonstrate that RSV F interacts with EGFR in a strain-specific manner , EGFR is a co-factor for infection , and EGFR plays a role in RSV-induced mucin expression , suggesting EGFR is a potential target for RSV disease .
Respiratory syncytial virus ( RSV ) is a human pathogen of the Pneumovirus genus within the Paramyxoviridae family . Worldwide , the virus causes over 30 million lower respiratory tract illnesses per year in children and is a leading cause of infant pneumonia mortality [1 , 2] . Despite a substantial clinical burden of disease , there are no available vaccines or RSV-specific therapeutics . A challenge to RSV vaccine and therapy strategies remains elucidation of the unclear relationship between RSV infection and pathogenesis . RSV is an enveloped , non-segmented , negative-strand RNA virus whose genome is approximately 15 . 2 kb in length and encodes 10 genes which are translated into 11 proteins . RSV attachment is mediated through host glycosaminoglycans ( GAGs ) , cellular protein nucleolin , association with cholesterol-rich microdomains , and CX3CR1 [3–9] . Mechanisms surrounding RSV entry remain unclear and other host receptors , co-receptors , and co-factors contributing to infection are likely to be identified . Two envelope proteins mediate RSV infection , the attachment glycoprotein ( G ) and the fusion ( F ) protein . Prior to infection , RSV F exists in a metastable pre-fusion conformation [10 , 11] . RSV F undergoes a series of conformational changes yielding a thermodynamically stable six-helix post-fusion bundle , which drives viral and host membrane fusion [11–13] . RSV G is mucin-like , having extensive N- and O-linked glycosylation , and G is responsible for facilitating RSV attachment through interactions with GAGs and CX3CR1 [4 , 6 , 7 , 9 , 14 , 15] . However , G is not absolutely required for viral entry into immortalized monolayer cells [16–18] . Mechanisms by which F and G mediate host cell entry and their interactions with other host cell targets remain uncertain . Epidermal growth factor receptor ( EGFR ) is a host glycoprotein comprised of an extracellular ligand receptor and intracellular kinase domain . The latter is activated through both Src-dependent phosphorylation and autophosphorylation [19 , 20] . In addition to a wide variety of host ligands including epidermal growth factor ( EGF ) and transforming growth factor alpha ( TGFα ) , several viruses have been identified that employ EGFR binding and activation for viral entry and replication . These pathogens include hepatitis B virus , human cytomegalovirus ( hCMV ) , and Epstein-Barr virus ( EBV ) [21–23] . Previous studies by others evaluating the role of EGFR in RSV infection have shown that RSV activates EGFR in lung epithelial cells [24 , 25] . EGFR activation in these cells promotes a pro-inflammatory response including increased survival of RSV-infected cells and suppression of interferon regulatory factor ( IRF ) 1-dependent CXCL10 production , an important event for recruitment of lymphocytes to infected airway epithelial cells [24 , 25] . Another study using a recombinant virus based on the RSV subgroup A prototypic strain A2 demonstrated that RSV cell entry is largely mediated through endocytotic macropinocytosis promoted by EGFR phosphorylation [26] . Respiratory failure is the critical consequence of RSV disease in children , and overabundant mucus obstruction of the airways contributes to this outcome . Our laboratory previously reported that clinical isolate RSV A2001/2-20 ( 2–20 ) causes more airway necrosis , inflammation , and mucin expression during infection in BALB/cJ mice than the A2 reference strain [27 , 28] . Transfer of the RSV 2–20 F protein into strain A2 recapitulated higher levels of airway mucin expression in mice [28] . These studies demonstrated that the RSV F protein plays a key role in airway epithelium infection and pathogenesis in vivo and suggests that RSV F plays a role in RSV strain-specific phenotypes . EGFR phosphorylation is known to play a role in mucin expression in airway epithelial cells during influenza and rhinovirus infections [29 , 30] . We hypothesized that mucin induction by RSV 2–20 F is mediated by a specific interaction with EGFR . To test this hypothesis , we evaluated the ability of A2 and 2–20 viruses and transiently expressed F proteins to activate EGFR , and we assessed the impact of disrupting these interactions on virus infectivity in vitro and mucin expression in vivo . We demonstrate that RSV 2–20 F protein specifically binds to and activates EGFR , EGFR contributes to RSV-2-20 F infectivity , and EGFR signaling mediates 2–20 F induction of airway mucin expression in mice .
As EGFR phosphorylation/activation mediates mucin expression in response to other respiratory viruses , RSV A2 is known to activate EGFR , and RSV 2–20 F is more “mucogenic” than A2 F in the context of infection , we hypothesized that the 2–20 strain F protein potently activates EGFR . Western blotting was performed to determine levels of total EGFR and phospho-EGFR ( p-EGFR ) after infection of HEp-2 cells with RSV strains A2 and A2-2-20F . We used serum-starved cells because serum is an activator of EGFR signaling in vitro [31 , 32] . A2-2-20F-infected cells had a higher ratio of p-EGFR to EGFR than A2-infected cells at 24 h post-infection ( Fig 1A ) . We performed similar experiments in serum-starved NCI-H292 ( H292 ) cells , and in an earlier time course we found that A2-2-20F infection resulted in a higher p-EGFR/EGFR ratio than A2 infection at 1 , 12 , and 24 hr post-infection ( Fig 1B ) . To define the role of RSV 2–20 F expression alone in EGFR activation , A2 F and 2–20 F expression constructs were transfected into serum-starved HEp-2 cells and blotted for EGFR and p-EGFR levels . There was a significantly higher 4 . 45-fold ratio of p-EGFR to EGFR after 2-20F expression than after A2 F expression ( Fig 1C ) . These data demonstrate that the 2–20 RSV F protein activated EGFR in cells to a greater extent than the A2 strain F protein . We tested whether EGFR can enhance RSV F protein fusion activity . We used a previously established cell-cell fusion assay [28 , 33 , 34] . The dual-split protein ( DSP ) assay is based on co-transfecting an “effector”/cis population of 293T cells with a construct expressing N-terminal domains of a Renilla luciferase and GFP fusion protein ( DSP1-7 ) and an F expression construct in the presence of specific fusion inhibitor . Another population of 293T cells ( “target”/trans ) is transfected with a construct expressing the C-terminal domains of the luciferase-GFP fusion protein ( DSP8-11 ) and , in this case , either equal molar amounts of empty vector ( pcDNA ) or an EGFR expression vector . The fusion inhibitor is washed out , the effector and target cells are mixed , and fusion is quantified by luciferase activity reconstituted by cell content mixing . A2 F had significantly more cell-cell fusion activity than 2-20F in this assay ( Fig 1D ) . Expression of EGFR in trans enhanced 2–20 F activity but not A2 F activity ( Fig 1D ) . Similar to published A2 F and 2–20 F fusion assay experiments [28] , we found no difference , as measured by flow cytometry , between A2 F and 2–20 F surface expression in 293T cells ( S1 Fig ) . To determine whether the boost in RSV 2-20F fusion is specific to a trans F-EGFR interaction , 2-20F was either co-expressed ( in cis ) with EGFR or in trans in the target cells . There was a significant boost to 2-20F fusion when EGFR was expressed in trans but not in cis , suggesting EGFR enhancement of 2–20 F activity does not occur when the proteins are overexpressed in the same cells or membrane ( Fig 1E ) . Co-expression of EGFR with RSV F did not alter F surface expression ( S1 Fig ) . There was no effect on 2–20 F activity by overexpression of signaling lymphocyte activation molecules ( SLAM ) , a receptor for measles virus ( MeV ) serving as an irrelevant transmembrane protein control . Collectively , the data show EGFR specifically enhanced the fusion activity of RSV strain 2–20 F protein but not A2 F protein . EGFR can function as receptor , co-receptor , or entry co-factor for other viruses , and EGFR depletion in HeLa cells was reported to reduce RSV A2 strain infectivity [26] . We explored the role of EGFR in RSV-2-20F infection . Cells were pre-treated with EGFR tyrosine kinase inhibitors ( AG1478 and PD153035 ) then infected with recombinant RSV A2 , RSV A2-2-20F , or RSV A2-2-20GF . Virus infectivity was measured by flow cytometry of virally expressed mKate2 . NCI-H292 ( H292 ) cells are a human tracheal epithelial cell line known to express mucin genes through activation of the EGFR pathway and known to support RSV replication [35–37] . Treatment of H292 cells with increasing concentrations of EGFR inhibitor AG1478 resulted in a dose-dependent reduction in infectivity of all three virus strains at an MOI of 1 , and the reduction in infection was greater against A2-2-20F and A2-2-20GF than against A2 ( Fig 2A ) . Treatment of H292 cells prior to RSV A2 infection at a higher MOI = 3 with either AG1478 or PD153035 resulted in no change in infection ( Fig 2B ) . In contrast , EGFR inhibition prior to A2-2-20F or A2-2-20GF infection at MOI = 3 reduced infection efficiency ( Fig 2B ) . Treatment in BEAS-2B cells , a human bronchial epithelial cell line , with AG1478 or PD153035 resulted in similar decreases in infectivity of RSV A2-2-20F and A2-2-20GF while having no effect on A2 ( Fig 2B ) . In order to test the effect of EGFR inhibition on RSV infectivity in a model more relevant to RSV biology , we utilized normal human bronchial epithelial cells differentiated at air-liquid interface ( NHBE-ALI ) [38] . We found that pre-treatment of NHBE-ALI cultures with 5 μM AG1478 was not toxic , as previously described , whereas PD153035 was toxic to NHBE-ALI [39] . Pre-treatment of NHBE-ALI cultures with AG1478 resulted in significant reduction in infectivity of RSV A2-2-20F and A2-2-20GF , and no significant effect on A2 compared to vehicle ( Fig 2C ) . Taken together , EGFR signaling mediated infection by RSV expressing the 2–20 F protein . To determine the role of EGFR expression in RSV infectivity , BEAS-2B cells were first transduced with lentivirus expressing either EGFR-specific shRNA or scrambled ( control ) shRNA . EGFR shRNA reduced expression of EGFR in BEAS-2B cells by 56% compared to the scrambled shRNA control ( Fig 3A and 3B ) . shRNA knockdown of EGFR in BEAS-2B cells resulted in lower infectivity of A2-2-20F and A2-2-20GF but not A2 ( Fig 3C ) . MeV infection is known to be EGFR-independent; therefore MeV was used as a control for RSV specificity [40] . The knockdown data show EGFR acted as a co-factor contributing to infectivity of RSV expressing the F protein of the clinical isolate 2–20 and suggest RSV F may be interacting with EGFR in a strain-dependent manner . We assessed whether a physical ( direct or indirect ) interaction can occur between overexpressed RSV F and EGFR . 293T cells were transfected with either EGFR or SLAM and RSV F . All proteins were detected at high levels in whole cell lysates ( Fig 4A ) . RSV F was immunoprecipitated ( IP ) well with motavizumab mAb and probed for the presence of co-precipitated EGFR . Motavizumab binds an epitope in RSV F that is conserved among RSV strains and between the prefusion and postfusion conformations of the protein [41 , 42] . EGFR was detected after precipitation of A2F and 2-20F , and there was a 2 . 8-fold higher ratio of EGFR bound to 2-20F than to A2F ( Fig 4B ) . We previously deposited the 2–20 genome sequence to GenBank and have modeled the residue differences between A2 F and 2–20 F [27] . To identify putative EGFR interaction sites for RSV 2–20 F , three sets of mutations were introduced into 2–20 F that changed amino acids to the corresponding A2 residues . Mutation sets were introduced into the F head region ( T63N , E66K , and G76I triple mutant ) , stalk region ( G519V and K524N double mutant ) , and in the cleaved 27 residue peptide ( pep27 ) between the furin cleavage sites ( N124K ) of 2–20 F . Introduction of either the head or the stalk mutation sets resulted in reductions in the efficiency of EGFR-bound to 2–20 F , as detected by IP ( Fig 4B ) . The N124K pep27 mutation did not affect the co-IP efficiency significantly ( Fig 4B ) . Taken together , residues 63/66/76 and residues 519/524 contributed to the co-IP interaction between 2–20 F and EGFR . We evaluated the Co-IP interaction of F proteins from other RSV strains with EGFR . Strain Line 19 is a laboratory strain that that is more pathogenic in mice , including high viral loads , lung IL-13 levels , and airway mucin expression in BALB/c mice , compared to prototypic strains A2 and Long [43 , 44] . RSV strain A2001/3-12 ( 3–12 ) is a clinical strain we previously reported to induce lower airway mucin expression in mice than 2–20 [27] . Similar to 2–20 , overexpression of line19 F resulted in higher efficiency of EGFR co-IP than did overexpression of A2 F , Long F , or 3–12 F ( Fig 4B ) The data correlate strength of co-IP F-EGFR interaction in vitro with in vivo mucogenicity in mice of the strain from which F was derived . We quantified molecular co-localization between EGFR and RSV F at the cell surface . H292 cells were inoculated with either mock , A2 , A2-2-20F , or A2-2-20GF at 4°C , which facilitates attachment but not viral fusion . After one hour and washes , the cells were fixed and stained for RSV F and EGFR then examined by superresolution microscopy at the plasma membrane . There was no significant overlap between EGFR and RSV F puncta in the A2 strain group ( Fig 5A and 5B ) . In contrast , there was significant overlap between RSV F and EGFR puncta on the surface of A2-2-20F- and A2-2-20GF-inoculated cells ( Fig 5A and 5B ) . There were more RSV F puncta per cell in the A2-2-20GF group than the A2-2-20F and A2 groups , consistent with published data that 2–20 G has greater attachment activity than A2 G ( Fig 5A ) [18] . A2-2-20GF attachment also resulted in greater co-localization of F and EGFR than A2-2-20F ( Fig 5A and 5B ) , suggesting the 2–20 G protein also directs RSV localization at the cell surface . Taken together , there was significant co-localization between 2–20 F and EGFR at the cell surface following virus attachment , which was enhanced by 2–20 G . To evaluate whether RSV co-localizes with EGFR in a highly relevant primary airway epithelium model , well-differentiated primary pediatric bronchial epithelial cells ( WD-PBECs ) [45] were analyzed for surface expression of p-EGFR . RSV BT2a induces mucus secretion and goblet cell hyperplasia/metaplasia in WD-PBECs [45] . p-EGFR expression in WD-PBEC cultures was largely focused in the cell membrane on ciliated structures as evidenced by co-localization with beta-tubulin ( Fig 5C ) . WD-PBEC cultures were infected with clinical isolate , BT2a , which has similar kinetics to A2 during infection in HEp-2 cells , but exhibits more cytopathogenesis in WD-PBEC cultures [45] . Many cells infected with clinical isolate BT2a qualitatively co-localized with p-EGFR ( Fig 5D ) . Interestingly , infection of WD-PBECs with BT2a resulted in an apparent increase in surface expression of p-EGFR in RSV-infected cells , but not neighboring non-infected cells ( Fig 5E ) . Collectively , the co-localization data suggest RSV F is closely associated with EGFR at the plasma membrane of H292 and apically in human primary airway epithelial cells . Infection of BALB/cJ mice with RSV A2-2-20F or the parental isolate 2–20 results in airway mucin expression that peaks approximately day 8 post-infection , a time point when infectious virus is not detectable by plaque assay [27 , 28] . We previously demonstrated that 2–20 infects the mouse airway epithelium , detectable by immunofluorescence day one post-infection [27] . We examined whether A2-2-20F also infects the mouse airway epithelium and whether virally expressed proteins can be detected in PAS-positive airways . Mice were mock-infected or infected with A2-2-20F that does expresses mKate2 or A2-2-20F lacking the mKate2-encoding gene . The mKate2 far-red fluorophore has extreme pH stability [46] , therefore we hypothesized it will remain functional after histology processing . When adjacent , serial lung sections were compared , mKate2 signal was evident day 8 post-infection in bronchial epithelium that was also producing mucin ( Fig 6 ) . To control for autofluorescence , lung sections from recombinant A2-2-2F that does not express mKate2 infected and mock-infected mice were also examined . The mKate2 signal was clearly distinguishable from background ( Fig 6 ) , marking cells that were either previously infected and harboring mKate2 or cells actively expressing RSV-encoded gene products . These results correlate A2-2-20F infection with airway mucin expression . Muc5ac is a major inducible and secreted mucin protein in the lung that is up-regulated by EGFR activation and during RSV infection [43 , 47–49] . We first tested whether RSV infection of these cells results in MUC5AC gene expression . In DMSO-treated serum-starved H292 cells , A2-2-20GF infection resulted in higher MUC5AC mRNA levels than mock , A2 , and A2-2-20F infection ( Fig 7A ) . Therefore H292 cells provide an in vitro model of RSV strain-specific induction of mucin expression . Serum-starved H292 cells were treated with EGFR pathway inhibitors PD153035 and AG1478 , mock-infected or infected with RSV strains A2 , A2-2-20F , and A2-2-20GF , and MUC5AC expression was quantified . In the presence of either inhibitor , MUC5AC mRNA fold-change in A2-2-20GF infected cells was ablated . The combination of RSV 2–20 F and 2–20 G was important for MUC5AC induction in this in vitro model , consistent with the efficient attachment function of 2–20 G ( Fig 5A and ref [18] ) , and the EGFR pathway was critical for MUC5AC induction . We tested the role of EGFR signaling in A2-2-20F-induced airway mucin expression . BALB/cJ mice were pre-treated with erlotinib , a specific quinazoline derivative that binds to and inhibits EGFR tyrosine kinase activity , or vehicle suspension , and tested for suppression of EGFR activation in mouse lung homogenates [50] . Erlotinib caused a reduction in the signal of p-EGFR in lungs from both mock-infected and A2-2-20F-infected mice ( Fig 7B ) . Mice pre-treated then treated daily with vehicle or erlotinib were infected with mock or A2-2-20F . Lungs were harvested day 8 post-infection and processed for periodic acid-Schiff ( PAS ) stains of goblet cell hyperplasia/metaplasia ( Supplemental Fig 2 ) , a surrogate of airway for mucin expression . Our lab uses a digital pathology system to morophometrically quantify PAS positivity in all airways involved in lung sections [27 , 28] . Mice treated with erlotinib and infected with A2-2-20F had significantly less airway goblet cell hyperplasia/metaplasia than vehicle-treated , A2-2-20F-infected mice ( Fig 7C and 7D ) . Taken together , these data identify a novel role for EGFR signaling in mediating RSV-induced mucin expression and airway pathology .
RSV disease in infants is associated with airway obstruction , lung inflammation , epithelial cell sloughing , and mucus production [51 , 52] . The relative contributions of sloughed epithelium and mucus production to airway obstruction remain unknown [53 , 54] . Distal airways are thought to have fewer mucin-secreting cells than larger airways , but overabundant mucus production is associated with infant bronchiolitis clinically [53–55] . The clinical isolate strain 2–20 to a degree recapitulates human RSV disease in the BALB/c mouse model of RSV infection [27 , 28 , 56] . The prototypical A2 strain does not cause airway mucin expression in mice , and chimeric RSV strains A2-2-20F is mucogenic like parental 2–20 , implicating the F protein in mucin induction in vivo [28 , 44] . RSV 2–20 has somewhat altered tropism in the mouse because it infects the airway epithelium and alveolar epithelial cells , whereas A2 infects predominantly alveolar epithelial cells [27] . 2–20 and A2-2-20F cause more airway necrosis in mice than A2 [28] . Therefore , infection of the mouse airways correlates with mucin induction in this model . Here , we investigated mechanisms of 2–20 F-induced airway mucin induction . In cultured cells , 2–20 F exhibited greater functional and physical interaction with EGFR than A2 F , and EGFR was able to enhance the fusion activity of the 2–20 F protein . Chemical inhibition of EGFR signaling reduced infectivity of 2–20 F-expressing RSV and ablated mucin induction in vitro and in vivo . We report that RSV F interacts with and activates EGFR and that EGFR contributes to infection in vitro and plays a critical role in RSV-induced mucin expression . Our study shows F and EGFR interact functionally and physically . Expression of the 2–20 F protein potently activated EGFR , as measured by p-EGFR levels in cells . The efficiency of co-IP of EGFR with F depended on strain specificity of the expressed F protein . EGFR had the highest co-IP efficiency with 2–20 and line 19 F proteins , strains previously shown to be mucogenic in mice [27 , 28 , 44] . We mapped the enhanced co-IP efficiency with 2–20 F to two domain differing between 2–20 F and A2 F , residues 63/66/76 and residues 519/524 . Amino acids 63 , 66 , and 76 cluster at the top of the prefusion F trimer . This region overlaps prefusion-specific antigenic site ∅ , and the adjacent residue 67 is important for RSV F prefusion stability [11 , 57] . As we discussed in Stokes et al , residues 519 and 524 in RSV F are membrane-proximal in the stalk , a region implicated in regulating Hendra virus F protein triggering via stabilization of the pre-fusion form [28 , 58] . In our co-IP experiments , we expressed functional F , which for RSV does not require triggering by the attachment protein , so prefusion and postfusion forms were present . We speculate that 63/66/76 and 519/524 regions regulate prefusion stability , which may relate to EGFR co-IP efficiency between F species and mutants . In the DSP fusion assay , EGFR boosted 2–20 activity , so we predict the functional interaction occurs prior to postfusion F formation . Additional studies and reagents will be required to further elucidate F-EGFR molecular interactions . RSV infection was previously shown to activate EGFR . The A2 strain activates EGFR in cells , resulting in delayed apoptosis by ERK activation and production of the pro-inflammatory cytokine IL-8 production [24] . RSV A2 strain was shown to activate EGFR following virus attachment , leading to macropinocytotic endocytosis [26] . In that study , EGFR depletion in HeLa cells by siRNA delivery resulted in reduced infectivity of the A2 strain , whereas we found that EGFR depletion in BEAS-2B cells reduced infectivity of RSV expressing 2–20 F but not of the A2 strain . The discordant findings may be related to the cell line and/or the efficiency of EGFR knockdown . Recently , RSV activation of EGFR , in addition to influenza A ( H1N1 ) and rhinovirus infections , led to suppression of IRF1-dependent CXCL10 production [25] . CXCL10 is expressed in airway epithelial cells , is a ligand of CXCR3 ( a key regulator of leukocyte trafficking ) , and when elevated is associated with obstructive airway diseases [25 , 45 , 59 , 60] . Using RSV 2–20 F-expressing viruses in future studies may shed additional light on entry mechanisms , such as macropinocytosis , and downstream immunopathogenesis such as IL-8 expression , neutrophil recruitment , and CXCL10 expression . The capacity of RSV F to engage EGFR during infection may depend in part on a function of the RSV G protein . We observed an appreciable increase in MUC5AC expression in H292 cells when using strain A2-2-20GF , not A2 or A2-2-20F . In these cells , 2–20 G conferred greater attachment , as measured by superresolution microscopy , consistent with the sequence-based prediction that 2–20 G has more glycosylation sites and our recent published data that 2–20 G has a higher apparent molecular weight than A2 G and confers enhanced cell attachment in vitro [18] . The co-localization quantification in H292 cells revealed that , irrespective of the abundance of RSV F puncta , 2–20 G greatly enhanced signal overlap between F and EGFR , suggesting 2–20 G alters location of RSV on the plasma membrane . Our current working model is that initial 2–20 G interactions with CX3CR1 , GAGs , and/or other factors mediates attachment that likely precedes F-EGFR interaction , EGFR activation , and infection ( Fig 8 ) . Further studies will need to evaluate the role of G in F-EGFR interactions during infection in cells and animal models . In summary , we for the first time identified a host protein that both interacts with the RSV F protein and promotes fusion . EGFR was expressed at the apical surface of differentiated pediatric bronchial epithelial cells , and RSV F and EGFR co-localized in infected cells . Using clinically relevant RSV strains and infection models , we found that EGFR is critical for RSV-induced airway mucin expression and laid the groundwork for defining the molecular interaction between F and EGFR .
All animal procedures were conducted according to the guidelines of the Emory University Institutional Animal Care and Use Committee , under approved protocol number 2001533 . The study was carried out in accordance with recommendations in the Guide for Care and Use of Laboratory Animals of the National Institute of Health , as well as local , state , and federal laws . The media components and origins of HEp-2 , BEAS-2B , 293T , and BSR-T7/5 cells we use are previously described [27 , 28] . NCI-H292 cells were purchased from ATCC ( CRL-1848 ) and propagated in RMPI-1640 supplemented with 10% FBS ( Hyclone , Thermo Scientific ) , 0 . 01 M HEPES , and 25 mM D-glucose . Normal human bronchial epithelial ( NHBE ) cells were obtained from Lonza ( Allendale , NJ ) and differentiated on collagen-coated 24-well transwell supports at air-liquid interface as we described [38] . The human codon bias-optimized RSV A2 F and 2–20 F expression plasmids are described , and the RSV 2-20GF-expression plasmid was generated using the same strategy [28] . EGFR cDNA in a pBABE retroviral vector was transferred into pTriEx-3 using standard restriction enzyme cloning to yield pTriEx-3-EGFR , and the sequence of EGFR was confirmed . A full-length SLAM cDNA was extracted from Vero-SLAM cells and cloned into the pCG expression plasmid . DSP1-7 and DSP8-11 plasmids were provided by Naoyuki Kondo and Zene Matsuda [61] . Lipofectamine 2000 ( Life Technologies ) was used according to the provided protocol for all cell transfections , with the exception that DNA/liposome mixture remained on cells overnight for RSV rescue . The anti-RSV F mAb motavizumab was generously provided by Nancy Ulbrandt ( MedImmune AZ ) . Mouse anti-SLAM was purchased from Abcam ( clone ab2604 ) and mouse anti-vinculin was purchased from Fisher Scientific ( clone VLN01 ) . HRP-conjugated secondary antibodies for immunoblotting were purchased from Jackson Immunoresearch . Compound AG1478 ( LC Laboratories ) was diluted in DMSO . Recombinant human EGR was obtained from Lonza ( Cat #00556827 ) . We previously reported generation of recombinant RSV encoding the far-red fluorescent protein monomeric Katushka-2 ( mKate2 ) in the first gene position [62] . This virus was recovered on BSR-T7/5 cells transfected with pSynkRSV-line19F BAC together with helper plasmids encoding codon-optimized N , P , L , and M2-1 [62] . The F gene of BAC pSynkRSV-line19F , flanked by SacII and SalI sites , was replaced with a synthetic cDNA ( GeneArt , Life Technologies ) encoding either the A2 strain F open reading frame , the 2–20 strain F open reading frame , or the 2–20 strain G and F open reading frames , flanked by non-coding regions identical to those in pSynkRSV-line19F BAC [18] . The recombinant A2-2-20F that does not encode mKate2 ( A2-2-20F –mKate2 ) was previously described [28] . The kRSV-A2 , kRSVA2-2-20F , and kRSVA2-2-20GF viruses were plaque-purified and amplified in HEp-2 cells . Virus stocks used were sequence confirmed for the G and F genes and determined to be Mycoplasma free using the Venor GeM Mycoplasma detection kit ( Sigma-Aldrich ) . RSV clinical isolate BT2a was used for infection of well-differentiated primary pediatric bronchial epithelial cells ( described below ) [45] . Measles virus expressing GFP ( MeV-GFP ) used in this study was previously described [63] . shRNA constructs targeting EGFR ( TRCN0000039635 , TRCN0000039634 , TRCN0000039633 , TRCN0000010329 , TRCN0000121067 ) were purchased from Sigma . The control plasmid pLKO . 1 , which has a scrambled shRNA , was also purchased from Sigma . Two lentivirus helper plasmids , psPAX2 , and pMD2 . VSVG , were kindly provided by Gregory Melikian ( Emory University ) . Lentiviruses for the puromycin pLKO constructs were produced in 293T cells with two helper constructs . 293T cells ( 6 . 5 × 105 ) were transfected with pLKO containing the EGFR shRNA , psPAX2 , and pMD2 . VSVG . As a control , 293T cells were transfected with pLKO . 1 containing a scramble shRNA . Supernatants were harvested at 24 h following transfection . For infection , 30 to 40% confluent BEAS-2B cells were spinoculated with virus-containing supernatant at 4°C , 2900 x g . Following overnight incubation , media containing 1 μg/mL puromycin was added for 48 hours post-infection . A kill curve of puromycin on BEAS-2B cells determined that 1μg/mL puromycin killed 100% of cells . Surviving BEAS-2B cells following puromycin treatment were used for RSV infection and subsequently used for determination of EGFR knockdown efficiency . Flow cytometry analysis was performed to quantify RSV infectivity and levels of EGFR surface expression . Phycoerythrin-EGFR antibody ( sc-101 PE , Santa Cruz ) was used for the detection of EGFR on non-permeabilized 293T or BEAS-2B cells using an LSRII flow cytometer ( BD Biosciences ) . Motavizumab and Alexa 488-anti-human secondary Ab ( H17101 , Life Technologies ) were used for measuring RSV F cell surface expression on non-permeabilized 293T cells . BEAS-2B cells were harvested , fixed , and acquired using a 561nm laser . Isotype control antibody ( PE-IgG ) was used as a negative control . For measuring of RSV and MeV infectivity , cells were harvested 24 hr post-infection and acquired using an LSRII , by detecting the mKate2 and GFP signals , respectively . Data were analyzed using FlowJo software ( TreeStar , Ashland , OR ) . 293T cells transfected with equal molar amounts of DNA were used for each group , and plasmid quality was checked via agarose gel and spectrophotometer quantitation . 24 h after treatment , cells were harvested . Cell pellets were thawed on ice and lysed with RIPA buffer ( Sigma ) plus protease inhibitors ( Thermo Scientific ) . Protein concentrations were determined using Bradford Reagent ( Sigma ) . 50% ( v/v ) protein-A magnetic beads ( Cell Signal ) were conjugated with motavizumab by mixing 50 μL of bead slurry with 500 μL ice cold PBS and 0 . 75 μg of motavizumab ( 1 . 75 mg/mL ) in a microcentrifuge tube rotating end-over-end for 4 h at 4°C . Excess antibody was washed from the beads by pelleting , aspirating the antibody suspension , and washing 3 times with 1 mL of RIPA buffer . 120 μL of cell lysate was pre-cleared with 30 μL of non-conjugated bead slurry . 100 μL of cleared lysate was then added to the conjugated bead pellet with 10 μL of 10% BSA ( Sigma ) . The lysate/bead slurry was allowed to mix overnight , rotating end over end at 4°C . Beads were removed using a magnetic tube rack on ice at 4°C , were washed 4x in ice cold RIPA , and washed 1x in ice cold PBS . Beads were then pelleted by and re-suspended in 3x SDS loading buffer . For Westerns , lysates or beads mixed with 3x SDS loading buffer were heated 95°C 5 min then fractionated on 10% SDS-Page gels ( Bio-Rad ) . Proteins were transferred to PVDF membranes ( Bio-Rad ) . Membranes were blocked with 2% non-fat milk , 1% FBS ( Gemini ) in TBST . Membranes were incubated in primary antibody overnight ( p-EGFR , EGFR , or SLAM ) or 2 h ( motavizumab or vinculin ) . The dual-split protein ( DSP ) reporter cell-cell fusion assay was previously adapted to measuring RSV F protein activity [28 , 33 , 34] . 293T “effector” ( cis ) cells were transfected with RSV A2 F or 2–20 F and DSP1-7 in the presence of fusion inhibitor BMS-433771 ( a gift from Jin Hong , Alios Biopharma , San Francisco , CA ) . 293T “target cells” ( trans ) were transfected with DSP8-11 and pCG-SLAM or pcDNA3 . 1 empty vector . Effector cis or target trans cells were transfected with pTriex3-EGFR . Effector and target cells were washed with PBS 24 h post-transfection and harvested by pipetting in media containing EnduRen live cell luciferase substrate ( Promega ) . Equal volumes of effector and target cells were mixed and placed into an opaque 96-well plate in quadruplicate . Plates were incubated at 37°C and luciferase activity as a measure of cell-cell fusion was assayed on a TopCount Luminescence counter ( Perkin Elmer ) 4 , 6 , and 8 h after cell mixing . A positive control of DSP1-7 and DSP8–11 transfected into the same cell population was used to validate replicates . H292 cell monolayers were serum-starved for 24 h then mock-infected or infected with RSV . The cells were washed with PBS and lysed with TRIzol reagent ( Life Technologies ) at 20 h post-infection . Total RNA was isolated according to the TRIzol protocol . Quantitative real-time PCR was performed using the AgPath-ID OneStep RT-PCR kit ( Applied Biosystems ) and an ABI 7500 sequence detector system ( Applied Biosystems ) . The primers and probes for MUC5AC gene ( forward , 5′CGTGTTGTCACCGAGAACGT3′; reverse , 5′ ATCTTGATGGCCTTGGAGCA 3′ , probe , 5′ Fam- CTGCGGCACCACAGGGACCA-BHQ-1 3′ ) were obtained from Integrated DNA technologies ( IDT ) [64] . The primers and probes for GAPDH , the control , were forward , 5’ GAAGGTGAAGGTCGGAGT 3’ , reverse , 5’ GAAGATGGTGATGGGATTTC 3’ , and probe , 5’ Fam CAAGCTTCCCGTTCTCAGCC 3’ . Threshold cycles ( Ct ) and ΔCt for each sample was calculated . Assays were performed in duplicate in 3 independent experiments . H292 cells were cultured on 35-mm glass-bottom dishes ( MatTek Corp ) . Cells were inoculated at MOI = 3 at 4°C rocking for 1 h . , conditions at which attachment but not fusion can occur , washed twice in chilled PBS , and fixed in 10% buffered formalin ( Thermo-Fisher ) for 10 min . Cells were then washed 3X at room temperature with PBS . The fixed cells were blocked overnight in serum-free protein block ( Dako ) . Antibodies were diluted in an antibody diluent with background reducing components ( Dako ) . For the detection of RSV-F , a 1:2 , 000 dilution of 1 . 75 mg/mL motiavizmab was utilized . EGFR was detected with a rabbit polyclonal ( Millipore ) used at a 1:500 dilution . Primary antibodies were allowed to bind to cells for 4 h rocking at 4°C . Secondary antibodies anti-human IgG , IgA FITC conjugated ( LifeTech ) and anti-rabbit Alexa-568 ( LifeTech ) were used at dilutions 1:5 , 000 and 1:2 , 000 respectively . Secondary antibodies were incubated for 1 h rocking at room temp , followed additional 3 wash steps . Cells were kept at 4°C under PBS until imaging . Super-resolution images were acquired using a DeltaVision OMX Blaze ( GE Healthcare Life Sciences ) for three-dimensional structured illumination microscopy ( 3D-SIM ) . 3D-SIM reconstructions were generated by softWoRx ( v6 . 1 . 3 ) . The reconstructed files were further analyzed in Imaris ( v8 . 1 . 2 ) where appropriate channel thresholds were manually set . An overlap mask channel was created using Imaris where the thresholded Mander's coefficient was calculated to quantify 3D overlap . For WD-PBECs , pediatric bronchial epithelial cells ( PBEC ) were obtained , via written parental consent , from bronchial brushings of children undergoing elective surgery at the Royal Belfast Hospital for Sick Children , and the procedures were approved by the Office of Research Ethics Committees Northern Ireland [45] . PBEC were expanded in collagen-coated flasks using airway epithelial cell media and supplements ( Lonza ) , then seeded onto transwell inserts ( Corning ) , and then air-liquid interface ( ALI ) cultures were initiated and maintained 21 days in order to establish well-differentiated ( WD ) -PBECs , as described in further detail [45] . Paraformaldehyde-fixed and permeabilized WD-PBEC were stained for anti-β-tubulin , MUC5AC , or RSV F protein expression as described [45] and were stained with anti-phospho- ( p ) -EGFR ( Abcam , ab40815 ) . WD-PBEC cultures were infected with RSV subgroup A clinical isolate BT2a as described [45] . Fluorescent images were obtained with a SP5 confocal DMI 6000 inverted microscope ( Leica ) . 7-week old female BALB/cJ mice ( The Jackson Laboratory ) were orally gavaged with 100 mg/kg of erlotinib ( Selleck Chemicals LLS , Catalog S1023 ) or vehicle ( 0 . 5% carboxymethylcellulose /0 . 1% Tween 80 ) in a total volume of 100 μL daily , beginning two days prior to infection , and continuing for the duration of the experiment . Mice were infected intranasally with 1 x 106 PFU of A2-2-20F or mock virus preparation . On day 8 post-infection , the lungs were harvested and placed in 10% neutral buffered formalin for histopathology sectioning and periodic acid Schiff ( PAS ) staining for goblet cell hyperplasia as a measure of airway mucin expression . PAS positivity was quantified for greater than 285 individual airways total from 5 separate mice per group by digital morphometric analysis as described previously [27] . In a separate group of mice , the left lung was harvested , snap frozen , and later homogenized in RIPA buffer containing a protease inhibitor cocktail . The amount of total protein the lung homogenates was determined by Bradford assay and equivalent proteins from each group were loaded on a 4–17% SDS-PAGE gel and separated by electrophoresis before Western blotting for total EGFR ( Abcam , AB15669 ) or p-EGFR Monoclonal ( Abcam , AB24928 ) . 7-week old female BALB/cJ mice ( The Jackson Laboratory ) were infected intranasally with 1 x 106 PFU of A2-2-20F ( -mKate2 ) , 1 x 105 FFU of A2-2-2-20F , or mock virus preparation . On day 8 post infection , lungs were harvested and prepared as above for PAS staining or , after sectioning , were deparaffinized with Clear-rite ( Thermo Scientific ) , rehydrated through graded alcohols to water , and then stained with Prolong DAPI Gold ( Life Technologies ) . PAS slides were imaged using a Mirax Imaging System as descried previously [27] . DAPI-stained slides analyzed by fluorescence microscopy using the Mirax Image System . Equal exposure time was used for each channel ( DAPI 120μs , mKate2 900μs ) across all groups . Excitation was provided by HXP-120 light source ( LEj ) through Zeiss filter set 2 ( DAPI ) or 45 ( mKate2 ) . Images were analyzed in Panoramic Viewer v1 . 15 . 2 ( 3DHISTECH ) , where pseudocoloring levels were kept constant for each channel across all groups . Images of representative airways from the central portion of each lung were exported as TIF files . | Respiratory syncytial virus ( RSV ) is responsible for severe lower respiratory disease in infants and young children . Overabundant airway mucus contributes to airway obstruction in RSV bronchiolitis , and a better understanding of RSV pathogenesis may contribute to needed therapies and vaccines . We reported previously that RSV clinical isolate strain 2–20 induces more airway mucin expression in mice than prototypic RSV strains and that the 2–20 fusion ( F ) protein mediates mucin induction . Epidermal growth factor receptor ( EGFR ) has been shown to play a role in lung mucin expression . We identified a functional interaction between 2–20 F and EGFR , in that 2–20 F expression activated EGFR and , reciprocally , EGFR expression increased 2–20 F fusion activity . RSV F and EGFR co-localized in infected cells . EGFR co-immunoprecipitated with RSV F protein from various RSV strains , and the strength of this in vitro interaction correlated with strain-specific airway pathogenicity in mice . EGFR inhibition abrogated 2–20 F-mediated infection in vitro and mucin expression induction in vivo . These data identify EGFR as a novel strain-specific co-factor of RSV infection and suggest EGFR may be a target for ameliorating RSV disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"flow",
"cytometry",
"cell",
"physiology",
"medicine",
"and",
"health",
"sciences",
"respiratory",
"infections",
"293t",
"cells",
"biological",
"cultures",
"egfr",
"signaling",
"pulmonology",
"epithelial",
"cells",
"animal",
"models",
"model",
"organisms",
"research",
"and",
"analysis",
"methods",
"animal",
"cells",
"proteins",
"biological",
"tissue",
"mouse",
"models",
"cell",
"lines",
"spectrophotometry",
"biochemistry",
"mucin",
"signal",
"transduction",
"cytophotometry",
"cell",
"biology",
"anatomy",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"cell",
"signaling",
"spectrum",
"analysis",
"techniques",
"cell",
"fusion"
]
| 2016 | EGFR Interacts with the Fusion Protein of Respiratory Syncytial Virus Strain 2-20 and Mediates Infection and Mucin Expression |
Beetle horns are attractive models for studying the evolution of novel traits , as they display diverse shapes , sizes , and numbers among closely related species within the family Scarabaeidae . Horns radiated prolifically and independently in two distant subfamilies of scarabs , the dung beetles ( Scarabaeinae ) , and the rhinoceros beetles ( Dynastinae ) . However , current knowledge of the mechanisms underlying horn diversification remains limited to a single genus of dung beetles , Onthophagus . Here we unveil 11 horn formation genes in a rhinoceros beetle , Trypoxylus dichotomus . These 11 genes are mostly categorized as larval head- and appendage-patterning genes that also are involved in Onthophagus horn formation , suggesting the same suite of genes was recruited in each lineage during horn evolution . Although our RNAi analyses reveal interesting differences in the functions of a few of these genes , the overwhelming conclusion is that both head and thoracic horns develop similarly in Trypoxylus and Onthophagus , originating in the same developmental regions and deploying similar portions of appendage patterning networks during their growth . Our findings highlight deep parallels in the development of rhinoceros and dung beetle horns , suggesting either that both horn types arose in the common ancestor of all scarabs , a surprising reconstruction of horn evolution that would mean the majority of scarab species ( ~35 , 000 ) actively repress horn growth , or that parallel origins of these extravagant structures resulted from repeated co-option of the same underlying developmental processes .
A variety of morphological novelties have arisen and diversified through the course of animal evolution . Where studied , preexisting genetic networks redeployed in new developmental contexts have often been found to underlie the origin of these novel morphological traits , helping explain how a restricted number of developmental genes produce a diversity of forms [1] . However , we still know very little about the details of redeployment—which genes are co-opted and why , and whether or how the particular genes co-opted facilitate or constrain the subsequent diversification of novel structures . Beetle horns are remarkable examples of novel body parts that , once gained , are capable of radiating into a wide variety of forms . Beetle horns project from the head and/or prothorax as rigid cuticular outgrowths . Horns develop from discrete patches of epidermal tissue that detach locally from the cuticle of late-stage third-instar larvae and undergo a burst of proliferation to form a densely folded disc [2 , 3] . As in the imaginal discs of Drosophila melanogaster , Manduca sexta , and other insects , the three-dimensional shape of the adult beetle horn forms first as an intricately patterned arrangement of folds in the epidermis , which then unfurls as the animal molts from a larva to a pupa [4] . Studies of horn development have focused on the genes responsible for spatial patterning and cell proliferation within these growing horn primordia . Previous work with dung beetles in the genus Onthophagus has shown that proximodistal patterning genes used in conventional ventral appendage development , such as antennae and legs , are important during horn development [5] . In addition , embryonic head patterning genes also likely contribute to horn formation in Onthophagus horns [6] . Together , these studies provide compelling evidence that redeployment of preexisting patterning gene networks underlies the evolutionary origin of beetle horns . However , studies to date have been confined almost entirely to a single genus of scarab beetles , Onthophagus . In fact , horns have arisen multiple times within the Scarabaeidae , and are today widespread and diverse within two divergent subfamilies of scarabs , the dung beetles ( which includes Onthophagus ) , and the rhinoceros beetles ( Dynastinae ) . Dung and rhinoceros beetles are distant clades within the otherwise-largely-hornless scarab beetles , separated from each other by approximately 150 million years [7] . Within each clade , horns appear to have arisen and been lost multiple times at different locations on the beetle ( e . g . , head , thorax ) [8] . For this reason , dung and rhinoceros beetle horns are considered to be independent and parallel radiations of similar novel structures . Understanding whether the same or different genes underlie horn development in rhinoceros beetles , and how these genes function to form the horn compared to what occurs in dung beetles , promises critical insights to the process of modularity in evolution through gene network co-option , as well as the repeatability of evolution as it unfolds in parallel origins of elaborate and extravagant novel forms . In the Japanese rhinoceros beetle Trypoxylus dichotomus , males develop a large “pitchfork” shaped horn extending from the dorsal surface of the head , as well as a shorter , curved and bifurcated horn that projects anteriorly from the prothorax . We investigated the developmental patterning and growth of T . dichotomus head and thoracic horns by performing a comprehensive search for transcription factors ( TFs ) and signaling molecules involved in horn formation , harnessing the power of RNA-seq . Subsequent RNAi-based functional evaluation identified 11 TFs ( including developmental limb patterning genes , and head patterning genes ) that contribute to horn formation in T . dichotomus , and revealed important similarities and differences in gene function between dung ( Onthophagus ) and rhinoceros ( Trypoxylus ) beetle horns . Our results point to a deep parallelism in the origin and subsequent diversification of scarab beetle horns .
In T . dichotomus , males develop exaggerated horns on both the head and thorax while female beetles are hornless ( Fig 1A ) . In order to determine when male-specific horn morphogenesis begins , we first compared the development of male horn primordia and tissue from the same region in females . In T . dichotomus , sexually dimorphic horn development becomes apparent during the prepupal stage [9] . Approximately ten days prior to the end of the last ( third ) larval instar of males , cells of the dorsal head epidermis begin to evaginate to form a sac ( Fig 1B ) . The surface of the sac continues to grow and fold during prepupal development , and forms four concentric circles at its distal tips that correspond to the branched tips of the adult head horn [3] ( arrowheads in Fig 1C–1E ) . The development of the thoracic horn follows a similar progression , although the onset of evagination occurs later than in the head horn ( Fig 1F–1I ) . In addition , the thoracic horn forms a different surface folding pattern than head horns , reflecting the differences in adult horn shape . In T . dichotomus , the female pupa has a small protrusion on the head , and no visible horn on the thorax . This small pupal head horn in females disappears after eclosion , likely through programmed cell death during the adult molt [10] . The primordial tissue for the head structure in prepupal females displays less folding and lacks the four concentric circles typical of male horns ( Fig 1J–1M ) . Prepupal female tissue located at the same region as male thoracic horn tissue , while still displaying a folded morphology , displays less folding than male tissue and lacks the morphology of male thoracic primordia ( Fig 1N–1Q ) . Consistent with their different morphology at the pupal stage , females show no clear evagination in their horn primordia late in development ( Fig 1M and 1Q ) . To discover horn formation genes we sampled horn primordia at the onset of differentiation ( Fig 1C , 1G , 1K and 1O ) because ( i ) male-specific tissue folding is present during this stage , and ( ii ) the primordia are more clearly recognizable than in earlier stages , enabling consistent tissue collection among samples . We then performed RNA-seq analysis to identify genes that contribute to elaborate horn morphology . To construct a T . dichotomus transcriptome for read mapping , we assembled four cDNA libraries , comprising male head horn tissue ( 111 . 2M reads , see Materials and methods for detail ) , male thoracic horn tissue ( 107 . 8 M reads ) , female head horn tissue ( 113 . 6 M reads ) and female thoracic horn tissue ( 115 . 1 M reads ) . The summary of sequencing and de novo transcript assembly is shown in S1 Table . The total number of trinity transcripts is 82 , 108 and the contig N50 based on all transcript contigs is 3 , 158 ( S1 Table ) . We evaluated the quality of the assembled transcript model by performing a BLAST search against both the NCBI nr database as well as the OrthoDB 5 database . The assembled transcripts showed the highest similarities to Coleopteran ( beetle ) genes , particularly to genes of the red flour beetle Tribolium castaneum in the NCBI nr database search ( S1A–S1C Fig ) . In the OrthoDB search , 75 . 3% of the assembled transcripts had putatively orthologous genes in D . melanogaster and 89 . 3% of the transcripts were orthologous to T . castaneum ( S1D and S1E Fig ) . As T . dichotomus and T . castaneum are both beetles , the increased percentage of transcripts orthologous to T . castaneum demonstrates the quality of the T . dichotomus transcriptome constructed in this study . We further evaluated our transcriptome with BUSCO [11] . Our transcriptome indicates 97 . 8 and 95 . 7% coverages over complete BUSCOs in Metazoa and Insecta , respectively ( S1F Fig ) , reflecting the high quality of the T . dichotomus transcriptome . We mapped short read sequences to the transcriptome and calculated mRNA abundance . We checked the distribution of count data with a multi-dimentional scaling plot , and found that gene expression between males and females was distributed distinctly , and that at least two biological replicates for each sample clustered together ( S2 Fig ) . We then made two different types of comparisons to identify transcripts involved in horn formation . First , we made an intersexual comparison between the same horn types of male and female beetles ( e . g . between head horns in male and female ) . This was performed in order to identify genes driving the development of different morphologies between male and female horns . Next , we compared tissue intrasexually , between different horn types in either males or females ( e . g . between head horn and thoracic horn in males ) . Our goal was to identify genes that contribute to the unique horn morphologies present in each segment . Our intersexual comparison identified 739 differentially expressed genes ( DEGs ) , and our intrasexual comparison identified 814 DEGs at a false discovery rate lower than 0 . 05 ( S2 Table; S1–S4 Appendices ) . To understand the developmental processes each tissue undergoes during the stage we investigated , we categorized genes enriched in each comparison by gene set analysis using ErmineJ software [12] . In our intersexual comparisons , Gene Ontology ( GO ) terms associated with muscle formation ( e . g . myofibril assembly , striated muscle cell development ) and metabolism ( e . g . cellular amino acid catabolic process , lauric acid metabolic process ) were overrepresented ( S3 Table; S3 Fig ) . This suggests that sex differences in horn morphology ( male versus female head horn , male versus female thorax horn ) , at least during the prepupal period ( Fig 1C , 1G , 1K and 1O ) , arise primarily from differences in the amount of growth of each horn type . In contrast , differences between horn types ( head horn versus thorax horn ) are associated with differential expression of appendage patterning genes . GO terms associated with morphological differentiation , such as cell fate specification , leg disc pattern formation and head development , were clearly enriched in the comparisons between head and thoracic horns within each sex ( S3 Table; S3 Fig ) . We evaluated the function of DEGs during horn formation by using RNAi-mediated gene knockdown . Given the important functions of transcriptional regulation in animal development , we focused on DEGs annotated either as transcription factors ( TFs ) or as signaling molecules for our RNAi screening . In our annotation based on BLAST search against the NCBI nr database , we identified 49 candidate horn formation genes comprising 38 TFs and 11 signaling molecules ( Fig 1R–1U; S4 Table ) . We first performed RNAi for all 49 candidate horn formation genes as an initial screening , and repeated experiments for 13 TFs and a signaling molecule in which we observed visually detectable effects on head and/or thoracic horn morphology ( S4 Table ) . We consequently obtained 11 TFs with clear functional roles in horn development ( S4 Table ) . Among the 11 genes we identified , SP8 and pannier ( pnr ) RNAi most drastically affected horn phenotypes . SP8 is a member of the SP family of transcription factors , and has an orthologous amino acid sequence to D . melanogaster Sp1 ( S4 Fig ) . In D . melanogaster , SP family genes Sp1 and buttonhead ( btd ) play partially redundant roles in development of ventral appendages and mechanosensory organs [13–15] . D . melanogaster Sp1 is involved in leg disc fate determination and postembryonic growth of ventral appendages [13 , 14] . The role of a D . melanogaster Sp1 ortholog on growth of ventral appendages appears to be conserved in the beetle T . castaneum [16] . There are three sets of SP family genes in metazoans [17] , and while members of all three families were present in our transcriptome , SP8 was the only SP gene identified as differentially expressed . SP8 knockdown induced an extra horn-like outgrowth from the anterior proximal region of the male head horn ( Fig 2A and 2B ) . Head horns in T . dichotomus are unusual in having four tips , suggesting two successive bifurcation events ( Fig 3A ) . This new RNAi-induced horn outgrowth—a horn on a horn—exhibited a bifurcated tip ( Fig 2C ) , similar to the bifurcated tip of T . dichotomus thoracic horns ( Fig 3A ) , as well as head horns of other , more typical , Dynastinae species . In D . melanogaster pnr expression is localized to the dorsal midline , where it acts as a selector gene specifying dorsomedial identity within the head and thorax [18] . pnr also specifies dorsal regions of eye-antennal and wing imaginal discs and is an upstream regulator of both decapentaplegic ( dpp ) and wingless ( wg ) , making it an ideal candidate for a master regulator of patterning and growth of head and thoracic horns [19] . Knockdown of pnr led to the development of a furrow along the dorsal midline of the T1 segment of the thorax , instead of a horn ( Fig 2D and 2E ) . This complete loss-of-horn phenotype strongly suggests that pnr also functions as a dorsal-medial selector in T . dichotomus prepupal development . Interestingly , pnr did not affect development of the head horn , which suggests that cells giving rise to head horns may not be dorsal in origin—a possibility we discuss further below . The remaining genes analyzed in our RNAi screening displayed only modest phenotypes , and thus we quantitatively assessed the effect of RNAi treatment by measuring both horn shape and length ( S4 Table ) . To investigate the regulatory and functional relationships among our 11 genes of interest , we searched the integrated D . melanogaster genomics database FlyMine using D . melanogaster orthologs as a query [23] ( S7 Table ) . Although we found no pathway enrichment that suggests cooption of a specific developmental signal from this analysis , we found two terms from the Berkeley Drosophila Genome Project ( BDGP ) that were significantly enriched . These terms are assigned based on expression patterns of the genes in D . melanogaster embryonic development , and both the terms “clypeolabrum” ( P = 2 . 022393e-4 , Holm-Bonferroni test ) and “clypeo-labral primordium” ( P = 0 . 020819 , Holm-Bonferroni test ) were significantly enriched ( S7 Table ) , suggesting that our 11 candidate genes are involved in formation of this region . This finding agrees with data from Onthophagus beetles , where it has been reported that head horns are anatomically positioned around the pre-segmental ocular and clypeolabral regions , and genes involved in the patterning of embryonic pre-segmental regions are important for post-embryonic head horn differentiation [6] . The arthropod labrum originates as an ectodermal outgrowth arising just in front of the mouth ( i . e . , pre-segmental ) , in a domain of the head defined by expression of Optix [24] . Whether the labrum is a true appendage , or instead a non-appendicular projection , is debated [25]; but it forms in many insects ( including T . castaneum beetles ) from a pair of appendage-like outgrowths that later fuse medially into a single structure [24] . In T . castaneum , appendage patterning genes including dpp and wg are expressed in labral buds , but their domains of expression are reversed compared with other trunk appendages , leading Posnien et al . ( 2009 ) to propose that the labrum arose as an anterior outgrowth of the head from an ectopic redeployment of the appendage patterning network [24] . Our observation that genes functionally involved with head horn growth are associated with clypeolabral identity led us to hypothesize that the novel head horn in T . dichotomus is at least partially derived from pre-segmental regions , and that some of the RNAi induced changes in horn shapes we observed in this study were a consequence of perturbing development in the clypeolabral region . To test this hypothesis , we first analyzed the phenotype of the anterior head in RNAi treated females , as the morphology of the pre-segmental region is much clearer in hornless females than in males ( Fig 4A ) . We analyzed female heads for genes which were assigned the BDGP term “clypeolabrum” in the FlyMine analysis including Rx , Optix , SP8 and Tbx20 ( S7 Table ) . Rx and Optix are required to form the larval clypeolabral region in T . castaneum [26] . Sp1 loss-of-function mutants in D . melanogaster lack the mandibular head segment tissue ( note that the D . melanogaster Sp1 ortholog in beetles is SP8 , see S4 Fig for SP family gene phylogenetic tree ) [17 , 27] . Although midline , a Tbx20 homolog , is expressed in the clypeolabral region in D . melanogaster embryos , no head patterning function has yet been reported for any Tbx20 homologs in insects . In female T . dichotomus , the anterior region of the clypeus has a characteristic curved shape , and has shorter hairs on the surface than the posterior region ( Fig 4A and 4A’; clypeolabral region is in red ) . Rx RNAi in females created a furrow in the clypeolabral dorsal midline , and dramatically changed both the shape and the surface hair pattern ( Fig 4B and 4B’ ) . Optix RNAi females showed a wider anterior end compared to the control counterparts ( Fig 4C and 4C’ ) . Tbx20 RNAi resulted in a less pronounced anterior curved shape , and the posterior part of the clypeolabral plate was less extended laterally ( Fig 4D and 4D’ , arrowheads ) . We found that male beetles exhibited similar shape changes in the anterior head region compared to controls , indicating that these gene functions are shared between sexes , despite the corresponding body wall changes in males that ultimately form the horn ( Fig 4F–4I ) . We further discovered that SP8 RNAi , which forms a small ectopic horn in males , also affected the clypeolabrum shape in both males and females . In SP8 RNAi females , the anterior central part of the head has a u-shaped form , a rough surface , and long hair ( Fig 4E and 4E’ ) . Similar changes in anterior head shape were observed in male SP8 RNAi beetles ( Fig 4J ) . The labrum and clypeus are separate in control beetles , but were instead fused in SP8 RNAi males ( Fig 4K and 4L ) . The labrum segment lost nearly completely the normal dorsal structure , and the hairy ventral region was expanded in treated animals ( Fig 4M and 4N ) . These drastic changes of the clypeolabrum structure that coincide with ectopic horn formation suggest that the anterior-most tissue of the head has lost its identity , and that both dorsal and ventral characters are juxtaposed in atypical ways in SP8 RNAi animals . Artificial juxtapositions of dorsal-ventral signals in D . melanogaster imaginal discs can lead to new axes of outgrowth and ectopic miniature wings or legs that form at the base of existing structures [28 , 29] . It is conceivable that altered dorsal-ventral patterning in the T . dichotomus clypeolabral region created a new center for horn growth , and thus induced the formation of a small horn following SP8 RNAi . Combined , our analyses reveal that Rx , Optix , Tbx20 and SP8 function in the formation of both the male and female clypeolabral region and in the male head horn , and changes in the expression level of these genes alter horn shape and size . Previous work suggests that appendage patterning gene networks were coopted to form novel horn outgrowths in Onthophagus beetles , as both appendages and horns deploy similar developmental pathways [5] . We thus hypothesized that differentially expressed genes in our study would include appendage patterning genes . Four of our 11 genes are homologs of known appendage formation genes in D . melanogaster: BarH1 , dac , SP8 and ab [13 , 30–32] . RNAi knockdown of these four genes led to defects in both antenna and leg development , confirming their functional role in appendage growth in T . dichotomus ( Fig 5A–5E , 5G–5K and 5G’–5K’ ) . In addition , Sox14 RNAi led to fusion of appendage segments in the distal tip , a phenotype that has not been reported for this gene in any other insect ( Fig 5F , 5L and 5L’ ) . RNAi knockdown of dac , and ab affected both head and thoracic horn shape ( Fig 3E , 3H and 3K ) , and ab affected the size of both horns ( Fig 3Q ) . Our finding that dac plays a role in horn shape formation in T . dichotomus is noteworthy because RNAi knockdown of dac does not affect Onthophagus taurus horn formation [5] . While SP8 was assigned the BDGP term “clypeolabrum” , and functions in patterning this region , BarH1 , dac , ab , and Sox14 were not assigned this term ( S7 Table ) , suggesting independent recruitment of appendage patterning genes into the horn development program . To understand the ancestral function of genes involved in T . dichotomus head horn development , we performed RNAi analysis in the red flour beetle T . castaneum . Although T . castaneum is a member of the Tenebrionidae , and not a direct ancestor of the Scarabaeidae , this beetle is considered to represent the ancestral head shape [24] . In addition , we used this species for comparison because of the availability of both genome sequence and the ability to easily perform systemic RNAi [33 , 34] . We analyzed orthologs of the T . dichotomus clypeolabrum patterning genes we identified , Rx , Optix , Tbx20 and SP8 , and appendage-patterning genes , BarH1 , dac , Sox14 and ab . Contrary to the drastic change in horn and clypeolabral morphologies in T . dichotomus , Rx RNAi caused no detectable changes to adult head morphology compared to control injections in T . castaneum ( S7A and S7B Fig; n = 22 and 20 for EGFP and Rx , respectively ) . We could find all three SP family gene orthologs in the T . castaneum genome , which enabled us to target the SP8 gene specifically [17] ( S4 Fig ) . RNAi treatment for SP8 in T . castaneum affected appendages as previously reported [16] . SP8 RNAi resulted in fused appendage segments similar to results seen in T . dichotomus . ( S7I Fig; n = 22 ) . Notably , after SP8 RNAi , the clypeolabral region of the flour beetle was unaffected ( S7F–S7H Fig ) . In fact , we detected no obvious morphological changes in the head surface of T . castaneum after RNAi for either Optix or Tbx20 ( S7C and S7D Fig; n = 22 and 18 for Optix and Tbx20 , respectively ) . We note that Optix RNAi affected compound eye formation , as has been reported ( S7E Fig; t = 7 . 69 , degree of freedom = 42 , P = 1 . 53e-09 , student’s t-test ) [6] . These results suggest that the clypeolabrum patterning genes we examined , although critical for formation of the embryonic head , no longer exert detectible effects when knocked down in late-stage larval T . castaneum . This constitutes an important difference between head patterning in Tribolium flour beetles and both Trypoxylus rhinoceros beetles and Onthophagus dung beetles , and suggests that the origin of head horns in beetles may involve heterochronic shifts in the timing of patterning of the clypeolabral region . In contrast to the non-conserved function of clypeolabral patterning genes , the function of appendage patterning genes was well conserved between T . dichotomus and T . castaneum , as we observed similar effects between both species for all five genes ( Fig 5G–5L; S7I Fig ) .
Recent advances in developmental genetics have elevated the flour beetle T . castaneum to become a model system for studying development in insects generally [35 , 36] . In particular , studies of embryonic expression of trunk and appendage patterning genes recently led to a new model for the formation of segments and sutures in the insect head [35] . This “bend and zipper” model proposes that the flat epithelial band containing head segments and ventral appendage primordia migrates anteriorly , folding upwards and backwards ( Fig 6A and 6B ) [37] . The anterior head lobes grow around the clypeolabral region , eventually fusing with each other along the anterior midline of the head . This now-inverted epithelium fuses with the maxillary and labial segment regions of the layer below , completing the head capsule ( Fig 6C and 6D ) . The bend and zipper model accounts for the mysterious placement of frontal/ clypeolabral appendages in Paleozoic Euarthropoda , and for the coronal , frontal , and subgenal sutures demarcating head capsules of many insects [35 , 38] . Importantly , this model also provides a basis for proposing the developmental origins of horns in beetles , possibly explaining the paradoxical finding of our study as well as studies of dung beetles in the genus Onthophagus [6] , that head horns , though appearing to lie on the top , or dorsal region of the beetle head , in fact express genes typical of anterior “pre-segmental” ( clypeolabral ) body regions and ventral imaginal discs . For example , Optix , Rx , and Tbx20 , genes whose expression is confined to the pre-segmental clypeolabral region in diverse bilaterians including T . castaneum [26] , are expressed in head horn tissues in Trypoxylus dichotomus and appear functionally involved with specifying horn size and shape ( Fig 3 ) . Similarly , the zinc-finger transcription factor Sp8 ( D . melanogaster Sp1 ) is known to affect relative amounts of growth of ventral appendages in D . melanogaster [13] , T . castaneum [16] and milkweed bugs [39] , and we show that disruption of Sp8 through RNAi knockdown is sufficient to induce formation of an ectopic head horn in T . dichotomus ( Fig 2B and 2C ) . Together these results suggest that the head horns in both Onthophagus and Trypoxylus beetles form from appendage-like outgrowths in the clypeolabral region of the head , a pocket of anterior ( pre-segmental ) cells with ventrally-patterned outgrowths that folds upwards and backwards during embryogenesis such that the novel appendages growing from this region extend vertically from the top of the head in adult beetles ( Fig 6E ) . Whether this means that beetle head horns are homologous with the various non-appendicular clypeo-labral evaginations of fossil and extant panarthropods ( e . g . , primary antennae of onychophorans ) remains to be investigated [38] . In T . castaneum and other insects , including D . melanogaster , the homeotic gene Scr is localized to the dorsal ridge , the anterior-most region of the body capable of having a dorsal fate ( note the consistency of this expression pattern with the “bend and zipper” model of head development , Fig 6 ) [40] . The dorsal ridge forms the boundary of the head and thorax , and is comprised of regions of the maxillary and labial segments , as well as parts of the first thoracic ( T1 ) segment ( prothorax ) . Although Scr retains this regional specification in insects pre-dating the origin of wings ( e . g . , firebrats ) , its best-studied function in pterygote species is to repress growth of wing primordia in the prothorax [41] . RNAi knockdown of Scr leads to vestigial T1 wings on the prothorax of D . melanogaster , milkweed bugs , cockroaches , mealworm beetles , T . castaneum as well as Onthophagus beetles [21 , 41–45] . Wasik et al . ( 2010 ) showed that Scr RNAi knockdown affected development of prothoracic , but not head horns , in Onthophagus , and we demonstrate here that Scr RNAi leads to reduced growth of prothoracic , but not head horns in T . dichotomus [21] ( Fig 3O ) . Similarly , pnr , a gene involved in embryonic dorsal closure in D . melanogaster , is typically expressed along the dorsal midline of the thorax and abdomen , where it acts as a selector gene specifying dorsal-medial identity to tissues including the heart [18 , 19] . Here we show that pnr expression is necessary for growth of the prothoracic horn , as RNAi knockdown resulted in a complete loss of the dorsal-medial region of the thorax , including loss of the entire horn ( Fig 2E ) . Together , these results suggest the developmental locus of the thoracic horn in T . dichotomus is the anteriormost zone of the dorsal midline , a region specified by intersecting domains of expression of Scr and pnr ( a region defined by both light green and dark green in Fig 6C–6E ) . The Scarabaeidae contain approximately 35 , 000 species , the overwhelming majority of which are hornless . Yet , horns are thought to have arisen many times independently within this clade , such that today several thousand species bear elaborate weapons . The extreme sizes of these structures , and their concentration within a single family of beetles , led Darwin to conclude that sexual selection acted especially effectively in scarab beetles [46] , and Arrow suggested they have a “special tendency” to the acquisition of horns [47] . Arrow went so far as to conclude “it is certain that these horns have had no common origin” [47] . Horns are assumed to have arisen multiple times for two reasons: most scarab species ( 80% ) lack horns; and the sub-families with the majority of horned species ( “dor” beetles [Geotrupinae]; dung beetles [Scarabaeinae]; rhinoceros beetles [Dynastinae] ) are too widely dispersed within the Scarabaeidae ( Fig 6F ) . Our study provides the first detailed characterization of horn development from a rhinoceros beetle , T . dichotomus , permitting the parallel origins of rhinoceros and dung beetle horns to be contrasted at a mechanistic level . Although a few genes clearly show lineage specific differences in function ( e . g . , dac affects horns in T . dichotomus but not Onthophagus ) , the overwhelming pattern is one of similarity . Head horns in both lineages arise from ventral appendage-like outgrowths in the anteriormost , pre-segmental clypeolabral region of the head; in both lineages the expression and function of these clypeolabral patterning genes appear to involve a heterochronic shift markedly divergent from head development in T . castaneum ( S7 Fig ) ; and thoracic horn outgrowths in both lineages appear to extend from the anteriormost region of the dorsal midline , a zone specified by the homeotic gene Scr , and , we now show , the domain of expression of pnr . In addition to the precise locations of horn outgrowth being similar , the formation of the outgrowths themselves appears similar , involving in both horn types and in both lineages the partial deployment of appendage patterning networks . Finally , recent studies of the mechanisms of sexual dimorphism in beetles suggest that sexually dimorphic growth of both types of horns is regulated by the same pathway . Alternative splice forms of doublesex in males and females regulate sex specific patterns of growth of enlarged mandibles in stag beetles ( Lucanidae ) , as well as horns in Onthophagus and Trypoxylus , consistent with a shared capacity for female-specific repression of weapon growth across the scarabs [9 , 48 , 49] . Consequently , our results reveal many layers of mechanistic parallelism between the horns of rhinoceros and dung beetles ( S8 Fig ) , and point to a surprisingly repeatable path to the evolution of these extreme , sexually selected structures . An alternative explanation is that horns arose once , before the diversification of the scarabs , and that the repeated evolution of horns in diverse lineages represents “taxic atavism” [50 , 51] as has been described recently for amphibian teeth [52] and supersoldier castes in Pheidole ants [53] . Indeed , several clues suggest the ancestral scarab beetles may have been horned . First , most of the primarily-hornless subfamilies contain at least a few species with either rudimentary horns ( e . g . , Pleocomidae , Passalidae , Ochodaidae , Orphninae ) or with fully-developed horns ( e . g . , Melolonthinae , Cetoniinae , Rutelinae; Fig 6G ) . Second , the pupal stages of many scarabs have thoracic ‘horns’ , and these are often present in individuals ( e . g . females ) or species that lack this horn as adults . Pupal ‘horns’ may serve a current function as support structures protecting animals during the vulnerable metamorphic molt [54 , 55] , but they may also represent developmental carry-overs from a horn that was present in the adult stages of an ancestor [56 , 57] . Third , even within completely hornless species—in one case a species within a completely hornless subfamily , the Ceratocanthidae , which have been a distinct clade for at least 65 million years—mutant adult individuals occasionally appear with fully developed horns , and these horns also resemble the horns of other scarabs [56 , 58] ( Fig 6G ) . These observations led Emlen et al . ( 2006 ) to propose that perhaps the ancestral scarabs did have horns , as well as a developmental capacity to shut off horn growth ( e . g . , in females ) [59] . If true , this would mean that the hornless state of most present-day scarab species reflects a derived condition entailing the repression of horn growth . Future studies will be needed to distinguish between these alternatives , including examining horn growth in additional horned scarab lineages such as the Cetoniidae and Geotrupidae , and testing whether the putative ancestral developmental potential to produce horns remains in currently hornless species .
We purchased larvae of T . dichotomus from Kuwagata Koubo Mushikichi ( Fukuoka , Japan ) , and Roiene ( Gunma , Japan ) . Larvae were sexed as previously described [9] , individually fed on humus in plastic containers , and kept at 10 °C until use . T . castaneum was reared on flour powder in plastic containers at 30 °C . After dsRNA injections , each larva was separated in 24-well plates until adulthood . Head and thoracic horn primordia were manually dissected out from T . dichotomus larvae in ice-cold 0 . 75% sodium chloride , snap-frozen in liquid nitrogen and stored at -80 °C until use . The developmental stage of each tissue was determined from the external morphology of dissected horn primordia . Total RNA was extracted using the RNeasy mini kit ( Qiagen , Valencia , CA , USA ) according to manufacturer’s instruction . On-column DNase I treatment was performed . RNA purity was assessed with Qubit RNA HS assay kit ( Thermo Fischer Scientific , MA , USA ) . RNA integrity was analyzed on a Bioanalyzer 2100 ( Agilent Technologies , CA , USA ) , and RNA quality is shown in S8 Table . One μg of total RNA from a single beetle was used for each paired-end cDNA library construction using the TruSeq RNA sample preparation kit ( Illumina , San Diego , CA , USA ) . Three biological replicates were sequenced for each sample . The cDNA library was sequenced on an Illumina HiSeq 2000 ( Illumina , San Diego , CA ) generating 150 bp paired-end reads . We performed de novo assembly of short read sequences using Trinity ( version r2012-06-08 ) with default configurations without any additional options [60] . Raw RNA-seq data and the assembled transcripts are deposited in DDBJ Sequence Read Archive under project accession number PRJDB6456 , and in DDBJ Transcriptome Shotgun Assembly division under accession numbers IADJ01000001-IADJ01127986 ( 127986 entries ) , respectively . The BLASTnr version released on Oct 30 , 2012 was used for annotation of the transcript model . The same version of BLASTnr , and OrthoDB 5 databases were used for the qualification of transcriptome shown in S1 Fig . Cutoff e-values for the BLAST searches against these databases were 1 . 0e-4 . Both Metazoa ODB9 and Insecta ODB9 datasets were used for BUSCO analysis [11] . We performed read counting and differential expression analysis using RSEM ( version 1 . 1 . 21 ) with default configurations [61] . Multidimensional scaling of raw count values was performed and visualized with R ( version 3 . 3 . 3 ( 2017-03-06 ) ) [62] . For identification of differentially expressed genes ( DEGs ) between samples , we used the TCC package with default options , and multi-step normalization and edgeR-based DEG analysis . In this strategy , normalization of count data and DEG estimation are iterated for avoiding false positives , and we repeated this cycle three times in this study [63–65] . DEGs between samples were defined as genes at false discovery rate lower than 0 . 05 . For MA plots in Fig 1 , M and A values were calculated using TCC package , and visualized with R ( version 3 . 3 . 3 ( 2017-03-06 ) ) [62 , 65] . Raw numerical data for the DEG analysis are provided as S1–S4 Appendices . As several new methods have been developed after our initial analysis , we now include summaries of sequence data analyses with these alternative methods , which include analysis using a newer version of Trinity , and with tag quantification with Kallisto and Salomon , in S7 Appendix [60 , 66 , 67] . Alignment and phylogenetic neighbor-joining ( NJ ) tree of SP family genes was constructed by Clustal X using putative amino acid sequences [68] . We searched orthologous genes to the assembled transcripts from OrthoDB database . GO terms were assigned to assembled transcripts based on GO information on orthologous genes found in the FlyBase database . We then analyzed GO terms over-represented in each comparison between RNA-seq data set using ErmineJ ( version 3 . 0 . 2 ) [12] . GO terms enriched at an FDR < 0 . 5 for each comparison in ErmineJ anlaysis were summarized with REVIGO and visualized with R ( version 3 . 3 . 3 ( 2017-03-06 ) ) [62 , 69] . 953 bp , 482 bp and 841 bp cDNA fragments of esg , B-H1 and dac were first subcloned into the plasmid pCR4-TOPO ( Invitrogen ) , respectively . We in vitro transcribed double-stranded RNAs ( dsRNAs ) for these genes from purified PCR products using primers 5´-TAATACGACTCACTATAGGGAGACCACGTCCTGCAGGTTTAAACG-3´ and 5´-TAATACGACTCACTATAGGGAGACCACCGAATTGAATTTAGCGGC-3´ . For the remaining genes , first-stranded cDNA ( fs cDNA ) was synthesized with the SuperScript III Reverse Transcriptase ( Thermo Fischer Scientific , MA , USA ) using one μg total RNA extracted from head and prothoracic horn primordia of three males and three females at the prepupal stage . Sample collection and total RNA extraction were performed as described above . Equal amounts of each fs cDNA were mixed and used as a template for PCR . The PCR was performed using gene specific primers with T7 sequence at 5´ end listed in S9 Table , and in vitro transcribed RNAs were made from purified PCR products using the AmpliScribe T7-Flash Transcription Kit ( Epicentre , WI , USA ) . These gene-specific primers were designed in open reading frames to produce a product of 300–400 bp length , for which the target specificity was confirmed by BLAST search against the transcriptome . We ensured that the expected size of a PCR product was amplified on an agarose gel , cut a single band from the gel , and purified the product prior to in vitro transcription . A single dsRNA was used for each target gene , and a dsRNA targeting EGFP gene was used as a control . We injected dsRNA into the hemocoel of last instar larvae through the intersegmental membrane at the anterior-lateral position of the prothoracic segment using a syringe ( Terumo Corporation , Tokyo , Japan ) with a 30-gauge needle ( Becton , Dickinson and Company , NJ , USA ) . Last instar beetle larvae were moved from 10°C to room temperature ( about 25 °C ) at least 2 days before dsRNA injection , and reared at room temperature after the injection until they developed into adults . Our timing of dsRNA injection preceded the onset of horn formation , as larvae normally form pupal chambers several days after moving from 10°C to room temperature , and development of sexually dimorphic horns begins during prepupal period after pupal chamber formation [70] . We note that beetles need to be stored at 10 °C as the supply is seasonal , and that storage duration of larvae at low temperature might influence the survival rate of injected beetles . The data shown include all experiments performed on both short-stored and long-stored larvae . For T . castaneum , dsRNA was injected into late larvae using glass needles with a Femtojet ( Eppendolf , Hamburg , Germany ) . We injected approximately 0 . 83 μg of dsRNAs into each larva . We repeated the experiment for Rx , Tbx20 and SP8 because we did not find any change after the first injections for Rx and Tbx20 , and because a sufficient number of beetles were unable to eclose in SP8 RNAi . For this second experiment , the amount of dsRNAs were changed to approximately 3 . 32 μg for Rx and Tbx20 , and 0 . 083 μg for SP8 . Horn area was measured in pictures of each horn region ( i . e . central and side grooves in head horn , and thoracic horn groove ) on a flat surface by using ImageJ 64 . For the side grooves on the head horn , two areas were separately measured and averaged . Horn length was analyzed from image data of either heads or prothoraxes separated from the other body segments and imaged from the lateral aspect . Both dorsal and ventral length were separately measured using the “SegmentMeasure” plug-in for ImageJ 64 developed by Hosei Wada . Note that we included non-horn body wall parts to obtain “horn length” data in order to measure corresponding segments among different dsRNA treated animals . Dorsal and ventral length were summed after values from right and left views were averaged . Body length , from the anterior tip of clypeus to the posterior most region of the body , was measured with a digital caliper model DN-100 ( Niigata seiki , Co . , Ltd . , Niigata , Japan ) . We utilized logistic regression and a Wald test in R ( version 3 . 3 . 3 ( 2017-03-06 ) ) to test for significance [62] . We omitted some eclosed individuals from the measurement because we were unable to apply this scheme for measurement due to highly malformed horns . Although the number of available larvae from the supplier is limited , we analyzed at least five surviving individuals per gene . Raw numerical data for the area and length measurements are provided as S5 and S6 Appendices , respectively . Photographs were taken with digital microscopes; either the VHX-900 or VHX-5000 ( KEYENCE , Co . , Osaka , Japan ) . Scanning electron micrograph were taken with a VHX-D500 ( KEYENCE , Co . , Osaka , Japan ) . Adobe Photoshop CS5 . 1 and Adobe Illustrator CS5 . 1 ( Adobe Systems , Inc . , San Jose , CA ) were used for image processing and assembly . | Goliath and Hercules beetles include some of the largest insects known , and the horns they wield are spectacular . These ‘rhinoceros’ beetles form a subfamily within the Scarabaeidae , a clade containing ~35 , 000 primarily hornless species . The other subfamily of horned scarabs , dung beetles , is distantly related and their horns are considered a separate origin and parallel radiation . We characterize horn development in a rhinoceros beetle and show that the details are surprisingly similar to the horns of dung beetles . Our results reveal exciting parallels at the level of underlying developmental mechanism . The superficial similarity of these two types of beetle horns mirrors an even deeper similarity in the pathways and genes responsible for their construction . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
]
| [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"rna",
"interference",
"animals",
"dung",
"beetles",
"animal",
"models",
"developmental",
"biology",
"drosophila",
"melanogaster",
"model",
"organisms",
"experimental",
"organism",
"systems",
"epigenetics",
"drosophila",
"research",
"and",
"analysis",
"methods",
"genetic",
"interference",
"animal",
"studies",
"gene",
"expression",
"life",
"cycles",
"beetles",
"insects",
"arthropoda",
"biochemistry",
"rna",
"eukaryota",
"anatomy",
"nucleic",
"acids",
"thorax",
"genetics",
"biology",
"and",
"life",
"sciences",
"larvae",
"organisms"
]
| 2018 | Rhinoceros beetle horn development reveals deep parallels with dung beetles |
Most tissues in multicellular organisms are maintained by continuous cell renewal processes . However , high turnover of many cells implies a large number of error-prone cell divisions . Hierarchical organized tissue structures with stem cell driven cell differentiation provide one way to prevent the accumulation of mutations , because only few stem cells are long lived . We investigate the deterministic dynamics of cells in such a hierarchical multi compartment model , where each compartment represents a certain stage of cell differentiation . The dynamics of the interacting system is described by ordinary differential equations coupled across compartments . We present analytical solutions for these equations , calculate the corresponding extinction times and compare our results to individual based stochastic simulations . Our general compartment structure can be applied to different tissues , as for example hematopoiesis , the epidermis , or colonic crypts . The solutions provide a description of the average time development of stem cell and non stem cell driven mutants and can be used to illustrate general and specific features of the dynamics of mutant cells in such hierarchically structured populations . We illustrate one possible application of this approach by discussing the origin and dynamics of PIG-A mutant clones that are found in the bloodstream of virtually every healthy adult human . From this it is apparent , that not only the occurrence of a mutant but also the compartment of origin is of importance .
Many tissues have a hierarchical multi compartment structure in which each compartment represents a cell type at a certain stage of differentiation . This architecture has been well described for hematopoiesis [1] , [2] and epidermal cell turnover in the skin [3] , [4] or in the colonic crypt [5] . At the root of this process are the tissue specific stem cells that have the capacity to differentiate into more specialized cells [6] . Each cell undergoes a series of cell divisions and differentiation steps until the whole diversity of the tissue is obtained [1] , [2] , [7]–[11] . The model presented here closely follows this concept . We introduce in total compartments , where each compartment represents a certain stage of cell differentiation with representing the stem cell pool . Each cell in a compartment replicates at a rate . If a cell in a non stem cell compartment replicates , it can undergo three different processes: With probability , it divides into two more differentiated cells that migrate into the adjacent downstream compartment . With probability , the cell dies . With probability , it divides into two cells that retain the properties of their parent cell and therefore remain in the same compartment ( self-renewal ) , as shown in Fig . 1 . Thus in compartment , the number of cells is increased by influx from the adjacent upstream compartment and self-renewal within compartment , and decreased by cell death in compartment and cell differentiation into the adjacent downstream compartment . One could also allow asymmetric cell divisions in non stem cell compartments . But the average dynamics in this case can be captured by modifying the differentiation probabilities . Thus , this case is implicitly included in our model . In the following , we shall assume a constant number of stem cells , following [1] , [12] . This can be achieved via asymmetric cell division [13] , [14] . However , one can also assume a process at the stem cell level in which cell differentiation , cell death and self renewal are balanced such that the average number of cells remains constant , i . e . . For immortal stem cells , , this means . However , for our purpose details of the dynamics in the stem cell compartment are not relevant , as long as the number of stem cells is constant . This model does neglect several aspects that may have an impact on the dynamics of the system under consideration , such as biochemical feedback or spatial population structure [15]–[19] . However , due to the generality , our model can be seen as a benchmark and thus allows to infer when such aspects are of importance and when they can be ignored by a comparison between the different model classes . One special case of our framework is the model of hematopoiesis discussed in [1] . There , cell death is neglected , for all . Furthermore , an exponentially increasing proliferation rate and a constant differentiation probability are assumed for all non stem cell compartments . In this work , we relax these conditions and therefore our analytical arguments hold for general values of , and in each compartment and thus for a wide class of related models and different tissues .
The individual cell model is based on a finite number of cells that divide and differentiate with certain probabilities . Thus , it is a stochastic process [20] and fits the current view of the stochastic nature of such cell differentiation processes [21] . However , the average cell numbers can be captured by a system of coupled differential equations that is deterministic . These equations follow from a proper counting of incoming and outgoing cells within each compartment . Let us assume that the number of stem cells is constant , following [1] , [12] . The number of cells in the first non stem cell compartment increases by influx from the stem cell pool at a rate and due to self renewal at a rate . In addition , the average number of cells in the compartment is lowered by cell differentiation into the next compartment at rate . Cell death in compartment occurs at rate . The dynamics in all other compartments is the same , except that the number of cells in the compartment increases due to influx from the adjacent upstream compartment at rate . Self renewal occurs at rate . decreases due to cell death at rate and cell differentiation at rate . Combining these terms and assuming in total compartments , we obtain a system of coupled differential equations ( 1a ) ( 1b ) ( 1c ) where and the dots denote derivatives with respect to the time . From now on , we use the abbreviation to denote the difference between the loss from compartment due to differentiation and cell death and the gain from self renewal . Thus , . Typically , we will have , and this net loss of cells will be compensated from the influx of cells from the upstream compartment . The simulations presented in this paper are individual based stochastic simulations . We implement all elements of the first compartments separately , thus we are able to record the dynamics of every single cell . Every cell division is called an event . We use a standard Gillespie algorithm [22] to determine in which compartment the next event takes place . After the compartment is determined , one cell in this compartment is chosen to divide proportional to the reproduction rate . The outcome of this event is determined by the cell death and differentiation probabilities and . The dynamics in the stem cell compartment is different: in our realization stem cells are allowed to divide asymmetrically only , thus we keep the number of stem cells constant . One could implement a Moran process on the stem cell level also and therefore allow dynamics on the stem cells [14] . However this would not change the aspects we look at in this paper . The number of stem cell events determine the time scale . We define 1 time unit as stem cell events . For example , in the hematopoietic system of a healthy adult human we assume that there are approximately stem cell divisions a year .
The equilibrium of the process is obtained from setting the left hand side of our system of differential equations to zero . Biologically , this corresponds to tissue homeostasis . In this case , we have ( 2 ) Next , we turn to the process of filling empty compartments by a continuous influx from the stem cell pool . Because we do not consider interactions between different cell clones in our differential equations , this corresponds also to the dynamics of a mutation arising in the stem cell pool . Thus , we choose the initial condition ( 3 ) The differential equation ( 1b ) for the compartment is an inhomogeneous linear differential equation of first order and can be solved by methods such as the variation of parameters . Assuming initial condition ( 3 ) , one obtains the solution for compartment ( 4 ) Because the differential equation for compartment depends on and only , one can insert ( 4 ) into ( 1c ) for and solve the resulting inhomogeneous equation through variation of parameters again , ( 5 ) Continuing with this procedure one can find the general pattern , which leads to a solution for general , ( 6 ) where we have introduced to shorten our notation . Equation ( 6 ) allows any choice of , and . Within the basic model assumptions depicted in Fig . 1 , this represents the most general case . All thinkable stem cell driven effects can now be described and followed in detail , as for example any change in the equilibrium compartment sizes or any change of cell division properties during cell differentiation . Compartments are continuously filled with cells until they reach the equilibrium described above . This can easily be deduced from ( 6 ) , because all terms involving decaying exponential functions in time will ultimately be irrelevant for the cell counts . If we choose ( i ) an exponentially increasing proliferation rate , ( ii ) constant differentiation probability and ( iii ) constant cell death for each non stem cell compartment , solution ( 6 ) simplifies to ( 7 ) with as a short cut . In Fig . 2 , equation ( 7 ) is compared to averages obtained from an individual based stochastic simulation . Note that is required to maintain an equilibrium . In this case we have , the cell count in a compartment under equilibrium conditions . This is in agreement with former results [23] . While we focus on the biologically relevant case of , we can also consider more general values of . For , the compartment produces more cells than it loses even in the absence of cell influx from upstream . Thus , the number of cells would grow exponentially according to equation ( 7 ) in each non stem cell compartment . For the gain of cells due to self renewal and the loss of cells due to differentiation and cell death in a compartment is equal . Thus the number of cells are not changed by processes in the compartment , despite a continuous output of cells into the next downstream compartment . The case can be solved directly from Eqs . ( 1a ) – ( 1c ) , which gives . Solution ( 6 ) describes the deterministic process of filling empty compartments within hierarchical organized tissue structures , as can occur during wound healing , recovery from hematopoietic stem cell transplantation [18] or of in vitro experiments with fetal liver cells [24] . However , it can also be viewed as the dynamics of a mutant clone arising from a single cell in the stem cell pool , . Thus , it is also possible to describe the average time development of diseases caused by mutations at the stem cell level such as the chronic myeloid neoplasms . Again , because we assume there is no interaction between normal cells and mutated cells , the dynamics of mutated cells proceeds independently , albeit with different differentiation parameters . Next , we turn to mutations occurring downstream of the stem cell compartment . The occurrence of a mutation in a non stem cell compartment is more likely than a mutation in the stem cell pool due to the higher numbers and proliferation rates of non stem cells . The dynamics of such a mutant is not driven by the stem cell pool and thus is not described by the solution form above , equation ( 6 ) . However , the compartment structure is unchanged and thus the dynamics of such mutants is also described by equations ( 1a ) – ( 1c ) , but with altered initial conditions . Assuming there is a mutation in compartment , the initial condition isHere represents the number of mutant cells in compartment at time , whereas the mutation occurred in compartment at time . According to this initial condition , the system of coupled differential equations ( 1a ) – ( 1c ) turns into a homogenous system and the dynamics of mutant cells is independent from the first equations , see Text S1 for details . Using the same tools as above , we obtain the general solution ( 8 ) with . Note that this equation describes the dynamics of chronic myeloid leukemia clones in [10] analytically and reduces to the solution in [17] in a special case . If , as in [1] , we assume ( i ) an exponentially increasing proliferation rate , ( ii ) a constant differentiation probability , ( iii ) constant cell death across all compartments , then the solution simplifies to ( 9 ) Fig . 3 a ) shows the dynamics of mutants in the first compartments , when the mutation arises in compartment . Note that for the most biologically plausible case , for large the exponential functions in ( 8 ) vanish . Thus the mutants will be washed out from the non stem cell compartments . Thus , the absence of mutants is a stable state of such hierarchical compartment structures . However , this equilibrium may not be of any biological or medical relevance , since the time to get rid of the last mutant cells of the clone may be longer than the normal expected lifetime of the healthy organism , cf . Fig . 4 . Fig . 3 b ) shows how the maximum of equation ( 9 ) and the time to reach it depends on , , and . As the proliferation rate of the mutant population decreases , the size of the mutant population in downstream compartments increases , although it will take ‘longer’ for the population to reach high levels . A mutation that increases the net loss of cells ( either by increasing cell differentiation or cell death ) in a compartment lowers the number of mutants at maximum size of the clone in downstream compartments , which is also reached earlier . Note also that mutations occurring later in the cell differentiation process will lead to smaller maxima that vanish faster [25] . Based on equation ( 8 ) , other mutant dynamics are also possible . If in a single compartment , mutant counts diverge exponentially in all downstream compartments . If in a single compartment and otherwise , in the long run mutants will reach an equilibrium in all downstream compartments , which is given by ( 10 ) This equilibrium is robust against variations of and thus is a stable fixed point . However a small change in would lead either to extinction or the divergence of the mutant cell count . For a more detailed discussion , see Text S1 . Initially , the difference between the dynamics of a clone arising from the stem cell compartment and an early non stem cell compartment is small , see Fig . 4 . In the long run the average mutant cell count is given by the dynamics of the slowest decaying exponential function of equation ( 8 ) . It is often natural to assume that this corresponds to the dynamics in the compartment of the mutant origin . Thus , if we assume that for all ( as in the hematopoiesis model in [1] ) , in the long run mutants will die out at a rate ( 11 ) This is shown in Fig . 5 a ) . For this special choice of parameters equation ( 11 ) becomes ( 12 ) Thus , the mutant cell count in the long run is given by a decaying exponential function . This enables us to calculate the average extinction time of mutants in the -th compartment , if the mutation occurred in compartment , ( 13 ) If we assume a constant differentiation probability , constant cell death and an exponential increasing proliferation rate again this simplifies to ( 14 ) In Fig . 5 b ) we compare the extinction time due to equation ( 14 ) to simulation results . This approximation does not allow to calculate the extinction time of the mutant of the compartment of origin , but a more detailed consideration of this case can be found in [23] . A special case of interest is a mutation with and . This results in a mutant cell that shows stem cell like properties in compartment and non stem cell like properties in higher compartments . In this case the set of differential equations ( 1a ) – ( 1c ) becomes ( 15 ) Equation ( 15 ) transforms into equation ( 1a ) – ( 1c ) if one shifts the index to . Thus we have to shift the index of the general solution ( 6 ) and find ( 16 ) Thus , the average dynamics of such a mutation is exactly as described above for a stem cell mutation . The averages of the simulation are described by equation ( 6 ) and ( 8 ) , but a single run is still stochastic . The relative standard deviation of lower compartments is of order , but decreases with increasing compartment number . Stochastic effects on the stem and the early progenitor cell level are important and can be crucial in a medical context [14] , [26] , but to understand some fundamental properties , the deterministic view seems to be sufficient . Here , we will utilize the model to illustrate the dynamics of a mutation that is seen in virtually every healthy human being . Sensitive flow cytometric analysis of circulating blood cells will identify a small clone that lacks expression of CD55 and CD59 ( amongst others ) [27] . CD55 and CD59 belong to a class of proteins that inhibit complement activation and their absence renders red blood cells sensitive to intravascular destruction . These proteins are normally displayed on the surface of cells since they are anchored to the plasma membrane via a glycosylphosphatidyl inositol ( GPI ) moiety . Synthesis of GPI requires a series of steps . The PIG-A gene encodes a protein that is an essential component of the complex responsible for the first step of GPI biosynthesis . Mutations in this gene can lead to a partial or complete deficiency of GPI synthesis resulting in low level or complete absence of such proteins from the cell surface , as for example the complement inhibitors CD55 and CD59 [28] , [29] . Red blood cells lacking CD55 and CD59 are destroyed by complement , leading to hemolytic anemia . As a result , mutations in PIG-A can explain the phenotype of paroxysmal nocturnal hemoglobinuria ( PNH ) , an acquired hematopoietic stem cell disorder characterized by anemia , hemoglobinuria and other manifestations [30] . A recent mathematical model suggests that a PIG-A mutation in a HSC is sufficient to explain the incidence and natural history of PNH [31] . However circulating blood cells with the PNH phenotype ( due to a mutation in PIG-A ) can be found in virtually every healthy adult human [27] . Such clones generally disappear with time . With this background , we will now apply the analytical solution ( 8 ) , to assess extinction times of PIG-A mutants and compare these results to in vivo data derived from healthy adult humans . The model parameters were fixed to represent hematopoeisis following [1] . In this approach , cell death is neglected , for all , and an exponentially increasing proliferation rate as well as a constant differentiation probability is assumed for all non stem cell compartments . Further , limited self-renewal is considered across many stages of differentiation , a prediction that is finding increasing support . For example , this was noted recently for cells at the proerythroblast stage , a cell type far removed from the stem cell or primitive progenitor cell pools [32] . Finally , the model parameters for human hematopoiesis become , , and . The number of cells per compartment increases exponentially and one needs compartments [1] to ensure that in a healthy adult human , on average , the daily bone marrow output is of the order of blood cells [33] . The same model can also be fitted to other mammals [34] . PIG-A mutants are considered to be neutral [35] , supported by in vivo measurments [36] , [37] . Thus we chose , as for normal cells , and as mutant parameters . For fully neutral mutants , the clone would either be present for too much time or it would not reach the level observed in vivo . We explored various values of and found that a slightly lower differentiation probability gave the best results . Note that this slight difference compared to healthy cells is still consistent with the experimental evidence . In Araten et al . [27] the blood of 19 healthy adult humans was sampled and tested for clones with PIG-A mutations . Mutants were found in every person ranging from 8 to 51 mutants per million ( with an average of 21 ) normal blood cells . Blood samples from the same patients were taken at later times to determine survival of these clones . The lower limit of detection in [27] was approximately 7 . 5 mutant cells per million . The detected maximum of 51 mutants per million healthy cells decreased after 164 days and was undetectable after 192 days . Individuals with the average cell count of 21 PIG-A mutants per million still had the clone present after 65 days , but it was not detectable after 174 days . We need to determine the compartment , where a mutation in PIG-A occurred , such that the clone that arises would grow to reach the detection threshold and remain detectable for a time compatible with observations . Using equation ( 8 ) , we record the dynamics of mutant cells in compartment 31 for different compartments of origin . In Fig . 6 , the mutant cell count per healthy cells in compartment 31 is shown , where the mutation took place in a ) compartment 10 , b ) compartment 11 and c ) compartment 12 . With these curves , one can predict extinction times for different origins of the mutation . Comparing the total size of the mutant population and the corresponding extinction times to values obtained in humans [27] allows to predict the compartments where the mutant clone originated . In Fig . 6 , we show the corresponding times from the mathematical model calculated from equation ( 8 ) . The same figure also illustrates that the time of origin of the mutation can be much earlier than the detection time . For example , if the mutation occurred in compartment 10 , we predict an extinction time of 230 days for the maximum of 51 mutants per million , when the initially sample was taken at time , see figure 6 a ) . The extinction time for the average cell count is 100 days . However , it should be clear that such mutant cells will survive for significantly longer than what is detectable by technology due to issues of sensitivity . With this in mind , there is good agreement between what the model predicts and the results described in [27] , since it is unlikely that the clones in all the individuals were either found as soon as they were detectable or when they were at their peak concentration . Thus what is relevant are ( i ) the distribution of times that these mutant cells remain in circulation and ( ii ) the size of the clones one observes . In this respect our model provides a very good approximation of the dynamics of such clones and is able to infer the cell of origin . If the mutation occurred in earlier compartments , the clone would be expected to expand to a higher cell count and will stay in the circulation for a longer time , but such clones are less likely to occur due to the lower number of progenitor cells and slower proliferation rate . Mutation events in higher compartments as are more likely to happen but these mutants would not be detectable by most current clinical flow cytometry techniques due to the small size of such clones in compartment 31 although they may be detected perhaps with polymerase chain reaction technology . Thus , compartments are the most likely compartments of the mutant origin for the cases described in [27] . Mutations arising in these compartments correspond to mutations in early progenitor cells . This agrees with the experimental results , since the PNH phenotype is present in several different cell lineages and thus has to occur early in the hematopoietic tree . Note that besides the compartment of origin , the only free parameter is the differentiation probability of mutated cells . Here we assumed that these circulating mutant cells all originate from one founder cell . Two or more independent , simultaneous or contemporaneous clones in early compartments would be unlikely [23] , [38] . If a second independent mutation occurs during the presence of an earlier mutant population , the total mutant cell number is the sum of both single populations , see Text S1 and Figure S1 for more details . Moreover the hierarchical structure of hematopoiesis provides an explanation why almost all humans carrying PIG-A mutations do not have symptoms of PNH . Only mutations in the most ‘primitive’ compartments have an impact and only mutations in a HSC will lead to disease . In general , one can predict the dynamics for mutants with very different properties using equation ( 8 ) . The compartment of the mutant origin can be inferred if one follows the mutant count by taking blood samples at regular intervals .
In this work , we presented closed analytical solutions for the deterministic dynamics of stem cell and non stem cell driven mutants in a multi compartment model of tissues such as hematopoiesis , the skin and the colon . This enables us to describe the dynamics of mutant cells in a general approach . We can predict the time development of a mutant depending on its origin and its specific proliferation properties . The process of cell differentiation is conceptually fairly well understood , but it is of course a challenge to estimate the various parameters in our model for real systems . Fortunately , very often , simplifying assumptions , e . g . exponentially increasing cell proliferation rates , can lead to insights [26] . However , our analytical solution allows us to incorporate more involved parameter dependencies , which could immediately be analyzed . Let us turn to hematopoiesis to address some of the implications of our model because recent technological developments allow the detection of well known mutations in many otherwise healthy people . Perhaps the best examples are derived from blood disorders , since repeated blood sampling is a minimal invasive procedure and molecular probes for many blood disorders are available . The case of PIG-A mutant cells present in healthy humans has been analyzed extensively in an earlier section . There are several other specific examples [25] . Our model provides a mathematical abstraction of hierarchically structured tissues and neglects many factors that can have an important impact on the dynamics , as for example spatial population structure or temporal changes of cell division properties , e . g . due to aging or injury . Nonetheless , the most important aspects of such tissue structures are captured by our model . It takes the form of ordinary differential equations that allows analytical solutions in many cases . An alternative would be a numerical solution , but such a solution has to be implemented for specific sets of parameters . We are convinced that our model can readily be applied to various hierarchical tissues and expect that general features of mutant dynamics will be conserved across different tissues . | We investigate the average stem cell driven dynamics of cell counts in an abstract multi compartment model . Within this framework one can represent different tissue structures , as for example hematopoiesis , the skin or the colonic crypt . Our analysis is based on an individual cell model in which cells can differentiate , reproduce or die . We give closed solutions to the corresponding system of coupled differential equations , that describe the average dynamics of all cell types . There are three cases of interest: ( i ) Mutations at the stem cell level , ( ii ) Mutations in downstream compartments associated with more mature , non stem cell types , ( iii ) Mutations in downstream compartments with cells acquiring stem cell like properties . The average dynamics shows for ( i ) and ( iii ) an increase of mutants towards an equilibrium , in case ( ii ) the average mutant cell count goes through a maximum , but mutants die out in the long run . We calculate the corresponding extinction times for every compartment . We discuss applications to hematopoietic diseases such as , PIG-A mutant cells or the classic oncogene BCR-ABL . Although the abstract model is a simplified sketch of cell differentiation , it is capable of describing many aspects of a wide variety of such tissues and associated diseases . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"theoretical",
"biology",
"disease",
"dynamics",
"population",
"dynamics",
"biology",
"population",
"biology"
]
| 2011 | Dynamics of Mutant Cells in Hierarchical Organized Tissues |
Salmonella enterica subspecies can establish persistent , systemic infections in mammals , including human typhoid fever . Persistent S . enterica disease is characterized by an initial acute infection that develops into an asymptomatic chronic infection . During both the acute and persistent stages , the bacteria generally reside within professional phagocytes , usually macrophages . It is unclear how salmonellae can survive within macrophages , cells that evolved , in part , to destroy pathogens . Evidence is presented that during the establishment of persistent murine infection , macrophages that contain S . enterica serotype Typhimurium are hemophagocytic . Hemophagocytic macrophages are characterized by the ingestion of non-apoptotic cells of the hematopoietic lineage and are a clinical marker of typhoid fever as well as certain other infectious and genetic diseases . Cell culture assays were developed to evaluate bacterial survival in hemophagocytic macrophages . S . Typhimurium preferentially replicated in macrophages that pre-phagocytosed viable cells , but the bacteria were killed in macrophages that pre-phagocytosed beads or dead cells . These data suggest that during persistent infection hemophagocytic macrophages may provide S . Typhimurium with a survival niche .
Salmonella enterica are Gram-negative bacteria that are acquired from contaminated food or water . Certain S . enterica subspecies can traverse the gut lumen of some mammals and then colonize lymphatic tissue , causing systemic infection . S . enterica subspecies Typhi colonize the human liver , spleen , and mesenteric lymph nodes , causing Typhoid fever . Approximately 5% of people with acute Typhoid fever progress to an asymptomatic chronic infection . These individuals intermittently shed the pathogen into community sewers and thereby serve as a reservoir for dissemination to naïve hosts [1] . Little is known about how bacteria establish chronic infections in otherwise healthy mammals . S . enterica subspecies Typhimurium cause infections of the liver , spleen , and mesenteric lymph nodes in mice . Like humans , mice can develop acute infections that progress to chronic infections . Historically , researchers have focused on the acute phase of infection using mouse strains that are homozygous for a loss of function mutation in the vacuolar cation transporter Slc11a1 ( Nramp1 ) . Slc11a1G169D mutant mice serve as a good model for acute infection because they are exquisitely sensitive to intravacuolar eukaryotic and bacterial pathogens [2] . For instance , they generally die within a week of inoculation with virulent S . Typhimurium . In contrast , Slc11a1 wild-type mice infected with S . Typhimurium survive acute infection and develop chronic infections that last for months or longer [3 , 4] . In this report we exploit Slc11a1 wild-type mice to investigate how S . Typhimurium establish chronic infection . To determine where S . Typhimurium reside during the early stages of chronic infection , we examined tissue sections from orally inoculated Slc11a1 wild-type mice . The bacteria were found within macrophages that had ingested other cell types . Macrophages that have ingested other cell types are also known as hemophagocytic macrophages . S . Typhimurium infection of hemophagocytic macrophages was modeled using primary mouse macrophages and a macrophage-like tissue culture cell line . Data suggest that S . Typhimurium survive and replicate within macrophages that phagocytosed viable host cells but are killed by macrophages that phagocytosed nothing or that phagocytosed dead host cells . These results indicate that hemophagocytic macrophages may provide S . Typhimurium with a survival niche in vivo during persistent infection .
To gain insight into how acute infections can become persistent infections , we examined the known sites of infection , livers , spleens , and mesenteric lymph nodes , in S . Typhimurium-infected 129SvEv Slc11a1 wild-type mice at 1- , 3- and 8-weeks post-infection . This time span represents several disease stages , including a period of strong innate immune response ( 1-week ) , strong adaptive immune response ( 3-weeks ) , and the beginning of clearance in some animals ( 8-weeks ) [3 , 5–7] . Mice were inoculated intragastrically with 5 × 108 virulent bacteria , a dose at which most individuals survive and become colonized for months or longer [4] . This resulted in approximately 104 bacterial colony-forming units ( CFU ) per gram of spleen or liver at 1- and 3-weeks post-infection , and 103 CFU per gram at 8-weeks post-infection . Fixed tissue sections were processed for immunofluorescence microscopy . S . Typhimurium was found within macrophages at all time points , consistent with previous reports [4 , 8 , 9] . Phalloidin labeling of actin facilitated visualization of tissue architecture and cell boundaries by confocal microscopy ( Figures 1 and 2 , Videos S1 and S2 ) . The macrophages within the livers and spleens of infected mice appeared multinucleate at 1- , 3- , and 8-weeks post-infection . Multinucleate macrophages were not observed in mock-infected mice , indicating that they are a distinct feature of infection . Quantification of the bacteria within mouse tissues was difficult due to the low number of S . Typhimurium in persistently infected mice . For instance , previous researchers were routinely able to detect S . Typhimurium in acutely infected mice with thick-section confocal microscopy at bacterial loads of 105 CFU/gram of tissue but could not visualize bacteria at 104 CFU per gram of tissue [9] . The bacterial load in our mice , which had not undergone pre-selection for high levels of colonization [4] , was 104 CFU per gram of tissue . A confocal microscope with an acousto beam splitter enabled limited quantification of bacteria at this level of colonization . Data indicate indicated that most S . Typhimurium that were clearly intracellular were within multinucleate macrophages at 1- and 3- weeks post-infection ( Table 1 ) . At 8-weeks post-infection , there were too few visible bacteria ( 103 CFU/gram of tissue ) per 50 μm section to quantify , but the occasional bacteria we did find were within multinucleate macrophages ( data not shown ) . Several observations suggest that macrophages within infected tissues were multinucleate due , at least in part , to phagocytosis of other host cells . First , actin rings were observed around many of the nuclei ( Figure 1I and 1J ) , consistent with phagocytosis [10 , 11] . Second , confocal microscopy with cell-type specific markers indicated that the nuclei represented cells of diverse types . Some of the nuclei likely represented engulfed macrophages , as the area immediately around them but within an actin ring was recognized with the macrophage-specific antibodies F4–80 ( cell surface ) and MOMA-2 ( cytoplasmic ) ( data not shown ) . Other nuclei co-localized with a marker that recognizes neutrophils , specifically an antibody to Ly-6G/Gr-1 , which stains peripheral granulocytes , including neutrophils ( Figure 2A–2C ) [12] . This suggests that some of the nuclei within macrophages were derived from phagocytosed neutrophils , which are normally recruited to sites of bacterial infection [9] . Additionally , hemophagocytic macrophages contained lymphocytes , as evidenced by staining with T or B cell specific markers ( Figure 2D–2H , Video S3 , and data not shown ) . The results collectively suggest that macrophages in S . Typhimurium-infected mice engulf multiple types of leukocytes and that such macrophages could provide S . Typhimurium with an in vivo niche . A normal function of tissue macrophages is to phagocytose and destroy dead or dying cells . However , several observations suggest that the engulfed host cells were not degraded at the time of tissue fixation . First , cell surface markers for different leukocytes were sufficiently intact to be detected ( Figure 2 ) . Second , the leukocyte nuclei within macrophages of S . Typhimurium infected mice appeared intact ( Figures 1 and 2 ) even though nuclear fragmentation is a known indicator of cell death . A highly sensitive method of detecting broken DNA , nick-end labeling , revealed few damaged nuclei within inflammatory lesions at 1- and 3-weeks post-infection ( Figure 3 and data not shown ) . Since nuclear and DNA fragmentation are late-stage markers of cell death , it was possible that the leukocytes ingested by macrophages in infected mice were at an earlier stage of death upon fixation . Tissue sections were examined for the presence of mature caspase-3 , which can be detected prior to and after DNA breakage in apoptotic cells [13] . Few caspase-3 positive cells were seen in liver sections at 4-days or at 1- , 3- , or 8-weeks post-infection . During this time frame , small diffuse inflammatory lesions ( 4-days ) developed into larger dense lesions ( 1- and 3-weeks ) and finally resolved into small lesions ( 8-weeks ) ( Figure 4 ) . However , significant death or degradation of the engulfed cells in infected tissues was not observed . Collectively , these observations suggest that ingested cells may have been alive upon phagocytosis and/or were not degraded by the hemophagocytic macrophages . To establish whether S . Typhimurium could preferentially survive within macrophages that have ingested viable versus dead host cells , an in vitro tissue culture infection assay was developed . Primary bone marrow–derived mouse macrophages ( BMDMs ) were generated from Slc11a1 wild-type mice . BMDMs were activated with the cytokine interferon-gamma ( IFNγ ) and LPS on the premise that in vivo S . Typhimurium are likely to encounter activated macrophages after the first few days of infection [14 , 15] . Activated BMDMs were incubated with media , polystyrene beads , apoptotic cells , necrotic cells ( data not shown ) , or live cells . Within thirty minutes , both beads and cells were phagocytosed by the activated BMDMs ( Figure 5B ) . As expected , many of the beads or cells added to the unactivated BMDMs were not phagocytosed and were therefore removed with washing ( Figure 5A ) . S . Typhimurium was added to the BMDMs 1-hour after the addition of beads or cells . Thirty minutes later , gentamicin was added to kill extracellular bacteria . Two hours post-infection , intracellular S . Typhimurium were enumerated by plating lysed BMDMs on selective media . There were up to 2-fold differences in the number of bacteria in BMDMs across samples ( Figure 6A ) , but the patterns of these differences varied between experiments and were not considered significant . By 18-hours post-infection , the number of intracellular S . Typhimurium declined in activated BMDMs that were pre-incubated with media only , beads , or dead cells ( Figure 6B ) . This is consistent with previous observations that activated BMDMs effectively kill S . Typhimurium [16–18] . However , BMDMs that phagocytosed viable cells prior to infection exhibited 2-fold bacterial replication by 18-hours and 35-fold replication by 42-hours ( Figure 6B and 6C ) . Similar results were obtained when BMDMs were incubated with Jurkat E6–1 cells , a human T cell derived line ( Figure 6 ) , or with DG-75 cells , a human B cell derived line ( data not shown ) . These results indicate that S . Typhimurium survives and replicates preferentially within BMDMs that have phagocytosed viable cells . The plating assay described above is a population assay . To determine the status of bacterial replication in individual BMDMs upon infection with S . Typhimurium , the number of bacteria per BMDM was determined by immunofluorescence confocal microscopy . BMDMs were incubated with live host cells and scored based on whether or not live cells had been phagocytosed . The number of intracellular S . Typhimurium rods within each BMDM was enumerated . By 18-hours ( data not shown ) and 42-hours post-infection , BMDMs that had ingested viable human ( Figures 5E , 5F , and 7A ) or mouse ( Figure 7B ) T cells contained more bacteria than BMDMs on the same cover-slip that had ingested nothing . The observation that both mouse and human T-lymphocyte derived tissue culture cells have similar effects suggests that this phenomenon is not species specific . These results corroborate the colony-forming unit analyses ( Figure 6 ) and indicate that macrophages which have phagocytosed viable cells could provide S . Typhimurium with a niche for replication . The BMDMs used above were derived from Slc11a1 wild-type mice . Many researchers work with Slc11a1G169D ( homozygous loss-of-function ) mouse macrophage-like cell lines , such as J774s or RAW264 . 7s . These cells allow S . Typhimurium to replicate to much higher levels than their wild-type counterparts [19] . S . Typhimurium replication was compared in RAW264 . 7 cells that did , or did not , phagocytose viable leukocytes . RAW264 . 7 cells were activated with IFNγ , incubated with viable human or mouse T-lymphocyte derived tissue culture cells , and then infected with S . Typhimurium . Intracellular bacteria were enumerated as described above , but consistent results were not obtained . Observation of individual cells by fluorescence microscopy indicated that relative to activated BMDMs , fewer activated RAW264 . 7 cells phagocytosed live T cells; human and mouse T cells were engulfed by 52 ± 3% and 87 ± 5% , respectively , of BMDMs , compared to only 8 ± 2% and 68 ± 2% , respectively , of RAW264 . 7 cells . To determine whether individual RAW264 . 7 cells that did ingest viable T cells were permissive for S . Typhimurium replication , intracellular bacteria were enumerated using fluorescence microscopy 18-hours ( data not shown ) and 42-hours post-infection . Experimental variation was minimized by comparing RAW264 . 7 cells with or without ingested T cells from the same infection-wells . As expected , RAW264 . 7 cells were quite permissive for S . Typhimurium replication and multiple bacteria were enumerated per cell . Nevertheless , uptake of either human or mouse T cells correlated with increased bacterial load ( Figure 8 ) . This suggests that S . Typhimurium survival in macrophages that have phagocytosed viable cells is a phenomenon that can occur in cell lines as well as in primary cells and is Slc11a1-independent .
Hemophagocytosis is the phenomenon of activated macrophages engulfing cells of the hematopoietic lineage [20] . It can occur in patients infected with evolutionarily diverse microbial agents , including Staphylococci , Mycobacteriae , Leishmaniae , Epstein-Barr virus , and influenza virus [20 , 21] . Hemophagocytosis is also associated with genetic , neoplastic , and rheumatic disorders . Patients with hemophagocytosis typically experience fever , splenomegaly , and cytopenias , specifically leukopenia , anemia , and thrombocytopenia [20] . Bone marrow , liver , or spleen biopsies may reveal numerous hemophagocytic macrophages in these patients [21] . While it is clear that in clinical situations hemophagocytosis is pathological [20] , it is unknown whether the phenomenon could benefit the host in certain situations or at sub-clinical levels . Hemophagocytosis is an established clinical feature of human typhoid fever . English-language observations of hemophagocytosis in typhoid patients date back to 1898 with the description of large phagocytic cells containing red and white blood cells in livers obtained from patients who died during the first couple weeks of infection [22] . More recent papers also describe hemophagocytosis in typhoid patients , sometimes referring to the hyperphagocytic macrophages as “typhoidal cells” [23–29] . For example , in one study , bone marrow biopsies were performed on 40 juvenile patients who tested positive for typhoid , paratyphi A or paratyphi B by blood culture and agglutination . Thirty-four patients ( 85% ) had macrophages that contained multiple cell types , including granulocytes , lymphocytes , blood platelets , and erythrocytes [30] . Thus , hemophagocytosis occurs in a significant subset of typhoid patients . We observed hemophagocytosis in a mouse model of typhoid fever . Tissue sections from infected mice revealed macrophages with multiple nuclei ( Figure 1 ) . Many of the nuclei represented phagocytosed leukocytes , as indicated by the staining of material around these nuclei with cell-surface markers for neutrophils , T cells ( Figure 2 ) and B cells ( data not shown ) . It seems unlikely that the phenomenon observed is macrophage ingestion of dead leukocytes , as the leukocyte nuclei , DNA , and cell surfaces appeared intact . Moreover , infected tissues did not contain significant numbers of cells with activated caspase-3 ( Figures 1–4 ) . Based on these data , we hypothesized that infected tissues contained macrophages that had phagocytosed viable leukocytes , indicating that they were hemophagocytic . It is difficult to experimentally determine within an animal model whether engulfed cells of infected tissues were alive or dead upon phagocytosis . Therefore a cell culture assay was developed to test the corollary that S . Typhimurium survives preferentially in macrophages that phagocytosed viable leukocytes versus dead or no leukocytes . This assay relied upon activating macrophages with the inflammatory cytokine interferon-gamma ( IFNγ ) , which can play a major role in maintaining and possibly establishing hemophagocytosis in humans [31] . Severe systemic hemophagocytosis is often rapidly fatal due to a dramatic reduction in circulating red blood cells . Patients with severe hemophagocytosis have high IFNγ blood serum levels and can be successfully treated with a combination of inhibitory anti-IFNγ antibodies and blood transfusion . This indicates that IFNγ is important for maintenance of the pathological state , likely via macrophage activation [21] . One consequence of in vitro macrophage activation with IFNγ is increased phagocytosis of particles , including beads , dead cells , and live cells ( Figure 5 and data not shown ) . Therefore , IFNγ-activated tissue culture macrophages that had engulfed different particles were used to establish whether macrophages that ingested viable host cells preferentially allow S . Typhimurium to survive . First , activated primary bone marrow–derived macrophages were incubated with beads , viable lymphocytes , or dead lymphocytes , each of which became phagocytosed ( Figures 5 and 6 ) . The macrophages were infected with S . Typhimurium and bacterial survival was evaluated over time . Bacteria were not visualized within lymphocytes under our experimental conditions . This is consistent with a report that S . Typhimurium does not infect T cells [32] , but another group found that under some conditions , the bacteria can infect B cells [33] . In our experiments , S . Typhimurium survived and replicated only in macrophages that ingested viable cells ( Figures 5–7 ) . This was not likely a function of the number of phagocytosed live versus dead cells because similar numbers of each were engulfed by macrophages across experiments ( Figure 6A ) . Analogous experiments were performed with RAW264 . 7 cells , a commonly used macrophage-like mouse cell line . Individual RAW264 . 7 cells that had phagocytosed viable mouse or human T-lymphocytes contained more intracellular S . Typhimurium than RAW264 . 7 cells that did not engulf T cells ( Figure 8 ) . These observations suggest that tissue culture macrophages as well as primary macrophages will be useful for identifying molecular mechanisms of S . Typhimurium replication in macrophages that engulfed viable cells . Moreover , S . Typhimurium survival in macrophages that engulfed viable mouse or human cells suggests that the phenomenon is not limited to mice . Thus , macrophage phagocytosis of viable cells and/or subsequent infection with S . Typhimurium may inhibit S . Typhimurium killing by the macrophage and could provide the bacteria with a survival niche in vivo . It is surprising that the observation that S . Typhimurium can reside within hemophagocytic macrophages in mice has not been previously reported , particularly since immunofluorescence microscopy of tissue sections from S . Typhimurium infected mice has been performed by multiple laboratories [8 , 9 , 34] . One explanation for this discrepancy may be that salmonellae researchers historically work with mouse strains that are homozygous for Slc11a1G169D , and significant hemophagocytosis may not occur in these mice ( e . g . C57Black6 and Balb/c strains ) . In light of this , it is interesting that activated Slc11a1G169D RAW264 . 7 cells that phagocytosed viable lymphocytes do allow S . Typhimurium to replicate ( Figure 8 ) . This could suggest that the apparent absence of significant hemophagocytosis in Slc11a1G169D mice is due to differences between the mutant and wild-type mice at the tissue and/or organismal level . Slc11a1G169D mice experience abnormally high levels of bacterial replication in macrophages [9] and B cells [33] , massive inflammatory infiltration into infected tissues , and death within a week [35] . It is possible that inflammation or death masks or prevents hemophagocytosis in these animals . Wild-type mice survive acute infection and become carriers . At 11 weeks post-infection , S . Typhimurium were found within macrophages in wild-type mice , but multinucleate macrophages or hemophagocytosis were not reported [4] . We observed bacteria in multinucleate macrophages as late as 8-weeks post-infection ( data not shown ) , but have not examined tissues from later time points . One possibility is that hemophagocytic macrophages may represent a niche for S . Typhimurium survival early , but not late , during infection . This is of interest because salmonellae survival in hemophagocytic macrophages could play a role in the establishment of chronic infection . Future experiments will be needed to resolve these issues . How might hemophagocytosis lead to the alteration of a macrophage such that it can no longer effectively control S . Typhimurium ? One possibility is based on observations that macrophage interactions with viable cells involve receptor-ligand responses that can alter macrophage activation states [36 , 37] . Surface proteins on viable cells activate SIRPα/SHPS-1 on macrophages . SIRPα/SHPS-1 activation initiates an inhibitory tyrosine phosphatase cascade that blocks FcγR- and CR3-mediated phagocytosis , which are functional markers of macrophage activation [38 , 39] . Viable cell surface proteins that activate SIRPα/SHPS-1 include immunoglobulin superfamily member CD47/IAP and integrins [40] . These data suggest that viable cell activation of SIRPα and/or other macrophage receptors could partially or wholly inactivate macrophages such that they cannot control S . Typhimurium replication . Activation of surface receptors could also condition macrophages such that they are vulnerable to inactivation by an S . Typhimurium-specific mechanism . This is intriguing in part because there is evidence that systemic salmonellae can delay and/or blunt immune responses [41] . For instance , Salmonella enterica serotype Typhi has a capsule , Vi-antigen , that down-regulates the host TLR response , a major arm of the innate immune system [42 , 43] . Finally , it is also possible that cell surface receptor-ligand interactions alone are insufficient to alternatively-activate or condition macrophages , and that the actual engulfment of viable cells is required . Additional evidence that macrophage activation states are altered upon interaction with large , live particles is found upon examination of NCBI GEO DNA microarray datasets . There are currently no datasets that examine macrophage gene expression changes upon exposure to viable leukocytes . However , analyses that quantify macrophage and dendritic cell responses to the single-celled eukaryotic pathogens Leishmania donovani , Leishmania major , and Toxoplasma gondii reveal multiple changes in genes that regulate or are markers of macrophage activation state , such as EST2 , STX11 , LST1 , and HLA-DMB ( see Table S1 ) . This is consistent with the idea that the response of macrophages to interaction with other live eukaryotic cells is complex and can involve changes in activation state . The in vivo and in vitro evidence suggest that S . Typhimurium may use hemophagocytic macrophages as a survival niche in mice , and that this phenomenon may model a clinical feature of human typhoid fever and other infectious diseases . This provides researchers with an opportunity to study a poorly understood feature of human typhoid fever in a tractable animal model . Moreover , tissue culture hemophagocytosis assays will allow for the dissection of the molecular mechanisms by which the phagocytosed viable cells and/or the bacteria manipulate activated macrophages such that they become permissive for bacterial survival and replication .
Salmonella enterica serovar Typhimurium wild-type strain SL1344 [44] was grown overnight at 37 °C with aeration prior to infections . Antibiotics were used at the following concentrations: streptomycin , 30 μg/ml; kanamycin , 30 μg/ml; chloramphenicol , 20 μg/ml . For mouse infections , 7-week-old female 129SvEvTac mice ( Taconic Laboratories ) were without food for 10–12 hours prior to intragastric inoculation with 5 × 108 bacteria in 100uL of PBS . Liver and spleen samples were fixed in 4% paraformaldehyde , embedded in 2% agarose , and cut into 50 μm sections on a Leica Vibratome VT1000S . Sections were incubated in serum-free protein block ( Dako Cytomation ) containing 0 . 2% saponin and then with subsets of the following primary antibodies: rabbit anti-S . Typhimurium LPS O-antigen Group B polyclonal antisera ( 1:500; BD Biosciences ) , rat anti-mouse F4–80 and MOMA-2 ( 1:10; Serotec ) , phalloidin-Alexa488 ( 1:200; Molecular Probes ) , biotin-conjugated anti- Gr-1/Ly-6G/RB6-8C5 ( 1:25; MCA771B , Serotec ) , and biotin-conjugated hamster anti-CDɛ ( 1:25; BioLegend ) . Anti-Gr-1/Ly-6G/ RB6-8C5 recognizes a low-molecular-weight phosphatidylinositol-anchored cell surface glycoprotein expressed on granulocytes [12] , a subset of eosinophils , plasmacytoid dendritic cells ( which produce IFNα and IL-12 in response to viruses but not bacteria ) [45] , and transiently in the bone marrow during developmental stages of monocytes . This antibody does not cross-react with Ly-6C [46] , as previously reported [47] . Sections were incubated with the following secondary antibodies: goat anti-rabbit-Alexa568 , goat anti-rat-Alexa680 , streptavidin-Alexa514 , anti-hamster-Alexa546 ( Molecular Probes ) . Sections were incubated with DAPI and mounted in ProLong Gold anti-fade reagent ( Molecular Probes ) . Nick end-labeling was performed using Formalin-Fixed , Paraffin-Embedded ( FFPE ) tissues sectioned at 4 μm on a Leica Microtome RM2035 , processed according to the Fluorescein FragEL DNA fragmentation Detection kit ( Calbiochem ) instructions , and counterstained with DAPI . Samples were analyzed on a Leica TCS SP2 Confocal Laser Scanning Microscope ( CLSM ) and processed with Image Analysis software . For activated caspase-3 labeling , FFPE 4-μm thin sections were stained with hematoxylin and eosin , incubated with rabbit anti-cleaved-caspase-3 antibody ( Serotec ) and anti-rabbit-Alexa568 , and analyzed with light microscopy . Throughout experiments both liver and spleen were examined , but only liver images are shown because the regularly shaped hepatocytes facilitate visualization of irregularly shaped macrophages . Tissues from 4 mice at 1-week post-infection , and 2 mice at 3- , 4- , and 8-weeks post-infection were examined . Bone marrow–derived macrophages ( BMDMs ) were isolated as previously described [48] . Briefly , marrow was flushed from femurs and humeri of 3 . 5–4 . 5 week old 129SvEvTac ( Taconic ) mice . Stem cells were isolated by overlaying on Histopaque-1083 ( Sigma-Aldrich ) and grown in Dulbecco modified Eagle medium ( DMEM; Sigma-Alrich ) supplemented w/ 10% heat-inactivated fetal bovine serum ( FBS; HyClone ) , glutamine , sodium pyruvate , and 10 ng/ml granulocyte macrophage colony stimulating factor ( GM-CSF; PeproTech ) at 37 °C , 5% CO2 for 6 days [49 , 50] . Cells were assayed for expression of macrophage-specific markers , specifically a mixture of F4–80 and MOMA-2 as described above . BMDM or RAW264 . 7 cells ( both are referred to as macrophages here , for clarity ) were seeded at 105 cells per well in poly-L-lysine-coated 24- or 96- well tissue culture plates . Cells were activated with 20 ng/ml lipopolysaccharide ( S . enterica Typhimurium LPS; Sigma-Aldrich ) and/or 20 U/ml IFNγ ( PeproTech ) for 18 hr and activation was measured with Griess assays . Activated and unactivated macrophages were incubated with media alone , polystyrene beads ( 2μm; Molecular Probes ) , or lymphocytes that were necrotic ( 30 min −80 °C ) , apoptotic ( 30 min at 56 °C [51 , 52] ) , or live . The lymphocytes included human T-cell-derived Jurkat E6–1 cells [53] , human B-cell derived DG-75 cells [54] , and mouse transgenic T-cells expressing RFP ( Remi Creusot , Stanford University ) . Beads or cells were added to the macrophages at a ratio of 10:1 ( beads/cells: macrophages ) . After 30 min ( RAW264 . 7s ) or 1 hr ( BMDMs ) macrophages were washed and infected for 30 min with normal mouse serum ( Sigma ) -opsonized S . Typhimurium at a multiplicity of infection of 20 ( BMDMs ) or 10 ( RAW264 . 7s ) . Cells were washed and incubated for 1 . 5 hr at 37 °C in fresh media supplemented with gentamicin ( 100μg/ml ) to kill extracellular bacteria . Media was exchanged for media supplemented with gentamicin ( 10 μg/ml ) to prevent extracellular bacterial growth . At 2 , 18 or 42 hr , wells were washed twice with pre-warmed PBS , incubated with 1% Triton X-100 for 5 min , lysed , and serial dilutions plated for colony-forming units . Percent survival was calculated by dividing CFUs obtained after 18 or 42 hr , by the initial number of intracellular bacteria after 2 hrs . Release of lactate dehydrogenase ( LDH ) , a eukaryotic cytoplasmic enzyme , into the media correlates with cell death and was measured with the Cytotox-One kit ( VWR ) according to the kit instructions . There were not significant differences in BMDM cell death between samples or over the course of the experiments ( data not shown ) . For immunofluorescence visualization of macrophages , the cytoplasm of Jurkat E6–1 or DG-75 cells was pre-labeled with CMFDA ( Molecular Probes ) . Co-cultures were fixed with 4% paraformaldehyde and permeabilized with ice-cold methanol . Bacteria and/or macrophages were stained and visualized as described above . Statistical analyses were performed using a Students t-test .
Derived from EntrezGene ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=gene ) . Homo sapiens CD4/IAP/MER6/OA3 ( 961 ) ; Homo sapiens Leukocyte Specific Transcript 1 LST1 ( 7940 ) ; Homo sapiens Major Histocompatibility Complex , Class II , DM Beta HLA-DMB ( 3109 ) ; Homo sapiens Signal-Regulatory Protein Alpha /SIRPalpha/SHPS-1 ( 140885 ) ; Homo sapiens Syntaxin 11/ STX11 ( 8676 ) ; Homo sapiens V-Ets Erythroblastosis Virus E26 Oncogene Homolog 2 ( avian ) /ETS2 ( 2114 ) ; Mus musculus CD3/ Cd247/ 4930549J05Rik/ AW552088/ CD3-eta/ CD3-zeta/ Cd3h/ Cd3z/ T3z/ TCRk/ Tcrz ( 12503 ) ; Mus musculus F4–80/ Cell Surface Glycoprotein F4/80; Lymphocyte Antigen 71/ DD7A5–7/ EGF-TM7/ F4/80/ Gpf480/ Ly71/ TM7LN3 ( 13733 ) ; Mus musculus Lymphocyte Antigen 6 Complex , Locus G/ Ly-6G/Gr-1/Gr1/Ly6g ( 17072 ) ; Mus musculus Slc11a1/Nramp1 ( 18173 ) . | Microbes that establish persistent infections present serious problems for world health but are not well understood . The bacteria Salmonella enterica cause asymptomatic chronic infection in humans . Carriers shed the bacteria into the environment , leading to periodic acute typhoid fever epidemics . Antibiotics are effective at treating typhoid fever , but Salmonellae strains resistant to multiple antibiotics have caused recent epidemics . New therapeutic strategies are needed and may develop from a molecular understanding of how the bacteria avoid killing by our immune systems . During acute and chronic infection , Salmonellae reside within macrophages , a kind of white blood cell type that normally destroys bacteria . Evidence is presented that during the establishment of chronic infection of mice , the bacteria can live within a special kind of macrophage . Hemophagocytic macrophages are macrophages that have ingested white and red blood cells . They are a clinical marker of typhoid fever and many other kinds of microbial infections . Cell culture assays showed that Salmonellae preferentially survive in hemophagocytic macrophages . These data suggest that hemophagocytic macrophages may provide S . Typhimurium with a survival niche during chronic infection . Moreover , a natural mouse model and a cell culture assay now exist for studying the medically important phenomenon of hemophagocytosis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
]
| [
"infectious",
"diseases",
"microbiology",
"mus",
"(mouse)",
"homo",
"(human)",
"eubacteria"
]
| 2007 | Hemophagocytic Macrophages Harbor Salmonella enterica during Persistent Infection |
Viruses diversify over time within hosts , often undercutting the effectiveness of host defenses and therapeutic interventions . To design successful vaccines and therapeutics , it is critical to better understand viral diversification , including comprehensively characterizing the genetic variants in viral intra-host populations and modeling changes from transmission through the course of infection . Massively parallel sequencing technologies can overcome the cost constraints of older sequencing methods and obtain the high sequence coverage needed to detect rare genetic variants ( <1% ) within an infected host , and to assay variants without prior knowledge . Critical to interpreting deep sequence data sets is the ability to distinguish biological variants from process errors with high sensitivity and specificity . To address this challenge , we describe V-Phaser , an algorithm able to recognize rare biological variants in mixed populations . V-Phaser uses covariation ( i . e . phasing ) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity . Overall , V-Phaser achieved >97% sensitivity and >97% specificity on control read sets . On data derived from a patient after four years of HIV-1 infection , V-Phaser detected 2 , 015 variants across the ∼10 kb genome , including 603 rare variants ( <1% frequency ) detected only using phase information . V-Phaser identified variants at frequencies down to 0 . 2% , comparable to the detection threshold of allele-specific PCR , a method that requires prior knowledge of the variants . The high sensitivity and specificity of V-Phaser enables identifying and tracking changes in low frequency variants in mixed populations such as RNA viruses .
Genetic differences can arise among individual viral particles within an infected host , and detecting these viral genetic variants can reveal how viruses adapt to challenges such as host immune responses , antiviral medications , and transmission bottlenecks . However , detecting rare variants is difficult with existing sequencing technologies due to low sensitivity , high error rates , and/or poor scalability . For example , bulk-sequencing approaches generate a consensus assembly , but they have limited sensitivity to detect intra-host variation [1] . One approach to increase sensitivity is to amplify and clone selected fragments of viral nucleic acids into proliferating targets that are subsequently isolated and sequenced [2] , but this method has a higher false positive rate and poor scalability . To reduce errors , the single genome amplification ( SGA ) method isolates individual viral genomes through dilution , and then amplifies and sequences each genome individually to minimize introduced errors [3]–[5] , although scalability remains an issue . Rare variant detection requires deep coverage that is not cost-effective with current methods of cloning or SGA . To address scalability , massively parallel sequencing technologies can isolate and sequence individual DNA or cDNA molecules en masse from the population of viral genomes and generate millions of short read sequences that can increase the sensitivity and decrease the cost to detect variants [6] , [7] . Still , increased error rates can somewhat impact potential gains in sensitivity . Here , we report on a novel method to detect rare variants that increases sensitivity even in the presence of process errors . Detecting biological variants involves not only finding them , which deep sequencing technologies can do with high sensitivity , but also differentiating them from process ( i . e . amplification or sequencing ) errors . One way to do this is to compare variants to a distribution of errors . For example , several authors have reported using a Poisson or binomial probability model to define the error distribution , and they can call candidates that fall outside the distribution variants [8]–[13] . These models , however , assume that all bases have equal quality scores , where the base quality score is a measure of how accurate the base call is . This assumption is invalid for bases measured by massively parallel sequencing technologies , as Brockman et al . [14] have shown , since base quality can vary by several criteria; in fact , sequencing technologies take criteria such as these into account when assigning base quality scores . To avoid this assumption , probability models can incorporate base quality scores . Such probability models exist in tools that call single nucleotide polymorphisms ( SNPs ) in human and other diploid genomes , including MAQ [15] , SoapSNP [16] , Unified Genotyper [17] , SNVMix [18] , or Slider [19] . In contrast , instead of an explicit error probability model , Hoffman et al . [20] compare variants to an empirical control data set . Archer et al . [21] and Rozera et al . [22] report methods that correct read sequences for suspected process errors prior to calculating variant frequencies . Archer et al . [21] use a k-mer mapping approach to position reads on a consensus template and refine alignments locally , and Rozera et al . [22] turn to heuristic rules to filter out errors based on cutoffs for base quality scores and other criteria . Both strategies avoid using an explicit probability model of error and hence assume that all process errors take a specific form , and that no biological variants take the same form as the process errors . The above models separate variants from error using specific forms or heuristics or a probabilistic distribution . An alternative approach is to consider patterns of candidate variants . For example , Eriksson et al . [9] use Fisher's exact test to find patterns that occur more frequently than expected by chance to call variants . Refining this approach further , several authors probabilistically cluster patterns to infer variant haplotypes [9] , [11] , [12] , [23]; the cluster centers are haplotypes , and process errors can be removed by collapsing variation within the cluster . Since patterns of variants are essentially groups of variants that occur at the same loci on multiple reads , i . e . in phase , we can analyze them together as a group of phased variants , and we can compare them to phased errors in the same pattern . Phased errors presumably occur much less frequently than errors in general , making it easier to recognize phased variants . To address the challenge of calling rare genetic variants in diverse populations in the presence of error , we introduce V-Phaser , a single nucleotide variant calling tool that uses phase and quality filtering with a probability model that incorporates and recalibrates individual base quality scores . To increase sensitivity , V-Phaser looks not only for variants that fall outside the distribution of errors but also for patterns of variants in phase . To increase specificity , it incorporates individual base quality scores into a composite Bernoulli model that allows error rates to vary from base to base . It also uses a pre-processing filter to screen out low quality bases and improve the fit of the model . We calculate the theoretical gain in sensitivity of detecting variants using phase to increase specificity . We then validate V-Phaser on read sets with known variation generated by the 454 sequencing platform to estimate sensitivity and specificity . To determine the effect of each algorithmic step on performance , we evaluate the method with each of three features ( phasing , recalibration , and filtering ) turned off and compare these results to those achieved on the same data with several other viral variant callers . Finally , we use V-Phaser on data from a chronically HIV-1 infected subject to demonstrate its utility to detect low frequency variants in viral populations .
Variant calling algorithms typically use a probabilistic or empirical error model to define the distribution of errors , and they recognize those candidates that fall outside of this distribution as variants . We define the boundary between variants and errors to be the variant detection threshold frequency ( VDTF ) . To this definition , we add the concept of phasing , where phased variants co-occur on the same reads , to distinguish unphased VDTFs from phased VDTFs , which separate phased variants from phased errors . V-Phaser uses both phased and unphased VDTFs to increase sensitivity . If errors are distributed uniformly at a rate p , we cannot use unphased VDTFs to find variants that occur below this rate no matter how deeply we sequence the population , since the unphased VDTF cannot fall below p . In contrast , paired errors occur at the much lower rate of p2 , and correlated variant pairs can be detected at much lower frequencies than p , so long as that frequency remains above p2 and the depth of sequencing is sufficient . Theoretically , we can define phased VDTFs for any pattern of variants , but in practice the only patterns V-Phaser considers are paired variants in phase . V-Phaser can call paired variants at a lower frequency using phased VDTFs compared to unphased VDTFs , and given comparable frequencies , V-Phaser can call phased variants at lower coverage . We can calculate phased and unphased VDTFs as a function of coverage and error rate ( Figure 1 ) . At any level of coverage and error rate , the phased VDTF is lower than the unphased VDTF . In addition , the phased VDTF remains relatively flat over a range of error rates , whereas the unphased VDTF manifests more dynamic range , which suggests that compared to the unphased VDTF , even if the probability model grossly misspecifies error rates , the phased VDTF is relatively robust . V-Phaser detects variants in phase when they occur on the same reads , so to be on the same reads , variants need to be close to each other . When variants are close together , many of the reads that cover one variant will also cover the other variant , but when variants are farther apart , fewer and fewer reads will begin and end in just the right places to span both . At some point , the gain from phase will be offset by the loss of shared coverage . To capture this concept , we define the phase distance to be the farthest distance from a locus such that compared to the unphased VDTF at that locus , the phased VDTF is lower ( i . e . more informative ) . If variants are farther apart than the phase distance , they do not have enough shared coverage to increase sensitivity . We show that the phase distance is longer than half of the average read length for coverage more than 65-fold , and as coverage increases , it approaches the length of the average read ( Figure 2 ) . Just as increasing coverage increases sensitivity to detect variants , it also increases the chances to detect phased variants that are farther apart . Errors introduced by massively parallel sequencing technologies can be correlated , and models to detect correlated variants can also detect correlated errors , as well . On control read sets without variants , we found that errors vary with base quality score , the position of the base on the read , and the transition from the previous base ( Figure 3 a–c ) . DePristo et al . [17] use recalibration equations in their Unified Genotyper to adjust for these associations and call SNPs . V-Phaser minimizes false positive correlated errors by filtering out errors and modeling the correlations among errors . First , as detailed in the Materials and Methods section , V-Phaser uses a read cleanup algorithm , ReadClean454 [24] , to identify and correct process errors in the reads and then utilizes a Neighborhood Quality Standard ( NQS ) criteria to filter out low quality bases . From the remaining high quality bases , V-Phaser builds an error probability model to adjust for correlations . Modeling base quality well is the key to achieving high specificity , but highly variable viral sequences pose a difficult challenge . To estimate the parameters , models are often fit to highly conserved genomic regions without variation , but such regions do not exist for small , diverse viral genomes . Models can also be fit to empiric negative controls , but error rates can vary from lane to lane or from run to run . Instead , V-Phaser uses an expectation-maximization ( EM ) algorithm to iteratively fit its probability model as it calls variants . Initially , V-Phaser treats all mismatches as errors and estimates the parameters accordingly using the recalibration equations of the Unified Genotyper [17] . In the E step , V-Phaser uses the model to calculate the VDTFs to call variants and remove them from the error list . Then in the M step , V-Phaser updates the parameters to the model . V-Phaser iterates until the number of variants called stabilizes . To evaluate V-Phaser's performance , we used read sets with known variability generated by the 454 FLX sequencing platform . Using these control data , we validated the variants called by the comprehensive algorithm and also evaluated the contribution of each core component of V-Phaser to the model's sensitivity and specificity . First , we assessed the effect of using or not using phased variants by invoking a version of V-Phaser that only used unphased VDTFs to identify variants . Second , we measured the effect of using individual base quality scores utilizing a version of V-Phaser that estimates two uniform error rates , for homopolymer and nonhomopolymer regions . Finally , we tested the impact of low quality base filtering invoking a version of V-Phaser without NQS pre-processing filters . The positive control data were 454 read sets derived from an artificial mixture of eight strains of West Nile Virus ( WNV ) for which we knew the individual strain sequence . We limited our analysis to regions of the genome covered by all eight individual sequences . Differences among the individual consensus assemblies defined the WNV variant set; a total of 110 variants were defined . We scored any error call that V-Phaser made on this set of variants as a false negative , and any variant call as a true positive . Of the 110 variants in the WNV variant set , 102 variants were detected with frequencies ranging from 0 . 3% to 47 . 5% , and a median frequency of 11 . 3% ( Table S1 ) . Eight variants were not observed on any sequence reads . V-Phaser called 100/102 variants present in the data resulting in a sensitivity of 98% ( Figure 4a ) , including 15/17 ( 88% ) of the variants at frequencies under 1% ( Figure 5 a–d ) . All versions of V-Phaser could detect 100% of the variants above 2 . 5% , but without phased VDTFs V-Phaser could detect only 9/17 ( 53% ) of the minor variants present at less than 1 . 0% in frequency , and it still missed other variants with frequencies as high as 2 . 4% . Of the remaining 10 , 004 loci assumed to be non-variant based on consensus sequence comparison , V-Phaser called 143 variants , for a putative specificity of 99% . Out of 555 loci that showed variation in the mixture read set but not in the parental strains , V-Phaser correctly called 74% of them as errors . It is possible that many or most of the mistaken variant calls could be artificial variants or mutations that were introduced somewhere in the process of creating the mixture , rather than sequencing errors . Because of the unknown rate of novel variants introduced during passage of the WNV samples , we used an infectious clone ( HIV NL4-3 ) as a negative control to more accurately measure the specificity of V-Phaser . We scored any variant calls that V-Phaser made on the negative control as a false positive , and any error calls as a true negative . Among all loci in the negative control read set , 87% had no mismatches . Considering only the loci that harbored variation , all versions of V-Phaser maintained specificity greater than 97% if they incorporated individual base quality scores , but for the version using uniform errors , the specificity fell to 91% ( Figure 4b ) . Among these sites with variation , V-Phaser called 29 sites that ranged in frequency from 0 . 4% to 5 . 6% as true variants; some of these sites may actually be biological variants and not process errors ( see discussion below ) . If the composite Bernoulli model correctly described the error distribution , then 95% of the time V-Phaser would not make any false positive calls on the entire sample . Clearly , the composite Bernoulli model fits the error distribution better than a uniform error model , but the false positives are evidence that at least some errors did not follow the model . We tested the validity of the composite Bernoulli model by assessing how well the model fit the error distribution with and without filtering using a quantile-quantile ( q-q ) plot as described in the Materials and Methods section . Compared to the unfiltered data , the filtered data produced a model that fit the observed error distribution better ( Figure 6 a–b ) . Without pre-processing filters , V-Phaser systematically overestimated the probability of error . This overestimation of the model seemed to be a function of the number of low quality bases . As we sampled without replacement from 1% to 100% of the reads , we saw an increasing skew in the q-q plot ( Figure S1 a–f ) . In addition , to test whether homopolymer related artifacts in 454 sequencing were causing V-Phaser to overcall variants , we examined the error calls made by V-Phaser on the clonal HIV NL4-3 data . Since homopolymer related artifacts systematically violate model assumptions , the resulting overcalls would be expected to cluster in or near homopolymer regions . False positives were not significantly more likely to be observed in homopolymer nucleotide runs , nor in regions proximal to these runs , as compared to residues outside of these regions , regardless of whether a variant is called with phase or without phase ( χ2 test p>0 . 4 in all comparisons ) . These results were consistent if we extended the homopolymer flanking regions to three or four instead of two bases . Therefore , false positives appear to be unrelated to homopolymer related artifacts and V-Phaser appears to have no strong susceptibility to errors induced by system error on the 454 sequencing platform . We ran several other variant calling programs on our control data sets . The programs ShoRAH [12] and ViSPa [25] both generated compute errors that were not easily resolved by the software's authors when run on our data set . We successfully ran Segminator II [21] and QuRe [13] . QuRe filters regions of the genome that have less than 30-fold read coverage or are below the 5th percentile of coverage ( defaults ) ; as such comparison of sensitivity and specificity across the various algorithms was computed only across the bases interrogated by QuRe . Sensitivity and specificity of the three programs , in addition to results for V-Phaser with phasing turned off , are shown in Table 1 . V-Phaser outperformed both of the other programs in specificity . Although Segminator II had 100% sensitivity , it achieved this at the expense of a very high false positive rate , calling variants at more than 20% of the examined invariant sites . Notably , the counting of inserted or deleted bases as false positives can significantly impact reported specificities . V-Phaser reports a deleted base in two instances while QuRe and Segminator II report 841 and six respectively . Inclusion of indels in the measure of specificity decreases QuRe's specificity considerably ( Table 1 ) , but this likely a less accurate measure of the algorithms specificity since such errors could be easily filtered . We applied V-Phaser to data from an individual with chronic HIV-1 infection taken from a larger study [24] , and we analyzed called variants by the frequency of these variants among the reads at that position . Using just unphased VDTFs , V-Phaser called only 485 variants , none of which were <1%; using no filtering , V-Phaser called 1 , 778 variants; with phased VDTFs and filtering , V-Phaser detected 2 , 015 variants , including 603 variants with frequency <1% ( Figure 7 ) . Notably , V-Phaser detected variants down to 0 . 2% , a detection threshold comparable to allele-specific PCR [26] . More than one out of every five loci had a recognized variant . Assuming the specificity of V-Phaser remained constant , the positive predictive value was estimated as 98% . Of note , V-Phaser identified 42 insertions or deletions ( indels ) as variants , and V-Phaser detected 198 triallelic loci and 19 quadallelic loci .
V-Phaser called rare variants in the presence of error in massively parallel sequencing data of highly diverse viral populations with >97% sensitivity and >97% specificity . Applied to a sample from a chronically HIV-1 infected individual , V-Phaser could identify as many as 603 minor variants at population frequencies of <1% . These variants were detected without a priori knowledge of the specific mutations , and biological variants at frequencies as low as 0 . 2% were identified , comparable to the detection threshold of allele-specific PCR , which is restricted to assaying known mutations . In these data , V-Phaser called 42 indels and identified more than 200 loci ( roughly 2% of the genome ) with more than one variant . In direct comparisons to two other recently published variant callers , Segminator II [21] and QuRe [13] , V-Phaser outperformed both algorithms on specificity and outperformed QuRe on both specificity and sensitivity . This is not surprising since QuRe implements the error correction model of Wang et al . [27] , which only considers pileup information . In fact , if we turn off the phasing portion of V-Phaser , it performs identically to QuRe on sensitivity , but even better on specificity ( Table 1 ) . Segminator II identifies all the true variants in the WNV mixed population data set , but at the cost of an unacceptably high false positive rate . Its alignment-based read filtering only removes errors arising from process-based indels while ignoring errors from other sources , such as random substitution errors due to sequence misreads or PCR errors in library construction . The comprehensive V-Phaser model clearly outperformed the model using uniform error rates , but the number of false positives was higher than expected with the Bonferroni correction . Some of these false positives detected in the negative-control might actually be low level variants present in the HIV NL4-3 cDNA libraries used to generate the read set that had not been previously detected . Some might be errors introduced early and amplified to create correlated errors not modeled by V-Phaser . Particularly when considering the 454 sequencing process , which generates systematic errors in regions around homopolymers , correlated error may occur in such regions . However , we have multiple reasons to believe that this has a small impact on the final V-Phaser calls . First , V-Phaser's base context model in the quality recalibration accounts for some amount of homopolymer error . Second , our ReadClean454 read cleanup step during the alignment phase removes or marks as low quality the majority of errors derived from homopolymer misreads or “carry forward and incomplete extension” ( CAFIE ) errors ( a related 454 error mode ) [24] . Third , we examined the error calls made by V-Phaser on the clonal HIV NL4-3 data to see if they clustered in or near homopolymer regions , and we found them to be randomly distributed with respect to homopolymer regions . While the false positive rate was very low for the highly diverse clinical sample we used , it could be higher for samples with very low diversity since the false positive rate is inversely proportional to the total number of true variants . Another potential weakness of the model is the modeling of indels . DePristo et al . [17] suggest that indel errors distribute differently from other errors and need to be modeled differently . We did not explicitly test V-Phaser for indel detection in the artificial mixture used as the positive control , since the set of variants had no indels . V-Phaser uses phase information to increase sensitivity . Correlated errors under the model are rarer than errors in general , making it easier to call correlated variants . One potential problem is the presence of chimeras , where one read is a composite from two different genomes . Chimeras can decrease the correlation between variants , but data from Hedskog , et al . [28] suggest that chimeras occur rarely , making it difficult to significantly obscure any correlation . The biggest limitation to using phase information is the read length generated by the sequencing platform . We saw that correlated variants need to be close enough to add to the sensitivity , and that this phase distance is bounded by the average read length ( Figure 2 ) . For the 454 platform the average read length is over 500 bp , but for other platforms the average read length is much shorter . This limitation could be overcome by utilizing paired-end reads to extend the phase distance to cover variants significantly farther apart . V-Phaser is a variant calling tool that uses phase information to increase sensitivity and models base quality to increase specificity . V-Phaser is an effective tool to call variants in the presence of errors from massively parallel sequencing data with high specificity and high sensitivity . We designed V-Phaser to overcome specific challenges to calling variants in small , diverse viral genomes , but V-Phaser is general enough to analyze read sets from other populations as well , such as metagenomic data and tumor sequencing data , making it a novel algorithm with wide utility .
The subject gave written informed consent and the study was approved by the Massachusetts General Hospital and granted exemption by the Massachusetts Institute of Technology Review Boards . We construct a composite model of independent Bernoulli random variables that are not identically distributed to allow error rates to vary from base to base . We suppose that the base bik at genomic locus i and read k is measured at an error rate pik , where reads are aligned to a reference assembly with loci numbered from 1 to l , and reads at locus i are numbered from 1 to ni , the coverage at locus i . We define the error random variable Eik to be 1 if bik is measured incorrectly , and 0 otherwise . Let the random variable Xi be the number of errors that occur at locus i: ( 1 ) Under the special case that the errors are independent and identically distributed Bernoulli random variables , such that pik = pi for all reads k at locus i , Xi follows a binomial distribution , so the probability fi ( x ) that x or more errors occur at locus i with coverage of ni reads is as follows: ( 2 ) More generally , if Xi is the sum of independent Bernoulli random variables that are not identically distributed , we can calculate fi ( x ) with the recursive application of the discrete convolution formula , where we define the random variable Uir as the number of errors that occur at locus i in the first r reads: ( 3 ) We define the unphased variant detection threshold ti as the smallest t such that fi ( t ) is statistically significant . To adjust for multiple testing , we use the Bonferroni correction since errors are uncorrelated under the null hypothesis . At a significance level α , and applying the Bonferroni correction for testing the total number of positions sequenced c , we calculate ti as follows: ( 4 ) If mismatches at position i occurred in t reads , we can infer if the mismatches are variants by comparing t to ti . If t is greater than or equal to ti , then we infer that not all of the mismatches are errors , and at least one of them is a variant . If t is less than ti , then we infer that we cannot distinguish these mismatches from error . Under the probability model , errors are independent , but variants can be phylogenetically related . Thus , we can also distinguish variants from errors if mismatches at one locus are correlated with mismatches at a different locus . In particular , we define the error random variable Eijk to be 1 if errors occur at both loci i and j on read k and 0 otherwise . Then the number of errors Xij that occur in phase at both loci i and j , with shared coverage of nij reads that span both i and j , is the sum of error random variables as before . In the special case that errors are identically distributed Bernoulli random variables , where pik = pi and pjk = pj for all reads k that cover both loci i and j , we calculate the phase probability gij ( x ) as follows: ( 5 ) In the more general case , we can calculate gij ( x ) by recursively applying the discrete convolution formula as before , where we define the random variable Uijr as the number of reads with errors that occur at locus i and locus j among the first r shared reads: At a significance level α and applying the Bonferroni correction for testing the total number b of pairs of loci i and j such that gij is defined , we can calculate the phased variant detection threshold tij as follows: ( 6 ) If we find phased mismatches at locus i and locus j on t reads , we can infer if they are variants by comparing t to tij . If t is greater than or equal to tij , then we infer that not all of these mismatches are errors . Otherwise , we cannot distinguish these mismatches from errors . We define the unphased variant detection threshold frequency ( VDTF ) Fi to be the frequency at which we begin to distinguish variants from errors at locus i and depth ni . Similarly , we define the phased VDTF Fij to be the frequency at which we begin to distinguish phased variants from errors at loci i and j and shared depth nij . We calculate Fi and Fij as follows: ( 7 ) We sequenced an HIV infectious clone ( NL4-3 ) to serve as a negative control for our validations and HIV RNA derived from a clinical sample as previously described [24] . We derived the positive control read set from eight individual primary WNV strains isolated from mosquitoes and birds . Individual strains were passaged once in C6/36 cells for amplification , and equal concentrations of each strain were then pooled and used to infect C6/36 cells at a multiplicity of infection of 0 . 1 . Viral RNA was isolated from these cultures ( QIAmp viral RNA mini kit , Qiagen ) and the RNA genome reverse transcribed to cDNA using Superscript III reverse transcriptase ( Invitrogen ) , random hexamers ( Roche ) and a specific oligonucleotide targeting the 3′ end of the target genome sequences . Four overlapping PCR products , each of size ∼3 kb , were designed to capture the WNV coding region . PCR products were then pooled and sheared prior to library construction . To generate each read set , whole viral genomes were sequenced using the Broad Institute's viral genome sequencing and assembly pipeline . Pooled PCR products ( ∼3 kb ) were amplified using primer sets specific to either HIV or WNV , acoustically sheared , and sequenced on the 454 Genome Sequencer FLX Titanium ( Roche ) using standard protocols . The library was loaded into a picotiter plate ( PTP ) to yield >200-fold read coverage . Resulting sequence reads were trimmed of primer sequences , filtered for high quality , assembled de novo and annotated using the Broad Institute's AssembleViral454 algorithm [24] and an in-house annotation algorithm . Reference consensus assemblies used in analyses are available from NCBI's GenBank under accessions HQ505665 , JN819311 , JN819312 , JN819313 , JN819315 , JN819318 , JN819319 , JN819320 , JN819315 , JQ403053 , and JQ403055; read data are available from NCBI's Short Read Archive ( Project Accessions SRA045000 and SRA045569 ) . Once we generated the sequence data , we aligned and processed them using ReadClean454 algorithm as previously described [24] . In particular , the algorithm corrects typical errors introduced by the 454 sequences , including carry forward and incomplete extension errors , homopolymer errors , and indels that break the open reading frame ( ORF ) . Any base rearrangements do not affect the assigned base quality . Any insertions to preserve the ORF consist of N bases with an assigned base quality of 1 . We then flagged each base to indicate if it passed the NQS criteria [29] . A base met NQS criteria if its quality score was 20 or higher and the five bases to either side all had quality scores of 15 or higher . We omitted the final NQS criterion that at least nine of the ten flanking bases were perfect matches , since we expected the HIV genome to be variable enough that variants among the flanking bases could be relatively common [24] . For calling variants , V-Phaser ignored any bases flagged as not meeting the NQS criteria . Once we aligned and preprocessed the sequence data , we estimated the model parameters and applied the model to the data to call variants . In the uniform error case , we estimated error rates in homopolymer and nonhomopolymer regions , where homopolymer regions are defined by runs of 3 or more identical nucleotides in a row . In the general case , we estimated error rates overall , per base transition ( where each transition was a dinucleotide consisting of a base and its preceding base in the read sequence ) , and per read position ( distance from the start or end of the read , whichever was closer ) . We then used these estimates in calibration equations [17] to estimate the error rate for each base . As V-Phaser called variants , it iteratively adjusted model parameters using an EM algorithm . It initialized the algorithm by treating all mismatches as errors to estimate error rates . In the E step , it derived phased and unphased thresholds , called variants , and removed these variants from the list of errors . In the M step , it updated error rates due to the removal of called variants from the error list . V-Phaser continued to iterate until it could call no more variants . To test whether homopolymer-related artifacts in 454 sequencing which violated the model assumptions were causing V-Phaser to overcall variants , we divided the reference sequence of the clonal HIV NL4-3 genome into three categories: homopolymeric regions ( defined as 3 or more of the same nucleotide in a row ) , homopolymer flanking regions ( defined as 2 bases to either side of an homopolymeric region , representing the region in which CAFIE errors are expected to occur ) , and non-homopolymeric regions ( the remainder of the genome ) . We then assigned each of the false positive calls made by V-Phaser to one of these categories and used a χ2 test ( 2 d . f . ) to determine whether any region had more variants than expected . We use quantile-quantile ( q-q ) plots to assess the fit of our probability models under the null . To assess the fit of the model to the observed data , we compute the probability of observing each datum under the model using F ( x ) , the cumulative distribution function ( CDF ) , and compare the distribution of these observed probabilities against the expected distribution of cumulative probabilities under the null . For random variables with continuous CDFs , the expected distribution under the null is the uniform distribution between 0 and 1 . For our probability models , the expected distribution of probabilities under the null is more difficult to calculate , since the CDF is discrete and varies from locus to locus with the base qualities at that locus . Since our models use discrete rather than continuous random variables , we redistribute the mass of the probability mass function to construct a uniform distribution . We define G ( x ) , a projection of the cumulative distribution function ( PCDF ) that maps the CDF probabilities onto the uniform distribution in the following way:Conceptually , the PCDF redistributes massed probabilities uniformly to bridge discontinuities in the CDF . For example , if X is a random Bernoulli variable with success probability p and failure probability q = 1−p and CDF FX , then FX ( k ) = 0 for k<0 , FX ( k ) = q for 0≤k<1 , and FX ( k ) = 1 for k≥1 . The discontinuities at k = 0 and k = 1 correspond to the massed probabilities for X at those values . G redistributes this mass uniformly to bridge the discontinuity . So whenever X = 0 , G ( FX ( k = 0 ) ) uniformly takes on a value between 0 and q , and whenever X = 1 , G ( FX ( k = 1 ) ) uniformly takes on a value between q and 1 . X can take on no other values , so G ( FX ( k = X ) ) follows a uniform distribution between 0 and 1 . So if we have n observations of X , about p/n of them will be 1 and about q/n of them will be 0 . The expected cumulative probabilities for each observed 0 will all be q , but their projected probabilities will be uniformly distributed between 0 and q . Similarly , the projected probabilities for each observed 1 will be uniformly distributed between q and 1 . If we sort the observations by their projected probabilities , then the projected probability for the ith observation will be very close to i/n . By construction , these projected probabilities are uniformly distributed between 0 and 1 under the null . So even though the CDFs vary by locus with the mix of error rates among bases at that locus , the PCDF remains uniform . Thus , we can compare the projected distribution of PCDF probabilities against the expected distribution under the null , which by design is the uniform distribution . We evaluated V-Phaser's performance in terms of sensitivity and specificity to detect variants relative to four other programs designed for variant detection in viral quasispecies populations: ShoRAH [12] , ViSPa [25] , Segminator II [21] , and QuRe [13] . All programs were run according to standard parameters defined by the software authors . In all cases , we used the alignment and read cleaning ( if any ) methods recommended by the authors . Only the Segminator II and QuRe software packages successfully ran on our datasets . For V-Phaser , we used our standard process including ReadClean454 [24] to error correct and align the reads . In all cases , we used our sample-specific consensus assemblies as the reference for alignment . Sensitivity was computed using our WNV mixed population control data set and specificity was determined using the HIV NL4-3 infectious clone control data set . In scoring the resulting calls , we ignored all inserted and deleted bases called ( 6 by Segminator II , 2 by V-Phaser , and 841 by QuRe ) , because we could not determine the exact number of discrete indel events called by QuRe and felt that it would be unfair to count all 841 as errors since such errors could be filtered ( the data have no known indels based on the input strain sequences ) . | New sequencing technologies provide unprecedented resolution to study pathogen populations , such as the single stranded RNA viruses HIV , dengue ( DENV ) , and West Nile ( WNV ) , and how they evolve within infected individuals in response to immune , therapeutic , and vaccine pressures . While these new technologies provide high volumes of data , these data contain process errors . To detect biological variants , especially those occurring at low frequencies in the population , these technologies require a method to differentiate biological variants from process errors with high sensitivity and specificity . To address this challenge , we introduce the V-Phaser algorithm , which distinguished the covariation of biological variants from that of process errors . We validate the method by measuring how frequently it correctly identifies variants and errors on actual read sets with known variation . Further , using data derived from a patient following four years of HIV-1 infection , we show that V-Phaser can detect biological variants at frequencies comparable to approaches that require prior knowledge . V-Phaser is available for download at: http://www . broadinstitute . org/scientific-community/software . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"genomics",
"evolutionary",
"biology",
"population",
"modeling",
"population",
"genetics",
"biology",
"computational",
"biology",
"microbiology",
"genetics",
"and",
"genomics"
]
| 2012 | Highly Sensitive and Specific Detection of Rare Variants in Mixed Viral Populations from Massively Parallel Sequence Data |
Evolutionary outcomes depend not only on the selective forces acting upon a species , but also on the genetic background . However , large timescales and uncertain historical selection pressures can make it difficult to discern such important background differences between species . Experimental evolution is one tool to compare evolutionary potential of known genotypes in a controlled environment . Here we utilized a highly reproducible evolutionary adaptation in Saccharomyces cerevisiae to investigate whether experimental evolution of other yeast species would select for similar adaptive mutations . We evolved populations of S . cerevisiae , S . paradoxus , S . mikatae , S . uvarum , and interspecific hybrids between S . uvarum and S . cerevisiae for ~200–500 generations in sulfate-limited continuous culture . Wild-type S . cerevisiae cultures invariably amplify the high affinity sulfate transporter gene , SUL1 . However , while amplification of the SUL1 locus was detected in S . paradoxus and S . mikatae populations , S . uvarum cultures instead selected for amplification of the paralog , SUL2 . We measured the relative fitness of strains bearing deletions and amplifications of both SUL genes from different species , confirming that , converse to S . cerevisiae , S . uvarum SUL2 contributes more to fitness in sulfate limitation than S . uvarum SUL1 . By measuring the fitness and gene expression of chimeric promoter-ORF constructs , we were able to delineate the cause of this differential fitness effect primarily to the promoter of S . uvarum SUL1 . Our data show evidence of differential sub-functionalization among the sulfate transporters across Saccharomyces species through recent changes in noncoding sequence . Furthermore , these results show a clear example of how such background differences due to paralog divergence can drive changes in genome evolution .
Understanding how organisms adapt to their environment is a fundamental goal of evolutionary biology . This goal has been complicated by the dependence on the reconstruction of historical events to make inferences about selective pressures and evolutionary mechanisms . Furthermore , it can be difficult to pinpoint genetic variation that causes new phenotypes of interest amid very divergent genomes . One approach to circumventing this limitation is to study evolution in the laboratory , where growth , environment , and population parameters can be controlled and dynamic adaptation events can be followed in real time [1–5] . However , experimental evolution has its own limitations , such as being too far removed from natural environmental factors and extending over only limited time scales . Merging laboratory evolution and comparative genomics could provide a more comprehensive view of processes that underlie evolution . In addition , comparative experimental evolution allows us to determine to what degree genetic background may result in differential functional innovation in the future [4 , 6] . One source of genetic novelty that may vary across divergent species is gene duplication . Gene duplicates can have different fates , either through dosage effects of an extra copy , splitting ancestral functions or regulatory patterns over duplicates ( sub-functionalization ) , or acquiring novel function ( neo-functionalization ) [7 , 8] . Alternatively , they can provide genetic redundancy to endow organisms with mutational robustness [9–11] . Duplications occur frequently during evolution and are commonly linked to genome innovations that result in an adaptive or phenotypic change to a particular environment [12 , 13] . After a duplication event , adaptation may result through the accumulation of mutations in the non-coding or protein coding regions of the genome , which may alter gene function , protein-protein interactions , or expression profiles . Accumulation of mutations in the coding region of each paralog may potentially modify active sites , affecting biochemical functionality , or alter binding interfaces and thus their interaction specificity [14] . Mutations in the non-coding region of each paralog may cause regulatory interactions in networks to be lost or re-wired , potentially leading to expression divergence between paralogs [15–17] . The Saccharomyces clade of species provides a particularly appealing platform for comparative studies of gene function . The last common ancestor of this group existed approximately 20 million years ago , with approximately 80% identity in coding sequences between S . cerevisiae and S . uvarum [18] . The Saccharomyces species are experimentally tractable , have high quality genome sequences [19–21] , contain largely syntenic chromosomes [22] , and can mate to form hybrids , including with the laboratory workhorse S . cerevisiae , providing access to a huge knowledge base and extensive toolkit of genetic and genomic resources . Additionally , the Saccharomyces genus is a result of a well-studied WGD event , which occurred just before the separation of Vanderwaltozyma polyspora from the S . cerevisiae lineage [23] and was itself probably a result of a hybridization event [24] . In this study , we compared the evolutionary outcomes upon sulfate-limited growth in chemostat culture between S . cerevisiae , S . paradoxus , S . mikatae , S . uvarum , and S . cerevisiae/S . uvarum hybrid strains and used whole genome sequencing and species-specific microarrays to identify resultant genetic changes . We discovered differential amplification of sulfate transporter gene paralogs SUL1 and SUL2 in the different species . The species-specific amplification preference correlated with the selective effects of amplification and deletion of each sulfate transporter gene . Analysis of functional divergence of the two paralogs across these species provides evidence for differential sub-functionalization between the SUL1 and SUL2 paralogs of S . cerevisiae and S . uvarum , driven largely by lineage-specific acquired changes in the non-coding region of SUL1 in S . uvarum . In this work , we discovered an example of recent paralog divergence between two gene duplicates with altered gene expression between S . cerevisiae and S . uvarum , and demonstrated that such differences can alter the genetic mechanisms by which these species adapt to future challenges .
As described previously [25–28] , evolved clones of S . cerevisiae selected during long-term continuous culture under sulfate-limitation reproducibly carry amplification events near the right telomere of chromosome II containing the high affinity sulfur transporter gene SUL1 ( representative event shown in Fig 1B ) . This mutation confers one of the highest ( 20–40% increase ) and most reproducible ( 25/25 populations ) fitness advantages known in the experimental evolution literature [25–28] . In order to determine whether other yeast species would follow this same evolutionary path , we performed two evolution experiments with a sister species , S . uvarum , in chemostats using the same condition in which the SUL1 amplification has been observed for S . cerevisiae . Each experiment was initiated with a prototrophic diploid S . uvarum strain that had never before been exposed to long-term sulfate limitation in the laboratory ( see materials and methods ) . In contrast to the amplification of SUL1 in the S . cerevisiae clones , no amplification of this locus was observed in the two populations of S . uvarum evolved under sulfate limitation for 500 generations . However , the locus containing the gene SUL2 was amplified in both populations as determined through microarray-based comparative genomic hybridization ( aCGH ) ( S1 Fig ) . Two clones from one population were analyzed further by deep sequencing , revealing an internal segment of chromosome X containing the gene SUL2 at an increased copy number of 5 in one of the two clones ( Fig 1C ) . The fitness benefit of this evolved clone was 20% when competed against the ancestral strain ( n = 4 , Table 1 , see Materials and Methods for further details of how fitness was measured in S10 Fig ) . Although the exact function of the protein Sul2 has never been experimentally tested in S . uvarum , Sul2 has been identified as a lower affinity transporter of sulfate in S . cerevisiae [29] . We next set out to explain the differential amplification of SUL1 and SUL2 in these closely related species . We hypothesized that the different evolutionary outcomes could result from divergence in gene function—SUL2 may encode the higher affinity transporter gene in S . uvarum and so its amplification causes a higher fitness benefit—or from changes in chromosomal context that affect amplification rate or amplicon fitness . We test these hypotheses below . We hypothesized that the preference for the amplification of SUL1 in S . cerevisiae could be due to changes in chromosomal context between the two species that might affect the propensity of the region to amplify . Although the SUL1 orthologs are largely syntenic between the two genomes , some differences do exist . SUL1 in S . cerevisiae is located on the right arm of chromosome II , near the telomere . The S . uvarum ortholog is located in a syntenic region , on chromosome IV where , as compared with the S . cerevisiae genome , the left portion of this chromosome contains a reciprocal translocation with a region syntenic to the right arm of S . cerevisiae chromosome IV [22 , 30] . The regions immediately adjacent to SUL1 are largely syntenic , though the gene just distal to SUL1 in S . cerevisiae , PCA1 , is missing in S . uvarum ( S2 Fig ) . Adjacent sequences to the telomeric repeats , including X and Y’ elements as well as subtelomeric gene families , have been shown to be rapidly evolving across species of the Saccharomyces clade , possibly contributing to a difference in mutation rate [19 , 31–33] . In S . cerevisiae , this region also contains a DNA replication origin ( ARS228 ) , which we previously demonstrated to be involved in ( though not necessarily required for ) the generation of the amplification [34 , 35] . To test for replication origin function , we cloned the corresponding region from S . uvarum and tested it for the ability to support plasmid replication ( i . e . , an assay for Autonomously Replicating Sequences , or ARSs ) . Like S . cerevisiae , S . uvarum does contain an ARS in this region ( S3 Fig ) . However , there do appear to be differences in activity among a minority of ARSs between S . cerevisiae and S . uvarum , determined through whole genome replication assays [36] . The SUL2 gene is located on chromosome XII in S . cerevisiae and X in S . uvarum , though the immediate surrounding region is mostly syntenic . From comparisons with the reconstructed ancestral genome , SUL2 appears to be the ancestral copy of the sulfur transporter , with SUL1 being a more recent gene duplicate after a small-scale duplication ( SSD ) event [37] . Amino acid conservation between SUL1 and SUL2 in S . cerevisiae is 62 . 5% and 61 . 3% shared identity in S . uvarum , whereas SUL1 from S . cerevisiae and SUL1 from S . uvarum share 84% identity and SUL2 from S . cerevisiae and SUL2 from S . uvarum share 87% identity , indicating that the sulfate transporter genes are correctly annotated . Although the origin of replication is present , there may be other differences near SUL1 in S . uvarum that might explain why this region has not been observed to amplify in the evolved strains . To test if SUL1 is capable of amplification , we evolved four haploid sul2Δ strains of S . uvarum in sulfate-limited media and tested the evolved populations for copy number variation using aCGH . At 260 generations , we identified an amplification of the SUL1 locus in one of the four populations and no other amplifications in the other three populations ( Fig 2D ) . This result indicates that the SUL1 locus in S . uvarum has the capacity for amplification , but does not attain high frequency in populations initiated with strains containing both SUL1 and SUL2 genes . Alternatively , the SUL2 locus cannot amplify in S . cerevisiae . To test if the SUL2 locus can amplify in S . cerevisiae , we evolved four haploid strains of S . cerevisiae in which SUL1 has been deleted ( sul1Δ ) in sulfate-limited media and tested the evolved populations for copy number variation using aCGH . We identified an amplification of the SUL2 locus in all four populations ( including a whole chromosome aneuploidy event that occurred in one population ) indicating that SUL2 can amplify in S . cerevisiae , but these amplifications do not attain high frequency in evolution experiments performed with strains in which the SUL1 gene is present ( Fig 2C and S4 Fig ) . We note that these experiments leave open the possibility that differences in amplification rate might contribute to the observed differences in amplification propensity . We have so far been unable to measure the amplification rate of these loci and so have not tested this hypothesis . However , another possible explanation for these results is that SUL2 amplification may have a greater selective effect in S . uvarum . To test this , we performed additional experiments to determine the functional contribution of each gene from both species . To test whether the functions of these genes may have diverged between these species , we measured the fitness effects of having additional copies of each gene . Previous studies have shown that the addition of SUL1 on a low copy plasmid in S . cerevisiae increases the fitness of the strains by ~40% [26] . To determine the effect of additional copies of SUL1 and SUL2 from S . cerevisiae and S . uvarum , we transformed S . cerevisiae with ARS/CEN plasmids individually containing each SUL gene along with 500bp upstream of the coding region . We performed chemostat competition experiments between GFP+ and dark strains harboring additional copies of each gene in S . cerevisiae ( Fig 3A ) . The fitness cost of expressing GFP , determined by competing isogenic wt strains with and without a GFP construct , is negligible ( -0 . 02 ) . The pairwise competitions provided fitness data that allowed us to more precisely determine the rank order of the fitness benefit of each gene amplification . The strain with an extra copy of SUL1 from S . cerevisiae ( ScSUL1 ) outcompeted all other strains , followed by SUL2 from S . cerevisiae ( ScSUL2 ) , which had a comparable fitness effect to SuSUL2 . The strain with the SuSUL1 gene had the lowest fitness effect of all genes tested ( Fig 3B ) . This result suggests that SUL2 may have maintained a similar function between the two species , but SUL1 function may have diverged . In support of our original hypothesis , the SUL2 gene from S . uvarum ( SuSUL2 ) conferred a greater fitness effect than the S . uvarum SUL1 ( SuSUL1 ) . This is also consistent with our predictions based on the evolution experiments , suggesting that SuSUL2 amplification may have a greater selective benefit than amplification of SuSUL1 . To determine if these results were consistent across genetic backgrounds , we performed chemostat competition experiments between GFP+ and dark strains harboring additional copies of each gene integrated at the URA3 locus in S . uvarum ( Fig 4A ) . The strain with an extra copy of SUL1 from S . cerevisiae ( ScSUL1 ) outcompeted all other strains , followed by SUL2 from S . uvarum ( ScSUL2 ) , which had a greater fitness effect than SuSUL1 ( Fig 4B ) . The strain with the additional copy of ScSUL2 gene had the lowest fitness effect of all genes tested , which differs from the S . cerevisiae background results . These results suggest that other epistatic interactions may also contribute to the differences in the fitness effects of each allele between genetic backgrounds ( Fig 4B ) . In addition to testing the fitness effects of each SUL1 and SUL2 gene independently , we also investigated the amplification preference in the context of having all alleles present in one genome . Given the results from the single gene plasmid experiments above , we predicted that ScSUL1 would be the preferred allele for amplification . We had previously created de novo S . cerevisiae/S . uvarum hybrid strains and subjected them to hundreds of generations of growth in sulfate-limited continuous culture . Evolved strains were then analyzed by aCGH to determine differences in genome content from their ancestral strains ( see [38] for additional analysis ) . Amplification of segments containing the SuSUL1 or SuSUL2 gene was never observed in 16 clones from 8 independent populations , and SuSUL1 was even found deleted in one evolved clone , displaying loss of heterozygosity at this locus ( S5 Fig ) . In contrast , the S . cerevisiae copy of SUL1 was found amplified in 14/16 evolved clones ( Fig 5B ) . Copy numbers estimated from the array CGH data ranged from 3 to as many as 20 copies of SUL1 . Centromere-proximal breakpoints varied from population to population , but amplicons extended to the most distal telomeric probe in all cases . Additional rearrangements were rarely observed in these strains ( S5 and S6 Figs ) . When all four alleles are present in the same genome , ScSUL1 amplifications are preferentially recovered , suggesting that ScSUL1 amplification yields the greatest fitness advantage in this particular environment and genomic context . We have shown that the addition of extra copies of each gene results in an increased fitness in S . uvarum and S . cerevisiae , with ScSUL1 yielding the greatest fitness increase , a result that corresponds to the amplification preferences in evolved strains derived from an interspecific hybrid . In addition , we deleted SUL1 and SUL2 in both S . cerevisiae and S . uvarum backgrounds to determine the relative fitness contributions of these loci in each background . We created sul1Δ and sul2Δ haploid strains and measured the competitive fitness of each null mutant in sulfate-limited conditions . We competed the sul1Δ and sul2Δ strains within each species against each other to calculate the fitness effect of each mutant . In S . cerevisiae , the sul2Δ strain outcompeted the sul1Δ strain , suggesting that SUL1 in S . cerevisiae is the gene that is more important for growth in sulfate-limited conditions . Conversely , in S . uvarum , the sul1Δ strain outcompeted the sul2Δ strain , suggesting that SuSUL2 , rather , is the gene that is more important for growth in sulfate-limited conditions ( Fig 6B ) . Taken together with the fitness data from increasing the copy number of each gene , these data suggest differential SUL1 and SUL2 fitness contributions across these two species despite the genes’ similarity in amino acid composition and genomic context . In order to determine where the divergence in relative fitness effects between SUL1 and SUL2 in S . cerevisiae and S . uvarum occurred in evolutionary history , we tested the fitness of SUL1 and SUL2 from S . paradoxus and S . mikatae—two other species of the sensu stricto clade—and SUL2 from Naumovozyma castellii , a more distant species that has not undergone gene duplication of this locus . We cloned the genes along with 500bp upstream of the coding region from each species into an ARS/CEN plasmid and determined the relative fitness effect of the addition of the SUL genes in S . cerevisiae when competed against a plasmid-free strain . This experiment allowed us to calculate the relative fitness coefficient of each strain . All strains showed significantly higher fitness than wild type S . cerevisiae , with the relative fitness coefficients ranging from 18 . 1% to 43 . 8% , after correcting for the cost of carrying a plasmid ( -5 . 4% ± 0 . 59 ) . The S . cerevisiae SUL1 ( ScSUL1 ) plasmid conferred a fitness benefit of 42 . 6% ( Fig 7B ) . The strains containing SUL1 from S . paradoxus and S . mikatae conferred a greater fitness advantage than SUL2 from the respective species . In N . castellii , the singleton SUL2 conferred the greatest fitness advantage of 43 . 8% ( Fig 7B ) . One possible scenario to explain these results is that the new SUL1 duplicate in the last common ancestor of S . cerevisiae , S . paradoxus , S . mikatae and S . uvarum may have maintained the high affinity function of the ancestor , while SUL2 subfunctionalized or lost specificity . Alternatively , the S . uvarum SUL1 paralog may have acquired mutations that decreased its fitness only in that lineage . From these data , we can make predictions about the types of genomic events that would occur if we evolved S . paradoxus and S . mikatae under sulfate limited conditions . Since SUL1 from both species resulted in the highest fitness benefit , we would expect to select for amplifications of the SUL1 locus . To test this , we grew four populations of S . paradoxus , S . mikatae , and S . uvarum for 200 generations in sulfate limited chemostats and determined the copy number variation between evolved populations and each ancestral strain using deep sequencing . We did not detect amplification events at the SUL1 nor the SUL2 locus in any of the four populations of S . uvarum . One explanation for this result could be due to the 200-generation timescale . The detection of the original SUL2 amplification event occurred after 500 generations . However , consistent with expectations , we did identify two populations with an amplification containing the SUL1 locus in S . paradoxus and one population in S . mikatae ( Fig 8 ) . Other aneuploidy and segmental amplifications occurred in addition to the SUL1 locus amplification in the evolved populations ( S7 and S8 Figs ) ; however , none of these copy number variants included the SUL2 locus . Overall , these data are consistent with the previous gene function measurements of each allele in S . cerevisiae , indicating that SUL1 is more adaptive when amplified in S . paradoxus and S . mikatae . Based on the similar results across S . cerevisiae , S . mikatae , and S . paradoxus , we decided to focus on understanding what is different about the paralogs in S . cerevisiae vs . S . uvarum . To identify the genetic region responsible for the differences in fitness effects of SUL1 and SUL2 between the two sister species , we created chimeric constructs composed of different combinations of the promoter and open reading frame ( ORF ) of each gene . Rich et al recently used a deep mutational scanning approach to identify the functional elements of the ScSUL1 promoter that are crucial for growth in sulfate limitation [40] . Based on their results , we cloned 500bp upstream of each ORF ( the region encompassing all elements that positively influence SUL1’s fitness contribution ) and cloned the ORF until the stop codon . We then cloned all 12 combinations of promoter and ORF into a low copy ARS/CEN plasmid . Wild-type S . cerevisiae strains were transformed with the individual plasmids carrying chimeric SUL constructs and competed against a plasmid-free strain to calculate the relative fitness coefficient of each strain in sulfate-limited media . Additionally , the non-chimeric alleles were also tested against a plasmid-free strain , with a total of 16 alleles tested . As seen in Fig 9 , the fitness coefficient values ranged from 0 . 2 to 38% after correcting for the cost of carrying a plasmid ( -5 . 4% ± 0 . 59 ) , which was calculated by competing a strain with an empty plasmid against a WT strain . When placed under the same promoter , the SuSUL1 ORF had a greater fitness advantage than the SuSUL2 ORF , opposite to the result obtained when each ORF was driven by its native promoter . All chimeras containing the promoter region of SuSUL1 showed substantial decreases in fitness . This result suggests that expression differences between the two species may largely explain the differential fitness effects of the two SUL1 genes . Interestingly , the chimeric allele containing the SuSUL2 promoter with the SuSUL1 ORF ( PSuSUL1-SuSUL1 ) recapitulates the fitness effect of ScSUL1 . Additionally , strains containing the promoter of ScSUL1 or ScSUL2 resulted in similar fitness patterns when paired with the three other ORFs , with the ScSUL1 coding region yielding the highest relative fitness . However , when promoters of SuSUL1 or SuSUL2 were paired with the other three ORFs , we identified a different ranking of fitness patterns , with the SuSUL1 coding region yielding the highest fitness . We did not attempt to further dissect these apparent epistatic interactions between the promoters and coding regions; however , such complex genetic interactions have been observed in other contexts [41–44] . Since the results from the chimeric constructs suggested that the promoter region is largely responsible for the differences in fitness , we sought to measure gene expression levels driven by each promoter . We used reverse transcriptase real time PCR ( RT-PCR ) to determine the expression level of ScSUL1 under the control of all four promoters in S . cerevisiae strains grown at steady state in sulfate-limitation . We found that the expression level of the ScSUL1 chimera with the promoter from SUL1 from S . uvarum ( PSuSUL1-ScSUL1 ) was significantly reduced in comparison to the other promoters ( Fig 10A ) . We also found a modest correlation between expression level and the fitness value of each construct ( R2 = 0 . 55 ) ( Fig 10B ) . This result demonstrates that the differences between the fitness contributions of the two transporter genes may be due to gene expression differences .
In this work , we used comparative experimental evolution to investigate how genetic background influences the genetic mechanisms of adaptation to sulfate limitation across different species of yeast in the Saccharomyces clade . We identified differential amplification of gene duplicates that encode sulfate transporters in S . cerevisiae and S . uvarum . Collectively , our results display an example of adaptation via amplification of different genomic loci , likely driven by regulatory divergence of paralogs . Specifically , we have shown SUL1 amplification during long-term growth in sulfate-limited conditions occurs in all species tested in the Saccharomyces clade except S . uvarum . While the number of S . paradoxus , S . mikatae , and S . uvarum populations that were used for the laboratory evolution experiments was small ( n = 4–6 ) , we have repeatedly identified SUL1 locus amplifications in all reported evolution experiments of wild type S . cerevisiae ( n = 25/25 ) . Therefore , it is surprising that even within two evolved populations of S . uvarum , we did not identify SUL1 amplification , but instead identified SUL2 locus amplifications in both populations after 500 generations . Additionally , two of the evolved populations in S . paradoxus and one population of S . mikatae amplified SUL1 . The other populations that did not amplify SUL1 or SUL2 may contain other events that may be equally or more beneficial than either amplification , or additional time may be required for the amplification event to occur and rise to high frequency ( >200 generations ) ( S7 and S8 Figs ) . This point is further supported by additional evolution experiments we performed in S . uvarum for 200 generations where neither SUL1 nor SUL2 amplifications were detected , suggesting that amplification events are dynamic and may depend on longer time scales to occur and/or achieve high frequency ( S9 Fig ) . These findings also demonstrate that other means of adaptation to sulfate limitation may exist , since populations from both S . paradoxus and S . mikatae amplify other regions of the genome in addition to SUL1 or do not amplify either of the SUL genes at all ( S7 and S8 Figs ) . Further work will be required to understand the genetic differences that mediate these other evolutionary trajectories and connect them definitively to fitness changes . Our results contribute to ongoing efforts to understand the mutations that drive adaptation , a long-standing question in evolutionary biology . There are examples of parallel molecular evolution that occur across genetic backgrounds for many traits [4 , 45–49] , suggesting that genetic background plays a relatively unimportant role in determining the outcome of adaptation at the molecular level . A more recent study , however , tested how genetic differences between strains of bacteria influence their adaptation to a common selection pressure and found that parallel evolution was more common within-strains than between-strains , implying that genetic background has a detectable impact on adaptation [50] . Taken together , it is unclear to what degree genetic background impacts the mechanism and rate of adaptation to a novel selection pressure . Our study has identified differential locus parallelism between sulfate transporter loci in S . cerevisiae and S . uvarum , demonstrating one example where genomic background influences the route taken to adapt to sulfate limitation during experimental evolution . To further investigate the effect of genetic context and whether this was due to coding or non-coding variation , we generated chimeric alleles of promoter and coding regions between S . cerevisiae and S . uvarum SUL1 and SUL2 genes . We identified poor fitness outcomes associated with the non-coding region of the SUL1 gene in S . uvarum , along with other complex interactions with the coding regions . These results suggest that the accumulation of mutations in the non-coding region of S . uvarum SUL1 may have resulted in reduced expression , thus driving selection for SUL2 amplification during adaptation of S . uvarum to sulfate limited conditions . Rich et al recently used a deep mutational scanning approach to identify the functional elements of the ScSUL1 promoter that are crucial for growth in sulfate limitation [40] . This same approach could be applied to the promoter region of SUL1 in S . uvarum to determine which sequences are responsible for these differences in activity . Many studies have aimed to determine whether adaptation and phenotypic change typically occur from mutations in non-coding or coding regions in the genome [51–55] . In the case of gene duplicates , it has been proposed that their retention provides genetic redundancy , buffering the mutational space to either acquire new function , or to partition the ancestral function between duplicates . Gradual stochastic changes in expression level may lead to an eventual imbalance in the selective pressure between the two duplicates [56] . These gradual changes in gene expression may play a significant role in shaping the adaptive landscape over time , resulting in different adaptation outcomes across diverse genetic backgrounds . Our results provide an example of divergent adaptation through changes in expression of one duplicate in the S . uvarum lineage in the Saccharomyces clade . In the case of nutrient limitation , a simple modification in expression may be more likely to suffice , since the metabolic pathway for uptake and utilization already exists , and increasing uptake is a straightforward solution [4] . Alternatively , differential tradeoffs between toxic metal resistance and ion transport may exist between species and result in altered sulfur biosynthesis requirements to synthesize glutathione , a key factor in the cell's defense against oxidative stress and metal toxicity , and/or other sulfur-containing compounds [57–59] . In addition to metal exposure , nutrient limitation is also a likely scenario experienced by wild and industrial yeast strains . Growing evidence suggests that domesticated Saccharomyces species have been exposed to sulfate related selective pressures through the selection for favorable characteristics associated with brewing . In lager brewing yeast , increased sulfite production is important for its antioxidant properties and for preserving favorable flavor profiles [60] . Saccharomyces pastorianus is a lager brewing species found only in the brewing environment and appears to be an allotetraploid hybrid between S . cerevisiae and S . eubayanus [61] . Interestingly , S . pastorianus carries inactive copies of SUL1 from S . cerevisiae and S . eubayanus , while retaining functional copies of SUL2 which have been shown previously to improve sulfite production when overexpressed [62–64] . Identifying the genetic basis of traits under selection in a particular environment may not only help highlight the emergence of new traits but also inform ways to engineer further improvement .
The strains used in this study are listed in S1 Table . The S . cerevisiae strains used in this study were from the FY series FY4 in the S288c background , with the exception of the interspecific hybrids , which utilized GRF167 . The S . uvarum strains used were derived from the CBS 7001 background . The S . mikatae strain was IFO 1815 and the S . paradoxus strain was CBS 432 . The N . castellii strain was CBS 4309 . The SUL1 and SUL2 deletion strains were created in S . cerevisiae and S . uvarum by targeting 50bp upstream of the ATG and 100bp upstream of its stop codon . The deletions were confirmed with primers targeting approximately 175bp upstream of the ATG ( S3 Table ) . To test the fitness due to the amplification of SUL1 or SUL2 from each species , we transformed DBY7283 , a ura3 S . cerevisiae MATα strain , with a low-copy plasmid [65] . Phusion PCR was used to amplify 500bp upstream and 5bp downstream of the stop codon of SUL1 and SUL2 from S . cerevisiae , S . uvarum , S . paradoxus , and S . mikatae , and SUL2 from S . castelli . Each SUL1 and SUL2 gene was blunt cloned into pIL37 using primers listed in S3 Table . All plasmids used in this study are listed in S2 Table . The haploid S . cerevisiae strain used in the competition experiments was a haploid FY MATα where the HO locus had been replaced with eGFP as previously described [26] . The diploid S . cerevisiae GFP+ strain was made by crossing the haploid FY MATα strain , where the HO locus had been replaced with eGFP , to a MATa FY strain . The S . uvarum GFP+ haploid strain was created by replacing the HO locus with eGFP by amplifying the NatMX-GFP construct from the plasmid YMD1139 . The strain was verified using primers that target 600pb upstream of the HO locus . The fitness of the haploid S . uvarum GFP+ strain , YMD2869 , was 0 . 388% +/- 0 . 33 ( n = 2 ) . The fitness of the diploid S . uvarum GFP+ strain , YMD2869 , was 2 . 33% +/- 0 . 19 ( n = 2 ) . To directly compete two strains each containing an additional copy of either SUL1 or SUL2 from S . cerevisiae or S . uvarum , a GFP+ ura3 S . cerevisiae strain was transformed with plasmids containing either SUL1 or SUL2 from S . cerevisiae or S . uvarum . These GFP+ strains were used in a competitive assay ( see below ) against strains also containing additional copies of each gene . To test the fitness due to the amplification of SUL1 or SUL2 from each species in the S . uvarum background , we integrated each SUL allele into YMD2823 , a ura3Δ S . uvarum MATα strain , at the URA3 locus due to the high loss rate of S . cerevisiae CEN plasmids [20] . We used primers listed in S3 Table to amplify 700bp upstream and 214bp down stream of each SUL allele ORF and cloned the construct into a CEN plasmid with the URA3 marker . To create homology to the URA locus , we amplified each allele and the URA3 marker using primers indicated in S3 Table . Strains were verified using primers that target 200 bp upstream and downstream of the URA3 locus and Sanger sequenced . The chimeric plasmids were created by amplifying 500 bp upstream of the start codons of the SUL1 and SUL2 ORFs from S . cerevisiae and S . uvarum and cloning each upstream region into YMD2307 using primers with added SnaBI sites at the 3’ end ( S3 Table ) . Each plasmid was digested with SnaBI and SUL1 or SUL2 from S . cerevisiae or S . uvarum was ligated immediately adjacent to the previously cloned upstream region , creating a total of twelve different chimeric strains . de novo hybrids between S . uvarum and S . cerevisiae were created by mating . Pulsed field gel analysis of the resulting strains confirmed the presence of both sets of chromosomes with no apparent size polymorphisms . Microarray analysis ( see protocol below ) of the hybrid DNA versus purebred DNA from each species also confirmed that these strains contained a complete haploid genome from each parent . Microarray data are deposited in the Gene expression Omnibus ( GEO ) repository under accession number GSE87401 and in the Princeton Microarray Database . The S . cerevisiae and S . uvarum genomes were downloaded from the Saccharomyces Genome Database and concatenated to create a hybrid genome . The program Array Oligo Selector was used to design 70mers to each open reading frame in both genomes . Under the default stringency settings , 711 genes were too similar to another sequence in the combined genomes for a sufficiently unique oligo to be designed . For these cases , the program was rerun in the context of each single genome in order to provide more complete coverage of the purebred genomes . 485 genes were still too similar to other sequences in the single genomes to pass this test and were left off the array . The resulting 4840 S . uvarum and 6423 S . cerevisiae 70mers were purchased from Illumina . 70mer DNA was resuspended at 40 μM in 3X SSC and printed using a pin-style arraying robot onto aminosilane slides in a controlled-humidity environment . Slides were UV crosslinked at 70 mJ . On the day of hybridization , the slides were blocked by agitating for 35 minutes at 65C with 1% Roche blocking agent in 5X SSC and 0 . 1% SDS . Slides were then rinsed with water for 5 minutes and spun dry . Hybridization conditions were optimized to maximize specificity . DNA from S . cerevisiae was labeled with one fluor and DNA from S . uvarum labeled with another and competitively hybridized to the arrays under a variety of DNA quantity , hybridization volume and temperature , and wash stringency conditions . As expected because of the 2-tier design strategy , less than 5% ( 563/11263 ) showed evidence of cross-hybridization with signal significantly over background levels in both channels . These probes were filtered out of all hybrid datasets . All microarray manipulations were performed in an ozone-free environment . 4 μg DNA was sonicated to a size range near 1 kb then purified by Zymo DNA clean and concentrator columns . Labeling of 2 μg sonicated DNA was done by random-primed klenow incorporation of Cy-nucleotides either with the Invitrogen Bioprime kit according to the manufacturer's instructions , or with individually purchased reagents as previously reported [66] . The labeled reactions were purified by Zymo columns and measured for labeling yield and efficiency using a nanodrop spectrophotometer . 1 μg of each labeled DNA were mixed with Agilent blocking reagent and 2X hybridization buffer in a total volume of 400 μl , heated at 95°C for 5 minutes , and hybridized to a prepared microarray using an Agilent gasket slide . Hybridizations were performed overnight at 65°C in a rotating hybridization oven . Gaskets slides were removed in 1X SSC and 0 . 1% SDS solution . Arrays were agitated for 10 minutes in a 65°C bath of the same wash buffer , then washed on an orbital shaker for 10 minutes in a new rack in 1X SSC , ending with 5 minutes in 0 . 1X SSC . Arrays were then spun dry and scanned in an Agilent scanner . The resulting images were analyzed using Axon Genepix software version 5 . Complete microarray data are available for download from the Princeton Microarray Database and GEO under accession GSE87401 . Data were linearly normalized and filtered for spots with intensity of at least 2 times over background in at least one channel . Manually flagged spots were also excluded . These filters were adequate to routinely filter out >95% of empty spots and retain >95% of hybridizing spots . A single colony of S . mikatae and S . paradoxus and S . uvarum was inoculated into sulfate-limited chemostat medium with ura supplemented , grown overnight at 30°C , and 100 μL of the culture was inoculated into a ministat chamber [27] containing 20 mL of the same medium at 30°C . After 30 hr , the flow of medium was turned on at a dilution rate of 0 . 17 ± 0 . 01 hr−1 . Four chemostats were inoculated from four individual colonies for each species and cell samples ( glycerol stock and dry pellet ) were passively collected every day from fresh effluent for ~200 generations . DNA was isolated by a modified Smash-and-Grab protocol from each endpoint population [67] . Whole genome sequencing of the evolved and ancestral populations was performed as described below . Longer term S . uvarum and hybrid evolution experiments were performed in ATR Sixfors fermentors modified to run as chemostats , as described [25] , with the exception that S . uvarum populations were held at 25°C . Prior experiments comparing this system with the ministat system demonstrated that they are nearly equivalent [27] . To determine if SUL1 would amplify in S . uvarum , four individual colonies of a sul2Δ S . uvarum strain were inoculated into four sulfate-limited ministat chambers as previously described . Array CGH was performed on the four populations after 260 generations using the ancestral sul2Δ deletion strain as the reference . Yeast samples for real-time PCR analysis were collected directly from the culture vessels , when the cultures reached steady state ( approximately 3 days at ~25 generations ) . The cells were filtered on Nylon membrane ( 0 . 45 μm pore size ) and immediately frozen in liquid nitrogen and stored at -80°C until RNA extraction . The pairwise competition experiments were performed in ministats . Each competitor strain was cultured individually . Upon achieving steady state , the competitors were mixed in 50:50 ratio . Each competition was conducted in two biological replicates for 15 generations after mixing . Samples were collected and analyzed three times daily . The proportion of GFP+ cells in the population was detected using a BD Accuri C6 flow cytometer ( BD Biosciences ) . The data were plotted as ln[ ( dark cells/GFP+ cells ) ] vs . generations . The relative fitness coefficient was determined from the slope of the linear region by the use of linear regression analysis ( see schematic in S10 Fig ) [68] . The gene deletion competition assays were performed using two different drug resistant markers . For testing the fitness of either the sul1Δ or sul2Δ deletion strain in S . cerevisiae or S . uvarum , a spontaneous canavanine-resistant mutant ( CanR ) was selected . Two 20 mL chemostats were inoculated with either deletion strain marked with either CanR or the canavanine sensitive ( CanS ) strain containing the alternate deleted allele . Cultures were brought to steady-state conditions over a period of 15 generations . 10 mL from the chemostat containing the canavanine sensitive ( CanS ) strain ( containing the alternate deleted allele ) was removed and replaced with 10 mL from the chemostat containing the CanR marked clone . We sampled the chemostat an average of every 5 generations for approximately 30 generations . Cells were sonicated , diluted , plated on rich nonselective media , and grown for 2 days at 30°C . We counted >200 colony forming units using sterile methods . Cells were then replica-plated to synthetic complete minus arginine media containing 60 mg/L canavanine and allowed to grow at 30°C or 25°C for 3 days . CanR cells were identified as fully formed colonies [25] . RNA was extracted from the filtered sample by acid phenol extraction and quantified using a nanodrop spectrophotometer . 90 μg of RNA was cleaned-up using the Qiagen RNA easy kit according to the manufacturer's instructions ( Qiagen ) . Contaminating DNA was removed by using Rapid DNase out removal kit on 2 μg of RNA in a 100 μL reaction ( Thermo ) . Oligonucleotides for real-time PCR are listed in S3 Table . One microgram of total RNA was reverse-transcribed into cDNA in a 20 μL reaction mixture using the SuperScript VILO cDNA synthesis kit ( Life ) . The cDNA concentrations were then analyzed using the nanodrop . For the RT-PCR , each sample was tested in triplicate in a 96-well plate using SYBR . The reaction mix ( 19 μL final volume ) consisted of 10 μL of LightCycler 480 SYBR Green I Master ( Roche ) , 2 μL of each primer ( 5 mM final concentration ) , 5 μL of H2O , and 1 μL of a 1/100 dilution of the cDNA preparation . The absence of genomic DNA in RNA samples was verified by real-time PCR using the DNase free RNA . A blank was also incorporated in each assay . The thermocycling program consisted of one hold at 95°C for 4 min , followed by 50 cycles of 10 sec at 95°C and 45 sec at 56°C . The quantification of the expression level of SUL1 was normalized with ACT1 and the standard deviation was taken between four replicates . Genomic DNA libraries were prepared for Illumina sequencing using the Nextera sample preparation kit ( Illumina ) . Barcoded libraries were quantified on an Invitrogen Qubit Fluorometer and submitted for 150 bp paired end sequencing on an Illumina HiSeq 2000 . Read data have been deposited at the NCBI under the Bioproject accession number PRJNA297229 . The reads were aligned against the reference strain of S . uvarum ( CBS 7001 ) , S . mikatae ( IFO 1815 ) , and S . paradoxus ( CBS 432 ) using Burrows-Wheeler Aligner [69] . The sequence coverage of the nuclear genome ranged from 70 to 300x . Copy-number variations ( CNVs ) were detected by averaging the per-nucleotide read depth data across 100bp windows . For each window , the log2ratio in read depth between the evolved and parental strain was calculated . The copy number was calculated from the log2ratios and plotted using the R package DNAcopy [70] . | Both comparative genomics and experimental evolution are powerful tools that can be used to make inferences about evolutionary processes . Together , these approaches provide the opportunity to observe evolutionary adaptation over millions of years where selective history is largely unknown , and over short timescales under controlled selective pressures in the laboratory . We have used comparative experimental evolution to observe the evolutionary fate of an adaptive mutation , and determined to what degree the outcome is conditional on the genetic background . We evolved several populations of different yeast species for over 200 generations in sulfate-limited conditions to determine how the differences in genomic context can alter evolutionary routes when challenged with a nutrient limitation selection pressure . We find that the gene encoding a high affinity sulfur transporter becomes amplified in most species of Saccharomyces , except in S . uvarum , in which the amplification of the paralogous sulfate transporter gene SUL2 is recovered . We attribute this change in amplification preference to mutations in the non-coding region of SUL1 , likely due to reduced expression of this gene in S . uvarum . We conclude that the adaptive mutations selected for in each organism depend on the genomic context , even when faced with the same environmental condition . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
]
| [
"genome",
"evolution",
"chemical",
"compounds",
"salts",
"fungi",
"model",
"organisms",
"fungal",
"evolution",
"experimental",
"organism",
"systems",
"saccharomyces",
"research",
"and",
"analysis",
"methods",
"mycology",
"sulfates",
"molecular",
"evolution",
"chemistry",
"comparative",
"genomics",
"evolutionary",
"genetics",
"genetic",
"loci",
"yeast",
"genetics",
"biology",
"and",
"life",
"sciences",
"yeast",
"and",
"fungal",
"models",
"saccharomyces",
"cerevisiae",
"genomics",
"evolutionary",
"biology",
"physical",
"sciences",
"gene",
"amplification",
"computational",
"biology",
"organisms"
]
| 2017 | Differential paralog divergence modulates genome evolution across yeast species |
Factor H binding protein ( fHbp ) is an important antigen for vaccines against meningococcal serogroup B disease . The protein binds human factor H ( fH ) , which enables the bacteria to resist serum bactericidal activity . Little is known about the vaccine-potential of fHbp for control of meningococcal epidemics in Africa , which typically are caused by non-group B strains . We investigated genes encoding fHbp in 106 serogroup A , W-135 and X case isolates from 17 African countries . We determined complement-mediated bactericidal activity of antisera from mice immunized with recombinant fHbp vaccines , or a prototype native outer membrane vesicle ( NOMV ) vaccine from a serogroup B mutant strain with over-expressed fHbp . Eighty-six of the isolates ( 81% ) had one of four prevalent fHbp sequence variants , ID 4/5 ( serogroup A isolates ) , 9 ( W-135 ) , or 74 ( X ) in variant group 1 , or ID 22/23 ( W-135 ) in variant group 2 . More than one-third of serogroup A isolates and two-thirds of W-135 isolates tested had low fHbp expression while all X isolates tested had intermediate or high expression . Antisera to the recombinant fHbp vaccines were generally bactericidal only against isolates with fHbp sequence variants that closely matched the respective vaccine ID . Low fHbp expression also contributed to resistance to anti-fHbp bactericidal activity . In contrast to the recombinant vaccines , the NOMV fHbp ID 1 vaccine elicited broad anti-fHbp bactericidal activity , and the antibodies had greater ability to inhibit binding of fH to fHbp than antibodies elicited by the control recombinant fHbp ID 1 vaccine . NOMV vaccines from mutants with increased fHbp expression elicit an antibody repertoire with greater bactericidal activity than recombinant fHbp vaccines . NOMV vaccines are promising for prevention of meningococcal disease in Africa and could be used to supplement coverage conferred by a serogroup A polysaccharide-protein conjugate vaccine recently introduced in some sub-Saharan countries .
For more than 100 years devastating epidemics of meningococcal disease have occurred in sub-Saharan Africa [1] . In the decade 1988 to 1997 , more than 700 , 000 cases and over 100 , 000 deaths were reported . Public health responses were limited by scarce resources [2] . Further , the only vaccines available , un-conjugated ( plain ) polysaccharides , elicited incomplete and short duration of protection in young children [3] , [4] , and had a minimal effect on decreasing transmission of the organism [3] , [5] , [6] . Control of epidemic meningococcal disease in Africa , therefore , remains an unsolved public health challenge . Most meningococcal disease in industrialized countries is caused by strains producing capsular serogroups B , C or Y , whereas most disease in sub-Saharan Africa is caused by serogroup A strains . After more than ten years of work [7] , [8] , a promising serogroup A polysaccharide-protein conjugate vaccine recently was developed for sub-Saharan Africa [9] , [10] . As of January 21 , 2011 , nearly 20 million people had been immunized as part of demonstration projects in three countries ( http://www . path . org/menafrivac/index . php ) . While this vaccine has the potential to eliminate serogroup A epidemics , widespread vaccination may result in selective pressure for replacement of strains with other capsular serogroups such as X or W-135 , which have caused epidemics in this region [11]–[14] . However , with the possible exception of Spain [15] , there is little evidence of serogroup replacement after widespread use of monovalent serogroup C meningococcal conjugate vaccines in Europe [16] , [17] . Pneumococcal serotype replacement , in contrast , has been a problem in many countries where pneumococcal polysaccharide-protein conjugate vaccines were introduced [18]–[22] . A number of protein antigens are being developed for prevention of meningococcal serogroup B disease ( Reviewed in [23] , [24] ) . These antigens also are prevalent in meningococcal strains with other capsular serogroups [7] , [25] , [26] . Therefore , the vaccines have the potential to prevent disease caused by non-group B strains . One of the most promising of the new protein vaccine candidates is factor H binding protein ( fHbp , which was previously referred to as GNA1870 [27] or LP2086 [28] ) . Recombinant fHbp is part of two vaccines in clinical development for prevention of serogroup B disease [28]–[30] . Native outer membrane vesicle vaccines from meningococcal mutants with over-expressed fHbp also are under investigation [31]–[36] . The fHbp antigen is a surface-exposed lipoprotein that binds complement fH [37] , which down-regulates complement activation and enhances the ability of the organism to escape complement-mediated bacteriolysis [37]–[39] . In immunized mice and humans , antibodies to recombinant fHbp vaccines elicited complement-mediated serum bactericidal activity [27] , [28] , [30] , [40]–[45] , which in humans is the hallmark of protection against meningococcal disease [46]–[48] . The present investigation was undertaken to determine the vaccine-potential of fHbp for control of meningococcal epidemics in Africa caused by serogroup A , W-135 and X isolates .
In a previous study , we characterized fHbp sequence variants in a small collection of serogroup A , W-135 and X isolates from patients in sub-Saharan Africa [49] . The objectives of the present study were to determine fHbp sequence variants in an expanded panel of case isolates from Africa , to measure levels of fHbp expression , which in previous studies had been reported to be important for predicting susceptibility of serogroup B strains to anti-fHbp bactericidal activity [50] , and to investigate the immunogenicity in mice of recombinant fHbp vaccines representative of sequence variants prevalent among invasive African strains . The recombinant vaccine immunogenicity results were compared to that of a prototype native outer membrane vesicle vaccine ( NOMV ) prepared from a serogroup B mutant strain engineered to over-express fHbp . Our hypothesis was that the NOMV would elicit broader serum bactericidal antibody responses against the strains from Africa than the recombinant fHbp vaccines since in previous studies , mutant NOMV vaccines with over-expressed fHbp elicited broader bactericidal activity against serogroup B strains [33]–[36] , [51] . Vaccine immunogenicity was evaluated in CD 1 mice in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Children's Hospital & Research Center at Oakland Institutional Animal Care and Use Committee . Blood collection was performed under anesthesia , and all efforts were made to minimize suffering . The human complement source for measuring serum bactericidal activity was serum from an adult who participated in a protocol that was approved by the Children's Hospital Oakland Institutional Review Board ( IRB ) . Written informed consent was obtained from the subject . Antibody concentrations were transformed ( Log10 ) . For calculations of geometric mean antibody concentrations , concentrations below the limit of the assay were assigned a concentration half of the lower limit . Two-tailed Student's t tests were used to compare the geometric mean antibody concentrations between two independent groups of mice . All statistical tests were two-tailed; probability values of less than or equal to 0 . 05 were considered statistically significant .
The distributions of the prevalent clonal complexes , fHbp variant groups , and fHbp sequence variants are shown in Figure 2 . The serogroup A isolates were derived from three clonal complexes: CC 1 ( three isolates from 1963 to 1967 and one from 1989 ) , CC 4 ( ten from 1966 to 1990 ) , and CC 5 ( three from 1988–1999 , and fourteen from 2003–2007 ) . In contrast , the 53 serogroup W-135 isolates were predominately from CC 11 ( N = 47 from 1994 to 2007 ) . Among the 22 serogroup X isolates , four were members of a new CC ( designated CC 181 ( http://pubmlst . org/neisseria/ ) . Of the remaining 18 isolates , three from the 1970′s were sequence type ( ST ) 3687 , and 15 from 2006–2007 were ST 5403 . These two STs differed by only a single nucleotide change in one of the seven loci ( fumC ) and , thus , may represent an undesignated CC , which accounted for 82% of the serogroup X isolates . There also were a limited number of PorA VR types ( Figure S2 ) . Among the serogroup A isolates , P1 . 20 , 9 was present overall in 55 percent , and in 89 percent of 18 serogroup A isolates obtained since 1990 . Among the serogroup W-135 isolates , P1 . 5 , 2 and related types such as 5-1 , 2-2 predominated ( 98% ) , and among the serogroup X isolates , P1 . 19 , 26 and related type P1 . 19 , 26-4 accounted for 68% . The PorA VR typing results are consistent with previous studies of strains from sub-Saharan Africa [58] , [59] . One hundred percent of the serogroup A isolates , 95% of the X isolates , and 34% of the W-135 isolates had fHbp in the variant 1 group ( Figure 2 , middle column ) . The remaining W-135 isolates had fHbp variant 2 ( 58% ) or 3 ( 8% ) ; one serogroup X isolate had fHbp variant 3 . The distribution of the major fHbp amino acid sequence variants is shown in Figure 2 ( right column ) . All of the group A isolates had fHbp sequence variants ID 4 or 5 , which differed from each other by a single amino acid . The most prevalent sequence variants in the serogroup X isolates were ID 74 ( 67% ) and ID 73 ( 12% ) ; two of the serogroup X isolates in the category “other” had fHbp ID 4 , which was prevalent among serogroup A isolates . With only a few exceptions the W-135 isolates were clonal based on having a common clonal complex ( CC 11 ) and PorA VR type ( P1 . 5 , 2 , see Figure S2 ) . This clone could be subdivided into multiple subclones based on genes encoding fHbp ID 9 ( variant 1 ) , 22/23 ( variant 2 , and which differed from each other by one amino acid ) , or 349 or 111 ( variant 3; included in the category “other” , Figure 2 ) . Collectively , four fHbp sequence variants ( or related variants , each differing from the respective prevalent variant by 1 amino acid ) were present in 81% of the 106 isolates . These were fHbp ID 4/5 , 74 or 9 ( variant 1 group ) , or ID 22/23 ( variant 2 group ) . The percent amino acid identities between each of these sequence variants , and between fHbp sequence variants ID 1 , 28 and 77 , which were used as control vaccines , are summarized in Table 1 . We determined fHbp expression levels in bacterial cells from 44 isolates ( Figure 3 ) using a quantitative Western blot . Six of the 16 group A isolates tested ( Panel A ) , and nine of the 14 W-135 isolates tested with fHbp in the variant 1 group ( Panel B ) , had low fHbp expression ( defined by ≤33% of fHbp expressed by the group B reference strain H44/76 , which is a naturally high expresser of fHbp ID 1 in variant group 1 ) . All but one of seven group W-135 isolates tested with fHbp in the variant group 2 expressed ≤33% of the group B reference strain 8047 , which is a naturally high expresser of fHbp ID 77 in variant group 2 ( Panel D ) . In contrast , all but one of the seven group X isolates tested with fHbp in variant 1 group had high fHbp expression ( >100% expression compared with H44/76 , Panel C ) . The seventh isolate had ∼80% of the fHbp expressed by H44/76 , which was considered intermediate . We prepared isogenic mutants with different levels of fHbp expression from three isolates: a serogroup B reference strain , and serogroup A and W-135 African isolates . Figure 4 panels A , B and C illustrate relative expression of fHbp on the surface of live bacteria as measured by flow cytometry . Panels D , E and F show the corresponding fHbp expression measured in solubilized bacterial cells by a quantitative Western blot [50] . For each set of mutants , there was a 5- to 10-fold range between lowest and highest fHbp expression . The wildtype serogroup B reference isolate had naturally high expression of fHbp ID 1 . Sera from mice immunized with each of the recombinant fHbp ID 1 , 4 , 9 , or 74 ( variant 1 group ) vaccines had high bactericidal titers against this strain ( Figure 4 , Panel G ) . As expected , this strain was resistant ( titer <1∶10 ) to the serum from mice immunized with the recombinant fHbp ID 22 vaccine ( variant group 2 ) . Against the isogenic mutant of H44/76 with 58% fHbp expression by Western blot as that of the wildtype H44/76 strain , the respective anti-fHbp ID 1 , 4 , 9 , or 74 bactericidal titers were ∼10-fold lower than the corresponding titers measured against the higher fHbp expressing wildtype strain ( Panel G ) . In contrast , none of these antisera was bactericidal against the H44/76 mutant with low fHbp expression ( 10% of the wildtype isolate ) . The H44/76 wildtype strain and two fHbp mutants were equally susceptible to bactericidal activity of a positive control mAb against PorA ( Panel G , yellow horizontal hatched bars ) . Similar respective results were observed with the mutants of the serogroup A ( A3 ) and serogroup W-135 isolate ( W13 ) . For example , the W-135 wildtype strain with 45% fHbp expression relative to that of the reference strain was susceptible to anti-fHbp bactericidal activity only by the antiserum prepared to the recombinant fHbp vaccine ID 9 that matched that of the target isolate ( titer >1∶1000 , Panel I ) . In contrast , the isogenic mutant with increased fHbp expression ( 126% relative to H44/76 ) was susceptible to anti-fHbp bactericidal activity by antisera to any of the recombinant fHbp vaccine sequence variants in variant group 1 . While the data from the mutants do not permit a precise definition of the level of fHbp expression required for homologous and cross-reactive anti-recombinant fHbp bactericidal activity , collectively the results indicated that fHbp expression below 30% of H44/76 was associated with resistance and , with increasing fHbp expression , the isolates became more susceptible to anti-fHbp cross-reactive bactericidal activity . Figure 5 shows the anti-fHbp bactericidal titers of antisera prepared to the different recombinant fHbp sequence variants in variant group 1 when measured against wildtype serogroup A , W-135 and X isolates with fHbp in variant group 1 . The isolates were generally susceptible to anti-fHbp antibodies elicited by the recombinant fHbp vaccine that matched the sequence variant of the vaccine ( blue bars ) but not to antibodies elicited by mismatched recombinant fHbp variants . The lack of cross-reactive bactericidal activity was most notable for the anti-fHbp ID 1 and ID 74 antisera ( Panels A and D , respectively ) . This result was surprising since these vaccines had 92 to 96% amino acid identity with the fHbp expressed by the test strains ( Table 1 ) . In Figure 5 the order of the isolates from left to right is with increasing fHbp expression ( as shown in Figure 2 ) . While there were trends for increased susceptibility to anti-fHbp bactericidal activity with increasing fHbp expression ( for example , the W-135 isolates and the respective anti-fHbp ID 9 titers ) , the relationship is not linear , being confounded by other variables . The lack of linearity is most evident with the serogroup A isolates where some of the lowest fHbp expressers ( i . e . , A1 and A2 ) were killed by the anti-fHbp ID 4 antiserum that matched that of the isolates , while some of the highest expressers ( i . e . , A13 and A14 ) were resistant . Figure 6 shows the anti-fHbp bactericidal titers of antisera prepared to different recombinant fHbp sequence variants in variant groups 2 or 3 when measured against wildtype serogroup W-135 isolates with fHbp ID22/23 in variant group 2 . The anti-fHbp ID 22 antiserum was bactericidal against all of the isolates , even though nearly all of these isolates were low fHbp expressers . Although these isolates also were killed by the control mismatched anti-fHbp ID 77 ( variant 2 ) or fHbp ID 28 ( variant 3 ) antisera ( 84 to 94 percent amino acid identity with fHbp ID 22/23 , Table 1 ) , the respective titers were 10 to 100-fold lower than those of the anti-fHbp ID 22 antiserum ( compare anti-ID 22 bactericidal titers in Panel B to those in Panels C and D , Figure 6 ) . We measured susceptibility of 12 African isolates to bactericidal activity of an antiserum from mice immunized with a prototype NOMV vaccine prepared from a mutant of group B strain H44/76 with over-expressed fHbp ID 1 ( Figure 7 ) . As controls , we tested antisera from mice immunized with an NOMV vaccine from an isogenic fHbp knock-out mutant ( NOMV fHbp KO ) or a recombinant fHbp ID 1 vaccine . Against the wildtype serogroup B H44/76 strain , which was a high expresser of fHbp ID 1 that matched the recombinant fHbp vaccine , the bactericidal titers of the control antisera to the NOMV vaccine from the fHbp knock-out mutant , or the recombinant fHbp ID 1 vaccine , were both ∼1∶10 , 240 . Only one of the 12 heterologous African isolates ( X4 ) was susceptible to bactericidal activity of the antiserum to the recombinant fHbp ID 1 vaccine ( white bars ) . In contrast , 11 of the 12 isolates were killed by the antiserum from mice immunized with the NOMV vaccine from the serogroup B mutant with over-expressed fHbp ID 1 ( orange bars ) , which included both serogroup A isolates resistant to bactericidal activity of the antiserum to the recombinant fHbp ID 4 vaccine that matched that of the isolate ( gray bars ) . The one W-135 strain , W1 , which was not killed by any of the antisera , was the lowest expresser of fHbp ( Figure 3 ) . The remaining three serogroup W-135 tested and all four serogroup X isolates were killed by sera from the mice immunized with the NOMV vaccine from the mutant with over-expressed fHbp ID 1 , but the respective bactericidal titers were lower than the corresponding titers elicited by the recombinant fHbp vaccine with a sequence variant that matched that of the strain . With one exception ( X5 ) , the bactericidal titers elicited by the control NOMV vaccine from the fHbp knockout mutant were negative ( titers <1∶10 , gray hatched bars ) . Although not shown in Figure 7 , when we mixed this antiserum with the antiserum to the recombinant fHbp ID 1 vaccine , the serum bactericidal titers remained negative ( <1∶10 , 11 isolates ) or unchanged ( titer 1∶50 , isolate X5 ) . Thus , there was no evidence of cooperative bactericidal activity between anti-fHbp antibodies elicited by the recombinant fHbp ID 1 vaccine and antibodies elicited to other antigens in the NOMV . These results are in contrast to a previous report of cooperative bactericidal activity observed between human antibodies to recombinant fHbp ID 1 and Neisserial Heparin binding antigen , which individually lacked bactericidal activity [60] . By ELISA , the mice immunized with the NOMV vaccine from the mutant with over-expressed fHbp ID 1 had higher serum anti-fHbp ID 1 antibody concentrations than mice immunized with the recombinant fHbp ID 1 vaccine ( respective geometric means of 2203 and 746 U/ml , P<0 . 02 , Figure 8 , Panel A ) . By ELISA , the sera from the mice immunized with the mutant NOMV vaccine also showed greater inhibition of binding of fH to fHbp ID 4 , which was the sequence variant expressed by serogroup A strains ( Panel B , P<0 . 05 at each dilution tested ) . The increased fH inhibition was not only a result of the higher serum anti-fHbp concentrations in the mutant NOMV vaccine group , but also appeared to be from a different anti-fHbp antibody repertoire , since on average the anti-fHbp antibody concentration required for inhibition of fH in this group was nearly 4-fold lower than in the recombinant fHbp vaccine ID 1 group ( respective geometric means of 1 . 17 vs . 4 . 04 U/ml , P<0 . 05 , Panel C ) .
There are several important limitations to the present study . First , we investigated only 106 case isolates . Although these isolates were from 17 countries , 30 percent were from Burkina Faso . Africa is a large and diverse continent with a complex ecology affecting meningococcal transmission and disease . Development of a mutant NOMV fHbp-based vaccine for sub-Saharan Africa will require ongoing surveillance of meningococcal strains to assure that the vaccine antigens match those of the prevalent strains . Second , for investigation of anti-fHbp antibody activity elicited by the recombinant fHbp vaccines , we prepared hyperimmune antiserum pools in mice immunized with the vaccines given with Freunds complete and incomplete adjuvant . This adjuvant is unsuitable for humans and the high anti-fHbp titers in the hyperimmune mouse serum pools are unlikely to be achieved in humans . The poor cross-reactive bactericidal activity of these mouse antisera is , therefore , likely to be even lower in humans immunized with recombinant vaccines given with aluminum adjuvants . The broad serum bactericidal antibody responses to the mutant fHbp NOMV vaccine , however , were in mice given the vaccine with aluminum hydroxide . A third limitation was that while the data from the isogenic mutants with different levels of fHbp showed a clear relationship between fHbp expression levels and susceptibility to anti-fHbp bactericidal activity , the relatively small number of wild type strains tested , and the presence of potential confounders such as capsular serogroup [70] , LOS [71] , [72] or alternative fH binding ligands [73] , did not provide sufficient statistical power for a formal analysis of the relationship between fHbp expression and anti-fHbp bactericidal activity . A fourth limitation of the present study is that we used a prototype NOMV vaccine from a mutant group B strain with over-expressed fHbp ID 1 . We chose this vaccine since it had worked well in previous studies of group B strains , and an NOMV vaccine from a similar mutant African strain was not yet available . Neither the PorA VR type of the group B vaccine strain nor the fHbp sequence variant was present among the African isolates . We would anticipate that even higher serum bactericidal antibody responses would be elicited by NOMV vaccines prepared from mutant African isolates where the antigens in the vaccine would be matches to the Africa strains . Finally , although the serum bactericidal titers of the control mice immunized with the NOMV from the fHbp knockout were negative , we had insufficient sera from mice immunized with the NOMV vaccine from the mutant with over-expressed fHbp to prove that the bactericidal antibodies were directed against fHbp . In several previous studies however , we demonstrated that bactericidal activity against serogroup B strains was greatly or completely diminished after depletion of anti-fHbp antibodies by solid phase adsorption [34] , [36] , [51] . | Epidemics of meningococcal meningitis are common in sub-Saharan Africa . Most are caused by encapsulated serogroup A strains , which rarely cause disease in industrialized countries . A serogroup A polysaccharide protein conjugate vaccine recently was introduced in some countries in sub-Saharan Africa . The antibodies induced , however , may allow replacement of serogroup A strains with serogroup W-135 or X strains , which also cause epidemics in this region . Protein antigens , such as factor H binding protein ( fHbp ) , are promising for prevention of meningococcal serogroup B disease . These proteins also are present in strains with other capsular serogroups . Here we report investigation of the potential of fHbp vaccines for prevention of disease caused by serogroup A , W-135 and X strains from Africa . Four fHbp amino acid sequence variants accounted for 81% of the 106 African isolates studied . While there was little cross-protective activity by antibodies elicited in mice by recombinant fHbp vaccines from each of the four sequence variants , a prototype native outer membrane vesicle ( NOMV ) vaccine from a mutant with over-expressed fHbp elicited antibodies with broad protective activity . A NOMV vaccine has the potential to supplement coverage by the group A conjugate vaccine and help prevent emergence of disease caused by non-serogroup A strains . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"vaccines",
"medicine",
"vaccination",
"clinical",
"immunology",
"immunity",
"vaccine",
"development",
"biology",
"microbiology",
"immunology",
"bacterial",
"pathogens"
]
| 2011 | Meningococcal Factor H Binding Proteins in Epidemic Strains from Africa: Implications for Vaccine Development |
Trinucleotide hereditary diseases such as Huntington disease and Friedreich ataxia are cureless diseases associated with inheriting an abnormally large number of DNA trinucleotide repeats in a gene . The genes associated with different diseases are unrelated and harbor a trinucleotide repeat in different functional regions; therefore , it is striking that many of these diseases have similar correlations between their genotype , namely the number of inherited repeats and age of onset and progression phenotype . These correlations remain unexplained despite more than a decade of research . Although mechanisms have been proposed for several trinucleotide diseases , none of the proposals , being disease-specific , can account for the commonalities among these diseases . Here , we propose a universal mechanism in which length-dependent somatic repeat expansion occurs during the patient's lifetime toward a pathological threshold . Our mechanism uniformly explains for the first time to our knowledge the genotype–phenotype correlations common to trinucleotide disease and is well-supported by both experimental and clinical data . In addition , mathematical analysis of the mechanism provides simple explanations to a wide range of phenomena such as the exponential decrease of the age-of-onset curve , similar onset but faster progression in patients with Huntington disease with homozygous versus heterozygous mutation , and correlation of age of onset with length of the short allele but not with the long allele in Friedreich ataxia . If our proposed universal mechanism proves to be the core component of the actual mechanisms of specific trinucleotide diseases , it would open the search for a uniform treatment for all these diseases , possibly by delaying the somatic expansion process .
Trinucleotide diseases are hereditary disorders in which a gene that harbors a trinucleotide repeat is inherited with a number of repeats that exceeds a disease-specific threshold [1 , 2] . In the so-called polyglutamine diseases , including Huntington disease ( HD ) [3] , spinocerebellar ataxia ( SCA ) of various types [4] , and others , the expanded repeat CAG codes for glutamine in a gene's coding region . Polyglutamine diseases are manifested by neuronal symptoms [1] . In other diseases , the repeat is located in noncoding regions: in the muscle disease myotonic dystrophy type 1 ( DM1 ) [1 , 5] the CTG repeat is located in the 3′ untranslated region ( UTR ) of the gene DMPK , and in Friedreich ataxia [1 , 5 , 6] ( FRDA ) a GAA repeat is located within the first intron of the gene FRDA . The genes associated with the various diseases are structurally and functionally unrelated . Despite their differences , many of the trinucleotide diseases share intriguing phenotype characteristics [2 , 7] . The disease has no symptoms for many years until a sudden onset at an age that is inversely correlated with the number of inherited repeats [2 , 4 , 8–11] . For example , in HD , the median onset age may change from 67 y for patients with 39 repeats to 27 y for patients with 50 repeats [11] . When the number of repeats exceeds 70 , the disease has juvenile onset; there are also cases of childhood onset for even longer repeats [12 , 13] . These relations of onset age and the number of repeats are similar in other diseases , and are typically characterized by an exponential curve in which the change in the age of onset as a result of additional inherited repeat reduces with the number of repeats [4 , 8 , 14] . A larger number of repeats also directly correlates with the severity and the rate of symptom progression of the disease [12 , 15 , 16] . In addition , many diseases show genetic anticipation , where the number of inherited repeats increases significantly from generation to generation , usually via paternal transmission , thus causing earlier onset and faster progression [1 , 2 , 7] . The mechanism , which leads to such genetically encoded delay in disease onset , is yet unknown . For polyglutamine diseases , it is currently assumed that the extended polyglutamine has a gain of a toxic function which leads to cumulative damage in the affected cells , possibly in the form of glutamine aggregate formation [1 , 17–19] . It is assumed that the level of toxicity depends on the number of repeats , such that longer repeats are more toxic and lead to a faster damage and earlier cell death , implying that both disease and delay in onset are governed by the same mechanism [1 , 17–19] . This suggested mechanism of cumulative damage has several shortcomings and is unlikely to explain the strong correlations of onset and repeat length . First , the strong correlations of repeat length and age of onset are also apparent in nonpolyglutamine diseases such as DM1 and FRDA , suggesting a mechanism that is unrelated to the specific gene function or expression level . Second , in the rare case of patients with homozygous mutation ( two expanded alleles ) , the cumulative damage mechanism would predict a significant decrease in age of onset , which is in contradiction with recent clinical findings that homozygousity does not result in earlier onset [20–22] . Unlike onset , disease progression after onset was found to be notably faster in homozygote patients with HD , leading to the suggestion that two different mechanisms account for the delayed onset and the disease pathology [20] . Furthermore , aggregate accumulation mechanism is highly sensitive to differences in expression level and thus unlikely to show such precise correlations . Finally , it is unclear how such a mechanism would result in the exponential onset curve often seen in polyglutamine diseases . Several previous studies in trinucleotide diseases animal models , including mouse [23–25] and fruit fly [26] , have highlighted the fact that trinucleotide repeats present significant somatic instability , which is specifically significant in the disease-affected tissues . Somatic length instability was also shown in lymphoblastoid cell lines of HD subjects with intermediate length [27] . This somatic instability is a result of either slippage mutation or a mishybridization of the two DNA strands due to the high complementarities of the repeating sequence followed by a DNA repair process [2 , 26 , 28] . This process was shown to have strong bias toward expansion [23–26] . The repeat instability increases with the number of repeats [29 , 30] , as the likelihood of mishybridization grows with repeat length . Thus , as the disease allele somatically expands within a cell , its probability for further expansion increases , leading to an accelerated expansion process . Understanding the mechanism by which the number of inherited repeats affects the onset age and disease progression is highly desirable , as it may open new treatment opportunities . Here , we propose that a universal mechanism of length-dependent somatic mutation underlies trinucleotide diseases and accounts for these striking genotype–phenotype correlations .
Our proposed mechanism specifies that onset and progression of the disease are determined by the rate of expansion of the trinucleotide repeat in certain cells in the patient's body . The disease manifests when the trinuecleotide repeat has expanded beyond a certain threshold in a sufficient number of these cells , and progresses as more and more cells do so . For each disease , our universal mechanism , described in Figure 1 , assumes that a patient inherits the disease gene in which one allele ( if the disease is dominant , or two alleles if it is recessive ) has a trinucleotide repeat larger than the disease-specific initial threshold , and predicts that: ( 1 ) the patient has a disease-specific group of cells , the dynamics of which determines the onset age and progression rate of the disease ( Figure 1A–F ) ; ( 2 ) the disease alleles of cells in this group stochastically expand at a rate that increases linearly with the number of repeats ( Figure 1G ) ; ( 3 ) when the number of repeats in one allele ( if it is dominant; two if recessive ) is larger than a disease-specific pathological threshold , the cell enters a disease-specific pathological state ( Figure 1D ) ; ( 4 ) disease onset occurs when a critical portion of the cells in the group has entered the pathological state ( Figure 1E ) ; and ( 5 ) the disease progresses in severity , toward death , as more cells enter the pathological state ( Figure 1F ) . We studied the dynamics of the mechanism and its implications on various disease-related properties using computer simulations and a mathematical analytical model ( see Materials and Methods and Figure 1H ) . We have conducted computer simulations and mathematical analysis of our proposed mechanism ( see Materials and Methods and Text S1 ) and used them to compute the expected age of onset for patients with various inherited repeat lengths . Our results show that such a process leads to an exponentially decreasing age of onset curve typically seen in clinical data of trinucleotide diseases . Furthermore , by fitting our model parameters to previously published clinical data of each disease ( see Figures 2A and S1 ) , we can estimate both the length of the pathological threshold assumed by our mechanism and the rate of somatic trinucleotide expansion ( the initial threshold of each disease can be accurately determined from the clinical data ) . Figure 2A shows the onset curve predicted by the mechanism , with mechanism parameters fitted to Huntington clinical data [8] . The predicted pathological threshold for HD is 115 repeats ( see Text S1 ) . Figure 2B demonstrates how the trinucleotide repeats of patients with HD with various inherited repeat lengths are predicted by our mechanism to expand exponentially during the patient's lifetime toward the pathological threshold , leading to the observed onset age differences . The slow expansion rate associated with smaller number of inherited repeats eventually leads to a large change in the onset age as a result of a single difference in the number of inherited repeats as seen in the clinical data . While most trinucleotide diseases are autosomal dominant , FRDA is the only known autosomal recessive trinucleotide disease . In this disease , the repeat sequence GAA is found in the first intron of the gene coding for Frataxin . A patient with FRDA has inherited two expanded disease alleles , which typically range in size from 200 repeats and up to more than 1 , 000 repeats . Previous studies [10 , 31 , 32] of FRDA showed that onset age is in strong correlation with the size of the shorter allele but not with the longer allele size or with the average size of both alleles . A mechanism based on a slow accumulation of toxicity cannot account for this unique phenomenon , as both alleles contribute to the level of Frataxin in the cell . In contrast , our mechanism of somatic expansion toward a pathological threshold provides a simple explanation . The long allele somatically expands beyond the pathological threshold earlier , as it is not only inherited with a number of repeats closer to that threshold but also starts with a faster expansion rate . Being a recessive disease , the cell enters its pathological state only when the shorter allele also expands beyond this threshold . Computer simulations of our mechanism in patients with various size combinations of two alleles ( see Figure 3 ) demonstrate that in a recessive disease , onset age is in strong correlation with the size of the short allele . In contrast , our mechanism predicts that in patients of dominant diseases with two diseased alleles ( so-called homozygous patient ) , onset age correlates with the size of the longer allele , as reported previously [33] ( see Figure 3 ) . In trinucleotide diseases there is also a correlation between the number of repeats and the rate of symptoms progression [12 , 15 , 16] . A patient with HD with more than 70 CAG repeats may manifest a juvenile onset before the age of 20 y and a much more aggressive course of disease progression compared to a late-onset patient with 40 inherited CAG repeats . Our proposed mechanism provides a simple explanation to this difference in progression rate . At birth , all disease-related cells in a patient's body carry the inherited allele size , and the repeat length variability between cells is negligible . However , this variability grows during the patient's lifetime as the trinucleotide repeat in each cell in this group expands independently and stochastically . Disease onset occurs when enough cells enter the pathological state by expanding beyond the pathological threshold , and the disease progresses as more and more cells enter the pathological state . A wide repeat size distribution near the time of onset implies that many of the cells are still far from the pathological threshold and hence accounts for a slow progression rate . In contrast , a narrow distribution near the onset time implies that many cells are about to exceed the pathological threshold leading to a fast progression rate ( see Figure 4 and Videos S1 and S2 ) . Computer simulations of the length-dependent expansion process quantify the effect of the number of inherited repeats on disease progression . In these simulations , we arbitrarily defined disease onset to be the time when 20% of the cells have entered a pathological state and calculated the time until 80% of the cells enter a pathological state ( shorter time indicates faster progression ) . The results ( see Figure 4 ) show that the progression is much slower for late-onset patients ( CAG40 ) than for patients with juvenile onset ( CAG70 ) . Since all patients are born with negligible variability in the size of the trinucleotide repeat , the shorter time to onset and the fast expansion in the juvenile case leads to a smaller variability near the time of onset and thus accounts for the faster progression . In rare cases , patients with polyglutamine diseases carry two copies of the disease allele and are considered homozygote to the disease . One would expect that if polyglutamine toxicity damage accumulated from the patient's birth time , having two copies of a disease allele would have a tremendous effect on the age of onset . However , recent clinical studies of homozygote patients did not find any reduction in the expected age of onset due to homozygousity [20–22] . On the other hand , the rate of progression was significantly higher in HD homozygote patients compared to nonhomozygote patients . The fact that homozygousity increases the rate of progression but has no effect on age of onset cannot be explained only by toxicity or aggregate accumulation , and requires additional explanations . Our proposed mechanism provides one of the first explanations for this puzzling phenomenon . According to this mechanism in dominant diseases , a cell in the disease-related group of cells enters a pathological state when the first of its two alleles has expanded beyond the pathological threshold . We have conducted computer simulations of the somatic expansion mechanism that compares the longer allele size distribution in a patient homozygous for the disease ( extreme value distribution of the two-allele sizes ) with the long allele of a patient with heterozygous mutation . The simulations show that the distributions at the time of onset are nearly similar for the most expanded alleles ( see Figure 5A and 5B ) , which accounts for the similar onset age . However , as we go toward the least expanded alleles in the distribution , the homozygote distribution is narrower and closer to the pathological threshold , explaining the faster progression . Simulation of patients with various allele sizes ( see Figure 5C ) show that the reduction in onset age in the homozygote case is minor ( ∼6% ) , while the change in the disease progression is significant ( ∼30% ) . Mouse models of trinucleotide diseases demonstrate that somatic mutations exist in the disease-associated tissue and that those mutations expand with age [23–25] . If indeed mouse and human disease-related biochemistry is similar at the cellular level , our mechanism predicts that for a mouse to show disease symptoms during its short lifespan , it must be born with a disease allele very close to the pathological threshold ( 115 in HD according to our prediction ) . Indeed , mouse models of polyglutamine diseases typically require a number of repeats larger than 100 in order to show disease symptoms [34 , 35] . The creation of symptomatic mouse models with a number of repeats similar to that of human failed despite efforts to significantly increase the expression of the diseased gene [36 , 37] . This is consistent with our mechanism and in addition suggests that toxicity of short repeats cannot be increased by higher expression and that pathology is only seen when the repeat is much longer than typical inherited human genotype . In contrast to patients with HD , knock-in HD mice homozygous for HD mutation show anticipated age of onset compared to heterozygotes in addition to a more progressive disease [38] . This further supports our prediction that the mouse models that manifest HD symptoms are born with a number of repeats larger than , or very close to , the pathological threshold . In such a case , our model indeed predicts that homozygosity would have a stronger effect on onset age . Clinical studies [39–41] show that if an inherited CAG repeat is interrupted by another trinucleotide sequence , age of onset is delayed significantly compared to patients without such an interrupt . For example , a patient with SCA type 1 ( SCA1 ) who had a CAG58 repeat with an interrupt of a CAT repeat after 45 repeats had an onset age of 50 y rather than the expected onset age of 22 y . Other studies of DNA repeats showed that such an interrupt significantly slows the repeat's rate of mutation [29 , 30] . In addition , it was shown that the rate of mutation of tandem repeats depend on the length of the pure uninterrupted repeat segment [29 , 30] ( 45 in the above case ) ; thus , our mechanism , which is based on the rate of somatic mutation , accurately predicts the observed change in onset age resulting from the above interrupt location .
We suggest that a length-dependent somatic expansion mechanism underlies the genetically encoded delayed onset of trinucleotide diseases . According to the mechanism , the inherited disease allele has no toxic implications on the disease-related cells before it expands beyond a disease-specific pathological threshold , leading to cell pathology . Several clinical and experimental findings provide support for this mechanism . First , it provides a simple explanation to the correlation between age of onset and number of inherited repeats uniformly for both polyglutamine and nonpolyglutamine diseases . In addition , the disease dynamics implied by our mechanism explains the exponential shape of the onset curves , the faster progression associated with juvenile onset , the correlation with the short allele only in the recessive disease FRDA , and the similar onset but faster progression for patients with HD with homozygous mutations . The commonly assumed mechanism of cumulative damage or slow aggregate formation does not seem to be able to explain most of these disease-related phenomena . Our mechanism does not contradict studies in mouse models , which are focused on understanding the pathology of the different diseases , showing that this pathology occurs only when the number of repeats is sufficiently long . Thus , it provides explanation to the large repeat number that is required for symptomatic mouse models . The universal mechanism suggested in this work may apply to many trinucleotide diseases . Nevertheless , it provides several predictions that may be subject to further experimental validation in a disease-specific context , possibly by the use of animal models . One challenge is to identify for each disease which group of cells triggers disease onset . Our mechanism predicts that the somatic expansion in this group of cells would be particularly high . Another prediction is that somatic repeat expansion is expected to progress with the age of an affected animal even prior to disease onset . Finally , the model predicts that the rate of repeat expansion increases with time , and that at any time is a function of the repeat length at that time . Newly available technologies that facilitate the amplification and measurement of the repeat length at a single-cell resolution may characterize and accurately measure the mutation progress rate for various cell populations in the affected organ of mouse models for various diseases . Our mechanism suggests that the disease gene is not toxic for many years and that the time to onset is counted by a silent expansion of the repeat with no physiological implication on the cell . This may have significant clinical implications on the effort to find therapies for these cureless inherited diseases . Rather than addressing direct causes of pathology such as polyglutamine aggregates , therapeutic effort may focus on delaying the onset by slowing the somatic expansion process , which is known to be mediated by DNA repair mechanisms [26 , 28] . Our mechanism predicts that an ability to slow this expansion process may provide a common therapy to patients of most trinucleotide disease , as it addresses the universal component of the mechanism rather than the disease-specific component .
Disease-specific parameters . I—the initial threshold . A patient that inherits allele with number of repeats longer than this threshold will have the disease during his lifetime . T—the pathological threshold . Cells , which their alleles have somatically expanded beyond this threshold , become pathological . In the recessive version of the model both allele needs to expand beyond this threshold to become pathological . R—the basal expansion rate . This parameter determines the contribution of single additional repeat to the length dependent rate of expansion . C—the critical portion . The portion of pathological cells that is required for the disease onset . Patient parameters . t—the patient's age . L0—the patient's number of inherited repeats . Computer simulations were performed on a group of 1 , 000 cells in which the number of repeats was initialized to L0 , the number of inherited repeats . At each time point t , the number of repeats in each cell , Lt , was expanded based on its value at the beginning of the simulation time unit . The size of expansion was taken from a Poisson distribution with expectance E ( Lt ) = ( Lt − I ) × R where E is the expected expansion per time unit based on the cell current allele size Lt , the initial threshold I , and the rate of mutation R . The simulation sampling rate , 5 y−1 , is much larger than the typical mutation rate , 0 . 05 y−1 , and thus is sufficient for accuracy . The critical portion of cohort size C = 20% was used as a threshold for onset , although other choices for C ( C = 5%–50% ) gave qualitatively similar results . To simulate recessive disease and compare it with dominant disease patient with two disease alleles ( Figure 3 ) , we simulated the two alleles independently for all combinations of two allele sizes between 39 and 50 using the disease parameter from the example in Figure 1 ( I = 38 , T = 150 , R = 0 . 06 ) . The onset was determined according to the model , and the correlations with the small and large alleles were measured . The qualitative results hold for any disease-specific parameter set . The same simulation parameters were used to create the distribution of homozygote versus heterozygote patients . The percent difference in onset O and duration D was calculated as follows: Duration Difference = 100 × ( DHeteroozygote − DHomozygote ) / DHeteroozygote Onset Difference = 100 × ( OHeteroozygote − OHomozygote ) / OHeteroozygote We have derived an analytical model that describes the dynamic behavior of the mean and the standard deviation of allele size distribution that is stochastically expanding under the length-dependent expansion rate assumed by the mechanism we describe . The equations ( shown in Figure 1 ) derived by the model were used to fit clinical data of the various diseases ( see Text S1 ) . Detailed derivation of the model equations is described in Text S1 .
The Entrez Gene ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=gene ) accession numbers for the genes discussed in this paper are HD ( 3064 ) , FRDA ( 2395 ) , SCA1 ( 6310 ) , SCA2 ( 6311 ) , SCA3 ( 4287 ) , SCA6 ( 773 ) , SCA7 ( 6314 ) , DM1 ( 1760 ) , and DRPLA ( 1822 ) . | Trinucleotide diseases are a broad family of hereditary diseases characterized genetically by an expanded DNA region consisting of a repeated three-letter code . Patients inheriting such an abnormal DNA region experience sudden disease onset at an age that inversely depends on the size of the expanded region , followed by inevitable and highly predictable suffering and death . Despite more than a decade of research , the underlying mechanism of these diseases remains an enigma . Although the genes implicated with the various trinucleotide diseases are unrelated , and the defects in these genes occur in different parts of the DNA coding for the gene , the diseases' shared characteristics suggest a common mechanism underlies their root cause . We suggest a mechanism that uniformly explains how the inherited DNA repeats genetically encode the time of onset and the rate of progression of trinucleotide diseases . It suggests the disease manifests and progresses through the further expansion of the inherited abnormally expanded DNA region . It explains the clinical data of many diseases in this family , including previously unexplained onset-related phenomena . It also predicts that a general therapy for these diseases would be a drug or procedure that successfully interferes with the ongoing expansion of the disease trinucleotide repeat . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
]
| [
"homo",
"(human)",
"genetics",
"and",
"genomics",
"computational",
"biology"
]
| 2007 | A Universal Mechanism Ties Genotype to Phenotype in Trinucleotide Diseases |
Early malacological literature suggests that the outbreak of schistosomiasis , a parasitic disease transmitted by aquatic snails , in the Senegal River basin occurred due to ecological changes resulting from the construction of the Diama dam . The common treatment , the drug praziquantel , does not protect from the high risk of re-infection due to human contact with infested water on a daily basis . The construction of the dam interfered with the life cycle of the prawn Macrobrachium vollenhovenii by blocking its access to breeding grounds in the estuary . These prawns were demonstrated to be potential biological control agents , being effective predators of Schistosoma-susceptible snails . Here , we propose a responsible restocking strategy using all-male prawn populations which could provide sustainable disease control . Male prawns reach a larger size and have a lower tendency to migrate than females . We , therefore , expect that periodic restocking of all-male juveniles will decrease the prevalence of schistosomiasis and increase villagers' welfare . In this interdisciplinary study , we examined current prawn abundance along the river basin , complemented with a retrospective questionnaire completed by local fishermen . We revealed the current absence of prawns upriver and thus demonstrated the need for restocking . Since male prawns are suggested to be preferable for bio-control , we laid the molecular foundation for production of all-male M . vollenhovenii through a complete sequencing of the insulin-like androgenic gland-encoding gene ( IAG ) , which is responsible for sexual differentiation in crustaceans . We also conducted bioinformatics and immunohistochemistry analyses to demonstrate the similarity of this sequence to the IAG of another Macrobrachium species in which neo-females are produced and their progeny are 100% males . At least 100 million people at risk of schistosomiasis are residents of areas that experienced water management manipulations . Our suggested non-breeding sustainable model of control—if proven successful—could prevent re-infections and thus prove useful throughout the world .
Schistosomiasis is a chronic parasitic disease caused by blood flukes of the genus Schistosoma , which are dependent on two hosts to complete their life cycle , an intermediate host ( a freshwater snail ) and a definitive host ( a vertebrate ) . The adult parasites can live for decades and cause increasing damage to organ tissues ( bladder , liver or intestine ) and can result in mortality of the host [1] . One of the most heavily infected areas in the world is the Senegal River basin in which the outbreak of the disease was reported following the construction of the Diama dam , ∼50 km from the mouth of the river , in 1986 . The dam is a saltwater barrier and was built to support agricultural expansion in the delta and upriver by preventing saltwater intrusion during the dry season [2] . As a result of dam construction , the Senegal River basin ecosystem experienced major changes , such as habitat expansion for fresh water species , like aquatic snails hosting schistosomiasis [3]–[6] . Since the appearance of the dam , rates of Schistosoma haematobium infection have risen from 0–3 . 6% to 11 . 5% , and from 10 . 4–27 . 2% to 51 . 6% in different areas of the river basin [7] . Moreover , while S . mansoni was absent in the river basin before the construction of the dam , it was first reported 18 months after the dam was completed , with the associated infection rates now reaching up to 71 . 8% in some villages [7] . The ecological changes related to the separation of the upriver region from the estuary also are unfavorable for catadromous species , such as the native river prawn Macrobrachium vollenhovenii . M . vollenhovenii is a decapod crustacean belonging to the Palaemonidae family , endemic to the west coast of Africa from the Senegal River in the north to Angola in the south [8]–[10] . The northern habitat border of the prawn , the Senegal River basin , supported artisanal prawn fishery extending from the coast to more than 400 km inland prior to dam construction [11] . This natural habitat was confronted with an insurmountable challenge following construction of the dam due to the prawn's dependence on brackish water and access to the estuary to complete their life cycle . Ovigerous females of this species must migrate to the estuary in order to release their larvae , which in turn complete their larval development period in brackish water before migrating upriver as post-larvae [12]–[14] . The increased snail numbers after construction of the dam could be explained by a slowing of the river flow and decreased saltwater intrusion , thereby expanding regions of suitable habitat for the snails . This , together with the human migration seeking employment in the expanded rice and sugar cane fields of the new agricultural zone , resulted in a spread of schistosomiasis ( bilharzia ) among human populations living or working upriver of the Diama dam [4] , [5] , [15] . Chemotherapy-based campaigns using praziquantel , the primary drug used today to fight schistosomiasis , have been carried out by the Senegalese government . However , to eliminate the disease , an integrated management program is required . While praziquantel effectively kills adult worms inside the definitive host's body , rapid reinfection can occur upon re-exposure to cercariae from infected snails in the environment . [16]–[18] . Snail population abundance and distribution are mediated by predators in several aquatic systems [19]–[22] . Accordingly , Macrobrachium rosenbergii , the most commonly aquacultured freshwater prawn in the world [23] , is an effective predator of medically important freshwater snails under laboratory conditions [24]–[26] . Similarly , due to its relatively large size and tendency to consume medically important snails , M . vollenhovenii has been proposed both as a candidate for commercial aquaculture [27]–[29] and as an agent for biological control of schistosomiasis [25] . Indeed , M . vollenhovenii prawns were successful in controlling schistosome-susceptible snail populations under laboratory conditions [25] . Like other freshwater prawns , M . vollenhovenii exhibits clear sexual dimorphism , with males achieving larger maximum size than females [30]–[32] . Sexual dimorphism in many crustaceans is mediated by secretions of the androgenic gland ( AG ) , a masculinizing endocrine organ unique to this sub-phylum [33]–[36] . The masculinity-regulating hormone secreted by this gland in decapod crustaceans is the insulin-like hormone of the androgenic gland ( IAG ) . The gene encoding the hormone is uniquely expressed in males , with the function of the protein having been studied in several species [37]–[40] . Following discovery of the AG in M . rosenbergii [41] , a full functional sex reversal was achieved by bilateral ablation of the gland [42] , [43] . The discovery and sequence of the IAG-encoding gene in M . rosenbergii ( Mr-IAG ) [44] opened a path for the development of an innovative method of sex reversal through temporal RNA interference ( RNAi ) using double-stranded Mr-IAG RNA [42] , [45] . In this manner , sex reversed males ( neo –females ) are created that , when crossed with normal males , produce all-male progeny . Since male prawns grow faster than females and reach a larger size , these findings were translated into a commercialized biotechnology , namely the first use of RNAi in aquaculture , initially applied for the production of all-male prawn populations [46] . We hypothesized that the same could be achieved with other Macrobrachium species , such as M . vollenhovenii . In this multi-disciplinary study , we assessed the current abundance of M . vollenhovenii prawns in the Senegal River basin through capture using baited prawn traps . Such trapping efforts were supplemented by collaboration with local fisherman via a program offering purchase of their prawn catches throughout the course of the study period . We also conducted retrospective interviews with fishermen regarding the abundance of prawns along the Senegal River basin before and after construction of the Diama Dam . Studies of prawn catches and earlier literature on male superior size [31] , [32] suggested that both prawn fisheries and their biological control functions could benefit from restocking with all-male populations . A further objective of this study was thus to lay the required molecular foundation for the production of all-male populations . Accordingly , we characterized the AG and the IAG-encoding gene of M . vollenhovenii as a first step towards producing all-male populations for mass restocking of biological control agents .
To monitor the current abundance of prawns upstream of the Senegal River basin , 2–4 large crayfish traps were placed for 17–24 hours per site-visit at 15 sites throughout the lower Senegal River basin ( Fig . 1B , marked with white and grey stars ) . Sites were visited bimonthly between February , 2011 and June , 2012 . The traps used were commercial cylindrical crayfish traps constructed of a collapsible metal frame 30 cm in diameter and 60 cm in length , surrounded by fishing-net material . Traps were equipped with bait ( either dead fish or meat plus vegetables , such as cassava root or local plant material , as recommended by local prawn fishermen ) . The traps and baits were tested in 9 m2 prawn tanks at the Senegalese National Aquaculture facility prior to deployment and were found to successfully capture prawns within a few hours . To compare the abundance and distribution of prawns upstream of the Diama Dam in the Senegal River basin with abundance in the vicinity of the Diama Dam , prawns were purchased from local fisherman . All M . vollenhovenii prawns used for the present study were collected from September 12 , 2012 to August 31 , 2013 ( excluding April and July , 2013 , due to budgetary obstacles ) by a group of six fishermen who work regularly both up- and downstream of the Diama Dam ( Fig . 1A ) . All prawns caught by fisherman were captured near the Diama Dam in the Saint-Louis region , Senegal ( N 16°12′52 , 65″ W 25°20′16 , 07″ , marked as “Fisherman's location” in Fig . 1A ) . The fishermen used three types of fishing techniques , including baited traps ( 60 cm high , 80 cm diameter , made of metal and covered with fishing net ) , “sleeping nets” ( 200×6 m nylon net , 36 mm mesh with a 2 mm string ) and a “drifting net” ( same material as the sleeping net ) . The drifting nets are built of three nets attached together ( 600×6 m ) , so as to cover the width of the river . Since little quantitative information on prawn abundance in the past was available for this study , attempts to compare current abundance with the situation before construction of the dam relied on retrospective interviews with fishermen in villages along the Senegal River ( Fig . 1B , marked with black stars ) . This complementary approach included a standard questionnaire ( see supplemental appended item S1 ) designed to solicit information on the prawn catch today , compared to the past . The fishermen were asked twenty questions , including verification of their fishing experience ( years of activity ) and whether fishing is their primary activity ( in order to estimate their reliability ) . Fishermen were shown pictures of M . vollenhovenii to confirm or reject prior recollection of the prawns by appearance . Locations where both trapping and interviews were conducted are marked with grey stars on the map in Fig . 1B . Non-parametric statistical analysis was conducted to compare the reported abundance of the prawns before the construction of the dam and today in five villages upstream of the dam . Concomitant with the decline in prawn abundance , the number of active fishermen in the five villages , was reported by the fishermen to have declined from 175 before construction of the dam to only 18 today that were approached . Of these , the five who were active before construction of the dam and remain active today were selected for the study ( one from each village ) . To examine the relationship between sex and body weight , a two-sample Kolmogorov–Smirnov test , comparing the data distribution of both sexes , was initially conducted . An R×C test of independence was then performed to determine whether there was a dependency between sex and body weight , relying on the frequency of males and females weighing above 100 g . All analyses were conducted using STATISTICA 10 ( StatSoft software , Tulsa , OK ) . All Prawns used in the molecular study were anesthetized on ice for 5 min prior to dissection . Species determination was based on a molecular analysis using PCR for amplification of M . vollenhovenii mitochondrial 16S rRNA sequence ( GenBank accession numbers see Table 1 . ) . RNA samples from animals caught by the fishermen ( see “Monitoring prawn abundance in the Senegal River basin” ) were extracted and cDNA was prepared for PCR amplification as previously described [47] . For PCR amplification , the forward and reverse primers listed in Table 2 were used . PCR products were separated on agarose gels and bands were excised , purified and cloned as previously described . Sequences were obtained and compared to the known sequences using the BLAST algorithm . AGs were dissected from mature males , together with the attached terminal ampullae , under laboratory conditions in Senegal . Tissue samples were fixed in modified Carnoy's II for 72 h while being transported to Ben-Gurion University , Israel , where they were further processed according to conventional procedures . Five µm-thick sections were prepared . One out of five consecutive slides was stained by hematoxylin and eosin as previously described [47] . The other four slides were analyzed by immunohistochemistry using rabbit α-rec-Mr-IAG antibodies ( 1∶1500 ) as previously described [50] .
Due to the above size/weight differences found between M . vollenhovenii males and females and the notion that restocking with an all-male population will be advantageous , the AG and hormone that mediate maleness in this species were studied . Full-length Mv-IAG cDNA was found to be 1 , 213 bp-long ( Fig . 3A , Accession number KJ524578 ) . The sequence was isolated from a hAG by means of RT-PCR using Mr-IAG-based primers , followed by 5′ and 3′ RACE . The results showed that Mv-IAG consists of an open reading frame ( ORF ) of 531 bp flanked by a 5′ UTR ( 231 bp ) and a 3′ UTR ( 451 bp ) containing the putative polyadenylation site AATAAA . The Mv-IAG ORF was also predicted by ORF Finder ( http://www . ncbi . nlm . nih . gov/gorf/gorf . html ) . A 28 amino acid-long signal peptide was predicted by SignalP ( http://www . cbs . dtu . dk/services/SignalP ) . The predicted Mv-IAG ORF encodes a preprohormone , a signal peptide , the B chain , the C peptide , and the A chain in linear order ( Fig . 3B ) . The B and A chains of Mv-IAG are thought to be connected by two putative inter-chain disulfide bridges formed between Cys12 and Cys23 residues of the B chain and Cys15 and Cys32 of the A chain . Two other cysteine residues located in the A chain , Cys14 and Cys23 , are suggested to form an intra-chain disulfide bridge . Two putative cleavage sites of RR and KR at amino acids 69 and 129 , flanking the C peptide were joined to the B and A chains , respectively . The Mv-IAG sequence was compared with those from four other decapod crustacean species ( M . rosenbergii , P . pelagicus , C . quadricarinatus and F . chinensis ) in a multiple sequence alignment ( Fig . 4 ) . The positions of twenty amino acids were conserved . These included six cysteine residues , with two found in the B chain and four in the A chain . A phylogram generated using neighbor-joining methods [49] segregated the different decapod IAGs in accordance to their genus ( Fig . 5 ) . Protein INS-1 of C . elegans was used as an out-group to all of the twelve decapod IAGs known to date . It is clear that Mv-IAG is more related to Mr-IAG than to any other sequence . The different clades in the phylogram , reflecting the similarities of the proteins in the different species , were found to correlate with taxonomic relations in the cases of the Macrobrachium , the Palaemon and the Cherax species . The AG is located next to the sperm duct ( Fig . 6 middle ) . The sperm duct wall is rich in muscle fibers and filled with mature spermatozoa ( Fig . 6 left ) . Mv-IAG transcription was demonstrated by RT-PCR of cDNA from the AG but not from the male hepatopancreas or female ovary . The M . rosenbergii housekeeping gene β-actin served as a positive control ( Fig . 7 ) . Based on immunohistochemical analysis , Mv-IAG was localized to hAGs ( Fig . 8 ) , using rabbit anti-Mr-IAG specific antibodies [50] . A specific signal was observed only in the cytoplasm of the AG cells ( Fig . 8A ) , as nuclei were only stained by DAPI and not by the antibodies ( Fig . 8B ) . The specificity of the anti-Mr-IAG antibodies was further validated when no signal could be observed upon incubation of normal rabbit serum with the AG sections ( Fig . 8C ) . Sections were also stained with DAPI , which enabled nuclear localization as negative controls ( Fig . 8D ) .
Early malacological literature suggests that the outbreak of schistosomiasis in the Senegal River basin occurred due to ecological changes resulting from the construction of the Diama and Manantali Dams , which were completed in 1986 and 1990 , respectively [4] , [6] , [15] . Our current surveys in the Senegal River basin , including retrospective information from fishermen , appear to confirm the notion that the abundance of M . vollenhovenii was negatively influenced by construction of the Diama Dam . Although the historical , interview-based data could not be confirmed with independent fisheries or catch data prior to the appearance of the dam , research has consistently shown fishermen's knowledge to be a reliable estimate of relative abundance and distribution of fished species [51] , [52] . Moreover , during the present study , fishermen in the Diama Dam region received an incentive to fish M . vollenhovenii in the form of a reward offered by the current project . This presented yet further evidence supporting the reduction in abundance reported by fishermen as these individuals now devoted considerable effort to the prawn catch . Still , despite the increased effort , the data collected were comparable to those reported in the interviews . However , the causal relationship between prawn scarcity and the increased abundance of the snails and schistosomiasis infections upriver of the Diama Dam could not be established using our correlative data and should be further investigated . The use of prawns as biological control agents has been suggested and tested with both M . rosenbergii and M . vollenhovenii , showing that freshwater prawns are effective predators of schistosome-susceptible snails under laboratory conditions [24]–[26] . The novel approach of restocking populations of an indigenous prawn for its biological control abilities could become a powerful complement to chemotherapy campaigns . Today , campaigns for the distribution of this drug focus on periodic administration of the anthelminthic , praziquantel , to kill the adult worms [53] . What is lacking is a sustainable control strategy to prevent re-infection from snail to man [16] , [54] . The ability of an invasive , non-native crustacean to eliminate snails was shown in Kenya , with a concomitant reduction of prevalence and intensity of urinary schistosomiasis in school children [55] . To the best of our knowledge M . vollenhovenii is the first indigenous crustacean-predator proposed for such purposes . Our study suggests a strategy of restocking all-male prawns at a significant scale in the Senegal River basin involving a population that could be bred , hatched , and nurtured to the post-larval or juvenile stage in aquaculture facilities and then released into schistosomiasis transmission foci . If all-male prawn populations show an advantage in terms of yield and biological control effectiveness in the field , this strategy could have broad application in West African public health , fisheries and aquaculture sectors . Moreover , RNAi has been demonstrated to be a potent method for temporal gene manipulation in crustaceans [56] and indeed , a sexual shift has been achieved in all cases of IAG RNAi in crustaceans tested thus far [45] , [57] . Furthermore , the present study shows the high similarity between the IAG of the African prawn and that of other species , including that species in which RNAi has been successfully performed . Results of the present study also suggest that male M . vollenhovenii prawns reach larger sizes than females , as has been reported in the past [31] . Thus , the strategy of monosex culture could prove advantageous , similar to the proven production advantages of such cultures in M . rosenbergii aquaculture . These proven aquaculture benefits include the faster growth rate of males [13] , [43] , [58] , the ability to selectively harvest non-growing large males in order to stimulate a growth spurt in the subordinate morophtypes [59]–[61] , and the premium market prices acquired by large specimens [30] , [62] . All of these advantages also apply to the sustainable restocking of prawns for biological control of snails . Because there is a need to ensure that the prawns will feed within specific , snail-infested sites , it is logical to use all-male non-migrating agents , as suggested with other Macrobrachium species [12] , [14] . The sustainability of the solution proposed here will depend on a fisheries policy encouraging the harvesting or culling of the largest dominant males in order to boost the growth of smaller males and to maximize yields , as is routinely done in prawn aquaculture [63] . Such a policy will enable avoidance of over-population of the river since the size of the population will depend on the ratio between stocking and fishing rates . Moreover , since different-sized prawns have been found to be differentially efficient in snail predation [25] , a continuous restocking with younger , fast-growing male prawns will also support the biological control task . To achieve the all-male cohorts desired for restocking and fisheries , the current biotechnology relies on molecular manipulation of the IAG [46] . Here , we characterize M . vollenhovenii AG and IAG as a first step towards the ultimate goal of enabling routine , all-male M . vollenhovenii culture via recently established temporal RNAi-based biotechnology [46] . M . vollenhovenii IAG , has been completely sequenced in the present study and was found to share high similarity with homologous molecules in other decapod crustaceans [44] , [47] , [64] . Mv-IAG contains all the components of an insulin family member [38] . The M . vollenhovenii AG is anatomically and histologically similar to that described in M . rosenbergii [44] . Of the known decapod IAGs , Mv-IAG had the highest similarity to the IAG of its congener , M . rosenbergii ( 85% identity ) . Immunohistochemical analysis using anti-Mr-IAG anti-serum demonstrated the presence of Mv-IAG in the cytoplasm of AG cells . The high sequence similarity of Mv-IAG and Mr-IAG , as shown by bioinformatics tools in this study , provided the lead to pursue what turned out to be a successful use of anti-Mr-IAG antibodies to localize Mv-IAG in immunohistochemistry . Based on our results and the high similarity of M . vollenhovenii to M . rosenbergii , it is realistic to assume that the biotechnology proven to be effective for mass production of M . rosenbergii all-male populations in prawn aquaculture [46] can be directly implemented to the production of all-male M . vollenhovenii populations . At least 90% of the 243 million people currently infected with schistosomiasis in the world are in Africa [1] and at least 100 million of the more than 700 million people at risk of infection reside in areas that experienced major water management manipulations ( i . e . dams and irrigation schemes ) , as was the case in the Senegal River basin [4] , [7] . A meta-analysis [7] found that schistosomiasis risk in Africa was doubled for people living near dams and irrigation schemes , compared with people far from these schemes . Our suggested sustainable model of control , namely restocking native all-male prawn populations in the Senegal River using aquaculture and biotechnology , both as biological control agents and as an augmented fisheries crop , if proven successful locally , could be useful at other locations throughout the west coast of Africa where M . vollenhovenii is native ( Fig . 1C ) and where they may have been recently extirpated by dams . It is noteworthy that the use of all-male populations could permit responsible and sustainable restocking in other regions of Africa where these prawns are non-native , given that they have little invasion risk because the all-male prawns cannot revert to females and , therefore , cannot reproduce . | Schistosomiasis is a chronic parasitic disease that infects millions of people , especially in Africa . Schistosomes are transmitted by direct contact with water sources infested by freshwater snails , which are intermediate hosts for the parasite . The cure in humans is a drug , praziquantel , that kills the mature parasites inside the human body . The main problem with controlling the parasite by drug treatment is the high re-infection rate , since individuals are in contact with infected water on a daily basis . To efficiently combat the disease , an integrated management program is needed that includes control of infection in the intermediate host snails . We suggest the use of non-migrating , all-male populations of freshwater prawns that efficiently prey on these snails . Here , we describe the case of the Senegal River basin as an example of human actions ( dam construction ) that resulted in severe ecosystem changes , including exclusion of the native river prawns and expansion of snails hosting schistosomiasis . We have conducted an interdisciplinary study that documents the reduction of prawn abundance in the Senegal River and lays the molecular foundation for technology to produce all-male prawn populations to be used as part of an integrated disease control program , including both periodic stocking of juvenile prawns and chemotherapy . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"biotechnology",
"developmental",
"biology",
"fisheries",
"science",
"ecology",
"agriculture",
"ecosystems",
"coastal",
"ecosystems",
"aquaculture",
"life",
"cycles",
"biology",
"and",
"life",
"sciences",
"intestinal",
"parasites",
"freshwater",
"ecology",
"marine",
"biology",
"parasitology",
"shrimp",
"farming",
"parasitic",
"life",
"cycles"
]
| 2014 | The Prawn Macrobrachium vollenhovenii in the Senegal River Basin: Towards Sustainable Restocking of All-Male Populations for Biological Control of Schistosomiasis |
MITA ( also called STING ) is a central adaptor protein in innate immune response to cytosolic DNA . Cellular trafficking of MITA from the ER to perinuclear microsomes after DNA virus infection is critical for MITA activation and onset of innate antiviral response . Here we found that SNX8 is a component of DNA-triggered induction of downstream effector genes and innate immune response . Snx8-/- mice infected with the DNA virus HSV-1 exhibited lower serum cytokine levels and higher viral titers in the brains , resulting in higher lethality . Mechanistically , SNX8 recruited the class III phosphatylinositol 3-kinase VPS34 to MITA , which is required for trafficking of MITA from the ER to perinuclear microsomes . Our findings suggest that SNX8 is a critical component in innate immune response to cytosolic DNA and DNA virus .
Innate immune response is pivotal for host defense against microbial pathogens . Cytosolic DNA derived from invading pathogens triggers signaling events that lead to induction of downstream anti-microbial effector genes [1–3] . It has been demonstrated that the nucleotidyltransferase family member cyclic GMP-AMP ( cGAMP ) synthase ( cGAS ) is a ubiquitously expressed sensor of cytosolic DNA in various cell types [4 , 5] . Upon recognition of cytosolic DNA , cGAS utilizes ATP and GTP as substrates to catalyze the synthesis of the second messenger cGAMP , which binds to and activates the endoplasmic reticulum ( ER ) -located central adaptor protein MITA ( also called STING , MPYS , ERIS , or TMEM173 ) [6–9] . After binding to cGAMP , MITA traffics from the ER via Golgi apparatus to perinuclear microsomes , and in these processes TBK1 and IRF3 are recruited to MITA . In the MITA-associated complex , TBK1 firstly phosphorylates MITA at Ser366 and then phosphorylates IRF3 , leading to activation of IRF3 and induction of downstream effector genes such as type I interferons ( IFNs ) and proinflammatory cytokines [5 , 10–12] . The trafficking of MITA is critical for its activity in response to cytosolic DNA [13] . It has been demonstrated that the TRAPβ , Sec61β and Sec5 containing translocon complex as well as the class III phosphatylinositol 3-kinase ( PI3K ) VPS34 play critical roles for the trafficking and activation of MITA [7 , 12 , 14 , 15] . However , the regulatory mechanisms for MITA trafficking are still not well understood . Sorting nexin 8 ( SNX8 ) belongs to the sorting nexin protein family , which is involved in endocytosis and endosomal sorting [16 , 17] . Recently , we have demonstrated that SNX8 is a component of IFNγ-triggered noncanonical signaling pathway [16 , 17] . In this report , we identified SNX8 as a component of DNA-triggered innate immune response . Deficiency of SNX8 inhibited DNA- or DNA virus-triggered induction of downstream antiviral genes and innate antiviral response . Biochemical analysis indicated that SNX8 links VPS34 to MITA , which is required for trafficking and activation of MITA . Our findings reveal that SNX8 is a critical component in innate immune response to cytosolic DNA and DNA virus .
Our previous study has demonstrated that SNX8 mediates IFNγ-triggered non-canonical signaling pathway and host defense against Listeria infection [16] . In this study , we investigated the role of SNX8 in innate antiviral response . In reporter assays , overexpression of SNX8 dose-dependently activated the IFN-β promoter , which requires coordinative and cooperative activation of the transcription factors IRF3 and NF-κB . Consistently , overexpression of SNX8 activated ISRE ( an enhancer motif for IRF3 ) and NF-κB in a dose-dependent manner in reporter assays ( Fig 1A ) . Further experiments indicated that SNX8 potentiated HSV-1-triggered activation of the IFN-β promoter in a dose-dependent manner in HeLa cells ( Fig 1B ) . Overexpression of SNX8 also potentiated the transcription of IFNB1 , CXCL10 and IL6 genes induced by HSV-1 and transfected synthetic DNAs ISD ( IFN-stimulating DNA ) and HSV120 ( DNA of 120 basepairs representing the genomes of HSV-1 ) ( Fig 1C ) . These results suggest that SNX8 is involved in DNA virus-triggered induction of downstream antiviral genes . To investigate the role of endogenous SNX8 in DNA virus-triggered signaling , we constructed SNX8-deficient human foreskin fibroblasts ( HFFs ) by the CRISPR-Cas9 method . We found that transcription of IFNB1 , ISG56 , CXCL10 and IL6 genes induced by HSV-1 and transfected ISD and HSV120 was dramatically inhibited in SNX8-deficient HFFs in comparison to wild-type cells ( Fig 1D ) . In contrast , the transcription of TGF-β following HSV-1 infection and transfection of cytosolic dsDNA was comparable between SNX8-deficient and control HFFs ( Fig 1D ) . In similar experiments , SNX8-deficiency did not affect IFN-β-triggered induction of downstream genes such as CXCL10 , RIG-I , and ISG56 ( Fig 1E ) . Consistently , HSV-1-induced phosphorylation of TBK1 , IRF3 and IκBα , which are hallmarks of activation of the DNA-triggered signaling pathways , were markedly inhibited in SNX8-deficient cells ( Fig 1F ) . These results suggest that SNX8 is essential for DNA virus-triggered induction of downstream genes . We next investigated innate immune response to DNA virus in primary cells from Snx8+/+ and Snx8-/- mice . We found that induction of Ifnb1 , Isg56 , Cxcl10 and Il6 genes were significantly inhibited in Snx8-/- bone marrow-derived macrophages ( BMDMs ) and mouse lung fibroblasts ( MLFs ) in comparison with their wild type counterparts after infection with three different types of DNA viruses , including HSV-1 , vaccinia virus ( VV ) , and ectromelia virus ( ECTV ) ( Fig 2A and S1A Fig ) . In similar experiments , the mRNA levels of Cxcl10 , Rig-I , Isg54 and Isg56 induced by IFN-β was comparable between Snx8-/- and Snx8+/+ BMDMs ( Fig 2B ) . In addition , the secretion of IFN-β and IL-6 cytokines were impaired in Snx8-/- BMDMs following HSV-1 infection ( Fig 2C ) . Consistently , HSV-1-induced phosphorylation of TBK1 , IRF3 and IκBα was markedly inhibited in Snx8-/- BMDMs and MLFs ( Fig 2D and S1B Fig ) . In addition , transcription of Ifnb1 , Cxcl10 , Isg56 and Il6 genes induced upon transfection of DNAs including ISD , DNA90 ( dsDNA of approximately 90 basepairs ) , HSV60 and HSV120 ( dsDNA 60- or 120-mers representing the genomes of HSV-1 ) was impaired in Snx8-/- BMDMs and MLFs ( Fig 2E and S1C Fig ) . Consistently , secretion of IFN-β and IL-6 cytokines induced by transfected ISD and HSV120 was significantly inhibited in Snx8-/- BMDMs ( Fig 2F ) . Collectively , these results suggest that SNX8 is essential for induction of downstream antiviral genes following DNA virus infection or cytosolic DNA stimulation in primary murine cells . To investigate the roles of SNX8 in host defense against viral infection in vivo , we infected Snx8+/+ and Snx8-/- mice with HSV-1 by intraperitoneal ( i . p . ) injection and monitored their survival . The results indicated that Snx8-/- mice were more susceptibility to HSV-1-induced death than their wild-type littermates ( Fig 3A ) . In addition , the serum cytokines induced by HSV-1 infection , including IFN-α , IFN-β and IL-6 , were severely impaired in Snx8-/- in comparison to their wild-type littermates ( Fig 3B ) . Moreover , the brain tissues of Snx8-/- mice infected with HSV-1 ( a neurotropic DNA virus ) for 6 days showed higher HSV-1 viral loads and genomic DNA copies in comparison to those of infected wild-type littermates ( Fig 3C ) . Collectively , these data suggest that SNX8 is essential for host defense against HSV-1 infection in mice . To investigate the mechanisms of SNX8 in DNA virus-triggered signaling , we examined whether SNX8 is involved in the induction of downstream genes triggered by intracellular cGAMP . We found that cGAMP-induced transcription of Ifnb1 , Isg56 , Cxcl10 and Il6 genes were significantly dampened in Snx8-/- in comparison with wild-type MLFs ( Fig 4A ) . In addition , cGAMP-induced phosphorylation of TBK1 and IRF3 was also impaired in Snx8-/- MLFs ( Fig 4B ) . These results suggest that SNX8 acts downstream of cGAMP and upstream of TBK1-IRF3 . Consistently , knockdown of SNX8 inhibited cGAS- and MITA- but not TBK1- or IRF3-5D- ( a constitutive active mutant of IRF3 ) mediated activation of the IFN-β promoter in a dose-dependent manner ( Fig 4C ) . We next determined whether SNX8 is associated with signaling components in DNA virus-triggered pathways . Transient transfection and co-immunoprecipitation experiments indicated that SNX8 was associated with MITA , but not cGAS , TBK1 or IRF3 ( Fig 4D ) . Endogenous co-immunoprecipitation experiments indicated that SNX8 constitutively interacted with MITA in un-infected and early-infected cells , but their association was undetectable at late phase ( 6–9 h ) of HSV-1 infection due to down-regulation of MITA ( Fig 4E ) . Domain mapping analysis indicated that the N-terminal transmembrane domains of MITA ( aa1-190 ) and the N-terminal domain of SNX8 ( aa1-180 ) were required for their association ( Fig 4F & 4G ) . qPCR analysis indicated that reconstitution of SNX8 and SNX8 ( 1–180 ) but not the MITA binding-defective mutant SNX8 ( 178–465 ) rescued HSV-1-induced transcription of Ifnb1 in Snx8-/- MLFs , suggesting that the interaction of SNX8 with MITA is important for the its role in modulating MITA activation ( S1D Fig ) . Cellular trafficking of MITA from the ER via Golgi to perinuclear microsomes is critically involved in its activation . We investigated whether SNX8 is involved in the trafficking of MITA . Confocal microscopy indicated that SNX8 was localized in the ER , ER-Golgi intermediate compartment ( ERGIC ) , Golgi and endosome ( Fig 5A ) , which is similar with the localization of MITA . In addition , we found that SNX8 and MITA co-localized in the cytoplasm ( Fig 5B ) . Notably , HSV-1 infection or transfection of synthetic ISD caused the accumulation of MITA in perinuclear microsomes in Snx8+/+ MLFs , but this accumulation was completely inhibited in Snx8-/- MLFs ( Fig 5C ) . Furthermore , confocal microscopy revealed that SNX8-deficiency markedly inhibited the trafficking of MITA from the ER via Golgi to perinuclear microsomes induced by infection of HSV-1 ( Fig 5D ) . These data suggest that SNX8 is required for the trafficking of MITA from the ER to perinuclear microsomes . It has been demonstrated that during its trafficking , MITA is phosphorylated , activated and recruits IRF3 [18] . We found that SNX8-deficiency dramatically inhibited HSV-1-induced phosphorylation of MITA at Ser366 in MLFs ( Fig 5E ) . These date suggest that SNX8 is critical for the trafficking and activation of MITA upon DNA virus infection . Several studies have demonstrated that the PI3K VPS34 as well as iRhom2-mediated recruitment of the TRAPβ translocon complex play important roles in the trafficking of MITA [7 , 12 , 14 , 15] . In co-immunoprecipitation experiments , SNX8 interacted with MITA , VPS34 , iRhom2 , Sec5 but not TRAPβ ( Fig 6A ) . Interestingly , VPS34 interacted with MITA and SNX8 but not TRAPβ ( Fig 6B ) . Consistently , confocal microscopy indicated that VPS34 was co-localized with SNX8 and MITA in the cytoplasm ( Fig 6C ) . Furthermore , endogenous co-immunoprecipitation experiments indicated that VPS34 was associated with MITA and SNX8 in un-infected and early-infected ( 3 h ) but not late-infected ( 6–9 h ) cells , which might be caused by the degradation of MITA in late-infected cells ( Fig 6D ) . Knockdown of SNX8 inhibited the association of MITA with VPS34 but not that of MITA with TRAPβ , Sec5 or iRhom2 ( Fig 6E ) . In addition , confocal microscopy indicated that SNX8-deficiency markedly inhibited the co-localization of VPS34 and MITA in HeLa cells ( Fig 6F ) . These results suggest that SNX8 mediates the MITA-VPS34 but not the MITA-iRhom2-TRAPβ association . Consistently , knockdown of VPS34 inhibited HSV-1-induced trafficking of MITA to microsomes ( Fig 6G ) . In reporter assays , knockdown of VPS34 inhibited MITA- and MITA/SNX8-mediated activation of ISRE ( S2A Fig ) , as well as HSV-1-induced transcription of downstream genes including IFNB1 , CXCL10 , ISG56 and IL6 ( Fig 6H ) . In addition , VPS34-IN1 , a selective inhibitor of VPS34 , inhibited HSV-1-induced transcription of downstream genes in a dose-dependent manner ( S2B Fig ) . These results suggest that SNX8-mediated recruitment of VPS34 is essential for the trafficking and activation of MITA .
In this study , we investigated the roles of SNX8 in DNA virus- or cytosolic dsDNA- triggered signaling . SNX8-deficient cells failed to effectively produce IFNs and other cytokines in response to infection with DNA virus and transfected with cytosolic DNA . Snx8-/- mice exhibited lower serum cytokine levels and higher viral titers in brain , resulting in higher lethality . These findings establish a critical role for SNX8 in innate immune response to cytosolic dsDNA and DNA virus . In our preliminary study , we also found that SNX8 was involved in RNA virus- triggered induction of downstream antiviral genes , which will be reported in a separate study . Several evidences suggest that SNX8 targets MITA for its regulation of DNA virus-triggered signaling . First , SNX8 deficiency impaired cGAMP-induced signaling , which provided us with the clue that linked SNX8 to MITA pathway . Second , knockdown of SNX8 inhibited cGAS- and MITA-mediated activation of the IFN-β promoter , but not TBK1 or IRF3-5D . Third , SNX8 interacted with MITA in un-infected or early infected with HSV-1 . These data suggest that SNX8-mediated DNA virus-triggered signaling is dependent on MITA . Our results suggest that SNX8 acts as a link for VPS34-mediated trafficking and activation of MITA . The trafficking of MITA from ER to perinuclear microsomes was impaired in Snx8-/- MEFs infected with HSV-1 or transfected with ISD , suggesting that SNX8 is essential for trafficking of MITA . Co-immunoprecipitation experiments indicated that SNX8 was constitutively associated with VPS34 and MITA . Knockdown of SNX8 impaired the association of VPS34 with MITA . In addition , knockdown of VPS34 inhibited the synergetic effects of SNX8 on MITA-mediated activation of ISRE . These data suggest that SNX8 acts as a link for MITA-VPS34 translocation complex and promotes the trafficking of MITA , which is essential for effective activation of MITA-mediated signaling . In conclusion , our findings uncover previously uncharacterized roles of SNX8 in mediating MITA-dependent innate immune response against DNA virus .
Snx8-/- mice on the C57BL/6 background were generated by the CRISPR/Cas9 method and obtained from the Wuhan University A3 Animal Center . All mice were maintained in Specific Pathogen Free facility of Wuhan University College of Life Sciences . The animal care and use protocol was adhered to the Chinese National Laboratory Animal-Guideline for Ethical Review of Animal Welfare . The protocols and procedures for mice experiments in this study were approved by the Wuhan University College of Life Sciences Animal Care and Use Committee guidelines ( approval number WDSKY0200902-2 ) . 3′3′-cGAMP , Lipofectamine 2000 ( InvivoGen ) ; poly ( dA:dT ) , DNA90 , ISD , HSV60 , HSV120 ( Sangon Biotech ) ; polybrene ( Millipore ) ; SYBR ( Bio-Rad ) ; FuGene and Dual-Specific Luciferase Assay Kit ( Promega ) ; digitonin and VPS34-IN1 ( Sigma ) ; puromycin ( Thermo ) ; recombinant IFN-β ( R&D Systems ) ; ELISA kits for murine IFN-α and IFN-β ( PBL ) and IL-6 ( Biolegend ) . Mouse monoclonal antibodies against FLAG and β-actin ( Sigma ) and HA ( Covance ) ; rabbit monoclonal antibodies against phospho-TBK1 , TBK1 and SNX8 ( Abcam ) ; MITA , VPS34 , IRF3 , phospho-IRF3 , phospho-IκBα and phosphor-MITA ( Ser366 ) ( Cell Signaling Technology ) , rabbit polyclonal antibody against ERGIC-53/p58 ( Sigma ) ; Alexa Fluor 555 Mouse anti GM130 ( BD Biosciences ) were purchased from the indicated companies . Rabbit and mouse anti-SNX8 sera were raised against a recombinant human SNX8 protein . HEK293 cells were provided by Dr . Gary Johnson ( National Jewish Center ) . THP-1 cells and HeLa cells were purchased from ATCC . HFFs were provided by Dr . Minhua Luo ( Wuhan Institute of Virology ) . HSV-1 ( KOS strain ) ( China Center for Type Culture Collection , Wuhan , China ) , vaccinia virus ( Tian-Tan Strain ) ( China Center for Type Culture Collection , Wuhan , China ) and ECTV ( Han-Zhong Wang , Wuhan Institute of Virology , Wuhan , China ) viruses have been obtained from the indicated resources . Mammalian expression plasmids for HA- or FLAG-tagged VPS34 were constructed by standard molecular biology techniques . The other expression and reporter plasmids were previously described [16 , 19–22] . HEK 293 cells were seeded in 24-well dishes and transfected the following day by standard calcium phosphate precipitation method . HeLa cells were transfected by FuGENE . HFFs , MLFs and MEFs were transfected by Lipofectamine 2000 . To normalize for transfection efficiency , pRL-TK ( Renilla luciferase ) reporter plasmid was added to each transfection . Luciferase assays were performed using a dual specific luciferase assay kit . Firefly luciferase activities were normalized on the basis of Renilla luciferase activities . All reporter assays were repeated for at least three times . Data shown were average values ± SD from one representative experiment . Total RNA was isolated from cells using TRIzol reagent ( TAKARA ) , reverse transcribed , and subjected to real-time PCR analysis to measure mRNA levels of the tested genes . Data shown are the relative abundance of the indicated mRNA normalized to that of GAPDH . Gene-specific primer sequences were as follows: Cells were lysed in 1 ml NP40 lysis buffer ( 20 mM Tris-HCl , 150 mM NaCl , 1 mM EDTA , 1% Nonidet P-40 , 10 μg/ml aprotinin , 10 μg/ml leupetin , and 1mM phenylmethylsufonyl fluoride ) . For each immunoprecipitation , 0 . 4 ml aliquot of lysate was incubated with 0 . 5–2 μg of the indicated antibody or control IgG and 25 μl of a 1:1 slurry of Protein-G sepharose ( GE healthcare ) for at least 2 hr . The sepharose beads were washed three times with 1 ml of lysis buffer containing 500 mM NaCl . The precipitates were fractionated on SDS-PAGE , and immunoblot analysis were performed as described [23 , 24] . Double-stranded oligonucleotides corresponding to the target sequences were cloned into the pSuper-Retro RNAi plasmid ( Oligoengine ) . The following sequences were targeted for human SNX8 cDNA: #1 ( 5′-CGGCAGATCTTCTCATATT-3′ ) and #2 ( 5′-CGGCAGATCTTCTCATATT-3′ ) . Human VPS34 cDNA: #1 ( 5′-GATCTGAAACCCAATGCTG-3′ ) and #2 ( 5′-GGAATGTGAAGATCAAGAT-3′ ) . Mouse Vps34 cDNA: ( 5′- GCTGTCCTAGAAGATCCCA-3′ ) HEK293 cells were transfected with two packaging plasmids , pGag-Pol ( 10 μg ) and pVSV-G ( 3 μg ) , and control or VPS34-RNAi retroviral plasmid ( 10 μg ) by calcium phosphate precipitation . The cells were washed 12 hr after transfection and new medium without antibiotics was added for additional 24 hr . The recombinant virus-containing medium was filtered and used to infect THP-1 cells in the presence of polybrene ( 4 μg/ml ) . The infected THP-1 cells were selected with puromycin ( 0 . 5 μg/ml ) for 2 weeks before additional experiments were performed . Establishment of SNX8-deficient HeLa cells and HFFs were performed as described [16] . Briefly , double-stranded oligonucleotides corresponding to the target sequences were cloned into the lentiCRISPR-V2 vector and cotransfected packaging plasmids into HEK293 cells . Lentiviral particles were collected and used to transduce HeLa cells and HFFs . The infected HeLa cells and HFFs were selected with puromycin ( 0 . 5 μg/ml ) for 2 weeks before additional experiments were performed . The following sequences were targeted for human SNX8 cDNA: #1 ( 5′-GGGCAGGCACCATACGGTAG-3′ ) and #2 ( 5′-GACCTGCTGCACGATGGCCT-3′ ) . BMDMs were infected with HSV-1 for 18 hr . The culture media were collected for measurement of IFN-β and IL-6 cytokines by ELISA . For preparation of BMDMs , the bone marrow cells were cultured in 10% M-CSF-containing conditional medium from L929 cells for 3–5 days . MEFs were prepared from day 12 . 5 embryos and cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% FBS . Primary lung fibroblasts were isolated from approximately 4- to 6-week-old mice . Lungs were minced and digested in calcium and magnesium free HBSS containing 10 μg/ml type II collagenase ( Worthington ) and 20 μg/ml DNase I ( Sigma-Aldrich ) for 3 hours at 37°C with shaking . Cell suspensions were filtered through progressively smaller cell strainers ( 100 and 40 μm ) and then centrifuged at 1500 rpm for 5 min . The cells were then plated in culture medium ( 1:1 DMEM/Ham's F-12 containing 10% FBS ) . After 1 hour , adherent fibroblasts were rinsed with HBSS and cultured in media . Mice were infected with HSV-1 i . p . The viability of the infected mice was monitored for 12 days . The mouse sera were collected at 6 hr after infection to measure cytokine production by ELISA . Confocal microscopy was performed as previously described [22] . In Brief , cells were fixed with 4% paraformaldehyde for 15 min and then permeabilized in 0 . 2% Triton X-100 and stained with antibodies by standard protocols . The stained cells were observed with Carl Zeiss microscopy under a 60× oil objective . Student’s t test was used for statistical analysis with Microsoft Excel and GraphPad Prism Software . For the mouse survival study , Kaplan-Meier survival curves were generated and analyzed by Log-Rank test; P < 0 . 05 was considered significant . | Infection by virus , such as the DNA virus herpes simplex virus 1 , induces the host cells to express proteins that mediate antiviral immune response . The protein called MITA plays an essential role in the process of antiviral immune response . After viral infection , MITA traffics from the endoplasmic reticulum ( ER ) to perinuclear punctuate structures , a process required for its activation and induction of downstream antiviral genes . How the trafficking of MITA is regulated remains enigmatic . In this study , we found that a protein called SNX8 plays a critical role in regulation of MITA trafficking from the ER to perinuclear punctuate structures . SNX8 does this by recruiting the VPS34-containing translocon machinery to MITA . Deficiency of SNX8 impaired antiviral immune response and rendered the mice more sensitive to DNA virus-induced death . Our results suggest that SNX8 is a critical component in antiviral immune response to DNA virus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
]
| [
"luciferase",
"assay",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"cytokines",
"hela",
"cells",
"molecular",
"probe",
"techniques",
"biological",
"cultures",
"immunology",
"biochemical",
"analysis",
"developmental",
"biology",
"enzyme",
"assays",
"immunoprecipitation",
"molecular",
"development",
"cell",
"cultures",
"bioassays",
"and",
"physiological",
"analysis",
"dna",
"molecular",
"biology",
"techniques",
"co-immunoprecipitation",
"research",
"and",
"analysis",
"methods",
"microsomes",
"immunoblot",
"analysis",
"cell",
"lines",
"molecular",
"biology",
"precipitation",
"techniques",
"immune",
"response",
"immune",
"system",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"physiology",
"genetics",
"biology",
"and",
"life",
"sciences",
"cultured",
"tumor",
"cells"
]
| 2018 | SNX8 modulates innate immune response to DNA virus by mediating trafficking and activation of MITA |
The obligate intracellular pathogen Chlamydia trachomatis replicates within a membrane-bound inclusion that acquires host sphingomyelin ( SM ) , a process that is essential for replication as well as inclusion biogenesis . Previous studies demonstrate that SM is acquired by a Brefeldin A ( BFA ) -sensitive vesicular trafficking pathway , although paradoxically , this pathway is dispensable for bacterial replication . This finding suggests that other lipid transport mechanisms are involved in the acquisition of host SM . In this work , we interrogated the role of specific components of BFA-sensitive and BFA-insensitive lipid trafficking pathways to define their contribution in SM acquisition during infection . We found that C . trachomatis hijacks components of both vesicular and non-vesicular lipid trafficking pathways for SM acquisition but that the SM obtained from these separate pathways is being utilized by the pathogen in different ways . We show that C . trachomatis selectively co-opts only one of the three known BFA targets , GBF1 , a regulator of Arf1-dependent vesicular trafficking within the early secretory pathway for vesicle-mediated SM acquisition . The Arf1/GBF1-dependent pathway of SM acquisition is essential for inclusion membrane growth and stability but is not required for bacterial replication . In contrast , we show that C . trachomatis co-opts CERT , a lipid transfer protein that is a key component in non-vesicular ER to trans-Golgi trafficking of ceramide ( the precursor for SM ) , for C . trachomatis replication . We demonstrate that C . trachomatis recruits CERT , its ER binding partner , VAP-A , and SM synthases , SMS1 and SMS2 , to the inclusion and propose that these proteins establish an on-site SM biosynthetic factory at or near the inclusion . We hypothesize that SM acquired by CERT-dependent transport of ceramide and subsequent conversion to SM is necessary for C . trachomatis replication whereas SM acquired by the GBF1-dependent pathway is essential for inclusion growth and stability . Our results reveal a novel mechanism by which an intracellular pathogen redirects SM biosynthesis to its replicative niche .
Chlamydia species are obligate intracellular pathogens that cause a wide range of diseases in humans , including sexually transmitted , ocular , and respiratory tract infections [1] . The capacity of Chlamydia infections to lead to infertility and blindness , their association with chronic diseases such as atherosclerosis , and the extraordinary prevalence and array of these infections make them public concerns of primary importance [1] , [2] . All Chlamydia species share a dimorphic developmental cycle that allows them to survive within the hostile environment of the host cell ( reviewed in [3] ) . Chlamydia alternate between an extracellular , spore-like infectious form termed the elementary body ( EB ) , and an intracellular , metabolically active but non-infectious form termed the reticulate body ( RB ) . Infection is initiated by binding of the EB to the host cell where it is taken up by an actin and Rho family GTPase-dependent process and sequestered within a unique membrane bound compartment called the inclusion [4] . Subsequently , the EB differentiates into an RB and replicates by binary fission within the inclusion . Concomitantly , the bacteria begin remodeling the inclusion membrane by insertion of bacterial proteins that promote segregation of the inclusion from the classical endosomal/lysosomal transport pathway , that facilitate interactions of the inclusion with the exocytic transport pathway , and that promote migration of the inclusion along microtubules to the peri-Golgi region [5] , [6] . The developing inclusion expands to accommodate increasing numbers of bacteria and is stabilized by recruitment of host cytoskeletal structures primarily composed of F-actin and intermediate filaments [7] . After 24–72 hours ( hrs ) of replication , RBs redifferentiate back to EBs and are released from the host cells by cell lysis or active extrusion [8] . Chlamydiae are one of the few known bacterial pathogens that require host-derived membrane lipids , including sphingomyelin ( SM ) and cholesterol , for intracellular growth and development [6] , [9] , [10] , [11] , [12] , [13] , [14] . Recent work suggests that SM biosynthesis is also required for homotypic fusion of multiple inclusions within the same cell as well as for inclusion membrane stability [15] . Chlamydia are thought to acquire SM by vesicular trafficking via multiple routes , including ( 1 ) the interception of SM-containing Golgi-derived exocytic vesicles destined for the plasma membrane ( PM ) [16] , [17] , [18] , ( 2 ) fusion with multivesicular body ( MVB ) -derived vesicles [15] , [19] , [20] , and/or ( 3 ) Golgi fragmentation [21] , [22] . SM acquisition by the inclusion is observed as early as 2 hrs post infection ( hpi ) , with incorporation of SM into the inclusion membrane and into the bacterial cell wall [6] , [10] , [12] , [13] , [15] , [19] , [20] , [23] . Golgi fragmentation induced by C . trachomatis has been shown to play an important role in SM acquisition during the later stages of infection and may be required for subsequent fusion with exocytic vesicles by a mechanism that involves Rab6 and Rab11 [21] , [22] . SM transport to the inclusion is partially abrogated by Brefeldin A ( BFA ) , an inhibitor of vesicular transport [6] , [24] , [25] and is accompanied by a decrease in inclusion size [6] , indicating that SM transport to the inclusion via vesicular trafficking is important for inclusion growth . Importantly , BFA treatment has no effect on bacterial replication [6] , suggesting that SM acquisition by a BFA-sensitive pathway is not essential for replication and that C . trachomatis may acquire SM by additional routes that involve BFA-insensitive and/or non-vesicular trafficking pathways . ADP ribosylation factors ( Arfs ) are small GTPases that are key players in the regulation of vesicular transport where they function to recruit coat proteins necessary for vesicle formation ( reviewed in [26] ) . Arf1 is localized in all three Golgi compartments , cis- , medial- , and trans-Golgi , and cycles between an active GTP bound form and an inactive GDP-bound form [27] . Arf1 activation is spatially and temporally regulated by specific guanine nucleotide exchange factors ( GEFs ) , GBF1 ( Golgi-specific BFA resistance guanine nucleotide exchange factor 1 ) and BIG1/BIG2 ( BFA-inhibited guanine nucleotide exchange proteins 1 and 2 ) which are present in the cis- and trans-Golgi , respectively [26] , [28] , [29] , [30] , [31] , [32] , [33] . BFA inhibits the activation of Arf1 by targeting GBF1 and BIGs [29] , [34] , [35] , [36] . GBF1 is required for the assembly and maintenance of the Golgi stack whereas BIGs are required for maintenance of the trans-Golgi network [33] , [37] . Besides regulating coat protein recruitment , Arf1 controls the actin cytoskeleton by modulating the lipid microenvironment via recruitment of various phosphoinositide kinases [38] , and it also regulates the vimentin architecture in cells [39] . Although Arf1 localizes to the inclusion and has recently been implicated in Chlamydia infection [40] , [41] , the specific roles of Arf1 and its cis- and trans-Golgi-specific GEFS in inclusion biogenesis and/or acquisition of SM has not been previously investigated . Until recently , vesicular trafficking of ceramide from the ER to the trans-Golgi was believed to be essential for SM biosynthesis; however , it is now appreciated that the major pathway of de novo SM biosynthesis involves a cytosolic lipid transfer protein , called CERT , that transports ceramide ( the precursor for SM ) from the ER directly to the trans-Golgi , where it is converted to SM by one of two SM synthases , SMS1 or SMS2 [42] , [43] , [44] , [45] , [46] . CERT has three functional domains , an N-terminal PH ( pleckstrin homology ) domain that binds phosphoinositide-4-phosphate ( PI4P ) at the trans-Golgi , a FFAT ( two phenylalanines in an acidic tract ) motif that binds integral membrane proteins , VAP-A and VAP-B , at the ER , and a C-terminal START ( steroidogenic acute regulatory protein-related lipid transfer ) domain that binds to and extracts ceramide from the ER membrane [47] . CERT is subjected to regulation within a serine repeat ( SR ) domain where phosphorylation by Protein Kinase D and hyperphosphorylation by Casein Kinase Iγ2 ( CKIγ2 ) inactivates CERT while dephosphorylation by Protein Phosphatase 2Cε activates CERT [48] , [49] , [50] . Transfer of ceramide from the ER to the Golgi via CERT is thought to occur at ER-Golgi membrane contact sites ( MCS ) , sub-regions of the ER located very close to trans-Golgi stacks [51] , [52] . The role of non-vesicular lipid transport pathways during Chlamydia infection has not been previously explored . In this study , we examined the role of specific host proteins required for vesicular and non-vesicular lipid transport during infection to define their contribution to SM acquisition , bacterial replication , and inclusion biogenesis . We found that C . trachomatis selectively co-opts GBF1 within the cis-Golgi compartment for vesicle-mediated SM acquisition and that this source of SM contributes to inclusion membrane growth and stability but that GBF1 function is not required for bacterial replication . Unexpectedly , we discovered that C . trachomatis coordinates recruitment of CERT and SM synthases to the inclusion and that subversion of this lipid synthesis pathway is required for bacterial replication and for efficient SM acquisition by C . trachomatis . We propose that C . trachomatis utilizes separate lipid trafficking pathways to acquire SM for distinct roles during infection: SM acquired by the GBF1-dependent pathway is essential for inclusion growth and stability while CERT-dependent transport of ceramide and subsequent conversion to SM is necessary for C . trachomatis replication .
In an RNA interference ( RNAi ) screen for host factors required for C . trachomatis L2 infection of Drosophila S2 cells , homologs of Arf1 and its associated GEFs , GBF1 ( localized to the cis-Golgi ) and BIG1 ( localized to the trans-Golgi ) were identified [53] . Since Chlamydia is thought to occupy a post Golgi compartment and since C . trachomatis induced Golgi fragmentation enhances replication [6] , [21] , [22] , we tested the hypothesis that spatially-localized Arf1 function within the Golgi might be important for SM acquisition and/or for C . trachomatis intracellular growth . We first examined the localization of Arf1-GFP , GBF1 , and BIG1 during C . trachomatis infection of HeLa cells . We found that GBF1 and BIG1 were localized to the fragmented Golgi around the inclusion in C . trachomatis-infected cells and appeared to maintain their cis and trans polarity , as observed in uninfected cells ( Figure 1A ) . GBF1 ( Figure 1B ) and BIG1 ( data not shown ) were primarily localized adjacent to but not on the inclusion membrane . Similar results were obtained in cells expressing GFP-tagged GBF1 ( data not shown ) . In contrast , Arf1-GFP was found not only on Golgi vesicles adjacent to the inclusion but was also localized to the inclusion membrane , where it formed a thin rim of circumferential staining ( Figure 1B ) , consistent with previously published results [40] , [41] . We noted a striking concentration of Arf1-GFP at the region of two closely apposed inclusion membranes ( Figure 1B ) . To evaluate whether GBF1 or BIG1/2 activity was required for Arf1-GFP recruitment to the inclusion membrane , we treated cells with BFA , which inhibits both GBF1 and BIGs , and examined the localization Arf1-GFP and GBF1 . Exposure of the cells to BFA resulted in dispersion of GBF1 and Arf1-GFP throughout the cell and loss of the circumferential rim staining on the inclusion; however , BFA had no effect on Arf1-GFP localization at abutting inclusion membranes ( Figure 1B ) . This finding suggests that GBF1 and/or BIG1/2 activities are required for Arf1 recruitment to the outer region of the inclusion but that they are not necessary for Arf1 maintenance at closely apposed inclusions . To determine the specific contribution of the cis- versus trans-Golgi compartments to SM acquisition , HeLa cells were depleted of GBF1 or BIG1/2 by siRNA , infected with C . trachomatis , and then examined by live-cell microscopy for SM acquisition as described in the Materials and Methods . Protein depletion was assessed by western blot analysis ( Figure 1C ) . As shown in Figure 1D , labeled lipids were readily visible inside bacterial inclusions in control siRNA-treated cells and in cells depleted of BIG1 and/or BIG2 . In contrast , depletion of GBF1 resulted in ∼60% reduction in SM acquisition by the inclusion ( Figures 1D and S1A; p<0 . 001 ) . SM acquisition was similarly reduced in cells treated with Golgicide A ( GCA ) , a GBF1-specific inhibitor [37] and with BFA ( [17] and Figures 1E and S1B ) . As GCA targets GBF1 selectively and does not inhibit BIGS , these results suggest that the relevant target of BFA during C . trachomatis L2 infection is GBF1 . We next tested whether GBF1 or BIG1/2 were required for intracellular growth . GBF1 depletion ( Figure S1B ) or inhibition with GCA or BFA ( Figure S2 ) resulted in inclusions that were smaller than those observed in control siRNA- or untreated cells . Quantitation of inclusion size , as described in Materials and Methods , revealed that the average inclusion size in GBF1-depleted cells was decreased by 40% ( p<0 . 001 ) compared to control siRNA-treated cells ( Figure S1D ) . In contrast , depletion of BIG1 and/or BIG2 gave rise to inclusions that were slightly larger than those observed in control siRNA depleted cells ( Figures S1C and S1D ) . Despite the changes in inclusion size , depletion of any of the Arf1 GEFS did not decrease production of progeny; in fact , a small increase in progeny was observed ( Figure 1F ) . The reason for the increase in progeny is not currently known . Together , these results demonstrate that GBF1 , but not BIG1/2 function , is required for vesicle-mediated SM acquisition and inclusion growth but is dispensable for bacterial replication . These findings indicate that the impact of disrupting GBF1 function on SM acquisition is not an indirect result of altering Arf1-dependent trafficking in the trans-Golgi , since depletion of BIG1 and/or BIG2 did not reduce SM acquisition or inclusion growth . We note that in Drosophila S2 cells , depletion of either GBF1 or the single BIG1 was sufficient to decrease inclusion formation [53]; this finding may reflect differences in Golgi organization between Drosophila S2 and mammalian cells , and/or it may reflect the fact that these proteins may have additional roles beyond the secretory pathway in host cells . We observed that interference with GBF1 function resulted in a defect in SM acquisition by the inclusion , and recent studies have shown that the inclusion membrane integrity is compromised when host SM biosynthesis is inhibited [15] . Therefore , we tested whether GBF1 plays a role in maintaining the integrity of the inclusion membrane . Depletion of GBF1 ( Figure 2A ) or exposure to GCA or BFA ( Figure S2A ) resulted in a loss of inclusion membrane integrity , as evidenced by discontinuities in 14-3-3β staining ( which binds to the inclusion membrane protein , IncG ) and release of bacteria into the host cytoplasm at the broken membrane sites . Quantitative analysis ( as described in Materials and Methods ) revealed that ∼80% of infected cells in which GBF1 function was inhibited exhibited broken inclusions ( Figures 2 and S2A ) . The loss of inclusion membrane integrity was observed at mid- to late times of infection ( data not shown ) , after significant replication has occurred . Recent studies have shown that the inclusion is stabilized by a cage-like structure composed of F-actin and vimentin [7] . Although it has been reported that inhibition of SM biosynthesis did not result in alteration of the vimentin or actin structure around the inclusion [15] , experiments using BFA have revealed that perturbations in vesicular trafficking lead to reciprocal changes in the architecture of the vimentin network in the host cell , indicating a tight link between membrane trafficking and the cytoskeleton [39] . Furthermore , Arf1 has been shown to regulate actin dynamics at Golgi membranes [54] . We therefore considered whether the loss of inclusion integrity observed upon disruption of GBF1 function was due solely to the decrease in SM acquisition or whether disruption of GBF1 function also affected the vimentin and actin cage surrounding the inclusion . We examined the localization of these cytoskeletal proteins in infected cells treated with GBF1 siRNA or GCA . Disruption of GBF1 function caused both the vimentin and actin cages to collapse to a thin , hemispheric cup-like structure on the nuclear side of the inclusion ( Figures 2B , S2B , and S2C ) . In cells treated with either BFA or GCA , we also observed less GBF1 staining surrounding the inclusion , and GBF1 displayed a more compact Golgi-like localization ( Figure S2A ) . GBF1 inhibited cells were more sensitive to Triton extraction ( data not shown ) , similar to what has been reported following disruption of the actin and vimentin cage during infection [7] , suggesting that the cytoskeletal cage is compromised upon GBF1 inhibition . Together , these results indicate that GBF1 function contributes to the stability of the inclusion membrane by a mechanism that involves SM acquisition and/or maintenance of the vimentin and F-actin cage around the inclusion; however , GBF1 function is not required for intracellular replication . Given that SM biosynthesis is essential for replication [6] , [12] , our findings suggest that other lipid transport mechanisms may be involved in the acquisition of host SM . Since ceramide transport from the ER to the trans-Golgi by the cytosolic lipid transporter , CERT , is the predominant pathway for SM biosynthesis [42] , [43] , [44] , [45] ) , we tested whether CERT-dependent trafficking was involved in SM acquisition by C . trachomatis . We first examined the localization of CERT during C . trachomatis infection of HeLa cells that were transiently transfected with CERT-GFP . In uninfected cells , CERT-GFP preferentially localized to the Golgi as well as to punctate , vesicle-like structures outside the Golgi ( Figure S4 and [45] , [55] ) , which are thought to be ER-derived ( K . Kumagai and K . Hanada , personal communication ) . In C . trachomatis-infected cells , CERT-GFP was recruited to the inclusion , exhibiting a non-homogeneous , patchy distribution on the inclusion membrane surface ( Figure 3A , B , D , E , Video S1 ) . CERT-GFP appeared on the same plane as but distinct from localization of the C . trachomatis inclusion membrane protein , IncA ( Figure 3B ) . This pattern of CERT recruitment to the inclusion was confirmed using an antibody to endogenous CERT ( Figure S3 ) , demonstrating that localization was not an artifact of ectopic expression . CERT-GFP also localized to C . trachomatis serovar D inclusions ( Figure 3B ) , suggesting that the recruitment to the inclusion was not serovar-specific . CERT-GFP was recruited to inclusions as early as 2 hpi , where nascent inclusions were enveloped by CERT-GFP-containing vesicles that partially overlapped with p58 , a marker of the ER-Golgi intermediate compartment ( ERGIC ) ( Figure S4A ) . CERT binding to VAP at the ER is required for CERT to extract ceramide from the ER and deliver it to the trans-Golgi [45] , where ceramide serves as a substrate for SM synthesis by trans-Golgi localized SMS1 and SMS2 [46] , [56] . Previous work suggests that there is an intimate association of the ER with the chlamydial inclusion [57] , [58] . We examined the localization of CERT and VAP-A by co-expressing CERT-GFP and HcRed-VAP-A [45] in infected cells and found that HcRed-VAP-A colocalized with CERT-GFP at the inclusion at 2 , 8 , and 24 hpi ( Figures 3C–E ) . These results suggest that CERT may function to bring the ER membrane in close apposition to the inclusion or that VAP-A is recruited directly to the inclusion . Given the proximity of the Golgi and the striking recruitment of CERT to the inclusion , we used three complementary approaches to determine whether CERT plays a role in C . trachomatis infection: ( 1 ) pharmacological inhibition of CERT by HPA-12 , a synthetic analog of ceramide that specifically inhibits CERT-mediated ceramide transfer [59] , [60] , ( 2 ) overexpression of Casein Kinase Iγ2 ( CKIγ2 ) , which plays a crucial role in down-regulating CERT activity [48] , and ( 3 ) depletion of CERT by siRNA . We first examined inclusion morphology and progeny production in cells treated with increasing concentrations of HPA-12 . Cells were infected for 1 hr and then incubated in the absence or presence of HPA-12 for an additional 23 hrs . We observed a dose-dependent decrease in inclusion formation ( Figures 4A ) and progeny production ( Figure 4B ) in cells exposed to HPA-12 , resulting in a 96% reduction in progeny at 10 µM HPA-12 compared to DMSO ( p<0 . 001 ) . At the doses employed , HPA-12 did not affect host or bacterial cell viability nor did it affect bacterial binding and entry ( data not shown ) . CKIγ2 regulates CERT by hyperphosphorylation of a SR motif located within the middle region , which induces an autoinhibitory interaction between the PH and START domains . This event interferes both with the ability of CERT to bind PI4P and with its ability to transport ceramide , though CERT is still able to interact with VAP-A at the ER [48] , [61] . Overexpression of CKIγ2 is sufficient to inhibit CERT function and to cause dissociation of CERT from the Golgi complex [61] . In cells co-transfected with CERT-GFP and HA-tagged CKIγ2 , we observed a significant defect in inclusion development , with the appearance of small inclusions ( Figure 4C ) , similar to the results observed with HPA-12 ( Figures 4A and 6C ) . Importantly , CERT-GFP was still recruited to the inclusion in cells expressing HA-tagged CKIγ2 ( Figure 4C ) . These results are consistent with the notion that CERT activity is required for inclusion development and suggest that PI4P binding is not required for CERT recruitment to the inclusion . We next examined inclusion morphology in HeLa cells depleted of CERT for 72 hrs and subsequently infected with C . trachomatis for 24 hrs . The efficiency of CERT depletion was determined in uninfected cells by western blot analysis ( Figure 4D ) . Loss of CERT activity was confirmed by demonstrating decreased accumulation of BODIPY FL-SM and BODIPY FL-Ceramide at the Golgi apparatus ( Figure 4E ) . CERT depletion resulted in a ∼66% ( p<0 . 001 ) reduction in inclusion size as well as a ∼60% ( p<0 . 001 ) decrease in progeny production ( Figures 4F–G ) . Depletion or inhibition of CERT sometimes resulted in the appearance of inclusions containing aberrant appearing RBs ( Figure 4F and data not shown ) . Progeny formation was decreased more robustly in HPA-12 treated cells than in CERT-depleted cells , most likely because CERT depletion was not complete , although we cannot rule out the possibility that HPA-12 may have additional off-target effects . We next explored whether CERT was required for the SM accumulation in the inclusion by monitoring incorporation of BODIPY FL-Ceramide . Since depletion of CERT led to a significant reduction in inclusion size ( Figure 4F ) , we monitored lipid transport in the presence of the CERT inhibitor , HPA-12 . HeLa cells were infected for 20 hrs to allow inclusions to develop to a large enough size for robust visualization by phase microscopy , then exposed to HPA-12 for 3 hrs , and subsequently pulse-labeled with BODIPY FL-Ceramide . The fluorescent lipid readily accumulated in inclusions within DMSO-treated cells , however , inclusions in HPA-12-treated cells showed ∼56% ( p<0 . 001 ) decrease in fluorescence intensity ( Figure 5 ) . Similar results were observed in control cells treated with D609 , an inhibitor of SMS1 and SMS2 [62] , [63] , [64] or with BFA ( Figure 5; p<0 . 001 ) . Since CERT is localized to the Golgi during infection , it is possible that CERT is indirectly involved in C . trachomatis infection based on its role in host SM biosynthesis at the Golgi . In addition or alternatively , as CERT and VAP-A are also localized at the inclusion , CERT may be directly involved in C . trachomatis infection by mediating the transfer of a portion of host ceramide from the nearby ER directly to the inclusion . We examined the localization of ceramide in infected cells expressing CERT-GFP using a ceramide antibody . This antibody does not cross-react with SM , cholesterol , or other phospholipids under physiologic in vivo conditions [65] . Ceramide appeared to be highly enriched in the region adjacent to the inclusion in what is likely to be the surrounding ER ( Figure S5 ) . Importantly , we also found that a fraction of ceramide localized at the inclusion membrane as evidence by co-staining with IncA antibodies and Arf1-GFP ( Figure S5A and S5B ) . Although we found ceramide in close association with CERT-GFP at the inclusion membrane , ceramide and CERT-GFP did not overlap on the inclusion membrane ( Figure S5C ) , possibly reflecting conversion of ceramide to SM and loss of reactivity with the ceramide antibody . Taken together , these results indicate that CERT is required for C . trachomatis replication and that interference with CERT correlates with a block in the SM acquisition by the inclusion . In addition , the localization of ceramide during infection suggests that CERT may be functioning at the Golgi as well as at the inclusion . To determine the mechanism of CERT recruitment to the inclusion , we examined the consequences of inactivating specific domains of CERT . The ability of CERT to transfer ceramide for SM biosynthesis requires: ( 1 ) interaction with the Golgi membrane ( PI4P ) via its PH domain , ( 2 ) an interaction with the ER membrane proteins VAP-A and VAP-B via its FFAT motif , and ( 3 ) the ability to bind ceramide via its START domain [47] . We were particularly interested in the PH domain , as recent studies have shown that multiple proteins that regulate PI4P metabolism are recruited to the inclusion , including OCLR ( oculocerebrorenal syndrome of Lowe protein 1 ) and the PI4P kinases , PI4KIIα and to a lesser extent PI4KIIβ [40] . The presence of PI4P at the inclusion combined with our finding that VAP-A colocalizes with CERT at the inclusion ( Figures 3C–3E ) raises the possibility that CERT could be recruited to the inclusion by its interaction with PI4P and/or with VAP-A . We examined the localization of two GFP-tagged CERT mutants: CERT ( G67E ) , which contains a mutation in the PH domain that prevents CERT from binding PI4P at the Golgi , or CERT ( D324A ) , which harbors a mutation in the FFAT motif that prevents CERT from interacting with VAPs at the ER [43] , [45] during C . trachomatis infection . In uninfected cells , CERT ( G67E ) -GFP is distributed throughout the cell while CERT ( D324A ) -GFP is concentrated at the Golgi ( data not shown and [45] ) . As shown in Figure 6A , both CERT ( G67E ) -GFP and CERT ( D324A ) -GFP localized to the inclusion at 24 hpi in a manner indistinguishable from CERT-GFP . While CERT-GFP and CERT ( D324A ) -GFP show some Golgi localization during infection , none is observed for CERT ( G67E ) -GFP ( Figure 6A ) . Co-staining with antibodies to Calnexin , an ER marker , revealed that although the ER appears in close proximity to the inclusion , it does not overlap with CERT-WT , D324A , or G67E at the inclusion ( Figure S6 ) . CERT was still recruited to inclusions formed in a mutant cell line of Chinese hamster ovary cells , LY-A , which carries a mutation in the PH domain of endogenous CERT ( G67E ) ( Figure S3 ) [43] , [66] . Despite the fact that LY-A cells have reduced SM biosynthesis [43] , [66] , SM acquisition by C . trachomatis inclusions was normal in these cells ( data not shown ) . We next tested whether Arf1 activity was required for CERT recruitment , since Arf1 localizes to the inclusion and since the PH domains of some lipid transfer proteins , such as FAPPs ( four-phosphate-adaptor protein 2 ) and OSBP ( oxysterol binding protein ) , can simultaneously bind PI4P and Arf1 [67] . C . trachomatis-infected HeLa cells were treated with Exo1 , a specific inhibitor of Arf GTPase activity [68] , from 1 to 24 hpi , and CERT-GFP localization was examined at 24 hpi . Although quantitation of inclusion size in Exo1 treated cells revealed a ∼64% decrease ( p<0 . 001 ) , Exo1 had no effect on CERT recruitment to the inclusion ( Figure 6B ) . Together , these results indicate that interaction with PI4P , VAP-A , or Arf1 alone is not essential for CERT recruitment to the inclusion , though PI4P , VAP-A , or Arf1 binding may function redundantly or there may be other determinants within CERT . In addition , CERT-GFP was still recruited to inclusions in cells treated with GCA , BFA , or Nocodazole , which disrupts microtubules ( Figures S4B and 7C ) , indicating that GBF1 function , vesicular trafficking , and microtubules are not required for CERT recruitment . To test whether CERT lipid transfer activity and/or ceramide binding is required for CERT recruitment to the inclusion , we treated cells expressing CERT-GFP with 5 µM HPA-12 from 1 to 24 hpi . In control experiments , HPA-12 treatment did not affect CERT-GFP localization to the Golgi region in uninfected cells ( data not shown ) . In C . trachomatis-infected cells exposed to HPA-12 , the patchy distribution of CERT-GFP was lost from the inclusion ( Figure 6C , top row ) , although CERT-GFP was still present in the Golgi surrounding the inclusion , as evidenced by colocalization with p230 , a trans-Golgi marker ( Figure 6C , bottom row ) . These results indicate that ceramide binding and/or the transfer activity of CERT is required for its recruitment to the inclusion . Since CERT cannot directly transfer SM [44] , we considered the possibility that SMS1 or SMS2 are directly recruited to the inclusion , where they could synthesize SM following ceramide transfer to the inclusion by CERT . In uninfected cells , both SMS1 and SMS2 are found at the trans-Golgi while SMS2 also localizes to the plasma membrane ( Figure S7 and [46] , [69] ) . We examined the localization of CERT-GFP and C-terminally 3xFLAG-tagged SMS1/SMS2 or SMS1-V5 and C-terminally 3xFLAG-tagged SMS2 during C . trachomatis infection at 24 hpi . In uninfected cells , C-terminally 3xFLAG-tagged SMS1 and 3xFLAG-tagged SMS2 were located in close apposition to CERT-GFP at the Golgi ( Figure 7A ) . In infected cells , C-terminally 3xFLAG-tagged SMS1 primarily localized to the fragmented Golgi surrounding the inclusion and displayed no overlap with CERT-GFP at the inclusion ( Figure 7A ) . In contrast , C-terminally 3xFLAG-tagged SMS2 localized to both the fragmented Golgi as well as to the inclusion membrane , where it exhibited a distinct punctate pattern that partially overlapped with CERT-GFP ( Figure 7A ) . Localization of SMS2 to the inclusion membrane was confirmed by co-staining infected cells expressing SMS2-V5 with antibodies to IncA; inclusion-membrane localized SMS2-V5 appeared to partially overlap with IncA whereas no overlap was seen between IncA and SMS1-V5 ( Figure 7B ) . To further distinguish between inclusion membrane versus Golgi localization , we examined the distribution of SMS1-V5 or SMS2-V5 in infected HeLa cells following disruption of the Golgi by BFA or by Nocodazole . As shown in Figure 7C , exposure of C . trachomatis-infected cells to either BFA or to Nocodazole caused SMS1-V5 to disperse throughout the cell while SMS2-V5 remained primarily associated with the inclusion , where a rim-like staining was observed . These results indicate that SMS2 is tethered to the inclusion during infection whereas SMS1 is primarily associated with the Golgi around the inclusion . In addition , SMS2 recruitment to the inclusion is independent of microtubules and of Arf1/GBF1-dependent vesicular trafficking . The mechanism by which SMS1 or SMS2 localize to the trans-Golgi has not yet been elucidated . However , it was recently shown that SMS2 is palmitoylated at the carboxyl terminus and that this modification plays a role in targeting SMS2 to the plasma membrane [69] . We therefore tested whether SMS2 recruitment to the inclusion required palmitoylation by examining the localization of a C-terminally 3xFLAG-tagged SMS2 construct in which all four of the potential palmitoylation sites were mutated ( C331 , 332 , 343 , 348A ) [69] . Although this mutant displays a decrease in PM distribution ( data not shown and [69] ) , it was recruited to the inclusion in a similar fashion as C-terminally 3xFLAG-tagged SMS2 wild-type ( Figure S7 ) . This result suggests that palmitoylation is not required for SMS2 recruitment to the inclusion . Both SMS1 and SMS2 are required for host SM production , however their relative contributions to subcellular SM levels are distinct , with SMS1 generating the bulk of SM at the Golgi while SMS2 is responsible for SM biosynthesis at the plasma membrane [46] , [56] . Our observation that SMS2 but not SMS1 is recruited to the inclusion membrane prompted us to determine their relative contributions to infection . Cells were depleted of SMS1 or SMS2 by siRNA [56] for 72 hrs and subsequently infected with C . trachomatis for 24 hrs . The efficiency of depletion was verified by western blot analysis in cells expressing C-terminally 3xFLAG-tagged SMS1 and 3xFLAG-tagged SMS2 ( Figure 8A ) . Loss of SMS1 activity was confirmed by demonstrating decreased accumulation of BODIPY FL-Ceramide and BODIPY FL-SM at the Golgi apparatus ( Figure 8B ) [56] . Depletion of either SMS1 or SMS2 resulted in a significant reduction in relative inclusion size and progeny formation ( Figures 8C–D ) . These results demonstrate that both SMS1 and SMS2 contribute to C . trachomatis replication and inclusion growth . Our results support the hypothesis that at least a portion of SM acquired by C . trachomatis may be directly synthesized on the inclusion membrane . In addition , SM synthesized at the nearby Golgi by SMS1 and SMS2 transported by a BFA-insensitive pathway may also contribute to replication .
Different pathogens have evolved a variety of unique strategies to establish a protective intracellular niche in which to replicate and to obtain essential nutrients from the host . For example , Legionella pneumophila replicates within a vacuole that is closely associated with the ER , while Salmonella and Mycobacteria species replicate within compartments that have characteristics of endosomes [70] . Chlamydiae are among the few known pathogens that occupy an exocytic compartment from which they acquire host-derived nutrients , including SM and cholesterol [6] , [9] , [10] , [11] , [12] , [13] , [14] . While previous work supports a role for canonical vesicular trafficking in the acquisition of SM by Chlamydia from the host , it has remained a mystery as to why inhibitors of vesicular trafficking have no effect on Chlamydia replication , given that host SM biosynthesis is necessary for bacterial replication [6] , [12] . Here , we demonstrate that C . trachomatis co-opts key proteins involved in both vesicular and non-vesicular lipid trafficking pathways to acquire SM for distinct roles during infection , providing an intriguing explanation for this apparent paradox . We found that C . trachomatis recruits CERT , its ER binding partner VAP-A , and SM synthases to establish an on-site SM biosynthetic factory at or near the inclusion that is critical for C . trachomatis replication . In addition , we show that C . trachomatis co-opts the function of GBF1 , a regulator of Arf1-dependent vesicular trafficking within the early secretory pathway , to further provide SM . This source of SM contributes to inclusion membrane growth and stability but is not essential for bacterial replication . We found that depletion or inhibition of CERT significantly impaired production of infectious progeny and SM acquisition . While these treatments decrease SM biosynthesis and would thus be expected to affect C . trachomatis replication , unexpectedly and most remarkably , we found that CERT was recruited to the inclusion membrane . Our results are consistent with two non-mutually exclusive mechanisms by which CERT could promote SM acquisition during infection ( Figure 9 ) . CERT could transport ceramide directly from the ER to the inclusion , where this lipid would serve as a substrate for inclusion membrane localized SMS2 , allowing SM biosynthesis to proceed on the inclusion membrane ( Figure 9 , step A ) . SM would then be transferred from the inclusion membrane to intracellular RBs . Alternatively , or in addition , by virtue of its ability to bind to the ER and to the Golgi , CERT could help to coordinate recruitment of these organelles to the inclusion ( Figure 9 , step B ) . This process would bring the trans-Golgi localized SMS1 and SMS2 in close proximity to the ER and to the inclusion , thereby promoting efficient SM synthesis in the vicinity of the inclusion . Since CERT cannot transport SM [44] , it is likely that this source of SM is subsequently transferred to the inclusion by a BFA-insensitive vesicular trafficking pathway . It is also possible that SM could be transferred from the Golgi at MCS to the inclusion by one of several mechanisms involving non-vesicular lipid exchange between membranes , such as transient hemifusion and/or stochastic collision with the Golgi [71] . Our results suggest that both SMS1 and SMS2 contribute to infection . The involvement of SMS1 was not surprising , since this enzyme is responsible for the bulk of total host cell SM biosynthesis ( 60–80% ) [46] , [56] . However , our finding that SMS2 was localized to the inclusion membrane and played a role in intracellular growth was unexpected since SMS2 participates primarily in SM biosynthesis at the plasma membrane and plays a minor role in overall host cell SM biosynthesis ( 20–40% ) [46] , [56] . In contrast to SMS1 , inclusion membrane localized SMS2 and CERT are not disrupted by BFA , GCA , or Nocodazole , which may explain why bacterial replication is not affected by these drugs . We note that it is difficult by current methods to demonstrate that SM is synthesized directly on the inclusion , since SM can also arrive via vesicular trafficking [46] , [56] . We speculate that by preferentially recruiting SMS2 over SMS1 during infection , C . trachomatis ensures a source of SM for itself without placing a huge burden on the host's ability to synthesize SM since SMS1 would still be active at the Golgi . Future studies will be required to unravel how SMS2 is recruited to the inclusion membrane . How is CERT , a multi-domain protein , recruited to the inclusion ? Although the recruitment of CERT to Golgi and ER membranes requires the PH and FFAT domains , respectively [47] , we found that only the ceramide binding and/or ceramide transfer activity of CERT is required for its recruitment to the inclusion . While CKIγ2-induced phosphorylation of CERT inhibits its activity , CERT was still recruited to the inclusion , presumably because CERT is still able to bind ceramide . On the other hand , exposure to HPA-12 prevented CERT recruitment to the inclusion , possibly through its known ability to inhibit interaction of the START domain with membranes in vitro [60] . An alternative scenario is that HPA-12 binding to CERT could alter its ability to bind PI4P , VAP-A , or potentially a bacterial factor at the inclusion membrane through steric hindrance or conformational change . We favor the hypothesis that CERT binds to ceramide at the ER , prior to its recruitment to the inclusion , and subsequently interacts with the inclusion by an as yet to be identified bacterial or host protein . It is also possible that a small amount of ceramide and/or VAP-A may initially be incorporated into the inclusion membrane from either the plasma membrane during endocytosis or by fusion with the ER or ER-derived vesicles containing ceramide , which would then promote CERT recruitment . These initial events could then set up an amplification mechanism for subsequent ceramide transfer and SM synthesis . Further studies are needed to determine whether CERT transfer activity or ceramide binding alone is necessary for its recruitment to the inclusion , and whether either of these functions cooperates with a bacterial protein present in the inclusion membrane . It was surprising that PI4P binding was not required for CERT recruitment to the inclusion , given the recent observation that PI4P binding is necessary and sufficient to recruit the PH domain of OSBP [40] . However , it should be noted that those studies were performed with only the PH domain of OSBP whereas our studies were performed in the context of the entire CERT protein containing a single point mutation ( G67E ) that abolishes PI4P binding . It is likely that there are multiple localization signals for CERT recruitment to the inclusion . The dispensable requirement of PI4P binding for CERT recruitment to the inclusion likely explains why inclusions still form in LY-A cells expressing CERT ( G67E ) and why CERT still localizes to the inclusion in these cells . It is worth noting that recombinant CERT ( G67E ) is still able to transfer ceramide in an in vitro assay [45] , suggesting that although SM synthesis is reduced in the LY-A cell line due to the inability of CERT to bind the Golgi and transfer ceramide [43] , [66] , SM synthesis may proceed at the inclusion because this specialized compartment has access to ceramide via CERT and at least one SMS isoform . While the role of CERT in ceramide transfer and SM biosynthesis is clearly established , we cannot rule out the possibility CERT may have other functions in host cells relevant to Chlamydia infection . Previous work has suggested that there is an intimate association of the ER or ER proteins with the chlamydial inclusion [57] , [58] . It is possible that CERT and VAP-A together may be important for coordinating ER-inclusion membrane contact sites . These membrane contact sites could be important for the inclusion to obtain other essential lipids or nutrients for growth . Although loss of CERT function in individual cells has no effect on cell survival [43] , [66] , knockout of CERT in mice is embryonic lethal as a result of severe defects in both the ER and mitochondria function [72] . In addition , D . melanogaster flies lacking functional CERT have a short lifespan and show enhanced susceptibility to oxidative stress [73] . Our studies provide further insights into the mechanism of BFA-sensitive SM acquisition . While it was previously suggested that Chlamydia occupies a post Golgi compartment with the prediction that trafficking from the trans-Golgi would be essential , our results demonstrate that acquisition of SM by Chlamydia requires Arf1 activation within the cis-Golgi but not Arf1 activation within the trans-Golgi ( Figure 9 , step C ) , suggesting that Chlamydia is not necessarily co-opting the entire organelle . Loss of SM acquisition following inhibition of GBF1 function correlated with a loss of inclusion membrane integrity and alterations in the localization of actin and vimentin cytoskeletal components surrounding the inclusion ( Figure 9 , step D ) . Whether the loss of inclusion integrity is a direct or indirect consequence of the collapse of the closely associated cytoskeletal cage or whether it results from the loss of SM acquisition , as suggested by Beatty and coworkers [15] , remains to be determined . Of note , GCA was not reported to affect the stability of serovar E inclusions [15]; however , this disparity may reflect strain differences . We observed that GBF1 and Arf1 have little overlap at the inclusion membrane during infection even though they extensively colocalize at the Golgi surrounding the inclusion . This observation could reflect a transient and dynamic interaction at the inclusion . Alternatively , we favor the hypothesis that GBF1 activates Arf1 at the Golgi and then Arf1-vesicles containing SM traffic to and fuse with the inclusion . Under these circumstances , GBF1-dependent activation of Arf1 at the Golgi rather than at the inclusion would be important , which could explain why there is little colocalization of these proteins at the inclusion . Consistent with this notion , we found that inhibition of GBF1 activity resulted in a loss of inclusion localized Arf1 , except at the region of abutting inclusion membranes . This finding suggests that GBF1 is required for Arf1 recruitment to the outer region of the inclusion but not for Arf1 maintenance at closely apposed inclusions . We are currently investigating the role of Arf1 at this region . Recent studies have revealed that Arf1 and GBF1 also play important roles in lipid droplet biogenesis [74] , [75] . As lipid droplets are translocated into the lumen of the inclusion [76] , [77] , interference with Arf1 and/or GBF1 function could also affect Chlamydia interaction with lipid droplets . Notably , GBF1 colocalizes with Lda3 ( unpublished results ) , a chlamydial protein that localizes to lipid droplets [77] . Chlamydia now joins the growing list of pathogens that subvert GBF1 or Arf1 function to establish an intracellular replicative niche , including poliovirus , coxsackievirus , coronavirus , hepatitis C virus , and Legionella pneumophila [26] . In contrast to these pathogens where depletion or inhibition of GBF1 by BFA significantly impairs replication , our findings demonstrate that GBF1 is more important for establishing a stable intracellular niche in which C . trachomatis replicates . Both poliovirus and coxsackievirus encode a protein ( 3A ) that binds to and co-opts GBF1 function , while L . pneumophila secretes into the cell RalF , an Arf GEF , that regulates Arf1 localization to the L . pneumophila-containing phagosome [78] , [79] . Since GBF1 was not localized on the inclusion membrane , it is possible that GBF1 communicates indirectly with the inclusion through a variety of host proteins or lipids that have been shown to bind GBF1 and that localize at or near the inclusion , including Rab1 [80] , PI4P [81] , and the Golgi matrix proteins , golgin 84 and p115 [26] . It will be important to determine whether the alterations in the actin and vimentin cytoskeleton during infection are directly or indirectly modulated by GBF1 . In summary , we show that C . trachomatis hijacks components of both vesicular and non-vesicular lipid trafficking pathways for SM acquisition , and that the SM obtained from these pathways is utilized in different ways by the pathogen . We hypothesize that SM acquired by CERT-dependent transport of ceramide and subsequent conversion to SM is necessary for C . trachomatis replication whereas SM acquired by the GBF1-dependent pathway is essential for inclusion growth and stability . Together , these observations provide an intriguing explanation for why inhibition of vesicular trafficking alone fails to affect intracellular replication . Moreover , our results describe the first example of a bacterial pathogen to co-opt CERT and reveal a novel strategy by which this organism creates its own SM biosynthetic factory . This work has identified novel targets that may prove useful in combating Chlamydia infections .
D609 was purchased from Calbiochem . Brefeldin A , Nocodazole , and Exo1 were purchased from Sigma . Golgicide A ( GCA ) was kindly provided by D . Haslam ( Washington University School of Medicine , St . Louis , MO ) . BODIPY FL C5 Ceramide was obtained from Molecular Probes . Construction of Arf1-GFP , Arf1-mCherry , and GFP-GBF1 were previously described [82] , [83] . CERT-GFP , CERT ( D324A ) -GFP , CERT ( G67E ) -GFP , HcRed-VAP-A , HA-CKIγ2 , and HPA-12 were kind gifts from K . Hanada ( National Institute of Infectious Disease , Tokyo ) and have been previously described [45] , [59] . C-terminally V5-tagged SMS1 and SMS2 constructs were kind gifts from J . Holthuis ( Utrecht University ) and have been described previously [46] . C-terminally 3xFLAG-tagged SMS1 and SMS2 constructs were kind gifts from M . Tani ( Kyushu University ) and have been previously described [69] . GFP-p58 was kindly provided by C . Roy ( Yale University ) . Unless otherwise indicated , the following concentrations of inhibitors were used: 5 µM HPA-12 , 25 µg/ml D609 , 10 µM GCA , 10 µM BFA , 10 µM Nocodazole , and 50 µg/ml Exo1 . Antibodies were obtained from the following sources: mouse anti-Chlamydia FITC conjugate ( Meridian Diagnostics ) , goat anti-C . trachomatis MOMP ( Cortex Biochem ) , mouse anti-GAPDH ( Chemicon ) , mouse anti-GBF1 ( BD Transduction Laboratories ) , rabbit anti-BIG1 ( Santa Cruz ) , rabbit anti-BIG2 ( Bethyl Laboratories , Inc . ) , chicken anti-CERT ( Sigma ) , rabbit anti-14-3-3β ( Santa Cruz ) , mouse anti-vimentin ( Sigma ) , mouse anti-FLAG ( Sigma ) , rabbit anti-V5 ( Sigma ) , mouse anti-HA ( Covance ) , rabbit anti-p58 ( Sigma ) , mouse anti-ceramide IgM ( Clone15B4 ) , rabbit anti-calnexin ( Cell Signaling ) , rabbit anti-goat IgG horseradish peroxidase ( HRP ) ( Calbiochem ) , goat anti-rabbit IgG HRP ( Amersham Biosciences ) , goat anti-mouse HRP ( Amersham Biosciences ) , donkey anti-goat Alexa 594 ( Molecular Probes ) , donkey anti-goat Alexa 488 ( Molecular Probes ) , chicken anti-mouse 594 ( Molecular Probes ) , goat anti-mouse IgG1 Alexa 594 ( Molecular Probes ) , donkey anti-goat Alexa 488 ( Molecular Probes ) , Texas red-conjugated donkey anti-chicken IgY ( Jackson Laboratories ) , and donkey anti-rabbit Alexa 488 ( Molecular Probes ) . Texas red-conjugated phalloidin was obtained from Molecular Probes . Mouse anti-IncA antibodies were kind gifts from T . Hackstadt ( Rocky Mountain Laboratories ) , D . Rockey ( Oregon State University ) , and G . Zhong ( University of Texas Health Science Center at San Antonio ) . All siRNAs , including non-targeting negative controls , were obtained from Dharmacon: Human GBF1 ( J-019783-06 ) , human BIG1 ( D-012207-02 , D-012207-03 ) , and human BIG2 ( MQ-012208-01 ) and have been previously described [33] , [84] . Human SMS1 and SMS2 siRNA sequences have been previously described [56] . Human CERT siRNA was performed using siGENOME SMARTpool . HeLa 229 cells and L929 cells were obtained from ATCC and passaged as previously described [85] . LY-A and LY-A/hCERT cells were obtained from RIKEN BRC through the National Bio-Resource Project of MEXT , Japan and have been previously described [43] , [66] . CHO cells were maintained at 37°C with 5% CO2 in Ham's F12 containing 10% fetal bovine serum ( FBS ) . C . trachomatis serovar L2 ( LGV 434 ) and D ( UW3-Cx ) were propagated in L929 cells . C . trachomatis EBs were harvested from infected cells and purified using a Renografin step-gradient as described [86] . Unless otherwise indicated , experiments were performed with serovar L2 . Cells were grown on glass coverslips in 24-well plates and infected at a multiplicity of infection ( MOI ) of approximately 1–5 with C . trachomatis as described in the text . For expression of tagged constructs , cells were transfected with the indicated plasmid constructs using Effectene ( Invitrogen ) for 18 hrs prior to infection , following manufacturer's instructions . Cells were fixed in 4% paraformaldehyde ( PFA ) or methanol and stained with the appropriate antibodies . For anti-GBF1 , anti-14-3-3β , anti-MOMP , anti-HA , anti-ceramide , anti-calnexin , anti-p58 , and anti-V5 staining , cells were permeabilized with 0 . 05% saponin/0 . 2% bovine serum albumun ( BSA ) in phosphate buffered saline ( PBS ) for 30 minutes and blocked with 0 . 2% BSA/PBS for 30 minutes . For anti-CERT staining , cells were permeabilized with 0 . 5% Triton X-100/PBS for 3 minutes and blocked with 1% BSA/PBS for 20 minutes . For phalloidin staining , cells were permeabilized with 0 . 2% Triton/PBS and blocked with 1% BSA/PBS for 1 hr . For anti-vimentin and anti-IncA staining , cells were post-fixed in methanol for 5 minutes and blocked in 2% BSA/PBS for 1 hr . Cells were incubated with the appropriate primary and fluorophore-tagged secondary antibodies for 1 hr each , followed by washing 3 times in PBS . Anti-FLAG staining was performed as previously described [69] . Coverslips were mounted in Vectashield mounting media containing DAPI ( Vector Laboratories ) to identify bacteria and host cell nuclei . Images were acquired under 20 , 40 or 100× oil objective using a Nikon Eclipse TE2000-E fluorescence microscope with Simple PCI imaging software ( Compix , Inc . ) or acquired under 100× oil objective using Nikon TE2000-PFS spinning disk confocal inverted microscope with Nikon Elements software . For each set of experiments , the exposure time for each filter set for all images was identical . Images were processed with Adobe Photoshop CS , Nikon Elements , or Imaris Software . Cells were lysed in lysis buffer ( 50 mM Tris HCl , pH 7 . 5 , 150 mM NaCl , 0 . 1% SDS , 1% Nonidet P-40 , 1% sodium deoxycholate , 1 mM sodium orthovanadate , 1 mM sodium fluoride , 1 mM okadaic acid , and Complete protease inhibitors ( Roche Diagnostics ) ) . After centrifugation at 20 , 800× g for 5 minutes to remove cell debris , the supernatants were transferred to fresh tubes . Proteins were separated on 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) gels and transferred to 0 . 45-µm pore Trans-blot nitrocellulose membranes ( BioRad Laboratories ) . Membranes were blocked with 5% milk and probed with the indicated antibodies . Proteins were detected by enhanced chemiluminescence ( ECL ) ( Amersham Biosciences ) according to the manufacturer's protocol . HeLa cells grown in 6-well plates were transfected with specific or control siRNAs according to manufacturer's protocol . At 72 hrs post transfection , cells were infected with C . trachomatis for 1 hr and then incubated for an additional 24 hrs . Knockdown efficiency was measured by western blot analysis for endogenous protein , or in the case of SMS1 and SMS2 , knockdown efficiency was determined using HeLa cells expressing C-terminally 3xFLAG-tagged SMS constructs as previously described [56] . Knockdown efficiency for SMS1 was also determined using BODIPY FL-Ceramide ( see below ) . HeLa cells were incubated with the indicated siRNA for 3 days and subsequently infected with C . trachomatis ( MOI of 1–5 ) for 24 hrs . For drug treated samples , HeLa cells were infected with C . trachomatis for 1 hr and then incubated for an additional 23 hrs in the presence of the indicated drug . To quantify primary inclusion formation , cells were fixed and processed by immunofluorescence and processed as described below . To quantify production of infectious progeny , infected cells were scraped into media , lysed with sonication , and 5-fold serial dilutions were used to infect fresh HeLa monolayers that had been plated on coverslips for 24 hrs . For assessment of drug effects on bacterial viability and internalization , progeny was harvested from DMSO treated samples and left untreated or treated with the various inhibitors during the 1 hr infection of fresh HeLa monolayers . Cells were then washed free of the inhibitor and incubated for an additional 24 hrs . Inclusions were visualized by immunofluorescence with goat anti-MOMP antibodies and donkey anti-goat Alexa 594 or mouse anti-Chlamydia FITC conjugate . Images were analyzed using MetaMorph software ( Molecular Devices , Sunnyvale , CA ) from a minimum of 10 fields per sample . The relative inclusion forming units ( IFUs ) were expressed as a percentage of control siRNA or DMSO controls . The data is presented as mean ± standard error . To quantify the relative size of inclusions , approximately 200–350 cells were counted from 7–10 fields , the inclusion area analyzed by Metamorph using the mean area function , and the data is normalized to the percent area of control siRNA or DMSO-treated cells . To quantify inclusion disruption , approximately 350 cells were scored for apparent breaks in the inclusion membrane by staining with 14-3-3β and for concomitant release of bacteria into the cytosol . The data is normalized to percentage of similarly scored control or DMSO-treated cells . HeLa cells were infected with C . trachomatis for 21 hrs and then treated with the indicated drug for an additional 3 hrs . At 24 hpi , live cells were then incubated for 30 minutes in serum-free MEM containing 1 µM BODIPY FL-Ceramide ( Molecular Probes ) complexed with 0 . 034% defatted bovine serum albumin ( dfBSA ) in MEM at 4°C in the dark . Cells were washed 3 times with serum free MEM and back exchanged with MEM/0 . 34% dfBSA for 1 hr . Cells were immediately visualized by fluorescence microscopy under a 40× objective . To assess SM accumulation in the Golgi upon depletion of CERT or SMS1 , BODIPY FL-Ceramide labeling was performed at 72 hrs post siRNA treatment and fluorescence intensity in the Golgi region of siRNA-treated samples was compared to control siRNA . To assess SM accumulation by the inclusion upon depletion and inhibition of GBF1 and BIGs , cells were treated with the appropriate siRNA for 72 hrs , infected with L2 , and BODIPY FL-Ceramide labeling was performed at 24 hpi . For quantitation of SM accumulation in the inclusion , mean fluorescence intensity of the inclusion was determined by defining regions of interest within the inclusion and dividing by the mean fluorescence intensity of the nucleus , which remained constant with all drug treatments . At least 5 fields were examined , and values were obtained from a total of 10–15 inclusions . Data represented the mean ± standard error of n experiments . Statistical analysis was performed using the software program Instat . The significance between groups was determined by ANOVA . A p value less than 0 . 05 was considered to be statistically significant . Arf1 ( GeneID 375 ) , GBF1 ( GeneID 8729 ) , BIG1 ( GeneID 10565 ) , BIG2 ( GeneID 10564 ) , CERT ( GeneID 10087 ) , VAP-A ( GeneID 9218 ) , SMS1 ( GeneID 259230 ) , and SMS2 ( GeneID 166929 ) . | C . trachomatis is the leading cause of non-congenital blindness in developing countries and is the number one cause of sexually transmitted disease and non-congenital infertility in Western countries . The capacity of Chlamydia infections to lead to infertility and blindness , their association with chronic diseases , and the extraordinary prevalence and array of these infections make them public concerns of primary importance . This pathogen must establish a protective membrane-bound niche and acquire essential lipids from the host cell during infection in order to survive and replicate . This study identifies novel mechanisms by which C . trachomatis hijacks various lipid trafficking proteins for distinct roles during intracellular development . Disruption of these lipid trafficking pathways results in alterations in the growth and stability of its protective niche as well as a defect in replication . Understanding the molecular mechanisms of these host-pathogen interactions will lead to rational approaches for the development of novel therapeutics , diagnostics , and preventative strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"medicine",
"women's",
"health",
"biology",
"microbiology",
"molecular",
"cell",
"biology"
]
| 2011 | Chlamydia trachomatis Co-opts GBF1 and CERT to Acquire Host Sphingomyelin for Distinct Roles during Intracellular Development |
Cytokinins and gibberellins ( GAs ) play antagonistic roles in regulating reproductive meristem activity . Cytokinins have positive effects on meristem activity and maintenance . During inflorescence meristem development , cytokinin biosynthesis is activated via a KNOX-mediated pathway . Increased cytokinin activity leads to higher grain number , whereas GAs negatively affect meristem activity . The GA biosynthesis genes GA20oxs are negatively regulated by KNOX proteins . KNOX proteins function as modulators , balancing cytokinin and GA activity in the meristem . However , little is known about the crosstalk among cytokinin and GA regulators together with KNOX proteins and how KNOX-mediated dynamic balancing of hormonal activity functions . Through map-based cloning of QTLs , we cloned a GA biosynthesis gene , Grain Number per Panicle1 ( GNP1 ) , which encodes rice GA20ox1 . The grain number and yield of NIL-GNP1TQ were significantly higher than those of isogenic control ( Lemont ) . Sequence variations in its promoter region increased the levels of GNP1 transcripts , which were enriched in the apical regions of inflorescence meristems in NIL-GNP1TQ . We propose that cytokinin activity increased due to a KNOX-mediated transcriptional feedback loop resulting from the higher GNP1 transcript levels , in turn leading to increased expression of the GA catabolism genes GA2oxs and reduced GA1 and GA3 accumulation . This rebalancing process increased cytokinin activity , thereby increasing grain number and grain yield in rice . These findings uncover important , novel roles of GAs in rice florescence meristem development and provide new insights into the crosstalk between cytokinin and GA underlying development process .
Rice panicle architecture , a valuable composite agronomic trait that includes grain number per panicle ( GNP ) , panicle length and so on , is strongly associated with rice grain yield . GNP is one of the most important agronomic characteristics of ideal plant architecture [1] . To improve rice grain yields to meet the needs of the rapidly growing population , numerous studies have focused on identifying and cloning genes/QTLs contributing to rice panicle architecture development . Many genes and pathways have recently been identified , including transcriptional and plant hormone regulators that contribute to the reproductive meristem activity maintenance processes . Cytokinins play a fundamental role in regulating reproductive meristem activity by promoting cell division [2] . Grain number 1a ( Gn1a ) , a cytokinin metabolism-related gene , encodes a cytokinin oxidase/dehydrogenase ( OsCKX2 ) that catalyzes the degradation of active cytokinins in reproductive meristems . Thus , a null allele of Gn1a leads to improved rice grain yield through increased active cytokinin levels and reproductive meristem activity [3] . Another gene , LONELY GUY ( LOG ) encodes a cytokinin nucleoside 5’-monophosphate phosphoribohydrolase . LOG transcripts are specifically enriched in the apical regions of vegetative and reproductive meristems . LOG functions in the activation of cytokinin , catalyzing the conversion of inactive cytokinins to biologically active forms . Reduced active cytokinin levels in the meristem due to malfunctioning of cytokinin activation is likely responsible for the defective meristem activity in the log mutant [4] . In additions , the zinc finger transcription factor DROUGHT AND SALT TOLERANCE ( DST ) directly induces the expression of OsCKX2 in the inflorescence meristems . The mutant allele DSTreg1 reduces OsCKX2 expression , thus increasing cytokinin levels in the inflorescence meristem , and therefore , the number of panicle branches and grains [5 , 6] . Gibberellins ( GAs ) are crucial for plant growth and developmental processes , such as seed germination [7] , grain setting [8] and so on . However , unlike cytokinins , GAs are primarily associated with high yield rice breeding due to their roles in plant height promotion . Most mutants or RNAi transgenic lines of GA biosynthesis genes , including CPS , KS , KAO [9] , KO [10] , GA20oxs [11–13] and GA3oxs [14] , show dwarfism phenotypes , which results in improved lodging resistance , a valuable trait for rice breeding under high inputs [15] . At the same time , transgenic-activated expression of GA catabolism genes , GA2oxs , also leads to dwarfism [16 , 17] . However , GA signals are also active in inflorescence meristems . OsGA20ox2 , OsGA3ox2 , Gα and SLR1 are highly expressed in inflorescence meristems and leaf primordia [18] . In maize , the expression domains of GA2ox1 and KN1 ( a maize KNOX gene ) overlap , mainly at the base of the shoot apical meristem . The KNOX gene KN1 directly induces GA2ox1 expression in reproductive meristems [19] . In tobacco and Arabidopsis , GA20ox expression could be directly excluded from the corpus of the shoot apical meristem [20 , 21] . These findings suggest that GAs are detrimental to meristem activity . Although the importance of GAs in meristem establishment and maintenance has been recognized , the GA biosynthesis and regulatory networks underlying this process are largely unknown , and it also remains to be determined whether certain GA biosynthesis and regulatory genes can be useful for increasing grain number and yield in rice . KNOX proteins are a class of homeodomain transcription factors that function in meristem establishment and maintenance . OSH1 ( a rice KNOX gene ) can directly activate the expression of other KNOX paralogs ( OSH15 , for example ) and itself . The positive autoregulation of KNOX genes and activation by cytokinin are both essential for meristem maintenance [22] . In rice and Arabidopsis , KNOX proteins can activate cytokinin biosynthesis in the meristems through the induction of genes encoding adenosine phosphate isopentenyltransferase ( IPT ) . IPTs are important enzymes that convert ATP , ADP and AMP to the iP riboside 5’-triphosphate ( iPRTP ) , iP riboside 5’-diphosphate ( iPRDP ) and iP riboside 5’- moophosphate ( iPRMP ) forms [23 , 24] . As KNOX proteins reduce GA activity , they play an indispensable role in maintaining shoot apical meristem activity , probably by balancing cytokinin and GA activity in the meristems , increasing cytokinin levels and reducing GA levels [25 , 26] . Here , we report the identification and characterization of a QTL , Grain Number per Panicle1 ( GNP1 ) , which encodes rice GA biosynthetic protein OsGA20ox1 . We propose that the upregulation of GNP1 in the inflorescence meristems may increase cytokinin activity via a KNOX-mediated feedback regulation loop and increase GA catabolism activity through inducing the expression of GA2oxs . This process would result in increased cytokinin activity , rebalancing cytokinin and GA activity and increasing grain number and grain yield . These results provide insights into the mechanism underlying KNOX-mediated cytokinin and GA crosstalk during rice inflorescence meristem development , and they suggest that GNP1 is a suitable target gene for high yield rice breeding .
To identify QTLs , we constructed two sets of reciprocal introgression lines ( ILs ) derived from a japonica rice variety Lemont ( LT ) and an indica variety Teqing ( TQ ) , TQ-ILs and LT-ILs . In these two ILs , multiple QTLs for Grain Number per Panicle ( GNP ) were identified in Beijing and Sanya , respectively ( S1 Table ) . Among these , QTLs affecting GNP in the RM227–RM85 region on chromosome 3 were detected in both TQ- and LT-ILs , suggesting that this QTL is stable for the grain number trait in rice . This QTL was designated Grain Number per Panicle1 ( GNP1 ) . From 201 LT-ILs , an IL named GG306 ( BC3F4 ) , containing chromosome segment RM227–RM85 from TQ and 92 . 6% of the genetic background of LT , was selected ( Fig 1A ) and backcrossed twice to LT . Self-pollination of BC5F1 plants heterozygous for this fragment resulted in heterozygous near-isogenic lines ( NILs ) with almost all of the genetic background of LT except for the introgressed segment ( Fig 1A ) . The BC5F2 was successively self-pollinated several times to obtain segregating NIL-F2 ( BC5F3 , BC5F4 and BC5F5 ) populations for fine mapping of GNP1 and construction of NILs , NIL-GNP1LT and NIL-GNP1TQ ( Fig 1B ) . An analysis of a BC5F3 population of 163 individuals derived by self-pollination of the BC5F2 heterozygotes at the region RM227–RM85 showed that the trait segregated as a single locus with a Mendelian ratio , which was confirmed by data from BC5F4 families ( S1 Fig and S2 Table ) . Through map-based cloning of GNP1 , we narrowed the GNP1 locus down to a 33 . 7 kb region between SL65 and SL54 ( Fig 1C and S2 Fig ) . This region contains four predicted genes ( LOC_Os03g63970 , LOC_Os03g63980 , LOC_Os03g63990 and LOC_Os03g63999 , http://rice . plantbiology . msu . edu/cgi-bin/gbrowse ) . To further investigate the effects of the GNP1 locus on grain number and other traits , we analyzed near-isogenic lines , NIL-GNP1LT and NIL-GNP1TQ , in the LT genetic background , which only differed in the ~66 . 1 kb region containing GNP1 derived from LT and TQ ( Fig 1A ) . We observed a significant increase in the total grain number per panicle ( GNP; +56% ) , filled grain number per panicle ( FGN; +28% ) and secondary branch number ( SBN ) in NIL-GNP1TQ ( Fig 1D , Fig 1E and S3F Fig , the same pattern in SBN between LT and TQ ( S3G Fig ) ) , but only a small increase in plant height ( +8%; Fig 1B and S3A Fig ) , a slight decrease in grain length ( -4%; S3B Fig ) , grain width ( -5%; S3C Fig ) and 1 , 000-grain weight ( -12%; S3D Fig ) and no effect on panicle length ( S3E Fig ) and primary branch number ( S3F Fig the same pattern in PBN between LT and TQ ( S3G Fig ) ) compared with the NIL-GNP1LT isogenic control in plants grown in Shanghai . These results indicate that the GNP1TQ locus in NIL-GNP1TQ has pleiotropic effects on rice development , primarily on inflorescence development , especially secondary branch number and grain number . To determine whether GNP1TQ affects grain yield , we evaluated the grain yields of NIL-GNP1TQ and the isogenic control ( Lemont ) , together with other related traits . In different fields , the grain number was still substantially higher in NIL-GNP1TQ than in the control , leading to a significant increase in grain yield ( 5 . 7–9 . 6% ) despite the slightly reduced grain weight ( Table 1 and S3 Table ) . These results suggest that the GNP1TQ locus can potentially be used in high yield rice breeding . According to the mapping results , LOC_Os03g63980 and LOC_Os03g63990 are predicted to encode transposon and retrotransposon proteins , LOC_Os03g63999 encodes a small peptide with unknown function and LOC_Os03g63970 encodes GA 20-oxidase 1 , which is thought to catalyze the conversion of GA12 to GA20 within a multi-step process . Therefore , LOC_Os03g63970 is the most likely candidate for the GNP1 locus . We sequenced the promoter ( 2 kb before ATG ) and LOC_Os03g63970 in both TQ and LT . The two parents exhibited base differences at 21 positions in the promoter region , including 17 single-base substitutions , as well as two single-base and two multi-base insertions and deletions . The coding region contains two single-base substitutions , one of which leads to an amino acid substitution ( S4 Fig ) . These results suggest that the sequence differences in the promoter and coding region of this gene might lead to changes in gene expression levels and protein function and may help increase grain number in NIL-GNP1TQ . To validate this hypothesis , we obtained the LOC_Os03g63970 T-DNA gain-of-function mutant gnp1-D from the Rice T-DNA Insertion Sequence Database . TAIR-PCR screening showed that the T-DNA was inserted at position -514 to -492 of the LOC_Os03g63970 promoter relative to the start codon ATG ( Fig 2A ) , which constitutively induces the expression of LOC_Os03g63970 throughout the plant . We analyzed traits of the homozygous gnp1-D mutant and control via PCR with specific primers designed based on the insertion sequence ( Fig 2A and Fig 2B ) , finding a significant increase in plant height ( Fig 2C and Fig 2D ) with increasing LOC_Os03g63970 expression in flag leaves ( Fig 2E ) . Interestingly , a substantial increase in GNP ( +51 . 5% ) and FGN ( +71 . 6% ) were also observed ( Fig 2F and Fig 2G ) . These results suggest that LOC_Os03g63970 is the gene for GNP1 and that the increased GNP1 expression in this mutant might influence GA biosynthesis during rice panicle meristem development . We then constructed a binary vector harboring the GNP1TQ coding sequence ( CDS ) driven by a CaMV 35S promoter , which we used to transform japonica rice ( O . sativa L . ) variety Zhonghua 11 ( ZH11 ) , whose GNP1 CDS matches that of LT . GNP1 was expressed at levels several hundred- to over a thousand-fold that of CK ( transgenic negative control ) in flag leaves ( Fig 3A ) . Compared with CK , the GNP of line p35S::GNP1TQ-3 increased by 36 . 3% , accompanied with hugely increased height ( S5A and S5B Fig ) and greatly increased sterility , while lines p35S::GNP1TQ-1 and p35S::GNP1TQ-2 had significantly increased GNP ( FGN ) by 27 . 8% ( 35 . 5% ) and 26 . 5% ( 33 . 4% ) ( Fig 3B and Fig 3C ) , and slightly increased height ( S5A and S5B Fig ) . These results indicate that the expression disturbances associated with the promoter activity variations at the GNP1 locus are responsible for the phenotypic variation in GNP and plant height with a dose-dependent manner and a very high expression level of GNP1 may have a negative effect on seed setting rate . Then , in order to find out whether decreased expression of GNP1 could show some negative effect on grain number phenotype , we transformed ZH11 with the mimic artificial microRNA oligo sequence designed for GNP1 silencing driven by the CaMV 35S promoter . Interestingly , the grain number of six transgenic-positive independent lines increased ( S6A Fig ) , which was negatively correlated with GNP1 expression ( S6B Fig ) . These lines also had reduced plant height ( S6C and S6D Fig ) . These results indicate that the reduced expression of GNP1 might contribute to attenuated GA biosynthesis activity , leading to reduced GA levels and partially reducing the negative effects of GAs on maintaining inflorescence meristem activity [26] , which might be responsible for the higher grain number in these mimic artificial miRNA transgenic lines . To further confirm the function of GNP1LT CDS , we transformed NIL-GNP1LT with GNP1LT CDS driven by the GNP1 promoter from Lemont ( pGNP1LT ) . Similar to gnp1-D gain-of-function mutant and GNP1TQ overexpression lines , as the expression level of GNP1 increased ( up to nearly ten-fold compared to the control; Fig 3D ) , we observed an increase in GNP and FGN ( Fig 3E ) , as well as plant height ( S5C Fig ) . These results indicate that both GNP1LT and GNP1TQ could affect panicle development . These results indicate that the accumulation of GNP1LT or GNP1TQ transcripts ( or both ) in the plant has a positive effect on grain number and plant height . To determine whether the differences between the GNP1LT and GNP1TQ promoter regions ( S4 Fig ) influence GNP1 expression , and account for the differences in grain number , we analyzed the expression patterns of GNP1 between NIL-GNP1LT and NIL-GNP1TQ in different tissues during panicle initiation to the booting stage . GNP1 was mainly expressed in developing panicles and nodes ( S7 Fig ) , which is consistent with effects of this gene on grain number and plant height . In addition , compared to NIL-GNP1LT , GNP1 transcripts were much more abundant in NIL-GNP1TQ tissues ( S7 Fig ) . Meanwhile , GNP1 expression in seedling leaf sheaths was negatively correlated with the dose of GA3 used for treatment ( Fig 4A and Fig 4C ) and positively correlated with that of the GA biosynthesis inhibitor uniconazole-P ( Fig 4B and Fig 4D ) , suggesting that GNP1 expression is controlled by biologically active GA levels . The GNP1LT allele was much more sensitive to uniconazole-P treatment and endogenous GA signal feedback regulation ( Fig 4B and Fig 4D ) , probably due to the sequence variations among promoters . We also investigated GNP1 expression in the shoot apical meristems and inflorescence meristems . Similar to OSH1 , a key factor in rice meristem maintenance and regulation , GNP1 was also expressed in the apical regions of these meristems ( S8 Fig ) . OSH1 expression signal in NIL-GNP1TQ meristems is still strong and specific ( S8 Fig ) , These results suggest that during NIL-GNP1TQ inflorescence meristem development , the sequence variations of the promoter might lead to a failure to maintain low GNP1 expression level , resulting in induced GNP1 expression in the panicle meristems of NIL-GNP1TQ . The above findings demonstrate that the variations in promoters leading to changes in GNP1 expression in the panicle meristems are the main contributor to the differences in grain number between NIL-GNP1TQ and NIL-GNP1LT . Moreover , the total GNP was positively correlated with the expression level of GNP1 . In vitro , GNP1 ( GA20ox1 ) directly catalyzes the biosynthesis of GA53 , GA44 , GA19 and GA20 in the early-13-hydroxylation pathway with various catalyzing efficiency for each steps [27] . GA20 is then used for GA1 and GA3 biosynthesis via catalyzing by GA3oxs ( Fig 5A ) [28] . We therefore measured the contents of five endogenous GA biosynthesis intermediates , finding that GA20 and GA12 accumulated preferentially in the panicle meristems of NIL-GNP1TQ , whereas GA44 levels were much lower and there were no changes in GA19 levels relative to NIL-GNP1LT ( Fig 5B and Fig 5C ) , indicating that GA20 biosynthesis was accelerated . GNP1 mRNA levels were much higher in NIL-GNP1TQ , suggesting that the catalytic activity of GNP1 markedly increased as well , leading to higher accumulation of the GA biosynthesis intermediate GA20 . The increased accumulation of GA12 suggests that GA biosynthesis activities including GA12 biosynthesis and previous steps might have been activated in this line . However , in the panicle meristems of NIL-GNP1TQ , bioactive GA1 and GA3 were not detected although they were detected in NIL-GNP1LT ( Fig 5D ) , indicating that GA1 and GA3 levels in the NIL-GNP1TQ panicle meristems were too low to quantify . Consistent with this result , the GA signal transduction-related genes RGL3 and SLR1 were induced in this line ( S9 Fig ) . RGL3 and SLR1 are DELLA proteins and negative regulators of GA signaling , whose degradation by GAs in collaboration with GID1 ( gibberellin receptor ) [29 , 30] and F-box protein is a key event in GA signaling activation [31–33] . Indeed , bioactive GA1 and GA3 levels were reduced in NIL-GNP1TQ panicle meristems . By contrast , most GA biosynthesis-related genes were upregulated , including OsKAO , OsKO , OsKS , OsCPS and OsGA3ox2 ( S9 Fig ) , leading to increased GA12 levels ( Fig 5B ) , likely due to feedback activation by reduced bioactive GA ( GA1 and GA3 ) levels . At the same time , most bioactive GA catabolism genes , i . e . , OsGA2oxs ( Fig 5E ) , were induced . As GA2oxs directly catalyze progressive catabolic processes that convert active GAs into inactive forms ( Fig 5A ) , the increased catabolic activities in NIL-GNP1TQ panicle meristems regulate GA levels much more effectively , regardless of the activated GA biosynthesis process described above . Based on these findings , during NIL-GNP1TQ panicle meristem development , GA ( GA1 and GA3 ) levels happened to be reduced , although the catabolic activities of GNP1 were enhanced . Cytokinins significantly affect reproductive meristem activity [2] . The abnormal GA metabolism in NIL-GNP1TQ observed in the current study might be caused by KNOX-mediated responses . To investigate this possibility , we analyzed the expression of five rice KNOX genes , including OSH1 , OSH6 , OSH15 , OSH43 and OSH71 . The expression of these genes significantly increased in the panicle meristems of NIL-GNP1TQ ( Fig 6A ) . OsIPTs , which are directly regulated by KNOX proteins , were also upregulated in NIL-GNP1TQ , as was the cytokinin activating gene LOG ( Fig 6B ) , perhaps leading to cytokinin accumulation . We also examined endogenous cytokinins levels in NIL-GNP1TQ , finding that the levels of several cytokinins and cytokinin biosynthesis intermediates increased in this line ( Fig 6C to 6F ) , leading to increased expression of cytokinin signal response factors ( Fig 6G ) . These results indicate that cytokinin activity was substantially enhanced in NIL-GNP1TQ panicle meristems , resulting in increased grain number compared to NIL-GNP1LT .
A previous in vitro study showed that recombinant OsGA20ox1 could catalyze the conversion of GA12 and GA53 to GA9 and GA20 , but it acts more effectively on GA53 [27] . The present study shows that GNP1 encodes a rice OsGA20ox1 protein . OsGA20ox1 activity is induced via increased expression of GNP1 , which increases GA20 levels in vivo . Moreover , GNP1 transcript levels in seedling leaf sheaths were positively correlated with the treatment dose of uniconazole-P and negatively correlated with that of GA3 ( Fig 4C and 4D ) , suggesting that GNP1 expression is controlled by biologically active GA levels . Moreover , NIL-GNP1LT was much more susceptible to endogenous GA signal feedback regulation than NIL-GNP1TQ , likely due to the sequence variations among promoters leading to altered expression of GNP1 . GNP1 transcripts were mainly detected in newly initiated panicles and in apical regions of meristems overlapping with OSH1 ( a rice KNOX gene ) expression ( S8 Fig ) . This specific expression pattern implies that GNP1 also plays a fundamental role in regulating panicle meristem activity that is similar to that of cytokinin biosynthesis and signaling genes . The increased grain number of NIL-GNP1TQ due to enhanced expression of GNP1 supports this notion . Cytokinins positively regulate reproductive meristem activity [2] , GAs are detrimental to meristem activity [20 , 21] and KNOX proteins play an irreplaceable role in balancing cytokinin and GA activity in the meristem [25] . We observed increased cytokinin activity in the panicle meristems of NIL-GNP1TQ , including KNOX-mediated induction of OsIPTs and increased levels of cytokinins and cytokinin biosynthesis intermediates , together with enhanced cytokinin responses . In additions , these plants failed to accumulate bioactive GA1 and GA3 and exhibited significantly increased KNOX transcript levels . Taken together , these results demonstrate that increased GNP1 activity positively induces the expression of KNOX genes via a feedback loop ( Fig 7 , red arrow ) . This promotion of KNOX gene expression leads to increased cytokinin activity through directly inducing OsIPT expression , as well as upregulation of GA2oxs , which negatively regulate GA biosynthesis , thereby reducing GA1 and GA3 levels . The activation of GA biosynthesis might be due to feedback regulation compensating for the defects in GA1 and GA3 accumulation , leading to increased accumulation of GA12 . The tendency for activated GA biosynthesis may be much less effective than that for GA catabolism . This feedback mechanism rebalances cytokinin and GA activity , resulting in increased cytokinin levels and contributing to the higher GNP1 expression level of NIL-GNP1TQ . On the other hand , decreased expression of GNP1 could lead to lower GA1 and GA3 level in those positive GNP1 mimic artificial miRNA transgenic lines , which might eliminate the suppression effect of higher GA1 and GA3 level on meristem activities , and increase grain number in turn ( Fig 7 ) . We propose that during inflorescence meristem development and maintenance processes , increased expression of GNP1 in those NILs leads to promoted cytokinin activities and gives increased grain number and yield , while decreased expression of GNP1 in those mimic artificial miRNA transgenic lines most probably contributes to alleviation of the detrimental effect of gibberellins to meristem activity , according to those previous reports , which in turn also gives increased grain number . Numerous efforts aimed at increasing food production to sustain the growing population have focused on elucidating the mechanisms underlying the development of several important agronomic traits in rice , such as panicle architecture . In this study , we cloned a rice GA20ox1 gene , GNP1 , whose expression strongly increases rice grain number . Increasing GNP1 expression may be useful for high yield rice breeding , as these GNP1 higher-expressed NILs exhibited increased grain number and grain yield , although they were also slightly taller than the controls . When we overexpressed GNP1 in ZH11 , similar results were obtained , thus representing a new strategy for high yield rice breeding .
Two sets of reciprocal introgression lines ( ILs ) derived from a japonica rice ( O . sativa L . ) variety Lemont and an indica variety Teqing were used as materials for QTL mapping [34] . ZH11 and Lemont were used for the transgenic experiments . The gnp1-D T-DNA mutant line PFG_2D-41474 . R was identified from the Rice Functional Genomic Express Database ( RiceGE , http://signal . salk . edu/cgi-bin/RiceGE ) and obtained from the Rice T-DNA Insertion Sequence Database ( RISD DB , http://cbi . khu . ac . kr/RISD_DB . html ) [35] . Oligo sequences used for genotyping the progeny of gnp1-D T-DNA insertional line are shown in S4 Table . For map-based cloning of GNP1 , we performed genotyping of 5 , 500 BC5F3 individuals from five BC5F2 plants that were heterozygous only at the region RM227–RM85 , harboring five markers . We identified 16 informative recombinants of four genotypes within this region . Using multiple comparisons of the homozygous recombinant BC5F4 lines for GNP with the non-recombinant controls , we localized GNP1 to a 309 . 5kb region between SL13 and RM85 . Further fine mapping using 9 , 500 BC5F4 plants with six new markers between SL13 and RM85 identified six informative recombinants and four genotypic classes in the target region . We localized GNP1 to a high-resolution linkage map by progeny testing of BC5F5 homozygous recombinant plants and narrowed the GNP1 locus down to a 33 . 7 kb region between SL65 and SL54 . Primers used for fine mapping are shown in S5 Table . GA3 and uniconazole-P treatment were carried out as previously described [36] with minor modifications . For GA3 treatment , manually dehulled seeds were sterilized with 75% ethanol for 1 min , washed three times with distilled water , sterilized with 2 . 5% sodium hypochlorite for 35 min , washed five times with sterile distilled water and incubated on 1/2 MS medium at 4°C for 3 days in the dark . The germinated seeds were transferred to plastic containers containing 1% ( w/v ) agar with various concentrations of GA3 ( 63492-1G , Sigma-Aldrich ) . For uniconazole-P treatment , the seeds were incubated in distilled water with various concentrations of uniconazole-P ( 19701-25MG , Sigma-Aldrich ) at 4°C for 24 h , followed by 26°C for an additional 24 h . The seeds were washed three times with distilled water and incubated for an additional 24 h in distilled water at 26°C . The germinated seeds were grown in 1% ( w/v ) agar in plastic containers . Seedlings were grown for 7 days under fluorescent light with a 12 h light/12 h dark photoperiod at 26°C . The second leaf sheath lengths of 48 seedlings per treatment were measured and analyzed . For qRT-PCR analysis , second leaf sheaths were also used , with six pooled replicates for each treatment . To produce the overexpression constructs , the full-length coding sequence of GNP1 was amplified from NIL-GNP1TQ and cloned into plant binary vector pCAMBIA1300 under the control of single CaMV 35S promoter . The artificial microRNA oligo sequences used for GNP1 silencing were designed as previously described [37] ( http://wmd3 . weigelworld . org/cgi-bin/webapp . cgi ? page=Home;project=stdwmd ) and amplified using primer set G-11491 and G-11494 . The oligo sequences were inserted into the XbaI and KpnI sites of pCAMBIA1300 containing one CaMV 35S promoter . Oligo sequences for three different target sites were independently used for construction and transformation . The overexpression and silencing plasmids were introduced into Agrobacterium tumefaciens strain EHA105 and transferred into the japonica variety ZH11 . To produce the construct for the complementary test , 2 . 2 kb promoter sequence with full-length coding sequences of GNP1 were amplified from NIL-GNP1LT . The sequences were then cloned into pCAMBIA1300 , introduced into Agrobacterium tumefaciens strain EHA105 and used for transformation of NIL-GNP1LT . All constructs were confirmed by sequencing . The primer sets are shown in S6 Table , and plant transformation processes were carried out as previously described [38] . Total RNA was extracted from various plant tissues using TRIZOL Reagent ( Invitrogen ) . Approximately 500 ng of total RNA was transcribed into first-strand cDNA using ReverTra Ace qPCR RT Master Mix with gDNA Remover ( TOYOBO ) . Real-time PCR data were obtained using an ABI 7300 Real Time PCR System with Fast Start Universal SYBR Green Master Mix with ROX ( Roche ) and analyzed using the ΔΔCt method . The cycling parameters were 10 min at 95°C , followed by 40 cycles of amplification ( 95°C for 10 s and 60°C for 1 min ) . The ubiquitin and actin genes were used for normalization . The standard amplification slope for real-time PCR primer OsGA2ox1f/OsGA2ox1r was -3 . 498971 , which was used to calculate amplification efficiency . All analyses were repeated at least three times . Primer sets are shown in S7 Table . NIL-GNP1TQ and NIL-GNP1LT plants were grown in open fields for approximately 5 weeks . Freshly initiated panicles approximately 1 cm long were harvested , and ~1 g samples were used for measurements , with three independent biological repeats per sample . Quantification of endogenous GAs [39] and cytokinins [40] was performed as previously described . NIL-GNP1TQ plants were grown in open fields for approximately 3 weeks . Samples ~0 . 5 cm in length including the meristem region were harvested and fixed in 4% ( w/v ) paraformaldehyde with 0 . 1% Tween-20 , 0 . 1% Triton-x-100 and 1% ( v/v ) 25% glutaraldehyde solution in 0 . 1 M sodium phosphate buffer ( pH 7 . 4 ) overnight at 4°C . The samples were then dehydrated with a graded ethanol series followed by a dimethylbenzene series . The samples were then embedded in Paraplast Plus ( Sigma , P3683 ) , cut into 10 μm sections and mounted on pre-coated poly-prep slides ( Sigma , P0425 ) . Digoxigenin-labeled RNA probes were prepared following the instructions of the DIG RNA labeling kit ( SP6/T7 ) ( Roche , 11175025910 ) . Hybridization and signal detection were performed as previously described [41] . The primer sets are shown in S8 Table . Yield and related traits for NIL-GNP1TQ and the isogenic control ( Lemont ) were evaluated at five locations: Beijing ( 40 . 2°N , 116 . 2°E ) ; Nanning ( 22 . 1°N , 107 . 5°E ) , Guangxi province; Jingzhou ( 30 . 3°N , 112 . 2°E ) , Hubei province; Pingxiang ( 27 . 6°N , 113 . 9°E ) , Jianxi province and Sanya ( 18 . 3°N , 109 . 3°E ) , Hainan province , China . NIL-GNP1TQ and Lemont plants were grown in a randomized plot design with three replications per line . The area of each plot was 13 . 2 m2 , with a single plant transplanted per hill at 25 d after sowing and a spacing of 17 cm between hills and 25 cm between rows . As a basal dressing , 50 kg ha-1 each of N , P and K was applied the day before transplanting , and 30 kg ha-1 of N was applied twice as topdressing at 1 and 5 weeks after transplanting . At the heading stage , heading date ( HD ) and plant height ( PH ) were recorded when 30% of plants contained panicles in each line . At maturity , whole plots were harvested for yield measurements based on a 14% moisture content after air drying . Eight plants were sampled and dried in an oven at 70°C for 5 d for trait investigation , including panicle number per plant ( PNP ) , panicle length ( PL ) , filled grains per panicle ( FGP ) , grain number per panicle ( GNP ) , thousand grain weight ( TGW ) , grain length ( GL ) and grain width ( GW ) . QTLs affecting GNP were identified using IciMapping 3 . 0 [42] , combined with genotypic data for 157 SSRs and three morphological markers ( Ph , gl-1 and C ) for the ILs [34] . The permutation method was used to obtain empirical thresholds for claiming QTLs based on 1 , 000 runs in which the trait values were randomly shuffled [43] . | Grain number per panicle , a valuable agronomic trait for rice yield improvement , is profoundly affected by reproductive meristem activity . This activity , in turn , is controlled by transcriptional and plant hormone regulators , especially KNOX proteins and cytokinins . However , little is known about the roles of GAs in these processes in rice and how the regulatory network functions due to the complexity of crosstalk between plant hormone regulators . In this study , we identify a novel GA biosynthesis gene in rice and demonstrate its role in improving grain number and grain yield . We also propose that the KNOX-mediated cytokinin-GA activity rebalancing mechanisms regulate inflorescence meristem development and maintenance processes , providing a possible tool for high-yield rice breeding . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"plant",
"anatomy",
"panicles",
"floral",
"meristem",
"plant",
"physiology",
"hormones",
"cereal",
"crops",
"plant",
"science",
"rice",
"model",
"organisms",
"plant",
"hormones",
"crops",
"inflorescences",
"plants",
"research",
"and",
"analysis",
"methods",
"cytokinins",
"grasses",
"crop",
"science",
"gibberellins",
"leaves",
"agriculture",
"biochemistry",
"plant",
"biochemistry",
"plant",
"and",
"algal",
"models",
"biology",
"and",
"life",
"sciences",
"biosynthesis",
"meristems",
"organisms"
]
| 2016 | The QTL GNP1 Encodes GA20ox1, Which Increases Grain Number and Yield by Increasing Cytokinin Activity in Rice Panicle Meristems |
Visceral leishmaniasis ( VL ) is the most severe form of leishmaniasis and is potentially fatal if not diagnosed and treated . Accurate and timely diagnosis is considered one of the pillars needed for the reduction in disease-related lethality . Brazil is currently one of the three eco-epidemiological hotspots for this disease . Several serological tests are commercially available in this country for VL diagnosis , although information on the performance of these tests is fragmented and insufficient . The aim of this study was to directly compare the performance of six commercial kits: three enzyme-linked immunosorbent assays ( ELISAs ) , two immunofluorescence antibody tests ( IFATs ) , one immunochromatographic test ( ICT ) , besides one ICT , currently not commercially available in Brazil and one in-house direct agglutination test ( DAT-LPC ) , not yet marketed . A panel of 236 stored samples from patients with clinically suspected VL , including 77 HIV-infected patients , was tested . IT-LEISH and DAT-LPC showed the highest accuracy rate among the non-HIV-infected patients , 96 . 2% [CI95%: 92 . 8–99 . 7%] and 95 . 6% [CI95%: 91 . 9–99 . 3%] , respectively . For the ELISA tests evaluated , the maximum accuracy was 91 . 2% , and in the inter HIV-status group analysis , no significant differences were observed . For both IFATs evaluated , the maximum accuracy was 84 . 3% , and a lower accuracy rate was observed among the HIV-infected patients ( p = 0 . 039 ) than among the non-HIV-infected patients . The DAT-LPC was the most accurate test in the HIV-infected patients ( p≤0 . 115 ) . In general , no significant difference in accuracy was observed among the VL-suspected patients stratified by age . In summary , the differences in the performance of the tests available for VL in Brazil confirm the need for local studies before defining the diagnostic strategy .
Human visceral leishmaniasis ( VL ) is a neglected tropical disease ( NTD ) endemic to more than 65 countries with an average of 25 , 000 new cases reported per year from 2013–2017 . Over 90% of global VL cases were reported from seven countries: Brazil , Ethiopia , India , Kenya , Somalia , South Sudan and Sudan . If left untreated , VL is fatal in more than 95% of the cases within two years after the onset of the disease [1] . Leishmaniasis is linked to environmental changes such as deforestation , building of dams , irrigation schemes and urbanization . For these reasons , despite the advances in diagnosis and even with taking the successfully implemented control programmes into account , in recent years , the disease has expanded mainly on the Indian continent [2 , 3] . Approximately 96% of the VL cases in South America are reported in Brazil , with an average of 3 , 749 cases reported annually with a lethality of 6 . 9% [4 , 5] . Since the clinical features of VL mimic several other common diseases and the treatment is associated with significant toxicity , an accurate diagnosis is crucial . The gold standard for VL diagnosis remains the demonstration of Leishmania parasites , in bone marrow aspirate or in other biologic specimens , as spleen or liver . This strategy requires expertise of health professionals to perform both the biological sample collection and execution of parasitological exam [6]; in addition to a time from hours to days , depending on the conditions of the service , until the release of the result . On the other hand , a wide range of serological tests are available and are considered the main tools for the diagnosis of VL . Serology exhibits variable performance in diagnosis of VL depending upon antigens , and immune status of the human host . In this sense , the human immunodeficiency virus ( HIV ) associated with Leishmania infection represents a further challenge due to the reduction in the antibody levels in this subgroup of patients [7] . In turn , immunochromatographic tests using recombinant K39 antigen ( rK39-ICT ) represent a breakthrough in VL diagnostics in recent years because of the high performance and low cost of the test , coupled with the fast and easy execution profile [8] . The Brazilian Visceral Leishmaniasis Surveillance and Control Programme have been providing the immunofluorescence antibody test ( IFAT ) for the diagnosis of VL in recent decades . In 2009 , the first rK39-ICT was incorporated , and since then , the brand has already been replaced twice . In parallel , several other commercial tests based on enzyme-linked immunosorbent assays ( ELISAs ) and IFAT are registered in the national agency—ANVISA ( Agência Nacional de Vigilância Sanitária ) —that regulates the commercialization of diagnostic kits , and are widely used , mainly in the private health sector [9] . No commercial test based on direct agglutination test ( DAT ) is available in Brazil for the diagnosis of VL , but this test has been improved and has been used with good results in research [10 , 11] . Despite this diversity of serological tests for VL diagnosis , no study so far has comparatively evaluated the performance of these tests under the same conditions and in the same population . The goal of this study is to present a comparative analysis of the performance of the serological tests available for VL diagnosis in Brazil .
The study was conducted at the Laboratory of Clinical Research and Public Policy in Infectious and Parasitic Diseases at the Instituto René Rachou of the Oswaldo Cruz Foundation ( IRR/Fiocruz ) –a national reference centre for leishmaniasis in Belo Horizonte in the state of Minas Gerais , Brazil . The tested samples came from patients with suspected VL residing in three VL endemic Brazilian states: Minas Gerais ( Southeastern region ) , Piauí and Bahia ( Northeastern region ) . A range of 236 serum samples stored at -70°C from patients with clinical manifestations compatible with VL ( including HIV co-infected patients ) recruited in previous clinical studies was used to assess the tests’ performance . The sample size estimation was calculated using Stata software version 9 . 2 ( STATA Corporation , College Station , Texas , USA ) following the rationale for the two main objectives of the study , in both cases using a power of 80% and an alpha error of 5%: I . to assess the performance of tests among patients without immunodeficiency , according to the performance proposed as the minimum required for a VL serological test: around 95% for sensitivity [13] , a minimum one-sample size of 150 samples for proportion comparison was estimated; II . to compare the performance of the tests between HIV infected and uninfected patients , for the identification of a difference equal to or greater than 10% between the groups , an estimated size of 72 samples ( in each group ) was calculated for two-sample comparison of proportions . All sample included in this study were derived from patients presenting clinical suspicion of VL . According to the Brazilian definition [14] , a suspected VL case has fever and at least one clinical sign as splenomegaly , hepatomegaly , leukopenia , anaemia or thrombocytopenia . The samples from patients with a previous history of VL were excluded as well as the serum samples with aliquots with insufficient volume to perform all the tests . The samples were divided into VL cases and non-cases . The criteria for the VL case definition was based on parasitological confirmation of Leishmania infection in the bone marrow aspirate ( 118 samples ) and for the non-VL cases , a negative parasitological examination with the confirmation of another disease , such as malaria , schistosomiasis , mycobacteria infection and leukemia ( 118 samples ) . In total , 77 samples from HIV-infected patients were included in this study , being 38 in the VL cases group and 39 in the non VL cases group . All the samples were anonymized , and the diagnostic test operators were blinded to the nature of the serum sample . At the first stage of this study , in January 2017 , a search for the tests registered for VL diagnosis was performed via the electronic database of the Brazilian agency for registration of health products , ANVISA [9] . Then , the commercial availability of each product in Brazil was checked with the manufacturer or its legal distributor . Six kits ( three ELISA kits , two IFAT kits and one rK39-ICT ) were identified . Even without registration in force in Brazil , we chose to include Kalazar Detect , the first rapid test used in Brazil between 2009 and 2014 . In the same way , the prototype of a direct agglutination test ( DAT ) produced in our laboratory ( DAT-LPC ) –a non-commercial kit–was also included among the tests to be evaluated , a decision based on the promising results observed in several validation studies previously performed [10 , 15] . In the end , eight test kits were selected for testing in this study , and their characteristics are listed in Table 1 . The test’s manufacturers had no role in study design , analysis , decision to publish , or preparation of the manuscript . All tests were performed in strict accordance with the manufacturer’s instructions . The prototype DAT-LPC kit was performed as described by Oliveira et al . ( 2017 ) [10] . The study was carried out in conformity with the Helsinki Declaration and the Brazilian rules ( RDC 466/2012 ) . Ethical approval was obtained from the Research Ethics Committee of IRR/FIOCRUZ ( CAEE 44549915 . 2 . 0000 . 5091 –Approval number 1 . 808 . 889 ) . During the original clinical studies , written informed consent was obtained from all the participants/parents or guardians before collecting samples . Confidentiality was assured by assigning a study code to each sample , and no confidential information was shared . Data analysis was performed using MedCalc for Windows , version 15 . 0 ( MedCalc Software , Ostend , Belgium ) and IBM SPSS Statistics ( Chicago , IL , USA ) software . Sensitivity , specificity and accuracy were calculated using two-by-two contingency table with exact binomial 95% confidence interval ( 95% CI ) , and explored in different age and HIV-status groups . Considering sensitivity as the probability of being a test positive when disease is present , the sensitivity rate was calculated as the number of patients with VL who tested positive divided by the total number of patients with VL . Considering specificity as the probability of being a test negative when disease is absent , the specificity rate was calculated as the number of non-VL patients who tested negative divided by the total number of non-VL patients . The accuracy rate is proportion of patients presenting a correct test result and was calculated as the number of patients with VL who tested positive plus the number of non-VL patients who tested negative divided by the total number of patients tested . These parameters were compared using the χ2 test at a significance level of 0 . 05 . We used the Cohen kappa index for agreement testing between the test results . The values of the Cohen κ coefficients were interpreted according to Landis and Koch: 1 . 00–0 . 81: excellent , 0 . 80–0 . 61: good , 0 . 60–0 . 41: moderate , 0 . 40–0 . 21: weak and 0 . 20–0 . 00: negligible agreement .
Sixty-six percent of the 236 suspected VL cases were male , with an average age of 25±19 . 7 years ( range: 1 month to 76 years ) . Among 159 non-HIV-infected patients , the mean age was 18±19 . 14 years , and 40 patients ( 25 . 2% ) were under 3 years . In the HIV-infected group , the mean age was 40 ± 9 . 8 years ( ranging from 20 to 65 years ) , and 74% of the patients were male . No statistically significant difference was observed between the accuracy of the tests according to the gender of the patients . The sensitivity , specificity and accuracy of the VL tests according to the patient’s HIV status are shown in Table 2 . The tests evaluated exhibited lower sensitivity rates among the HIV-infected patients than among the non-HIV-infected patients ( p≤0 . 05 ) , except for the Leishmania ELISA IgG+IgM test ( p = 0 . 104 ) and the DAT-LPC test ( p = 0 . 412 ) . Furthermore , when the accuracy rates were compared , significant differences were observed for the NovaLisa Leishmania infantum IgG test ( p = 0 . 019 ) , Leishmania IFA IgG test ( p = 0 . 035 ) , IT-LEISH test ( p = 0 . 001 ) and Kalazar Detect test ( p≤0 . 0001 ) , which also exhibited significantly lower accuracy among the HIV-infected patients than among the non-HIV-infected patients . The agreement among all tests evaluated for HIV-infected and HIV-uninfected patients was calculated by Cohen kappa index ( S1 Table ) . Table 3 shows the VL tests’ performance for the HIV-uninfected group stratified according to age . The sensitivity , specificity and accuracy rates of the testes were no statistically different in the comparison between patients grouped by age , except for Kalazar Detect , which exhibited significantly lower sensitivity among the children under 3 years old compared to patients upper 3 years old: 86 . 2% [CI95%:69 . 4–94 . 5] versus 96 . 1% [CI95%:86 . 78–98 . 9] , p = 0 . 046 .
The main contribution of this study is the confirmation of significant differences in the performances of different serological tests available for VL diagnosis in Brazil . This observation confirms that the recommendation of the use of serology as one of the main diagnostic strategies of the national programme to achieve lethality reduction needs to be qualified with specific information about the test performance . There is no consensus about the minimum sensitivity and specificity rates required for a VL diagnostic test . According to Boelaert et al . ( 2007 ) [13] , for a VL-screening test , the minimum sensitivity and specificity required would be 95% and 98% , respectively . Considering these parameters , none of the diagnostic tests evaluated here are satisfactory . In this sense , questions emerge from these results surrounding the adequacy ( or inadequacy ) of the current tests as efficient tools for tracking the disease . Another contribution of this work is to demonstrate variations in test performance when applied to different populations through a direct test comparison using a well-defined panel of samples controlling for the heterogeneity of this population under the same conditions . Our results confirm that the differences in test performance are related to the test’s methodology and to the HIV infection status and age of patients . From these , the HIV co-infection was the factor that more impacted in the performance of the serological tests . The higher frequency of false-negative results in the VL/HIV co-infected patients may be explained by the functional impairment of cell-mediated immunity due to viral infection that result in the absence or lower response to Leishmania spp . infection [16] . In this study , the tests that presented the best performance for the diagnosis of VL among the non-HIV-infected patients were IT-LEISH , DAT-LPC and Kalazar Detect , each with a sensitivity and specificity above 90% . These results are in agreement with those found in a meta-analysis involving populations mainly infected by Leishmania donovani , which estimated the sensitivity of the rK39-ICT and DAT to be 93 . 9% and 94 . 8% , respectively [17] . Concerning rK39-ICT , it is widely known that there are differences in the performance of these tests in different endemic regions as well as among tests produced by different manufacturers in the same endemic region [18–21] . Here , no significant differences were observed between the accuracy of the IT-LEISH and Kalazar Detect tests , independent of the HIV infection status . However , an unsatisfactory rK39-ICT performance was confirmed among the HIV-infected individuals ( 63 . 2% and 47 . 4% sensitivity for IT-LEISH and Kalazar Detect , respectively ) , similar to the results described in other studies [15 , 21–24] . This finding overrides the potential advantages of a rapid , inexpensive and easy-to-perform test , making this diagnostic strategy insufficient for HIV co-infected or unknown HIV status patients . On the other hand , the high performance of the DAT in this patient group has been systematically reported , with sensitivity varying from 87 . 8 to 91 . 7% and specificity varying from 82 . 3 to 83 . 3% [15 , 22–24] , similar to that observed here–a DAT-LPC sensitivity of 89 . 5% and specificity of 89 . 7% . Although ELISAs and IFATs are important diagnostic techniques for infectious diseases in general , we verified an unsatisfactory accuracy of these tests for the diagnosis of VL . For non-HIV-infected patients , the IFATs and ELISAs showed the lowest performance among all the tests evaluated , especially in sensitivity , as demonstrated by Mniouil et al . ( 2018 ) and Mikaeili et al . ( 2007 ) [25 , 26] . In contrast , the Ridascreen Leishmania Ab test exhibited a higher sensitivity ( 93 . 8% ) compared to that of the other ELISA tests evaluated here , which was also observed in other studies [25 , 27 , 28] . However , the low specificity ( 77 . 2% ) of the Ridascreen Leishmania Ab test results in a low final accuracy . For the IFATs , both tests evaluated exhibited insufficient accuracy , similar to that reported by Pedras et al . ( 2008 ) and Bangert ( 2018 ) [24 , 27] . It is important to note that the IFATs evaluated here use distinct species of Leishmania-promastigote forms as antigens: Leishmania major in IFI-Leishmaniose Humana and Leishmania infantum in Leishmania IFA IgG , which apparently did not impact their low performance . The insufficient IFATs’ performance , particularly that of IFI-Leishmaniose Humana , needs to be highlighted in Brazil , since this test is still available to the public health service and is widely used[4] . The variation in the test performance according to the patient's age was explored by testing several age cut-off points . The accuracy of the Kalazar Detect test was significant lower among children under 3 years old comparison to patients over three years old . Few studies have evaluated the performance of rapid tests in very young paediatric populations . In the study conducted by Cruz et al . , the performance of the rapid test did not differ between individuals over 10 years in age in relation to children of up to 10 years in age [29] . Overall , in this study , the observed sensitivity was higher than we have shown ( 75 . 9 versus 93 . 1% ) , which may be explained by differences in the case definition criteria and by the higher age of the children evaluated . The use of accurate diagnostic tests is especially important for VL , a disease in which the misdiagnosis could be extremely dangerous in both scenarios: a false-positive result would lead to an unnecessary toxic treatment and a false negative test result would leave untreated patients with a lethal disease . The performances of serological diagnostic tests are expected to vary with the methodology of the test , the type of antigen , infection length and characteristics of the individual , such as immune status and age . In the case of VL , a disease with a global distribution , several of these determinants act simultaneously to influence the performance of the tests . In summary , these results have direct implication in public health care policies in Brazil . In addition to confirming the high performance of rapid immunochromatographic tests in general , the results show important exceptions , people living with HIV and children younger than 3 years old , specific groups in which a rapid test cannot be used to rule out the VL diagnosis safely . For these groups , a specific algorithm is required , and DAT-LPC emerges as the best performing serological test , which adds to its advantage in terms of national autonomy in production . In addition , these results suggest the presence of significant differences in the performance of tests from different manufacturers using the same methodology , which reinforces the need for local validations of the different tests before their use in large scale . Our findings highlight the need for more stringent criteria for the registration of diagnostic products in Brazil , including the requirement to carry out validation studies before marketing . In a future , broader analysis , in addition to performance , other aspects of these tests should be considered before a diagnostic strategy is defined , such as cost-effectiveness , national production/autonomy and accessibility . In this context , this study represents the first step of a wider evaluation required . | Visceral leishmaniasis ( VL ) is a tropical disease distributed worldwide . In the Americas , Brazil reports about 96% of VL cases , which has been highlighted by the increase in lethality in last years . Accurate VL diagnosis is of utmost importance . Despite this , the performance of some commercial tests currently available in Brazil is unknown , especially for HIV-infected patients . Accordingly , in this study we present a comparative performance analysis of six commercial kits available in Brazil for the diagnosis of VL in non-HIV and HIV-infected patients , besides one immunochromatographic test ( ICT ) and one in-house direct agglutination test ( DAT-LPC ) currently not commercially available in Brazil . ICTs and DAT-LPC showed better performance among non-HIV infected patients . Despite the known limitation of serological tests for the diagnosis of patients with HIV , the direct agglutination test was more accurate in this specific group of patients . Our results demonstrate significant differences in the performance of different serological tests and confirm that the use of serology should be qualified with previous information on the performance of the tests . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
]
| [
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"pathology",
"and",
"laboratory",
"medicine",
"tropical",
"diseases",
"geographical",
"locations",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"protozoans",
"leishmania",
"neglected",
"tropical",
"diseases",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"zoonoses",
"serology",
"south",
"america",
"serodiagnosis",
"immunoassays",
"protozoan",
"infections",
"brazil",
"people",
"and",
"places",
"hiv",
"diagnosis",
"and",
"management",
"leishmania",
"infantum",
"eukaryota",
"diagnostic",
"medicine",
"leishmaniasis",
"biology",
"and",
"life",
"sciences",
"organisms"
]
| 2019 | Performance of serological tests available in Brazil for the diagnosis of human visceral leishmaniasis |
Ciliopathies represent a broad class of disorders that affect multiple organ systems . The craniofacial complex is among those most severely affected when primary cilia are not functional . We previously reported that loss of primary cilia on cranial neural crest cells , via a conditional knockout of the intraflagellar transport protein KIF3a , resulted in midfacial widening due to a gain of Hedgehog ( HH ) activity . Here , we examine the molecular mechanism of how a loss of primary cilia can produce facial phenotypes associated with a gain of HH function . We show that loss of intraflagellar transport proteins ( KIF3a or IFT88 ) caused aberrant GLI processing such that the amount of GLI3FL and GLI2FL was increased , thus skewing the ratio of GLIFL to GLIR in favor of the FL isoform . Genetic addition of GLI3R partially rescued the ciliopathic midfacial widening . Interestingly , despite several previous studies suggesting midfacial development relies heavily on GLI3R activity , the conditional loss of GLI3 alone did not reproduce the ciliopathic phenotype . Only the combined loss of both GLI2 and GLI3 was able to phenocopy the ciliopathic midfacial appearance . Our findings suggest that ciliopathic facial phenotypes are generated via loss of both GLI3R and GLI2R and that this pathology occurs via a de-repression mechanism . Furthermore , these studies suggest a novel role for GLI2R in craniofacial development .
Midfacial disorders encompass a spectrum of conditions that affect the development of the facial midline . The full spectrum of medial craniofacial dysplasias range from conditions that exhibit tissue deficiencies or agenesis ( hypotelorism , cyclopia ) to those that exhibit tissue excess or duplication ( hypertelorism , frontonasal dysplasia ) . The etiology of these conditions are heterogeneous; however , there has been an established linkage between activity of the Hedgehog ( HH ) pathway and midfacial growth [1–7] . Reduced levels of HH activity are associated with a collapse of midfacial tissues [2 , 8 , 9] , whereas increased levels of HH activity are associated with a widening or duplication of midfacial tissues [1 , 7 , 10 , 11] . Although this correlation between the HH pathway and midfacial disorders is well established , the cellular and molecular mechanisms by which these disorders occur remain nebulous . GLIs are the major transcriptional effectors of the HH pathway . In vertebrates , the GLI family of proteins consists of three members ( GLI1-3 ) . GLI2 and GLI3 act as bifunctional transcription factors that contain an N-terminal repression domain , as well as a C-terminal transcriptional activation domain . Both proteins can be converted from full-length ( GLIFL ) transcriptional activators ( GLIA ) into truncated repressors ( GLIR ) through regulated proteolytic processing [12 , 13] . GLI2 acts as the primary transcriptional activator of the HH pathway [14] , yet it has been reported to contribute to some repressor activity [15–17] . GLI3 primarily functions as a transcriptional repressor of the HH pathway [18–21] , although it has been shown to exert weak activator activity as well [22–24] . There is evidence of GLI2 and GLI3 having distinct , partially redundant roles during development [25] , yet it is unclear if and how they compensate for one another during craniofacial development . The ratio of GLIA to GLIR is believed to dictate the net activity of the HH pathway [26 , 27]; however , the exact mechanism by which the graded intracellular activity of GLIA and GLIR is generated remains unknown and is the subject of several ongoing studies . Recently , several groups have contributed to piecing together a potential primary cilia-dependent mechanism for processing of a HH signal ( reviewed in [28–30] ) . Integration of data from these studies allows for the following hypothesized mechanism . Prior to processing , GLI proteins associate with Suppressor of Fused ( SUFU ) , a conserved protein known to regulate the activity of GLI transcription factors via modulating GLI processing , stabilization and subcellular localization [31–34] . In the presence of a HH signal , Smoothened ( SMO ) is translocated to the cilium [35] and the SUFU-GLIFL complex traffics through the cilium [36] . Activated ciliary SMO then works through KIF7 to promote the dissociation of the inhibitory SUFU-GLIFL complex [33 , 34 , 37] . Unbound GLIFL is then processed into an activator and moves to the nucleus to promote expression of downstream targets . In the absence of the HH ligand , SMO is not translocated into the cilium and thus cannot antagonize SUFU . SUFU remains in complex with GLI; GLI is proteolytically processed into GLIR and the SUFU-GLIR complex moves to the nucleus where it recruits the Sap18-Sin3 co-repressor complex to repress GLI target genes [38–41] . Loss of functional primary cilia results in a wide range of disorders called ciliopathies . 30% of ciliopathies can be primarily defined by their craniofacial phenotypes . Furthermore , 70% of craniofacial ciliopathies have midfacial defects , reminiscent of those with HH or GLI mutations [42] . To understand how ciliary defects affect GLI-mediated HH pathway activity during midfacial patterning , we conditionally knocked out intraflagellar transport ( IFT ) proteins in cranial neural crest cells ( NCCs ) and examined GLI isoform expression and function . Taken together , our data add mechanistic insights into how the cilium and GLI proteins function during midfacial development .
Our previous work showed that the conditional loss of Kif3a in NCCs ( Kif3afl/fl;Wnt1-Cre ) generated severe midfacial defects , including midfacial widening , duplicated nasal septum , agenesis of the corpus callosum and aglossia [7] . To determine if the midfacial phenotypes were Kif3a-specific , or if the loss of other anterograde intraflagellar transport proteins produced this phenotype , we generated Ift88fl/fl;Wnt1-Cre embryos and compared the midfacial phenotypes to that of Kif3afl/fl;Wnt1-Cre embryos . Ift88fl/fl;Wnt1-Cre embryos had a striking resemblance to Kif3afl/fl;Wnt1-Cre embryos ( Fig 1 , Tables 1 and 2 ) . Relative to wild-type embryos , both Kif3afl/fl;Wnt1-Cre ( n = 28 ) and Ift88fl/fl;Wnt1-Cre ( n = 26 ) exhibited significant midfacial widening , as determined by internasal width ( Fig 1A–1C; 1J ) . Furthermore , both mutants exhibited a bilateral cleft secondary palate ( Fig 1D–1F ) . We had previously determined that the facial skeleton of Kif3afl/fl;Wnt1-Cre mutants was highly dysmorphic [7] . Specifically , there was a duplication of the nasal septum underlying the severe midfacial widening . Safranin-O staining revealed that similar to Kif3afl/fl;Wnt1-Cre embryos , Ift88fl/fl;Wnt1-Cre embryos also had a duplicated nasal septum ( Fig 1G–1I , n = 2 ) . This striking similarity between the midfacial phenotypes of Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants suggested that the underlying mechanisms for midfacial widening were not Kif3a-specific , but rather due to a ciliary process in which anterograde IFT proteins participated . Based on data linking midfacial development and primary cilia with the HH pathway , we next examined if GLI post-translational processing was affected in the frontonasal prominence ( FNP ) of Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre embryos . Primary cilia have previously been implicated in GLI processing [26 , 30] . Several ciliary mutants exhibit aberrant production of full-length ( FL ) and cleaved GLI isoforms , which affects the overall ratio of full-length GLI activator ( GLIA ) to GLI repressor ( GLIR ) [26 , 43] . To determine if loss of Kif3a and Ift88 impacted the production of GLIFL and GLIR , e11 . 5 FNPs ( area medial to the medial aspect nasal pit and ventral to the dorsal-most aspect of the nasal pit; inset Fig 2A ) from each mutant were isolated for Western blot analysis of full-length GLI3 ( GLI3FL ) , cleaved GLI3 ( GLI3R ) , full-length GLI2 ( GLI2FL ) and cleaved GLI2 ( GLI2R ) isoforms ( Fig 2 ) . The amount of both GLI3FL and GLI2FL isoform was increased in the FNP of both Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants ( Fig 2A ) , whereas production of GLI3R and GLI2R in Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre embryos was reduced relative to that in the wild-type FNPs ( Fig 2A ) . We next performed densitometry to quantitate how altered production of GLI isoforms affected the overall ratio of GLIFL to GLIR protein . Densitometry analysis determined that there was an approximate 8-fold and 5 . 5-fold increase of the GLI3FL:GLI3R ratio in the FNPs of Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants , respectively ( Fig 2B ) and an approximate 9-fold and 4 . 7-fold increase in GLI2FL:GLI2R ratio in the FNPs of Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants , respectively ( Fig 2C ) . We speculate that the more significant increase in GLI expression in Kif3afl/fl:Wnt1-Cre mutants relative to Ift88fl/fl;Wnt1-Cre mutants stems from the independent function of these proteins within the cilium . The more significant increase in GLI protein expression is in line with the Kif3afl/fl:Wnt1-Cre phenotype being slightly more severe than the Ift88fl/fl:Wnt1-Cre phenotype . The specificity of the antibodies for GLI2 and GLI3 and the presence of GLI3 and GLI2 full-length and repressor isoforms was confirmed by performing Western blot analysis and noting the absence of bands in Gli3fl/fl;Wnt1-Cre and Gli2fl/fl;Wnt1-Cre embryos ( S1 Fig ) and allowing for longer exposure of film ( S2 Fig ) , respectively . GLI proteins can be degraded in the cytoplasm or shuttled to the nucleus , where they act as transcriptional activators or repressors . To determine if the robust increase in GLI3FL and GLI2FL detected via Western blot was conserved in the nucleus , we performed nuclear fractionation analysis ( Fig 2D ) . Nuclear GLI3FL and GLI2FL were increased in the FNP of both Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre embryos , whereas nuclear GLI3R was slightly reduced in the FNP of both Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre embryos . Levels of nuclear GLI2R appeared similar between wild-type and Kif3afl/fl;Wnt1-Cre embryos , yet appeared to decrease slightly in Ift88fl/fl;Wnt1-Cre embryos . Overall , the loss of cilia in NCCs caused an increase in the amount of nuclear GLI3FL/GLI2FL and a decrease in nuclear GLI3R/GLI2R . Furthermore , this alteration in the production of GLI isoforms resulted in a skewing of the GLIFL to GLIR ratio in favor of the GLIFL isoform . It is well established that a net gain of HH function produces midfacial widening [1] . The increased GLIFL to GLIR ratio posed two possible molecular mechanisms for producing HH-dependent midfacial widening: ( 1 ) a loss of GLIR activity ( de-repression ) [44] , or ( 2 ) a gain of GLIA activity . Using genetic and biochemical approaches , we attempted to determine which mechanism was responsible for midfacial widening in ciliopathic mutants . GLI3 predominantly acts as the repressor of the HH pathway ( reviewed in [45] ) , and loss of GLI3R has been linked to midfacial widening [11 , 46 , 47] . To test if reduced GLI3R is causal for ciliopathic midfacial phenotypes , we genetically increased GLI3R levels with the addition of one allele of Gli3Δ699 [48] into Kif3afl/fl;Wnt1-Cre mutants . Gli3Δ699 encodes a C-terminally truncated GLI3Δ699R that mimics the cleaved GLI3R [48] . Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ ( n = 25 ) embryos showed a reduction in the internasal width , relative to Kif3afl/fl;Wnt1-Cre mutants ( Fig 3A–3C; Table 1 ) . The reduction in midfacial widening was accompanied by a less patent palate ( Fig 3D–3F; Table 1 ) . Most notably , the duplicated nasal septum of the Kif3afl/fl;Wnt1-Cre mutants was restored to a singular cartilaginous element in Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos ( Fig 3G–3I; Table 1 ) . We repeated these rescue experiments by crossing Gli3Δ699 into Ift88fl/fl;Wnt1-Cre mutants . Not surprisingly , given the lack of axonemal extension in Ift88fl/fl;Wnt1-Cre mutants ( S3A and S3B Fig ) , we observed a similar improvement in the midfacial phenotypes of Ift88fl/fl;Wnt1-Cre;Gli3Δ699/+ embryos ( n = 21 ) relative to Ift88fl/fl;Wnt1-Cre mutants ( S3C–S3E Fig; Table 2 ) . Thus , based on these three phenotypic characteristics , we concluded that genetic addition of GLI3R produced a partial rescue of ciliopathic midfacial widening . We referred to the rescue as partial , because , despite a phenotypic improvement , Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ and Ift88fl/fl;Wnt1-Cre;Gli3Δ699/+ embryos still exhibited craniofacial anomalies: midfacial widening ( compare Fig 3A and 3C ) and shorter proximal-distal length along the nasal septum ( compare Fig 3G and 3I; S3C and S3E Fig ) . Gli3Δ699/+ and Gli3Δ699/∆699 embryos did not display any obvious craniofacial defects , despite the reduction or absence of GLI3A ( S4 Fig ) , further supporting the hypothesis that loss of the GLI3R rather than the gain of the GLI3A contributed to the midfacial widening . To confirm that addition of one Gli3Δ699 allele molecularly restored the amount of GLI3R to wild-type levels , we performed Western blot analysis on e11 . 5 wild-type , Kif3afl/fl;Wnt1-Cre and Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ facial prominences ( inset Fig 3J ) . Introduction of GLI3Δ699R ( Fig 3J asterisk ) increased the total amount of GLI3R in Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos to a level comparable to wild-type , and thus restored the ratio of GLI3FL to GLI3R closer to that of wild-type embryos ( Fig 3K ) . Again , these results were similar to what was observed in Ift88fl/fl;Wnt1-Cre;Gli3Δ699/+ facial prominences ( S3F Fig ) . Finally , our previous work suggested that the onset of midfacial widening in Kif3afl/fl;Wnt1-Cre was due to increased midfacial proliferation within the FNP prior to condensation of the nasal septum [7] . Phospho-Histone H3 ( pHH3 ) staining confirmed increased proliferation in the distal FNP of Kif3afl/fl;Wnt1-Cre , relative to wild-type embryos ( Fig 3L and 3M ) . Introduction of the Gli3Δ699 allele significantly reduced pHH3 staining , suggesting that proliferation was restored to that of wild-type levels ( Fig 3N and 3O; n = 3 ) . These data , in conjunction with partial rescue of the craniofacial phenotype , suggested that aberrant HH pathway activity , via reduced GLI3R activity , was the molecular basis of ciliopathic midfacial widening . GLI proteins act as the key transcription factors for transduction of a HH signal . To do so , GLI proteins bind to consensus GLI binding regions ( GBRs ) within the regulatory regions of target genes . To determine if GLI3 generated in ciliary mutants was able to recognize and occupy GBRs , we generated a biotin-labeled oligo for the Patched ( Ptch ) promoter containing one endogenous GBR ( Fig 4A ) . The Ptch oligo was incubated with streptavidin conjugated Dynabeads and then with cell lysate ( Fig 4A ) from the facial prominences of wild-type , Kif3afl/fl;Wnt1-Cre mutants or Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ rescue embryos ( inset Fig 4A and 4B ) . Pull-down and subsequent Western blot experiments revealed that in wild-type facial prominences , GLI3R dominated the binding of GBRs ( Fig 4B ) . We repeated this experiment in Kif3afl/fl;Wnt1-Cre mutant facial prominences and found two interesting results . First , despite lacking proper ciliary-dependent post-translational processing , GLI3 generated in ciliary mutants was still able to recognize and bind to GBRs . Second , the distribution of GLI3FL to GLI3R binding to the synthesized GBRs was altered in the facial prominences of the Kif3afl/fl;Wnt1-Cre mutant relative to wild-type facial prominences . In Kif3afl/fl;Wnt1-Cre mutants GLI3R binding was reduced while GLI3FL binding was increased relative to the wild-type ( Fig 4B ) . We next repeated this experiment with the facial prominences of Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos that partially rescued the Kif3afl/fl;Wnt1-Cre midfacial phenotypes . GLI3FL binding to GBRs was reduced and GLI3R binding to GBRs was drastically increased in Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos , making the GBR binding profile of the Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ more equivalent to wild-type embryos than Kif3afl/fl;Wnt1-Cre mutant embryos ( Fig 4B ) . Thus , Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos appeared to rescue the ciliopathic midfacial defects phenotypically , molecularly , and at the level of chromatin binding . Our GBR pull-down assay was performed in vitro with a synthesized Ptch oligo containing a GBR . To test if altered levels of GLI isoform binding to regulatory regions could impact target gene expression in vivo , we performed Ptch in situ hybridization on e11 . 5 wild-type , Kif3afl/fl;Wnt1-Cre and Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos ( Fig 4C ) . As expected , wild-type embryos displayed Ptch expression in previously defined areas of the facial prominences . Interestingly , Ptch expression was decreased throughout the facial prominences in Kif3afl/fl;Wnt1-Cre embryos ( Fig 4C ) . Furthermore , GLI1 protein expression was not changed in Kif3afl/fl;Wnt1-Cre embryos ( S5 Fig ) . These data suggest that the loss of cilia disrupts the pathway in such manner that expression of the two pathway ‘readouts’ are neither synchronized , nor accurate representations of pathway dynamics , a trend we have observed before with ciliary mutants [49] . The addition of the Gli3Δ699 allele in Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos did not restore Ptch expression to that of the wild-type ( Fig 4C ) . Thus , despite having increased levels of GLI3FL produced in Kif3afl/fl;Wnt1-Cre mutants ( Fig 2 ) and increased enrichment of GLI3FL on the GBRs of a synthesized oligo , we did not see increased expression of Ptch in vivo . These data suggest that the partial rescue of midfacial widening in Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos is not via restoration of Ptch expression and that increased production of GLI3FL does not translate into increased activator activity in Kif3afl/fl;Wnt1-Cre mutants . We next attempted to determine why GLI3FL generated in Kif3afl/fl;Wnt1-Cre mutants was not functioning as an activator . When HH ligand is present , SMO is trafficked into the cilia . Activated SMO , in conjunction with KIF7 , dissociates GLIFL from SUFU , allowing GLIFL to function as an activator . Absence of HH ligand or loss of cilia prevents SMO localization to the ciliary axoneme , and thus SMO/KIF7 mediated dissociation of the GLI-SUFU complex is impaired , rendering GLIFL under constitutive inhibition , leading to the loss of GLI activator activity [50] . To determine if excess GLIFL produced in Kif3afl/fl;Wnt1-Cre embryos remained associated with SUFU , we examined the association of SUFU with GLI3 in the developing FNP ( inset Fig 4D ) . In wild-type embryos , low levels of SUFU protein were detected . There was an increase of total SUFU protein in Kif3afl/fl;Wnt1-Cre mutant embryos ( Fig 4D , input ) , as well as an increase in the amount of GLI3-SUFU association ( Fig 4D , IP:αGLI3 ) . Furthermore , we performed nuclear fractionation and found levels of nuclear SUFU were increased in the Kif3afl/fl;Wnt1-Cre mutant ( Fig 4E ) . Taken together , these data suggest that despite increased amounts of GLI3FL in Kif3afl/fl;Wnt1-Cre mutant embryos , GLI3FL was rendered inactive , possibly due to a failure to dissociate from SUFU . Multiple studies have suggested GLI3 predominantly functions as a repressor , and that GLI3R activity is required for midfacial patterning [11 , 46 , 47] . Based on these findings and the partial rescue of ciliopathic midfacial phenotypes in Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos , we hypothesized that loss of GLI3 in NCCs should recapitulate the ciliopathic midfacial widening observed in Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants . To test this hypothesis , we generated Gli3fl/fl;Wnt1-Cre embryos . Interestingly , we found that Gli3fl/fl;Wnt1-Cre embryos did not phenocopy the midfacial phenotypes of ciliary mutants ( Fig 5 ) . Gli3fl/fl;Wnt1-Cre embryos did not show midfacial widening ( Fig 5A and 5B ) , a cleft secondary palate ( Fig 5G and 5H ) or a bifurcated nasal septum ( Fig 5M and 5N ) . The lack of midfacial phenotypes in Gli3fl/fl;Wnt1-Cre embryos suggested that perhaps GLI3 was not the only factor driving the midfacial ciliopathic phenotype . Given that GLI2 processing is also disrupted in Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants ( Fig 2 ) , and that GLI2 null animals have craniofacial defects [25] , we hypothesized that GLI2 may also be playing an important role in midfacial patterning . A precedent exists for GLI proteins compensating for one another in several biological contexts and for GLI2 having repressor activity [16 , 17] . To determine if loss of GLI2 function also contributed to the ciliopathic midfacial phenotype , we generated Gli2fl/fl;Wnt1-Cre and Gli2fl/fl;Gli3fl/fl;Wnt1-Cre mutants and assayed embryos for characteristic midfacial phenotypes of ciliopathic mutants . Similar to Gli3fl/fl;Wnt1-Cre embryos , Gli2fl/fl;Wnt1-Cre embryos did not show midfacial widening ( Fig 5C ) , but did have a medial cleft of the secondary palate ( Fig 5I ) . Furthermore , although a small split in the most ventral aspect of the nasal septum was detected in Gli2fl/fl;Wnt1-Cre embryos ( S6 Fig ) , these embryos did not present with a bifurcated septum similar to that of the Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants ( Fig 5O ) . Overall , Gli2fl/fl;Wnt1-Cre embryos did not phenocopy Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants . We next tested if conditional loss of both GLI2 and GLI3 in NCCs would recapitulate the midfacial ciliopathic phenotype . Gli2fl/fl;Gli3fl/fl;Wnt1-Cre double mutants exhibited facial phenotypes very similar to Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants . Gli2fl/fl;Gli3fl/fl;Wnt1-Cre embryos exhibited midfacial widening ( Fig 5D; Table 3 ) , bilateral clefting of the secondary palate ( Fig 5J; Table 3 ) and a bifurcated nasal septum ( Fig 5P; Table 3 ) , giving the Gli2fl/fl;Gli3fl/fl;Wnt1-Cre embryos a strikingly similar phenotype to Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre embryos ( compare Fig 5D , 5E; 5J , 5K; and 5P , 5Q; Table 3 ) . These data , together with our biochemical analysis showing aberrant GLI2 and GLI3 processing ( Fig 2 ) , suggest that the midfacial widening in ciliopathic mutants is caused by the combinatorial loss of GLI2R and GLI3R function . Our data suggested loss of GLIR function was causal to the midfacial widening in the examined ciliary mutants , as we were able to rescue the phenotypes with addition of GLI3R . We next tested if the craniofacial complex in Kif3afl/fl;Wnt1-Cre embryos would be sensitive to altering the GLI ratio ( e . g . , would increasing the amount of GLIA in these mutants exacerbate midfacial widening ) . To do so , we utilized ΔNGli2 mice . ΔNGli2 encodes a constitutively active form of GLI2 that mimics the action of GLI2A independent of ciliary processing [51] . We first generated ΔNGli2;Wnt1-Cre embryos and examined facial phenotypes with one copy of constitutively active GLI2 in NCCs . ΔNGli2;Wnt1-Cre embryos did not exhibit a widened midline or duplicated nasal septum ( S7A–S7D Fig ) , presumably due to overriding GLIR activity . However , when we genetically increased the amount of GLI2A on a background with reduced GLIR via generating Kif3afl/fl;Wnt1-Cre;∆NGli2 embryos , we observed a significant exacerbation of the internasal width , relative to Kif3afl/fl;Wnt1-Cre mutants ( Fig 5F ) . The exacerbation of midfacial widening was accompanied by a severe midfacial cleft ( Fig 5L ) and two completely separate nasal septa in Kif3afl/fl;Wnt1-Cre;∆NGli2 embryos ( Fig 5R ) . Western blot analysis confirmed the presence of the ΔNGLI2 protein in Kif3afl/fl;Wnt1-Cre;∆NGli2 embryos ( S7E Fig ) . Taken together , these data contribute to the proposal of the following mechanism for midfacial patterning and the observed ciliopathic midfacial phenotypes . In the wild-type FNP , cilia effectively process GLI3FL into GLI3R . GLI3R-SUFU complexes occupy the GBRs within GLI target genes ( Fig 6A ) . A ratio of high GLIR binding relative to GLIFL activator binding at GBRs of target genes is established . Thus , there are high levels of GLIR activity during normal growth of the FNP ( Fig 6A ) . The loss of cilia ( in both Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre mutants ) impairs normal processing of GLIFL and GLIR , resulting in higher levels of GLIFL and lower levels of GLIR , relative to control embryos . Lack of ciliary-localized SMO prevents GLIFL from dissociating from SUFU . Thus , in ciliary mutants there is a reduction in GLIR occupation of GBRs , and the GLI3FL that now occupies the GBR is not functional due to maintained association with SUFU ( Fig 6B ) . This disruption in processing causes a loss of required GLIR activity and a “de-repression” in the FNP . Loss of GLI repression in the FNP then results in midfacial widening , a classic gain of HH phenotype . We further hypothesize that genetic addition of the Gli3Δ699 allele can partially rescue the phenotype via cilia-independent production of a truncated GLI3 protein that functions like GLI3R . This GLI3Δ699R is able to compete with GLIFL to occupy the majority of the GBRs within the regulatory regions of target genes ( Fig 6C ) . Binding of GLI3Δ699R at GBRs restores the amount of GLIR enrichment and the GLI ratio distribution in the FNP to levels similar to wild-type ( Fig 6C ) . We suggest the rescue is only partial because some amount of GLIFL-SUFU complex still occupies GBRs ( supported by data in Fig 4B ) . Addition of the ΔNGli2 allele produces GLI2A free from SUFU suppression , and increases the amount of GLI2A at GBRs ( Fig 6D ) . The disruption in cilia-dependent GLI processing combined with addition of functional GLI2A shifts the GLI ratio heavily in favor of the GLIA . The facial phenotype manifests as an exacerbated midfacial widening due to a de-repression combined with increased GLI2A activity in the FNP ( Fig 6D ) . Finally , our hypothesized model also explains how Gli2fl/fl;Gli3fl/fl;Wnt1-Cre could phenocopy the Kif3afl/fl;Wnt1-Cre midfacial condition ( Fig 6E ) . Loss of GLI2 and GLI3 would prevent any GLIFL or GLIR from binding to GBRs within the regulatory regions of target genes , thus mimicking the Kif3afl/fl;Wnt1-Cre loss of GLIR binding and increased binding of a non-functional GLIFL-SUFU complex . Loss of both GLI2 and GLI3 in NCCs had the same effect on the GLI production as loss of cilia- loss of the predominant repressor resulting in a de-repression . Loss of GLI repression in the FNP then results in the gain of Hedgehog phenotype of midfacial widening ( Fig 6E ) . Together , these data support the hypothesis that , rather than being dependent upon GLI3R alone , GLI2R also plays a role in midfacial patterning .
Midfacial defects encompass a spectrum of diseases ranging from midline collapse ( cyclopia ) to midline expansion/duplication ( frontonasal dysplasia/diprosopus ) [1] . The current understanding of HH-mediated midfacial growth hypothesizes a mechanism by which HH activity in the ventral forebrain directly signals to the adjacent facial ectoderm , inducing a competency in the ectoderm prior to NCC arrival into the FNP . As NCCs migrate in between the forebrain and facial ectoderm , NCCs of the FNP signal to the overlying facial ectoderm establishing a Sonic Hedgehog ( SHH ) -expressing signaling center in the facial ectoderm coined the Frontonasal Ectodermal Zone ( FEZ ) . The FEZ in turn establishes growth zones within the underlying NCC-derived mesenchyme of the FNP that regulate the size and shape of the midface [52–57] . Gain or loss of SHH activity in either signaling center can cause an expansion or loss of midfacial structures , respectively . Our studies explore a novel mechanism for the generation of midfacial expansion- a NCC-specific , ciliary-dependent mechanism . The loss of functional primary cilia on NCCs recapitulates the midfacial widening similar to those reported when there is a gain of SHH activity in the forebrain or FEZ ( Fig 1; [6] ) . Several explanations exist to explain how these two different mechanisms can generate similar midfacial phenotypes . First , it is possible that midfacial phenotypes in our conditional ciliary mutants are autonomous to the NCC . Second , it is possible that changes in NCC-derived facial mesenchyme induce changes in the neuroectoderm and FEZ that then contribute to the ciliary midfacial phenotype . There is precedence for NCCs exerting a critical effect on the developing forebrain [58–61] , and we have previously observed aberrant morphology in the neuroectoderm of Kif3afl/fl;Wnt1-Cre mutant embryos [7]; however , how those dysmorphologies arise is unknown . Exploring if the establishment and/or maintenance of SHH signaling centers in the brain and face are disrupted in ciliary mutants is a focus of our ongoing work . Third , the midfacial phenotypes observed in anterograde intraflagellar transport mutants could be due to a combinatorial effect of loss of cilia in both NCCs and neural tissue . Despite being the predominantly used driver to examine NCCs , recombination is also known to occur in the dorsal diencephalon and perhaps other areas of the developing brain in Wnt1-Cre animals [62] . Given the significant role cilia have in neural patterning [28] , it is possible that the midfacial phenotypes observed in Kif3afl/fl;Wnt1-Cre and Ift88fl/fl;Wnt1-Cre embryos are due to defects in the developing brain that then have secondary , compounding effects on NCCs that lack cilia . Understanding the role of primary cilia in transducing signals between NCCs and adjacent tissues during craniofacial development will be important for better understanding the relationship between NCCs , the forebrain and facial ectoderm during craniofacial development . Several studies have established that the GLI proteins function to regulate the output of the HH pathway; however , the mechanisms by which these proteins do so are not fully understood . For example , both GLI2 and GLI3 can function as repressors or activators , and there is precedence for organ systems requiring the function of either a GLIR or GLIA for proper development and patterning [18 , 48] . The existence of both repressor and activator isoforms allows for several mechanisms of action including activation , de-repression and ratio sensing to establish the proper amount of activity required for development of an organ system . The range of mechanisms used by GLI proteins is fully evident when examining GLI function in organ systems such as the limb and neural tube . In mouse and chick limb buds , a gradient of GLI3R forms inversely to a SHH source in the ZPA [63 , 64] and loss of GLI2 has no effect on digit patterning , whereas loss of GLI3 results in polydactyly [65] , a phenotype associated with a gain of HH function [66] . Embryos lacking both SHH and GLI3 also have polydactyly similar to that generated when GLI3 alone is lost , suggesting that limb patterning is due to the suppression of GLI3R function [63 , 67] . Interestingly , a separate mechanism exists in the neural tube . Both GLI2 and GLI3 are expressed in the neural tube; however , their expression becomes confined to discrete regions . GLI3 is only expressed in the medial and dorsal regions , whereas GLI2 is expressed throughout the entire dorsal-ventral axis . Loss of function studies support a mechanism in which GLI3R activity is required for patterning the dorsal neural tube , while GLI2A activity is required for patterning the ventral regions of the neural tube [18 , 68 , 69] . Our study suggests that GLIR activity is particularly important in the craniofacial complex , and that both GLI3R and GLI2R can contribute to this repressive activity . Furthermore , our data support both de-repression and cilia-dependent ratio sensing as essential mechanisms for interpreting the net GLI output in the developing face . In addition to gaining mechanistic insight into how cilia and GLI contribute to the patterning of the craniofacial complex , these data further support the hypothesis that GLI proteins use various mechanisms to exert their function on different organ systems . Our future studies will continue to examine how loss of cilia affect GLI protein function . For example , does loss of cilia impact GLI acetylation , phosphorylation , or sumoylation ? Would the loss of these modifications impact the ability of the GLI to exert repressor or activator function ? GLI proteins have also been reported to interact with co-regulators [70–72] . Does the loss of ciliary processing affect the ability of GLI to interact with co-activators or co-repressors ? Examining GLI processing at multiple levels and understanding how each modification impacts overall protein function is an important challenge that lies ahead . Although our study centers on the role of primary cilia in GLI processing and subsequent HH pathway activity , there are several pieces of evidence that suggest other pathways may also contribute to midfacial phenotypes observed in ciliopathic mutants . First , primary cilia do not function exclusively to transduce the HH pathway; rather , they are considered a hub for several signaling pathways [73–76] . Thus , it is possible that loss of the cilium also affects the Wnt , TGFβ , Notch and/or PDGF pathways; each of which have previously been implicated in midfacial patterning and craniofacial development [77–80] . Second , despite a marked improvement in midfacial patterning with the addition of the GLI3Δ699R , a total rescue was not achieved ( Fig 3 ) . We reason this could be due to the fact that we are only ‘rescuing’ the GLI deficit , and not addressing other pathways that may be affected by the loss of functional cilia . Examining how pathways other than the HH pathway are affected in craniofacial ciliopathies is a topic of our future work . Our data explore both the ciliary- and GLI-dependent molecular mechanisms for the onset of midfacial phenotypes in craniofacial ciliopathies . Further understanding how the cilium processes GLI transcription factors , as well as the distinction between GLI2 and GLI3 and their capacity to compensate for one another is a topic that will undoubtedly be useful for future studies and will perhaps provide avenues for therapeutic intervention when considering treatment for craniofacial ciliopathies .
Wnt1-Cre and Gli3fl/fl ( from Jackson laboratory ) , Kif3afl/fl and Ift88fl/fl ( from Dr . Bradley Yoder , University of Alabama at Birmingham ) , Gli2fl/fl ( from Dr . Alexandra Joyner , Memorial Sloan-Kettering Cancer Center ) , Gli3Δ699 ( from Dr . Chi-Chung Hui , the Hospital for Sick Kids , Canada ) , ΔNGli2 ( from Andrzej Dlugosz , University of Michigan ) were maintained by Veterinary Services of Cincinnati Children’s Hospital Medical Center with IACUC approval . All transgenic lines were outbred and maintained on a CD1 background . e15 . 5 embryos were harvested and fixed in Bouin’s solution overnight . For Safranin-O staining , tissue was processed for paraffin embedding and staining procedures were followed as described ( http://www . ihcworld . com _protocols/special_stains/safranin_o . htm ) . For phospho-Histone H3 ( pHH3 ) staining ( Ser10 , Santa Cruz ) , e11 . 5 heads were fixed in 4% PFA , paraffin embedded and cut transversely . Staining was done according to manufacturer’s instruction . pHH3-positive cells in the FNP were counted from multiple consecutive sections ( total thickness 60 μm ) . The following antibodies were used per manufactures’ instructions: GLI2 ( R&D systems; primary concentration 1:500 , secondary concentration 1:5 , 000 ) , GLI3 ( R&D systems primary concentration 1:1 , 000 , secondary concentration 1:5 , 000 ) , SUFU ( H300 , Santa Cruz primary concentration 1:2 , 000 , secondary concentration 1:5 , 000 ) , MYC ( 9B11 , Cell Signaling primary concentration 1:2 , 000 , secondary concentration 1:5 , 000 ) , GAPDH ( FL335 , Santa Cruz primary concentration 1:10 , 000 , secondary concentration 1:10 , 000 ) , LAMIN A/C ( H110 , Santa Cruz primary concentration 1:2 , 000 , secondary concentration 1:5 , 000 ) , a-TUBULIN ( DM1A , Abcam primary concentration 1:15 , 000 , secondary concentration 1:20 , 000 ) , PHH3 ( JBW301 , Millipore primary concentration 1:1 , 000 , secondary concentration 1:1 , 000 ) , ( ARL13b , Protein Tech primary concentration 1:1 , 000 , secondary concentration 1:1 , 000 ) , gamma-TUBULIN ( GTU88 , Sigma primary concentration 1:1 , 000 , secondary concentration 1:1 , 000 ) . e11 . 5 embryos were harvested and either the frontonasal prominence ( FNP; area medial to nasal pits ) alone or the FNP , maxillary ( MXP ) and mandibular prominence ( MNP ) combined were dissected out . Whole cell lysate from the frontonasal prominence or combined facial prominences were sonicated in RIPA buffer ( 50 mM Tris-HCl , pH 7 . 4 , 1% NP-40 , 0 . 25% sodium deoxycholate , 150 mM NaCl , 1 mM EDTA ) containing protease ( Roche ) and phosphatase inhibitors ( 1 mM PMSF , 1 mM Na3VO4 , 10 mM NaF , 60 mM β-glycerophosphate ) . For nuclear fractionation , FNPs from multiple e11 . 5 embryos were freshly isolated and digested by 2 mg/mL Collagenase D ( Roche ) for 20 minutes at 37°C , with gentle shaking . Cytoplasmic protein was extracted using NE-PERTM reagents ( CERI buffer , Thermo Scientific ) according to manufacturer’s instruction . The remaining nuclear pellet was re-suspended in CERI buffer and sonicated to obtain nuclear protein . For immunoprecipitation , protein samples were first incubated with antibody for 2 hours at 4°C . Dynabeads were then added and incubated for overnight at 4°C . GLI3FL to GLI3R ratio was measured by quantitating bands in ImageJ . For the ratio of GLI3FL to GLI3R in Kif3afl/fl;Wnt1-Cre;Gli3Δ699/+ embryos , the amount of GLI3R used to calculate the ratio included the amount of GLI3Δ699R plus endogenous GLI3R . DNA oligos containing partial sequence of mouse Ptch1 promoter ( -911bps to -970bps ) were synthesized with or without Biotin labeled at the 5’ end of the anti-sense strand ( sense strand: CGCCCCCCCACCCCCAAGCCTGGGATGCACACACGGGGGTTGCCTACCTGGGTGGTCTCT; anti-sense strand: Biotin-AGAGACCACCCAGGTAGGCAACCCCCGTGTGTGCATC CCAGGCTTGGGGGTGGGGGGGCG , underline indicates GLI binding site ) . 100 pmole of each sense and anti-sense strand ( with or without Biotin labeled ) was annealed to dsDNA . The Biotin-dsDNA was then incubated with 30 μL of Streptavidin-coupled Dynabeads ( Invitrogen ) for 15 minutes at room temperature . DNA-Dynabead complex was incubated with whole cell lysate for two hours at 4°C . GLI proteins pulled down by the DNA/beads complex were detected by Western blot . | Primary cilia are ubiquitous organelles that serve to transduce molecular signals within a cell . Loss of functional primary cilia results in a disease class called ciliopathies . Ciliopathies have a broad range of phenotypes; however , severe facial anomalies are commonly associated with this disease class . The facial midline is particularly sensitive to loss of primary cilia , frequently undergoing a significant widening . This phenotype is similar to that which occurs when there are gain-of-function defects in the Sonic Hedgehog pathway . This manuscript addresses the molecular basis for midfacial widening in ciliopathies . Importantly , we determine mechanisms to both rescue and phenocopy the ciliopathic midfacial phenotype . In sum , this work provides novel insight into the molecular mechanisms of midfacial patterning and the extent to which loss of cilia impact that process . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"safranin",
"staining",
"gene",
"regulation",
"regulatory",
"proteins",
"dna-binding",
"proteins",
"cytoplasmic",
"staining",
"developmental",
"biology",
"transcription",
"factors",
"embryos",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"separation",
"processes",
"ectoderm",
"specimen",
"preparation",
"and",
"treatment",
"embryology",
"staining",
"proteins",
"gene",
"expression",
"hedgehog",
"signaling",
"biochemistry",
"signal",
"transduction",
"cell",
"biology",
"phenotypes",
"cilia",
"genetics",
"biology",
"and",
"life",
"sciences",
"cell",
"signaling",
"fractionation"
]
| 2016 | Craniofacial Ciliopathies Reveal Specific Requirements for GLI Proteins during Development of the Facial Midline |
Cellular and viral S-adenosylmethionine-dependent methyltransferases are involved in many regulated processes such as metabolism , detoxification , signal transduction , chromatin remodeling , nucleic acid processing , and mRNA capping . The Severe Acute Respiratory Syndrome coronavirus nsp16 protein is a S-adenosylmethionine-dependent ( nucleoside-2′-O ) -methyltransferase only active in the presence of its activating partner nsp10 . We report the nsp10/nsp16 complex structure at 2 . 0 Å resolution , which shows nsp10 bound to nsp16 through a ∼930 Å2 surface area in nsp10 . Functional assays identify key residues involved in nsp10/nsp16 association , and in RNA binding or catalysis , the latter likely through a SN2-like mechanism . We present two other crystal structures , the inhibitor Sinefungin bound in the S-adenosylmethionine binding pocket and the tighter complex nsp10 ( Y96F ) /nsp16 , providing the first structural insight into the regulation of RNA capping enzymes in ( + ) RNA viruses .
Most eukaryotic cellular and viral mRNAs are modified by the addition of a polyadenine tail at the 3′- terminal and a cap structure at the 5′-terminal . The RNA cap protects mRNA from degradation by 5′ exoribonucleases , ensures efficient mRNA translation , and prevents recognition of viral RNA via innate immunity mechanisms[1] , [2] , [3] , [4] . The RNA cap is made of an N7-methylated guanine nucleotide connected through a 5′-5′ triphosphate bridge to the first transcribed nucleotide , generally an adenine . Through 2′-O methylation of the latter , this cap-0 structure ( 7MeGpppA… ) may be converted into a cap-1 structure ( 7MeGpppA2′-O-Me… ) . In the eukaryotic cell , the cap is added co-transcriptionally in the nucleus by three sequential enzymatic reactions[1] , [5]: ( i ) an RNA triphosphatase ( RTPase ) removes the 5′ γ-phosphate group of the nascent mRNA; ( ii ) a guanylyltransferase ( GTase ) , dubbed capping enzyme , catalyses the attachment of GMP to the 5′-diphosphate mRNA; and ( iii ) an S-adenosylmethionine ( SAM ) -dependent ( N7-guanine ) -methyltransferase ( N7MTase ) methylates the cap onto the N7-guanine , releasing S-adenosylhomocysteine ( SAH ) . In general , a SAM-dependent ( nucleoside-2′-O- ) -methyltransferase ( 2′-O-MTase ) further intervenes , in higher eukaryotes , to yield a cap-1 structure . The viral RNA capping machinery is structurally and mechanistically diverse , and RNA viruses often deviate from the paradigmic eukaryotic mRNA capping scheme . For example , alphaviruses methylate GTP onto the N7-guanine before the presumed attachment of 7MeGMP to the nascent viral 5′-diphosphate mRNA[6] . In the case of single-stranded negative-sense ( - ) RNA viruses , such as the vesicular stomatitis virus , the L polymerase attaches GDP rather than GMP to a nascent viral 5′-monophosphate mRNA , covalently linked to the viral capping enzyme[7] . Other viruses , such as influenza virus capture a short capped RNA oligonucleotide from host cell mRNAs and use it as an RNA synthesis primer . This process is known as « cap snatching »[8] . In 2003 , a novel coronavirus named Severe Acute Respiratory Syndrome coronavirus ( SARS-CoV[9] ) was responsible for the first viral pandemic of the new millennium with ∼8000 cases globally and a 10 % case-fatality rate . Coronaviruses encode an unusually large membrane-associated RNA replication/transcription machinery comprising at least sixteen proteins ( nsp1-to-16 ) [10] . For SARS-CoV , the RNA cap structure likely corresponds to a cap-1 type[11] , [12] , [13] . As in many other ( + ) RNA viruses , the RTPase activity is presumably embedded in the RNA helicase nsp13 , whereas the GTase remains elusive . RNA cap 2′-O-MTase activity was first discovered in the feline coronavirus ( FCoV ) nsp16[14] . Shortly after , SARS-CoV nsp14 was shown to methylate RNA caps in their N7-guanine position[15] . Curiously , although closely homologous to that of FCoV , recombinant SARS-CoV nsp16 alone was devoid of enzymatic activity . It was demonstrated[16] , [17] , [18] , [19] that nsp10 interacts with nsp16 , conferring 2′-O-MTase activity to nsp16 on N7-methyl guanine RNA caps selectively[16] . The latter selectivity implies that RNA cap methylation obeys an ordered sequence of events during which nsp14-mediated N7-guanine methylation precedes nsp10/nsp16 RNA 2′-O methylation . Nsp10 is a double zinc finger protein of 148 residues whose crystal structure is known[20] , [21] . Together with nsp4 , nsp5 , nsp12 , nsp14 , and nsp16 , nsp10 has been found to be essential in the assembly of a functional replication/transcription complex[22] . Drawing on these observations , nsp10 has been proposed to play pleiotropic roles in viral RNA synthesis[23] and polyprotein processing through interaction with the main protease nsp5[24] . SAM-dependent MTases belong to a large class of enzymes present in all life forms . These enzymes catalyze the transfer of the SAM methyl group to a wide spectrum of methyl acceptors , indicating that a common chemical reaction is used on a variable active-site environment able to activate the methyl acceptor atom . Although SAM-dependent MTases share little sequence identity , 2′O-MTases exhibit a KDKE catalytic tetrad and a very conserved folding made of a seven-stranded β-sheet surrounded by one to three helices on each side[25] , always similar to the paradigmatic catechol-O-MTase[26] . The SAM binding site general location is conserved , suggesting that evolutionary pressure on the MTase fold has maintained the same SAM-binding region whilst accommodating the versatile chemistry of the methyltransfer reaction . Structural and functional studies of viral MTases involved in RNA capping is an expanding research area , since these enzymes show unexpected diversity relative to their cellular counterparts , and thus constitute attractive antiviral targets . Crystal structures of viral RNA cap MTases exist for only three viral families , namely Poxviridae , Reoviridae , and Flaviviridae . The Vaccinia virus VP39 crystal structure was the first to be elucidated in 1996[27] . The structure of this DNA virus RNA 2′-O-MTase revealed a conserved MTase fold similar to that of RrmJ ( also named FtsJ ) , the canonical reference folding for RNA cap MTases[26] . More recently , the crystal structure of a second Vaccinia virus N7-guanine RNA cap MTase domain ( D1 ) was determined in complex with its activator protein D12[28] . The study revealed that D12 also bears an MTase fold , but has lost catalytic capability due to truncation of its SAM binding site . In turn , Reoviridae provided the first RNA cap MTase structures at 3 . 6 Å resolution as forming part of the reovirus core[29] . Another RNA cap machinery was more recently described for the non-turreted orbivirus Bluetongue virus VP4 protein at 2 . 5 Å resolution[30] , which revealed a three-domain protein , with a “head” guanylyltransferase domain , a central N7-guanine MTase , and a “bottom” 2′-O-MTase domain . This architecture illustrates the sequence of three out of the four chemical reactions involved in RNA capping described above . Regarding ( + ) RNA viruses , MTase structural information at the atomic level is only available for a single genus . The flavivirus N-terminus domain ( residues 1–265 ) of the NS5 RNA-dependent RNA polymerase harbors an RrmJ fold with an N-terminus extension able to accommodate RNA cap structures[31] , [32] . This enzyme carries both N7-guanine MTase and 2′-O-MTase activities on a single domain with one shared active site[33] . Homologous domains have been crystallized for a number of flaviviruses , revealing a conserved fold and activity[34] , suggesting that MTases might represent interesting targets for drug design . No other ( + ) RNA virus RNA cap MTase crystal structures have as yet been defined . In 2003 , the identification of the 2′-O-MTase signature sequence in the SARS-CoV genome added nsp16 to the list of putative targets for antiviral drugs[35] . Several compounds have been shown to inhibit viral MTases , such as the co-product of the MTase reaction SAH , Sinefungin , and aurintricarboxylic acid ( ATA ) [14] , [36] , [37] , [38] , [39] . In this paper , we report the crystal structure of the SARS-CoV 2′-O-MTase nsp16 in complex with its activator , the zinc finger protein nsp10 , at 2 . 0 Å resolution , in conjunction with mutagenesis experiments , binding and activity assays . These results lay down the structural basis for the nsp10 function as an activator of nsp16-mediated 2′-O-MTase . We identify residues playing key roles in the nsp10/nsp16 interaction , as well as other residues involved in 2′-O-MTase catalysis and RNA binding . We also report the crystal structure of the nsp10/nsp16 complex bound to the inhibitor Sinefungin . Comparison with known cellular SAM binding sites points to the nsp16 nucleobase binding pocket as a possible target for the design of selective antiviral molecules .
We observed that purified nsp16 was unstable in solution , impeding crystallogenesis . Yeast double-hybrid and co-immunoprecipitation experiments on purified SARS-CoV nsp10 and nsp16 have uncovered the reciprocal interaction of these two proteins[16] , [17] , [18] . Indeed , SARS-CoV nsp16 exhibits 2′-O-MTase activity only when complemented with SARS-CoV nsp10 , raising the interesting possibility that nsp10 acted as a scaffold for nsp16 . Co-expression of nsp10 and nsp16 using a bi-cistronic prokaryotic expression vector facilitated affinity chromatography purification and crystallization of the complex[16] , [40] . Crystals diffracted to ∼1 . 9 Å . The position of the nsp10 protein was determined using molecular replacement with the SARS-CoV nsp10 protein structure[20] as a search model . Strong peaks in both the residual and anomalous Fourier maps confirmed the presence of two zinc ions . Nsp16 was well defined by its electron density except for two flexible loops ( residues 19–35 and 135–137 ) with high B factors and weak or missing electron density . These loops are solvent-exposed at each side of the putative RNA-binding groove ( see below ) . Structure determination data and refinement statistics are reported in Table 1 . The heterodimer can be conveniently viewed as nsp16 sitting on top of a nsp10 monomer ( Fig . 1A ) . The nsp10 overall structure in the complex remains essentially unchanged relative to published structures of nsp10 alone , with its N-terminus comprising two α-helices , a central β-sheet domain , and a C-terminus domain containing various loops and helices ( see[20] , [21] , Fig . 1B ) . Comparison with existing crystal structures of nsp10 using DaliLite[41] rendered nsp10 atomic coordinates very similar to those of nsp10 in our nsp10/nsp16 complex . The average RMSD is about 0 . 77 Å in 118 residues ( PDB codes 2FYG , 2G9T and 2GA6[20] , [21] ) . This indicates that neither significant conformational change nor surface modification occurs in nsp10 when binding to nsp16 . The nsp10 structural Zn2+ ions are not directly involved in the nsp10/nsp16 interface ( Fig . 1A ) . Nsp16 adopts a canonical SAM-MT fold ( Figs . 1B , 2A and B ) , as defined initially for the catechol O-MTase[25] . The seven-stranded β-sheet MTase fold has been described as having a secondary structure topology defining two binding domains , one for SAM and the other for the methyl acceptor substrate ( Fig . 2A ) . The nsp16 topology matches those of dengue virus NS5 N-terminal domain and of vaccinia virus VP39 MTases[27] , [31] . Nsp16 lacks several elements of the canonical MTase fold , such as helices B and C ( Fig . 2B ) . Electron density corresponding to one molecule of S-adenosylhomocysteine ( SAH ) , the co-product of the methylation reaction , was identified in the putative SAM-binding site ( Figs . 1A and 3A ) . Neither SAM nor SAH was added to the purification or crystallization buffers , therefore it must have been captured from the medium by nsp16 during bacterial growth . The SAH molecule is found with its adenine in an anti conformation and the ribose pucker in a southern ( 2′-endo/3′-exo ) conformation . All the residues involved in SAM/SAH binding are absolutely conserved in coronavirus np16s ( Fig . S1 ) . Binding specificity for SAM/SAH is achieved by holding distal SAM/SAH carboxylic and amino groups through five hydrogen bonds ( G81 , N43 , Y47 , G71 , and D130 ) ( Fig . 3A and Fig . S2A ) . The ribose moiety is held by three hydrogen bonds involving Y132 , G73 , and D99 . As in the case of other MTases[25] , the SAH binding cleft is globally positively charged . However , an aspartic acid ( D99 ) acts as the ribose-sensing residue with its side chain carboxyl making strong hydrogen bonds with both ribose hydroxyls ( Fig . S2A ) . Binding of the adenine base involves few contacts . The nucleobase occupies a loose hydrophobic pocket engaging two hydrogen bonds of moderate strength with side chain and main chain atoms of conserved residues D114 and C115 , respectively . Soaking the crystals into a Sinefungin-containing buffer captured this MTase inhibitor in the SAH binding site almost perfectly superimposable on SAH ( Fig . 3B and Fig . S2B ) . Binding involved the same residues and contacts as SAH . Inhibition of the MTase reaction by Sinefungin therefore probably occurs competitively . The Sinefungin amino group quasi-isosteric to the donated SAM methyl group indicates a cavity where the 2′-hydroxyl of the capped RNA is expected to bind . Lining this empty substrate cavity are the residues proposed to be involved in the catalytic reaction: K46 , D130 , K170 , and E203[16] . Alanine substitutions in the catalytic tetrad ( K46 , D130 , K170 , or E203 ) almost completely block 2′-O-MTase activity without jeopardizing binding to nsp10 ( Table 2 , and [16] ) . Several SAM-binding residues ( N43 , G73 , D99 and Y132 , Fig . S2A ) were substituted by alanine . Although they conserve their specific nsp10 binding properties , indicating that they are correctly folded , they all show a drastically reduced MTase activity ( Table 2 ) , validating the structural description of the nsp10/nsp16/SAH ternary complex . We recently reported[16] that nsp10/nsp16 MTase activity requires Mg2+ . Although the crystallization buffer contains Mg2+ , we were unable to locate any such cation in the nsp16 active site . In enzyme activity assays , the Mg2+ ion can be substituted by Mn2+ or Ca2+ , but not Zn2+ ( data not shown , see also[16] ) . A peak of electron density presumably corresponding to Mg2+ is localized onto nsp16 , distant from the SAH-binding cavity . The Mg2+ coordination mode is through six first-shell water molecules in an octahedral geometry ( Fig . 4 ) . Binding via water molecules , involves T58 and S188 side chain hydroxyls and the main chain carbonyl of E276 . Since there are no carboxylic acids involved in binding this cation , it was suspected that its presence resulted from the crystallization procedure[42] , with no biological relevance . However , the T58A , T58N , T58E and S188A substitutions show 43 , 70 , 99 and 72% loss of activity , respectively ( Table 2 ) , with no significant effect on the stability of the nsp10/nsp16 complex except for T58E whose association was 54 % that of wild-type . These residues are located on three distinct structural elements at the C-terminus of helix Z ( T58 ) , the N-terminus of β6 ( S188 ) , and in the central part of helix A3 ( E276 ) , respectively ( Fig . 4 and Fig . S3 ) . The cation may thus hold these elements together . The nsp10/nsp16 complex absolutely requires an N7-methyl guanine capped RNA substrate to exhibit MTase activity[16] . The structural basis for the preferential binding to methylated N7-guanine versus non-methylated caps has been elucidated in four cases , those of VP39[27] , eIF4E[43] , CBC[44] , and PB2[45] proteins ( PDB codes 1AV6 , 1EJ1 , 1H2T , and 2VQZ , respectively ) bound to cap analogues or capped RNAs . In these cases , the methylated base specificity is achieved through increased binding energy resulting from the stacking of the N7-methyl guanine between parallel aromatic residues of the cap binding protein . The presence of the methyl group greatly enhances π-π stacking , providing a dominant effect over unmethylated guanine[46] . Despite numerous attempts , cap analogues ( m7GpppA , GpppA , m7GpppG , GpppG ) and short capped RNA substrates ( m7GpppA ( C ) n ) could neither be co-crystallized with nsp10/nsp16 nor soaked and bound onto preformed nsp10/nsp16 crystals . However , the atomic coordinates of the N7-methyl guanine RNA oligomer in complex with VP39[47] provided data from which a model of RNA binding to the nsp16 protein was derived . SAM molecules identified in both structures were superimposed , and the VP39-bound RNA was positioned onto the nsp16 structure . After minimal manual adjustments not exceeding 5 Å , the VP39 RNA was a reasonably good fit into an nsp16 hydrophobic groove radiating from the catalytic site ( Fig . 5 ) , establishing very few contacts with nsp10 . We note that the protein side diametrically opposite to the proposed hydrophobic RNA binding groove is highly positively charged ( not shown ) , an observation that may account for the difficulty of achieving experimental RNA binding in the proposed RNA binding site . In the absence of robust data to guide docking of the guanine cap , the m7Gpp cap structure was not positioned in the structure but two possible N7-methylated cap guanine binding areas are indicated by arrows ( Fig . 5 ) . The first transcribed nucleotide together with its ribose receiving the methyl group fit well in the active site ( Fig . 5 , panel B ) as predicted in the proposed mechanism . The same holds for the immediately preceding three nucleotides . The base of the first transcribed nucleotide may be held by contact with P134 and Y132 , bending the extending RNA cap structure . Accordingly , the substitution of Y132 greatly depresses MTase activity ( Table 2 ) . We also note that Y132 is located in the vicinity of a highly mobile loop ( residues 135-138 ) not always visible in our crystal structures suggesting that this loop may move in order to wrap the triphosphate moiety of the RNA cap and/or the RNA cap itself . The solvent exposed side chain of Y30 may also participate in RNA binding . In the model , the highly mobile side chain of Y30 was flipped out in an alternative conformation in order to open the groove . In that position , Y30 should specifically contact the third transcribed nucleotide . Our mutagenesis data confirms the importance of Y30 since its replacement with either Ala or Phe severely impairs MTase activity without affecting the interaction with nsp10 ( Table 2 ) . All nsp10 secondary structure elements but helices 2 and 5 contact nsp16 ( Fig . 1 ) . The nsp10 contact points can be viewed as 5 small patches A to E ( residues 40–47 , 57–59 , 69–72 , 77–80 , and 93–96 , respectively , Fig . 6A ) . In turn , these five patches contact most of the nsp16 SAM-binding structural elements in 4 areas , I to IV ( Fig . 6B , Fig . S3 ) ) , mainly involving β2 , β3 , αA , αZ , and for area IV , the loop connecting helices A2 and A3 at the C-terminus ( Figs . 1 and 6 ) . In total , the interface of the heterodimer involves 53 residues , 23 and 30 from nsp10 and nsp16 , respectively . In nsp10 , a single residue ( Asn10 , at the edge of the interaction surface ) is not conserved out of 23 ( 4 . 3 % ) , whereas in nsp16 there are 8 non-conserved residues out of 30 ( 26 . 7 % ) ( Fig . S1 ) . The interface has a buried surface area of 1820 Å2 , with nsp10 contributing to 930 Å2 and nsp16 to 890 Å2 . Four nsp10 patches included in the 5 interaction patches identified here were recently mapped using reverse yeast two-hybrid methods coupled to bioluminescence resonance energy transfer and in vitro pull-down assays ( see[18] and below ) . To probe the observed crystal structure of the interface further , we engineered 5 new nsp10 alanine mutants ( N40A , L45A , T58A , G69A , and H80A , see Table S1 ) sitting in patches A , B , C and D . Whereas T58A , G69A and H80A showed limited effect on nsp16 binding , N40A reduced it to 64% of wild type affinity , and L45A almost abrogated it . The crystal structure indicates that the nsp10 interface proposed by Lugari et al . [18] , is a correct and conservative estimation , as the interface also includes L45 belonging to patch A . We also confirm the positive co-relation of the detected nsp10/nsp16 interaction with MTase activity . In no instance can nsp16 be active in the absence of nsp10/16 complex formation . The Y96 position is of particular interest . Alanine substitution ( Y96A ) abrogates interaction whereas a phenylalanine ( Y96F ) increases both interaction and MTase activity[18] . In order to understand how residue 96 plays such a pivotal role , we determined at 2 . 0 Å resolution the crystal structure of this nsp10 ( Y96F ) /nsp16 complex ( Table 1 ) . Strikingly , the absence of the hydroxyl group does not alter the topology of the interface . Wild-type and Y96F residues superimpose without significant difference at all atomic positions ( not shown ) . Either Y96 or F96 is in direct contact with nsp16 helix αZ , which carries the catalytic residue K46 . Detailed surface analysis using PISA indicates that the position of K46 in Y96F nsp10 is identical to that of K46 in wild-type nsp10 , ruling out a better alignment of catalytic residues of the Y96F mutant . The nsp10 ( Y96F ) /nsp16 differs from wild-type nsp10/nsp16 in the SAH binding site , though . When compared to that of wild-type , the SAH occupancy is much lower ( ∼0 . 3 versus ∼1 ) , leading to poor density definition . SAH is known to be a fairly good inhibitor of the methylation reaction . Therefore , a lower binding affinity might translate into less end-product inhibition , and account for the observed increased activity . We measured the affinity of nsp16 for SAH using fluorescence spectroscopy , but no significant differences were found ( not shown ) . Likewise , the MTase inhibition pattern by SAH was identical for wild-type and nsp10 ( Y96F ) /nsp16 ( not shown ) . We therefore infer that the previously observed ∼10-fold increased stability of the heterodimer[18] may be responsible for the increased activity . A more hydrophobic character of the interaction may appear upon the loss of the tyrosine hydroxyl which , in the wild-type protein , was not engaged in any polar contact . We thus attribute the increased activity of the nsp10 ( Y96F ) /nsp16 complex relative to wild-type to a stronger equilibrium association of nsp10 ( Y96F ) with nsp16 than that of wild-type nsp10 with nsp16 . Mutation analysis was also conducted on nsp16 residues presumably involved in the interface and interfacial activation ( Tables 2 and S2 ) . Several mutants ( V78A , V104A , L244A , M247A ) in patches II , III and IV completely disrupt the nsp10/nsp16 complex and annihilate nsp16 MTase activity . Interestingly , we also identified nsp16 mutants still interacting with nsp10 , but with a strongly reduced 2′-O-MTase activity ( I40A , M41A , V44A , T48A , Q87A , D106A ) suggesting , that these mutations in the nsp10/nsp16 interface may alter the fine positioning of catalytic residues without any significant effect on nsp10 binding . Accordingly , most of these mutants are localized in αZ helix of patch I which contains the K46 catalytic residue . On the other hand , patch II and III mutants tend to have more mitigated phenotypes , yielding to full-blown interaction with only about half of the expected activity . Finally , patch IV mutants were totally inactive . Using all mutants reported in Table 2 , a plot ( Fig . 6C ) of interaction versus activity shows that the nsp10/nsp16 interaction is strictly required to obtain significant nsp16 MTase activity .
The SARS-CoV RNA cap 2′-O-MTase is a heterodimer comprising SARS-CoV nsp10 and nsp16 . When bound to nsp10 , nsp16 is active as a type-0 RNA cap-dependent 2′-O-MTase , ie . , active only when the cap guanine is methylated at its N7 position[16] . The nsp10/nsp16 crystal structure shows that nsp16 adopts a typical fold of the S-adenosylmethionine-dependent methyltransferase family as defined initially for the catechol O-MTase[25] . A good alignment ( 170° ) is found between the SAH sulfur atom , a water molecule present in both SAH- and sinefungin-bound nsp16 structures , and the K46 ε-amino group ( Fig . 3A and B ) . This geometry provides interesting hints for a catalytic mechanism , as the positions of the catalytic residues ( K46 , D130 , K170 , E203 ) match spatially those of the vaccinia virus VP39 2′-O-MTase[47] . At the initial stage of the reaction the 2′-hydroxyl of the capped RNA substrate would occupy the position of the water molecule . In turn , E203 and K170 decrease the pKa of the K46 ε-amino group that becomes a deprotonated general base ( -NH2 ) able to activate the RNA 2′-hydroxyl at neutral pH . In VP39 , K175 has been identified as the general base catalyst[47] with a pKa depressed by ∼ 2 pH units by the neighbouring D138 and R209 residues[48] . These findings indeed suggest a related mechanism: once K46 has activated the 2′-hydroxyl group , the 2′-oxygen would produce an in line attack through a SN2-like mechanism onto the electrophilic SAM methyl group . The methyl group would pass through a pentavalent intermediate with the 2′-O and sulfur at apical positions . D130 is positioned to stabilize the transient positive charge on the donated methyl atom of SAM before the sulfur recovers a neutral electric charge during SAH generation ( Fig . 3B ) . Unlike most SAM-dependent MTases , the SARS-CoV nsp10/nsp16 enzyme requires a divalent cation , either magnesium , manganese or calcium[25] . We have found that this cation does not reside in the active site . Instead , the cation is coordinated through water molecules by three residues located on three distinct structural elements . It is thus possible that one divalent cation , presumably Mg2+ , present in the host cell at millimolar levels , plays a structural role in holding these three nsp16 structural elements together and so regulate the enzyme activity . It is intriguing that T58A is more active than T58N or T58E that can still bind the water that chelates to the metal . Alternatively , it is possible that divalent cations such as Mg2+ or Ca2+ act as a phosphodiester charge shield to allow RNA binding in the hydrophobic binding groove[49] . The main regulation mechanism of nsp16 is through its physical association with nsp10 . Nsp16 is unstable in solution , and nsp10 acts as a scaffold for nsp16 , yielding a stable dimer active as an RNA cap-dependent ( nucleoside-2′-O ) -MTase . The complex is assembled through a ∼890 Å2 contact surface in nsp16 , an area typically in the intermediate zone differentiating strongly from weakly associated dimers[50] . This finding is consistent with a Kd estimated at ∼0 . 8 µM[18] that qualifies the nsp10/nsp16 complex as a rather weak heterodimer . The nsp10 interaction surface identified in the crystal structure was confirmed by site-directed mutagenesis and overlaps that previously identified by indirect methods[18] . Remarkably , the nsp10 surface in the nsp10/nsp16 complex is essentially identical to that of uncomplexed nsp10 crystallized alone by others[20] , [21] ( Fig . S4 ) . It is therefore reasonable to see this heterodimer as a non-permanent species which would tolerate nsp10 or nsp16 engaging in interactions with other partners . This notion is actually in line with the involvement of nsp10 in a network of protein-protein interactions that we and others have proposed[17] , [19] . Donaldson et al . [23] have engineered mutations in nsp10 using reverse genetics . Out of eight mutations that turned out to be in the nsp10/nsp16 interface ( this work ) , five , two and one rendered lethal , debilitated , and viable phenotypes , respectively[23] . Interestingly , the nsp10 ( Q65E ) mutant providing a temperature-sensitive phenotype[22] , [23] does not map in the nsp10/nsp16 interface , confirming that nsp10 has a pleïotropic role . Our mutagenesis analysis shows that the formation of an nsp10/nsp16 complex is a pre-requisite for MTase activity ( Fig . 6C ) indicating that physical association of nsp10 and nsp16 is essential to activate nsp16 2′-O-MTase activity and foster efficient virus replication . We note that most interface mutants exhibit a severe loss of their 2′-O-MTase activity , whereas the apparent association affinity is often only modestly affected . That minor changes in the interface translate into potent effects is also dramatically illustrated by the Y96F mutation , where the loss of a single hydroxyl provokes a significant change in affinity[18] . Remarkably , it is not the most active complex that was selected in nature , since the nsp10 ( Y96F ) /nsp16 complex is both more stable and more active than the wild-type heterodimer ( this work and[18] ) . This is yet another observation hinting at the involvement of nsp10 in protein-protein interaction networks including other partners than nsp16 , such as nsp5 and nsp14[17] , [19] . In most other coronaviruses , the nsp10 residue at position 96 is a phenylalanine . It would be interesting to determine whether this polymorphism is relevant to the SARS-CoV pathogenicity at any ( direct or indirect ) level , or if compensating polymorphisms in other coronaviral nsp10 ( or nsp16 ) restore a weaker nsp10/nsp16 association equivalent to that of the SARS-CoV pair . Since a bona fide viral RNA cap is key in evading the host cell innate immunity[4] , [51] , a minimal level of 2′-O-MTase activity would be expected to be critical to virus survival . MTase activation through dimerisation of two viral protein partners has already been reported in the case of the vaccinia virus D1/D12 N7-guanine MTase[28] . However , the activating D12 subunit does not contact the D1 subunit through a homologous surface mainly defined by canonical αA and αZ helices . Rather , the D1/D12 activation surface would be located at a 90° clockwise rotation relative to the nsp10/nsp16 interface depicted in Fig . 1A . In the case of dengue virus , the bi-functional N7-guanine and 2′-O-MTase is part of the N-terminus of the dengue NS5 protein . Based on reverse genetic data and modeling[52] , the MTase domain would be associated with the Pol domain through an interface topologically similar to that of nsp10/nsp16 , i . e . , involving mainly helices αA , αZ and strands β2 and β3 as depicted in Fig . 1A . We have previously shown that the nsp10/nsp16 is only active as N7-guanine methylated capped RNA , implying that RNA cap methylation obeys to an ordered sequence of events where nsp14-mediated N7-guanine methylation precedes nsp10/nsp16 RNA 2′-O methylation[16] . In the absence of data regarding the RNA substrate , we built a model of RNA binding based on that of the vaccinia virus VP39 ternary complex structure . Interestingly , our model proposes that the RNA interacts only with nsp16 residues , in keeping with what was recently suggested based on RNA binding assays[14] . Although the position of the cap structure on the nsp16 surface remains to be determined , our model suggests a well-defined position for the ribose of the first transcribed nucleotide in the active site . In agreement with mutagenesis analysis , the model also suggests that the transcribed RNA 5′-end stacks between Y132 and Y30 . Furthermore , this model is consistent with the observation that coronavirus MTase requires RNA substrates of at least 3 transcribed nucleotides in length[14] . It is also worth to know that a comparison of nsp16 and VP39 electrostatic surfaces reveals that the putative RNA-binding groove of nsp16 is mostly hydrophobic , whereas the VP39 RNA-binding groove is positively charged . This variation would imply a change in the nature of the RNA/protein interaction . Viral MTases are increasingly evaluated as potential drug design targets[34] , [37] , [53] . We have crystallized the inhibitor Sinefungin with the nsp10/nsp16 complex . Sinefungin exhibits an IC50 of 0 . 74 µM , 16-fold lower than that of SAH as reported by Bouvet et al . [16] using purified nsp10/nsp16 . Analysis of the structure suggests a likely mechanism of action that also accounts for the observed inhibitory effect of this drug . We note that the adenine nucleobase does not fit snugly into its binding pocket , raising interest regarding structure-based drug design . Preliminary examination of eukaryotic non-viral MTase structures from main classes as defined in Martin and McMillan[25] indicates that the SAH adenine is bound tighter in any of the latter enzymes than in the nsp16 SAM-binding site , indicating a possible breach to achieve anti-coronavirus selectivity with a small molecule inhibitor of nsp16 . In conclusion , the crystal structures presented here extend our general understanding of the mechanism and regulation of viral RNA cap MTases in ( + ) RNA viruses , and point to both the nsp10/nsp16 interface and the substrate binding sites as putative antiviral targets .
Both nsp10 and nsp16 were expressed from the same dual expression vector pmCOX [16] . Nsp10 had a N-terminal strep-tag ( WSHPQFEK ) , and nsp16 a N-terminal hexa-histidine tag . The purification and crystallogenesis of the nsp10/nsp16 complex was performed as described in [40] . Typical crystals of the wild-type nsp10/nsp16 appear in hanging drops after 24 h at 20°C in 0 . 1 M CHES pH 9 , 1 . 52 M MgCl2 hexahydrate . Crystals ( a = 68 . 53 Å , b = 184 . 74 Å , c = 129 . 01 Å , C2221 ) contain one nsp10/nsp16 complex per asymmetric unit , with a solvent content of 70 % and Vm of 4 . 17 Å3/Da . Crystals of nsp10 ( Y96F ) /nsp16 were grown in 67 mM CHES pH 8 . 5 , 0 . 99 M MgCl2 hexahydrate , 33 mM Tris-HCl , 8 . 3 % PEG 8000 . Both crystallization conditions yielded crystals diffracting to 1 . 9 Å when exposed to synchroton radiation at the ID14-1 beamline of the European Synchrotron Radiation Facility , Grenoble , France . Crystals were cryo-cooled in the same buffer supplemented with 15 % glycerol . Crystal soaking was performed in the same buffer supplemented with 5 mM SAH or Sinefungin during 24 h . The position of the nsp10 protein was unambiguously determined by molecular replacement using the program PHASER[54] with the nsp10 protein ( 2FYG ) , as search probe[20] . Strong peaks in both the residual and anomalous Fourier maps confirmed the presence of two Zinc ions at the expected positions within the nsp10 protein , thus giving confidence in the validity of the MR solution . Phases calculated from this partial model were combined with SAD phases from the Zn atoms using PHASER . To ameliorate the resulting low quality density map , phases were improved with PARROT[55] . An initial model , comprising both nsp10 and nsp16 , was automatic built by successive use of BUCCANEER[56] and ARP/wARP[57] . The resulting model was subject to several cycles of manual rebuilding using COOT [58] and refinement with REFMAC [59] . The protein structure model could be built , except the strep and hexahistidine tags . In nsp16 , density was too weak for the mobile , solvent exposed nsp16 loop 136–139 , Y30 ( see “Results” ) , and 2 and 6 residues in N- and C-terminus , respectively . Likewise , nsp10 solvent exposed 9 and 8 residues in N- and C-terminus were missing , respectively . Overall , the chain traces are unambiguous , with clear electron density including for a single SAH residue bound to the nsp16 protein . Solvent accessible surfaces were calculated using program AREAIMOL [60] with a 1 . 7 Å radius sphere as the probe ( Table 1 ) and values rounded to the nearest 5 Å2 . Conformational differences were analyzed using the DynDom server ( http://www . cmp . uea . ac . uk/dyndom/main . jsp ) . Figures were created using PYMOL ( http://www . pymol . org ) . The coordinates of the wild-type/SAH , mutant , and wild-type/Sinefungin structures have been deposited at the Protein Data bank under PDB codes 2XYQ , 2XYV , and 2XYR , respectively . The modeling of the RNA cap structure in the nsp10/nsp16 complex structure is derived from the analysis of the structure of the vaccinia virus methyltransferase VP39 crystallized in complex with a capped RNA and a S-Adenosylhomocysteine[47] ( SAH ) ( pdb code: 1AV6 ) . The two structures are manually aligned using COOT[58] based on the position of SAH binding sites , as well as SAH , and Sinefungin ( SFG ) molecules . The RNA binding site of VP39 is only partly overlapping that of nsp16 whilst the shape of the cavity is similar; thus local adjustments necessary to accommodate the RNA molecule in its binding groove were done manually using COOT . The side chain of tyrosine 30 of nsp16initially pointed to the putative RNA binding site , preventing any bona fide modeling . In order to fit the RNA molecule in the cavity , an alternative conformation was sought for this side chain . The second most common conformation for the tyrosine side chain was selected . Due to the biochemistry data and surface electrostatic analysis , it is not possible to describe with certainty the final position of the cap , thus the cap was removed and replaced by arrows symbolizing possible positions . No other modification was performed on the RNA , the Sinefungin molecule or the nsp16 structure . The SARS-CoV nsp10 and nsp16-coding sequences were amplified by RT-PCR from the genome of SARS-CoV Frankfurt-1 ( accession number AY291315 ) as previously described[16] . The nsp10 and nsp16 genes ( encoding residues 4231–4369 , 5903–6429 , and 6776–7073 of replicase pp1ab ) were cloned into a Gateway modified dual-promotor expression plasmid and in the gateway pDest 14 expression vector . In this backbone , SARS CoV nsp10 can be expressed under a tet promoter and encodes a protein in fusion with a N-terminal strep tag , whereas nsp16 is expressed under a T7 promoter and encodes a protein in fusion with a N-terminal hexahistidine tag . The mutants were generated by PCR using the Quickchange site–directed mutagenesis kit ( Stratagene ) , according to the manufacturer's instructions . AdoMet and cap analogs GpppA and 7MeGpppA were purchased from New England BioLabs , the[3H]-AdoMet was purchased from Perkin Elmer and Sinefungin ( adenosylornithine ) from Sigma-Aldrich . E . coli C41 ( DE3 ) cells ( Avidis SA , France ) , containing the pLysS plasmid ( Novagen ) , were transformed with nsp10 or nsp16 cloned in pDest14 , or nsp10/nsp16 cloned in pmCox , and grown in 2YT medium supplemented with appropriate antibiotics . The expression of strep-tagged nsp10 or 6His-tagged nsp16 mutants was induced ( DO600 = 0 . 6 ) by adding 50 µM IPTG , and the expression of the nsp10/nsp16 complex by adding 50 µM IPTG and 200 µg/L of anhydrotetracycline . After an incubation for a 16 h at 24°C , the cell were pellets , frozen and resuspended in lysis buffer ( 50 mM HEPES , pH 7 . 5 , 300 mM NaCl , 5 mM MgSO4 , 5 mM β-mercaptoethanol ( only for nsp10 ) supplemented with 1 mM PMSF , 40 mM imidazole , 10 µg/ml DNase I , and 0 . 5% Triton X-100 . After sonication and clarification , proteins were purified either by IMAC ( HisPurTM Cobalt Resin; Thermo Scientific ) chromatography[16] ( nsp10 mutants and nsp16 mutants ) , and the nsp10/nsp16 complex was purified by using Strep-Tactin sepharose ( IBA Biotagnology ) as previously described[16] . All purified proteins were analyzed by SDS-PAGE . The binding of wild-type nsp10 to mutant nsp16 , and that of mutant nsp10 to wild-type nsp16 was quantified using ImageJ as described[16] . MTase activity assays were performed in 40 mM Tris-HCl , pH 8 . 0 , 5 mM DTT , 1 mM MgCl2 , 2 µM 7MeGpppAC5 or GpppAC5 , 10 µM AdoMet , and 0 . 03 µCi/µl [3H]AdoMet ( GE Healthcare ) . Short capped RNAs ( 7MeGpppAC5 , GpppAC5 , were synthesized in vitro using bacteriophage T7 DNA primase and were purified by high-performance liquid chromatography ( HPLC ) as previously described[61] . In the standard assay , nsp10 and nsp16 were added at final concentrations of 600 nM , and 200 nM , respectively , and the amount of 3H-CH3 transferred onto 7MeGpppAC5 substrates was determined by filter binding assay as previously described[16] . | A novel coronavirus emerged in 2003 and was identified as the etiological agent of the deadly disease called Severe Acute Respiratory Syndrome . This coronavirus replicates and transcribes its giant genome using sixteen non-structural proteins ( nsp1-16 ) . Viral RNAs are capped to ensure stability , efficient translation , and evading the innate immunity system of the host cell . The nsp16 protein is a RNA cap modifying enzyme only active in the presence of its activating partner nsp10 . We have crystallized the nsp10/16 complex and report its crystal structure at atomic resolution . Nsp10 binds to nsp16 through a ∼930 Å2 activation surface area in nsp10 , and the resulting complex exhibits RNA cap ( nucleoside-2′-O ) -methyltransferase activity . We have performed mutational and functional assays to identify key residues involved in catalysis and/or in RNA binding , and in the association of nsp10 to nsp16 . We present two additional crystal structures , that of the known inhibitor Sinefungin bound in the SAM binding pocket , and that of a tighter complex made of the mutant nsp10 ( Y96F ) bound to nsp16 . Our study provides a basis for antiviral drug design as well as the first structural insight into the regulation of RNA capping enzymes in ( + ) RNA viruses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"biomacromolecule-ligand",
"interactions",
"biochemistry",
"antivirals",
"proteins",
"virology",
"enzymes",
"protein",
"structure",
"emerging",
"viral",
"diseases",
"viral",
"enzymes",
"biology",
"microbiology"
]
| 2011 | Crystal Structure and Functional Analysis of the SARS-Coronavirus RNA Cap 2′-O-Methyltransferase nsp10/nsp16 Complex |
Mammalian genomes harbor millions of retrotransposon copies , some of which are transpositionally active . In mouse prospermatogonia , PIWI-interacting small RNAs ( piRNAs ) combat retrotransposon activity to maintain the genomic integrity . The piRNA system destroys retrotransposon-derived RNAs and guides de novo DNA methylation at some retrotransposon promoters . However , it remains unclear whether DNA methylation contributes to retrotransposon silencing in prospermatogonia . We have performed comprehensive studies of DNA methylation and polyA ( + ) RNAs ( transcriptome ) in developing male germ cells from Pld6/Mitopld and Dnmt3l knockout mice , which are defective in piRNA biogenesis and de novo DNA methylation , respectively . The Dnmt3l mutation greatly reduced DNA methylation levels at most retrotransposons , but its impact on their RNA abundance was limited in prospermatogonia . In Pld6 mutant germ cells , although only a few retrotransposons exhibited reduced DNA methylation , many showed increased expression at the RNA level . More detailed analysis of RNA sequencing , nascent RNA quantification , profiling of cleaved RNA ends , and the results obtained from double knockout mice suggest that PLD6 works mainly at the posttranscriptional level . The increase in retrotransposon expression was larger in Pld6 mutants than it was in Dnmt3l mutants , suggesting that RNA degradation by the piRNA system plays a more important role than does DNA methylation in prospermatogonia . However , DNA methylation had a long-term effect: hypomethylation caused by the Pld6 or Dnmt3l mutation resulted in increased retrotransposon expression in meiotic spermatocytes . Thus , posttranscriptional silencing plays an important role in the early stage of germ cell development , then transcriptional silencing becomes important in later stages . In addition , intergenic and intronic retrotransposon sequences , in particular those containing the antisense L1 promoters , drove ectopic expression of nearby genes in both mutant spermatocytes , suggesting that retrotransposon silencing is important for the maintenance of not only genomic integrity but also transcriptomic integrity .
The mouse genome harbors millions of copies of transposable elements , the majority of which are retrotransposons . The retrotransposons include long terminal repeat ( LTR ) elements , long interspersed elements ( LINEs ) , and short interspersed elements ( SINEs ) [1] . The LTR elements include endogenous retroviruses ( ERVs ) . Some retrotransposons , such as the intracisternal A particle ( IAP , which is an ERV ) and LINE-1 ( L1 , a LINE ) , are active in transposition and have a potential to cause insertion mutations . Thus , retrotransposons present a threat to genomic integrity . In general , the expression of retrotransposons is epigenetically regulated at the transcriptional level by DNA methylation at CpG sites and by histone modifications , such as histone H3 methylation at lysine 9 ( H3K9me ) . For example , a knockout ( KO ) mutation of Dnmt1 , which encodes a maintenance-type DNA methyltransferase , causes derepression of IAPs in whole mouse embryos because of a passive loss of DNA methylation [2] . When DNA methylation is completely lost from embryonic stem cells ( ESCs ) by KO mutation of all three DNA methyltransferase genes ( Dnmt1 , Dnmt3a , and Dnmt3b ) , the expression of L1 elements , but not of IAP elements , is increased [3 , 4] . Moreover , a KO mutation of Setdb1/Eset , which encodes an H3K9 methyltransferase , causes increased expression of LTR elements , such as IAP elements , in mouse ESCs and brain , but not in embryonic fibroblasts [4–6] . Thus , although both DNA methylation and histone modifications are important for silencing , the dominant mechanism differs among cell types and retrotransposons . During germ cell development , the DNA methylation profile changes dynamically [7] ( Fig 1A ) . Initially , primordial germ cells ( PGCs ) have a low methylation level at embryonic day 13 . 5 ( E13 . 5 ) ; subsequently , global de novo methylation occurs in mitotically arrested prospermatogonia , between E13 . 5 and the newborn stage ( postnatal day 0 or P0 ) . A few days after birth , the arrested cells resume mitosis , giving rise to spermatogonia , and , at around P9 , they start to differentiate into spermatocytes and initiate meiosis . The global CpG methylation level established in late prospermatogonia is maintained throughout spermatogenesis , although there are local changes at regulatory elements [8 , 9] ( Fig 1A ) . Dnmt3l and Dnmt3a , which respectively encode a DNA methyltransferase-like protein and a de novo DNA methyltransferase , are highly expressed in prospermatogonia [10 , 11] . KO mutations of Dnmt3l result in a failure in de novo methylation of the IAP and L1 promoters and the centromeric and pericentromeric repeats [12–14] . Moreover , Dnmt3l KO mice show increased expression of the IAP and L1 family members in postnatal testes [12 , 15] , suggesting the important role of this gene in retrotransposon silencing . In prospermatogonia , PIWI-interacting RNAs ( piRNAs ) are generated by the actions of many proteins , including PLD6/MitoPLD/ZUC , PIWIL2/MILI , and PIWIL4/MIWI2 [16] . PLD6 is a phospholipase D/nuclease family protein that contributes to the generation of primary piRNAs by cleaving piRNA precursors [17–19] . PIWIL2 binds primary piRNAs and cleaves target RNAs that are complementary to the bound piRNAs . The cleaved RNAs are further processed to yield secondary piRNAs , which are used for another round of piRNA production via the so-called ping-pong cycle [20] . A large part of the piRNAs in mouse prospermatogonia is derived from retrotransposons and other transposable elements: therefore , the piRNA system is considered as a host defense system [21 , 22] . In fact , the L1 and IAP families are derepressed in Pld6 KO testes at P14 [19] . Interestingly , prospermatogonia from Pld6 KO , Piwil2 KO , and Piwil4 KO mice fail to achieve de novo methylation at the L1 promoters [19 , 20 , 23–25] , suggesting that piRNAs may guide de novo methylation [26] . Thus , the piRNA system likely silences retrotransposons at both the transcriptional and posttranscriptional levels . In a previous study , we showed that DNA methylation is virtually unaffected at the IAP , MMERVK10C , and SINE B1 sequences in Pld6 KO spermatogonia [27] . It was also reported that Piwil2 KO mutants properly gain methylation at many retrotransposon sequences , with the exception of the L1 promoters [28 , 29] . Thus , the piRNA-guided de novo methylation and , in turn , transcriptional silencing , may occur only at limited families of retrotransposons , such as the L1 family , in prospermatogonia . In Mael and Hsp90aa1/Hsp90α KO prospermatogonia , in which the amount of piRNA is severely reduced , the methylation of the L1 promoters is unchanged , whereas the L1 protein level is increased [30 , 31] , suggesting the presence of piRNA-mediated posttranscriptional regulation . In the present study , we attempted to clarify the contributions of the piRNA system and DNA methylation to retrotransposon silencing during male germ cell development in greater detail . We performed whole-genome bisulfite shotgun sequencing and RNA sequencing ( RNA-seq ) in germ cells obtained from Pld6 KO and Dnmt3l KO mice . Our results demonstrate an interesting shift from a posttranscriptional to a transcriptional mechanism as the major contributor to retrotransposon silencing during male germ cell development . We also show that retrotransposon silencing is important not only for genomic integrity , but also for transcriptomic integrity .
To examine the effect of Pld6 KO and Dnmt3l KO mutations on de novo DNA methylation of retrotransposons , we performed whole-genome bisulfite shotgun sequencing in wild-type ( WT ) , Pld6 KO , and Dnmt3l KO spermatogonia that were collected by fluorescence-activated cell sorting ( FACS ) at postnatal day 7 ( P7 ) . Using uniquely mapped reads , we determined the average methylation level at CpG sites ( the methylated cytosine calls divided by the sum of the methylated and unmethylated cytosine calls ) , which were 0 . 73 , 0 . 72 , and 0 . 36 ( in fraction values ) in WT , Pld6 KO , and Dnmt3l KO spermatogonia , respectively ( Fig 1B , S1 Fig ) . This suggests that , while Dnmt3l is important for de novo methylation at unique sequence regions , Pld6 is dispensable for this process . Although the impact of the Dnmt3l mutation was global , GC-poor ( or AT-rich ) regions were more severely affected ( Pearson’s R = −0 . 68 ) ( Fig 1C , S2 Fig ) . These regions overlapped well with regions that were poorly methylated at E16 . 5 [32] in WT prospermatogonia ( Fig 1D , S2 Fig ) , indicating that the de novo methylation of the GC-poor regions is more dependent on Dnmt3l and occurs later in normal development . Next , the DNA methylation levels of relatively young ( i . e . , mouse-specific ) retrotransposons ( n = 263 ) were determined in each genotype by mapping the reads onto their consensus sequences in the RepeatMasker library ( for some retrotransposons , the sequences were divided into portions; e . g . , 5end , orf2 , and 3end ) . To determine the extent of de novo methylation in individual retrotransposons , we subtracted the methylation levels determined in E13 . 5 WT PGCs ( onset of de novo methylation ) [32] from those determined in P7 spermatogonia ( after de novo methylation ) . In Dnmt3l KO spermatogonia , almost all retrotransposons were severely affected ( Fig 1E , S3 and S4 Figs and S1 Table ) , whereas the Pld6 mutation had a lesser impact ( Fig 1F , S3 Fig and S1 Table ) . The methylation levels of most retrotransposons in Pld6 KO spermatogonia were very similar to those observed in Piwil2 KO spermatogonia ( S5 Fig ) [28 , 29] , and only a subset of retrotransposons , including L1Md_A_5end and L1Md_Gf_5end ( the 5′ regions of A- and GF-type L1 , respectively ) , were severely affected ( ≥0 . 3 ) . To determine whether the Pld6 and Dnmt3l mutations affect the RNA levels of retrotransposons in prospermatogonia , we performed deep sequencing of polyA ( + ) RNAs ( mRNA-seq ) with strand discrimination in newborn ( P0 ) testes , where prospermatogonia are the only germ cell component . ( Note that LINE and LTR elements have a polyadenylation signal [33 , 34] and their RNAs are polyA ( + ) . ) The mapping of the reads to the consensus sequences revealed that the expression of the L1 and IAP family members was increased by >2-fold in Pld6 KO testes ( P < 0 . 05 , t test; Fig 2A , S1 Table ) . In Dnmt3l KO testes , the expression of only a small subset of retrotransposons was increased by >2-fold , albeit with little significance ( P > 0 . 05 for all; Fig 2B , S1 Table ) . Thus , the Pld6 mutation had a greater impact on retrotransposon silencing than did the Dnmt3l mutation ( Fig 2C ) , despite the observation that the Dnmt3l mutation affected de novo methylation more severely ( Fig 1E and 1F ) . These results suggest that Pld6 silences retrotransposons predominantly via a mechanism that is independent of methylation . The mapping of RNA reads to the full-length L1Md_A sequence revealed that the increase in expression was most prominent in the 5′ region ( up to 10-fold ) in Pld6 KO testes ( Fig 2D , blue line ) , whereas it was constant ( 1 . 8-fold ) throughout the L1Md_A sequence in Dnmt3l KO testes ( Fig 2D , orange line ) . As the RNA-seq libraries were constructed from polyA ( + ) RNAs , our results suggest that full-length plus near-full-length L1Md_A RNAs are more abundant , thus RNA cleavage is less frequent in Pld6 KO testes . When both cleaved and uncleaved RNAs were together measured in total RNA from P0 testes by random priming followed by quantitative PCR ( qRT-PCR ) , a uniform 1 . 7- to 2 . 8-fold increase was observed along the L1Md_A sequence in Pld6 KO testes ( S6 Fig ) . Next , we quantified the nascent RNAs of L1Md_A in newborn testes by ethynyl uridine labeling , purification , and subsequent qRT-PCR and found that transcription was increased only 1 . 1- to 2 . 3-fold in Pld6 KO testes ( Fig 2E ) . Thus , the higher expression detected in these regions by RNA-seq ( 5- to 10-fold , Fig 2E ) was more likely caused by increased RNA stability ( less RNA cleavage ) rather than increased transcription . In contrast , in Dnmt3l KO testes , the increase in the nascent RNAs was not much different from that observed in the steady-state RNA by RNA-seq ( Fig 2F ) , suggesting regulation at the transcriptional level . Moreover , the introduction of the Pld6 mutation in addition to the Dnmt3l mutation further increased the RNA levels of the L1 and IAP family members ( compare Pld6/Dnmt3l double KO with Dnmt3l KO; Fig 2G , S7 Fig and S1 Table ) . This suggests that , in prospermatogonia , retrotransposon silencing by Pld6 does not require Dnmt3l , is largely independent of DNA methylation , and , thus , perhaps occurs through piRNA-guided RNA cleavage . To evaluate the role of piRNA-guided RNA cleavage in retrotransposon silencing in prospermatogonia , we profiled the piRNAs ( 24- to 33-nt small RNAs ) from WT and Pld6 KO testes at P0 by small RNA sequencing . The profile of retrotransposon-derived piRNAs in WT P0 testes was very similar to that in E16 . 5 testes [31] ( R = 0 . 95 ) , with 45% of them being derived from the L1 family ( S1 Table ) . In Pld6 KO P0 testes , the retrotransposon-derived piRNAs were severely reduced ( S8 Fig ) , as was observed in Pld6 KO E16 . 5 testes [19] . Importantly , retrotransposons with a large drop ( >1 , 000 RPM ) in the amount of antisense piRNAs in Pld6 KO testes showed a significant increase in RNAs in the mutants ( S8 Fig and S1 Table ) . Next , we tried to identify and quantify the cleaved L1 RNAs in WT and Pld6 KO testes by 5'-RACE sequencing ( 5'-RACE-seq ) ( Fig 3 ) . The method includes RNA adaptor ligation where only cleaved RNAs with a 5' monophosphate group can accept the adapter: non-cleaved RNAs with a triphosphorylated or a capped 5' end are not reactive [35 , 36] ( Fig 3A ) . We mapped the 5'-RACE-seq reads to the full-length L1Md_A sequence and to the whole genome and revealed a number of RNA cleavage sites throughout the L1Md_A sequence in WT testes at P0 ( Fig 3B ) . Importantly , about half of the cleaved RNAs showed a 10-nt complementarity with an antisense piRNA at their 5’ portions ( Fig 3C ) , a feature of the piRNA-guided RNA cleavage . The fraction of L1-derived RNA fragments in the total cleaved RNAs ( mapped to the whole genome ) was reduced 3 . 1-fold in the Pld6 KO testes ( Fig 3B ) . Given that L1 transcription increased 2-fold in the mutant testes ( Fig 2E ) , the above results suggest that the observed L1 RNA cleavage largely depended on Pld6 . Thus , our findings suggest that the piRNA-guided RNA cleavage , which directly or indirectly involves Pld6 , is important for retrotransposon silencing in prospermatogonia . Next , we studied the impact of the Pld6 KO and Dnmt3l KO mutations on retrotransposon silencing in meiotic germ cells . WT testes at P21 contained all of the premeiotic ( spermatogonia ) , meiotic ( spermatocytes ) , and postmeiotic ( spermatids ) germ cells ( Fig 1A ) , as confirmed by FACS profiling ( S9 Fig ) . However , only spermatogonia and spermatocytes ( at the preleptotene , leptotene , and zygotene stages ) were present in Pld6 KO and Dnmt3l KO testes ( S9 Fig ) , because of the cell death that occurs at the late zygotene stage [14 , 19] . Thus , we collected leptotene/zygotene ( L/Z ) spermatocytes by FACS and performed polyA ( + ) RNA-seq . The results of this experiment showed that the Pld6 KO and Dnmt3l KO mutations caused a >2-fold increase in the expression of 21 and 46 retrotransposons , respectively ( P < 0 . 05; Fig 4A and 4B , S1 Table ) . In particular , the expression of some L1 family members was increased by >30-fold in both mutants and the expression of some IAP , MERVK , and MMERGLN family members was increased by >10-fold in the Dnmt3l mutants ( Fig 4C ) . Thus , both mutations had a greater impact on retrotransposon silencing in L/Z spermatocytes than they did in prospermatogonia ( P0 testes ) ( compare Figs 4A and 4B and 2A and 2B ) . However , the Dnmt3l mutation had a greater impact than did the Pld6 mutation in spermatocytes ( Fig 4A and 4B ) . Thus , the relative importance of Pld6 and Dnmt3l in retrotransposon silencing was reversed between the stages ( compare Figs 2C and 4C ) . Moreover , in contrast to the observations from P0 testes ( S8 Fig ) , the increase in expression observed in Dnmt3l KO spermatocytes correlated well with the decrease in methylation detected in Dnmt3l KO spermatogonia ( Fig 4D ) . In addition , the expression levels of the L1Md_A , L1Md_Gf , and MMERGLN family members were similar between Pld6 KO and Dnmt3l KO spermatocytes ( Fig 4C ) , which agrees well with the similar hypomethylation observed in their promoters ( Fig 1E and 1F , S3 Fig and S1 Table ) . The expression levels of the IAP and MERVK family members were higher in Dnmt3l KO spermatocytes ( Fig 4C ) and correlated well with the differences in methylation observed between the mutants ( S3 Fig and S1 Table ) . Comparisons of the RNA-seq data of Pld6 KO spermatocytes with those of Piwil2 and Piwil4 KO testes at P10 [29] revealed that similar sets of retrotransposons were derepressed in the Pld6 and Piwil2 KO mutants ( S5 Fig ) . In contrast , only retrotransposons with reduced DNA methylation showed increased expression in Piwil4 KO testes ( S5 Fig ) . For example , the Piwil4 KO mutation reduced the methylation level of L1Md_Gf_5end , but not L1Md_A_5end , and consistently , the expression of L1Md_Gf_5end , but not L1Md_A_5end , was elevated in the Piwil4 KO testes ( S5 Fig ) . All of these results further support the important role of DNA methylation in retrotransposon silencing in L/Z spermatocytes . Despite the importance of DNA methylation during meiosis , it was reported that PIWIL2 regulates L1 at the posttranscriptional level in spermatocytes [37] . In addition , the expression of some retrotransposons , such as IAPA_MM , was increased in Pld6 KO mutants without a large decrease in DNA methylation ( S1 Table ) . Therefore , we conducted a couple of analyses to examine the contribution of piRNA-guided RNA cleavage to retrotransposon regulation in spermatocytes . First , we mapped the RNA-seq reads obtained from Pld6 KO spermatocytes to the full-length L1Md_A sequence and found that the increase in expression was highest in the 5′ region ( up to 100-fold ) ( Fig 4E , blue line ) and declined toward the 3′ end , resembling the pattern observed in P0 testes ( Fig 2D ) . This suggests that RNA cleavage contributes to silencing , at least partly , in spermatocytes . Second , we profiled small RNAs in FACS-purified L/Z spermatocytes from P21 WT and Pld6 KO testes by deep sequencing , and detected a severe loss of piRNAs derived from LINE and LTR elements , but not of those derived from SINE elements , in Pld6 KO spermatocytes ( Fig 4F ) , which was consistent with our previous data from P10 testes [19] . Importantly , the degree of decrease in piRNA abundance in Pld6 KO spermatocytes correlated well with the degree of increase in target RNA abundance ( Fig 4G ) , which is consistent with the direct involvement of piRNAs in posttranscriptional silencing . We then examined whether the RefSeq expression profile ( transcriptome ) is affected by the Pld6 KO and Dnmt3l KO mutations . In Pld6 KO L/Z spermatocytes , 41 and 32 genes showed increased ( >2-fold ) and decreased ( <1/2 ) expression , respectively ( q-value < 0 . 05 , calculated by Cuffdiff; Fig 5A , S2 Table ) . In contrast to round spermatids , in which many RefSeq genes are directly regulated by piRNAs antisense to the genes [38] , only a weak correlation was observed between the changes in gene expression in Pld6 KO spermatocytes and the abundance of antisense piRNAs in WT spermatocytes ( Fig 5B ) . Thus , the altered expression did not appear to involve piRNAs against the RefSeq genes themselves . In Dnmt3l KO spermatocytes , 442 and 184 genes , respectively , showed increased ( >2-fold ) and decreased ( <1/2 ) expression ( Fig 5C , S2 Table ) . Thus , 10 times more RefSeq genes were upregulated in Dnmt3l KO spermatocytes compared with Pld6 KO spermatocytes . Only a small proportion of the upregulated genes exhibited significantly decreased levels of promoter methylation in Dnmt3l KO spermatogonia ( Fig 5D ) , suggesting that promoter methylation of the genes themselves is largely irrelevant . In P0 testes , a smaller number of genes were affected compared with that observed in L/Z spermatocytes: 50 and 143 genes were affected ( >2-fold and <1/2 , q-val < 0 . 05 ) in Pld6 KO and Dnmt3l KO mutants , respectively , and antisense piRNAs and promoter methylation seemed irrelevant ( S10 Fig ) . Retrotransposons may provide alternative promoters for neighboring genes , and such fusion transcripts have been observed in Setdb1 KO ESCs and PGCs [5 , 39] . Although we did not find direct evidence for fusion transcripts in our single-end RNA-seq data , Pld6 KO and Dnmt3l KO spermatocytes contained derepressed L1 , IAP , and MERVK copies whose transcription appeared to extend beyond their 3’ end and reach the nearby RefSeq genes . For example , an ectopic activation of Tecrl in both mutants was accompanied by derepression of an L1Md_Gf copy located 35-kb upstream of this gene and the region between them ( Fig 6A ) . Similarly , a MMERVK10C copy appeared to drive its downstream gene in Dnmt3l KO spermatocytes , with a lesser effect observed in Pld6 KO spermatocytes ( Fig 6B ) . We also found that the antisense promoter [40] of a full-length copy of L1Md_T ( TF-type L1 ) , located in the eighth intron of Afm in an antisense orientation , likely drove the transcription of downstream exons in both mutants ( Fig 6C ) . In another example , an intronic IAP copy in Slc15a2 appeared to drive the transcription of downstream exons in both mutants , but at a much lower level in Pld6 KO spermatocytes ( Fig 6D ) . Thus , of the 19 genes that exhibited a >3-fold increase in expression in Pld6 KO spermatocytes , 13 ( 68% ) were accompanied by an activated L1 ( 11 genes ) or RLTR10 ( two genes ) copy ( P = 4 . 4 × 10−7 , Fisher’s exact test; Fig 6E , S3 Table ) . Of the 91 genes that exhibited a >3-fold increase in expression in Dnmt3l KO spermatocytes , 37 ( 41% ) were accompanied by an activated L1 ( 23 genes ) , IAP ( seven genes ) , MERVK10/RLTR10 ( six genes ) , or MMERGLN ( one gene ) copy ( P = 2 . 2 × 10−16; Fig 6E , S4 Table ) . Of the remaining 54 genes , nine showed a large decrease ( <0 . 3 ) in promoter methylation in Dnmt3l KO spermatocytes . These results demonstrate that , in spermatocytes , the derepression of retrotransposons disrupts the integrity of the transcriptome by generating fusion transcripts . In contrast , we did not find such fusion transcripts in P0 testes of either mutants , presumably because of the lower levels of derepression of retrotransposons at this stage . It is possible that the higher expression of the RefSeq genes observed here resulted from regional activation , rather than from the derepression of individual retrotransposons . However , regional activation is unlikely because we did not observe increased expression of the neighboring RefSeq genes ( <100 kb ) ( Fig 6F ) . We further examined whether the presence or absence of retrotransposons affects the induction of the RefSeq genes using strain hybrids . As some of the retrotransposon copies that drive the nearby genes were absent from the genome of the MSM/Ms strain [41] ( S11 Fig ) , we generated Pld6 KO and Dnmt3l KO mice in the F1 hybrid background ( MSM/Ms × C57BL/6J ) . PolyA ( + ) RNA-seq analyses of L/Z spermatocytes using single-nucleotide polymorphisms revealed that the RefSeq alleles that are activated in both mutants are almost exclusively of C57BL/6J origin ( S11 Fig ) . These results strongly suggest that the increased gene expression results from the derepression of nearby retrotransposons .
Robust silencing of retrotransposons is most likely achieved by a combination of DNA methylation , histone modifications , and small-RNA-mediated RNA degradation in mammalian cells . Among these mechanisms , DNA methylation is predominant in most cell types [2 , 3 , 12 , 42] . Previous studies suggest that piRNAs guide the de novo methylation of some retrotransposons in prospermatogonia and that the increased expression of retrotransposons in piRNA-deficient germ cells may be caused by the failure in de novo methylation [20 , 23–25] . However , in these studies , the expression was studied only in postnatal testes at P10 or later , and not in prospermatogonia . The present study showed that the loss of methylation caused by a Dnmt3l mutation did not severely affect the expression of any retrotransposons in newborn prospermatogonia , and that the Pld6 mutation had a greater impact than did the Dnmt3l mutation ( Fig 2 ) ; therefore , it is likely that posttranscriptional silencing is the primary strategy used for retrotransposon silencing in this cell type . In E13 . 5 PGCs , many retrotransposons are in fact active , but their expression levels decrease in E16 . 5 prospermatogonia [28] , in which de novo methylation has not yet been completed ( Fig 1B , S4 Fig ) . Of note , de novo methylation is especially delayed in retrotransposon-rich , GC-poor genomic regions at this stage ( S2 Fig ) . Instead , the decline in retrotransposon expression appears to coincide with the accumulation of PIWI proteins and piRNAs [20 , 28 , 43 , 44] . It should be noted that prospermatogonia are mitotically arrested: after the activation of retrotransposons , their RNA can persist without dilution via cell division . Thus , RNA cleavage guided by piRNAs would be beneficial to the host . Mammalian oocytes , which remain in the meiotic prophase for a long period and maintain a low methylation level [7] , also use this strategy , and defects in the piRNA system in oocytes cause increased expression of some retrotransposons [45] . In contrast , DNA methylation is more important than RNA cleavage for retrotransposon silencing in spermatocytes . Decreased methylation had a great impact on the silencing of many retrotransposons , even in the presence of an intact piRNA system ( Fig 4 ) . It should be noted that the increased expression of specific retrotransposons ( i . e . , L1 and MMERGLN ) observed in Pld6 KO spermatocytes correlates well with the failure in de novo methylation . Similarly , disruption of Piwil4 ( expressed only in the fetal stage ) results in a severe decrease in L1Md_Gf methylation and in an increase in its expression in postnatal testes [23] . In contrast , the same mutation does not affect methylation or expression of L1Md_A and IAP [23 , 29] . These results indicate that the methylation pattern established in prospermatogonia has a long-term effect on retrotransposon silencing . The observed switch in the relative contribution of the different silencing mechanisms during germ cell development seems reasonable because methylation can be more easily maintained in postreplicative meiotic cells , despite the presence of dynamic histone modification changes and replacements . However , we note that piRNA-guided RNA cleavage did play a role in spermatocytes , which was consistent with previous work [37] . It should be noted that the ETn , MusD , and MERVL families , the transcriptional competence of which has been demonstrated in other cells [46–48] , are not activated strongly in either Pld6 KO or Dnmt3l KO mutants . Therefore , other mechanisms should compensate for the lack of piRNAs or DNA methylation . H3K9me3 is a likely candidate , because KO mutations of Setdb1 , an enzyme that is responsible for this modification , increase the expression of some LTR elements , including the IAP , MMERVK10C , and ETn families , in ESCs and PGCs [4 , 49] . In contrast , a role for H3K9me2 in this process is unlikely as its level is very low in PGCs and undifferentiated spermatogonia [50 , 51] . Another candidate is H4R3me2 , because a KO mutation of its responsible enzyme , PRMT5 , leads to the activation of retrotransposons in PGCs [52] . However , PRMT5 has a large number of targets for arginine methylation , including PIWIL4 , PIWIL2 , and TRIM28/KAP1 [53]; thus , the manner via which this enzyme contributes to retrotransposon silencing needs to be clarified . In addition to the repressive chromatin modifications and the piRNA system , specific transcriptional activators and/or repressors may be involved here . For example , the L1 family can be repressed by ZBTB16/PLZF and SOX2 and activated by LEF1 [54–56] . ZBTB16 , a factor involved in L1 silencing in testes [55] , is expressed in prospermatogonia and undifferentiated spermatogonia , but not in spermatocytes [57] , suggesting that a combinatorial loss of this repressor and methylation may allow L1 expression in spermatocytes . Expression of SOX2 is high in PGCs , but very low in E15 . 5 prospermatogonia [58] , and its role in retrotransposon silencing in germ cells remains to be explored . Finally , the derepression or activation of retrotransposons affected RefSeq genes in Pld6 KO and/or Dnmt3l KO spermatocytes , thus disrupting the integrity of the transcriptome ( Fig 6 ) . The L1 family is the most predominant retrotransposon species that affected nearby genes . In particular , the antisense promoter of L1Md_T [40] often drives fusion transcripts in the mutant spermatocytes . Our results indicate that transcriptional silencing of retrotransposons is important for the maintenance of not only genomic integrity , but also transcriptomic integrity in meiotic male germ cells . In conclusion , our results indicate that the combinatorial use of the piRNA system and DNA methylation effectively counteracts retrotransposon activities in developing male germ cells . In prospermatogonia , piRNA-guided de novo DNA methylation occurs only in a small subset of retrotransposons , and piRNA-guided RNA cleavage is the major mechanism underlying retrotransposon silencing . In spermatocytes , however , although piRNA-guided RNA cleavage also plays a role , transcriptional silencing by DNA methylation becomes far more important . Thus , there is a shift in the relative contribution of transcriptional and posttranscriptional mechanisms to retrotransposon silencing along with the dynamic epigenome changes during male germ cell development .
Pld6 KO and Dnmt3l KO mice were described previously [19 , 59] . These mutant lines were backcrossed to C57BL/6J for more than 12 generations . To obtain mutant mice of the ( MSM/Ms × C57BL/6J ) F1 background , we crossed heterozygous mice with MSM/Ms mice for six generations , followed by crossing with heterozygous mice of the C57BL/6J background . The animal experiments were conducted according to Japanese Act on Welfare and Management of Animals , Guidelines for Proper Conduct of Animal Experiments ( published by Science Council of Japan ) , Fundamental Guidelines for Proper Conduct of Animal Experiment and Related Activities in Academic Research Institutions ( published by Ministry of Education , Culture , Sports , Science and Technology , Japan ) , Regulation for Animal Experiments at Kyushu University , and Regulation for Animal Experiments at Nagoya University . The protocols have been approved by Animal Experiments Committee in Kyushu University ( A26-010-3 ) and Nagoya University ( 2016070501 , 2017030268 ) . Spermatogonia were isolated from P7 testes by FACS , using a FACSAria II instrument ( BD Biosciences ) , an anti-EpCAM antibody , and a secondary antibody labeled with Alexa Fluor 488 , as described previously [27] . The high purity of cell preparations was validated by measuring the DNA methylation level at the Lit1 differentially methylated region ( S12 Fig ) , which should be unmethylated in male germ cells and 50% methylated in somatic cells . Spermatogonia , spermatocytes , and round spermatids were isolated from P21 testes by FACS , as described previously [60] . The differentiation stages of the collected cells were confirmed by immunostaining with anti-SYCP3 and anti-γ-H2AX antibodies . Genomic DNA ( 60–130 ng ) was prepared from P7 spermatogonia ( about 15 , 000 cells ) using the standard procedure involving proteinase K digestion , phenol/chloroform extraction , and ethanol precipitation . Libraries for whole-genome bisulfite shotgun sequencing were prepared using the post-bisulfite adaptor tagging method [61] . Single-end 100-bp sequencing was performed using HiSeq2500 ( Illumina ) . The software used to generate bcl files included RTA 1 . 71 . 21 . 3 and HCS 2 . 0 . 12 . 0 . Approximately 130–150 million reads were obtained for each sample , with an average sequencing depth of about 1 . 7 . After removing the first 6 nt , last 15 nt , and any additional low-quality stretches ( Q score < 30 ) , Bismark [62] was used with default parameters for the analysis of methylation in unique regions of the mouse genome ( mm10 ) . The mapping efficiency of the datasets was 54 . 1%–69 . 9% and the bisulfite conversion rate of the samples was 99 . 2%–99 . 3% . Biological replicates for each category ( [WT] n = 3 , Pld6+/+ , Pld6+/- , and Dnmt3l+/- mice; [Pld6 KO] n = 2; [Dnmt3l KO] n = 2 ) showed highly concordant DNA methylation patterns ( R = 0 . 94 to 0 . 97 ) . To analyze methylation at retrotransposons specific to the genus Mus , the consensus sequences listed in the RepeatMasker library were downloaded from RepBase [63] . The retrotransposon sequences and sequencing reads were appropriately processed , and mapping was performed using Bowtie2 [64] with the default parameters . The output files were used to determine the methylation statuses of individual CpG sites . The published whole-genome bisulfite shotgun sequencing data for E13 . 5 PGCs and E16 . 5 prospermatogonia [32] , Piwil2 KO spermatogonia [28] , and Pwil4 KO spermatogonia [29] were downloaded from the DDBJ Sequence Read Archive ( http://trace . ddbj . nig . ac . jp/dra/index . html ) . Total RNA was isolated from P0 testes and FACS-purified P21 germ cells by acid/phenol extraction using Isogen ( Nippon Gene ) . The libraries for polyA ( + ) RNA-seq were prepared from 2 μg ( P0 testes ) or 20 ng ( L/Z spermatocytes ) of total RNAs ( RIN score > 9 ) using the TruSeq stranded mRNA LT Sample Prep kit ( Illumina ) , and were sequenced using HiSeq1500 in a rapid mode . The sequencing runs were 100-bp paired-end for P0 testes ( 15–40 million read pairs per sample ) and 50-bp single-end for L/Z spermatocytes ( 9–21 million reads per sample ) . TopHat2 and Cuffdiff [65 , 66] were used for gene expression analysis . The mapping efficiency was 90%–98% . To analyze retrotransposon expression , reads were mapped to a list of retrotransposon consensus sequences by Bowtie2 using the following options: -L 10 , -a , -D 20 , -R 20 , and -i S , 1 , 1 . 15 . For reads that showed the best mapping score with more than one retrotransposon , the read counts were divided by the number of the corresponding retrotransposons , to calculate reads per kilobases per million mapped reads ( RPKM ) . For retrotransposons showing very low expression in the WT control ( RPKMWT <1 ) , the fold change was calculated by adding an identical number to both the numerator and denominator of RPKMKO/RPKMWT , so that the denominator became 1 . This alleviated possible experimental fluctuations in fold change calculated using low RPKMs . For P0 testes , we used the first 50-bp sequences of the first read of the 100-bp paired-end reads . Small RNA-seq libraries were prepared from 400–600 ng of total RNA from P0 testes using NEBNext small RNA kit ( New England Biolabs ) . Small RNA-seq libraries for spermatogonia and L/Z spermatocytes were prepared from 6–42 ng of total RNAs ( 24 , 000–150 , 000 cells ) using the TruSeq Small RNA Sample Prep kit ( Illumina ) . These libraries were sequenced using HiSeq1500 in a rapid mode for 50-bp single-end runs ( yielding 14–62 million reads per sample ) . Reads corresponding to abundant noncoding RNA sequences , such as rRNAs and tRNAs , and miRNAs were removed using SeqMap [67] , and the remaining reads with a length of 24–33 nt were then mapped to the retrotransposon consensus sequences that were used for the mRNA analysis by SeqMap , allowing two mismatches . For reads showing the least mismatches with more than one retrotransposon , the read counts were divided by the number of the corresponding retrotransposons , to calculate reads per million reads ( RPM ) . 5'-RACE-seq libraries were prepared from 700 ng of total RNA from P0 testes as described previously [35 , 36] with some modifications . After removal of rRNAs using Ribo-zero Gold H/M/R kit ( Illumina ) , an RNA adaptor ( 5'- GUUCAGAGUUCUACAGUCCGACGAUC-3'; SR adaptor ) was ligated to RNAs using NEBNext small RNA kit . The ligated RNAs were used as a template for reverse transcription with random primers ( 5'-AGACGTGTGCTCTTCCGATCTNNNNNN-3' ) . Resulting cDNAs were amplified by PCR using NEBNext small RNA kit , and DNA fragments of 250–400 bp were purified from an agarose gel . The libraries were sequenced using HiSeq1500 in a rapid mode for 50-bp single-end runs . After removing the adaptor sequence and low quality bases , the reads were mapped to the retrotransposon consensus sequences by bowtie2 and to the mouse genome ( mm10 ) by TopHat2 . Testes collected at P0 were treated with 0 . 125% trypsin and 0 . 5 mM EDTA to obtain cell suspensions . After washing with phosphate-buffered saline supplemented with 10% fetal bovine serum , the cells were incubated in DMEM supplemented with 10% fetal bovine serum and 0 . 5 mM ethynyl uridine for 30 min at 32°C using the Click-iT Nascent RNA Capture Kit ( Life Technologies ) . The treated cells were harvested and RNA was extracted immediately using Isogen . Ethynyl uridine-labeled nascent RNAs were purified according to the manufacturer’s instructions . cDNA was synthesized on beads using the PrimeScript RT reagent Kit with gDNA Eraser ( Takara Bio ) , and analyzed by real-time PCR using primers specific for each retrotransposon ( S5 Table ) . The deep-sequencing data have been deposited in the Gene Expression Omnibus ( GEO ) under the accession number GSE70891 . | Retrotransposons are a class of transposable elements , of which mobility has mutagenic potential . Therefore , it is important to regulate the expression of retrotransposons for maintaining the genomic integrity . In male germ cells , DNA methylation and the piRNA system are thought to play roles in retrotransposon silencing . However , genome-wide DNA methylation is once erased ( in primordial germ cells ) and reestablished ( in prospermatogonia ) during development . In prospermatogonia , piRNAs guide de novo DNA methylation at some retrotransposons . To clarify the contribution of DNA methylation and the piRNA system to retrotransposon silencing in the course of male germ cell development , we analyzed DNA methylation and RNA expression in Dnmt3l and Pld6 knockout mice , which are defective in de novo DNA methylation and piRNA biogenesis , respectively . Our results reveal that , in prospermatogonia , the piRNA system works mainly at the posttranscriptional level , and plays a more important role than does DNA methylation in retrotransposon silencing . However , DNA methylation becomes much more important in later stages when germ cells enter meiosis ( in spermatocytes ) . We also found that hypomethylated retrotransposons can drive ectopic expression of nearby genes; therefore , their transcriptional silencing by DNA methylation is important for maintaining the transcriptomic integrity as well . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
]
| [
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"spermatocytes",
"retrotransposons",
"spermatogonia",
"germ",
"cells",
"genetic",
"elements",
"epigenetics",
"dna",
"molecular",
"biology",
"techniques",
"rna",
"sequencing",
"dna",
"methylation",
"chromatin",
"sperm",
"research",
"and",
"analysis",
"methods",
"chromosome",
"biology",
"animal",
"cells",
"gene",
"expression",
"chromatin",
"modification",
"dna",
"modification",
"molecular",
"biology",
"biochemistry",
"testes",
"cell",
"biology",
"nucleic",
"acids",
"anatomy",
"genetics",
"transposable",
"elements",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"genomics",
"mobile",
"genetic",
"elements",
"genital",
"anatomy"
]
| 2017 | Switching of dominant retrotransposon silencing strategies from posttranscriptional to transcriptional mechanisms during male germ-cell development in mice |
DNA methylation is an epigenetic modification involved in regulatory processes such as cell differentiation during development , X-chromosome inactivation , genomic imprinting and susceptibility to complex disease . However , the dynamics of DNA methylation changes between humans and their closest relatives are still poorly understood . We performed a comparative analysis of CpG methylation patterns between 9 humans and 23 primate samples including all species of great apes ( chimpanzee , bonobo , gorilla and orangutan ) using Illumina Methylation450 bead arrays . Our analysis identified ∼800 genes with significantly altered methylation patterns among the great apes , including ∼170 genes with a methylation pattern unique to human . Some of these are known to be involved in developmental and neurological features , suggesting that epigenetic changes have been frequent during recent human and primate evolution . We identified a significant positive relationship between the rate of coding variation and alterations of methylation at the promoter level , indicative of co-occurrence between evolution of protein sequence and gene regulation . In contrast , and supporting the idea that many phenotypic differences between humans and great apes are not due to amino acid differences , our analysis also identified 184 genes that are perfectly conserved at protein level between human and chimpanzee , yet show significant epigenetic differences between these two species . We conclude that epigenetic alterations are an important force during primate evolution and have been under-explored in evolutionary comparative genomics .
The genomic era is characterized by different comparative approaches to understand the effect of genomic changes upon phenotypes . In the context of human evolution , the genomes of all species of great apes have now been sequenced [1]–[4] allowing nucleotide resolution comparisons to understand the evolution of our genome . However , in contrast to these advances in comparative genomic analyses , there has been relatively little progress in the understanding of the evolution of genome regulation [5]–[9] . DNA methylation is an important epigenetic modification found in many taxa . In mammals , it is involved in numerous biological processes such as cell differentiation , X-chromosome inactivation , genomic imprinting and susceptibility to complex diseases [10]–[13] . Promoter hypermethylation is generally thought to act as a durable silencing mechanism [14] . However , the exact relationship between DNA methylation and gene expression is not clear since recent studies have also linked gene body methylation with transcriptional activity and alternative splicing [15]–[17] . At some loci DNA methylation patterns are influenced by the underlying genotype [18]–[20] . However , due to the fact that patterns of DNA methylation can change during development [16] , [21] , [22] or as a result of environmental factors [23] , [24] , the exact mechanisms governing DNA methylation states remain unclear . Most efforts to understand DNA methylation changes in primates have focused on the comparison of human with chimpanzee or macaque [6] , [7] , [9] , [25] . This is largely attributable to the difficulty of obtaining samples from endangered species and the lack of genome sequence for the great apes . The publication last year of draft sequences of the gorilla [2] and bonobo [3] genomes facilitates a more accurate characterization of the species-specific events in all the great ape phylogeny , and interrogation of this epigenetic modification from an evolutionary point of view . Studies to date have found that DNA methylation profiles are , in general , more similar between homologous tissues than between different tissues of the same species [9] . However , differentially expressed genes between human and chimpanzee are often associated with promoter methylation differences , regardless of tissue type , establishing that some differences in the expression rates of genes between the species are associated with differences in DNA methylation . It is estimated that around 12–18% ( depending on the tissue ) of interspecies differences in gene expression levels could be explained by changes in promoter methylation [9] . Here we present the first comparative analysis of DNA methylation patterns between humans and all great ape species , allowing us to recapitulate the evolution of CpG methylation over the last 15 million years in these species . We used Illumina Methylation450 BeadChips to profile DNA methylation genome-wide in blood-derived DNA from a total of 9 humans and 23 wild-born individuals of different species and sub-species of chimpanzee , bonobo , gorilla and orangutan . We observed that the methylation values recapitulate the known phylogenetic relationships of the species , and we were able to characterize methylation differences that have occurred exclusively in the human lineage and among different great apes species . We also identified a significant positive relationship between the rate of coding variation and alterations of methylation at the promoter level , indicative of co-occurrence between evolution of protein sequence and gene regulation
We obtained cytosine methylation profiles of peripheral blood DNA isolated from a set of males and females of nine humans , five chimpanzees , six bonobos , six gorillas and six orangutans ( Table S1 ) using the Illumina HumanMethylation450 DNA Analysis BeadChip assay . Because the probes on the array are designed using the human reference genome , we performed a set of strict filters to remove divergent probes that could bias our methylation measurements . The filtering was based on the number and location of mismatches with their target site in each species genome assembly tested [1]–[4] ( Figure S1 and Figure S2 , see Methods ) . This resulted in the retention of 326 , 535 probes ( 72% ) in chimpanzee , 328 , 501 probes ( 73% ) in bonobo , 274 , 084 probes ( 61% ) in gorilla and 197 , 489 probes ( 44% ) in orangutan , consistent with their evolutionary distance to human . We also applied a second filtering step to remove probes that overlapped with intra-species common variation ( see Methods ) [26] . Cell heterogeneity may also act as a confounder when measuring DNA methylation , particularly from whole blood [27] . Due to the difficulty of obtaining fresh blood samples from wild-born great apes , we were unable to either isolate a specific blood cell type or measure the cellular composition of the blood samples from which our DNA was extracted . To minimize false positives resulting from different cellular compositions or other confounders , we performed two filtering steps . First , we removed CpG sites that showed differential methylation in human between whole blood and each of the two most abundant subtypes of blood cell ( CD4+ T-cells and CD16+ neutrophils , see Methods ) . Second , we required a minimum threshold of at least 10% change in mean methylation ( mean β-value difference ≥0 . 1 ) at each CpG in order to define differential methylation between species . As a result of this threshold , differences in other cell types that account for <10% of the cellular composition of blood , are unlikely to affect our results ( see Methods ) . In this work we used two different datasets: i ) we confined our analysis to 114 , 739 autosomal probes and 3 , 680 probes on the X chromosome that were directly comparable across all the species to facilitate an unbiased comparison of human and all great apes ( 32 individuals ) , and ii ) we used 291 , 553 shared autosomal probes between humans and chimpanzees to compare these two species . We performed separate analyses of autosomal and sex-linked probes to prevent confounding effects of X chromosome inactivation on DNA methylation between males and females [13] . Unless specifically mentioned , all results presented below refer to analysis of autosomal probes only . To investigate the global correspondence of DNA sequence differences between species and the degree of methylation changes , we examined the Enredo-Pecan-Orthus ( EPO ) whole-genome multiple alignments of human , chimpanzee , gorilla , and orangutan [Ensemble Compara . 6_primates_EPO] [28] , [29] and we calculated pairwise distances between these four species . Upon comparison of these sequence distances and methylation data ( see Methods ) , we observed a high global correlation between sequence substitution and methylation divergence ( R2 = 0 . 98 , p = 0 . 0003 ) ( Figure 1A ) . We then constructed a neighbor-joining phylogenetic tree based on the methylation levels of the 114 , 739 autosomal CpGs measured in all individuals and species ( Figure S3 ) . This tree accurately recapitulates the known evolutionary relationships of great apes , including the separation at sub-species level of the Pan , Gorilla and Pongo genera . These results are also maintained when using only the subset of probes that have a perfect match ( n = 31 , 853 ) to each of the primate reference genome and contain no common polymorphisms suggesting that that methylation levels are associated with the evolutionary history of these species ( Figure 1B ) . Due to the relatively recent origin of all partitions within genera of great apes [2]–[4] and our sample size , we focused our analysis on changes at the genus taxonomic level . To identify only those methylation differences that represent fixed changes between these groups and to avoid possible artifacts due to intraspecific polymorphism , we retained only those CpGs with low methylation variance within each genus ( intragenus standard deviation <0 . 1 ) . This filtering step resulted in the removal of 1 , 377 CpGs in human , 5 , 224 in the Pan sp . , 5 , 289 in Gorilla sp . and 5 , 740 in Pongo sp . , with the resulting final set being 99 , 919 CpGs shared across all five species , covering 12 , 593 genes ( ≥2 probes within a 1 kb interval and overlapped with RefSeq genes , −1500 bp transcription start site ( TSS ) to 3′UTR ) . The proportion of sites removed in this step are consistent with the relative population diversity within each of these species [2]–[4] , [30] . Approximately 22% of the sites tested ( n = 21 , 884 CpGs ) showed no significant changes among any of the species ( conserved sites: Wilcoxon rank-sum test , FDR-adjusted p>0 . 05 and mean β-value difference all cases <0 . 1 ) . Comparison of genes linked with these sites showed an enrichment of Gene Ontology ( GO ) categories for fundamental cellular processes . In contrast , we identified 2 , 284 human-specific ( 2 . 3% ) differentially methylated CpGs , 1 , 245 specific to Pan species ( 1 . 2% ) , 1 , 374 specific to Gorilla species ( 1 . 4% ) . and 5 , 501 changes specific to Pongo species ( 5 . 5% ) ( Wilcoxon rank-sum test , FDR-adjusted p<0 . 05 and mean β-value difference ≥0 . 1 , see Methods ) ( Figure 2 and Table S2 ) . We clustered these sites into regions with at least two nearby differentially methylated CpGs ( <1 kb interval ) and overlapped with RefSeq genes ( −1500 bp from TSS to 3′UTR ) . Doing this , we identified 171 genes that show human specific methylation patterns , 101 genes in Pan species , 101 genes in Gorilla species and 445 genes in Pongo species ( Table S3 ) . We observed that this spatial aggregation of differentially methylated sites is significantly non-random ( random permutation compared to all 99 , 919 CpGs used in our analysis , p<0 . 0001 , see Methods ) and a simple Likelihood Ratio Test also suggested a non-homogenous rate of methylation changes in the human and great ape evolution ( LRT , p<10−5 , see Text S1 ) . Using the Genomic Regions Enrichment of Annotation Tool ( GREAT ) [31] ( see Methods ) we identified significant enrichments ( FDR-corrected p<0 . 05 ) for several biological processes associated with lineage-specific differentially methylated genes . Within the human-specific differentially methylated regions most of the categories found were related with the circulatory system , as expected from testing blood-derived DNA . However , we also found enrichment for terms related to development and neurological functions , including semicircular canal formation and facial nucleus development ( Table S4 ) . The use of disease ontology terms showed that mutations in several of these genes are known to be associated with diseases including Möbius syndrome , Asperger's syndrome and malignant hyperthermia . In the Pan genus ( chimpanzee and bonobo ) we observed significant enrichments among genes involved in epithelial development and the respiratory system , while in Pongo species ( orangutan ) enriched categories included a variety of basic metabolic and reproductive processes ( Table S4 ) . We found a particular set of genes with methylation changes specifically in the human lineage including examples such as ARTN , COL2A1 and PGAM2 ( Figure 3 ) . ARTN is a neurotrophic factor which supports the survival of sympathetic peripheral neurons and dopaminergic neurons . COL2A1 encodes the alpha-1 chain of type II collagen , which is found primarily in the cartilage , the inner ear and the vitreous humor of the eye . Mutations in this gene are associated with several developmental syndromes [32] . PGAM2 is an enzyme involved in the glycolytic pathway , mutations in which are associated with glycogen storage disease [MIM: 261670] , a defect that causes muscle cramping , myoglobinuria and intolerance for strenuous exercise . In addition to the identification of regions showing changes in a single species , we also detected loci with more complex changes in methylation profiles among great apes . One example is the promoter region associated with different isoforms of the GABBR1 gene ( Figure 3D ) . This gene encodes the GABAB receptor 1 , a G protein-coupled receptor involved in synaptic inhibition , hippocampal long-term potentiation , slow wave sleep , muscle relaxation and sensitivity to pain . While human and gorilla have GABBR1 promoter methylation patterns that are broadly similar to each other , orangutan shows relative hypomethylation across this region . In contrast chimpanzee and bonobo show increased methylation specifically at the TSS of long GABBR1 isoforms , and intermediate methylation levels associated with the short isoform . These data suggest some epigenetic differences among primates are associated with isoform regulation . We observed a highly non-random distribution of the differentially methylated CpGs ( Figure 4A and 4B ) in relationship to gene annotations and CpG density . From the functional distribution standpoint , there was a significant excess of changes ( p<0 . 0001 , permutation test , see Methods ) for sites located within 1 , 500 bp upstream of gene TSSs , gene bodies and intergenic regions , and from the CpG content standpoint , differential methylation occurred preferentially in CpG shores ( ±2 kb CpG island ) and non-CpG island regions . These results highlight CpG shores as epigenetically variable regions , as it has been observed in human development and disease [12] , [33] . In contrast , the regions immediately surrounding gene TSSs ( −200 bp of the TSS and 1st exon ) and CpG islands showed relative conservation of methylation . We also observed a significant difference in the distribution of methylation levels at differentially methylated sites compared to the rest of the genome ( Figure 4C ) . While the overall genome-wide pattern of methylation levels shows a strongly bi-modal distribution , with most sites having either very high or very low methylation levels , sites of evolutionary change have a significantly different distribution to genome wide distribution ( p = 2 . 2×10−16 Kolmogorov-Smirnov test ) , showing intermediate methylation levels , which has been shown to be a hallmark of distal regulatory elements . [34] . In female mammals , X chromosome inactivation ( XCI ) is maintained via a number of epigenetic marks , including altered DNA methylation [35] , [36] . Consistent with a role in XCI , the majority of sites we identified on the X chromosome in great apes showed relatively higher methylation levels in females versus males due to the contribution from the inactive X chromosome ( 63% , p = 0 . 005 , Figure S4A ) . We searched for CpG sites on the X chromosome presenting no significant changes between males and females in a specific lineage ( mean β-value difference <0 . 1 ) but showing significant gender differences in all the other species ( see Methods ) . This analysis identified 22 CpGs in human , 59 in chimpanzee and bonobo , 72 in gorilla and 41 in orangutan ( Table S5 ) . Some regions are particularly interesting such as the MID1 gene which has been previously reported as a gene subject to X-inactivation in humans but not in mouse [37] . Our results suggest that this gene may escape XCI in the Pan lineage , but not in all other great apes . Similarly the HTR2C gene shows multiple probes upstream of the TSS with similar patterns of methylation in both male and female humans , potentially suggesting that this gene escapes XCI in the human lineage . In contrast , the same sites show significantly higher methylation levels in females compared to males in all other primate species , suggesting that in these species HTR2C may be subject to XCI ( Figure S4B ) . Using published RNAseq data [38] , we did not observe a female-specific increase in HTR2C gene expression for in humans , although we note that many genes escaping XCI show no clear sex differences in expression levels [39] . To maximize the identification of altered methylation patterns between human and our closest living relative , the chimpanzee , we performed a pairwise comparison of these two species using a larger dataset of 289 , 007 filtered probes common to human and chimpanzee . We used the chimpanzee species and not the whole genera to make use of the better annotation in the genome reference assembly for this species compared to the rest of non-human primate genome reference assemblies [1] . We identified 16 , 365 sites that showed significant hypermethylation in human , and 9 , 693 sites showing significant hypomethylation ( FDR-adjusted p<0 . 05 , β-value difference ≥0 . 1 ) . This represents ∼9% of the total number of sites tested , and includes ∼2 , 500 genes ( ≥2 differentially methylated CpGs within a 1 kb interval and overlapped with RefSeq genes , −1500 bp TSS to 3′UTR ) . Using this larger dataset , we then investigated the relationship between the evolution of protein coding sequences and epigenetic change at promoter level . Using a curated set of 7 , 252 human∶chimpanzee 1∶1 orthologs [1] we identified 745 genes ( ∼10% of those tested ) that showed at least two differentially methylated sites at the promoter ( −1500 bp from the TSS to 1st exon , see Methods ) . We then compared both the number of amino acid changes and the KA/KI ratio ( the number of coding base substitutions that result in amino acid changes as a fraction of the local intergenic/intronic substitution rate ) of these differentially methylated genes against the remainder [1] ( Figure 5 ) . We observed a significant difference in both the number and rate of non-synonymous amino acid changes between genes with altered promoter methylation compared to those without significant methylation differences ( p<0 . 0001 , permutation test ) suggesting that rapid evolution at the protein coding level is frequently coupled with epigenetic changes in the promoter . We also observed similar results when using only those probes with a perfect match to the chimpanzee reference genome ( Figure S5 ) . An interesting example is the BRCA1 gene , which contains 32 amino acid changes between human and chimpanzee and has a KA/KI ratio of 0 . 69 ( three times the average of all orthologous genes ) . This gene shows large differences in methylation ∼1–1 . 5 kb upstream of the TSS ( Figure 6 ) . Previous studies have shown that methylation changes of this same region are associated with altered BRCA1 expression [40] . In contrast , we also observed 184 genes that show perfect human:chimpanzee conservation at the amino acid level , yet they show significant epigenetic differences at their promoter ( Table S6 ) . Within this set of genes , we observed significant enrichments for categories related with gene expression ( table S7 ) [41] , [42] . As our survey of evolutionary changes in primate DNA methylation patterns utilized DNA derived from whole blood , we tested whether these changes are also present in other somatic tissues by comparing against an independent dataset . A previous study [9] utilized a similar array platform , although with a much reduced probe density , to compare DNA methylation levels in humans and chimpanzees using DNA isolated from heart , liver and kidney . Comparing the 457 sites common to both datasets that we defined as differentially methylated in blood samples versus these three other tissues , we observed a highly significant trend for methylation differences identified between human and chimpanzee to be conserved across all four tissue types ( Figure 7 ) .
The primary focus to date for understanding human evolution from a comparative genomic perspective has been the study of changes in DNA sequence and gene expression levels [43]–[45] . Our study of DNA methylation profiles among human and great apes adds to this wealth of information , reinforcing the view that epigenetic changes contribute significantly to species divergence , and therefore they should be considered in studies of human evolution . In this study , one of the main challenges was the technical limitation stemming from the use of arrays designed against the human genome to profile methylation patterns in great ape species with divergent genomes . We utilized a set of filters to account for these differences , and were also able to replicate the results even after limiting our analysis to those probes with 100% identity in each of the non-human reference genome assemblies . Supporting a biological role for our findings , we observed that the clustering of differential methylation within each species was highly non-random , and showed significant enrichments within functional genomic elements . From a biological perspective , it is conceivable that differences in the constitutive fractions of whole blood between species might introduce a bias due to the fact that different cell types possess distinct epigenomes [27] . This limitation is shared by nearly all comparative molecular studies of primary tissues from endangered species ( i . e . great apes ) due to the difficulty of obtaining relevant samples , especially in the case of wild-born individuals as the ones used in this study . However , in order to minimize this problem we removed all CpG sites that vary significantly between whole blood and the most abundant cell populations in blood . We further required a minimum threshold of 10% change in global methylation between sites in these species in order to identify differentially methylated sites , meaning that changes in the prevalence of minor cell fractions would not influence the results . Finally , while all samples were obtained from adult individuals , we could not match the ages perfectly among all samples , so in order to compensate for this effect , and to minimize the effects of intraspecific polymorphism , we focused our study on sites with low intragenus variance . Our results show that ∼9% of the CpGs we assayed showed significant methylation differences between human and chimpanzee , including the promoter regions of 745 genes ( 10% of those tested ) . We estimate that over 2 , 500 genes present at least some methylation changes between human and chimpanzees ( ≥2 differentially methylated sites separated by ≤1 kb ) , a higher number than that known to be affected by copy number variation or under positive selection in the same species [46]–[49] . Although the arrays we used do not provide a complete and unbiased coverage of the genome , these data suggest that epigenetic changes have been frequent during recent primate evolution and represent an important substrate for adaptive modification of genome function . Underlining this idea , the changes we observed among primates are highly enriched for sites showing intermediate DNA methylation levels . Previous studies have shown that such methylation values are often a hallmark of distal regulatory elements [34] , suggesting that many epigenetic changes occurring among human and great ape species impact transcriptional regulation . Consistent with these findings , we detected global enrichments for epigenetic change within known regulatory regions , including distal regions upstream of gene transcription start sites and regions flanking CpG islands ( termed ‘CpG shores’ ) . We observed that the great ape phylogeny can be recapitulated from methylation data alone . Potential explanations for this are that methylation values could be driven by proximal DNA changes that were not controlled in this study , or that epigenetic changes have occurred independently of DNA sequence but are subject to similar rates of change either through selective pressures or neutral drift . Interestingly , we also identified a significant positive relationship between the rate of coding variation within genes and alterations of promoter methylation , suggesting a co-occurrence between changes in protein sequence and gene regulation that may be related to expression changes in fast evolving genes [50] . In contrast , and consistent with previous analysis indicating the importance of regulatory changes in evolution [51] , our study also identified scores of genes that are perfectly conserved at the amino acid level between human and chimpanzee , yet showing significant epigenetic change between these two species . Furthermore , gene ontology analysis of this set showed that they are significantly enriched for the functional category of gene expression . These observations highlight the evolutionary importance of epigenetic changes that affect gene regulation , and also demonstrate that sequence-based studies are insufficient to capture the full spectrum of evolutionary change . Overall our analysis identified >800 genes with significantly altered methylation patterns specifically within each species of human and great apes , including 171 with a methylation pattern unique to humans . Analysis of these 171 genes identified interesting enrichments for a number of functional categories that could suggest a relationship to human-specific traits . For example , we observed that genes involved in the regulation of blood pressure and development of the semicircular canal of the inner ear among others , were all highly enriched for DNA methylation changes specifically in the human lineage . While major changes in circulatory physiology are required for upright locomotion , the inner ear provides sensory input crucial for maintaining balance . Furthermore , a previous study of primates and other mammals has shown that the size of the semicircular canals is correlated with locomotion and with relatively larger canals found in species that utilize fast or agile movement [52] . While these trends hint at the potential importance of epigenetic changes in the evolution of several human-specific features , we caution that at this stage they should be considered as preliminary , as our studies were performed using DNA derived from whole blood , and it is well known that epigenetic patterns often vary widely between different tissues of an organism [27] . Therefore further studies in physiologically relevant tissues will be required to confirm the significance of these findings . However , we note that comparison with previously published data [9] suggests that many of the changes in DNA methylation that we detected between blood of human and chimpanzee appear to be conserved across several other tissues , suggesting that inter-specific differences observed in blood can in some cases be informative for other tissues . Although sequencing studies have undoubtedly provided major advances in our understanding of primate evolution , our analysis of primate epigenomes unveils many novel differences among the great apes that are not apparent from purely sequence-based approaches . Of particular note is the fact that we identify enrichments in multiple independent functional gene categories which suggests that regulatory changes may have played a key role in the acquisition of human-specific trait . Therefore , epigenetic alterations likely represent an important facet of evolutionary change in primate genomes . Future studies that integrate epigenetic data with recent detailed maps of functional elements , selective constraint and chromatin interactions in the human genome [53]–[55] will likely provide many novel insights into genomic and phenotypic evolution .
The non-human research has been approved by the ethical committee of the European Research Union . No living animal has been used and DNA has been obtained during standard veterinary checks . Methylation profiling of human subjects was approved by the Institutional Review Board of the Icahn School of Medicine at Mount Sinai ( HS#: 12-00567 HG ) . We obtained methylation data from peripheral blood DNA extracted from 9 adult humans , 5 chimpanzees , 6 bonobos , 6 gorillas and 6 orangutans . All individuals were unrelated adults and the non-human primates were all wild born . DNA samples were bisulfite converted , whole-genome amplified , enzymatically fragmented , and hybridized to the Infinium HumanMethylation450 BeadChip which provides quantitative estimates of methylation levels at 482 , 421 CpG sites distributed genome-wide . The assay was performed according to the manufacturer's instructions . The BeadChip array data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE41782 . Due to the low density of probes targeting non-CpG dinucleotides ( <0 . 7% ) on the array , we focused our study on CpG sites . Since the 50 bp probes on the array were designed against the human reference genome but we performed hybridizations utilizing DNA from different great ape species , we first mapped the probe sequences to the chimpanzee ( panTro3 ) , bonobo ( panPan1 ) , gorilla ( gorGor3 ) and orangutan ( ponAbe2 ) reference genomes using BWA [56] , allowing a maximum edit distance of 3 . We then assessed probe performance as a function of the number and relative location of sequence differences at the probe binding site in each primate genome ( Figure S1 and Figure S2 ) . Based on this analysis , in each species we only retained those probes that had either a perfect match , or had 1 or 2 mismatches in the first 45 bp but no mismatches in the 3′ 5 bp closest to the CpG site being assayed . We also removed all probes that contained human SNPs with minor allele frequency ≥0 . 05 within the last 5 bp of their binding site closest to the CpG being assayed [57] . Using published SNP data [26] for each species we removed probes containing SNPs with minor allele frequency ≥0 . 15 within the last 5 bp of their binding site closest to the CpG being assayed . We also removed all probes that contained more than two SNPs with minor allele frequency ≥0 . 15 in the first 45 bp . Methylation values for CpG sites in each sample were obtained as β-values , calculated as the ratio of the methylated signal intensity to the sum of both methylated and unmethylated signals after background subtraction ( β-values range from 0 to 1 , corresponding to completely unmethylated and fully methylated sites , respectively ) . Within each individual , probes with a detection p>0 . 01 were excluded . We performed a two color channel signal adjustment and quantile normalization on the pooled signals from both channels and recalculation of average β-values as implemented in “lumi” package of R [58] . The Illumina Infinium HumanMethylation450 BeadChip contains two assay types ( Infinium type I and type II probes ) which utilize different probe designs . As the data produced by these two assay types shows distinct profiles ( Figure S6 ) , to correct this problem we performed a BMIQ ( beta mixture quantile method ) [59] on the quantile normalized data sets . Using a published human data set [27] we identified differentially methylated sites between whole blood and CD4+ T-cells , and between whole blood and CD16+ neutrophils , representing the two most abundant cell fractions of blood ( comprising ∼13% and ∼65% , respectively ) ( Wilcoxon rank-sum test , FDR-adjusted p<0 . 05 and mean β-value difference in each case ≥0 . 1 ) . These sites ( n = 10 , 151 ) were removed to mitigate potential confounders due to differing proportions of blood cell types among primates , leaving for comparison only those sites that do not significantly vary among the most abundant cell types of blood . β-values can be interpreted as the percentage of methylation at a given site . A β-value of 0 . 1 indicates that there has been a change in methylation in 10% of the molecules tested . Because our analyses required a mean β-value difference >0 . 1 to achieve significance , this threshold means that changes in blood cell fractions representing <10% of whole blood will be unlikely to affect our results . The final dataset after all filtering steps comprised 114 , 739 probes shared across all great ape species , and 291 , 553 probes shared between human and chimpanzee . To investigate the global correspondence of DNA sequence differences between species and the degree of methylation changes , we examined the Enredo-Pecan-Orthus ( EPO ) whole-genome multiple alignments of human , chimpanzee , gorilla , and orangutan [Ensemble Compara . 6_primates_EPO] [28] , [29] . Considering only those blocks with alignments for all great apes , we first excluded regions containing gaps or indels and then calculated pairwise distances between these four species based on the frequency of single nucleotide substitutions . To calculate the global changes in methylation we used a distance matrix , we first averaged the β-values per probe within a species and then calculated the difference between two species using Euclidean distances . We built phylogenetic trees based on the methylation states of 114 , 739 filtered probes ( perfect match probes and probes containing 1 or 2 mismatches in the first 45 bp ) ( Figure S3 ) . We used the “ape” R package to construct the phylogenetic tree using the Neighbor-Joining algorithm and 1 , 000 bootstraps of the resulting tree [60] . We repeated the analysis using only the subset of probes with a perfect match to each of the primate reference genome assemblies ( n = 31 , 853 ) ( Figure 1B ) . To identify only those methylation differences that represent fixed changes between genera , we retained only those CpGs with low methylation variance within each genus ( intragenus standard deviation <0 . 1 ) . This filtering step resulted in the removal of 1 , 377 CpGs in human , 5 , 224 in the Pan genus , 5 , 289 in Gorilla and 5 , 740 in Pongo , with the resulting final set being 99 , 919 CpGs shared across all five species . We performed six pairwise comparisons among groups ( Human-Pan species/Human-Gorilla species/Human-Pongo species/Pan species-Gorilla species/Pan species-Pongo species/Gorilla species-Pongo species ) . We defined a site to be genus-specific differentially methylated if all three comparisons with other groups were significant ( Wilcoxon rank-sum test , FDR-adjusted p<0 . 05 ) and mean β-value difference in each case ≥0 . 1 . We also tried other statistical approaches ( linear modeling , limma package , [59] ) and obtained very similar results ( concordance for 98% of the sites ) . All coordinates quoted are based on hg19 . We intersected human probe coordinates provided by Illumina with RefSeq genes , retaining CpG sites overlapping genes ( −1500 bp from TSS to 3′UTR ) . We defined a gene to be differentially methylated if there were at least two differentially methylated CpG sites separated by ≤1 kb . To assess significance of these observations we performed a permutation test , as follows . Based on the number of differentially methylated sites detected in each species ( Human = 2 , 284; Pan = 1 , 245; Gorilla = 1 , 374; Orangutan = 5 , 501 ) we randomly sampled from the 99 , 919 CpGs and then determined the number of clusters ( at least two differentially methylated CpG sites separated by ≤1 kb ) , repeating this process 10 , 000 times to create the null distribution . The p-value corresponded to the number of times that differentially methylated clusters appeared within the null distribution divided by the number of permutations ( n = 10 , 000 ) . The Genomic Regions Enrichment of Annotations Tool ( GREAT version 2 . 0 . 1 ) [31] was utilized to identify significant enrichments ( FDR-corrected p<0 . 05 ) for Gene Ontology biological processes . While tools for identifying enriched GO terms are usually based on genes , GREAT permits the assignment of biological function to non-coding genomic regions by analyzing the annotations of nearby genes . For this analysis regulatory regions were associated to the single nearest gene situated within 10 kb . The background data set was the 99 , 919 CpG sites interrogated in all great ape species . In order to evaluate the positional context of the differentially methylated sites , we compared the distribution of these 10 , 404 sites detected among the primate species with all 99 , 919 CpGs tested . Permutation p-values were calculated as described above using 10 , 000 iterations . We performed two color channel signal adjustment and quantile normalization on males and females separately . Due to the different methylation pattern in females no BMIQ normalization was done in this data set . For studies of DNA methylation on the X-chromosome that might be linked with XCI between species , we searched for CpG sites presenting no significant changes between males and females in a specific lineage ( mean β-value difference <0 . 1 ) but showing significant changes in all the other species ( mean β-value difference between sexes >0 . 1 ) . The number of probes shared between human and chimpanzee after applying our mapping and SNP filters was 291 , 554 . Based on this set of probes , we performed a separate two color channel signal adjustment and quantile normalization of the raw data using only human and chimpanzee samples . We performed a BMIQ normalization to correct the probe design bias . After excluding probes with a standard deviation within either species >0 . 1 we retained a total of 289 , 007 probes . Differentially methylated sites were those with p<0 . 05 ( Wilcoxon rank-sum test , FDR-adjusted p<0 . 05 ) and a mean β-value difference ≥0 . 1 . From the total set of 13 , 454 human:chimpanzee orthologous genes [1] , we removed genes with <150 or >1500 amino acids , and then compared the number of amino acid changes and the KA/KI ratio of genes with robust alterations of promoter methylation ( mean β-value difference of top 2 probes within promoter ≥0 . 1 , considering CpGs located ≤1 , 500 bp upstream of Refseq gene TSSs , in the 5′UTR or the 1st exon , n = 745 ) versus those without methylation changes ( n = 6 , 507 ) . The Gene Ontology enRIchment anaLysis and visuaLizAtion tool ( GOrilla ) [41] , [42] was utilized to obtain the functional enrichments within the 184 genes conserved at amino acid level , yet having significant epigenetic differences at their promoter . The data set containing 7 , 252 human∶chimpanzee 1∶1 orthologs was used as a background . | Differences in protein coding sequences between humans and their closest relatives are too small to account for their phenotypic differences . It has been hypothesized that these differences may be explained by alterations of gene regulation rather than primary genome sequence . DNA methylation is an important epigenetic modification that is involved in many biological processes , but from an evolutionary point of view this modification is still poorly understood . To this end , we performed a comparative analysis of CpG methylation patterns between humans and great apes . Using this approach , we were able to study the dynamics of DNA methylation in recent primate evolution and to identify regions showing species-specific methylation pattern among humans and great apes . We find that genes with alterations of promoter methylation tend to show increased rates of divergence in their protein sequence , and in contrast we also identify many genes with regulatory changes between human and chimpanzee that have perfectly conserved protein sequence . Our study provides the first global view of evolutionary epigenetic changes that have occurred in the genomes of all species of great apes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| []
| 2013 | Dynamics of DNA Methylation in Recent Human and Great Ape Evolution |
The rate of germline mutation varies widely between species but little is known about the extent of variation in the germline mutation rate between individuals of the same species . Here we demonstrate that an allele that increases the rate of germline mutation can result in a distinctive signature in the genomic region linked to the affected locus , characterized by a number of haplotypes with a locally high proportion of derived alleles , against a background of haplotypes carrying a typical proportion of derived alleles . We searched for this signature in human haplotype data from phase 3 of the 1000 Genomes Project and report a number of candidate mutator loci , several of which are located close to or within genes involved in DNA repair or the DNA damage response . To investigate whether mutator alleles remained active at any of these loci , we used de novo mutation counts from human parent-offspring trios in the 1000 Genomes and Genome of the Netherlands cohorts , looking for an elevated number of de novo mutations in the offspring of parents carrying a candidate mutator haplotype at each of these loci . We found some support for two of the candidate loci , including one locus just upstream of the BRSK2 gene , which is expressed in the testis and has been reported to be involved in the response to DNA damage .
The rate of germline mutation is a key parameter in molecular evolution and population genetics . As the ultimate source of genetic novelty , germline mutations provide the raw material on which selection acts and the basis for genetic drift over time . Mutation rates are known to differ substantially between species [1] , and in eukaryotes , the single nucleotide mutation rate , fundamental to many demographic and evolutionary analyses , ranges over two orders of magnitude [2] . Methods to estimate the rate of de novo mutation predate the knowledge that DNA carries hereditary information . If the frequency of a deleterious allele is in mutation-selection balance , the rate at which deleterious alleles are removed through selection is equal to the rate at which novel alleles arise through mutation . This idea was used by Haldane to provide an indirect estimate of the rate of spontaneous haemophilia from prevalence estimates [3] . Subsequent estimates incorporated knowledge of the physical size of the locus ( or more precisely , the number of target nucleotides giving rise to the phenotype of interest ) to obtain per-base and per-generation mutation rate estimates ( e . g . [4 , 5] ) . More recently , whole genome resequencing methods have been used to obtain direct measurements of the human mutation rate from parent-offspring trios [6–9] . As well as enabling measurement of the genome-wide mutation rate , these studies have opened up the possibility of investigating factors contributing to variation in mutation rate between individuals . A study of 78 Icelandic parent-offspring trios estimated that 97% of variation in the number of de novo mutations called in offspring could be explained by the age of the father [8] , and other whole-genome studies have shown similar , albeit weaker , relationships between parental age and germline mutation rate [10–12] . Genetic and environmental factors may also influence the rate of germline mutation [10 , 13] , and a number of recent studies point to differences in the germline mutation spectrum between human populations [13–15] , consistent with a genetic contribution to variation in germline mutation . Indeed , several cancer-associated germline human mutations are known to affect genes involved in DNA proofreading and mismatch repair , increasing cancer risk through an elevated rate of somatic mutation [16 , 17] . For example , Lynch syndrome , which is associated with a very high lifetime risk of development of cancer of the colon and several other organs , results from germline mutations in a number of mismatch repair genes [18] . Although the effects of such mutations on the rate of germline mutation in human have not yet been determined , deficiency in mismatch repair is known to result in an elevated per generation mutation rate in yeast [10] . Here we investigate the feasibility of detecting genetic polymorphism associated with increased germline mutation rate by looking for an increase in the number of derived alleles in haplotypes carrying the mutator allele . Using simulation , we show that a mutator allele can result in a localized peak in the numbers of derived alleles in a subset of haplotypes , against a background of haplotypes with typical numbers of derived alleles . This is because in the region linked to the mutator allele , haplotypes containing it are always subject to the elevated mutation rate , whereas other haplotypes are only affected when they occur together with the mutator allele in a heterozygote , and this will occur only rarely if the mutator allele is rare . Detecting this pattern depends on persistence of the mutator allele for a large number of generations , and we discuss the likelihood of this , given estimates of the selective disadvantage of a mutator allele . We search for this pattern in data from the 1000 Genomes Project ( G1K ) and report and characterise a number of candidate mutator loci . For a subset of the candidate loci the highly derived haplotypes , characteristic of a mutator allele , were found among parents of two human trio datasets obtained from the Genome of the Netherlands ( GoNL ) [19] and G1K [20] projects and in each case we tested for an elevated number of de novo mutations in the offspring of parents carrying a putative mutator haplotype .
Because the number of additional derived alleles is independent of the time since the mutator allele arose , but the segment length is not , the power to detect a mutator allele above the background is greater for alleles that arose a large number of generations before the present . Given that the mutator allele is also required to be associated with a large effect on the germline mutation rate , it is necessary to consider whether such an allele could persist for a large number of generations despite the reduction in fitness that will result from an increased germline mutation rate . Although the increased burden of de novo mutations associated with the mutator allele is likely to reduce the fitness of individuals carrying it , this reduction may be relatively small [23] , and the per generation germline mutation rate is capable of sustaining substantial variation in animals , for example ranging widely across mammalian species [2] . Bear in mind also that de novo mutations will remain linked with the mutator allele only for an average of two generations before recombining away [2] . Indeed , a mutator allele , even at relatively low frequency , may contribute a large number of novel mutations , exerting a disproportionate influence on the evolution of the population as a whole while it persists . The selective disadvantage associated with a ( heterozygous ) allele that increases the germline mutation rate can be approximated as 2sd ΔU [2] , where sd is the mean selective disadvantage of a heterozygous deleterious mutation , U is the genomic deleterious mutation rate and ΔU is the change in U resulting from the mutator allele . Thus given a value for U , a mutator allele that increases the germline mutation rate by a factor ϕ will be associated with a selective coefficient ( for heterozygotes ) of s = −2sd ( ϕ − 1 ) U . The parameters sd and U are not straightforward to estimate . Lynch [2] provides values for multicellular eukaryotes from the literature that range over orders of magnitude from 10−3–10−2 for sd and from 0 . 01–1 for U . For values at the lower ends of these ranges , even a mutator allele associated with a large value of ϕ would have a relatively small effect on fitness . For example , ϕ = 5 gives s = −0 . 00008 , which is in the region in which genetic drift begins to dominate over selection for an effective population size on the order of 104 . In this case a strong mutator allele could persist in a population for a large number of generations , sufficient to have an impact on the number of mutations that accumulate in the region linked to the mutator allele . By contrast , values of sd and U at the higher ends of these ranges would give s = −0 . 08 , indicating strong selection against a mutator allele . Such an allele would not persist for long and would rapidly reduce in frequency in an organism with even a moderate effective population size . Estimates of U in particular genomic regions have been made by comparing between-species sequence divergence within these regions with divergence in putatively neutral regions ( such as fourfold degenerate coding sites , pseudogenes or inactive transposable elements ) . Using this approach , Keightley [24] estimated a relatively high value of 2 . 2 deleterious mutations per generation across the whole genome in humans . There are caveats associated with this estimate ( such as the assumption that genomic mutation rates are the same across sequence categories and possible bias from alignment in calculating genome-wide divergence ) , but it does seem likely that U is not substantially less than one . The mean selective disadvantage is more difficult to estimate , due in part to the confounding influence of demographic factors . Boyko et al . [25] estimated values of around -0 . 03 for the mean selective disadvantage of heterozygous amino acid changing mutations in African Americans . However , this is likely to be unrepresentative of mutations genome wide and heavily influenced by strongly deleterious mutations . Also , although Boyko et al . [25] assumed a codominant selection model , many of these mutations are likely to be recessive and thus may not be deleterious when they arise de novo ( as they are almost certain to be heterozygous ) . For Drosophila García-Dorado and Caballero [26] estimated a mean of 0 . 1 for the coefficient of dominance , h , defined such that the fitnesses of heterozygotes and homozygous mutants are 1 + hs and 1 + s respectively , and h = 0 . 5 corresponds to codominance . The best-fitting model of Boyko et al . [25] for the distribution of selective effects of amino acid changing mutations included a normally distributed component of weakly deleterious mutations with mean of -0 . 0002 , as well as a point mass representing strongly deleterious mutations . Consistent with this , Do et al . [27] have shown that the absence of differential removal of deleterious mutations from the genomes of Africans and non-Africans implies that selection coefficients acting on non synonymous substitutions are either strong ( s < −0 . 004 ) or very weak ( s > −0 . 0004 ) . In our context it may be possible to neglect strongly deleterious mutations on the grounds that they are typically recessive and rare outside protein-coding regions , where most de novo mutations occur . Mean selection coefficients for mutations outside protein coding regions are likely to be much smaller . If we set sd = −0 . 0001 , U = 1 , ϕ = 5 and set h = 0 . 1 ( to allow for the fact that even weakly deleterious mutations may have a coefficient of dominance well below 0 . 5 ) , the selective disadvantage of the mutator allele in heterozygotes is approximately -0 . 0001 . Such a mutation is affected by selection , but in a species with an effective population size similar to that of humans it can persist for a large number of generations by chance , particularly if it attains a high initial frequency due to a population bottleneck . The expected number of generations for which a mutation persists , under codominant selection , can be calculated [28] . For example a mutation with initial frequency of 0 . 2 and a selective coefficient of -0 . 0002 ( -0 . 0001 in heterozygotes ) has an expected time to extinction of 13 , 000 generations . We used simulation to investigate the effect of a mutator allele on the numbers of derived alleles in the region in linkage disequilibrium with it . We considered a genomic region of 100 Kb , with a mutator locus at 50 Kb , and implemented simulations in a coalescent framework , with recombination and selection ( see Materials and Methods for details ) . As expected , where mutations with a large impact on the germline mutation rate remained polymorphic in the population for a sufficient time , the genomic region close to the mutator locus was frequently found to exhibit a peak in the number of derived alleles , with this peak shared by a subset of haplotypes and the remaining haplotypes having a typical number of derived alleles across the region . An example of a simulation that exhibits such a peak is shown in Fig 1 , and the results of ten simulation runs are provided as supplementary information S1 Fig . Whether a simulation displays this characteristic signature of a mutator locus depends on the history of recombination and coalescence in the surrounding genomic region . For example , recombination events close to the locus can disrupt linkage , eroding the increase in the number of derived alleles associated with the mutator locus . For the simulation shown in Fig 1 the mutator allele arose 20 , 000 generations before the present and resulted in a five-fold increase in the germline mutation rate in heterozygotes ( and a ten-fold increase in homozygotes ) . To search for human loci that may have carried mutator alleles , we obtained data from phase 3 of the G1K project [20] . The data consisted of inferred haplotypes for a total of 2504 individuals in 26 populations , along with putative ancestral alleles for each variable site inferred from Ensembl Compara release 59 [29] . For each haplotype in a population we counted the number of derived alleles in a sliding window of size 10 Kb along the genome . In each window we then calculated the maximum number of derived alleles in the window and the interquartile mean number of derived alleles across all haplotypes in the population ( i . e . excluding the upper and lower 25th percentiles ) . As expected , there was a strong linear correlation between the maximum and the interquartile mean ( Fig 2 ) . Our further expectation , supported by the above simulations , is that loci at which a mutator allele has existed in a population for a large number of generations can be identified from a characteristic pattern of variation , comprising a number of haplotypes with an unusually large number of derived alleles against a background of haplotypes with a typical number of derived alleles . Diversifying selection is likely to increase both the interquartile mean and the maximum number of derived alleles . By contrast , a low-frequency mutator allele should affect only the maximum , and consequently we excluded from consideration windows with large values of the interquartile mean ( >75th percentile ) . The remaining windows were arranged in order of a statistic M , defined as the residual distance to the regression line ( the latter shown in red in Fig 2 ) , with windows showing the greatest M-value considered as the best candidates for sites of ancient germline mutator alleles . We also removed candidate loci in which a high proportion of SNPs ( >5% ) were not in Hardy-Weinberg equilibrium , as potentially indicative of artefacts . The top 20 genomic regions by M-value are shown in Table 1 . To reduce the likelihood that divergent haplotypes that give rise to the entries in the table are the result mapping difficulties resulting from structural variants , we used the indel calls provided by the G1K project [30] . Two of the candidate loci in Table 1 ( on chromosome 14 and at 192 . 6 Mb on chromosome 1 ) overlapped with a large structural variant ( >50bp ) . The structural variant on chromosome 14 is very rare , occurring in just 15 haplotypes from the G1K Phase 3 data , while the highly derived haplotype at this locus is much more frequent ( occurring in 302 haplotypes ) . The opposite is the case for the structural variant at 192 . 6 Mb on chromosome 1 , which is much more frequent ( 1871 haplotypes ) than the highly derived haplotype at this locus ( 88 haplotypes ) . Therefore , in neither case is it plausible that mapping errors created by these structural variants are the cause of the observed signal . However , we cannot rule out that unreported structural variants are the cause of some of the candidate mutator loci that we identified . It is also necessary to consider other mechanisms , not included in our demographic simulations , that might have lead to excess divergence in a subset of haplotypes . One example is a polymorphic gene conversion event affecting a subset of haplotypes , which could result in a block of divergent sequence . This would require gene conversion involving a segmentally duplicated region , in which the duplication of the target locus occurred prior to the divergence of humans from the other primates used to infer the ancestral state of genomic variants in this data . Under this scenario we may expect to find a subset of haplotypes that appear to be highly derived . A single gene conversion event giving rise to the diverged haplotypes would result in a phylogenetic tree of haplotypes consisting of one long internal branch separating two clades consisting of only short branches . The peak on chromosome 18 in Table 1 fits this description , but the remaining peaks do not ( S3 Fig ) . For all of the remaining peaks , there is substantial diversity among the highly derived haplotypes , consistent with a greater rate of divergence than for the less highly derived haplotypes . Multiple gene conversion events would result in polyphyly of the diverged haplotypes , and this is not observed for most of the peaks; however , accurate phylogenetic inference may be exceptionally difficult in the gene conversion scenario . Another possible mechanism might be an extreme multi-nucleotide mutation event , more substantial than those included in our simulations , giving rise to multiple mutations simultaneously . However , similarly to gene conversion , this would also create two clades , both consisting of short branches , separated by a long branch that reflects the multi nucleotide mutation . Thus for trees of this nature we cannot rule out either gene conversion or extreme multi-nucleotide mutation . However we note that such trees are also consistent with the effects of a mutator allele , since a relatively recent coalescence of the highly derived haplotypes would also produce a long internal branch separating clades consisting mostly of short branches . Indeed , this was frequently observed in the proof-of-concept simulations ( S4 Fig ) . Introgression of haplotypes derived from hybridisation between modern humans and Neanderthals as well as other ancient hominins has been reported [36–39] . However , this introgression is not likely to result in a subset of haplotypes with an excess of derived alleles . Alleles were designated as derived or ancestral by comparison to other primates and the number of derived alleles on a given Neanderthal haplotype should be similar to the number for a haplotype from modern humans . Moreover , almost all of the peaks in Table 1 are found in Africans ( S5 Fig ) , but only non-Africans have Neanderthal ancestry . The one exception is again the peak on chromosome 18 . Indeed , for this peak we found that the highly derived haplotypes are enriched for haplotypes of likely Neanderthal origin ( as determined by [40] ) . This peak is not close to any DNA repair genes and its removal from the list of candidates would in fact slightly increase the statistical significance of the association with DNA repair genes discussed above . As a further potential alternative explanation , we also considered the case of a genomic segmental duplication segregating within the population , which could give rise to mapping errors in individuals carrying it , such that derived alleles called in the candidate locus are due to incorrectly mapped sequence reads . However , a simple duplication could cause at most an apparent doubling of divergence on affected haplotypes in this way , and therefore would be unlikely to be included as a candidate under the thresholds we have applied . A more complicated tandem duplication with several copies might give rise to higher apparent divergence , but such sites are very unlikely to have passed the filtering criteria used in the G1K calling protocol . The peaks we observe in the derived allele count could potentially be caused by extremely strong selective pressure acting on a subset of populations from the G1K dataset . Such strong and localised selective pressure could cause a population or group of populations to have a large number of derived alleles within a genomic region , while the remainder of the populations in the dataset that did not experience the strong selective pressure have a typical number of derived alleles . Even if selective pressures strong enough to bring multiple linked derived alleles to high frequencies had acted on human populations we find no evidence for this hypothesis in the data , as , for most of the peaks , the highly derived haplotypes are found in human populations from multiple populations ( S5 Fig ) . Our proof-of-concept simulations suggest that most of the peaks in Table 1 are consistent with the effects of mutator alleles with large effect size which were maintained in the population for a large number of generations . The difference between the maximum and inter-quartile mean number of derived alleles in Table 1 is below 30 for all but the first locus , and in the low twenties for loci towards the bottom of the table . The proof-of-concept simulations also had large differences in the maximum and interquartile mean when the signal was detectable ( just over 20 for simulations 8 and 9 in S1 Fig ) . The top locus in the table , however , has a much larger difference between the maximum and interquartile mean number of derived alleles . Although we could not find an alternative explanation for this signal , it seems difficult to reconcile with the effects of a mutator allele whose effect size would have enabled it to persist in the population for a large number of generations . Although we have considered the approach described here primarily as a means of detecting the remnant signatures of ancient mutator alleles , if such an allele were still active and linked to a highly-derived haplotype , then it might be possible to find supporting evidence in whole genome sequence data from trios . In principle , an active a mutator allele would give rise to an association between the presence of the highly-derived haplotype in the parents and an elevated number of de novo mutations in the offspring . We investigated this possibility for the candidate loci shown in Table 1 by obtaining counts of de novo mutations in the offspring of complete trios and genome-wide parental genotype data from the G1K ( n = 59 ) and GoNL ( n = 248 ) projects [19 , 20] . For each of the loci shown in Table 1 we tested for a positive association between the number of de novo mutations in the offspring and the presence in the corresponding parent of the highly derived haplotypes at the putative mutator locus ( Table 2 ) . P-values from two tests are reported for each peak , one based on fitting a robust linear model and the second based on permutation ( referred to as pt and pperm in Table 2 , respectively; see Materials and Methods for details ) . The analysis was carried out separately for male and female parents and for the two projects , to avoid biases resulting from differences in study methodology confounded with differences in populations of origin . In each case we required at least five parents with a highly derived haplotype to perform the test . In the case of the G1K dataset 21 tests were performed ( 10 paternal and 11 maternal ) . Of these , two showed a nominally significant positive association between the number of de novo mutations and the presence of the highly derived haplotype in the parent in both statistical tests ( Table 2 ) . The most significant of these was for the peak just upstream of BRSK2 on chromosome 11 ( illustrated in Fig 3 ) . With the GoNL dataset , 15 tests could be performed ( seven paternal and eight maternal ) , but no significant associations were observed ( Table 2 ) . Considering that 36 tests in total were carried out in total , the associations in Table 2 do not remain significant following Bonferroni correction , and additional diverse trio datasets may be necessary to confirm these associations definitively . Data forming the basis for the tests shown in Table 2 are provided as S1 and S2 Tables . Paternal age at conception is strongly correlated with the number of de novo mutations in offspring [8]; however , paternal age was not available for the G1K data , while for the GoNL data we included the ages of both parents at conception in the linear model relating de novo mutation count to the presence of the highly derived haplotype . There remained no significant positive associations in the case of this dataset . For the G1K dataset population of origin information was available for the samples; however , there was no statistically significant relationship between population of origin and the de novo mutation count ( S6 Fig ) . Interestingly , the candidate locus that shows the strongest evidence of an association with the de novo mutation count in the G1K trios is just upstream of BRSK2 ( Fig 3b ) , a highly conserved serine threonine kinase which is preferentially expressed in brain and testis and shows enhanced activity in response to DNA damage [41 , 42] . The observed association was maternal rather than paternal; however , expression in testis is consistent with a role in germline DNA replication . At the same time , this haplotype is not associated with an elevated de novo mutation count in the GoNL data ( Table 2 ) . A possible explanation for this is that despite its much larger sample size , the GoNL cohort , which comprises individuals of Dutch ancestry , is substantially less diverse than the G1K cohort . As noted above , although the highly derived haplotype was observed in parents of trios in the GoNL cohort , its presence does not necessarily imply the presence of the causative mutator allele: a recombination event on the ancestral lineage leading to the highly derived haplotype in the Dutch population may have separated the two . Indeed , in this way it is possible for an extinct mutator allele to leave a fossil peak in the derived allele count of a contemporary population , and it is plausible that many of the signals we have detected are of this nature . The candidate mutator locus on chromosome 14 is also noteworthy . This peak overlaps precisely with an exon of the tyrosyl-DNA phosphodiesterase 1 ( TDP1 ) gene ( S7 Fig ) . The product of TDP1 has a role in the repair of stalled topoisomerase I-DNA complexes . This candidate is not supported by the available trio datasets , despite the highly derived haplotypes being found in sufficient numbers in both cohorts , suggesting that its occurrence on the TDP1 gene is either a striking coincidence or that it may be a fossilised peak resulting from a mutator allele that is extinct or at least much rarer than the highly derived haplotypes to which it has given rise . We have shown that when a genomic locus contains a polymorphism in which one allele increases the germline mutation rate , a characteristic signature may result , comprising a subset of haplotypes that are locally more divergent than other haplotypes at that locus . Whether this signature can be detected depends on the magnitude of the effect and on the time since the mutator allele arose . The number of additional mutations on an unrecombined segment of expected length carrying the mutator allele is independent of the time since the mutator allele arose , but the likelihood of detecting a fixed number of additional mutations over the background increases as the segment length decreases and is thus higher for older mutations . We searched for such signatures in data from Phase 3 of the 1000 Genomes Project , and found candidate human genetic loci that may have once contained ( and in some cases may still contain ) germline mutator alleles . Consistent with this expectation , the set of genes located in the vicinity of these loci is enriched for genes involved in DNA repair . A test for the continued presence of active mutator alleles in these haplotypes , by looking for association with high numbers of de novo mutations passed to offspring in two trio sequencing cohorts , found support for association in two cases , but no associations with overall significance . It may therefore be that mutator alleles are no longer present , or are now very rare , in most of the candidate loci we identified . However the trio cohorts we examined were small relative to what may be feasible in future studies , and in one case ( the GoNL data ) represented a population which is comparatively homogeneous in terms of its genetic ancestry . Further trio cohorts from more genetically diverse populations may be necessary to test some of the candidates we identified . Such studies will also shed light on the full extent of polymorphism in the rate of de novo mutation in human populations . It is difficult to conclusively exclude alternative explanations for the signals we have detected . However we have considered several such explanations and have shown that they are either far less likely to generate signals of this nature ( in the case of neutral demographic processes ) or would generate loci with characteristics not seen in these cases ( for example in the case of a mis-mapped segmental duplication ) . More generally , it is far from implausible a priori that there are genetic factors affecting the germline mutation rate in humans , and that there may arise from time to time mutator alleles with a moderately large effect size . We have also shown that such alleles can plausibly persist for many generations and leave a detectable signal that survives after they have disappeared . It may also be that different such alleles affect the mutation process in different ways , and that as well as changing the overall germline mutation rate they give rise to different mutational signatures . Indeed recent studies have found variation in the mutational spectrum ( expressed in terms of the relative frequency of different triplet sequence contexts for de novo mutations ) between human populations and over time , including evidence for past activity of mutator alleles that are now rare or extinct ( [15 , 43] ) . On longer timescales , comparison of mutation rates estimated from inter-specific divergence ( e . g . [44] ) with measurements of the mutation rate in contemporary populations points to a slowdown in human and great ape evolution [45] . Some of this may be due to changes in life history parameters such as generation time; however , there may also have been changes in the underlying per-generation mutation rate , or more specifically , changes in cellular mutation rates during development and gametogenesis , perhaps driven by genetic variation at loci such as those detected here . Indeed , it is worth considering that the higher mutation rate inferred for some haplotypes at candidate loci may represent the ancestral rather than derived state , such that the mutations in question resulted in anti-mutator rather than mutator alleles .
We used a coalescent approach to simulate the effects of a mutation that increases the germline mutation rate . The mutator allele is introduced at an initial frequency p0 at time t0 and we assume no mutation between the mutator allele and wild-type after time t0 . We use a constant effective population size of 10 , 000 individuals and incorporate recombination , occurring at a constant rate across the simulated genomic region ( see below for simulations of null models with variable recombination rate and demographics ) . The mutator allele is associated with a selective coefficient , s , and we incorporate selection against the mutator allele using an approach described by Kaplan et al . [46] . We first obtained the deterministic allele frequency trajectory in the forward direction , with s = −0 . 0002 . Because we require that chromosomes carrying the mutator allele have an elevated mutation rate the simulations could not be performed using existing implementations of the coalescent with recombination and selection ( e . g . [47] ) and were instead implemented as a Perl script , which is available from the authors , on request . To allow for the effects of the mutator allele on the rate of mutation of the homologous chromosome in heterozygous individuals , at each generation the mutation rate was elevated for chromosomes that did not carry the mutator allele with a probability equal to the frequency of the mutator allele in that generation . To examine the genome-wide distribution of maximum and inter-quartile mean number of derived alleles per window , we simulated populations evolving under a model of neutral evolution and without mutation rate polymorphism . Two simulations of 5000 haploid whole genome sequences were carried out , one comprising a single population with constant effective population size , and another comprising five separate populations diverging 100 kyr ago , to explore the potential effects of population structure on the signal of interest . All populations simulated were of equal size with a scaled mutation rate θ = 0 . 001 , matching the mean genome-wide heterozygosity in modern human populations . Simulations were carried out using the coalescent simulator msprime [48] , with a variable recombination rate matching that found in the human genome ( including recombination hotspots ) [49] , and a uniform genome-wide mutation rate . Simulations suggested that a mutation that increases the rate of germline mutation and is maintained in a population for a sufficiently long time can result in a genomic region in which haplotypes linked to the mutator allele have a large number of derived alleles and the remaining haplotypes have typical numbers of derived alleles ( i . e . the effect of the mutator allele on unlinked haplotypes is the same as its effect on the rest of the genome ) . Variant calls in . vcf format were downloaded for phase 3 ( release v5a ) of the 1 , 000 Genomes project [20] . Putative ancestral alleles ( derived by the 1 , 000 Genomes project from Ensembl Compara release 59 [29] ) were obtained from the vcf files , restricting to SNPs with high confidence ancestral alleles . Using a sliding window ( window size: 10 Kb; step size: 1 Kb ) we calculated the interquartile mean ( also called the 25% trimmed mean ) and the maximum number of derived alleles in each window . There was a linear relationship between the maximum and the interquartile mean , reflecting the fact that windows within which the interquartile mean number of derived alleles was large also had a large value for the maximum number of derived alleles ( over all haplotypes ) . We fitted linear regression models to the maximum , as a function of the interquartile mean and calculated the distance from the regression line for each window . Windows with an elevated value of the interquartile mean ( >75th percentile ) were excluded and the remaining windows were sorted by distance to the regression line . χ2 tests of Hardy-Weinberg equilibrium ( at alpha = 0 . 05 ) were carried out separately within each population for each SNP within the candidate regions . Windows within which a high proportion ( >5% ) of SNPs rejected Hardy-Weinberg equilibrium were removed . This resulted in the removal of nine loci . We obtained de novo mutation counts in offspring and parent variant call data for human parent-offspring trios from the G1K ( n = 59 ) and GoNL ( n = 248 ) projects [19 , 20] ( see [43] for details of methods used to call de novo mutations from the G1K trios ) . For each candidate mutator locus we determined the number ( 0 , 1 or 2 ) of copies of the highly derived haplotype in each parent . For the phased G1k data we plotted the number of derived alleles per haplotype and if this distribution included a subset of highly derived haplotypes we selected a threshold that gave the best separation between the highly derived and typically derived haplotypes ( S8 Fig ) and used this to classify each haplotype . For unphased data ( GoNL ) we plotted the number of derived alleles per individual . When the highly derived haplotypes occurred at low frequency this distribution was typically bimodal ( all individuals had 0 or 1 highly derived haplotype ) , becoming trimodal when the highly derived haplotypes occurred at higher frequency ( corresponding to individuals with 0 , 1 or 2 highly derived haplotypes ) . In each case we set a threshold number of derived alleles that gave the best separation of the peaks in the distribution of the derived allele count ( S9 Fig ) . We then tested for an association between the offspring de novo mutation count and the number of copies of the highly derived haplotype by fitting robust linear regression models using the rlm function from the MASS package [50] in R [51] , with the default M-estimation method . The G1K and GoNL trios were analysed separately to avoid the potential for confounding between study methodologies and population of origin and results are reported separately for each parent ( to enable the detection of parent-of-origin specific effects ) . Parental age at conception was available for the GoNL trios and included as a covariate in the linear models . P-values for the null hypothesis that the regression coefficient for the offspring de novo mutation count as a function of the number of copies of the highly derived haplotype was not greater than zero were calculated in two ways: from the right tail of the distribution of the regression coefficient t-statistic ( referred to as pt in Table 2 ) and by permutation . For the latter we performed 10 , 000 permutations of the de novo mutation counts and determined the proportion of times the t-statistic from the robust regression for the permuted data was greater than or equal to the value for the unpermuted data ( reported as pperm in Table 2 ) . Correction for multiple testing was carried out using the Bonferroni method . We identified all genes within 100 Kb of each of the candidate mutator loci ( excluding pseudogenes ) and tested for functional enrichment using DAVID [52 , 53] , version 6 . 8 with default parameters ( on 3/11/2016 ) . Given that there were several genes found within 100 Kb of many of the candidate mutator loci the phenomenon of functional gene clustering means that this set of genes is not independent , creating a potential for bias in the statistical tests of enrichment . We therefore also devised a test based on randomisation of the genomic loci . For this test 20 loci ( equal to the number of loci in Table 1 ) were sampled at random from the regions of the data for which we had variant call data and the number of DNA repair genes ( annotated with GO term GO:0006281 in Ensembl 84 ) within 100 Kb of these random loci was determined . This was repeated 100 , 000 times and the numbers of DNA repair genes in the random data was compared to the observed number of genes with this GO term within 100 Kb of the candidate mutator loci . | Each time a genome is replicated there is the possibility of error resulting in the incorporation of an incorrect base or bases in the genome sequence . When these errors occur in cells that lead to the production of gametes they can be incorporated into the germline . Such germline mutations are the basis of evolutionary change; however , to date there has been little attempt to quantify the extent of genetic variation in human populations in the rate at which they occur . This is particularly important because new spontaneous mutations are thought to make an important contribution to many human diseases . Here we present a new way to identify genetic loci that may be associated with an elevated rate of germline mutation and report the application of this method to data from a large number of human genomes , generated by the 1000 Genomes Project . Several of the candidate loci we report are in or near genes involved in DNA repair . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"biochemistry",
"mutation",
"deletion",
"mutation",
"haplotypes",
"nucleic",
"acids",
"heredity",
"germline",
"mutation",
"gene",
"identification",
"and",
"analysis",
"genetics",
"mutation",
"detection",
"biology",
"and",
"life",
"sciences",
"dna",
"dna",
"repair",
"alleles",
"genetic",
"loci",
"genetic",
"mapping"
]
| 2017 | Inference of Candidate Germline Mutator Loci in Humans from Genome-Wide Haplotype Data |
We performed a Phenome-wide association study ( PheWAS ) utilizing diverse genotypic and phenotypic data existing across multiple populations in the National Health and Nutrition Examination Surveys ( NHANES ) , conducted by the Centers for Disease Control and Prevention ( CDC ) , and accessed by the Epidemiological Architecture for Genes Linked to Environment ( EAGLE ) study . We calculated comprehensive tests of association in Genetic NHANES using 80 SNPs and 1 , 008 phenotypes ( grouped into 184 phenotype classes ) , stratified by race-ethnicity . Genetic NHANES includes three surveys ( NHANES III , 1999–2000 , and 2001–2002 ) and three race-ethnicities: non-Hispanic whites ( n = 6 , 634 ) , non-Hispanic blacks ( n = 3 , 458 ) , and Mexican Americans ( n = 3 , 950 ) . We identified 69 PheWAS associations replicating across surveys for the same SNP , phenotype-class , direction of effect , and race-ethnicity at p<0 . 01 , allele frequency >0 . 01 , and sample size >200 . Of these 69 PheWAS associations , 39 replicated previously reported SNP-phenotype associations , 9 were related to previously reported associations , and 21 were novel associations . Fourteen results had the same direction of effect across more than one race-ethnicity: one result was novel , 11 replicated previously reported associations , and two were related to previously reported results . Thirteen SNPs showed evidence of pleiotropy . We further explored results with gene-based biological networks , contrasting the direction of effect for pleiotropic associations across phenotypes . One PheWAS result was ABCG2 missense SNP rs2231142 , associated with uric acid levels in both non-Hispanic whites and Mexican Americans , protoporphyrin levels in non-Hispanic whites and Mexican Americans , and blood pressure levels in Mexican Americans . Another example was SNP rs1800588 near LIPC , significantly associated with the novel phenotypes of folate levels ( Mexican Americans ) , vitamin E levels ( non-Hispanic whites ) and triglyceride levels ( non-Hispanic whites ) , and replication for cholesterol levels . The results of this PheWAS show the utility of this approach for exposing more of the complex genetic architecture underlying multiple traits , through generating novel hypotheses for future research .
Genome-wide association studies ( GWAS ) have led to the discovery of thousands of variants associated with disease and phenotypic outcomes [1] . GWAS focus on investigating the association between hundreds of thousands to over a million single nucleotide polymorphisms ( SNPs ) and a single , or small set , of phenotypes and/or disease outcomes . While a wealth of information about the relationship between SNPs and phenotypes has been revealed , an extensive picture of the complex genetic architecture underlying common disease has yet to be elucidated . In addition , the relationship between SNPs and multiple phenotypes ( pleiotropy ) is only beginning to be explored . A complementary approach to GWAS are phenome-wide association studies ( PheWAS ) , an approach for investigating the complex networks that exist between human phenotypes and genetic variation , through testing a series of SNPs for association with a large and diverse set of phenotypes [2]–[5] . These analyses can be used to investigate the relationship between genetic variants and presence/absence of disease and phenotypic outcomes as well as the association between genetic variation and intermediate clinically measured variables such as cholesterol levels , blood pressure measurements , and total iron binding capacity . PheWAS can be used to replicate relationships found in GWAS as well as to discover novel associations and generate hypotheses for further research . This approach also allows for the detection of SNPs with pleiotropic effects , where one genetic variant is associated with multiple phenotypes [6] , [7] . Investigating the interrelationships that exist between phenotypes as well as between genetic variation and phenotypic variation has the potential for uncovering the complex mechanisms underlying common human phenotypes . Here we describe a PheWAS using epidemiologic data from the National Health and Nutrition Examination Surveys ( NHANES ) collected by the Centers for Disease Control and Prevention and accessed by the Epidemiological Architecture for Genes Linked to Environment ( EAGLE ) study as part of the Population Architecture using Genomics and Epidemiology ( PAGE ) network [8] . A major focus of the PAGE network is the replication and generalization of GWAS-identified variants in diverse populations , as the majority of published GWAS have been performed in populations of European-descent with little generalization across other racial/ethnic groups . Thus , the PAGE network has pursued investigating associations for genetic variants that have been well replicated in previous research across ancestry groups beyond European-descent . As a part of PAGE , EAGLE genotyped 80 GWAS-identified variants in two NHANES datasets representing three surveys: NHANES III , collected between 1991 and 1994 , and Continuous NHANES which was collected between 1999–2000 and 2001–2002 across three race-ethnicities . The majority of the SNPs within our study were chosen for genotyping based on published lipid trait genetic association studies ( 51 SNPs ) , but our study also included SNPs previously associated with phenotypes such as C-reactive protein levels , coronary heart disease , and age-related macular degeneration , with detailed information about these SNPs in S1 Table . Genotyping was performed in a total of 14 , 998 NHANES participants with DNA samples including 6 , 634 self-reported non-Hispanic whites , 3 , 458 self-reported non-Hispanic blacks , and 3 , 950 self-reported Mexican Americans . Similar to the PheWAS framework outlined by the PAGE study [3] , we performed comprehensive unadjusted tests of association for 80 SNPs with 1 , 008 phenotypes , using linear or logistic regression , depending on the phenotype , stratified by race-ethnicity . With this approach we replicated many previously reported associations and identified novel genotype-phenotype relationships . We have performed our analyses across multiple genetic ancestries . Most importantly , we have also found indications of pleiotropy for a number of the SNPs included in our investigation . Contrasting the association results for SNPs with multiple phenotypes , interesting direction of effect differences were identified . We further explored the relationship between SNPs , genes , and known biological relationships between the genes , identifying network relationships within these results . The findings in this paper demonstrate that PheWAS is a useful method for both validating findings from GWAS and discovering previously unknown genotype-phenotype relationships in diverse populations , enriching our understanding of the complex underpinnings of human phenotypes .
As a positive control , we first sought evidence for associations that replicate findings from the literature . Replication of previously reported associations validates our PheWAS pipeline and data integrity . Thirty-nine out of the 69 ( 56 . 5% ) of our PheWAS associations have previously been described in the literature with the same direction of effect , and our results for these associations are presented in S2 and S3 Tables as well as visualized in Fig . 2 . A proportion of the phenotypes could have phenotypic harmonization such that we could explore the association result for the phenotype across both surveys , NHANES III and Continuous NHANES , which we refer to as NHANES Combined . A Combined NHANES result was not available for every phenotype , as not all phenotypes could be harmonized across both surveys even if phenotypes could be binned into phenotype classes across both surveys . Our result tables contain this NHANES Combined information when available . The majority of the SNPs within our study ( 51 out of 80 ) , but not all of the SNPs , were chosen for genotyping based on published lipid trait genetic association studies ( for example , [10]–[12] ) , and of these , 19/23 lipid-associated SNPs were associated with lipid traits in this PheWAS . For example , total cholesterol levels and LDL cholesterol levels have been previously associated with the SNP rs646776 near CELSR2 in European-descent populations [13]–[15] . In this PheWAS , we observed a significant association between rs646776 ( coded allele G ) and total cholesterol levels in NHANES III ( p = 3 . 17×10−6 , β = −7 . 66 , n = 2 , 224 ) and Continuous NHANES ( p = 9 . 15×10−7 , β = −0 . 014 , n = 3 , 943 ) for non-Hispanic whites with the same direction of effect as the association previously reported for this SNP and LDL cholesterol levels . The association between rs646776 and total cholesterol remained significant in Combined NHANES ( p = 1 . 0×10−10 , β = −0 . 029 , n = 6 , 389 ) . After determining results where the phenotype of our association matched that of the same SNP-phenotype association in the GWA catalog , we evaluated whether any of our phenotypes were extremely similar to previously published SNP-phenotype associations . There were a total of 9/69 ( ∼13% ) PheWAS results where the SNPs had been previously associated with lipid measurements not exactly matching the respective lipid measurements of our study ( S4 Table and Fig . 3 ) . For example , the SNP rs515135 near APOB/KLHL29 has been previously reported to be associated with LDL cholesterol ( LDL-C ) levels in European-descent populations [16] , [17] . In this PheWAS , rs515135 ( coded allele G ) was associated with total cholesterol levels in non-Hispanic whites . For this SNP , the most significant results meeting our PheWAS replication criteria from NHANES III were: p = 0 . 0024 , β = 4 . 85 , n = 2 , 569 and Continuous NHANES were: p = 1 . 06×10−5 , β = 0 . 026 , n = 3959 . This variant was also associated with total cholesterol levels in Combined NHANES ( p = 1 . 39×10−7 , β = 5 . 13 , n = 6 , 528 ) . Another example of a closely related association was for SNP rs7557067 near APOB , previously found to be associated with triglyceride levels in European-descent populations [17] . In this PheWAS , rs7557067 ( coded allele G ) was associated with total cholesterol levels in non-Hispanic whites from NHANES III ( p = 0 . 0050 , β = −0 . 012 , n = 2 , 436 ) and Continuous NHANES ( p = 0 . 0053 , β = −0 . 015 , n = 3 , 966 ) . In the larger sample size of Combined NHANES , this association with total cholesterol levels was maintained ( p = 1 . 1×10−4 , β = −0 . 014 , n = 6 , 404 ) . Given that total cholesterol includes HDL-C and that HDL-C is inversely correlated with triglycerides [18] , [19] , this PheWAS finding was also expected . The remainder of the PheWAS results with phenotypes that did not match previously reported SNP-phenotype associations had phenotypes very distinct from previously reported phenotypes . A total of 21/69 ( ∼30% ) PheWAS results are potentially novel findings . These are associations with a greater divergence between the previously associated phenotype for a given SNP and the associated phenotype found in this study ( Table 3 ) . We found novel results for all three racial/ethnic groups . However , only one novel result meeting our PheWAS significance criteria generalized across two or more populations showing the same direction of effect: protoporphyrin levels in both non-Hispanic whites and Mexican Americans for the ABCG2 SNP rs2231142 ( coded allele C ) . Of the replicating measures for protoporphyrin levels , the most significant results for this association in Mexican Americans for NHANES III was: p = 2 . 61×10−7 , β = −0 . 075 , n = 2 , 029 , for Continuous NHANES was: p = 2 . 0×10−4 , β = −0 . 079 , n = 968 , and for Combined NHANES: p = 9 . 41×10−8 , β = −5 . 21 , n = 3 , 897 . The most significant result for this association in non-Hispanic whites was for NHANES III: p = 6 . 0×10−6 , β = −0 . 062 , n = 2 , 587 and for Continuous NHANES was: p = 6 . 6×10−4 , β = −0 . 06 , n = 1 , 667 . This SNP was previously associated with uric acid [20]–[23] . We also found this SNP to be associated with uric acid in non-Hispanic whites and Mexican Americans with the same direction of effect as previously reported associations , as well as an additional novel result for blood pressure measurements only in Mexican Americans with an opposite direction of effect . The number of novel results was similar across race-ethnicities , even with the difference in sample size across non-Hispanic whites , non-Hispanic blacks , and Mexican Americans that could affect power for detection of novel associations . An example novel result showing a very unique divergence from previously reported associations was for the SNP rs11206510 ( coded allele T ) near the gene PCSK9 . This SNP has been previously associated with coronary heart disease [24] , LDL-C [16] , [17] , [25] , and myocardial infarction [26] in European-descent populations , but we did not replicate any of those previously reported associations . In this study we found this SNP was associated with serum globulin levels in Mexican Americans from NHANES III ( p = 0 . 0095 , β = 0 . 0120 , n = 2 , 023 ) , Continuous NHANES ( p = 0 . 0042 , β = 0 . 012 , n = 1871 ) , and Combined NHANES ( p = 8 . 7×10−4 , β = 0 . 015 , n = 3 , 894 ) . We contrasted the direction of effect of this SNP with the previously reported associations for this SNP and the direction of effect was the same . Another example of novel divergence from previously reported results involved two SNPs we found to be associated with white blood cell count in non-Hispanic blacks . The SNP rs1800795 ( coded allele G ) near IL6 previously was associated with C-reactive protein levels [27]–[29] . In our study , this SNP was associated with white blood cell counts in non-Hispanic blacks from NHANES III ( p = 0 . 0047 , β = −0 . 34 , n = 2038 ) and Continuous NHANES ( p = 0 . 0048 , β = −0 . 071 , n = 1 , 316 ) . We also found that rs4355801 in TNFRSF11B was associated with white blood cell counts in non-Hispanic blacks from NHANES III ( p = 0 . 0036 , β = 0 . 30 , n = 6 , 991 ) , Continuous NHANES ( p = 0 . 0079 , β = 0 . 378 , n = 3 , 728 ) , and Combined NHANES ( p = 5 . 77×10−5 , β = 0 . 042 , n = 3 , 411 ) . Previously , TNFRSF11B rs4355801 ( coded allele G ) was associated with bone mineral density in women of European-descent [30] . We did not observe a significant PheWAS association with C-reactive protein or bone mineral density in our study for these two SNPs , respectively . We found a total of six novel PheWAS-significant results associated with circulating vitamin levels ( vitamin E , vitamin A , and folate ) . For example , a PheWAS-significant association for the missense SNP rs1260326 ( coded allele T ) in the gene GCKR was found with vitamin A levels in non-Hispanic whites from NHANES III ( p = 6 . 1×10−3 , β = 1 . 30 , n = 2 , 250 ) , Continuous NHANES ( p = 1 . 11×10−4 , β = 2 . 34 , n = 1 , 639 ) , and Combined NHANES ( p = 1 . 06×10−5 , β = 1 . 65 , n = 4 , 189 ) . This SNP was previously associated with serum albumin levels and serum total protein levels in European- and Japanese-descent individuals [31] , non-albumin protein levels in Japanese-descent individuals [32] , platelet counts [33] , cardiovascular disease risk factors [34] , C-reactive protein levels [35] , urate levels [20] , total cholesterol and triglyceride levels [36] , and chronic kidney disease [37] in individuals of European ancestry , and liver enzyme levels in European- and Asian-descent populations [38] . None of these previously reported associations replicated in our study . We compared the positive direction of effect of this SNP rs1260326 , associated with vitamin levels , with previously reported associations . Associations with the same coded allele ( T ) with urate levels [20] , serum albumin levels [31] , serum total protein levels [31] , platelet counts [33] , liver enzyme levels[38] , cardiovascular disease risk factors [34] , C-reactive protein levels [35] , total cholesterol and triglyceride levels [36] , chronic kidney disease [37] all had a positive direction of effect . This SNP was associated with non-albumin protein levels [32] with a negative direction of effect . While any of the novel PheWAS associations indicate potential pleiotropy as all of the SNPs of this study have previously reported genome-wide associations , within our study , we found 13 SNPs with more than one significant PheWAS phenotype class ( Table 4 and Fig . 4 ) . While the majority of these were SNPs were associated with more than one lipid phenotype , there were nine SNPs associated with other phenotypes . For example , the missense SNP in ABCG2 rs2231142 , also described in novel results , was found to have two novel associations , protoporhyrin ( in non-Hispanic whites and Mexican Americans ) and blood pressure levels ( Mexican Americans ) , and one replication of a previously known association with uric acid levels ( non-Hispanic whites and Mexican Americans ) . The results for this SNP are plotted in Fig . 5 . For another example , rs2338104 , an intronic SNP in KCTD10 , which was previously associated with HDL cholesterol ( HDL-C ) in European-descent populations [17] , [25] , was associated here with hemoglobin and hearing levels , both novel results in non-Hispanic whites ( Fig . 6 ) . Another example of potential pleiotropy was for SNP rs1800588 near LIPC , previously associated HDL-C in European-descent populations [15] . We observed significant associations between this SNP and the novel phenotypes of folate ( in Mexican Americans ) and vitamin E levels ( in non-Hispanic whites ) , as well as replication for cholesterol and the related phenotype of triglycerides ( both in non-Hispanic whites; Fig . 7 ) . The intronic SNP rs174547 of FADS1 provides another example . This SNP was previously associated with phospholipid levels [39] , resting heart rate [40] , phosphatidylcholine levels [41] , HDL-C and triglyceride levels [17] in individuals of European ancestry . Here , this SNP is associated with ferritin levels in Mexican Americans and with folate levels in non-Hispanic blacks . To further characterize these putative pleiotropic relationships , we compared and contrasted direction of effect for each association ( Table 4 ) . We found variants related to potentially protective effects for certain traits , and a potential risk effects for other traits . For example , intergenic SNP rs12678919 near LPL was associated with HDL cholesterol levels in non-Hispanic whites with a positive direction of effect and hearing in non-Hispanic blacks with a negative direction of effect ( coded allele G ) . Intronic SNP rs174547 in FADS1 was associated with ferritin levels in Mexican Americans with a positive direction of effect and folate ( in non-Hispanic blacks ) and triglycerides ( in non-Hispanic whites ) with a negative direction of effect ( coded allele T ) . The intronic SNP rs6855911 in SLC2A9 was associated with uric acid ( in both non-Hispanic blacks and Mexican Americans ) with a negative direction of effect and thigh circumference measurements ( non-Hispanic blacks ) with a positive direction of effect ( coded allele G ) . PheWAS-significant results provide an opportunity to explore the relationships between SNPs , genes , traits/outcomes , and pathways or other known relationships between genes and gene-products . We used the software tool Biofilter to identify the genes the PheWAS-significant SNPs were within or closest to . We then used Biofilter to annotate the resultant genes using the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) [42] , Gene-Ontology ( GO ) [43] , and NetPath [44] which allowed us to identify any known connections between genes due to shared biological pathways or other known biological connections . After stratifying the results by race-ethnicity , we used Cytoscape [45] to visualize the connections between genes based on their annotation . We present here the networks where there were two or more SNPs significant in our PheWAS connected via genes and those two or more genes were connected by a pathway or other gene-gene connection . For example , Fig . 8 shows one example for PheWAS results in Mexican Americans , where LPL SNP rs328 had a significant association with HDL-C levels , and the FADS1 SNP rs17547 had an association with ferritin levels . Both genes are found in the TGF-β receptor regulated NetPath pathway . Fig . 9 shows another example in Mexican Americans in which three SNPs were associated with uric acid levels: rs2231142 , rs7442295 , rs685911 . One of the SNPs is located within the gene ABCG2 , and the other two SNPs are located within SLC2A9 ( blue boxes ) . Both ABCG2 and SLC2A9 are found within the GO biological process “urate metabolic process” , a collection of the gene products involved in the chemical reactions and pathways involving urate . These same connections were also found for non-Hispanic whites , as this group had a PheWAS-significant association between these SNPs and uric acid levels . One of the SNPs , rs2231142 , was also associated with diastolic blood pressure and protoporphyrin levels . Fig . 10 displays an example using KEGG and the Mexican American PheWAS results . LPL and LIPC both are involved in the KEGG biological process “glycerolipid metabolism” . LPL SNP rs328 was associated in this study with HDL-C , while LIPC SNP rs1800588 was associated with folate levels . LPL was also involved in the KEGG pathway “Peroxisome Proliferator-Activated Receptor ( PPAR ) signaling pathway” , along with APOA5 , which was associated with triglyceride levels through its SNP rs3135506 . PPARs are transcription factors activated by lipids .
For this PheWAS , performed using the data of NHANES , we have replicated a number of previously published results and have found novel and pleiotropic associations . For example , for rs2231142 , a missense SNP in ATP-binding cassette subfamily G member 2 ( ABCG2 ) , we replicated previous associations with uric acid levels observed in European-descent populations and in Mexican Americans with the same direction of effect . Additionally , we identified a novel association for this SNP with protoporphyrin in both the European-descent population and Mexican Americans , where the coded allele ( C ) was associated with increased uric acid levels as well as increased protoporphyrin . This PheWAS finding is intriguing in light of some of the known connections that link protoporhyrin with uric acid levels , suggesting the potential for this SNP to have an impact on the levels of one or both resulting in the associations identified here . Protoporhyrin combines with heme to form iron-containing proteins . This gene is in the bile secretion pathway [42] , and bile consists of substances including bilirubin , which is converted from heme/porphyrin [43] . Thus , the observed association is consistent with a known biological process . There is also a known correlation between ferritin levels and uric acid levels , and urate forms a coordination complex with iron to diminish electron transport , acting as an iron chelator and antioxidant [46] . This correlation implies an expected link between protoporphyrin and uric acid association results; however , we did not observe an association with ferritin levels in this study for this SNP . The PheWAS significant association between rs2231142 and blood pressure levels was only observed in Mexican Americans . However , the direction of effect is opposite as seen for uric acid levels and protoporphyrin . There is a demonstrated positive correlation between high blood pressure and high serum uric acid levels [44] , [45] , but the relationships between rs2231142 and diastolic blood pressure compared with serum uric acid levels in our study were inconsistent , suggesting an independent relationship between this SNP and the two phenotypes . Thus , this is an example of the novel discoveries that can occur with the PheWAS approach that would not be found through only investigating the association between multiple SNPs and a single trait outcome or phenotype . Another intriguing result was for rs2338104 , an intronic SNP in the potassium channel tetramerisation domain containing 10 ( KCTD10 ) gene , which is a member of the polymerase delta-interacting protein 1 gene family . KCTD10 has been previously associated with DNA synthesis/cell proliferation [46] , HDL cholesterol levels [13] , [21] , and interaction with an ubiquitin ligase [47] . In this study , KCDT10 rs2338104 was associated with right ear hearing levels and mean cell hemoglobin levels in non-Hispanic whites . The biological function of KCDT10 has not been extensively studied; consequently , biological explanations for the relationship between this variant and hearing or mean cell hemoglobin do not yet exist . Novel associations for hematologic traits were found in this PheWAS . The SNP rs1800795 near gene interleukin 6 ( IL6 ) and rs4355801 in tumor necrosis factor receptor superfamily , member 11b ( TNFRSF11B ) had significant association with white blood cell counts in non-Hispanic blacks . There are known associations between hematologic traits and genetic variants on chromosome 1 in African Americans , spanning a wide region of chromosome 1 [47] . This region of association is due to the presence of the African-derived Duffy Null polymorphism , a genetic variant protective against Plasmodium vivax malaria . Presence of this variant explains the lower white blood cell and neutrophil counts in African Americans [48] . However , neither rs1800795 nor rs4355801 are located on chromosome 1 and therefore represent potentially unique associations with hematologic traits . Further novel associations with circulating vitamin levels were found . The SNP rs1260326 was associated with vitamin A in non-Hispanic whites . Vitamin E was associated with rs13266634 , rs28927680 , and rs1800588 in non-Hispanic whites and rs964184 in non-Hispanic whites and Mexican Americans . Additionally , folate levels were associated with rs174547 in non-Hispanic blacks and rs1800588 in Mexican Americans . When considering the direction of effect for the vitamin levels , we found that rs174547 , an intronic SNP in fatty acid desaturase 1 ( FADS1 ) , was associated with ferritin and iron levels with different direction of effect in Mexican Americans . Conversely , vitamin E showed the same direction of effect as triglycerides . Recent findings indicate a potential relationship between vitamin E intake and triglyceride levels for certain SNPs [49] . Thus , these results may be reflective of an interaction between variability in vitamin E intake and genetic variance . Other SNPs with pleiotropic effects showed associations with different directions of effect . For example , rs780094 in the intron of glucokinase regulator ( GCKR ) was associated with serum glucose levels with a positive direction of effect ( 0 . 67 ) and potassium and vitamin B6 intake levels with a negative direction of effect ( β = −0 . 05 and −0 . 11 , respectively ) in Mexican Americans . This result is consistent with the demonstrated inverse relationship between potassium intake and glucose intolerance [50] . Likewise , glucose tolerance has been found to increase upon vitamin B6 supplement intake in women with gestational diabetes mellitus [51] , [52] . One possibility , requiring further investigation , is that this SNP modulates the effect of vitamin B6 and potassium on glucose levels . Fourteen of our results showed both a significant PheWAS association and the same direction of effect for a different race-ethnicity . We did not investigate non-significant results with a similar direction of effect for this study . We evaluated the differences in allele frequency across the two surveys , across race-ethnicity , for the SNPs that met our criteria for PheWAS replication ( S5 Table ) . There were not consistent trends between similar or markedly different allele frequencies and whether we did or did not see the same SNP-phenotype associations across more than one race-ethnicity . The reason for differences in association may lie in the variation between linkage disequilibrium patterns across populations . Additionally , as genetic architecture can vary across different race-ethnicities , there is the potential for finding novel associations that exist in only one population . Low power due to sample size could have also contributed to fewer significant associations in non-Hispanic black and Mexican American populations , when compared to non-Hispanic whites , as the sample sizes were generally smaller . Further , phenotypic outcome is impacted by both genetic variation and environmental exposure variation , and thus some associations may not replicate across race-ethnicity in part due to potentially different environmental exposure across racial/ethnic groups . Also , there are differences in the median age across race-ethnicity for the two surveys that could contribute to being unable to detect SNP-phenotype associations across different race-ethnicities . We found examples of gene-gene connections that link our PheWAS results from the SNP to gene to pathway level . These examples show the utility of applying known information about genes to provide biological context for individual PheWAS results through visually linking the information together . Multiple connections not readily apparent when exploring tabular results can be highlighted with this approach . For example , Fig . 9 shows three SNPs within two different genes that are within the GO biological process of “urate metabolic process” , a group of gene products involved in the chemical reactions and pathways involving urate . These SNPs are all associated with uric acid levels in our PheWAS . These SNPs have previously reported associations with uric acid levels , and these genes are known to be involved with pathways that contain urate . However , through connecting phenotypes , SNPs , genes , and pathways , and visualizing the results , we can more clearly show how single genetic variants are likely biologically linked to outcome variation . Further , this example shows the SNP rs2231142 associated with two other phenotypes , as described earlier in this discussion . We also presented network results in Figs . 8 , 9 and 10 . The results presented in Fig . 8 show two SNPs in different genes that both are found in the TGF-β receptor regulated NetPath pathway . This would not have been evident in the PheWAS without applying annotation from known pathways . Fig . 10 shows one example of two genes involved in the KEGG biological process “glycerolipid metabolism” . Here , one SNP is associated with HDL-C levels , and , interestingly , a separate SNP in the network is associated with folate levels . Plasma folate levels have been associated with lipoprotein profiles [49] . Further , the LPL SNP rs328 was associated in this study with HDL-C and is also involved in the KEGG pathway “Peroxisome Proliferator-Activated Receptor ( PPAR ) signaling pathway” , along with a SNP in APOA5 , which was associated with triglyceride levels . PPARs are transcription factors activated by lipids . In the future we will continue to use this network approach , to highlight both the biological context that supports results found in PheWAS and the biological annotation that may identify relationships that forge new hypotheses about the connection between genetic variation and complex outcomes . One limitation to the current PheWAS approach is the risk of false-positive associations due to the large number of tests for association between SNPs and phenotypes . For this analysis , we required replication of association results across NHANES to reduce the type-1 error rate . Correcting for multiple hypothesis testing to account for the comprehensive associations in PheWAS , and thus potentially inflated Type I error , based on the number of tests/studies/groups can be problematic for multiple reasons . Most multiple testing calculations assume independent tests , which we do not have here as phenotypes are correlated across our PheWAS studies . Also , our power from one result to another can vary in part due to variations in sample size for the specific phenotype . In addition we used phenotype-class binning of results which results in different numbers of sub-phenotypes in each bin for potential replication . Future work includes research into identifying additional methods for multiple testing burden in PheWAS , such as permutation testing . Another limitation to the PheWAS approach is the high-throughput nature of the analysis . For instance , adjustments were not made for participants on medication that could modify or lower measurements such as lipids . The results are considered preliminary and bear further inquiry . However , it is notable that we observed replication of a number of previously published results with the same direction of effect indicating that our high-throughput approach is functional for a number of measures . Because we chose to seek replication across NHANES surveys , we did not explore results unique to any one survey . A major strength of the PheWAS approach is the potential for novel discoveries about genetic variants and their relation to phenotypes for future investigation as well as to replicate results found in GWAS . Phenome-wide associations provide the opportunity to uncover complex networks of phenotypes involved in disease through tests of association between genetic variants and a broad range of phenotypes . Utilizing existing epidemiologic collections such as the diverse NHANES allows for potential generalization of variant-phenotype relationships across race-ethnicities . We have found novel associations for phenotypes such as white blood cell count and vitamin levels for SNPs with different previously known associations . We also have found indications of pleiotropy . Further , because this approach investigates single SNPs with multiple phenotypes , results with contrasting direction of effect can be investigated . We explored the results of this PheWAS within the context of additional biological information including the use of network diagrams . In addition , we were able to pursue this across multiple race-ethnicities , whereas much of the approach in GWAS has been within European Americans . The results described here demonstrate the utility of the PheWAS approach to expose relevant results that contrast what is known about the relationships between multiple phenotypes and between genotype and phenotype to uncover the complex nature of human traits .
Two NHANES surveys [53] were included in the PheWAS analyses . The epidemiological survey data and DNA samples of NHANES III were collected between 1991–1994 and Continuous NHANES was collected between 1999–2000 and 2001–2002 . For some of the phenotypes , harmonization across NHANES III and Continuous NHANES was possible . Thus , for a subset of phenotypes , we were able to use the two surveys combined in analyses we refer to as NHANES Combined . NHANES measures the health and nutritional habits of U . S . participants regardless of health status across race-ethnicity , by collecting medical , dietary , demographic , laboratory , lifestyle , and environmental exposure data via questionnaire , direct laboratory measures , and a physical exam . In NHANES , specific age groups ( such as the young elderly ) and racial/ethnic groups are oversampled . The epidemiological data of NHANES and the associated DNA samples were collected by the National Center on Health Statistics ( NCHS ) at the Centers for Disease Control and Prevention ( CDC ) . All procedures were approved by the CDC Ethics Review Board and written informed consent was obtained from all participants . Because no identifying information is available to the investigators , Vanderbilt University's Institutional Review Board determined that this study met the criteria of “non-human subjects . ” For this study , EAGLE genotyped 80 GWAS-identified variants in two NHANES datasets representing three surveys: NHANES III , collected between 1991 and 1994 , and Continuous NHANES , collected between 1999–2000 and 2001–2002 . The majority of the SNPs within our study were chosen for genotyping based on published lipid trait genetic association studies . Also included in this study are SNPs previously associated with a range of other phenotypes , and we detail information about these SNPs in S1 Table , including the genotyping method for each SNP ( unless the SNP was already available within NHANES before EAGLE genotyping , and there we cite the lab that provided the genotypic data to NHANES ) . Genotyping was performed in a total of 14 , 998 NHANES participants with DNA samples including 6 , 634 self-reported non-Hispanic whites , 3 , 458 self-reported non-Hispanic blacks , and 3 , 950 self-reported Mexican Americans . Genotypes included in this study were accessed from ( 1 ) genotyping performed using Sequenom by the Vanderbilt DNA Resources Core , or ( 2 ) existing data in the Genetic NHANES database . In addition to genotyping experimental NHANES samples , blinded duplicates provided by CDC and HapMap controls ( n = 360 ) as part of the PAGE study were also genotyped . Quality control , which included concordance and Hardy Weinberg Equilibrium , was performed on all SNPs by the CDC . All SNPs that passed quality control are available for secondary analyses through NCHS/CDC . Single SNP unadjusted tests of association were performed for 80 SNPs available in NHANES III and Continuous NHANES and 1 , 008 phenotypes . When the exact phenotype was measured in NHANES III and Continuous NHANES , the unadjusted tests of association were also performed for all samples as part of Combined NHANES . As outlined in the PAGE Study [7] tests of association between all SNPs and phenotypes were performed using linear or logistic regression , depending on whether the phenotype was binary or continuous . For categorical phenotypes , binning was used to create new variables of the form “A versus not A” for each category , and logistic regression was used to model the new binary variables . All continuous phenotypes were natural log transformed , following a y to log ( y+1 ) transformation of the response variable with +1 added to all continuous measurements before transformation to prevent variables recorded as zero from being omitted from analysis . All analyses were stratified by self-reported race-ethnicity . Analyses were performed remotely in SAS v9 . 2 ( SAS Institute , Cary , NC ) using the Analytic Data Research by Email ( ANDRE ) portal of the CDC Research Data Center in Hyattsville , MD . A wide range of phenotypic variables was available for both NHANES III and Continuous NHANES . We used only phenotypes for this study that could be binned into phenotype classes across more than one NHANES ( see phenotype classes section for more details ) , so that we could seek replication for association results across surveys . The phenotypes of this study are listed in S6 Table . Detailed information on the collection of each of the phenotypes is available through the CDC , for NHANES III ( http://www . cdc . gov/nchs/nhanes/nh3data . htm ) and for Continuous NHANES ( http://wwwn . cdc . gov/nchs/nhanes/search/nhanes_continuous . aspx ) To facilitate comparisons across NHANES , similar phenotypes from each of the NHANES were binned into 184 “phenotype-classes” ( Table 2 ) via manual inspection of one person and reviewed by a second individual , similar to the phenotype binning of [4] . The development of phenotype-classes was necessary for several reasons . First , not all phenotypes and exposures were surveyed or collected in the same way for each iteration of NHANES , and thus could not be completely harmonized . However , some of these phenotypes were similar enough across surveys and to be binned into the same phenotype-class ( for example , “Arm Circumference” and “Upper Arm Length” were both binned in the “Body Measurements ( Arm ) ” phenotype-class ) . Second , when matching phenotypes and exposures , the labels across and within NHANES vary even for the same phenotypes . For example “Vitamin A” and “Serum Vitamin A” both measured the same phenotype and thus were both classified in the “Vitamin A” phenotype-class . For the majority of PheWAS results , there were multiple significant NHANES measures for each phenotype class , and we reported the lowest p-value in descriptions of the PheWAS results within the figures and the results . Our list of the phenotypes of this study also includes their respective phenotype class , listed in S6 Table . A significant PheWAS result met all of the following criteria: 1 ) a SNP-phenotype association was observed in both NHANES III and Continuous NHANES , 2 ) with p-value <0 . 01 , 3 ) allele frequency >0 . 01 , 4 ) sample size >200 , 5 ) for the same race-ethnicity , 6 ) phenotype class , and 7 ) direction of effect . For each of these consistent associations , we examined tests of association results for Combined NHANES . Significant PheWAS results were then plotted using Phenogram [50] and PheWAS-View[51] , software specifically developed for visualization of PheWAS results ( http://ritchielab . psu . edu/ritchielab/software/ ) . The expanded results for all 69 results meeting our PheWAS significance criteria are presented in S2 Table . We calculated pairwise Pearson correlations between all phenotypes that had a significant PheWAS result , for NHANES III and Continuous NHANES , stratified by race-ethnicity . For any significant PheWAS phenotype , we listed correlations for any phenotypes with a correlation >0 . 6 with the significant PheWAS phenotype list . We took the absolute value of the correlations and used the statistical package R [52] to create a clustered heat map of the correlations with color ranging from light yellow to dark blue . We present our correlation matrices in S1–S6 Figures . The most correlated phenotypes are shown in a light yellow color , the less correlated a phenotype pair , the more blue on the heatmap . Biofilter [53] , [54] is a software package that allows the user to download and automatically integrate several different knowledge databases into a single accessible database called the Library of Knowledge Integration , and then run queries via Biofilter with the resultant integrated data ( https://ritchielab . psu . edu/ritchielab/software/ ) . We used Biofilter to annotate the SNPs of this study with the location and identification of the nearest genes to each of our SNPs , from NCBI dbSNP and NCBI Gene ( Entrez ) ( http://www . ncbi . nlm . nih . gov/ ) . We also applied information from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) [42] , Gene Ontology ( GO ) [43] , and NetPath [44] . This allowed us to highlight known connections between genes . Thus , we were able to identify any biological pathway or grouping connections between the genes SNPs were in or near in our study . After we used Biofilter to annotate the genes as described above , we stratified the results by race-ethnicity . We used Cytoscape [45] to visualize the connections between genes based on their annotation . Using this visualization tool , we explored networks where one or more SNPs were connected , via genes , to mutual pathways or genes , and we did not further investigate any resultant networks comprised of single SNPs . RegulomeDB [55] was used to annotate PheWAS-significant SNPs in this study with functional and regulatory information for our analyses . The results of this analysis are included in Table 4 . | The Epidemiological Architecture for Genes Linked to Environment ( EAGLE ) study performed a Phenome-Wide Association Study ( PheWAS ) to investigate comprehensive associations between a wide range of phenotypes and single-nucleotide polymorphisms using the diverse genotypic and phenotypic data that exists across multiple populations in the National Health and Nutrition Examination Surveys ( NHANES ) , conducted by the Centers for Disease Control and Prevention ( CDC ) . In this study , we replicated known genotype-phenotype associations , identified genotypes associated with phenotypes related to previously reported associations , and most importantly , identified a series of novel genotype-phenotype associations . We also identified potential pleiotropy; that is , SNPs associated with more than one phenotype . We explored the features of these PheWAS results , characterizing any potential functionality of the SNPs of this study , determining association results that were found in more than one racial/ethnic group for the same SNP and phenotype , identifying novel direction of effect relationships for SNPs demonstrating potential pleiotropy , and investigating the association results in the context of gene-based biological networks . Through considering the SNP associations on multiple phenotypic outcomes , as well as through exploring pleiotropy , we may be able to leverage the results of PheWAS to uncover more of the complex underlying genomic architecture of complex traits . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"genome-wide",
"association",
"studies",
"statistical",
"analysis",
"of",
"genetic",
"association",
"genetic",
"networks",
"genomics",
"genome",
"analysis",
"trait",
"locus",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"genomics",
"statistics",
"alleles",
"genetic",
"loci"
]
| 2014 | Detection of Pleiotropy through a Phenome-Wide Association Study (PheWAS) of Epidemiologic Data as Part of the Environmental Architecture for Genes Linked to Environment (EAGLE) Study |
Paracoccidioides spp . , a dimorphic pathogenic fungus , is the etiologic agent of paracoccidioidomycosis ( PCM ) . PCM is an endemic disease that affects at least 10 million people in Latin America , causing severe public health problems . The drugs used against pathogenic fungi have various side effects and limited efficacy; therefore , there is an inevitable and urgent medical need for the development of new antifungal drugs . In the present study , we evaluated the transcriptional profile of Paracoccidioides lutzii exposed to argentilactone , a constituent of the essential oil of Hyptis ovalifolia . A total of 1 , 058 genes were identified , of which 208 were up-regulated and 850 were down-regulated . Cell rescue , defense and virulence , with a total of 26 genes , was a functional category with a large number of genes induced , including heat shock protein 90 ( hsp90 ) , cytochrome c peroxidase ( ccp ) , the hemoglobin ligand RBT5 ( rbt5 ) and superoxide dismutase ( sod ) . Quantitative real-time PCR revealed an increase in the expression level of all of those genes . An enzymatic assay showed a significant increase in SOD activity . The reduced growth of Pbhsp90-aRNA , Pbccp-aRNA , Pbsod-aRNA and Pbrbt5-aRNA isolates in the presence of argentilactone indicates the importance of these genes in the response of Paracoccidioides spp . to argentilactone . The response of the P . lutzii cell wall to argentilactone treatment was also evaluated . The results showed that argentilactone caused a decrease in the levels of polymers in the cell wall . These results suggest that argentilactone is a potential candidate for antifungal therapy .
The genus Paracoccidioides , which comprises the species lutzii and brasiliensis , is the etiological agent of paracoccidioidomycosis ( PCM ) , an important systemic mycosis in Latin America . The inhalation of mycelia fragments , infectious forms of the fungus , is the common route of infection that primarily affects the lungs [1] . PCM has been reported to affect individuals from northern Argentina to southern Mexico , with prevalence in Brazil , Colombia , Venezuela and Argentina . The most cases of PCM occur in men , rural workers and individuals between 30–50 years of age , although it affects individuals at any age [2] . In Brazil , PCM is responsible for over 50% of deaths caused by fungal infections [3] . Fungal infections are a serious threat to public health due to their association with high rates of morbidity and mortality [4] . Despite the existence of potent antifungal agents , the development of antifungal resistance by the fungal species , as well as cytotoxicity and collateral effects , has limited the use of current antifungals [5] . In addition , PCM treatment is a slow process that extends over months or years depending on the severity of the disease and the site of injury [6] . Given these facts , it is important to search for and identify novel antifungals . New therapeutic approaches have been suggested for PCM [7] . In this way , our group has identified new antifungal targets , such as the enzymes 1 , 3-β-D-glucan synthase [8 , 9] , malate synthase [10 , 11] , isocitrate lyase [12] and ( S ) -adenosyl-L-methionine: C24 sterol methyl transferase [13] , from Paracoccidioides spp . In addition , we have investigated new antifungal compounds , including thiosemicarbazide [14] and oenothein [15 , 16] . Here , as part of a continuing search for diverse chemicals from plants , we have examined argentilactone , a bioactive metabolite isolated from Hyptis ovalifolia , which is renowned for its wide range of anticancer , insecticidal and antimicrobial activities [17 , 18] , including those against P . lutzii . In our previous work , we have investigated the antifungal potential of argentilactone and its semi-synthetic derivatives on P . lutzii [19] . Argentilactone and the tetrahydro derivative inhibited native and recombinant isocitrate lyase from P . lutzii in the presence of glucose and acetate . Additionally , argentilactone and the tetrahydro derivative exhibited inhibitory activity against P . lutzii yeast cells and dose-dependently interfered with the dimorphic transition from the mycelium to the yeast phase . Argentilactone interfered with the viability of Paracoccidioides spp . , but was not toxic to MRC5 cells at the IC50 concentration in the fungus . In silico studies showed that argentilactone and reduced argentilactone bound to the catalytic site of PbICL , and the amino acids involved in their binding were identified . The data obtained indicate that argentilactone is a potential candidate for antifungal therapy . In this study , we investigated the transcriptional profile of P . lutzii yeast cells grown in the presence of argentilactone using the Illumina/Hiseq™2000 platform ( Illumina , San Diego , CA , USA ) . A total of 1 , 058 genes were identified , of which 208 were up-regulated and 850 were down-regulated in response to argentilactone treatment . The genes identified were classified by the biological function of the encoded protein products . The main categories identified in the up-regulated genes were metabolism , cell rescue , defense and virulence , energy and cell cycle and DNA processing . The down-regulated gene categories were related to metabolism , transcription , protein fate and cell cycling and DNA processing .
The essential oil of H . ovalifolia was obtained as described previously , and the NMR data are consistent with the literature [18] . P . lutzii ( ATCC-MYA-826 ) was used in the experiments described in this study , except for the silenced mutant experiments , which were performed with P . brasiliensis strains ATCC 60855 and Pb339 . The yeast phase was maintained at 36°C in Fava Netto’s semi-solid medium [20] containing 1% ( w/v ) peptone , 0 . 5% ( w/v ) yeast extract , 0 . 3% ( w/v ) proteose peptone , 0 . 5% ( w/v ) beef extract , 0 . 5% ( w/v ) NaCl , 4% ( w/v ) glucose , and 1 . 4% ( w/v ) agar , pH 7 . 2 . For experiments , cells were transferred to Fava Netto’s liquid medium , where they remained for 72 h at 36°C under agitation at 150 rpm . Afterwards , the fungus was transferred into McVeigh Morton ( MMcM ) chemically defined liquid medium [21] and incubated for 16 h with agitation at 150 rpm . The viability of P . lutzii cells grown in the absence or presence of 9 μg/mL argentilactone was determined using the trypan blue method [22] , in which viable and non-viable cells are counted in a Neubauer chamber . Except for the transcriptional profiling of P . lutzii yeast cells , all experiments were done using yeast cells from three different seedings from different days . All procedures for extraction and manipulation of total RNA were performed in RNAse-free conditions . Total RNA from Paracoccidioides spp . yeast cells not treated or treated with 9 μg/mL argentilactone for 6 h at 36°C in MMcM liquid medium was extracted using Trizol ( Invitrogen , Carlsbad , CA , USA ) according to the supplier's instructions . Each experiment ( not treated and treated ) was performed in triplicate and pooled . The mRNA was purified using the GenElute mRNA kit ( Sigma Aldrich , St . Louis , MO , USA ) . Total RNA was quantified on a NanoDrop 8000 Spectrophotometer and stored at -80°C . Total RNA integrity was visualized using an agarose gel . The cDNA libraries were prepared from poly ( A ) -fragment selected mRNA and processed on the Illumina HiSeq™2000 Sequencing System ( http://www . illumina . com ) . The pipeline was performed as described previously [23] . Briefly , the sequencing reads were mapped to reference the P . lutzii genome ( http://www . broadinstitute . org/annotation/genome/paracoccidioides_brasiliensis/Multiome . html ) using the Bowtie 2 tool . Mapped read data were analyzed by the DEGseq package . Each read was allowed to align to just one site of the genome , and the reads were counted . The default parameters were used to perform the alignment . The number of mismatches allowed in seed alignment ( -N ) was 0 , and the length of each seed ( -L ) was 20 . The fold change selection method was used for differentially expressed gene selection using Fisher’s exact test , and a p-value of 0 . 001 was considered to select the genes . From the selected genes , a 1 . 5-fold change cut-off was considered . Genes with log2 ( fold change ) higher than 0 . 58 or less than -0 . 58 were selected and classified as up- and down-regulated genes , respectively . Genes’ identifications and annotations were determined from the P . lutzii genome database ( http://www . broadinstitute . org/annotation/genome/paracoccidioides_brasiliensis/MultiHome . html ) . The biological processes were obtained using the Pedant on MIPS ( http://pedant . helmholtz-muenchen . de/pedant3htmlview/pedant3view ? Method=analysis&Db=p3_r48325_Par_brasi_Pb01 ) , which provides a tool to browse and search the functional categories ( FunCat ) of proteins . Additionally , hypothetical proteins were annotated using the Blast program ( https://blast . ncbi . nlm . nih . gov/Blast . cgi ? PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome ) . The argentilactone sensitivity assay was performed from three independent experiments using P . brasiliensis , Pb339 , Pb60855 , Pb60855EV , Pb339EV and the silenced mutants for superoxide dismutase ( SOD ) protein ( Pbsod-aRNA ) [14] , heat shock protein HSP90 ( Pbhsp90-aRNA ) [24] , cytochrome C peroxidase protein ( Pbccp-aRNA ) [25] and hemoglobin ligand protein RBT5 ( Pbrbt5-aRNA ) [26] were grown in liquid Fava-Netto for 72 h with agitation at 150 rpm at 36°C , and then transferred to MMcM liquid medium , where they remained overnight . The cells were then washed with phosphate buffered saline ( PBS 1X ) and diluted to concentrations of 104 , 105 and 106 . The cells were plated in solid Fava-Netto medium supplemented with argentilactone at concentrations of 4 . 5 μg/mL , 9 μg/mL , 18 μg/mL and 36 μg/mL and incubated for 6 days at 36°C . The control was prepared in the absence of argentilactone . Total RNA was extracted using Trizol reagent ( Invitrogen , Carlsbad , CA , USA ) following the manufacturer’s protocol . The RNA was reverse transcribed using the high-capacity RNA-to-cDNA kit ( Applied Biosystems , Foster City , CA , USA ) . The cDNA was quantified by qRT-PCR using a SYBR green PCR master mix ( Applied Biosystems Step One Plus PCR System ) . α-tubulin was used as endogenous control for data normalization , and its amplification was presented as relative expression in comparison to that of the experimental samples , whose value was set to 1 . Data were expressed as the mean ± standard deviation of the biological triplicates of independent experiments . Standard curves were generated by diluting the cDNA solution 1:5 . Relative expression levels of genes of interest were calculated using the standard curve method for relative quantification . Statistical comparisons were performed using Student’s t test and p-values < 0 . 05 were considered statistically significant . The specific sense and antisense primers are listed in S1 Table . Argentilactone-regulated transcripts were selected for qRT-PCR validation assays . The protein extraction was performed with cells grown in the presence and absence of argentilactone . After incubation with 9 μg/mL argentilactone for 6 h in MMcM liquid medium , the cells were centrifuged at 10 , 000 x g for 15 min at 4°C and protein was extracted using extraction buffer ( 20 mM Tris-HCl pH 8 . 8; 2 mM CaCl2 ) containing a mixture of protease inhibitors ( GE Healthcare ) . After the addition of glass beads ( 0 . 45 mm ) , the cells were lysed in a bead-beater , followed by centrifugation at 10 , 000 x g for 15 min at 4°C . The supernatant was collected , and the protein concentrations were determined using Bradford reagent ( Sigma-Aldrich ) [27] . The superoxide dismutase activity was quantified using an SOD assay kit ( Sigma-Aldrich ) that determine to production of formazan dye upon reduction with O2- by colorimetric detection at 440 nm . The levels of SOD activity were quantified using 1 μg of total protein extract . Enzyme activity data were plotted as the mean of three independent experiments . The statistical analysis was performed using Student’s t-test and samples with a p-value ˂ 0 . 05 were considered statistically significant . Fluorescence microscopy assays were performed as previously described [28] . Calcofluor White ( CFW ) and Congo Red ( CR ) ( Sigma-Aldrich ) were used to label the cell wall of P . lutzii yeast cells . A total of 106 cells/mL P . lutzii yeast cells were inoculated in Fava Netto’s liquid medium and grown for 3 days with agitation at 150 rpm . Afterwards , the cultures were incubated in MMcM liquid medium overnight at 36°C with shaking for 16 h . The cells were centrifuged at 5 , 000 x g for 5 min and then transferred to MMcM liquid medium containing 9 μg/mL argentilactone for 6 h . Control cells were incubated in MMcM liquid medium without argentilactone . Cells were fixed in 100% methanol at -80°C for 20 min , and then at -20°C for 20 min , and finally they were washed and centrifuged . The collected cells were stained with 100 μg/mL CR and CFW in PBS for 15 min and washed with 1x PBS . The samples were analyzed under a fluorescence microscope at 345 nm and 500 nm for CR and CFW , respectively ( Zeiss Axiocam MRc—Scope A1 , Carl Zeiss , Jena , Germany ) . The level of ethanol was measured as previously described [23] using an enzymatic detection kit UV-test for ethanol ( RBiopharm , Darmstadt , Germany ) . A total of 1 x 106 cells were grown in the presence and absence of the argentilactone . Afterwards , the protein extract was obtained after cell lysis using glass beads and a bead beater apparatus ( BioSpec , Oklahoma , USA ) in 5 cycles of 30 sec . The cell lysate was subjected to centrifugation for 15 min , at 4°C , at 10 , 000 × g . The enzyme assay was performed in triplicate using the supernatant according to the manufacturer's instructions . The mitochondrial membrane was monitored using the rhodamine 123 fluorescent dye . A total of 106 cells/mL were treated with 9 μg/mL argentilactone for 6 h . After treatment , the cells were centrifuged and incubated with 20 μM rhodamine 123 for 20 min at room temperature . Afterwards , the cells were washed 2 times with 1X PBS and resuspended in 1 mL PBS for analysis using a guava easyCyte flow cytometer with excitation and emission wavelengths of 488 and 530 nm , respectively .
Next-generation sequencing was used to produce the transcriptional profile . Approximately 46 million reads of 100-bp single-end sequences were obtained . The data were submitted in the ncbi . nlm . nih . gov , generating access number SRP064389 . The reads were mapped using the reference genome of the P . lutzii genome database ( http://www . broadinstitute . org/annotation/genome/paracoccidioides_brasiliensis/MultiHome . html ) and were analyzed using the DEGseq package . For the global analysis , plotting graphs were prepared . The number of reads counted for each transcript in the presence or absence of argentilactone was represented by scattered dots . The transcripts are represented by dots , which could represent a different number of reads in each condition ( S1A Fig ) . The statistical test was applied to identify differentially expressed transcripts , represented by red dots ( S1B Fig ) . To determine the up- and down-regulated transcripts ( S2 and S3 Tables , respectively ) , a cut-off of 1 . 5-fold change was used , resulting in 1 , 058 differentially expressed transcripts in P . lutzii yeast cells . A biological process classification was performed to gain a general understanding of the functional categories affected by argentilactone . A total of 54% ( 567 transcripts ) were represented by proteins of unknown function ( Fig 1A ) . After 6 h of exposure to argentilactone , transcripts associated with metabolism ( 23 . 6% ) were the most represented . Other groups were also regulated , such as transcription ( 12 . 6% ) , cell rescue , defense and virulence ( 11 . 2% ) and cell cycle and DNA processing ( 11% ) ( Fig 1B ) . The genes related to metabolism ( 20% ) , transcription ( 15 . 8% ) , protein fate ( 12 . 3% ) , cell cycle and DNA processing ( 11 . 7% ) and cellular transport , transport facilities and transport routes ( 11 . 2% ) ( Fig 1C ) were down-regulated . The up-regulated genes were mainly related to metabolism ( 32 . 4% ) , cell rescue , defense and virulence ( 18 . 3% ) , cell cycle and DNA processing ( 9 . 2% ) , energy ( 9 . 2% ) and protein fate ( 5 . 6% ) ( Fig 1D ) . From the transcriptional data obtained ( S2 and S3 Tables ) , we could infer that , we could infer that P . lutzii down-regulated glutamine synthase , which catalyzes the condensation of glutamate and ammonia to glutamine , and glutamate dehydrogenase , which converts glutamate to α-ketoglutarate , and vice versa . In addition , two glutamate-1-semialdehyde 2 , 1-aminomutase , which converts glutamate 1-semialdehyde to 5-aminolevulinate , was down-regulated . Glutamate 1-semialdehyde is a molecule formed from glutamate and is a precursor to ornithine and proline . The cofactor of glutamate-1-semialdehyde 2 , 1-aminomutase is pyridoxal phosphate , which is produced from pyridoxal by pyridoxine kinase , was also down-regulated . On the other hand , in the presence of argentilactone , P . lutzii seems to use the amide group of xanthine , a purine base , in the synthesis of uric acid , which is excreted and metabolized into allantoic acid , allantoin and urea through an amphibian-like uricolytic pathway [29] , as xanthine dehydrogenase , uricase and allantoinase were up-regulated . The production of putrescine and polyamines from ornithine is absent , as ornithine decarboxylase is down-regulated . Glycolysis is induced in P . lutzii in the presence of argentilactone , as class II aldolase and glyceraldehyde-3-phosphate dehydrogenase are up-regulated . It is noteworthy that several transporters , including sugar and amino acid transporters , were down-regulated . Although alcoholic fermentation is up-regulated in P . lutzii yeast cells [30] , the presence of argentilactone repressed it because two alcohol dehydrogenases are down-regulated . The ethanol dosage assay confirmed these data , as ethanol is reduced in the presence of argentilactone ( Fig 2 ) . Homogentisate 1 , 2-dioxygenase catalyzes the conversion of homogentisate to 4-maleylacetoacetate . Hydroxymethylglutaryl-CoA lyase , which converts β-hydroxy-β-methylglutaryl-CoA to acetoacetate and acetyl-CoA , and acetyl-CoA hydrolase , which converts acetyl-CoA to acetate , were up-regulated . P . lutzii utilizes β-oxidation to obtain energy , as acyl-CoA dehydrogenase , which converts fatty acyl-CoA into trans-2-enoyl-CoA , four enoyl-CoA hydratases , which convert trans-2-enoyl-CoA to L-β-hydroxy-acyl-CoA , and 3-ketoacyl-CoA thiolase B , which converts β-ketoacyl-CoA to acyl-CoA-fatty acid and acetyl-CoA , were up-regulated . The methylcitrate cycle , one of the major pathways for propionyl-CoA metabolism , is an alternative source of carbon through pyruvate production [31] . Here , 2-methylcitrate dehydratase , which participates in the methylcitrate pathway , was up-regulated . The P . lutzii cell wall seems to be affected by argentilactone , as several transcripts associated with the synthesis of chitin and glucan , the major components of the fungal cell wall , including that of Paracoccidioides [32 , 33] , were down-regulated . Among the enzymes are endo-1 , 3 ( 4 ) -β-glucanase , two glucan 1 , 3-β-glucosidases , β-glucan synthesis-associated protein and glucanase , which are related to the biosynthesis of glucan , and endochitinase , chitin deacetylase , UDP-N-acetylglucosamine pyrophosphorylase , UDP-N-acetylglucosamine transporter YEA4 , chitin synthase B , chitin synthase regulator 3 and chitin synthase export chaperone , which are related to the biosynthesis of chitin . To investigate the chitin and glucan levels in the cell wall , the cells were stained with CFW and CR and visualized by fluorescence microscopy . The fluorochrome CR and CFW interact with polysaccharides of cell wall [34] exhibiting strong affinity for chains of chitin [35 , 36] . P . lutzii yeast cells after treatment with argentilactone showed a decrease in fluorescence , indicating a reduction in the levels of this polymer ( Fig 3 ) , corroborating the transcriptional data . Several genes responding to stress were up-regulated . Among these genes are sod , rbt5 , ccp , hsp90 , heat shock protein 10 ( hsp10 ) and heat shock protein SSC1 ( hspssc1 ) . The increased expression levels of hsp90 , sod , rbt5 and ccp were confirmed by qRT-PCR in P . lutzii ( Fig 4 ) and P . brasiliensis ( S2 Fig ) , corroborating the transcriptional data . The growth of hsp90 , sod , ccp and rbt5 silenced mutants was evaluated in the presence of argentilactone ( Fig 5 ) . Due the difficulty in obtaining mutants in P . lutzii , the mutants used here were obtained to P . brasiliensis from studies performed previously ( 14 , 24 , 25 , 26 ) . Pb339 , Pb60855 , Pb339EV , Pb60855EV , Pbsod-aRNA , Pbhsp90-aRNA , Pbccp-aRNA and Pbrbt5-aRNA were grown for 6 days in the presence of 4 . 5 , 9 , 18 and 36 μg/mL argentilactone . All mutants presented greater sensitivity to argentilactone compared to wild-type cells and cells containing the empty vector . Argentilactone inhibits Pb60855 and Pb60855EV cells in the presence of 18 μg/mL of the compound , as a dose of 36 μg/mL was necessary to affect the cell growth of Pb339 and Pb339EV . For the silenced mutants Pbsod-aRNA , Pbhsp90-aRNA , Pbccp-aRNA and Pbrbt5-aRNA , 9 μg/mL argentilactone was sufficient to inhibit the growth of the Pbhsp90-aRNA-silenced mutant . These results suggest that overexpression of the hsp90 , sod , ccp and rbt5 transcripts are important for the response of Paracoccidioides spp . to argentilactone and that the silencing of these genes directly influences the growth of the fungus in the presence of argentilactone . Due to the induction of genes related to oxidative stress , SOD enzymatic activity was investigated . The enzymatic activity was measured after growing P . lutzii for 6 h in the presence or absence of argentilactone . A significant increase of enzymatic activity was observed in the presence of argentilactone ( Fig 6 ) . The ability of argentilactone to cause damage to the mitochondrial membrane potential was compared to antimycin A , a potent inhibitor of the electron transport chain . The mitochondrial membrane was monitored using rhodamine 123 , which decreases its fluorescence when there is depolarization of the mitochondrial membrane potential . Flow cytometry data showed that argentilactone exerted a similar effect on cells as antimycin A . In the presence of argentilactone , the intracellular fluorescence due to rhodamine 123 was decreased ( Fig 7 ) .
Argentilactone seems to be able to penetrate into Paracoccidioides spp . yeast cells and modulate cellular targets . Argentilactone seems to induce oxidative stress and interfere with the biosynthesis of the Paracoccidioides spp . cell wall . Given the overall stress caused by argentilactone in Paracoccidioides spp . , other studies must be performed to better elucidate the mode of action of argentilactone in Paracoccidioides spp . | Paracoccidioidomycosis ( PCM ) is a neglected human systemic mycosis caused by Paracoccidioides spp . fungus that invades the host’s lungs and can disseminate to many other organs . Treatment usually involves amphotericin B , sulfadiazine , trimethoprim-sulfamethoxazole , itraconazole , ketoconazole or fluconazole for six months to two years . In this way , many adverse effects are associated with treatment , and patients can have many co-morbidities and difficulties in complying with treatment . For those reasons , more effective and less toxic drugs are needed . The discovery of a potentially bioactive molecule and its correlation with a biological target is an important step in the research and development of drugs . One of the ways in which cells adjust to environmental change is by changing the pattern of gene expression . Thus , the transcriptome is potential experimental strategy to elucidate the mode of action of bioactive molecules . Here , Paracoccidoides spp . altered the expression of genes , leading to a further understanding of the action of the compound argentilactone in the fungal cells . Argentilactone seems to be able to modulate cellular targets , to induce oxidative stress and to interfere with the biosynthesis of the P . lutzii cell wall . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| []
| 2016 | Effects of Argentilactone on the Transcriptional Profile, Cell Wall and Oxidative Stress of Paracoccidioides spp. |
The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems . Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems . Nevertheless , it still remains unclear whether chaos requires specific cellular properties or network architectures , or whether it is a generic property of neuronal circuits . We investigate the dynamics of networks of excitatory-inhibitory ( EI ) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition . Combining Dynamical Mean-Field Theory with numerical simulations , we show that chaotic , asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses . Two different mechanisms can lead to these chaotic fluctuations . One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations . The decorrelation time of these fluctuations is proportional to the time constant of the inhibition . The second mechanism relies on the recurrent E-I-E feedback loop . It requires slow excitation but the inhibition can be fast . In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation . Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population . This is not necessarily the case in the I-I mechanism . Finally , we discuss the neurophysiological and computational significance of our results .
Single cell recordings [1] and electro-encephalography [2 , 3] suggest the existence of chaotic dynamics in the brain . Consistent with chaotic dynamics , in-vivo experiments have demonstrated that cortical circuits are sensitive to weak perturbations [4 , 5] . Remarkably , the misplacement of even a single spike in a cortical network has a marked effect on the timing of subsequent spikes in the network [6] . Chaotic states in extended dynamical systems can be classified as synchronous or asynchronous , depending on the spatial patterns of the dynamics . In synchronous chaos the temporal fluctuations exhibit spatial correlations . If the temporal fluctuations are spatially incoherent , the chaotic state is classified as asynchronous . EEG measures the activity of a large population of neurons . Therefore , it is probable that chaoticity observed in EEGs reflects synchronous chaos in brain regions of rather large size . Models of local cortical circuits exhibiting synchronous chaos have been studied in [7–12] . A computational advantage of synchronous chaos in the brain is that it enables neuronal populations to respond quickly to changes in their external inputs [7] and facilitates the access of the network to states ( e . g . limit cycles or fixed points ) that encode different stimuli [3] . A large body of experimental data , however , has reported that cortical neurons exhibit very weak correlations [13 , 14] and thus are more compatible with asynchronous than with synchronous chaos . Moreover , recent studies have demonstrated that the richness , the complexity and the high dimension of the dynamics in systems operating in asynchronous chaos endows them with remarkable computational capabilities [15–18] . The present paper focuses on the mechanisms underlying the emergence of asynchronous chaos in local neuronal circuits . Asynchronous chaos was studied in a seminal work by Sompolinsky , Crisanti and Sommers ( SCS ) [19] , who investigated a large network of N neuronal-like units fully connected with random weights drawn from a zero mean Gaussian distribution ( called hereafter as the SCS model ) . The dynamics of the network are those of a “rate” model [20] , in which the activity of a unit , S ( t ) , is characterized by a continuous variable which is a non-linear function , S = ϕ ( h ) , of the total input to the unit . In the SCS model the activity variables take values between [-1 , 1] and the function ϕ ( h ) is sigmoidal and odd . Using Dynamical Mean-Field Theory ( DMFT ) SCS showed that if the standard deviation of the weight distribution is sufficiently large , the dynamics bifurcate from fixed point to asynchronous chaos . The SCS model in its original form or in its discrete time version has been used in numerous studies in theoretical and computational neuroscience [15–17 , 21–25] . However , the connectivity of the SCS model violates Dale’s Law , whereby in biological networks a given neuron is either excitatory or inhibitory [26] . Also , the equation of the SCS model dynamics are invariant under the transformation h → −h , a symmetry not fulfilled in more realistic neuronal network models . More importantly , as this is the case frequently for rate models , the physiological meanings of the dynamical “neuronal” variables and of the parameters are not clear in the SCS network . Should these variables and the time constant of their dynamics—which sets the time scale of the chaotic fluctuations—be interpreted as characterizing neurons , or synapses ? In this paper we address the following general and fundamental issues: To what extent are asynchronous chaotic states generic in networks of spiking neurons ? How does this depend on single neuron properties ? How do excitation and inhibition contribute to the emergence of these states ? To what extent these chaotic dynamics share similarities with those exhibited by the SCS model ? We first study these questions in one population of inhibitory neurons receiving feedforward excitation . We then address them in networks of two populations , one inhibitory and the other excitatory , connected by a recurrent feedback loop . A major portion of the results presented here constitutes the core of the Ph . D thesis of one of the authors ( O . H ) [27] .
We consider N randomly connected inhibitory spiking neurons receiving an homogeneous and constant input , I . The voltage of each neuron has nonlinear dynamics , as e . g . in the leaky integrate-and fire ( LIF model , see Materials and Methods ) or in conductance-based models [20] . The connection between two neurons is Jij = JCij ( i , j = 1 , 2…N ) , with J ≤ 0 , and Cij = 1 with probability K/N and 0 otherwise . The outgoing synapses of neuron j obey τ s y n d S j ( t ) d t = - S j ( t ) + J ∑ t j s < t δ ( t - t j s ) ( 1 ) where Sj ( t ) is the synaptic current at time t and τsyn the synaptic time constant . When neuron j fires a spike ( time t j s ) , Sj increments by J . Thus , the total input to neuron i , hi ( t ) = I+∑j Jij Sj ( t ) , satisfies: τ s y n d h i ( t ) d t = - h i ( t ) + I + ∑ j ∑ t j s < t J i j δ ( t - t j s ) ( 2 ) We assume K ≫ 1 , hence the number of recurrent inputs per neuron is K ± O ( K ) . Scaling J and I as: J = − J 0 / K , I = K I 0 , the time-averaged synaptic inputs are O ( K ) and their spatial ( quenched ) and temporal fluctuations are O ( 1 ) [28 , 29] . Finite neuronal activity requires that excitation and inhibition cancel to the leading order in K . In this balanced state , the mean and the fluctuations of the net inputs are O ( 1 ) [28 , 29] . The properties of the balanced state are well understood if the synapses are much faster than all the typical time constants of the intrinsic neuronal dynamics [30] . Temporally irregular asynchronous firing of spikes is a hallmark of this regime [13 , 28 , 29 , 31 , 32] . However , this stochasticity does not always correspond to a true chaotic state [28 , 29 , 33–36] . In fact , this depends on the spike initiation dynamics of the neurons [37] . The opposite situation , in which some of the synapses are slower than the single neuron dynamics , remains poorly understood . This paper mostly focuses on that situation . When the synaptic dynamics is sufficiently slow compared to the single neuron dynamics , the network dynamics can be reduced to the set of non-linear first order differential equations: τ s y n d h i ( t ) d t = - h i ( t ) + I + ∑ j J i j r j ( t ) ( 3 ) r i ( t ) = g ( h i ( t ) ) ( 4 ) where ri ( t ) is the instantaneous firing rate of neuron i and g ( h ) is the neuronal input-output transfer function [20] . These are the equations of a rate model [20 , 38] in which the activity variables correspond to the net synaptic inputs in the neurons . Eqs ( 3 ) – ( 4 ) differ from those of the SCS model in that they have a well defined interpretation in terms of spiking dynamics , the time constant has a well defined physiological meaning , namely , the synaptic time constant , the transfer function quantifies the spiking response of the neurons and is thus positive , the interactions satisfy Dale’s law and the neuronal connectivity is partial . The above considerations show that when synapses are slow , the dynamics of inhibitory networks is completely determined by the transfer function of the neurons . Therefore , to gain insights into the way dynamics become chaotic in such systems we proceed by investigating various spiking models that differ in the shape of their transfer functions . We now consider EI spiking networks with recurrent feedback interactions between the two populations . The synaptic strengths and time constants are J 0 α β / K and ταβ ( α , β ∈ {E , I} ) . Assuming slow synapses , the dynamics can be reduced to four sets of equations for the four types of synaptic inputs , h α β i ( t ) ( Materials and Methods , Eq ( 17 ) ) . The DMFT yields self-consistent equations for the statistics of these inputs . These equations can be analyzed straightforwardly for the fixed point state . In contrast to purely inhibitory networks where the fixed point loses stability only via a bifurcation to chaos , it can now also lose stability via a Hopf bifurcation . This depends on the synaptic time constants . When this happens the network develops synchronous oscillations which break the balance of excitation and inhibition ( the oscillation amplitude diverges for large K ) . We focus here on instabilities which lead to chaos . Their locations in the 6 dimensional parameter space ( 4 synaptic strengths , 2 external inputs ) of the model can be derived for a general transfer function ( Eqs ( 54 ) – ( 55 ) ) . Differential equations for the PAC functions , σαβ ( τ ) , can also be derived in the chaotic regime . However , general analytical characterization of their solutions is particularly difficult . Leaving such study for future work , we mostly focus below on numerical simulations . Our key result is that in EI networks asynchronous chaos emerges in two ways , one driven by I-I interactions ( II mechanism ) and the other by the EIE loop ( EIE mechanism ) .
Networks of neurons operating in the so-called balanced regime exhibit spiking activity with strong temporal variability and spatial heterogeneity . Previous theoretical studies have investigated this regime assuming that excitatory and inhibitory synapses are sufficiently fast compared to the neuronal dynamics . The nature of the balanced state is now fairly well understood in this case . By contrast , here we focused on networks in which some of the synapses are slow . To study the dynamics in these networks , we reduced them to a rate dynamics that we investigated by combining Dynamical Mean-Field Theory and simulations . Our key result is that when synaptic interactions are sufficiently strong and slow , chaotic fluctuations on the time scales of the synaptic dynamics emerge naturally from the network collective behavior . Moreover , the nature of the transition to chaos and the behavior in the chaotic regime are determined only by the neuronal f − I curve and not by the details of the spike-generation mechanism . We identified two mechanisms for the emergence of asynchronous chaos in EI neuronal networks . One mechanism relies on II interactions whereas in the other the EIE feedback loop plays the key role . These mechanisms hold in rate models ( Eq ( 3 ) ) as well as in LIF spiking networks . By computing the maximum Lyapunov exponent , we provided direct evidence that in rate models these states are indeed chaotic . For LIF spiking networks , we argued that when the synapses are sufficiently slow , the observed activity fluctuations are chaotic since their statistics are quantitatively similar to those observed in the corresponding rate model . This similarity persists for synaptic time constants as small as the membrane time constant . This is in agreement with [33–35] which relied on numerical integration of the LIF model to compute the Lyapunov spectra of networks of various sizes and increasing synaptic time constants . They found that the LIF dynamics are chaotic only if the synapses are sufficiently slow . In these two mechanisms , the dynamics of the synaptic currents play the key role whereas dependence on the intrinsic properties of the neurons only occurs via their nonlinear instantaneous input-output transfer function . Since the synaptic currents are filtered versions of the neuronal spike trains , and that the temporal fluctuations of the activity occur on the time scales of the synaptic currents , it is natural to qualify the dynamical regime as rate chaos . Although the features of the bifurcation to chaos may depend on the shape of the transfer function , as we have shown , the qualitative features of the chaotic state are very general , provided that the synaptic currents are sufficiently slow . Rate chaos is therefore a generic property of networks of spiking neurons operating in the balanced regime . We show in S3 Text that rate chaos occurs also in networks of non-leaky integrate-and-fire spiking neurons . In that case , the statistics of the fluctuations are similar to those of the model in Eq ( 3 ) with a threshold-linear transfer function . We also found rate chaos in biophysically more realistic network models in which the dynamics of the neurons and of the synapses are conductance-based ( results not shown ) . In these cases , the dynamics of the synaptic conductances give rise to the chaotic fluctuations . Quantitative mappings from spiking to rate models have been derived for networks in stationary asynchronous non chaotic states [38] or responding to external fluctuating inputs [48] . Spiking dynamics also share qualitative similarities with rate models for networks operating in synchronous states [9–11 , 38 , 43] . To our knowledge , the current study is the first to report a quantitative correspondance between spiking and rate model operating in chaotic states . The SCS model [19] has been widely used to explore the physiological [22 , 49] and computational significance of chaos in neuronal networks . Recent works have shown that because of the richness of its chaotic dynamics , the SCS model has remarkable learning capabilities [15–18] . Our work paves the way for an extension of these results to networks of spiking neurons with a connectivity satisfying Dale’s law , which are biologically more realistic than the SCS model . Another interesting implication of our work is in the field of random matrices . Given a dense NxN random matrix , A , with i . i . d elements with zero mean and finite standard deviation ( SD ) , in the large N limit , the eigenvalue of A / N with the largest real part is real , and it is equal to SD [50 , 51] ( more generally , the eigenvalues of A / N are uniformly distributed within a disk of radius SD centered at the origin [50 , 51] ) . Several results regarding the spectra ( bulk and outliers ) of dense random matrices with structures reflecting Dale’s law have been derived recently [52–54] . Less is known when the matrices are sparse . A byproduct of our approach are two conjectures for the maximal eigenvalue of such sparse random matrices , namely Eqs ( 7 ) and ( 62 ) that we verified numerically . Neuronal spiking statistics ( e . g . , firing rate , spike counts , inter-spike intervals ) exhibit a very broad range of time scales during spontaneous or sensory evoked activity in-vivo ( see e . g [55 , 56] ) . Fluctuations on time scales larger than several 100s of millisecond can be accounted for by neuromodulation which changes the global excitability of the cortical network or changes in behavioral state . Very fast fluctuations are naturally explained in the framework of the standard model of balance of excitation and inhibition [28–30] . By contrast , it is unclear how to explain modulations in the intermediate temporal range of a few 10s to several 100s of milliseconds . In fact , the standard framework of balanced networks predicts that fluctuations on this time scale are actively suppressed because the network state is very stable . Our work extends this framework and shows two mechanisms by which modulations in this range can occur . In the II mechanism , inhibitory synapses must be strong and slower than 10 − 20 ms . GABAA inhibition may be too fast for this [57] ( see however [58] ) , but GABAB[59] are sufficiently slow . In contrast , the EIE mechanism is achieved when inhibition in fast . It requires slow recurrent excitation to inhibitory neurons , with a time constant of a few to several tens of ms , as is typically the case for NMDA receptors ( see e . g [60–62] ) . Hence , the combination of GABAA and NMDA synapses can generate chaotic dynamics in the cortex and fluctuations in activity on a time scale of several tens to a few hundreds of ms . Note added in production: Following a request from the editors after formal acceptance of our article , we note that a recent paper [63] claims that spiking networks with instantaneous delayed synapses exhibit an asynchronous state similar to the chaotic state of the SCS model . However , this claim is incorrect and has been shown to rely on flawed analysis [64] .
The two population network of leaky integrate-and-fire ( LIF ) neurons considered in this work consists of NE excitatory ( E ) and NI inhibitory neurons . The subthreshold dynamics of the membrane potential , V i α , of neuron i in population α ( i = 1 , … , Nα; α , β ∈ {E , I} ) obeys: τ m d V i α ( t ) d t = - V i α ( t ) + I α + J α E ∑ j C i j α E S j α E ( t ) - ∑ j J α I C i j α I S j α I ( t ) ( 11 ) where τm is the membrane time constant ( we take τm = 10 msec for both populations ) , C i j α β and Jαβ are respectively the connectivity matrix and the strength of the connections between the ( presynaptic ) population β and ( postsynaptic ) population α and Iα the external feedforward input to population α . For simplicity we take NE = NI = N . However , all the results described in the paper are also valid when the number of neurons is different in the populations ( provided both numbers are large ) . , The variables S j α β , which describe the synapses connecting neuron j in population β to population α , follow the dynamics: τ α β d S j α β d t = - S j α β + ∑ t j β δ ( t - t j β ) ( 12 ) where ταβ is the synaptic time constant and the sum is over all the spikes emitted at times t j β < t . Eqs ( 11 ) , ( 12 ) are supplemented by a reset condition . If at time tsp , V i α ( t s p ) = 1 , the neuron emits a spike and V i α ( t s p + ) = 0 . For simplicity we do not include the neuronal refractory period . We assume that the connectivity is random with all the C i j α β uncorrelated and such that C i j α β = 1 with probability K/N and 0 otherwise . Hence each neuron is connected , on average , to K neurons from its population as well as to K neurons from the other population . When varying the connectivity K we scale the interaction strength and the feedforward inputs according to: J α β = J 0 α β / K and I α = I 0 α K[29] . The dynamics of the network of the one-population spiking LIF neurons considered in the first part of the paper are: τ m d V i ( t ) d t = - V i ( t ) + I + J ∑ j C i j S j ( t ) ( 13 ) supplemented with the reset condition at threshold . The elements of the connectivity matrix , Cij , are uncorrelated and such that Cij = 1 with probability K/N and 0 otherwise . All neurons are inhibitory , thus J < 0 . The synaptic dynamics are: τ s y n d S j d t = - S j + ∑ t j δ ( t - t j ) ( 14 ) where τsyn is the synaptic time constant of the inhibition and the sum is over all the spikes emitted at times tj < t . The interaction strength and the feedforward inputs scale with K as: J = − J 0 / K and I = I 0 K with J0 > 0 . We consider briefly this model in S3 Text . The network architecture as well as the synaptic dynamics are as above . The single neuron dynamics of non-leaky integrate-and-fire ( NLIF ) neurons are similar to those of LIF neurons except for the first terms on the right-hand side of Eqs ( 11 ) , ( 13 ) which are now omitted . If the synapses are much slower than the membrane time constant , the full dynamics of a spiking network can be approximated by the dynamics of the synapses driven by the instantaneous firing rates of the neurons , namely: τ α β d S i α β d t = - S i α β + g ( J β E ∑ j C i j β E S j β E - J β I ∑ j C i j β I S j β I + I β ) ( 15 ) where g ( x ) is the transfer function of the neuron ( the f − I curve ) [20] . In particular , for the LIF networks , g ( x ) = - 1 τ m log ( 1 - 1 / x ) H ( x - 1 ) ( 16 ) with H ( x ) = 1 for x > 0 and H ( x ) = 0 otherwise . For the NLIF networks , the transfer function is threshold-linear: g ( x ) = xH ( x ) . Defining h i α β ≜ J α β ∑ j C i j α β S j α β , the dynamics of h i α β are given by τ α β d h i α β d t = - h i α β + ∑ j J α β C i j α β g ( h j β E ( t ) - h j β I ( t ) + I β ) ( 17 ) We will denote by h i β the total input into neuron i in population β: h i β = h i β E − h i β I + I β . For networks comprising only one population of inhibitory spiking neurons we will drop the superscript β = I and denote this input by hi . The dynamics then yield: τ s y n d h i d t = - h i + I - J ∑ j = 1 N C i j g ( h j ) ( 18 ) where τsyn is the inhibitory synaptic time constant .
A Dynamical Mean-Field Theory ( DMFT ) can be developed to investigate the rate model , Eq ( 17 ) , for a general transfer function under the assumption , 1 ≪ K ≪ N . Here we provide a full analysis of a one-population network of inhibitory neurons whose dynamics are given in Eq ( 18 ) . We take I = I 0 K as the external input and J = J 0 / K as the coupling strength . In this case , a functional integral derivation shows that these dynamics can be written as: τ s y n d h i ( t ) d t = - h i ( t ) + η i ( t ) , i = 1 , . . . , N ( 19 ) where ηi ( t ) is a Gaussian noise: η i ( t ) = μ + J 0 q z i + ξ i ( t ) ( 20 ) with zi , i . i . d Gaussian quenched variables with zero mean and unit standard deviation ( SD ) , ξi ( t ) are Gaussian noises with ⟨ξi ( t ) ⟩t = 0 , and ⟨ξi ( t ) ξj ( t+τ ) ⟩t = Cξ ( τ ) δi , j where ⟨ ⋅ ⟩t stands for averaging over time . Therefore , in general , the inputs to the neurons display temporal as well as quenched fluctuations . The self-consistent equations that determine the mean , temporal correlations and quenched fluctuations yield: μ = K ( I 0 - J 0 [ ⟨ g ( h i ( t ) ) ⟩ ] ) ( 21 ) q = [ ⟨ g ( h ) ⟩ 2 ] ( 22 ) C ξ ( τ ) = J 0 2 ( [ ⟨ g ( h ( t ) ) g ( h ( t + τ ) ⟩ ] - q ) ( 23 ) where ⟨ ⋅ ⟩ and [⋅] stand for averaging over noise and quenched disorder , respectively . Thus the quantities q and μ obey: q = ∫ - ∞ ∞ [ ∫ - ∞ ∞ g ( μ + J 0 q z + σ 0 - J 0 2 q ξ ) D ξ ] 2 D z ( 24 ) and: 1 J 0 ( I 0 - μ K ) = ∫ - ∞ ∞ g ( μ + σ 0 z ) D z ( 25 ) where σ ( τ ) = [⟨h ( t ) h ( t+τ ) ⟩] − μ2 is the population-averaged autocovariance ( PAC ) of the input to the neurons and we define: σ0 = σ ( 0 ) and D x = e − x 2 2 2 π d x . In the limit K → ∞ , μ must remain finite . This implies that the population averaged firing rate , [⟨g ( h ) ⟩] = I0/J0 does not depend on the specifics of the transfer function of the neurons and varies linearly with I0 . This is a key outcome of the balance between the feedforward excitatory and the recurrent inhibitory inputs to the neurons . To express Cξ ( τ ) in terms of σ , we note that the vector ( h ( t ) , h ( t+τ ) ) T is a bivariate Gaussian , so in fact we need to calculate E[g ( μ+x ) g ( μ+y ) ] where ( x , y ) T has zero mean and a covariance matrix Σ x y = ( σ 0 σ σ σ 0 ) and E[⋅] stands for averaging over temporal noise and quenched disorder . Defining [ x y ] = [ σ 0 - | σ | 0 | σ | 0 σ 0 - | σ | sign ( σ ) · | σ | ] · [ ξ θ z ] where ξ , θ and z are independent Gaussian variables with zero mean and unit variance yields E [ g ( μ + x ) g ( μ + y ) ] = = E [ E [ g ( μ + σ 0 - | σ | ξ + | σ | z ) | z ] E [ g ( μ + σ 0 - | σ | θ + sign ( σ ) · | σ | z ) | z ] ] = = ∫ - ∞ ∞ [ ∫ - ∞ ∞ g ( μ + σ 0 - | σ | ξ + | σ | z ) D ξ · ∫ - ∞ ∞ g ( μ + σ 0 - | σ | θ + sign ( σ ) · | σ | z ) D θ ] D z ( 26 ) A straightforward derivation shows that σ ( τ ) obeys: τ s y n 2 d 2 σ d τ 2 = = σ - J 0 2 ∫ - ∞ ∞ ∫ - ∞ ∞ ∫ - ∞ ∞ g ( μ + σ 0 - | σ | ξ + | σ | z ) g ( μ + σ 0 - | σ | θ + sign ( σ ) · | σ | z ) D ξ D θ D z ( 27 ) with initial conditions: σ ( 0 ) = σ 0 ; d σ d τ ( 0 ) = 0 ( 28 ) where the last condition results from σ ( τ ) = σ ( −τ ) . Eq ( 27 ) can be rewritten as: τ s y n 2 d 2 σ d τ 2 = - ∂ V ( σ ; σ 0 ) ∂ σ ( 29 ) where the “potential” V ( σ;σ0 ) which depends parametrically on σ0 is: V ( σ ; σ 0 ) = = - σ 2 2 + J 0 2 ∫ - ∞ ∞ ∫ - ∞ ∞ ∫ - ∞ ∞ G ( μ + σ 0 - | σ | ξ + | σ | z ) G ( μ + σ 0 - | σ | θ + sign ( σ ) · | σ | z ) D ξ D θ D z ( 30 ) with G ( x ) = ∫g ( x ) dx . Note that for positive σ this equation yields V ( σ ; σ 0 ) = - σ 2 2 + J 0 2 ∫ - ∞ ∞ [ ∫ - ∞ ∞ G ( μ + σ 0 - σ ξ + σ z ) D ξ ] 2 D z ( 31 ) Therefore the quantity E = 1 2 ( τ s y n d σ d τ ) 2 + V ( σ ; σ 0 ) ( 32 ) is conserved under the dynamics , Eq ( 29 ) . Hence: 1 2 ( τ s y n d σ d τ ) 2 + V ( σ ; σ 0 ) = V ( σ 0 ; σ 0 ) ( 33 ) To simplify notations , we drop the parameter σ0 and denote the potential by V ( σ ) . The first , second and third order derivatives of the potential with respect to σ are denoted V′ ( σ ) , V′′ ( σ ) and V′′′ ( σ ) . For illustrative purpose , we consider a sigmoid transfer function , g ( x ) = ϕ ( x ) ≜ 1 2 [ 1 + erf ( x 2 ) ] . In this case we have G ( x ) = Φ ( x ) ≜ x 2 [ 1 + erf ( x 2 ) ] + e - x 2 2 2 π Using the identities: ∫ - ∞ ∞ ϕ ( a + b z ) D z = ϕ ( a 1 + b 2 ) and ∫ - ∞ ∞ ϕ ( a + b z ) z D z = b 1 + b 2 e - a 2 2 ( 1 + b 2 ) 2 π the potential V ( σ ) can be written as: V ( σ ) = - σ 2 2 + J 0 2 ∫ - ∞ ∞ ( 1 + σ 0 - | σ | ) Φ ( μ + | σ | z 1 + σ 0 - | σ | ) Φ ( μ + sign ( σ ) · | σ | z 1 + σ 0 - | σ | ) D z Fig 17A1–3 plots V for σ ∈ ( −σ0 , σ0 ) for J0 = 4 , fixed I0 = 1 and different values of σ0 . When V′ ( σ0 ) > 0 ( Fig 17A1 ) , the solution to Eq ( 29 ) , σ ( τ ) , decreases monotonically from σ0 to −σ0 that it reaches in finite time with a strictly negative velocity; this solution does not correspond to an autocovariance function . For σ0 such that V′ ( σ0 ) = 0 ( Fig 17A2 ) the solution is σ ( τ ) = σ0 . It corresponds to a fixed point of the dynamics , Eq ( 18 ) in which all the inputs to the neurons are constant in time , h i ( t ) = h i 0 , and h i 0 has a Gaussian distribution . Finally , for σ0 such that V′ ( σ0 ) < 0 ( Fig 17A3 ) , there is no solution to Eq ( 33 ) with σ ( 0 ) = σ0 . Fig 17B1–3 plots V for J0 = 15 . For small σ0 , the solution Eq ( 33 ) does not correspond to an autocovariance function . As σ0 increases , V ( σ ) becomes non-monotonic in the vicinity of σ = σ0 with local maxima and minima at σ = σmax and σ = σmin , respectively ( Fig 17B2 ) . However , here also the solution for σ ( τ ) does not correspond to an autocovariance because σ0 is the global maximum in the range σ ∈ [−σ0 , σ0] . For σ 0 = σ 0 * , such that V ( σ m a x ; σ 0 * ) = V ( σ 0 * ; σ 0 * ) ( Fig 17B3 ) an acceptable solution appears , in which σ decays monotonically from σ 0 * and converges to σmax as τ → ∞ , i . e . σmax = σ∞ . This solution corresponds to a chaotic state of the network . If σ0 is further increased beyond σ 0 * , V ( σmax , σ0 ) > V ( σ0 ) ( Fig 17B4 ) , and the solution exhibits oscillations around σmin . For σ0 ≈ 11 . 77 , V′ ( σ0 ) = 0 , and the solution corresponds to a fixed point ( Fig 17B5 ) . Finally , for σ0 larger , V′ ( σ0 ) is negative ( Fig 17B6 ) and there is no solution to Eq ( 18 ) with σ ( 0 ) = σ0 . A bifurcation between these behaviors occurs at some critical value , Jc , such that for J0 < Jc the self-consistent solutions of Eq ( 29 ) are either oscillatory or constant as a function of τ , whereas for J0 > Jc they are either oscillatory or decay monotonically . A stability analysis of these different solutions is beyond the scope of this paper; instead , we rely on numerical simulations of the full dynamics . They indicate that the network dynamics always reach a fixed point for sufficiently small J0 . For sufficiently large J0 the fixed point is unstable and the network settles in a state in which σ ( τ ) decays monotonically with τ . Simulations also show that the maximum Lyapunov exponent in these cases is positive ( see below ) ; i . e . the network is in a chaotic state . For values of J0 in between these two regimes , the network displays oscillatory patterns of activity . However , for increasing network sizes , N , the range of J0 in which oscillations are observed vanishes ( not shown ) . Therefore for large N the bifurcation between a fixed point and chaos occurs abruptly at some critical value Jc . A similar phenomenology occurs for other non-linear positive monotonically increasing transfer functions . In summary , for a fixed feedforward input , I0 , there are two regimes in the large N limit: for J0 < Jc: the stable state is a fixed point . The distribution of the inputs to the neurons is a Gaussian whose mean , μ , and variance , σ are determined by the self-consistent mean-field equations: μ = K ( I 0 - J 0 ∫ - ∞ ∞ g ( μ + σ z ) D z ) ( 34 ) σ = J 0 2 ∫ - ∞ ∞ [ g ( μ + σ z ) ] 2 D z ( 35 ) For a transfer function , g ( x ) , which is zero when x is smaller than some threshold T ( functions without threshold correspond to T = −∞ ) , the distribution of the neuronal firing rates , ri , in this state is given by: p m ( x ) = d d x [ Pr ( r i ≤ x ) ] = = 1 2 π σ e - μ 2 2 σ · δ ( x - T ) + 1 2 π σ e - ( g - 1 ( x ) - μ ) 2 2 σ · 1 g ′ ( g - 1 ( x ) ) · H ( x - T ) ( 36 ) for J0 > Jc: the stable state is chaotic . The distribution of time average inputs is Gaussian with mean μ and variance σ ∞ = J 0 2 q and the autocovariance of the inputs is determined by Eq ( 29 ) which depends on σ0 . The quantities μ , σ0 and σ∞ are determined by the self-consistent equations: σ ∞ = J 0 2 ∫ - ∞ ∞ [ ∫ - ∞ ∞ g ( μ + σ 0 - σ ∞ ξ + σ ∞ z ) D ξ ] 2 D z ( 37 ) and σ 0 2 - σ ∞ 2 2 = = J 0 2 ∫ - ∞ ∞ G ( μ + σ 0 z ) 2 D z - - J 0 2 ∫ - ∞ ∞ [ ∫ - ∞ ∞ G ( μ + σ 0 - σ ∞ ξ + σ ∞ z ) D ξ ] 2 D z ( 38 ) together with Eq ( 25 ) .
A DMFT approach can also be developed to investigate the dynamics of the two population network model , Eq ( 17 ) . To that end , the last term in Eq ( 17 ) is written as a Gaussian random process with mean μαβ and autocorrelation function Cαβ ( τ ) and derives the self-consistent equations that these quantities satisfy . The quantity μαβ is therefore μ α β = J 0 α β K E [ g ( h β ) ] where: h i β = h i β E - h i β I + I β ( 39 ) is the net input to neuron i in population β . The synaptic inputs h i α β is also a Gaussian random process . We denote its mean over time and over all the neurons in population α by μαβ = E[hαβ ( t ) ] and its PAC by σαβ ( τ ) = E[hαβ ( t ) hαβ ( t+τ ) ]− ( μαβ ) 2 . Taking I β = I 0 β ⋅ K we can write the mean of h j β as μ β = μ β E - μ β I + I β = = K ( J 0 α E E [ g ( h E ) ] - J 0 α I E [ g ( h I ) ] + I 0 β ) ( 40 ) The PAC of h j β then reads: σ β ( τ ) ≜ E [ h β ( t ) h β ( t + τ ) ] - ( μ β ) 2 = = σ β E ( τ ) + σ β I ( τ ) We can now write the balance condition in the large K limit: I 0 α + J 0 α E r E - J 0 α I r I = μ α K ( 41 ) where r β = E [ g ( h α ) ] = ∫ - ∞ ∞ g ( μ α + σ 0 β z ) e - z 2 2 2 π d z ( 42 ) is the neuronal firing rate averaged over cells in population α . Here , σ 0 β = σ β ( 0 ) . We can also express Cαβ ( τ ) in terms of σα ( τ ) as: C α β ( τ ) = E [ ∑ j J i j α β g ( h j β ( t ) ) ∑ j J i j α β g ( h j β ( t + τ ) ) ] = ( J 0 α β ) 2 C ˜ β ( σ β ( τ ) ) + ( μ α β ) 2 ( 43 ) where: C ˜ β ( σ β ) = = ∫ - ∞ ∞ ∫ - ∞ ∞ ∫ - ∞ ∞ g ( μ β + σ 0 β - σ β ξ + sign ( σ β ) | σ β | z ) g ( μ β + σ 0 β - σ β θ + | σ β | z ) D θ D ξ D z ( 44 ) Let us denote by Δαβ ( τ ) the autocorrelation of hαβ ( t ) . We can express the relation between Cαβ ( τ ) and Δαβ ( τ ) by their Fourier transforms as Δαβ ( ω ) = H ( ω ) H* ( ω ) Cαβ ( ω ) , where H ( ω ) = 1/ ( 1+iταβ ω ) . Transforming back to the time domain yields: ( τ α β ) 2 d 2 Δ α β d τ 2 = Δ α β - C α β ( 45 ) Since Δαβ = σαβ+ ( μαβ ) 2 we get: ( τ α β ) 2 d 2 σ α β d τ 2 = σ α β - ( J 0 α β ) 2 C ˜ β ( 46 ) Thus we get a set of self-consistent equations for the four PACs σαβ . The relevant soutions have to satisfy the four boundary conditions: lim τ → ∞ d σ α β ( τ ) d τ = 0 ( 47 ) In general , these dynamical equations cannot be written like those of a particle in some potential . This makes the study of their solutions substantially more difficult than in the one population case . A potential function can be written for the DMFT if the time scale of one type of synapses is substantially larger than the others , which makes it possible to consider the latter as instantaneous . We carry out this analysis below assuming τIE ≫ τEI , τEE , τII . Setting all the synapses except those from E neurons to I neurons to be instantaneous implies that except for σIE one has: σ α β = ( J 0 α β ) 2 C ˜ β ( 48 ) where C ˜ β is defined in Eq ( 44 ) . Since τIE is now the only time scale we can take τIE = 1 . Also , σEE , σEI , σII and the potential V are now functions of a single variable , σIE . Therefore , the differential equation for σIE can be written as d 2 σ I E d τ 2 = - d V d σ I E where d V d σ I E = - σ I E + ( J 0 I E ) 2 C ˜ E ( σ I E ) ( 49 ) The instability of the fixed point occurs when , V′ ( σIE ) and V′′ ( σIE ) , the first and the second derivatives of V with respect to σIE , vanishes . Using Eq ( 49 ) one has: V ′ ′ ( σ I E ) = - 1 + ( J 0 I E ) 2 d C ˜ E d σ E · d σ E d σ I E ( 50 ) Since σα = σαE+σαI: d σ E d σ I E = ( J 0 E E ) 2 d C ˜ E d σ E · d σ E d σ I E + ( J 0 E I ) 2 d C ˜ I d σ I · d σ I d σ I E ( 51 ) and d σ I d σ I E = 1 + ( J 0 I I ) 2 d C ˜ I d σ I · d σ I d σ I E ( 52 ) where d C ˜ β d σ β = ∫ - ∞ ∞ [ ∫ - ∞ ∞ g ′ ( μ β + σ 0 β - σ β ξ + σ β z ) e - ξ 2 2 2 π d ξ ] 2 e - z 2 2 2 π d z From Eqs ( 51 ) – ( 52 ) one gets: d σ E d σ I E = ( J 0 E I ) 2 d C ˜ I d σ I ( 1 - ( J 0 E E ) 2 d C ˜ E d σ E ) ( 1 - ( J 0 I I ) 2 d C ˜ I d σ I ) and: V ′ ′ ( σ I E ) = - 1 + ( J 0 I E ) 2 d C ˜ E d σ E ( J 0 E I ) 2 d C ˜ I d l σ I ( 1 - ( J 0 E E ) 2 d C ˜ E d σ E ) ( 1 - ( J 0 I I ) 2 d C ˜ I d σ I ) ( 53 ) Thus at chaos onset , together with Eq ( 41 ) , J 0 α β , σα and μα obey: σ α = ( J 0 α E ) 2 C ^ ( μ E , σ E ) + ( J 0 α I ) 2 C ^ ( μ I , σ I ) ( 54 ) 1 = ( J 0 E E ) 2 C ^ ′ ( μ E , σ E ) + ( J 0 I I ) 2 C ^ ′ ( μ I , σ I ) + + [ ( J 0 E I J 0 I E ) 2 - ( J 0 E E J 0 I I ) 2 ] C ^ ′ ( μ E , σ E ) C ^ ′ ( μ I , σ I ) ( 55 ) where: C ^ ( μ , σ ) = ∫ - ∞ ∞ [ g ( μ + | σ | z ) ] 2 e - z 2 2 2 π d z C ^ ′ ( μ , σ ) = ∫ - ∞ ∞ [ g ′ ( μ + | σ | z ) ] 2 e - z 2 2 2 π d z For instance for the threshold-linear transfer function we have C ^ ( μ , σ ) = F 2 ( μ , σ ) C ^ ′ ( μ , σ ) = ϕ ( μ σ ) and r α = F 1 ( μ α , σ α ) where Fi ( a , b ) are defined in Eq ( 28 ) . It should be noted that if the transition to chaos occurs for the same parameters for which the fixed point loses stability and that this is controlled by a real eigenvalue crossing zero , the location of the transition will not depend on the synaptic time constant . If this is the case , Eq ( 54 ) will characterize the location of the transition to chaos in the parameter space of the network in general and not only under the assumption of the separation of time scales under which we have established this condition . Let us denote the fixed point solution of the dynamics , Eq ( 17 ) , by: h α β ( t ) = h ‾ α β . Writing h α β ( t ) = h ‾ α β + δ h α β with δ h α β ≪ h ‾ α β , linearizing the dynamics and looking for solution of the form δ h ∝ eλt ) one gets: λ τ E E δ h E E = - δ h E E + J 0 E E C ˜ E E ( δ h E E - δ h E I ) λ τ I E δ h I E = - δ h I E + J 0 I E C ˜ I E ( δ h E E - δ h E I ) λ τ E I δ h E I = - δ h E I - J 0 E I C ˜ E I ( δ h I E - δ h I I ) λ τ I I δ h I I = - δ h I I - J 0 I I C ˜ I I ( δ h I E - δ h I I ) ( 56 ) where the C ˜ α β ( α = E , I , β = E , I ) are N×N sparse matrices with elements C ˜ i j E E = g ′ ( h ¯ j E E - h ¯ j E I ) C i j E E C ˜ i j I E = g ′ ( h ¯ j E E - h ¯ j E I ) C i j I E C ˜ i j E I = g ′ ( h ¯ j I E - h ¯ j I I ) C i j E I C ˜ i j I I = g ′ ( h ¯ j I E - h ¯ j I I ) C i j I I ( 57 ) ( Cαβ is the matrix of connectivity between populations β ( presynaptic ) and α ) . We are interested in instability onsets at which a real eigenvalue crosses 0 . Using Eq ( 56 ) , it is straightforward to show that such an instability happens if the synaptic strength are such that: det [ I - J 0 I E J 0 E I C ˜ I E ( I - J 0 E E C ˜ E E ) - 1 C ˜ E I ( I - J 0 I I C ˜ I I ) - 1 ] = 0 ( 58 ) If J 0 E E = 0 , one can rewrite Eq ( 58 ) as: det [ I - M ] = 0 ( 59 ) with: M = J 0 I I C ˜ I I + J 0 I E J 0 E I C ˜ I E C ˜ E I ( 60 ) Let us assume that J 0 I I is fixed and such that for small enough J 0 I E J 0 E I the fixed point is stable . When increasing , J 0 I E J 0 E I the fixed point loses stability when the value of J 0 I E J 0 E I is the smallest for which Eq ( 59 ) is satisfied , that is for which the largest real eigenvalue , λmax of the matrix M crosses 1 . If this instability also corresponds to chaos onset , Eq ( 54 ) , this would imply that the condition λmax = 1 is equivalent to: 1 = ( J 0 I I ) 2 C ^ ′ ( μ I , σ I ) + ( J 0 E I J 0 I E ) 2 C ^ ′ ( μ E , σ E ) C ^ ′ ( μ I , σ I ) ( 61 ) Interestingly , this condition means that the variance of the elements of the matrix N M is equal to one leading us to conjecture that more generally the eigenvalue of the latter which has the largest real part and is given by: λ m a x = ( J 0 I I ) 2 C ^ ′ ( μ I , σ I ) + ( J 0 E I J 0 I E ) 2 C ^ ′ ( μ E , σ E ) C ^ ′ ( μ I , σ I ) ( 62 )
The integration of differential equations , Eq ( 15 ) and Eq ( 18 ) ( Eq ( 3 ) in main text ) , was performed with a C code using the Euler method with fixed Δt = τsyn/20 ( the validity of the results was verified using smaller values of Δt ) . Simulations of the LIF spiking networks were done using a second-order Runge-Kutta integration scheme supplemented by interpolation of spike times as detailed in [65] . In all the spiking network simulations the time step was Δt = 0 . 1 ms . Self-consistent mean-field equations were solved with MATLAB function fsolve , which implements a ‘trust-region-dogleg’ algorithm or the Levenberg-Marquardt algorithm for non-square systems . Numerical calculations of integrals was done with MATLAB function trapz . The population average autocovariance ( PAC ) functions of neuronal quantities fi ( t ) ( i = 1…N ) were computed as σ ( τ ) = σ ( k Δ τ ) = = 1 N ∑ i = 1 N 1 N t - | k | ∑ n = 0 N t - 1 f i ( n Δ τ ) f ( ( n + k ) Δ τ ) - [ 1 N 1 N t ∑ i = 1 N ∑ n = 0 N t - 1 f i ( n Δ τ ) ] 2 where Nt is the number of time samples for the calculation of the PAC . In all figures fi ( t ) = hi ( t ) except in Fig 16 where f i ( t ) = I α + h i α E ( t ) − h i α I ( t ) . All PACs of spiking networks were calculated over 163 . 84 sec , and averaged over 10 realizations of the connectivity . For models Eq ( 15 ) and Eq ( 18 ) , PACs were calculated over 2048τsyn after discarding 200τsyn of transient dynamics and averaged over 8 realizations . To calculate the maximal Lyapunov exponent , Λ , of the inhibitory network , Eq ( 3 ) , we simulated the system for a sufficiently long duration ( 200τsyn ) so that it settled on the attractor of the dynamics . Denoting by h ⃗ * the network state at that time , we then ran two copies of the dynamics , one with initial conditions h ⃗ 1 ( t = 0 ) = h ⃗ * and the other with slightly perturbed initial conditions , h ⃗ 2 ( t = 0 ) = h ⃗ * + ϵ / N ( ∣ ∣ h ⃗ 1 ( 0 ) − h ⃗ 2 ( ( 0 ) ∣ ∣ = ϵ , where ∣∣⋅∣∣ is the l2 norm ) . Monitoring the difference , d ⃗ ( t ) = h ⃗ 1 ( t ) − h ⃗ 2 ( t ) we computed T r e s e t ( 1 ) = min ( arg ( ∣ ∣ d ⃗ ( t ) ∣ ∣ = D m a x ) , T m a x ) and D r e s e t ( 1 ) = ∣ ∣ d ⃗ ( T r e s e t ( 1 ) ) ∣ ∣ . We then reinitialized the dynamics of the second network copy to h ⃗ 2 ( T r e s e t ( 1 ) ) + d ⃗ ( T r e s e t ( 1 ) ) ∣ ∣ d ⃗ ( T r e s e t ( 1 ) ) ∣ ∣ ⋅ ϵ . We iterated the process n times and estimate the Lyapunov exponent according to: Λ = ∑ i = 1 n ln ( D r e s e t ( i ) ϵ ) ∑ i = 1 n T r e s e t ( i ) A similar method was used for two population networks , Eq ( 15 ) , the only difference being that the vector h ⃗ now had dimension 4N . Throughout the article we take n = 100 , Tmax = 5τsyn , Dmax = 10−3 and ϵ = 10−6 . The Lyapunov exponent values reported in this article are averages over 5 realizations of the networks . Fig 10D in the main text plots the lines in the J0 − I0 phase diagrams of the threshold-power law rate model , for which 5% , 50% , 95% of randomly chosen networks have dynamics which converge to a fixed point . To compute these lines we simulated , for each value of γ and J0 , 100 realizations of the network . For each realization , we computed the population average of the temporal variance the synaptic inputs , ρ: ρ = 1 N ∑ i = 1 N [ 1 N t o t ∑ k = 0 N t o t - 1 h i ( k Δ t ) 2 - ( 1 N t o t ∑ k = 0 N t o t - 1 h i ( k Δ t ) ) 2 ] where Ntot is the total number of time steps of the simulations after discarding a transient with a duration of 256τsyn . The fixed point was considered to be unstable if ρ > 10−9 . The fraction of unstable networks , Fu , was fitted with a logistic function: Fu ( J0 ) = 100[1+exp ( − ( J0 − Jm ) /ΔJ ) ]−1 . The thick red line and red dots plot the values of Jm vs . γ , and the dashed lines are the values of J0 for which Fu = 95 and Fu = 5 . | Cortical circuits exhibit complex temporal patterns of spiking and are exquisitely sensitive to small perturbations in their ongoing activity . These features are all suggestive of an underlying chaotic dynamics . Theoretical works have indicated that a rich dynamical reservoir can endow neuronal circuits with remarkable computational capabilities . Nevertheless , the mechanisms underlying chaos in circuits of spiking neurons remain unknown . We combine analytical calculations and numerical simulations to investigate this fundamental issue . Our key result is that chaotic firing rate fluctuations on the time scales of the synaptic dynamics emerge generically from the network collective dynamics . Our results pave the way in the study of the physiological mechanisms and computational significance of chaotic states in neuronal networks . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models",
"Dynamical",
"Mean-Field",
"Theory",
"of",
"the",
"Single",
"Inhibitory",
"Population",
"Two-population",
"networks",
"Numerical",
"simulations"
]
| []
| 2015 | Asynchronous Rate Chaos in Spiking Neuronal Circuits |
Sex determination in mammals is controlled by the presence or absence of the Y-linked gene SRY . In the developing male ( XY ) gonad , sex-determining region of the Y ( SRY ) protein acts to up-regulate expression of the related gene , SOX9 , a transcriptional regulator that in turn initiates a downstream pathway of testis development , whilst also suppressing ovary development . Despite the requirement for a number of transcription factors and secreted signalling molecules in sex determination , intracellular signalling components functioning in this process have not been defined . Here we report a role for the phylogenetically ancient mitogen-activated protein kinase ( MAPK ) signalling pathway in mouse sex determination . Using a forward genetic screen , we identified the recessive boygirl ( byg ) mutation . On the C57BL/6J background , embryos homozygous for byg exhibit consistent XY gonadal sex reversal . The byg mutation is an A to T transversion causing a premature stop codon in the gene encoding MAP3K4 ( also known as MEKK4 ) , a mitogen-activated protein kinase kinase kinase . Analysis of XY byg/byg gonads at 11 . 5 d post coitum reveals a growth deficit and a failure to support mesonephric cell migration , both early cellular processes normally associated with testis development . Expression analysis of mutant XY gonads at the same stage also reveals a dramatic reduction in Sox9 and , crucially , Sry at the transcript and protein levels . Moreover , we describe experiments showing the presence of activated MKK4 , a direct target of MAP3K4 , and activated p38 in the coelomic region of the XY gonad at 11 . 5 d post coitum , establishing a link between MAPK signalling in proliferating gonadal somatic cells and regulation of Sry expression . Finally , we provide evidence that haploinsufficiency for Map3k4 accounts for T-associated sex reversal ( Tas ) . These data demonstrate that MAP3K4-dependent signalling events are required for normal expression of Sry during testis development , and create a novel entry point into the molecular and cellular mechanisms underlying sex determination in mice and disorders of sexual development in humans .
Sex determination is the process by which an embryo develops into a male or female , namely , the formation of testes in an XY embryo and ovaries in an XX embryo . In the mouse , this process begins with commitment of cells of the bipotential genital ridge to either the testicular or ovarian fate at 11 . 5 d post coitum ( dpc ) [1] . In mammals such as mice and humans , this commitment depends on the presence or absence of the Y-linked testis-determining gene , SRY [2]–[4] . During the search for the elusive mammalian testis-determining factor , it was a criterion of correct identification that any candidate gene be associated with mutations that cause pure ( gonadal ) XY sex reversal: the development of an ovary in an XY individual . Such mutations in SRY were readily discovered in mice [5] and humans [6] exhibiting sex reversal , and this link with sex reversal has been a constant theme in the subsequent identification of novel , mostly autosomal , genes functioning in sex determination . Instances of XY sex reversal in the mouse associated with single gene mutations remain relatively uncommon . Excluding Sry , they include targeted mutations of Sox9 [7] , [8] , Dax1 [9] , Fgf9 [10] , Fgfr2 [11] , [12] , Gata4/Fog2 [13] , [14] , Cbx2 ( M33 ) [15] , and Wt1 ( +KTS ) [16] . Mice harbouring targeted mutations in three members of the insulin-receptor signalling pathway also exhibit XY sex reversal [17] . In several of these cases , variability exists in the degree of sex reversal observed , depending on genomic context . The C57BL/6J background often biases gonadal development in favour of ovarian tissue in mutant XY embryos and this “B6 sensitivity” increases still further if the AKR/J Y chromosome ( YAKR ) is present [14] . Additional genes have been identified that disrupt testis development , affecting testis cord formation or the differentiation of testis-specific cell lineages . These include Dhh [18]–[20] , Pdgfra [21] , Pod1 [22] , Arx [23] , Wnt4 [24] , and Spry2 [25] . The contribution of other protestis genes to sex determination , such as Sf1 [26] , Dmrt1 [27] , and Sox8 [7] , can be difficult to discern owing to functions of such genes earlier in gonad development or functional redundancy . In addition to the contribution of specific genes , other autosomal loci have been reported to control sex determination in the mouse . Such loci have been identified on the basis of genetic segregation in cases of sex reversal observed when the Y chromosome of the C57BL/6 strain is replaced by that of Mus domesticus poschiavinus [28] , or on the basis of their modifying the phenotypic effect of another sex determining locus [29] , [30] . The search for novel sex determining genes has been driven in recent years by the transcriptional properties of candidate genes identified by expression profiling [31]–[35] . However , such gene-driven approaches have not yielded a significant number of novel sex reversal phenotypes or abnormalities of gonadal differentiation that could act as important models for the investigation of the molecular genetic basis of sex determination . Notable exceptions to this general observation include the genes Cyp26b1 [36] , [37] and Pgds [38] , [39] , whose roles in germ cell and somatic cell development , respectively , were established partly on the basis of earlier observations on their male-specific expression derived from systematic expression screens . As an alternative to expression-based screens , we have employed a forward genetic approach to identifying loci controlling sexual development in the mouse . Using N-ethyl-N-nitrosourea ( ENU ) mutagenesis and a three-generation ( G3 ) breeding scheme , we screened for abnormalities of the developing gonads in embryos homozygous for induced mutations . In one mutant pedigree , RECB/31 , we identified XY embryos exhibiting abnormal testis cord development and , in some cases , gonadal sex reversal . We have named this mutant line boygirl ( byg ) . Genetic mapping placed byg on the proximal region of mouse Chromosome 17 and molecular studies revealed that the byg phenotype is caused by a point mutation in the Map3k4 ( Mekk4 ) gene . Embryos doubly heterozygous for both the Map3k4byg allele and a targeted null allele of Map3k4 ( Map3k4tm1Flv ) exhibited neural tube defects and XY gonadal sex reversal , confirming that Map3k4 is the causal gene . Map3k4 encodes a mitogen-activated protein kinase ( MAPK ) kinase kinase , demonstrating for the first time a role for MAPK signalling in mammalian sex determination . We describe molecular and cellular studies on the byg mutant that demonstrate a requirement for mitogen-activated protein kinase kinase kinase 4 ( MAP3K4 ) in regulating XY gonadal growth , mesonephric cell migration , and the expression of Sry , and hence Sox9 , during XY gonad development . We also describe genetic experiments that suggest that loss of Map3k4 is responsible for a previously reported autosomal sex reversal phenomenon , T-associated sex reversal ( Tas ) [40] , [41] .
Line 31 ( RECB/31 ) was identified in a forward genetic ( phenotype-driven ) screen for embryonic gonad abnormalities after ENU mutagenesis ( see Materials and Methods for details ) . Embryos homozygous for ENU-derived mutations were isolated and examined for a variety of morphological abnormalities . One RECB/31 embryo , dissected at 13 . 5 dpc , exhibited spina bifida , mild oedema , and also contained gonads shaped like normal testes but with no visible testis cords ( Figure 1A and 1B ) . A second , independent RECB/31 litter contained an embryo with spina bifida and testes that had fewer cords than normal with an irregular morphology ( Figure 1C ) . Having identified these individuals , subsequent RECB/31 embryos were examined and gonads were collected for sexing and wholemount in situ hybridisation ( WMISH ) . In this manner , another XY individual was identified in which the gonads were morphologically ovarian at the same stage ( Figure 1D ) . WMISH analysis of gonads from these three abnormal embryos using the Sertoli cell marker Sox9 confirmed the disruption to testis development and its variable severity as described above ( Figure 1B–1D ) . In each case , Sox9 expression was still prominent . However , in the case of the XY gonad with an ovarian appearance , expression was restricted to the central portions of the gonad and absent from the poles . This observed phenotypic variability , and that of subsequent mutants identified in the RECB/31 pedigree , is likely due to the mixed genetic background of the embryos examined . All embryos with abnormal XY gonads exhibited failure of neural tube closure , either spina bifida or exencephaly ( unpublished data ) . Embryonic death of homozygous mutants was commonly observed after 15 . 5 dpc . Because of the observed gonadal abnormalities and apparent XY gonadal sex reversal , this mutant line was named boygirl ( byg ) . During subsequent generations of backcrossing onto C3H/HeH the gonadal phenotype was still robust , although the majority of RECB/31 XY gonads had the appearance of ovotestes , in which the central portion of the gonad shows evidence of cord formation , but the poles are ovarian in both appearance and marker expression ( Figure 1E–1N ) . No overt abnormalities were observed in XX byg/byg gonads in these marker studies . Identification of additional affected XY gonads permitted mapping of the byg mutation . Abnormal embryos ( n = 9 ) were typed with a genome-wide panel of 55 SNP markers in order to identify chromosomal regions that were consistently homozygous for the C57BL/6-derived allele . Only one region , on proximal mouse Chromosome 17 , showed this feature of genetic association with byg . This initial linkage was refined by subsequent backcrossing of byg carrier males with C3H/HeH females and intercrossing of carrier progeny , identified by SNP haplotype analysis . Additional SNPs were then used to identify a critical region , in which the byg mutation must reside , between 9 . 66 Mb ( rs3665053 ) and 15 . 32 Mb ( rs13482889 ) on Chromosome 17 . We used an informatics-based approach to identify candidate genes in the byg critical region . One such candidate was the gene Map3k4 ( also known as Mekk4 , GenBank [http://www . ncbi . nlm . nih . gov/Genbank] number NM_011948 ) , which encodes a MAPK kinase kinase [42] , [43] . Mice lacking this gene , which were generated previously by gene targeting , are associated with perinatal lethality on the C57BL/6 background [44] . Because homozygous Map3k4 mutant embryos also exhibit neural tube defects and because Map3k4 is expressed in most embryonic tissues between 9 . 5 and 15 . 5 dpc [42] , [44] , [45] , including the gonads ( Figure 2A and 2B ) , we examined the sequence of Map3k4 in affected byg/byg embryos . A single nucleotide substitution at nucleotide position 1 , 144 of the Map3k4 open reading frame was identified in the homozygous form in two independent byg/byg mutants ( Figure 2C and 2D ) . This substitution replaces an arginine with a premature stop codon at amino acid position 382 of the 1 , 597 amino acid MAP3K4 protein . The predicted truncated protein lacks the critical kinase domain of MAP3K4 and , therefore , lacks any MAPKKK function ( Figure 2E ) . Absence of full-length ( 180 kDa ) MAP3K4 protein in byg homozygous mutants was confirmed by Western blotting with an anti-MAP3K4 antibody ( Figure 2F ) . A kinase-inactive allele of Map3k4 has previously been shown to have very similar phenotypic consequences to the null allele [45] . Thus , because of the effect of the premature stop codon causing loss of the kinase domain , we conclude that the Map3k4byg allele is a null allele . The entire colony of byg mice was typed for the presence of the mutation in Map3k4 and all known byg carriers were heterozygous for the mutation . The mutation was not found in any wild-type C57BL/6J or C3H/HeH mice . We concluded , therefore , that the gonadal phenotype in mutant byg embryos is caused by loss of MAP3K4 function . To confirm this , and discount the possibility that a second , closely linked mutation in an unrelated gene was responsible for the gonadal phenotype , we studied a second Map3k4 mutant allele ( Map3k4tm1Flv ) generated by gene targeting [44] . Embryos homozygous for the Map3k4tm1Flv allele exhibit neural tube defects and die perinatally , although there have been no descriptions of sexual development in these individuals . Embryos doubly heterozygous for both Map3k4byg and Map3k4tm1Flv were examined at 14 . 5 dpc and exhibited neural tube defects ( unpublished data ) . All XY embryos contained gonads with an overt ovarian appearance ( Figure 2G ) , and these failed to express Sox9 at significant levels ( Figure 2H ) . The absence of any overt testicular tissue in these XY embryonic gonads is likely due to the increased contribution from the C57BL/6J genome in these individuals . Embryos homozygous for the targeted allele , which was maintained on the C57BL/6J background , also exhibited gonadal sex reversal , with affected XY embryos containing gonads with ovarian morphology , lacking Sox9 and expressing Wnt4 , a marker of ovarian differentiation ( Figure 2I and 2J ) . Thus , these data confirm that Map3k4 is the gene disrupted in the byg mutant and that MAP3K4 is required for testis determination in mice . Evidence exists that the C57BL/6J background is sensitised to disruptions to XY gonad development , and this conclusion appeared to be supported by the increased severity of the XY gonadal phenotype observed in embryos heterozygous for both Map3k4byg and Map3k4tm1Flv , and homozygous for Map3k4tm1Flv , in which the contribution from C57BL6/J was greater . Thus , we performed a detailed examination of embryos homozygous for Map3k4byg after backcrossing to C57BL6/J for at least two generations . We examined cell proliferation , mesonephric cell migration , and cellular differentiation in mutant and wild-type gonads because all these processes are required for normal testis development [1] , [46] , [47] . Cellular proliferation is an important component of the organogenetic programme of testis development [48] , [49] . Gonadal cell proliferation was examined at 11 . 5 dpc ( 17–18 tail somites [ts] ) , 12 . 0 dpc ( 20–22 ts ) , and 12 . 25 ( 24 ts ) in the coelomic region of gonads from byg/byg and control littermates using immunostaining with an antibody for the mitotic marker phosphorylated histone H3 ( pHH3 ) . Somatic cell proliferation in XY byg/byg gonads appeared reduced in the coelomic region in comparison to wild-type XY embryonic gonads at all stages examined ( Figure 3; Table 1 ) . Moreover , at the 22- and 24 ts stages ( 12 . 0–12 . 25 dpc ) , the coelomic region of control XY gonads was thickened and contained a larger number of somatic cells , in contrast to byg/byg XY gonads , which had fewer cells in this region and resembled wild-type XX gonads of the same stage ( Figure 3 ) . We conclude that cellular proliferation , and thus gonadal growth , in the coelomic region is severely compromised in byg/byg XY gonads at an early stage . Increased levels of apoptosis have previously been described in the neural tube of mice lacking Map3k4 [44] . For this reason we examined levels of apoptosis in the byg/byg XY gonad and controls at 17 ts using an antibody to cleaved caspase 3 . We observed very few positive cells in mutant and control gonads , although large numbers of apoptotic cells were observed in a positive control ( interdigital mesenchyme of the developing limb ) using this assay ( unpublished data ) . Thus , we cannot attribute impaired gonadal growth in XY byg/byg embryos to increased levels of apoptosis . Testis cord formation in the mouse requires cell migration from the associated mesonephros into the XY gonad in a male-specific fashion [50]–[53] . To examine mesonephric cell migration into the XY byg/byg gonad we first examined development of mutant gonads when explanted from the embryo at 11 . 5 dpc and cultured in vitro . Control gonads ( wild-type and byg/+ littermates ) formed clear testis cords after 2 d of culture and expressed the Sertoli cell marker , Sox9 ( n = 3 ) ( Figure S1A ) . In contrast , we did not observe any testis cords in cultured XY gonads from byg/byg embryos ( n = 3 ) ( Figure S1B and S1D ) . WMISH analysis revealed that these cord-free XY gonads failed to express Sox9 ( Figure S1B ) , indicating a failure to execute the program of testis differentiation . In contrast , high levels of Wnt4 expression in the mutant XY gonads indicated an activation of the ovarian pathway ( Figure S1D ) . To examine whether the severe disruption to cord formation in the byg/byg gonad was associated with any loss of mesonephric cell migration , we performed recombination experiments in which mesonephroi ubiquitously expressing green fluorescent protein ( GFP ) were cultured adjacent to a byg/byg XY gonad from 11 . 5 dpc . Cell migration into control XY gonads was prominent after 48 h of culture ( Figure S1E ) . In contrast , little or no cell migration was detected in cultured byg/byg gonads ( Figure S1F ) . These data suggest that two early cellular processes associated specifically with XY gonad development , cell proliferation in the coelomic growth zone and mesonephric cell migration , are disrupted in the absence of MAP3K4 . In order to address the molecular basis of these defects , we next investigated the expression of key male- and female-determining genes and gene-products between 11 . 5 and 14 . 5 dpc , stages of gonad development between which the male and female fates are established and the programme of sexually dimorphic morphogenesis is executed . Several molecules have been shown to be required for normal testis determination , including SRY [5] , fibroblast growth factor 9 ( FGF9 ) [10] , FGFR2 [11] , [12] , and SRY-like HMG box 9 ( SOX9 ) [7] , [8] . Current understanding suggests that SRY , in concert with SF1 , acts to up-regulate Sox9 expression in the XY gonad at 11 . 5 dpc [54] , [55] . Sox9 expression is then maintained at a high level by a positive feedback loop with FGF9/FGFR2 , and acts to antagonise function of the ovary-determining gene Wnt4 [56] . A role for prostaglandin D2 in the regulation of Sox9 expression has also been proposed [12] , [39] , [57] , [58] . Downstream of SOX9 , genes such as Amh [59] and Vanin-1 [31] , [32] , [60] , with male-determining effects , are transcriptionally activated , and germ cell fate is established by modulation of retinoic acid signalling [36] , [37] . These molecular events are associated with precise spatial ( cellular and subcellular ) and temporal expression profiles of genes and their protein products , often in a sexually dimorphic manner . Given its central role in testis development we began our study with an analysis of Sox9 expression . From 11 . 5 dpc onwards Sox9 transcription in control XY gonads is prominent , initially in pre-Sertoli cells and subsequently in Sertoli cells of the seminiferous cords/tubules . However , analysis of byg/byg homozygotes revealed dramatically reduced levels of Sox9 transcript ( Figure 4 ) . At 14 . 5 dpc the byg/byg XY gonad resembles an ovary morphologically and no significant Sox9 transcription was detectable ( Figure 4A ) . This loss of a Sertoli cell marker in mutant XY gonads was accompanied by elevated expression of two known female-specific markers at the same stage , Stra8 and Wnt4 ( Figure 4B and 4C ) . Expression of these genes indicates that the ovarian pathway of development , including entry of germ cells into meiosis , is activated in vivo in the absence of MAP3K4 . At 11 . 5 dpc , the sex-determining stage of gonadogenesis , little or no Sox9 transcript was observed ( Figure 4E ) , and this loss of expression was confirmed by immunostaining of mutant and control gonads at the same stage with an anti-SOX9 antibody ( Figure 4G–4I ) . However , Wnt4 expression was prominent in the XY byg/byg gonad at 11 . 5 dpc , in contrast to wild-type controls ( Figure 4F ) . Interestingly , Sox9 transcription at 11 . 5 dpc in mutant gonads on the C3H/HeH background was reduced in comparison to controls ( Figure 4D ) , but not to the same degree as the C57BL/6J-derived mutant gonads at the same stage . Loss of Sox9 expression is associated with XY sex reversal in a number of genetic contexts , and mice homozygous for a loss-of-function allele of Sox9 targeted to the developing XY gonads by Cre-mediated excision exhibit immediate , complete gonadal sex reversal , as evidenced by the expression of female-specific markers and the absence of testis cord formation [8] . Thus , loss of Sox9 expression is sufficient to explain the failure of male-specific events in XY byg/byg homozygotes , such as enhanced coelomic region growth , mesonephric cell migration , and testis cord formation . We next analysed expression of several other important markers of male and female gonad development around 11 . 5 dpc ( 16 to 19 ts ) using immunohistochemical staining of transverse sections . SF-1 ( NR5A-1 ) is thought to mediate up-regulation of Sox9 transcription in the early XY gonad by acting on a specific gonadal enhancer ( TESCO ) in synergy with SRY [55] . We observed no significant difference in the expression of SF-1 between wild-type and byg/byg XY gonads at this stage , with large numbers of somatic cells exhibiting nuclear staining in both genotypic classes ( Figure S2A and S2B ) . FGFR2 , a gonadal receptor for FGF9 , has been reported to exhibit a sexually dimorphic profile of expression in the gonads at 11 . 5 dpc , with somatic cells in the body of the XY gonad exhibiting nuclear localisation of the protein and XX somatic cells , in contrast , exhibiting a cytoplasmic localisation [12] , [61] . We also observed nuclear localisation of FGFR2 in somatic cells of control XY gonads at 11 . 5 dpc ( Figure S2F and S2G ) , but in XY byg/byg gonads , although FGFR2 expression was still prominent , its localisation was cytoplasmic , resembling XX control gonads at the same stage ( Figure S2H–S2J ) . Next , we examined the early expression of FOXL2 , a protein required for normal ovary development [62]–[65] . Foxl2 transcription has been reported to be up-regulated in the developing mouse gonad around the time of sex determination [66] and restricted to somatic cells [62] , [63] . In newborn mice FOXL2 protein is expressed in the nuclei of pregranulosa cells [63] . We observed expression of FOXL2 in the nuclei of somatic cells in wild-type XX gonads at 11 . 5 dpc ( Figure S2E ) , but negligible expression was observed in wild-type XY gonads ( Figure S2C ) . However , striking up-regulation of FOXL2 was observed in the nuclei of somatic cells of byg/byg XY mutants ( Figure S2D ) . Together with prominent expression of Wnt4 transcript in mutant gonads at the same stage ( Figure 4F ) , these data suggest that the ovarian determining pathway is activated at an early stage in the gonads of XY byg/byg embryos lacking MAP3K4 . Absence of a number of molecules has been reported to cause reduction or loss of Sox9 expression in mutant mouse gonads , including FGF9 [56] , FGFR2 [12] , WT1 [16] , [67] , and DAX1 [9] , [29] . Recently , it has been shown that SRY and SF-1 cooperatively bind a specific enhancer element ( TESCO ) to up-regulate Sox9 transcription during XY gonad development and that SOX9 subsequently acts to maintain its own expression by binding to the same enhancer [55] . Because of this central role for SRY in regulation of Sox9 expression , we investigated the expression of Sry in XY byg/byg gonads ( Figure 5 ) . Sry transcription reaches a peak at 11 . 5 dpc ( 17–18 ts ) in XY mouse gonads , and so we studied expression at this stage using in situ hybridisation . At 17 ts we observed Sry transcripts in wild-type XY gonads using WMISH . However , no significant Sry transcription was observed in XY mutant gonads at the same stage ( Figure 5A ) . At the 19 ts stage , Sry transcription is reduced in the wild-type gonads and still absent from mutant ( Figure 5B ) . We utilised quantitative real time-PCR ( qRT-PCR ) to confirm this reduction in Sry expression in mutant gonads at 11 . 5 dpc ( Figure 5C ) . This qRT-PCR study revealed an almost 3-fold reduction in Sry transcript levels in XY byg/byg gonads . Sf1 transcript levels did not differ significantly between mutant and control gonads , in line with our immunohistochemistry data . Fgf9 transcript levels appeared to be reduced in XY byg/byg gonads , although this difference was not statistically significant . We then studied the expression of SRY protein in mutant and control gonads at the same stage using a specific antibody to SRY [39] , [68] . Expression of SRY was observed in somatic cells of the developing gonad at 11 . 5 dpc in control XY gonads ( Figure 5D and 5F ) . In contrast , very few SRY-positive cells were detected in XY byg/byg gonads , which resembled XX control gonads at the same stage of development ( Figure 5E , 5G , and 5H ) . High magnification examination of XY byg/byg gonads at these stages also revealed that those cells that did express SRY did so at a greatly reduced level ( Figure 5I and 5J ) . In contrast to wild-type controls , no SRY-positive cells were detected at 11 . 0 dpc ( Figure 5K and 5L ) . These studies suggest that appropriate expression of Sry in XY gonads , at both the transcript and protein level , is dependent on the presence of MAP3K4 . In the absence of MAP3K4 , Sry expression is delayed and , at 11 . 5 dpc , severely reduced . Reduced or delayed expression of Sry is known to be a cause of XY gonadal sex reversal [69] , [70] . MAP3K4 activity results in activation of the p38 and JNK MAPK pathways as part of a three-kinase phosphorelay module [71] . This signalling module is thought to regulate , amongst other things , the cell's response to stress including ultraviolet radiation , heat shock , and osmotic stress [72] . MAP3K4 regulates the MAPKs p38 and JNK via the phosphorylation of the MAP2Ks MKK3/MKK6 and MKK4/MKK7 , respectively [42] , [43] . A reduction in the number of cells positive for activated MKK4 activity has been reported in the neuroepithelium of embryos lacking MAP3K4 [44] . Therefore , we assayed for the presence of activated MKK4 in wild-type XY gonads at 11 . 5 dpc using antibodies specific for the phosphorylated form of this protein ( pMKK4 ) . pMKK4-positive cells were observed in the gonad , but these were primarily found in the coelomic region of the gonadal periphery ( Figure 6A and 6B ) , a profile reminiscent of pHH3-positive mitotic cells ( Figure 3A ) . A similar distribution was observed when pMKK7-positive cells were imaged ( Figure 6H ) . Given these observations , we assayed directly for co-expression of pMKK4 and pHH3 in the gonad at 11 . 5 dpc using immunostaining of sections . pMKK4-positive cells were found to be positive for pHH3 too , both in the gonad and adjacent mesonephros ( Figure 6B–6D ) . We then assayed for the presence of activated p38 ( pp38 ) and pMKK7 in the same tissue sections , and observed a similar pattern of pp38- and pMKK7-positive cells at the gonadal periphery , which were also positive for pHH3 ( Figure 6E–6J ) . The co-expression of pMKK4 and pHH3 was also observed in XY byg/byg gonads at the same stage . In the case of pMKK4 , pMKK7 , pp38 , and pJNK , cells positive for these activated proteins were still detectable in XY byg/byg gonads at 11 . 5 dpc ( Figure S3 ) , consistent with residual pMKK4 expression in the neural tube of embryos lacking MAP3K4 [44] . These data suggest that MAPK signalling is active in the developing XY gonad at early stages , and is associated with proliferating cells of the coelomic growth zone , but that alternative pathways exist for MAPK activation in the gonad in the absence of MAP3K4 . Moreover , given our observation that mitotic somatic cells in the coelomic region are those cells with activated MKK4/7 and activated p38 , the reduction in the number of proliferative cells in the XY byg/byg gonad ( Figure 3 ) corresponds to a reduction in the number of pMKK4/7- and pp38-positive cells . Whilst it is possible that a gonadal somatic cell activates the MAPK pathway only once it enters mitosis , it is more consistent with the known role of MAPK signalling in cell proliferation to conclude that male-enhanced proliferation in the coelomic region is a MAPK-dependent process . The reduction of coelomic region growth in the XY byg/byg gonad at 11 . 5 dpc is thus explicable by a reduction in the number of cells exhibiting MAP3K4-mediated phosphorylation of MKK4/7 , p38 , and possibly other MAPK signalling components . In order to address whether disruption to components of MAPK signalling can disrupt testis development in vitro , we cultured wild-type embryonic XY gonads from 11 . 5 dpc for 48 h in the presence of highly selective inhibitors of the MAPKs extracellular signal-related kinase ( ERK ) ( U0126 ) and p38 ( SB202190 ) [73] . We then assayed for the expression of Sox9 using WMISH ( Figure 6K–6M ) . Similar experiments to address the role of JNK were not performed because of the unavailability of highly specific small molecule inhibitors . We observed little affect on Sox9 expression in gonads treated with ERK inhibitor when assayed by WMISH , although testis cord formation did not occur in treated samples with the same efficiency as samples cultured in vehicle control ( Figure 6K ) . These data are consistent with other reports that MEK1/ERK inhibitors fail to significantly disrupt testis development in vitro [74] . In contrast , culturing in the presence of p38 inhibitor resulted in dramatic reduction of Sox9 expression , including an almost complete loss of signal in 50% of treated samples ( n = 8 ) ( Figure 6L and 6M ) . Examination of Wnt4 expression in SB202190-treated cultured explants ( n = 3 ) also revealed robust expression of this ovarian marker in contrast to vehicle controls ( Figure 6N ) , suggesting that at least partial gonadal sex reversal was occurring during culture of XY explants because of abrogation of p38 activity . In this context , it is interesting to note that human SRY has been recently identified as a possible target of p38 MAPK signalling in cultured keratinocytes [75] . Given the importance of two components of the FGF signalling pathway , FGF9 and FGFR2 , in XY gonad development , we next studied whether byg/byg gonads exhibited defects in this pathway by determining whether FGF9 was able to activate Sox9 transcription in XY byg/byg gonads . It has previously been reported that FGF9 is capable of activating Sox9 transcription in developing XX gonads if they are cultured in the presence of beads coated in this growth factor [56] . In an attempt to address the question of which upstream , extracellular signals employ MAP3K4-dependent phosphorylation during XY gonad development , we determined whether FGF9-mediated activation of Sox9 transcription was abrogated in MAP3K4-deficient gonads . XY gonads from byg/byg and control embryos were cultured at 11 . 5 dpc for 48 h in the presence of FGF9-coated beads ( or beads coated in bovine serum albumin [BSA] ) and were then analysed for the presence of Sox9 transcripts in cells contacting the bead using in situ hybridisation . BSA-coated beads did not induce Sox9 transcription in any samples . In contrast , Sox9 transcripts were clearly detected in the vicinity of beads in both cultured wild-type XX gonads and in XY byg/byg gonads ( Figure 7A and 7B ) . These data suggest that MAP3K4 is not an obligatory component of signal transduction pathways employed by FGF9 to activate transcription of Sox9 in the developing gonad . However , failure of normal SRY , and thus SOX9 , expression in byg/byg XY gonads may result in failure to establish the positive feedback loop between SOX9 and FGF9/FGFR2 [56] . A locus on mouse Chromosome 17 associated with XY sex reversal and ovotestis formation has previously been described [40] . This mutation , known as Tas , was identified in a mouse stock carrying the hairpin-tail ( Thp ) deletion whilst being crossed to the C57BL/6J background . The presence of an AKR/J-derived Y chromosome is also required for the development of ovarian tissue in XY C57BL/6J Thp/+ individuals . It has been hypothesized that the Tas mutation resides within the region of the t complex deleted in Thp and hemizygosity for the relevant locus causes varying degrees of sex reversal when on the C57BL/6J YAKR background [41] . This genetic background is known to be very sensitive to disturbances in the early events of testis development induced by gene mutation [14] , and thus one potential explanation for the Tas phenotype is haploinsufficiency for a t complex locus that is ordinarily testis determining . Map3k4 is located on proximal mouse Chromosome 17 in the region of the t complex and , in the form of the previously anonymous DNA marker D17Rp17 ( still a synonym of Map3k4 , see http://www . informatics . jax . org/searches/accession_report . cgi ? id=MGI%3A1346875 and GenBank entry NM_011948 ) , has been shown to map within the Thp deletion [76] . Given this map position and the gonadal phenotype of XY embryos lacking functional Map3k4 on C57BL/6J , we hypothesized that haploinsufficiency for this gene might be , at least partially , responsible for the Tas gonadal sex reversal phenotype . We tested this model in two ways . First , we generated embryos doubly heterozygous for the byg mutation and the Thp deletion . If Map3k4 resides within the Thp deletion these embryos will lack Map3k4 function because of failure of complementation and will recapitulate the phenotype of byg/byg homozygous embryos . Figure 8 shows that XY byg/+ , Thp/+ embryos exhibited abnormalities of testis development . XY gonads dissected from doubly heterozygous embryos at 13 . 5/14 . 5 dpc showed disruption to cord morphology or gonadal sex reversal , in which Sox9 transcription is lost ( Figure 8A ) and Wnt4 transcription is activated ( Figure 8B ) . Doubly heterozygous mutants also exhibited neural tube defects ( unpublished data ) . We performed this cross on the C3H/HeH background because this strain has not previously been associated with sensitisation to events disrupting testis development , even given the presence of the YAKR chromosome [41] . We confirmed , therefore , that Map3k4 resides in the Thp deletion and that this deletion , combined with a loss-of-function allele of Map3k4 , causes varying degrees of disruption to XY gonad development even in the absence of any other predisposing genetic factors . Secondly , we performed a cross to test directly whether Map3k4 haploinsufficiency might account for the development of ovarian tissue in XYAKR Thp/+ C57BL/6J individuals . We generated XYAKR mice after backcrossing of YAKR to C57BL/6J for six generations . These males were then crossed with females heterozygous for the targeted null allele of Map3k4 ( Map3k4tm1Flv ) , also on C57BL/6J , to generate XYAKR Map3k4tm1Flv/+ heterozygotes on a C57BL/6J background . Nine of these individuals were generated in five litters and seven were scored as normal males based on examination of the external genitalia . However , two were scored as phenotypic females on the basis of external genitalia morphology . Examination of these sex-reversed individuals revealed the presence of ovaries and uterine horns . Histological examination of the ovaries from one of these individuals showed them to be smaller than controls and lacking clearly discernible follicles or ova ( unpublished data ) . Examination of four other heterozygous males at approximately 11 wk of age revealed that these had testes of reduced size ( ranging from 0 . 03 g to 0 . 08 g , mean = 0 . 06 g±0 . 015 ) , in contrast to wild-type controls ( n = 6 , ranging from 0 . 08 g to 0 . 11 g , mean = 0 . 093 g±0 . 009 ) . Small testes are sometimes an indication of earlier ovotestis development . To test this possibility , we performed timed matings in order to examine gonadal morphology in XYAKR Map3k4tm1Flv/+ embryos at 14 . 5 dpc . Of four XYAKR Map3k4tm1Flv/+ embryos examined , one contained gonads with an overt ovarian morphology , whilst three contained ovotestes identified by morphology and the familiar variegated expression of Sox9 and Wnt4 ( Figure 8C ) . On the basis of the XY gonadal sex reversal , complete and partial , observed in adult and embryonic Map3k4tm1Flv/+ individuals on C57BL/6J-XYAKR , we conclude that haploinsufficiency for Map3k4 is a major contributory factor to male-to-female sex reversal observed in XYAKR C57BL/6J Thp/+ individuals .
Here we describe evidence demonstrating , for the first time to our knowledge , an in vivo role for the phylogenetically ancient MAPK signalling cascade in mammalian sex determination . XY embryos lacking functional MAP3K4 on a predominantly C57BL/6J background exhibit embryonic gonadal sex reversal associated with failure of a number of cellular and molecular events , paramount amongst these being failure to transcriptionally up-regulate Sry and , presumably as a consequence , Sox9 in the developing gonad at 11 . 5 dpc . Previous studies , often involving analyses of Mus domesticus-derived Sry alleles on a C57BL/6 background , have suggested that the testis determining pathway is exquisitely sensitive to levels and timing of Sry: if a threshold level is not met in a critical time window , ovary development is likely to ensue [69] , [70] , [77] . Thus , attention is naturally focussed on the possible explanation for reduced Sry expression , at the transcript and protein levels , in XY byg/byg gonads . Three potential explanations exist: ( i ) that a transcriptional regulator ( or regulators ) required for transcription of Sry in pre-Sertoli cells does not function appropriately because of , either directly or indirectly , the absence of MAP3K4-mediated signalling; ( ii ) that insufficient numbers of pre-Sertoli cells are established in the XY byg/byg gonad; ( iii ) a combination of both of the above effects . With respect to the second hypothesis , the coelomic epithelium is thought to be a source of pre-Sertoli cells in the early XY gonad ( prior to 11 . 5 dpc ) [78] . Thus , the reduction in cell proliferation and gonadal growth in the coelomic region of XY byg/byg mutant embryos might be considered evidence of a wider range of defects in the developmental potential of the mutant coelomic epithelium and associated mesenchyme , perhaps extending to a reduction in the provision of pre-Sertoli cells , or the provision of pre-Sertoli cells competent to activate transcription of Sry . This hypothesis is consistent with the active MAPK signalling that we report in the coelomic region at 11 . 5 dpc in XY gonad . However , it should be noted that in other genetic contexts in which cell proliferation in the coelomic region of the developing XY gonad is disrupted , such as in gonads lacking Fgf9 [10] , [61] , Sry transcription is reported to be unaffected [56] . Thus , there is no established mechanistic link between prior proliferative defects in the early gonad and subsequent loss of Sry expression . However , given the reported role of FGF9 in promoting gonadal cell proliferation [61] , it is possible that loss of MAP3K4 results in an inability of coelomic region cells to efficiently transduce FGF9 signal produced by initial SRY-positive pre-Sertoli cells . This , in turn , would result in failure to establish a positive feedback mechanism by which cell proliferation and SRY expression mutually promote each other , causing insufficient provision of pre-Sertoli cells . This model would explain the reduced numbers of SRY-positive cells detected in XY byg/byg gonads between 11 . 0 and 11 . 5 dpc ( Figure 5 ) . In order to establish whether there is a paucity of cells migrating into the XY byg/byg gonad at around 11 . 2–11 . 4 dpc to populate the pre-Sertoli cell niche , it will be necessary to perform single-cell labelling experiments similar to those used to establish the role of the coelomic epithelium in this process [78] . However , establishing whether a marked cell was undergoing , or had undergone , active MAPK signalling of the appropriate sort would be technically daunting . With respect to the first hypothesis , little is known about the transcriptional control of Sry , although several potential activators have been described including M33 , WT1 ( +KTS ) , GATA4/FOG2 , and SF1 [79] . This hypothesis is supported by the presence of a few SRY-positive cells in the XY byg/byg gonad at 11 . 5 dpc that exhibit a significant reduction in the intensity of the SRY signal , and also the existence of FOXL2-positive cells in the XY byg/byg gonad at 11 . 5 dpc , since this lineage is arguably the ovarian equivalent of the pre-Sertoli cell lineage of the testis . Evidence already exists for MAPK-dependent phosphorylation of SF1 [80] , [81] and GATA4 [82] in other contexts , as a means of increasing their transcriptional activation potency . It is also noteworthy that SRY , which is phosphorylated in humans [83] , has recently itself been proposed to be a target of p38-mediated signalling pathways on the basis of cell line studies in vitro [75] . We are currently attempting to identify reduced phosphorylation of candidate testis-determining proteins in MAP3K4-deficient embryonic gonads . However , we cannot rule out the possibility that previously uncharacterised molecules are the key effectors of MAPK-mediated events during gonadogenesis . Moreover , MAP3K4-mediated events required for normal Sry transcription may occur in the progenitors of pre-Sertoli cells , in the form of programming , rather than pre-Sertoli cells themselves . In conclusion , the data suggest that the third hypothesis may best explain the observations concerning SRY expression . The similarity in the phenotypes of mice lacking the Map3k4 gene [44] and those merely lacking a functional kinase domain of the same gene [45] , strongly argues that MAP3K4 functions primarily to regulate MAPK signalling through its kinase domain . Thus , although we cannot formally exclude additional functions , we conclude that loss of functional MAP3K4 in the byg mutant results in disrupted MAPK signalling during gonad development . Although ours is the first report of a requirement for MAPK signalling in sex determination in vivo , one previous report has implicated a MAPK scaffolding protein , Vinexin-γ , in regulation of Sox9 transcription during gonad development [84] . However , the fetal gonads of both XX and XY embryos lacking Vinexin-γ are morphologically normal and adult mice of the same genotypes are viable and fertile . Moreover , Sox9 transcript levels in Vinexin-γ −/− XY gonads at 12 . 5 dpc are 75% that of Vinexin-γ −/+ gonads , suggesting that any modulation of Sox9 transcription by Vinexin-γ is relatively modest . These data appear to be consistent with reported organ culture studies in which the MAPK inhibitor PD98059 did not significantly inhibit testis cord formation in XY gonad explants [74] . In contrast to the Vinexin-γ studies , we observe an almost complete absence of Sox9 at the sex determining stage of gonad development ( 11 . 5 dpc ) in C57BL/6J XY embryos lacking MAP3K4 and a complete failure of testis cord formation at later stages . One possible explanation of the apparent discrepancy in these observations with respect to the role of MAPK signalling in testis development is the focus in other studies on the MEK-ERK pathway of MAPK signalling , sometimes called the classical MAPK cascade [73] . It has been proposed that Vinexin-γ mediates its effects on Sox9 transcription in vitro via male-specific activation of the MAPK , ERK [84] , and PD98059 is a specific MEK-ERK inhibitor [73] , [85] . The focus on MEK-ERK in other studies is likely a consequence of the inviting similarities between requirements for Sox9 up-regulation during gonad development and chondrogenesis . FGF-mediated activation of Sox9 transcription during chondrogenesis has been shown to be blocked by the MAPK inhibitor U0126 [86] . U0126 is also a specific MEK-ERK inhibitor [73] , [85] . Given that MAP3K4 is thought to act ultimately by activation of the MAPKs p38 and JNK [42] , [43] , the focus on ERK activation and the consequences of its disruption as a means of determining the role of MAPK signalling during gonad development may have been overly restrictive and resulted in misleading conclusions . Our studies utilising specific small molecule inhibitors of MAPK signalling in organ culture assays corroborate previous observations that MEK-ERK inhibition does not significantly disrupt Sox9 expression in vitro . However , in contrast , they do suggest a possible role for p38 in gonadal Sox9 transcriptional regulation and testis cord formation . The significance of these in vitro observations for the possible role of p38 in the aberrant phenotype of the MAP3K4-deficient gonad is unclear , given that Sry transcription is already at its peak at 11 . 5 dpc , the approximate stage at which gonadal explants were employed for in vitro culture experiments . Inhibition of p38 at these stages may disrupt testis-determining events downstream of regulation of Sry transcription , perhaps related to regulation of Sox9 expression , in a manner analogous to that reported for the IL-1β-dependent induction of SOX9 expression in human articular chondrocytes [87] , or disruption of SOX9 function itself . Mice constitutively lacking the alpha isoform of p38 die at around 10 . 5 dpc , before gonadogenesis can be fully examined [88] . For this reason , it is important to remain open-minded about how many distinct steps in testis development require MAPK-dependent events . Teasing these out genetically will require a conditional null allele of Map3k4 ( and genes encoding other MAPK signalling elements ) and inducible , cell-type-specific Cre lines . It will also be important to determine whether disruption to individual MAP2Ks and MAPKs also results in abnormal gonad development in vivo , or whether loss of MAP3K function is disruptive to a broader range of MAPK signalling events , including potential compensatory ones , and thus more likely to result in phenotypic abnormalities . In addition to downstream events mediated by MAP3K4 , it is not yet clear which upstream signals employ MAP3K4 for their transduction . Analogies with chondrogenesis , as described above , have tended to focus attention on the role of FGF signalling and its use of MAPK for its transduction . Moreover , FGF9 is known to be required for the male-specific elevated proliferation rate in the gonadal coelomic region at around 11 . 5 dpc [61] . However , we have demonstrated that the ability of exogenous FGF9 to activate Sox9 transcription during gonad culture remains unaltered in the absence of MAP3K4 . These data do not definitively demonstrate that FGF9 does not employ MAP3K4-mediated signal transduction during regulation of Sox9 expression during male gonad development in vivo , but they do suggest that such a pathway is not obligatory . Moreover , initial up-regulation of Sox9 transcription , along with Sry transcription , proceeds as normal in embryonic gonads lacking FGF9 [56] . It is , rather , the maintenance phase of Sox9 transcription in developing male gonads that is disrupted in the absence of FGF9 . Taken together , these observations suggest that we should look at other pathways , in addition to FGF , for the activating signals that require MAP3K4 for their transduction . Although activation of MAPK is a widespread phenomenon , ligand binding to receptor tyrosine kinases ( RTK ) is commonly associated with activation of this intracellular signalling cascade [89] . Interestingly , the insulin receptor tyrosine kinase gene family ( Ir , Igf1r , and Irr ) has previously been shown to be required for testis determination through its regulation of Sry expression [17] , and a number of reports describe a requirement for MAPK in signal transduction through this family of receptors in different biological contexts [90] , [91] . Similarly , loss of another RTK , PDGFRα , also disrupts testis development [21] and PDGF signalling is reported to employ MAPK [92] . Finally , in addition to RTK activity , prostaglandin D2 ( PGD2 ) has been shown to influence Sertoli cell differentiation and SOX9 activity [39] , [57] , [58] , presumably through its G-protein coupled receptors , DP and CRTH2 [93] , although this is not established . PGD2 signalling in other contexts has been shown to require MAPK [94] , [95] . Although the details of MAPK activation in these disparate systems vary , they are all potentially relevant to the phenotype of MAP3K4-deficient gonads because evidence suggests cross-talk between distinct MAPK pathways [75] . Despite the above observations , we cannot rule out the possibility of a role for a hitherto unrecognised growth factor or other extracellular signal in the employment of MAP3K4 during testis development . One virtue of invoking a requirement for MAP3K4 in FGF9-mediated signalling during gonadogenesis in vivo is that this model does not predict a requirement for sexually dimorphic expression of MAP3K4 , consistent with Map3k4 expression data . We observed near ubiquitous expression of Map3k4 , including male and female gonads , although higher levels were detected in particular cell types . Because of a lack of the relevant antibodies , we were unable to assay for the presence of activated MAP3K4 specifically in XY gonads , although such activation is predicted by the existence of MAP4Ks [96] . It should also be noted that the same explanatory virtue applies to invoking a requirement for MAP3K4 in activation of Sry transcription . We also report here data indicating that haploinsufficiency for Map3k4 is sufficient to account for Tas [40] , a phenomenon that has remained unexplained at the molecular level since its discovery more than 20 y ago . XY embryos heterozygous for the Map3k4tm1Flv mutation on the C57BL/6J-YAKR background exhibit testicular abnormalities , including XY ovary and ovotestis development , reminiscent of XYAKR C57BL/6J Thp/+ embryos [40] . Moreover , two adult XY Map3k4tm1Flv/+ individuals developed as phenotypic females , and both contained ovaries . Four others exhibited testicular hypoplasia , which is associated with prior ovotestis development . It is unclear , however , despite the role for Map3k4 haploinsufficiency established here , whether additional testis-determining genes exist in the region deleted in Thp , or whether chromosome deletions themselves predispose XY embryos to sex reversal by inhibitory effects on fetal growth [97] . Significantly , it has been demonstrated that , on the appropriate genetic background , the loss of a single copy of a male-determining gene can result in XY gonadal sex reversal [14] . It has been proposed that such phenotypic effects in mice caused by a single disrupted allele mimic the more common situation in humans , where loss of a single functional copy of genes such as SF1 , SOX9 , or WT1 can result in the development of XY females [98] , [99] . Our findings suggest that the loss of a single copy of Map3k4 , caused by the Thp deletion or targeted gene deletion , is another example of such a case . Thus , we propose that haploinsufficiency of MAP3K4 could be the cause of previously unassigned cases of XY gonadal dysgenesis in humans [100] . A second , independent case of Tas on the C57BL/6J XYAKR background ( B6-TAS ) is caused by the T-Orleans deletion ( TOrl ) , which overlaps with the hairpin tail deletion and also includes Map3k4/D17Rp17 [41] . Interestingly , it has been proposed that B6-TAS in TOrl/+ XYAKR mice is due to biologically insufficient levels of Sry expression [101] . An analogous explanation of the mechanism underlying the Thp/+ XYAKR phenotype is consistent with the report here of delayed and reduced levels of Sry transcription in byg/byg XY gonads at 11 . 5 dpc . Levels of Sry transcription in XY embryonic gonads of Map3k4byg/+ or Map3k4tm1Flv/+ heterozygotes on the C57BL/6J-YAKR background have yet to be determined , but this experiment will form part of a more extensive analysis of gonadogenesis in these individuals . Our data have opened a novel entry point into the molecular genetic control of mammalian sex determination and , in particular , the regulation of Sry expression . We know of no other higher organisms in which MAPK signalling is thought to regulate sexual development , although pheromone response during mating in yeast and other fungi is known to require a highly related pathway of kinase activity [102]–[104] . We are currently investigating the role of other proteins required for MAPK signalling in mouse gonad development , utilising in vivo and in vitro methods . The ultimate aim of these studies is to clarify the pathway of MAPK signalling that operates during gonadogenesis and determine precisely how it interacts with the molecular events constituting sex determination . Finally , our study suggests that forward genetic screens in the mouse should be considered as another important tool for identifying vertebrate sex determining genes .
We have previously described the mutagenesis and screening methodology employed here [105] . Briefly , a three-generation ( G3 ) recessive mutagenesis screen was used in which C57BL/6J males were injected with ENU and outcrossed to C3H/HeH females; F1 ( founder ) males were used to establish pedigrees by mating to C3H/HeH and F2 female offspring were backcrossed to their father . Using this breeding scheme it is expected that approximately one in eight embryos in a pedigree will be homozygous for any given ENU-induced mutation . G3 embryos were examined at 13 . 5 and 14 . 5 dpc for developmental abnormalities . Examination of pedigree RECB/31 ( byg ) revealed several embryos with abnormal male gonad development . Affected embryos were used for genetic mapping with a 55-marker genome wide SNP panel ( sequences available on request ) . byg was maintained by backcrossing to C3H/HeH and , following identification of the Map3k4 mutation , genotyped for the mutant SNP by pyrosequencing . Timed matings were used to generate embryos at specific stages . Breeding pairs were set up at approximately 3 pm and vaginal plugs were checked the following morning . Noon on the day of the plug was counted as 0 . 5 dpc . Embryos were typed for chromosomal sex as previously described [106] . Genotyping for the byg mutation was performed using a PCR-based pyrosequencing assay using the following primers: Forward PCR primer: 5′-AGGACTATGAACGGTACGC-3′; Reverse PCR primer: 5′-Bio-CGCAGCTTCTGATTTAGATC-3′; Sequencing primer 5′-GCCAAGGACTTTGAGG-3′ . byg was backcrossed to C3H/HeH and C57BL/6J . Analysis of byg/byg embryos on C57BL/6J was performed between generations n = 2 to n = 5 . The generation and maintenance of mice lacking Map3k4 has been previously described [44] , [107] . Map3k4-deficient mice utilised here were maintained on a C57BL/6J background . Hairpin tail ( Thp ) mice , originally archived on a mixed genetic background , were rederived using independent in vitro fertilisation ( IVF ) with both C57BL6/J and C3H/HeH oocytes . Thp was maintained on both C57BL/6J and C3H/HeH . XY sex reversal was observed on the former , but not the latter , genetic background . Thp carriers were identified by the shortened tail [108] . Confirmation of the presence of the AKR-derived Y chromosome was performed by using a PCR assay based on that described in [109] , which exploits a Zfy-2 polymorphism between M . domesticus and M . musculus . WMISH to explanted gonads was performed as previously described [31] , [106] . The following probes were used for WMISH: Sox9 [110]; Oct4 [111]; 3β-HSD [112]; Wnt4 ( IMAGE clone 40044945 ) , Sry [113] , Stra8 ( IMAGE clone 40045823 ) , Map3k4 ( IMAGE clone 5705378 ) . Total RNA and protein were extracted from individual 11 . 5 dpc ( 17–18 ts ) mouse urogenital ridges ( comprising gonad and mesonephros ) using the Nucleospin RNA/protein isolation kit ( MACHEREY-NAGEL ) following manufacturer's instructions . The quantity and quality of the RNA was assessed using the Nanodrop ND1000 ( Isogen Life Science ) and by gel electrophoresis . A two-step real-time analysis approach was taken . First , cDNA was synthesised using the AB High Capacity cDNA Reverse Transcription Kit using 1 µg of total RNA . The following TaqMan assays ( Applied Biosystems [AB] ) were used: Sf1 ( Mm00496060_m1 ) ; Fgf9 ( Mm00442795_m1 ) ; Sry ( Mm00441712_s1 ) ; Hprt1 ( Mm01545399_m1 ) . For each assay , reactions were performed in triplicate using AB Fast Mastermix in a final volume of 20 µl ( 5 µg of cDNA added ) . Real-time amplification was performed on an AB 7500 Fast machine , using the manufacturer's recommended program for Fast Mastermix . Analysis of the results was performed using AB software , employing a ddCt method with the gene Hprt1 as the endogenous control . For each assay four biological replicates and three technical replicates were performed . Statistical analysis was performed using a non-paired t-test on the average dCt values calculated for the three technical replicates of each independent sample ( biological replicate ) . The following antibodies were used in this study: SRY [39]; SOX9 [39]; FGFR2 ( Santa Cruz number sc-122 ) ; SF1 , a kind gift from K . Morohashi; FOXL2: antibodies were raised in rabbits against the peptides MMASYPEPEDAAGAALL and WDHDSKTGALHSRLDL , previously utilised in [114] . Antibodies were affinity purified and tested prior to use: platelet/endothelial cell adhesion molecule ( PECAM ) ( BD Bioscience number 553708 ) ; phospho-histone H3 ( pHH3 , Sigma number HH908 or Upstate number 06-570 ) ; phospho-MKK4 ( Cell Signalling number 9151 ) ; phospho-MKK7 ( Cell Signalling number 4171 ) ; phospho-p38 ( Cell Signalling number 4631 ) ; phospho-JNK/SAPK ( Cell Signalling number 9251 ) ; cleaved caspase-3 ( Cell Signalling number 9661S ) ; MAP3K4 ( Sigma m7194 ) . Wholemount immunohistochemistry was performed as previously described [106] . Section immunohistochemistry was performed on the basis of protocols described in [39] . Wholemount samples were imaged using a Leica TCS SP5 confocal microscope . Sections were visualised using a Zeiss Axiophot 2 . After immunostaining with anti-PECAM and anti–pHH3 ( Upstate , number 06-570 ) and nuclear counterstaining with aqueous DAPI , the central third of each gonad was imaged using a Leica TCS SP5 confocal microscope ( 40× ) . A Z-stack series ( 10 µm steps ) was generated for each sample and then three central sections were chosen for cell counts in the coelomic region ( pHH3-positive cells and DAPI-stained nuclei ) . Sections were separated by 20 µm to ensure that no cell was counted twice . Differences between samples were assessed using a two-tailed t-test . Culturing of embryonic gonads and recombination experiments between subdissected gonads and marked mesonephroi were performed based on methodologies described in [50] and [106] . Briefly , XY urogenital ridges ( UGRs ) , consisting of gonad and attached mesonephros , were collected at 11 . 5 dpc ( 16–19 ts stage ) and cultured to establish conditions under which testis cords formed reliably after 48 h culture . Samples were incubated on 1 . 5% agar blocks at 37°C/5% CO2 in Dulbecco's Minimal Eagle's Medium ( DMEM ) /10% fetal calf serum ( FCS ) /50 µg/ml ampicillin/200 mM L-glutamine in the presence of MAPK inhibitors or vehicle control . For recombination cultures , 11 . 5 dpc XY male UGRs from byg/byg mutant embryos were subdissected into component gonad and mesonephros in PBS . The gonads were recombined with mesonephroi from XY Tg ( GFPU ) 5Nagy/J embryos ( ubiquitously expressing GFP ) and cultured for 48 h , as above . Migration from the marked mesonephros into the attached gonad was imaged using a Leica TCS SP5 confocal microscope . No migration was observed into control XX gonads during these experiments . The following MAPK signalling inhibitors were used: SB202190 ( p38 inhibitor , Sigma ) and U0126 ( ERK [Mek1] inhibitor , Sigma ) . SB202190 was used at a final concentration of 25 µM in culture medium , in line with previously reported in vitro studies employing this inhibitor [115]–[117] . U0126 was also used at a final concentration of 25 µM [86] , [118] , [119] . To examine the effects of exogenous FGF9 expression in XX gonad development we employed the methodology described in [56] . Briefly , agarose beads ( Sigma-Aldrich ) were incubated in culture medium containing 50 ng/ml FGF9 protein ( R&D Systems ) , or 0 . 1% BSA , in a humidified chamber at room temperature for at least 5 h . Beads were then placed adjacent to gonads ( n = 3 for each genotypic class ) and cultured for approximately 42 h . Animal procedures employed in this study were authorized by UK Home Office Project License PPL 30/2381 . | In mammals , whether an individual develops as a male or female depends on its sex chromosome constitution: those with a Y chromosome become males because of the development of the embryonic gonad into a testis . The Y-linked sex determining gene SRY regulates this process by initiating a pathway of gene and protein expression , including the expression of critical autosomal genes such as SOX9 . We identified a mouse mutant that causes embryonic gonadal sex reversal: the development of ovaries in an XY embryo . This mutant , which we called boygirl ( byg ) , was shown to contain an early stop codon that disrupts the autosomal gene encoding MAP3K4 , a component of the mitogen-activated protein kinase ( MAPK ) signaling pathway . Analysis of embryonic XY gonads suggests that sex reversal is caused by delayed and reduced expression of the sex-determining gene SRY . Our data indicate , for the first time , a requirement for MAPK signaling in the developing XY gonad in order to facilitate normal expression of SRY and the downstream testis-determining genes and also suggest that reduced dosage of MAP3K4 may be the cause of a previously described autosomal sex-reversing mutation in the mouse . We predict that loss of MAP3K4 or other MAPK components may underlie disorders of sexual development ( DSD ) in humans as well . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"developmental",
"biology/germ",
"cells",
"developmental",
"biology/embryology",
"genetics",
"and",
"genomics/animal",
"genetics",
"developmental",
"biology",
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/disease",
"models",
"genetics",
"and",
"genomics",
"developmental",
"biology/molecular",
"development",
"developmental",
"biology/organogenesis",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
]
| 2009 | Loss of Mitogen-Activated Protein Kinase Kinase Kinase 4 (MAP3K4) Reveals a Requirement for MAPK Signalling in Mouse Sex Determination |
The treatment of Buruli ulcer ( BU ) that is caused by Mycobacterium ulcerans , is currently based on a daily administration of rifampin and streptomycin ( RIF-STR ) . A fully oral intermittent regimen would greatly simplify its treatment on the field . The objective of this study was to assess the bactericidal and sterilizing activities of intermittent oral regimens in a murine model of established M . ulcerans infection . Regimens combining rifapentine ( RFP 20 mg/kg ) with either moxifloxacin ( MXF 200 mg/kg ) , clarithromycin ( CLR 100 mg/kg ) or bedaquiline ( BDQ 25 mg/kg ) were administrated twice ( 2/7 ) or three ( only for RFP-CLR 3/7 ) times weekly during 8 weeks . The bactericidal but also the sterilizing activities of these four intermittent oral regimens were at least as good as those obtained with control weekdays regimens , i . e . RFP-CLR 5/7 or RIF-STR 5/7 . A single mouse from the RFP-MFX 2/7 group had culture-positive relapse at the end of the 28 weeks following treatment completion among the 157 mice treated with one of the four intermittent regimens ( 40 RFP-CLR 2/7 , 39 RFP-CLR 3/7 , 39 RFP-MXF 2/7 , 39 RFP-BDQ 2/7 ) . These results open the door for a fully intermittent oral drug regimen for BU treatment avoiding intramuscular injections and facilitating supervision by health care workers .
Buruli Ulcer ( BU ) , is an infectious disease caused by Mycobacterium ulcerans that is mostly prevalent in Africa , but also found in Australia , Southeast Asia and South America [1] . Recently , cases have also been reported in Japan [2] . Until 2004 , surgery to remove infected tissue followed by skin grafting was the only effective treatment [3] but recurrence rates ranged between 16% and 28% [4] . In 2004 , the World Health Organization recommended to treat BU with a combination of rifampin ( RIF ) and streptomycin ( STR ) administered daily during 8 weeks [5] . This standard drug regimen appeared to be effective [6] , well tolerated , and of low cost . However , this regimen is not fully satisfactory because it requires daily injection of STR , which is operationally demanding in most countries where BU is endemic , especially in rural areas , and exposes to aminoglycosides toxicity but also to the risk of transmission of blood-borne viral infection . An effective , easy to organize , orally administered regimen would greatly simplify the BU treatment under field conditions . Oral regimen based on the daily administration of RIF in coordination with clarithromycin ( CLR ) or fluoroquinolone has been shown to be sterilizing in mice [7] . In humans , the RIF-CLR combination has been successfully used in a continuation oral phase after an initial 2 or 4-week RIF-STR phase in Ghana [8 , 9] , as well as a fully oral treatment of a small cohort of patients with limited BU lesions in pilot studies carried out in Benin [10] and Australia [11] . Another oral regimen combining RIF and either ciprofloxacin or moxifloxacin ( MXF ) , has been successfully used in Australia [12] . A fully oral treatment administered intermittently , e . g . twice or three times weekly , would further simplify the management of BU treatment , particularly in areas with limited access to health care facilities . In a previous study in mice , we have shown that twice-weekly administration of rifapentine ( RFP ) , a long half-life ansamycin , in combination with streptomycin or moxifloxacin , was as bactericidal as daily administration of the corresponding regimens containing RIF in place of RFP [7] . However , the sterilizing activity of intermittent oral regimens has not been evaluated so far . The present study aimed at evaluating , in a murine model of established infection by M . ulcerans , the sterilizing activity of fully oral intermittent ( twice or three times weekly ) regimens based on RFP combined with either CLR , MXF or bedaquiline ( BDQ ) , a new antimycobacterial drug , which has been shown to be active against M . ulcerans [13 , 14] .
Four hundred and eighty 4 weeks-old female balb/c/j mice ( Janvier Labs , Le Genest Saint-Isle , France ) , weighing around 20g , were inoculated in the left hind footpad according to the Shepard method [15] with 0 . 03 ml of a bacterial suspension containing 4 . 3 log10 Colony Forming Unit ( CFU ) of M . ulcerans strain Cu001 . The strain Cu001 was isolated from a Buruli ulcer patient in Adzopé , Ivory Coast , in June 1996 [16] , and was maintained in our laboratory by regular passage into mice footpads since then . The strain was kindly provided by the local laboratory without any identification data regarding the patient . This strain is susceptible to all drugs used in BU treatment . According to the European directive 2010/63/UE , mice were held by 5 in II L cages with shaving of cellulose proposed as enrichment . Water and food were given ad libitum . A temperature of 22 +/-3°C , a hygrometry of 55 +/- 5% and a light/dark cycle 12/12 were maintained in the animal facility . Seven weeks after the inoculation , the infection was well established in mice as they had developed a swelling in their inoculated footpad . As previously described [17] , infected footpads were scored by using a lesion index from 0 ( no lesion on the inoculated footpad ) to 5 ( death of the mouse likely related to M . ulcerans infection ) . Treatment of mice began when infected footpad reached a lesion index between 2 ( i . e . , inflammatory swelling limited to the inoculated footpad ) and 3 ( i . e . , inflammatory swelling involving the entire inoculated footpad ) . The 480 inoculated mice were randomly allocated into eight groups ( randomization . com ) : one untreated control group of 60 mice and seven treated groups of 60 mice each . The day of treatment initiation , 20 mice of the control group were sacrificed for CFU enumeration in the footpad in order to establish the pretreatment value ( D0 ) . Four fully oral intermittent treatment regimens ( twice or three times a week ) , RFP-MXF 2/7 , RFP-BDQ 2/7 , RFP-CLR 2/7 and RFP-CLR 3/7 , as well as the intermittent RFP-STR 2/7 , were compared to reference regimens , the oral regimen RFP-CLR 5/7 [7] and RIF-STR 5/7 [18] . Treatment began immediately after randomization . RIF was purchased from Sandoz laboratory , France; MXF from Bayer Santé , France; CLR from Abbott France , France; STR from Sigma , France; RFP from Sequoia Research , United Kingdom; and BDQ was kindly provided by Janssen Pharmaceutica , Belgium . Dosages were as follows: RIF 10 mg/kg; MXF 200 mg/kg; CLR 100 mg/kg; RFP 20 mg/kg when administered twice a week and 10 mg/kg when administered 5 times per week; STR 150 mg/kg; and BDQ 25 mg/kg . RIF , MXF , RFP and CLR were re-suspended in 0 . 05% agar-distilled water; STR was diluted in normal saline; and BDQ was directly provided by Janssen Pharmaceutica in a 20% hydropropyl-β-cyclodextrin formulation . All drugs were orally administered by gavage in a volume of 0 . 2 ml except for STR , which was injected subcutaneously in the same volume . Ten mice per group were sacrificed after 4 weeks of treatment and ten others after 8 weeks of treatment . The other 40 mice from each group that had been treated during 8 weeks were held without treatment and observed during an additional 28-week period to monitor relapses of M . ulcerans infection . Mice were sacrificed by cervical dislocation as recommended by the French decree n°2013–118 and the European directive 2010/63 . The severity of the infection and the effectiveness of treatment were assessed in the different groups ( i ) clinically by measuring the lesion index , and ( ii ) bacteriologically by cultivating M . ulcerans on an appropriate medium to get the mean number of CFU per inoculated footpad after 4 and 8 weeks of treatment . The sterilizing activity of the treatment was assessed by ( i ) measuring the lesion index during the 28-week observational period and ( ii ) culturing the footpad at the end of this period . For CFU enumeration , the tissues from the footpad were removed aseptically and homogenized in a Hank’s balanced salt solution in a final volume of 2 ml . Suspensions were then plated onto Lowenstein-Jensen medium . For untreated control groups , suspensions were serially diluted in 10-fold steps ( from pure to 10−4 ) and plated in duplicate onto the medium with 0 . 1 ml of the diluted suspension . For the treated groups , the entire volume of the footpad suspension was plated onto 10 tubes of the medium each with 0 . 2 ml of the suspension . All tubes were incubated at 30°C up to 90 days . Mice were examined weekly to assess the evolution of the lesion index in order to monitor the relapse during the post-treatment follow-up period . A rebound of the lesion index to ≥3 of the inoculated footpad suggested clinical relapse and led to immediate sacrifice for footpad culture . For that purpose , the entire volume of the footpad suspension was cultivated onto Lowenstein Jensen medium as described above . A culture positive for M . ulcerans within 90 days of incubation was taken as a confirmation of relapse . At the end of the 28 weeks of observation following the end of the treatment , all the remaining mice were systematically sacrificed and their footpads were cultivated as described above . A positive culture at the end of the incubation time was considered to be a culture-positive relapse . In order to assess a possible acquisition of resistance to the antibiotics used during treatment , the bacilli cultivated from relapsing mice ( during the observation period ) were tested for in vitro susceptibility and molecular detection of mutation implicated in antibiotic resistance . The bacilli cultivated from relapsing mice as well as the susceptible strain Cu001 , were re-suspended in distilled water and the turbidity was adjusted to Mac Farland 3 ( 1 mg/ml ) . RIF , RFP , MXF and CLR were tested on a 7H11 + 10% OADC ( Oleic-Acid-Dextrose-Catalase ) medium ( pH 7 . 4 ) . CLR has also been tested on MH media ( pH 6 . 6 ) . RIF and RFP were dissolved in dimethylformamide , and MXF and CLR in distilled water to obtain a final concentration of 4 μg/ml in medium . They were then twofold diluted in their own solvent and incorporated to the culture media to obtain a final range from 4 to 0 . 12 μg/ml . 0 . 1 ml of 2 bacilli suspensions ( pure and 10−2 ) were plated onto drug-containing media and drug-free media used as a growth control . All media were incubated at 30°C and examined after 60 and 90 days [13 , 19] . A part of the bacterial suspension from relapsing mice was inactivated at 95°C during 30 min , to allow bacterial DNA extraction by heat shock ( 5 cycles of 2 min at 95°C and 2 min at 4°C ) following by 15 min at 95°C and 15 min in an ultrasonic bath . IllustraTM PuReTaq Ready-To-Go PCR beads ( GE Healthcare ) was used to perform PCRs . For the rpoB gene , we used primers as previously reported [17] RPOBMuS 5’-GCGCACGGTGGGTGAGCTG-3’ RPOBMuAS 5’-CGAGACGCCCTACCGCAAGG-3’ . Other primers were designed according to information on the complete genome of M . ulcerans ( GenBank accession number CP000325 ) : for the gyrA gene , MugyrAS 5’-CGCCGTGTGCTCTATGCCATG-3’ and MugyrAAS TCGCCGGGTAATGACCCGCCA-3’ , and for the ARN23S gene 23 . 1 , 5’-AATGGCGTAACGACTTCTCAACTGT-3’ and 23 . 2 5’-GCACTAGAGGTTCGTCCGTCCC-3’ . PCR-amplified fragment were purified by using Qiagen DNA purification kit ( Qiagen ) and sequenced by the dideoxychain termination method with the ABI PRISM BigDye Terminator V3 . 1 Cycle Sequencing Kit ( Life Technologies ) . The oligonulceotide primers used for DNA sequencing were the same as those used for PCR . The sequences were analyzed with the software Seqscape 2 . 0 ( Life Technologies ) . The Student’s t test and the Fisher exact test were used for groups’ comparison . A p-value <0 . 05 was considered as statistically significant . A regimen was considered to be bactericidal if its mean value of CFU per footpad was significantly lower than in the untreated group . Our animal facility received in April , 24th 2012 the authorization to carry out animal experiments ( license number B-75-13-01 ) . The persons who carried out the animal experiments had a personal authorization delivered by the Direction Départementale de la Protection des Populations de Paris . We followed the experimental guidelines of the Faculté de Médecine Pierre et Marie-Curie to carry out the experimental project .
The evolution of the footpads swelling is shown in Fig 1 . Inoculated footpads of the untreated control mice had swollen from a mean lesion index 3 at D0 ( beginning of treatment ) to index 4 at week 2 , where mice were sacrificed before predictable death due to M . ulcerans infection and to avoid culture contamination . In all treated mice , footpad lesions drastically decreased from a mean index 3 at D0 to mean indexes ranging from 1 . 2 ( RFP-MXF 2/7 group ) to 1 . 6 ( RFP-CLR 5/7 group ) after 4 weeks of treatment . During the next 4 weeks of treatment ( i . e . till completion of 8 weeks treatment ) , the mean lesion indexes remained globally stable ranging from 1 . 2 ( RFP-MXF 2/7 group ) to 1 . 5 ( RFP-BDQ 2/7 group ) . The results of the footpad CFU counts are shown in Table 1 . All untreated mice had culture-positive footpads at D0 ( beginning of treatment ) and the mean number of CFU per footpad had increased from 6 . 39±0 . 30 to 6 . 63±0 . 29 log10 at week 4 . After 4 weeks of treatment with the RIF-STR 5/7 reference regimen , all mice remained culture positive , although the mean CFU counts dramatically decreased to reach <1 log10 . In the RFP-CLR 5/7 reference group , as well as in all test groups , the mean CFU was constantly <1 log10 . After 8 weeks of treatment , mice were culture negative in all treated group , except in the RIF-STR 5/7 reference group where three mice still had positive footpads with only 1 , 2 and 12 colonies , respectively , recovered from these entire footpads . The proportion of culture-negative mice at 8 weeks , as well as the mean CFU count per footpad were not statistically significant among all groups including the RIF-STR 5/7 group ( p>0 . 05 ) . During the 28 weeks of follow-up after treatment completion , 4 mice of the RIF-STR 5/7 group exhibited a rebound of their footpad lesion and were also culture-positive , confirming relapses . These relapses occurred between 12 and 17 weeks after the end of treatment . At the end of the 28-week follow-up , all mice were sacrificed for CFU determination . Concerning the RFP10-CLR 5/7 , 1/40 mice were culture-positive whereas among the 5 oral intermittent test groups , only one ( 1/39 in the RFP-MXF 2/7 group ) mouse was culture-positive among the 157 observed mice . Compared to the STR-RIF 5/7 group , the mean CFU count at the end of the follow-up in the RFP-CLR 5/7 group and in the RFP-MXF 2/7 group was not statistically significant ( p = 0 . 17 and 0 . 16 , respectively ) . On the opposite , all other intermittent regimen had significantly lower mean CFU count than the STR-RIF 5/7 group ( p = 0 . 04 for all comparisons ) . Initial antibiotic MICs against M . ulcerans Cu001 reference strain were in the susceptible range: 0 . 5μg/ml for RIF , 0 . 5–1 μg/ml for RFP , 0 . 25–0 . 5 μg/ml for MXF , 0 . 5 μg/ml for STR , and 0 . 5 μg/ml for CLR ( same value on both 7H11 and MH media ) . MICs remained unchanged for all drugs against bacilli grown from relapsing mice in orally treated groups , and results were as follow: RIF 0 . 5–1 μg/ml , RFP 0 . 5μg/ml and MXF 0 . 5 μg/ml against bacilli from the sole positive mouse in the RFP20-MXF 2/7 group; RIF 0 . 5–1 μg/ml , RFP 0 . 5–1 μg/ml and CLR 0 . 5 μg/ml ( on 7H11 ) and 0 . 5–1 μg/ml ( on MH ) against bacilli from the sole positive mouse in the RFP10-CLR 5/7 group . The results were similar for bacilli isolated from relapsing mice treated with RIF-STR 5/7 . Finally , no mutations conferring resistance were detected by molecular biology in bacilli grown from relapsing mice .
The bactericidal activity of an intermittent 8-week treatment regimen with 2/7 administration of RFP 20 mg/kg in combination with either STR or MXF has been previously reported , raising the hope that Buruli ulcer might be treated with twice-weekly therapy [7] . However , the sterilizing activity , i . e . the lack of relapse after treatment completion , of the RFP-MXF 2/7 oral intermittent regimen was unknown . The present study showed that three fully oral intermittent regimens , based on rifapentine combined with moxifloxacin , clarithromycin , on the one hand , or bedaquiline , on the other hand , were highly bactericidal after 8 weeks of treatment but also had sterilizing activity against M . ulcerans infection in mice . The RIF-STR 5/7 regimen , which is the main reference drug regimen used for treating BU in endemic countries , was bactericidal . However , it lacks of sterilizing effect when compared to the other regimens in the present experiment , although the differences did not reach statistical significance ( p>0 . 05 ) . This lack of sterilizing activity has been already reported in a previous study where the reduction in the RIF-STR dosing frequency from 7/7 to 5/7 led to a relapse rate of 12% in the mouse model [18] . In the present study , the intermittent 2/7 or 3/7 fully oral regimens were as active as the control oral regimen ( RFP-CLR 5/7 ) . We confirm by the present study that the latter regimen is sterilizing in the mouse model ( a single relapse among 40 mice ) , as previously found in the same model of established infection [7] as well as in a model of incubating infection [20] . Therefore , the RFP-CLR 5/7 regimen is worth comparing to the daily RIF-CLR oral regimen , which has been successfully tested in the field [10] and is currently evaluated in a clinical trial ( NCT01659437 , clinicaltrials . gov ) under the supervision of WHO . We did not test RIF-CLR intermittent regimens in order to compare RIF and RFP activities when combined to CLR . However , we have already reported the lack of sterilizing effect of RIF-STR intermittent regimens [18] . This previous result together with the present experiment suggest that the difference is due to the RFP effect , and likely to its long half-life . We report here that three fully oral intermittent regimens were sterilizing in the BU mouse model . The combinations using CLR instead of MXF have the potential interest to be used in children , who represent a high proportion of BU patients . Of note , in a case report from Australia [21] , the use of a RIF-CLR intermittent regimen has been successful for treating a child . In addition , CLR is currently under investigation in a clinical trial of BU in Ghana and Benin [10] . The use of BDQ in the treatment of BU has already been investigated in the mouse model [13] , and has shown to be sterilizing when used daily with RIF . Here , we demonstrated its sterilizing activity when administered 2/7 combined with a long half-life ansamycin , RFP . Intermittent administration of combined regimen raises the question of pharmacokinetic mismatch between the drugs combined in the regimen . A large mismatch will result in effective monotherapy and potentially favor the selection of resistant mutants . The half-lives of MXF and BDQ are very close to that of RFP , thus 2/7 administration of RFP-MXF or RFP-BDQ should be effective in preventing the selection of ansamycin-resistant mutants . The half-life of clarithromycin in humans is shorter ( 4–5 hours ) than that of rifapentine ( around 14–16 hours ) . Therefore , this discrepancy between the two half-lives suggests using the RMP-CLR combination three times weekly rather than twice weekly , a rhythm that can still coincide with visits to health care centers . The 20 mg/kg RFP dosage used in the present experiment was higher than the classical 10 mg/kg dosage used for daily administration . The higher dosage was justified for a twice-weekly administration since it aims at counterbalancing the lower number of administration . High RFP dosages have been used in the treatment of tuberculosis in humans and were well tolerated when administrated weekly [22–24] . Moreover , BU occurs very often in children and higher weight-normalized RFP dosages are required to obtain kinetic properties equivalent to those seen in adults [25] . The present study was conducted by using a single M . ulcerans strain . MICs of the different antibiotic against this strain are in the middle range of what was previously reported [13] and therefore the present results are likely to be valid for a majority of strains . However , treatment outcome may vary if antibiotic MICs are on the higher range . In our study , data on pharmacokinetic parameters of antibiotics used in treatment regimens were not performed , and would have helped in interpreting bacteriological results . Nevertheless , the choice of drug concentrations in the experimental model was based on data of previous studies performed in mice and derived from human pharmacokinetic results [22–24] . The results of the present work are encouraging and suggest the possibility to combine two advantages over the actual reference RIF-STR daily regimen ( i ) oral administration that would avoid injections and ( ii ) intermittent administration that would facilitate the supervision in the field . Among the intermittent oral regimens that have shown sterilizing activity , the RFP-CLR combinations are likely to be the most interesting to be tested in the field because CLR is already used in the BU treatment especially in children . In addition , the use of MXF and BDQ for the treatment of BU in countries with high tuberculosis prevalence is questionable since both drugs are essential for the treatment of multidrug resistant tuberculosis . | Buruli ulcer , caused by Mycobacterium ulcerans , is the third most frequent disease due to a bacteria of the mycobacteria family , after tuberculosis and leprosy . The disease consists of a cutaneous infection leading to skin ulcerations that can cause severe and debilitating scars . It occurs mostly in Africa where access to healthcare is still difficult . Until recently , surgery was the only way to treat Buruli ulcer . Recently , a medical treatment based on two antibiotics ( rifampin and streptomycin ) was found to be active in an experimental infection model and was recommended by WHO in 2004 for humans . A drawback of this new medical approach is a long and daily administration ( 2 months ) of streptomycin , an aminoglycoside , which requires daily injections and can have secondary effects . In our research , we sought to find a fully oral and intermittent treatment to facilitate the management of the patients on the field . The antibiotics combinations we studied were efficient in our model and would need now to be tested on the field . | [
"Abstract",
"Introduction",
"Material",
"and",
"methods",
"Results",
"Discussion"
]
| [
"antimicrobials",
"body",
"weight",
"medicine",
"and",
"health",
"sciences",
"animal",
"models",
"of",
"disease",
"drugs",
"tropical",
"diseases",
"microbiology",
"animal",
"models",
"bacterial",
"diseases",
"model",
"organisms",
"physiological",
"parameters",
"pharmaceutics",
"antibiotics",
"drug",
"administration",
"neglected",
"tropical",
"diseases",
"pharmacology",
"bacteria",
"research",
"and",
"analysis",
"methods",
"animal",
"models",
"of",
"infection",
"infectious",
"diseases",
"buruli",
"ulcer",
"animal",
"studies",
"mouse",
"models",
"actinobacteria",
"mycobacterium",
"ulcerans",
"physiology",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy",
"organisms"
]
| 2016 | Sterilizing Activity of Fully Oral Intermittent Regimens against Mycobacterium Ulcerans Infection in Mice |
Riboviruses ( RNA viruses without DNA replication intermediates ) are the most abundant pathogens infecting animals and plants . Only a few riboviral infections can be controlled with antiviral drugs , mainly because of the rapid appearance of resistance mutations . Little reliable information is available concerning i ) kinds and relative frequencies of mutations ( the mutational spectrum ) , ii ) mode of genome replication and mutation accumulation , and iii ) rates of spontaneous mutation . To illuminate these issues , we developed a model in vivo system based on phage Qß infecting its natural host , Escherichia coli . The Qß RT gene encoding the Read-Through protein was used as a mutation reporter . To reduce uncertainties in mutation frequencies due to selection , the experimental Qß populations were established after a single cycle of infection and selection against RT − mutants during phage growth was ameliorated by plasmid-based RT complementation in trans . The dynamics of Qß genome replication were confirmed to reflect the linear process of iterative copying ( the stamping-machine mode ) . A total of 32 RT mutants were detected among 7 , 517 Qß isolates . Sequencing analysis of 45 RT mutations revealed a spectrum dominated by 39 transitions , plus 4 transversions and 2 indels . A clear template•primer mismatch bias was observed: A•C>C•A>U•G>G•U> transversion mismatches . The average mutation rate per base replication was ≈9 . 1×10−6 for base substitutions and ≈2 . 3×10−7 for indels . The estimated mutation rate per genome replication , μg , was ≈0 . 04 ( or , per phage generation , ≈0 . 08 ) , although secondary RT mutations arose during the growth of some RT mutants at a rate about 7-fold higher , signaling the possible impact of transitory bouts of hypermutation . These results are contrasted with those previously reported for other riboviruses to depict the current state of the art in riboviral mutagenesis .
Riboviruses ( RNA viruses with no DNA replication intermediates ) infect organisms from prokaryotes to higher eukaryotes and frequently cause deadly diseases . The mortality , morbidity , and economic burden of ribovirus-borne diseases strongly impact human society , especially in developing countries where neither sanitation nor treatment may be adequate [1] . Although extensive efforts have focused on developing countermeasures to prevent or treat riboviral diseases , only a few of these diseases can be effectively controlled by vaccination or antiviral drugs . In addition , control or eradication of riboviral diseases is soon balanced by the emergence of new riboviral pathogens or treatment-resistant strains of old ones ( reviewed in [2] ) . Thus , we seek to understand which special features of these viruses contribute to their success . One key feature is their high mutation rate ( reviewed in [3] ) . Although the evolutionary forces driving high riboviral mutation rates remain unclear ( reviewed in [1] ) , three mechanistic factors play important roles: the higher error-insertion rates of RNA replicases compared to DNA replicases , the lack of proofreading activity in RNA replicases , and the nonexistence of post-replicative RNA mismatch repair . The estimated mean rate per infection cycle is about 1 . 3 for several common single-stranded RNA ( ssRNA ) human pathogens [4] , roughly 0 . 1 for ssRNA tobacco viruses [5] , and 0 . 03 for the double-stranded RNA ( dsRNA ) bacteriophage φ6 [6] . Unfortunately , most of these estimates were based on studies in which small , potentially unrepresentative sequences were used as mutation reporters . In some cases , estimated rates in excess of 1 per infection cycle are probably incompatible with viability [7] . A further problem is the scarce information on the mode ( linear , exponential , or mixed ) by which riboviruses replicate their genomes within the host cell . Distinct modes of genome replication impact the pattern of intra-cell mutation accumulation in the riboviral genome ( and hence the mutation rate per infection cycle ) differently . The only two empirical studies published to date on riboviral replication strategy , one conducted with the phage φ6 [8] and the other with the ssRNA turnip mosaic virus [9] , suggest that riboviruses replicate their genome mostly in a linear fashion , but further results are needed based on other riboviral systems . In addition , there are limited data on the kinds and relative frequencies of spontaneous mutations ( the mutation spectrum ) in riboviruses , again a reflection of mutation reporters that do not sufficiently sample the genome . Only three spontaneous mutation spectra based on a cognate riboviral gene of adequate size are available and , unfortunately , none seems to be fully illustrative . The tobacco mosaic virus rate and spectrum [10] were derived under conditions of multiple sequential infections . The tobacco etch potyvirus spectrum [11] probably contains a large fraction of mutations resulting from methodological manipulations rather than from virus replication errors . Finally , the phage φ6 spectrum [6] was obtained from a mutation-accumulation experiment in the absence of gene complementation in trans , which tends to discriminate against strongly deleterious mutations . A complete portrait of spontaneous mutagenesis in riboviruses is important not only for understanding their prevalence but also for improving ways to prevent and to treat riboviral diseases . For instance , accurate information on riboviral mutation kinds and rates may facilitate the creation of more stable attenuated vaccines [12] . Similarly , it seems likely that antiviral treatments based on mutagenic base analogs may prove to be more effective if the base analogs specifically increase the rate of those errors that riboviral replicases already generate most frequently . Although pathway-directed mutagenesis is unlikely to prevent the appearance of riboviral resistance to specific base analogs , it may enlighten the development of more efficient combinatory therapies [13] and at least slow disease progression , thus enhancing the immune response . The main aims of the present study were to characterize the mutation spectrum , to determine the mode of genome replication , and to estimate the spontaneous mutation rate of a ribovirus using the bacteriophage Qß as an experimental model . Qß has been well characterized physiologically [e . g . ] , [ 14]–[17] , physiochemically [e . g . ] , [ 18]–[21] , structurally [e . g . ] , [ 22]–[27] , and molecularly [e . g . ] , [ 28]–[32] . It is a linear ( + ) -strand ssRNA phage whose natural host is Escherichia coli , although it can also propagate in other gram-negative bacteria with an F pilus . Its 4217-nt long genome is organized in three cistrons that encode ( from 5′ to 3′ ) the A2 or Maturation protein , which mediates both the binding of Qß to the host and post-replicative host lysis; the Coat protein and its elongated A1 or Read-Through ( RT ) protein , which is required for Qß capsid assembly and for host infection; and the catalytic ß subunit of the Qß replicase . ( RT is translated when a ribosome incorporates tryptophan at the natural UGA stop codon of the Coat-coding gene at a frequency of ≈3% [33] . ) Qß's life cycle may be summarized as follows: i ) the phage attaches to the F pilus of E . coli and the genome enters the cytoplasm; ii ) cellular components translate the ß subunit of the phage replicase , which then polymerizes with four host subunits ( the ribosomal protein S1 , the translation elongation factors EF-Tu and EF-Ts , and the host factor HF ) and binds the Qß genome; iii ) the ß subunit copies the ( + ) -strand genome to produce a ( − ) -strand RNA that in turn is used as template to produce more ( + ) strands; iv ) ( + ) strands serve as templates for the production of the phage proteins; v ) 40–60 minutes after infection , by which time the host cell is filled with phage particles , partially assembled virions , and phage-specific side products , the cell lyses , releasing ( 10–40 ) ×103 particles of which only 10–50% are infectious ( reviewed in [14] , [34] ) . Here , we used the gene encoding the RT protein ( excluding the portion that encodes the Coat protein ) as an in vivo mutation reporter . Selection against RT− mutants was ameliorated by using a complementing system in trans based on a plasmid that encodes the entire Coat/RT mRNA with the natural UGA stop codon replaced with a TGG tryptophan codon [35] . To further reduce the effect of selection , the experimental Qß populations were established after a single cycle of infection . We assessed the Qß genome mutation rate ( μg ) in three different ways: i ) a forward-mutation test in which mutants carrying phenotypically detectable RT mutations were isolated and sequenced and μg was estimated from the frequency of observed nonsense mutations and indels; ii ) single-burst reversion tests in which two different RT− mutants were employed ( one carrying a single-base substitution and the other a four-base insertion ) and μg was estimated from the corresponding reversion rates; and iii ) a phenotype-blind forward-mutation test in which some first-generation progeny of the RT mutants detected by the first method were isolated and sequenced and μg was estimated from the frequency of all secondary RT mutations generated de novo . The distributions of RT+ revertants observed in the reversion tests were used to infer the mode in which Qß replicates its genome , and the spontaneous Qß mutation spectrum was obtained from the RT mutations collected through the forward-mutation tests .
The basics of the experimental system and the strains used in this study are described in Figure 1 and Table 1 , respectively . Mutations arising in a mutation-reporter ( target ) sequence can be of two types . “Detectable” mutations are those that display the mutant phenotype when present as a single mutation . “Undetectable” mutations lack the mutant phenotype when present as a single mutation but may nevertheless be observed when they arise in the presence of a detectable mutation , in which case they are sometimes called “hitchhiker” mutations ( and their detectable partner may be called a “driver” mutation ) . Sometimes , especially with mutants with equivocal phenotypes , no mutation is found in the target , reflecting either some imperfection in the screening method or a mutation elsewhere in the genome whose effect mimics that of the reference mutation; such isolates are thereafter included in the non-mutant total . Another distinction is often relevant: some mutations produce a fully mutant phenotype but others produce an intermediate phenotype ( and are therefore often called “leaky” mutations or are said to produce a “weak” mutant phenotype ) . In this study , yet another dimension is added . Each Qß mutant originally isolated as requiring a helper host to generate a plaque or each of a number of non-mutant control plaques was re-plated and up to four next-generation plaques were harvested and sequenced . When all members of such a family contain the same mutation , we call it the “primary” mutation , and if some of the next-generation plaques contain additional mutations , we call them “secondary” mutations , which may arise when mutation rates are sufficiently high . One-step growth curves of wild-type ( wt ) Qß in RT-helper ( RTH ) cells , which complement RT− mutations , indicated that Qß requires ≈75 min to lyse an infected RTH cell ( Figure 2 ) . Thus , to limit the number of infection cycles to one before seeking RT mutants , RTH lysates were generated by adding chloroform 75 min after infection with wt Qß . Samples of these lysates were plated with RTH cells and the resulting plaques were harvested and tested for the RT− phenotype ( impaired growth on non-complementing NR16205 cells but normal growth on RTH cells ) . Among 7517 plaques tested in four independent experiments , 47 candidate RT mutants were recovered and sequenced . Of these , 30 contained at least one primary RT mutation ( Table 2 ) . ( The 17 candidates with no primary RT mutation may have carried an RT−-mimicking mutation elsewhere in the genome or , because Qß grows better on RTH cells than on NR16205 cells , might have carried weak non-RT mutations and showed enhanced growth on RTH cells . ) Most of the primary mutations were missense but two ( one in mutant RT23 and one in RT37 ) were indels consisting of single-base additions . Two mutants each carried two primary mutations; in RT18 , both were missense; in RT41 , one was missense and the other was a synonym . Three mutants ( RT10 , RT40 and RT46 ) each carried a nonsense mutation that generated a stop codon; RT40 is a special case because it converted the leaky UGA codon that terminates the Qß Coat protein to a far less leaky UAA stop codon . In two cases , the primary mutation displayed at most a very weak phenotype upon re-plating: the primary mutation of RT27 was a synonym and that of RT33 was missense . These mutants are included in Table 2 and dependent calculations because their mutations could in principle produce a deleterious effect but would have no significant impact if disregarded . The 13 RT secondary mutations ( Table 2 ) presumably arose sufficiently early during the growth of the screened plaques on RTH lawns to be detected among the next-generation progeny . They include 6 missense mutations and 7 synonyms , a ratio that deviates from the approximately 3 . 3∶1 ratio expected from the set of RT codons . Applying the binomial distribution , finding 6 missense among a total of 13 mutations has P = 0 . 014 and finding ≤6 has P = 0 . 018 . This result presumably signals selection against RT mutations with strong effect during plaque growth on RTH lawns , consistent with the smaller burst sizes of RTIN ( a mutant Qß strain carrying a four-bases insertion in RT; see Table 1 ) than wt Qß in helper cells . ( The average burst sizes of RTIN and wt Qß are 328 and 847 , respectively , estimated from three different one-step curves per phage type . ) Table 3 lists the kinds of mutations in the entire set of 45 . The 2 indels are strikingly less frequent than the 43 single-base substitutions . The general expectation that frameshifting indels generate a detectable mutant phenotype when arising in a protein-coding sequence reduces the chances of having missed other indels during the scoring of mutants . In addition , pQßRT , the RT-expressing plasmid used in this study , can complement RT deletions comprising up to 447 nt [36] , which reduces the probability of having missed indels >1-nt long . The 39 transitions were almost 10-fold more frequent than the 4 transversions; if both transversions and transitions were to arise at equal frequencies among base-substitution pathways , the expected ratio would be 1∶0 . 5 ( each site being able to generate two kinds of transversions and one kind of transition ) , a 20-fold difference from the observed ratio . Transitions , when ranked in decreasing order of observed numbers , were U→C ( 16 ) >G→A ( 10 ) >A→G ( 8 ) >C→U ( 5 ) . The numbers of the four bases in the target decrease in the same order , U ( 175 ) >G ( 147 ) >C ( 139 ) >A ( 130 ) , but this trend cannot quantitatively explain the normalized frequencies of mutated bases , which is 0 . 091>0 . 068>0 . 058>0 . 038 . Thus , the intrinsic mutability of the four bases , presumably reflecting the error propensities of the Qß replicase , is likely to be the main determinant of the relative frequencies of observed mutations . The mutations were widely distributed over the target ( Figure 3 ) . Because only 4 RT positions out of the observed 38 hosted more than one substitution , the spectrum is clearly far from saturation . Both indels arose within short homopolymeric runs , a common pattern in mutation spectra that presumably reflects misaligned primer-templates [37] , [38] . The substitutions showed no correlation with their nearest neighbors either individually or as purines versus pyrimidines ( analyses not shown ) . However , because a tendency towards enhanced mutability of any base within a G/C-rich sequence has been observed in both E . coli [39] , [40] and the T-even coliphage RB69 [41] , we also examined the base composition ( G+C versus A+T ) of the local sequence environments where substitutions were observed . G•C base pairs are more stable than A•U pairs , so that G/C-rich sequences might help to stabilize secondary structures containing hairpin loops , where unpaired bases may be more sensitive to oxidative damage . In addition , duplexes richer in G•C pairs may be slower to unwind , which might render replication more error-prone in currently unknown but perhaps generally applicable ways . A recent description of the structure of the Qß replicase [31] suggested that the replicating Qß genome ( template+complement ) forms a 6–7 base-pair duplex in the internal cavity of the replicase before both the single-stranded product and template exit the enzyme . Accordingly , we analyzed the base composition of the sequences six and seven bases upstream of the observed substitutions ( Figure 4 ) . Both the 6-mers and the 7-mers contain more ( G+C ) than expected from the target content of bases . The difference for the 6-mers has P = 0 . 059 and for the 7-mers has P = 0 . 034 ( replicated G-test for goodness-of-fit , P-values for “pooled G” , GP , 1 df ) . Nevertheless , these small differences , combined with the homogeneity in base composition of the analyzed sequences , made the “total G” ( GT ) non-significant in both analysis ( GT = 0 . 409 , 6 df , and GT = 0 . 420 , 7 df , for 6- and 7-mers , respectively ) . Overall , a larger sample of mutations would probably indicate more clearly the existence ( or absence ) of any effect of the G/C content of the local sequence on Qß-replicase error tendencies . To determine how mutations accumulate in the Qß ( + ) -strand progeny during replication and thus to estimate the rate of spontaneous mutation per genome replication in Qß , it is necessary to know the mode by which Qß produces its progeny during cell infection . Two distinct modes are possible . One is linear , wherein the infecting ( + ) -strand genome is used repeatedly as a template and then at least some of the resulting ( − ) -strand RNAs are each used repeatedly as templates; consequently , at the end of the infection cycle , each of the many ( + ) -strand progeny has experienced only two replications , from ( + ) to ( − ) and from ( − ) to ( + ) . In this model , due to the many ( + ) -strand progeny contributed by the fewer ( − ) -strand templates , most replication errors will produce a single mutant during the second round of replication and only a small fraction of errors will generate a clone of mutants when a replication error occurs in the first round of replication and is further repeatedly copied in the second round [4] . The other mode is classical exponential replication , in which case the numbers of mutants recovered from single viral bursts display an exponential distribution [42] . Intermediate models combining linear and exponential replication in different proportions are also conceivable . To determine which model best fits the distribution of mutants in Qß , two separate single-burst reversion tests were conducted ( see Materials and Methods ) , one using the mutant RTIN and the other using RTSUB ( described in Table 1 ) . The tests involved plating cultures , containing bursts from infected RTH cells , onto NR16205 cells , aiming to deliver roughly 1 revertant-yielding burst on each of many plates . With RTIN , among the 250 cultures plated , six did not form plaques well and were discarded , and five had the following number of Qß plaques: 1254 , 585 , 345 , 342 and 105 . Because the plating efficiency of wt Qß with NR16205 is ≈1 . 5-fold lower than with the RTH strain , those numbers correspond to about 1881 , 818 , 518 , 513 and 158 plaques , respectively . Since the frequency of RT+ revertants in the starting RTIN population was ( 4 . 63±1 . 50 ) ×10−6 ( mean ± SD , n = 5 ) and roughly 4560 infected cells were introduced into each of the 250 experimental cultures , the expected total number of revertant bursts produced by preexisting RT+ phages was ≈5 . 3 , in close agreement with the observed five large revertant bursts . The variation in numbers of revertants among these five bursts is consistent with the observed variation in burst sizes of wt Qß growing in the RTH host , 847±308 ( mean ± SD , n = 3 one-step curves ) , and individual bursts with sizes from 300 to 3000 have been observed . In the case of RTSUB , among the 500 cultures assayed , one had to be discarded and another contained 935 plaques; in this case , the expected number of bursts from preexisting RT+ revertants was 3 . 5 . None of the cultures containing bursts attributable to preexisting RT+ phages were included in the analyses . The revertant distributions differed for the two mutants ( Table 4 ) . With RTIN , the distribution closely fitted a Poisson , supporting a linear mode of genome replication for Qß and strongly inconsistent with an exponential mode . With RTSUB , the distribution deviated significantly from a Poisson , showing an excess of plates containing ≥3 revertants . Even within a linear replication mode , however , these results may reflect either or both of two causes . The first is that different reversion pathways during the first and second rounds of replication will tend to have different rates at any particular site . With RTIN , the reversion target for the first replication consists of 5′-UCUUAAUUAAGU-3′ where the target is underlined and reversion to wild-type would probably occur by the deletion of UUAA or , perhaps less likely , by pseudoreversion by the loss of one base from any of the four homo-dinucleotides , producing a gene with one extra codon . Unusually , the second-replication target is 5′-ACUUAAUUAAGA-3′ , which is identical to the first-replication target except for the outermost flanking bases . Thus , the two error rates might be very similar and the ratio of ( + ) -strand to ( − ) -strand products might have been large enough so that errors accumulated mostly during the second replication and the resultant revertant bursts were largely composed of clones of size 1 . With RTSUB , however , reversion must have occurred along the available single-base-substitution pathways ( up to 8 for a UAG stop codon , depending on the functional competence of the encoded amino acids ) , each of which differs between replications and which might therefore have displayed large rate asymmetries , which can easily exceed 100-fold in the case of DNA genomes [e . g . , 43] . The second cause is that the total number of copying events probably differ between ( − ) -strand and ( + ) -strand synthesis during cell infection; in Qß , for instance , the number of accumulated ( + ) strands was estimated to be about 10 times greater than the number of ( − ) strands [14] , [44] , so that revertant bursts of size 1 from the ( + ) -strand synthesis would then be more frequent than the larger bursts from the first rounds of replication . Notably , however , these larger bursts , once they appear , are expected to exhibit variable sizes that depend on , among other factors , the growth conditions [45] , and that might therefore impact the observed distribution . To estimate the rate of spontaneous mutation per genome replication ( μg ) for a ribovirus , it is necessary to know ( i ) the mutation frequency f , ( ii ) the number of infection cycles c that elapse between the initial infection and the scoring of mutants , ( iii ) the average number of times n that each genome is replicated per infection cycle , ( iv ) the number of detectably mutable bases in the mutational target ( T ) , and ( v ) the genome size ( G ) . In the present case , G = 4217 nt , c = 1 , and , from our results , n≈2 . Although T = 591 RT bases for estimating the indel mutation rate ( μI ) , that number cannot be used when estimating the corresponding base-substitution rate ( μSUB ) because , while nearly all indels are detectable , many substitutions fail to produce a mutant phenotype . Instead , μSUB may be estimated from the number of substitutions that generate a stop codon ( nonsense mutation ) because , like indels , nonsense mutations are generally detectable . When considering nonsense mutations , T equals one-third of the number of paths in the mutational target that may generate a stop codon ( one-third because each base can mutate by three different paths ) [46] . In this study , 3 nonsense mutations were found among 7517 Qß isolates , and T = 66 paths leading to a stop codon in the RT target . Thus , fpath = 3/ ( 7517 ) ( 66 ) = 6 . 047×10−6 , fSUB = 3fpath = 1 . 814×10−5 per base , and μSUB = fSUB/cn = 9 . 0704×10−6 . Because 2 indels were found , fI = 2/ ( 7517 ) ( 591 ) = 4 . 502×10−7 , and μI = fI/cn = 2 . 251×10−7 . Hence , μg = ( μI+μSUB ) G = 0 . 039 . In addition to the primary mutations detected by their phenotypes , some hitchhiking mutations were found . These secondary mutations may be used for an independent estimate of μg , in which case T = 591 bases . A total of 9 secondary mutations ( all base substitutions ) were detected among 112 sequenced sub-isolates . ( The remaining secondary mutations from the 13 described in Table 2 were observed in RT− isolates lacking any detectable primary RT mutation and thus were excluded from these calculations . ) Thus , fSUB = 9/ ( 112 ) ( 591 ) = 1 . 36×10−4 , μSUB = fSUB/cn = 6 . 80×10−5 , and μSUBg = μSUBG = 0 . 287 . This value is greater than the corresponding value from the nonsense-mutation method by 7 . 4-fold and may , as discussed later , signal the impact of transient hypermutation . Mutation rates can also be estimated for the reversion of the mutants RTIN and RTSUB using the results of the single-burst reversion tests . First , some definitions are needed: the number of cultures = C; the average number of infected cells per tube = N; the average burst size = B; the number of initial [ ( + ) -strand→ ( − ) -strand] copies = c1 with an error rate μ1 per copy; the number of succeeding [ ( − ) -strand→ ( + ) -strand] copies = c2 with an error rate μ2 per copy and a burst size B = c2 that ignores unpackaged genomes; and there are n = 2 two rounds of replication per infection . Then the average total number of mutational events per infected cell will be c1μ1+c2μ2; however , these components cannot be disentangled with our data , so we will assume that c2μ2≫c1≥1 ( e . g . , most of the mutations are generated in the second replication , as indicated by the results from the single-burst reversion tests ) , in which case the average total number of mutations per infected cell will be c2μ2 = Bμ2 . For a set of cultures of which some contain 0 mutants , the fraction of null tubes is e−m where m is the average number of mutational events per culture [47] . The total number of replication events per culture≈NB , whence μ2≈μ = m/NB . For RTIN , the fraction of null tubes was 31/239 , m = 2 . 04 , N≈4560 infected cells per tube , and B = 328±93 ( mean ± SD , n = 3 one-step curves ) , so that μ ( RTIN ) = ( 1 . 37±0 . 39 ) ×10−6 . For RTSUB , the fraction of null tubes was 238/498 , m = 0 . 738 , N≈35 , B = 859±165 ( mean ± SD , n = 3 one-step curves ) , and μ ( RTSUB ) = ( 2 . 46±0 . 47 ) ×10−5 . The ratio μ ( RTSUB ) ∶μ ( RTIN ) ≈18 which , given the indel sample size of 2 , agrees well with the corresponding ratio of the two kinds of mutations ( substitutions and indels ) in the spectrum ( 43∶2≈22 ) or by rate ( 9 . 07×10−6 ) ∶ ( 2 . 25×10−7 ) ≈40 ) . Another way to estimate these reversion rates is to use μ = f/2 but , as directly above , to assume that all detected mutations arose in the second round of replication , those arising in the first round being too infrequent to be readily observed , in which special case , μ = f as above . Here , f is simply the sum of all observed RT+ revertants divided by all the Qß progeny in all tubes , NBC . For RTIN , the total number of revertants was 510 ( Table 4 ) , so that μ ( RTIN ) = 510/ ( 4560 ) ( 328±93 ) ( 239 ) = ( 1 . 43±0 . 40 ) ×10−6 , a value close to the null-class value because of the excellent agreement between the observed distribution and the Poisson expectations ( Table 4 ) . For RTSUB , the total number of revertants was 446 ( Table 4 ) , so that μ ( RTSUB ) = 446/ ( 35 ) ( 859±165 ) ( 498 ) = ( 2 . 98±0 . 58 ) ×10−5 , again a value close to the null-class value but slightly higher due to the occurrence of a small excess of plates with larger numbers of revertants compared to the expectations of the Poisson distribution ( Table 4 ) . Because the number of paths in which the RTSUB mutated codon ( UAG ) may change producing an RT+ revertant is not known , the estimated μ ( RTSUB ) is an upper limit corresponding to 8 paths or 2⅔ substitutions .
We have obtained a spontaneous mutation spectrum for the RNA coliphage Qß using a cognate mutational target , the RT-coding gene minus the portion encoding the Coat protein . This 591-nt target generously samples the 4217-nt Qß genome , and the RT and genome base compositions are indistinguishable ( G-test of independence , P = 0 . 9719 , 3 df ) . The spectrum , based on 45 single-base changes , is a mixture of 32 primary mutations plus 11 secondary mutations found hitchhiking on some primary mutations , plus 2 single synonymous mutations ( at target sites 18 and 294 ) arising during sequencing that showed no primary mutation . This spectrum has three defining characteristics . One is its strikingly low frequency of indels , only 2 among 30 RT mutants and 45 mutations , thus representing only about 4% of the total mutations , while in spectra from several DNA-based microbes ( phages λ and T4 , E . coli , Saccharomyces cerevisiae , and Schizosaccharomyces pombe ) , indels comprise about 40% of the mutations ( average 41% , range = 27–59% [39] , [46] , [48]–[51] ) . Another characteristic is its unusually high transition∶transversion ratio ( 39∶4 = 9 . 75 ) compared to a random expectation of 1∶2 = 0 . 5 . This transition bias contrasts with the transition∶transversion ratios observed for the same DNA-based microbes mentioned above ( mean 0 . 87 , range 0 . 08–1 . 67 ) . Finally , normalized to target-base frequencies , the spectrum reveals a biased mutation tendency consisting of U→C>G→A>A→G>C→U> all transversions . Taking into account the dynamics of Qß genome replication with most mutations arising during the second round of replication , this mutation bias reflects a mismatch formation/extension bias in the template•progeny sense of A•C>C•A>U•G>G•U> transversions mismatches . This bias does not seem to reflect either cytosine deamination ( which promotes C→U ) or guanine oxidation ( which promotes G→U ) , but rather the insertion of ionized , tautomerized , wobbled or syn-conformation bases . Several other spectra of spontaneous riboviral mutations have been described previously . Taken together , the informative parts of these spectra indicate that riboviral mutation spectra differ from those characteristic of DNA viruses and cellular organisms in displaying many more transitions than transversions and an even smaller proportion of indels . With the aims of determining the way in which mutations accumulate during riboviral replication and estimating the rate of spontaneous mutation per genome replication , we investigated the mode in which Qß replicates its genome . Results from two independent single-burst reversion tests indicated that this mode is essentially linear , with the genome of each Qß progeny resulting from only two replications: from the original parental ( + ) strand to a ( − ) strand and then to a new ( + ) strand . Our results further suggest that most replication errors occur during the second round of replication , which in turn reveals the specific mismatches that produced the substitutions in the mutational spectrum . An interesting result is that the distribution of RT+ revertants deviated significantly from the expected Poisson distribution for RTSUB but not for RTIN . With no reason to suspect that the two strains replicate their genomes differently , this discrepancy may reflect intrinsic differences between their reversion targets . The reversion target in RTIN is the same in both rounds of replication , while reversion in RTSUB may occur through up to 8 different single-base-substitution pathways in each round of replication . Thus , reversion rate asymmetries between the two rounds of replication may be anticipated for RTSUB , allowing some reversion to occur during the first round of replication and thus producing some revertant clones of size >1 during the second round . The mode of genome replication in riboviruses has been addressed in only a few instances . Using the single-burst reversion test , a predominantly linear mode was reported for the phage φ6 [8] . In that study , however , the observed distribution of mutants ( i . e . , revertants ) differed somewhat from the expected Poisson for a linear mode of replication , suggesting an exponential component in the replication dynamics that was estimated to generate ≈1% of the total progeny [8] . Such discrepancies between observed and Poisson distributions may occur because of sampling errors or the presence of a small exponential component . A way to discriminate among these is to plot the logarithm of the cumulative frequency distribution of observed mutants against the logarithm of the sizes of the mutant classes [53] . Figure 5 shows such plot for T2 , φ6 , RTIN , and RTSUB . In a log-log plot , exponential replication will display a linear relationship between the cumulative distribution of mutants and the number of mutants per class , with a slope close to −1 . In linear replication , however , the plot will not be linear and the slope for the cumulative distribution will be steeper because most mutants arise in clones of size 1 . In agreement with this reasoning , the data for T2 , which replicates exponentially [42] , exhibits a linear relationship with slope −1 . 20±0 . 03 , based on the sum of the r and the w mutants and excluding all classes containing ≥16 mutants ( i . e . , classes starting to approach the T2 burst size ) , while the plots for φ6 , RTIN , and RTSUB display nonlinear relationships . In a recent report [9] , the dynamics of ( + ) -strand and ( − ) -strand accumulation during cell infection were quantitatively analyzed for the ( + ) -strand RNA turnip mosaic virus using strand-specific quantitative real-time PCR . The results indicated that the virus replicates its genome in a mostly linear mode , in agreement with other quantitative results from in silico modeling of the optimal riboviral replication strategy in response to the error rate and the availability of resources , among other parameters [45] , [54] . However , the continuous accumulation of turnip mosaic virus ( − ) strands throughout infection suggests that a purely linear mode of replication may have been unlikely; indeed , the occurrence of a trace of exponential replication was reported . While we cannot exclude a trace of exponential replication in the case of Qß , our results suggest that the RTSUB revertant distribution may depart from a Poisson distribution mostly due to asymmetries in the reversion rates at the first and the second rounds of replication . Overall , the empirical data gathered to date on the riboviral mode of replication indicate that , regardless of the single- or double-stranded genome structure of the virus , the strategy of preference is mainly linear . The advantages that this mode of replication may confer to riboviruses over an exponential mode have been evaluated previously ( e . g . , [8] ) . Our results provide several independent estimates of the spontaneous Qß mutation rate per genome replication ( μg ) . The first is based mainly on the small set of three nonsense mutations detected among 30 RT mutants . Because many base substitutions do not produce a detectable phenotype , the estimation of the μSUB fraction of μg = ( μSUB+μIN ) G from the frequency of nonsense mutations is a preferred method because nonsense mutations are highly detectable and their target size is easy to determine from the codon composition of the mutation target . However , this method has two drawbacks: nonsense mutations are typically a small fraction of all substitutions , so that sufficient mutants must be harvested and sequenced for a reliable estimate [46]; and no substitutions to C ( in the coding strand ) can generate a stop codon , so that the average rate per base from the nonsense-generating pathways must be assumed to apply to all pathways . Using the nonsense-mutation method and adding the small component due to indel mutagenesis , the Qß genomic rate was estimated to be μg = 0 . 039 per replication or about 0 . 08 per infection cycle . For this nonsense-based estimate of μg , RT mutants were collected after one-step growth of wt Qß , so that c = 1 in the calculations . While some prior RT mutations may have arisen during the growth of the wt Qß stocks in non-complementing NR16205 lawns , lethal indels and nonsense mutations would have been subjected to strong negative selection . In some riboviruses , mutants bearing lethal mutations can grow in the presence of complementation in trans provided by a plasmid ( e . g . , this study ) or by a gene inserted into a host chromosome ( e . g . , [10] ) , and thus it may be assumed that complementation can also be provided by a co-infecting wild-type phage , which means that even de novo Qß mutants carrying an RT− mutation may have expanded during the growth of the original wt Qß stocks , rendering 1<c≤3 . In the case where only one RT− mutant and one wild-type co-infect the same host cell and up to 50% of the resulting progeny are RT− mutants , the consequent selection coefficient ( s ) of the RT− mutant will be 0 . 50 per infection cycle . Applying the method described in Burch et al . [6] to estimate the effect of selection within the plaque and considering μg = 0 . 039 , the probability of loss of an RT− mutant with s≥0 . 50 arising in the first infection cycle of wt Qß on a host lawn would be ≥42% at the end of the growth phase ( Figure S1 ) . However , previous reports [22] , [55] indicate that co-infection by distinct Qß mutants and consequent complementation occur at low to undetectable frequencies . Even if any RT− mutation arose during the last cycle of growth in NR16205 lawns , the small fraction of each wt Qß isolate used to establish the one-step lysates further reduced the frequency of preexisting RT− mutations in our starting wt Qß populations . The second method for estimating μg is based on the single-burst reversion tests . Here , the mutation rate is based on the null-class method [47] . For RTIN , μtarget = 1 . 37×10−6 and the number of mutating bases in the four-base duplication may be taken as 4 ( although it may be argued to be 1 ) ; then μINg = ( 4217/4 ) ( 1 . 37×10−6 ) = 0 . 0014 . For RTSUB , μtarget = 2 . 46×10−5 and the potential number of mutating bases is 3; then μSUBg = ( 4217/3 ) ( 2 . 46×10−5 ) = 0 . 035 . The sum of the indel and the substitution rates is μg = 0 . 036 ( 0 . 041 if the indel reversion target size is taken as 1 ) , a value ( perhaps deceptively ) close to that of μg = 0 . 039 calculated from the spectrum . The third method for estimating μg applies only in cases where the mutations are not required to produce a mutant phenotype , which can arise when a target is sequenced without regard to phenotype ( provided the mutation is not a dominant lethal ) or when , as in the present case , hitchhiker mutations arise secondarily to and in combination with a driver mutation , the target then consisting of the entire sequence of the mutation reporter . Hitchhikers could arise during any of the roughly 3 infection cycles that generate a Qß plaque but , in order to be detected , most would have to arise in the first cycle with μg = 0 . 287 . This value may be an underestimate because the low ratio ( 6/7 ) of missense mutations to synonymous mutations among the secondary RT mutations suggests significant selection against missense mutations during plaque growth even in RTH lawns; if the hitchhikers were a random set , then missense mutations would comprise about ¾ , or 9 . 75 , of 13 substitutions . In fact , RTIN has an average burst size in RTH cells that is 2 . 9-fold smaller than that of wt Qß , a difference that implies a selection coefficient of s≈0 . 65 per infection cycle . An RT mutant with s≥0 . 65 arising with μg = 0 . 287 in the first infection cycle on a RTH lawn would have a ≥40% probability of being lost by the end of the growth phase ( Figure S1 ) . Thus , μg = 0 . 287 estimated from hitchhikers might be significantly underestimated . The presence of more mutants with multiple mutations than expected from a random distribution is remarkably widespread among DNA and RNA genomes and is probably more often due to transient hypermutation caused by some temporary perturbation of replication-fidelity factors than due to mutator mutations [56] , [57] . A notable example is the considerably higher frequency of mutations than expected among mutants already carrying a driver mutation produced by the replicase of the DNA phage RB69 [56] . However , because the Qß replicase gene occupies 42% of the genome and the estimated μg is high , we considered that some mutants might have arisen in a mutator background and then gone on to produce hitchhikers at an increased frequency . Therefore , we examined whether the gene encoding the ß subunit of the Qß replicase harbored mutations in the 28 RT mutants carrying detectable primary mutations and their four parental wild-types . We observed three T→C substitutions ( at ß-subunit position 75 of mutant RT32 , position 550 of RT42 , and position 1668 of RT20 ) but all were synonyms , so that replicase mutators were apparently not impacting our set of RT mutations . Instead , the excess of secondary mutations among our RT mutants may have arisen by the action of an abnormal replicase ß subunit produced by an error of translation or protein conformation . Among the three values , our best estimate of μg was obtained by the nonsense-mutation method . While the rate obtained from the reversion tests was similar , its accuracy depends on the extent to which the two mutants fairly sample the whole genome , and the similarity may have been fortuitous . Both selection and transient hypermutation may have played an important role in the production of the RT mutations considered in the third method . Unfortunately , even our favored μg estimate is based on small samples of mutations ( 3 nonsense and 2 indels ) , which enlarges the margin of potential sampling error . When our first and second μg estimates are combined with the fraction ( 0 . 4 ) of random mutations that are lethal for the ( − ) -strand-RNA vesicular stomatitis virus [58] , ( 0 . 075 mutations per infection cycle ×591 nt per target ×7517 targets tested ×0 . 4 of mutations detectable ) /4217 nt per genome = 32 RT mutants , in close agreement with the 30 observed and providing modest further support for μg≈0 . 04 . While the mutation rates per genome replication estimated here and reported for TMV [10] and phage φ6 [6] are all in the neighborhood of 0 . 04 , rates for mammalian riboviruses center around 0 . 7 and display a wide range [7] . However , the latter rates were based on tiny targets often consisting of a single base or pathway and may have been reported because they were large and thus more easily measured; alternatively , as has been frequently suggested , immune surveillance in mammals may drive higher mutation rates . It is interesting that while the mutation frequency can be increased over the background with nitrous acid by up to 80-fold in tobacco mosaic virus with retention of some viability [59] , it can be increased only about 2 . 5-fold in poliovirus and vesicular stomatitis virus before extinction begins [60] , suggesting that mammalian riboviruses do indeed sustain mutation rates substantially higher than those of phage and plant riboviruses . Finally , although co-infection and complementation do not seem to occur at a detectable frequency with Qß , it may occur with other riboviruses , perhaps somewhat elevating mutant frequencies and thus causing mutation rates to be overestimated .
Plasmids and bacterial strains are listed in Table 1 . All three pQß plasmids express the indicated Qß components constitutively and have been described [35] , [36] , [61] . The RTIN mutant carries a tandem duplication of 2158-UUAA-2161 that corresponds to 416–419 in the target sequence . Cell transformations with the plasmids were performed using CaCl2 [62] . Unless otherwise indicated , RTH cells were grown in Luria-Bertani medium ( LB ) supplemented with 2 mM CaCl2 and 100 µg/ml trimethoprim ( TMP ) , while NR16205 cells were grown in LB containing 15 µg/ml tetracycline . Cells and phages were plated using LB bottom agar with 2 . 0% Bacto agar . The top agar was always made up in distilled water . For counting plaques or scoring mutants , the top agar contained 0 . 4% Sigma-Aldrich Noble agar; for other uses , it contained 0 . 8% Bacto agar . All growth was at 37°C . NR16205 cells were transformed with pQßm100 ( which expresses wt Qß ) and plated on NR16205 lawns to yield wt Qß plaques , which were independently harvested into tubes containing 1 ml D broth ( 0 . 2% Bacto tryptone , 0 . 5% NaCl ) and 25 µl of chloroform . For one-step growth curves in RTH cells , 10 µl of phage suspension from a wt Qß isolate was mixed with 1 ml of cells at OD600≈0 . 5 ( 108 cells/ml ) at a multiplicity of infection ( MOI ) ≈0 . 01 for 20 min at room temperature , centrifuged to remove non-adsorbed phages , resuspended , and serially diluted in LB+TMP . Samples diluted 103- and 105-fold were held for 3 h at 37°C with gentle shaking , and 100-µl aliquots were removed from each dilution every 10 min and plated with RTH cells . Plates were incubated overnight and the follow-on titers were used to estimate Qß densities over time . Three one-step growth experiments were conducted in parallel for each wt Qß isolate used to generate one-step lysates . Visual inspection of the resulting curves sufficed to determine the time ( ≈75 min ) for Qß to complete one infection cycle in RTH cells . These one-step curves were also used to estimate the burst size of wt Qß in RTH cells according to the protocol detailed for RTIN and RTSUB ( see Table S1 ) . The distribution of RT+ revertants among RT− bursts was monitored as in a previous study [8] using two different RT− mutants ( RTIN and RTSUB as described in Table 1 ) . Preliminary measurements provided their burst sizes ( Figure S2 , Table S1 ) and revertant frequencies , which are needed to conduct the burst experiments . Ten and five independent experiments were carried out with RTSUB and RTIN , respectively , and ≈500 RT+ revertants were scored per mutant . In each experiment , ≈106 phages were added to 1 ml of RTH cells at OD600≈0 . 5 . After 20 min of adsorption at room temperature , the mixture was centrifuged for 1 min at 8 , 000 g and the pellet was resuspended in 1 ml LB broth . From the supernatant , 100 µl were collected to estimate the amount of non-adsorbed phages . The resuspended pellet was further diluted and 50 aliquots of 100 µl each were distributed into individual tubes , where infection was allowed to continue for ≈75 min at 37°C and then stopped with 15 µl of dichloromethane . Lysates were aerated for 30 min at 37°C to allow the dichloromethane to evaporate and their entire volumes were then independently plated on NR16205 lawns . The observed distributions of RT+ revertants were compared to the expected Poisson distributions using G-tests for goodness-of-fit . To limit the number of infection cycles to one before seeking spontaneous mutants , RT mutants were scored among the progeny of one-step growth of wt Qß in RTH cells . RTH cells were infected with wt Qß ( MOI≈0 . 01 ) as above and one-step lysates were recovered by adding chloroform after 75 min of growth . Samples from the lysates were plated on RTH lawns at ≈70 plaques per plate and well-isolated plaques were independently sampled into 96-well plates containing 0 . 6 ml D-broth per well ( reserving six un-inoculated wells as cross-contamination controls ) . For each of four independent lysates , three different rounds of 630 isolations each were performed . In each round , a control plate containing 8 wt and 82 RTIN isolates was also established to confirm the ability of RTH cells to complement RT− mutants and the inability of any RTH cells remaining in the isolates to grow in LB supplemented with tetracycline . Isolates were spotted in parallel on lawns of NR16205 and RTH cells using a 6×8-array replica plater . After a few losses , a total of 7517 plaques were tested . Isolates that grew poorly in NR16205 cells were re-tested in both bacterial strains and the RT-coding genes of two independent sub-isolates per putative RT mutant were sequenced . After this first round of sequencing , two additional sub-isolates as well as the original isolate were sequenced for each verified RT mutant . The original wt Qß isolate used to develop each lysate and two sub-isolates of it were also sequenced . Plate lysates were prepared from RTH cells ( 0 . 25 ml at OD600≈0 . 5 ) mixed with phages at MOI≈0 . 1 in Noble top agar . After overnight incubation , the plates were covered with 7 ml of SM buffer with gelatin [62] and were gently rocked for 30 min . The SM buffer was recovered and 100 µl of chloroform were added to each sample . Cell debris was removed by centrifugation at 12 , 000 g for 10 min . The supernatant was supplemented with polyethylene glycol ( PEG 8000 ) to 10% w/v and NaCl to 1 M , incubated for 1 h on ice , and centrifuged at 3 , 000 g for 15 min at 4°C [63] . The pellets were resuspended in 2 ml of 10 mM MgSO4 , 10 mM Tris-HCl , pH 8 , and the resulting concentrated phages were used as sources for RNA purification . Phage RNA was isolated using the QIAamp Viral RNA Mini Kit . From the extracted RNAs , 10 µg were then treated with DNase I ( New England BioLabs ) to degrade residual host DNA . The DNase-treated product was purified using the RNAeasy Mini Kit . From the purified RNA , 1 µg was subjected to reverse transcription with the Omniscript RT Kit and about 25 ng of the RT product was amplified with PfuTurbo DNA polymerase ( Stratagene ) . PCR products were confirmed by agarose gel electrophoresis , purified with the QIAquick PCR Purification Kit , and sequenced using BigDye Terminator v3 . 1 ( Applied Biosystems ) . All kits were purchased from Qiagen and were used according to the manufacturer's recommendations . Sub-isolates showing secondary mutations were subjected to a second round of RT , amplification and sequencing . The primers utilized in the RT , PCR , and sequencing reactions and the PCR cycling parameters are listed in Table S2 . The RT and genome base compositions were compared using the G-test of independence . This test was also applied to compare the observed distributions of RT+ revertants among the single-burst reversion tests conducted with each of two different RT− mutants , RTSUB and RTIN . The G-test for goodness of fit was used to compare the observed and expected Poisson distributions of RT+ revertants among RT− single-bursts , and the replicated G-test for goodness of fit was applied to compare the G+C content of the local sequence environment ( six to seven bases upstream ) of the base substitutions observed in RT with the expected content according to the base composition of the whole gene . When applying this last test , each upstream position ( from +1 to +6 or +7 ) was considered as an independent replicate . All tests were performed as per Sokal and Rohlf [64] . | Viral disease is a subject of major concern in public health . Diseases produced by riboviruses ( RNA viruses sensu stricto ) represent a special urgency , because these viruses display an exceptional capability to generate resistance mutations against antiviral drugs . Unfortunately , little is known about the rate and nature of spontaneous mutation in riboviruses . Thus , characterization of their mutation process may be helpful in the development of improved ways to counteract riboviral diseases . In this study , we investigated the mutation process in vivo of a model ribovirus , the bacteriophage Qß , focusing on three key aspects: i ) the kinds and relative frequencies of mutations , ii ) the mode of genome replication , and iii ) the rate of spontaneous mutation . Our results , combined with other information about riboviral mutagenesis , depict a ribovirus mutation spectrum largely dominated by transitions , a predominantly linear mode of genome replication , and a mutation rate per genome replication on the order of 0 . 04 for bacteriophages and plant viruses but perhaps an order of magnitude higher for mammalian riboviruses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"model",
"organisms",
"genetics",
"biology",
"microbiology",
"genetics",
"and",
"genomics"
]
| 2012 | The Three Faces of Riboviral Spontaneous Mutation: Spectrum, Mode of Genome Replication, and Mutation Rate |
β-defensin peptides are a family of antimicrobial peptides present at mucosal surfaces , with the main site of expression under normal conditions in the male reproductive tract . Although they kill microbes in vitro and interact with immune cells , the precise role of these genes in vivo remains uncertain . We show here that homozygous deletion of a cluster of nine β-defensin genes ( DefbΔ9 ) in the mouse results in male sterility . The sperm derived from the mutants have reduced motility and increased fragility . Epididymal sperm isolated from the cauda should require capacitation to induce the acrosome reaction but sperm from the mutants demonstrate precocious capacitation and increased spontaneous acrosome reaction compared to wild-types but have reduced ability to bind the zona pellucida of oocytes . Ultrastructural examination reveals a defect in microtubule structure of the axoneme with increased disintegration in mutant derived sperm present in the epididymis cauda region , but not in caput region or testes . Consistent with premature acrosome reaction , sperm from mutant animals have significantly increased intracellular calcium content . Thus we demonstrate in vivo that β-defensins are essential for successful sperm maturation , and their disruption leads to alteration in intracellular calcium , inappropriate spontaneous acrosome reaction and profound male infertility .
β-defensins are cationic peptides with a canonical six cysteines in their mature secreted peptide that were first isolated as antimicrobials and their presumed function is host defence . The β-defensin gene family consists of 40 family members at 5 gene loci in human and more than 50 genes over 4 loci in the mouse [1] , [2] . The main cluster is on chromosome 8 in both human and mouse with 10 and 31 β-defensin genes respectively . In human , seven of the chromosome 8 genes lie at two distinct loci approximately 5 Mb apart as a highly copy number variable ( CNV ) cluster , which vary between 2 and 7 copies per genome [3] . Increased copy number above the mean number of 4 has been associated with increased risk of psoriasis [4] . It is evident that the evolutionary history of this gene family is complex with evidence for both rapid positive as well as negative selection [5] . The functional repertoire of β-defensin peptides has expanded recently to include involvement in pigmentation , immune cell attraction and immunomodulation [6] . However , the physiological function of mammalian β-defensins in vivo has not been determined . β-defensins are highly expressed under normal conditions in different regions of the epididymal epithelia ( see http://mrgd . org/index . cgi & [7]–[10] ) . They are secreted into the lumen and have been shown to be present on the plasma membrane of sperm [9] , [11] , [12] . It seems likely that they are involved in reproductive function and a few studies suggest that β-defensins influence sperm motility . The rat β-defensin Bin1b ( SPAG11or EP2 ) has been shown to induce immature and immotile sperm to become progressively motile in vitro [9] . In addition , the β-defensin DEFB126 on chromosome 20 has recently been linked to the ability of sperm to penetrate hyaluronic acid gel ( a mimic of female cervical secretions ) . Men homozygous for a frameshift mutation in DEFB126 are not infertile , but have reduced chance of successful fertilisation in the first year [13] . DEFB126 is quite different to other β-defensins , as it has an extensive C-terminal tail containing O-linked glycosylation sites that are not seen in other defensins . It is presumed this glycosylation is important for its function . Additionally in the rat , incomplete knockdown of Defb15 suggests that this peptide influences sperm motility , but not the capacitation process or acrosome reaction ( AR ) [12] . Single gene deletion of Defb1 on chromosome 8 in mice has led to animals with a subtle gross phenotype , leading to the assumption that functional redundancy may reduce the severity of the expected phenotype [14] , [15] . In order to address this issue and ascertain in vivo function , we aim to use gene targeting and lox/cre MICER ( Mutagenic Insertion and Chromosome Engineering Resource ) technology to selectively delete the β-defensin gene clusters in the mouse [16] , [17] . In this study , we describe deletion of nine genes from the main thirty one β-defensin gene cluster on chromosome 8 ( Figures 1A and S1 ) . These are Defb1 , Defb50 , Defb2 , Defb10 , Defb9 , Defb11 , Defb15 , Defb35 and Defb13 , which are the nine most telomeric genes of the cluster found adjacent to the intestinal α-defensin ( cryptdins ) genes . Defb1 , Defb15 , Defb35 and Defb13 are orthologous to the human genes DEFB1 , DEFB106 , DEFB105 , and DEFB107 respectively , but Defb2 , 10 , 9 , 11 ( closely related paralogues ) and Defb50 are in murine restricted clades [5] . All nine deleted genes and their human orthologues are most strongly expressed in the male reproductive tract [18] .
The gene targeting strategy used to delete the genomic DNA encompassing the nine β-defensin genes is shown schematically in Figure 1A and described in detail in Materials and Methods and Figure S1 . The genotypes of the offspring derived from intercrosses of heterozygous mice carrying the 175 kb DefbΔ9 nine gene deletion were obtained at the expected Mendelian frequencies of 1∶2∶1 for wild-type , heterozygous and homozygous mutant animals ( data not shown ) . PCR analyses on genomic DNA or RT-PCR on cDNA synthesised from RNA from epididymides demonstrate the presence and expression of the nine deleted defensin genes in both heterozygous and wild-type mice , but their absence in homozygous mutant animals ( Figure 1B and C ) . Importantly , genes that are expressed in the epididymis and are adjacent to , but outside the deletion ( including Bin1b and Defb33 ) , were not altered in their expression level ( Figure S2 ) . The DefbΔ9/DefbΔ9 mice homozygous for the deletion have no obvious gross phenotype compared to littermate controls after 8 generations of backcrossing to C57Bl/6 . No change in size , weight or appearance is apparent , except mice carrying the mutant allele have paler tails by virtue of the Agouti transgene included in the MICER vector ( data not shown ) . β-defensins are classically known as antimicrobial peptides , so we tested the mature peptides in vitro against a Gram-negative bacterium ( Pseudomonas aeruginosa , PAO1 ) . Table 1 illustrates the sequences of the mature synthetic peptides and Figure S3 reveals that all the peptides except from Defb15 are strong antimicrobials in either oxidised or reduced form . The active antimicrobials include Defb50 , which does not have the canonical six cysteines and is missing the second cysteine of the motif ( Table 1 ) . Defb50 has poor antimicrobial activity in its oxidised form , but this improves under reduced conditions ( Figure S3 ) . These results support recent work by Schroeder et . al . ( 2011 ) , which suggests that some β-defensins display improved activity following reduction [19] . Despite deleting the expression of these antimicrobials from the homozygous mice , there is no indication that the mutant mice have an increased inflammatory profile under normal animal housing conditions . There is no elevation in levels of TNF-α or IL-6 or type I interferon in sera from mutant versus wild-type mice ( Figure 1D ) . The breeding of homozygous DefbΔ9 males to wild-type CD1 females reveals an inability to produce offspring ( Figure 2A ) , but the homozygote mutant females have comparable fecundity to wild-type and heterozygote littermates when mated to CD1 males ( Figure 2B ) . The male phenotype is not sperm-cell autonomous , as heterozygous male mice when crossed to wild-type females produce similar numbers of wild-type and heterozygous offspring ( 56 heterozygotes and 61 wild-type ) . This demonstrates that haploid sperm cells with the mutant allele are not disadvantaged compared to sperm carrying the wild-type allele when produced in DefbΔ9 heterozygous mice . Despite the inability of mutant male mice to reproduce , the tissue histology of testis and epididymis shows no obvious structural abnormalities or differences from wild-type littermates at 5 weeks , 10 weeks or 20 weeks . Testes are not significantly altered in weight ( data not shown ) , and spermatogenesis appears normal with sperm being produced and subsequently stored in the epididymides ( Figure 2C ) . Epididymal sperm cells from the cauda of homozygous mutant animals were present in similar numbers to those from wild-type animals . However , the mutant derived sperm are more fragile compared to sperm from wild-types , resulting in significantly higher numbers of headless tails when exacerbated by dropping the sperm suspension onto a glass slide ( Figure 3A ) . In mammals , ejaculated sperm need to complete capacitation before being competent to fertilize a mature oocyte . This process occurs in the female reproductive tract . It involves several changes in membrane properties and an increase in intracellular calcium that drives motility and induction of the AR [20] . Only capacitated sperm can bind glycoproteins of the zona pellucida ( ZP ) , undergo AR and fertilize a mature oocyte . Sperm were freshly isolated into modified Tyrode's complete medium to induce sperm capacitation [21] and subjected to analysis using computer assisted sperm analyses ( CASA ) at various time points . Spinning or vigorous pipetting was avoided to minimise any ex vivo effects on sperm viability and/or motility . The sperm from the mutants have a very obvious and significant lower percentage of progressive motility both before capacitation at time 0 minutes ( T0 ) and after capacitation induction at time 90 minutes ( T90 ) ( Figure 3B ) . We determined the capacitation and AR state of the mutant and wild-type sperm to ascertain whether the maturity of the sperm was altered . Pisum sativum ( PSA ) lectin binds to the outer acrosomal membranes of the sperm head and loss of binding indicates that the sperm cells have undergone the AR [22] . We find reduction in the ability of PSA-FITC to bind the DefbΔ9/DefbΔ9 derived sperm directly after dispersal from the cauda ( without induction of capacitation ) , indicating significant increase in spontaneous AR ( 20% for DefbΔ9/DefbΔ9 derived sperm versus 8% for wild-type ) ( Figure 4A ) . At time 90 minutes , after sperm capacitation there are twice as many acrosome-reacted mutant sperm compared to wild-type derived sperm cells ( Fig . 4A ) . We confirmed the above result using a recently described measure of the ability of sperm to successfully fertilize . Tardif et . al . demonstrated that zonadhesin ( ZAN ) epitopes are only revealed by AR when the sperm are competent to undertake fertilization [23] . We observe that ZAN was already exposed ( and able to bind to an antibody against ZAN ) on 20% of the sperm from the DefbΔ9 mutant mice immediately after isolation from the cauda ( T0 ) , and this percentage does not increase over time in capacitation medium ( Figure 4B ) . In contrast , the sperm from wild-type animals show a continuum of ZAN exposure , from 3% at T0 to a maximum level of 22% after 90 minutes incubation in capacitating medium ( Figure 4B ) . Coomassie blue G250 stains the acrosome of the sperm . This technique allows direct visualisation of the acrosome or its absence following AR under light microscopy . Analysis and quantification is determine by scoring at least 150 sperm with the presence or absence of an intense blue stain on the anterior sperm head [24] . The results show that the mutant sperm have significantly increased and premature spontaneous AR with 28% of mutant sperm showing AR compared to 5% of wild-type sperm at T0 ( Fig . 4C ) . This mirrors and further supports the PSA lectin and ZAN exposure assessments of AR . No direct procedure is available to determine capacitation status , but AR induction informs on the rate of sperm capacitation . Therefore , rate of sperm capacitation can be evaluated indirectly by measuring the number of cells without an acrosome following induction of the AR , as only capacitated cells can undergo this process . We find that the rate of capacitation is very different between sperm from wild-types and homozygotes . The mutants display the optimal percentage of capacitation at T0 as estimated by measuring AR by PSA-FITC binding , following calcium ionophore induced capacitation and AR . This level does not increase after 90 mins whereas the status of sperm capacitation for wild-type derived sperm increases from 6% to 23% over this same period of time ( Figure 4D ) . One might expect that sperm that are prematurely capacitated may bind to the zona pellucida ( ZP ) of oocytes more effectively than wild-type sperm . Paradoxically the sperm from the mutants are extremely poor at binding firmly to the ZP of eggs , whereas wild-type sperm bind effectively ( Figure 4E ) . Recent studies in the mouse have shown that sperm that have undergone the AR can penetrate an egg , although this was not an efficient process [25] , [26] and mouse sperm from several KOs cannot strongly bind to the ZP and yet are still able to fertilize [27] , [28] . Ultrastructural analyses using transmission electron microscopy ( TEM ) reveals an abnormally high number of cells with disruption of the classic 9+2 microtubule arrangement in the tail axoneme of sperm from mutants compared to the sperm from wild-type littermates ( 41% for −/− vs 4% for +/+ ) ( Figure 5A and data not shown ) . To reduce potential artefacts introduced by processing of purified sperm cells , we analysed tissue samples from testes , caput and cauda , and examined the structure of the sperm tails still within these tissues . These analyses reveal that sperm present in the cauda ( but not in caput or testes ) of DefbΔ9/DefbΔ9 mutants show an increase in disruption of the microtubule structure , where the 9+2 arrangement has disintegrated ( Figure 5A ) . This phenotype is reminiscent of sperm cells that have undergone hyperactivation following capacitation , where increased tail movement can result in disintegration of the axonemal filaments in sperm that are demembranated and stimulated with calcium [29] . A very similar microtubule disruption phenotype is also seen in sperm of mice with deletion of the group III secreted phospholipase A2 ( sPLA2-III ) , and like the DefbΔ9 mutants this is only present in sperm cells isolated from the cauda [30] . Interestingly , secreted phosphlipase A2 enzymes have a cysteine rich structure and like defensins have antimicrobial activity [31] . We sought to understand why sperm from the DefbΔ9/DefbΔ9 have this precocious capacitation and increased spontaneous AR . Sperm require an increase in intracellular calcium , which causes hyperactivation and allows progression to the AR . Mice deleted for any of the components of the calcium CatSper ( cation channels of sperm ) channel are infertile due to their lack of ability to transport calcium into the cell , resulting in an inability to undergo capacitation , hyperactivation and prepare sperm for the AR [32] . Conversely , sperm exposed to a ten-fold increase in cauda epididymal calcium concentration in mice mutant in TRPV6 ( transient receptor potential vanilloid 6 ) that is a calcium ion selective channel , display increased intracellular calcium and have a markedly reduced motility and fertilization capacity [33] . In addition , sperm induced with 10 µM of the calcium ionophore A23187 will undergo the AR , but will become immotile [34] . We treated sperm from wild-type littermates ( +/+ ) with A23187 to release calcium and induce AR , and examined their ultrastructure by TEM at the T90 minutes time point ( Figure S4 ) . The A23187 treatment resulted in 53% of these wild-type sperm being acrosome reacted as judged by Coomassie G250 stain ( 157 sperm undergone AR out of 293 ) , compared to 14% ( 38 sperm undergone AR out of 271 ) of the untreated wild-type controls . The TEM revealed that a phenotype of microtubule disruption was evident in 52% ( 101/195 tails in focus ) of the A23187 treated sperm compared to 3% ( 3/105 tails in focus ) in the controls ( Figure S4 ) . This strongly suggests that excessive intracellular calcium may induce microtubule defects similar to those we observe in the DefbΔ9 mutant sperm . We determined the intracellular calcium concentration of freshly isolated sperm from both DefbΔ9/DefbΔ9 and wild-types to see if this could explain the increased spontaneous AR and microtubule defect present in the mutants . The sperm from mutant animals show significantly increased calcium concentration compared to wild-type littermates ( Figure 5B ) . The altered calcium concentration does not reflect increased numbers of non-viable sperm , as sperm killed using heat , demonstrate only a low background level of intracellular calcium comparable to the negative vehicle-treated control ( data not shown ) . Thus , in the absence of the deleted β-defensins , there is significant increase in intracellular calcium ( Figure 5B ) and the likely consequence of this is an increase in spontaneous AR and microtubule disruption . Defensins have diverse receptor-binding activity [6] , and pertinently the defensin-like molecule MsDef1 from Alfalfa seed has been shown to have the ability to block mammalian L-type calcium channel activity [35] . It is therefore possible that the rise in calcium that we observe is due to the lack of β-defensin ( s ) from the membrane , allowing transport of the ion through the CatSper ( or other ) calcium channel . In wild-type cells this does not happen until the membrane remodelling occurs during capacitation . Recent work on the secreted seminal vesicle protein SPINK3 supports this idea [36] . SPINK3 has calcium transport inhibitory activity and when added to capacitated mouse sperm , the number of acrosome reacted sperm is significantly lower compared to sperm not exposed to this peptide . The implication of this is that sperm in the male reproductive tract is inhibited from undergoing the AR until near the egg [36] . β-defensins may act as an additional protection against inappropriate activation of sperm in the epididymis , a site where sperm are mature but not placed for fertilization . Taken together , the results from the deletion mice , demonstrate for the first time that β-defensins are important for suppression/regulation of spontaneous AR and are essential for fertility . The sequence of the mouse genome ( GRC m39 ) reveals that the region we have deleted specifically contains only these nine β-defensin genes and no other annotated feature expressed in the male reproductive tract are found . However , we do not know which of the deleted gene ( s ) in the cluster are responsible for the unique fertility phenotype . It is suggested that epididymal maturation of sperm cells occurs most likely in the caput or corpus region of the epididymis rather than the cauda [9] . Of the 9 genes , only Defb15 , Defb35 and Defb13 are strongly expressed in these regions with the others being expressed predominantly in the cauda [10] . We have evidence from mass spectrometry analyses that Defb2 , Defb11 and Defb15 are present on isolated wild-type cauda sperm ( data not shown ) , but have not detected the other peptides . Rat Defb15 binds the acrosomal region of caput sperm , and incomplete knockdown results in rats with reduced sperm motility , but no defect in capacitation or AR [12] . Interestingly , Defb15 has an extended carboxyl tail containing an extra cysteine and six potential serine or threonine residues that would support O-linked glycosylation , which is considered a key feature of the function of DEFB126 in human sperm [13] . However , the motility defect described in the sperm isolated from DEFB126 homozygous men is quite different to the phenotype we describe here . Unlike Tollner's study , where the sperm from homozygous men only display abnormal motility when tested in the cervical mucus mimic using viscous hyaluronic acid , our DefbΔ9/DefbΔ9 derived sperm have an obvious motility defect even in isolation medium . It may be that due to the deletion of several genes we are observing a compound or additive phenotype , indeed deletion of a single gene might not demonstrate a strong enough phenotype to be easily recognised . The three human orthologues to Defb15 , Defb35 and Defb13 ( DEFB106 , DEFB105 and DEFB107 respectively ) are on the chromosome 8 8p23 . 1 highly CNV block [3] . In primates , protection against loss of fertility from mutation might be a selective advantage that allows the increase in copy number of these genes to be maintained [37] . Pertinently , human sperm samples with a high spontaneous AR may have significantly lower fertilisation rate by in vitro fertilization ( IVF ) compared to samples used with a normal and low AR [38] . For the first time , we show here that β-defensin genes have a profound effect on sperm function in vivo , and this is manifested in the DefbΔ9/DefbΔ9 mice by increased intracellular calcium , precocious capacitation and increased spontaneous AR , which results in microtubule destabilisation , lack of motility and profound infertility . This provides evidence for the β-defensin ( s ) in this cluster being essential for control of intracellular calcium and regulation of AR . This improved understanding of the function of these antimicrobial peptides leads the way not only towards increased understanding of male infertility , but also the development of novel and highly effective contraceptives with additional antimicrobial action for local use in the female reproductive tract .
Animal studies were performed under UK Home Office license and permission and local ethical approval . The DefbΔ9 mice used in the studies were derived from C57BL/6N and 129 strain background , subsequently backcrossed to C57BL/6N for at least 4 generations . We chose to use the lox/cre double targeting strategy described originally by Adams et . al . [16] , and used successfully in several reported studies to introduce precise deletions of the genome [17] . The defensin cluster on chromosome 8:A1–A2 in the mouse consists of 31 β-defensin or α-defensin-like genes from 8∶18 , 974 , 940 to 8∶20 , 922 , 071 . Within the cluster is an expansion of genes from 8∶21 , 025 , 545–8∶21 , 735 , 471 that are derived from the β-defensins and are termed α-defensins ( cryptidins ) due to their different cysteine spacing and connectivity [39] . There are nine β-defensin genes telomeric to the cryptidins and these are the genes we deleted . A MICER clone carrying exons 1 and 2 of the HPRT gene , a neo selection cassette and the Tyrosinase gene and 7 Kb of homology to the genomic region downstream of Defb13 was constructed in house and was linearized with SalI and electroporated into 129/Ola E14 ( IV ) cells ( kind gift of Austin Smith ) . Targeted clones were isolated with a long range PCR from vector DNA to genomic DNA outwith the vector and hybridised to an internal oligonucleotide to validate the PCR fragment ( Figure S1A ) . Clones were isolated at a low frequency of 1 in 203 . This clone was then subjected to a second round of targeting to the region upstream of Defb1 using the MICER clone MHPP423o12 , which has 9 Kb of homology to the mouse genome and carries the HPRT exons 3–8 and puromycin selection gene . Correctly targeted clones were isolated at a high frequency of 1 in 4 ( Figure S1B ) and correctly targeted clones were isolated for cre recombinase treatment and selection in HAT . Only clones that undergo the lox site-mediated recombination in the presence of cre create a functional HPRT gene that will allow the growth of the HPRT mutant E14 cells in HAT selection . Some clones produced HAT resistant clones at a frequency that was at least 10 fold lower than other clones . We presumed this was due to intra versus inter-chromosomal recombination as described previously indicating that the targeting events were on the same chromosome [16] . We isolated HAT resistant clones from targeted cell lines that were most efficient at producing colonies after cre exposure and selection . As expected , these HAT resistant clones were now puromycin and G418 sensitive , as the plasmid sequences containing these selection cassettes were lost during the recombination ( Figure 1A ) and PCR of the HPRT gene was successful and showed sequence consistent with the expected lox-mediated recombination event ( Figure S1C ) . PCR analysis of genomic DNA isolated from tail tips from DefbΔ9 mice for the 9 defensin cluster deletion were carried out using the sequence specific mouse primers as in Table S1 . The primers were used at a final concentration of 0 . 2 µM each in the PCR reaction , which were carried out under standard conditions using Platinum Taq polymerase ( Invitrogen ) . PCR products were visualised on ethidium bromide stained 2% agarose gels . Total ribonucleic acid ( RNA ) was extracted from cells using the method of Chomczynski and Sacchi [40] . The epididymides were removed from mutant and C57Bl/6 mice , homogenised in 1 ml of RNAzol ( Biogenesis , Dorset , U . K . ) in a 2 ml RNAse free tube ( Sarstedt , Leicester , U . K . ) , into which 100 µl of chloroform was added , vortexed and left on ice for 5 mins . Following centrifugation at 10 , 000 rpm for 15 mins , the upper aqueous layer was removed into a fresh tube and an equal volume of ice-cold isopropanol added . The solution was mixed and left at −20°C for 30 mins . The RNA was then precipitated by centrifugation at 10 , 000 g for 20 mins . The resulting RNA pellet was washed twice with 75% ice cold ethanol ( 2×5 mins 10 , 000 rpm spins ) and resuspended in 20–100 µl of RNAse free water . The concentration and purity of the RNA was determined by spectrophotometry ( GeneQuant II , Pharmacia Biotechnology , St Albans , U . K . ) . Complementary DNA ( cDNA ) was made by the process of reverse transcription using a cDNA synthesis kit ( Roche Applied Science ) . Briefly , 1 µg of RNA in a volume of 8 . 2 µl was reverse transcribed by mixing with the following components , 2 µl oligo dT primer ( 0 . 8 µg/µl ) , 2 µl reaction buffer ( ×10 ) , 2 µl dNTP mix ( 40 mM ) , 4 µl 25 mM MgCl2 , 1 µl RNase inhibitor ( 50 U/µl ) and 0 . 8 µl reverse transcriptase ( 200 u/µl ) . The reaction was carried out at 25°C for 10 mins and then at 42°C for 60 mins . The tube was then placed at 95°C for 5 mins , after which time the cDNA was used for PCR . cDNAs were amplified using sequence specific mouse primers . Sequences of the primer pairs are shown below . The primers were used at a final concentration of 0 . 2 µM each in the PCR reaction , which were carried out under standard conditions using Platinum Taq ( Invitrogen ) . The thermal cycling protocol for all primers comprised an initial denaturation step at 94°C for 2 minutes followed by 35 cycles of 94°C for 1 minute , 55°C ( Vary according to primer set ) for 1 and 72°C for 1 . 5 minute . The final cycle consisted of a re-annealing at 72°C for 10 minutes . PCR products were visualised on ethidium bromide stained 2% agarose gels . Quantitative PCR was carried out using the Lightcycler 480 Real-time PCR System ( Roche ) . Primers were designed using the Roche Universal ProbeLibrary Assay Design Center ( www . roche-applied-science . com/sis/rtpcr/upl/index . jsp ? id=UP030000 ) . The reference gene used was the Universal ProbeLibrary Mouse GAPDH Gene Assay to allow for quantification of gene expression levels using dual-color real-time PCR . cDNAs were amplified using sequence specific mouse primers . The primers were used at a final concentration of 0 . 2 µM each in the PCR reaction , which were carried out under standard conditions using LightCycler 480 Probes Master ( Roche Applied Science ) . The relative quantification of each target gene was determined using the expression levels of the reference gene . All samples were analysed in triplicate . The primers and annealing temperature for PCR amplification of cDNAs and primer sets used for quantitative RT-PCR are given in Supplementary material S4 . Caudal epididymides sperm were dispersed in modified Tyrode's medium [21] after mincing the cauda and incubating at 37°C ( 5% CO2 ) for approximately 15 minutes . Following cell dispersion and removal of tissue , sperm concentration was assessed by using a cell counter chamber . Fragility of the caudal epididymis sperm of mutant and wild-type mice was determined by dropping approximately 30 µl sperm suspension onto a microscope glass slide from a determined height using a ruler . A coverslip was placed on top and subsequently sealed with nail varnish . The number of intact sperm and detached heads were quantified . A total of 200 sperm were analysed for each slide , which represented one animal . An average of 3 pairs was analysed . The calcium levels were measured using Fluo-3 AM ester ( Molecular Probes F14218; Invitrogen ) calcium fluorescent indicator ( methodology adapted from [36] ) . One of the advantages of using Fluo-3 is that it exhibit large fluorescent intensity increases on binding calcium ( typically >100-old ) . Unlike the ultra-violet light-excited indicators fura-2 and indol-1 , there is no accompanying spectral shift . Caudal epididymis sperm were isolated as described above in calcium-free modified Tyrode's medium from sexually mature males ranging from 11–19 weeks old ( mean age: 14 . 9 weeks ) . The respective wild-type and knockout male mice chosen for each set of experiment are all matched for age , diet , living conditions and are housed separately from female mice . After isolation , sperm aliquots at 20 million/ml concentration were transferred to pre-warmed 1 . 5 ml eppendorf tubes and incubated in the presence of 5 uM Fluo-3 AM ( 1∶200 dilution from 1 mM stock ) or DMSO as control at 37°C for 30 mins . Samples were washed by centrifugation ( 3×0 . 7 g for 5 mins ) and loaded onto Black Greiner 96-well bottom plate ( Sigma-Aldrich ) in duplicates at 100 µl/well . Heat-killed sperm with Fluo3 AM ester and sperm with DMSO were used as controls . The plates were read by BMG Labtech FluoSTAR Omega fluorescent reader where the fluorescent intensity was measured using appropriate wavelength settings ( excitation at 485 nm , emission at 520 nm ) . Fluorescent intensity of total intracellular calcium of DefbΔ9 +/+ and DefbΔ9 −/− sperm after subtracting the background levels of the DMSO controls were shown ( mean ± SD; n = 3 pairs ) . Male DefbΔ9 mice were set to breed with CD1 females mice , while female DefbΔ9 mice were set up to breed with CD1males over a period of approximately 3 months . For each genotype 3–6 individual breeding pairs were set up , and the average pups per litter was calculated for both male and female mice . Caudae epididymides from DefbΔ9 +/+ and −/− mice were isolated and placed in pre-warmed modified Tyrode's medium supplemented with 4 mg/ml BSA at 37°C ( 5% CO2 ) as described previously [21] . Aliquots of sperm were taken at 2 time points ( T0 and T90 mins ) , diluted fourfold with the same media and placed in pre-warmed 80 µm glass chamber for computer-assisted sperm analysis ( CEROS; Hamilton Thorne Biosciences Beverly , MA ) . For each animal , 3–4 microscope fields from each of the 2 chambers were video-recorded , capturing 200–400 sperm . Images were captured at 60 fps Hz for 30 frames and sperm parameters such as percentage of total motile , progressive motile , average path velocity ( VAP ) , straight line velocity ( VSL ) and curvilinear velocity ( VCL ) were analysed . Epididymides and decapsulated testis were fixed with 2% paraformaldehyde ( PFA ) , 2 . 5% glutaraldehyde in 0 . 1M sodium cacodylate buffer + 0 . 04% CaCl2 for 30 mins at room temperature . The tissues were cut roughly into 1 mm cubes and further fixed overnight at 4°C . Fixed cells were rinsed in 0 . 1M sodium cacodylate buffer + 0 . 4% CaCl2 , post-fixed in 1% osmium tetroxide ( OsO4 , Agar Scientific ) for one hour , and dehydrated in sequential steps of acetone ( 25% , 50% , 75% and 100% twice ) prior to impregnation in increasing concentrations ( 25% , 50% , 75% ) of resin ( TAAB Lab Equipment ) in acetone followed by 100% resin for 3 times , placed in moulds and polymerised at 60°C for 24 hrs . Ultrathin sections of 70 nm were subsequently cut using a diamond knife on a Leica EM UC7 ultramicrotome . Sections were stretched with chloroform to eliminate compression and mounted on Pioloform filmed copper grids prior to staining with 1% aqueous uranyl acetate and lead citrate ( Leica ) . They were viewed on a Philips CM100 Compustage ( FEI ) Transmission Electron Microscope with images collected using an AMT CCD camera ( Deben ) . Sperm-ZP binding was assessed by a gamete co-incubation assay . Mouse oocytes and 2-cell embryos were collected from superovulated CD1 female mice into M2 media . The cumulus cells were removed from the oocytes by incubating the cumulus masses in M2 containing 1% hyaluronidase . The cumulus free oocytes were washed through several drops of M-199M ( M199 media supplemented with 4% BSA and 30 µg/ml of sodium pyruvate ) to remove any loose cumulus cells and any hyaluronidase media . They were then grouped with the 2-cell embryos in 50 µl microdrops of M-199M media , each group contains twelve oocytes and three 2-cell embryos , and put into the 37°C , 5% CO2 incubator until required . The sperm from the caudal epididymides of wild-type ( +/+ ) and DefbΔ9 ( −/− ) mice were collected into 5 mls of M-199M and capacitated for 90 minutes at 37°C in 5% CO2 . Approximately 5000 capacitated sperm ( 5 µl of 106/ml sperm suspension ) were added to each 50 µl microdrop containing the oocytes and 2-cell embryos as control and incubated for 45 minutes at 37°C in 5% CO2 . After this time the oocytes and 2-cell embryos were removed from the sperm drops into fresh M-199M media microdrops using a 120 µM diameter Pasteur pipette ( Bio Medical Instruments ) and were washed through several microdrops of media to remove the non-specific bound sperm . The oocytes and 2-cell embryos were transferred to microdrops of fixative made from 1∶1 mix of M199 media with 2% formaldehyde in PBS/PVP solution . This fixes the oocytes and 2-cell embryos with any sperm bound to them and allows the sperm bound to be counted and analysed . Sperm proteins were detected in methanol-fixed and permeabilized mouse . Sperm capacitation of caudae epididymides was determined by the ability of sperm to undergo the acrosome reaction ( AR ) in the presence of calcium ionophore A23187 ( Molecular Probes ) as previously described Calcium ionophore A23187 or DMSO alone as vehicle was added to the sperm samples ( 10–20×106 sperm/ml ) at 10 µM concentration and incubated for 15 mins at 37°C in a 5% CO2 incubator to induce the AR . Percentage of spontaneous and A23187-induced AR following this was determined using FITC-conjugated Pisum sativum lectin ( PSA-FITC ) labelling ( 0 . 1 mg/ml; Sigma ) at T0 and T90 minutes using sperm from wild-type +/+ and DefbΔ9 −/− mice as previously described [41] . Briefly , ∼30 µl sperm suspension was smeared onto a slide , air dried then fixed and permeabilized in 100% methanol for 15 mins at room temperature . After methanol fixation , 100 ul PSA-FITC lectin ( 0 . 1 mg/ml ) was added to the slide and incubated in the dark for 30 mins at room temperature . The slides were rinsed with PBS and mounded with fluorescent mounting media . One to two hundred sperm were scored and classified as “acrosome-intact” or “acrosome reacted” using epifluorescence and phase contrast microscopy at ×40 magnification . Zonadhesin ( ZAN ) is a sperm-specific protein located in the acrosome and is critically involved in sperm-ZP adhesion . Live , motile sperm expose ZAN at the surface when cells are capacitated . Therefore ZAN exposure is an alternative indirect way of measuring sperm capacitation . ZAN was detected by incubating cells in suspension with anti-zonadhesin D3p18 domain ( 1 µg/ml ) affinity-purified antibodies at 37°C for 30 mins as previously described [42] . After spinning cells at 500× g for 5 mins , bound antibodies were visualised with a goat anti-rabbit IgG conjugated to Alexa Fluor 594 ( 3 µg/ml; Molecular Probes ) on cells in suspension or on cells smeared and dried on slides as for immunofluorescence on fixed cells . Cauda sperm were isolated in modified Tyrode's medium , fixed in 4% formalin for 15 mins . Sperm were spun down ( 1000× g 5 mins ) then washed once in 0 . 1 M ammonium acetate ( pH 9 . 0 ) and resuspended in a final volume of approximately 50 µl . An aliquot of 20 µl was smeared on glass slide , allowed to air dry then stained for 2 mins with a solution of 0 . 22% Coomassie blue G250 ( wt/v ) in 50% methanol ( v/v ) and 10% acetic acid ( v/v ) . Slides were washed 3 times in distilled water , air-dried and mounted in glycerol . Acrosome integrity of at least 150 sperm per animal was assessed by the presence or absence of an intense blue stain on the anterior sperm head ( mean ± SD; n = 3 pairs ) . Results are expressed as mean± standard deviation ( SD ) . Statistical differences between groups were tested by the Student t-test or non-parametric Mann-Whitney test as appropriate . A p-value of <0 . 05 was considered to be significant . | β-defensins are small molecules , considered primarily to be antimicrobials and important in the first defence response to invading organisms . They are predominantly produced at surfaces in contact with the outside environment and these include skin , airway and reproductive tract . We show here that when we delete from the mouse a subset of nine β-defensin genes , surprisingly the main consequence is that the male mice are completely infertile . When normal sperm leave the male and enter the female reproductive tract they are triggered to undergo a reaction that alters the membrane properties of the sperm and allows fertilisation . We show here that sperm isolated from the male mice , that no longer make these β-defensins , are prematurely ready to fertilise an egg . It is far too early for this to happen and as a consequence the sperm are severely reduced in their ability to move and have a major defect in the structure of their tail . We provide evidence that the reason this has happened is due to a dysregulation of calcium transport . This work is important for understanding defensin gene function in a living organism and may enable the design of novel contraceptives with additional antibiotic ability . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
]
| []
| 2013 | Partial Deletion of Chromosome 8 β-defensin Cluster Confers Sperm Dysfunction and Infertility in Male Mice |
A community-based longitudinal study was performed in the Eastern Province of Zambia , in which repeated serological samplings were done to determine the incidence of human cysticercosis . Three sampling rounds were carried out at six months intervals . A total of 867 participants presented for all three samplings . All samples were tested for the presence of cysticercus antigens using a monoclonal antibody-based enzyme-linked immunosorbent assay ( sero-Ag-ELISA ) , while a randomly selected sub-sample of 161 samples from each sampling round was tested for specific antibodies using a commercial enzyme-linked immunoelectrotransfer blot ( EITB ) assay . Stool samples ( n = 226 ) were also collected during the final round of sampling for taeniosis diagnosis by coprology and coproantigen ELISA . Cysticercosis seroprevalence varied from 12 . 2% to 14 . 5% ( sero-Ag ) and from 33 . 5% to 38 . 5% ( sero-Ab ) during the study period . A taeniosis prevalence of 11 . 9% was determined . Incidence rates of 6300 ( sero-Ag , per 100000 persons-year ) and 23600 ( sero-Ab , per 100000 persons-year ) were determined . Seroreversion rates of 44% for sero-Ag and 38 . 7% for sero-Ab were recorded over the whole period . In conclusion , this study has shown the dynamic nature of T . solium infections; many of the people at risk become ( re ) infected due to the high environmental contamination , with a high number turning seronegative within a year after infection . An important number of infections probably never fully establish , leading to transient antibody responses and short-term antigen presence .
Human ( neuro ) cysticercosis , an infection caused by the metacestode larval stage of the pork tapeworm Taenia solium , is a serious but neglected zoonotic disease and a major public health problem in many developing countries of Latin America , Asia and Africa [1] , [2] . Humans are the definitive hosts harbouring the adult tapeworm ( leading to taeniosis ) . Carriers of the tapeworm shed eggs into the environment that are infective not only to the pig intermediate host ( leading to porcine cysticercosis ) but also to humans who then act as an accidental intermediate host [3] leading to human cysticercosis . When the larval stages invade the nervous system they cause neurocysticercosis ( NCC ) , which is the most important parasitic disease affecting the nervous system and accounts for about 30% of all acquired epilepsy cases in endemic areas [4] . In terms of Disability Adjusted Life Years ( DALYs ) , the global burden of epilepsy is estimated at 7 . 8 million DALYs with 6 . 5 million of these occurring in T . solium endemic regions of the world [5] . The few community based human prevalence studies carried out in Africa have indicated sero-prevalences of human cysticercosis ranging from 7–22% [e . g . 6] , [7] , . In a recent study in Zambia , a sero-prevalence of 5 . 8% has been recorded in a rural community in the eastern part of Zambia [9] . Studies that report incidence of human cysticercosis are even more scarce and absent for Sub-Saharan Africa . Two longitudinal studies in villages in Peru indicated human cysticercosis incidence rates of 25% and 8% by specific antibody analysis [10] . In a simulation model based on data obtained in a rural community in Ecuador an annual incidence rate of 14% was described [11] . Obviously , more information is needed on the transmission dynamics of this parasite . The present study aimed at determining the incidence of human cysticercosis in an endemic area .
The University of Zambia Biomedical Research Ethics Committee granted ethical clearance ( IRB0001131 ) for the study . Further approval was sought from the Ministry of Health of Zambia , from the local district health authorities and the area chief . Meetings were held with the people in the villages through their leaders ( headmen ) to explain the purpose of the study , request their permission to conduct the study and also to invite them to participate . Participation was requested of individuals of all ages after written informed consent . For individuals below the age of 16 , permission was sought from their parents or guardians by way of written informed consent . All participants found positive for taeniosis and other helminths were provided with treatment , namely niclosamide and mebendazole respectively . Those positive for cysticercosis were referred to the District hospital for follow-up and the recommended standard of care provided to them if required . The study was carried out in the Vulamkoko community in Katete district of the Eastern province of Zambia ( figure 1 ) . The Vulamkoko Rural Health Center ( RHC ) provides health care in this community with a catchment population of 23 , 613 ( clinic headcount records ) . The climate is tropical with two main seasons , the rainy season ( November to April ) and the dry season ( May to October/November ) . The mean rainfall varies from 500 to 1200 mm/year with temperatures above 20°C most of the year . The most common ethnic group in Katete is the Chewa people . They practice subsistence agriculture raising animals and growing crops . People's homes in this area are of adobe and have no sanitary facilities . Pigs have access to the nearby bushes that are used as latrines by the villagers . The community was selected for the study on the basis of known endemicity for porcine cysticercosis [12] , presence of free roaming pigs , backyard slaughter of pigs without meat inspection , continued observation of cysticerci in the meat , absence of any cysticercosis related control programs and the community's willingness to participate . All willing villages within a radius of 7 km from the RHC were selected . The willingness of the RHC to collaborate , and the availability of staff and adequate working space was also taken into account . A community-based longitudinal study was carried out between October 2009 and October 2010 , with three main sampling rounds ( R1 , R2 , R3 ) with six months intervals ( figure 2 ) . Participants who were not sampled in the first round of sampling and willing to participate were entered in the study only during the second round of sampling . Meetings were held in the selected villages and individuals of all ages of all households invited to participate in the study . The sampling unit was an individual in a household . Each willing participant , after written informed consent , was registered and had a blood sample taken by qualified health personnel every six months during a 12-month period ( a total of 3 samples ) . During the last sampling round , a stool sample was also requested from the participants . A questionnaire was administered to each participating household to obtain information on general household characteristics , pig management and sanitation ( Mwape et al . , submitted ) . About 5 ml of blood were collected , serum extracted , aliquoted and stored at -20°C until use . Submitted stool samples were divided into two aliquots , one placed in 10% formalin and the other in 70% ethanol and stored until use . All the samples were transported to Lusaka for analysis [9] . The serum samples were tested for circulating cysticercal antigens using the monoclonal antibody-based B158/B60 antigen enzyme linked immunosorbent assay ( sero-Ag-ELISA ) as described by Dorny et al . ( 2004 ) [13] . To determine the test result , the optical density of each serum sample was compared with a series of 8 reference negative human serum samples at a probability level of P<0 . 001 [13] . Due to budgetary restrictions , not all samples could be analysed for presence of specific antibodies . Therefore , from the individuals that gave samples at all the sampling rounds , a Stata® ( Stata Corp . , College Station , TX ) generated random subset sample , taking into account the age and sex distribution , was tested for presence of specific antibodies against cysticercosis using a commercial kit , Immunetics® ( Immunetics Inc . ) . The assay was performed according to the manufacturer's instructions . The stool samples , only collected during the last sampling round , were microscopically examined for Taenia ova using the formalin-ether concentration technique as described by Ritchie ( 1948 ) [14] . Additionally , the samples were analysed for the presence copro-antigens using a polyclonal antibody based antigen ELISA ( copro-Ag-ELISA ) as described by Allan et al . ( 1990 ) [15] with slight modifications [9] . All collected data were entered into an excel ( Microsoft Office Excel 2007® ) spreadsheet and analyses were conducted in Stata 10 ( Stata Corp . , College Station , TX ) . The sampled population was distributed in 10 age categories of 10 years intervals and in function of sex .
A total of 3167 serum samples ( from 1206 individuals from 32 villages ) and 226 stool samples were examined for cysticercosis and taeniosis , respectively . Entrees and exits of participants are explained in figure 2 . A total of 1129 individuals were sampled at baseline ( R1 ) , 1069 at R2 and 969 at R3 . A total of 867 ( 76 . 8% ) gave samples during all sampling rounds . Reasons for lack of follow up at R2/R3 consisted of refusal to continue participating ( 2 . 7%/5 . 3% ) , away at time of sampling ( 3 . 8%/6 . 6% ) , reported sick and could not be sampled ( 1 . 2%/1 . 1% ) , died of other causes , as assessed by the RHC ( 0 . 3%/0 . 7% ) , relocated to other areas ( 1 . 2%/2 . 2% ) and those that could not be traced ( 2 . 8%/3 . 1% ) . From the 867 individuals sampled at R1 , R2 and R3 , 358 ( 41 . 3% ) were men and 509 ( 58 . 7% ) women; the age ranged from 2 to 87 years with a median age of 18 years . The number of people living in a HH ranged from 1 to 15 with a median of 6 . From the 867 individuals that gave samples for all the sampling rounds , a random sample of 161 individuals were tested for specific antibodies against cysticercosis in each round ( the same 161 participants were tested in each round ) . Household characteristics ( recorded from 516 HH ) included; 69% of the HH kept pigs with 98% of these rearing on free-range , 46 . 6% of the HH did not have latrines . About 72 . 2% slaughter pigs in their backyards , 96 . 2% had at least one individual who consumed pork ( boiled , fried or roasted ) . Only 0 . 6% had the meat inspected . The data obtained in the questionnaire are described in more detail in another report ( Mwape et al . , submitted article ) . Table 1 shows the overall and by sex cysticercosis sero-Ag and sero-Ab prevalences per sampling . Sero-Ab prevalence figures ( 33 . 5–38 . 5% ) were significantly higher than sero-Ag prevalence figures ( 12 . 2–14 . 5% ) . No significant differences were observed in sero-Ab and sero-Ag prevalences between males and females . The sero-Ab prevalence does not change between sampling rounds , the sero-Ag prevalence was significantly higher in sampling round 2 than in round 1 . The probability of being sero-Ag positive increased with age for men for all three sampling rounds . Taeniosis prevalence was determined to be 11 . 9% by copro-Ag-ELISA . Eleven and a half percent of the participants that tested copro-Ag positive , were also sero-Ag positive . Thirteen percent of the participants that tested copro-Ag negative , tested sero-Ag positive . Taenia eggs were not detected by coprological examination in any of the stool samples . Other helminth ova detected included hookworms in 20 of the samples ( 8 . 8% ) , Schistosoma spp . in 7 ( 3 . 1% ) and Trichuris trichiuria in 2 ( 0 . 9% ) .
The present study is the first to estimate the incidence of human cysticercosis based on specific antibody as well as antigen detections; adding to the very short list of publications reporting the incidence of human cysticercosis [16] . The high taeniosis prevalence ( 11 . 9% ) in this study is strongly indicative for a high environmental contamination with T . solium eggs , and subsequent high exposure risk . The high sero-antibody results ( 33 . 8–38 . 5% sero-Ab prevalence ) as well as the fact that less than half of the sampled population ( 44 . 7% ) remained negative ( sero-Ab ) throughout the study period corroborate this finding , as presence of specific antibodies is indicative for exposure to infection [17] . About 32% ( 34/106 ) of the participants negative at the start of the study turned Ab positive at one point; an additional 6 tested participants positive at R1 , but negative at R2 , turned positive again at R3 ( table 3 ) , indicating that more than one on three people have been ( re ) exposed and reacted to infection during the study period . The sero-Ag results present a different picture . A much higher percentage ( 78% ) of people remained negative throughout the study; and only 11 . 5% of the participants negative at the start of the study turned positive at one point ( table 3 ) . As presence of antigen indicates establishment of infection rather than exposure , these results strongly indicate that about one on three people are exposed to infection , whereas the infection only establishes in about one on ten people . Findings from studies in Peru in pigs and human and in Ecuador in human [11] also suggest exposure without infection or mild infections that are aborted by the natural immunity of the individual , expressed by the presence of transient antibodies [18] . The higher levels of sero-Ab prevalence and seroconversion in comparison with sero-Ag prevalence/conversion , as well as the high seroreversion levels , identified in this study , support this finding . Another interesting outcome from this study is the rather short-term presence of antigen in 31 participants ( negative at R1 , positive at R2 , and again negative at R3 , table 3 ) . Whether this is due to an only partial establishment of infection ( immature cysticerci ) , or establishment and quick degeneration ( self cure ? ) of cysticerci is not clear . It was noted that individuals who became seronegative were those with samples that had low antigen titers ( Data not shown ) . In humans , it is described that cysticerci in the brain usually stay viable during years , while probably cysticerci in the muscles tend to degenerate more quickly [19] . However later , Garcia et al . ( 2010 ) [20] challenged this theory in the case of single cysticercal granuloma's , for which they hypothesize that instead of being caused by a late degenerative process , the granulomas are rather due to an early parasite death . In experimental infections in pigs often infections do not establish , or ( partially ) establish ( with the corresponding increase in antigen levels ) and abort shortly afterwards . Deckers et al . , ( 2008 ) [21] indeed demonstrated circulating cysticercus antigens as early as three weeks after experimental infection , which is before full maturation of the cysticerci . Many factors , among which the size of the ( re ) infection , the immune status of the host , age and sex play a determining role in the ( non ) establishment of infection [22] . Results from this study suggest that presence of antigen doesn't necessarily always signify presence of a viable , well established infection , however could be indicative for short term partial establishment , and perhaps a ‘transient’ antigen presence should be considered . As such , serological results from field studies , should be looked at critically . Individuals with positive test results shouldn't be automatically considered as ‘infected with T . solium’ , as is often done in reports from field studies . Significantly higher sero-Ag reversion than seroconversion was determined up to the age of 60 years . Previous studies have indicated higher levels of active infection in elderly people , which was suggested to be due to a lowered host immune response [11] . The higher seroreversion rates than seroconversion rates observed in younger people , but not in older people in this study , could indeed be indicative of an improved clearing of the infection in younger people . The simulation models described in Praet et al . ( 2010 ) [11] suggest a continuous exposure of the population with seroreversion ( antibody ) rates depending on the number of exposures , which relates to age as well as the immunological status of the individual . Antibody seroreversion rates of 60% after first exposure and 20% after second and subsequent exposures were obtained . This is the first study to report cysticercosis incidence based on sero-Ag analysis ( 6300 per 100000 persons-year ) . The sero-Ab incidence rate ( 23600 per 100000 persons-year ) is comparable to that reported in Peru by Garcia et al . ( 2001 ) [10] and in Ecuador [11] . A higher average porcine cysticercosis sero-Ab incidence rate of 53% has been reported in Peru [18] . Since pigs are highly coprophagic , it is expected that they would be exposed more frequently and to higher levels of infection as compared to humans and hence record a higher incidence rate especially for sero-Abs . In conclusion , this study has shown the dynamic nature of T . solium infections , many of the people at risk become ( re ) infected due to the high environmental contamination , with a high number turning seronegative within a year after infection . An important number of infections probably never fully establish , leading to transient antibody responses and possibly even ‘transient’ antigen presence . | Human neurocysticercosis is an infection of the central nervous system caused by the larval stage of the pork tapeworm ( Taenia solium ) . The infection occurs mainly in developing countries and is associated with poverty , poor sanitation and free-range pig management . It is estimated to be responsible for 30% of cases of acquired epilepsy in endemic areas . The limited number of human studies on this infection in Sub-Saharan Africa determined a high occurrence of cysticercosis . This study aimed to learn more about the transmission dynamics of this parasite in a rural endemic area in Eastern Zambia . A longitudinal study was carried out in which 867 participants were blood sampled three times , with a 6-month interval . Samples were analysed for the presence of cysticercal circulating antigens and specific antibodies . Results indicate that about 1 on 3 people get exposed to infection while only 1 on 10 people actually acquire infection . The study shows the dynamic nature of T . solium infections; many of the people at risk become ( re ) infected due to the high environmental contamination , with a high number turning seronegative within a year after infection . An important number of infections probably never fully establish , leading to short-term antibody and antigen presence . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"taeniasis",
"medicine",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"zoonoses",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"cysticercosis",
"neglected",
"tropical",
"diseases",
"parasitic",
"diseases"
]
| 2013 | The Incidence of Human Cysticercosis in a Rural Community of Eastern Zambia |
The p12 protein is a cleavage product of the Gag precursor of the murine leukemia virus ( MLV ) . Specific mutations in p12 have been described that affect early stages of infection , rendering the virus replication-defective . Such mutants showed normal generation of genomic DNA but no formation of circular forms , which are markers of nuclear entry by the viral DNA . This suggested that p12 may function in early stages of infection but the precise mechanism of p12 action is not known . To address the function and follow the intracellular localization of the wt p12 protein , we generated tagged p12 proteins in the context of a replication-competent virus , which allowed for the detection of p12 at early stages of infection by immunofluorescence . p12 was found to be distributed to discrete puncta , indicative of macromolecular complexes . These complexes were localized to the cytoplasm early after infection , and thereafter accumulated adjacent to mitotic chromosomes . This chromosomal accumulation was impaired for p12 proteins with a mutation that rendered the virus integration-defective . Immunofluorescence demonstrated that intracellular p12 complexes co-localized with capsid , a known constituent of the MLV pre-integration complex ( PIC ) , and immunofluorescence combined with fluorescent in situ hybridization ( FISH ) revealed co-localization of the p12 proteins with the incoming reverse transcribed viral DNA . Interactions of p12 with the capsid and with the viral DNA were also demonstrated by co-immunoprecipitation . These results imply that p12 proteins are components of the MLV PIC . Furthermore , a large excess of wt PICs did not rescue the defect in integration of PICs derived from mutant p12 particles , demonstrating that p12 exerts its function as part of this complex . Altogether , these results imply that p12 proteins are constituent of the MLV PIC and function in directing the PIC from the cytoplasm towards integration .
Reverse transcription and integration are the hallmarks of the retroviral life cycle . These steps include reverse transcription of the genomic RNA into a linear double-stranded DNA and the subsequent integration of this DNA into the genome of the infected cell . These events are part of the ‘early’ stages of the retroviral life cycle , starting with the binding of the virus to its cellular receptor and ending once the integration step has occurred . Reverse transcription and integration are mediated by the viral enzymes; reverse transcriptase ( RT ) and integrase ( IN ) , respectively; both are cleavage products of the polyprotein encoded by the viral pol gene . Reverse transcription occurs in a cytoplasmic complex , termed reverse transcription complex ( RTC ) , which transforms to the PIC ( reviewed in [1] , [2] ) . The PIC harbors the viral DNA and travels from the cytoplasm to the nucleus , to target the chromatin of the infected cell for integration . The full composition of the RTC and PIC is not known; this is true not only for the cellular components , but also for the viral constituents of these complexes [2] , [3] . Some of the known cellular components identified in RTC/PIC of different retroviruses include: the barrier of auto-integration factor ( BAF ) [4] , [5] , high-mobility group proteins ( HMGs ) [6] , [7] , Ku [8] , lamina-associated polypeptide 2α ( LAP2α ) [9] , and lens epithelium-derived growth factor ( LEDGF/p75 ) [10] , [11] . To date , the viral protein components identified in the RTC/PIC of the simple MLV include: RT [12] , nucleocapsid ( NC ) [13] , capsid ( CA ) [12] and IN [7] , [12] , [13] , [14]; while in the complex human immunodeficiency virus type-1 ( HIV-1 ) , NC [15] , matrix ( MA ) [16] , [17] , [18] , [19] , RT [15] , [16] , [17] , [18] , [19] , IN [6] , [15] , [16] , [19] , [20] and Vpr [15] , [19] are present . The trafficking of the PIC towards the chromatin of infected cells might be substantially variable between different retroviruses . This is demonstrated in part , by the fact that the nuclear entry by the MLV PIC is strictly dependent on mitosis [21] , whereas nuclear entry by HIV-1 PIC can occur efficiently in nondividing cells [22] , [23] , [24] , [25] , [26] . Differences in PIC composition may contribute to such a variance; it has been shown that the MLV PIC contains the viral CA protein [14] , while the HIV PIC does not [18]; this may contribute to the inability of wt MLV PICs to enter the nucleus through the nuclear pores [27] , and render the MLV PICs dependent on nuclear envelope breakdown during mitosis for nuclear entry [21] . One viral protein thought to influence the trafficking of the MLV PIC , is the p12 protein . This protein is a cleavage product of the Gag precursor , which is the major structural protein in the virion . A few thousand Gag molecules assemble to form one viral particle , and these are cleaved by the viral protease during the virion maturation step . For MLV , this cleavage results in the release of the following viral proteins: MA , p12 , CA and NC . The p12 domain is known to act in the late budding process of the Gag precursor , and accordingly mutations in the p12 hamper virion release [28] , [29] , [30] , [31] . However , additional mutations in this protein have specifically affected early stages of MLV infections , revealing a critical role for p12 in these stages [3] , [28] , [31] , [32] , [33] . Analysis of a subset of these mutants revealed normal generation of the linear genomic DNA but no generation of circular viral DNA forms . Such circular forms are thought to be formed by nuclear enzymes from a portion of the linear viral DNA and although these circles are not substrates for the IN [34] , [35] , [36] , they serve as a marker for nuclear entry by the viral DNA . Their absence during the infection of the p12 mutants suggested that p12 may function in directing the PIC into the nucleus or alternatively affects unknown nuclear steps needed for integration . Importantly , for at least one of these p12 mutants , the PIC appeared competent for integration in an in vitro assay [3] . A later study provided genetic evidence that p12 may function in concert with CA in early stages of infection: using swap mutants between the different domains of the Gag of MLV and its closely related virus , the spleen necrosis virus ( SNV ) , it was demonstrated that productive infection can be achieved only when the p12 ( or p18 from SNV ) and CA domains are from the same virus [37] . Although these studies strongly indicate p12 participation in the early events of MLV infection , the precise mechanisms of p12 action is not known . Here we provide evidence that p12 is a part of the PIC and functions from within this complex in early stages of infection .
To study p12 function during the early stages of MLV life cycle , we constructed epitope-tagged p12 to enable its visualization in infected cells . Former mutagenesis studies have demonstrated that MLV tolerates mutations in the central region of p12 rather than in other parts of this protein [28] , [31] , [33] , [38] ( Fig . 1 ) . In addition , our phylogenetic analysis revealed that the central region of p12 is relatively less conserved between members of the MLV family , further suggesting that this region may tolerate changes in its sequence ( Fig . 1 ) . We then inserted into this region a triple repeat of the Myc epitope ( 3xMyc; Fig . 1 ) to enhance detection of tagged p12 . The p12 protein of this virus could easily be detected by Western blot analysis ( Fig . 2A ) , but the replication of this mutant was greatly reduced in comparison to wt ( Fig . 2B ) . However , we were able to recover a revertant virus with normal replication kinetics , after repeated passages of the 3xMyc virus on naïve NIH3T3 cells . A BsrGI-XhoI fragment of the revertant , containing the entire p12 domain and flanking Gag sequences , was PCR amplified and sequenced , revealing the existence of only one in-frame copy of the Myc epitope in p12 with no other mutations . This fragment was used to replace the wt sequence between the BsrGI-XhoI restriction sites in a molecular clone of MLV ( pNCS , Materials and Methods ) , and the resulting virus was named 1xMycR ( Fig . 1 ) . Further analysis of purified virions of the 1xMycR virus demonstrated that its p12 could be detected with anti-Myc antibodies by Western blot analysis and , as expected , the protein had a reduced molecular weight compared to p12 of the 3xMyc virus ( Fig . 2A ) . Importantly , unlike the 3xMyc virus , the 1xMycR virus replicated with wt-like kinetics in NIH3T3 cells ( Fig . 2B ) , demonstrating that the reduction in the number of repeats of the epitope tag in p12 , accounted for the improved replication of the latter virus . Next , we examined whether p12 proteins can be detected by immunofluorescence in 1xMycR-infected cells , using anti-Myc antibodies . To unambiguously distinguish signals resulting from authentic infection and background signals , we preformed the infections with human cells ( osteosarcoma; U2OS ) that cannot be normally infected due to the lack of the MLV receptor; or with a cognate , U2OS-derived , cell line ( U/R ) that stably expresses the mCAT-1 murine receptor for MLV [39] , and is susceptible to MLV infection ( Materials and Methods ) . Parental U2OS or U/R cells were infected either with 1xMycR or wt viruses , fixed and immunostained with anti-Myc antibody . Clear punctate fluorescence could be detected in the cytoplasm of 1xMycR infected U/R cells ( Fig . 3A ) . We also observed a punctate staining of p12 that was in close proximity to the chromosomes , in dividing cells that were identified by their condensed chromosomes ( Fig . 3B and see below ) . In the control U/R cells , p12 staining was not observed following infection with the wt virus ( Fig . 3C ) ; and very low p12 staining was observed in 1xMycR-infected parental U2OS cells ( Fig . 3D ) , likely representing a low level of adherence of the virus to the cells . In addition , no immunofluorescence was observed in mock-infected U/R cells or in 1xMycR-infected U/R cells that were stained with the secondary , but not with the primary anti-Myc antibody ( data not shown ) . Thus , the majority of the punctate staining represents a signal that is derived from an authentic infection , and not from background staining or non-specific adherence of viral particles or antibodies to the cell membrane . Further support to this idea came from the direct correlation that was found between the number of p12 puncta in infected cells and the amount of 1xMycR virions that were used for infection ( Fig . S1 ) , suggesting that these dots represent p12 proteins that originated from physical particles . The punctate pattern of staining as opposed to a diffuse staining , suggests that the p12 molecules are associated in a complex . Similar punctate staining was observed when cells derived from the natural host of MLV ( murine NIH3T3 cells ) were infected with the 1xMycR virus ( Fig . 3E ) . Of note , whereas clear immunofluorescence signal was detected in 1xMycR-infected cells , no such signal was observed in cells that were infected with the previously described replication-competent MLV that contained a Flag-tagged p12 [33] , and that were stained with anti-Flag antibodies ( data not shown ) . This discrepancy may result from the different locations of the tags in p12 and from the fact that the Flag epitope replaced few residues in p12 , while the Myc epitope was inserted into the complete sequence of this protein , rendering this tag in the in 1xMycR virus more accessible to the antibodies . The early detection of p12 at 12 h postinfection ( Fig . 3 ) , suggested that the immunofluorescence detected the p12 proteins of the incoming virus and not the p12 domain of Gag precursors that were synthesized in late stages of the infection . To further examine this issue , we arrested the cell cycle of U/R cells by serum starvation and aphidicolin treatment , as this procedure was shown to block nuclear entry and integration of the incoming MLV , averting Gag synthesis in later stages of infection [21] . We then infected the cells with 1xMycR virus and observed the same punctate fluorescent signal for p12 in the aphidicolin-treated cells ( Fig . 3F ) , confirming that p12 staining is of the incoming virus and not of newly synthesized Gag precursors . The p12 staining in aphidicolin-treated cells ( Fig . 3F ) was mainly restricted to the cytoplasm , as was observed for cells that were not treated with aphidicolin and that were in the interphase stage of the cell cycle ( Fig . 3A ) . In contrast , p12 was detected in both the cytoplasm and in the vicinity of chromosomes of dividing cells ( Fig . 3B ) . To further study and quantify this observation , cycling non-synchronized U/R cells were infected with 1xMycR virus and 12 h postinfection the cells were fixed and stained for p12 and the cellular DNA as described above . Mitotic cells were identified on the basis of chromosome condensation and the typical configuration of each mitotic stage . In addition , the extent of p12 staining ( red ) that overlapped the staining of cellular DNA ( blue ) was determined ( see Materials and Methods ) , to quantify the distribution of p12 proteins between the cytoplasm and the chromosomes . Fig . 4 shows images of 1xMycR-infected cells at different stages of the cell cycle , and the quantification of p12 distribution is represented in Fig . S2 . As can be seen , p12 was mainly detected in the cytoplasm of interphasic cells ( Fig . 4A ) with only 6% overlap between p12 and the chromosomes ( Fig . S2 ) , similar to what was shown above ( Fig . 3A and F ) . In contrast , cells at different stages of mitosis showed a much higher overlap between the p12 and the chromosomes ( Fig . 4B–E ) ; the percentages of this overlap were quantified to be 49 , 75 , 68 and 72% at prometaphase , metaphase , as well as early and late anaphase , respectively ( Fig . S2 ) . No p12 signal was detected in U/R cells that were infected with wt virus and used to control for the immunofluorescence specificity ( Fig . 4F ) . Overall , complexes of p12 proteins appeared to migrate from the cytoplasm to the nucleus and to accumulate at the vicinity of the chromosomes in dividing cells . To further study the association of p12 with the chromosomes , optical sections were generated for the mitotic chromosomes in infected cells ( inset , Fig . 4E ) . This microscopy analysis clearly demonstrated that p12 proteins were detected at the same plane of the chromosomes ( Fig . 4G ) . Reconstitution of the serial optical sections into a 3D image further showed the close proximity between p12 proteins and the chromosomes ( Fig . 4H ) . Thus , p12 proteins appear to be closely associated with , and even imbedded in , the mitotic chromosomes of infected cells . The above results implied that p12 proteins are found in complexes that migrate from the cytoplasm to the chromosomes in dividing cells . These resemble the characteristics of the PIC [21] , suggesting that p12 may be a constituent of this complex . If this notion is true then in infected cells p12 should co-localize with components of the PIC but not with virion constituents that are not part of the PIC . To test this , we performed immunofluorescence analysis to detect the spatial relationships between p12 and MA - a virion constituent that is thought to become dispersed in the infected cell upon the uncoating step and is not known to be part of the MLV PIC; and CA - a virion constituent that is part of the MLV PIC [12] . 1xMycR-infected U/R cells were stained with anti-Myc antibodies , together with anti- MA or CA antibodies . This analysis revealed that , as expected for virion constituents , the p12 ( red fluorescence ) and MA ( green fluorescence ) proteins co-localized ( yellow fluorescence ) in particles that adhered to the cover glass in regions that were free of cells ( Fig . 5A , B ) . In contrast , no such overlap could be detected between the p12 and MA in the infected cells . The lack of co-localization between p12 and MA in the infected cells was further emphasized in conditions where p12 proteins migrated towards the mitotic chromosomes ( Fig . 5B ) . When the distribution of CA and p12 was tested , co-localization of the two proteins was clearly detected both in virions that adhered to the glass outside the cell and in the infected cell ( Fig . 5C ) . Altogether , these immunofluorescence analyses demonstrated that in the virions p12 proteins are co-localized with MA and CA , yet in the infected cells p12 is associated with the CA but not with the MA proteins , suggesting that p12 puncta in infected cells are derived from uncoated virions and not from internalized virus particles , and further hinting for p12 association with the PIC . To further address the possibility that p12 molecules are indeed part of the MLV PIC we set to visualize the PICs in infected cells and to test the possible co-localization of p12 with these PICs . Since the presence of the viral DNA genome is the hallmark of the PIC , its detection by FISH has been used to identify MLV PICs by microscopy [21] . Thus , we aimed to detect the incoming PICs in early stages of infection , using DNA FISH in combination with immunofluorescence to define the time-dependent spatial correlation between the viral genomic DNA and the p12 protein . For this analysis we used U/R cells , to avoid the problem of cross-hybridization with endogenous MLV-like elements found in mouse cells [40] . In addition , to avoid detection of carry-over of MLV plasmid DNA from transfected cells , we used viruses from chronically infected NIH3T3 cells . In preliminary FISH experiments we could readily detect clear and punctate fluorescence staining in U/R , but not in U2OS , cells that were infected with wt virus and that were hybridized with a MLV-derived , biotin-labeled probe that was detected with a Cy3-conjugated avidin , demonstrating the specificity of the detection method ( Fig . S3 and see below ) . This staining was also reminiscent of the previously reported staining of MLV PICs by FISH in Rat-1 cells [21] . We then established conditions for immunofluorescence combined with DNA FISH ( see Materials and Methods ) . U/R or U2OS cells were infected with 1xMycR or wt viruses , and 12 h postinfection , cells were stained with anti-Myc antibody and a secondary Cy3-conjugated antibody . FISH analysis followed , using a MLV-derived biotinylated probe and FITC-labeled avidin for the detection of the probe . A clear overlap ( yellow ) between the p12 proteins ( red ) and the MLV genomic DNA ( green ) was observed in U/R cells infected with 1xMycR virus ( Fig . 6A–E ) . This overlap could be observed in the cytoplasm of infected cells ( Fig . 6A , B , D and E ) , as well as in the vicinity of chromosomes ( Fig . 6C ) , including condensed chromosomes of mitotic cells ( Fig . 6B , E and S4 ) . In contrast , such broad overlapping signals were absent in 1xMycR-infected U/R cells that were processed for immunofluorescence/FISH analysis at two hours postinfection ( Fig . 6F ) . In this setting , only extensive punctate staining of p12 was observed , probably reflecting the lack of complete reverse transcription at this early time point . Quantification of more than 600 fluorescent dots in infected cells ( Fig . S5 ) confirmed the above observations: at 12 h postinfection approximately 60% of the fluorescent puncta showed an overlapping signal between the p12 proteins and the viral genomic DNA , while approximately only 10 and 30 percents of the fluorescent dots showed such an overlap at earlier time points ( 2 and 6 h postinfection , respectively ) . In addition , at 12 h post infection about 10% of the total fluorescent dots , showed overlapping p12 and genomic DNA signals that could be located with the chromosomes . In contrast to these results , more than 99% of the extracellular dots ( representing extracellular virions attached to the glass in cell-free regions , see Fig . 5 ) , showed only p12 staining both at early ( 2 h ) and late ( 12 h ) time point postinfection ( Fig . S5 ) , likely due to the absence of efficient reverse transcription in these particles . Additional negative controls showed no overlapping signals between the p12 and the genomic DNA staining further emphasizing the genuineness of this analysis . These controls included: 1xMycR-infected U/R cells that were processed as above but without the addition of the primary anti-Myc antibody , showing only the green FISH signal ( Fig . 6G ) ; U/R cells , infected with the wt virus that lacks the Myc epitope , showing only the green FISH signal ( Fig . 6H ) and 1xMycR-infected U2OS cells , showing neither red nor green punctate fluorescence ( Fig . 6I ) . Thus , the overlapping staining in 1xMycR-infected U/R cells , which was absent from the extracellular particles and from the negative controls , suggested that p12 proteins associate with viral genomic DNA and hence , p12 is indeed a component of the MLV PIC . Moreover , the overlap between p12 protein and viral DNA that was detected in close proximity to the condensed chromosomes ( Fig . 6B , E and S5 ) suggested that p12 proteins escort the viral DNA until very close to the chromatin of dividing cells . As was suggested above , the absence of intracellular overlapping immunofluorescence/FISH signals at two hours postinfection ( Fig . 6F ) likely reflected the lack of complete reverse transcription at this early time point , and may further suggest that reverse-transcription is not a prerequisite for the formation of p12 puncta in infected cells . We further investigated this point by the generation of MLV virus-like particles ( VLPs ) , with or without the genomic RNA , and that their p12 proteins were Myc-tagged as the 1xMycR virus ( Materials and Methods ) . These VLPs were then used to infect U/R cells , which were examined by immunofluorescence for the generation of p12 puncta . This analysis revealed a similar formation of p12 puncta , and similar accumulation of these dots close to mitotic chromosomes , for both conditions ( Fig . S6 ) . These results suggest that the presence of the genomic RNA , and its subsequent reverse transcription , are not a prerequisite for the formation and migration of p12 puncta in infected cells . It should be noted that in 1xMycR-infected U/R cells , we also observed signals for the viral DNA and the p12 proteins that did not overlap ( Fig . 6D and E; triangles and ellipses , and Fig . S5 ) . Since only a fraction of the incoming viruses establish a productive infection [2] , [41] , [42] , our microscopic analysis cannot resolve between the following two options: 1 ) only PICs that include functional p12 proteins have the potential to complete the early steps of infection or; 2 ) PICs that do not contain p12 are the infectious ones . Since p12 is crucial for MLV infection [3] , [31] , [33] , in the latter scenario , p12 proteins function not as part of the PIC but autonomously , outside of this complex . To address the question whether p12 modifies PIC function as part of the PIC or by acting separately from the PIC , we designed and applied a genetic ‘rescue’ assay based on complementation of wt and mutant p12 proteins . If p12 functions as a constituent of the PIC , upon co-infection of wt and p12 mutant viruses the wt p12 proteins should not rescue the defect in integration of PICs derived from the p12 mutant virus ( Fig . 7A ) . In contrast , rescue should occur if p12 proteins act autonomously of the PIC , particularly in conditions where wt p12 proteins are present in large excess over mutant p12 proteins ( Fig . 7B ) . Two types of MLV particles were generated for this assay: The first , named MLVIRES-GFP , was expressed from the pNCAIRES-GFP clone , a replication-competent MLV with a GFP marker [43] , allowing the accurate determination of its titer . MLVIRES-GFP expresses wt p12 proteins . The second type was made of VLPs that were generated from helper plasmids expressing the MLV Gag with or without the PM14 mutation in p12 ( Fig . 1 ) , and the Pol and Env ( ecotropic ) proteins; these VLPs also encapsidated the pQCXIN retroviral vector ( Clontech ) , expressing the neomycin resistance gene ( Neor ) . Importantly , pQCXIN is a self-inactivating vector due to a deletion in the U3 sequence of the 3′ LTR , which renders the vector compatible for only a single cycle of infection even in the presence of replicating virus such as the MLVIRES-GFP . We chose the PM14 mutation for this analysis since a virus that carries this mutation is capable of normal reverse transcription but is defective in integration [31]; yet , in our hands , VLPs with the PM14 mutation could transduce the pQCXIN vector at low but detectable efficiencies , allowing the quantification of p12 function in this complementation assay . NIH3T3 cells were infected with dilutions of wt VLPs ( MOI of 0 . 2 , 0 . 1 or 0 . 01 ) , or PM14 VLPs ( with RT activities that matched the ones of the wt VLPs dilutions ) , in the presence or absence of MLVIRES-GFP ( MOI of 10 ) . In these settings , the wt p12 proteins , derived from MLVIRES-GFP , are present in large excess over decreasing amounts of VLP-derived p12 proteins ( wt or mutant ) in infected cells . Infected cultures were then selected in G418- containing medium for two weeks , after which the number of Neor colonies was determined ( Fig . 7C ) . The results of this experiment showed that in all tested ratios , no increase in the number of Neor colonies was observed for the PM14 VLPs ( Fig . 7C , white columns ) in the presence of MLVIRES-GFP , indicating that p12 proteins act from within the PIC . In fact , co-infection of MLVIRES-GFP with either wt ( Fig . 7C , black columns ) or mutant VLPs resulted in a reduction in the number of Neor colonies; the level of this decrease augmented with the increase of MLVIRES-GFP/VLP ratio . This phenomenon likely represents competition between MLVIRES-GFP and the VLPs over cellular factor ( s ) needed to establish the infection . Of note , the reduction in the number of Neor colonies in the presence of MLVIRES-GFP was greater for the PM14 VLPs , compared to the wt VLPs , at all the tested ratios . For example , when the presence of MLVIRES-GFP reduced the infectivity of wt VLPs by less than two-fold , a reduction of more than eight-fold was observed for PM14 VLPs . These results suggest that the PM14 mutant is more sensitive to the competition exerted by the MLVIRES-GFP virus . Similar results were obtained when we repeated this experiment using VLPs ( wt and PM14 ) that were pseudotyped with the vesicular stomatitis virus G glycoprotein ( VSV-G ) instead of the ecotropic MLV envelope protein , to avoid direct competition between the VLPs and the MLVIRES-GFP virus on the mCAT-1receptor , ( Fig . 7D ) . No rescue of the infectivity of the p12 mutants was observed when these experiments were repeated using different conditions that included lower MOIs , the use of wt VLPs ( instead of the replicating MLVIRES-GFP virus ) as a source for the wt p12 proteins , and PM14 VLPs encapsidating a vector with a different selection marker ( data not shown ) . Overall , these experiments provide genetic evidence that p12 functions as part of the PIC . As mentioned above , the exact composition of retroviral PIC is not known , however the presence of the viral genomic DNA is the hallmark of the PIC . To further demonstrate that p12 is a component of the PIC we aimed to co-immunoprecipitate the viral genomic DNA with the p12 proteins . To immunoprecipitate p12 , we infected NIH3T3 cells with wt or 1xMycR virus , lysed the cells and performed IP with anti-Myc , or control anti-Flag monoclonal antibodies . We then analyzed the cell lysates and the immunoprecipitates for the presence of the viral genomic DNA by PCR . This analysis revealed preferential IP of the genome of the 1xMycR virus when anti-Myc antibodies were used , compared to the controls ( Fig . 8A ) . To better quantify this , we measured the amount of the viral DNA genome in the cell lysates and the immunopellets by quantitative PCR ( qPCR ) and calculated the relative efficiency of this IP ( Fig . 8B and S7A ) . The average IP efficiency , obtained from three independent experiments , showed that when anti-Myc antibodies were used , the genome of the 1xMycR virus was immunoprecipitated approximately 7 fold higher than the genome of the wt virus . In addition , the average IP efficiency of the 1xMycR genome by the anti-Myc antibodies was approximately 25 fold higher than the one obtained for IP using anti-Flag antibodies . This specific IP of the viral genomic DNA with antibodies against p12 strongly suggested that p12 is indeed part of the PIC . Since CA was identified as a component of the MLV PIC [14] we also tested if this protein co-immunoprecipitates with the p12 proteins . Co-IP experiments were performed similarly to what was described above for the IP of the viral genomic DNA . The presence of CA in cell lysates and in immunopellets was determined by Western blot analysis with polyclonal antibodies against CA ( Fig . 8C ) . This analysis revealed that CA could be detected in the precipitate only when the precipitation included the lysates of the 1xMycR-infected cells and the anti-Myc antibodies; CA protein was absent from control precipitates obtained from wt-infected cell lysates that were reacted with anti-Myc antibodies , or from lysates of 1xMycR-infected cells that were reacted with the control anti-Flag antibodies . To verify that the detected p12-CA interaction reflects an authentic , intracellular interaction and not an interaction present in internalized virus particles , we carried out an analogous pull-down experiment on both extracellular virions and infected cells . The results of this experiment clearly demonstrate that CA of the 1xMycR virus was immunoprecipitated by anti-Myc antibodies only from lysates of infected cells and not from lysates of extracellular virions ( Fig . S7B and C ) . These results indicate co-association of CA - a known component of the MLV PIC - and p12 proteins , further providing evidence that p12 is indeed a component of the PIC . Our data provide strong evidence that p12 is a functional component of the PIC . Yet , the exact function of p12 is currently unknown . In principle , p12 may influence one or more steps that include: migration of the PIC along the cytoplasm , nuclear entry and targeting the chromatin for integration . Elaborate biochemical analysis of cells infected with wt MLV or with an integration-defective p12 mutant - the PM14 virus - demonstrated that the two viruses have the same distribution of the viral genomic DNA in cytoplasmic and nuclear fractions; yet no circular genomic DNA was detected for the mutant virus , suggesting that PM14 virus is defective in nuclear steps required for productive infection [3] . However , as the authors suggested , association of the PIC of the PM14 virus to the external side of the nuclear envelope and/or nuclear retention could not be dismissed . We tested if the immunofluorescence procedure that we developed to investigate 1xMycR infection could be applied to the analysis of mutant p12 proteins . For this we introduced the PM14 mutation ( Fig . 1 ) into p12 of the 1xMycR clone to generate the 1xMycR/PM14 virus . We then separately transfected the 1xMycR and the 1xMycR/PM14 clones into 293T cells , harvested the virion-containing supernatants and infected sub-confluent U/R cultures with equal amounts of virions ( normalized by an exogenous RT assay ) . The infected cells were processed for immunofluorescence analysis 12 h postinfection with anti-Myc antibodies to detect the p12 proteins . This analysis revealed that both viruses showed similar intracellular distribution in interphasic cells , where the majority of the p12 proteins were cytoplasmic ( Fig . 9 ) . However , a remarkable difference between the wt and the mutant virus was observed in mitotic cells , where the p12 proteins of the 1xMycR/PM14 virus were localized mostly to the cytoplasm , in contrast to the substantial association of the p12 proteins of the 1xMycR virus with the chromosomes ( Fig . 9 ) . Further quantification of this phenomenon revealed that in interpahsic cells about 8% of the p12 proteins that were derived from the 1xMycR/PM14 virus overlapped the chromosomes , similar to the value ( 6% ) obtained for the 1xMycR virus ( Fig . S8 ) . In mitotic cells , however , where almost 70% of the p12 proteins of the 1xMycR virus overlapped the chromosomes , the p12 proteins with the PM14 mutation showed only 11% overlap - a level that was similar to the one obtained in interphasic cells ( Fig . S8 ) . These results demonstrated that the PM14 mutation hindered the movement of the p12 proteins from the cytoplasm towards the chromosomes and strongly suggest a role for p12 in PIC trafficking .
In this work we provide microscopic , genetic and biochemical evidence that the p12 protein - a Gag cleavage product - is a functional constituent of the MLV PIC . We used fluorescent microscopy analysis to detect Myc-tagged p12 proteins of a replication-competent virus ( 1xMycR ) , in infected cells . The comparison between the epitope-tagged virus and the untagged wt virus , and between infected cells that express , or do not express , the mCAT-1 receptor for MLV , allowed us to distinguish unambiguously between background signals and immunofluorescent staining that was related to authentic infection of the incoming virus . This is important since MLV particles with the ecotropic envelope may bind human cells independently of specific receptor-envelope interactions , and such binding can be detected by immunofluorescence [42] . When susceptible U/R and NIH3T3 cells were infected with the 1xMycR virus , our immunofluorescence analysis revealed punctate p12 staining at an early time of infection . This staining was attributed to the incoming virus , and not to newly synthesized Gag proteins since it could be detected as early as two hours postinfection , and in aphidicolin-treated cells , where integration and the subsequent Gag expression are inhibited . Of note , throughout the immunofluorescence experiments , the cells were infected with MOI of approximately 3 , yet many of the infected cells exhibited hundreds to thousands of p12-positive dots This likely reflects the fact that for retroviruses only a minute fraction of the virions is infectious [41] , [42] . The number of these dots was in a direct correlation with the amount of the virus used for infection . Thus , it is likely that the majority of these dots represent p12 proteins that originated from physical particles that are defective in their infectivity . The punctate , rather than diffuse , pattern of the staining of p12 suggested that p12 proteins are found in a complex in early stages of the infection cycle . Furthermore , this staining was mainly cytoplasmic in aphidicolin-treated cells or in mitotic cells early after infection , in contrast to its localization adjacent to mitotic chromosomes 12 h postinfection . Thus , the pattern and the distribution of p12 proteins in dividing , and in cell-cycle arrested cells , resemble the one of the MLV genomic DNA [21] , suggesting that p12 is part of the MLV PIC . In support of this idea are the findings that whereas p12 staining overlapped the staining of MA or CA in virions , in the infected cells p12 staining co-localized only with that of CA , which unlike the MA protein is thought to be part of the MLV PIC . However , in principle , p12 proteins can migrate towards the cellular chromatin as PIC-independent complexes . Therefore , to better evaluate p12-PIC interaction we combined immunofluorescence with FISH analyses , and detected co-localization of p12 proteins with the viral genomic DNA , both in the cytoplasm and in close proximity to the cellular chromatin of infected cells . Since the viral genomic DNA is the hallmark of the PIC , this co-localization suggested that p12 is indeed part of the PIC . The early detection of punctate p12 staining at two hours postinfection , when almost no FISH signal for the genomic DNA could be detected , hints for organization of p12 proteins in a complex that precede the PIC , most likely in the RTC . Moreover , the appearance of p12 puncta in cells that were infected by MLV VLPs with no encapsidated genomic RNA suggests that the presence of the genomic RNA is not necessary for the early organization of p12 in complexes and their trafficking towards the chromosomes . In this scenario , the protein components of the RTC and the PIC may have intrinsic assembly ability which is independent of the presence of the genomic RNA and/or cDNA . Our analysis revealed , however , that not all the signals of the p12 proteins and the viral DNA genome overlap , raising the possibility that p12 proteins may function in early stages of infection not as constituents of the PIC but as elements that are separated from this complex; in any scenario , such variation in the association of p12 with the viral genomic DNA may account in part for the fact that only a fraction of MLV particles are infectious . Since our microscopic examination could not unambiguously distinguish between these two scenarios describing p12-PIC interactions , we performed a genetic ‘rescue’ assay to evaluate if p12 proteins exert their activity in trans , in respect to the PIC , or as constituents of this complex . In this assay , VLPs that were defective in early steps of infection - between reverse transcription and integration - due to a mutation in p12 ( PM14 mutation; [31] ) were used together with wt virus ( pNCAIRES-GFP ) to co-infect NIH3T3 cells . The presence of wt p12 proteins in the infected cells did not improve the infectivity of the mutant VLPs and this was true for all wt-to-mutant ratios tested , including conditions where wt p12 proteins were present in a very large excess over the mutant p12 proteins . These results strongly suggest that p12 proteins function as part of the PIC . The co-infection experiments also revealed that excess of pNCAIRES-GFP particles repressed the infection of the VLPs regardless if the latter harbored wt or mutant p12 proteins , and that the level of this inhibition rose with the increase in the pNCAIRES-GFP-to-VLPs ratio . This probably reflects a competition between the pNCAIRES-GFP virus and the VLPs on cellular factor ( s ) needed to establish infection and that are found in limiting amounts . The ecotropic receptor may serve as such a factor since more Neor colonies were obtained when the pNCAIRES-GFP virus and the VLPs ( transducing the Neor vector ) had different ( the ecotropic and the VSV-G glycoprotein , respectively ) , instead of the same , envelopes , resulting in the use of different receptors to initiate infection . However , even in such settings , excess of pNCAIRES-GFP over the VLPs resulted in a dose-dependent reduction in the infectivity of the latter , suggesting that saturable cellular factors other than the ecotropic receptor exist . Notably , the PM14 VLPs were more sensitive to the pNCAIRES-GFP-mediated suppression , compared to wt VLPs . This phenomenon may indicate that PM14 VLPs are kinetically slower than wt VLPs in transducing the vector . In this scenario , wt PICs may traffic towards integration faster than the PICs with mutant p12 proteins and accordingly , saturate cellular factors needed for infection , before such factors will be available for the mutant PICs . The above results suggest that p12 is a constituent of the PIC and thus , it is anticipated that the viral genomic DNA should be co-immunoprecipitated with p12 proteins . Indeed , preferential IP of the genome of the 1xMycR virus with anti-Myc antibodies from infected cells was identified , compared to the controls ( made of the wt virus or anti-Flag antibodies ) , providing strong evidence that p12 proteins are found in a complex with the viral genomic DNA . Further biochemical support to the interaction of p12 with the PIC came from the detection of CA , another component of the MLV PIC [14] , in the p12 immunoprecipitates , as CA proteins were immunoprecipitated from lysates of cells that were infected with the 1xMycR virus , using anti-Myc antibodies . It should be noted that we failed to directly detect the Myc-tagged p12 proteins in the pellets where CA proteins were found . This may be the result of the use of a monoclonal antibody ( anti-Myc ) to detect p12 , unlike CA detection that was performed using polyclonal sera that may enhance the sensitivity of the detection . Support for this explanation comes from the fact that Western blot analysis of virions showed an intense signal for CA but a relatively weak signal for p12 , although the two proteins are present in equimolar quantities inside the viral particles; the difference in detection sensitivity by Western blot analysis was also observed when only CA , but not p12 , was detected in extracts of the 1xMycR-infected cells prior to the IP step ( data not shown ) . Importantly , although this analysis was not sensitive enough to detect the tagged p12 proteins , CA IP was specific since it was observed only when the 1xMycR virus and the anti-Myc antibody were used , and not when the co-IP was performed with controls that included an isotype-matched control antibody ( IgG1 , anti-Flag ) or the untagged , wt virus . In the co-IP experiments , only low amounts of CA were detected in the pellets , compared to its level in the cell lysates . This may reflect a labile p12-CA interaction and/or the possibility that not all of the CA molecules interact with p12 proteins . At this stage , it is not clear whether the p12 and CA proteins are complexed together through direct or indirect interactions; yet , our success to detect this interaction in infected cells but not in extracellular virions implies that at least some of the p12 and CA molecules in the virion form new mutual interactions during or after the uncoating step . In any case , these co-IP experiments provide a biochemical support to the notion that p12 and CA proteins have a cooperative effect in early stages of infection , as was concluded from the analysis of swap mutants between the different domains of the Gag of MLV and its closely related virus - the SNV [37] . Because CA is a component of the MLV PIC and since our analysis suggests that p12 is also a functional constituents of the same complex , the two proteins may act in concert to direct the PIC towards nuclear entry and integration . In line with this idea is the observation , made by Yuan et al . [3] , and Lee et al . [37] , regarding the phenotypic similarity that exists between wt MLVs that are restricted by the Fv1 restriction factor and mutant MLVs harboring either specific mutations in p12 , or p12/CA chimeras derived from MLV and SNV . In each of these cases virus infection is characterized by normal levels of reverse transcription but defective production of genomic circular forms and integration . If p12 proteins act in directing the PIC towards integration , their function ( s ) may be required during PIC migration through the cytoplasm , nuclear entry and/or nuclear trafficking . Remarkably , we demonstrated here a clear accumulation of the p12 proteins adjacent to mitotic chromosomes , and this accumulation appeared to increase along the steps of mitosis; ranging from 6% of p12 proteins that overlapped the chromatin during interphase , to about 70% overlap during mitosis . Moreover , the PM14 mutation in p12 that renders the virus integration-defective in vivo [3] , also hindered the accumulation of p12 proteins with the mitotic chromosomes , as the majority ( ∼90% ) of the puncta of the p12 mutant proteins remained cytoplasmic in dividing cells . These results are in agreement with the postulation , discussed above , that wt PICs are kinetically faster in their trafficking towards integration , than PICs that are derived from the infection of the PM14 virus . Yet , it should be noted that Yuan et al . [3] , observed that the distribution of the viral genomic DNA between the cytoplasm and the nuclear fractions was similar for both the wt and the PM14 mutant viruses . While our microscopic analysis revealed such similarity for the p12 content in the nucleus of interphasic cells , our results also showed differential nuclear-cytoplasmic distribution of p12 in mitotic cells , for the two viruses . This difference can be explained by the different methodologies used: whereas our microscopy analysis monitored the infection in individual mitotic cells , in which the nuclear envelope is broken , the biochemical fractionation used by Yuan et al . , relayed on the lysis of unsynchronized infected cultures and the subsequent separation of intact nuclei from the cytoplasm . In such conditions , mitotic cells with no intact nuclei can be overlooked , especially if these cells present only a fraction of the unsynchronized culture . Altogether , our results suggest that p12 proteins are associated with the viral genomic DNA and indicate for a role ( s ) of p12 along the course of trafficking of this DNA from the cytoplasm to the chromosomes . One possibility of such a role is that p12 interacts with cytoplasmic factors that are required for trafficking towards the nucleus . Such specific factors have not been identified for MLV , but may involve cytoskeleton proteins , as was described for HIV and other retroviruses [3] . In addition , the extensive accumulation of p12 in close proximity to the chromosomes hints for a role of p12 very close to the integration step and may even indicate for a direct function in the integration process itself . However , Yuan et al . [3] , have demonstrated that PICs derived from a MLV with a mutation in p12 that render the virus integration-defective in vivo , were integration-competent in an in vitro assay; demonstrating that a defect in p12 function does not necessarily hamper PIC-mediated integration . Thus , p12 may be needed at the vicinity of the chromatin for steps that precede the integration reaction itself . What could be such a role ? For HIV-1 , it has been demonstrated that the cellular protein LEDGF is a component of the PIC [10] , [11] and may serve to tether the IN proteins to the chromatin [10] , [44] , [45] , [46] , [47] . LEDGF interacts with IN proteins derived from several lentiviruses but not with MLV IN [10] , [48] , and no factor with such tethering activity has been described for MLV . In light of the similarity of the N-termini of p12 and the histone H5 protein [49] , and because our microscopic data showed an intimate association of p12 with the chromatin it is tempting to assume that p12 also functions in tethering the MLV PIC to the chromatin . Such a scenario may explain why specific mutations in p12 block MLV integration in live cells but do not interfere with integration into naked DNA , in vitro [3] . The ability to detect MLV PICs by immunofluorescence through the detection of p12 proteins provides a new tool that may assist the analysis of several issues concerning MLV infection: ( i ) such microscopic analysis should complement the biochemical approach used to study p12 mutants and resolve , for example , the question whether PICs of specific p12 mutants are capable of entering the nucleus or are trapped on the external side of the nuclear envelope [3]; ( ii ) p12 tagging may also be applied for the analysis of the recently discovered human retrovirus , the XMRV , which expresses p12 protein with a high degree of similarity to the MLV p12; suggesting a way to monitor XMRV infection in human cells; ( iii ) Means were developed to visualize HIV-1 RTC/PIC , which were also exploited to study the interaction of these complexes with the TRIM5α restriction factor [50] . Similarly , tagging p12 proteins of N or B -tropic MLVs with the Myc epitope , as was described here for the 1xMycR virus , should allow the analysis of the restriction of these MLVs by Fv1 [51] , and TRIM5α-mediated restriction of N-tropic MLV [52] , [53] . In summary , the combination of microscopic , genetic and biochemical assays described here provide strong evidence that p12 is a component of the MLV PIC and this interaction is crucial for the progression of the PIC towards integration .
The pNCS plasmid contains an infectious molecular clone of the Moloney MLV [54] , and a simian virus 40 origin of replication in the plasmid backbone . Overlapping PCR was used to insert three tandem repeats of the Myc epitope ( EQKLISEEDL ) , between amino acids 45 and 46 of p12 in pNCS , generating the 3xMyc clone . Virions of a faster replicating revertant , which was derived from the 3xMyc virus , were collected and the genomic RNA was extracted and reverse transcribed with MLV RT and random primers ( Promega ) . The cDNA was amplified by PCR using Ex-Taq ( Takara Bio Inc . ) and a forward primer ( 5′CCCAGGTTAAGATCAAGG3′ , derived from the matrix sequence ) , together with a reverse primer ( 5′CTTGGCCAAATTGGTGGG3′ , derived from the capsid sequence ) . The resulting 875 bp fragment was cloned into pTZ57R ( Fermentas ) and sequenced , revealing that the revertant virus retained only a single , in-frame copy of the Myc epitope in p12 . The cloned PCR fragment was digested with BsrGI and XhoI and the resulting 640 bp fragment , containing the entire p12 and the Myc epitope sequence , was used to replace the BsrGI-XhoI wt sequence in pNCS , to generate the 1xMycR clone . Myc-tagged proteins were detected by Western blot analysis using mouse monoclonal anti-Myc antibody ( supernatant of 9E10 hybridoma ) , and a secondary donkey anti-goat horseradish peroxidase ( HRP ) -conjugated antibody ( Jackson Immunoresearch Laboratories , product no . 705-035-147 ) . The PM5 or PM14 mutations in p12 [31] were introduced by overlapping PCR into the BsrGI-XhoI fragment of pNCS to generate PM5 or PM14 clones , respectively . Overlapping PCR was also used to combine the Myc epitope of the 1xMycR virus with the PM14 mutation ( Fig . 1 ) in the above BsrGI-XhoI fragment to generate the 1xMycR/PM14 clone . pNCAIRES-GFP encodes for a replication-competent MLV , which expresses the green fluorescent protein ( GFP ) under the translational control of the encephalomyocarditis virus internal ribosome entry site ( IRES ) [43] . This clone was generously provided by Jeremy Luban ( University of Geneva ) . To generate MLV VLPs with Myc-tagged p12 proteins , containing or lacking the genomic RNA , the following plasmids and procedure were used: the helper plasmid pVSV . G expresses the VSV-G glycoprotein . The pGag-PolGpt . p12 1xMycR , is a derivative of the pGag-PolGpt helper plasmid that expresses the MLV Gag and Pol proteins , and its p12 sequences was modified to include the Myc epitope tag as in 1xMycR virus ( Fig . 1 ) . The pQCXIP plasmid encodes an MLV-based vector ( Clontech ) . The pQCXIPΔ5′ encodes for a defective vector that was generated by deleting an internal BsrGI fragment from pQCXIP , resulting in the removal of the 5′ LTR and the packaging signal . VLPs were generated by transfecting subconfluent 293T cells in 60 mm plates with 10 µg of pQCXIP or pQCXIPΔ5′ together with 7 . 5 µg of pGag-PolGpt . p12 1xMycR and 2 . 5 µg of pVSV . G DNAs , using the calcium phosphate procedure . Culture supernatants were harvested two days posttransfection and used for infection . Human embryonic kidney 293T cells , human osteosarcoma U2OS cells , and the mouse NIH3T3 fibroblasts were cultured in Dulbecco's Modified Eagle Medium ( DMEM ) , supplemented with 10% heat-inactivated fetal calf serum ( FCS ) , 2 mM L-glutamine , penicillin ( 20 U/ml ) , streptomycin ( 20 µg/ml ) and nystatin ( 2 . 5 U/ml ) , in a humidified incubator at 37v and 5% CO2 . All tissue culture products were purchased from Biological Industries ( Beit Haemek , Israel ) . To generate human cells stably expressing the murine receptor for MLV , U2OS cells were transfected with 7 . 5 µg of pCDNA 3 . 1 Zeo ( + ) that encode for the murine receptor for MLV ( mCAT-1; [39] ) , using the calcium phosphate precipitation method . Transfected cells were selected in the presence of 100 µg/ml zeocin for two weeks and zeocin-resistant colonies were expanded and screened for their susceptibility to MLV infection . One of the clones ( clone #12 , named hereafter U/R ) , was chosen for further experiments since it could efficiently be infected with MLV particles encapsidating a MLV vector that expresses the GFP reporter gene ( pQCXIP-gfp-C1 vector [55]; data not shown ) . To determine the kinetics of spreading of various MLV clones in NIH3T3 cultures , the cells were either infected or transfected with these clones as indicated . The transfection was carried out using the DEAE-dextran method , as described before [56] . When required , cell synchronization was achieved by arresting cell division before S phase by serum starvation and aphidicolin treatment , as previously described [21] and briefly explained here . On day 1 , cells were plated on 13 mm round cover-slips in a 24-well plate at approximately 5% confluency per well . On day 2 , cells were serum-starved by removing the media , washing the dish with serum-free media and then adding DMEM containing 0 . 25% FCS . On day 4 , serum was added to a concentration of 10% . 6 h after the addition of serum , aphidicolin ( 2 µg/ml ) was added . After 6 h , the medium was removed and fresh medium containing the virus , 10% serum , polybrene ( hexadimethrine bromide; 8 µg/ml ) and aphidicolin ( 2 µg/ml ) was added . After 2 h , the virus-containing medium was removed and replaced with 10% serum-containing medium and aphidicolin ( 2 µg/ml ) . After an additional 12 h , cells were fixed and subjected to immunofluorescence analysis . Cells were grown on 13 mm round cover-slips in a 24-well plate to ∼1 . 5×105 cells/well and were infected with the indicated virus with multiplicity of infection ( MOI ) of approximately 3 [virus preparations were estimated to have ∼5×106 infectious units ( IU ) /ml , based on comparisons of their RT activity , determined by RT exogenous assay [57] , to a standard MLV stock with known IU concentration . This stock was made of pNCAIRES-GFP , a replication-competent MLV that expresses the GFP marker [43] , allowing the determination of its titer by measuring the number of GFP-positive cells in infected cultures , by fluorescence-activated cell sorting ( FACS ) analysis] . Cells were incubated with the virus for the indicated time , after which they were washed three times with PBS and fixed with 4% paraformaldehyde for 20 min . After three washes with 50 mM glycine in PBS , the cells were permeabilized with 0 . 1% Triton in PBS for 2 min and immediately washed three times with PBS . The cells were then incubated with blocking solution [1∶10 normal goat serum in Tris-Buffered Saline ( TBS; 50 mM Tris-HCl , 150 mM NaCl pH 7 . 5 ) ] for 30 min , followed by one hour incubation with a mouse monoclonal anti-Myc antibody ( supernatant of 9E10 hybridoma , diluted 1∶6 in TBS ) , and washed once with TBS and twice with PBS ( 5 min each wash ) . The cells were then incubated for one hour with a Cy-3-conjugated goat anti-mouse antibody ( Jackson Immunoresearch Laboratories , product no . 115-166-072 ) diluted 1∶500 in TBS and washed once with TBS and twice with PBS ( 5 min each wash ) . The nuclei were stained for 20 min at room temperature with DAPI ( 1 µg/ml in PBS ) , followed by two washes with PBS . The cover-slips were glued to glass slides with aqueous mounting media containing anti-fading agent ( BIOMEDA ) . All the above steps were carried out at room temperature . For co-localization experiments of p12 and MA or CA proteins , the immunofluorescence procedure was performed as described above with the following modifications: the blocking solution was made of 3% bovine serum albumin ( BSA ) in PBS and the supernatant of the 9E10 hybridoma was diluted 1∶6 in PBS . Goat polyclonal anti-MA antiserum ( American National Cancer Institute , product no . 78S-282 ) or goat polyclonal anti-CA antiserum ( American National Cancer Institute , product no . 81S-263 ) , were used at a 1∶1000 dilution in PBS containing 3% BSA . These antisera were raised against the MA or the CA proteins of the Rauscher MLV , but cross-react with the MA and CA proteins of the Moloney MLV , respectively . Secondary antibodies included the FITC-conjugated F ( ab' ) 2 fragment donkey anti-mouse IgG ( H+L ) ( Jackson Immunoresearch Laboratories , product no . 715-096-150 ) , or Red-X- conjugated F ( ab' ) 2 fragment donkey anti-goat IgG ( H+L ) ( Jackson Immunoresearch Laboratories , product no . 705-296-147 ) , both antibodies were used at a 1∶200 dilution in PBS containing 3% BSA . BX50 microscope ( Olympus ) , LSM 510 META confocal microscope ( Zeiss ) or spinning disk confocal ( Yokogawa CSU-22 Confocal Head ) microscope ( Axiovert 200 M , Carl Zeiss MicroImaging ) were used in this study where indicated . Quantification of the overlap between p12 and chromatin signals was achieved through the following procedure: Cells , infected with the 1xMycR virus were processed for immunofluorescence analysis as described above . Then , the entire cell volume was imaged by confocal microscopy and the picture was deconvolved with 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 DAPI-based staining were identified through intensity based segmentation , the total signal intensity was calculated for each signal and the percentage of overlapping signal was deduced by subtraction of the DAPI region of interest ( ROI ) from the p12 ROI . Approximately 400 dots of p12 signal per image were analyzed . All the above steps were performed employing the SlideBook software ( Intelligent Imaging Innovations ) . Cells were grown on 20×20 mm cover-slips in 6-well plates to 30% confluency and were infected with 1 ml media , containing equal amounts of 1xMycR or wt viruses , normalized by RT activity using the exogenous RT assay [57] . Unless otherwise indicated , all the following steps were performed at room temperature . At the indicated time postinfection , slides were washed with PBS , fixed with 4% paraformaldehyde in PBS for 10 min , permeabilized with 0 . 1% Triton in PBS for 10 min , washed for 10 min in 0 . 1 M Tris pH 7 . 4 , incubated for 20 min in 20% glycerol in PBS , freeze-thawed three times in liquid nitrogen , washed once with PBS and once with 0 . 1% Triton in PBS , blocked for 30 min with normal goat serum that was diluted 1∶10 with PBS , incubated for 60 min with mouse anti-Myc monoclonal antibody ( supernatant of hybridoma 9E10 , diluted 1∶6 in TBS ) , and washed once with TBS for 5 min and twice for 5 min in PBS . Slides were then incubated for 60 min with a Cy-3-conjugated goat anti-mouse antibody ( Jackson Immunoresearch Laboratories , product no . 115-166-072 , diluted 1∶500 ) and washed once with TBS and twice with PBS ( each wash for 5 min ) . The slides were re-fixed in 4% paraformaldehyde in PBS for one min , rinsed with PBS and incubated in 70% ethanol overnight . To detect the viral DNA in situ , DNA FISH was performed as described previously [58] with the following modifications: The following day , the cover-slips were dried and glued to glass slides with the cells facing up . The slides were then immersed for 10 min in 0 . 1M HCl , 10 min in 0 . 5% Triton X-100 in PBS at 37°C and washed three times in PBS . Slides were dehydrated by a series of ethanol washes ( 70% , 90% and 100%; 5 min each ) , and incubated at 37°C for 1 hour , followed by denaturation in 70% ( v/v ) deionized formamide ( F9037 , Sigma ) in 2xSSC , at 75°C , for 5 min . The slides were then briefly washed with 70% ethanol and dehydrated once again in a series of ice-cold ethanol washes ( 70% , 90% and 100% , 5 min each ) , air dried and warmed to 37°C . Each slide was spotted with 10 µl of biotin-labeled probe in hybridization solution ( see below ) , sealed with glass cover-slips and rubber cement and incubated in a moist chamber overnight at 37°C . Following hybridization , the sealing was gently removed and the slides were washed three times with 50% ( v/v ) formamide in 2xSSC ( prewarmed to 42°C , 5 min each wash ) , followed by three washes with 0 . 1XSSC ( prewarmed to 60°C , 5 min each wash ) . The slides were then incubated with blocking solution ( 3% BSA in 4xSSC , 30–60 min at 37°C ) . All stages following blocking were carried out in the dark . The hybridized biotin-labeled probe was detected with FITC-conjugated avidin ( A-2011 , Vector Laboratories , 1∶400 dilution ) , which was incubated with the slides for 30 min at 37°C in 1% BSA/4xSSC and 0 . 1% Tween 20 . Slides were then washed three times with 4xSSC and 0 . 1% Tween 20 ( prewarmed to 42°C , 5 min each wash ) , and covered with antifade ( VECTASHIELD , Vector Laboratories ) containing DAPI ( 200 ng/ml ) under glass cover-slips . The biotin-labeled probe was prepared in a nick-translation reaction ( 100 µl , 2 h at 16°C ) , containing nick-translation buffer ( 50 mM Tris-HCl pH 7 . 8 , 5 mM MgCl2 , 50 ng/ml BSA ) , plasmid DNA template ( pNCS , 2 µg ) , dATP , dGTP , dCTP ( 50 nM of each nucleotide , Sigma ) , and biotin-11-dUTP ( 50 nM , Roche ) , β-mercaptoethanol ( 10 mM ) , DNaseI ( 30 ng/ml , freshly diluted ) , Klenow polymerase ( 20 U , New England Biolabs ) . A sample ( 8 µl ) of this reaction was separated in a 2% agarose gel to verify the generation of a smear , made of approximately 150–500 bp-long DNA products . The rest of the reaction was kept frozen at −20°C until used . On the day of the hybridization the probe was ethanol precipitated with 10 µg of salmon sperm DNA , resuspended in 50 µl of 100% deionized formamide ( Sigma , F9037 ) , thoroughly mixed with 50 µl of 20% dextran sulfate in 2xSSC , denatured ( 75°C , 5 min ) , and immediately applied to the denatured slides ( 10 µl/slide ) . For Co-IP of the viral genomic DNA , supernatants of sub-confluent NIH3T3 cultures , chronically infected with the wt or the 1xMycR virus , were harvested , diluted 1∶1 with growth medium and complemented with polybrene ( 8 µg/ml final concentration ) . 2 ml of the virus-containing media were used to infect 5×106 NIH3T3 cells in 10 cm plates for 2 h , after which the media were replaced with fresh growth media , followed by additional 5 h incubation . For each IP , 2 plates were trypsinized and the cells were washed once with PBS . Cell pellets were resuspended in 1 ml of IP lysis buffer [50 mM Tris pH 7 . 5 , 150 mM NaCl , 0 . 5% NP-40 , 10 mM MgCl2 , 1x protease inhibitor cocktail ( Roche , product no . 1183614500 ) ] and incubated for 30 min at 4°C with constant agitation . Lysed samples were centrifuged at 20 , 800 g for 10 min . 5 µl sample of each of the cleared lysates was diluted 1∶10 , and triplicates ( 5 µl each ) were analyzed by qPCR . The remaining lysates were then incubated for 30 minutes at 4°C with 50 µl of magnetic polystyrene Dynabeads Protein G ( Invitrogen , product no . 100 . 03D ) , pre-conjugated to specific antibodies [the magnetic beads were pre-incubated with 200 µl of supernatant of the anti-Myc hybridoma ( 9E10 ) , or with 0 . 2 µl ( in 200 µl PBS ) of anti-Flag monoclonal antibody ( Sigma , F1804 ) , for 10 minutes at room temperature with a constant agitation and washed once with PBS] . The samples were placed on a magnet and the beads-free lysates were discarded . The magnetic beads were washed once with IP washing buffer ( 50 mM Tris pH 7 . 5 , 150 mM NaCl , 0 . 1% NP-40 , 10 mM MgCl2 ) , twice with PBS , and then transferred into a new tube . The beads were resuspended in 50 µl TE pH 7 . 4 ( 10 mM Tris-Cl pH 7 . 4 , 1 mM EDTA ) and 5 µl of the bead slurry were analyzed by PCR with MLV-specific primers ( forward primer 5′CCCAGGTTAAGATCAAGG3′ , and reverse primer 5′CTTGGCCAAATTGGTGGG3′ ) . For qPCR , triplicates ( 5 µl each ) of the bead slurry were analyzed; real-time PCR reactions were performed with MLV-specific primers ( forward primer 5′-AGCCCTTTGTACACCCTAAGC-3′ and reverse primer 5′-GAGGTTCAAGGGGGAGAGAC-3′ ) and Fast SYBR Green Master Mix ( Applied Biosystems , product no . 4385612 ) , and analyzed with StepOnePlus Real-Time PCR System ( Applied Biosystems ) . Standard curves were used to determine the absolute DNA quantity in the samples . To calculate the relative efficiency of the immunoprecipitation of the viral genomic DNA in the different experimental settings , the levels of the viral genomic DNA in the cell extracts and in the IP pellets were quantified by qPCR , and the background signal obtained in the ‘mock-infected’ sample was subtracted . The level of the immunoprecipitated viral genomic DNA ( IP sample ) was divided by the level of this DNA in the cell lysate ( input sample ) , to give normalized IP value . The normalized value for the genome of the 1xMycR virus that was immunoprecipitated with anti-Myc antibodies was set as 100% and was compared to the normalized values obtained for the genome of the 1xMycR virus that was immunoprecipitated with anti-Flag antibodies , and for the genome of the wt virus that was immunoprecipitated with anti-Myc antibodies . The average value of this comparison , obtained from three independent Co-IP experiments , gave the ‘Relative IP Efficiency’ index . For Co-IP of CA , infections and lysis of the infected cells were carried out as described above . To reduce non-specific binding to the agarose beads that were used for this procedure , the cell lysates were first incubated for 30 minutes at 4°C with a mixture of 7 . 5 µl of protein A-agarose beads ( Roche , product no . 11 134 515 001 ) and 7 . 5 µl of protein G-agarose beads ( Roche , product no . 11 243 233 001 ) , Then , the lysate-bead slurries were centrifuged at 20 , 800 g for 2 minutes , the beads were discarded and the cleared supernatants were incubated overnight at 4°C with 15 µl protein A and 15 µl G beads , pre-conjugated to specific antibodies [beads were pre-incubated with 0 . 5 ml of supernatant of the anti-Myc hybridoma ( 9E10 ) or with 0 . 5 µl ( in 0 . 5 ml of PBS ) of anti-Flag monoclonal antibody ( Sigma , F1804 ) ] . Samples were centrifuged at 10 , 000 rpm for 2 min at 4°C and the pelleted beads were washed once with IP washing buffer , twice with PBS , and directly boiled in 2x Sample Buffer . The CA protein was detected by Western blot , using goat anti-MLV CA polyclonal antibody ( National Cancer Institute , product no . 81S-263 ) and a secondary donkey anti-goat HRP-conjugated antibody ( Jackson Immunoresearch Laboratories , product no . 705-035-147 ) . For Co-IP of CA from extracellular virions , supernatants of sub-confluent NIH3T3 cultures , chronically infected with the wt or the 1xMycR virus , were harvested and virions were purified from 2 ml of undiluted medium by ultracentrifugation ( 107 , 000 g for 2 h ) through 25% sucrose cushions . Lysis of the virion pellets and CA Co-IP were performed as described above for the infected cells . The p12 domain of the Gag polyprotein of the Moloney MLV ( SWISS-PROT: P03332 ) was aligned with 12 homologous sequences that were retrieved from the SWISS-PROT database , using a PSI-BLAST search . The sequences were then multiple aligned ( MSA ) using the CLUSTALW program , and a phylogenetic tree consistent with the MSA was constructed . Calculation of the conservation scores for each residue was carried out by the Rate4Site algorithm [59] , which is based on the Maximum Likelihood method . All these stages were curried out automatically by the ConSeq server ( http://conseq . tau . ac . il/ ) , as described in [60] . | All retroviruses reverse transcribe their RNA genome to a DNA copy in the cytoplasm of the infected cell . To be expressed , the viral genomic DNA has to travel to the cell nucleus and to integrate into the cellular chromosomes . This trafficking is governed by cellular and viral proteins that associate with the viral genome to form a ‘pre-integration complex’ ( PIC ) , yet the full composition of this complex is unknown . Former studies showed that for the murine leukemia virus ( MLV ) , mutations in a viral protein named p12 abrogate MLV infection , after reverse transcription and prior to the integration step , suggesting a role for this protein in early stages of infection . However , the precise mechanism of p12 action is not known . We combined microscopic , genetic and biochemical techniques to provide evidence that the p12 protein is part of the MLV PIC and that it exerts its function from within this complex . These analyses also suggest a role for p12 in the trafficking of the PIC from the cytoplasm to the chromosomes of the infected cell . Altogether , these findings highlight an important ‘building block’ of a complex that is essential for MLV infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"virology/virion",
"structure,",
"assembly,",
"and",
"egress",
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"virology/immunodeficiency",
"viruses",
"virology/viral",
"replication",
"and",
"gene",
"regulation"
]
| 2010 | The Gag Cleavage Product, p12, is a Functional Constituent of the Murine Leukemia Virus Pre-Integration Complex |
Following envelope mediated fusion , the HIV-1 core is released into the cytoplasm of the target cell and undergoes a series of trafficking and replicative steps that result in the nuclear import of the viral genome , which ultimately leads to the integration of the proviral DNA into the host cell genome . Previous studies have found that disruption of microtubules , or depletion of dynein or kinesin motors , perturb the normal uncoating and trafficking of the viral genome . Here , we show that the Kinesin-1 motor , KIF5B , induces a relocalization of the nuclear pore component Nup358 into the cytoplasm during HIV-1 infection . This relocalization of NUP358 is dependent on HIV-1 capsid , and NUP358 directly associates with viral cores following cytoplasmic translocation . This interaction between NUP358 and the HIV-1 core is dependent on multiple capsid binding surfaces , as this association is not observed following infection with capsid mutants in which a conserved hydrophobic binding pocket ( N74D ) or the cyclophilin A binding loop ( P90A ) is disrupted . KIF5B knockdown also prevents the nuclear entry and infection by HIV-1 , but does not exert a similar effect on the N74D or P90A capsid mutants which do not rely on Nup358 for nuclear import . Finally , we observe that the relocalization of Nup358 in response to CA is dependent on cleavage protein and polyadenylation factor 6 ( CPSF6 ) , but independent of cyclophilin A . Collectively , these observations identify a previously unappreciated role for KIF5B in mediating the Nup358 dependent nuclear import of the viral genome during infection .
Human Immunodeficiency Virus Type-1 ( HIV-1 ) , like all primate lentiviruses , possesses the ability to infect non-dividing cells . The ability to infect non-dividing cells is conveyed by the viral capsid ( CA ) protein which makes up the viral core that houses the viral genome [1 , 2 , 3] . CA has important functions during the early stages of HIV infection . Specifically , it acts to shield the viral genome from cytoplasmic sensors capable of inhibiting infection and activating innate immune signaling pathways[4 , 5 , 6 , 7] . The ability to protect the viral genome from host factors in the cytoplasm and also mediate the nuclear import of the viral genome is complicated by the dimensions of the viral core , which at ~120nm x 60 nm [8 , 9] , significantly exceeds the size limitation of nuclear pore cargoes , which is ~39 nm [10 , 11] . These findings collectively suggest that core disassembly , known as uncoating , must be properly regulated so that the viral genome can be delivered to the nucleus while keeping the genome shielded from host factors in the cytoplasm . CA must therefore interact with numerous host factors to ensure that these functions are performed in a spatiotemporally appropriate fashion . Genome wide screens for host factors required for replication identified numerous proteins associated with the nuclear import machinery of the cell , including the nuclear pore complex ( NPC ) proteins Nup358 and Nup153 [12 , 13 , 14] . In the context of the nuclear pore , Nup358 forms a basket on the cytoplasmic side of the NPC , while Nup153 serves a similar function on the opposite side of the NPC ( reviewed in [15] ) Functional studies demonstrate that the virus preferentially relies on NUP358 and Nup153 to enter the nucleus of non-dividing cells [16 , 17 , 18 , 19 , 20 , 21 , 22] . In the case of Nup153 , the ability to support HIV-1 infection maps to the viral CA protein [21] , and structural studies have found that the phenylalanine/glycine repeats ( FG repeats ) present on Nup153 are able to bind a conserved “pocket” on assembled CA[22 , 23 , 24 , 25] . This binding pocket , which is formed by inter-molecular association of N-terminal and C-terminal domains in adjacent CA proteins in the CA hexamer , is also the binding site for numerous cellular factors and antiviral compounds , including cleavage and polyadenylation specificity factor 6 ( CPSF6 ) [20 , 23 , 24] . In contrast to Nup153 , the role of Nup358 in HIV-1 infection remains less clear . Nup358 has a cyclophilin ( Cyp ) homology domain , which is capable of binding CA at the conserved Cyp binding loop present on CA , and has been reported to induce isomerization of the peptide bond at P90 to facilitate infection and core disassembly ( uncoating ) [18 , 26] . However , other studies have shown that the dependence of HIV-1 infection on Nup358 does not require the Cyp homology domain [27] , leaving the mechanism by which Nup358 interacts with the viral ribonucleoprotein complex and facilitates infection unclear . We and others have recently observed that microtubule motors dynein and the kinesin-1 motor KIF5B are required for HIV-1 uncoating and infection[28 , 29 , 30] . Specifically , uncoating in the cytoplasm was inhibited by microtubule disruption or by depletion of dynein heavy chain or KIF5B[28 , 30] . However , these studies did not determine the mechanism by which microtubule trafficking was coupled to the disassembly of the viral core . Here , we show that depletion of theKinesin-1 motor KIF5B leads to the accumulation of HIV-1 viral cores at the nucleus . During infection , KIF5B mediated anterograde trafficking of viral cores is accompanied by the displacement of Nup358 from the nuclear membrane to the cytoplasm , where it associates with viral cores . Critically , this cytoplasmic trafficking of Nup358 and its association with viral cores is perturbed by mutations which disrupt the hydrophobic binding pocket in assembled CA ( N74D ) and mutations which disrupt the conserved Cyp binding loop on CA ( P90A ) . The functional relevance of this interaction was demonstrated by the observation that KIF5B depletion inhibits nuclear import of the viral genome in a CA dependent manner , as N74D and P90A CA mutants are not sensitive to KIF5B depletion . These studies provide evidence of a bipartite interaction between CA and Nup358 , in which the hydrophobic binding pocket and Cyp binding loop both contribute to this association . These studies demonstrate , for the first time , the determinants required for the interaction of Nup358 and the viral core , and identify a critical role for KIF5B in the nuclear import of HIV-1 during infection .
HeLa and 293T cells ( ATCC ) were cultured in Dulbecco’s modified Eagle medium ( DMEM ) ( Cellgro ) supplemented with 10% fetal bovine serum ( FBS ) , 1000 U/ml penicillin , 1000 U/ml Streptomycin and 10 μg/ml Ciprofloxacin Hydrochloride . HeLa TZM-bl cells stably expressing CD4 and CCr5 were obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH: TZM-bl from Dr . John C . Kappes , Dr . Xiaoyun Wu and Tranzyme Inc . To generate monocyte derived macrophages ( MDM ) , peripheral blood mononuclear cells ( PBMC ) were obtained from peripheral blood ( Loyola University IRB 208423 ) immediately after collection by layering over lymphocyte separation medium ( Corning ) and spinning at 2000rpm for 30min . The PBMC were washed twice in PBS and monocytes positively selected using the EasySep Human CD14 positive selection kit ( STEMCELL ) following the manufactures protocol . Isolated monocytes were resuspended in RPMI media supplemented with 10% fetal bovine serum ( FBS ) , 1000 U/ml penicillin , 1000 U/ml Streptomycin and 10 μg /ml Ciprofloxacin Hydrochloride . Monocytes were differentiated to macrophages by resuspending in RPMI media containing 50 ng/ml granulocyte macrophage colony-stimulating factor ( GM-CSF ) and 50 ng/ml macrophage colony-stimulating factor ( M-CSF ) . Cells were plated on culture dishes and differentiated for 8 days before performing experiments . Human blood obtained for this study was de-identified prior to our work . We did not have any interactions with the human subject , or protected information , and therefore no informed consent was required . To generate pseudotyped HIV-1 , 293T cells seeded in a 15cm dish at 60% confluency were transfected with 8 . 25 μg pCMV-VSVg and 16 . 75 μg of R7ΔEnvGFP using polyethylenimine ( PEI , MW 25000 Polysciences ) . HIV-1 virus harboring the HXB2 glycoproteins were generated by transfecting 293T cells on 10cm dish with 7 μgR7ΔEnvGFP and 3 μg HXB2 envelope glycoproteins using PEI . P8 . 9NDSB is a minimal HIV-1 packaging plasmid for gag and pol expression and described before [31] . p8 . 9NDSB wildtype ( WT ) and capsid mutants N74D and P90A was kindly provided by Jeremy Luban ( University of Massachusetts Medical School ) [32] . To generate pseudotyped reporter virus , 293T cells seeded on 10cm dish were transfected with 3 ug p8 . 9NdSB WT or mutants , 2 μg pCMV-VSVg and 5 μg pLVX-GFP ( clontech ) . MLV reporter virus were produced by PEI transfection of 293T cell on 10cm plate with 5 μg of pCigB ( generous gift of Dr . Greg Towers ) , 3 μg of GFP reporter vector [33] and 2 μg of pCMV-VSVg . Gag-integrase-Ruby was kindly provided by Thomas J . Hope ( Northwestern University ) and virus were produced by PEI transfection of 293T cell on 10cm plate with 5 μgR7ΔEnvGFP , 2 . 5 μg Gag-intergrase-Ruby and 2 . 5 μg pCMV-VSVg . Viruses were harvested 48 hours after transfection , spun for 5 minutes at 1200 rpm and filtered through a 0 . 45 μm filter ( Milipore ) . Synchronized infection was performed as described previously [34] . In brief , cells were spinoculated at 13°C for 2 hours at 1200xg , after which virus containing medium was removed and replaced with 37°C media . Infectivity was measured 48 hours post synchronized infection and percentage of GFP positive cells was determined using BD FACSCanto II cytometer ( BD Bioscience ) . Multiplicity of infection ( MOI ) was calculated based on the equation: MOI = -lnP ( 0 ) where P ( 0 ) is the proportion of uninfected cells . HIV-1 capsid protein p24 was stained using either anti-p24 AG3 . 0 ( mAb from Dr . Jonathan Allan ) or 183-H12-5C ( HIV-1 p24 hybridoma from Dr . Bruce Chesebro ) and were obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH . Rabbit polyclonal antibodies against Nup358 ( ab64276 ) and Nup153 ( ab84872 ) were purchased from Abcam . Rabbit anti-KHC to detect kinesin-1 heavy chain ( sc-28538 ) were from Santa Cruz Biotechnology . Rabbit polyclonal to CPSF6 ( ab99347 ) was purchased from Abcam and rabbit polyclonal to cyclophilin A ( PA1-025 ) was purchased from ThermoScientific . Secondary antibodies conjugated to fluorophore for immunofluorescence studies were purchased from Jackson Immunoresearch Laboratories . Cyclosporine A ( CsA; Sigma Aldrich ) was used at a final concentration of 2 . 5 μM . DAPI to stain nucleus was obtained from Sigma Aldrich . Cell lysates were prepared by lysing cells with NP-40 lysis buffer ( 100mM Tris pH 8 . 0 , 1% NP-40 , 150 mM NaCl ) containing protease inhibitor cocktail ( Roche ) for 10 minutes on ice . Following incubation , lysates were spun down at 13 , 000 rpm for 10 min and supernatant collected for western blot analysis . In brief , 2x Laemmli sample buffer were added to the lysed sample and incubated at 100°C for 5min . Protein concentration was measured using Pierce BCA protein assay kit ( Thermo Scientific ) and equal amount of protein was loaded in to an 8% polyacrylamide gel for SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) . To detect Nup358 , proteins were loaded on to a 4–15% gradient gel ( Bio-Rad ) . Upon separation , the proteins were transferred to nitrocellouse membrane ( Bio-Rad ) . Membranes were probed using specific primary antibodies and then probing with secondary antibodies conjugated to Horseradish Peroxidase ( HRP ) ( Thermo Scientific ) . Antibody complexes were detected using SuperSignal West Femto Chemiluminescent Substrate ( Thermo Scientific ) . Chemiluminescence was detected using the ChemiDoc Imaging System ( Bio-Rad ) . siRNA sequences targeting KIF5B heavy chain [35] and Nup358 [36] has been described before . siRNA targeting human cyclophilin A ( sc-105263 ) and CPSF6 ( sc-72990 ) were obtained from Santa Cruz Biotechnology , Inc . A control siRNA targeting luciferase was purchased from Fisher Scientific . siRNA’s were transfected in to HeLa or HeLa TZM-bl cells plated on 6 well plate using Lipofectamine 2000 ( Thermo Fisher ) as per the manufactures protocol . A second transfection was performed 24 h later . 72h after the first siRNA transfection , cells were collected and seeded on to 24 well plate for subsequent experiments a day later . Western blotting was performed to monitor knockdown efficiency of the siRNA . Z-stack images were collected with a DeltaVision wide field fluorescent microscope ( Applied Precision , GE ) equipped with a digital camera ( CoolSNAP HQ; Photometrics ) , using a 1 . 4-numerical aperture 100× objective lens . Excitation light was generated with an Insight SSI solid state illumination module ( Applied Precision , GE ) and were deconvolved with SoftWoRx deconvolution software ( Applied Precision , GE ) . In any experiment , identical acquisition conditions were used to acquire all images . Following deconvolution , images were analyzed by Imaris 7 . 6 . 4 ( Bitplane ) . An algorithm was designed using the surface feature function in Imaris to generate surfaces around signal of interest and the maximum fluorescence intensity associated within these surfaces were measured . The algorithm was applied to all images within the same experiment . For live cell experiments , cells were plated in delta DPG dishes ( Thermo Fisher Scientific ) . Cells were maintained at 5% CO2 at 37°C in an environmental chamber on a DeltaVision microscope , and images were captured in a z series on an electron multiplied charge coupled device digital camera ( EMCCDCascade 2; Photometrics ) and deconvolved using SoftWoRx deconvolution software . Images were acquired every 15 seconds for 10 minutes . The in situ uncoating assay has been described before [37] . For the assay , fluorescently labeled HIV-1 was generated by transfecting 293T cells on a 25cm dish with 6 . 25 μg S15-mCherry , 5 . 25 μg GFP-Vpr , 7 . 5 μg R7ΔEnvGFP , and 6 μg pCMV-VSVg using PEI . Following fusion , S15-mCherry labeled viral membrane is lost which allows to effectively discriminate between viruses that have non- productively endocytosed by the target cells ( S15-mCherry+ , GPF-Vpr+ ) from those that have productively fused into the cytoplasm ( S15-mCherry− , GFP-Vpr+ ) . A synchronized infection performed on HeLa cells with these fluorescent labeled virions ( Labeling efficiency , 94% ) . Following spinoculation , media was aspirated and changed to 37°C warm media . Cells were incubated at 37°C and fixed at various time points post infection . Coverslips were then subjected to indirect immunofluorescence as described before [37]to stain for viral capsid protein p24 using the anti-p24 mAb AG3 . 0 and a Cy5 conjugated secondary antibody ( Jackson Immunoresearch ) and mounted on glass slides . Images were acquired at 100x magnification using DeltaVision wide field fluorescent microscope . 20 fields acquired per coverslip . After deconvolution , GFP-Vpr viral complexes was identified using the surface function in Imaris ( Bitplane ) software and the maximal S15-mCherry and p24 signal present within these individual GFP-Vpr generated surfaces was determined . From the large sets of data acquired , the average maximal p24 intensity of fused ( S15-mCherry negative ) populations of virions was determined . Cells were infected with equal amount of virus as determined through a p24 Elisa kit ( Advanced Bioscience laboratories ) and RT-PCR was performed to determine the late reverse transcription ( Late RT ) and 2-LTR products and carried out as described before [38] . GAPDH was used as a housekeeping gene for normalization . Primers for GAPDH were forward , GCACCGTCAAGGCTGAGAAC and reverse , GCCTTCTCCATGGTGGTGAA . Genomic DNA from cells was extracted following the DNeasy Blood and Tissue Kit protocol ( Qiagen ) . DNA concentration was determined and equal amount of DNA was digested with Dpn1 ( New England BioLabs ) before performing RT-PCR . Duolink proximity ligation assay ( PLA ) kit was purchased from Sigma and assay performed as described by the manufacturer ( Olink Bioscience ) . In brief , cells grown on coverslips were fixed with 3 . 7% PFA 3h post synchronized infection . To detect interaction between viral capsid protein p24 and Nup358 , cells were permeabilized and blocked in 3% BSA followed by incubation with primary antibodies targeting viral protein p24 ( mouse monoclonal ) and Nup358 ( rabbit polyclonal ) . After primary staining , coverslip containing cells were washed and incubated ( 1h , 37°C ) with secondary anti-mouse conjugated with minus and anti-rabbit conjugated with plus Duolink II PLA probes . Coverslips were washed again and incubated with ligation-ligase solution ( 30min , 37°C ) followed by washing and subsequent incubation with amplification-polymerase solution ( 100min , 37°C ) containing Duolink II insitu detection reagent Red . Finally , coverslips were washed and mounted with Duolink II mounting medium containing DAPI . Interactions were detected as fluorescent spots ( λ excitation/emission 598/634 nm ) under a fluorescence microscope . To determine the amount of p24 or Nup358 signal present in the cytoplasm and around the nucleus , the DAPI staining to detect nuclei along with the masking function in Imaris was utilized . An algorithm was designed using the DAPI channel and surface function to detect the cell nuclei . To detect nuclear or perinuclear signal , all signal outside the nuclei surface generated was masked using the masking tool in Imaris and saved as a separate channel . Then a new algorithm was designed utilizing this new channel and the surface function to detect the p24 or Nup358 signal present around the nucleus . The total sum intensity of these surface masks was calculated to determine the amount of protein present around the nucleus . Similarly , to detect cytoplasmic signal , all signal outside of the nuclear surface mask was determined . The relative fraction of the perinuclear signal was calculated as a percentage of the total signal ( perinuclear + cytoplasmic ) . GraphPad Prism version 5 . 00 ( GraphPad Software , Inc . ) was employed for statistical analysis and to make graph . Statistical significance was assessed using the One-Way or Two-way ANOVA and Bonferroni posttest . P<0 . 05 was considered significant in our experiments . Data is represented as mean +/- SEM depending on the graph .
To investigate the role of Nup358 and KIF5B in HIV-1 uncoating , we looked at the uncoating states of individual virions following a synchronized infection in cells depleted of Nup358 , KIF5B or both proteins by siRNA using an in situ uncoating assay . This assay measures the amount of CA which remains associated with individual viral particles during infection . Viral particles are labeled with GFP-Vpr , and unfused viral particles are removed from analysis using a fluorescent marker of viral membranes ( S15-mCherry ) , allowing the relative amount of p24CA which remains associated with populations of cytoplasmic viral particles to be analyzed [28] . Cells were infected with VSV pseudotyped virus labeled with GFP-Vpr ( for labeling viral complexes ) and S15-mCherry ( to determine fusion state ) and fixed at various time points post infection . The amount of p24 associated with individual virions that have productively entered the cells ( S15 Negative ) was determined by staining with a monoclonal antibody to p24 and measuring the p24 fluorescence intensity using wide field deconvolution microscopy . Western blot analysis of Nup358 and KIF5B showed efficient depletion of each protein at the time of infection ( Fig 1A ) . Depletion of either or both protein was sufficient to substantially inhibit HIV-1 infection ( Fig 1B ) . Following synchronized infection , we observed that depletion of KIF5B delayed HIV-1 uncoating , as measured by p24 staining of individual GFP-Vpr puncta . This effect was apparent in individual experiments ( Fig 1C ) and in the normalized average of three independent experiments ( Fig 1D ) . Similarly , we observe a significant increase in CA staining in the cytoplasmic virion population in Nup358 knockdown cells ( Fig 1C and 1D ) . A similar effect on uncoating was also observed following depletion of both KIF5B and Nup358 , and the effects of depletion of both proteins was similar to the effects observed when either was depleted ( Fig 1C and 1D ) . This delay in uncoating was not due to upstream perturbation of fusion , as a similar percentage of virions had lost their S15 membrane label at the time points examined ( S1 Fig ) . These results demonstrate that Nup358 depletion delays HIV-1 uncoating . The observation that knocking down both Nup358 and KIF5B did not increase the infectivity defect or uncoating defect observed when these proteins were depleted individually is consistent with these proteins acting at a similar step in infection . In the experiments described in Fig 1 , we also observed that KIF5B depletion and Nup358 depletion ( Fig 2A ) led to the accumulation of viral cores around the nucleus , rather than the dispersed localization observed three hours following a synchronized infection ( Fig 2B ) . To quantify this phenotype , we developed a localization assay in which a perinuclear surface mask is generated using DAPI fluorescence . An algorithm which reliably overestimated the boundary of the nuclear DAPI stain was used to measure the fraction of virions within the perinuclear region and in the cytoplasm ( Fig 2C ) . The algorithm developed typically generated a surface mask 2μm from the boundary of the nucleus ( DAPI ) . This method allowed us to quantify the number of p24 puncta around the perinuclear region ( inside the perinuclear mask ) and in the cytoplasm ( outside the nuclear mask ) in a large number of cells in an automated and unbiased manner . Using this assay , we observed a significant increase in the amount of capsid localized around the perinuclear region in KIF5B and NUP358 knockdown cells relative to control cells at three hours post-infection ( Fig 2D ) . To ensure that this effect was not due to the mode of viral entry , we performed similar experiments as above in HeLa TZM-bl cells , which express CD4 and coreceptors required for HIV-1 envelope fusion . Depletion ofKIF5B and Nup358 from these cells ( Fig 2E ) led to a similar decrease in infectivity following infection of these cells with viruses using the HXB2 envelope glycoproteins for entry ( Fig 2F ) . As was observed with VSV-g pseudotyped virions , KIF5B and Nup358 depletion led to a perinuclear accumulation of viral particles around the nucleus , where a more dispersed localization of particles was observed in the control siRNA infected cells ( Fig 2G and 2H ) . To understand the functional relevance of KIF5B mediated trafficking of HIV-1 during infection , we measured viral reverse transcription and nuclear import ( as measured by 2-LTR formation ) products in cells depleted of KIF5B or Nup358 by siRNA using quantitative PCR . Infection with wildtype virus showed no change in the amount of late reverse transcription products following KIF5B or Nup358 depletion ( Fig 3A ) . However , we observed that Nup358 or KIF5B knockdown reduced nuclear import and infection of WT virus ( Fig 3B and 3C ) , consistent with previous reports [18 , 20 , 29] . As previously reported , Nup358 depletion did not significantly influence nuclear import or infection by the N74D or P90A mutants [18 , 20] . Strikingly , we also observed that depletion of KIF5B did not affect nuclear import or infection by the P90A and N74D mutant viruses ( Fig 3B and 3C ) . Collectively , these data demonstrate that WT HIV-1nuclear entry is mediated by a Nup358 , KIF5B dependent mechanism , and that nuclear entry of the N74D and P90A virus occur in a Nup358 and KIF5B independent manner . Given the defective nuclear entry observed following KIF5B and Nup358 knockdown , we examined the ability of HIV to associate with Nup358 during infection . We hypothesized that WT , but not the N74D or P90A mutants , might interact with Nup358 more efficiently during infection . To ensure the specificity of our antibody , we transfected HeLa cells with control siRNA or siRNA targeting Nup358 . Nup358 depletion effectively abrogated staining observed using a Nup358 polyclonal antibody , demonstrating that this antibody is not cross-reacting with another cytoplasmic or NPC protein ( S2A Fig ) . In HIV-1 infected monocyte derived macrophages ( MDMs ) , infectivity of the N74D and P90A mutants was attenuated ( Fig 4A ) , while infectivity was not affected in HeLa cells ( Fig 3C ) , as previously reported [5 , 20] . In both cell types , Nup358 localized to the nuclear membrane and to cytoplasmic puncta three hours after a synchronized infection . In contrast , Nup358 localization following infection with viral particles lacking envelope , or with the N74D or P90A mutants , appears less cytoplasmic and predominated around the nuclear envelope ( Fig 4A ) . Algorithm assisted quantification of Nup358 relocalization confirmed that WT induced the cytoplasmic accumulation of Nup358 in both MDMs and HeLa cells , while virus lacking envelope , or harboring the N74D or P90A mutations , did not ( Fig 4A and 4B ) . Infection with murine leukemia virus ( MLV ) , which cannot infect non-dividing cells , and is not thought to interact with NUP358 during infection , was similarly unable to induce NUP358 relocalization ( S2B Fig ) . This result was not specific for the mode of entry , as HXB2 pseudotyped HIV-1 was able to induce NUP358 redistribution in TZM-bl cells ( Fig 4C and 4D ) . This result was also specific for NUP358 , as NUP153 did not colocalize appreciably with HIV-1 capsid ( S3C Fig ) , nor was its localization impacted by HIV-1 infection ( S3D Fig ) . It is known that Nup358 interacts dynamically with the NPC and Nup358 is also known to be a cargo adapter for KIF5B [39] , so we next asked if the cytoplasmic relocalization of Nup358 induced by HIV-1was KIF5B dependent . KIF5B knockdown inhibited the NUP358 relocalization induced by HIV-1 infection ( Fig 4E and 4F ) . Collectively , these data demonstrate that HIV-1 induces NUP358 relocalization during infection in a CA and KIF5B dependent manner . We also assessed the degree of colocalization between viral cores and Nup358 following infection of MDMs . Colocalization of Nup358 and HIV-1 cores was readily detectable following WT infection in MDMs ( Fig 5A ) and HeLa cells ( Fig 5B ) . However , this pattern of colocalization was not apparent after infection with the capsid mutants or WT/ΔEnv ( S4 Fig ) . To quantify the association between Nup358 and HIV-1 cores , we determined the percentage of p24 puncta which were positive for Nup358 in these infections . Three hours following infection of MDMs , a higher percentage of WT viral particles were positive for Nup358 than WT/ΔEnv , N74D or P90A ( p<0 . 001 ) ( Fig 5C ) . A similar trend was observed 1 hour following infection , although this difference was less significant ( p<0 . 05 ) ( Fig 5C ) . The degree of colocalization between both the N74D and P90A mutants was similar to the degree of background colocalization between Nup358 and WT/ΔEnv virus ( Fig 5C ) at both time points . Similar results were also observed in HeLa cells ( Fig 5D ) . To further validate this observation , we performed similar experiments as above but employed the proximity ligation assay ( PLA ) . This assay utilizes species specific secondary antibody probes conjugated to complementary oligonucleotides . When two such antibodies are in close proximity ( <30-40nm ) , the complementary strands can be ligated and amplified and detected as bright fluorescent puncta , thus measuring protein-protein interaction with high specificity and sensitivity[40 , 41 , 42 , 43 , 44] . In a PLA using primary antibodies to CA and NUP358 , PLA puncta were readily detected in the cytoplasm of MDMs and HeLa cells infected with WT virus , while a smaller number of puncta were observed following infection with WT/ΔEnv virus . The number of puncta quantified following WT/ΔEnv infection was similar to the number of background puncta in uninfected control cells ( Fig 6A and 6B ) . The N74D mutant also failed to induce PLA puncta following infection ( Fig 6A and 6B ) , consistent with the lack of colocalization observed by immunofluorescence analysis ( Fig 5A and 5B ) . However , we did observe that infection with the P90A mutant induced more PLA puncta than the N74D mutant and other controls ( Fig 6A and 6B ) , although the number of puncta induced by this mutant was consistently lower than WT in MDMs and HeLa cells ( Fig 6B ) . Collectively , these experiments demonstrate a specific association between WT viral cores and Nup358 in the cytoplasm of target cells that is dependent on the hydrophobic pocket disrupted by the N74D mutation and facilitated by the Cyp binding loop disrupted by the P90A mutation . We next used the NUP358/CA PLA assay to determine if KIF5B was necessary to promote the interaction between CA and NUP358 . Infection of HeLa cells transfected with control or KIF5B specific siRNA did not influence the number of PLA puncta observed following infection with WT virus ( Fig 6C and 6D ) . However , the PLA puncta were predominantly localized to a perinuclear region following KIF5B depletion . Finally , siRNA depletion of NUP358 reduced the number of PLA puncta observed , demonstrating the specificity of the assay . These data demonstrate that engagement of NUP358 by HIV cores occurs independently of KIF5B . The data above demonstrates that HIV-1 cores can induce the cytoplasmic accumulation of NUP358 , and this relocalization of NUP358 is prevented by KIF5B depletion . Taken together with the observation that KIF5B depletion results in the perinuclear accumulation of viral cores , this suggests that the viral core reaches the nuclear pore and is subsequently trafficked away from the NPC by KIF5B . To test this hypothesis , we labeled synchronously infected these cells with HIV-1 virions labeled with a Gag-Integrase-Ruby ( GIR ) construct . This Ruby variant of a recently described GFP construct [45] , contains a protease cleavage site between Gag and Integrase , such that viral maturation results in the liberation of Integrase-Ruby when the construct is expressed in virus producing cells . We used GIR labeled HIV-1 to infect HeLa cells transfected cells with a bacterial artificial chromosome expressing NUP358-GFP [46] . In cells , this NUP358-GFP construct localized to the nuclear envelope and to cytoplasmic puncta which were present in the presence or absence of HIV-1 infection . Live cell imaging was performed 1–3 hours following infection . At this time following infection , numerous examples of viruses associated with the nuclear envelope could be observed . Such viruses were frequently observed to traffic along the periphery of the nucleus , consistent with a previous report [47] . Viruses exhibiting this behavior could frequently be observed to become displaced from the nuclear envelope . As shown in S1 Movie , the indicated virus can be observed to traffic away from the nucleus and ultimately return to the area of the nuclear envelope during a 10-minute acquisition period ( S1 Movie ) . As seen in this movie , we did not detect sustained trafficking of NUP358-GFP with HIV-1 , although it is possible that the imaging conditions required for live cell imaging were insufficient to detect small amounts of NUP358-GFP associated with HIV-1 cores in these situations . We did , however , frequently observe cytoplasmic associations between HIV-1 and NUP358-GFP that are consistent with the displacement of NUP358-GFP from the nuclear envelope ( Fig 7 , S2 Movie ) . In the sequence provided , an accumulation of NUP358-GFP appears and colocalizes with a viral particle that was associated with the nuclear envelope in the previous frame . As apparent in the movie , this association does not appear uniformly stable throughout the acquisition period . However , some amount of NUP358-GFP signal remains present on this particle 90 seconds after it becomes displaced from the nuclear envelope . These data are consistent with the displacement of NUP358-GFP from the nuclear pore . However , we cannot exclude the possibility that the colocalization observed was between HIV-1 and one of the numerous preexisting , cytoplasmic accumulation of NUP358 in these cells , which could be seen trafficking considerable distances during the 15 second acquisition intervals . However , these experiments do demonstrate anterograde trafficking of HIV-1 from the nuclear envelope and frequent but transient periods of colocalization between HIV-1 and cytoplasmic NUP358-GFP .
In this study , we demonstrate that HIV-1 infection induces the KIF5B dependent relocalization of Nup358 . This relocalization of Nup358 , and cytoplasmic association with HIV-1 , was dependent on entry of HIV-1 into the target cell cytoplasm , as this relocalization was not observed using WT/ΔEnv HIV-1 . Although the role of Nup358 in facilitating HIV-1 infection has been observed by others [12 , 14 , 16 , 17 , 18 , 26 , 27] , a clear understanding of the molecular interactions occurring between CA and Nup358 during infection has not emerged . As a large , multi-domain protein associated with the cytoplasmic side of the NPC , numerous functions have been ascribed to Nup358 with respect to its role in HIV-1 infection[16 , 18 , 26 , 27] . Two studies have supported a role for Nup358 driving HIV-1 uncoating via interaction with the Cyp homology domain in Nup358 [18 , 26] . However , another study which demonstrated that the Cyp homology domain is not required for the Nup358 dependent enhancement of HIV-1 infection suggests that the role of Nup358 in HIV-1 infection cannot be fully explained by the Cyp homology domain [27] . To understand the determinants in Nup358 required for the interaction with HIV-1 cores , we exploited the measurable association between Nup358 and HIV-1 cores in the cytoplasm , and CA mutants which disrupt putative Nup358 binding surfaces . Specifically , we observed that mutations which disrupt the hydrophobic binding pocket in assembled CA ( N74D ) and mutations which disrupt the conserved Cyp binding loop on CA ( P90A ) both perturb the relocalization of Nup358 induced by HIV-1 infection ( Fig 4 ) and the association of CA and Nup358 during infection ( Figs 5 and 6 ) . The association of CA and Nup358 in the cytoplasm was measured in two distinct but related methods . When the intensity of Nup358 staining associated with individual viral cores was measured , neither the N74D or P90A mutant exhibited significantly more colocalization with Nup358 than WT/ΔEnv control virus ( Fig 5 ) , suggesting that both CA epitopes are required for Nup358 association . However , when the interaction between CA and Nup358 was measured by PLA , we observed a significant degree of interaction between P90A CA and Nup358 , while the N74D mutant did not induce significantly more puncta than was observed following WT/ΔEnv or mock infection ( Fig 6 ) . This suggests that although the association between CA and Nup358 may be facilitated by both the hydrophobic binding pocket and Cyp binding loop of CA , the hydrophobic pocket is required for this interaction . Of note , the PLA , by its nature , detects the frequency but not the magnitude of interactions between the viral core and Nup358 , as the puncta identified are generated by enzymatic amplification of nucleotides when individual secondary antibodies are in close enough proximity to enable the reaction . As such , the PLA sensitively detects the interaction between two proteins , rather than the degree of association . Despite these methodological differences , both methods demonstrated that the N74D mutation prevented the association of Nup358 with viral cores . Differences in the colocalization analysis and PLA analysis suggest that the P90A mutation reduces , but does not eliminate , the binding of Nup358 to CA . Multivalent binding of Nup358 and the viral core may help to reconcile previously discordant observations regarding the role of the Cyp homology domain in infection [18 , 26 , 27] . We also observe that CPSF6 depletion similarly prevents the redistribution of NUP358 to the cytoplasm and HIV-1 association . This suggests that CPSF6 somehow facilitates the interaction between HIV-1 and NUP358 , consistent with a role for CPSF6 in HIV-1 nuclear import observed in other imaging based approaches [48 , 49] . CPSF6 is known to bind to the hydrophobic pocket , formed between adjacent CA monomers , and this pocket is known to be disrupted by the N74D mutation [22 , 23 , 24 , 25 , 50] . The similarity in the results obtained following CPSF6 depletion and using the N74D mutant suggest a common mechanism . However , given the large number of hydrophobic pockets present in the viral core , it remains unclear if CPSF6 acts as an adaptor linking the viral core and NUP358 , or if CPSF6 engagement somehow facilitates the ability of NUP358 to bind the core in similar , adjacent hydrophobic pockets present on the core . One limitation in imaging based studies of viral trafficking , including this one , is that such methods analyze bulk populations of viruses at early times following infection , and therefore analyze many viral particles which are not destined to lead to productive infection . However , in this study , the phenotypes observed using imaging based approaches were in good agreement with qPCR based detection of reverse transcription ( Late RT ) , nuclear import ( 2 LTR-circles ) and infection . We observe that infection and nuclear import of WT HIV-1 is perturbed by KIF5B knockdown , consistent with recent observations by us and others [28 , 29] . Critically , however , we also observe here that the N74D and P90A mutants are not similarly affected by KIF5B depletion ( Fig 3 ) . Notably , nuclear entry of the N74D and P90A virus has previously been shown to be Nup358 independent [18 , 20] . The observation that these mutants are similarly not dependent on KIF5B for nuclear import and infection suggests that KIF5B and Nup358 act cooperatively to facilitate nuclear entry of the viral complex . In considering the mechanisms by which NUP358 and KIF5B might mediate this effect , two aspects of infection should be considered . First , the intact HIV-1 core is too large , by half , to fit through the nuclear pore [8 , 9 , 10 , 11] , making it very unlikely that an intact core can translocate through an intact nuclear pore . However , numerous studies have recently observed intranuclear HIV-1 complexes containing CA [45 , 48 , 49] , which strongly suggests that some CA must remain associated with the viral genome during nuclear import . As such , there are two mechanisms by which KI5B and Nup358 may promote the nuclear entry of the viral genome during infection ( Fig 9 ) . First , cooperative core uncoating , as we measured using an in situ uncoating assay ( Fig 1 ) , may reorganize the CA associated with the viral genome to achieve a complex with dimensions capable of translocating through the nuclear pore . Alternatively , KIF5B mediated displacement of NUP358 from the NPC may disrupt the NPC in such a way that the ability of the viral core to traverse the nuclear pore is increased . This would be consistent with a recent study by Chin and coworkers , who observed that association with CSPF6 enhanced nuclear entry and potentiates the ability of the viral genome to traffic deeper into the nucleus[48] . Taken together with the observation that KIF5B depletion inhibits nuclear import ( Fig 3 ) [28 , 29] , and the finding of others that Nup358 and CPSF6 depletion alters the genomic distribution of HIV-1 integration sites [18 , 51] , we speculate that the ability of the viral core to induce the translocation of Nup358 away from the NPC in a CPSF6 dependent fashion may alter the NPC environment in a way that allows the nuclear translocation of larger cargoes , thus facilitating the deeper nuclear penetration of viral genomes , as observed by Chin et al . Such a model might also explain other studies which seem to observe alternate pathways of HIV-1 nuclear import [18 , 20] ( Fig 8 ) . In that regard , it is notable that studies examining the uncoating and nuclear entry of adenovirus have reported that a Kinesin-1 dependent , Nup358 dependent mechanism mediates the uncoating of adenovirus at the NPC [52] . This study observed that anterograde trafficking by Kinesin-1 induced the gross disruption of the NPC , such that the nuclear entry of large molecules above the diffusion barrier was facilitated by adenovirus infection [52] . In our study , we were not able to demonstrate a similar disruption of the NPC induced by HIV-1 infection . However , these negative data do not preclude a similar NPC disruption from occurring during HIV-1 infection , as the study demonstrating NPC disruption at a cellular level used a much higher MOI than was used in our studies [52] . Our analysis into the cellular determinants which are required for this interaction between Nup358 and CA revealed that CPSF6 is required for this association , while CypA is not required for this interaction ( Fig 7 ) . Understanding how KIF5B and Nup358 cooperatively coordinate the trafficking , uncoating and nuclear import of the viral genome may provide opportunities for therapeutic interventions expected to induce an antiviral response when this process is disrupted . In support of this possibility , Rasaiyaah and coworkers have observed that the same N74D and P90A mutants which we observe fail to associate with Nup358 ( Figs 5 and 6 ) and enter the nucleus independently of KIF5B ( Fig 3 ) also activate cytoplasmic host cell sensors and initiate interferon response [5] . Taken together , this suggests that NUP358 association is necessary to avoid activation of innate sensors by HIV-1 during infection . A better understanding of the cytosolic interactions which allow HIV-1 uncoating and nuclear import to occur without activation of these sensors may provide opportunities to leverage the innate and adaptive immune response to HIV-1 , in the context of treatment or vaccine development . | Fusion of viral and target cell membranes releases the HIV-1 viral capsid , which houses the viral RNA and proteins necessary for viral reverse transcription and integration , into the cytoplasm of target cells . To complete infection , the viral capsid must ultimately traffic to the nucleus and undergo a process known as uncoating to allow the nuclear import of the viral genome into the nucleus , where it subsequently integrates into the genome of the target cell . Here , we show that the concerted actions of microtubule motor KIF5B and the nuclear pore component Nup358 cooperatively facilitate the uncoating and nuclear import of the viral genome . Moreover , we also identify the determinants in the viral capsid protein , which forms the viral capsid core , that are required for KIF5B dependent nuclear entry . These studies reveal a novel role for the microtubule motor KIF5B in the nuclear import of the viral genome and reveal potential intervention targets for therapeutic intervention . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"nuclear",
"import",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"hela",
"cells",
"gene",
"regulation",
"pathogens",
"biological",
"cultures",
"cell",
"processes",
"microbiology",
"viral",
"structure",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"cytoplasmic",
"staining",
"non-coding",
"rna",
"rna",
"viruses",
"cell",
"cultures",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"viral",
"core",
"medical",
"microbiology",
"hiv",
"gene",
"expression",
"microbial",
"pathogens",
"viral",
"packaging",
"hiv-1",
"cell",
"lines",
"viral",
"replication",
"cytoplasm",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"virology",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"cultured",
"tumor",
"cells",
"lentivirus",
"organisms"
]
| 2016 | KIF5B and Nup358 Cooperatively Mediate the Nuclear Import of HIV-1 during Infection |
Protozoan pathogens of the genus Leishmania have evolved unique signaling mechanisms that can sense changes in the host environment and trigger adaptive stage differentiation essential for host cell infection . The signaling mechanisms underlying parasite development remain largely elusive even though Leishmania mitogen-activated protein kinases ( MAPKs ) have been linked previously to environmentally induced differentiation and virulence . Here , we unravel highly unusual regulatory mechanisms for Leishmania MAP kinase 10 ( MPK10 ) . Using a transgenic approach , we demonstrate that MPK10 is stage-specifically regulated , as its kinase activity increases during the promastigote to amastigote conversion . However , unlike canonical MAPKs that are activated by dual phosphorylation of the regulatory TxY motif in the activation loop , MPK10 activation is independent from the phosphorylation of the tyrosine residue , which is largely constitutive . Removal of the last 46 amino acids resulted in significantly enhanced MPK10 activity both for the recombinant and transgenic protein , revealing that MPK10 is regulated by an auto-inhibitory mechanism . Over-expression of this hyperactive mutant in transgenic parasites led to a dominant negative effect causing massive cell death during amastigote differentiation , demonstrating the essential nature of MPK10 auto-inhibition for parasite viability . Moreover , phosphoproteomics analyses identified a novel regulatory phospho-serine residue in the C-terminal auto-inhibitory domain at position 395 that could be implicated in kinase regulation . Finally , we uncovered a feedback loop that limits MPK10 activity through dephosphorylation of the tyrosine residue of the TxY motif . Together our data reveal novel aspects of protein kinase regulation in Leishmania , and propose MPK10 as a potential signal sensor of the mammalian host environment , whose intrinsic pre-activated conformation is regulated by auto-inhibition .
Leishmaniasis is an infectious disease characterized by a variety of pathologies , affecting more than 12 million people worldwide and ranging from self-healing cutaneous lesions to fatal visceral infection [1] . This disease is caused by pathogenic protozoa of the genus Leishmania , which show two major life cycle stages depending on the host . The extracellular promastigote stage develops inside the midgut of sandflies and is transmitted during blood feeding to a vertebrate host where they are ingested by phagocytic cells , notably macrophages . Inside the host cell phagolysosome , promastigotes develop into proliferating intracellular amastigotes . These developmental transitions are triggered by environmental changes , mainly pH ( 7 . 4 to 5 . 5 ) and temperature ( 26°C to 37°C ) , encountered in insect and vertebrate hosts , respectively , and can be mimicked in vitro [2]–[4] . Interfering with amastigote stage development and proliferation by altering the parasite's ability to sense its environment could be a very efficient way to eliminate intracellular Leishmania and thus signaling proteins involved in extra- or intracellular signal transduction are interesting drug target candidates . In eukaryotes , environmental signals are generally sensed and transduced by signaling cascades involving receptors and downstream-regulated protein kinases . The MAPK signaling pathway is a good example of such a phosphorylation cascade [5] as it is composed of mitogen-activated protein kinase kinase kinases ( M3Ks ) , which activate mitogen-activated protein kinase kinases ( M2Ks ) , which in turn activate mitogen-activated protein kinases ( MAPKs ) by dual phosphorylation on the highly conserved TxY motif present within the MAPK activation loop [6] , [7] . MAPKs regulate various important cellular functions , such as cell cycle progression and differentiation , through phosphorylation of a large number of substrates , including transcription factors and MAPK-activated protein kinases , thus modifying gene expression and post-translational regulation , respectively [8]–[14] . While the core cascade M3K-M2K-MAPK is conserved in Leishmania , the absence of classical transcription factors and the largely constitutive gene expression suggest that the response to environmental signals occurs mainly post-translationally through the regulation of the level of protein phosphorylation , rather than through modulation of gene expression [15] . As in higher eukaryotes , the Leishmania MAPK pathway comprises two distinct kinase families , the STE family , which includes five putative M2K and M2K-like and 20 putative M3K members , and the CMGC family , including 17 putative MAPK and MAPK-like members [16] . The comparison between the Leishmania major and the human kinomes revealed an evolutionary expansion relative to genome size of these two kinase families in the parasite . The STE and CMGC families represent 19% and 25% in the Leishmania kinome , compared to 9% and 12% in the human kinome , respectively [17] . These expansions indicate that the MAPK pathway could be of particular importance to parasite development and survival , a possibility that is supported by recent investigations showing that Leishmania MAP kinases are required for flagellar development , intracellular survival and viability [16] , [18]–[23] . Thus , the study of Leishmania MAPKs could hold the key to the understanding of the mechanisms that allow the adaptation of Leishmania to environmental changes required for extra- and intracellular parasite survival during host infection . The activity of the three Leishmania MAPKs MPK4 , MPK7 and MPK10 have been shown to be induced in a stage-specific manner in axenic amastigotes , which occurred concomitant to an increase of their phosphorylation as expected for this class of kinases [24]–[26] . The importance of MPK4 and MPK7 for cell survival and infectivity , respectively , has been established [21] , [26] , [27] , but neither the mode of regulation nor the functions of MPK10 have been studied . MPK10 is conserved in all Leishmania species ( Figure S1A ) with a percentage of identity above 90% . This percentage is even higher if we compare the sequence of L . donovani MPK10 with that of L . major , L . mexicana and L . infantum ( 99% , 98% and 100% , respectively , Figure S1B ) . The close relationship between this kinase and human ERK2 or p38 kinase suggests a potential role in cell differentiation or in response to stress [28] , [29] . Leishmania MPK10 is a highly conserved member of the eukaryotic MAPK family . However , MPK10 does not resemble a classical eukaryotic MAPK with respect to two structural features . First , the alignment of the protein sequence with its mammalian orthologs revealed the presence of a long carboxy-terminal extension [16] , [24] . This is a common feature for Leishmania MAPKs ( observed in 15 out of 17 putative Leishmania MAPKs ) , whereas only five mammalian MAPKs have such an extension . They are usually regulatory domains implicated in the control of the kinase activity , localization or auto-inhibition [30]–[34] . Second , a structural analysis of MPK10 published recently by Horjales et al . provided evidence that recombinant MPK10 adopts an activated conformation , despite the absence of TxY phosphorylation [29] . Following phosphorylation of this motif by upstream M2Ks , mammalian MAPKs are activated by the switch of a conserved DFG motif from an inactive ( DFG-out ) to an active ( DFG-in ) conformation . This change leads to the alignment of two structural motifs comprising non-consecutive hydrophobic residues that are referred to as the regulatory and catalytic spines [35] , [36] . In contrast to mammalian MAPKs , the Leishmania DFG motif of MPK10 is replaced by DFN , causing the R and C spines to be aligned thus stabilizing an apparent active conformation in the absence of TxY phosphorylation [29] . These findings suggest that MPK10 has a strikingly different mode of regulation compared to mammalian MAPKs , which could be particularly adapted to adjust quickly to environmental changes thus acting as a signaling switch . Here , by utilizing a transgenic approach we unraveled an unusual mechanism of MPK10 regulation that tightly controls kinase activity . Stage-specific increase in MPK10 activity during the pro- to axenic amastigote conversion did not follow the largely constitutive phosphorylation status of the regulatory tyrosine residue in the kinase activation loop , suggesting additional and non-classical mechanisms of MPK10 regulation . Combining limited tryptic digestion with mutagenesis analysis and phosphoproteomics investigation , we uncovered an essential role of the C-terminal domain of MPK10 in limiting stage-specific kinase activity , and identified a novel regulatory phospho-serine residue that is required for axenic amastigote viability . Together our data propose MPK10 as a potential signal sensor of the mammalian host environment whose intrinsic pre-activated conformation is regulated by auto-inhibition . These results shed important new light on Leishmania-specific signaling mechanisms , and significantly advance our understanding on parasite-specific protein kinase biology .
To investigate the biochemical properties of MPK10 , we generated non-mutated recombinant MPK10 ( NM ) and the corresponding MPK10-K51A enzymatically dead mutant , both tagged with GST-Strep . We used the bacterially expressed and purified proteins in an in vitro kinase assay at 30°C or 37°C , monitoring the transfer of radiolabeled phosphate from γ-33P-ATP to MPK10 ( auto-phosphorylation ) or to the canonical MAPK substrate myelin basic protein ( MBP ) [37]–[39] . The kinase reactions were subjected to SDS-PAGE , proteins were visualized by Coomassie staining for normalization ( Figure 1A , upper panel ) , and phosphotransferase activity was revealed by auto-radiography ( Figure 1A , lower panel ) . We observed a weak auto-phosphorylation signal at 30°C that was slightly enhanced when the kinase reaction was performed at 37°C ( Figure 1A , lower panel ) . We did not observe any significant activity towards MBP at both temperatures . Likewise , performing kinase assays at 37°C and pH 7 . 5 with other substrates , including casein , histone H1 or Ets1 , revealed only a very weak substrate-specific activity , with casein giving rise to the strongest signal , which still was faint compared to the levels of auto-phosphorylation ( Figure 1B , lower panel ) . By contrast , recombinant human MEK1 ( a M2K ) , used as a positive control , revealed strong substrate-specific activity towards MBP . No substrate phosphorylation or auto-phosphorylation could be detected with the kinase dead MPK10-K51A control , suggesting that the signals observed were specific for MPK10 and not due to a co-purified bacterial kinase . Based on these results , casein appears to be the most suitable substrate for recombinant MPK10 . We next varied the pH of the kinase assay in an attempt to improve MPK10 activity . Kinase assays performed with casein and MPK10 or MPK10-K51A at pH 5 . 5 , 6 . 5 , 7 . 5 and 8 . 5 revealed different pH optima for auto- and substrate-specific phosphorylation at pH 6 . 5 and 7 . 5 , respectively ( Figure S2 , lower panel ) . In conclusion , recombinant MPK10 shows some minor auto-phophorylation activity , but largely fails to phosphorylate the canonical MAPK substrate MBP [37]–[39] , which may either depend on activation through an upstream M2K , parasite-specific kinase substrate interactions , or auto-inhibition . We performed a limited tryptic digestion of purified recombinant GST-Strep-tagged MPK10 to investigate the presence of potential auto-inhibitory accessory domains by delineating the structured , protease-resistant kinase core . We treated recombinant MPK10 with 0 . 25 µg of trypsin for 150 minutes , analyzing samples after 2 . 5 , 5 , 15 , 30 , 60 and 150 minutes by SDS-PAGE and Coomassie staining . MPK10 was sensitive to partial digestion as we observed the appearance of several bands within the first 15 minutes corresponding to different tryptic MPK10 products ( Figure 2A ) . After 30 min of treatment , only one band remained , revealing the core of the kinase . N-terminal sequencing and SELDI-TOF analysis of four of the digestion products ( as marked by arrowheads in Figure 2A and represented by the cartoon shown in Figure 2B ) revealed that both ends of the tagged protein were cleaved by trypsin at lysines 12 , 24 , and 30 , arginine 392 . We also identified a digestion product that resulted from cleavage at aspartate 387 and thus lacked the last 46 amino acids of MPK10 ( Figure 2C ) . This product is likely due to a miscleavage or cleavage by a contaminating bacterial protease . To investigate the activity of the MPK10 kinase core alone and to test whether deletion of the C-terminal region ( 46 aa ) increases its activity , we generated and purified recombinant non-mutated His6-MPK10 ( NM ) or mutated His6-MPK10 deleted for the last 46 amino acids ( ΔC ) and monitored their activity towards canonical substrates by performing in vitro kinase assays in the presence of γ-33P-ATP . We changed from the GST-Strep to His6 tag to show that the lack of MPK10 substrate phosphorylation activity is independent from this modification . The auto-radiogram shown in Figure 2D ( left panel ) confirms weak auto- and substrate phosphorylation activity of His6-MPK10 NM similar to GST-STREP-MPK10 NM ruling out tag-specific interference with kinase activity . In contrast to His6-MPK10 NM , His6-MPK10-ΔC presented a stronger auto-phosphorylation activity and enhanced phosphorylation of Ets1 and casein . These differences reflect a true increase in phosphotransferase activity as judged by Coomassie staining , which showed equal loading of both recombinant proteins and substrates ( Figure 2D , right panel ) . The signals were specific for His6-MPK10-ΔC and not caused by a co-purified kinase , as we did not detect any signal with the His6-MPK10-ΔC_K51A control . Yet again , the auto-phosphorylation signals of NM or truncated MPK10 were significantly stronger than those for substrate phosphorylation . Moreover , no 33P incorporation could be detected for MBP . Thus , deleting the C-terminal tail substantially increases the ability of MPK10 to phosphorylate itself , suggesting a potential role of this domain in negative regulation . The C-terminal domain of L . donovani MPK10 is conserved in Leishmania species ( 84 to 100% , Figure S3B ) but not in Trypanosoma species ( 38 to 53% , Figure S3B ) , except for the conserved motif ( DHMxRTxSxME ) , of unknown function ( underlined , Figure S3A ) . This motif is only conserved in trypanosomatid MPK10 as extending the analysis of the C-terminal extensions to the 14 Leishmania and 2 human MAPKs ( ERK5 and ERK8 ) as well as T . brucei TbECK1 [32] by pattern recognition analysis ( Pratt version 2 . 1 ) and multiple sequence alignment ( ClustalW ) did not reveal this motif or any other conserved motifs or patterns ( data not shown ) . As our study reveals important limitations of the bacterially expressed protein with regard to kinase activity , we analyzed in the following in situ activated MPK10 isolated from transgenic L . donovani parasites . To gain insight into MPK10 regulation and activity in a physiologically relevant context , we generated transgenic parasites expressing a GFP-MPK10 fusion protein from the episomal vector pXG-GFP2+ ( kindly provided by S . Beverley ) . We first performed a time course experiment to investigate MPK10 activity in promastigotes and during axenic amastigote differentiation . GFP-MPK10 was purified using a monoclonal anti-GFP antibody from L . donovani promastigotes harvested from logarithmic ( log ) or stationary ( stat ) phase cultures , and from cultures at different time points between 12 h to 120 h after induction of axenic amastigote differentiation by pH and temperature shift . Immuno-purified proteins were incubated for 30 min at 37°C in the presence of radiolabeled ATP and MBP , and the kinase reaction was subjected to SDS-PAGE . The phosphotransferase activity was determined by auto-radiography ( Figure 3A , a and b ) , and MPK10 and MBP were visualized by Coomassie staining for normalization ( Figure 3A , c and d ) . After exposure , the bands corresponding to the signals of phosphorylated MPK10 or MBP were recovered from the dried gel and 32P incorporation was measured using a scintillation counter . The results were expressed relative to GFP-MPK10 NM from promastigotes of logarithmic culture set to 100% ( the relative counts are represented by the numbers in Figure 3A a and b ) . As opposed to recombinant MPK10 we observed that MBP phosphorylation was higher than MPK10 auto-phosphorylation when using protein purified from parasite extracts . This finding suggests that the purification of GFP-MPK10 from transgenic parasites allows the assessment of biologically relevant kinase activity . MPK10 purified from parasites undergoing axenic amastigote differentiation showed a higher level of MBP phosphorylation compared to promastigotes , especially during the first 48 h following temperature and pH shift ( 202% versus 100% respectively , Figure 3A , b ) . Afterwards , the level of activity decreases to reach its lowest point at 96 h ( 31% ) , and increases again at 120 h ( 144% ) . These data suggest that MPK10 activity is stage-specifically regulated . The signals observed are specific to MPK10 and not due to a co-purified kinase as no signals were observed with the K51A mutant ( data not shown ) . The mean values with standard deviation of three independent experiments are represented by the histogram plot shown in Figure 3B . As judged by statistical analysis ( see Figure S4 ) , significant differences in MPK10 kinase activity are observed ( i ) between promastigotes and parasites at 48 h of axenic amastigote differentiation confirming its stage-specific regulation , ( ii ) between parasites at 48 h and 72 h or 96 h , demonstrating a transient reduction in MPK10 activity during later stages of axenic differentiation , and ( iii ) between parasites at 96 h and 144 h providing evidence for a second peak in MPK10 activity in axenic amastigotes . Conversely , no significant difference in MPK10 activity was observed between promastigotes from logarithmic or stationary phase culture . Phosphorylation of both the threonine and the tyrosine residues of the TxY motif have been shown to be required for eukaryotic MAPK activation and dual phosphorylation is correlated with kinase enzymatic activity . As an alternative read out for MPK10 activity we therefore assessed tyrosine phosphorylation using an anti-phosphotyrosine antibody . First , we tested whether phosphorylated Tyr192 was the only residue recognized by the antibody in MPK10 . We compared the signals obtained with GFP-MPK10 NM , GFP-MPK10-K51A and the corresponding TxY motif mutants T190A , Y192F and T190A_Y192F purified from respective transgenic promastigotes harvested in logarithmic growth phase ( low level of MPK10 activity , Figure 3B ) , or cells during axenic amastigotes differentiation harvested at 48 h after induction of differentiation ( high level of MPK10 activity , Figure 3Ab and B ) . The western blot presented in Figure 3C shows that GFP-MPK10 NM , GFP-MPK10-K51A and GFP-MPK10-T190A are recognized by the anti-phosphotyrosine antibody as documented by the detection of a strong signal at 75 kDa ( Figure 3C ) . The absence of this signal in GFP-MPK10-Y192F and GFP-MPK10-T190A_Y192F demonstrates that pY192 is the only residue recognized by this antibody in GFP-MPK10 . Moreover , Y192 phosphorylation does not require T190 phosphorylation as Y192 is phosphorylated in the GFP-MPK10-T190A single mutant . Remarkably , we did not observe any difference in the level of tyrosine phosphorylation between MPK10 purified from log promastigotes or from 48 h axenic amastigotes despite their difference in activity , suggesting that the phosphorylation state of Y192 is dissociated from the regulation of kinase activity . In contrast , no tyrosine phosphorylation was observed for recombinant MPK10 NM , which further supports the observed inactive state of the bacterially purified kinase ( Supplementary Figure S5 ) . We next studied dissociation of MPK10 activity and Y192 phosphorylation state in a more detailed time course experiment by western blot analysis of pY192 levels in promastigotes ( log and stat ) and during axenic amastigote differentiation ( Figure 3D ) . Again , no difference was observed in the phosphorylation state of Y192 between MPK10 purified from log promastigotes , stat promastigotes or axenic amastigotes during the first 48 h of differentiation , despite their significant differences in activity . After 72 h we observed a decrease in tyrosine phosphorylation , which this time correlated with decreased MPK10 activity . These findings were supported by an independent experiment performed with Leishmania mexicana using a targeted quantitative phosphoproteomic analysis termed Selected Reaction Monitoring ( SRM , [40] ) , from which results concerning MPK10 were extracted and presented in Figure 3E . In this analysis the phosphorylation states of the two regulatory phosphorylation sites T190 and Y192 were quantified in L . mexicana late log phase promastigotes and axenic amastigotes at 72 h of differentiation . Peptides containing T190-H-pY192 ( unphosphorylated T190 and phosphorylated Y192 ) were found at similar levels in promastigotes and axenic amastigotes , whereas peptides containing pT190-H-pY192 ( pT190 and pY192 dual phosphorylation ) showed significantly increased abundance in the axenic amastigote fraction . Peptides containing a single pT190 phosphorylation were not detected . These results show that phosphorylation of T190 and Y192 occurs independently: while pY192 is identified both in promastigotes and in axenic amastigotes , pT190 is mostly identified in amastigotes and thus this phosphorylation event may be the rate limiting step for MPK10 activation . Altogether , these findings strongly suggest that the phosphorylation of the Y192 residue is largely constitutive and occurs independently from MPK10 activation , which is in contrast to MAPK regulation in most other eukaryotes . This characteristic of MPK10 is likely a conserved feature among Leishmania species as we found similar results with independent experiments performed with L . donovani and L . mexicana . In most eukaryotes , both residues of the TxY motif need to be phosphorylated for MAPK activation and as a consequence their mutation abrogates kinase activity [41]–[43] . To analyze the requirement of T190 and Y192 phosphorylation for MPK10 activity , we used our transgenic cell lines to investigate the impact of the overexpression of the three different MPK10 TxY-motif mutants on parasite growth and survival as well as to measure MPK10 kinase activity , using GFP-MPK10 and GFP-MPK10-K51A as positive and negative controls , respectively . We first followed the growth and percentage of cell death in promastigotes by flow cytometry analysis and observed no differences between the untransfected L . donovani control ( UC ) and parasites over-expressing GFP-MPK10 NM or mutant forms ( Figure S6 ) . We next measured the kinase activity of GFP-MPK10 NM and GFP-MPK10 mutant proteins purified from promastigotes . The results presented in Figure 4A show that GFP-MPK10 NM undergoes weak auto-phosphorylation ( exposure 3 h ) , but catalyzes robust MBP phosphorylation ( exposure 1 h ) . It is interesting to note that although the auto-phosphorylation signals are much weaker than the MBP phosphorylation signals , they are similarly regulated . No MBP phosphorylation could be detected with the GFP-MPK10-K51A , -T190A , -Y192F or -T190A_Y192F mutants . These data demonstrate that , similarly to other eukaryotic MAPKs , T190 and Y192 are essential for MPK10 activity , as their mutation considerably reduced the activity of the kinase . We next investigated the impact of these MPK10 mutants on amastigote growth and survival . As presented in Figure 4B ( upper panels ) , untransfected control ( UC ) parasites showed a decrease in cell growth during the first 24 hours of differentiation , but started to grow thereafter to reach a plateau at around 96 h . This profile was the consequence of a high level of cell death at 24 h ( 26±13% ) , which decreased to reach a percentage of cell death of 15±2% at 96 h . This phenomenon has been previously documented and is likely due to the adaptation of parasites to elevated temperature and acidic pH [44] . Parasites overexpressing GFP-MPK10 NM ( Figure 4B , upper panels ) resumed growth after 48 h of differentiation to finally reach the same cell concentration than UC parasites , a profile similar to that obtained with GFP-MPK10-K51A . We observed a higher percentage of cell death of GFP-MPK10 NM ( 41±5% ) compared to UC parasites . This difference is largely attributed to the over-expression of MPK10 , as there is only a slight yet statistically significant difference between the percentage of cell death of untransfected parasites and transgenic parasites expressing the empty vector during axenic amastigote differentiation ( supplementary Figure S7 ) . This finding , which was not observed in promastigotes , indicates that the over-expression of MPK10 , regardless of its activity , is somewhat detrimental to axenic amastigotes . We next compared the phenotype of strains over-expressing GFP-MPK10 to those over-expressing GFP-MPK10-Y192F , -T190A or -T190A_Y192F ( Figure 4B , lower panels ) . All mutant strains showed a 24 hours growth delay , which corresponded to a higher percentage of cell death compared to that observed for GFP-MPK10 NM . At 24 h , strains expressing GFP-MPK10-Y192F and -T190A_Y192F showed a percentage of cell death of 51±7% and 48±6 . 1% , respectively ( Figure 4B , lower panel right ) , but recovered thereafter and showed growth characteristics similar to the GFP-MPK10 NM used as control ( Figure 4B , lower panel left ) . Parasites expressing GFP-MPK10-T190A showed a more severe phenotype with 61±7 . 6% of cell death ( Figure 4B , lower panel right ) , from which they recovered slower than the other mutants ( Figure 4B , lower panel left ) . This dominant negative effect , observed during the first 48 h after pH and temperature shift , corresponds to the period where MPK10 is the most active . Altogether , these data indicate that MPK10 could be important only transiently during differentiation . Moreover , these findings show that not only T190 phosphorylation is essential for the catalytic activity of MPK10 in promastigotes but also for axenic amastigote viability . Strikingly , increased cell survival can be restored in the GFP-MPK10-T190A mutant by Y192F mutation . We then measured the kinase activity of the purified mutant proteins from parasites at 48 h of differentiation , when the percentage of cell death was the highest , and at 96 h after differentiation , when parasites have recovered . As shown in Figure 4C , we obtained similar results from amastigotes at 48 h ( left panel ) and 96 h ( right panel ) to those obtained from promastigotes , i . e . no kinase activity was detected for GFP-MPK10-K51A , -T190A , -Y192F or -T190A_Y192F . We did not find a clear correlation between over-expression phenotype and MPK10 kinase activity , as all mutant kinases were inactive , yet only the over-expression of GFP-MPK10-T190A was detrimental for the amastigotes , suggesting that the effect on growth is not entirely due to whether the protein is active or not . We only observed this phenotype in axenic amastigotes but never in promastigotes . This phenomenon may be due to the level of GFP-MPK10 expression , which was two- to five-fold lower in promastigotes compared to axenic amastigotes ( data not shown ) . Thus , the level of transgenic MPK10 NM and MPK10 mutant protein could be too low to efficiently compete with the endogenous MPK10 in promastigotes , masking a dominant-negative effect . Why and how MPK10 levels are regulated in promastigotes and not in axenic amastigotes remains to be established . This observation seems independent from the vector , as expression of other kinases was not regulated the same way ( data not shown ) . Altogether , these data confirm the essential role of both residues of the TxY motif for MPK10 activity . Our observation that auto-phosphorylation activity of recombinant MPK10 is enhanced after removal of the 46 C-terminal amino acids primed us to investigate the role of this domain in the regulation of MPK10 NM in its physiological context using our transgenic system . Compared to GFP-MPK10 NM transgenic parasites , GFP-MPK10-ΔC over-expression had neither an effect on promastigote growth ( Figure 5A , left panel ) nor cell death ( Figure 5A , right panel ) . We next measured the kinase activity of this mutant . As judged by quantification using imageJ , a 3 . 5 fold increase in phosphorylation of MBP by GFP-MPK10-ΔC compared to that obtained by GFP-MPK10 was observed ( Figure 5B ) , demonstrating that transgenic GFP-MPK10-ΔC has a higher activity than GFP-MPK10 . Strikingly , GFP-MPK10-ΔC showed a strong increase of MBP phosphorylation but a moderate increase of auto-phosphorylation , suggesting that part of the increase in activity could be due to a better affinity for the substrate rather than enhanced phosphotransferase activity . This finding supports the hypothesis that the C-terminal domain has an auto-inhibitory function in situ . Surprisingly , Y192 phosphorylation was reduced by 80% in GFP-MPK10-ΔC compared to GFP-MPK10 NM ( Figure 5C ) . Thus , although only 20% of GFP-MPK10-ΔC showed Y192 phosphorylation and thus can be considered active , the truncated kinase still phosphorylated MBP 3 . 5 fold more efficiently than GFP-MPK10 NM , suggesting a dramatic increase in kinase catalytic function after removal of the C-terminal domain . We investigated in the following the effect of GFP-MPK10-ΔC over-expression on axenic amastigotes . Contrary to promastigotes , the over-expression of GFP-MPK10-ΔC caused an important reduction of cell growth after temperature and pH shift ( Figure 5D , left panel ) , which resulted from an increase in the percentage of parasite death during the differentiation process , reaching 78±8 . 5% at 48 h . In contrast to the over-expression of the other mutants ( Figure 4B ) , GFP-MPK10-ΔC transgenic parasites did not recover and maintained a high level of cell death . This result indicates that GFP-MPK10-ΔC is toxic for axenic amastigotes and as a consequence we were not able to investigate the kinase activity of GFP-MPK10-ΔC at this parasite stage . However , based on the enhanced activity detected in promastigotes , we hypothesized that the toxicity could be the consequence of a non-physiologically high level of MPK10 kinase activity . To test this possibility we generated two double mutants , GFP-MPK10-ΔC_K51A and -ΔC_Y192F that lack activity by different means . Over-expression of GFP-MPK10-ΔC_Y192F was no longer toxic for axenic amastigotes , suggesting that rendering GFP-MPK10-ΔC inactive is sufficient to rescue the parasites from the lethal phenotype as the percentage of cell death was significantly reduced compared to the active kinase ( Figure 5D , right panel ) . GFP-MPK10-ΔC_K51A also showed a reduction in toxicity but not as complete as GFP-MPK10-ΔC_Y192F . This discrepancy was due to a higher percentage of cell death from 48 h to 144 h , which was not observed with the over-expression of GFP-MPK10-ΔC_Y192F . We next investigated whether this difference between GFP-MPK10-ΔC_K51A and -ΔC_Y192F could be explained by a difference in kinase activity but , as shown in Figure 5B and expected from the results obtained with the mutants of full length MPK10 , both mutants showed a weak or no activity towards MBP , respectively . Thus , the different phenotypes observed with these two mutants are not linked to a difference in kinase activity . We further showed that in contrast to hyperactive GFP-MPK10-ΔC , the level of Y192 phosphorylation of inactive GFP-MPK10-ΔC_K51A corresponds to wild-type level ( Figure 5C ) , thus revealing a negative feedback loop between MPK10 activity that controls the level of Y192 phosphorylation . Overall , these findings demonstrate that the toxicity following over-expression of GFP-MPK10-ΔC in amastigotes is due to the high activity of the truncated kinase , which can be compensated by reduction of tyrosine phosphorylation . After demonstrating the importance of the C-terminal domain for auto-inhibition of MPK10 activity and parasite viability , we investigated the potential mechanisms involved in its regulation . Several mechanisms have been described to release kinases from auto-inhibition , including protein phosphorylation [45] . We performed a qualitative phospho-peptide analysis using L . donovani axenic amastigote extracts ( 48 h of axenic differentiation ) to test this possibility . Our analysis identified the expected phospho-peptides encompassing the TxY motif ( supplementary Figure S8A ) , but revealed a novel phospho-peptide located in the C-terminal domain of MPK10 showing a single phosphorylation at residue S395 ( supplementary Figure S8B ) . This serine is conserved across MPK10 orthologs in trypanosomatids ( Figure 6A , gray arrow ) , and part of the conserved sequence motif DHMxRTxSxME ( underlined , Figure S3A ) , which suggests an important role for MPK10 function . If this serine were implicated in the regulation of MPK10 auto-inhibition , we would expect its phosphorylation to be stage regulated . To test this hypothesis we took advantage of the previously described quantitative phospho-peptide analysis . SRM analysis confirmed the presence of phosphorylated S395 , which was mostly identified in promastigotes . Besides T190 of the activation loop this is the second stage-specifically regulated residue in MPK10 and thus may be crucial to control kinase activity ( Figure 6B ) . As this promastigote-specific phosphorylation occurs when MPK10 is least active , dephosphorylation of S395 could be important to release MPK10 from the auto-inhibition . We next investigated this possibility and studied the role of this residue in MPK10 regulation by generating transgenic parasites over-expressing GFP-MPK10-S395A . No difference was observed between GFP-MPK10 NM and -S395A with respect to promastigote growth ( Figure 6C , left panel ) or percentage of cell death ( Figure 6C , right panel ) . Comparison of the kinase activities of GFP-MPK10 NM and -S395A purified from promastigotes did not reveal any difference based on quantification of the signals by a scintillation counter as detailed in Figure 3A ( Figure 6D ) . We next compared the kinase activity of GFP-MPK10 NM and -S395A purified from parasites at different time points during axenic amastigote differentiation ( Figure 6D ) . At 48 h , GFP-MPK10 NM phosphorylated MBP more efficiently than GFP-MPK10-S395A ( 100% and 54% respectively ) , whereas the activity of the mutant protein was slightly stronger than that of GFP-MPK10 NM at 96 h ( 127% and 100% respectively ) . These findings provide evidence for a delay in GFP-MPK10-S395A activation , supporting a role of this residue in proper kinase regulation . The mean values with standard deviation of three independent experiments assessing activity for GFP-MPK10 NM and -S395A of parasites at 48 h and 96 h during axenic amastigote differentiation is shown in Figure 6E . Statistically significant differences were observed at 48 h with a reduction by twofold of GFP-MPK10-S395A activity ( p-value<0 . 001 ) , and at 96 h where on the contrary GFP-MPK10-S395A activity is increased by 75% ( p-value<0 . 05 ) . No significant difference was observed in promastigotes ( p-value of 0 . 06 ) . These data strongly suggest that in axenic amastigote , GFP-MPK10-S395A is differentially regulated compared to GFP-MPK10 NM , which could cause the observed effect on parasite viability . Parasites expressing GFP-MPK10-S395A presented a delay of 24 h in cell growth compared to GFP-MPK10 NM but thereafter resumed growth with kinetics slightly slower than that of parasites overexpressing GFP-MPK10 NM ( Figure 6F , right panel ) . This delay is caused by a significantly higher percentage of cell death ( 69±16% , p-value<0 . 01 ) observed for parasites expressing GFP-MPK10-S395A at 48 h of axenic amastigote differentiation compared to GFP-MPK10 NM ( 44±12% , Figure 6F , right panel ) . In conclusion , our findings demonstrate the importance of S395 residue for axenic amastigote survival . The fact that the GFP-MPK10-S395A phenotype resembles that obtained with the over-expression of GFP-MPK10-T190A attributes similar importance for kinase regulation to S395 in the C-terminal domain as to T190 in the activation loop .
MAPKs are proline-directed serine/threonine kinases that phosphorylate substrates containing proline in the P+1 site [46] . Classically , MAPKs are inactive enzymes that are solely activated by M2Ks , which phosphorylate both the threonine and the tyrosine of the TxY motif present in the activation loop [9] , [46] . This phosphorylation allows conformational changes that lead to the alignment of the R and C spines required for the activation of the kinase [29] , [35] , [36] . Moreover , the phosphorylation of the tyrosine residue of the TxY motif is important to permit the formation of the proline-directed P+1 specificity site required for substrate recognition and restriction of specificity [46] . The threonine residue possesses a structural role by stabilizing MAPK conformation and improving the geometry of the active site [46] , [47] . MPK10 retains certain characteristics of classical MAPKs such as the conserved motif typical of MAPKs , TxYxxxRxYRxPE , including the TxY motif and the ( P+1 ) -specificity pocket [16] . We demonstrated the importance of the T190 residue for the catalytic activity of MPK10 , as alteration of this site abrogates phosphotransfer and severely reduces axenic amastigote survival . We also showed the essential role of the Y192 residue for MPK10 activity although its mutation does not have an impact on axenic amastigote survival unlike mutation of T190 . Aside these conserved MAPK features , MPK10 regulation presents many non-classical characteristics . We have previously shown that based on its structural conformation , MPK10 appears to be in an active conformation , without the need for dual-phosphorylation of the TxY motif , as the replacement of the DFG motif by a DFN motif results in the alignment of the R and C spines , similar to eukaryotic MAPKs after phosphorylation of the TxY motif by M2Ks [29] . Our study supports these findings revealing alternative modes of regulation of this intrinsic MPK10 activation state . We first showed that phosphorylation of Y192 is largely dissociated from MPK10 activity as its phosphorylation state does not show any significant stage-specific change , even though the activity of MPK10 increases by about twofold between log phase promastigotes and axenic amastigotes at 48 h after initiation of differentiation . In classical MAPKs , kinase activity correlates with phosphorylation of both the threonine and the tyrosine residues of the TxY motif , which is tightly regulated by environmentally induced upstream M3Ks and M2Ks [9] . By contrast , our data suggest that Leishmania MPK10 is mostly constitutively phosphorylated on Y192 in promastigotes and during amastigote development and proliferation . Consequently , this residue seems not to be implicated in regulating the observed stage-specific activation of MPK10 . These findings were confirmed by proteomics identification of a mono-phosphorylated T190-H-pY192 activation loop in both pro- and axenic amastigotes , whereas dual pT190-H-pY192 phosphorylation of the activation loop was mainly identified in axenic amastigotes , where MPK10 is most active . However , we observed a decrease in Y192 phosphorylation in axenic amastigotes at stationary phase , which correlated with a decrease in MPK10 activity , suggesting that inactivation of MPK10 at this stage requires Y192 dephosphorylation . Altogether , these findings indicate that the two residues of the TxY motif are differentially regulated , raising the question on how MPK10 is phosphorylated by M2Ks . There are at least five possibilities: ( i ) Y192 could be auto-phosphorylated in cis , a possibility that we rule out since kinase dead MPK10-K51A still shows phosphorylation on this residue . ( ii ) Classically , the phosphorylation of the TxY motif by M2Ks is sequential , initiating with the tyrosine residue [48] . M2Ks could constitutively phosphorylate the tyrosine residue of MPK10 , but require additional signals or interactions to complete phosphorylation of the adjacent threonine residue . ( iii ) Each regulatory residue could be phosphorylated independently by two different M2Ks as it was demonstrated for human JNK kinase , whose threonine of the TxY motif is preferentially phosphorylated by MKK7 , whereas its tyrosine is preferentially phosphorylated by MKK4 [9] . ( iv ) Without activation of the MAPK cascade , only Y192 could be accessible for phosphorylation , whereas access to T190 could be blocked by the C-terminal domain . ( v ) Both sites could be phosphorylated by the same M2K but only T190 would be constantly dephosphorylated until the MAPK pathway is fully activated . Three types of phosphatases target MAPKs , the dual-specificity phosphatases also called the MAP kinase phosphatases ( MKPs ) , the tyrosine phosphatases such as PTP-SL and STEP , and finally the serine/threonine phosphatase such as PP2A [49] . PP2A could likely be responsible for this dephosphorylation; a possibility supported by recent findings that T . cruzi PP2A blocks axenic amastigote differentiation [50] . Dissociation of Y192 phosphorylation from stage-specific induction of MPK10 activation during amastigote differentiation raises the question of its importance for MPK10 function . We showed that similar to mammalian MAPKs , the Y192 residue is essential for MPK10 enzymatic activity . However , the dominant negative effect generated by overexpression of MPK10-T190A on axenic amastigote growth and viability was more detrimental than the one observed by overexpression of MPK10-Y192F , suggesting a more important role for the T190 residue for MPK10 activity . We can only speculate on the role of the largely constitutive phosphorylation of the Y192 residue . As described in the literature , phosphorylation of the tyrosine of the TxY motif is important to form the proline-directed ( P+1 ) -specificity pocket [46] , [49] . Consequently , maintaining Y192 mostly phosphorylated could allow a constant and dynamic interaction of MPK10 with its substrates allowing for faster phosphorylation once the pathway is activated . A second striking feature of the regulation of MPK10 is represented by its C-terminal domain . Whether we used the recombinant or the transgenic protein , removal of this domain resulted in a significant increase in kinase activity . While this increase translates mainly into higher auto-phosphorylation levels for the recombinant kinase , we observed a dramatic increase in substrate phosphorylation for the transgenic kinase . One main difference between the two kinases is the absence of Y192 phosphorylation of the recombinant protein , which could explain the inability of recombinant kinase to efficiently transfer phosphate onto the substrate ( Figure S5 ) . Thus , our findings firmly establish regulation of MPK10 through auto-inhibition . This is a common feature in the regulation of kinase activities , and has been described for example for Twitchin kinase or CaMKI ( Ca2+/calmodulin-dependent kinase I ) but is a rare feature for MAPKs [48] . Although five human MAPKs have a long C-terminal domain ( ERK3 , ERK4 , ERK5 , ERK7 and ERK8 ) , only that of ERK5 has an auto-inhibitory function [9] . The authors have shown that deletion of the last 100 amino acids of ERK5 leads to an increase of its kinase activity and they postulated that the deletion of the C-terminal domain could facilitate its activation by the upstream kinase [51] . Evidence for a different auto-regulatory mechanism of MPK10 arises from our analysis using transgenic parasites expressing the hyper-active MPK10 C-terminal deletion mutant: the strongly reduced phosphorylation levels of the regulatory Y residue in this mutant despite its significantly increased phosphotransferase activity demonstrates that the pool of active MPK10 is reduced , while the activity of each single active kinase protein is dramatically increased . Thus the deletion of the C-terminal domain increases the basal activity of the truncated kinase rather than allowing better access for the M2K . How the C-terminal domain regulates MPK10 activity is still elusive since no conserved pattern or domains such as SH2 or SH3 have been identified . One possible explanation could be that the C-terminal domain masks the active site acting as a pseudosubstrate such as described for Twitchin kinase [52] . It is interesting to note that in T . brucei , a hybrid kinase between a CDK ( Cyclin dependent kinase ) and a MAPK termed TbECK1 is also auto-inhibited by its C-terminal domain [32] . Procyclic parasites expressing the truncated protein showed growth defects and the parasites presented aberrant karyotypes . The expression of truncated TbECK1 was toxic to the bloodstream form , which is reminiscent to the toxic effect of MPK10-ΔC in axenic amastigotes . Our data show that activating MPK10 via T190 is important for axenic amastigote survival , especially during the first 48 h after induction of axenic amastigote differentiation by temperature and pH shift . However , inhibition of MPK10 activity is equally essential as absence of auto-inhibition caused massive cell death in axenic amastigotes expressing MPK10-ΔC . These results therefore demonstrate the transient requirement for MPK10 during axenic amastigote differentiation and reveal a new mechanism to regulate MAPK activity by auto-inhibition , which is crucial for amastigote viability . Finally , we identified a highly conserved , trypanosomatid-specific serine at position 395 inside the MPK10 C-terminal domain as a novel , stage-specific regulatory residue that is mainly phosphorylated in promastigotes and whose mutation causes an important dominant negative effect on axenic amastigote survival . Because kinases can be released from auto-inhibition by phosphorylation or dephosphorylation [45] , we hypothesized that S395 could be implicated in the regulation of the auto-inhibition of MPK10 by its C-terminal domain . We did not confirm this hypothesis but we have two pieces of evidence suggesting that this residue could have an important role in the regulation of MPK10 activity . First , we showed that S395 is conserved in all trypanosomatids and is part of a sequence motif inside the C-terminal domain , which is also conserved in all trypanosomatids . Second , we clearly established the importance of the phosphorylation of S395 for axenic amastigote survival , which is as important as the regulation of T190 , and partially mimics the viability defect caused by MPK10-ΔC . The function of this residue is still elusive and may be studied in the future by using an anti-phospho-S395 specific antibody to investigate the link between the phosphorylation kinetics of this residue and MPK10 activity , and address the question whether the release from auto-inhibition is regulated by environmental signals or whether it is alleviated before signal sensing , leaving the kinase in a semi-activated state , relying only on T190 phosphorylation for activation . Because MPK10 shows a transient peak of activity during the first 48 h of axenic amastigote differentiation , its activity seems to be tightly regulated . The decrease in MPK10 activity after 48 h of induction of axenic amastigote differentiation is concomitant with the decrease of Y192 phosphorylation , suggesting that dephosphorylation of Y192 and probably T190 are required for MPK10 inactivation . We have evidence suggesting that MPK10 could directly or indirectly regulate its activity through the phosphorylation or the dephosphorylation of its TxY motif . Indeed , we have shown that to reduce toxicity caused by the over-expression of the hyperactive MPK10-ΔC , its level of Y192 phosphorylation was decreased . The level of pY192 was restored to wild-type level only after rendering the truncated kinase inactive , suggesting the existence of a feedback loop , where MPK10 could either inactivate the M2K that phosphorylates Y192 or activate the phosphatase that dephosphorylates this residue . This kind of feedback regulation between MAPKs and M2Ks or MKPs has not been extensively studied , as only three types of feedback loop have been described that are linked to regulation of MKPs , involving ( i ) rescue of MKP from degradation through phosphorylation by MAPK [53] , ( ii ) activation of MKP by MAPK binding [54] , and ( iii ) induction of MKP transcription by activated MAPK [55] , [56] , a possibility that can likely be discarded as transcription is constitutive in Leishmania [57] . In addition , Mody et al . have reported the phosphorylation of MKK5 by ERK5 , revealing the existence of a potential feedback loop between a MAPK and its upstream activating kinase [58] . Future identification of the M2K and MKP that regulate the phosphorylation dynamics of the TxY motif of MPK10 will provide more insight into the mechanism controlling this feedback loop . In conclusion , our transgenic study identifies novel mechanisms of MPK10 regulation that are unusual for MAPKs and document once more the stunning capacity of Leishmania to adapt highly conserved signaling proteins to its parasitic life style . Our data propose a model in which MPK10 is not inactivated but partially active in promastigotes as judged by tyrosine phosphorylation and structural conformation ( Figure 7 ) . At this stage the kinase is kept in a standby configuration by auto-inhibition . During the first 48 h of axenic amastigote differentiation , MPK10 is released from auto-inhibition , which correlates with T190 phosphorylation and S395 dephosphorylation . This activity seems to be controlled by a feedback loop where MPK10 regulates its own tyrosine phosphorylation levels . Thereafter , MPK10 activity is decreased likely due to dephosphorylation of the TxY motif and phosphorylation of S395 . These regulatory residues may fine tune MPK10 regulation according to environmental signals and differentiation state through activating and inhibitory mechanisms . Future studies combining null mutant analysis and complementation assays for a detailed structure-function analysis of these residues and the auto-inhibitory C-terminal domain will uncover the contribution of these sequence elements in regulating MPK10 functions relevant for parasite differentiation and infectivity .
Leishmania donovani strain 1S2D ( MHOM/SD/62/1S-CL2D ) , clone LdB was cultured and axenic amastigotes were differentiated as previously described [4] [59] [60] . Briefly , 106 logarithmic promastigotes per mL were grown at 26°C in M199 media ( supplemented with 10% heat-inactivated FCS , 25 mM HEPES pH 6 . 9 , 4 . 2 mM NaHCO3 7 . 5% , 2 mM glutamine , 8 µM 6-biopterin , 1× RPMI 1640 vitamin mix , 10 µg/mL folic acid , 100 µM adenine , 30 µM hemin , 100 U/mL of Penicillin/Streptomycin ( Pen/Step ) ) and differentiated in axenic amastigotes by incubation at 37°C and 5% CO2 with RPMI media ( supplemented with 20% of heat-inactivated FCS , 28 mM MES , 2 mM glutamine , 1× RPMI 1640 amino acid mix , 10 µM folic acid , 100 µM adenine , 100 U/mL of Pen/Step ) . Parasites were harvested at different time points between 12 h to 144 h after induction of axenic amastigote differentiation . For kinetic analysis , one flask ( Corning ) containing 200 mL of culture medium was used for each time point to avoid internal variations . Leishmania mexicana MNYC/BZ/62/M379 promastigotes and axenic amastigotes were grown as described previously [61] . Cultures were incubated at 27°C until late-log phase ( 4–5×107 parasites/ml ) , and either harvested or differentiated into amastigotes by inoculation in Schneider's Drosophila medium ( PAN Biotech , Aidenbach , Germany ) supplemented with 20% heat-inactivated FCS ( PAN Biotech ) , 2 mM L-glutamine , 100 U/ml Pen/Strep , and 20 mM 2-morpholinoethanesulfonic acid monohydrate [MES] ( Serva , Heidelberg , Germany ) for a final pH of 5 . 5 . Cultures were incubated at 34°C , 5% CO2 , for 72 h . Parasites were harvested by centrifugation at 2 , 000× g at 4°C , and washed consecutively in ice-cold HEPES and ice-cold HEPES with protease and phosphatase inhibitors ( 1 mM Na-orthovanadate , 0 . 1 µM okadaic acid , 10 mM NaF , 10 mM o-phenanthroline , EDTA-free protease inhibitors ( Roche ) ) . Parasite pellets were snap-frozen in liquid nitrogen and stored at −80°C . Episomal tranfectants were generated by electroporation of 5×107 L . donovani LdB promastigotes from logarithmic culture with 20 µg of plasmid [62] . Transfected cells were selected in liquid media containing 20 µg/ml geneticin ( Invitrogen ) and resistant parasites were expanded in liquid culture at drug concentrations up of to 100 µg/ml of geneticin . Parasites were then frozen one passage after selection and all experiments were performed with parasites issued from the same electroporation . To avoid any potential bias due to adaptation or compensatory responses , parasites were used for all the experiments at passage 2 after selection . Pools of transfectants were used to avoid clonal variation that can bias transgenic studies . Cultured parasites were incubated for 15 min with 10 µg/ml propidium iodide ( Sigma-Aldrich ) and diluted in PBS ( Gibco ) . Cells were analyzed with a FACSCalibur flow cytometer ( Beckman Coulter ) to determine the incorporation of propidium iodide ( excitation wave length λex = 488 nm; emission wave length λem = 617 nm ) . The percentages of cell death and cell growth were calculated using FlowJo ( v7 . 6 ) software ( Tree Star , Inc . , San Carlos , CA ) . 109 parasites were washed with ice cold RPMI and lysed in 1 ml lysis buffer containing 150 mM NaCl , 1% Triton X-100 , 50 mM Tris HCl pH 8 and inhibitor cocktails for proteases ( Complete Mini EDTA-free tablets , Roche Applied Science , IN ) and phosphatases ( Phosphatase Inhibitor Cocktails I and II , Sigma–Aldrich , MO ) . Clear lysates were obtained after sonication and centrifugation at 12 000× g for 10 min and stored at −80°C . Purified GFP-MPK10 wild-type and mutant proteins were isolated from crude cell extracts of respective transgenic parasites using the μMACS Epitope Tag Protein Isolation Kit , according to the manufacturer's specifications ( Miltenyi Biotec Inc . , CA ) . Briefly , equal amounts of total proteins were incubated with 50 µl of magnetic bead-conjugated mouse monoclonal anti-GFP antibody for 1 hour at 4°C , immuno-complexes were immobilized on the μMACS separator , washed four times with 150 mM NaCl , 1% Igepal CA-630 ( formerly NP-40 ) , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 50 mM Tris HCl ( pH 8 . 0 ) , and once with 20 mM Tris HCl ( pH 7 . 5 ) . Bound GFP-MPK10 protein was eluted in 75 µl PBS after removing the columns from the magnetic field . Purified proteins were separated by SDS–PAGE ( NuPAGE gel 4–12% Bis-Tris , Invitrogen ) and visualized either by Coomassie staining or SYPRO Ruby Protein Gel Stain ( Invitrogen ) using a Typhoon 9400 scanner ( Amersham Biosciences ) with λex = 457 nm and λem = 610 nm . Alternatively , proteins were separated by SDS–PAGE on NuPAGE 4–12% Bis-Tris gels ( Invitrogen ) and blotted onto polyvinylidene difluoride ( PVDF ) membranes ( Pierce ) . Proteins were revealed using the following antibodies at the indicated dilutions: i ) polyclonal anti-MPK10 antibody , generated by rabbit immunization using recombinant MPK10 protein produced in E . coli transformed with pGEX-Strep3-MPK10 plasmid ( Eurogentec ) , 1∶10 , 000; ii ) anti-phospho-tyrosine antibody 4G10 Platinum from Millipore , 1∶1 , 000; and iii ) secondary goat anti-rabbit-HRP and anti-mouse-HRP antibodies from Thermo Scientific , 1∶20 , 000 . The visualization was performed on X-ray film ( Roche ) at various exposure times . E . coli BL21 Rosetta ( VWR ) transformed with pQE80-His6-MPK10 or pGEX-GST-Strep3-MPK10 were grown at 37°C and induced with IPTG ( 0 . 2 µM final ) overnight at RT . Cells were harvested by centrifugation at 15 , 000× g for 10 min at 4°C . For the purification of GST-Strep3-MPK10 , bacteria were resuspended in a pre-chilled buffer containing 25 mM Tris-HCl pH 8 , 150 mM NaCl , 1 mM DTT , 10 µg/mL apoprotein , 1 µM leupeptin , 1 µM pepstatin , 1 mM PMSF . Samples were sonicated ( Bioruptor system , Diagenode ) for 2 min at 20 V setting on ice ( 10 s on/10 s off cycle ) . After addition of 500 µg/mL of lysozyme and 500 U of benzonase , lysates were incubated on ice for 30 min with 140 µM EDTA , 0 . 0035% Triton-X100 and centrifuged at 15 , 000× g for 30 min at 4°C . The supernatant was immediately subjected to GST affinity chromatography ( GSTrap , GE Healthcare Life Sciences , Waukesha , WI , USA ) . GST-tagged proteins were washed with a buffer containing 50 mM Tris pH 8 , 100 mM NaCl , 1 mM DTT and eluted with the same buffer supplemented with 30 mM L-glutathione . Appropriate fractions were pooled and the GST-tag was cleaved by incubation of the fractions with 5 µg/mL Xa factor in presence of 1 mM CaCl2 . The reaction was stopped by adding 5 µg/mL of Glutamyl-glycyl-arginine chloromethyl ketone GGACK ( Calbiochem ) . A second purification step was performed using a StrepTrap column ( Strep-tactin , GE Healthcare Life Sciences , Waukesha , WI , USA ) . Elution was performed with E Strep buffer ( 100 mM Tris-HCl pH 8 , 150 mM NaCl , 1 mM EDTA and 2 . 5 mM Desthiobiotin ) and appropriate fractions were collected , and stored at 4°C until used . For His6-MPK10 purification , the bacterial pellet was resuspended in PBS containing 60 mM β-glycerophosphate , 1 mM sodium vanadate , 1 mM sodium fluoride , 1 mM disodium phenylphosphate , 150 mM sodium chloride , 10 mM imidazole supplemented with protease inhibitor cocktail ( Complete EDTA free tablets , Roche Applied Science ) . The sample was sonicated for 2 min at 20 V setting on ice ( 10 s on/10 s off cycle ) . Triton X-100 ( 0 . 1% final ) was added , the sample was incubated for 30 min at 4°C ( shaking ) and centrifuged at 15 , 000× g for 30 min at 4°C . The supernatant was purified on Co-NTA agarose ( Pierce ) . The beads were washed with PBS containing 60 mM β-glycerophosphate , 1 mM sodium vanadate , 1 mM sodium fluoride , 1 mM disodium phenylphosphate , 300 mM sodium chloride , 30 mM imidazole , 1% Triton X-100 at pH 7 . 5 . Elution was performed with 300 mM imidazole in elution buffer pH 7 . 5 ( PBS containing 60 mM β-glycerophosphate , 1 mM sodium vanadate , 1 mM sodium fluoride , 1 mM disodium phenylphosphate ) . The eluate was supplemented with 15% glycerol and stored at −80°C . Ten percent of the GFP-MPK10 purified protein was incubated on a shaker for 30 min at 37°C with 25 µg myelin basic protein ( MBP ) substrate , 200 µM of ATP , 50 mM of MOPS pH 7 . 5 , 100 mM NaCl , 10 mM MgCl2 and 1 µCi [γ-32P] adenosine-triphosphate ( ATP ) ( 3000 Ci/mmol ) in final volume of 20 µl . The phosphotransferase reaction was then stopped by adding Laemmli loading buffer . Reaction mixtures were separated by SDS–PAGE , which was stained by Commassie and dried . 32P incorporation was monitored by exposing the dried gel on an X-ray sensitive film ( Roche ) at −80°C . After exposure , the bands corresponding to MPK10 or MBP were excised from dried gels and radioactivity was quantified by a scintillation counter . Recombinant His6-MPK10 and respective mutants were assayed with 36 µg dephosphorylated casein , 12 µg histone H1 , 9 µg of Ets1 or 9 µg MBP as substrates in a Tris buffer at pH 7 . 5 ( 50 mM Tris-Cl pH 7 . 5 , 10 mM MnCl2 and 100 mM NaCl ) in 20 µl final volume and in the presence of 15 µM [γ-33P]-ATP . After 30 min incubation at 37°C , the reaction was stopped by adding an equal volume of 2× electrophoresis loading buffer to the 20 µl reaction mix . Incorporated 33P was monitored by auto-radiography . 50 µg of Strep3-MPK10 were digested with 0 . 25 µg trypsin at RT . Aliquots were taken at 0 , 2 . 5 , 5 , 15 , 30 , 60 , and 150 min and the reaction was stopped by adding Laemmli loading buffer . The polypeptides were then separated by SDS-PAGE , transferred to PVDF membrane , stained with amidoblack , and N-terminal sequencing was performed . For the mass determination of cleavage products , pH of the cleavage reaction was lowered to 5 . 0 and mass determination was performed by SELDI-TOF analysis after immobilizing the samples on a H4 ProteinChip Array ( C16 reversed phase surface ) . A Leishmania amastigote cell pellet was submitted for qualitative phosphoproteomic analysis using titanium dioxide phosphopeptide enrichment followed by an iTRAQ labeling experiment for analysis of phosphopeptides by LC-MS/MS . Briefly , 400 µg of Leishmania proteins were reduced with DTT and the free cysteines were alkylated with iodoacetamide for 30 min at 37°C in darkness . Proteins were then digested overnight at 37°C using Porcine trypsin ( sample:enzyme ratio of 50∶1 ) . Following the digestion , the peptides were acidified , concentrated and de-salted using a Waters HLB Oasis SPE cartridge . The peptides were then enriched for phosphopepitdes using a TiO2 affinity column and splited into two 100 µL aliquots . Each aliquot was then reduced and alkylated with MMTS followed by iTRAQ labeling ( 114 , 116 ) . The 116 labeled sample was then treated with FastAPalkaline phosphatase before the samples were combined , fractionated by SCX chromatography and analyzed by ESI-Q-TOF MS/MS . Sample were then analyzed by reversed phase nanoflow ( 300 nL/min ) HPLC with nano-electrospray ionization using a quadrupole time-of-flight mass spectrometer ( QSTAR Pulsar I , Applied Biosystems ) operated in positive ion mode and a 2 hour gradient . 1×107 parasites were lysed in a solution of 7 M urea , 2 M thiourea , 40 mM Tris , 1% n-octyl-β-D-glycopyranoside , 1 mM MgCl2 , 1 mM o-phenanthroline , 300 U benzonase , 1 mM Na-pervanadate ( Na-orthovanadate activated in 18% H2O2 ) , EDTA-free protease inhibitors ( Roche ) and phosphatase inhibitor cocktails ( P2850 and P5726 from Sigma ) , and sonicated for 3×15 s on ice . Lysates were incubated at −80°C for 30 min prior to reduction ( DTT , 20 mM , incubation at room temperature for 60 min ) and alkylation ( iodoacetamide , 40 mM , incubation at room temperature in the dark for 45 min ) . Proteins were precipitated in 8 fold excess of ice-cold acetone-ethanol ( 1∶1 , v/v ) by overnight incubation at −20°C . Proteins were reconstituted in 6 M urea/2 M thiourea and diluted in 50 mM NH4HCO3 for digestion with trypsin at a 75∶1 substrate-enzyme ratio over night . For selected reaction monitoring ( SRM ) analyses , 3×330 µg digested protein from whole cell lysates of promastigotes , axenic amastigotes were subjected to TiO2 enrichment as described in Rosenqvist et al . [40] . The TiO2 eluates were pooled prior to SRM analysis . The discovery analyses were conducted in triplicates for each of the sample types using LTQ Orbitrap XL mass spectrometers ( Thermo Fisher Scientific , Bremen , Germany ) . The discovery data were processed with DTASuperCharge ( Mortensen , P . DTASuperCharge , an MSQuant application . http://msquant . alwaysdata . net/msq/ ) and searched against a customized L . mexicana 6-frame translation library as well as a predicted protein list ( GeneDB http://www . genedb . org ) using an in-house Mascot server ( version 2 . 2 . 06 , Matrix Science , London , UK ) . For SRM analyses , the discovery data were processed in ProteomeDiscoverer , and the MPK10 phosphopeptides identified were imported into Pinpoint , version 1 . 0 . 0 ( Thermo Scientific ) . Leishmania spp and Trypanosoma spp MPK10 gene and protein sequences were retrieved from the web databases GeneDB ( www . genedb . org ) and TriTrypDB ( http://tritrypdb . org/tritrypdb/ ) [63] . Homology searches were carried out using BLAST Program with the default BLOSUM-62 substitution matrix [64] , and pattern recognition analysis using the program PRATT v2 . 1 [65] . Multiple sequence alignments were performed using built-in algorithm ClustalXv2 . Additional sequence analyses were carried out using the BioEdit Program suite ( Tom Hall , North Carolina State University ) . Statistical analysis and data plotting were performed using Rstudio software ( http://www . rstudio . org/ ) and R language ( R Development Core Team ( 2005 ) . R: A language and environment for statistical computing . R Foundation for Statistical Computing , Vienna , Austria . ISBN 3-900051-07-0 , URL: http://www . R-project . org ) . Statistical analyses were performed using the t-test on mean values when samples followed a Normal distribution , otherwise the Mann-Whitney Rank Sum Test was used . Differences were considered significant when p value<0 . 05 . | Leishmaniasis is an important human disease caused by Leishmania parasites . A crucial aspect of Leishmania infectivity is its capacity to sense different environments and adapt for survival inside insect vector and vertebrate host by stage differentiation . This process is triggered by environmental changes encountered in these organisms , including temperature and pH shifts , which usually are sensed and transduced by signaling cascades including protein kinases and their substrates . In this study , we analyzed the regulation of the Leishmania mitogen-activated protein kinase MPK10 using protein purified from transgenic parasites and combining site-directed mutagenesis and activity tests . We demonstrate that this kinase is activated during parasite differentiation and regulated by an atypical mechanism involving auto-inhibition , which is essential for parasite viability . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"mutagenesis",
"signal",
"transduction",
"kinetoplastids",
"cell",
"biology",
"molecular",
"biology",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecular",
"genetics",
"microbiology",
"protozoology",
"microbial",
"genetics",
"cell",
"signaling",
"mapk",
"signaling",
"cascades",
"parasitology",
"signaling",
"cascades"
]
| 2014 | Transgenic Analysis of the Leishmania MAP Kinase MPK10 Reveals an Auto-inhibitory Mechanism Crucial for Stage-Regulated Activity and Parasite Viability |
Aedes albopictus is a very invasive and aggressive insect vector that causes outbreaks of dengue fever , chikungunya disease , and yellow fever in many countries . Vector ecology and disease epidemiology are strongly affected by environmental changes . Urbanization is a worldwide trend and is one of the most ecologically modifying phenomena . The purpose of this study is to determine how environmental changes due to urbanization affect the ecology of Aedes albopictus . Aquatic habitats and Aedes albopictus larval population surveys were conducted from May to November 2013 in three areas representing rural , suburban , and urban settings in Guangzhou , China . Ae . albopictus adults were collected monthly using BG-Sentinel traps . Ae . albopictus larva and adult life-table experiments were conducted with 20 replicates in each of the three study areas . The urban area had the highest and the rural area had the lowest number of aquatic habitats that tested positive for Ae . albopictus larvae . Densities in the larval stages varied among the areas , but the urban area had almost two-fold higher densities in pupae and three-fold higher in adult populations compared with the suburban and rural areas . Larvae developed faster and the adult emergence rate was higher in the urban area than in suburban and rural areas . The survival time of adult mosquitoes was also longer in the urban area than it was in suburban and rural areas . Study regions , surface area , water depth , water clearance , surface type , and canopy coverage were important factors associated with the presence of Ae . albopictus larvae . Urbanization substantially increased the density , larval development rate , and adult survival time of Ae . albopictus , which in turn potentially increased the vector capacity , and therefore , disease transmissibility . Mosquito ecology and its correlation with dengue virus transmission should be compared in different environmental settings .
Aedes albopictus ( Skuse ) ( Diptera: Culicidae ) , the Asian tiger mosquito , is an aggressive , strongly anthropophilic , exophagic , and exophilic mosquito . As an important vector of dengue fever , chikungunya disease , and yellow fever , Ae . albopictus has emerged as a global public health threat [1]–[3] . Ae . albopictus is indigenous to both tropical and temperate regions of Southeast Asia and islands of the western Pacific and Indian Oceans , but it has recently expanded its range to every continent except Antarctica [4] , [5] . Unlike wetland mosquito species that oviposit and develop in habitats that are large , predictable , and easy to identify , Ae . albopictus is difficult to locate and control because this species utilizes small , different types of habitats including small containers and spare tires [6]–[8] . Ae . albopictus originated at the edges of forests and bred in natural habitats ( e . g . , tree holes , bamboo stumps , and bromeliads ) and was previously considered a rural vector [9] . However , this species has adapted well to urban environments with larvae now breeding in artificial containers ( e . g . , tires , cemetery urns , and water storage containers ) and has become the most important and sometimes sole vector in urban areas [8] , [10] , [11] . Ae . albopictus is found almost everywhere , especially in urban areas in southern and southwestern China [12]–[15] . The frequent outbreaks of dengue fever in the cities in southern ( mainly Guangdong province ) and southeastern coastal ( mainly Fujian and Zhejiang provinces ) China in the past few decades have caused serious public health concerns [16]–[18] . Although Aedes albopictus is described as a minor vector of dengue and possibly chikungunya in the world , it is emerging as a major dengue vector in China and was responsible for most outbreaks of dengue in China [19] , [20] and chikungunya in 2010 in Guangdong , China [21] . Similar to other mosquito vectors , Ae . albopictus needs aquatic habitats to breed and develop , and therefore , it is sensitive to environmental changes [8] , [22] , [23] . Destruction of breeding habitats is an important strategy to reduce the Aedes mosquito population; eliminating suitable breeding habitats reduces larval development and thus the adult mosquito population . Equally importantly , environmental changes , such as changes in temperature , affect habitat productivity , larval and adult development times , and survival , which in turn directly and indirectly affect disease transmissibility [24]–[32] . Urbanization refers to the increasing population of urban areas . Urbanization predominantly results in the physical growth of urban areas , leading to environmental changes . Urbanization is a global trend that results from economic development . Asian countries including China and India , countries in Southeast Asia , and African countries such as Nigeria are the fastest growing areas in the world , and the unprecedented movement of people into these areas is predicted to intensify in the future [33] . Many problems have emerged as a result of urbanization , including environmental pollution , crowding , and the destruction of natural ecology . The socioeconomic effects of urbanization have been extensively studied by socio-ecologists [34]–[36]; however , the ecological effects and their impact on vector biology and vector-borne infectious disease transmission remain unclear . Most dengue fever outbreaks occur in the urban areas of China , and these outbreaks have become more frequent over the past decade [18] , [20] , [37] . There is an accelerating trend of urbanization in China; will this process of urbanization accelerate dengue fever outbreaks ? Changes in environmental conditions as a result of urbanization may directly and/or indirectly affect the ecology of mosquitoes , e . g . , larval habitat availability and suitability , development , and survivorship . Because Ae . albopictus has invaded Europe ( e . g . , Italy and France ) and the Americas ( e . g . , USA ) , which increases the global vulnerability to dengue fever outbreak , therefore , it is crucial to evaluate its adaptations to urban environments . We hypothesized that urbanization increases Ae . albopictus larval habitats and survivorship and accelerates the development of larvae and adults . This study explored the ecology of Ae . albopictus in different settings ( urban , suburban , and rural ) in the Great Guangzhou area , China . Field surveys of larval habitat availability , larval development and adult mosquito life-table experiments were conducted in semi-natural conditions to test the hypothesis .
The field surveys of larval habitat availability and semi-natural condition larval development and adult mosquito life-table experiments were carried out in Guangzhou , the capital city of Guangdong province , China . Guangzhou is the largest city in southern China , and it is located in the Pearl River Delta , where numerous cities form a Canton-Macao-Hong Kong economic development zone . The annual average temperature in Guangzhou is 21 . 6°C , and its annual rainfall is approximately 1 , 980 mm . This climate is ideal for the development and reproduction of Ae . albopictus . The city has experienced rapid expansion during the recent regional economic development . Several major dengue fever outbreaks have occurred in this area since 1980 , and Ae . albopictus is the sole dengue vector [13] , [38] , [39] . Therefore , Great Guangzhou is an ideal place to study the impacts of urbanization on Ae . albopictus . The study was conducted in three areas that represented urban , suburban , and rural settings in Guangzhou ( Figure 1 and Figure S1 ) . Each study area was approximately 1 . 8 km2 . The distance between each area was approximately 24 km . Tonghe ( 113°19′E , 23°11′N , 31 m above sea level ( a . s . l . ) ) is an urban area with a population density of >3 , 000 people/km2 . The land use types are primarily residential and commercial buildings and public services such as schools and hospitals , filled with trees and grasses . Liangtian ( 113°23′E , 23°21′N , 25 m a . s . l . ) is a suburban area with a population density of approximately 1 , 000 people/km2 , and land use includes a mixture of residential , manufacturing , and farmland . Dengcun ( 113°33′E , 23°30′N , 42 m a . s . l . ) is a rural area and has a population density of <100 people/km2 , where land use is primarily agricultural ( rice and vegetable planting ) and forest . Aquatic habitat surveys were conducted in the three areas from May to November 2013 . We surveyed all aquatic containers in the study areas monthly with three teams of four trained personnel per team . All properties within the site ( i . e . , residential , abandoned , commercial , and public services ) and alleyways were surveyed , except for parcels whose owners refused access or places that were inaccessible due to physical barriers ( e . g . , fallen structures ) . We provided a detailed explanation of the purpose of the study to the residents , and after obtaining consent , we inspected the indoor , outdoor , and surrounding areas for aquatic habitats . The location and the physical characteristics of the habitats were recorded . Their chemical and biological characteristics were sampled , and mosquito larval availability and counts were recorded . The geographic location of each habitat was located using a hand-held GPS unit ( GARMIN Corporation , Taibei , Taiwan ) . For each habitat identified , the depth and surface area of the water was measured; for small containers ( <0 . 25 L ) , water was emptied into a separate container to measure the actual volume . Habitats were subjectively characterized by coverage of canopy ( direct sunlight , full shade that can be exposed to sunshine , full shade that cannot be exposed to sunshine ) , habitat type , substrate type ( soil , sand , leaf , moss , no substrate ) , and turbidity ( clear = colorless , tinted = in between , polluted = opaque and odoriferous ) . Immature Ae . albopictus samples were classified as young larvae ( 1–2 instar ) , old larvae ( 3–4 instar ) , or pupae . Immature mosquito abundance was determined using the standard 350 ml dippers . Once dipped , larvae and pupae were collected using a pipette , and individual numbers were counted . Samples were transported to the laboratory , where they were reared until emergence for species identification . All mosquitoes that emerged were pooled by site ( Urban , Suburban , and Rural ) and species . The BG-Sentinel trap with lure ( bought from Solbrite Resources Pte Ltd , Singapore; produced by BioGents , Regensburg , Germany ) was used for adult surveillance in this study because it is a very efficient tool for capturing adult Ae . albopictus [40]–[42] . During the surveys , 12 BG-Sentinel traps were placed in each study area . In each study site , we chose three typical environmental settings for the traps: in the urban area , a residential area , public park , and commercial district; in the suburban area , a residential area , factory , and garden; and in the rural area , a residential area , farmland , and forest . The distance between two traps was at least 50 meters . Traps were placed in the same location for three consecutive days during the first week of each month; they were shifted to different locations for another three days during the third week of each month . The adult population was monitored continuously from July to November 2013 . Trapped mosquitoes were collected every 24 hours , transported to the laboratory , and frozen for species identification . Frozen mosquitoes were placed on a piece of white filter paper in a petri dish on a chill table , and the species was identified morphologically using taxonomic keys [43] . Blood-fed females were identified visually by their dilated red abdomens , and they were stored at −80°C for further analysis . Mosquitoes used for adult life-table experiments were all F0 individuals who originated from different habitats in the study areas . Newly emerged ( <24-h-old ) adults were transferred to a 30×30×30 cm microcosm covered with nylon netting; 20 females and males each were placed in each microcosm . We used the thumbtack to fix the twine on the ceiling , and cages were lifted 1 . 0 m above the ground . The twine was smeared with grease to prevent the reach of ants and other insects which may cause interference with the experiment . Cotton wool soaked in 10% sucrose solution was supplied to the mosquitoes daily . Dead mosquitoes were recorded and removed from the cage daily . There was no other mosquito coils or spray near the cages during the experimental period . The experiments were performed in July–August 2013 and repeated in October–November 2013 . There were 15 replicates in each site during each season . Air temperature , humidity , and water temperature were measured using the HOBO data loggers . Data were offloaded using a Hobo Shuttle Data Transporter ( Shuttle , Onset Computer Corporation , Bourne , MA ) and then downloaded to the computer using BoxCar Pro 4 . 0 software ( Onset Computer Corporation ) . The monthly rainfall amount in each area was obtained from local meteorological stations ( Figure S2 ) . The daily average , minimum , and maximum temperatures and relative humidity were calculated from the hourly records . ANOVA post hoc Tukey's honestly significant difference ( HSD ) tests were used to determine the statistical significance of differences in mean temperature and relative humidity in different areas for each season . Water temperature in larval habitats was analyzed in the same manner . Mosquito larval density was standardized as the number of larvae per liter of water . Differences in immature mosquito density among different areas were tested using the Tukey's HSD test after logarithmic transformation of larval densities . Differences in adult mosquito density among different areas were tested using one-way analysis of variance ( ANOVA ) with repeated measures after square root transformation of raw data . Survival rates of Ae . albopictus larvae were calculated as the proportion of first-instar larvae that survived to emergence of adult . Mean larval development time was defined as the average duration from first-instar larvae to emergence of adult , and was computed separately for each sex . Kaplan-Meier survival analysis was used to determine the effect of different environmental conditions on adult mosquito daily survivorship . Stepwise logistic regression was used to identify the factors significantly influencing the occurrence of immature Ae . albopictus in aquatic habitats . We used the χ2-test to determine the significance of differences in stage-specific survival rates of immature mosquito from different places , and Tukey's HSD tests of ANOVA post hoc were used to determine the statistical significance of differences in stage-specific development times . Statistical analysis was performed using JMP statistical software ( JMP 9 . 0 , SAS Institute Inc . , USA ) . All entomological surveys and collections conducted on private lands or in private residential areas were done with the owners'/residents' permission , consent and presence . These studies did not involve endangered or protected species .
During our survey period , we found 2639 , 2523 , and 1760 aquatic habitats in urban , suburban , and rural areas , respectively . χ2-test indicated that habitat Ae . albopictus positive rate varied significantly ( P<0 . 0001 ) among the three areas , urban area had the highest positive rate ( 44 . 0% ) , then suburban area ( 37 . 7% ) , and rural area had the lowest rate ( 31 . 5% ) . A wide variety of container types were present in the three study sites ( Table 1 ) . The most abundant container types in urban areas were plastic buckets ( 412 ) , and the least abundant were tarps ( 6 ) . Flower pots , disposable food tins and gutters were also abundant in urban areas ( Table 1 ) . The most abundant container types in suburban areas were disposable food tins ( 665 ) , and the least abundant were pools ( 2 ) . Abandoned tires , plastic buckets , and clay pottery were also abundant in the suburban area ( Table 1 ) . The most abundant container types in rural areas were clay pots ( 445 ) and plastic buckets ( 324 ) ; disposable food tins ( 276 ) were also found frequently in this area . Overall , the variety of containers and habitat types was less abundant in rural areas ( Table 1 ) . Immature Ae . albopictus were most often found in abandoned tires ( positive rate 67 . 3% ) and flower pots ( 65 . 1% ) in urban areas ( Table 1 , Figure S3 ) . In suburban areas , Ae . albopictus larvae were common in abandoned tires ( 54 . 2% ) and clay pots ( 53 . 4% ) ( Table 1 ) . Whereas in rural areas , Ae . albopictus larvae were frequently found in plastic buckets ( 29 . 3% ) and plastic basins ( 27 . 7% ) ( Table 1 ) . The number of Ae . albopictus-positive habitats varied over time and between different study areas ( Figure 2 ) . The urban area had the highest aquatic habitat positive rate in every month except October . Over the seven-month survey period , the aquatic habitat positive rate in urban areas ( monthly-mean ± SD 43 . 8±4 . 4% ) was significantly higher than in rural areas ( 28 . 4±7 . 6% ) ( Tukey's HSD test , P<0 . 05 ) but not significantly different from suburban areas ( 36 . 9±7 . 3% ) . Densities of immature mosquitoes also varied significantly among study areas ( Table 2 ) . The urban area had significantly higher 1–2 instar larvae density than that in the suburban and rural areas , but the difference in 1–2 instar larvae density between suburban and rural areas was statistically insignificant ( Tukey HSD test , Table 2 ) . The density of 3–4 instar larvae in urban and suburban areas was significantly higher than that in rural areas , but the difference in 3–4 instar larvae density between urban and suburban areas was statistically insignificant ( Tukey HSD test , Table 2 ) . Urban areas also had a significantly higher pupae density than that in suburban and rural areas , but the difference in pupae density between suburban and rural areas was statistically insignificant ( Tukey HSD test , Table 2 ) . The monthly average density of Ae . albopictus adults was significantly higher in urban areas than that in suburban and rural areas , and it was significantly higher in suburban area than that in rural areas ( ANOVA with repeated measures , P<0 . 05 ) ( Figure 3 ) . The monthly density of adult Ae . albopictus was significantly higher in urban areas than that in the other two sites in all months ( Tukey HSD test , P<0 . 05 ) ; and it was significantly higher in the suburban area than that in the rural area every month ( Tukey HSD test , P<0 . 05 ) except November ( P>0 . 05 ) . Ae . albopictus adult emergence rates were significantly different among urban , suburban , and rural areas regardless of in natural habitats or with food supplement groups ( χ2-test , all P<0 . 001 ) ( Figure 4 ) . In the natural habitat , Ae . albopictus adult emergence rates was the highest in the urban area ( 51 . 5% ) , then suburban area ( 19 . 3% ) , with the lowest rate in the rural area ( 13 . 9% ) ( χ2-test , all P<0 . 001 ) ( Figure 4A ) . In the food supplement group , urban areas had the highest adult emergence rate ( χ2-test , all P<0 . 001 ) , but the difference in adult emergence rates between suburban and rural areas were statistically insignificant ( P>0 . 05 ) ( Figure 4B ) . For the natural habitat group , larval development time in urban areas was significantly shorter than that in both the suburban and rural areas ( male F = 19 . 0 , d . f . = 2 , 92 , P<0 . 001; female F = 20 . 5 , d . f . = 2 , 98 , P<0 . 001 ) ( Figure 4 , Table S1 ) . The mean developing time from 1st instar larval to adult were 21 . 4 and 24 . 2 days for males and females , respectively , in urban areas , but those values were 28 . 3 days ( male ) and 32 . 8 days ( female ) in suburban areas and 31 . 7 days ( male ) and 34 . 0 days ( female ) in rural areas . Habitat water temperature in urban areas ( 25 . 8±2 . 7°C ) was significantly higher than that in suburban ( 20 . 9±3 . 4°C ) and rural areas ( 20 . 5±4 . 1°C ) ( F = 48 . 5 , d . f . = 2 , 174 , P<0 . 001 ) . For the food supplemental group , the larval development time from 1st instar larvae to adults in urban areas was significantly shorter than that in suburban and rural areas ( male F = 20 . 3 , d . f . = 2 , 23 , P<0 . 001; female F = 9 . 8 , d . f . = 2 , 23 , P<0 . 001 ) ( Figure 4 , Table S1 ) . Overall , the larvae to adult development time was >50% shorter in control groups than it was in natural habitat groups in all study sites ( Figure 4 , Table S1 ) . The larval stage-specific development time were shown in Figure 5 . Young larval ( 1st and 2nd instar ) and pupa developed significantly faster in urban areas than that in suburban and rural areas ( Tukey HSD test , all P<0 . 001 ) ; old larval ( 3rd and 4th instar ) showed similar development time in the three areas ( Figure 5 ) . The stage-specific survival rates also varied among the three areas ( Figure 5 ) . Overall , young larvae survived significantly better in urban areas than in suburban and rural areas ( χ2-test , all P<0 . 001 ) . Whereas , survival rates in old larvae and pupae did not show such a difference ( all P>0 . 05 ) ( Figure 5 ) . From August to September , the life span of female adult mosquito was significantly longer in urban areas than that in suburban and rural areas , but the difference in median survival time between suburban and rural areas was insignificant ( Figure 6 , Table S2 ) . Adult male mosquito survival time was significantly different among study sites ( χ2 = 17 . 4 , d . f . = 2 , P<0 . 001 ) . Male survival time was longest in the suburban area and shortest in the rural area ( Figure 6 , Table S2 ) . The average outdoor temperatures in urban ( 29 . 4±1 . 7°C ) and suburban areas ( 29 . 2±0 . 9°C ) were significantly higher than that in rural areas ( 28 . 1±1 . 8°C ) ( F = 5 . 4 , d . f . = 2 , 106 , P = 0 . 0016 ) ( Table S2 ) . Relative humidity were significantly different among rural ( 87 . 5±9 . 4% ) , urban ( 82 . 1±11 . 1% ) , and suburban areas ( 75 . 9±7 . 6% ) ( F = 10 . 5 , d . f . = 2 , 106 , P<0 . 001 ) ( Table S2 ) . The mean daily survival rates were similar in all study sites and similar between males and females ( Tukey HSD test , all P>0 . 05 ) ( Table S2 ) . Survival curves were similar in females between urban and suburban areas but different from those in rural areas ( Figure 6C and 6D ) . From October to November , the median survival of adult female mosquitoes in urban and suburban areas was significantly longer than in rural areas but the difference between urban and suburban areas was insignificant ( Figure 6 , Table S2 ) . The median survival of males was significantly different among the three sites ( χ2 = 181 . 1 , d . f . = 2 , P<0 . 001 ) , with the longest and shortest survival times in urban and suburban areas , respectively ( Figure 6 , Table S2 ) . The average outdoor temperature in urban areas ( 24 . 8±2 . 6°C ) was significantly higher than that in suburban ( 22 . 8±3 . 6°C ) and rural areas ( 21 . 9±2 . 4°C ) , and there was no difference in temperature between suburban and rural areas ( Table S2 ) . There was no significant difference in the relative humidity among the three areas . ( F = 1 . 9 , d . f . = 2 , 134 , P = 0 . 15 ) . Mean daily survival rates were similar in all study sites but differed between males and females ( Figure 6 , Table S2 ) . Survival curves were similar in females between rural and suburban areas but very different in those from urban areas , which showed prolonged survivorship ( Figure 6D ) . Stepwise logistic regression revealed that six factors were significantly associated with the presence of immature mosquitoes in the study sites ( Table 3 ) . Habitat Ae . albopictus larval presence rate was significantly greater in the urban area than in suburban and rural areas ( OR = 1 . 71 , P<0 . 001 ) , and the suburban area was significantly higher than the rural area ( OR = 1 . 67 , P<0 . 001 ) . The presence of Ae . albopictus larvae was significantly in negative correlation with habitat water depth ( OR = 0 . 03 , P<0 . 001 ) ; whereas , it showed positive correlation with habitat water surface area ( OR = 3 . 95 , P<0 . 001 ) . The presence of Aedes larvae was significantly greater in clean water than that in tinted or polluted water ( OR = 1 . 889 , P<0 . 001 ) , and greater in tinted water than polluted water ( OR = 1 . 78 , P = 0 . 034 ) . Shading ( regard less of fully shaded or half-shaded ) , compare to open area , was positively affecting the presence of Aedes larvae ( OR = 2 . 29 , P<0 . 001 ) . The presence of Ae . albopictus larvae was also positively correlated with habitats that have leaves on water surfaces ( OR = 2 . 25 , P<0 . 001 ) , and with habitats that have soil and moss substrates ( OR = 1 . 71 , P<0 . 001 ) .
Outbreaks of dengue fever in China were reported in Hainan province and southern Guangdong province in the 1980s and have been reported in Zhejiang province in 2004 , illustrating a 2 , 000 km expansion from subtropical to temperate areas over 30 years [16] . Among these outbreaks , Ae . albopictus was the only vector reported [13] , [20] , [44] . Although the causes of dengue fever outbreaks are multi-factorial , environmental changes such as urbanization may be one of the leading factors . We found that in urban areas , there are more Ae . albopictus habitats . In addition , urban areas promoted faster larval and pupal development , and higher larval-to-adult survival rate compared to rural areas . Ae . albopictus mosquito is strongly anthropophilic and has a higher blood-feeding rate in urban areas , where human population density is great , than that in rural areas [29] , making it a more susceptible vector in urban areas . Because there is no effective drug therapy or vaccine for dengue fever , vector population control is by far the only effective method for reducing dengue virus transmission . In this context , understanding the vector ecology and biology is essential for developing dengue control strategies . Unfortunately , it is unclear how urbanization impacts the ecology of Ae . albopictus , and the lack of this key knowledge hinders disease control efforts . We found that , in the similar sampling area , the total number of potential habitats and the number of Aedes-positive habitats were significantly higher in urban areas than in suburban and rural areas . Urban areas have 10-fold higher human population density and more frequent human activities than do suburban and rural areas , leading to a larger number of artificial containers such as abandoned tires , disposable food tins , and flowerpots , which are all favorable breeding habitats for Ae . albopictus [7] , [23] , [45] . Larger size and higher density of human populations also mean more opportunities for Ae . albopictus blood feeding . Previous study found that Ae . albopictus has a higher blood-feeding rate in urban areas than in rural areas , most likely due to host availability [29] . Additionally , the existence of stable and abundant artificial containers produced by human activities serve as larval development sites , facilitating large mosquito densities in urban areas [11] . Urbanization shifts mosquito breeding sites from natural habitats to artificial habitats . These artificial habitats are usually small containers such as used tires and disposable containers , and they are often directly exposed to sunlight . Therefore , the water temperature in these habitats is higher than in rural areas . In our study sites , the average water temperature of urban habitats was 5°C higher than in suburban and rural areas . Similarly , vegetation changes and land use changes in urban areas may affect the radiation budget and energy balance of the land surface and thus may modify the microenvironments , e . g . , food sources that enhance larval survival . These changes facilitate the development of immature Ae . albopictus , i . e . , shorten the larval-to-adult development time and enhance the larva survival rate . Our findings are consistent with other studies conducted in different countries and for different mosquito species [24] , [30] , [46]–[48] . Compared with the food supplemental group , we found that added food sources significantly affect the developmental time and survival rate of immature mosquitoes , which implies that the habitat types in different areas may affect larval development differently due to the difference in availability of nutrients . However , the effects were more pronounced in urban areas than in suburban and rural areas , implying that other factor such as water temperature may play a more important role than food supply in urban areas . These results demonstrated that larvae develop and survive better in urbanized areas , in other words , Aedes larvae is better adapted to urban environment . Similar to a study conducted in the United States [49] , the urban area had a higher pupal and larval density than other two areas; thus , the urban habitats had a higher capacity to support larval development . The reason might be that urban areas had less predators , more nutrition from a “dirtier” environment , or even less drift from agricultural insecticides . Pupal productivity is a good indicator of the abundance of adult mosquitoes [50]–[52] . The surveillance of adult mosquitoes in this study supports this conclusion , i . e . , urbanization leads to a higher population density of adult Ae . albopictus . Higher mosquito density does not necessarily lead to increased disease transmission if adult mosquitoes have a very short life span . We found that both male and female mosquitoes in urban areas had the longest life spans . This result may be due to environmental factors such as air temperature and humidity . The average temperature in urban areas is higher than in suburban and rural areas . Longer adult life spans may enhance disease transmission , although the exact correlation between vector capacity and adult life span needs to be further explored . In this study we fed the adult mosquito with 10% sugar solution without blood , which might have led to exerted stress on the females during multiple gonotrophic cycles and affected the longevity of the female mosquitoes . We observed that the mortality of adult mosquitoes in rural area changed dramatically around day 15 , because the air temperature in rural area showed a 5 . 7°C increase from day 11 to day 15 compared to the first 11 days . This drastic increase in temperature might have influenced the mortality rate of the adult mosquito . In our survey , we found that the distribution of immature Ae . albopictus was not random . Habitat surface area , canopy coverage , water turbidity , water depth , and substrate type were all important factors influencing habitat selection . These findings confirmed other studies reporting preferences for urban areas [11] , [49] , shaded containers [49] , clean water [53] , water with foliage [49] , and larger surface area [49] , [54] . These results illustrated the complex ecology of Ae . albopictus , which makes controlling this mosquito species difficult in light of its recent global expansion . In conclusion , the results of this study indicated that urbanization has a significant impact on the ecology of Aedes albopictus . In the urbanizing and urbanized area , the changed environment became more suitable for the growth and development of Ae . albopictus , the condensed population produced more kinds of containers for larval habitats and more blood sources for adult replication . This might be the reason for quick adaptation of Ae . albopictus in urban areas . The epidemic of dengue is largely dependent on vector population . Developing countries such as China and other Southeastern Asian countries experiencing rapid urbanization are under sustained risk of dengue outbreaks . | Aedes albopictus has expanded its ecological habitat range throughout the world . Although Ae . albopictus was previously considered a rural vector , this species has adapted well to suburban and urban environments , and it has become the most important and sometimes the sole vector of dengue virus transmission in urban areas . Dengue is a vector-borne disease that has become a severe global public health problem during the last decade . We explored the effect of ecology in different ecological settings ( urban , suburban , and rural ) on Ae . albopictus larval habitat and mosquito development in Guangzhou , where recently dengue has caused serious public health concerns . The environmental changes caused by urbanization had a significant impact on the ecology of Ae . albopictus . Compared with rural and suburban areas , urban areas had more Ae . albopictus larval habitats , shorter larval development time , higher adult emergence rate , and longer lifespan . These results imply that urbanization significantly increases the potential for dengue outbreaks . Because urbanization is a global trend resulting from economic development , the elucidation of Ae . albopictus adaptation to different environments in China also reveals the potential for this important vector to colonize other parts of the world . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"ecology",
"epidemiology",
"vector",
"biology",
"dengue",
"fever",
"neglected",
"tropical",
"diseases",
"biology",
"and",
"life",
"sciences",
"vector-borne",
"diseases",
"viral",
"diseases",
"tropical",
"diseases",
"parasitic",
"diseases",
"disease",
"vectors",
"parasitology"
]
| 2014 | Urbanization Increases Aedes albopictus Larval Habitats and Accelerates Mosquito Development and Survivorship |
Plasma insulin oscillations are known to have physiological importance in the regulation of blood glucose . In insulin-secreting β-cells of pancreatic islets , K ( ATP ) channels play a key role in regulating glucose-dependent insulin secretion . In addition , they convey oscillations in cellular metabolism to the membrane by sensing adenine nucleotides , and are thus instrumental in mediating pulsatile insulin secretion . Blocking K ( ATP ) channels pharmacologically depolarizes the β-cell plasma membrane and terminates islet oscillations . Surprisingly , when K ( ATP ) channels are genetically knocked out , oscillations in islet activity persist , and relatively normal blood glucose levels are maintained . Compensation must therefore occur to overcome the loss of K ( ATP ) channels in K ( ATP ) knockout mice . In a companion study , we demonstrated a substantial increase in Kir2 . 1 protein occurs in β-cells lacking K ( ATP ) because of SUR1 deletion . In this report , we demonstrate that β-cells of SUR1 null islets have an upregulated inward rectifying K+ current that helps to compensate for the loss of K ( ATP ) channels . This current is likely due to the increased expression of Kir2 . 1 channels . We used mathematical modeling to determine whether an ionic current having the biophysical characteristics of Kir2 . 1 is capable of rescuing oscillations that are similar in period to those of wild-type islets . By experimentally testing a key model prediction we suggest that Kir2 . 1 current upregulation is a likely mechanism for rescuing the oscillations seen in islets from mice deficient in K ( ATP ) channels .
Insulin is secreted from pancreatic islet β-cells in response to elevated blood glucose . Islet activity is oscillatory , with periods ranging from tens of seconds to several minutes , and this is reflected in the reported periods of pulsatile insulin secretion [1–4] . Plasma insulin oscillations play a physiological role in blood glucose regulation [5–8] . A recent study showed that the action of insulin on the liver to lower plasma glucose is more profound when insulin is delivered to the liver in a pulsatile fashion [9] , and earlier studies showed that plasma insulin oscillations are disrupted in type II diabetics and their near relatives [10–12] . At stimulatory levels of glucose β-cells exhibit electrical bursting , and Ca2+ that enters the cells during each burst evokes a pulse of insulin secretion [7 , 13 , 14] . Several mechanisms have been proposed to explain this bursting electrical activity [15–18] . A recent mathematical model that combines two of these mechanisms can reproduce bursting having a wide range of periods , as seen in experimental studies [19] . One mechanism produces fast oscillations , while the other produces slow oscillations and both can oscillate independently , prompting the name Dual Oscillator Model ( DOM ) . In the DOM , the fast component of bursting results from the negative feedback of Ca2+ on the membrane potential via Ca2+-activated K+ channels and , indirectly , via K ( ATP ) channel activation . The slow component , in contrast , is due to oscillations in glycolysis that occur as the result of actions of the allosteric enzyme phosphofructokinase ( PFK ) [20 , 21] . The subsequent oscillatory ATP production acts through ATP-sensitive K+ channels ( K ( ATP ) channels ) to produce oscillations in K ( ATP ) current , which turns the bursts of electrical activity on and off [22 , 23] . K ( ATP ) channels play a crucial role coupling cell metabolism to membrane potential . These channels are comprised of four inwardly rectifying K+ channel subunits ( Kir6 . 2 ) and four sulfonylurea receptor subunits ( SUR1 ) arranged in an octomeric array ( for review see [24] ) . A mutation in the genes coding for either subunit prevents K ( ATP ) channels from being trafficked normally to the plasma membrane or alters their sensitivity to adenine nucleotides , leading to persistent hyperinsulinemic hypoglycemia of infancy ( PHHI ) in humans , a condition characterized by high insulin secretion that occurs even when blood glucose is low [25–27] . High secretion results from the permanent depolarization of the β-cell membrane that is due to the lack of normally hyperpolarizing K ( ATP ) current . Surprisingly , in SUR1 homozygous knockout mice ( SUR1-/- mice ) , lacking K ( ATP ) channels , islets typically still exhibit electrical bursting ( although the glucose sensitivity of bursting in these islets is largely abrogated ) , and blood glucose levels are relatively normal unless the animals are metabolically stressed [28 , 29] . Similarly , islets from Kir6 . 2 knockout mice exhibit slow Ca2+ oscillations , similar to those observed in wild-type islets which are known to be due to bursting electrical activity [30] . In these mice , compensation must therefore occur to overcome the loss of the large hyperpolarizing K ( ATP ) current . Indeed , when the K ( ATP ) channels of wild type islets are acutely blocked by sulfonylurea drugs , β-cells spike continuously from a sustained depolarized level [31–33] . We hypothesized that such compensation could be achieved through the upregulation of another hyperpolarizing K+ channel that impersonates K ( ATP ) channels in sensing cellular metabolism [34] . In a companion study ( Vadrevu et al , manuscript in preparation ) , we demonstrated that the upregulation of Kir2 . 1 channel protein in islets from SUR1-/- mice ( KO islets ) could mediate this compensation . In the current report , we demonstrate that SUR1 KO islets exhibit sustained Ca2+ oscillations at stimulatory levels of glucose , and that the amount of inward rectifying K+ current is increased in these K ( ATP ) channel KO cells . Using mathematical modeling , we explored the functional role of this current on the electrical activity of islet β-cells when K ( ATP ) channels are absent . In particular , we investigated whether this inward-rectifying K+ current has the ability to rescue normal electrical bursting pattern in β-cells of SUR1-/- mouse islets . Kir2 . 1 channels conduct large inward currents at voltages below the K+ Nernst potential ( VK ) and smaller outward currents at voltages above VK . This diode-like property , or inward rectification , is caused by blockade of the channels by intracellular ions and polyamines when the cell membrane is depolarized [35–37] . Kir2 . 1 channels also contain consensus sites for phosphorylation by protein kinase A ( PKA ) and studies show that PKA potentiates Kir2 . 1 current [38–40] . One study shows that a phosphatase inhibitor can prevent rundown of the Kir2 . 1 current that is activated by PKA , which indicates activation of the channels is regulated by protein phosphorylation [41] . Since PKA activity is cAMP-dependent , changes in the cAMP concentration in the β-cell can in principle regulate Kir2 . 1 channel activity . Recent studies employing FRET-based sensors and TIRF microscopy showed that glucose induces cAMP oscillations in mouse β-cells [42 , 43] , which may be accounted for by oscillations in metabolism [44] . It is therefore possible that , in KO cells , metabolic oscillations drive cAMP oscillations which in turn drive oscillations in Kir2 . 1 current , and this replaces oscillations in K ( ATP ) current as the mechanism for bursting electrical activity . We illustrate how this works with the model , and make predictions that are subsequently confirmed experimentally and thereby support the hypothesis that Kir2 . 1 channel upregulation is a feasible mechanism which can rescue electrical bursting in SUR1-/- mouse islets lacking K ( ATP ) channels .
The animal protocol used was in accordance with the guidelines of the University of Michigan Institutional Animal Care and Use Committee ( IACUC ) . Pancreatic islets were isolated from 3–4 month old male Swiss-Webster mice as in [45] . Islets were hand picked into fresh Kreb’s solution and then transferred to culture dishes containing RPMI-1640 supplemented with 10% FBS , glutamine and penicillin-streptomycin . Islets were cultured overnight at 37°C in an incubator . Electrophysiological recordings were made from islets cultured for 72 hours or less . Patch electrodes were pulled ( P-97 , Sutter Instrument Co . , Novato , CA ) from borosilicate glass capillaries ( Warner Instrument Inc . , Hamden , CT ) and had resistances of 8–10 M-ohm when filled with an internal buffer containing ( in mM ) : 28 . 4 K2SO4 , 63 . 7 KCl , 11 . 8 NaCl , 1 MgCl2 , 20 . 8 HEPES and 0 . 5 EGTA at pH7 . 2 . The electrodes were then backfilled with the same solution but containing amphotericin B at 0 . 3 mg/ml to allow membrane perforation . Islets were transferred from culture dishes into a 0 . 5 ml recording chamber held at 32–34°C . Islets were visualized using an inverted epifluorescence microscope ( Olympus IX50 , Tokyo , Japan ) . Pipette seals obtained were > 1 G-ohms . Recordings were made using an extracellular solution containing ( in mM ) : 135 NaCl , 2 . 5 CaCl2 , 4 . 8 KCl , 1 . 2 MgCl2 , 20 HEPES , and 11 . 1 glucose . After the establishment of a perforated patch , cells were voltage-clamped to a holding potential of -60 mV , and a 2-second voltage ramp from -120 to -50 mV was applied , as in [32] . Evoked currents were digitized at 10 kHz after filtering at 2 . 9 kHz . The protocols were generated using Patchmaster software ( v2x32; HEKA Instruments ) . Pancreatic islets were cultured overnight in RPMI medium containing 5 mM glucose and on the day of experiments were transferred to fresh media containing 2 . 5 μM Fura-PE2-AM for 30 min . Following incubation , islets were loaded into a glass-bottomed chamber mounted onto the microscope stage . The chamber was perfused at 0 . 3 mL/min with 11 mM glucose solution and the ambient temperature was maintained at 33°C using inline solution and chamber heaters ( Warner Instruments ) . Excitation was provided by a TILL Polychrome V monochromator ( TILL Scientific , Germany ) with light output set to 10% maximum . Excitation ( x ) or emission ( m ) filters ( ET type; Chroma Technology , Bellows Falls , VT ) were used in combination with an FF444/521/608-Di01 dichroic ( Semrock , Lake Forest , IL ) as follows: Fura-2 , 340/10x and 380/10x , 535/30m ( R340x/380x – 535m ) ; A single region of interest was used to quantify the average response of each islet using MetaMorph software ( Molecular Devices ) . In one set of experiments , after three oscillations were recorded , the solution was switched to a solution containing 11 mM glucose with thapsigargin ( 1 μM ) . In another set of experiments , the solution was switched to one containing 11 mM glucose and 8-Bromoadenosine 3’ , 5’-cyclic monophosphate ( 8-Br-cAMP ) ( 50 μM ) . We used an 8-variable model consisting of ordinary differential equations , illustrated in Fig 1 . This Dual Oscillator Model ( DOM ) has electrical , Ca2+ , and metabolic components [23 , 46] . We focus our description on elements of the model that are most important for this study , but all equations and tables of parameter values are given in Supporting Information . ( The computer codes , using the CVODE solver implemented in XPPAUT , can be downloaded as freeware from www . math . fsu . edu/~bertram/software/islet . ) In the DOM , the fast oscillatory component is based on negative Ca2+ feedback onto the membrane potential through Ca2+-sensitive K+ current ( IK ( Ca ) ) . This mechanism can drive fast bursting . The second oscillatory component is due to metabolic oscillations , which result from the activity of the allosteric enzyme phosphofructokinase ( PFK ) . In the process of glycolysis , PFK catalyzes the phosphorylation of fructose 6-phosphate ( F6P ) to fructose 1 , 6-bisphosphate ( FBP ) . The activity of PFK is increased by its product FBP , so that increased FBP increases the reaction rate and causes a sharp rise in FBP . This eventually depletes the substrate of the reaction , F6P , and turns off flux through PFK , resulting in a reduction in FBP . This allows the substrate , F6P , to recover and the cycle to restart . Oscillatory FBP levels in turn cause oscillations in pyruvate , the end product of glycolysis and the substrate for mitochondrial respiration . The oscillatory glycolytic input results in oscillatory levels of the nucleotide concentrations ( ATP , ADP and AMP ) . The membrane potential is then affected through the action of ATP and ADP on K ( ATP ) channels , which can drive slow bursting in the model . Equations for the dynamics of cAMP were recently added to an earlier version of the DOM [44] and it was shown that this version was capable of producing cAMP oscillations in model β-cells . We employed these equations , where the cAMP concentration is determined by the difference between its production by adenylyl cyclase ( VAC ) and degradation by phosphodiesterases ( VPDE ) : dcAMPdt=VAC−VPDE ( 1 ) where , VAC=v¯AC ( αAC+βACc3c3+KACca3 ) ( βampKamp2AMPc2+Kamp2 ) ( 2 ) VPDE=v¯PDE ( αPDE+βPDEc3c3+KPDEca3 ) cAMPcAMP+KPDEcamp ( 3 ) where c is the cytosolic free Ca2+ concentration , which stimulates both AC and PDE . Cytosolic AMP ( AMPc ) inhibits AC and thus the production of cAMP [47–49] . We modified the VAC equation from the original model to incorporate a higher-order dependence on AMP . In the model , slow cAMP oscillations are the result of AMP oscillations and the accompanying Ca2+ oscillations , which are both the product of glycolytic oscillations . The details of the cAMP dynamics are given in [44] . In the DOM , the rate of change of the membrane potential of a wild type β-cell is given by a conductance-based Hodgkin-Huxley type equation: dVdt=− ( IK+ICa+IK ( Ca ) +IK ( ATP ) ) /Cm ( 4 ) where , Cm is the membrane capacitance , IK is the delayed rectifier K+ current , ICa is a voltage-sensitive Ca2+ current , IK ( Ca ) is a Ca2+-sensitive K+ current and IK ( ATP ) is an ATP-sensitive K+ current . The rate of change of the free cytosolic Ca2+ concentration is: dcdt=fcyt ( −αICa−kpmcac⏞Jmem+kleak ( cer−c ) −kSERCAc⏞JER ) ( 5 ) where terms labeled by Jmem and JER represent the Ca2+ flux across the plasma membrane and net flux out of the endoplasmic reticulum ( ER ) , respectively . Here , fcyt is the fraction of free to total cytosolic Ca2+ , α converts current to flux , kpmca is the Ca2+ pumping rate across the plasma membrane , kleak is the rate of the Ca2+ leak from the ER and kSERCA is the Ca2+ pumping rate into the ER . The ER Ca2+ concentration ( cer ) is also dynamic and given by: dcerdt=−ferVcte ( kleak ( cer−c ) −kSERCAc ) ( 6 ) where fer is the ratio of the free Ca2+ in the ER and Vcte is the ratio of the volume of the cytosol to the volume of the ER compartment . The equation for the Ca2+-sensitive K+ current ( IK ( Ca ) ) is , IK ( Ca ) =gK ( Ca ) ω ( V−VK ) ( 7 ) where , gK ( Ca ) is the maximal conductance of the current , and ω is the following Ca2+-dependent activation function , ω=c2c2+Kc2 ( 8 ) where Kc is the affinity constant . In the KO-cells lacking K ( ATP ) channels there is no IK ( ATP ) present . In the model KO-cells , K ( ATP ) current is replaced by the following Kir2 . 1-mediated inward-rectifying K+ current: IKir=gKirk∞c∞ ( V−VK ) . ( 9 ) Here gKir is the maximal Kir2 . 1 channel conductance , k∞ is the voltage-dependent block of the channel by polyamines which is the cause of the inward rectification , and c∞ is the cAMP-dependent activation of the channels . We use a Boltzmann function to describe k∞: k∞=11+exp ( V−Vkirskir ) ( 10 ) where VKir is the half activation potential and sKir is the slope factor that determines the sensitivity to the voltage . The resulting voltage-dependent k∞ curve is shown in Fig 2A and is parameterized according to [50] . Kir2 . 1 current has both cAMP dependent and independent components [38] , which are incorporated into the activation function c∞ as follows: c∞=αcamp+βcampcAMP4cAMP4+Kcamp4 ( 11 ) where αcamp is the cAMP independent component , and the cAMP dependency of the current is described by the second term . The c∞ function is illustrated in Fig 2B .
Ca2+ and membrane potential oscillations in SUR1-/- islets lacking functional K ( ATP ) channels were reported previously [28 , 51] . Our fura-2 Ca2+ measurements verified that slow cytosolic Ca2+ oscillations persisted in both wild-type ( Fig 3A ) and KO-islets ( Fig 3B ) perfused with 11 mM glucose . These data show that our SUR1-/- islets recapitulate the Ca2+ oscillations observed in [28 , 51] . We recently identified an increase in Kir2 . 1 channel protein in islets isolated from SUR1-/- mice ( Vadrevu et al , manuscript in preparation ) . To verify the electrophysiological functionality of these channels in the β-cell membrane of KO islets , we measured current-voltage relations of wild-type and KO cells using the perforated patch clamp technique in peripheral islet β-cells . Fig 3C shows current recordings elicited by voltage ramp commands from -120 mV to -50 mV ( see Materials and Methods ) applied to wild-type islets ( black ) and K ( ATP ) KO islets ( red ) . In wild-type islets , the current-voltage relation is largely linear beyond about -110 mV ( Fig 3C , black ) ( n = 6 islets from 4 mice ) , while in the KO cells the evoked current was more nonlinear , exhibiting inward rectification ( Fig 3C , red ) . The strong inward rectification is likely due to current from the upregulated Kir2 . 1 inward-rectifying K+ channels that we report in a companion study ( Vadrevu et al , manuscript in preparation ) , supporting a functional role for the upregulated Kir2 . 1 channel protein . Fig 4 illustrates slow bursting produced by the model for the case of wild-type cells . The oscillations in the free Ca2+ concentration observed here ( Fig 4A ) result from the bursting electrical activity described earlier . The burst timing in this case is controlled by the slow glycolytic oscillations , which are reflected by the FBP time course as shown ( Fig 4E ) . FBP oscillations in turn cause oscillations in downstream metabolic components , including cytosolic AMP and ATP ( Fig 4C and 4D ) . The conductance of K ( ATP ) channels ( gK ( ATP ) ) is dependent on ADP and ATP levels , and oscillations in the concentrations of these nucleotides cause K ( ATP ) conductance ( Fig 4B ) and concomitantly K ( ATP ) current to oscillate and drive slow busting . The slow cAMP oscillations are modulated by Ca2+ and AMP , but in the model of the wild-type β-cells cAMP has no impact on the cell’s electrical activity . If the key K ( ATP ) channels are removed , the model cell spikes continuously , as is seen experimentally when a K ( ATP ) channel blocker like tolbutamide is applied to a wild-type islet [31–33] . The upregulated Kir2 . 1 conductance shown in Fig 3C would be expected to also provide hyperpolarizing current , but can it rescue the bursting oscillations that are normally driven by K ( ATP ) current ? To answer this , we replaced K ( ATP ) current in the model with Kir2 . 1 current to simulate the case for KO cells . The properties of this model current are discussed in Materials and Methods and are shown in Fig 2 . A key feature of the Kir2 . 1 channels is their activation by cAMP [38–40] . In Fig 5 we show that if Kir2 . 1 is sufficiently up-regulated , it can rescue slow bursting in model cells lacking K ( ATP ) . In the model of the KO condition , slow glycolytic oscillations now drive slow AMPc oscillations ( Fig 5C ) that cause the cAMP concentration to oscillate ( Fig 5A , red ) . cAMP in turn activates the Kir2 . 1 channels and results in oscillations in the Kir2 . 1 conductance ( Fig 5B ) . This causes the membrane potential to switch between the active and silent phases , which drives bursting and Ca2+ oscillations as in the wild-type case ( Fig 5A , black ) . The shape of the burst is largely determined by the details of the V and cAMP dependence of the Kir2 . 1 channels , which in our model is calibrated by data from a human isoform of the channel expressed in human embryonic kidney cells . Differences of channel properties between mouse and human would change the shape of the burst , but not the burst mechanism ( unless channel differences were drastic ) . A robust property of the burst mechanism is that the cAMP concentration peaks during the silent phase in the KO model cells , unlike the wild-type model cells where cAMP peaks at the beginning of the active phase . This peak in cAMP is reflected in the Kir2 . 1 conductance . Fig 5B shows the moving average of this conductance , where averaging is done over a window of 6 s to filter out fast V-dependent changes . Like cAMP , the Kir2 . 1 conductance peaks during the silent phase , and the subsequent decline in this conductance starts the next burst . Although the ATP concentration also oscillates ( Fig 5D ) , it does not affect the membrane potential in this case since there are no K ( ATP ) channels to sense changes in nucleotides . In the wild-type model cells , cAMP had no effect on membrane potential or any other components of the model . However , in the model we made of the KO cells , cAMP , acting through Kir2 . 1 channels , is now the key to slow bursting . To further understand how this occurs , a slow burst is shown in more detail in Fig 6 . In this figure , voltage is averaged over the duration of each spike to illustrate mean voltage ( Fig 6A , red ) . This allows us to focus on the slower burst waveform . The figure begins with the system in the silent phase , where Kir2 . 1 conductance is high ( Fig 6D ) due to elevated cAMP concentration ( Fig 6B , red ) and a relatively hyperpolarized voltage ( Fig 6A , red ) . As glycolytic activity declines near the end of the silent phase AMPc slowly increases ( Fig 6B , black ) . This , in turn , reduces the cAMP concentration by inhibiting adenylyl cyclase , thereby reducing Kir2 . 1 channel activation ( Fig 6C , red ) . The resulting decline in Kir2 . 1 conductance initiates an active phase of electrical activity , further reducing Kir2 . 1 conductance due to voltage-dependent channel blockade as the cell depolarizes ( Fig 6C , black ) . Cytosolic Ca2+ now increases due to Ca2+ influx through voltage-dependent Ca2+ channels and this activates Ca2+-ATPase pumps through ATP hydrolysis , further increasing the AMPc . This causes cAMP to decline rapidly . By the middle of the active phase AMP reaches its peak and then starts to decline . This decline , despite the continued rise in c , is due to the upstroke of the glycolytic oscillator , which facilitates the production of ATP at the expense of ADP and AMP . Decreased AMPc disinhibits adenylyl cyclase and cAMP again starts to increase . The cytosolic Ca2+ concentration starts to decrease only after cAMP is elevated enough to significantly activate Kir2 . 1 current ( Fig 6C , red ) , eventually terminating the active phase . The KO model relies on the action of cAMP oscillations on Kir2 . 1 channels to drive electrical bursting and Ca2+ oscillations in the SUR1-/- islets . If cAMP is tonically elevated , then the subsequent tonic activation of Kir2 . 1 should hyperpolarize the islet , terminating electrical bursting and Ca2+ oscillations , and bringing the intracellular Ca2+ concentration to a low level . We performed this manipulation by adding 8-Bromoadenosine 3’ , 5’-cyclic monophosphate ( 8-Br-cAMP ) to wild-type and SUR1-/- islets . This is a membrane permeant brominated derivative of cAMP that is resistant to degradation by cAMP phosphodiesterase , and is thus long lasting . Application of 8-Br-cAMP ( 50 μM ) to wild-type islets ( N = 10 ) had little or no effect on Ca2+ oscillations , as shown in three representative islets ( Fig 7A ) . In contrast , the same concentration applied to SUR1-/- islets terminated Ca2+ oscillations in all islets tested ( N = 9 ) , reducing the intracellular Ca2+ level to what is expected from a hyperpolarized islet ( Fig 7B ) . This is consistent with the hypothesis that cAMP activates Kir2 . 1 channels , and that oscillations in cAMP drive oscillations in Ca2+ in SUR1-/- islets , but not wild-type islets . To better understand the dynamics of the bursting mechanism , and to help facilitate the design of new experiments , we performed a fast/slow analysis of the Kir2 . 1 model . Fast/slow analysis separates system variables into component fast and slow subsystems based on their respective time scales [52] . The slow variables are almost constant on the time scale of changes in the fast variables . Therefore , these variables can be treated as slowly-varying parameters of the fast subsystem . In our model , the fast variables are voltage ( V ) , the activation variable for voltage-gated K+ current ( n ) and cytosolic Ca2+ ( c ) . The variables that change on much slower time scales are fructose 6-phosphate ( F6P ) , fructose 1 , 6-bisphosphate ( FBP ) , ATPc , AMPc , cAMP and the Ca2+ concentration of the ER ( cer ) . For comparison , Fig 8A shows a fast variable ( c ) shown together with a slow variable AMPc . At the start of a burst active phase c immediately jumps to a plateau and exhibits small oscillations due to the voltage spikes , and jumps down at the end of the active phase . In contrast , AMPc exhibits a slow rise and fall , with a peak near the middle of the active phase . We start the fast/slow analysis by setting cer to its mean value , since it is not a part of the primary oscillatory mechanism . The slow variables other than cer interact according to the following scheme: F6P→FBP→ATP→AMP→cAMP where only cAMP directly affects the fast subsystem , through the cAMP-dependent activation variable of IKir ( c∞ ) . We first generate a bifurcation diagram of the fast subsystem with c∞ as the bifurcation parameter ( Fig 8B ) , since the curve is simpler than that obtained using cAMP itself as the bifurcation parameter . For small values of c∞ the system is at a depolarized steady state , since the Kir2 . 1 current is largely turned off . These stable steady states make up the initial segment of the upper branch of the z-shaped curve ( solid line ) , which we refer to as the z-curve . As c∞ is increased two branches of periodic solutions , one stable ( bold solid curve ) and one unstable ( bold dashed curve ) , emerge at a saddle node of periodics ( SNP ) bifurcation . The branch of unstable limit cycles is created at a subcritical Hopf Bifurcation ( HB ) , at which point the branch of stable steady states becomes unstable ( dashed curve ) . The branch of unstable steady states turns at a saddle-node bifurcation ( SN1 ) , forming the middle branch of the z-curve . This branch turns at another saddle-node bifurcation ( SN2 ) and forms the stable lower branch of the z-curve . The stable branch of periodic solutions reflects tonic spiking , and the minimum and maximum voltage values during a spike are shown as two separate curves . This branch terminates at the left knee of the z-curve at a saddle-node on invariant circle ( SNIC ) bifurcation . The burst trajectory is shown projected into the c∞-V plane in Fig 8C . The left portion of the trajectory reflects the active phase of the burst when the model cell is spiking . When the cell enters the silent phase c∞ first increases and then decreases to start a new active phase . This is the right portion of the trajectory . The burst trajectory is superimposed onto the z-curve in Fig 8D , along the c∞ curve ( Eq 11 ) . This curve depends on the cAMP concentration , which has the following steady state function: cAMPss=kPDEcampVACv¯PDE ( αPDE+βPDECiss3Ciss3+KPDEca3 ) −VAC ( 12 ) where VAC is the rate of adenylyl cyclase production and is inhibited by AMPc ( Eq 2 ) . AMPc changes slowly during a burst ( Fig 8A , blue ) due to the activity of the glycolytic oscillator . The steady-state cytosolic Ca2+ concentration in Eq 12 ( ciss ) is given by: ciss=αICa+kleakcerkpmca+kleak+kSERCA ( 13 ) where ICa is a function of V and cer is clamped at its mean value . This gives the voltage dependence to the c∞ curve . During the burst , the glycolytic oscillator moves the c∞ curve back and forth . In Fig 8D the curve is plotted for values of AMPc at its minimum and its maximum during a burst . During a burst AMPc moves between these minimum and maximum values and shifts the c∞ curve back and forth . For small values of AMPc , the c∞ curve is shifted to the right ( magenta dashed curve ) , intersecting the z-curve on the bottom stationary branch . At this point the system is in its hyperpolarized silent phase . As AMPc slowly increases the c∞ curve shifts to the left and the phase point follows it . When the curve passes the knee , the phase point is attracted to the periodic spiking branch , starting the active phase . The phase point follows the periodic branch to the left until AMPc reaches its maximum ( green dashed curve ) . From here AMPc declines and shifts the c∞ curve rightward , bringing the phase point with it . The c∞ curve eventually reaches SN2 again and intersects the stable stationary branch initiating a silent phase . It keeps moving rightward as AMPc continues to decline , bringing the phase point with it . Eventually AMPc begins to rise , restarting the cycle . This is parabolic bursting since the spike frequency during a burst follows a parabolic time course , low at the beginning and the end as the phase point passes near the infinite-period SNIC bifurcation [53] . As the fast subsystem bifurcation diagram lacks a bistable region , the glycolytic oscillations are necessary for the production of bursting in the Kir2 . 1 model . To address whether the upregulation of other types of K+ channels might yield effects similar to those of Kir2 . 1 , we examined the effects of replacing K ( ATP ) current with an alternative hyperpolarizing constant-conductance or “leak” K+ current , instead of Kir2 . 1 current , and increased the K ( Ca ) channel conductance ( Fig 9 ) . With these modifications , bursting could be produced in the absence of K ( ATP ) due to Ca2+ feedback onto K ( Ca ) channels ( Fig 9A ) . In this model , ER Ca2+ , which played little or no role in bursting produced using the Kir2 . 1 model , became absolutely essential in driving the burst . Glycolytic oscillations are now irrelevant since they do not change the membrane potential or contribute to burst generation in any way . The fast subsystem consists of three variables in this case , V , n , and c , and a slow variable cer , which we consider as a slowly-varying parameter of the fast subsystem . The fast-subsystem bifurcation diagram is shown in Fig 9B . Unlike with the Kir2 . 1 model ( Fig 8 ) , there is a bistable interval in the z-curve , where stable steady states coexist with stable periodic solutions ( between the saddle-node bifurcation SN2 and the homoclinic bifurcation HC ) . The burst trajectory is projected into the cer-V plane in Fig 9C , and superimposed on the fast-subsystem bifurcation diagram in Fig 9D . Also superimposed is the cer nullcline , the curve where the cer derivative is 0 . Bursting is produced as the trajectory moves to the left along the bottom stationary branch of the z-curve during the silent phase and to the right along the periodic branch during the active phase , utilizing the fast-subsystem bistability . This is standard square-wave or type 1 bursting that has been described previously for other models of bursting in β-cells and in neurons [52 , 54] . We have thus far described two possible ways in which the upregulation of hyperpolarizing K+ channels can rescue bursting in SUR1-/- β-cells . As one clear difference between the two alternative mechanisms is their dependence on ER Ca2+ concentration , we explored the consequences of manipulating the ER Ca2+ concentration as a way of determining which model is more likely the correct one . This can be done experimentally by blocking the Ca2+ pumps on the ER membrane ( the SERCA pumps ) using the agent thapsigargin [55] . In the model , the parameter kSERCA is the Ca2+ pumping rate into the ER from the cytosol . To mimic the effect of thapsigargin we reduced kSERCA by a factor of 4 . In the ER bursting model , this greatly lowered cer ( Fig 10A , blue trace ) and converted slow bursting into fast two-spike bursting ( Fig 10A , black trace ) . In terms of the fast/slow analysis ( Fig 9B ) , the reduction in kSERCA shifts the z-curve and cer nullcline far to the left . In addition , the periodic tonic spiking branch is destabilized through a period doubling bifurcation , and the resulting period doubled branch itself loses stability at a period doubling bifurcation . In fact , there is a period doubling cascade ( green curve ) , leading ultimately to a branch of fast two-spike bursting ( blue curve ) . The trajectory ( red curve ) moves to this latter curve at the new equilibrium value of cer . Thus , the slow bursting is replaced by very fast 2-spike bursting . In the Kir2 . 1 model , in contrast , bursting persisted even when SERCA pumps were inhibited ( Fig 10C , black ) . This is because bursting in this case is driven by the activity of the glycolytic oscillator . Blocking SERCA pumps lowers mean cer , which affects the cytosolic Ca2+ level , but this only modulates the slow bursting pattern rather than abolishing it . Indeed , the fast/slow analysis illustrates that the burst mechanism is very similar in this case to what it was before the reduction in kSERCA ( Fig 10D ) . The main difference is that the period of bursting is now increased , since the c∞ curve moves further to the right during the silent phase ( Fig 10D , dashed magenta curve ) . These simulations make a testable prediction that can eliminate one or the other of the compensation models . We subsequently tested the predictions in the lab , by treating oscillating SUR1-/- islets with thapsigargin ( TG ) . Fig 11 shows the model prediction obtained with the Kir2 . 1 model on the top row and the results of the experiments on the bottom three rows ( three SUR-/- islets and three wild-type islets are shown ) . TG application did not terminate slow Ca2+ oscillations in any of the SUR1-/- islets shown ( Fig 11B ) , as predicted by the Kir2 . 1 model ( Fig 11A ) . In fact , Ca2+ oscillations persisted in all 10 of the KO islets tested , with only a small change in the properties of the oscillations . Before TG treatment the oscillation period was 7 . 3 ± 1 . 2 min and the duty cycle ( duration of elevated Ca2+ divided by the period ) was 0 . 4 ± 0 . 08 . After TG application there was a slight increase in period to 7 . 6 ± 1 . 1 min and the duty cycle increased to 0 . 6 ± 0 . 06 . The slow Ca2+ decline that occurs at the end of each active phase prior to TG application , characteristic of Ca2+ leaking out of the ER and into the cytosol , was eliminated by the application of TG , as expected [56 , 57] . The persistence of oscillations when TG is applied is in clear contrast with the wild-type model ( the model that has K ( ATP ) current ) and wild-type islets , where in most of the wild-type islets tested TG converted slow oscillations ( with period 10 . 6 ± 0 . 9 min and duty cycle 0 . 5 ± 0 . 06 ) to continuous spiking or fast bursting with an elevated cytosolic Ca2+ level ( in 13 of 14 islets tested ) ( Fig 11C and 11D ) . A similar effect of TG on slow Ca2+ oscillations was previously observed in islets [58] . Since the response to TG confirms the prediction of the Kir2 . 1 model , but not the ER bursting model , we conclude that the Kir2 . 1 model is a more likely candidate to account for the compensation that occurs in SUR1-/- islets . That is , the data support the hypothesis that bursting observed in KO islets is due to compensatory upregulation of Kir2 . 1 channels .
The primary aim of this modeling study was to help understand how islet β-cells can compensate for the genetic knockout of K ( ATP ) channels in SUR1-/- mice . One focus was on Kir2 . 1 channels , which we found to be upregulated in the SUR1-/- mice ( Vadrevu et al , manuscript in preparation ) . We showed that upregulation of these channels can maintain bursting , even though the K ( ATP ) channels that normally couple metabolic oscillations to plasma membrane K+ channel activity are missing . This requires that the Kir channels have a dependence on cAMP , as has been reported previously for Kir2 . 1 channels [38–41] . It has also been reported that cAMP exhibits slow oscillations in insulin-secreting MIN6 cells [43] and in islet β-cells [42 , 43] , a behavior which could reflect oscillations in the nucleotide AMP [44] . Indeed , we were not able to observe bursting in simulations of K ( ATP ) KO islets if AMP regulation of cAMP was omitted . We did , however , show an alternative mechanism that could produce bursting in the KO islets in a manner that is independent of Kir2 . 1 current . The two models made very different predictions for the effects of blocking Ca2+ pumps in the ER membrane , and subsequent experiments with the SERCA pump blocker thapsigargin supported the Kir2 . 1 model over the alternate model . Of course , we do not suggest that these are the only two models that might be capable of mediating bursting in the absence of K ( ATP ) . For example , there are data showing that Mg:ATP can stimulate Kir channels , providing another means by which metabolic oscillations could cause bursting electrical activity [38] . We do show , however , that the two models examined herein are both feasible , and that they are experimentally discernable . A key hypothesis that we make in the Kir2 . 1 model is that cAMP regulates Kir2 . 1 current in SUR1-/- islets , likely through PKA as described in [38–41] , rather than direct metabolic regulation of the channels . A consequence of this hypothesis is that manipulations that increase the cAMP level should hyperpolarize the islet and terminate Ca2+ oscillations . Indeed , we found this to be the case . Application of 8-Br-cAMP had no apparent effect on Ca2+ oscillations in wild-type islets ( Fig 7A ) , but terminated oscillations and brought Ca2+ to a resting level in SUR1-/- islets . This is what we predict , since we expect little or no expression of Kir2 . 1 channels in wild-type islets , but significant expression in SUR1-/- islets ( Fig 3 ) . The data of Fig 7 do not preclude the possibility that cAMP activates another type of K+ channel in SUR1-/- islets , but other data show that the upregulated current is an inward-rectifying K+ current ( Fig 3 ) . If this upregulated Kir current were regulated directly by metabolism rather than cAMP , it is hard to explain why increasing the cAMP level with membrane permeable 8-Br-cAMP would terminate Ca2+ oscillations and bring Ca2+ to a resting level . Another hypothesis that we make is that cAMP oscillates in SUR1-/- islets . This has not yet been demonstrated , as it has been in wild-type islets [42 , 43] . However , we have previously reported that slow NAD ( P ) H oscillations persist in the SUR1-/- islets ( Merrins et al , 2010 ) , indicating the existence of metabolic oscillations which could drive cAMP oscillations as in our model . In glucose-stimulated wild-type islets the glycolytic product fructose 1 , 6-bisphosphate exhibits oscillations coincident with electrical bursting and Ca2+ oscillations [59] , and there are slow oscillations in oxygen consumption [60] and NAD ( P ) H [61 , 62] . At present , we do not yet know if the metabolic oscillations in fact result in cyclic AMP oscillations in SUR1-/- islets . The upregulation of Kir2 . 1 channels we propose might result from the expected increase in β-cell electrical activity that occurs when K ( ATP ) channel formation is disrupted by the genetic deletion of SUR1 , although when this occurs developmentally is not clear . It is well established that dramatic changes in electrical activity can regulate the expression of ion channels in excitable cells [63–65] . This may result from the increased intracellular Ca2+ concentration that accompanies increased electrical activity , which can enhance gene expression [66 , 67] . This feedback process would guard against the production of excessive Ca2+ levels in the cell , which can in turn induce apoptosis [68] . One prediction of the Kir2 . 1 model is that the Ca2+ and cAMP oscillations should be 180° out of phase with one another in the KO cells ( Fig 5A ) . This differs considerably from the wild-type case , where cAMP has a saw-tooth pattern and declines during the burst active phase and then rises during the silent phase ( Fig 4A ) . While cAMP levels have been measured simultaneously with Ca2+ in MIN6 cells and the time course is in general agreement with the model [43] , such measurements have not yet been made in SUR1-/- islets . A study performed in MIN6 β-cells in which Ca2+ oscillations were induced with the aid of the K+ channel blocker tetraethylammonium ( TEA ) showed oscillations in protein kinase A activity that was generally in phase with cAMP oscillations , indicating that the kinase kinetics were sufficiently fast to resolve the roughly 6-min oscillations in the cAMP concentration [69] . Our model would predict this , for both wild-type and SUR1-/- islets . The PKA oscillations could affect islet β-cells in ways other than or in addition to phosphorylation of Kir channels , such as phosphorylation of L-type Ca2+ channels as has been demonstrated in the TC3 β-cell line [70] . Glycolytic oscillations are well established in yeast [71] , but until recently there was no direct evidence that they occur in islet β-cells . However , recent studies using a FRET sensor for the glycolytic enzyme pyruvate kinase provide direct evidence for the existence of glycolytic oscillations in islets [72 , 59] . These metabolic oscillations are readily transmitted to the membrane potential through the cyclic activity of K ( ATP ) channels [46] ( Fig 4 ) , and we have now illustrated how these can drive bursting even in the absence of K ( ATP ) channels by utilizing the cAMP dependence of upregulated Kir2 . 1 channels . It is not obvious how Kir2 . 1 channel expression increases to an appropriate level so that bursting is produced when K ( ATP ) channels are missing , but it is plausible that channel compensation is achieved through the actions of Ca2+ on Ca2+-dependent activators or inhibitors of transcription factors . A further modeling study for the dynamic regulation of Kir2 . 1 channel expression is currently under way . | Pulsatile insulin secretion is important for the proper regulation of blood glucose , and disruption of this pulsatility is a hallmark of type II diabetes . An ion channel was discovered more than three decades ago that conveys the metabolic state of insulin-secreting β-cells to the plasma membrane because it is blocked by ATP and opened by ADP , and thereby controls the activity of these electrically-excitable cells on a rapid time scale according to the prevailing blood glucose level . In addition to setting the appropriate level of insulin secretion , K ( ATP ) channels play a key role in generating the oscillations in cellular activity that underlie insulin pulsatility . It is therefore surprising that oscillations in activity persist in islets in which the K ( ATP ) channels are genetically knocked out . In this combined modeling and experimental study , we demonstrate that the role played by K ( ATP ) current in wild-type β-cells can be taken over by an inward-rectifying K+ current which , we show here , is upregulated in β-cells from SUR1 knockout mice . This result helps to resolve a mystery in the field that has remained elusive for more than a decade , since the first studies showing oscillations in SUR1-/- islets . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
]
| [
"medicine",
"and",
"health",
"sciences",
"action",
"potentials",
"body",
"fluids",
"chemical",
"compounds",
"diabetic",
"endocrinology",
"membrane",
"potential",
"electrophysiology",
"carbohydrates",
"neuroscience",
"organic",
"compounds",
"glucose",
"hormones",
"ion",
"channels",
"cellular",
"structures",
"and",
"organelles",
"insulin",
"proteins",
"endocrinology",
"chemistry",
"blood",
"sugar",
"biophysics",
"cell",
"membranes",
"physics",
"biochemistry",
"genetic",
"oscillators",
"blood",
"cell",
"biology",
"organic",
"chemistry",
"anatomy",
"physiology",
"genetics",
"monosaccharides",
"biology",
"and",
"life",
"sciences",
"potassium",
"channels",
"physical",
"sciences",
"neurophysiology"
]
| 2017 | Upregulation of an inward rectifying K+ channel can rescue slow Ca2+ oscillations in K(ATP) channel deficient pancreatic islets |
Vesicle trafficking systems play essential roles in the communication between the organelles of eukaryotic cells and also between cells and their environment . Endocytosis and the late secretory route are mediated by clathrin-coated vesicles , while the COat Protein I and II ( COPI and COPII ) routes stand for the bidirectional traffic between the ER and the Golgi apparatus . Despite similar fundamental organizations , the molecular machinery , functions , and evolutionary characteristics of the three systems are very different . In this work , we compiled the basic functional protein groups of the three main routes for human and yeast and analyzed them from the structural disorder perspective . We found similar overall disorder content in yeast and human proteins , confirming the well-conserved nature of these systems . Most functional groups contain highly disordered proteins , supporting the general importance of structural disorder in these routes , although some of them seem to heavily rely on disorder , while others do not . Interestingly , the clathrin system is significantly more disordered ( ∼23% ) than the other two , COPI ( ∼9% ) and COPII ( ∼8% ) . We show that this structural phenomenon enhances the inherent plasticity and increased evolutionary adaptability of the clathrin system , which distinguishes it from the other two routes . Since multi-functionality ( moonlighting ) is indicative of both plasticity and adaptability , we studied its prevalence in vesicle trafficking proteins and correlated it with structural disorder . Clathrin adaptors have the highest capability for moonlighting while also comprising the most highly disordered members . The ability to acquire tissue specific functions was also used to approach adaptability: clathrin route genes have the most tissue specific exons encoding for protein segments enriched in structural disorder and interaction sites . Overall , our results confirm the general importance of structural disorder in vesicle trafficking and suggest major roles for this structural property in shaping the differences of evolutionary adaptability in the three routes .
The well-organized network of vesicle trafficking pathways is the basic communication mechanism between the different intracellular compartments and the environment , and as such it is crucial for the efficient transport of macromolecules within and between cells and their environment . Transport vesicles deliver various cargo molecules ( proteins , lipids , signalling molecules , etc . ) to the plasma membrane and to specific membranous compartments , while being also responsible for maintaining the appropriate protein and lipid composition of various organelles . There are several specialized routes present in all eukaryotic cells playing distinct roles , being responsible for different directions of traffic between various source and target membranes of several organelles and for carrying various types of cargo . These systems have their own well-conserved macromolecular machinery with a specific coat protein complex on the surface of their vesicles , which usually lends the name to the whole system . While endocytosis and the late secretory route are mostly mediated by clathrin-coated vesicles , the early secretory pathway from the ER ( endoplasmatic reticulum ) to the Golgi apparatus and the retrograde transport backwards are mediated by the COPII ( COat Protein II ) and COPI ( COat Protein I ) routes , respectively [1]–[4] . Although the protein machinery of these pathways vastly differs , with almost no common members , they do share several main structural and mechanistic characteristics . In all three cases there is a specific multisubunit protein coat complex on the outer surface of the vesicles which can self-assemble as a lattice collecting and concentrating the appropriate adaptor-cargo complexes into membrane patches . This process is not only responsible for cargo selection , but through the generation of membrane curvature , it also enhances the budding and fission of vesicles [5] . The coat also determines the shape and size of vesicles , which vary a lot in the three main systems [6] , [7] . The architecture and assembly mechanism of these cages have been extensively studied by electron microscopy and crystallography [7]–[11] , and are extensively reviewed [5] , [6] , [12] , [13] . Despite limited sequence similarity , proteins involved in coat assembly have a common underlying structural design using β-propeller domains and long stretches of α-solenoid motifs as basic building blocks [5] . The clathrin cage assembles from triskelion assembly units , trimers of clathrin heavy chains ( CHCs ) , that are centered on cage vertices with their long α-solenoid legs extending along whole edges until reaching adjacent vertices , while also overlapping with the legs of neighboring triskelions [8] , [9] . The N-terminal β-propeller domains of CHCs project inwards in order to interact with the adaptor proteins residing on the surface of the vesicle membrane [14] , [15] . Clathrin light chains ( CLCs ) form an extended single helix on the surface formed by the interhelical loops of the CHC α-solenoid legs with their termini occupying ambiguous localizations [8] . Despite containing the same types of domains as CHCs , the COPII cage architecture is much different from that of clathrin . In this case , the assembly unit is a heterotetramer of two Sec13-31 ( Protein transport protein Sec13 and 31 ) dimers forming a long rod by the α-solenoid legs of those , enclosed by two β-propeller domains at each end . The cage is cuboctahedron shaped with the rods forming the edges and four rods converging into each vertex without any overlaps between them [7] , [16] , [17] . The COPI cage shows a design intermediate between COPII and clathrin , with its domain organization and vertex interactions similar to those of COPII , and the triskelion shape ( with curved legs converging from three-fold centers ) resembling clathrin coat subunits [10] . In all three pathways vesicles are uncoated after formation , i . e . their cage-forming scaffold proteins and the adaptor proteins are stripped off either stepwise ( COPII and clathrin ) or at once ( COPI ) . The precise timing of this procedure is still under debate [18] . The pathways also share the dependency of the coat assembly on different small GTPases of the ARF/SAR ( ADP-ribosylation factor/Secretion-associated RAS-related protein ) family , as well as on their corresponding activating or nucleotide exchanging factors [19] . Cargo handling also shows many features in common . Adaptor proteins link the scaffold to the cargo and to the membrane , and communicate with other accessory proteins involved in other functions necessary for the formation and fission of the transport vesicle ( e . g . deforming and sensing membrane curvature , causing scission , and up- or down-regulating any of the previous steps ) [20] , [21] . In addition , cargo-specific receptor proteins are also present in all three systems . Other similarities between these routes include the basic mechanisms of vesicle transport ( driven by motor proteins along the actin cytoskeleton elements ) and vesicle fusion with the target membrane . The key players of vesicle fusion are SNARE ( Soluble N-ethylmaleimide-sensitive factor attachment protein receptor/SNAP receptor ) proteins in all the three systems . SNAREs behave like molecular engines; they generate the pulling force required for placing the two membranes close enough for fusion . Instead of using ATP in force generation , a huge conformational change occurs when the SNARE protein in the vesicle membrane interacts with the appropriate SNARE proteins in the target membrane ( also providing specificity for the fusion process ) . The four SNARE coiled-coil homology domains assemble into a stable four-helix bundle , which is then disassembled in an ATP-dependent manner [22] , [23] . The basic regulatory mechanisms of fusion are also common between the different systems . For instance , there are multisubunit tethering complexes or huge coiled-coil tethers [24] that help COPI and COPII vesicles to get close enough to the target membrane for the SNARE proteins to interact and arrange fusion [18] , [25] . Despite this array of mechanistic , structural , and regulatory similarities , there are fundamental functional and evolutionary differences between the clathrin-mediated system and the other two . While COPI and COPII are essential for the viability of eukaryotic cells ( without any of them the transport between the ER and the Golgi is completely abolished ) and they only occur in the ER-Golgi-ER routes , the clathrin system seems to be less indispensable and more broadly relied on . Knock-outs of certain main clathrin components , such as AP-2 ( Adapter-related protein complex 2 ) are unviable in case of multicellular organisms [26] . On the other hand , yeast cells could get along without AP-2 [27] or even without clathrin [28] , but not without some other clathrin pathway associated adaptors , such as the epsin-homologs [29] , HIP1 ( Huntingtin-interacting protein 1 ) and Hip1R ( Huntingtin-interacting protein 1-related protein ) [30] . Trypanosomes for instance , do not have any AP-2 like proteins , yet they rely on endocytosis [31] . Instead of implying that clathrin-mediated routes are not crucial , these observations emphasize their vast plasticity providing alternative ways for the cargo to get to the right place , by slightly altering the original pathway . In general , the clathrin-mediated system shows more plasticity and robustness than COPI and COPII , which from an evolutionary point could translate into more adaptability . There are several differences in the complexity of the three pathways too . In the COPI and COPII systems , the adaptor subunits are part of the multisubunit coat complex and there is only one set of them for each pathway [32] . However , there are slightly different versions or isoforms for some of the subunits in both systems showing different cargo specificity or differential localization , which suggests distinct functional roles for the variously composed coat complexes [33] , [34] . Instead , the clathrin system adaptors comprise a highly diverse and dynamic set of proteins , which may share similar functions ( e . g . binding the clathrin coat and the cargo at the same time ) , or play individual roles in the assembly and transport of vesicles [21] , [35] . They can participate in different routes , showing preference for different sorting signals and organelle membranes , sorting different cargo types into the same population of vesicles cooperatively , or recruiting cargo into different populations of vesicles on the same membrane in a mutually exclusive manner . Clathrin adaptors form extensive interaction networks on the membrane donor surface , many of them having copious known interaction partners . Although the functional and evolutionary differences between the systems have been previously studied , the inherent structural characteristics that could account for them have not been fully explored . Some previous works allude that long regions of certain clathrin-associated adaptor proteins are disordered , unstructured or unfolded [36]–[39] , but this phenomenon has never been investigated in a systematic manner . In a large-scale study protein transport was suggested to have the strongest correlation with structural disorder [40] . In our view structural disorder could have a pronounced role in certain functionalities important for vesicle trafficking systems , which would mean that their proteins could be at least partly accounted for the observed correlation . Interestingly , in the same study transport mechanisms in general were found to be depleted in predicted disordered regions . This would be logical because transportation of ions and small molecules requires a huge variety of large multi-pass membrane proteins ( ion channels and different transporters ) and these are usually very well-structured and consequently predicted as completely ordered . We believe that the presence of such disordered/unstructured regions may be a key phenomenon in vesicle trafficking; these disordered protein segments could account for many of the functional and evolutionary differences of the transport routes discovered so far , and hence , their abundance , as well as their locations and functions , should be adequately studied . Intrinsically disordered proteins and regions lack a well-defined structure; they function via an ensemble of possible conformations [41] , [42] . Being freed from the restrains of maintaining a folded structure , disordered protein segments show increased tolerance against mutations [43] , which confers them the possibility of fast evolutionary changes . Indeed , structural disorder could allow for many functional and structural advantages for vesicle trafficking proteins . Due to their enlarged capture radius , disordered regions could offer the ability to bridge large distances via the “fly-casting mechanism” of protein binding [44] promoting effective assembly of the vesicle coat . In this mechanism loosely structured protein segments reach out and bind their partners from larger distances due to their many exposed protein interaction motifs . Since short linear protein interaction motifs [45] , [46] , posttranslational modification sites [47] , [48] , and tissue-specific disordered binding regions of splice variants [49] , [50] , usually reside in disordered protein segments , these regions could be especially important in facilitating specific binding to partner proteins and in displaying important regulatory roles [21] , [38] . All the above mentioned characteristics of disordered regions , along with other advantages they provide – such as their conformational freedom and their ability to bind many interactions partners ( moonlighting [51] ) – make them excellent candidates for the efficient assembly [52] and transport of macroscopic organelles . Given all the possible implications and advantages intrinsic disorder can have on the different vesicle trafficking mechanisms , studying its role in them is of indisputable importance . To our knowledge , the abundance of disordered regions was only improperly assessed for proteins in the clathrin pathway by secondary structure prediction methods [38] , inappropriate for identifying structurally disordered protein segments . Furthermore , the presence of structural disorder was not addressed in the other two major vesicle trafficking systems . Hence a quantitative assessment of protein disorder content using adequate methods is still lacking . In the recent years , the field of intrinsic structural disorder has flourished and the importance of unstructured protein regions has been finally recognized in many key cellular processes [41] , [42] . In this work , we present a systematic study of protein disorder in all three main vesicle trafficking systems using adequate methods . The quantification of intrinsic structural disorder together with a comparison of disorder content in the different pathways should aid in understanding how these structural characteristics affect the functional and evolutionary features of vesicle trafficking proteins .
Clathrin route proteins were collected from comprehensive reviews [20] , [35] , while the main components of the COPI and COPII vesicle coats were collected from their specific literature [3] , [4] , [53] . Proteins involved in vesicle fusion regulation ( multisubunit tethering complexes , coiled coil tethering proteins , SM ( Sec1/Munc18-like ) proteins and other regulatory proteins ) were compiled from fusion-specific literature [18] , [24] , [54] , just like SNARE proteins [22] . In case of the two latter groups , proteins and their corresponding functions were collected regardless of their specific pathway , because they function in a very similar manner in all cases [18] . Following literature-based data collection , we extended our protein datasets with their interaction partners reported to function in the three systems by the Universal Protein Knowledgebase ( UniProtKB ) [55] . We also included proteins annotated with vesicle trafficking-related terms of the Gene Ontology ( GO ) Database [56] ( namely GO:0048208 , GO:0012507 , GO:0006892 , GO:0030126 , GO:0030130 , GO:0030132 , GO:0030136 GO:0048205 , GO:0006890 ) after manually checking their UniProtKB annotation and the literature to make sure that they mainly function in vesicle trafficking routes . The resulting dataset contains only manually curated proteins . The protein sequences of the complete human ( Homo sapiens ) and yeast ( Saccharomyces cerevisiae; strain ATCC 204508/S288c ) proteomes were obtained from the UniProtKB release 2012_09 ) [55] and filtered for fragmented proteins and 95% sequence identity . Finally , 20213 human proteins and 6221 yeast proteins were used to calculate whole-proteome reference data . Whenever possible , the classification of proteins found in the literature was used . Seven large functional groups were defined: four of them were budding and fission-associated: i ) coat proteins; ii ) adaptors and sorting proteins; iii ) enzymes and enzymatic activity related proteins; and iv ) a general group for unclassified proteins . Only the proteins of these were classified according to the three main systems ( Clathrin-mediated , COPI and COPII mediated ) . The other three functional groups correspond to fusion-associated proteins: v ) SNAREs; vi ) multisubunit tethering complexes; and vii ) other fusion regulators . In case of the human proteins , an extra functional group of fusion regulators playing a specific role in the regulation of neurotransmitter transport was added . The unclassified group includes all the proteins for a given system that could not be classified into the first three budding and fission-associated groups but still take part in these processes , many of them being transmembrane cargo-specific receptors . GEFs ( Guanine nucleotide exchange factors ) were not included in the analysis since they were only reported to act on their specific small GTPase . Although sometimes highly specific for the process , these proteins were not considered as part of the transport vesicles themselves . Prediction of intrinsic protein disorder was carried out using the IUPred method [57] , [58] . IUPred predicts intrinsic structural disorder using sequence information alone , based on the possible pairwise interaction energies ( calculated from statistic potentials ) between a given residue and the residues of the surrounding sequence windows . If possible favourable interactions prevail with the given protein segment , the amino acid is predicted to be ordered , otherwise it is predicted to be intrinsically disordered . The predictor provides a disorder probability value in the 0 . 0–1 . 0 range for each residue as an output . Using the standard 0 . 5 value as threshold , we mapped the prediction into a binary ( “ordered” vs . “disordered” ) classification at the residue level . Prediction of disordered binding regions ( DBRs ) possibly involved in protein-protein interactions was carried out using the ANCHOR method [59] , [60] . This method predicts putative binding regions within a protein sequence if they are intrinsically disordered in isolation and may undergo a disorder-to-order transition upon binding . As well as IUPred , the method is based on energetic estimations . The format of the output of this method is similar to that of IUPred and hence it was transformed in the same manner . Standard measures generally used in the literature to describe the disorder content of proteins were calculated: i ) relative disorder content or ratio of disordered residues ( predicted number of disordered residues/total number of residues ) , ii ) ratio of residues in Long Disordered Regions ( LDRs ) ( ratio of residues belonging to continuous stretches of at least k disordered residues; k = 30 , 50 and 100 ) , and iii ) ratio of residues in ANCHOR-predicted Disordered Binding Regions ( DBRs ) . In case of the transmembrane proteins , residues belonging to the reported transmembrane segments were not taken into account for the calculation of any disorder metric . Comparisons of disorder metrics among the different groups of vesicle trafficking proteins ( functional groups or complete systems ) were performed using Wilcoxon Rank Sum Test . Comparisons of disorder measures in specific groups to the corresponding reference proteome values , were performed using Fisher's Exact Test . Transmembrane regions were assigned according to UniProtKB [55] annotations , while protein domains and their corresponding locations were assigned using the PfamScan method [61] for all sequences . Default domain coordinates were assigned from the HMMER3 alignment coordinates using only high quality Pfam-A HMM profiles for the search . Orthology identification between human and yeast proteins was performed by the Inparanoid v7 program [62] . Interaction partners with the highest confidence ( confidence level = 0 . 9 ) were obtained from the STRING database 9 . 0 [63] for all budding and fission-associated human proteins . The resulting Ensemble identifiers were mapped to UniProtKB accession numbers and the corresponding sequences were obtained from the UniProtKB [55] . Two filtering criteria were applied to the data . In the first filtering step , interaction partners showing more than 70% sequence identity ( according to the CD-HIT algorithm [64] ) with any of the vesicle trafficking proteins listed in our database were excluded . Additionally , for each cluster of partners of the same protein with at least 70% sequence identity , only the one retained by CD-HIT ( generally the longest one ) was kept . In the second filtering step , a manual curation was performed on the resulting set of potential non-vesicle trafficking related interaction partners . Proteins involved in intracellular protein transport , endocytosis or vesicle trafficking according to their UniProtKB annotation were also removed . Proteins of the GTP-ase family ( present in the three main routes ) were excluded from further statistical analysis because of their non-specific nature and their extremely high number of identified interaction partners . All tissue specific exons ( TSEs ) for the budding- and fission-associated human proteins of our dataset were collected from Wang et al . [65] . Exons were classified as being tissue specific using Wang et al . 's criteria: if they scored at least 0 . 25 on a 0–1 exon switch score scale ( calculated based on the difference between the two most extreme tissue inclusion level values measured for the given exon [65] ) . Only budding- and fission-associated proteins were included in this analysis because they were the only ones that could be grouped into the three main pathways according to the adopted classification schema . 45 tissue specific exons were identified . These TSEs were mapped onto Ensembl transcripts ( one transcript for each ) . One TSE was excluded because it was a non-coding exon according to Ensembl [66] ( exon: chr1:54127505-54127724:- , corresponding to the YIPF1 gene ) . Structural disorder and disordered binding sites were predicted for the whole proteins by IUPred and ANCHOR , respectively , and the ratios of disordered residues within the regions corresponding to the TSEs and the predicted binding sites overlapping with them were calculated and identified . A comprehensive search in the Protein Data Bank ( PDB ) [67] was performed to identify complexes of distinct pairs of vesicle trafficking-related proteins in which the binding region of at least one of the partners is predicted to be intrinsically disordered by IUPred . All data processing in this study was performed using scripts written in the Perl programming language . All analyses were implemented in the statistical analysis programming language R ( www . r-project . org ) . The Pymol molecular graphics tool [http://www . pymol . org/] and the DOG2 protein domain representation tool [68] were also used for figure preparation .
A comprehensive dataset of proteins functioning in the main vesicle trafficking systems in human and yeast was assembled . To the best of our knowledge , this is the largest and most complete such collection , containing 244 human and 162 yeast proteins . Three different disorder metrics were used to explore the abundance of structural disorder and functionally important disordered sites in vesicle trafficking proteins: 1 ) the ratio of predicted disordered residues ( hereafter referred to as disorder content ) , 2 ) the ratio of residues in predicted long consecutive disordered regions ( LDRs ) of different lengths ( k = 30 , 50 and 100 residues ) , and 3 ) the ratio of residues in predicted disordered binding regions ( DBRs ) . All collected and calculated data are provided in the Supporting Information ( Table S1 and S2 for human and yeast , respectively ) . Each protein was identified by name and UniProt accession number , and classified according to the functional classification scheme ( see Methods ) . The different types of disorder-related measures described in Methods as well as the number of transmembrane segments and different Pfam entities are provided for all proteins . The number of proteins and the means and medians of all calculated disorder metrics for each functional class and each main pathway of vesicle trafficking proteins in human and yeast are summarized in Table 1 . The ratios of proteins having LDRs of various length ( k = 30 , 50 , 100 ) in the different functional classes and pathways , together with corresponding background data calculated on human and yeast complete proteomes are presented in Table 2 . Overall , proteins involved in vesicle trafficking tend to be slightly more disordered in human than in yeast proteins ( mean disorder content of 20 . 85% vs 17 . 77% , respectively; one tailed Wilcoxon Rank Sum Test , p-value = 2 . 67E-02 ) . The mean number of DBR residues is also higher for human than for yeast proteins ( p-value = 1 . 80E-02 ) . The overall disorder content of vesicle trafficking proteins ( 20 . 85% and 17 . 77% for human and yeast , respectively ) is not statistically significantly different from that of the corresponding complete proteome ( 22 . 81% and 16 . 96% for human and yeast , respectively; Fisher's Exact Test ) in either species . The disorder content of the equivalent functional categories in human and yeast shows no statistical differences for any of the pairs as assessed by the Wilcoxon Rank Sum Test . All disorder metrics showed similar tendencies ( and absolute values ) for the main functional groups of the two species ( Figure 1 , Table 1 ) . Thus , for the sake of clarity in the following section only data related to human proteins are described in detail . Fusion regulation proteins are among the least disordered ones ( Table 1 ) since their members are mostly subunits of large complexes . Proteins in the “coat” and in the “unclassified” groups ( the latter containing many transmembrane cargo-specific adaptors ) are also rather structured . These two groups , however , show larger deviations than the previous two . Although coat proteins form a completely folded , rigid , cage-like structure on the surface of the vesicles – and in good agreement with this , most of their subunits are predicted as completely structured using IUPred – , some of their subunits are predicted to be largely disordered . Such is the case of clathrin light chains ( CLCs ) ( 60 . 08% and 74 . 67% for clathrin light chains A and B , respectively ) , which in their bound form adopt a long α helical structure on the surface of the clathrin heavy chain ( CHC ) α-solenoid legs . However , in their unbound form they are likely to be highly disordered , which enables them to gain such an extended arrangement in the complex . Although to a lesser degree , the Sec31 subunit of the COPII type coat is also considerably disordered ( 33 . 61% and 27 . 40% for Sec31 A and B paralogs ) . The predicted disordered region matches well the long , low-complexity , proline-rich region of Sec31 proteins , which was shown to be unstructured even within the assembly unit by limited proteolysis [7] and also to mediate the interaction with the Sec23/24 adaptor subunit complex [69] . The “SNARE” group is composed of proteins containing at least one v- or t-SNARE coiled coil homology domain and various types of family-specific domains . Even so , they show a surprisingly high deviation in disorder content with a few of them being mostly disordered and others being well structured . The members of the syntaxin family of SNAREs have disordered N-terminal regulatory regions that can be used by their direct regulatory partners ( e . g . SM proteins ) to modify their function . Several PDB complexes demonstrate how the disordered syntaxin N-tail folds up when bound to a globular SM partner ( Figure 2 ) . Our results agree with the general view in the literature that SNARE motifs are unfolded in monomeric state , forming the four helix bundle only upon vesicle fusion [22] , [23] . Although its median disorder content is only the second largest ( 21 . 49% ) , the group of “adaptor and sorting proteins” ( ASPs ) has the most highly disordered ( >50% ) members . This group is very diverse , containing completely structured subunits of larger adaptor complexes ( such as the sigma and mu subunits of the AP complexes , the zeta subunit of the COPI coatomer complex , and the Sec23 subunit of the COPII coat adaptor ) and also highly disordered adaptor proteins such as the epsins , DAB1 and DAB2 ( Disabled homolog 1 and 2 ) , HRS ( Hepatocyte growth factor-regulated tyrosine kinase substrate ) and NUMB ( Protein numb homolog ) . The latter ones all have long LDRs enriched in binding motifs . Interestingly , the group of enzymatic activity related proteins ( EARPs ) is the most disordered ( 22 . 84% ) . This might be counterintuitive at the first glance , since enzymes are thought to be typically well-folded proteins . Indeed , this is the case for domains carrying enzymatic activity or for small single-domain enzymes , like the small GTPases . However , their direct regulators , the long GAPs ( GTPase activating proteins ) , for instance , are considerably disordered . The increased structural disorder content of GAPs in general was already reported and accounted to their long flexible inter-domain linker regions [40] . Other members of this group such as the synaptojanins , the AAK1 ( AP2-associated protein kinase 1 ) and GAK ( Cyclin-G-associated kinase ) kinases , and auxillin , all contain very long disordered regions ( at least one LDR≥100 residues ) outside their structured domains and show more than 30% overall disorder content . In addition to their enzymatic activity , they are likely involved in protein-protein interactions or other disorder mediated functions as reflected by their enrichment in predicted DBRs . Finally , the group of “neurotransmitter transport specific regulators” ( NTSRs ) contains distinct protein families: synaptotagmins , complexins , several neurotransmission-specific SM proteins , synaptophysin and tomosyn . Complexins are the most disordered family of our entire protein dataset ( d . c . 76 . 25–98 . 51% ) , while the other members are highly ordered . The statistical comparison of human functional groups against their whole proteome reference value ( Table 2 ) showed that the group of ASPs is significantly enriched in proteins with LDRs of different length ( LDR≥30 , 50 and 100 amino acids; Fisher's Exact Test p-values: 9 . 89E-03 , 2 . 84E-02 , and 2 . 18E-04 , respectively ) . Similar enrichment was found for the group of EARPs ( LDR≥50 and 100 amino acids , p-values: 4 . 16E-02 , and 2 . 15E-03 , respectively ) . This enrichment is also valid for yeast proteins: ASPs are enriched in LDRs of all three lengths ( Fisher's Exact Test p-values: 2 . 69E-03 , 5 . 25E-03 and 5 . 64E-03 , respectively ) and EARPs are also enriched in LDRs≥100 residues ( p-value = 1 . 84E-02 ) with respect to the whole proteome . The disorder content of all budding- and fusion-associated proteins involved in the three main vesicle trafficking systems regardless of their functional classification was also compared ( Figure 3 , Table 1 ) . Proteins associated with the clathrin-mediated route are the most disordered , with 23 . 33% and 22 . 58% median disorder content in human and yeast , respectively . This disorder content is significantly higher than the disorder content of COPI ( 9 . 20% and 8 . 22% in human and yeast , respectively; Wilcoxon Rank Sum Test; p-value = 9 . 89E-03 in human , 4 . 68E-02 in yeast ) and COPII proteins ( 9 . 27% and 6 . 99% in human and yeast , respectively ( p-value = 4 . 09E-02 in human , and 5 . 99E-03 in yeast ) ) in both species . The disorder content of the proteins in the COPI and COPII routes is not significantly different in either species . Similarly , the average disorder content of the proteins in each of the three main pathways does not significantly differ between human and yeast ( as assessed by the Wilcoxon Rank Sum Test ) . The higher disorder content found in the clathrin-mediated route is partly due to the highly disordered clathrin light chains and mainly stemming from the several highly disordered proteins in the ASP and EARP groups . Overall , many of its proteins contribute to the observed effect . In case of the COPI and COPII pathways , most of the disorder contribution comes from a few outliers; namely , the huge , largely disordered Sec16 homologs for the COPII route , and a few highly disordered ArfGAPs ( ADP-ribosylation factor GTPase-activating proteins ) for the COPI route . Only the clathrin-mediated route showed significant enrichment in proteins with LDRs of all three lengths ( LDR≥30 , LDR≥50 and LDR≥100 ) when compared to the corresponding whole proteome reference values for both species ( Fisher's Exact Test; p-values: 3 . 85E-04 , 1 . 66E-03 and 2 . 32E-06 , respectively , for human and p-values: 9 . 40E-04 , 6 . 28E-04 and 4 . 86E-04 , respectively , for yeast ) . In order to show that structural disorder provides enhanced evolutionary adaptability and plasticity in the clathrin pathway , two appropriate measures were introduced . The first measure reflects the capability of proteins to participate in multiple pathways and to mediate interactions with diverse partners ( moonlighting ) . Although the correlation between moonlighting ability and structural disorder of proteins has been previously described [51] , this work evidences that such relationship is present in proteins involved in vesicle trafficking and it compares the three main pathways from this point of view . The second measure introduced reflects the ability of proteins to show tissue specific functions/interactions . The increased preference of tissue specific exons ( TSEs ) for encoding disordered regions frequently embedding linear motifs , disordered binding sites or posttranslational modification sites has been recently demonstrated [49] . In order to investigate the proteins in the three main vesicle trafficking routes from this perspective , all their reported TSEs were collected and analyzed from both the structural and the functional point of views . High confidence , non-vesicle trafficking related ( off-pathway ) interaction partners were collected for all budding- and fission-associated human proteins in the dataset ( Table S3 ) . Out of the 363 collected interactions , most of them ( 297 ) belonged to the clathrin route , while 50 belonged to the COPII route and 19 to the COPI route . The mean and median of the identified interactions was also higher for the clathrin route ( 4 . 30/2 . 0 ) than for the COPII ( 1 . 72/1 . 0 ) , and the COPI ( 0 . 95/0 ) routes . The number of such interactions for the clathrin proteins is significantly higher than that of COPII proteins ( Wilcoxon Rank Sum Test , p-value = 0 . 016 ) and COPI proteins ( Wilcoxon Rank Sum Test , p-value = 0 . 0063 ) , while COPI and COPII proteins are not significantly different from this point . For the most “interactive” proteins these values were compared with different measures of predicted structural disorder and disordered binding sites ( Table 3 ) . Interestingly , there is only one protein for the COPII ( SEC13 ) and one for the COPI ( COPB2 ) route out of the total 21 , which have at least 5 off-pathway interaction partners , both of them functioning as coat complex subunits and showing very limited structural disorder content ( 6 . 52% and 9 . 19% , respectively ) . Out of the remaining 19 clathrin route associated proteins , there are 12 adaptor and sorting proteins , 4 enzymatic activity related proteins ( the three dynamins and synaptojanin-1; 22 . 32–33 . 76% disorder content , all containing at least one LDR>100 residues ) , 2 coat components ( clathrin light chain A with 60 . 08% disorder content and heavy chain 1 with 1 . 07% disorder content ) both having 10 such interaction partners , and the unclassified Endophilin-A1 also showing 10 such interactions ( disorder content ∼33% ) . Interestingly , among the 12 clathrin adaptors , the three AP-2 subunits show the least number of off-pathway interaction partners ( mu ( 5 ) , beta ( 6 ) and alpha-1 ( 7 ) ) and also the lowest disorder content ( 4 . 16–14 . 74% ) . Among these three , only the most “interactive” alpha-1 has LDRs ( >30 ) and predicted DBRs , and this protein has the highest disorder content also . The remaining 9 clathrin adaptors are altogether responsible for 135 ( more than 1/3 of the total ) off-pathway interactions , which is more than half of those mediated by the most “interactive” 21 proteins ( 266 interactions ) . These 9 clathrin adaptors do not form complexes , but act as single , they have quite high disorder content ( 15 . 65–74 . 16% ) , with most of them showing more than one LDR and plenty of DBRs ( with the exception of the two beta-arrestins ) and the majority of them being highly disordered ( >50% ) . All these results suggest that apart from some subunits of large complexes of coats and adaptors , most of the proteins showing moonlighting capabilities are often highly disordered and have several LDRs and many predicted DBRs . Among the three main routes , the clathrin pathway is the only one rich in such proteins , thus the vast majority of non-vesicle trafficking related interactions are mediated by this pathway . Further attesting to the functional adaptability of the clathrin route is that we observed the partners of proteins in this route being the most diverse with respect to their main pathways/molecular functions . For example , for the COPII proteins the 50 off-pathway interactions involve only 40 unique non-vesicle trafficking interaction partners ( more COPII proteins can interact with the same off-pathway partner ) out of which 15 are subunits of the nuclear pore complex alone ( this is not surprising , since the ER membrane is continuous with the nuclear membrane , which allows for the free “flow” of membrane-anchored protein complexes between them ) and there are also several lysosome-associated proteins and secreted ones . All reported examples of TSEs for all budding- and fission-associated proteins in our data set were collected from Wang et al . [65] . The resulting 44 coding exons mapped onto 1550 residues in Ensembl transcripts . Structural disorder and disordered binding regions for the corresponding whole protein sequences were calculated ( Table S4 in the Supplementary material ) . Our results show that clathrin route associated proteins seem to be the ones which have most frequently acquired tissue-specific exons during their evolution: 33 of the 44 identified exons ( 75% ) are located in 22 unique clathrin route associated proteins ( ∼31% of its proteins showing at least 1 TSE ) , while only 7 such exons were found for 5 COPII related proteins , 3 for 2 COPI associated proteins , and 1 in TMED2 , a transmembrane cargo receptor shared between the COPI and COPII routes ( 25 . 8% of the COPII , and 13 . 6% of the COPI route proteins having at least one TSE ) . TSEs showing the strongest tissue specificity ( measured by exon switch scores reported in Methods ) are even more enriched in clathrin associated proteins , since out of the 23 TSEs with an exon switch score ≥ 0 . 5 , 20 occur in clathrin proteins ( ∼87% ) and only 3 in COPII proteins ( one of these being shared with the COPI route ) . According to IUPred predictions , 45% of the 1550 residues encoded by the TSE exons are located in disordered regions ( the predictions were performed on whole proteins , and only TSE regions were taken into account from the predictions ) , almost twice the value for the complete human proteome ( 22 . 81% ) . According to ANCHOR predictions , there are 39 DBRs that are either completely encoded or overlapping with the protein regions corresponding to the 44 exons , and 4 additional binding regions were found to overlap with the 5 residue neighborhood of TSE boundaries , which are most probably also affected by the presence or absence of the given TSE . The 33 TSEs present in the clathrin pathway proteins encode for a total of 970 residues . The disorder content of these TSE-encoded protein regions is higher in the clathrin route proteins ( 47% ) than in the proteins associated with the other two routes ( 39 . 3% ) . 29 DBRs were found within the corresponding clathrin route related protein regions and the 4 binding regions that reside within 5 residues from the TSE boundaries are also found in association with these regions . The low number of TSEs in the proteins of the COPI and COPII routes do not allow statistical comparisons between the three pathways , but point to the fact that clathrin route proteins are far more prone to acquire such regions . Table 4 presents cases of strongly tissue specific TSEs ( exon switch score ≥ 0 . 35 ) with at least 7 amino acids contribution to the protein chain . We identified at least one Pfam ‘entity’ ( 143 families; 153 domains; 3 motifs; 9 repeats ) for 238 vesicle trafficking proteins in human . There were only 10 proteins for which no domain or family could be assigned . We further analyzed the Pfam patterns of highly disordered proteins , namely those with at least 70% disorder content or a high ratio of LDR residues ( ≥mean plus 2 times standard deviation or ≥50% ) . In some cases , such proteins are assigned to Pfam families based on evolutionary conservation , and yet they do not contain any folded domains . Such is the case of all the complexins ( 1–4 ) , which belong to the synaphin family and are highly disordered ( disorder content 76–98% ) according to our predictions . Another example is the family of clathrin light chains , where both proteins show very high predicted disorder content ( 60% and 74% ) and have no folded domains assigned . Our data clearly show that there are several folded domains that are typically located in highly disordered proteins . These structured “islands” are usually surrounded by extended disordered regions on either or both of their sides , and tend to be the sole domain of the protein . Examples of such kind of domains include the ENTH ( epsin N-terminal homology ) domain , the PID ( phosphotyrosine interaction domain ) , the Sec16 domain , and the muHD ( muniscin C-terminal mu homology domain ) , among others . The highly disordered epsin type clathrin adaptors ( 1–3 ) have the unique ENTH domain at their N-terminus , which serves as a membrane interacting module , while the remaining part of the protein is completely disordered . Epsins 1 and 3 also contain UIM motifs ( Ubiquitin Interaction Motifs ) within their disordered regions along with the many identified adaptor protein- and clathrin-binding motifs [21] , [35] , [38] . Another clathrin adaptor , DAB2 , has also one single domain at its N-terminus , the PID , which roughly coincides with the only structured region of this highly disordered protein ( 74% disorder content ) . NUMB ( 61 . 21% ) and DAB1 ( 51 . 36% ) also share this PID domain comprising the only structured region of these proteins . The muHD domain is also coupled with a long disordered segment , in this case from the N-terminal side . This domain is present in three disordered adaptors from the clathrin system: SGIP1 ( SH3-containing GRB2-like protein 3-interacting protein 1 , disorder content 62 . 68% ) , FCHO1 ( FCH domain only protein 1 , d . c . 47 . 58% ) and FCHO2 ( FCH domain only protein 2 , d . c . 34 . 57% ) . Another example of these structured island domains is the Sec16 domain found in Sec16A and Sec16B ( Protein transport protein Sec16A and B ) . Located approximately in the middle of these huge proteins ( ∼2000 residues ) , it is surrounded by highly disordered termini on both sides ( the structural characteristics of Sec16A will be further discussed in the next section ) . The ArfGAP domain is also usually located on the N-terminal end of the long , considerably disordered ArfGAP proteins ( d . c . 36 . 63–52 . 22% ) . Apart from the previously mentioned domains , we have found others , which are often surrounded by variable long disordered regions , but are also present in proteins that tend to have less disorder content . The BAR ( Bin-Amphiphysin-Rvs ) domain – involved in membrane curvature sensing – is present in the highly disordered amphiphysin ( d . c . 60 . 58% ) , but also in endophilins ( A1-3 , B1-2 ) , which have substantially variable disorder content ( d . c . 7 . 12–35 . 60% ) . The protein kinase domain is present in AAK1 ( AP2-associated protein kinase 1; d . c . 58% ) , but it is often part of other less disordered kinases as well . In summary , the vast majority of the domains that are always surrounded by highly disordered regions belong to clathrin pathway associated adaptor and sorting proteins ( all the epsins , NUMB , DAB1 , DAB2 , SGIP1 , FCHO1 and FCHO2 ) . Their structural properties – having a single folded domain located at one of their termini , while the rest of their chain is highly disordered with embedded functional motifs – make them excellent candidates for the fly-casting mechanism . Additionally , previous studies have shown that these adaptors are able to form extended adaptor networks on the surface of the budding vesicle , some of them being responsible for recruiting clathrin as well [20] , [21] , [35] . According to the ANCHOR prediction method , and as it was pointed out in previous works [21] , [38] , these long disordered regions contain a plethora of different binding motifs , which can facilitate specific interactions between the adaptor proteins or with clathrin , but also with other components of the system or possible “off-pathway” interaction partners . To further investigate the role of disordered binding regions in building the adaptor network , we performed a systematic PDB search looking for clathrin coat specific protein complexes . We found several structures where the interaction of two clathrin adaptors is shown , and one of the partners uses its disordered binding regions to bind to the folded domain of the other one . In case of the multisubunit AP-2 , one of the most studied key players of endocytosis , two long disordered regions connect the two α-adaptin ear domains to the major part of the huge complex . The recognition of cargo sorting signals is done by the major part , while the principal clathrin-binding region is located in the disordered β2-adaptin hinge [21] , [38] . The two α-adaptin ear domains are favoured targets of disordered tails of other clathrin adaptors and accessory proteins [70] . We found several distinct complexes showing these interactions ( Figure 4 A , B and C ) . In these complexes , usually very short peptide constructs ( lengths ranging between 6–12 residues ) of disordered regions in the partner proteins bind to the α-adaptin ear domain . In addition , we found an interesting case where a relatively long , highly disordered region of human stonin 2 binds to one of the small folded EF-hand domains of human EPS15 ( Epidermal growth factor receptor substrate 15 ) ( Figure 4 D ) . We also found structures where other non-adaptor clathrin pathway associated proteins interact with AP-2 or clathrin via their disordered segments . Amphihpysin , for instance , interacts with both ( PDB IDs 2VJ0 and 1UTC ) via two different disordered binding regions located in the long disordered segment following the BAR domain . Proteins from the EARP functional group also bind AP-2 , in case of synaptojanin 1 , two distinct constructs were shown to bind the α-adaptin domain ( PDB ID: 1W80 ) , while PIP5K1C ( Phosphatidylinositol 4-phosphate 5-kinase type-1 gamma ) interacts with the β subunit ( PDB ID: 3H1Z ) . We identified 56 human proteins that could be successfully matched to a yeast protein from our dataset using the Inparanoid 7 . 0 method . We focused on orthologous pairs involved in the budding and fission associated functional groups because they show higher abundance of disordered regions , they were the ones that could be reliably grouped according to the main routes and hence better distinguishing between those . We filtered these pairs for at least one of their members showing considerably high disorder content ( >30% ) . From the resulting 8 protein pairs ( this relatively small number well indicates the lower evolutionary conservation levels observed for disordered protein regions in general ) one showed very similar disorder content ( less than 5% difference ) ; 5 pairs showed more disorder in the human ortholog than in the yeast ortholog; and in two cases the yeast protein showed higher disorder content . We analyzed two protein pairs in detail ( Figure 5 ) . The first pair shows the largest difference in disorder content among all pairs: human Sec24A ( protein transport protein Sec24A ) and yeast SFB2 ( SED5-binding protein 2 ) with a sequence identity 22 . 66% ( Figure 5A ) . Here , the human sequence is considerably longer due to a long , disordered segment at the N-terminal region , which is missing in the yeast ortholog . Being also abundant in predicted disordered binding regions ( shown in blue ) , this region might be a result of adaptive evolution . In fact , this subunit of the COPII coat-adaptor complex is important for the recognition and binding of the cargo ( transmembrane cargo proteins , and transmembrane cargo receptors of soluble proteins ) [32] . Given that the repertoire of possible cargo proteins transported from the ER to the Golgi is considerably higher in human than in yeast , the presence of additional binding regions in the human ortholog could make sense . In the second pair , both proteins are highly disordered: human Sec16A ( Protein transport protein Sec16A ) and yeast Sec16 ( COPII coat assembly protein SEC16 ) , and according to their predicted disorder patterns ( Figure 5B ) , despite the rather low overall sequence identity ( 14 . 07% ) , their disordered nature is well conserved . Both members are extremely long ( >2000 residues ) , highly disordered proteins ( 74 . 44% and 71 . 4% disorder content in the yeast and human orthologs , respectively ) . Their domain maps show that the huge disordered regions surrounding the Sec16 ( and Sec16_C ) domains are almost entirely covered by DBRs . These two proteins are highly similar in length , and can be considered well conserved from the structural point of view . Their preserved disordered nature , with plenty of DBRs ( 54 . 8% ) and an even higher ratio of residues located in LDRs ( 62 . 1% ) is in a good agreement with their essential roles in COPII vesicle assembly [69] and cargo selection . For the coat assembly , the long disordered regions can be especially advantageous , because – being able to bridge very long distances through the fly-casting mechanism – they can reach for the components of the vesicle coat from the surrounding environment , and help them to acquire proper orientation for the assembly . In case of the clathrin system , it is usually the group of adaptor proteins that is responsible for this function . The same proteins can perfectly utilize their disordered regions to form the adaptor network on the vesicle surface and to attach the clathrin chains to the surface of this network as well . In the COPII system , however , the adaptors are part of the multisubunit adaptor-coat complex , and the two subunits playing the adaptor role are certainly not disordered enough to fulfil these roles . Hence , there is definitely a need for a large disordered protein , like Sec16 ( and its homologs ) to orchestrate the assembly of the coat components , especially when large distances need to be spanned .
Vesicle trafficking routes have fundamental roles in the eukaryotic cell , providing the possibility of targeted macromolecule transport between the various intracellular compartments and also the cell and its environment . The COPI , COPII and clathrin-mediated vesicle trafficking routes comprise the major part of the transport network , being responsible for the different types , locations and directions of traffic involved in endocytosis , the early and late secretory pathways and the retrograde Golgi-ER transport . Despite their similarities , there are fundamental functional and evolutionary differences that strongly distinguish these routes , yet the structural characteristics that could account for these differences have not been previously described . In this work , we provided a systematic assessment of the potential functional involvement of structurally disordered protein regions in the main vesicle trafficking systems . Based on the functional requirements of their proteins and the inherent advantages that structural disorder could offer them , we expected such systems to heavily rely on disordered protein regions . Such regions have been widely recognized to be abundant in proteins related to signalling and regulatory roles [41] , [42] . They can act as flexible linkers between structured domains to enhance their free movement and rotations [71] providing the possibility for large , multidomain proteins to acquire multiple supertertiary structures [72] . Due to their increased accessibility all types of proposed protein-protein interaction motifs [73] and posttranslational modification sites [47] , [48] tend to reside in disordered regions , hence they are also frequently involved in molecular recognition and regulatory functions [21] , [38] . Protein disorder also provides many advantages in fine-tuning the kinetics and thermodynamics of molecular recognition events [74] . Moreover , extended disordered regions are especially useful in the assembly of large macromolecular complexes [52] , similar to the ones involved in vesicle trafficking . The different measures of structural disorder that were used to describe the abundance and location of such protein regions in vesicle trafficking proteins allowed us to distinguish between major functional roles , in which disordered regions could be involved . While the overall disorder content of proteins provided a broad picture about the dependence of the given functional group or trafficking route on structural disorder , the ratio of residues located in predicted DBRs helped to estimate the involvement of these regions in protein-protein interactions . In those cases where the ratio of residues in LDRs is considerably more than those in DBRs , we could speculate that besides promoting protein-protein interactions , disordered regions might also serve as flexible linkers between structured domains , or as long spacers , assisting in fly-casting mechanism by providing the possibility for the motif-rich parts to reach farther . Despite the heterogeneity of proteins in the three major vesicle trafficking routes , we found that the proteins of these systems followed similar overall tendencies of structural disorder in the two species . The overall statistic comparison of human and yeast proteins showed that proteins involved in vesicle trafficking are only slightly more disordered in human . The equivalent functional groups in the two species showed very similar disorder contents , which reflect the well-conserved nature of vesicle trafficking proteins . These results are consistent with previous works showing that intrinsic disorder is not necessarily correlated with organism complexity [75] , [76] . In this case , structural disorder is intrinsic to the biological process rather than depending on the complexity of organisms , which also highlights the role of disorder in proteins involved in vesicle trafficking . The importance of disordered regions in vesicle trafficking is also well reflected by the fact that almost all the main functional groups have highly disordered ( >50% ) members in both species . The big differences observed between the disorder contents of different groups nonetheless imply that certain functions require the presence of disordered protein segments more than others . Not surprisingly , most coat proteins are mainly structured , since they tend to fold into rigid cage-like structures on the surface of all types of vesicles . Despite much different cage architectures , they all contain the same two types of folded building blocks: α-solenoids and β-propellers . Only the group of clathrin light chains ( CLCs ) was predicted to be largely disordered , while the different Sec31 COPII coat subunits possess a long disordered segment between their structured domains . The dynamic nature of the highly disordered CLC has an important role in the regulation of clathrin lattice assembly through allowing for large conformational switches , which also influence the conformation of the heavy chain knee regions [11] . The coat assembly is also influenced by the various interaction partners of the light chain , like HIP1 [77] , [78] . In our view , the flexibility of the CLC chain could be essential for promoting the right packing process of the extraordinarily tight , highly overlapping , clathrin triskelion cage [5] , [6] , which far exceeds the packing density of the other two types of coat complexes [6] , [10] . Furthermore , the CLCs could be important in the ability of self assembly as well [5] . Sec31 COPII coat subunits all have a very long predicted disordered region matching the proline-rich unstructured segment described in the literature as a flexible linker between the two long α-solenoid repeat regions [7] that mediates the interaction with the Sec23/24 subcomplex [69] . Some of the proteins involved in fusion-related functions also showed a considerable amount of disorder , although most of the functional groups ( like the MSTC , OFRP and NTSR groups ) here had rather low disorder content . The SNARE group was the most disordered among these in both species , since the different SNARE homology domains are unfolded in their monomeric form [22] , [23] , which was well-detected by the applied disorder prediction method . As it was pointed out in Figure 2 , many of the SNARE proteins , namely the syntaxin family members , also have disordered N-terminal regulatory segments that allow their regulatory binding partners ( SM proteins ) to modify their functions [23] . The NTSR group , although showing relatively low overall disorder content , contains the most disordered protein family of our data set , that of complexins . These SNARE regulatory proteins are predicted to be almost completely unfolded . In their complexes the ‘central helix’ of complexins is interacting with one SNARE complex , while their ‘accessory helix’ forms a bridge to another SNARE complex , occupying the empty v-SNARE binding site to inhibit vesicle fusion . Their accessory helix was thought to compete with the v-SNARE homology domain for binding the prefusion t-SNARE complex , but recently it was shown rather to help organizing the t-SNAREs into a zigzag topology that is incompatible with fusion ( PDB: 1KIL , 3RLO ) [79] , [80] . Considering their function , complexins prevent SNAREs from neurotransmitter release until an action potential arrives at the synapse . They are essential grappling/clamping proteins [81] that help stabilize SNAREs in an active , but yet frozen state , and only release them when synaptotagmins give a Ca2+-induced signal for this [23] , [82] . The mechanism by which synaptotagmins can pass the information about the Ca2+ signal to complexins is not yet fully understood . According to our predictions , complexin regions forming the helixes in the complexes are unfolded in their monomeric state , just like the SNARE coiled coil homology domains that they are mimicking . The group of “adaptor and sorting proteins” showed the highest number of extremely disordered members , especially because of the non-complex-forming clathrin adaptors . Although it contains many fully structured complex subunits as well ( especially due to the many , highly similar subunits of the four different AP complexes in the clathrin route ) , it is quite evident that intrinsic disorder has a fundamental role in maintaining many of the functions carried out by this group , such as linking the coat scaffold to the cargo and to the membrane , helping vesicle coat assembly by binding the coat subunits , and communicating with other accessory proteins . The dependency on structurally disordered regions must be the largest in case of the clathrin system , since most of the individual clathrin adaptors , many of the accessory proteins and also some of its enzymes ( like synaptojanins ) have extremely long disordered tails with a large collection of several protein interaction motifs . Also , when searching for those solitary folded domains , which behave like structured “islands” , surrounded by extended disordered regions on either or both sides , most of the examples that we could identify belonged to the ASP group of the clathrin-mediated system . These proteins seemed to be the best candidates for the fly-casting mechanism , since they could behave as a fishing stick , their folded domain being fixed to the surface of the vesicle or to bigger protein complexes , while their disordered , flexible tail can freely reach for their various partner proteins over relatively long distances . This binding mode can be especially advantageous in the vesicle assembly process because it may enhance the speed of recognition and bring the coat components into close proximity to the surface of the budding vesicle . Unstructured proteins have larger capture radius that helps them efficiently utilize their many interaction motifs . Additionally , we collected several examples from the PDB that provide structural evidence for protein interactions mediated by the induced folding of disordered binding regions in clathrin system related proteins . Many of these structures showed the same domain , the AP-2 α-adaptin ear domain , facilitating specific interactions with disordered binding motifs of its partner proteins ( Figure 4 ) . The partners were not exclusively adaptor proteins in this case; there are also structures about synaptojanin-1 and amphiphysin interacting with the ear domain . We found other examples of clathrin system related complexes as well , like human stonin 2 binding to the EPS15 EF-hand domain . All these observations are in a very good agreement with the previously described extended , dynamic protein network on the surface of clathrin coated pits [20] . The composition of this network is probably highly variable in a localization- , route- , and maybe cargo-specific manner , with several different functional groups represented among its members . The functional importance of disordered regions was also well reflected by the conserved nature of their location , while the variability in their length and their low sequence similarity showed their increased adaptability and tolerance against mutations compared to folded domains . When investigating orthologous protein pairs from human and yeast , the location of disordered regions was found to be quite conserved , while their length appeared to be more subject to evolutionary change . In case of the Sec16 pair , the long disordered regions surrounding the structured domains were very well preserved , and even the lengths of the two proteins were highly similar . Since almost the entire length of the two long disordered “arms” of the proteins is covered by predicted disordered binding sites in a well-conserved way , their essential role in the initiation of the COPII coat assembly is very likely . The level of conservation in these regions seems to heavily depend on the specific functional needs of the given protein . In case of the Sec24 orthologous pair , for instance , the human sequence has a considerably long N-terminal unstructured region , which is almost completely missing from the yeast counterpart . The presence of numerous predicted disordered binding regions implies that this region is the result of adaptive evolution . Since this protein is a key player in cargo recognition and binding , which obviously involves a larger repertoire of possible cargos in human , the emergence of such adaptive regions could provide indisputable benefits . Our results showing that the clathrin system is significantly more disordered than the COPI and COPII systems not only imply the larger dependence of this system on disordered protein segments and support the concept of highly dynamic networks formed by its proteins , but also explains much about the differences between the three routes from the evolutionary point of view [35] . Disordered regions not only have conformational freedom but also a kind of evolutionary freedom . Their increased tolerance against mutations gives them the possibility of fast evolutionary changes , providing exceptional adaptability . As already mentioned , the clathrin-mediated system shows marked plasticity and robustness compared to the other two systems . There are many observations emphasizing the increased adaptability of this system as well . It shows many species-specific characteristics [27] , [31] , and it has been extensively modified to assist other specialized pathways . Adaptors and the clathrin itself , for instance , are often manipulated to create novel types of organelles , such as the rhoptry secretory organelle in Toxoplasma gondii [83] , the contractile vacuoles of Dictyostelium species [84] , special vesicles for odorant receptors transport of Caenorhabditis elegans [85] , and the machinery for sorting proteins to the basolateral plasma membrane of vertebrate epithelial cells [86] , among others . Biogenesis of synaptic vesicles in animals and human is also performed by endocytic adaptors [87] . Similarly , there are other organelles as well that require these adaptors for their maturation [88] . Apart from the species and tissue-specific inventions , clathrin system adaptors are also frequently used for various functions during embryonic development [88] , [89] and often responsible for mitotic moonlighting functions of many kinds [90] . Taken together , these observations on the many different adaptive changes manifest on clathrin-route related proteins strongly support the idea that it has been favoured by evolution over the two other main trafficking systems . Until now , the structural background of this phenomenon had not been established . In this study we show how structural disorder found in this system underscores its exceptional adaptability . We analyzed the moonlighting abilities and tissue specific functions of the proteins in the three main vesicle trafficking routes , as they are both strong indicative measures of adaptability . The clathrin route clearly stands out , having the highest number of non-vesicle trafficking interaction partners and the most verified tissue specific exons . The correlation between these abilities and the disordered nature of the corresponding proteins/protein regions in general had been previously suggested [49] , [51] , and here we demonstrate this positive correlation specifically for vesicle trafficking proteins . Clathrin-route associated proteins have significantly more off-pathway interactions than COPI- and COPII-route associated proteins . Out of the most “interactive” 21 proteins , 19 are clathrin-route related , many of them having disorder regions ( at least 30 consecutive residues and/or higher disorder content than the whole proteome reference value ) . More than one third of all off-pathway interactions of the three routes are mediated by only 9 non-complex-forming clathrin adaptors , most of which have LDRs densely covered by predicted DBRs and/or are highly disordered , meaning that more than half of their residues reside in disordered regions . The clathrin-route proteins also show more ability to maintain tissue specific functions than the proteins in the other two routes . 75% of all TSEs were found in clathrin-route associated genes , together with approximately the same ratio of TSE encoded predicted DBRs residing in clathrin proteins . TSE-encoded protein regions are almost twice as disordered as the proteome average , and they have an increased capacity to host interaction sites ( one in every 40 residues in our dataset ) . These findings are in good agreement with the general view that TSE-encoded protein regions are enriched in protein disorder [49] and differentially spliced exons in general , and tissue specific ones in particular , are prone to specifically rewire interaction networks by coding for protein regions enriched in short linear motifs/interaction sites [49] , [50] , [91] . Our results also show that TSE-encoded regions in the clathrin-route associated proteins are more disordered than the rest ( encoded by the other two routes ) , which further points to the enhanced dependency of this route on structural disorder and the related advantages disorder offers . In summary , we found many functional modalities enabled by disordered regions to be present in vesicle trafficking proteins . These include regulatory roles , the use of flexible linkers , mediating protein-protein interactions with proteins of the same route or others , or the quick assembly of large macromolecular complexes by fly-casting . Taken together , our results provide compelling evidence for the functional involvement of structural disorder in the main vesicle trafficking systems . The presence of highly disordered proteins in almost all the main functional groups of vesicle trafficking proteins emphasized the unquestionable importance of disorder for this cellular process in general . The remarkable differences in its abundance between the three main trafficking routes , however , provided structural background for long standing observations on the functional and evolutionary differences of these systems . | Vesicle trafficking systems are fundamental among cellular transport mechanisms; various cargo molecules are transported via different coated vesicles to their specific destinations in every eukaryotic cell . Clathrin-coated vesicles mediate endocytosis and the late secretory route , while the COat Protein I and II ( COPI and COPII ) vesicle trafficking routes are responsible for the bidirectional traffic between the ER and the Golgi apparatus . Despite similar basic principles , regulatory mechanisms and structural features of the three systems , their molecular machinery , functions , and evolutionary characteristics vastly differ . We investigated and compared these three routes and their basic functional protein groups from the structural disorder point of view , since disordered protein regions could provide a broad variety of functional and evolutionary advantages for them . We found that structurally disordered protein segments are most abundant in the clathrin system , which might explain the observed inherent plasticity , increased adaptability and exceptional robustness of this route . We support our hypothesis by two analyses on protein multi-functionality and tissue specificity , both being indicative of evolutionary adaptability . Clathrin pathway proteins stand out in both measures , with their disordered regions being largely responsible for their outstanding capabilities . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"biochemistry",
"proteins",
"macromolecular",
"assemblies",
"biology",
"computational",
"biology",
"molecular",
"cell",
"biology"
]
| 2013 | Structural Disorder Provides Increased Adaptability for Vesicle Trafficking Pathways |
HIV-1 is transmitted primarily across mucosal surfaces and rapidly spreads within the intestinal mucosa during acute infection . The type I interferons ( IFNs ) likely serve as a first line of defense , but the relative expression and antiviral properties of the 12 IFNα subtypes against HIV-1 infection of mucosal tissues remain unknown . Here , we evaluated the expression of all IFNα subtypes in HIV-1-exposed plasmacytoid dendritic cells by next-generation sequencing . We then determined the relative antiviral potency of each IFNα subtype ex vivo using the human intestinal Lamina Propria Aggregate Culture model . IFNα subtype transcripts from the centromeric half of the IFNA gene complex were highly expressed in pDCs following HIV-1 exposure . There was an inverse relationship between IFNA subtype expression and potency . IFNα8 , IFNα6 and IFNα14 were the most potent in restricting HIV-1 infection . IFNα2 , the clinically-approved subtype , and IFNα1 were both highly expressed but exhibited relatively weak antiviral activity . The relative potencies correlated with binding affinity to the type I IFN receptor and the induction levels of HIV-1 restriction factors Mx2 and Tetherin/BST-2 but not APOBEC3G , F and D . However , despite the lack of APOBEC3 transcriptional induction , the higher relative potency of IFNα8 and IFNα14 correlated with stronger inhibition of virion infectivity , which is linked to deaminase-independent APOBEC3 restriction activity . By contrast , both potent ( IFNα8 ) and weak ( IFNα1 ) subtypes significantly induced HIV-1 GG-to-AG hypermutation . The results unravel non-redundant functions of the IFNα subtypes against HIV-1 infection , with strong implications for HIV-1 mucosal immunity , viral evolution and IFNα-based functional cure strategies .
The type I interferons ( IFNs ) are critical players in the innate immune response against viral infections . Shortly after infection , these cytokines are rapidly induced , stimulating an antiviral state through the induction of hundreds of interferon-stimulated genes ( ISGs ) [1] . This family of cytokines include IFNα , the first cytokine produced through recombinant DNA technology and tested in clinical trials against many infectious diseases [2] . Notably , IFNα is a collective term for 12 unique IFNα proteins or subtypes expressed by 13 IFNA genes that are tandemly arrayed on human chromosome 9 . However , most clinical trials only utilize recombinant IFNα2 , the subtype that is currently licensed for the treatment of hepatitis B virus ( HBV ) and HCV infection . IFNα2 was also evaluated for reducing HIV-1 plasma viral loads during chronic infection . However , the variable levels of efficacy observed [3–6] and the advent of potent and safer antiretroviral drugs reduced enthusiasm for the use of IFNα in the clinical management HIV-1 infection . Two major developments in recent years renewed interest in IFNα as a therapeutic for HIV-1 infection: ( 1 ) the discovery of antiretroviral restriction factors , most of which are induced by IFNα [7]; and ( 2 ) the improved prospects in achieving functional HIV-1 cure , which may be advanced through IFNα-based therapies [8 , 9] . However , this renewed interest also raised unanswered questions on the basic biology of IFNα , including the biological consequences of having an expanded IFNA gene family [10 , 11] . In fact , the relative expression , antiviral potency and restriction factor mechanisms employed by the various IFNα subtypes against HIV-1 infection remains unclear . One potential advantage for the expansion of the IFNA gene family could be the diversification of regulatory elements , which would allow the infected host to differentially express IFNA genes in response to diverse stimuli . Plasmacytoid dendritic cells ( pDCs ) are the primary producers of IFNα in vivo [12] , and exposure of pDCs to HIV-1 or HIV-1 infected cells resulted in a dramatic rise in IFNα production [13 , 14] . Measurements of total IFNα proteins rely on antibodies that may have different binding affinities to the IFNα subtypes . Furthermore , antibodies that can distinguish the various IFNα subtypes are not yet available . IFNα expression is primarily regulated at the mRNA level [15] . Innate sensing of viruses , for example through Toll-like receptors ( TLRs ) , results in a signaling cascade that leads to the activation and recruitment of transcription factors to the IFNA promoter ( s ) [16] . Thus , quantitative real-time PCR ( qPCR ) is a standard procedure used by many laboratories to measure IFNA gene expression , with increasing recognition on the importance of obtaining IFNA subtype distribution for understanding retroviral pathogenesis [17] . However , quantifying the expression of the different IFNA subtype genes is complicated by their high sequence homology ( 78 to 99% ) . Nevertheless , IFNA subtype expression profiles of pDCs were evaluated using quantitative real-time PCR assays developed for each IFNA gene [15 , 18–20] . Humanized mice exposed to TLR7 agonists showed prominent expression of IFNA2 and IFNA14 in pDCs [18] but other studies showed equal expression of all IFNA subtypes following TLR ligand stimulation [15 , 19] . These discrepancies suggested that measuring IFNA distribution by qPCR may be difficult to reproduce across laboratories . Moreover , performing 12 qPCR reactions for each IFNA subtype would not be ideal for limited biological samples . The lack of a robust method to quantify IFNA distribution is therefore a significant hurdle in understanding the role of IFNA subtypes in human health and disease . Functional diversification may be another evolutionary advantage for an expanded IFNα gene family . Although all IFNα subtypes signal through the same type I interferon receptor ( IFNAR ) , the IFNα subtypes exhibited different binding affinities for the IFNAR-1 and IFNAR-2 subunits [21 , 22] . This might result in different signaling pathways induced by IFNα subtypes [23] and in distinct expression patterns of ISGs in vitro [24] . In vivo , mouse IFNα subtypes exhibited different potencies against herpes simplex virus 1 , murine cytomegalovirus , vesicular stomatitis virus ( VSV ) , influenza virus and Friend retrovirus [11 , 25] . Altogether , the data indicate that the IFNα subtypes are not functionally redundant , raising the immediate question of which IFNα subtypes are most potent against HIV-1 . An early study revealed that IFNα2 may be the most potent , but only 6 IFNα subtypes were evaluated against an X4-tropic , lab-adapted HIV-1 strain in the MT-2 T cell line [26] , thereby raising issues regarding physiological relevance . IFNα is induced very early during HIV-1 infection [27] , and blocking IFNAR signaling in the SIV/rhesus macaque model resulted in higher viral loads and pathogenesis [28] . The impact of the early IFNα response against HIV-1 most likely manifests in the gut-associated lymphoid tissue ( GALT ) , as it is the major site of early HIV-1 amplification and spread that leads to a massive depletion of CD4+ T cells [29 , 30] . Prior success in infecting gut lamina propria mononuclear cells ( LPMCs ) with HIV-1 [31] led to the development of the Lamina Propria Aggregate Culture ( LPAC ) model [32 , 33] . The LPAC model allows for the robust infection of primary gut CD4+ T cells with CCR5-tropic HIV-1 strains , subsequently leading to CD4+ T cell depletion . Importantly , this model allowed for HIV-1 infection studies without the confounding effects of non-physiologic T cell activation , as HIV-1 can efficiently infect gut CD4+ T cells without exogenous mitogens [29–31] . Thus , the LPAC model is an ideal ex vivo platform to evaluate the relative potency of the various IFNα subtypes against HIV-1 . Identifying the key effectors behind the anti-HIV-1 activity of IFNα could pave the way for the design of novel IFNα-based therapeutics . The APOBEC3 proteins ( A3G , A3F , A3D and A3H ) , Tetherin/BST-2 and Mx2 were considered as bona fide HIV-1 restriction factors [7 , 34–37] . These factors were proposed as effectors of the IFNα treatment effect based on correlative studies using IFNα clinical trial data [38 , 39] as well as cell culture data [35 , 40–42] . However , their regulation by diverse IFNα subtypes in mucosal CD4+ T cells has not yet been explored . APOBEC3 and Tetherin are counteracted by the HIV-1 Vif and Vpu , respectively [7] , but it is important to note that these interactions are saturable . Induction of APOBEC3 and Tetherin expression may undermine the antagonism due to Vif and Vpu by offsetting the balance of these respective interactions . Tetherin and Mx2 inhibit HIV-1 in the infected cell , leading to a reduction in virus release [34–37] . In contrast , the APOBEC3 proteins are packaged into budding HIV-1 particles and inhibit replication in the next target cell by impeding reverse transcription and hypermutating reverse transcripts [43 , 44] . Thus , a strong case for APOBEC3 activity could be made if reduced HIV-1 virion infectivity and increased G→A hypermutation were both detected . We previously showed that treatment of Friend retrovirus-infected wild-type mice with IFNα reduced viral loads , but not in Apobec3 knock-out ( KO ) mice [45] . Given the longstanding evolutionary conflict between mammalian hosts and retroviruses [46] , we hypothesized that the human APOBEC3 proteins may also act as effectors of IFNα treatment against HIV-1 in mucosal CD4+ T cells . Here , we modeled the role of the IFNα subtypes during acute HIV-1 infection . Using a novel next-generation sequencing-based method , we quantified the relative expression of the IFNα subtypes following HIV-1 exposure in pDCs , and determined the relative antiviral potency of each IFNα subtype in the LPAC model . Moreover , we determined the induction profiles of known HIV-1 restriction factors following treatment with individual IFNα subtypes , and provide evidence that the APOBEC3 proteins may serve as key effectors for the antiviral activity of IFNα against HIV-1 .
Plasmacytoid DCs ( pDCs ) are the primary sources of IFNα in vivo , migrating to the GALT from the periphery during acute SIV infection [47] and accumulating in mucosal tissues during chronic HIV-1 infection stages [48 , 49] . To date , the IFNα subtypes produced by pDCs following HIV-1 sensing remain unknown . To determine the expression levels of each IFNα subtype , we designed 2 complementary assays using primers designed in the most conserved regions of the 13 IFNA genes ( S1 Fig ) . Using these primers , total IFNA expression relative to the housekeeping gene GAPDH could be measured by qPCR , whereas IFNA subtype distribution could be quantified by next-generation sequencing . We used negative selection to enrich pDCs from PBMCs from 4 healthy donors and exposed the cells to HIV-1 virions ( R5-tropic BaL strain ) for 6 hrs ( Fig 1A ) . A 6 hr timepoint was chosen to ensure the viability of the pDCs , which significantly decline by 24 h post-culture [50] , while capturing the initial burst of IFNA expression following viral sensing . Total IFNA expression was induced 485-fold in pDCs following HIV-1 exposure , but not in PBMCs lacking pDCs , confirming that pDCs are the main producers of IFNα ( Fig 1B ) . We next quantified the relative abundance of each IFNα subtype in pDCs ± HIV-1 . Primers in the conserved regions were modified with Illumina-sequencing adaptors , and the IFNA subtype designation for each sequence was determined based on the polymorphic regions in the amplicon . IFNA1 and IFNA13 encode identical proteins and had identical DNA sequences in the region amplified , so these genes were counted together as IFNA1/13 . On average , 9 , 543 IFNA sequence reads were analyzed per donor per condition . The IFNA genes were aligned according to their relative genomic positions and their proportional expression values are shown ( Fig 1C ) . The proportional expression of different IFNA subtypes by pDCs from different donors was very consistent both in naïve cultures ( Fig 1D ) and following HIV-1 exposure ( Fig 1E ) . Interestingly , there was a strong bias towards expression of IFNA genes at the centromeric half of the IFNA complex following HIV-1 exposure ( Fig 1E ) . Five out of six IFNA genes in this genomic cluster accounted for >70% of the IFNA subtypes expressed by pDCs following HIV-1 exposure ( Fig 1F ) . The exception was IFNA6 , which decreased as a percentage of the total IFNA . The augmented IFNA subtype expression levels were independent of genomic orientation , as IFNA2 and IFNA8 were both highly expressed yet had opposite genomic orientations ( Fig 1C ) . We then determined the absolute copy numbers of each IFNA subtype by multiplying the percentage values ( Fig 1D and 1E ) with the total copy numbers ( Fig 1B ) . The absolute copy numbers of all IFNA subtypes increased in pDCs following HIV-1 exposure , though to varying degrees ( S2 Fig ) . IFNA14 , IFNA2 and IFNA10 were induced over 1000-fold following HIV-1 exposure of pDCs , whereas IFNA6 was induced by ~100-fold . Overall , the results revealed a pattern of IFNA gene induction after HIV-1 exposure that appeared to be linked to chromosomal position . Since the GALT is the major site of early HIV-1 amplification and spread , we utilized LPAC as a physiologically relevant model to determine the relative anti-HIV potency of each IFNα subtype . In particular , we were interested in whether IFNα2 , the subtype approved for clinical use , was the optimal IFNα subtype for inhibiting HIV-1 . Fig 2A outlines the experimental infection protocol . Analyzing the HIV-1 potency of all 12 IFNα subtypes at multiple doses was not feasible in the LPAC model because of the limited number of LPMCs available per donor . Thus , initial dose-response tests were performed with IFNα14 , which potently inhibited HIV-1 in a pilot experiment . Following infection with HIV-1BaL , LPMCs were rinsed with culture media and resuspended to various IFNα14 concentrations . Infection levels were evaluated at 4 days post-infection ( dpi ) to capture not only the impact of restriction factors that inhibit HIV-1 virus production , but also those that inhibit virion infectivity , which would decrease infection after one round of replication ( S3 Fig ) . The percentage of infected CD4+ T cells was measured by detecting intracellular HIV-1 p24 capsid expression by flow cytometry , as we previously described [32 , 33] . To account for HIV-1 Nef and Vpu-mediated CD4 downregulation [51] , we gated on CD3+CD8- cells . A screen of LPMCs from 7 donors revealed that IFNα14 restricted productive HIV-1 infection , and that the inhibition was saturable at higher concentrations ( Fig 2B ) . The majority of the LPMC donors had similar sensitivity to IFNα14-treatment , with the exception of one donor who responded to lower concentrations . An IFNα concentration of 100 pg/ml was in the linear range of the dose response curve ( ~50% inhibition ) , and was chosen for the subsequent evaluation of all IFNα subtypes in 4 LPMC donors . This concentration was also within the range of IFNα levels in plasma following HIV-1 infection in vivo [52] . Majority of the cells in the LPMC donors used were CD3+ T cells ( 88% ± 3% ) . On average , 65% of the LP T cells were CD4+ . Myeloid DCs and gamma-delta T cells account for <1% of the total LPMC subpopulations , respectively . Recombinant IFNα subtypes were added to LPMCs ( 100 pg/ml ) after spinoculation ( Fig 2A ) . At 4 dpi , HIV-1 infected cells were quantified by detecting intracellular p24 by flow cytometry as in Fig 2B . There were clear differences in the potency of the IFNα subtypes in inhibiting productive HIV-1 infection ( Fig 2C ) . IFNα8 , IFNα14 and IFNα6 showed the highest levels of inhibition , whereas IFNα1 and IFNα2 had no significant effect . The supernatants were also tested for infectious HIV-1 titers using the TZM . bl assay ( S3 Fig ) . Again , the same 3 IFNα subtypes were most potent , whereas IFNα1 remained the least potent ( Fig 2D ) . The antiviral potency of the different IFNα subtypes as measured by p24 flow cytometry and the TZM . bl assay significantly correlated with each other ( S4A Fig ) . Although IFNα2 had no significant effect on cellular HIV-1 infection ( Fig 2C ) , it moderately reduced infectious titers ( Fig 2D ) . Overall , the LPAC data revealed differences in the potencies of IFNα subtypes in inhibiting HIV-1 infection . IFNα2 , the current subtype approved for clinical use , was one of the least potent subtypes . To investigate whether the IFNα response of pDCs following HIV-1 exposure was biased towards the expression of the most potent antiviral IFNα subtypes , we next determined the relationship between IFNα subtype expression levels and relative potency . Absolute IFNA subtype copy numbers were calculated by multiplying the total IFNA copies ( Fig 1B ) by the percentage of total IFNA for each subtype ( Fig 1E ) . This provided an estimated copy number of each IFNA subtype per 106 copies of GAPDH . Using these values , a significant inverse correlation was observed between IFNA subtype expression and potency ( Fig 3A and S4B Fig ) . This correlation can be exemplified as follows . IFNα1 was highly expressed but ineffective at inhibiting HIV-1 replication . IFNα6 , one of the most potent subtypes , was among the least abundant following HIV-1 exposure . IFNα2 showed a very high fold-increase following HIV-1 exposure relative to baseline but had weak antiviral efficacy . IFNα5 is expressed at higher relative abundance ( Fig 1F ) but was also weakly antiviral . These results revealed that the predominant IFNA subtypes produced by pDCs following HIV-1 exposure had low antiviral potency . Two notable exceptions were IFNα8 and IFNα14 , which exhibited strong anti-HIV-1 activity and also had high expression in pDCs following HIV-1 exposure ( Fig 3A ) . Exclusion of the IFNα8 and IFNα14 datapoints further strengthen the inverse correlation ( R2 = 0 . 62 , p = 0 . 007 ) . Data from the Schreiber group [22] revealed that different IFNα subtypes exhibited variable binding affinities to IFNAR as estimated by surface plasmon resonance against each subunit , IFNAR-1 and IFNAR-2 . We therefore determined if IFNα subtype anti-HIV-1 potency ( Fig 2C and 2D ) correlated with published binding affinity data to IFNAR [22] . There was a significant positive correlation between antiviral potency and binding affinity ( KA ) to IFNAR-2 ( Fig 3B and S4C Fig ) , but not the IFNAR-1 subunit ( Fig 3C and S4D Fig ) . These analyses suggested that following HIV-1 exposure , pDCs produced IFNα subtypes with relatively low antiviral activity and lower binding affinity to IFNAR-2 . In particular , IFNα1 was expressed at high levels by pDCs exposed to HIV-1 virions but had the weakest IFNAR-2 binding affinity and the lowest anti-HIV-1 potency in the LPAC model . The correlation between antiviral potency and IFNAR binding affinity suggested that the more potent IFNα subtypes might trigger higher ISG induction . To test this hypothesis , we quantified the mRNA expression levels of the IFNα-inducible HIV-1 restriction factors Mx2 , Tetherin and APOBEC3 in LP CD4+ T cells after stimulation with representative IFNα subtypes . We focused on CD4+ T cells , the major cellular targets of HIV-1 replication in the GALT , but not intestinal macrophages , which are non-permissive to HIV-1 infection [53] . We selected IFNα8 and IFNα14 as potent IFNα subtypes due to their high affinity , highest antiviral potency in the LPAC model and high expression level in pDCs . IFNα1 and IFNα2 were selected as weak IFNα subtypes due to their relatively low affinity , weaker antiviral activity in the LPAC model ( with IFNα2 being more potent than IFNα1 ) , but high expression level in HIV-1-exposed pDCs ( IFNα1 and IFNα2 ) . IFNα2 was also chosen because of its clinical relevance . LPMCs were infected with HIV-1BaL and 100 pg/ml IFNα was administered . After 24 hr , CD4+ T cells were negatively selected and ISG mRNA expression was evaluated by qPCR ( Fig 4A ) . The magnitude of ISG induction was donor-dependent so the data for each donor are presented . ( Fig 4B to 4E ) . The ISG expression that best correlated with the relative antiviral activities of the IFNα subtypes was Mx2 ( Fig 4B ) . IFNα8 ( 3 of 3 donors ) and IFNα14 ( 2 of 3 donors ) more significantly induced Mx2 compared to IFNα1 and IFNα2 . IFNα2 , which showed moderate antiviral activity ( Fig 2D ) , more significantly induced Mx2 compared to IFNα1 in 3 of 3 donors . Tetherin induction exhibited trends similar to Mx2 , but the differences were not as consistent between donors ( Fig 4C and 4D ) . Overall , the more antiviral IFNα subtypes induced Mx2 and Tetherin to higher levels . In contrast , A3G ( Fig 4E ) was not significantly induced by any of the IFNα subtypes . A3F and A3D expression were induced in a few cases with IFNα treatment ( S6 Fig ) , but the induction levels did not correlate with the relative anti-HIV-1 potency of the IFNα subtypes . We previously demonstrated that mouse Apobec3 was the primary effector of IFNα treatment against Friend retrovirus infection despite not being transcriptionally induced [45] . We therefore investigated the potential contribution of human APOBEC3 proteins to the IFNα-treatment effect . The APOBEC3 proteins A3G , A3F , A3D and A3H do not inhibit HIV-1 in the producer cell . Instead , these proteins get packaged into HIV-1 virions and inhibit replication in the next target cell . Thus , non-infectious virion release is a distinguishing feature of APOBEC3-mediated retrovirus restriction [54 , 55] . By contrast , most restriction factors such as Mx2 and tetherin inhibit virus particle production in the infected cell [7] . Virion infectivity is typically measured by determining the ratio of infectious titer as measured by the TZM . bl assay and the total viral particles released in the supernatant as measured by HIV-1 p24 ELISA ( S3 Fig ) . LPMCs from 6 donors were infected with HIV-1BaL and were treated with IFNα1 , IFNα2 , IFNα8 and IFNα14 . At 4 dpi , all 4 IFNα subtypes inhibited virus particle release to the same extent ( Fig 5A ) . By contrast , the infectious titers were reduced significantly more by IFNα8 and IFNα14 compared to IFNα1 and IFNα2 ( Fig 5B ) . Thus , inhibition of virion infectivity correlated with the antiviral efficacy of the IFNα subtypes ( Fig 5C ) . In particular , IFNα8 and IFNα14 were the most potent at inhibiting virion infectivity whereas IFNα1 had no significant effect . In order to confirm that the findings were not specific to HIV-1BaL , LPMCs were infected with transmitted/founder ( T/F ) HIV-1 strains , which are infectious molecular clones reconstructed from acute HIV-1 infection samples [56–58] . In 6 LPMC donors , the antiretroviral activity of IFNα1 and IFNα8 against the T/F HIV-1 strains CH470 , CH40 , and CH58 were compared . IFNα1 and IFNα8 inhibited virus particle release to similar extents for CH40 and CH58 ( Fig 5D ) , whereas CH470 particle release was slightly more inhibited by IFNα8 . In virion infectivity assays , IFNα8 more potently inhibited the 3 T/F HIV-1 strains ( Fig 5E ) . We also evaluated the impact of IFNα8 in 13 additional T/F HIV-1 strains in 2 LPMC donors . IFNα8 treatment resulted in a highly significant ( ~4-fold ) decrease in virion infectivity ( Fig 5F ) . IFNα14 treatment also significantly inhibited the virion infectivity of these T/F HIV-1 strains ( S5 Fig ) . These data indirectly suggested that the more potent IFNα subtypes augmented APOBEC3-mediated restriction of multiple HIV-1 strains . The APOBEC3 proteins A3F , A3D and A3H mutated HIV-1 reverse transcripts with a preferred TC context , leading to GA→AA mutations in the retroviral plus strand , whereas A3G preferentially mutated in the CC context , leading to proviral GG→AG mutations [59] . Thus , the magnitude of retroviral mutations in the GA→AA versus GG→AG context could be used to determine the APOBEC3 members responsible for HIV-1 G-to-A hypermutation and to provide additional evidence of APOBEC3 involvement in HIV-1 restriction . To quantify APOBEC3-mediated retroviral mutations , we recently developed a next-generation sequencing approach to quantify mouse retrovirus hypermutation [60] . To extend this method to HIV-1 , we designed barcoded Illumina primers encompassing gp41/nef ( 420–450 bp depending on the strain ) , a region that may be more susceptible to APOBEC3-mediated deamination due to longer retention in single-stranded form during reverse transcription [61] . We initially tested the method by infecting LPMCs with WT HIV-1 NL4-3 and NL4-3ΔVif , which cannot counteract the effects of APOBEC3 . The percentage of GG→AG and GA→AA mutations were computed against the mutations at C or G bases , which are directly modified by deaminases . As expected , there was a significant increase in GG→AG and GA→AA mutations in NL4-3ΔVif compared to WT at 4 dpi ( Fig 6A ) . Thus , A3G and A3F/A3D/A3H actively mutated HIV-1ΔVif in gut CD4+ T cells . Following the validation of the next-generation sequencing method , we next analyzed proviral HIV-1 sequences for evidence of GG→GA and GA→AA mutations following treatment with IFNα8 or IFNα1 . LPMCs were infected with T/F HIV-1 strains CH470 , CH40 , and CH58 . These strains were derived from infectious molecular clones and therefore allow for straightforward mutational analysis . These 3 HIV-1 strains also had reduced virion infectivity following IFNα8 but not IFNα1 treatment ( Fig 5E ) . Untreated and IFNα-treated infected cells were harvested at 4 dpi . Sequences were pooled for each of the HIV-1 CH470 , CH40 , and CH58 strains , respectively , to allow for a thorough analysis of mutational patterns . A 2×2 contingency analysis was performed to test if IFNα had any effect on A3F/D/H-type ( GA→AA ) or A3G-type mutations ( GG→AG ) relative to the total number of C or G mutations . Following IFNα8 treatment , both GG→AG and GA→AA mutations significantly increased in CH470 ( Fig 6B ) . GG→AG mutations also significantly increased in CH40 , and to a lesser extent in CH58 ( Fig 6B ) . Surprisingly , IFNα1 treatment also increased GG→AG mutations in CH40 , CH58 and CH470 ( Fig 6C ) . Thus , both IFNα8 and IFNα1 treatment increased proviral DNA mutations that were associated with A3G deaminase activity .
Acute HIV-1 infection is characterized by extensive virus replication in the GALT , suggesting that the innate immune response could have a considerable impact on early HIV-1 spread in this compartment . In particular , IFNα exhibited potent anti-HIV-1 properties in vitro and was one of the first cytokines induced during acute HIV-1 infection [27] . Blocking type I IFN signaling in the SIV/rhesus macaque model resulted in more severe pathogenesis [28] . T/F HIV-1 strains exhibited higher resistance to type I IFNs than counterpart chronic strains , suggesting that type I IFNs exerted a strong selective pressure during acute HIV-1 infection [57 , 58] . These studies suggested that the initial IFNα response may serve as a roadblock for HIV-1 replication and spread in the GALT . However , there were 12 IFNα subtypes , and to date , it remained unknown which IFNα subtypes were produced by pDCs , the professional IFNα-producing cells that rapidly migrate and reside in the GALT following HIV-1/SIV infection [47–49] . Moreover , only one subtype , IFNα2 , was evaluated in clinical trials to reduce HIV-1 viremia . In fact , the clinical use of IFNα2 was largely driven by its status as the first IFNα subtype cloned for large-scale production [2] , and not from a systematic evaluation of antiviral potencies in physiologically-relevant target cells . Thus , the current study was undertaken to investigate the relative expression of the different IFNα subtypes in pDCs and their antiviral potency in the LPAC model . A major finding from this work was that IFNα8 , IFNα6 and IFNα14 were the most effective at inhibiting HIV-1 replication in gut CD4+ T cells . By contrast , the antiviral activity of IFNα2 was weak at best . IFNα8 , IFNα6 , and IFNα14 exhibited strong binding affinities to IFNAR-2 [22] . This suggests that binding affinity to IFNAR-2 , proposed as the first IFNAR subunit that binds IFNα [62] , may contribute to the differential potencies of the IFNα subtypes . This notion was corroborated by the higher ISG induction profile for IFNα8 and IFNα14 compared to IFNα2 . Sequence analyses of IFNα8 , IFNα6 and IFNα14 in human populations revealed that DNA polymorphisms in these subtypes tend to preserve the amino acid sequence ( e . g . , purifying selection ) [63] , suggesting that these IFNα subtypes may have essential roles in vivo . Moreover , IFNα8 exhibited strong antiviral activity against other viruses [64] . Interestingly , using a novel method to quantify IFNA subtype distribution , we observed an inverse correlation between IFNα subtype expression in HIV-1-exposed pDCs and anti-HIV-1 potency . IFNα6 fit this trend–it was one of the least expressed IFNα subtypes in HIV-1-exposed pDC cultures . IFNα6 was also weakly expressed by pDCs stimulated with TLR ligands [15] . However , IFNα8 and IFNα14 were both potent and more abundantly produced by pDCs exposed to HIV-1 . IFNα8 and IFNα14 were encoded within the centromeric half of the IFNA complex , suggesting that epigenetic mechanisms may regulate their expression . The data suggest that IFNα8 and IFNα14 may constitute the most potent antiviral fraction of the initial IFNα response against HIV-1 infection . However , it should be noted that IFNα8 and IFNα14 only account for ~20% of the total IFNA transcripts produced by pDCs following HIV-1 exposure . The majority of the IFNα subtypes expressed by pDCs following HIV-1 exposure had relatively weak antiviral activity ( IFNA1 , 2 and 5 account for >40% of IFNA transcripts ) . In particular , the most expressed IFNα subtype , IFNα1 , had the weakest antiviral activity . IFNα1 also exhibited very weak activity against VSV and HCV , and the lowest binding affinity for IFNAR-2 [22 , 64–66] . IFNα2 was also highly induced in pDCs post-HIV-1 exposure , consistent with another study showing IFNα2 was upregulated in HIV-1-infected individuals [67] . We speculate that IFNα1 and IFNα2 induction may be a strategy used by HIV-1 to evade a more potent IFNα response . However , the rationale for why humans evolved weakly antiviral IFNα subtypes in the first place remains unknown . One possibility is that weakly antiviral IFNα subtypes may be better at modulating other immunological processes . If true , then these IFNα subtypes could potentially elicit more adverse effects if administered therapeutically . IFNα2 therapy was long known to have undesirable clinical side-effects including fever , fatigue and lymphopenia [2] . Moreover , in an intriguing paradox , high IFNα expression levels during chronic HIV-1 infection correlated with disease progression [52 , 68] . This led some to propose blocking IFNα signaling in chronic HIV-1-infected individuals to reduce immune activation [69] . However , the IFNα subtypes responsible for the link between IFNα and chronic immune activation remains unknown . The development of the IFNA subtyping method described here should facilitate revisiting this phenomenon . In addition , further studies would be required to evaluate the tolerability profile of IFNα8 , IFNα6 and IFNα14 relative to IFNα2 and IFNα1 . One possible strategy to harness the antiviral properties of IFNα for the design of safer HIV-1 therapeutics is to focus on its downstream antiviral effectors . Many ISGs were reported to have inhibitory activity against HIV-1 in vitro [70] , but transcriptional induction levels may not predict the most potent antiviral effectors of IFNα [45] . In this study , the more antiviral IFNα subtypes induced Mx2 and Tetherin to a greater extent . Mx2 and Tetherin act on the producer cell , decreasing viral production . Thus , if the IFNα subtypes were acting through these restriction factors to inhibit HIV-1 replication , we would expect higher inhibition of virus production by the more potent IFNα subtypes . Surprisingly , this was not the case: IFNα1 inhibited virus particle production to a similar extent as IFNα8 and IFNα14 . Thus , Mx2 or Tetherin may not be mediating the differences in antiviral potencies between the IFNα subtypes . In other words , the differential induction of Mx2 and Tetherin expression by potent versus weak IFNα subtypes may just reflect the magnitude of IFNAR signaling and not necessarily indicate the mobilization of these effector mechanisms . The IFNα subtypes did not significantly upregulate A3G , A3F and A3D transcription in gut CD4+ T cells , consistent with previous data using IFNα in PBMCs [71 , 72] . Nonetheless , the relative potencies of the IFNα subtypes were associated with reduced virion infectivity , thus pointing to the APOBEC3 proteins as a significant antiviral effector of IFNα . The notion that the APOBEC3 proteins could act as significant effectors of potent IFNα subtypes makes evolutionary sense based on our studies in mice [45] . However , the mechanism for how IFNα improved APOBEC3 function without transcriptional induction remains to be determined . Surprisingly , both the potent ( IFNα8 ) and weak ( IFNα1 ) subtypes induced retroviral GG→AG hypermutation , suggesting that the deaminase-dependent activity of A3G did not correlate with the relative antiretroviral potencies of the IFNα subtypes . A3G inhibits HIV-1 through a deaminase-independent and deaminase-dependent mechanism . The deaminase-independent mechanism acts upstream by inhibiting the elongation of reverse transcripts , thereby preventing the production of single stranded DNA substrates for deamination [43] . Our results raise the intriguing possibility that IFNα subtypes may differentially activate deaminase-independent and deaminase-dependent activities of the APOBEC3 proteins . Notably , several studies suggested that A3G deaminase activity could be a double-edged sword , as A3G may not only restrict HIV-1 replication but also promote viral evolution to evade antiretroviral drugs and adaptive immunity [73–76] . About 16% of transmitted/founder HIV-1 strains exhibit signatures of G→A hypermutation [56] , and APOBEC3-linked mutations in rapidly evolving sites may be linked to CTL escape [77] . Thus , the induction of weakly antiviral subtypes such as IFNα1 by pDCs during acute HIV-1 infection may have important consequences for early HIV-1 evolution . In conclusion , the differential expression , potency and restriction factor induction by the human IFNα subtypes suggest that these evolutionarily related cytokines play non-redundant roles during HIV-1 infection . These findings are particularly timely with respect to ongoing clinical trials that aim to leverage IFNα2 therapy as a potential HIV-1 curative strategy ( clinicaltrials . gov identifiers NCT00594880 , NCT01295515 , NCT01285050 and NCT01935089 ) . Our results suggest that evaluating IFNα subtypes that more potently augmented APOBEC3-mediated deaminase-independent restriction may yield better clinical outcomes on the road to a functional HIV-1 cure .
Blood collection from self-identified HIV-negative donors was approved by the Colorado Multiple Institutional Review Board ( COMIRB ) at the University of Colorado Anschutz Medical Campus . The use of discarded , macroscopically normal human jejunum tissue samples was granted exempt status by COMIRB and patients signed a pre-operative consent form allowing its unrestricted use for research purposes . Protected patient information was de-identified to laboratory personnel . HIV-1BaL stocks ( AIDS Research Reagent Program/ARRP Catalogue #4984 ) were prepared by passage in MOLT4-CCR5 ( ARRP #510 ) cells for 9 days . Virus containing supernatants were ultracentrifuged at 76 , 800g . T/F HIV-1 infectious molecular clones CH470 , CH40 , and CH58 , as well as AD17 , CH106 , CH607 , REJO , RHPA , THRO , STCOr1 , STCOr2 , WARO , MCST , RHGA , TRJO and WITO were generously provided by Beatrice Hahn [57 , 58] . NL4-3 and NL4-3ΔVif were obtained from ARRP . CH470 , CH40 and CH58 plasmids were re-transformed and amplified in Stbl3 cells ( Invitrogen ) and purified using Qiagen maxi kit . T/F maxi-preps were sequence-verified using 13 HIV-specific primers to cover the entire genome . 40 μg of T/F plasmids were used to transfect 293T cells in a T175 flask . Four flasks were transfected by CaCl2 transfection method for each virus [78] . Virus-containing supernatants were collected at 48 hrs , concentrated by ultracentrifugation at 76 , 800g over a 20% sucrose cushion . Virus stocks were titered using an HIV-1 Gag p24 ELISA kit ( Perkin Elmer ) . pDCs were isolated from peripheral blood of 4 healthy donors who self-identified as HIV-1-uninfected . All subjects voluntarily gave written , informed consent . This study was approved by the Colorado Multiple Institutional Review Board ( COMIRB ) at the University of Colorado Anschutz Medical Campus . pDCs were negatively selected using the EasySep plasmacytoid cell enrichment kit according to the manufacturer’s instructions . Purity was determined by flow cytometry . On average , the pDC-enriched fraction was 76% ( range: 53–92% ) BDCA-2+ . The other cell subpopulations were significantly depleted , with 2% CD3+ ( from 65% ) , 0 . 2% CD14+ ( from 6% ) , 0 . 7% CD19+ ( from 2% ) and 1 . 1% CD56+ ( from 11% ) . Zombie Aqua Viability Dye ( Biolegend ) exclusion was used to identify viable cells , and anti-BDCA2-PE ( Miltenyi ) was used to identify pDCs . pDCs or PBMCs with pDCs removed were resuspended to 106 cells/ml in complete RPMI ( RPMI with 10% human AB serum , 1% penicillin/streptomycin/glutamine , 500 μg/ml Zosyn ) . Cells were spinoculated with 250 ng/ml of cell-free HIV-1BaL for 2 hrs at 1700 rcf at room temperature . Cells were washed 1x with complete RPMI , resuspended to 106 cells/ml , and incubated at 37°C for 4 hr . Cells were then harvested and RNA extracted using Qiagen RNAeasy Micro kit . Primers were designed in conserved regions of the IFNA subtypes: Forward primer 5’TCCATGAGVTGATBCAGCAGA and reverse primer 5’ ATTTCTGCTCTGACAACCTCCC ( S1A Fig ) . cDNA was transcribed from RNA using random hexamers in the Qiagen Quantitect Reverse Transcription kit . cDNA was diluted 1:5 and 10 μl added to make a final concentration of 1× Quantitect SYBR green PCR reagent containing 8 pmol of each primer . qPCR was run on Biorad CFX96 real-time PCR machine under the following conditions: 95°C for 15 min followed by 40 cycles of 94°C 15 s , 55°C 30 s , 72°C 30 s . Specificity was determined by melt curve analysis . qPCR data was analyzed with CFX Manager software ( Biorad ) . Copy number was interpolated using a standard curve with 108–102 copies of IFNA8 plasmid . Copies of GAPDH were determined by Taqman primer/probe assay ( S1 Table ) . RNA from pDCs was reverse transcribed with Quantitect reverse-transcription kit ( Qiagen ) using a primer from a conserved region in the IFNA subtype alignment ( S1A Fig ) . RT primer: 5’-GATCTCATGATTTCTGCTCTGAC . cDNA was added to a PCR reaction containing Phusion Hi Fidelity Taq ( New England Biolabs ) according to manufacturers instructions containing 8 pmol of the following Illumina primers containing random nucleotides ( N ) and 6-bp barcodes ( INDEX# ) . Forward primer: 5’AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGAT CT NNNN INDEX1 TGCGTCTCCATGAGVTGATBCAGCAGA Reverse primer: 5’CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT NNNN INDEX2 ATTTCTGCTCTGACAACCTCCC PCR was run at the following conditions: 98°C for 30 min , followed by 35 cycles of 98°C 10 s , 58°C 15 s , 72°C 15 s , and a final elongation of 72°C 5 min . Sequences reads were generated in the Illumina MiSeq as recommended by the manufacturer . Resultant sequences were compared to a reference sequence database containing cDNA sequences from all members of IFNA gene family and identified as a particular IFNA subtype with a threshold of 90% identity . IFNA gene distribution was calculated based on a percentage of the total IFNA counts . IFNA1 and IFNA13 DNA sequences were identical in the amplified region and encode an identical protein and so were referred to as IFNA1/13 . For simplicity the recombinant protein was noted as IFNα1 . All 12 recombinant IFNα subtypes were purchased from PBL Assay Science , Cat . No . 11002–1 . The proteins were resuspended in PBS containing 0 . 1% BSA to 5 . 31 μg/ml according to product insert and stored at –80°C as single use aliquots . Macroscopically normal human jejunum tissue samples were obtained from patients undergoing elective abdominal surgery . The use of discarded tissue was granted exempt status by COMIRB and patients signed a pre-operative consent form allowing its unrestricted use for research purposes . Protected patient information was de-identified to laboratory personnel . LPMCs were obtained and processed as previously described [32 , 33] . Briefly , LP mucosa was separated from muscularis mucosa , EDTA was used to separate epithelial cells , and collagenase D treatment released LPMCs . Cells were cryopreserved in RPMI + 10% DMSO + 10% FBS . Cryopreserved LPMCs were thawed by gradual addition of thaw media ( 90 ml RPMI + 10% FBS + 1% penicillin/streptomycin/glutamine + 100 μl DNAse ) . LPMCs were resuspended to 2 . 5×106 cells/ml in complete RPMI . HIV-1 ( 10 ng p24/ml for Ba-L and T/F HIV-1 strains ) was added and spinoculated at 1700 rcf for 2 hr at room temperature . Cells were washed 1× in complete RPMI , resuspended , and plated onto V-bottom 96 well plates at a concentration of 106 cells/ml . IFNα subtypes ( PBL Assay Science ) were added once at a final concentration of 100 pg/ml immediately post-infection . Cells were incubated for 4 days at 37°C , then were harvested at 4 dpi for flow cytometry as previously described [32 , 33] . Zombie Aqua Viability Dye ( Biolegend ) exclusion was used to identify viable cells . The antibodies used for flow cytometry were: CD3-ECD ( Beckman Coulter ) or CD3-PerCP-Cy5 . 5 ( Tonbo Biosciences ) , CD8-APC ( BD Pharmingen ) , HIV-1 p24-PE ( Beckman Coulter ) . Data were collected on a Gallios 561 flow cytometer ( Beckman Coulter ) and analyzed using Kaluza version 1 . 2 ( Beckman Coulter ) . Supernatants were harvested at 4 dpi and infectious titer was determined in TZM . bl reporter cells . TZM . bl cells ( 1 x 104 ) were plated in a 96-well plate in 160 μl culture media ( DMEM + 10% FBS + 1% PSG ) with dextran sulfate ( 100 ng/ml ) . 4 dpi supernatants ( 40 μl ) were added directly to individual wells and incubated for 48 hrs at 37°C . Half of the media was removed and cells were lysed with 100 μl Britelite luciferase reagent ( Perkin Elmer ) , incubated for at least 1 minute , and Relative Light Units ( RLU ) of luminescence were determined in a VictorX5 plate reader ( Perkin Elmer ) . The supernatants were also titered using an HIV-1 Gag p24 ELISA kit ( Perkin Elmer ) . Taqman primer probe combinations were used to quantify A3G , A3D , A3F , Tetherin and Mx2 relative to GAPDH ( S1 Table ) . GeneExpression Mastermix ( Life Technologies ) was used according to instructions and contained 10 pmol of each primer and probe . Thermocycling conditions were as follows: 50°C 2 min and 95°C 10 min , then 40 cycles of 95°C 15 s and variable annealing temperatures ( GAPDH: 64 . 5°C 45 s; Mx2: 62 . 5°C 45 s; BST-2: 60 . 8°C 45 s; and A3G: 56°C 40 s; A3F: 59°C 90 s; A3D: 60°C 45 s ) . Plates were run in the Biorad CFX96 real-time PCR machine . Infection of LPMCs with HIV-1 T/F or NL4-3 virus stocks with or without IFNα treatment was performed as above . At 4 dpi , cell pellets were harvested and DNA extracted using Qiagen DNAEasy kit . Amplification of the gp41/nef region was performed by nested PCR assembled as Phusion Taq reaction according to manufacturer protocol containing 10 pmol of the following primers . External PCR: Forward 5’-TTGCTCTGGAAAACTCATYTGCAC; Reverse 5’-TCAGGGAAGTAGCCTTGTGTGT . Thermocycling conditions included 98°C for 30 min and 35 cycles of 98°C 10 s , 59 . 5°C 20 s , 72°C 35 s and final elongation at 72°C 7 min . Following preamplification , Phusion Taq nested PCR with MiSeq-configured primers was performed: Forward: 5’ATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT NNNN INDEX1 AGCAGTAGCTGARGGRACAGAT Reverse: 5’CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT NNNN INDEX2 AGTGAAYTARCCCTTCCAGTCC with the following conditions: 98°C for 30 min , 35 cycles of 98°C 10 s , 56 . 6°C 15 s , 72°C 17 s and final elongation at 72°C 7 min . Amplicons were sequenced by Illumina MiSeq following standard protocol . Sequences with >80% identity were matched to the corresponding reference T/F HIV-1 sequences and total , GG→AG and GA→AA mutations were evaluated using custom Perl scripts [60 , 79] . Data were analyzed using Prism 5 . 0 ( GraphPad ) . For comparisons of data with over 2 variables ( e . g . , IFNα subtypes ) obtained from the same donors ( matched observations ) , repeated measures ANOVA was used for statistical analyses , followed a Dunnett’s multiple comparison test . For data with non-Gaussian distribution ( evaluated using the Kolmogorov-Smirnov normality test ) , a nonparametric ANOVA using Friedman test was implemented followed by a Dunn’s posthoc pairwise analysis . For comparisons of two datasets , a two-tailed Student’s t-test was performed . Correlations between two datasets were determined by linear regression and evaluated by Pearson r . To compare the relative proportions of specific dinucleotide mutations , a 2 × 2 contingency analysis with Yates’ correction was used . For all statistical tests , P values < 0 . 05 were considered significant . Next-generation sequencing data were deposited at the NCBI Sequence Archive Bioproject PRJNA284609 . Accession numbers for IFNA genes used in this work are as follows . IFNA1 , NM_024013 . 2; IFNA2 , NM_000605 . 3; IFNA4 , NM_021068 . 2; IFNA5 , NM_002169 . 2; IFNA6 , NM_021002 . 2; IFNA7 , NM_021057 . 2; IFNA8 , NM_002170 . 3; IFNA10 , NM_002171 . 2; IFNA13 , NM_006900 . 3; IFNA14 , NM_002172 . 2; IFNA16 , NM_002173 . 2; IFNA17 , NM_021268 . 2; IFNA21 , NM_002175 . 2 . | The therapeutic potential of recombinant IFNα against HIV-1 infection has been explored for 25 years , but its effectiveness was inconsistent . However , these clinical trials administered IFNα2 , which is only one member of a 12-protein family of IFNα subtypes . More recently , IFNα was found to activate ‘restriction factors’–proteins that can directly inhibit HIV-1 . To date , it remains unknown which IFNα subtypes are produced by professional IFNα producing cells known as plasmacytoid dendritic cells and which IFNα subtypes are more effective in inhibiting HIV-1 infection in the gastrointestinal tract , the primary site of early HIV-1 replication . Here , we show that weaker IFNα subtypes were more highly expressed following HIV-1 infection . Using an infection platform that captures important characteristics of early HIV-1 infection in the gut , several IFNα subtypes were found to be more effective at inhibiting HIV-1 than IFNα2 . In particular , IFNα8 and IFNα14 more potently reduced the infectivity of HIV-1 virions , an activity that can be attributed to the APOBEC3 proteins . Our findings strongly support the evaluation of potent IFNα subtypes in currently evolving HIV-1 curative strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| []
| 2015 | Interferon-α Subtypes in an Ex Vivo Model of Acute HIV-1 Infection: Expression, Potency and Effector Mechanisms |
There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions . Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and , crucially , do not restore the original computations performed by the network . Thus , they are often accompanied by undesired side-effects . Here , we introduce delayed feedback control ( DFC ) , a conceptually simple but effective method , to control pathological oscillations in spiking neural networks ( SNNs ) . Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity . Importantly , DFC , besides steering the system back to a healthy state , also recovers the computations performed by the underlying network . Finally , using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system .
Open-loop brain stimulation has emerged as a common tool to restore aberrant neuronal activity . The most successful example is the application of high-frequency deep-brain-stimulation ( DBS ) used to ameliorate motor symptoms in Parkinson’s disease ( PD ) . However , even in this case the stimulation induces side-effects such as gait imbalance , cognitive impairment , speech impairment , depression etc . [1] . The main cause of these side-effects is likely to be the constant stimulation , but additional explanations are plausible , e . g . the inability of open-loop stimulation to recover the original computations carried out by the impaired brain area . Thus , there is a clear need for more sophisticated brain stimulation schemes [2–4] . Moreover , to exploit the full potential of external brain stimulation as a research and therapeutic tool it is important to obtain theoretical insights that can guide the design of novel stimulation protocols . The goal for these new stimulation methods should ideally be twofold: to alter the dynamical state of the brain activity in a desired manner and to recover the computations performed by the network . Here , we demonstrate that DFC , a method with its origin in chaos control [5 , 6] , can achieve these objectives . To show that DFC is effective in altering the global activity state , we focus on its ability to switch the network state between synchronous-irregular ( SI ) oscillatory and asynchronous-irregular ( AI ) non-oscillatory activity . This choice is motivated by the fact that several brain diseases are manifested as a transformation of the AI state to persistent SI oscillations , e . g . in PD [7] and in certain forms of epilepsy [8] , or as the inability of the network to generate transient SI activity , e . g . in schizophrenia [9] . To demonstrate that DFC facilitates the recovery of certain types of computations , we also illustrate how a network under DFC can effectively process and route rate as well as temporally coded signals . Thus , DFC not only steers the system to a more physiological activity regime , but it also recovers to a considerable degree the coding abilities of the network as they were present before the onset of the pathology . Previous theoretical models of closed-loop stimulation are not suitable to study the control of SI oscillations because the dynamics that arise in networks of phase oscillators [10–12] , in networks of Hodgkin-Huxley neurons [13 , 14] and in Wilson-Cowan type firing rates models [15] are qualitatively different from the SI oscillations [16 , 17] . In addition , the physiologically plausible SI oscillations are known to be robust to both noise and heterogeneities [18–20] and , therefore , require a more differentiated control approach . Our control strategy is applicable to any network with arbitrary connectivity that undergoes a Hopf bifurcation and it is useful but not critical to know the parameters a priori . In fact , we show how adaptive tuning methods can be used to estimate the control parameters if the precise network parameters are not known . The DFC based stimulation method proposed here can be in principle applied in all animal models where the use of optogenetic tools allows for direct modulation of the membrane potential . In human patients , where optogenetics is currently not an option , manipulation of the membrane potential can be achieved indirectly via subthreshold electrical stimulation . Importantly , the method is not restricted to stimulation of deep structures , but could be applied non-invasively to modulate activity in cortical layers as well [21] . Finally , the theoretical insights we provide into the mechanisms of feedback control in SNNs could also explain the recent success of event-driven stimulation schemes [22–24] .
While our goal is to reveal the mechanisms by which DFC controls SI activity in excitatory-inhibitory SNNs , it is more instructive to first demonstrate the concept in a simple , purely inhibitory SNN . In the AI state the population average of the firing rate is constant in time r ( t ) = r0 . Therefore , the mean recurrent input that each neuron receives is also constant: I r e c ( t ) = C · J / C · ( s ⋆ r ) [ t ] = C · J / C · ∫ s ( τ ) r ( t - τ ) d τ = J · r 0 · ∫ s ( τ ) d τ where C is the average in-degree , J/C the synaptic coupling strength and s ( t ) the postsynaptic current . In such a network , the emergence of SI oscillations can be investigated by analyzing the stability of the network firing rate in the AI state [18 , 19] . A small perturbation in the steady-state firing rate r ( t ) = r 0 + R e [ r ^ 1 ( λ ) e λ t ] where eλt is an eigenmode of the network dynamics with complex eigenvalue λ , leads to a perturbation in the recurrent input I ( t ) = I 0 + R e [ I ^ 1 ( λ ) e λ t ] with I ^ 1 ( λ ) = - J S ( λ ) r ^ 1 , where J is the synaptic coupling strength and S is the synaptic response function . In a recurrent network both perturbations have to be consistent , that is r ^ 1 ( λ ) = R ( λ ) I ^ 1 ( λ ) where R is the neuron response function . This results in the self-consistency equation: J · R ( λ ) · S ( λ ) = 1 ( 1 ) In a purely inhibitory network J is negative , but here the negative sign of J has been absorbed in the phase S ( λ ) . We can then compute the eigenvalue spectrum , that is the roots λ that satisfy Eq ( 1 ) . When the eigenvalues have a positive real part , the AI state is unstable and the SNN settles in the SI state . Note that due to the synaptic delays the spectrum is infinite . However , in time-delay systems of the retarded type that we are considering here , the total number of unstable eigenvalues is always finite [25] . Increasing J shifts the spectrum towards more positive values on the real axis . For a critical value Jcr a complex pair of eigenvalues crosses the imaginary axis and the system becomes unstable through a supercritical Hopf bifurcation [18] ( Fig 1 ) . In the following we consider a SNN in which J > Jcr , thus resulting in the emergence of SI oscillations . We aim at designing a controller that can alter the global activity state from SI to AI by placing the unstable eigenvalues back to the left half-plane ( Fig 1e ) . For the implementation of our DFC controller we made two assumptions . First , we chose the instantaneous population rate to be the output state of the system . This state needs to be continuously monitored by the controller . Based on this state , the controller evaluates an appropriate control signal . The second assumption is that the control signal can be applied via current injection directly to the somata of the neurons . Note that the control signal is identical for all neurons in the network . Thus , the synchrony in the network activity was not decorrelated because each neuron received different input . To include the contribution of DFC the self-consistency Eq ( 1 ) needs to be modified: J R ( λ ) S ( λ ) e - λ · d - K R ( λ ) M ( λ ) e - λ · d c = 1 ( 2 ) where K is the control gain , dc the control delay and M the control kernel . The roots of the above equation ( see methods ) yield the range of parameters K , dc that move the unstable eigenvalues back to the left-half plane ( Fig 1e ) , which results in a switch of activity from SI to AI . To verify the analytical solution we simulated an Erdös-Rényi type inhibitory recurrent network of N = 10 , 000 sparsely connected leaky-integrate-and-fire ( LIF ) neurons . The neurons were connected with a connection probability ϵ = 0 . 1 . Switching on the DFC with parameters estimated from Eq ( 2 ) , almost immediately results in suppression of oscillations and in a network state that resembles the AI regime ( Fig 2a–2d ) . The suppression of stochastic oscillations is evident both in the spiking activity of single neurons ( Fig 2a ) and in the population activity of the network ( Fig 2b ) . The spike count variability and the irregularity of single neuron interspike intervals , estimated by the Fano Factor ( FF ) and the coefficient of variation ( CV ) respectively , confirm that under DFC the firing of individual neurons in the network follows Poisson statistics ( AI: FF = 1 . 04 , CV = 1 . 01 , DFC: FF = 1 . 02 , CV = 0 . 99 ) . Moreover , the oscillation index that captures the degree of oscillatory activity ( see methods ) is in both conditions comparable ( AI: PT = 1 . 47 , DFC: PT = 1 . 45 ) and significantly smaller than in the SI state ( PT = 3 ) . The change in the network spiking activity is also observed in the subthreshold membrane potential of individual neurons ( Fig 2c and 2d ) . Next we demonstrate the applicability of DFC in changing the SI state in recurrent networks of excitatory and inhibitory neurons . To this end we simulated a SNN composed of 8000 excitatory and 2000 inhibitory neurons with Erdös-Rényi type connectivity and a connection probability of ϵ = 0 . 1 . As in the I-I network , we used the mean-field approach to derive appropriate values for the parameters to attain an SI state . The self-consistency equation for the coupled EI-network under DFC is given by [ ( J E I · S E I ( λ ) e - λ · d E I - K · M ( λ ) e - λ · d c ) · R E ( λ ) ] · J I E · R I E ( λ ) · S I E ( λ ) · e - λ · d I E = 1 ( 3 ) where Jij , dij is the synaptic coupling strength and delay from population j to population i and RE ( RI ) is the neuron response function of excitatory ( inhibitory ) neurons . Note that we ignored the recurrent couplings within E and I populations , because we were interested only in the oscillations created by the EI-loop . We implemented DFC by recording the activity of neurons in the inhibitory population while stimulating excitatory neurons . Here again K is the control gain , dc the control delay and M the control kernel . Switching on the controller yielded a near instantaneous transition in the network activity from SI to AI ( Fig 2e–2h ) . In this case the original physiological state that we wanted to recover was characterized by slightly less irregular firing of the individual neurons . Nevertheless , DFC successfully steered the network to a regime with statistics comparable to the AI activity ( DFC: FFE = 0 . 85 , CVE = 0 . 91 , AI: FFE = 0 . 83 , CVE = 1 . 03 ) . In a coupled network with more than one population additional possibilities for recording and stimulating neurons exist . For instance , we could both record and stimulate the excitatory population ( see below “stability and robustness of control domains” ) . Our results , however , do not depend on the exact identity of the recorded and stimulated neurons . The oscillation frequency in the SI state of the network was in the beta range , f ≈ 25–30Hz , which is characteristic for PD [7] . This suggests that if PD-associated beta oscillations are caused by a strong coupling between STN and GPe [26 , 27] , then our DFC approach could be used to suppress these beta band oscillations . The results presented here are general and the same approach can be applied to suppress oscillations in other frequency bands as well as long as the oscillations underlie a Hopf bifurcation . To determine the range of values that led to stable control we fixed the control kernel M , using a box function of width 1 ms , and parametrized the system by the control gain K and delay dc . For each pair of values we simulated the SNN and computed the oscillation index ( Fig 3a and 3c ) . The ( K , dc ) -plane shows that a stable control domain exists at 7 ms . That is , an effective control delay of dc , eff = 7 ms yields the maximum stability for the resulting AI state . The semi-analytical results from mean-field theory ( red contour lines derived from Eqs 2 and 3 for the I-I network and E-I network , respectively ) are in good agreement with the numerical simulations ( blue shades ) . The only discrepancy occurs when the difference between synaptic and control coupling is small . In such a scenario it is more difficult to maintain constant rates of the stimulated population and the system may become effectively excitatory leading to rate instabilities [28] . Moreover , fluctuations in the mean input that are ignored in our mean-field approach could also become more important . The analysis of these fluctuations is beyond the scope of this work and will be addressed in a future study . Despite the fact that one stable control domain exists , a compensation mechanism to maintain constant firing rates is required to achieve stable control . In real-life applications a detailed fine-tuning may not always be possible . Therefore , we modified our control protocol and introduced an additional delay term dc2 , thus , effectively feeding into the controller the difference between two time-delayed versions of the population activity . For such differential DFC scheme the control signal is given by ( see methods ) : I C ( t ) = K · M ( t ) ⋆ ( v ( t - d c 1 ) - v ( t - d c 2 ) ) Differential control has been previously used to control unstable periodic orbits [5 , 29] and to suppress synchrony in networks with discrete-time neuron models [30] . With the differential control we accounted for the fact that recording neural activity and injecting a control current into the neurons introduces a finite time-delay . Therefore , we used a small but non-zero value for dc2 , i . e . dc2 = 1 ms , which is close to the overall closed-loop delay introduced by current technologies [31 , 32] . A crucial advantage of differential DFC is that no additional rate compensation is required , because the mean contribution of the control signal vanishes l i m T → ∞ 1 T ∫ 0 T ( v ( t - d c 1 ) - v ( t - d c 2 ) ) d t = 0 Moving in the control parameter space ( K , dc ) , therefore , did not affect the firing rates of the neurons . This was reflected in the near perfect overlap of theoretical predictions and numerical simulations of the SNN ( Fig 3b ) . In addition , differential DFC introduced two positive effects on the stability of the control domains: ( i ) The first control domain was expanded , which amounts to an increase in the robustness in the parameter variation . That is , small deviations from the estimated values of the gain and the delay would not be critical for the stability of the AI state achieved by differential control . ( ii ) A new stable control domain appeared at t = 23 ms . Thus , with differential control there is an increase of the range of parameters that lead to stability . DFC also enhanced the robustness of the system to external disturbances , e . g . undesired signals at the controller output , measurement noise etc . This becomes evident when we consider the distance Bcr of the complex eigenvalues λi from the imaginary axis for the main stable control domain at t = 7 ms . A more robust closed-loop system is reflected in higher values of Bcr . Differential and direct control yielded B d i f f c r = m a x ( R e ( λ i ) ) = - 257 and B d i r e c t c r = m a x ( R e ( λ i ) ) = - 224 , respectively , clearly revealing a more robust system with differential DFC . Both direct and differential control were effective in a E-I network as well . The location of the stable control domains depended on the exact implementation ( Fig 3c–3e ) . When the activity of the inhibitory population was monitored while stimulating the excitatory population , the main stable control domain appeared at t = 7 ms ( Fig 3c ) . This location is identical with the purely inhibitory network and reflects the overall delay of the I-E path ( I-I loop ) in the E-I ( I-I ) network . Indeed for both the I-E path and I-I loop the effective delay is deff = 7 ms ( see methods ) . By contrast , when the excitatory population was both recorded and stimulated then the location of the domains shifted to around t = 14 ms reflecting the larger overall delay in the E-I-E loop . ( Fig 3d and 3e ) . Note that in this case the stable control domain for direct control was smaller . The reason is that the size of the stable control domains shrinks for larger delays . In both the I-I and E-I SNNs we applied an identical control signal to all stimulated neurons . That is , we did not disrupt oscillations and decorrelated network activity by injecting different currents to each of the neurons . This is in contrast with a widespread assumption that common input always tends to increase correlations in neural activity [33] . The results from the application of DFC reveal that common input can both increase or decrease correlations in SNNs . It is the timing and the amplitude of the common input that determines the direction in which correlations are affected . It is important to point out that injection of a control signal is not equivalent to the application of additive noise to the system . To demonstrate this we simulated an I-I network and injected Gaussian noise with the same mean and variance as the control signal to all neurons . This stimulation approach failed to suppress SI oscillations ( Fig 4a and 4b ) indicating that the temporal structure of the control signal is crucial for successful control . Increasing further the noise intensity , e . g . by a factor of ten , eventually resulted in desynchronization of the activity and in quenching of oscillations ( Fig 4d and 4e ) . However , with such strong external noise the network dynamics is predominantly influenced by the input rather than the recurrent activity . This condition is disastrous from a computational point of view , because any information processing taking place within the stimulated brain region would be severely impaired . To illustrate this we recorded the subthreshold dynamics of ten randomly selected neurons in the network ( Fig 4f ) . The huge fluctuations in the membrane potential under the influence of strong external noise are rather pathological . By contrast , the fluctuations in the case of DFC are comparable to those in the physiological AI regime . The detrimental effect of strong external noise became even more apparent when we studied the response of the network to incoming stimuli . We examined two scenarios . First , we tested how a series of incoming pulse packets composed of randomly distributed spikes are processed by the SNN . We evaluated the network response by the area under the curve ( AUC , see methods ) for each of the following network states: AI , SI quenched DFC and SI quenched by noise stimulation . A high AUC value reflects better separability of two conditions . It is evident that the AUC in the AI state and in the DFC condition is close to unity indicating that both conditions are comparable in terms of stimulus separability ( Fig 5a and 5b ) . By contrast , when the SI oscillations were quenched by the injection of strong external noise the AUC dropped significantly . That is , DFC , in contrast to strong noise stimulation , does not impair the ability of the network to detect incoming stimuli . Next we tested how DFC affects temporal aspects of the network response . To this end , we provided external correlated inputs to all stimulated neurons and measured the spike train similarity in the network response . We computed the spike distance D that captures the time-resolved degree of synchrony between individual spike-trains ( [34] , see methods ) . Again DFC did not impair the temporal processing as indicated by a clear separation of the two clusters during baseline DB and stimulation DS ( Fig 5c ) . For external noise , however , the two distributions of values strongly overlapped , showing that aspects of temporal processing as measured by pairwise synchrony are clearly compromised when the SI state is disrupted by open-loop noise injection . These results suggest that processing of incoming signals either locally or by downstream areas is feasible in a DFC scheme , but not in an external noise scheme . The above two results clearly demonstrate that DFC has multiple advantages compared to the open-loop noisy stimulation . DFC does not only suppress SI activity steering the network to an AI regime , it also facilitates the recovery of the network’s ability to process stimulus related information . From its design it is evident that DFC effectively counteracts the increase in coupling strength , which is one of the main causes for the emergence of SI activity . Indeed , the goal of the DFC design was to move the poles of the system at , or close , to their original positions . Ideally , the stimulation kernel M would match the synaptic kernel S with dc = d and the amplitude of the control gain K would be tuned to match the pathological increase of the coupling strength ΔJ . If this were the case , DFC would completely eliminate the effects on the mean recurrent input . This is evident if we consider the modulation to a perturbation in the average input to a neuron I ( λ ) = ( J + Δ J ) · R ( λ ) · S ( λ ) · e - λ d - K · R ( λ ) · M ( λ ) · e - λ d c = K = Δ J , M = S ( J + Δ J ) · R ( λ ) · S ( λ ) · e - λ d - Δ J · R ( λ ) · S ( λ ) · e - λ d = J · R ( λ ) · S ( λ ) · e - λ d That is , under DFC the effects of ΔJ are not visible in the perturbed current term . In practical applications , of course , a perfect match between the control parameters ( K , dc , M ) with the synaptic values is not feasible , because the exact shape of the synaptic kernels are not known a priori and have to be estimated . Nevertheless , within a certain reasonable range of parameters ( see also section “stable control domains” ) , DFC still places the eigenvalues close to the initial position they had before the onset of pathology . Therefore , as we showed above , aspects of both rate and temporal coding that the network may be performing are recovered . The understanding of the exact mechanisms by which DFC suppressed SI activity allowed us to precisely investigate how the neuron and synapse response function R and S respectively influence the stability of the closed-loop system . To this end , we again used a mean-field approximation , which explicitly incorporates the expressions for R and S . In general , the neuron response R depends on the specific neuron model as well as on the external input . Here , we did not change the neuron model , but altered the external Gaussian white noise input by using different values for the mean and variance ( μ , σ2 ) . We then assessed the stability of the system . It is apparent that for a given pair of coupling and control parameters ( J , d ) and ( K , dc ) , respectively , the system becomes unstable as we move in the two dimensional parameter-space spanned by the mean and variance ( Fig 6a ) . For meaningful comparison we used ( μ , σ2 ) -combinations that yield constant rates . In the ideal case where M ( λ ) = S ( λ ) and dc = d Eq ( 2 ) becomes: J · R ( λ ) · S ( λ ) · e - λ d - K · R ( λ ) · M ( λ ) · e - λ d c = 1 ( J - K ) · R ( λ ) · S ( λ ) · e - λ d = 1 ( J - K ) · G S · R n ( λ ) · S ( λ ) = 1 ( 4 ) where Gs is the slope ( or the static gain ) of the ‘f-I curve’ at the operating point and Rn the normalized neuron response ( see methods ) . The critical effective coupling is then given by L c r ( λ ) = ( J - K ) ( λ ) = 1 G s | R n ( λ ) · S ( λ ) | As we move along the constant output firing rate lines both Gs and |Rn ( λ ) | increase ( S1b and S1b Fig ) leading to a decrease of Lcr . The changes in Gs are significantly larger than those in |Rn ( λ ) | , implying that the static gain is the dominant factor that affects stability . The changes in |S ( λ ) | are negligible ( S1c Fig ) . This is expected , because the frequency range we are interested in is much smaller than the cut-off frequency of the synaptic filter ω < ω3db . Thus , when the system operates in a dynamic regime in which single neuron responses have a higher gain the control domains shrink and the range of K values that stabilizes the system decreases . Next , we investigated the interaction between the synaptic S ( λ ) and the control kernel M ( λ ) . The amplitude responses for different kernels do not vary significantly ( S2 Fig ) . Therefore , the important factor that influences stability is the phase difference or , alternatively , the difference Δd between the effective delays of the synaptic deff and the coupling kernel dc , eff . An optimal result is achieved if this difference vanishes ( see methods ) i . e . when Δ d = d e f f - d c , e f f = 0 This point is illustrated for the case where dc = d+1ms ( Fig 6c ) . These results show that DFC does not strongly depend on the shape but rather on the effective delay of the kernel . Interestingly , the same control strategy can be used to induce or enhance rather than to suppress oscillations . Choosing appropriate control parameters to increase the effective coupling , i . e . selecting K to have the same sign as J ( see methods ) , results in SI activity ( Fig 7 ) . This may be helpful for the treatment of symptoms in several pathological conditions that are characterized by impaired oscillations , e . g . gamma power decrease in schizophrenia [35] . Thus , DFC is a generic control approach and the control parameters ( K , dc , M ) can be tuned to quench or to enhance oscillatory activity , depending on the nature of aberrant activity .
Few studies have addressed the problem of suppressing oscillations in neural activity ( see [39] for a detailed review ) . They are based ( i ) on population dynamics [46][15] , ( ii ) on detailed single neuron descriptions [13] , ( iii ) on simplified but computationally efficient models ( e . g . Rulkov maps [47] ) , ( iv ) or on combinations thereof [14] . These approaches have their merits , but they come with limitations: ( i ) the parameters cannot be directly mapped to experimental measurable quantities ( ii ) it is not clear if the results scale to large networks of neurons . The approach that we presented here is a trade-off between biophysical realism and analytical tractability . We used the LIF model , which captures single-neuron dynamics to a sufficient degree , while at the same time allows computationally efficient simulations of large networks . We applied DFC that was originally proposed in the context of chaotic systems as a method to control unstable periodic orbits [5] . DFC has been also used to control dynamics in extended media [48] and has been applied in different contexts as well [49] . It was later used to control coherence [50] and to suppress synchronous activity in networks in which the neurons themselves act as oscillators [10–12] . Here , we did not use simplified population dynamics or phase oscillators . Instead , we used spiking neurons that fire irregularly and are nevertheless able to generate oscillations . We used a single proportional control term to provide a proof of principle of the method and to be able to delineate the control domains semi-analytically . Alternative approaches , e . g . linear PI/PID control [4] or non-linear control schemes [11] are also possible and may improve performance , however , their theoretical analysis is less straightforward . We also inserted realistic descriptions of synaptic dynamics and , therefore , were able to explicitly study their contribution to stability . This allowed us to design an appropriate control kernel , which resulted in increased control domains . In addition , by using a mean-field theory that explicitly incorporates the synaptic and neuronal response functions we could study their contribution in a systematic way . The neuronal response function enabled us to investigate the influence of external and recurrent inputs and to relate them to experimentally measurable quantities . Indeed , as we showed above , the statistics of the mean field for activity states with very similar firing rate profiles may be significantly different affecting stability . Therefore , feasible measurements of the population activity can be directly used to characterize the operating point of the network and to fine-tune the control parameters to achieve the desired results . Finally , the results presented here provide us with an understanding of the recent success of event-triggered control strategies . Event-triggered control can be placed between open-loop and continuous closed-loop control ( e . g . DFC ) . Open-loop provides constant magnitude stimulation independent of the ongoing activity . Event-triggered approaches provide also constant magnitude stimulation , but only if a certain event occurs , for instance the power of beta oscillations crosses a certain threshold . Thus , the overall stimulation time and , therefore , the undesired stimulation side-effects are reduced . In DFC the stimulation side-effects are likely to be further reduced , because the stimulation amplitude is continuously adjusted to the ongoing activity . That is , no excessive stimulation is applied . This is particularly true for differential DFC , in which the stimulation amplitude vanishes over time . We used DFC , a relatively simple form of control that includes only a proportional gain term , because it is still possible to analytically study the stability of the closed-loop control system . More sophisticated control strategies could further increase the performance of the system . They come , however , at the price of increasing the number of control parameters that have to be estimated and of increasing complexity precluding a formal proof of stability . Our approach spans multiple levels of analysis of neuronal dynamics , enabling an understanding of how the control stimulus interacts with both low-level synaptic and high-level properties of the population activity to influence stability . At the same time the complexity of the controller is low enough to be of practical relevance . Thus , here we have provided a general conceptual framework for future studies that address both theoretical and practical aspects of closed-loop control in neuronal systems .
We simulate networks of N LIF neurons randomly connected with a probability of ϵ = 0 . 1 . Thus each neuron receives on average C = ϵN connections from other neurons in the network . For the purely inhibitory network we use N = NI and for the coupled excitatory-inhibitory case N = NE+NI . The subthreshold dynamics of a neuron i in the network is given by τ m d v i ( t ) d t = ( v r e s t - v ( t ) ) + R m · I i , r e c ( t ) + R m · I i , e x t ( t ) ( 5 ) where Rm is membrane resistance , τm is the membrane time constant and vrest is the resting potential . The recurrent input term I i , r e c ( t ) = - ∑ j = 1 N J i j c i j ∑ k s ( t - t j k - d i j ) ( 6 ) describes the total synaptic current arriving at the soma due to presynaptic spikes . cij are elements of the binary connectivity matrix . Each presynaptic spike causes a stereotypical postsynaptic current s ( t ) modeled as an α-function [51] s ( t ) = t τ s e 1 - t t s H ( t ) ( 7 ) where τs is the synaptic time constant and H ( t ) the Heaviside function . The double sum in Eq 6 runs over all firing times t j k of all presynaptic neurons connected to neuron i . For all connections in the network we use the same synaptic coupling strength Jij = J/C , where C is the average in-degree and dij = d the transmission delay . The external input I i , e x t ( t ) = μ + σ τ m η i ( t ) ( 8 ) contains a mean term μ and a fluctuating term resulting from the Gaussian white noise ηi ( t ) that is uncorrelated from neuron to neuron with <ηi ( t ) > = 0 and <ηi ( t ) ηi ( t′ ) > = δ ( t−t′ ) . In the stable asynchronous state the population average of the firing rate is constant in time , r ( t ) = r0 . The mean recurrent input that each neuron receives is therefore also constant and given by μ r e c ( t ) = ⟨ J / C · ∑ c i j ∫ s ( τ ) ∑ k δ ( t j k - τ - d ) d τ ⟩ = J · r 0 · ∫ s ( τ ) d τ = μ r e c similarly the variance of the recurrent input is σ r e c 2 ( t ) = V a r [ J / C · ∑ c i j ∫ s ( τ ) ∑ k δ ( t j k - τ - d ) d τ ] = J 2 / C · r 0 · ∫ s 2 ( τ ) d τ = σ r e c 2 We study the stability of the asynchronous state following a linear perturbation approach [18] . A small oscillatory modulation of the stationary firing rate r ( t ) = r0+r1 e−λt with v1 ≪ 1 and λ = x+jω where ω is the modulation frequency leads to corresponding oscillation of the synaptic current I 1 = - J · r 1 · e · τ s ( 1 + λ · τ s ) 2 e - λ d ( 9 ) The firing rate in response to an oscillatory input is given by r 1 = I 1 · r 0 σ ( 1 + λ τ m ) ∂ U ∂ y ( y t , λ ) - ∂ U ∂ y ( y r , λ ) U ( y t , λ ) - U ( y r , λ ) ( 10 ) The function U is given in terms of combinations of hypergeometric functions U ( y , λ ) = e y 2 Γ ( 1 + λ · τ m 2 ) F 1 - λ · τ m 2 , 1 2 , - y 2 + e y 2 Γ ( λ · τ m 2 ) F 1 - λ · τ m 2 , 3 2 , - y 2 In a recurrent network the modulation of the firing rate and the modulation of the synaptic input must be consistent . Combining Eqs ( 9 ) and ( 10 ) we get 1 = - J · r 0 · e · τ s e - λ d σ ( 1 + λ τ m ) ( 1 + λ τ s ) 2 ∂ U ∂ y ( y t , λ ) - ∂ U ∂ y ( y r , λ ) U ( y t , λ ) - U ( y r , λ ) which we write as 1 = J · R ( λ ) · S ( λ ) · e - λ d ( 11 ) where the terms R ( λ ) = 1 σ ( 1 + λ τ m ) ∂ U ∂ y ( y t , λ ) - ∂ U ∂ y ( y r , λ ) U ( y t , λ ) - U ( y r , λ ) and S ( λ ) = e · τ s ( 1 + λ · τ s ) 2 describe the neuronal and synaptic response functions respectively . The negative sign of J is absorbed in the phase of S ( λ ) . The critical coupling values at which modes have marginal stability with frequency ωi can then simply be computed by J i = 1 R ( ω i ) · S ( ω i ) The smallest value Jcr = min{Ji} is the critical coupling at which the first complex pair of eigenvalues crosses the imaginary axis and the system becomes unstable . In the case of the inhibitory network for μ = 14 mV and σ = 6 mV we have Jcr ≈ 115 mV . In the simulations we used for the coupling between two neurons i and j , Jij = 0 . 2mV thus the total coupling is J = C ⋅ Jij = 1000 ⋅ 0 . 2 mV = 200 mV >Jcr ( Fig 2a–2d ) . In the simulations we implement DFC by recording and stimulating all neurons in the network . The subthreshold dynamics of a neuron i with DFC is given by τ m d v i ( t ) d t = ( v r e s t - v ( t ) ) + R m · I i , r e c ( t ) + R m · I i , e x t ( t ) + R m · I C ( t ) ( 12 ) where IC ( t ) is the control input . Note that IC ( t ) is identical for all neurons in the network given by I C ( t ) = K · m ( t ) ⋆ ( v ( t - d c ) ) ( 13 ) where v ( t ) is the instantaneous population activity at time t and ⋆ denotes the convolution operation ( f ⋆ g ) ( t ) = ∫ - ∞ ∞ f ( t - τ ) g ( τ ) d τ . We used as control kernel m ( t ) a box function m ( t ) = H ( t - a ) - H ( t - b ) where H ( t ) is the Heaviside function H ( t ) = 0 , t < 0 m s 1 , 0 ≤ t ≤ 1 m s Thus the control input IC ( t ) was updated in steps of 1ms . | Brain stimulation is being used to ease symptoms in several neurological disorders in cases where pharmacological treatment is not effective ( anymore ) . The most common way for stimulation so far has been to apply a fixed , predetermined stimulus irrespective of the actual state of the brain or the condition of the patient . Recently , alternative strategies such as event-triggered stimulation protocols have attracted the interest of researchers . In these protocols the state of the affected brain area is continuously monitored , but the stimulus is only applied if certain criteria are met . Here we go one step further and present a truly closed-loop stimulation protocol . That is , a stimulus is being continuously provided and the magnitude of the stimulus depends , at any point in time , on the ongoing neural activity dynamics of the affected brain area . This results not only in suppression of the pathological activity , but also in a partial recovery of the transfer function of the activity dynamics . Thus , the ability of the lesioned brain area to carry out relevant computations is restored up to a point as well . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"action",
"potentials",
"medicine",
"and",
"health",
"sciences",
"neural",
"networks",
"population",
"dynamics",
"membrane",
"potential",
"electrophysiology",
"neuroscience",
"systems",
"science",
"mathematics",
"algebra",
"computational",
"neuroscience",
"population",
"biology",
"computer",
"and",
"information",
"sciences",
"system",
"instability",
"animal",
"cells",
"cell",
"biology",
"linear",
"algebra",
"single",
"neuron",
"function",
"neurons",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"computational",
"biology",
"eigenvalues",
"neurophysiology"
]
| 2016 | Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control |
Biological sensory systems react to changes in their surroundings . They are characterized by fast response and slow adaptation to varying environmental cues . Insofar as sensory adaptive systems map environmental changes to changes of their internal degrees of freedom , they can be regarded as computational devices manipulating information . Landauer established that information is ultimately physical , and its manipulation subject to the entropic and energetic bounds of thermodynamics . Thus the fundamental costs of biological sensory adaptation can be elucidated by tracking how the information the system has about its environment is altered . These bounds are particularly relevant for small organisms , which unlike everyday computers , operate at very low energies . In this paper , we establish a general framework for the thermodynamics of information processing in sensing . With it , we quantify how during sensory adaptation information about the past is erased , while information about the present is gathered . This process produces entropy larger than the amount of old information erased and has an energetic cost bounded by the amount of new information written to memory . We apply these principles to the E . coli's chemotaxis pathway during binary ligand concentration changes . In this regime , we quantify the amount of information stored by each methyl group and show that receptors consume energy in the range of the information-theoretic minimum . Our work provides a basis for further inquiries into more complex phenomena , such as gradient sensing and frequency response .
In order to perform a variety of tasks , living organisms continually respond and adapt to their changing surroundings through diverse electrical , chemical and mechanical signaling pathways , called sensory systems [1] . In mammals , prominent examples are the neurons involved in the visual , olfactory , and somatic systems [2]–[5] . But also unicellular organisms lacking a neuronal system sense their environment: Yeast can sense osmotic pressure [6] , and E . coli can monitor chemical gradients [7] , temperatures [8] and pH [9] . Despite the diversity in biochemical details , sensory adaptation systems ( SAS ) exhibit a common behavior: long-term storage of the state of the environment and rapid response to its changes [10] . Intuitively , one expects that for these SAS to function , an energy source – such as ATP or SAM – is required; but is there a fundamental minimum energy needed ? To tackle this question , we first relate a generic SAS to a binary information processing device , which is tasked to perform fast information acquisition on the environment ( response ) and to record subsequently the information into its longer term memory ( adaptation ) . Since the foundational works of Maxwell , Szilard and Landauer , the intimate relationship between thermodynamic costs and information processing tasks has been intensely studied [11]–[17] . As a result , the natural mapping between a generic SAS and an information processing device allows us to quantify the minimal energetic costs of sensory adaptation . The idea of viewing biological processes as information processing tasks is not new [7] , [12] , [18] . However , rationalizing sensory adaptation is complicated by recent studies that have revealed that motifs in the underlying biochemical networks play a fundamental role in the thermodynamic costs . For instance , the steady state of feedback adaptive systems must be dissipative , with more dissipation leading to better adaptation [19] , an observation echoed in the analysis of a minimal model of adaptive particle transport [20] . Other studies have suggested that some feedforward adaptive systems may require dissipation to sustain their steady state [21] , while some may not [22] , [23] . Furthermore , past studies [18] , [24] have approached the notion of information by considering noisy inputs due to stochastic binding , a realm in which adaptation may not be relevant due to the separation of time-scales [25] . Here , we develop a different approach that avoids these caveats by considering a thermodynamically consistent notion of information that naturally incorporates the costs of sensing in sensory adaptation . Specifically , we derive a collection of universal bounds that relate the thermodynamic costs of sensing to the information processed . These bounds reveal for the first time that for a generic SAS , measuring an environmental change is energetically costly [ ( 6 ) below] , while to erase the memory of the past is energetically free , but necessarily irreversible [ ( 5 ) below] . By formalizing and linking the information processing and thermodynamics of sensory systems , our work shows that there is an intrinsic cost of sensing due to the necessity to process information . To illustrate our generic approach , we study first a minimal four-state feedforward model and then a detailed ten-state feedback model of E . coli chemotaxis . Owing to the symmetry of its motif's topology the four-state feedforward model does not require energy to sustain its adapted state . Instead , all the dissipation arises from information processing: acquiring new information consumes energy , while erasing old information produces entropy . By contrast , the E . coli model sustains its nonequilibrium steady state ( NESS ) by constantly dissipating energy , a requirement for adaptation with a feedback topology [19] . In this nonequilibrium setting , we generalize our thermodynamic bounds in order to pinpoint the additional energy for sensing over that required to maintain the steady state . We find with this formalism that in E . coli chemotaxis the theoretical minimum demanded by our bounds accounts for a sizable portion of the energy spent by the bacterium on its SAS .
To respond and adapt to changes in an environmental signal , a SAS requires a fast variable , the activity ; and a slow variable , the memory . For example , in E . coli the activity is the conformational state of the receptor , the memory the number of methyl groups attached to it , and the signal is the ligand concentration [7] . Without loss of generality , we consider in the following all three variables normalized such that they only lie between 0 and 1 , and that the signal can only alternate between two values: a low value 0 and a high value 1 . As a result of thermal fluctuations , the time-dependent activity and memory are stochastic variables . Yet , the defining characteristics of sensory adaptation are captured by their ensemble averages and , both at the steady state and in response to changes in the signal . At a constant environmental signal , the system relaxes to an adapted -dependent steady state , which may be far from equilibrium [19] . In this state , the memory is correlated with the signal , with an average value close to the signal , where is a small error . The average activity however is adapted , taking a value roughly independent of the signal , , with adaption error . Besides the ability to adapt , SAS are also defined by their multiscale response to abrupt signal changes , which is illustrated in Fig . 1 . For example , given a sharp increase in the signal from to 1 the average activity quickly grows from its adapted value to a peak characterized by the gain error . This occurs in a time , before the memory responds . After a longer time , the memory starts to track the signal , and the activity gradually recovers to its adapted value ( see Fig . 1A ) . For a sharp decrease in the signal , the behavior is analogous ( see Fig . 1B ) . We identify a SAS as any device that exhibits the described adapted states for low and high signals ( 0 or 1 ) and that reproduces the desired behavior to abrupt increases and decreases in the signal ( see Fig . 1C for a cartoon biochemical example ) . While SAS typically exhibit additional features ( such as wide range sensitivity [26] , [27] ) , they all exhibit the universal features illustrated in Fig . 1 . To facilitate the development of our formalism , we first present a minimal stochastic model of a SAS , where the activity and memory are binary variables ( 0 or 1 ) . This model is minimal , since it has the least number of degrees of freedom ( or states ) possible and still exhibits the required response and adaptive behavior . Treating the environmental signal as an external field that drives the SAS , the system can be viewed as evolving by jumping stochastically between its four states depicted in Fig . 2A . The rates for activity transitions from given at fixed are denoted , and those for memory transitions from given are . As an equilibrium model , it is completely characterized by a free energy function , which we have constructed in the Methods by requiring the equilibrium steady state to have the required signal correlations of a SAS , ( 1 ) is the energy penalty for the memory to mistrack the signal , ensuring adaptation ( with the temperature and Boltzmann's constant ) . In fact , one can show that . is the penalty for the activity to mistrack the signal when ; it thus becomes relevant after a signal change , but before the memory adapts to the new signal , ensuring response . In Figs . 2C and D the energy landscape is represented for low and high signals ( smaller radius corresponds to less probability and larger energy ) . Note that for fixed , the adaptation error is zero when the energy penalty to misstrack the signal becomes large , the system's configuration is then and takes on the values 0 and 1 with equal probability . Finally , the dynamics are set by fixing the kinetic rates using detailed balance , e . g . , , and then choosing well-separated bare rates to set the timescale of jumps: for activity transitions and for memory transitions , with , thereby enforcing the well-separated time-scales of adaptation . When there is a change in the signal , this model exhibits response and adaptation as characterized in Figs . 1A and B ( verified in S1 and S2 Figures ) , and relaxes towards a dissipationless equilibrium steady state in which detailed balance is respected . This is in contrast to previous studies on adaptive systems , which demonstrated that maintaining the steady state for a generic feedback system breaks detailed balance [19] , [20] . Our model , however , differs by its network topology . As depicted in Fig . 2B , it is a mutually repressive feedforward ( all rates depend explicitly on , and the actions of and on each other are symmetric ) . Similar topologies also underly recent suggestions for biochemical networks that allow for adaptation with dissipationless steady states [22] , [23] . Any sensory system that responds and adapts can naturally be viewed as an information processing device . In the steady state , information about the signal is stored in the memory , since knowledge of allows one to accurately infer the value of . The activity , on the other hand , possesses very little information about the signal , since it is adapted and almost independent of the signal . When confronted by an abrupt signal change , the activity rapidly responds by gathering information about the new signal value . As the activity decays back to its adapted value , information is stored in the memory . However , to make room for this new information , the memory must decorrelate itself with the initial signal , thereby erasing the old information . Thus sensory adaptation involves measurement as well as erasure of information . To make this intuitive picture of information processing precise , let us focus on a concrete experimental situation where the signal is manipulated by an outside observer . This is the setup common in experiments on E . coli chemotaxis where the signal ( the ligand concentration ) is varied in a prescribed , deterministic way [28] . To be specific , the initial random signal is fixed to an arbitrary value , either 0 or 1 , with probability , and the system is prepared in the corresponding -dependent steady state , characterized by the probability density . Then , at time , the signal is randomly switched to with final value ( which may be the same as ) according to the probability . The signal is held there while the system's time-dependent probability density , which conditionally depends on both the initial and final signals , irreversibly relaxes to the final steady state . During this relaxation correlations between the system and the final signal value develop while the correlations with the past value are lost . As we will see , the measure of information that captures this evolution of correlations and naturally enters the thermodynamics of sensory adaptation is the mutual information between the system and the signal . The mutual information is an information-theoretic quantification of how much a random variable ( such as the system ) knows about another variable ( such as the signal ) , ( 2 ) measured in nats [29] . Here , is the Shannon entropy , which is a measure of uncertainty . Thus , the mutual information measures the reduction in uncertainty of one variable given knowledge of the other . Of note , with equality only when and are independent . There are two key appearances of mutual information in sensory adaptation capturing how information about the present is acquired , while knowledge of the past is lost , which we now describe . At the beginning of our experiment at , the SAS is correlated with , simply because the SAS is in a -dependent steady state . Thus there is an initial information that the SAS has about the initial value of the signal . The signal is then switched; yet immediately after , the SAS has no information about the new signal value , so . Then for the SAS evolves , becoming correlated with , thereby gathering ( or measuring ) information , which grows with time . Concurrently it decorrelates from , thus erasing information about the old signal , which also grows with time . This conditioning only takes into account direct correlations between and , excluding indirect ones through . To illustrate this , we calculate the flow of information in the non-disspative feedforward model for , which is a 1-bit operation ( because ) . Fig . 3A displays the evolution of the measured information ( in black ) , which we decomposed as ( 3 ) where ( red ) is the information stored in the memory and ( blue ) in the activity . We see the growth of proceeds first by a rapid ( ) increase as information is stored in the activity ( grows ) while the system responds , followed by a slower growth as adaptation sets in ( ) , and the memory begins to track the signal . At the end , the system is adapted , and there is almost no information in the activity , . With the small errors we have , the information acquired reaches nearly the maximum value of 1 bit , which is stored in the memory . Fig . 3B shows the erasure of information , visible by the decrease of from an initial value of nearly one bit to zero when the system has decorrelated from the initial signal . We have seen that through an irreversible relaxation , an SAS first acquires and then erases information in the registry of the activity , followed by the memory . The irreversibility of these information operations is quantified by the entropy production , which we now analyze in order to pinpoint the thermodynamic costs of sensing . Specifically , we demonstrate in Methods that for a system performing sensory adaptation in response to an abrupt change in the environment , the total entropy production can be partitioned in two positive parts: one caused by measurement ( ) and the other by erasure ( ) . The second law thus becomes ( 4 ) with the reference set to an initial state at . The erasure piece ( 5 ) is purely entropic in the sense that it contains no energetic terms . It solely results from the loss of information ( or correlation ) about the initial signal . By contrast , the energetics are contained in the measurement portion , ( 6 ) where is the change in Shannon entropy of the system and is the average heat flow into the system from the thermal reservoir . A useful alternative formulation can be obtained once we identify the internal energy . For example , in the equilibrium feedforward model , a sensible choice is the average energy ( 1 ) . ( Recall , that there is no unique division into internal energy and work , though any choice once made is thermodynamically consistent [30] , [31] . ) By substituting in the first law of thermodynamics , with the work , we arrive at ( 7 ) This equation shows how the measured information bounds the minimum energy required for sensing , which must be supplied as either work or free energy . Thus , to measure is energetically costly; whereas , erasure is energetically free , but necessarily irreversible . In particular , for sensing to occur , the old information must be erased ( ) , implying that the process is inherently irreversible , ( 8 ) Together ( 5 ) and ( 7 ) quantify the thermodynamic cost of sensing an abrupt change in the environment by an arbitrary sensory system . We have demonstrated from fundamental principles that sensing generically requires energy . However , ( 7 ) does not dictate the source of that energy: It can be supplied by the environment itself or by the SAS . The distinction originates because the definition of internal energy is not unique , a point to which we come back in our analysis of E . coli chemotaxis . Using again our equilibrium feedforward model as an example , we apply our formalism to investigate the costs of sensory adaptation . Since this model sustains its steady state at no energy cost , the ultimate limit lies in the sensing process itself . We see this immediately in Fig . 4 where we verify the inequalities in ( 4 ) and ( 7 ) . Since in ( 1 ) is explicitly a function of the environmental signal , the sudden change in at does work on the system , which is captured in Fig . 4A by the initial jump in . This work is instantaneously converted into free energy and is then consumed as the system responds and adapts in order to measure . Thus , in this example the work to sense is supplied by the signal ( the environment ) itself and not the SAS , which is consistent with other equilibrium models of SAS [23] . Furthermore , Fig . 4B confirms that the erasure of information leads to an irreversible process with net entropy production . The bounds of ( 4 ) and ( 7 ) are not tightly met in our model , since we are sensing a sudden change in the signal that necessitates a dissipative response . Nonetheless , the total entropy production and energetic cost are on the order of the information erased and acquired . This indicates that these information theoretic bounds can be a limiting factor for the operation of adaptive systems . We now show that this is the case for E . coli chemotaxis , a fundamentally different system as it operates far from equilibrium . We have quantified the thermodynamic costs in any sensory adaptation system; however , for systems that break detailed balance and maintain their steady state far from equilibrium , ( 5 ) – ( 8 ) are uninformative , because of the constant entropy production . A case in point is E . coli's SAS , which enables it to perform chemotaxis by constantly consuming energy and producing entropy through the continuous hydrolysis of SAM . Nevertheless , there is a refinement of the second law for genuine NESS in terms of the nonadiabatic and adiabatic entropy productions , [32] . Crudely speaking , is the entropy required to sustain a nonequilibrium steady state and is never null for a genuine NESS; whereas is the entropy produced by the transient time evolution . When the system satisfies detailed balance always , be it at its equilibrium steady state or not; when its surroundings change , the entropy production is entirely captured by . We can refine our predictions for a NESS by recognizing that captures the irreversibility due to a transient relaxation , just as does for systems satisfying detailed balance . Analogously to Eqs . ( 6 ) and ( 8 ) , we derive ( see Methods ) : ( 9 ) ( 10 ) Here , is the excess heat flow into the system , roughly the extra heat flow during a driven , nonautonomous process over that required to maintain the steady state [33] . As a result , it remains finite during an irreversible relaxation to a NESS , even though the NESS may break detailed balance . E . coli is a bacterium that can detect changes in the concentration of nearby ligands in order to perform chemotaxis: the act of swimming up a ligand attractor gradient . It is arguably the best studied example of a SAS . At a constant ligand concentration , chemoreceptors in E . coli – such as the one in Fig . 1C – have a fixed average activity , which through a phosphorylation cascade translates into a fixed switching rate of the bacterial flagellar motor . When changes , the activity of the receptor ( which is a binary variable labeling two different receptor conformations ) increases on a time-scale . On a longer time-scale , the methylesterase CheR and methyltransferase CheB alter the methylation level of the receptor in order to recover the adapted activity value . In this way , the methylation level ( which ranges from none to four methyl groups for a single receptor ) is a representation of the environment , acting as the long-term memory ( see diagram in Fig . 5A ) . One important difference with the previous equilibrium model is that the chemotaxis pathway operates via a feedback . The memory is not regulated by the receptor's signal , but rather by the receptor's activity ( see motif in Fig . 5B ) . The implication is that energy must constantly be dissipated to sustain the steady state [19] , thus ( 9 ) and ( 10 ) are the appropriate tools for a thermodynamic analysis . There is a consensus kinetic model of E . coli chemoreceptors [7] , [27] , [34]–[36] whose biochemical network is in Fig . 5A . The free energy landscape of the receptor coupled to its environment is ( 11 ) ( 12 ) with the receptor's characteristic energy , the reference methylation level , and the active/inactive dissociation constants ( values in Methods ) . In ( 11 ) the first term corresponds to the energy of the receptor , and the second comes from the interaction with the environment ( de facto a ligand reservoir ) . The dynamics of this receptor consist of thermal transitions between the states with different activity , while transitions between the different methylation levels are powered by a chemical potential gradient due to hydrolisis of the methyl donor SAM ( see Methods ) . Continuous hydrolysis of SAM at the steady state sustains the feedback at the expense of energy , allowing accurate adaptation in the ligand concentration range , see Fig . 5B . To begin our study , we develop an equation analogous to ( 7 ) , which requires identifying the internal energy of our system . As stated above , we consider the binding and unbinding of ligands as external stimuli , and thus define the internal energy as . Using the excess heat , we consistently define the excess work through , analogous to the first law . Upon substitution into ( 9 ) gives ( 13 ) showing just as in ( 7 ) that measuring requires excess work and free energy . Because here the internal energy is not a function of the ligand concentration , is not due to signal variation: It represents the energy expended by the cell to respond and adapt to the external chemical force . In Fig . 5C , we compare and to during a ligand change of . The sudden change in produces a smooth , fast ( ) increase in the free energy as the activity transiently equilibrates with the new environment . The excess work driving this response comes mainly from the interaction with environment . As adaptation sets in ( ) , the receptor utilizes that stored free energy , but in addition burns energy by the consumption of SAM . Thus , in order to adapt the cell consumes the free energy stored from the environment , as well as additional excess work coming now mostly from the hydrolysis of SAM molecules . The inequality in ( 7 ) with the measured information is satisfied at all times . The energetic cost of responding and adapting to the ligand change is roughly , of which much has already been used by . In comparison , the cost to sustain the chemotaxis pathway during this time is roughly ( see Methods ) . This means that the cost to sensing a step change is about 10% of the cost to sustain the sensing apparatus at steady-state . During this process the cell measures ( and erases ) roughly bits , less than the maximum of 1 bit despite its very high adaptation accuracy . This limitation comes from the finite number of discrete methylation levels , so that the probability distributions in m-space for large and low ligand concentrations have large overlaps ( S3 Figure ) . In other words , it is difficult to discriminate these distributions , even though the averages are very distinct , which results in lower correlation between the methylation level and signal . The minimal energetic cost associated to measuring these bits ( nats ) is . E . coli dissipates roughly during this process , thus the energetic cost of sensory adaptation is slightly larger than twice its thermodynamic lower bound ( ) . We further explored the cost of sensing in E . coli by examining the net entropy production for ligand changes of different intensity . In Fig . 6A , we plot the amount of information erased/measured for different step changes of the signal up to taking as lower base . The green shading highlights the region where adaptation is accurate ( ) . The information erased is always below 1 bit and saturates for high ligand concentrations , for which the system is not sensitive . The total entropic cost ( that is , ) and its relation with the information erased appears in Fig . 6B . The dependence is monotonic , and thus reveals a trade-off between information processing and dissipation in sensory adaptation . Notably , for small acquisition of information ( small ligand steps ) it grows linearly with the information , an effect observed in ideal measurement systems [17] .
We have derived generic information-theoretic bounds to sensory adaptation . We have focused on response-adaptive sensory systems subject to an abrupt environmental switch . This was merely a first step , but the procedure we have outlined here only relies on the validity of the second law of thermodynamics , and therefore can be extend to any small system affected by a random external perturbation to which we can apply stochastic thermodynamics , which is reviewed in [37] . Our predictions are distinct from ( although reminiscent of ) Landauer's principle [11] , [12] , which bounds the minimum energy required to reset an isolated memory . By contrast , the information erased in our system is its correlations with the signal . There is another important distinction from the setup of Landauer , and more broadly the traditional setup in the thermodynamics of computation [11] as well as the more recent advancements on the thermodynamics of information processing in the context of measurement and feedback [15] , [38]–[45] . There the memory is reset by changing or manipulating it by varying its energy landscape . In our situation , the erasure comes about because the signal is switched . The loss of correlations is stimulated by a change in the measured system – that is the environmental signal; erasure does not occur because the memory itself is altered . Also relevant is [46] , which addresses the minimum dissipated work for a system to make predictions about the future fluctuations of the environmental signal , in contrast to the measured information about the current signal , which we have considered . Our results predict that energy is required to sense changes in the environment , but do not dictate that source of energy . Our equilibrium feedforward model is able to sense and adapt by consuming energy provided by the environment . E . coli's feedback , however , uses mostly external energy to respond , but must consume energy of its own to adapt . The generic bounds here established apply to these two distinct basic topologies , irrespective of their fundamentally different energetics . For E . coli , to quantify to what extent is affected by SAM consumption and ligand binding , a more detailed chemical model is required in conduction with a partitioning of the excess work into distinct terms . An interesting open question in this regard , is why nature would choose the dissipative steady state of E . coli , when theoretically the cost of sensing could be paid by the environment . For a ligand change of , in the region of high adaptation , the information measured/erased is bits . We observed that the corresponding average change in the methylation level for a chemoreceptor is , suggesting that a methylation level can store bits for such 1-bit step response operations . Despite the small adaptation error , information storage is limited by fluctuations arising from the finite number of discrete methylation levels . Receptors' cooperativity , which is known to reduce fluctuations of the collective methylation level , may prevent this allowing them to store more information . On the energetic side , we have shown that the cost of sensing these ligand changes per receptor is around 10% of the cost of sustaining the corresponding adaptive machinery . We also showed that the energetic cost of binary operations is roughly twice beyond its minimum for large ligand changes , in stark contrast with everyday computers for which the difference is orders of magnitude . Taken together these numbers suggest that 5% of the energy a cell uses in sensing is determined by information-thermodynamic bounds , and is thus unavoidable . Future work should include addressing sensory adaptation in more complex scenarios . One which has recently aroused attention is fluctuating environments , which so far has been addressed using trajectory information [44] , [45] , [47] . However , under physiological conditions this is unlikely to play a significant role given the large separation of time-scales between binding , response , and adaptation [25] . Another scenario is a many bits step operation , in which instead of high and low signals a large discrete set of ligand concentrations is considered . Frequency response and gradient sensing are also appealing [27] , since in them the system is in a dynamic steady state in which the memory is continuously erased and rewritten . Analysis of such scenarios is far from obvious , but the tools developed in this work constitute the first step in developing their theoretical framework .
We determine a collection of rates that exhibit response and adaptation as in Fig . 1 by first decomposing the steady state distribution as . As a requirement to show adaptation , the memory must correlate with the signal , which we impose by fixing . Next , in the steady state the activity is , or since is binary the probability is about . Recognizing that is small , the average is dominated by adapted configurations with . Thus , adaption will occur by demanding that and , with a model parameter . Finally , to fix the activity distribution for non-adapted configurations , , we exploit the time-scale separation . In this limit , after an abrupt change in the signal , the activity rapidly relaxes . To guarantee the proper response , we set and . Using the symmetry condition we complete knowledge of . The energy levels are obtained using the equilibrium condition , where we choose as reference . Equation ( 1 ) is an approximation of this energy to lowest order in the small errors . Finally , the kinetic rates are obtained using either the approximate or exact energy function , imposing detailed balance , and keeping two bare rates , and , for activity and memory transitions: for activity transitions and for memory transitions . The bounds in ( 5 ) and ( 6 ) follow from a rearrangement of the second law of thermodynamics [48] . Consider a system with states [ for SAS] with signal-dependent ( free ) energy function in contact with a thermal reservoir at temperature . The system is subjected to a random abrupt change in the signal . Specifically , the initial signal is a random variable with values ( which are in the main text ) , which we randomly change at to a new random signal with values . For times , we model the evolution of the system's stochastic time-dependent state as a continuous-time Markov chain . We begin our analysis by imagining for the moment that the signal trajectory is fixed to a particular sequence . Then our thermodynamic process begins prior to by initializing the system in its -dependent steady state . At , the signal changes to and remains fixed while the system's probability density , which conditionally depends on the entire signal trajectory , evolves according to the master equation [49] ( 14 ) where is the signal-dependent transition rate for an transition . The transition rates are assumed to satisfy a local detailed balance condition , , which allows us to identify the energy exchanged as heat with the thermal reservoir in each jump . Eventually , the system relaxes to the steady state corresponding to the final signal value . Since the signal trajectory is fixed , this process is equivalent to a deterministic drive by an external field , and therefore the total entropy production rate will satisfy the second law [48] ( 15 ) where is the rate of change of the Shannon entropy of the system conditioned on the entire signal trajectory; and ( 16 ) is the heat current into the system from the thermal reservoir given the signal trajectory . Since ( 15 ) holds for any signal trajectory , it remains true after averaging over all signal trajectories sampled from the probability density : ( 17 ) with , and nonconditioned thermodynamic quantities , such as , denote signal averages . We next proceed by two judicious substitutions of the definition of the mutual information ( 2 ) that tweeze out the contributions from the measured and erased information . First , we replace the Shannon entropy rate as , and then immediately repeat . The result is a splitting of the total entropy production rate as , with one part due to erasure ( 18 ) and one due to measurement ( 19 ) The bounds in ( 5 ) and ( 6 ) follow by integrating ( 18 ) and ( 19 ) from time to . To prove the positivity of ( 18 ) and ( 19 ) , we use the definition of entropy and heat to recast them in terms of a relative entropy [29] as ( 20 ) ( 21 ) Positivity then follows , since the relative entropy decreases whenever the probability density evolves according to a master equation , as in ( 14 ) [50] . To arrive at ( 9 ) and ( 10 ) for genuine NESS , we repeat the analysis above applied to the average nonadiabatic entropy production rate ( cf . ( 17 ) ) ( 22 ) where is the excess heat flow into the system [33] , taking special note that now is the nonequilibrium stationary state and cannot be related to the energy , as in the equilibrium case above ( 16 ) . The parameters for in ( 11 ) are taken from [7] for a Tar receptor: , , , . The kinetic rates are obtained using local detailed balance and restricting to two characteristic time-scales . For -transitions , the rates are , with the typical activation time . For -transitions , the rates for active states are , and for inactive states , . Here , is the chemical potential force for the hydrolyzation of a SAM fuel molecule , which occurs when a methyl group is added or removed by CheR and CheB respectively [19] , and at the steady state . | The ability to process information is a ubiquitous feature of living organisms . Indeed , in order to survive , every living being , from the smallest bacterium to the biggest mammal , has to gather and process information about its surrounding environment . In the same way as our everyday computers need power to function , biological sensors need energy in order to gather and process this sensory information . How much energy do living organisms have to spend in order to get information about their environment ? In this paper , we show that the minimum energy required for a biological sensor to detect a change in some environmental signal is proportional to the amount of information processed during that event . In order to know how far a real biological sensor operates from this minimum , we apply our predictions to chemo-sensing in the bacterium Escherichia Coli and find that the theoretical minimum corresponds to a sizable portion of the energy spent by the bacterium . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"physics",
"thermodynamics",
"biophysics",
"theory",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"biophysics"
]
| 2014 | Thermodynamic Costs of Information Processing in Sensory Adaptation |
Yra1 is an essential nuclear factor of the evolutionarily conserved family of hnRNP-like export factors that when overexpressed impairs mRNA export and cell growth . To investigate further the relevance of proper Yra1 stoichiometry in the cell , we overexpressed Yra1 by transforming yeast cells with YRA1 intron-less constructs and analyzed its effect on gene expression and genome integrity . We found that YRA1 overexpression induces DNA damage and leads to a transcription-associated hyperrecombination phenotype that is mediated by RNA:DNA hybrids . In addition , it confers a genome-wide replication retardation as seen by reduced BrdU incorporation and accumulation of the Rrm3 helicase . In addition , YRA1 overexpression causes a cell senescence-like phenotype and telomere shortening . ChIP-chip analysis shows that overexpressed Yra1 is loaded to transcribed chromatin along the genome and to Y’ telomeric regions , where Rrm3 is also accumulated , suggesting an impairment of telomere replication . Our work not only demonstrates that a proper stoichiometry of the Yra1 mRNA binding and export factor is required to maintain genome integrity and telomere homeostasis , but suggests that the cellular imbalance between transcribed RNA and specific RNA-binding factors may become a major cause of genome instability mediated by co-transcriptional replication impairment .
Messenger RNA ( mRNA ) is coated by RNA-binding proteins ( RBPs ) forming large messenger ribonucleoprotein particles ( mRNPs ) . Many mRNA processing factors that participate in 5′-end capping , splicing , 3′-end processing , and polyadenylation are loaded co-transcriptionally to the pre-mRNA through interactions with the carboxy-terminal domain ( CTD ) of the RNA polymerase II ( RNAPII ) [1 , 2] . Co-transcriptional RBP loading is required for efficient transcription and RNA processing and export , and contributes to the translation process and mRNA half-life ( reviewed in [3];[4 , 5] ) . Cells possess surveillance mechanisms linked to different steps of mRNP biogenesis as a way to co ntrol the quality of mRNP and the overall expression process [6] . A particularly critical feature of co-transcriptional mRNP assembly is its effect on transcription and mRNA export . Nascent transcripts are packaged by different adaptor proteins that allow the mRNP to bind the export receptor Mex67/NXF1 , as termed in yeast/vertebrates , resulting in an export-competent mRNP that is transported through the nuclear pore complex ( NPC ) to the cytoplasm [7]; [8 , 9] . In yeast , Mex67 and its adaptors Yra1 , Nab2 and Npl3 , are recruited during transcription through specific interactions with the transcription machinery [10 , 11 , 12 , 13] . According to the gene-gating hypothesis [14] some transcription and mRNA export factors interact with components of the NPCs promoting the attachment of transcribed genes to the periphery , which in turn facilitate mRNA export [15] [16] . The coordination between the different mRNP biogenesis steps allows an efficient gene expression and a rapid cell response to any stimulus . Co-transcriptional mRNP biogenesis has also been shown to be necessary for the maintenance of genome integrity . The correct formation of an mRNP particle has been proposed to prevent nascent pre-mRNA molecules from physical entanglement with DNA during transcription and the formation of R loops that would hinder transcription elongation and constitutes a block for replication fork progression [17] . One of the best-characterized examples relating mRNP biogenesis with genetic instability is provided by the THO complex , which functions at the interface transcription-mRNA export . Mutations in THO lead to a transcription-associated hyper-recombination phenotype that is R-loop dependent as shown by its partial suppression by overexpression of RNase H , an enzyme that degrades the RNA strand of DNA:RNA hybrids [18]; [19] [20] . Several studies in yeast and human cells have revealed that genome instability generated by the absence of another RNA processing factors is also R-loop-dependent [21] [22] [23] [24] [25] [26] . Yra1 is an essential nuclear RNA-binding protein that belongs to the evolutionarily conserved REF family of hnRNP-like export factors [27] . It contains an RNA-binding domain in the middle part of the protein ( RBD/RRM ) and two highly conserved sequences at their N- and C-termini referred as the REF-N and REF-C [28]; [27] . The REF motifs are necessary to interact with RNA [27] and the RBD/RRM domain is involved in both RNA and RNAPII CTD binding [29] . Yeast Yra1 and its metazoan counterpart , ALY/REF , play a role as an mRNA export-adaptor . It interacts with the RNA , the Sub2/UAP56 RNA-dependent ATPase and the THO complex , as well as with other mRNP factors , mediating the association of the Mex67/NXF1 RNA export factor with the mRNPs [12] . Yra1/ALY co-transcriptionally associates with mRNA contributing to its delivery to the nuclear pore complex . The loading of Yra1/ALY to active genes has been shown to be partially dependent on THO in yeast [30] [31] , and on the cap-binding protein CBP80 , the transcription factor Spt6 and the chromatin remodeling factor Iws1/Spn1 in mammals [32] [33] . Pcf11 , a subunit of the yeast cleavage-polyadenylation factor CF1A , also participates in the cotranscriptional recruitment of Yra1 to the nascent mRNA , linking RNA export to 3’-end formation [34] . In this sense , it has been proposed that Yra1 controls polyadenylation site choice by competing with the assembly of functional CFIA at the nascent pre-mRNA [35] . In addition , Yra1/ALY has also been linked to the splicing machinery , as deduced from the observation that human ALY releases spliced mRNA from the nuclear speckles for export into the cytoplasm [36] . It seems , therefore , that Yra1/ALY could serve as a bridge between early mRNP biogenesis steps and mRNA export . The cellular levels of Yra1 and other mRNA export factors are tightly regulated [37] . YRA1 is one of the 5% of yeast genes that undergo splicing , containing an unusual intron in size and branch-point sequences , and its expression is negatively auto-regulated by splicing of its unusual intron [38] [39] . High Yra1 levels inhibit YRA1 pre-mRNA splicing , and the YRA1 pre-mRNA is exported and degraded via a highly regulated process that is dependent on the Edc3 de-capping activator and specific sequences of the YRA1 intron [39] [40] . Interestingly , removal of the intron from the YRA1 gene causes overexpression of Yra1 , which results in a dominant-negative phenotype and an mRNA export defect [27] [41] [38] . Overexpression of other mRNP factors such as Sub2/UAP56 is also inhibitory to both cell growth and mRNA export [42] [43] . Importantly , expression of ALY and other related mRNP factors such as human THOC1/hHpr1 , URH49 , and CIP29/hTho1 is deregulated in tumor cells , suggesting a possible connection between mRNP metabolism and tumorigenesis [44 , 45];[46];[47] . Consequently , understanding the molecular basis of the growth inhibition caused by overexpression of specific RNA binding proteins would help decipher the regulation of mRNP biogenesis and its impact on cell homeostasis . Here we examined the effect of YRA1 overexpression in yeast . We show that YRA1 overexpression causes DNA damage and a transcription-associated-hyperrecombination phenotype mediated by RNA:DNA hybrids . Interestingly , overexpression leads to a cell senescence-like phenotype and telomere shortening . Indeed , Yra1 binds to telomeres and its overexpression increases its occupancy and that of the Rrm3 DNA helicase at the Y’ telomeric regions , as determined by ChIP-chip analyses , and impairs replication , as determined by BrdU incorporation . The genome-wide occupancy of Rrm3 reveals a DNA replication impairment that can explain the genome instability phenotype observed . Our data suggest that stoichiometric amounts of Yra1 are critical for the maintenance of genome integrity , preventing the formation of aberrant co-transcriptional structures that may cause replication impairment .
Given the interaction of Yra1 with the THO complex , and the role of THO at the interface of transcription and genetic instability [30] [48] , we first wondered if overexpression of YRA1 could interfere with these functions . For this purpose , we cloned the YRA1 gene ( YRA1 ) and a cDNA copy of YRA1 ( YRA1Δi ) under the control of the Tet-off promoter , which is repressed upon doxycycline addition , and under the strong GAL1 promoter . YRA1 overexpression is achieved when expression is driven from YRA1Δi constructs , in agreement with previous reports ( Fig 1A ) [41] [38] . In tet:YRA1Δi transformants an increase in YRA1 mRNA levels could be observed in medium without doxycycline ( ON condition ) and this effect was exacerbated in GAL::YRA1Δi cells grown in galactose-containing medium . To gain insight into the molecular basis of the growth inhibition phenotype associated with YRA1 over-expression we investigated whether this effect was increased in the absence of factors involved in the maintenance of genome integrity such as recombination factors , DNA-damage checkpoint proteins , DNA helicases and other factors involved in DNA metabolism ( Fig 1B ) . The selected mutants were transformed with GAL::YRAΔi and GAL::YRA1 constructs and cultured in 2% galactose-containing medium , with and without the presence of small amounts of glucose ( Fig 1B ) . Interestingly , a strong growth defect was observed in checkpoint ( RAD53 ) and recombination ( RAD51 , RAD52 ) mutants when YRA1Δi was expressed in 2% galactose-containing medium ( Fig 1B ) . Growth of rad51Δ mutants was severely affected even in the presence of small amounts of glucose . YRA1 overexpression also causes a little growth inhibition in checkpoint and DNA helicase mutants such as rad9Δ and sgs1Δ , but no effect was observed in other checkpoint and DNA helicase mutants such as mec3Δ and srs2Δ . We did not detect growth impairment in mutants of factors involved in DNA metabolism at the conditions assayed . The results suggest that YRA1 overexpression could have a specific impact on genome integrity , leading to DNA breaks that demand the DNA damage checkpoint and recombination machineries to prevent its negative effect on cell proliferation . Next , we investigated the effect of YRA1 overexpression on genome integrity by assaying spontaneous recombination levels . Using the chromosomal direct-repeat recombination system leu2-k::ADE2-URA3::leu2-k , we observed a 71 . 5—and 161-fold increase in recombination caused by YRA1 overexpression from the plasmid-borne systems tet::YRA1Δi and GAL::YRA1Δi , respectively ( Fig 2 ) . No significant increase in recombination was observed after expression of YRA1 intron-containing constructs tet::YRA1 and GAL::YRA1 . These data indicate that YRA1 overexpression confers a hyper-recombination phenotype . Finally , since overexpression of Sub2 , which forms a heterodimer with Yra1 , is also inhibitory to both cell growth and mRNA export [42] [43] , we wondered whether imbalance of these factors could be the cause of the cell growth inhibition . Since it has been shown that Sub2 over-abundance impairs mRNA export and reduces Yra1 recruitment to genes [49] , we assayed whether simultaneous overexpression of YRA1 and SUB2 could restore the wild-type phenotype . Interestingly , growth inhibition was slightly suppressed by co-overexpression of both proteins ( S1A Fig ) . However , the hyperrecombination phenotype conferred by multicopy YRA1 was not suppressed by SUB2 overexpression , suggesting that the effect of YRA1 overexpression is Sub2-independent ( S1B Fig ) . Next we investigated whether the hyper-recombination phenotype caused by YRA1 overexpression was associated with transcription . For this we measured the recombination frequencies in the L and LYΔNS direct-repeat recombination systems ( Fig 3A ) . These systems are based on the same direct-repeats ( 600-bp internal fragments of the LEU2 gene sharing 300-bp of homology ) that are transcribed from the LEU2 promoter , but they differ in the length of the transcribed intervening sequence ( 31 bp for L , and 3 . 7 kb for LYΔNS ) [50] . We found that the strong hyper-recombination phenotype was only observed with YRA1 overexpression ( tet::YRA1Δi and HA-YRA1Δi ) when the long 3 . 7kb sequence was transcribed ( LYΔNS system ) ( Fig 3A ) . The result suggests that YRA1 overexpression confers a transcription-dependent genetic instability phenotype since the longer the transcribed region is the higher the increase in recombination . To confirm this , we determined the effect of YRA1 overexpression in the L-lacZ and GL-lacZ recombination systems that contain the GC-rich sequence lacZ between 0 . 6-kb leu2 direct-repeats transcribed from different promoters , LEU2 promoter ( L-lacZ system ) or GAL1 promoter ( GL-lacZ system ) ( Fig 3B ) . We have previously used these systems to report the transcription-associated recombination ( TAR ) phenotype of different mRNP biogenesis mutants [51]; [52] . Recombination analyses were carried out in wild-type cells transformed with plasmids carrying the tet::YRA1 or tet::YRA1Δi constructs or with the empty vector . Transformants were grown in the absence of doxycycline to allow YRA1 expression ( ON conditions ) , and under conditions of low ( GAL1 promoter in 2% glucose ) , medium ( LEU2 promoter in 2% glucose ) and high levels of transcription ( GAL1 promoter in 2% galactose ) of the recombination system used in each case . As can be seen in Fig 3B , the recombination frequencies of tet::YRA1 expressing cells were similar to those of wild-type cells transformed with the empty plasmid . In contrast , in the case of tet::YRA1Δi cells , the higher the strength of transcription of the recombination system used , the stronger the increase in recombination . Altogether , these data indicate a statistically significant increase in recombination levels in YRA1 overexpressing cells with respect to the wild type that is transcription-dependent . Next , we studied the relevance of the conserved RBD/RRM and REF domains of the Yra1 protein in the genome instability phenotype . We determined the recombination frequency of the LYΔNS system in cells expressing , either the YRA1 gene ( HA-YRA1 ) , the YRA1 cDNA ( HA-YRA1Δi ) , or truncated YRA1 cDNA versions lacking either the RBD/RRM or the REF-N domains ( HA-YRA1ΔRBDΔi or HA-YRA1ΔNΔi , respectively ) ( Fig 3C ) ; [27] . The HA-tagged Yra1 constructs used are under the native YRA1 promoter instead of the GAL/tet promoters , and show protein levels expected for the conditions used . Cells carrying the HA-YRA1Δi construct showed higher levels of tagged Yra1 than those with HA-YRA1 ( S2 Fig ) . However , in the case of cells transformed with HA-YRA1ΔRBDΔi the levels of proteins were very low as detected by western blot with anti-HA antibody , in agreement with previous published data [27] . The recombination levels of cells overexpressing truncated Yra1 proteins were close to those of cells not overexpressing YRA1 ( HA-YRA1 ) , in contrast to the 76-fold increase in recombination shown by cells overexpressing the full Yra1 protein ( HA-YRA1Δi ) ( Fig 3C ) . Since the amount of HA-YRA1ΔRBD protein detected is too low , further experiments are needed to delineate which domains and properties of Yra1 are the most relevant for genome instability when the protein is overexpressed . Since YRA1 overexpression confers an increase in transcription-associated recombination ( TAR ) , we wondered whether this was dependent on the nascent mRNA and whether was mediated by R loops . We first used the GL-Rib+ and GL-ribm repeat recombination systems that contains an active ( Rib+ ) and inactive ( ribm ) hammerhead ribozyme , respectively [18] . Both the Rib+ and ribm constructs synthesize a long mRNA , but upon transcription the active hammerhead ribozyme cleaves the nascent transcript shortening the mRNA fragment that remains attached to RNAPII . Fig 4A shows that the tet::YRA1Δi construct leads to a 10-fold increase in recombination with respect to the tet::YRA1 construct , but the recombination frequencies were the same in the GL-Rib+ and in the GL-ribm systems , in contrast to the control hpr1Δ , a THO mutant [18] in which the ribozyme-mediated cleavage of the nascent RNA partially suppresses the hyper-recombination phenotype . The data suggests that the length of the nascent RNA does not influence genome integrity in cells overexpressing Yra1 . To assess the possibility that R loops could contribute to hyper-recombination in YRA1-overexpressing cells , we assayed whether hyper-recombination could be suppressed by overexpression of RNase H , which digests the RNA moiety of RNA:DNA hybrids ( Fig 4B ) . For this we tested recombination in the LYΔNS system in cells expressing GAL::YRA1 or GAL::YRA1Δi , transformed either with a plasmid carrying RNH1 under the GAL1 promoter , or with the corresponding empty vector . Overexpression of RNase H1 leads to a significant and clear reduction ( 9-fold ) in the recombination levels of cells overexpressing YRA1 ( GAL::YRA1Δi ) . A partial suppression in the hyper-recombination hpr1Δ mutant , used as positive control , and a reduction in the basal recombination levels in wild-type cells were also observed . We tried to see whether expression of the human cytidine deaminase AID could also be used as an indirect genetic measure of R-loop formation in YRA1-overexpressing cells . This enzyme acts on single stranded DNA and has been used as a tool to infer R-loop accumulation [53] by exacerbating hyper-recombination in different yeast RNA biogenesis mutants ( [53]; [52] [54] ) . Recombination was slightly increased under tet::YRA1Δi overexpression with AID ( S3 Fig ) but the increase was low . Since the accessibility of AID to co-transcriptional R loops is mediated by the transcription apparatus [55] , we cannot discard that an excess of Yra1 may indirectly interfere with AID accessibility . The data suggest that Yra1 overexpression causes more recombination events and consequently more DNA breaks . Consequently we tested this prediction by quantifying the number of Rad52 foci . As can be seen in Fig 4C overexpression of GAL::YRA1Δi leads to a significant increase in the percentage of cells with Rad52 foci compared with that of the GAL::YRA1 expression system in wild-type cells , indicating that YRA1 overexpression leads to an increase in the accumulation of recombinogenic DNA breaks . To determine whether the increase in DNA breaks was dependent on R loops we assayed whether Rad52 foci accumulation were suppressed by RNase H1 overexpression . To allow sufficient time for RNH1 overexpression and action , experiments were performed from mid-log growing cells after 15 hours of inducing overexpression of both YRA1 and RNH1 . As can be seen in Fig 4C , the significant increase caused by YRA1 overexpression ( 2-fold ) was suppressed by RNH1 overexpression . The result was similar to that of the positive R-loop-dependent hyper-recombinant control hpr1Δ mutant ( Fig 4C ) . Altogether , these data indicate that R loops mediate genome instability triggered by YRA1 overexpression . It has been established that genome instability contributes to cell lifespan by different mechanisms , such as rDNA loss , mitochondrial dysfunction , DNA replication defects and telomere erosion [17] [56] . We noticed that yeast cells transformed with the GAL::YRA1Δi construct showed a decrease in colony formation after successive passages in galactose-containing medium . As YRA1 overexpression inhibits cell growth , we wondered whether this dominant-negative effect could be due to a senescence-like process . Therefore we performed successive streak-outs of yeast cells transformed with the GAL::YRA1Δi construct or the corresponding empty plasmid ( Fig 5A ) . We compared the cell viability of transformants in medium with 2%-galactose supplemented with 0 . 05% glucose to achieve YRA1 medium-level overexpression and minimize the inhibitory effect caused by high overexpression . As shown in Fig 5A , the colony size of cells expressing YRA1Δi was progressively smaller compared with those of colonies from the first streak-outs . We observed a poor cell growth after passages 5–6 that seems to be recovered at passage 10 , suggesting that YRA1 overexpression was leading to a senescence-like phenotype . As telomere shortening has been commonly associated with loss of cell viability and senescence [57] [58] [59] , we tested whether the growth defect phenotype caused by YRA1 overexpression was accompanied by changes in telomere length . Southern analysis with XhoI-digested genomic DNA from transformants was carried out to analyze telomere length dynamics at different passages . Fig 5B shows that telomeres were shortened at early passages ( see passage 3 ) maintaining the small size after further passages . Given that Yra1 is an RNA-binding protein that plays a role in gene expression , we assayed whether the effect on telomere length was a consequence of deregulation of the RNA component of telomerase TLC1 . Northern analysis ( Fig 5C ) indicates that the levels of TLC1 RNA are similar in YRA1Δi overexpressing cells and control cells after 70–80 generations , whereas the telomere was reduced in cells overexpressing YRA1 ( Fig 5C ) . Next , we analyzed telomere length in different yra1 mutants to assay whether failure of specific Yra1 domains could be responsible of the observed telomere shortening . Southern analysis with genomic DNA from yra1Δ strains complemented either with YRA1 gene ( YRA1 ) , the yra1-1 allele [60] , the YRA1 cDNA ( YRA1Δi ) , or the truncated YRA1 cDNA lacking the RBD domain ( YRA1ΔRBDΔi ) revealed that telomere length was not significantly affected in yra1-1 mutants , but it was shorter under YRA1 overexpression ( see YRA1Δi strain in S4 Fig ) . Since this reduction in telomere length was not observed in YRA1ΔRBDΔi cells , despite the lower levels of expression of this construct ( S2 Fig ) , the data could suggest that the ability of Yra1 to bind to RNA could be necessary for the telomere shortening . To further analyze the possible relationship between hyper-recombination associated with YRA1 overexpression and the senescence-like phenotype , we performed recombination analyses of colonies from young ( 10 or less generations ) and aged cultures ( 70–80 generations ) carrying the chromosomal recombination system leu2-k::ADE2-URA3::leu2-k ( Fig 5D ) . As can be seen , cells from standard young cultures show recombination frequencies similar to those of aged cells ( 2 . 0x10-3 versus 2 . 5 x10-3 ) , implying that hyper-recombination is independent of aging . As Yra1 is an mRNP factor that binds to transcribed genes [10] [35] , to gain insight into the effect of YRA1 overexpression we decided to see if increased amounts of Yra1 protein had any impact on its binding profile along the genome . We performed ChIP-chip experiments using Yra1 proteins expressed from a plasmid at normal levels ( HA-YRA1 ) or overexpressed ( HA-YRA1Δi ) . Data from asynchronous log-phase cultures were subjected to statistical analysis to obtain the values for Yra1 signal and P-value and to determine the binding profile of Yra1 throughout the genome ( see Materials and Methods ) ( S5 Fig ) . This revealed that when YRA1 is overexpressed ( HA-YRA1Δi ) the protein binds to actively transcribed chromatin , it is significantly enriched in ORFs ( Fig 6A ) and peaks at the 3’ end of genes , as shown in global analysis of the percentage of cluster mapping on different segments along a given ORF ( Fig 6B ) . This profile along the ORF is similar to those of cells expressing Yra1 at normal levels ( HA-YRA1 ) and to those reported for Yra1 protein [35] . Moreover , overexpressed Yra1 protein was strongly enriched on the YRA1 intron ( Fig 6A upper panel ) , consistent with the self-regulated splicing mechanism of YRA1 gene [35] . More than 50% of the genes ( 1014 genes ) to which Yra1 binds overlap between cells expressing HA-YRA1 ( 1749 ORFs ) and HA-YRA1Δi ( 1923 ORFs ) ( Fig 6A; S6A Fig ) . The analysis of structural and functional features of genes to which Yra1 binds revealed that they were longer and more expressed than the genome average in both normal and overexpressing conditions ( Fig 6C ) . In addition to ORFs , the overexpressed Yra1 protein was also recruited to tRNA and RNAPII-driven non-coding genes , as was previously described for Yra1 protein [35] , but it was also enriched at rRNA genes ( S6B Fig ) . Therefore , an excess of Yra1 protein leads to a high accumulation at actively transcribed chromatin , consistent with the role of Yra1 in mRNA biogenesis . Interestingly the genome-wide ChIP-chip data reveals an association of Yra1 to telomeres ( Fig 7A ) . Yra1 binds preferentially to X’ elements in cells without YRA1 overexpression ( HA-YRA1 ) , and binds to most of the Y´elements in YRA1 overexpressing cells ( HA-YRA1Δi ) ( S5B Fig ) . The analysis of Yra1 distribution along the Y’ element-containing telomeres by subdividing them into ten segments of the same length is shown in Fig 7B , where it can be seen the high enrichment of Yra1 along the Y' elements but not at telomeric repeats when it is produced in excess . The genome occupancy of Yra1 protein at telomeric regions and in particular when it is overexpressed , is in agreement with a possible role of this mRNP factor in the maintenance of telomere integrity , at least of Y’-containing telomeres . We tried to determine the degree of dependency on transcription of Yra1 recruitment to chromatin by treating cells carrying the HA-YRA1 and HA-YRA1Δi constructs with the RNAP inhibitor actinomycin D ( ActD ) at concentrations that reduced the RNA levels of RNAPI- , II- and III-transcribed genes to 50–80% of the wild-type levels ( S7A Fig ) and by comparing recruitment to transcribed versus non-transcribed regions . ChIP followed of qPCR showed that ActD reduced Yra1 occupancy in different transcribed regions tested in the control cells expressing the complete version of YRA1 ( HA-YRA1 ) , but had no effect in cells overexpressing YRA1 ( HA-YRA1Δi ) ( S7B Fig ) . However , recruitment was lower at the non-transcribed meiotic gene IME1 ( not expressed in mitotically dividing cells ) with and without ActD . Since ActD inhibits transcription but may not release the transcript and RNAP from the gene , the data are consistent with the conclusion that Yra1 binds preferentially to transcribed chromatin ( Fig 6 and S7B Fig ) . Since overexpressed Yra1 protein is highly accumulated at active chromatin ( Fig 6 ) , we performed a comparative analysis of the transcriptome from cells expressing GAL::YRA1 and GAL::YRA1Δi constructs in order to know the impact of YRA1 overexpression on gene expression . Neither specific functional classes of genes nor any DNA damage response genes were deregulated in cells overexpressing YRA1 ( S1 Table ) , supporting the conclusion that the genome instability associated with overexpression of this mRNP factor is not an indirect consequence . Moreover , microarray analysis of cells overexpressing YRA1 did not identify a significant enrichment in deregulated genes with specific structural features such as high GC content or length ( S1 Table ) . Consistently , no transcription defect was observed in the reporter lacZ-URA3 , a sequence poorly expressed in several mRNP mutants [48]; ( S8 Fig ) . In summary our analysis revealed that YRA1 overexpression had no significant impact on global gene expression , although we cannot discard indirect effects derived from the mRNA export defects . As genome instability , measured by DSB accumulation and hyperrecombination , associated with YRA1 overexpression is transcription dependent ( Fig 3 ) , we wondered whether this phenotype was linked to a defect in replication progression , provided that it is known that replication impairment is a major cause of spontaneous DNA breaks . We first observed by FACS that cells overexpressing YRA1 progressed through S/G2 with a slight delay with respect to wild-type cells ( S9 Fig ) , which suggests that part of its growth defect could be linked to replication impairment . We then analyzed replication by monitoring BrdU incorporation in control ( GAL-YRA1 ) and Yra1-overexpressing ( GAL-YRA1Δi ) cells . G1-arrested cells were released from α-factor in galactose-containing medium , to allow entrance into S phase , and subjected to ChIP with anti-BrdU followed of qPCR . BrdU levels peaked at an average of 30 minutes after G1-release in control cells expressing YRA1 at the early replication origins ARS1211 and ARS508 , in which replication and transcription occur head-on ( Fig 8 and S10 Fig ) . In contrast , BrdU incorporation at these sites was dramatically reduced in cells overexpressing Yra1 ( YRA1Δi construct ) , indicating that YRA1 overexpression impairs replication . To gain insight into the effect of YRA1 overexpression on replication fork progression all over the genome we performed ChIP–chip experiments with an Rrm3-Flag fusion protein . Rrm3 is a helicase required for the progression of the RF through obstacles in the DNA , and its accumulation at specific DNA sites has been used to identify RF pauses or stalls [61];[62] . We found that clusters of Rrm3 accumulation were distributed all over the genome both in GAL::YRA1 and GAL::YRA1Δi expressing cells ( Fig 9A , S11 Fig ) . Detailed analysis of the ORFs in which Rrm3 is accumulated revealed that the helicase peaked at the 3´end of the ORFs ( Fig 9C ) . Cells overexpressing Yra1 accumulated Rrm3 at the same genes as cells expressing normal levels of Yra1 ( 913 genes ) , but in addition Rrm3 binds to a new group of 543 genes ( up to 1412 total ) ( S12A Fig ) , indicating that replication obstacles extend to more genes when Yra1 is overexpressed . 21% of genes occupied by Yra1 showed Rrm3 accumulation ( 370 of 1749 genes ) , while this proportion increases to 36% for the YRA1Δi constructs ( 686 of 1923 genes ) ( S12B Fig ) . Interestingly , in control cells expressing normal Yra1 levels , only a 6% of the genome occupied by Yra1 is also occupied by Rrm3 , whereas this overlap was 40% in Yra1-overexpressing cells ( YRA1Δi construct ) . These observations suggest that under Yra1 overexpression Rrm3 accumulates preferentially at Yra1-bound genomic regions , suggesting a link between the stable presence of Yra1 at genome and a negative impact on replication progression . Rrm3 was also detected in more rRNA and tRNA genes ( S12C Fig ) . Moreover , we detected an increase of Rrm3 at Y’ telomeric regions in cells expressing GAL::YRA1Δi , coincident with the Yra1 presence at these regions ( Fig 9B and 9D ) . Our data suggest that changes in Yra1 stoichiometry could lead to RF progression impairment all over the genome , preferentially at transcribed genes , including Y’ elements .
It has been reported that overexpressing of ~15% of proteins reduces growth rate [63] . In the case of Yra1 its overexpression could affect numerous processes in the cell , because mRNA export would be affected , which might lead to loss of fitness . Yeast Yra1 protein levels are tightly regulated in a splicing-dependent manner , and its overexpression is toxic as that of other mRNP and export factors partially due to its impact on mRNA export [64] [43] ) . On the other hand , overexpression of human Yra1 and Hpr1 orthologues ALY and THOC1 , respectively , in a broad range of tumors , highlights the ability of these proteins to undergo stoichiometry changes in tumor cells [44 , 45] . Moreover , ALY depletion in cells by siRNA leads to DNA damage as determined by an increase in γH2AX foci formation [19] . Interestingly , our results indicate that YRA1 overexpression also leads to genome instability ( Figs 1–4 ) , a hallmark of cancer cells . This instability , detected by hyper-recombination , is dependent on transcription and R loop accumulation . The genetic and physical interaction of Yra1 with Sub2 and THO RNA biogenesis and export factors , which suppress RNA-mediated genome instability by partially preventing R-loop formation , is consistent with the observation that Yra1 overexpression leads to a hyper-recombination phenotype of a similar molecular nature to those of tho and sub2 mutants . Nevertheless , co-overexpression of Yra1 and Sub2 proteins ( S1 Fig ) indicated that genome instability associated with Yra1 excess is not due to a potential sequestering of THO-Sub2 complex subunits by an excess of free Yra1 . Thus , the effect of YRA1 overexpression on genome instability seems to be independent of Sub2 , which is consistent with the observation that YRA1 overexpression leads to telomere shortening whereas SUB2 overexpression has been shown not to affect telomere length [65] . The hyper-recombination conferred by Yra1 overexpression may require the RNA- and CTD-binding domains of Yra1 ( Fig 3C ) , which would suggest that the ability of Yra1 to bind RNA and active chromatin might be critical for the phenotype caused by its overexpression . Yra1 was initially identified on the basis of its potent RNA annealing activity in vitro [66] , and binds to RNAPII-transcribed genes with a localization bias toward the 3´ends of genes as is the case of the profile of the RNAPII CTD Ser2-P mark and a number of transcription elongation and mRNP factors , including the THO complex with which Yra1 interacts [67] [68] [35] [25] [10] . Yra1 ChIP experiments performed in ActD-treated cells ( S7B Fig ) are in agreement with the idea that Yra1 is recruited to transcribed chromatin . An excess of Yra1 could in principle interfere with the transcription process , but this does not seem to be the case since the binding profile of Yra1 along the ORFs was quite similar regardless of whether or not YRA1 was overexpressed ( Fig 6 ) and a significant impact on gene expression was not revealed by microarray analysis ( S1 Table ) . It is likely that the excess of Yra1 bound to the transcribed chromatin could affect the replication process . In this sense , Yra1 has been previously identified as a partner of Dia2 , a protein associated with replication origins [69] , although the functional relationship between these two observations is unclear yet . In any case , our results of BrdU incorporation support that YRA1 overexpression leads to replication progression impairment ( Fig 8 ) . Moreover , under YRA1 overexpression , the genome-wide profile of Rrm3 helicase occupancy , used as a marker of replication forks , supports this hypothesis ( Fig 9 ) . In Yra1-overexpressing cells there is an enrichment of Rrm3 in a significantly higher number of ORFs , including those of the highly transcribed ribosomal protein genes , in which Yra1 is also enriched ( Fig 9 , S6 and S12 Fig ) . Genome-wide analyses reveal that Rrm3 accumulates preferentially at Yra1-bound genomic regions when it is overexpressed ( S12B Fig ) . Thus , our data suggest that the cellular stoichiometry of the hnRNP Yra1 is relevant for genome integrity and replication in a transcription-dependent and R loop-dependent manner . It is possible that the excess of Yra1 causes tightly bound transcription machinery at transcribed genes that may transiently cause a block of the replication fork as a source of genome instability [17] . Recent evidence indicates that R-loops might also be formed in human cells , for which DNA repair proteins such as BRCA1 , BRCA2 and other Fanconi Anemia factors could play a kind of back-up system to remove then [70 , 71 , 72 , 73] . An excess of Yra1 , being Yra1 an RNA-binding protein , could also bind to co-transcriptional RNA:DNA hybrids transiently formed , therefore promoting a tighter and more stable structure able to block the passage of the replication fork and responsible for the instability ( Fig 10 ) . Our results suggest that Yra1 could have a role in telomere maintenance , as deduced from the fact that it binds to telomeres and its overexpression causes telomere shortening ( Figs 5 and 7 ) . Several factors involved in different steps of transcription and chromatin remodeling , such as components of the RSC , Mediator , Paf1 and THO complexes , Set1 , RNAPII and factors involved in phosphorylation of its CTD , and some splicing and 3’-end processing factors have been identified in different screenings for telomere-length alterations in yeast [74] [75] [76] . Moreover , other RNA binding proteins also bind to telomeres [77];[65] . In addition , DDX39 , a human DEAD-box RNA helicase with a high homology to UAP56/Sub2 , binds to the shelterin subunit TRF2 implicated in telomere protection , and its depletion activates the DNA-damage response and leads to aberrant telomere structures in human cells [78] . However , nuclear factors may also affect telomere integrity by altering structural telomeric intermediates required for telomere function . In this context , deregulated TERRA transcription has been associated with telomere shortening , senescence and disease ( reviewed in [79] ) . There is evidence that RNA:DNA hybrids formed at telomeres can regulate telomere dynamics and accelerate senescence , and that mutations in THO subunits enhance these features [80]; [81] [82] . Here we show that the stoichiometry of specific RNA-binding factors such as Yra1 at telomeres is critical for telomere homeostasis . However , at this point we do not know whether this is due to a major presence at the telomeric repeats; therefore , it is unlikely that telomere shortening is due to direct action of Yra1 on TERRA RNA or RNA:DNA hybrids . We need to know if all telomeres , whether or not containing subtelomeric Y’ regions , shorten , and what causes this shortening . It is likely that , once cells reach a minimum telomere size for survival , this is maintained by a stable equilibrium between the action of telomerase and recombination . We cannot discard that the excess of Yra1 could bind to the TLC RNA interfering with the telomerase activity , a possibility that needs to be addressed experimentally . However , according to the consequences of Yra1 excess in the rest of the genome , it is likely that replication impairment caused by the transcription apparatus and/or RNA:DNA hybrids ( Fig 10 ) also contributes to telomere shortening . Finally , the senescence-like phenotype could be the result of DNA instability generated by replication impairment after several generations . Interestingly , a reduced life-span phenotype associated with increased genome instability has been reported in hpr1Δ , a mutant of the THO complex [83] . It is possible that the growth defect and telomere shortening linked to the overexpression of Yra1 are related to replication impairment , consistent with the increase of Rrm3 occupancy at Y’ telomeric regions ( Fig 9 ) . However , the cause-effect relationship between telomere shortening and the senescence-like phenotype is yet to be resolved .
Yeast strains used are listed in S2 Table . YRA1 gene ( YRA1 ) and YRA1 cDNA ( YRA1Δi ) were cloned in centromeric plasmids pCM184 , pCM189 [84] and pRS413GAL [85] in order to achieve different YRA1 expression levels , to obtain plasmids pCM184tet::YRA1 , pCM184tet::YRA1Δi , pCM189tet::YRA1 , pCM189tet::YRA1Δi , pRS413GAL::YRA1 and pRS413GAL::YRA1Δi . Plasmids pHA-YRA1 , pHA-YRA1Δi , pHA-YRA1ΔN and pHA-YRA1ΔRBD carried different versions of YRA1 tagged in its N-terminal end with HA epitope [27] . Plasmids pRS314L , pRS316L , pRS314LY , pRS316LY and pRS316-LYΔNS [50] , pRS314L-lacZ , pRS314GL-lacZ [51 , 85] pGL-ribm , pGL-Rib+ , pGAL:RNH1 [18] and p413GALAID [53] were used to determine recombination frequencies . Plasmid pWJ1344 containing the tagged RAD52-YFP fusion [86] was used for DNA-damage analysis . Plasmid pCM184-LAUR [48] was used for the analysis of mRNA expression levels . Spontaneous Rad52-YFP foci from mid-log growing cells carrying plasmid pWJ1344 were visualized and counted by fluorescence microscopy [86] . Experiments were performed in cells after 15 hours in medium containing galactose to achieve the overexpression of both Yra1 and Rnh1 . Cells transformed with Recombination frequencies were determined as described [87] . For each strain , the recombination frequencies are given as the average and standard deviation of the median recombination frequency value obtained from fluctuation tests performed in 3–4 different transformants using 6 independent colonies per transformant . Recombinants were selected as Leu+ colonies for the plasmid containing LEU2 truncated repeat systems . Recombination analyses for the chromosomal leu2-k::ADE2-URA3::leu2-k system ( Lk-AU ) [88] were performed in wild-type and congenic mutants using 6 to 12 independent colonies grown in synthetic complete medium SC , and recombinants were selected in SC + FOA . Transformants were streaked on SC medium with 2% galactose supplemented with 0 . 5% glucose for 3 days before being passaged onto plates with fresh media . After each passage cells were growth to obtain DNA genomic for Southern analysis . Analysis of young and aged cultures were performed with cells grown in liquid SC medium with 2% galactose supplemented with 0 . 5% glucose . In order to achieve a high amount of cell divisions , cultures were diluted after ten generations in fresh media , after which they were diluted in several rounds successively and maintained in exponential phase during 80 generations . Generations were estimated indirectly by measuring optical density of the cultures . Samples ( 2–5 ml ) were collected from cultures before diluting in fresh media at every step . After 70–80 generations , samples were taken for recombination test , DNA and RNA extraction . After extraction , DNA ( 2–3 μg ) was digested with XhoI overnight , separated by electrophoresis ( 16–18 h at 4 V/cm using 1% 20–25 cm agarose gel in TBE , and transferred to Hybond-N membranes [75] . Terminal restriction fragments were visualized by hybridization with 32P-labeled Y′-probes . Microarray analysis of total RNA was performed using GeneChIP Yeast Genome 2 . 0 Array Affymetrix as previously described [68] . Briefly , total RNA was isolated from wildtype cells transformed with the GAL:YRA1 or GAL1::YRA1Δi constructs growing in raffinose and shifted for four hours to galactose ( 2% ) . RNA was extracted using the RNeasy Midi kit ( Qiagen ) . RNA levels for all yeast genes were determined using Affymetrix microarray scanner . Microarray was conducted in triplicate and the values presented represent the average of these three determinations . The expression data can be accessed at Gene Expression Omnibus ( GSE68488; GSE68487 ) . Total RNA was extracted from exponentially growing cells in SC-Trp medium and treated or not with 10 μg/ml of ActD for 2 hours . RNA was extracted with acid phenol , treated with DNase I ( Invitrogen ) , and cDNA was obtained by the Superscript® III First-Strand Synthesis System ( Invitrogen ) from 1 μl of RNA following the manufacturer’s instructions . qPCR was performed and relative RNA levels were determined by absolute quantitation normalized to the total amount of DNA extracted from the same cultures . Primers used are listed in S3 Table . Average and SD of three independent experiments are shown . Recruitment of HA-YRA1 and HA-YRA1Δi to chromatin was analyzed in exponentially growing cells in SC-Trp medium and treated or not with 10 μg/ml of ActD for 2 hours . ChIP analyses were performed as previously described [89] with some modifications: 1 . 5 mg/mL pronase was used instead of proteinase K to remove proteins in the de-crosslinking step; and the QIAquick PCR purification kit ( Qiagen ) was used for the last DNA purification step . Anti-HA tag antibody ( ChIP Grade , Abcam ) was used and qPCR was performed as described [90] . The represented values were calculated as the log2 ratio between treated and untreated cultures . Primers used for qPCR are listed in S3 Table . Average and SD of three independent experiments are shown . Analysis of replication by BrdU incorporation was performed as previously described [91] . Briefly , strains carrying the mutation bar1Δ , several copies of the Herpes simplex thymidine kinase ( TK ) under the control of the strong constitutive GPD promoter , and the constructions GAL-YRA1 or GAL-YRA1Δi were grown in 2% raffinose-containing medium lacking His , added 2% galactose and synchronized in G1 with α-factor for 2 hours , and released into fresh 2% raffinose-2% galactose-containing medium . 200 μg/ml BrdU was then added and culture samples were taken at the indicated time points . BrdU-IP was carried out as described [89] , with some modifications . Sodium Azide ( 0 . 1% ) was added to each sample and cells were broken at 4°C in lysis buffer ( 50 mM HEPES-KOH pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 1% triton X-100 , 0 . 1% sodium deoxycholate ) and sonicated . Immunoprecipitation was performed using anti-BrdU antibody ( MBL ) attached to magnetic beads coated with Protein A ( Invitrogen ) . Input and precipitated DNA were analyzed by qPCR and relative BrdU incorporation at a given region was calculated by absolute quantification relative to the signal of the first time point ( without BrdU ) . Primers used for qPCR are listed in S3 Table . S . cerevisiae oligonucleotide tiling microarrays were provided by Affymetrix ( GeneChip S . cerevisiae Tiling 1 . 0R array ) . The high-density oligonucleotide arrays used allows the analysis of yeast chromosomes at a 300-bp resolution , each of the 300-bp region being covered by at least 60 probes . ChIP-chip of asynchronously growing cells was carried out as described [92] [93] . For immunoprecipitation with Rrm3-FLAG , cells growing in raffinose were shifted for four hours to galactose ( 2% ) to overexpress YRA1 . Overexpression after 4 hours was checked by Northern . Briefly , 1 . 5x107 cells were disrupted by multi-beads shocker ( MB400U , Yasui Kikai , Japan ) , which maintained cells precisely at lower than 4°C during disruption . Anti-HA tag antibody ( ChIP Grade , Abcam ) and anti-FLAG antibody M2 ( Sigma-Aldrich ) were used for ChIP . ChIP DNA was purified and amplified by random priming using a WGA2 kit ( Sigma- Aldrich ) and following the manufacturer’s procedure . A total of 4 μg of amplified DNA was digested with DNaseI to a mean size of 100 bp and the purified DNA fragments were end-labelled with biotin-N6-ddATP23 . The ChIP-chip data can be accessed at Gene Expression Omnibus ( GSE68488; GSE68486 ) . Microarray expression data were normalized by RMA ( robust microarray average ) and statistically analyzed by LIMMA ( linear models for microarray analysis ) , comparing the mutant expression profile with its isogenic wild-type strain . The genes showing at least a 1 . 5-fold expression change with a P-value < 0 . 01 with a false discovery rate ( FDR ) corrections were considered as significantly altered . ChIP–chip data were analyzed using the Tiling Array suite ( TAS ) software from Affymetrix . For each probe position , TAS produces the signal and the change P-value , taking into account the probes localized within a given band-width around the inspected probe . Protein chromosomal distribution was then analyzed by detecting binding clusters , which were defined as ranges within the chromosome respecting the following conditions: estimated signal ( IP/SUP-binding ratio ) positive in the whole range , P-value < 0 . 01 , minimum run of 100 bp , and maximum gap of 250 bp . The results were visualized with the UCSC Genome Browser , developed and maintained by the Genome Bioinformatics Group ( Center for Biomolecular Science and Engineering at the University of California at Santa Cruz; http://genome . ucsc . edu/ ) . Distribution of binding sites along genes was carried out as previously described [68] . Analysis of sensitivity to genotoxic agents , Southern , Northern , Western and FACS using a FACScalibur Becton Dickinson machine were performed using standard procedures . Primers used are detailed in S3 Table . Antibodies anti–HA ( Abcam ) and anti-actin ( Sigma ) were used in western analysis . | Yra1 is an essential nuclear RNA-binding protein that plays a role in mRNA export in Saccharomyces cerevisiae . The cellular levels of Yra1 are tightly auto-regulated by splicing of an unusual intron in its pre-mRNA , removal of which causes Yra1 overexpression that results in a dominant-negative growth defect and mRNA export defect . We wondered whether or not YRA1 overexpression has an effect on genome integrity that could explain the loss of cell viability . Our analyses reveal that YRA1 overexpression causes DNA damage , confers a hyperrecombination phenotype that depends on transcription and that is mediated by RNA:DNA hybrids . YRA1 overexpression also leads to a cell senescence-like phenotype and telomere shortening . We show by ChIP-chip analysis that Yra1 binds to active chromatin and Y’ telomeric regions when it is overexpressed , in agreement with a possible role of this mRNP factor in the maintenance of telomere integrity . Our data indicate that YRA1 overexpression correlates with replication impairment as inferred by the reduction of BrdU incorporation and the increase of Rrm3 recruitment , a helicase involved in replication fork progression , at transcribed genes and Y’ regions . We conclude that the stoichiometry of specific RNA-binding factors such as Yra1 at telomeres is critical for genome integrity and for preventing transcription-replication conflicts . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"genetic",
"networks",
"chromosome",
"structure",
"and",
"function",
"messenger",
"rna",
"telomeres",
"dna",
"replication",
"network",
"analysis",
"genome",
"analysis",
"protein",
"structure",
"dna",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"genomics",
"chromosome",
"biology",
"proteins",
"hyperexpression",
"techniques",
"recombinant",
"proteins",
"protein",
"structure",
"networks",
"molecular",
"biology",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"gene",
"expression",
"and",
"vector",
"techniques",
"biochemistry",
"rna",
"macromolecular",
"structure",
"analysis",
"cell",
"biology",
"transformation",
"associated",
"recombination",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"dna",
"recombination",
"computational",
"biology",
"chromosomes"
]
| 2016 | Excess of Yra1 RNA-Binding Factor Causes Transcription-Dependent Genome Instability, Replication Impairment and Telomere Shortening |
Echinoderms , which are phylogenetically related to vertebrates and produce large numbers of transparent embryos that can be experimentally manipulated , offer many advantages for the analysis of the gene regulatory networks ( GRN ) regulating germ layer formation . During development of the sea urchin embryo , the ectoderm is the source of signals that pattern all three germ layers along the dorsal-ventral axis . How this signaling center controls patterning and morphogenesis of the embryo is not understood . Here , we report a large-scale analysis of the GRN deployed in response to the activity of this signaling center in the embryos of the Mediterranean sea urchin Paracentrotus lividus , in which studies with high spatial resolution are possible . By using a combination of in situ hybridization screening , overexpression of mRNA , recombinant ligand treatments , and morpholino-based loss-of-function studies , we identified a cohort of transcription factors and signaling molecules expressed in the ventral ectoderm , dorsal ectoderm , and interposed neurogenic ( “ciliary band” ) region in response to the known key signaling molecules Nodal and BMP2/4 and defined the epistatic relationships between the most important genes . The resultant GRN showed a number of striking features . First , Nodal was found to be essential for the expression of all ventral and dorsal marker genes , and BMP2/4 for all dorsal genes . Second , goosecoid was identified as a central player in a regulatory sub-circuit controlling mouth formation , while tbx2/3 emerged as a critical factor for differentiation of the dorsal ectoderm . Finally , and unexpectedly , a neurogenic ectoderm regulatory circuit characterized by expression of “ciliary band” genes was triggered in the absence of TGF beta signaling . We propose a novel model for ectoderm regionalization , in which neural ectoderm is the default fate in the absence of TGF beta signaling , and suggest that the stomodeal and neural subcircuits that we uncovered may represent ancient regulatory pathways controlling embryonic patterning .
It is becoming increasingly apparent that most developmental processes are controlled by dozens or hundreds of regulatory genes assembled into complex gene regulatory networks ( GRNs ) , rather than by a small number of master genes . By describing the functional relationships between these genes , GRNs allow integration of various levels of information on the activity of transcription factors and signaling pathways that regulate developmental processes . Over the last few years , a number of GRNs have been elucidated , including regulatory networks that drive specification of germ layers or organs in various organisms [1]–[7] . Sea urchin embryos offer many advantages for GRN analysis [8] . Unlike vertebrates , sea urchin embryos have a relatively small number of cells ( about 800 cells in a gastrula ) are fully transparent , and their embryos , available in huge number , develop rapidly as free-swimming larvae . A panoply of techniques is available for the functional analysis of developmental genes including treatments with pharmacological inhibitors and exogenous ligands , microinjection of antisense morpholino oligonucleotides for gene loss of function , and overexpression of mRNA for gain of function . Analysis of the first full sea urchin genome sequence from Strongylocentrotus purpuratus has revealed that echinoderms have a vast genetic repertoire but a low level of genetic redundancy , with almost all developmental regulatory genes being present as single copy [9] . Furthermore the sea urchin embryo has a rich history of experimental embryology and a wealth of biological knowledge is available on various aspects of its development . Finally , echinoderms occupy a basal position within the deuterostome lineage and are more related to chordates than most other invertebrate phyla . These various properties mean that echinoderms are a key phylum to study the evolution of developmental mechanisms and to understand the evolutionary origin of certain features of the chordate body plan . Axis specification has been extensively studied in the sea urchin [10] . Pioneer studies on endomesoderm patterning have shown that it is possible to dissect a complex GRN without the use of classical genetics by combining cis-regulatory and functional analysis , embryological , cell biological and genomic/computational approaches [11] . However , while considerable knowledge is available regarding the functional relationships between genes controlling specification of the territories along the animal vegetal axis , much less was known until recently on the genes that regulate ectoderm patterning and morphogenesis of the embryo along the dorsal-ventral axis . This gap started to be filled recently by the identification in Paracentrotus lividus of the TGFβ Nodal , Univin , and BMP2/4 as key regulators of ectoderm patterning [12]–[15] . Nodal is expressed zygotically , starting at the 32-cell stage . Its expression is initially very broad then it is rapidly restricted to a discrete sector of the ectoderm that corresponds to the presumptive ventral ectoderm . The restricted expression of nodal is so far the earliest known regional difference in zygotic gene expression detectable along the dorsal ventral axis . However , experiments performed at the beginning of the century have shown that as early as the 8-cell stage , respiratory gradients , visualized by mitochondrial cytochrome oxidase activity , prefigure the dorsal-ventral axis of the early embryo [16] . In addition , orientation of the dorsal-ventral axis can be biased by using respiratory inhibitors or by culturing embryos in hypoxic conditions [17]–[19] . Recent studies reported that mitochondria are asymmetrically distributed in some batches of eggs of Strongylocentrotus purpuratus with the ventral side displaying the highest concentration , and that microinjection of purified mitochondria can bias orientation of the dorsal-ventral axis [20] , [21] . A possible link between the transcriptional activation of nodal and these redox gradients is suggested by the finding that the stress activated kinase p38 is required for nodal expression [22] . An attractive model therefore emerges in which an asymmetry in the distribution of mitochondria may generate a redox gradient , which would activate p38 anisotropically leading to the spatially restricted expression of nodal . However , strong experimental evidence supporting this model are presently lacking and experimental manipulations that perturb the redox gradient have very modest effects on the spatial expression of nodal [21] ( Thierry Lepage unpublished results ) . If the role of redox gradients in the establishment of nodal expression is still unclear , in contrast , the role of a reaction diffusion mechanism , which involves a short range Nodal positive autoregulation and a long range inhibition mechanism by the Nodal antagonist Lefty , is probably essential to convert a subtle initial anisotropy into a sharply defined pattern [12] . Overexpression of nodal strongly ventralizes the embryos and largely mimics the effects of treatments with nickel chloride [23] , knockdown of Nodal function using morpholinos or by overexpressing lefty , completely eliminates dorsal-ventral polarity and results in embryos with disorganized skeletal elements , no mouth and a straight archenteron . The same , strongly-radialized , phenotypes are obtained by blocking translation of the univin transcript which encodes a Vg1/GDF1 ortholog expressed maternally [14] , suggesting that Univin may either act upstream of nodal expression or that it may heterodimerize with Nodal as suggested in vertebrates [24] , [25] . Intriguingly , in the absence of Nodal , not only is the expression of ventral marker genes such as brachyury , goosecoid or lefty abolished , but the expression of dorsal marker genes such as tbx2/3 and of the novel transmembrane protein 29D is suppressed as well [13] . As a consequence , most of the ectoderm ( except the ectoderm surrounding the animal and vegetal poles ) of Nodal morphants differentiates into a thick ectoderm consisting of cuboidal ciliated cells that morphologically resembles the neurogenic ectoderm of the ciliary band . Injection of synthetic mRNA encoding either Nodal or an activated Nodal receptor into one blastomere of Nodal morphant embryos at the 8-cell stage is sufficient to rescue both the ventral and the dorsal side of these embryos , indicating that a distinct relay molecule specifies dorsal fates . This relay molecule was recently identified as BMP2/4 , which is transcribed in the ventral ectoderm downstream of Nodal signaling , has a strong dorsalizing activity when overexpressed , and mediates the “rescue” of dorsal structures when Nodal signaling pathway is ectopically activated in a cell-autonomous manner in a Nodal loss of function background [26] . Furthermore , despite its ventral transcription , BMP2/4 has been shown to trigger receptor mediated signaling exclusively on the dorsal side of the embryo . Based on this series of findings , a basic model for sea urchin embryo dorso-ventral patterning emerges in which the dorsal ectoderm is induced by BMP2/4 signals emanating from the opposite side of the embryo . The ventral side produces inducing factors such as Nodal and BMP2/4 but it is also a source of inhibitors such as Lefty , which restricts Nodal signaling to the ventral side , and Chordin , which prevents BMP2/4 signaling in the ventral ectoderm . In the absence of lefty function , Nodal signaling is unrestricted and propagates throughout a large belt of cells surrounding the embryo while in the absence of chordin , ectopic BMP2/4 signaling occurs on the ventral side and causes abnormal patterning of the embryo [12] , [26] . Therefore , in the sea urchin as in vertebrates patterning of the embryo critically relies on sequential inductive events mediated by Nodal and BMP2/4 and on the interplay between ligands and their antagonists . However , in the sea urchin embryo , both the ligands ( Nodal and BMP2/4 ) and their antagonists ( Chordin and Lefty ) are co-expressed in the ventral ectoderm , which may represent a D/V organizer , and D/V patterning requires translocation of BMP2/4 from the ventral side where it is produced to the dorsal side where it activates its receptor . Another pathway that plays a crucial role in ectoderm patterning is the Wnt pathway . Wnt signaling from the vegetal pole region is required to restrict formation of the animal pole domain . The animal pole domain is a small ectodermal territory made of thick ciliated ectoderm that forms in the apical region of the embryo . This six3 expressing neurogenic territory appears to be specified at mesenchyme blastula stage and is thought to be resistant to Wnt and TGF beta signaling [10] , [13] , [27] , [28] . When the Wnt pathway is blocked by overexpression of cadherin or of a dominant negative form of TCF , the animal plate expands towards the vegetal pole and most of the ectoderm differentiates into neuroectodem , which contains scattered serotonergic neurons normally restricted to the animal plate region [27] . In contrast , inhibition of Nodal/Vg1/Activin signaling with a pharmacological inhibitor of the Nodal receptor causes formation of a thickened ciliated ectoderm , but this ciliated ectoderm does not appear to be specified as animal plate ectoderm since serotonergic neurons remain localized to the animal pole in these embryos . Instead , this ectoderm may have a ciliary band like identity as first proposed by Duboc et al . [13] . This idea is supported by the finding that the ectoderm of Nodal morphants abundantly expresses the ciliary band marker tubulinß3 [13] and by the presence of ectopic neurons as revealed by staining for the pan-neural marker synaptotagmin [27] . However , more in depth analysis of the specification state of this ectoderm in the absence of Nodal signaling is required to further test this idea . Deciphering the gene regulatory network that controls patterning of the ectoderm is of special importance for several reasons . The first reason is that patterning of all three germ layers relies on the activity of a signaling center located in the ventral ectoderm and analyzing how this signaling center works is essential to understand how dorsal ventral polarity of the embryo is established . Another reason is that , despite a wealth of information available on establishment of D/V polarity during normal and regulative development , the GRN that controls specification of the main ectodermal territories ( ventral ectoderm , dorsal ectoderm and ciliary band ) remains incompletely described and the molecular mechanisms involved in regionalization of the embryo along the D/V axis in normal and perturbed embryos have just started to be investigated [29] . A third reason to study the D/V GRN comes from the basal evolutionary position of echinoderms within the deuterostome superclade , and of the notion that studying D/V axis formation in echinoderms will contribute to better understand the evolution of the patterning mechanisms that shaped the deuterostome body plan . Indeed , recent studies have shown that this GRN relies extensively on cell interactions mediated by TGF beta family members such as Nodal , Univin/Vg1 and BMP2/4 , molecules that play crucial roles during vertebrate development [13] , [14] , [26] . Finally , since major morphogenetic processes such as mouth formation , skeleton formation and elongation of the arms and apex of the larva occur along the D/V axis , dissecting the D/V GRN offers the promise to study how morphogenetic processes are encoded in the genomic program of development . This will help to fill the gap that presently exists between our understanding of cell fate specification and our knowledge of how genes work together to regulate morphogenesis . We previously described the core of the GRN that acts downstream of Nodal and is responsible for patterning of the ectoderm along the dorsal-ventral axis [13] . We showed that on the ventral side , Nodal acts at the top of this GRN by regulating the expression of lefty , bmp2/4 , goosecoid and brachyury while on the dorsal side BMP2/4 activates the expression of tbx2/3 . Although the functional relationships between these key genes was elucidated in this initial study , recent molecular screens conducted by us ( Thierry Lepage unpublished ) and others [30] revealed that many more downstream genes are likely involved in patterning of the ectoderm along the dorsal ventral axis . A large scale effort to dissect the ectoderm GRN in S . purpuratus was recently published by Su and colleagues who used the nanostring technology to monitor the effects of gene perturbations [29] . However , this technique , which measures RNA concentrations in whole embryos , lacks the spatial resolution that is required to analyze the changes in the complex spatial expression patterns of many developmental genes . To understand better how the ectoderm of the sea urchin embryo is patterned by Nodal and BMP2/4 signals and to expand our provisional GRN , we conducted a large-scale study . Using a combination of gain of function and loss of function studies , and taking advantage of the amenability of Paracentrotus lividus embryos to detailed phenotypic analyses and in situ hybridization studies , we analyzed at high spatial resolution the expression and regulation by Nodal and BMP2/4 of 18 transcription factors and 8 signaling molecules that displayed a restricted expression along the D/V axis . Using an assay with recombinant proteins , we identified direct targets of Nodal and BMP2/4 . Finally , by conducting a large-scale analysis of the epistatic relationships between these genes , we were able to start ordering them into a hierarchy and to identify key regulators acting downstream of Nodal and BMP2/4 . Not only our results uncover novel and probably ancient regulatory circuits that drive morphogenetic processes such as mouth formation and neural induction , but they elicit a model for patterning of the ectoderm in which two successive inductive events regionalize the ectoderm into three territories: the ventral ectoderm that is specified by Nodal , the dorsal ectoderm that is specified by BMP2/4 and the neurogenic ectoderm of the ciliary band , which forms between the ventral and the dorsal ectoderm in a region protected from Nodal and BMP signaling . In addition , these findings highlight a striking parallel between the mouse embryo and the sea urchin embryo by showing that in both models a neurogenic ectoderm is the default state of ectoderm differentiation in the absence of Nodal and BMP signaling . Our analysis provides a picture of this GRN significantly different from that proposed by Su et al . in S . purpuratus and stresses the importance of the spatial resolution level in the analysis of gene regulatory networks in early embryos .
To elucidate the gene regulatory network that controls specification and patterning of the ectoderm in Paracentrotus , we first performed large scale in situ hybridization screens . In addition to a random screen initiated several years ago , which allowed us to characterize the expression of 4000 randomly selected cDNAs ( Thierry Lepage unpublished ) , we screened a P . lividus EST database against S . purpuratus sequences encoding transcription factors and signaling molecules and analyzed the expression of all those that were expressed during development of the sea urchin embryo [30]–[34] ( Table 1 ) . This allowed us to assemble a list of 36 genes displaying a robust expression in either the ventral ectoderm , the dorsal ectoderm or in the ciliary band territory ( Table 1 ) ( Figure 1A , 1B ) . Genes expressed in the animal pole domain were largely excluded from this analysis since most of them do not display a restricted expression along the D/V axis . The expression patterns of a number of the genes presented in this study had previously been described at various degrees in S . purpuratus [30]–[34] but they had never been described in Paracentrotus . In addition , the expression of several genes analyzed here , including smad6 , gfi1 , id , admp2 , BMP1 , and oasis has not been described previously in either species . The earliest asymmetrically distributed transcript that we identified in the in situ screens is the maternal transcript encoding mitochondrial cytochrome oxidase , with cleavage stage embryos frequently displaying a graded distribution of transcripts in the presumptive ectoderm ( Figure 1A1 ) . This asymmetrical distribution of a mitochondrial transcript likely reflects the asymmetrical distribution of mitochondria previously reported by Coffman and colleagues [20] , [21] . At the zygotic level , the first signs of tissue regionalization within the ectoderm are seen at 64/128 cell-stage with nodal and lefty transcripts starting to accumulate in the presumptive ventral territory ( Figure 1A2 , 3 ) [12] , [13] . The second wave of zygotic genes displaying a restricted expression along the D/V axis starts at the prehatching blastula with bmp2/4 and goosecoid starting to be transcribed in the ventral ectoderm rapidly followed by fgfr1 , chordin and nk2 . 2 at the swimming blastula stage ( Figure 1A4–8 ) [15] , . In Paracentrotus , there is no known example of genes displaying a restricted expression in the dorsal ectoderm before the swimming blastula stage . The first genes to be expressed in the dorsal ectoderm are nk2 . 2 and tbx2/3 , whose expression increases abruptly in the presumptive dorsal territory after hatching ( Figure 1A8 , 9 ) [31] , [40] , [41] . These genes are therefore good candidates as immediate early targets of Nodal or BMP2/4 signaling and are likely to play an early role in specification of these territories . Soon after ingression of the primary mesenchyme cells , when the embryo acquires its bilateral symmetry , a third wave of zygotic genes starts to be expressed . This includes the largest number of genes such as foxA , brachyury , foxG , Delta , NK1 , in the ventral ectoderm ( Figure 1A10–14 ) [34] , [42]–[45] , onecut/hnf6 and fgfA ( Figure 1A15–16 ) [46]–[48] in the lateral ectoderm and glypican5 , irxA , hox7 , dlx , smad6 , msx , id , oasis , admp2 and cyIII in the dorsal ectoderm ( Figure 1A17–26 ) [26] , [30] , [31] , [36] , [39] . Based on the timing of their expression , genes in this category are likely secondary targets of Nodal or BMP2/4 signaling . Starting at the early gastrula stage , additional genes start to be expressed with a restricted pattern along the D/V axis , with ptb transcripts accumulating in the ventral ectoderm ( Figure 1A27 ) , bmp1 , deadringer ( dri ) , otx , and rkhd being expressed in a broad domain encompassing the ventral ectoderm and ciliary band territory ( Figure 1A28–31 ) [13] , [49]–[52] , and gfi1 , pax2/5/8 , wnt8 , univin transcripts starting to be expressed in the presumptive ciliary band ( Figure 1A32–36 ) [32] , [36] , [48] , [53] . Similarly , atbf1 , unc4 , wnt5 start to be expressed in the dorsal ectoderm at the early gastrula stage . ( Figure 1A37–39 ) . Finally , at prism stage , tubulinß3 transcripts accumulate in the presumptive ciliary band while transcripts encoding the sea urchin specific transmembrane protein 29D accumulate in the presumptive dorsal ectoderm ( Figure 1A 40 , 41 ) [13] , [54] . At the mesenchyme blastula stage , foxG ( also known as Brain factor1 or Bf1 ) is expressed in two broad ventro-lateral stripes that largely overlap with the goosecoid expression territory ( Figure 1A12 ) , while Delta is first expressed in the ectoderm in a cluster of cells at the animal pole as well as in individual cells , possibly neurons , first on the ventral side then on the dorsal side , within the vegetal part of the foxG expression domain . At the prism/early pluteus stage , the pattern of foxG resolves into a thin belt of cells on the ventral side of the presumptive ciliary band ( Figure 1A42 ) [34] while Delta expression now occurs in a salt and pepper pattern within the ciliary band and facial ectoderm ( Figure 1A43 ) [55] . For simplification we divide the ectoderm into three main territories along the dorsal-ventral axis , however there are additional regional differences in gene expression that show that more than three regions can be defined ( Figure 1C ) . For example , the homeobox gene nk1 is expressed in the ventral-vegetal ectoderm in a region fated to become the ventral supra-anal ectoderm ( Figure 1C1 ) . Similarly , several dorsally expressed genes such as msx , id , oasis , admp2 or unc4 are strongly expressed in the dorsal-vegetal region fated to become the dorsal supra-anal ectoderm ( Figure 1A22–25; 38; Figure 1D2 , 3 ) . Thus , the ectoderm near the vegetal pole is divided into at least two sub domains along the D/V axis . Gene expression patterns also revealed that the ventral and dorsal ectodermal regions are progressively regionalized into different domains . This is best illustrated by the dynamics of goosecoid expression . goosecoid and brachyury are initially co-expressed within the ventral ectoderm ( Figure 1A5 , 11 ) , but during gastrulation , the expression domain of goosecoid is progressively cleared from the center of the ventral ectoderm ( Figure 1C3 ) . While goosecoid expression is progressively shifted at the periphery of the ventral ectoderm , forming a belt of cells abutting the ciliary band , brachyury and foxA remain expressed at the center of the ventral ectoderm , where the stomodeum will form ( Figure 1C2 ) . Similarly , analysis of gene expression within the dorsal ectoderm revealed the existence of nested patterns , with genes like nk2 . 2 , tbx2/3 and dlx ( Figure 1D4 , Figure 1A , 9 , 20 ) being expressed in a broader domain than genes like msx , wnt5 or smad6 ( Figure . 1A22 , 29 , 39; 1D3 , 4 see also [26] ) and genes like irxA being expressed in a sub domain of the dorsal ectoderm that excludes the dorsal apex ( Figure 1D1 ) . Finally , sub regions can also be recognized within the ciliary band territory starting at the early gastrula stage , with genes like fgfA , vegf , pax2/5/8 and sprouty being expressed in the ventral lateral region ( Figure 1A33 , 34; Figure 1D6 and data not shown ) [48] , [56] , genes like onecut/hnf6 or gfi1 being expressed in the entire presumptive ciliary band territory ( Figure 1C5; Figure 1D5 ) , and genes like foxG , which in vertebrates is expressed in and required for specification of the ventral telencephalon [57] , [58] , being expressed in a ventral subdomain of the ciliary band ( Figure 1A42 ) . Interestingly , several genes whose expression is later confined to the ciliary band are initially expressed much more broadly in the ectoderm ( Figure 1E ) . This is particularly apparent for glypican5 , fgfA , univin , and wnt8 , which are expressed in a large belt of ectodermal cells at blastula stage and also for the neural marker onecut/hnf6 which is first expressed ubiquitously , then in a broad ventro-lateral domain , and only later in the ciliary band ( Figure 1E1–6 ) [26] , [46]–[48] . This suggests that the expression of these ciliary band marker genes is initiated by broadly distributed transcription factors and later repressed on the ventral and/or dorsal sides by additional factors . As a first step to dissect the ectoderm gene regulatory network , we analyzed the regulation of these broadly expressed ciliary band genes . Since SoxB1 plays a critical role in ectoderm patterning in the sea urchin [59] and in the specification and maintenance of neural regions in vertebrates [60] , we tested if SoxB1 is required for expression of ciliary band marker genes ( Figure 1F ) . Injection of morpholinos against SoxB1 abrogated the expression of most markers of the neurogenic ectoderm of the ciliary band including onecut , gfi1 , foxG , egip , fgfA , pax2/5/8 , univin , wnt8 and strongly affected the spatial expression of dri and otx [14] ( Figure 1F1–20 ) . This result supports the idea that transcription of at least a subset of ciliary band marker genes is initiated by broadly distributed transcription factors such as SoxB1 and later restricted to the ciliary band by zygotic factors induced by Nodal and/or BMP signaling . We next tested how Nodal and/or BMP2/4 regulate the expression of the 36 genes identified in the in situ screen . We focused on Nodal and BMP2/4 since previous studies showed that these two ligands are essential for specification and patterning of the ventral and dorsal territories . We first analyzed the effects of overexpressing nodal or bmp2/4 on the expression of ectodermal markers . Embryos were injected with nodal or bmp2/4 mRNA and the expression of the ventral , dorsal , or ciliary band markers was monitored at different stages . In most cases , results were confirmed by treatments with recombinant mouse Nodal or BMP4 . Overexpression of nodal mRNA or treatments with recombinant Nodal protein dramatically expanded the expression of nodal , bmp2/4 , chordin , lefty , goosecoid and brachyury as reported previously ( Figure 2 ) [13] , [26] . Overexpression of Nodal also expanded the ectodermal domain of expression of foxA and fgfr1 at mesenchyme blastula stages . Similarly , the expression domain of nk1 , which is normally restricted to the ventral vegetal ectoderm , became radial in nodal overexpressing embryos . Genes expressed in the ciliary band behaved differently depending on the gene . In the case of deadringer , bmp1 and univin , which are expressed in the ciliary band and in the ventral ectoderm , overexpression of nodal expanded their expression to the whole ectoderm . In the case of wnt8 , which is expressed in two broad lateral stripes at gastrula stages , as well as in the case of fgfA and its downstream target pax2/5/8 , which are expressed in the ventral sub domain of the ciliary band , all expression was eliminated by exogenous nodal . However , in the case of foxG , egip , onecut/hnf6 , gfi1 , otx , exogenous nodal suppressed expression in most of the ectoderm except in the animal and/or vegetal most domains of the ectoderm . Overexpression of nodal increased the number of ventral-vegetal cells that normally express Delta at the early gastrula stage and , at 48h , produced ventralized embryos in which most Delta expressing cells were located at the animal pole and in the vegetal most ectoderm . Largely similar phenotypes were obtained following treatments with nickel chloride ( Figure S3 ) although we noted intriguing differences in the behavior of a few genes including wnt8 , univin , fgfA and pax2/5/8 , in response to these perturbations . Overall , these data are consistent with the idea that in nodal-overexpressing or nickel treated embryos , radially expressed Nodal promotes specification of ventral ectodermal fates and suppresses specification of the ciliary band in a large equatorial region but not in the animal pole region or in the ectoderm surrounding the blastopore . One likely reason that may explain why the vegetal ectoderm is refractory to Nodal overexpression or to nickel treatment is that in these embryos , Nodal signaling is restricted to the equatorial region [13] . The vegetal ectoderm may therefore be protected from Nodal activity by Lefty which is thought to diffuse farther than Nodal [12] , . Consistent with this idea , in Nodal treated embryos and in nickel treated embryos , nodal expression expands to a large belt of cells in the equator and a ciliary band differentiates in the vegetal most ectoderm while in lefty morphants , which also display unrestricted Nodal signaling , ciliary band marker genes such as tubulinß3 and onecut/hnf6 are expressed in the animal pole region but not in the vegetal ectoderm ( Figure 2 ) [12] . Taken together , these results suggest that a Lefty dependent inhibition of Nodal signaling is required for ciliary band formation in the vegetal pole region . Finally , as expected , overexpression of nodal eliminated the expression of all the dorsal marker genes we tested including , nk2 . 2 , tbx2/3 , smad6 , msx , atbf1 , wnt5 , admp2 , unc4 , hox7 , dlx , and 29D ( Figure 2 ) . Reciprocally , overexpression of bmp2/4 or treatments with recombinant BMP4 protein eliminated expression of all the ventral marker genes we tested including nodal , bmp2/4 , chordin , goosecoid , foxA , lefty ( not shown ) , brachyury , and nk1 ( Figure 3 ) . As in the case of nodal overexpression , misexpression of bmp2/4 or of the activated Alk3/6 BMP receptor ( Alk3/6QD ) [26] strongly suppressed the expression of the ciliary band markers such as bmp1 , foxG , onecut/hnf6 , otx , gfi1 , tubulinß3 , egip , dri , univin , wnt8 , fgfA and pax2/5/8 . However , unlike in the case of nodal overexpressing or nickel treated embryos , which conserved expression of ciliary band markers in the animal pole and in vegetal ectodermal regions , overexpression of bmp2/4 or of the activated type I BMP receptor ( Alk3/6QD ) efficiently eliminated the expression of all the ciliary band markers at the animal pole and in the vegetal most ectoderm as well as the expression of animal pole specific markers such as foxQ2 highlighting the very strong antagonism existing between high level of BMP2/4 signaling and specification of the animal pole and ciliary band cell fates . Finally , misexpression of BMP2/4 dramatically expanded the expression of all the dorsal marker genes including tbx2/3 , smad6 , nk2 . 2 , wnt5 , oasis , msx , irxA , dlx , atbf1 , hox7 , unc4 , admp2 , id and 29D . We next sought to determine which genes are direct targets of Nodal and BMP2/4 signaling . Based on the timing of expression of the ventral or dorsal markers genes , it was expected that only a subset would be direct targets of Nodal or BMP2/4 signaling . For example , only lefty , bmp2/4 , chordin , goosecoid , nk2 . 2 , fgfr1 and tbx2/3 are expressed at swimming blastula stage , the expression of most of the other starting only at mesenchyme blastula stage . We therefore tested whether the ventral marker genes are transcribed in direct response to Nodal and whether the dorsal marker genes are transcribed in direct response to BMP2/4 signaling or if transcription of these genes requires protein synthesis . To achieve this , we treated embryos at the hatching blastula , mesenchyme blastula or gastrula stages with recombinant mouse Nodal or BMP2/4 proteins in the presence or absence of a protein synthesis inhibitor ( Figure 4 ) , and analyzed the expression of all the ventral and all the dorsal marker genes . Short treatments with recombinant Nodal protein at blastula stage strongly induced expression of nodal , lefty , bmp2/4 , chordin , goosecoid , nk2 . 2 and fgfr1 throughout most of the ectoderm ( Figure 4A ) . These effects were observed even in the presence of a translational inhibitor suggesting that these genes are direct targets of Nodal signaling . In contrast , short treatments with Nodal at either mesenchyme blastula or gastrula stages failed to induce any ectopic expression of the other ventral genes such as foxA ( Figure 4A ) foxG , nk1 , or deadringer ( data not shown ) , which are expressed in the ectoderm starting at or after mesenchyme blastula . This suggests that these genes are indirect targets of Nodal signaling that cannot be induced during the short interval of the treatment . Interestingly , in the case of brachyury , a weak but consistent broadening of the ectodermal domain of expression was observed following treatment with Nodal . However , this effect was abolished by treatment with the protein synthesis inhibitor , consistent with this gene being an indirect target of Nodal signaling . Similarly , among all the dorsal marker genes we tested , 3 genes were strongly induced by treatments with BMP2/4 , even in the presence of protein synthesis inhibitors . These were tbx2/3 , nk2 . 2 and smad6 ( Figure 4B ) . Short treatments with high doses of BMP2/4 failed to induce expression of irxA , dlx , msx , atbf1 , hox7 , id , unc4 , oasis , wnt5 , admp2 or glypican 5 ( data not shown ) suggesting that these genes may be indirect targets of BMP signaling . The very good correlation between the results of this induction assay and the timing of expression of the downstream targets of Nodal and BMP2/4 indicates that this assay predicts with good confidence the direct , and probably also the indirect , target genes of these ligands at swimming blastula stage . It should be kept in mind however , that at later stages , this assay does not allow to rule-out completely the existence of a direct input from Nodal or BMP2/4 to downstream target genes . An alternative explanation for the fact that several genes appear to be refractory to induction by recombinant Nodal or BMP4 proteins is that after swimming blastula stage , the ventral and dorsal ectoderm may no longer be competent to switch their gene regulatory networks to a state that supports expression of dorsal or ventral genes respectively . We next attempted to determine if the activity of Nodal and BMP2/4 accounts for the restricted expression of all of the ventral and all the dorsal genes . Embryos were injected with a nodal morpholino and the expression of ventral , dorsal or ciliary band markers analyzed at successive stages ( Figure 5 ) . Expression of all the ventral marker genes that we tested including , bmp2/4 , goosecoid , fgfr1 , nk1 , chordin , brachyury , foxA and lefty disappeared in the Nodal morphants , consistent with previous results ( Figure 5B ) [13] , [29] , [38] . Injection of the nodal morpholino also largely prevented expression of foxG , confirming that this gene is induced downstream of Nodal signaling [29] . We also found that in Nodal morphants , the expression of all dorsal markers genes was strongly downregulated in most of the ectoderm , with responses falling into two categories: for some genes , e . g . glypican5 , oasis , msx , dlx , hox7 , wnt5 , smad6 , or unc4 , expression completely disappeared in the Nodal morphants ( Figure 5C ) . Others , e . g . tbx2/3 , id , irxA , nk2 . 2 , atbf1 , admp2 and 29D displayed residual expression in the vegetal-most ectoderm and/or in the PMCs indicating Nodal-independent expression of these genes in the presumptive dorsal vegetal ectoderm . A striking result was obtained when we analyzed the expression of ciliary band markers in the nodal morphants ( Figure 5D ) . The expression of most ciliary band markers dramatically expanded to most of the ectoderm following inhibition of Nodal signaling . This was the case for fgfA , bmp1 , univin , wnt8 , otx , pax2/5/8 , onecut/hnf6 , gfi1 , dri , as well as of the late ciliary band marker tubulinß3 and the ciliary band antigen 295 . Importantly , expression of Delta , which at pluteus stages identifies individual neurons of the facial ectoderm and ciliary band region [26] , [55] , was expanded to the whole ectoderm in Nodal morphants , strongly suggesting that most of the ectoderm is converted into neurogenic ectoderm in these embryos . Largely similar results were obtained using a pharmacological inhibitor of the Nodal receptor [62] ( Figure S4 ) . Taken together , these results show that Nodal signaling is essential for expression of all the ventral and of all the dorsal marker genes within the ectoderm . In the absence of Nodal , expression of all the ventral and dorsal marker genes is abolished and ciliary band genes are ectopically expressed throughout most of the ectoderm . We also examined the effect of knocking down BMP signaling on the expression of the ventral , dorsal and ciliary band markers ( Figure 6 ) . As expected , we found that expression of all the ventral markers that we tested was independent of BMP2/4 signaling: nodal , bmp2/4 , chordin , brachyury or foxA were expressed at similar levels and in similar domains in the controls and in the alk3/6 morphants ( Figure 6B ) . Removing BMP2/4 or Alk3/6 function affected the expression of dorsal marker genes in a way very similar to that caused by removing Nodal: expression of most genes including wnt5 , atbf1 , hox7 , msx , dlx , smad6 , tbx2/3 , unc4 was abolished while for irxA , nk2 . 2 and id , residual expression was still observed in the vegetal most ectoderm on the presumptive dorsal side ( Figure 6C ) . These results confirm that expression of all the dorsal ectodermal genes stringently relies on BMP2/4 signaling and that in the absence of Nodal or BMP2/4 signals , no other signals compensate for the lack of these inducers . Again , a striking result was observed when we analyzed the expression of ciliary band markers in the bmp2/4 or Alk3/6 morphants . For all of them , including gfi1 , onecut/hnf6 , otx , deadringer , pax2/5/8 , foxG , wnt8 , fgfA , univin , bmp1 and tubulinß3 , loss of BMP2/4 signaling caused a dramatic ectopic expression in the dorsal ectoderm ( Figure 6D ) . This ectopic expression transformed the normally bilateral expression domains of fgfA , pax2/5/8 , foxG , gfi1 , univin , and wnt8 into a horseshoe shaped domain covering the lateral and dorsal regions and caused the expression domain of deadringer and otx to become radial . These results reveal that in addition to promoting specification of dorsal cell fates , an essential function of BMP2/4 signaling is to repress ciliary band gene expression within the dorsal ectoderm . To establish the functional hierarchy between key ventral , dorsal and ciliary band genes , we designed morpholinos against 17 transcription factors and 8 signaling molecules expressed within the ectoderm with a restricted pattern along the dorsal-ventral axis . Among these 25 morpholinos , 19 ( alk4/5/7 , alk3/6 , brachyury , bmp2/4 , chordin , foxA , foxG , fgfA , goosecoid , irxA , lefty , tbx2/3 , dlx , msx , nodal , onecut/hnf6 , soxB1 , univin , wnt8 ) gave a clearly recognizable morphological phenotype ( Figure 5–9 ) . The expression of 15 transcription factors ( goosecoid , brachyury , foxA , nk1 , nk2 . 2 , tbx2/3 , msx , smad6 , hox7 , irxA , onecut , gfi1 , dri , pax2/5/8 , foxG ) and 8 signaling factors ( nodal , bmp2/4 , fgfA , chordin , wnt8 , univin , wnt5 , glypican5 ) was analyzed at different stages in the 17 morphant backgrounds while in the case of nodal and bmp2/4 morphants we analyzed the expression of an additional set of 17 marker genes ( Tables S1 , S2 ) . In addition , we overexpressed a subset of genes encoding transcription factors ( goosecoid , foxA , foxG , deadringer , nk2 . 2 , tbx2/3 , msx , smad6 ) and signaling molecules ( nodal , bmp2/4 , chordin ) and analyzed the expression of ventral , dorsal and ciliary band genes in these embryos . Since many of the genes identified in our screens including brachyury , foxA , otx , smad6 , tbx2/3 , wnt5 , oasis , univin , wnt8 , rkhd , ptb , fgfA , Delta are expressed not only in the ectoderm but also in the mesendoderm and since many other markers such as atbf1 , irxA , nk2 . 2 or egip , oasis , wnt5 , glypican5 , wnt8 , Delta , otx or bmp1 are expressed in more than one region and sometimes in both the ventral and dorsal ectoderm , we used in situ hybridization rather than QPCR to monitor the consequences of the perturbations . In situ hybridization is usually not used as the primary technique in large-scale projects such as gene regulatory network analysis since it is time and effort consuming and requires large numbers of injected embryos . However , we believe it is the only technique that provides the necessary spatial resolution to accurately analyze the expression of genes with complex expression patterns in perturbed embryos . Furthermore , when used with appropriate controls , in situ hybridization can provide a good estimate of the level of expression in perturbed embryos compared to controls . To provide a temporal view of the consequences of these perturbations and avoid secondary effects , the expression of the genes analyzed in response to nodal or bmp2/4 overexpression was examined at two different stages , soon after the onset of their restricted expression , and at a later stage , most often early or late gastrula stage depending on the gene analyzed . Information derived from these perturbations analyses was combined with earlier results to build a provisional gene regulatory network . The main features of this gene regulatory network are described below . Low levels of goosecoid transcripts are present maternally then their abundance increases sharply at swimming blastula stage , shortly after the peak of Nodal expression [35] ( Figure S2 ) . Expression of lefty , chordin , bmp2/4 , fgfr1 and goosecoid , was unchanged in the goosecoid morphants consistent with these genes being direct targets of Nodal signaling and with previous studies [38] ( Figure 7A and data not shown ) . Interestingly , at gastrula stages , strong ectopic expression of wnt8 , univin and foxG was detected in the ventral ectoderm of goosecoid morphants indicating that one function of Goosecoid is to repress expression of these three genes in the ventral ectoderm between blastula and gastrula stages . In contrast , ectodermal expression of foxA and brachyury , two likely indirect targets of Nodal required for mouth formation , was lost in the goosecoid morphants , consistent with the lack of stomodeum in these embryos ( Figure 7A ) [42] . Reciprocally , overexpression of goosecoid caused a dramatic expansion of foxA and brachyury ( Figure 7B ) . Therefore , in the sea urchin as in vertebrates , brachyury and foxA are targets of Nodal signaling but unlike in vertebrates , in the sea urchin , they are not primary targets of Nodal since their expression depends on the zygotic expression of goosecoid [63]–[65] . Overexpression of goosecoid also expanded the expression of deadringer as reported previously by Bradham et al . [22] , [66] . In contrast , the two dorsal marker genes hox7 and msx failed to be expressed in the goosecoid overexpressing embryos consistent with previous studies showing that goosecoid overexpression suppresses expression of dorsal genes such as tbx2/3 and spec1 [35] , [40] . Overexpression of goosecoid also abolished the expression of all the other ciliary band genes that we tested including wnt8 , univin , foxG , egip , gfi1 and onecut/hnf6 . Taken together these observations suggest that goosecoid plays a double function , first by allowing expression of stomodeal genes such as foxA and brachyury and second by suppressing the expression of ciliary band and dorsal genes . Once goosecoid and foxA have been turned on , Brachyury and FoxA cross regulate each other so that brachyury maintains foxA expression while foxA promotes brachyury expression ( Figure 7C , 7D ) . When the function of either of the two genes was blocked with a morpholino , expression of the other gene was lost and the resulting embryos developed without a stomodeum . The role of these cross regulatory interactions between brachyury and foxA may be to stabilize and lock the specification of the ventral ectoderm that has been initiated by Nodal as described in the endomesoderm GRN , for example between the transcription factors hex and tgif [7] . Inhibition of tbx2/3 function strongly perturbed establishment of dorsal-ventral polarity resulting in embryos with a rounded shape , which lacked ventral arms and had a strongly reduced dorsal region ( Figure 8A ) . Molecular analysis revealed that ventral markers such as chordin , foxA or brachyury were expressed in tbx2/3 morphants , albeit with reduced levels compared to controls ( Figure 8A ) . A similar slight reduction was observed for the ciliary band markers onecut/hnf6 , fgfA and pax2/5/8 . In contrast , inhibition of tbx2/3 function abolished the expression of several dorsal genes encoding transcription factors including msx , dlx , irxA and atbf1 while the expression of other genes such as smad6 , glypican5 , oasis and wnt5 appeared unaffected . These results identify tbx2/3 as a key regulator of dorsal gene expression downstream of BMP2/4 . Since loss of BMP2/4 or Alk3/6 signaling causes ectopic expression of ciliary band genes in the dorsal ectoderm , it follows that in unperturbed embryos , a transcriptional repressor must act in the dorsal ectoderm downstream of BMP2/4 to prevent expression of ciliary band genes . Of the four transcription factors expressed in the dorsal ectoderm that we tested , only in the case of one of them did we observe robust ectopic expression of a ciliary band gene . This gene is irxA . In embryos injected with morpholinos against the irxA transcript , onecut/hnf6 expression was strikingly expanded in the dorsal ectoderm ( Figure 8B ) . This effect was very robust and the territory in which the ectopic expression of onecut/hnf6 was observed was congruent with the expression territory of irxA . Interestingly , a small number of embryos injected with irxA morpholinos later developed with a thickened ectodermal region on the dorsal side that resembled an ectopic ciliary band ( Figure 8B ) . This suggests that IrxA is a repressor of ciliary band genes downstream of BMP2/4 . onecut/hnf6 is of one of the earliest marker genes expressed in the presumptive ciliary band . onecut/hnf6 morphants developed with a slightly reduced D/V axis but they clearly displayed a D/V polarity and a well-developed ciliary band ( Figure 8C ) . Nevertheless , we found that the expression of several marker genes of the ciliary band was affected in the onecut/hnf6 morphants . A reduced level of expression in the onecut/hnf6 morphants was observed in the case of pax2/5/8 , foxG and dri while in the case of gfi1 , no expression was detected . onecut/hnf6 is thus an upstream regulator of gfi1 . Gfi proteins are conserved in C . elegans ( Pag3 ) , Drosophila ( Senseless ) and mice ( Gfi1 ) . In all three species , these zinc finger proteins play conserved roles in neural development [67] . Mice mutant for gfi1 are deaf and ataxic while flies mutant for senseless lack sensory organs indicating that Gfi proteins regulate sensory organ development [67] , [68] . One can therefore anticipate that Gfi1 likely plays a role in neural development in the sea urchin embryo as it does in vertebrates and in flies . Since gfi1 is downstream of onecut , the ciliary band network therefore appears to be composed of at least two layers of zygotic factors .
In this study , taking advantage of the detailed phenotypic analyses and robust in situ hybridization procedures available in Paracentrotus lividus , we analyzed with a high level of spatial resolution the expression , the regulation and the function of most of the zygotic transcription factors and signaling molecules displaying restricted expression within the ectoderm of the sea urchin embryo . This analysis allowed us to assemble a gene regulatory network , the D/V GRN , which describes the regulatory interactions between these genes and provides a framework for understanding the developmental program responsible for patterning the embryo along the dorsal-ventral axis . Several interesting conclusions emerged from the resultant GRN . First , it provides a clear demonstration that the activities of Nodal and BMP2/4 account fully for the spatially restricted expression of all the known genes of this network: Nodal controls the expression of all the genes expressed specifically in the ventral ectoderm , and through BMP2/4 , the expression of all the genes expressed specifically in the dorsal ectoderm . Both overexpression of these ligands and corresponding loss of function experiments produced very strong , all or none , effects consistent with the idea that Nodal and BMP2/4 are critical inputs that drive the D/V GRN . It should be noted that despite their essential roles , Nodal and BMP2/4 are certainly not the only ligands involved in D/V patterning of the ectoderm and other ligands more broadly expressed likely cooperate with Nodal and BMP2/4 to specify the ventral and dorsal regions . In particular , Nodal may bind to its receptor as a heterodimer with Univin , a GDF1/Vg1 ortholog , as shown in other models [24] , [25] while BMP2/4 may heterodimerize with BMP5/8 to specify the dorsal ectoderm as shown in vertebrates and in Drosophila [69] , [70] . Nevertheless , the key roles played by Nodal in this GRN together with the essential function of Nodal factors in D/V axis formation in vertebrates and basal chordates [71] reinforce the hypothesis that an ancestral function of Nodal may have been in the regulation of D/V axis formation in deuterostomes . A second key conclusion emerging from our D/V GRN is that in the sea urchin , Goosecoid is a key upstream element of a small regulatory circuit that controls mouth formation . In vertebrates ectopic expression of goosecoid promotes cell migration and induces incomplete secondary axes while loss of function studies implicate goosecoid in the function of the Spemann organizer and head formation [72] . The function of goosecoid during development of other deuterostome embryos has not been studied . In the sea urchin , previous studies reported that both overexpression and loss of function of goosecoid strongly perturbed establishment of the dorsal-ventral axis , however the target genes of goosecoid were not known and the role of this repressor within the ventral ectoderm remained largely unclear [35] , [38] , [40] . Our finding that goosecoid is a direct target of Nodal signaling strongly suggested that this gene could play a key role in specification of the ventral ectoderm downstream of Nodal . We have shown that Goosecoid likely regulates the expression of deadringer and foxG in the ventral ectoderm . Furthermore , we demonstrated that Goosecoid plays a critical role in mouth formation by regulating downstream target genes such as the stomodeal genes brachyury and foxA . This raises the possibility that an ancestral function of goosecoid may have been in the regulation of stomodeum formation . Consistent with this idea , goosecoid is expressed in the stomodeal region in both protostomes and deuterostomes and is co-expressed with brachyury and foxA in the oral region of cnidarians [73] . Since Goosecoid is a transcriptional repressor [74] , this suggests that zygotic goosecoid activates foxA and brachyury by repressing the expression of a transcriptional repressor , the identity of which is presently unknown ( Figure 10 ) . Similar double repression mechanisms have been described in different GRNs . For example , in the sea urchin the skeletogenic mesoderm GRN , the repressor pMar has been proposed to repress hes-C as well as unidentified repressors to allow expression of genes specific of the PMC lineage [75] , [76] . Similarly Schnurri , represses the expression of brinker to allow the expression of Dpp target genes in Drosophila imaginal discs [77] . One candidate for a repressor acting downstream of goosecoid is the transcriptional repressor ZEB1/Smad Interacting Protein 1 ( Sip1 ) [78] . In the sea urchin embryo , Sip1 is expressed early in the presumptive ectoderm and its expression is downregulated at blastula stage , coincident with the onset of goosecoid expression [31] ( see Figure S2 and S5 ) . Experiments are currently being carried out in different labs to test this hypothesis . Another important function of Goosecoid appears to be in the repression of ciliary band and dorsal genes . Overexpression of goosecoid potently repressed expression of ciliary band markers . Furthermore , knockdown of Goosecoid function caused ectopic expression of univin , wnt8 and foxG in the ventral ectoderm . However , additional repressors likely cooperate with Goosecoid in this repression since inhibition of goosecoid function , unlike inhibition of irxA on the dorsal side , was not sufficient to derepress ciliary band markers genes such as onecut within the ventral ectoderm . Tbx2/3 has a special status amongst dorsal genes since it is one of the earliest zygotic genes expressed on the presumptive dorsal side [40] , [41] . Previous studies had shown that tbx2/3 is expressed dynamically in a broad dorsal territory in all three germ layers and that its expression is regulated by BMP signaling [13] , [26] , [40] , [41] . Indeed we showed that tbx2/3 is a direct target of BMP2/4 signaling in the ectoderm and that its function is required for expression of several dorsally expressed transcription factors such as msx , dlx , irxA and atbf1 . Intriguingly , previous studies in Paracentrotus failed to detect any D/V polarity defect in tbx2/3 morphants [40] . In contrast , we found that tbx2/3 is essential for D/V axis formation in this species . The reasons for this discrepancy are unclear . Interestingly , in vertebrates , tbx2 is also a target of BMP4 signaling during D/V patterning of the optic cup [79] . Similarly , in hemichordates , which are positioned phylogenetically as the sister phylum of echinoderms , tbx2/3 is a target of BMP2/4 suggesting that key genes that drive the D/V GRN are conserved in these two closely related phyla [80] . In vertebrates , tbx2 and tbx3 , unlike brachyury , which is a transcriptional activator , act as transcriptional repressors due to the presence of a strong repressor domain in their C-terminal region [81] , [82] . It is therefore possible that the sea urchin Tbx2/3 protein also functions as a transcriptional repressor and that , like Goosecoid , it stimulates gene expression by relieving the repressive action of a transcriptional repressor . The identity of this hypothetical transcriptional repressor is presently unknown . One of the most important findings of this study is the identification of irxA as a gene which acts downstream of BMP signaling to repress the ciliary band gene onecut . We previously reported that inhibition of BMP2/4 or Alk3/6 function causes an expansion of the presumptive ciliary band territory towards the dorsal side , and that this expansion is accompanied by the ectopic expression of the neural gene onecut/hnf6 [26] . On the basis of this result we anticipated that one function of the BMP pathway in the dorsal ectoderm was to repress ciliary band gene expression and we postulated the existence of a BMP2/4 dependent repressor of ciliary band genes . We have now identified IrxA as one such repressor based on the following evidence . First , we showed that irxA expression is regulated by BMP2/4 signaling . Second , we showed that blocking irxA translation with morpholinos caused a robust ectopic expression of onecut in a sector of the dorsal ectoderm that coincides with the expression domain of irxA . Finally , it is established that Irx proteins can function as repressors by recruiting the Groucho Co-repressor [83] , [84] . Since irxA is downstream of tbx2/3 in the GRN , we might predict that blocking tbx2/3 function should also result in ectopic expression of ciliary band genes . Surprisingly , we never observed ectopic expression of ciliary band marker genes in tbx2/3 morphants . This observation is consistent with previous GRN studies , which reported that direct target genes are more strongly affected than indirect target genes or in other words , that when a perturbation affects the driver gene , it causes stronger effects on target genes than when the perturbation affects genes further upstream in the pathway [29] . However , the simplest explanation is that our tbx2/3 morpholino may not be completely effective and that residual irxA expression may prevent ectopic expression of onecut in these embryos . In vertebrates and in Drosophila , irx genes are involved in neural development [85] . In Xenopus for example , irx1 promotes neural development by repressing bmp4 expression in the neural plate . It was therefore surprising to find that in the sea urchin embryo , irxA acts downstream of BMP2/4 to negatively regulate neural marker genes . Nevertheless , the identification of irxA as a BMP2/4 dependent repressor of ciliary band gene expression strongly supports our proposal that the default state of the ectoderm in the absence of TGF beta signaling is the ciliary band and that the ectoderm is patterned by two successive inductive events that repress the ciliary band fate on the ventral and dorsal sides . The results obtained in this study largely support this idea that the default state of the ectoderm in the absence of Nodal and BMP signaling is a ciliary band-like ectoderm that expresses a number of neural genes and that Nodal and BMP2/4 restrict this ciliary band fate by specifying the ventral and dorsal ectoderm . The first hint that the default state of the ectoderm in the absence of TGF beta signaling is the ciliary band is that several genes whose expression is later restricted to the ciliary band territory are expressed throughout the ectoderm at earlier stages . This is for example apparent for fgfA , univin and wnt8 , which are expressed in a belt of cells that includes most of the presumptive ectoderm at blastula stages . The expression of fgfA , univin and wnt8 is subsequently repressed on the ventral and dorsal sides during gastrulation thereby restricting the expression of these genes to the ciliary band domain . Several additional lines of evidence support the idea that the default state of the ectoderm in the absence of TGF beta signaling is a ciliary band and neural fate and that alternative ectodermal fates must be induced by active signaling . First , overexpression of both nodal and bmp2/4 strongly antagonized the expression of ciliary band and neural markers such as onecut , foxG and gfi1 , with bmp2/4 leading to a very potent inhibition of ciliary band formation . Second , in the lefty morphants the ciliary band failed to form while in the absence of Nodal and BMP2/4 signaling , the ventral and dorsal ectodermal regions were not specified and most of the ectoderm differentiated instead into a thickened ciliated ectoderm that resembled the ciliary band ectoderm and expressed all tested ciliary band markers . These ciliary band markers were de-repressed throughout the ventral and dorsal ectoderm in the nodal morphants while in the absence of BMP2/4 , which acts as a dorsal inducer , or of alk3/6 , which is required to transduce BMP2/4 signals , only specification of the dorsal ectoderm was perturbed and ectopic expression of these ciliary band genes was detected only on the dorsal side . A third argument is that the presumptive ciliary band territory is also a region in which fgfA and vegf are expressed and where MAP kinase activity is high [48] , [56] , [94] . Studies in vertebrates have shown that the activity of the MAP kinase ERK inhibits both BMP signaling and neuralization by phosphorylating Smad1 in the linker region thereby preventing its nuclearization . We thus predict that during normal development of the sea urchin embryo , the high MAP kinase activity present in the lateral ectoderm promotes neural fates within the presumptive ciliary band by inhibiting the activity of pSMAD1/5/8 and pSMAD2/3 . Thus , in the absence of Nodal and BMP signaling , signals such as FGFA that are normally present at the level of the lateral ectoderm are ectopically expressed in the ventral and dorsal regions where they may promote ectopic neuron formation [26] , [27] . One last but crucial argument that supports our model of the ciliary band as a default state of the ectoderm in the absence of TGF beta signaling is that we identified irxA and possibly Goosecoid as repressors of a subset of ciliary band genes downstream of Nodal or BMP signaling . One read-out of Nodal and BMP2/4 signaling therefore appears to be active repression of the ciliary band fate as we had predicted [26] . Yaguchi and colleagues previously demonstrated that in the absence of Wnt signaling , most of the ectoderm differentiates as a neurogenic ectoderm that expresses markers of the animal pole [27] . Since many ciliary band genes are also expressed in the animal pole , it could be argued that the ectopic expression of ciliary band marker genes observed following inhibition of Nodal or BMP signaling also reflects an expansion of the animal pole domain . This can be ruled out for several reasons . First , we showed that the expression of animal pole markers such as foxQ2 , is unaffected in Nodal morphants or in embryos treated with a pharmacological inhibitor of the Nodal receptor . Second , Yaguchi et al . showed that the number and location of serotonergic neurons of the apical organ are unaffected by inhibition of Nodal signaling . Importantly , we showed that pax2/5/8 , which is expressed in the vegetal part of the ciliary band but not in the animal pole region behaved exactly like the other ciliary band marker genes and was strongly derepressed in the ventral and dorsal ectoderm of Nodal morphants . Taken together these observations indicate that the lateral ectoderm of the prospective ciliary band , not the animal pole domain , is expanded in the Nodal morphants . Our study suggests that specification of the ciliary band is likely initiated by a combination of maternal factors such as SoxB1 and by zygotic factors such as FGFA , Otx and Onecut/Hnf6 whose expression is initiated independently of the Nodal and BMP2/4 signals ( Figure 10 ) . These zygotic genes initially show a broad expression in the ectoderm , which then becomes restricted to the presumptive ciliary band by the activity of transcriptional repressors such as Goosecoid and IrxA expressed in the ventral or dorsal ectoderm downstream of Nodal or BMP2/4 . Collectively our results suggest that the neural ectoderm of the ciliary band forms in a territory that is devoid of Nodal and BMP2/4 signaling ( Figure 11 ) . On the dorsal side , inhibition of BMP signaling appears to be sufficient to trigger formation of the ciliary band as was observed in BMP2/4 or Alk3/6 morphants or in embryos injected with low doses of smad6 mRNA . Similarly , on the ventral side , inhibition of Nodal signaling is sufficient to initiate formation of a ciliary band since BMP signaling does not occur on the ventral side but on the dorsal side [26] . In this case , ectopic neural differentiation likely results from inhibition of ventral differentiation . This highlights that , in the sea urchin ectoderm , preventing ventral cells to differentiate downstream of Nodal signaling promotes neural differentiation just as efficiently as inhibiting BMP signaling on the dorsal side . Similarly , in zebrafish embryos , inhibition of Nodal signaling causes the transfating of prospective mesendodermal cells into neural cells [95] , [96] and in the mouse , lack of Nodal signaling causes precocious neural differentiation [97] . Therefore , in the sea urchin embryo like in vertebrate embryo models , neural differentiation can result both from inhibition of BMP signals as well as from inhibition of other signals that regulate the fate of early blastomeres and allocate cells to embryonic territories and germ layers . In summary , our results show that in the sea urchin embryo , the neurogenic territory of the ciliary band is not induced by an interaction between the ventral and dorsal territories as previously suggested [98] , but that it represents the default state of the ectoderm in the absence of Nodal and BMP signaling . Nodal and BMP2/4 may therefore be regarded as factors that are required to prevent premature differentiation of ectodermal cells into neural cells as much as factors that are required for specification of the ventral and dorsal ectoderm . Another recent GRN analysis of ectoderm specification in S . purpuratus was performed using nanostring technology [29] . A comparison of the architecture of the gene regulatory networks derived from this study and ours reveals the expected similarities but also some major differences . A common central element in the architecture of both networks is the critical dependence of dorsal genes on non-autonomous signaling by BMP2/4 , a feature already proposed previously [13] . Another point of convergence is that both studies pointed to goosecoid and tbx2/3 as important early zygotic genes downstream of Nodal and BMP2/4: both studies identified brachyury as a downstream target of Goosecoid , and dlx and irxA as downstream targets of Tbx2/3 . Finally , both studies identified foxG and deadringer as downstream targets of Nodal . The first important difference in the architecture of the two proposed networks is that whereas our study defines the default state of the ectoderm in the absence of Nodal and BMP signals as a ciliary band-like ectoderm , the network proposed by Su et al . largely ignores formation of the ciliary band . Another important difference between the two studies concerns the dependence of ventral genes on Nodal . Su et al . argued that only part of the oral ectoderm specification system is downstream of Nodal [29] . According to the authors , a number of regionally expressed genes including onecut/hnf6 , otx2 , lim1 , and foxA , are activated “specifically in the oral ectoderm…exactly the same with or without nodal” , leading them to speculate that hypothetical Nodal independent early oral ectoderm signals regulate these genes in the ventral ectoderm . We do not agree with this interpretation , since from our in situ analysis , it is clear that these genes cannot be considered as oral-specific markers . Furthermore , we showed that the expression of onecut/hnf6 , otx2 , lim1 , and foxA in the presumptive ectoderm region of Nodal morphants was not regionalized , consistent with the absence of any oral territory in these embryos . The expression of onecut/hnf6 and otx2 is first initiated in a territory much larger than the ventral ectoderm , before subsequently becoming restricted to either to the ciliary band ( onecut/hnf6 ) or to a broader territory that also includes the ventral ectoderm ( otx2 ) . We thus interpret the continued expression of onecut/hnf6 and otx2 in the ectoderm as reflecting adoption of a ciliary band character by the entire ectoderm . Concerning foxA , the nodal-independent detection of the mRNA reported by Su et al is undoubtedly due to the abundant expression of this gene in a distinct endodermal territory , which , unlike the oral ectoderm expression , is largely Nodal-independent . The foxA example highlights the importance of using methods that allow spatial resolution to analyze the expression of genes with complex expression patterns in epistasis experiments . According to Otim and colleagues and Su and colleagues , two genes , onecut/hnf6 and deadringer , play essential roles in the DV GRN . Using an “unconventional morpholino” that targeted a sequence 660 bp downstream of the first ATG but that did not target a splice junction , Otim et al . reported that “inhibition” of hnf6/onecut function eliminated D/V polarity and caused a radialized phenotype that strikingly resembled the Nodal loss of function . Using the same reagent , Su et al . expanded this analysis and further argued that a positive regulatory input from onecut/hnf6 is required for the expression of several key regulators such as nodal , goosecoid , lefty , chordin , and bmp2/4 [29] , [46] . These results are highly surprising since morpholinos are predicted to be ineffective at blocking translation when they target sequences after the first 25 bases following the initiator ATG [99] , [100] . Using two different and more conventional morpholinos targeting the 5′ leader or the translation start site of the P . lividus hnf6/onecut transcript , we were unable to reproduce either the striking hnf6/onecut morphant phenotypes originally reported by Otim and colleagues or the effects on nodal , goosecoid , lefty , chordin , and bmp2/4 reported by Su and colleagues . It is therefore very unlikely that onecut/hnf6 , which is expressed only transiently within the ventral ectoderm , plays the crucial role proposed by these authors in this gene regulatory network . Regarding deadringer , Su et al . found that deadringer morphants display a much reduced expression of ventral genes such as goosecoid , NK1 and hes as well as a strongly reduced expression of dorsal genes such as irx , nk2 . 2 and tbx2 . 3 . Again , these results are surprising since the published cDNA sequence of deadringer used by Su et al . to design their morpholino as well as the associated predictions of the translation start site of the protein are probably incorrect and correspond to a truncated protein sequence as suggested by our sequence analysis of the genomic S . purpuratus deadringer locus and the analysis of the deadringer cDNAs in Paracentrotus ( Figure S1 ) . In addition , using two different morpholinos against the P . lividus deadringer transcript , we were unable to reproduce the published drastic effects of deadringer morpholinos on the expression of ventral and dorsal marker genes . It is therefore also unlikely that deadringer plays the role that it had been previously attributed in the S . purpuratus GRN . Finally , it has been argued that specific aboral differentiation genes such as CyIIIa and spec1 are transcriptionally activated in the aboral ectoderm long before late blastula and that this implied the existence of an early asymmetry in the aboral ectoderm that affected transcriptional activity . Su et al . postulated that this asymmetry may be a redox gradient that would directly regulate the transcriptional activity of aboral genes such as CyIIIa and tbx2/3 . Our results oppose this view . In Paracentrotus , the ectodermal expression of tbx2/3 is essentially lost following inhibition of Nodal or BMP2/4 signaling . While it is true that a residual tbx2/3 expression is observed in the Nodal morphants at gastrula stage , this expression is restricted to the vegetal most regions and therefore likely reflects the response of this gene to signals that act along the animal-vegetal axis rather than response to a redox gradient along the D/V axis . Furthermore , in Paracentrotus , expression of CyIII genes is first ubiquitous and only becomes restricted to the dorsal ectoderm at mesenchyme blastula stage ( see Figure S5 ) , coinciding with the nuclear translocation of pSmad1/5/8 in dorsal cells . In other words , we never observed any marker gene that was expressed specifically in the dorsal ectoderm before the onset of BMP signaling i . e . at late blastula stage . Our observations therefore do not support the view that the asymmetrical CyIIIa or tbx2/3 expression is driven by an early red-ox gradient , at least not in Paracentrotus , but suggest that their expression is more likely driven by differential Nodal and BMP signaling along the dorsal-ventral axis . A comparison of the mechanisms of neural induction in different species reveals both similarities and divergences regarding the signaling pathways involved . In Xenopus , inhibition of both Nodal and BMP signaling appears to be essential for neural induction , although FGF signaling is likely implicated in the early steps of this process [101] , [102] . Similarly , in mammals , both Nodal and BMP signaling have been involved in neural differentiation , the strongest evidence being that most epibast cells of mouse embryos mutant for nodal or bmpr1 display widespread and precocious expression of anterior neural markers [97] , [103] . In the chick and in zebrafish , there is strong evidence that FGF signaling regulates neural induction partly through the regulation of expression of BMP ligands and of BMP antagonists [104] , [105] , [106] . In contrast in ascidians , which are basally branching but divergent chordates , FGF signals are the key players in neural induction by directly regulating the expression of neural markers such as otx [107]–[109] . Inhibition of BMP signaling does not appear to play a role in this process [110] while Nodal plays a distinct , inductive role in patterning of the neural plate [111] . Similarly , in hemichordates , which together with the echinoderms form a sister group of the chordates and have a diffuse neural system , BMP signaling does not appear to play a role in the choice between neural and epidermis [80] . Our experiments in the sea urchin embryo show that inhibition of Nodal and BMP signaling is central to neural induction in echinoderms and that in the absence of Nodal or BMP signaling , most cells of the ectoderm differentiate into a neurogenic ectoderm . Since BMP signaling also regulates neural differentiation in insects [112] and annelids [113] , it appears likely that inhibition of Nodal and BMP signaling may have been an ancestral mechanism to specify neural cells not only in deuterostomes but also perhaps in bilateria , and thus that the neural specification mechanisms used in ascidians and hemichordates have diverged during evolution . Although in the sea urchin inhibition of Nodal causes the ventral ectoderm to adopt ultimately a neurogenic ectodermal fate , it should be kept in mind that our experiments also suggest that Nodal may have an early and positive role in specification and/or patterning of the neurogenic territory of the ciliary band since we showed that Nodal promotes the expression of Delta in a subpopulation of ciliary band cells and drives the early expression of the neural gene foxG . Therefore , in the sea urchin as in chordates , in addition to its general inhibitory role on neural induction , Nodal may also play a positive role in specification and/or patterning of the neural territory [111] , [114] , [115] . In conclusion , this large scale , systematic GRN analysis has allowed us to identify a number of key gene regulatory interactions and to build a provisional gene regulatory network describing specification of the three main ectodermal territories of the sea urchin embryo . It has not only uncovered key and probably ancient regulatory sub circuits that drive morphogenesis of the ectoderm , but has also allowed us to propose a new model of how specific regions of the ectoderm are induced over a default state , and of how the ectoderm is patterned by successive rounds of induction by TGF beta ligands . This relatively simple model captures most of the results derived from the functional analyses of Nodal and BMP2/4 in the sea urchin embryo and provides testable predictions for futures studies . Finally , our study illustrates the power of the GRN based approaches which can provide a global perspective on a set of genes regulating a biological process , explaining how this process works and what happens when it fails .
Adults sea urchins ( Paracentrotus lividus ) were collected in the bay of Villefranche-sur-Mer . Embryos were cultured as described previously [116] , [117] . When required , fertilization envelopes were removed by adding 2mM 3-amino-1 , 2 , 4 triazole 1 min before insemination to prevent hardening of this envelope followed by filtration through a 75µm nylon net . SB431542 ( 10 µM in sea water ) was diluted from stocks solutions in DMSO , and embryos incubated in 24 well plates protected from light . In controls experiments , DMSO was added at 0 . 1% final concentration . NiCl2 was used at 0 . 5 mM . SB431542 and nickel treatments were performed continuously starting 30 min after fertilization . Continuous treatments with recombinant mouse Nodal ( 1µg/ml ) and BMP4 proteins ( 0 . 5 µg/ml ) ( R&D ) started at the 16-cell-stage and used embryos lacking the fertilization envelope . We verified with a set of 10 genes that RNA overexpression and recombinant proteins produced equivalent effects for both Nodal and BMP . To determine if marker genes are direct or indirect targets of Nodal or BMP4 signaling , embryos at the swimming blastula/late blastula , early mesenchyme blastula stage or at gastrula stage from which the fertilization envelope had been removed were treated for 2h with recombinant proteins in the presence or absence of protein synthesis inhibitors . To block protein synthesis , puromycin or emetine was added at a final concentration of 360µM ( 200µg/ml ) or 5µM ( 10µg/ml ) respectively using stock solutions prepared in DMSO . In control experiments embryos were treated with 0 . 1% DMSO or with Puromycin at 200µg/ml or emetine at 10µg/ml . Development of the treated embryos was usually arrested 30 min after addition of the inhibitor , an indication of the effectiveness of the reagent and after 3–4h , all the treated embryos underwent a massive and brutal apoptosis , an effect characteristic of treatments with protein synthesis inhibitors . In the case of nodal , bmp2/4 , lefty , goosecoid , fgfr1 , chordin , nk2 . 2 , tbx2 . 3 , treatments were performed at the swimming blastula stage . In the case of nk1 , foxA , brachyury , foxG , dlx , hox7 , id , irxA , glypican5 , cyIIIa , admp2 , smad6 and msx , treatments were performed at the early mesenchyme blastula sage . In the case of deadringer , atbf1 , msx , wnt5 , irxA and dlx treatments were also performed at gastrula stage . Short treatments with Nodal or BMP4 failed to induce ectopic expression of any marker gene at gastrula stage suggesting that most of the genes expressed at this stage are indirect targets of Nodal and BMP2/4 or alternatively that at this stage , ectodermal territories are resistant to respecification by exogenous Nodal or BMP4 . Most of the genes analyzed in this study were discovered in the course of a random in situ hybridization screen using cDNA libraries from various stages ( T . Lepage unpublished ) . Additional marker genes were discovered in a second in situ screen aimed at analyzing the expression profiles of all the transcription factors and signaling molecules expressed during early sea urchin development [30] using a Paracentrotus lividus EST library ( http://goblet . molgen . mpg . de/cgi-bin/webapps/paracentrotus . cgi ) . When the isolated clones were incomplete , full-length cDNA sequences were obtained either by screening cDNA libraries with conventional methods and sequencing the corresponding clones . In certain cases , 5′RACE was performed using the Smart RACE kit ( Clontech ) to obtain the 5′ sequences . A list of all the Paracentrotus transcripts analyzed in this study with a summary of their temporal and spatial expression patterns is provided in Table 1 together with the corresponding accession numbers and original references describing these genes . Note that in the case of deadringer , the sequence of the Paracentrotus lividus clones diverged significantly from the published Strongylocentrotus purpuratus sequence . The published S . purpuratus deadringer transcript is predicted to encode a 490 amino acid protein . However , all the 13 independent deadringer cDNA clones that we sequenced encoded a protein 100 amino acids longer on the N-terminal side . Furthermore , translation of the S . purpuratus genomic sequence upstream of the predicted first ATG revealed the presence of a much longer open reading frame compared to the published deadringer protein sequence that encoded a protein highly similar to the deduced protein sequence from Paracentrotus ( see Figure S1 ) . This indicates that the previously published deadringer mRNA sequence was probably incorrect on the 5′ end and that the predicted deadringer protein sequence deduced from this mRNA was truncated . Since morpholinos fail to block translation when their target sequence is located after the first 25 bp following the initiator ATG [100] , the conclusions derived from previous functional studies of deadringer in S . purpuratus , which relied on a truncated sequence , are probably erroneous . For each gene of the network , a detailed analysis of the expression pattern was performed using digoxygenin labeled probes and in some cases , the temporal expression was analyzed by Northern blotting to verify maternal expression and to determine the exact onset of zygotic gene expression ( Figure S2 ) . In situ hybridization was performed following a protocol adapted from Harland [118] with antisense RNA probes and staged embryos . For marker genes expressed in ventral or dorsal territories at early stages , and for genes with complex expression profiles , double in situ hybridization was performed to confirm the orientations of the expression pattern . In this case , the two probes were hybridized and developed simultaneously . Probes derived from pBluescript vectors were synthesized with T7 RNA polymerase after linearization of the plasmids by NotI , while probes derived from pSport were synthesized with SP6 polymerase after linearization with SfiI . Control and experimental embryos were developed for the same time in the same experiments . Two color in situ hybridization was used following the procedure of Thisse et al . [119] . For overexpression studies the coding sequence of the genes analyzed was amplified by PCR with the Pfx DNA polymerase ( Invitrogen ) using oligonucleotides containing restriction sites and cloned into pCS2 [120] . Capped mRNAs were synthesized from NotI-linearized templates using mMessage mMachine kit ( Ambion ) . After synthesis , capped RNAs were purified on Sephadex G50 columns and quantitated by spectrophotometry . RNAs were mixed with Tetramethyl Rhodamine Dextran ( 10000 MW ) or Texas Red Dextran ( 70000 MW ) or Fluoresceinated Dextran ( 70000 MW ) at 5 mg/ml and injected in the concentration range 100–800µg/ml . The nodal , bmp2/4 , fgfA , univin , alk3/6QD , and chordin pCS2 constructs have been described in Duboc et al . ( 2004 ) , Röttinger et al . ( 2008 ) , Range et al . ( 2007 ) and Lapraz et al . ( 2009 ) . The pCS2 goosecoid construct is described in [40] . RNA derived from the following additional constructs were made ( the cloning sites are indicated in parenthesis ) : pCS2foxA ( ClaI-XbaI ) ; pCS2deadringer ( EcoRI-XhoI ) ; pCS2foxG ( ClaI-XhoI ) ; pCs2smad6 ( EcoRI-XbaI ) ; pCS2pax2/5/8 ( BamHI-XhoI ) ; pCS2tbx2/3 ( BamHI-XhoI ) ; pCS2msx ( BamHI-XhoI ) ; pCS2nk2 . 2 ( BamHI-XhoI ) . Morpholino antisense oligonucleotides were obtained from GeneTools LLC ( Eugene , OR ) . The nodal , BMP2/4 , Alk4/5/7 , Alk3/6 , univin , lefty and soxB1 morpholinos are described in [12]–[14] , [26] . Since morpholinos can have side effects or display toxicity or produce variable reductions in gene activity [121] , we designed and tested several morpholinos for each gene . A pair of morpholinos that did not display toxic effects was selected for further use ( a morpholino was considered toxic if it caused developmental arrest during cleavage or a massive cell death at the onset of gastrulation when injected at low doses ( 0 . 1–0 . 3 mM ) ) . In the cases of nodal , bmp2/4 , alk3/6 , Alk4/5/7 , univin and soxB1 , the efficiency of the morpholino to downregulate the expression of previously characterized targets genes was systematically assessed in control experiments [13] , [14] , [26] . The phenotypes observed for nodal , bmp2/4 , brachyury chordin , foxA , fgfA , goosecoid , irxA , lefty , tbx2/3 , dlx , msx , onecut/hnf6 , soxB1 , univin , wnt8 morpholinos were considered specific since they were confirmed with a separate , non-overlapping morpholino . In the case of alk3/6 , alk4/5/7 and nodal , a rescue experiment had previously been performed demonstrating the specificity of these reagents [13] , [14] , [26] . The phenotypes observed were always consistent with the zygotic expression pattern of the targeted genes and with previous well-established functional data [13] , [14] , [26] , [35] , [42] , [122] . We did not observe inconsistent phenotypes among several knockdowns except in one case , in which knocking down Tbx2/3 , an upstream regulatory gene of irxA , did not cause the same effect on the IrxA target gene onecut/hnf6 as knocking down irxA itself suggesting that the tbx2/3 morphant phenotype is a hypomorphic phenotype and not a null . In the case of the ventrally expressed genes nodal and bmp2/4 , we observed strong non autonomous effects consistent with the demonstrated translocation of BMP2/4 from the ventral to the dorsal ectoderm and with the role of BMP2/4 as relay downstream of Nodal [26] . In contrast , we never observed strong effects on the expression of ventral markers by morpholinos targeting genes expressed dorsally . In three cases , ( dlx , msx , foxG ) a morphological phenotype was consistently observed but molecular analysis failed to detect significant perturbations in the expression of the genes analyzed . Other morpholinos pairs ( deadringer , hox7 , nk2 . 2 , oasis , wnt5 ) gave very weak or not always reproducible phenotypes . Molecular analysis on embryos injected with these morpholinos failed to detect significant and reproducible changes in gene expression in any of the ventral , dorsal or ciliary band markers genes that we tested . In a few cases , ( atbf1 , klf2/4 ) all the morpholinos synthesized were highly toxic and were not studied further . The loss of function phenotypes of 29D , tubulinß3 , egip , CyIIIa , admp2 , fgfr1 , pax2/5/8 , unc4 , nk1 , id , rkhd and ptb and otx were not analyzed in this study and these genes were only used as markers in the following experiments . The sequences of all the morpholino oligomers used in this study are listed below . The most efficient morpholino of each pair is labeled with a star . alk4/5/7 Mo 1: TAAGTATAGCACGTTCCAATGCCAT alk3/6: Mo1: TAGTGTTACATCTGTCGCCATATTC brachyury Mo1: AGCATCGGCGCTCATAGCAGGCATA brachyury Mo2*: CTGGCAGAAGATGTACTTCGACGAT bmp2/4 Mo1*: GACCCCAGTTTGAGGTGGTAACCAT bmp2/4 Mo2: CATGATGGGTGGGATAACACAATGT chordin Mo1*: GGTATAAATCACGACACGGTACATG chordin Mo2: CGAAGATAAAAACTTCCAAGGTGTC deadringer Mo1: TGCTCGCGGTAACAAGTGATTCCAT deadringer Mo2: TTATATGGCAAAGGACTTCTACAGC dlx Mo1: CCCACGTCAAATGAATACATCAACA dlx Mo2: AAACACGTTTAGAATCCTCACGACT fgfA Mo1: ACTTTCATCCATTTTCGCTTTCATG fgfA Mo2*: ACACATTTTGGATACTTACAGCTCC foxA Mo1: CATGGGTTCCTCCTTGAAATCCACG foxA Mo2*: TGAAAGATTAAAGTAGCACAGTCAG foxG Mo1*: TCCGATGAATGTGCATGAAAAACTG foxG Mo2: CTTCTTGCTAAATACCAAGTTGGAG goosecoid Mo1*: TGTCTGGAAGGTAATAGTCCATCTC goosecoid Mo2: AGATCAGAGCTAACCACTTAGGACG hnf6/onecut: Mo1: AGCCGCTGGACCTCAAACGCGAAGA hnf6/onecut Mo2*: AAAATGATAATGTGGTCTCCGTCGC hox7 Mo1: TGACGAAATACGAACTCGAACTCAT hox7 Mo2: ACCACTTCATTAATAGCCAAAACCT irxA Mo1: ATTGTGGATAACTGCTCGTCGTCAT irxA Mo2: TTGTTGAAATCAACTTTGAGACGAT Lefty Mo1: GGAGCGCCATGAGATAATTCCATAT Lefty Mo2: GGAGATGGGCAAAATATGAAGATAC msx Mo1: CGACTTGATGGAAGAAAATTATTCC msx Mo2 : TTATCGCTTTAAGAATGACCAAGGA NK1 Mo1: AAGCATTGAGAATCCCTAAAACTGC NK1 Mo2: CATGTGCTCTGTTCAGACGGTCAAC nk2 . 2 Mo1: ATCAACATTCATACGATGTCTCTAT nk2 . 2 Mo2: ATAGTTAATTCCACACCACCCACTT nodal Mo1*: ACTTTGCGACTTTAGCTAATGATGC nodal Mo2: ATGAGAAGAGTTGCTCCGATGGTTG tbx2/3 Mo1: TCGACGAACCACCAAATCTTGAGCA tbx2/3 Mo2* : TCGGCAAAAGCCTCCGAGTCCAAAT Oasis Mo1: CTCTTCACCTAAAAGCCCATCCATG Oasis Mo2: CCAATTTGGGCCGTAGTCGAGGGAC soxB1 Mo 1*: GACAGTCTCTTTGAAATTAGACGAC soxB1 Mo2: GAAATAAAGCCAAAGTCTTTTGATG univin Mo1*: ACGTCCATATTTAGCTCGTGTTTGT univin Mo2: GTTAAACTCACCTTTCTAAACTCAC wnt8 Mo1: GAACAACTGCCGTAAAGATATCCAT wnt8 Mo2*: AACAGTCCAAATATGAAGTTCAAAC As a control for defects related to injection and egg quality , we used morpholinos directed against the hatching enzyme gene: 5′-GCAATATCAAGCCAGAATTCGCCAT-3′ or against the Nemo like kinase transcript -5′-TCGGAGGCAGACCAGCAGCGAGAAA-3′ . Embryos injected with either of these morpholinos at 1mM normally develop into pluteus larvae . Morpholinos oligonucleotides were dissolved in sterile water and injected at the one-cell stage together with Tetramethyl Rhodamine Dextran ( 10000 MW ) at 5 mg/ml . For each morpholino a dose-response curve was obtained and a concentration at which the oligomer did not elicit non-specific defect was chosen . Approximately 2–4 pl of oligonucleotide solution at 0 . 5 mM were used in most of the experiments described here . For morphological observations , about 150–200 eggs were injected in each experiment . To analyze gene expression in the morphants a minimum of 50–75 injected embryos were hybridized with a given probe . All the experiments were repeated at least twice and only representative phenotypes observed in more than 80% of embryos are presented . | Echinoderms ( sea urchins , starfish , etc . ) are marine invertebrates that share a close ancestry with vertebrates . Their embryos offer many advantages for the analysis of transcriptional circuits that control developmental programs . During early development of the common sea urchin Paracentrotus lividus , a signaling center located within the ventral ectoderm sends two key signals , Nodal and BMP2/4 , that control patterning of the embryo along the whole dorsal-ventral axis . How this signaling center works is not understood . We have conducted a large-scale functional analysis of the genes responsible for patterning of the ectoderm along the dorsal-ventral axis . We identified direct targets of Nodal and BMP2/4 and identified several key regulators that mediate the effects of these factors and drive essential and probably ancient regulatory circuits that together constitute a transcriptional program controlling morphogenesis of the embryo . In addition , we uncovered a striking parallel between the mouse embryo and the sea urchin embryo by showing that in both models a neurogenic ectoderm is the default state of ectoderm differentiation in the absence of Nodal and BMP signaling . Our results support the idea that inhibition of Nodal and BMP signaling was probably an ancient mechanism to specify neural cells in the ancestor of vertebrates . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"developmental",
"biology/morphogenesis",
"and",
"cell",
"biology",
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/gene",
"expression",
"developmental",
"biology/developmental",
"evolution",
"developmental",
"biology/pattern",
"formation",
"genetics",
"and",
"genomics/gene",
"function",
"developmental",
"biology/molecular",
"development",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
]
| 2010 | Ancestral Regulatory Circuits Governing Ectoderm Patterning Downstream of Nodal and BMP2/4 Revealed by Gene Regulatory Network Analysis in an Echinoderm |
Access to nutrients is a key factor governing development , reproduction and ultimately fitness . Within social groups , contest-competition can fundamentally affect nutrient access , potentially leading to reproductive asymmetry among individuals . Previously , agent-based models have been combined with the Geometric Framework of nutrition to provide insight into how nutrition and social interactions affect one another . Here , we expand this modelling approach by incorporating evolutionary algorithms to explore how contest-competition over nutrient acquisition might affect the evolution of animal nutritional strategies . Specifically , we model tolerance of nutrient excesses and deficits when ingesting nutritionally imbalanced foods , which we term ‘nutritional latitude’; a higher degree of nutritional latitude constitutes a higher tolerance of nutritional excess and deficit . Our results indicate that a transition between two alternative strategies occurs at moderate to high levels of competition . When competition is low , individuals display a low level of nutritional latitude and regularly switch foods in search of an optimum . When food is scarce and contest-competition is intense , high nutritional latitude appears optimal , and individuals continue to consume an imbalanced food for longer periods before attempting to switch to an alternative . However , the relative balance of nutrients within available foods also strongly influences at what levels of competition , if any , transitions between these two strategies occur . Our models imply that competition combined with reproductive skew in social groups can play a role in the evolution of diet breadth . We discuss how the integration of agent-based , nutritional and evolutionary modelling may be applied in future studies to further understand the evolution of nutritional strategies across social and ecological contexts .
Access to nutrients is one of the most influential factors affecting reproductive development and ultimately fitness ( e . g . [1–4] ) . A range of factors can influence nutrient access , but for many organisms interactions with conspecifics are pivotal . Group living animals in particular face a complex trade-off between access to foods that provide them with a balanced diet , social interactions that enhance fitness via benefits of group cohesion , and competition [5] . Contest-competition , for example , where individuals directly engage one another for access to nutrients , is a source of inter-individual variance that can lead to clear dominance hierarchies [6 , 7] . In the extreme , contests may even lead to a reproductive division of labour , with only those individuals at the top of the hierarchy being able to access enough nutrients , and at the right balance , to reproduce [8–10] . The effects that competition over food access can have on inter-individual variation in reproduction are well known in arthropods . For example , colonies of social spiders ( e . g . Stegodyphus sp . ) tend to be characterised by body size asymmetries and reproductive skews [11–15] . As a result of contest-competition over food access , only larger females are able to attain enough of the right nutrients to reproduce [8 , 13] . It has even been proposed that the reproductive asymmetries that arise from competition over nutrients may constitute a reproductive division of labour , in which non-reproductive spiders provide alloparental care ( [8 , 16] c . f . [14 , 17] ) . In the burying beetle , Nicrophorus vespilloides , females compete for access to carcasses , which in turn leads to a dominance hierarchy where reproduction is skewed in favour of the dominant female [18] . Experimental data indicate that access to appropriate nutrition is the main factor determining reproductive output and also impacts performance in dominance interactions [18 , 19] . Although there is no such direct evidence in cooperative breeding vertebrates , correlative studies in mongooses ( Mungos mungo ) and meerkats ( Suricata suricatta ) show that subordinate females breed more frequently in periods of food abundance [20 , 21] . These results highlight the importance of contest competition as a potentially major ecological factor shaping the evolution of nutritional and social traits in animal groups . In recent years , a new understanding of interactions between an organism’s nutritional requirements and its environment has been gained using the state-space models of the Geometric Framework ( GF ) [22–24] . In the GF , nutrients are represented by a Cartesian coordinate system , which is referred to as the nutrient space [23] . In the simple case of a nutrient plane , the GF represents two food components ( e . g . , the macronutrients protein and carbohydrate ) on x and y axes ( Fig . 1 ) . The optimal amount and blend of nutrients that the animal requires over a specified period in its life are represented by a coordinate or region within the nutrient space called the Intake Target ( IT; [23] ) . Foods are represented by ‘food rails’ , which are radials through the nutrient space with a slope that reflects the ratio of macronutrients present within the food ( Fig . 1 ) . As an individual eats , its nutritional state ( x , y coordinate ) moves through the nutrient space in parallel with the rail of the food it consumes . A high quality food may be considered one with a food rail that will guide an individual’s nutritional state to the IT from its current state ( Fig . 1 ) ; i . e . , one that is nutritionally balanced . When confined to nutritionally imbalanced foods , the animal needs to resolve the trade-off between over-ingesting some nutrients and under-ingesting others . The strategy that it adopts in this situation , known as the ‘rule of compromise’ , is expected to vary within and between species depending on the relative costs of ingesting excesses and deficits of the different nutrients [23 , 25] . Recently , the GF has been combined with Agent-Based Models ( ABMs; simulations representing each individual explicitly , sometimes named individual-based models in ecological fields [26] ) to successfully demonstrate how social interactions and nutritional strategies affect one another [5] . With regard to the influence of competition on the emergence of reproductive asymmetries , Lihoreau et al . [5] link classic models of contest competition ( outlined by Bonabeau et al . [27] ) with the GF in an ABM . In that model , access to each food rail is limited , and individuals must displace competitors via dominance interactions before feeding . Performance in dominance interactions is a function of the individual’s fitness , which in turn is negatively correlated with the distance between an individual’s nutritional state and the IT . Reproductive asymmetry arises as individuals who ‘get lucky’ and are able to feed on high quality foods early experience a ‘winner effect’ ( see [28] ) . That is , a loop of positive feedback ensues whereby better-nourished individuals continue to perform well in dominance interactions and monopolise high quality foods . Ultimately , only certain individuals attain enough nutrients at the right balance to breed , a model outcome that is consistent with observations of reproductive skew in some social animals ( e . g . spiders [8 , 13] and burying beetles [18 , 19] ) . Interestingly , this model also clearly demonstrates how early stochasticity in nutrient access can lead to the emergence of a self-organised social structure from an initially homogeneous group [5 , 29] . The aforementioned mechanistic model , however , does not consider the optimal nutritional strategy that individuals should adopt . When feeding on a poor quality food , an individual may choose to stop eating and seek an alternative . However , the individual risks incurring costs; e . g . , the time spent attempting , but ultimately failing , to gain access to alternative better foods . Under some circumstances it is thus conceivable that an individual could get its nutritional state closer to the IT by consuming a poor quality food , rather than by frequently searching for better balanced alternatives . Ultimately , the optimal strategy for leaving a suboptimal food may be dependent on the level of competition and the kinds of food in the environment . The incorporation of evolutionary and genetic components into GF based ABMs has been identified as a promising method with which to understand how ecological factors interact with nutritional strategies [5 , 23 , 30] . Here , we present the first such model , which we used to explore how intra-specific competition might affect the evolution of animals’ nutritional strategies . In the model by Lihoreau et al . [5] , an individual’s nutritional strategy was governed by the fixed global parameter K , which we refer to here as ‘nutritional latitude’ . When eating a food that will not guide its nutritional state to the IT an individual has some probability of leaving , which is both a function of the balance of nutrients in the food being consumed , and K . Here , a high K means an individual is likely to consume the same imbalanced food until reaching a point of nutritional compromise ( at which point it then seeks an alternative ) . In contrast , a low K corresponds to a low probability that an individual will continue feeding on a food rail that will not guide its nutritional state directly to the IT . Individuals with extremely high or low values of K may , thus , be thought of as nutrient generalists or specialists , respectively ( sensu Raubenheimer and Simpson [31]; we note that K as we model it here is equivalent to 1—K in Lihoreau et al . [5] ) . In this study , we couple nutritional latitude with an evolutionary algorithm , whereby an individual’s K is governed by an individual-level , heritable and mutable value . Each generation consists of 150 individuals that must attain a certain level of fitness ( i . e . , nutritional state ) within a fixed number of model iterations for it to be considered fit enough to breed . Fitness-proportionate selection then operates among those individuals fit enough to breed , with proximity to the IT ( optimal point of nutrient intake in the nutrient space ) determining this fitness . We allowed K to evolve over 1000 generations under varying levels of competition and in differing nutritional environments ( i . e . , different abundance and nutritional compositions of food ) . In doing so , we aimed to explore the effects of contest competition and the number and composition of foods in the nutritional environment on the evolution of individual nutritional strategies .
We began by exploring the effect of intensity of competition on the evolution of nutritional latitude in 2- and 3-food environments . We performed 30 model runs under varying intensities of competition ( c , which is bounded at 0 and 1; all parameters are outlined in Table 1 , and their mode of action is described in Models ) . From each model run we recorded the population mean nutritional latitude ( K , also bounded at 0 and 1 ) after 1000 generations . We first looked at the effects of competition in environments containing one nutritionally balanced food , and two imbalanced but complementary foods ( i . e . , those which between them subtend a region of the nutrient space containing the IT ) . For the latter two complementary foods we varied the extent of their nutritional imbalance ( Fig . 2 ) . In these environments when c = 0 , K was stable at a range of values ( Fig . 2 ) . The high variance in stable values of K suggests that no one level of nutritional latitude is optimal where competition is weak , but most low levels are equally fit . In the face of increasing c , K was relatively stable up to a point . With mildly imbalanced foods at c = 0 . 7 , and with extremely imbalanced foods at c = 0 . 67 , K increased sharply to above 0 . 91 ( Fig . 2 ) . In both 3-food environments , increases in K were accompanied by declines in the variance of evolved K ( Fig . 2 ) . For example , in the environment with mildly imbalanced foods the 2 . 5th and 97 . 5th percentiles of K were 0 . 14 and 0 . 47 , respectively with c = 0 , but were 0 . 69 and 0 . 88 when c = 0 . 73 ( Fig . 2A ) . Thus , selection for a high level of nutritional latitude is strong at moderate to high c , with a further sharp decrease at extremely high levels of c ( i . e . , > 0 . 85 ) , largely being driven by a change in the lower 2 . 5th percentile of K ( Fig . 2 ) . At very high c the population could not support itself as no individuals could fulfil the fitness requirements to be considered in breeding condition by the end of the simulation ( Fig . 2 ) . We next considered competition in a 2-food environment , containing one balanced and one imbalanced food , the latter of which varied in the degree of nutritional imbalance ( Fig . 3 ) . With a mildly imbalanced food , absent or weak competition selected for a lower K ( and lower variance; Fig . 3A ) than in 3-food environments ( Fig . 2 ) . Thus , selection for low nutritional latitude was stronger in this 2-food environment than in the 3-food environments . That being said , in the 2-food environment with a mildly imbalanced food and low c , K was stable , before transitioning to high K under moderate to high c ( Fig . 3A ) , as was the case in 3-food environments ( Fig . 2 ) . In the 2-food environment that contained a balanced and a severely imbalanced food , nutritional latitude showed a quite different profile from that previously observed . Increasing c in this environment selected for low nutritional latitude ( and very low variance in K ) , reaching a minimum value of K = 0 . 06 at c = 0 . 625 ( Fig . 3B ) . These results clearly indicate the importance of access to a complementary food to correct the nutritional state associated with consuming large amounts of a severely nutritionally imbalanced food ( see S1 File for additional discussion ) . As part of experiment 1 , we also looked at environments containing two nutritionally imbalanced but complementary foods . In these environments , the response of K to increasing c resembled that in 3-food environments ( see S1 File ) . In our model , the parameter η ( see Details in Models and Table 1 ) is the power of the relative nutritional states ( fitness; F ) of the ith and jth individuals to predict the outcome of a dominance interaction between these individuals . Given that we are largely concerned with species for which ability in dominance interactions is strongly correlated with nutritional state ( see Lihoreau et al . [5] ) , in the above results η is assumed to be high ( η = 25 ) . We now explore the effects of the intensity of competition ( c ) in scenarios where there is greater stochasticity in the outcome of contests over food; η = 20 and η = 10 . Where η was set at a lower levels , increasing the intensity of competition had the same qualitative effect on the evolution of nutritional latitude ( K ) as described above; i . e . at low c a range of low levels of nutritional latitude appear optimal , but a transition to high K is favoured at c greater than 0 . 733 ( Figs . 2 and 6 ) . However , at lower levels of η the intensity of competition that lead to population extinction was decreased . With η = 10 the population could not consistently sustain itself above values of c of 0 . 833 ( Fig . 6B ) . In an equivalent nutritional environment with η = 25 the population could not consistently sustain itself above values of c = 0 . 8667 ( Fig . 2 ) . These results indicate that having a stable dominance hierarchy , which is based on nutritional state can allow the population to better survive poor nutritional environments . We further discuss the biological implications of this finding below ( see Future Directions in Discussion ) . In the results described above , selection acts via two mechanisms . First , only those individuals able to attain fitness greater than 0 . 5 within 500 iterations are assumed to be in good enough condition to breed . Second , among those individuals fit enough to breed , fitness-proportionate selection operates [32] . The sensitivity of our results to this general selection mechanism was assessed by running the model with an alternative mechanism , truncated selection [29] . In this instance , the first 10% of the population to attain fitness over a cut-off are assigned as parents for the next generation . In our experiments cut-offs of 0 . 5 and 0 . 9 were assessed . Under truncated selection extinctions do not occur as the population is given a flexible amount of time to reach the fitness cut-off . We evaluated the effects of truncated selection on the model’s output in a 3-food environment with severely imbalanced foods ( such an environment produced results typical of most other environments; Fig . 2B ) . In the absence of competition ( c = 0 ) there was little or no selection on nutritional latitude: mean K = 0 . 5 with large variance ( Fig . 7 ) . However , low and moderate levels of competition selected for very low nutritional latitude ( Fig . 7 ) . As was the case under our general selection mechanism , a transition to increased nutritional latitude was still favoured under moderate to high competition ( Fig . 7 ) . However , K was not increased to anywhere near as high a level as under an equivalent nutritional environment with our general selection mechanism ( Figs . 2 and 7 ) . Finally , at very high levels of competition a return to low K was favoured ( Fig . 7 ) . The contrasting results of experiments 1 and 4 clearly illustrate that the mode of selection affects how nutritional strategies respond to contest competition . The implications of this finding for the biological interpretation of our model are discussed below ( see Future Directions in Discussion ) .
We developed an ABM that combines principles of the GF with an evolutionary algorithm to explore how contest competition may affect the evolution of animal nutritional strategies . Specifically , we modelled the extent to which individuals consume nutritionally imbalanced foods that will not guide them directly to their intake target ( nutritional latitude , K ) . In most of the nutritional environments we modelled , no competition and weak to moderate competition favoured low consumption of a suboptimal food . However , given that we observed high variance in stable values of K , it seems likely that there is no single optimal strategy . Rather , any fairly low level of nutritional latitude performs well . In contrast , moderate to severe competition appears to favour the consumption of more of an imbalanced food before seeking an alternative , than when competition is weak ( i . e . , they evolve increased nutritional latitude ) , potentially even consuming that food until reaching the point of nutritional compromise ( see [23] ) . The balance of nutrients in the foods available also influences the optimal level of nutritional latitude . For example , in a 2-food environment that contained one highly imbalanced and one balanced food , a very low level of nutritional latitude was favoured regardless of competition ( Fig . 3B ) . Thus , considering the nutritional composition as well as the amount of available foods is essential if we are to understand the role of competitive interactions in shaping the evolution of nutritional strategies . Our model suggests that in social groups where the availability of nutrients is highly variable , plastic nutritional latitude should be adaptive so that individuals can alter their strategy in response to the intensity of competition . Several biological systems are well suited to empirical exploration of this idea . In social spiders , experimental evidence suggests that access to lipids governs reproductive asymmetry [13] . The manipulation described by Salomon et al . [13] ( creating prey that vary in lipid content ) could be employed , and then the behaviour of marked individuals within these groups observed ( such as described in Whitehouse and Lubin [12] ) . An alternative model is the house cricket ( Acheta domesticus ) , a species with well-studied nutritional requirements [33–38] . Males are known to compete with one another for food; moreover , sexual selection likely results in reproductive asymmetry , with larger males most likely able to meet the energy requirements for intra-sexual competition [39–43] . Contest-competition and aggression over food and mate access are also observable phenomena in male fruit flies ( Drosophila melanogaster; [44 , 45] ) . This species also offers numerous other advantages including , being a model organism in genetics , being well studied with regards to its nutritional requirements and fitness consequences of nutritional imbalance and being suitable for artificial selection [1 , 2 , 46–48] . Using our ABM it will be possible to generate predictions for any number of nutritional scenarios specific to the model organisms described above . For example , considering spiders one may wish to explore a situation in which as food becomes scarce ( i . e . , competition increases in intensity ) , certain food rails appear only sporadically [49 , 50] . At the inter-species level our model suggests a role for contest competition and reproductive skew in shaping the evolution of dietary breadth . Specifically , consistent intense competition for access to a food containing a limiting nutrient , which results in reproductive skew , can select for high nutritional latitude , hence contributing to nutritional generalism . This hypothesis could be tested in a comparative nutrition framework ( e . g . , [31] ) , focussing on the intensity of contest competition and reproductive skew within groups of social generalists and specialists . Such approaches have , in the past , proved useful for studying the evolutionary mechanisms underlying dietary breadth , specifically suggesting that nutritional heterogeneity may lead to the adoption of specialist/generalist-specific rules of compromise ( [23]; further discussed in Future Directions . ) . Additional to insights into evolving nutritional strategies , our model supports predictions that contest competition over foods can lead to dominance hierarchies and reproductive asymmetries in social groups , because dominant individuals monopolise key nutrients for reproduction [5 , 8 , 13] . Contests for limited food can cause between-individual variance in reproductive output , regardless of the level of nutritional latitude or the nutritional environment . Given an apparent link between limited resources and alloparental care [51] , contest competition over nutrients may be a mechanism forcing groups of animals on to the continuum from cooperative breeding , where helpers occasionally provide care to the offspring of breeders , to eusociality , characterised by a complete division of labour [52] . We note that our models represent a scenario in which individuals are unable to leave the group , even when competition becomes strong , due to some unstated ecological constraint . If future models explicitly focus on how nutrition and contest competition contribute to the evolution of sociality , they will likely want to vary the strength of constraints that keep individuals within the group . Our models highlight some interesting relationships between nutrition , individual-level fitness and mean population fitness . Specifically , these models show that where individual nutritional state is a strong predictor of performance in dominance interactions ( here η ) and in turn reproductive asymmetry ( i . e . a high variance in fitness ) , the population is better able to survive when nutrients are severely limiting . Accordingly , previous theoretical and experimental studies in social spiders have also suggested a strong dominance hierarchy ensures that the colony is able to survive resource poor periods , as at least a few females are able to monopolise enough nutrients to breed [8 , 11] . We also note that the spread of high nutritional latitude under strong competition bears some similarities to an evolutionary “tragedy of the commons” [53] , because once the strategy becomes highly prevalent the mean fitness of the population becomes depressed to a lower level than might be the case if all ( or the vast majority of ) individuals to maintain low nutritional latitude . Our evolutionary model could be further expanded to give a more detailed representation of specific biological systems . First , we assumed that the fitness payoffs surrounding the IT are symmetrical . Geometric nutritional studies have shown that in some instances the fitness landscapes associated with the intake of nutrients may be asymmetrical [25] . A case in point is the predatory ground beetle ( Anchomenus doralis ) , where a female’s egg production displays an asymmetrical response to protein and lipid intake when mapped as a response landscape onto a protein-lipid nutrient-space [54] . Models considering the effects that asymmetrical fitness landscapes have on the evolution of nutritional strategies themselves , and in turn the consequences for social structure , are particularly exciting avenues of investigation . Second , geometric nutritional studies also demonstrate that different species follow different rules of compromise ( the extent to which they consume excesses of one nutrient relative to the IT to gain another which is limiting in the diet ) . The model described here conforms to what is known as the ‘nearest distance’ rule of compromise [23]: individuals seek to attain a nutritional state that minimises the Euclidean distance from the IT ( see Models ) . Some species , such as the migratory locust ( Locusta migratoria ) , appear to conform to such a rule of compromise when confined to a single food [31] . However , other rules of compromise are also followed . For example , the desert locust ( Schistocera gregaria ) follows what is known as an ‘equal distance’ rule of compromise , eating more of an imbalanced diet , over-consuming the excess nutrient to a greater degree ( and under-consuming the deficient nutrient to a lesser degree ) than L . migratoria , under the same no-choice experiment [31] . Evidence from this and other examples using the comparative approach suggests that the adoption of these two different rules of compromise closely associates with dietary breadth , with nutrient specialists adopting the nearest distance rule we implement here [23] . The study of the co-evolution of nutritional rules of compromise , dietary breadth , fitness-landscapes and other nutritional strategies ( e . g . nutritional latitude ) remains largely theoretical [23 , 25] . However , with the increasing application of nutritional geometry to a wider range of species , both in the lab and in the field , the comparative studies required to untangle the co-evolution between the aforementioned traits should soon be possible [23] . Within the selection mechanism implemented here , individuals must first attain a certain nutritional state to breed . Amongst those individuals with a high enough fitness to breed , relative fitness ( determined by proximity to the IT ) then governs overall representation in the subsequent generation ( i . e . , fitness proportionate selection; [32] ) . Thus , what we term the ‘general’ selection mechanism is most analogous to systems where reproductive asymmetries arise when resources become limiting . This mechanism of selection is typical of experimental outcomes in some social systems . For instance , female social spiders that do not attain enough nutrients ( lipids ) to reach a mature size at the end of the season are not capable of breeding , and larger individuals produce more offspring ( [8 , 13 , 55–57] c . f . [15] ) . We also explored the effects of truncated selection on the model output . Whilst these two selection regimes produced some broadly similar results , there were also differences; namely , with truncated selection there was a lack of selection on nutritional latitude in the absence of competition , but low nutritional latitude was favoured under even weak competition . Truncated selection is most analogous to social systems , where dominance hierarchies and reproductive asymmetries are always present , regardless of food availability . For example , in eusocial wasps ( Polistes; the inspiration for the original manifestation of the contest competition model we implement [27] ) linear hierarchies form amongst females , with reproduction limited to the individual at the top ( or the top few; [58] ) . For such species , where reproduction is always limited to a few individuals ( perhaps those best able to track the IT ) , a model operating truncated selection may be most appropriate . Additionally , it occurs to us that artificial selection experiments can use truncated selection; i . e . , the top few performing individuals are selected for breeding ( e . g . [59] ) . In the future , geometric ABMs such as ours may be used to generate predictions for selection experiments on nutritional strategies . However , those models should explicitly incorporate truncated selection as other modes of selection may produce inaccurate predictions . The models described here make simplifying assumptions about within population variation in nutritional requirements and the effects of nutritional state on fitness . For example , we only consider a single sex although sex differences in nutritional requirements may be ubiquitous ( e . g . [60] ) . Such assumptions seem justifiable on the basis of the biological systems that we are interested in . Considering sex specifically , the relationship between contest competition , reproductive asymmetry and nutritional state is often only profound ( or well understood ) in one sex . For example , in populations of social spiders female sex ratio bias tends to be very strong and males seem largely absent [13 , 14] . Thus it seems reasonable to assume that males play a relatively minor role in competition over nutrient access . When modelling the relationship between nutritional state and other social phenomenon ( e . g . collective behaviour and communal feeding [5] ) , however , it may be more realistic to model such variation . To incorporate this variation , rather than modelling a single intake target as we do here , one could include a bi-modal distribution of intake targets representing the differential requirements of each sex and individual heterogeneity simultaneously . In this instance , the combination of evolutionary algorithms , ABMs and the GF has allowed us to produce testable experimental predictions for how intra-specific competition affects the evolution of nutritional latitude and dietary breadth . The wider application of this integrated approach could be applied to assess how other nutritional strategies and ecological factors interact [30] . For instance , Lihoreau et al . [5] explore how collective decision-making can optimise the nutritional decisions of entire groups , a phenomenon that may be applicable to animals exhibiting a range of social interactions [61–63] . By expanding that model with an evolutionary algorithm , it would be possible to generate predictions for how nutritional strategies and social phenomenon co-evolve . The next step in combining evolutionary algorithms with GF-based ABMs will be to use spatially explicit models [30] . In this way researchers will be able to model the evolution of nutritional strategies in complex environments that are closely representative of real world ecosystems . Models were programmed in the software Netlogo [64] . Graphs were created and statistical calculations performed with R version 3 . 1 . 1 [65] . The model is described following the overview , design and details format of ABM description as widely recommended [66–68] . The code for the model can be found in S2 File . | Getting enough nutrients and at the right balance is among the primary challenges that an animal has to overcome . Animals that live in groups have the added complexity of competition among individuals over foods . We used an evolutionary simulation to explore how the intensity of such competition interacts with the composition of available foods to influence the strategies that an animal should use to meet its nutritional requirements . We found that two general strategies emerged . When competition was weak , animals that only locate and consume foods with an ideal balance of nutrients were favoured . However , when competition was strong , a strategy with which animals meet their nutritional requirements by eating large amounts of nutritionally imbalanced , but complementary , foods was optimal . These results implicate a role for competition for foods between animals within social groups in shaping dietary breadth . Evolutionary simulations such as those described here are important for understanding how different species evolve to meet their nutritional requirements in a range of ecological circumstances . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
]
| []
| 2015 | Evolving Nutritional Strategies in the Presence of Competition: A Geometric Agent-Based Model |
Homologous recombination ( HR ) is initiated by DNA double-strand breaks ( DSB ) . However , it remains unclear whether single-strand lesions also initiate HR in genomic DNA . Chicken B lymphocytes diversify their Immunoglobulin ( Ig ) V genes through HR ( Ig gene conversion ) and non-templated hypermutation . Both types of Ig V diversification are initiated by AID-dependent abasic-site formation . Abasic sites stall replication , resulting in the formation of single-stranded gaps . These gaps can be filled by error-prone DNA polymerases , resulting in hypermutation . However , it is unclear whether these single-strand gaps can also initiate Ig gene conversion without being first converted to DSBs . The Mre11-Rad50-Nbs1 ( MRN ) complex , which produces 3′ single-strand overhangs , promotes the initiation of DSB-induced HR in yeast . We show that a DT40 line expressing only a truncated form of Nbs1 ( Nbs1p70 ) exhibits defective HR-dependent DSB repair , and a significant reduction in the rate—though not the fidelity—of Ig gene conversion . Interestingly , this defective gene conversion was restored to wild type levels by overproduction of Escherichia coli SbcB , a 3′ to 5′ single-strand–specific exonuclease , without affecting DSB repair . Conversely , overexpression of chicken Exo1 increased the efficiency of DSB-induced gene-targeting more than 10-fold , with no effect on Ig gene conversion . These results suggest that Ig gene conversion may be initiated by single-strand gaps rather than by DSBs , and , like SbcB , the MRN complex in DT40 may convert AID-induced lesions into single-strand gaps suitable for triggering HR . In summary , Ig gene conversion and hypermutation may share a common substrate—single-stranded gaps . Genetic analysis of the two types of Ig V diversification in DT40 provides a unique opportunity to gain insight into the molecular mechanisms underlying the filling of gaps that arise as a consequence of replication blocks at abasic sites , by HR and error-prone polymerases .
Homologous recombination ( HR ) contributes to genome maintenance by repairing double-strand breaks ( DSBs ) and single-strand lesions . It accomplishes this by associating the damaged DNA with intact homologous sequences ( reviewed in [1] ) . Genetic studies of Escherichia coli indicate that DSBs are recognized by the RecBCD enzyme at the initial step of HR , while single-strand gaps are loaded with RecA with the help of the RecF , RecO and RecR ( RecFOR ) proteins [2] ( reviewed in [3] ) . In yeast and vertebrate cells , however , it remains unclear whether single-strand lesions can also directly stimulate HR , or if their replication leads to DSBs , which then stimulate HR . The process of DSB-induced HR is well characterized in the budding yeast [4] . First , DSBs are resected by a nuclease to generate a 3′ overhang . A major nuclease in this process is thought to be a complex containing three proteins: Mre11 , Rad50 and Nbs1 ( called the MRN complex ) ( reviewed in [5] ) . The role of the 3′–5′ exonuclease activity of purified Mre11 in DSB repair remains enigmatic , as DSB resection is of opposite polarity in vivo [6] . Recent studies indicate that the MRN complex requires another factor to function: CtIP , the ortholog of Sae2 and Ctp1 in S . cerevisiae and S . pombe , respectively [7]–[9] . Biochemical study demonstrated that Sae2 , a cofactor of the MRN complex , can process a single strand nick , and expand it [10] . The single-strand DNA generated adjacent to the DSB is coated with polymerized Rad51 , resulting in the formation of nucleoprotein filaments . The assembly of RAD51 at DNA damage sites is regulated by a number of RAD51 cofactors , including the tumor-suppressor gene BRCA1 ( Breast Cancer Susceptibility Gene 1 ) , BRCA2 , and five RAD51 paralogs ( RAD51B/C/D and XRCC2/3 ) ( reviewed in [11] , [12] ) . The Rad51-containing single-strand DNA filaments play a role in the search for homologous DNA sequences and subsequent strand invasion into homologous duplex DNA . The importance of the role of the MRN complex in genome maintenance is indicated by a marked increase in the number of spontaneously arising chromosomal breaks followed by cell death after depletion of Mre11 in DT40 cells [13] , and is also indicated by the high incidence of tumorigenesis in certain hereditary diseases: ataxia-telangiectasia-like diseases ( ATLD ) and Nijmegen breakage syndrome ( NBS ) , which result from hypomorphic mutations in the MRE11 and NBS1 genes , respectively [14]–[17] . A combination of HR and non-templated single-base changes contributes to Ig V sequence variation in chickens and in some mammalian species such as rabbits and cattle [18] . Similarly , the chicken DT40 B lymphocyte line undergoes templated HR-dependent diversification ( hereafter called Ig gene conversion ) as well as non-templated single-base substitutions ( hereafter called Ig hypermutation ) during in vitro passage [19]–[21] . HR introduces tracts of templated mutations to rearranged variable ( V ) regions [22]–[24] . An array of “pseudo-Vλ” regions , located upstream from the functional rearranged VJλ , provides donors for this non-reciprocal sequence transfer . Since donor and recipient segments have a ∼10% sequence divergence , sequential Ig gene conversion events are able to substantially diversify Ig V [24] . Both types of Ig V diversification are initiated by activation-induced deaminase ( AID ) , which forms uracil from deoxycytidine ( dC ) [25]–[27] . Uracil is subsequently removed by uracil-DNA-glycosylase- ( UNG ) mediated hydrolysis , which generates abasic sites [28]–[30] . In UNG−/− DT40 cells , the rate of C to T transitions is more than ten times greater than in UNG+/+ cells , indicating that more than 90% of the AID-induced uracil is accurately eliminated , presumably by base excision repair [28] . Non-templated hypermutation is generated as a consequence of translesion DNA synthesis ( TLS ) past abasic sites [31] . It is currently unclear how Ig gene conversion is induced by abasic sites , although it is likely that the abasic sites are converted to either single-strand gaps or DSBs , which in turn stimulate HR with upstream pseudo-Vλ segments . Current evidence points towards single-strand gaps , rather than DSBs , as the main downstream intermediate of abasic sites in the induction of Ig gene conversion for the following reason . In cells deficient in BRCA1 , BRCA2 or Rad51 paralogs , where Rad51 is not accumulated efficiently at DNA lesions , the impaired HR causes a shift of Ig V diversification from HR- to TLS-dependent hypermutation [20] , [32] , [33] . Cleavage of template strands containing abasic sites cannot occur prior to TLS past the abasic sites . Thus , a common substrate for both Ig gene conversion and TLS is likely to be a single-strand gap and/or a stalled replication fork [34] . We hypothesized that if Ig gene conversion was triggered by single-strand lesions but not by DSBs , it would not involve the MRN complex ( which is currently proposed as being involved in double-strand-break resection to generate recombinogenic 3′ ends ) . To test this hypothesis , we generated nbs1 hypomorphic mutant DT40 cells , where Nbs1 null mutant cells were rescued by an NBS1p70 transgene . The resulting ΔNBS1/NBS1p70 cells shared a phenotype very similar to cell lines established from patients with Nigmegen-breakage syndrome [35] , including significant reduction in the frequency of HR-dependent DSB repair . Unexpectedly , the defect of Nbs1 also suppressed Ig gene conversion by two orders of magnitude . To further define the role of the MRN complex in Ig gene conversion , we next attempted to reverse the defective Ig gene conversion by ectopically overexpressing chicken Exo1 [36]–[40] or E . coli Exo1 ( SbcB ) [41]–[43] . Exo1 is an evolutionarily conserved double strand-specific 5′ to 3′ exonuclease , and involved in mismatch repair in the eukaryotic cells . Additionally , the eukaryotic Exo1 can promote HR by facilitating 3′ tail formation at DSBs [38] , [39] . Although both eukaryotic Exo1 and SbcB expand single-strand gaps from single-strand breaks in mismatch repair , SbcB can digest single-strand DNA at an opposite direction , 3′ to 5′ , and thereby suppress DSB induced HR by removing 3′ overhang at DSBs ( reviewed in [3] ) . Remarkably , the ectopic expression of SbcB normalized Ig gene conversion , but overexpression of chicken Exo1 did not . Conversely , the ectopic expression of chicken Exo1 , but not SbcB , increased the frequency of DSB-dependent gene-targeting [44] , [45] , presumably by promoting the resection of DSBs . These data argue against the possibility that SbcB promotes Ig gene conversion by processing DSBs . Hence , these data support the notion that single-strand gaps may be supported a common direct precursor of both Ig gene conversion and error-prone gap-filling . In addition , our study thus suggests that the MRN complex is involved in HR , probably in two different ways: by processing DSBs and by generating recombinogenic single-strand lesions .
The chicken NBS1 gene is located on chromosome 2 , which is trisomic in DT40 cells . To completely inactivate the NBS1 gene , we generated deletion constructs containing different marker genes , a procedure designed to remove the entire reading frame of the NBS1 gene , including all 16 exons ( ∼30 kb ) ( Figure 1A ) . These targeting plasmids were sequentially transfected into wild-type ( WT ) DT40 cells , and the NBS1−/−/+ cells were isolated . To generate conditional NBS1-disrupted cells , we employed Cre-recombinase-mediated deletion of a chicken NBS1 transgene . NBS1−/−/+ cells were transfected with the transgene containing the WT NBS1 cDNA flanked by loxP sites on both sides ( the loxP-NBS1p95 transgene ) together with a Cre-ER expression vector [46] . The resulting NBS1−/−/+/loxP-NBS1p95 clones were transfected with targeting constructs to disrupt exons 1–16 or exons 13–16 , which encodes the Mre11-binding domain of the third NBS1 allele ( Figure 1B ) . We were only able to obtain targeted integration with the latter construct , because Nbs1 overproduction from the loxP-NBS1p95 transgene substantially reduced gene-targeting efficiency . The genotype of the NBS1−/−/Δ13–16/loxP-NBS1p95 ( hereafter ΔNBS1/loxP-NBS1p95 ) clones was confirmed by Southern-blot analysis of HindIII-digested genomic DNA for the disappearance of a WT 5 kb band ( Figure 1C ) . Western-blot analysis showed that ΔNBS1/loxP-NBS1p95 cells expressed levels of Nbs1p95 that were about 50 fold higher than the WT cells ( Figure 1D ) . ΔNBS1/loxP-NBS1p95 cells tended to grow more slowly than did WT cells ( Figure 1E ) , a phenotype that may be attributed to the overexpressed NBS1p95 . To investigate whether Nbs1p95 is required for cellular proliferation , ΔNBS1/loxP-NBS1p95 cells were treated with tamoxifen to activate the Cre recombinase , resulting in the deletion of the loxP-NBS1p95 transgene . ΔNBS1/loxP-NBS1p95 cells ceased proliferating four days after the addition of tamoxifen ( Figure 1E ) , with substantial numbers of dead cells ( data not shown ) . These observations indicate that NBS1 is required for cellular proliferation , as previously reported [47] . To investigate the cause of the cell death , we scored spontaneous chromosomal aberrations when the cells were dying . The tamoxifen-treated ΔNBS1/loxP-NBS1p95 cells indeed exhibited extensive spontaneous chromosomal breaks ( Figure 1F ) , as did Mre11 deficient cells [13] , indicating an essential role for Nbs1 in repairing lethal double-strand breaks . We also made conditional Rad50-depleted cells and found that they too exhibited an increase in the level of chromosomal breaks before cell death ( Figure S1 ) . Thus , a loss of Mre11 , Rad50 and Nbs1 has a very similar effect on the maintenance of chromosomal DNA in cycling cells , suggesting that the three molecules form a functional unit , as do the yeast ortholog proteins [5] . We wanted to test whether or not expression of Nbs1p70 could rescue the cells from cell death . To this end , we complemented ΔNBS1/loxP-NBS1p95 cells with an NBS1p70 transgene and generated ΔNBS1/loxP-NBS1p95/NBS1p70 clones . The Nbs1p70 protein contains an Mre11-binding site , but lacks both the FHA and BRCT domains ( Figure 2A ) [5] . Western-blot analysis verified the Nbs1p70 expression , which was about 30 times higher than the expression of endogenous Nbs1 ( Figure 2B ) . To remove the loxP-NBS1p95 transgene , ΔNBS1/loxP-NBS1p95/NBS1p70 cells were exposed to tamoxifen for three days , and isolated clones were examined for the expression of the Nbs1 protein . All surviving colonies expressed Nbs1p70 , but not WT Nbs1p95 , showing that their genotype is ΔNBS1/NBS1p70 ( Figure 2B ) . The resulting clones proliferated with slightly slower kinetics than did the ΔNBS1/loxP-NBS1p95 cells ( Figure 2C ) . We therefore conclude that Nbs1p70 is sufficient to rescue NBS1-deficient cells . This conclusion implies that the viability of previously described Nbs1-deficient DT40 cells might be attributable to the leaky expression of an N-terminally truncated protein [48] . Two representative ΔNBS1/NBS1p70 clones were further studied for their HR capability by measuring their gene-targeting frequency and sensitivity to DNA-damaging agents . Table 1 shows the ratio of targeted-to-random integration events at two loci . No gene-targeting events were detectable in the ΔNBS1/NBS1p70 clones . We next measured cellular sensitivity to ionizing radiation and camptothecin , a DNA-topoisomerase-I inhibitor [49] . Ionizing-radiation-induced DSBs are repaired by the two major DSB repair pathways , HR and nonhomologous end-joining [50] , whereas camptothecin-induced DSBs are repaired exclusively by HR [51]–[53] . Compared with WT cells , the ΔNBS1/NBS1p70 cells showed a significant increase in damage sensitivity , particularly to camptothecin ( Figures 2D and E ) . This is consistent with previous reports showing that Nbs1 promotes HR-mediated DSB repair [47] , [48] . The rate of Ig gene conversion was assessed by measuring the re-expression of surface immunoglobulin M ( sIgM ) in DT40 clones that carry a defined frameshift mutation in the light-chain Vλ gene [21] . Since the frameshift is eliminated by superimposed Ig gene conversion , leading to the production of Igλ , the rate of Ig gene conversion can be evaluated by measuring the kinetics of sIgM gain ( Figure 3A ) . Thirty subclones from each genotype were analyzed for sIgM-gain after 3 weeks of clonal expansion [33] , [54] . The median value of the fraction of sIgM+ cells was 1 . 84% for WT , 1 . 91% for NBS1−/−/+ and 0 . 75% for NBS1−/−/+/loxP-NBS1p95 cells ( Figure 3B ) . The reduced Ig gene conversion rate in NBS1−/−/+/loxP-NBS1p95 cells may result from the toxic effect of the overproduced Nbs1p95 protein . Two ΔNBS1/NBS1p70 clones displayed a significant decrease in gene conversion , with only 0 . 1–0 . 2% of subclones gaining sIgM , a level close to the background of the flow-cytometric analysis . To accurately evaluate the Ig gene conversion rate , we exposed populations of cells to trichostatin A , a histone-deacetylase inhibitor that increases the Ig gene conversion rate ∼50 fold [55] , [56] . Following culture for 3 weeks in trichostatin A , the sIgM gain was only elevated to 2 . 15% in the ΔNBS1/NBS1p70 cells , while the WT cells exhibited an increase from 1 . 84 to over 90% ( Figure 3C ) . This suggests that the intact MRN complex might promote Ig gene conversion , as reported previously [57] . Alternatively , the accuracy of Ig gene conversion in the ΔNBS1/NBS1p70 cells might be reduced , leading to a decrease in the re-expression of sIgM . To examine the accuracy of Ig gene conversion , we determined the VJλ-nucleotide sequences from unsorted cells treated with trichostatin A for 4 weeks ( Figure 3D ) . In trichostatin-A-treated unsorted WT cells , at least 42 Ig gene conversion events were detected among the 40 analyzed Vλ segments ( 1 . 3×10−2 events per Vλ per division ) . In contrast , the number of Ig gene conversion tracts was only one in 40 analyzed Vλ ( 3×10−4 events per Vλ per division ) in ΔNBS1/NBS1p70 cells . This 42-fold difference is comparable to the difference observed in the sIgM-gain assay ( Figure 3C ) . Ig V sequence analysis showed that the accuracy of these events is unaffected , as neither aberrant recombination nor accumulation of point mutations was found in ΔNBS1/NBS1p70 cells . To characterize the nature of Ig gene conversion , we also analyzed the VJλ nucleotide sequences of sorted sIgM+ revertants from ΔNBS1/NBS1p70 trichostatin-A-untreated cell populations . The frame-shift mutation in Ig Vλ [21] was indeed eliminated by superimposed gene conversion in all 40 analyzed fragments derived from ΔNBS1/NBS1p70 cells ( data not shown ) . Furthermore , we found no change in the pattern of gene conversion , such as length of gene-conversion tracts ( 84 nucleotides on average for both ΔNBS1/NBS1p70 and WT [56] ) or usage of pseudo-V donor segments , and no aberrant recombination ( data not shown ) . Thus , although the defective Nbs1 function reduces the rate of Ig gene conversion , it compromises neither its accuracy nor donor gene preference . To analyze Ig V hypermutation in ΔNBS1/NBS1p70 cells , we increased the level of AID expression by introducing an AID transgene into DT40 cells through retroviral infection [31] , [58] . We assessed Ig V diversification by determining the nucleotide sequence of Ig Vλ in unsorted cells at 14 days post-infection ( Figure 3D ) . WT and ΔNBS1/NBS1p70 cells exhibited similar levels of non-templated hypermutation: about 5 . 0×10−4 per nucleotide per division ( Figure 3F ) . Thus , a defect in Nbs1 does not affect Ig V hypermutation . AID overexpression increased the rate of Ig gene conversion from 5 . 2×10−4 to 1 . 3×10−2 per Vλ per division in 40 analyzed Vλ in WT cells ( Figures 3E and F ) . Surprisingly , the frequency of Ig gene conversion in ΔNBS1/NBS1p70 cells reached the level of the WT cells , i . e . , 1 . 6×10−2 per Vλ per division in 40 analyzed Vλ sequences . Thus , the frequency of gene conversion was increased 25 fold in WT cells and 307 fold in ΔNBS1/NBS1p70 cells by the ectopic expression of AID . No aberrant recombination events were observed . We conclude that a defect in Ig gene conversion in ΔNBS1/NBS1p70 cells is completely normalized by the ectopic expression of AID . This observation suggests two scenarios , described as follows: DSBs might initiate Ig gene conversion in a manner similar to the way in which AID-dependent DSBs trigger Ig-class switch recombination ( reviewed in [59] ) . Thus , higher levels of AID expression may result in multiple deamination events on both strands , with the ensuing incisions more likely to generate DSBs carrying the 3′ tails even in the absence of the intact MRN complex . Alternatively , Ig gene conversion might be initiated by single-strand gaps . In the latter model , the formation of multiple abasic sites and incisions in one strand results in the generation of recombinogenic single-strand gaps , after which Nbs1p70 is no longer required for the processing of single-strand lesions to stimulate Ig gene conversion . There are two major DSB repair pathways: HR and nonhomologous end-joining ( NHEJ ) . Two studies previously reported the negative effect of NHEJ on Ig gene conversion [60] , [61] , which suggests that DSBs are an intermediate in Ig gene conversion . However , the IgV sequence from unsorted populations show only a two-fold increase [60] or no increase [61] in the rate of Ig gene conversion in NHEJ deficient clones in comparison with WT cells . Furthermore , another study [20] and our own work did not reproduce their data ( data not shown ) . In general , it is difficult to draw a conclusion from at best a two-fold difference due to possible clonal variations in DT40 cells . To determine the involvement of DSBs in Ig gene conversion more accurately , we performed two experiments: 1 ) Detection of deletions within Vλ in RAD54−/− and KU70−/−RAD54−/− clones [50] ( Figure 4A ) , and 2 ) terminal deoxytransferase ( TdT ) expression ( Figure 4B and C ) . In the first experiment , the effect of Ku70 depletion on Ig V diversification was investigated in the RAD54−/− background , where HR is not completed despite the accumulation of Rad51 at sites with DNA damage [62] , [63] . Since the loss of Rad54 is substantially suppressed by NHEJ in the repair of x-ray-induced DSBs [50] , we assumed that if Ig gene conversion is initiated by DSBs , a majority of such breaks would eventually be repaired by NHEJ in RAD54−/− cells , as are x-ray-induced DSBs . Thus , the additional inactivation of Ku70 in RAD54−/− cells would result in the deletion of Vλ sequences , as illustrated by the extensive deletion of the V ( D ) J coding joint in NHEJ-defective B precursors [64] . To detect deletion of Ig Vλ , we determined the nucleotide sequences of Vλ in AID overexpressing WT , RAD54−/− and KU70−/−RAD54−/− cells ( Figure 4A ) . The RAD54−/− and KU70−/−RAD54−/− cells exhibited only one ( 6 . 6×10−4 per Vλ per division in 36 analyzed Vλ sequences ) and three ( 1 . 7×10−3 per Vλ per division in 43 analyzed Vλ sequences ) single-nucleotide deletion events , respectively . There were no longer deletions . Thus , unlike the repair of x-ray-induced DSBs , this result does not support the idea that unrepaired AID-induced damage at the Vλ segment of RAD54−/− cells is subject to NHEJ-mediated DSB repair . In the second experiment , we overexpressed TdT , which added nucleotides at DSBs in a template-independent manner during V ( D ) J-joining [65] , [66] . TdT has been shown to access the Ig locus when expressed in a human cell line that undergoes constitutive Ig somatic mutation in vivo [67] . If DSBs are a frequent trigger for Ig gene conversion , TdT-mediated nucleotide additions should be readily demonstrated at Ig Vλ in DT40 cells expressing TdT . We therefore transfected a TdT expression plasmid into WT DT40 cells and performed an Ig Vλ sequence analysis . The TdT overproduction affected neither point mutation nor Ig gene conversion frequency ( Figure 4B ) . In contrast to the effect seen in hypermutating Ramos cells [67] , we could not detect any difference in insertion frequency between WT cells with or without TdT overproduction ( Figure 4C ) . Furthermore , all the insertions were of a single base pair , with the exception of one sequence where a deletion of 19 base pairs was associated with the insertion of CCC , which could not be accounted for by a pseudogene donor ( ACAACGTCCC . . 19 bp del…GACAACC ) . This is the only example within the analyzed 109 sequences that may reflect the activity of TdT . The absence of additional nucleotides at Ig Vλ indicates that DSBs are not intimately associated with Ig gene conversion . In summary , these data support the hypothesis that the initiating lesions for Ig gene conversion are predominantly single-strand gaps rather than DSBs . Hence , AID overexpression that normalizes the impaired Ig gene conversion of ΔNBS1/NBS1p70 cells ( Figure 3D ) possibly does so as a consequence of the formation of multiple incisions in one strand , which promotes the generation of recombinogenic single-strand gaps even in the absence of the intact MRN complex . This hypothesis is also supported by a previous biochemical study , which demonstrates that AID processively deaminates C residues on a single-strand DNA [68] . If Ig gene conversion is triggered by single-strand lesions , then the MRN complex is likely to contribute to Ig gene conversion , possibly by converting small single-strand lesions to larger , more recombinogenic gaps . To test this hypothesis , we attempted to normalize the impaired Ig gene conversion of the Nbs1-deficient cells by overproducing nucleases whose activity is precisely characterized . These nucleases included Exo1 [36]–[40] and SbcB [41]–[43] . Using a retroviral vector , we introduced individual nuclease transgenes into DT40 cells and established overproducing clones . We cultured individual clones for 2 weeks and determined the nucleotide sequences of the Vλ segment . Remarkably , SbcB dramatically increased the rate of Ig gene conversion in ΔNBS1/NBS1p70 ( Figure 5A–C ) . Unexpectedly , this increase was not observed in ΔNBS1/NBS1p70 cells overexpressing chicken Exo1 , presumably because this exonuclease can work only in a physiological context such as during mismatch repair in the chicken cell line . The frequency of Ig gene conversion in SbcB overproducing Nbs1-deficient cells reached 4 . 2×10−3 per Vλ per division in analyzed 45 Vλ sequences , a level higher than the gene-conversion frequency of the WT cells ( Figure 5B ) . Ectopic SbcB expression did not significantly change the position ( compare Figures 3D and 5A ) or pseudo-V usage ( Figure 5D ) of the Ig gene conversion . In contrast , the nature of the Ig gene conversion was distinctly different between trichostatin-A-treated WT cells and those overproducing AID ( Figure 3D and 5A ) . Presumably , this is because , according to a previous biochemical study [68] , overproduced AID can deaminate even “cold” spots at Ig V , thereby initiating HR from a wider range of nucleotide sequences than does the endogenous AID of DT40 cells . Thus , it is likely that SbcB promotes Ig gene conversion in the same physiological manner as does the MRN complex . SbcB has the 3′ to 5′ exonuclease activity specific for single-stranded DNA in vitro [41]–[43] , and can thereby expand single-strand gaps to stimulate HR in vivo . Hence , we conclude that the MRN complex contributes to Ig gene conversion in a similar manner by increasing the size of single-strand gaps . To test whether overproduced SbcB affects HR-dependent repair of DSBs in vivo , we measured the effect of SbcB overproduction on DSB repair . To this end , we measured I-Sce1-induced gene-targeting [44] . We inserted the S2neo fragment carrying the I-SceI recognition site [69] into the OVALBUMIN locus of DT40 cells , subsequently transfecting the 3′neo fragment [69] ( gene-targeting vector in Figure 5E ) together with an I-SceI expression plasmid . Since gene-targeting of 3′neo into S2neo leads to the restoration of the WT neomycin-resistance ( neoR ) gene , the efficiency of gene-targeting events can be analyzed by measuring the frequency of neoR colonies . As previously observed [45] , the co-transfection of the I-SceI-expression plasmid increases the gene-targeting frequency of 3′neo by more than three orders of magnitude . To test whether SbcB affects DSB-induced gene-targeting , we measured gene-targeting frequency following transfection of both the 3′neo gene-targeting fragment and the I-SceI-expression plasmids , along with either a nuclease-expression-plasmid ( SbcB or the chicken Exo1-expression plasmids ) or a negative control vector into WT DT40 cells . The ectopic expression of SbcB had no impact on DSB-induced gene-targeting ( Figure 5F ) . In contrast , overproduction of chicken Exo1 increased the frequency of gene-targeting events more than 10 fold . This observation argues against the involvement of overproduced SbcB in DSB repair .
Two mechanisms could underlie the AID-dependent initiation of Ig gene conversion . The first assumes that AID-dependent single-strand lesions are converted to DSBs ( possibly by blocking replication in one of the two sister chromatids ) , which stimulate Ig gene conversion . The second states that AID-dependent single-strand lesions directly trigger Ig gene conversion . The first scenario is unlikely for five reasons . First , in brca1 , brca2 and rad51-paralog DT40 mutants , which are defective in the accumulation of Rad51 at sites of DNA damage , inefficient repair of AID-induced lesions activates TLS associated with hypermutation at dC∶dG basepairs [20] , [32] , [33] . Thus , the AID-induced substrate for HR is also likely to be the substrate for TLS-dependent Ig V hypermutation . Since effective TLS requires that there is no cleavage of the abasic-site-containing strand , it seems therefore plausibe that unfilled gaps directly stimulate Ig gene conversion in HR-proficient cells . ( Figure 6 ) . Second , if AID directly causes DSBs in Ig Vλ , such breaks would likely be repaired primarily by NHEJ in HR-deficient cells . Although it has been shown that AID-mediated DSBs trigger Ig-class switch recombination , which is partially dependent on NHEJ-mediated DSB repair [70] , we did not obtain evidence for the involvement of NHEJ in Ig gene conversion , even in RAD54−/− cells ( Figure 4A ) , where a late step of HR is compromised [62] , [63] . This observation conflicts with the critical role NHEJ plays in the repair of X-ray-induced DSBs , as evidenced by the significant increase in sensitivity to x-rays in KU70−/−RAD54−/− cells compared with RAD54−/− cells [50] . Third , overexpression of terminal deoxytransferase failed to add extra-nucleotide sequences at the Ig Vλ of DT40 cells ( Figure 4B and C ) . This observation argues against the significant association of Ig gene converstion with DSBs , because N nucleotides are inserted at the DSBs , as observed in DSB-induced V ( D ) J recombination [65] , [66] . Fourth , although chicken Exo1 overproduction significantly increased the frequency of DSB-induced HR ( Figure 5F ) , as observed in yeast [38] , [39] , the overproduction of SbcB did not enhance DSB-induced gene-targeting . However , SbcB reversed the defective Ig gene conversion in the Nbs1-deficient DT40 cells . Moreover , it is believed that SbcB suppresses DSB-induced HR , because its 3′ to 5′ exonuclease activity may remove the 3′ protruded tails from DSBs ( reviewed in [3] ) . Collectively , these data suggest that DSBs do not play a major role in triggering Ig gene conversion , and that it is more likely that single-strand gaps formed by the sequential action of AID , UNG and the MRN complex directly stimulate Ig gene conversion . At one time , models for both DSB- and nick-initiated HR were proposed [71] , [72] ( reviewed in [1] ) . The finding of DSBs during meiosis , as well as the development of the restriction-enzyme-induced HR model , established the DSB as the main initiator of HR [73] , [74] . However , accumulating evidence indicates that single-strand lesions are indeed responsible for the initiation of HR in both RecFOR-dependent HR in E . coli and in mutant V ( D ) J recombinase-induced HR in episomal plasmids [2] , [75] . Adding to this evidence , our study indicates that Ig gene conversion is a form of HR that is directly stimulated by single-strand lesions on chromosomal DNA in higher eukaryotic cells . The question remains as to whether or not single-strand gap-induced HR effectively contributes to the release of the replication block in the absence of accompanying DSBs . The notion that single-strand lesions directly stimulate Ig gene conversion indicates that , like SbcB , the MRN complex may promote HR by converting single-strand breaks to more recombinogenic substrates such as single-strand gaps . In fact , according to the nick-initiating HR model , the initial nick is expanded into a single-strand gap to trigger HR [72] . Moreover , the presence of such activity is suggested by the biochemical study of CtIP , a protein that physically interacts with the MRN complex [8] , [10] . On the other hand , Larson et al . indicate that the MRN complex incises a strand near an abasic site [76] . However , if this activity plays a dominant role in the initiation of Ig gene conversion , one cannot explain why the subsequent defect in the accumulation of Rad51 at the incision in the rad51 paralog and brca mutant shift Ig V diversification from HR- to TLS-mediated hypermutation [20] , [32] , [33] . Nonetheless , it is possible that the incision activity accounts for a fraction of Ig gene conversion . A defect in this incision activity might be substituted by AID overexpression , as it could introduce multiple AP sites , which makes less effective AP endonuclease compensate for the defective incision activity of the mutant Mre11 complex in ΔNBS1/NBS1p70 cells . Figure 6 presents two models for the participation of the MRN complex in Ig gene diversification . In both models , AID-mediated catalysis and subsequent hydrolysis of uracil lead to the formation of abasic sites . The first model assumes an endonuclease that can cleave the opposite strand of the abasic-site-containing strand ( Figure 6A ) , while the second model hypothesizes single-strand gap formation as a result of stalled replication ( Figure 6B ) . The MRN complex facilitates HR by increasing the length of gaps in both models . Quick and copious recruitment of Rad51 at DNA lesions triggers Ig gene conversion , whilst poor recruitment leads to translesion DNA synthesis past abasic sites by error-prone polymerases . In the second model , it is still unclear why , despite the 10% sequence divergence between pseudo-V donor and V ( D ) J recipient fragments , competition between equal sister-chromatid HR and Ig gene conversion ( Figure 6B ) does not fully inhibit homologous recombination in the latter [24] . Presumably , extensive processing of single-strand lesions by the MRN complex and SbcB allows for homologous recombination , whilst impaired processing inhibits both TLS and Ig gene conversion ( Figure 6B ) . The overproduction of AID might form gaps between two adjacent abasic sites on one strand , thereby suppressing the defective processing of single-strand lesions in ΔNBS1/NBS1p70 cells ( Figure 6B ) . Additionally , the MRN complex contributes to Ig gene conversions through its incision activity [76] , and its defect in ΔNBS1/NBS1p70 cells is rescued by the formation of multiple AP sites in AID overexpressing cells .
All genomic fragments in the NBS1-targeting constructs were amplified from DT40 genomic DNA using LA-PCR ( Takara Bio , Kyoto ) with the primers indicated below . To make the NBS1Δ1–16 plasmid , the upstream and downstream arms were amplified with 5′-AGCGTCGACCCCGCGTATTTCAGCAGCCTG-3′ and 5′-AAAAGCTTTGGTTCCTCGGTGCTCCTCACC-3′ primers and 5′-ATCTGAAGCTTGCTCCACTGATATGTTTGC-3′ and 5′-AAGCGGCCGCTTTGTGATTCAAACACTGGA-3′ primers , respectively . The resulting amplified upstream fragment was cut at the NotI site ( derived from genomic sequence ) followed by Klenow treatment and subsequently a HindIII cut . The 2 . 5 kb blunt-end HindIII fragment was cloned into the XhoI ( blunt ended with Klenow treatment ) HindIII site of pBluescript II ( Stratagene ) ( named the pBS/NBS1 5′ arm ) . Two oligonucleotides , containing either EcoRI-BamHI-BglII-SalI or BamHI-BglII-HindIII , were inserted into the EcoRI-SalI or BamHI-HindIII site of the pBS/NBS1 5′ arm plasmid . The 3 . 5 kb 3′ arm was inserted into the HindIII-NotI site of the pBluescript ( pBS/NBS1 3′ arm ) . To make the NBS1Δ1–16 blasticidin ( Bsr ) gene-disruption construct , the BsrR marker cassette was cloned into the BamHI site of the pBS/NBS1 5′ arm plasmid ( with EcoRI-BamHI-BglII-SalI sites ) , followed by the ligation of the resulting plasmid ( cut with SalI and NotI ) with the SalI-NotI fragment containing the 3′ arm from the pBS/NBS1 3′ arm plasmid ( NBS1Δ1–16 Bsr ) . Similarly , a Puromycin- ( PuroR ) marker cassette was cloned into the BamHI site of the pBS/NBS1 5′ arm plasmid , followed by the insertion of the HindIII and NotI fragment of the 3′ arm from the pBS/NBS1 3′ arm between the HindIII and NotI sites ( NBS1Δ1–16 PuroR ) . To make the NBS1Δ13–16 gene-disruption construct , the upstream arm was amplified with 5′-TTGGAGGTCGACAAGCAAAACTGATGACGG-3′ and 5′-AAAGGATCCTCTTGGACAGCTGACAACCAG-3′ primers . The 7 . 5 kb SalI- ( in genomic sequence ) BamHI fragment of the amplified fragment was cloned into the XhoI-BamHI site of the pBS/NBS1 5′ arm plasmid ( named pBS/Δ13–16 5′ arm ) . A neomycin- ( Neo ) marker gene cassette was cloned into the BamHI site of the pBS/Δ13–16 5′ arm . The resulting plasmid was ligated with the SalI-NotI fragment of the 3′ arm used for the NBS1Δ1–16 Bsr construct ( NBS1Δ13–16 ) . A probe for Southern hybridization was amplified from DT40 genomic DNA using the primers 5′-AAGCTTGCATGCAAACCTTGTTTTATCTTC-3′ and 5′-TGACTGCACTCTGCTCATTCTGGTATCTTC-3′ . The following two expression vectors were generated: 1 ) pBluescript-loxP-chicken β-actin promoter-multiple cloning site-internal ribosomal entry site ( IRES ) enhanced green fluorescent protein ( EGFP ) gene-loxP ( named the plox vector ) , and 2 ) pBluescript-chicken β-actin promoter-multiple cloning site ( named the pβ-actin vector ) . Chicken Nbs1p95 cDNA was amplified from pBS-NBS1 by PCR with the 5′-AAGAATTCAGAAAGAACTAGAAGGTTAAG-3′ and 5′-TTTGGGCTCGAGTTACAGATCCTCTTCTGAGATGAGTTTTTGTTCTCTTCTCCTCTTCACATTAGG-3′ primers and cloned into the BglII-SalI site of plox ( plox/NBS1p95 ) . To make the NBS1p70 cDNA ( Figure 2A ) , NBS1p95 cDNA served as template DNA for PCR amplification using primers 5′-AAGGATCCATGGATGAGCCTGCCATTGG-3′ and 5′-TTTGGGCTCGAGTTAAGCGTAATCTGGAACATCGTATGGGTATCTTCTCCTCTTCACATTAGG-3′ , and the amplified fragment was inserted into the BamHI-NotI site of the pβ-actin plasmid ( pβ-actin/NBS1p70 ) . Chicken Rad50 cDNA was amplified by a standard RT-PCR method with primers 5′-ATGGCCAAGATTGAGAAAATGAGCATCC-3′ and 5′-TTAATGAACGTATGAGCCAAGGGAGC-3′ , and then cloned into pTRE2 ( Clontech ) ( pTRE2/RAD50 ) ( Accession #XM_414645 ) . Two RAD50 disruption constructs , RAD50-Bsr and RAD50-HisD , were expected to delete exon11 to 13 encoding amino-acid sequences from 579 to 735 . The 3 . 9 kb 5′ arm was amplified from DT40 genomic DNA using primers 5′-TGCCATCAAGAGGAATCCAACTGGCCGTTA -3′ and 5′-CTCAGTGCTTTTGCCATGAAGCCAGTCTTC-3′ and cloned into pBluescript KS ( + ) . The resulting plasmid was inserted with the 1 . 4 kb SpeI-SacI genomic fragment including exon 14 , which was excised from a phage clone derived from the chicken genomic DNA library , where it served as the 3′ arm in the RAD50 disruption construct . Lastly , marker cassettes , Bsr or HisD , were inserted into the BamHI site to generate the RAD50-Bsr or RAD50-HisD gene-disruption construct . The genomic 3 . 4 kb SacI-EcoRI fragment , which is located at downstream of the 3′ arm , was used as a probe for Southern-blot analysis . Cells were cultured in RPMI1640 , supplemented with 10−5 M β-mercaptoethanol , 10% fetal-calf serum and 1% chicken serum ( Sigma , St Louis , MO ) at 39 . 5°C . Methods for DNA transfection and genotoxic treatments are as described previously [77] . WT DT40 cells were sequentially transfected with NBS1Δ1–16 BsrR and subsequently with NBS1Δ1–16-PuroR-targeting constructs to obtain NBS1−/−/+ cells . They were then transfected with an expression vector containing Cre-estrogen receptor chimeric recombinase ( pANMerCreMer [46] ) together with the plox/NBS1p95 plasmid . The resulting NBS1−/−/+/loxP-NBS1p95 cells were transfected with the NBS1Δ13–16 gene-disruption construct to obtain ΔNBS1/loxP-NBS1p95 . ΔNBS1/loxP-NBS1p95 cells were transfected with the pβ-actin/NBS1p70 vector to make ΔNBS1/loxP-NBS1p95/NBS1p70 cells . ΔNBS1/NBS1p70 cells were generated by exposing ΔNBS1/loxP-NBS1p95/NBS1p70 cells to 100 nM tamoxifen for 3 days followed by subcloning , as described previously [46] . WT DT40 cells were transfected with the RAD50-Bsr disruption construct to generate RAD50+/− cells . They were co-transfected with the pTRE2/RAD50 and pTet-off ( Clontech ) plasmids simultaneously to make RAD50+/−/tetRAD50 cells . These cells were transfected with the RAD50-HisD construct to generate RAD50−/−/tetRAD50 cells . Conditional inactivation of the RAD50 transgene was done using tetracycline as previously described [13] . Clonogenic survival was monitored by a colony-formation assay , as described previously [77] . To measure sensitivity to camptothecin ( Topogene , Columbus , OH ) , appropriate numbers of cells were plated into six-well cluster plates containing the complete medium and 1 . 5% methylcellulose ( Aldrich , Milwaukee , WI ) , supplemented with camptothecin . Colony numbers were counted at 7 and 14 days , and the survival percentage was determined in terms of the number of colonies of untreated cells . To measure ionizing-radiation sensitivity , serially diluted cells were plated in the medium containing methylcellulose , irradiated with a 137Cs γ-ray source and then incubated . Measurement of chromosome aberrations was carried out as previously described [77] . Methods described previously were used for the preparation of whole-cell extracts and western-blot analysis , with the following modifications . For western-blot analysis , the mouse monoclonal anti-human Nbs1 antibody ( BD Transduction Laboratories catalog #611871 ) was used at a 1∶100 dilution , and HRP-conjugated donkey anti-mouse IgG antibody ( Santa Cruz Biotechnology catalog #sc-2314 ) was used at a 1∶5000 dilution . Chicken Rad50 antiserum was raised in a rabbit against a whole protein of chicken Rad50 . For the western-blot analysis , rabbit polyclonal anti-chicken Rad50 antibody was used at a 1∶100 dilution , and HRP-conjugated donkey anti-rabbit IgG antibody ( Santa Cruz Biotechnology catalog #sc-2004 ) was used at a 1∶5000 dilution . For the western-blot analysis , rat monoclonal anti-mouse AID antibody ( kindly provided by Dr . K . Kinoshita , Kyoto University ) was used at a 1∶500 dilution , and HRP-conjugated donkey anti-rat IgG antibody ( Jackson ImmunoResearch catalog #712-035-150 ) was used at a 1∶5000 dilution . To analyze the frequency of targeted integration events at the OVALBUMIN [78] and HPRT [79] loci , their disruption constructs were transfected into cells . Following selection of clones resistant to appropriate antibiotics , Southern-blot analysis was performed . We confirmed that ΔNBS1/NBS1p70 cells retained the same frame-shift mutation in the V sequence as do WT cells [21] . Generation frequency of surface IgM ( sIgM ) loss variants as well as sIgM-gain revertants were monitored by flow-cytometric analysis of cells that had been expanded for 3 weeks after subcloning and then stained with fluorescein isothiocyanate-conjugated ( FITC ) goat anti-chicken IgM ( Bethyl , Montgomery , TX ) . At least 30 subclones were analyzed in each genotype . To enhance Ig gene conversion , trichostatin A ( TSA , Wako Osaka , concentration: 1 . 25 ng/ml ) was added to a mixture of sIgM-negative subclones from WT and the ΔNBS1/NBS1p70 #1 genotypes shown in Figure 3B . The fraction of sIgM+ revertants was monitored over time , as described previously [55] . In each analysis , the abundance of sIgM-positive cells was determined as the percentage of live cells whose FITC fluorescence fell at least eight fold more than the FITC fluorescence peak of sIgM negative cells . Ig gene conversion frequency of unsorted cells was calculated based on the number of gene-conversion events , of analyzed Vλ clones and of cell divisions . For retrovirus infection , the pMSCV-IRES-GFP recombinant plasmid was constructed by ligating the 5 . 2 kb BamHI-NotI fragment from pMSCVhyg ( Clontech ) with the 1 . 2 kb BamHI-NotI fragment of pIRES2-EGFP ( Clontech ) . Mouse AID [58] or chicken ExoI ( Accession #AB084249 ) or SbcB cDNA was inserted between the BglII and EcoRI sites of pMSCV-IRES-GFP [58] . The preparation and infection of retroviruses were carried out as previously described [58] . Expression of the GFP was confirmed by flow cytometry . The efficiency of infection was more than 90%m as judged by GFP expression . Cells were sub-cloned into 96 well-plates a day after infection . After 2 weeks , clones displaying a bright GFP signal were determined by FACS analysis . WT DT40 cells were transfected with a pSV2neo-based plasmid containing human TdT under control of the β-globin promoter and IgH enhancer [67] by electroporation as previously described . Clones were analyzed for TdT expression by indirect immunofluorescence microscopy using a mouse monoclonal anti-TdT ( Dako ) followed by anti-mouse Igκ conjugated to FITC . TdT-positive clones were expanded for 4 weeks , following which Ig-negative loss variants were sorted by FACS and the rearranged light-chain gene sequenced and analyzed as previously described [20] . DNA was extracted from three to five clones from genotypes at 14 days after AID , Exo1 or SbcB retrovirus infection , or at 28 days after TSA treatment . PCR-amplified fragments of Vλ segments were cloned into a plasmid and subjected to base-sequence analysis . Rearranged Vλ was amplified by PCR with Pyrobest DNA polymerase ( Takara Bio ) ( 30 cycles of 94°C for 30 s , 60°C for 1 min , and 72°C for 1 min ) with 5′-CAGGAGCTCGCGGGGCCGTCACT-GATTGCCG-3′ and 5′-GCGCAAGCTTCCCCAGCCTGCCGCCAAGTCCAAG-3′ primers , as previously described [20] . PCR products were cloned into the TOPO pCR2 . 1 cloning vector ( Invitrogen ) and sequenced with the M13 forward ( −20 ) or reverse primer using an ABI PRISM 3100 sequencer ( Applied Biosystems ) . Sequence alignment using GENETYX-MAC ( Software Development , Tokyo , Japan ) allowed the identification of changes from the parental sequences in each clone . Differentiating between non-templated nucleotide substitutions and gene conversion was carried out as previously described [20] . The rate of hypermutation was calculated based on mutation frequency and number of cell divisions ( 42 cycles in WT and 37 cycles in ΔNBS1/NBS1p70 for 14 days ) . 107 cells were suspended in 0 . 1 ml Nucleofector Solution T ( amaxa ) , and electroporated using a Nucleofector ( amaxa ) at program B-23 . 2 µg of linear 3′ neo DNA and 2 µg of circular I-SceI expression vector ( pcBASce ) , together with 2 µg of either control ( pBluescript II KS+ ) , SbcB or chicken Exo1 expression vector , were transfected . 3′ neo DNA was amplified by PCR from the SCneo neo substrate plasmid [69] using Phusion DNA polymerase ( Finnzymes ) ( 30 cycles at 94°C for 30 s , 60°C for 30 s , and 72°C for 2 min ) , with 5′-GGATCGGCCATTGAACAAGATGGATTGCAC-3′ and 5′-GGAAACAGCTATGACCATGATTACGCCAAG-3′ primers . The amplified fragment was used for electroporation , as previously described [44] . 24 hours after electroporation , the number of live cells was counted by FACS and transferred to 96 well-cluster trays with or without 2 . 0 mg of G418 per ml . Cells were grown for 7 days , and HR frequencies were calculated by the following equation: HR frequency ( colonies/cell ) = number of G418-resistant colonies/ ( plating efficiency of transfected cells in the absence of G418×number of live cells determined by FACS at 24 hour after electroporation ) [44] . | An important class of chemotherapeutic drugs used in the treatment of cancer induces DNA damage that interferes with DNA replication . The resulting block to replication results in the formation of single-strand gaps in DNA . These gaps can be filled by specialized DNA polymerases , a process associated with the introduction of mutations or by recombination with an undamaged segment of DNA with an identical or similar sequence . Our work shows that diversification of the antibody genes in the chicken B cell line DT40 , which is initiated by localized replication-stalling DNA damage , proceeds by formation of a single-strand intermediate . These gaps are generated by the action of a specific nuclease complex , comprising the Mre11 , Rad50 , and Nbs1 proteins , which have previously been implicated in the initiation of homologous recombination from double-strand breaks . However , in this context , their dysfunction can be reversed by the expression of a bacterial single-strand–specific nuclease , SbcB . Antibody diversification in DT40 thus provides an excellent model for studying the process of replication-stalling DNA damage and will allow a more detailed understanding of the mechanisms underlying gap repair and cellular tolerance of chemotherapeutic agents . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"oncology",
"immunology",
"molecular",
"biology/dna",
"repair"
]
| 2009 | Genetic Evidence for Single-Strand Lesions Initiating Nbs1-Dependent Homologous Recombination in Diversification of Ig V in Chicken B Lymphocytes |
Transient Receptor Potential ( TRP ) channels serve as temperature receptors in a wide variety of animals and must have played crucial roles in thermal adaptation . The TRP vanilloid ( TRPV ) subfamily contains several temperature receptors with different temperature sensitivities . The TRPV3 channel is known to be highly expressed in skin , where it is activated by warm temperatures and serves as a sensor to detect ambient temperatures near the body temperature of homeothermic animals such as mammals . Here we performed comprehensive comparative analyses of the TRPV subfamily in order to understand the evolutionary process; we identified novel TRPV genes and also characterized the evolutionary flexibility of TRPV3 during vertebrate evolution . We cloned the TRPV3 channel from the western clawed frog Xenopus tropicalis to understand the functional evolution of the TRPV3 channel . The amino acid sequences of the N- and C-terminal regions of the TRPV3 channel were highly diversified from those of other terrestrial vertebrate TRPV3 channels , although central portions were well conserved . In a heterologous expression system , several mammalian TRPV3 agonists did not activate the TRPV3 channel of the western clawed frog . Moreover , the frog TRPV3 channel did not respond to heat stimuli , instead it was activated by cold temperatures . Temperature thresholds for activation were about 16 °C , slightly below the lower temperature limit for the western clawed frog . Given that the TRPV3 channel is expressed in skin , its likely role is to detect noxious cold temperatures . Thus , the western clawed frog and mammals acquired opposite temperature sensitivity of the TRPV3 channel in order to detect environmental temperatures suitable for their respective species , indicating that temperature receptors can dynamically change properties to adapt to different thermal environments during evolution .
Animals adapt to environmental temperature changes by sensing both their body and ambient temperatures . Thermal stimuli are detected by temperature receptors and transmitted by the peripheral nerves in which they reside [1]–[4] . Thus , temperature receptors must have served crucial roles in adaptation to thermal environments during the course of evolution . In mammals , temperature receptors are ion channels that are activated by thermal stimuli [1]–[4] . In humans and rodents , nine temperature receptors have currently been identified and all of them belong to the transient receptor potential ( TRP ) cation channel superfamily and are called “thermoTRPs” . These nine thermoTRPs are further classified into three subfamilies: four belong to the TRP vanilloid subfamily ( TRPV1-TRPV4 ) , four to the TRP melastatin subfamily ( TRPM2 , TMPM4 , TRPM5 , and TRPM8 ) and one to the TRP ankyrin subfamily ( TRPA1 ) [1] , [3] . Phylogenetic analysis of vertebrate thermoTRP homologs revealed that the genes encoding TRPV1-TRPV4 , TRPM2 , TRPM4 , TRPM5 , and TRPM8 are unique to vertebrates [5] . Most of these genes emerged in the common ancestor of teleost fishes and terrestrial vertebrates through repeated gene duplications; subsequent sequence divergence resulted in a thermoTRP repertoire with different physiological properties . In humans and rodents , TRPV1 and TRPV2 are activated by noxious high temperatures , TRPM8 and TRPA1 by cold temperatures , and TRPV3 , TRPV4 , TRPM2 , TRPM4 , and TRPM5 by warm temperatures [1]–[4] . In addition to thermal stimuli , thermoTRPs are also activated by various physical and chemical stimuli [1]–[4] . Thus , thermoTRPs are involved in various sensory transductions and required for the adaptation to ambient environments . TRPV3 and TRPV4 perceive warm temperatures in homeothermic animals such as mammals [6]–[8] and thus must play important roles in body temperature regulation . Consistent with this idea , TRPV3 knockout mice showed abnormalities in sensing ambient temperatures near their body temperature [9] . However , whether these warm-temperature receptors are also physiologically important for the ectothermic vertebrates remains unknown . With respect to the TRPV4 gene , all of the vertebrate species thus far examined possess TRPV4 orthologs with highly conserved amino acid sequences among these different vertebrate species [5] . Regarding the TRPV3 gene , although all terrestrial vertebrate species thus far examined possess one copy of a TRPV3 orthologous gene , it has been lost in two teleost fish species [5] . Additionally , in the genome sequence database of the western clawed frog Xenopus tropicalis ( belonging to the amphibian class ) [10] , the predicted gene for TRPV3 is much shorter than mammalian orthologs due to the lack of the N- and C-terminal portions . Sequences homologous to the mammalian terminal regions were searched , but such regions have not been found in the genome sequence of the western clawed frog [5] . This implies that the N- and C-terminal regions of western clawed frog TRPV3 have diverged from those of the mammalian orthologs , thus the complete coding sequence of the TRPV3 gene could not be annotated bioinformatically utilizing mammalian TRPV3s . Since amino acid sequences of the terminal regions of TRPV3s are well conserved among several mammalian species , divergence of TRPV3 may reflect a functional shift of the TRPV3 channel between mammals and the western clawed frog . Thus TRPV3 is a suitable model for understanding how thermoTRPs have changed their amino acid sequences as well as function during the course of evolution . Moreover , comparison of TRPV3 channel properties between homeothermic and ectothermic vertebrates may supply new insights into the functional evolution of thermoTRPs related to body temperature differences among species . In contrast to the well characterized TRPV3 channels in homeothermic animals such as mammals , information on the TRPV3 channel in ectothermic animals is quite limited . Grandl et al . ( 2008 ) cloned western clawed frog TRPV3 ( which lacked the N- and C-terminal regions ) into a mammalian expression vector [11] , but this truncated TRPV3 was nonfunctional . They subsequently fused the N- and C-terminal regions of mouse TRPV3 to western clawed frog TRPV3 and found that this chimeric TRPV3 responded to heat , camphor , and 2-aminoethoxydiphenyl borate ( 2-APB ) [11] , [12] , known activators of mammalian TRPV3 in cultured mammalian cells [6]–[9] , [13] , [14] . However , since these observations were obtained using chimeric TRPV3 , they did not show the native channel properties of western clawed frog TRPV3 . In order to characterize the amino acid sequence as well as the channel properties of native TRPV3 from the western clawed frog , determination of the entire cDNA sequence is necessary . The aim of the present study is to understand evolutionary changes in the TRPV3 channels . In this study , we sequenced cDNA of western clawed frog TRPV3 including the 5′- and 3′-untranslated regions ( UTR ) and compared the amino acid sequences among various terrestrial vertebrate species . To characterize its channel properties , we cloned TRPV3 into an expression vector and used a heterologous expression system to compare properties between mammals and amphibians . Additionally , we conducted comprehensive comparative and phylogenetic analyses of the vertebrate TRPV subfamily utilizing various genome sequence databases to elucidate the evolutionary processes that occurred within the vertebrate lineages . Here we report the evolutionary changes of the TRPV3 channels , and highlight the differences in the temperature sensitivities between mammals and anurans .
In order to understand the evolutionary changes within the TRPV subfamily , a comprehensive phylogenetic tree containing a broad range of vertebrate species including mammals , chicken , green anole , western clawed frog , and teleost fishes was reconstructed ( Figure 1 ) [15]–[17] . The TRPV5 and TRPV6 genes that code for non-temperature-sensitive channels first diverged from the TRPV1-TRPV4 genes . Among the TRPV1-4 genes , each member was monophyletic to each other with high bootstrap value ( >96% ) . The TRPV4 cluster was first to diverge , followed by a split of the TRPV3 cluster from the vertebrate TRPV1/2 cluster . However , the order of divergence between the TRPV3 and TRPV4 clusters was not clearly resolved since the connection between the vertebrate TRPV1/2 and TRPV3 clusters was supported only by a moderate bootstrap value ( 61% ) . Within the TRPV1/2 cluster , the terrestrial vertebrate TRPV2 cluster first split from the clusters containing the teleost fish TRPV1/2 and terrestrial vertebrate TRPV1 genes , although this branching order was supported by a moderate bootstrap value ( 61% ) . In dog , cow , and horse , one copy each of the TRPV1-TRPV6 genes were found . In the green anole , one copy each of the TRPV1-4 genes , and two copies of the TRPV6 gene were found . Unfortunately , due to low coverage in the green anole genome sequence database , only short sequenced fragments existed for the TRPV1 , TRPV2 , and TRPV4 genes . Thus , only the TRPV3 gene and two copies of the TRPV6 genes were included in the phylogenetic tree ( Figure 1 ) . One copy of the TRPV6 gene ( TRPV6a ) clustered with the chicken TRPV6 gene; the other copy of TRPV6 ( TRPV6b ) clustered with the former two genes with high bootstrap value ( 82% ) . Thus the gene duplication event which created the two copies of the TRPV6 gene in the green anole occurred within the reptile/bird lineages independent from the gene duplication event that produced the mammalian TRPV5 and TRPV6 genes . This latter duplication event likely occurred within the common ancestor of mammals since opossum also possesses TRPV5 and TRPV6 genes . Teleost fish TRPV1/2 genes showed copy number variation among the different species . Zebrafish and three-spined stickleback possessed only one copy , while torafugu , spotted green pufferfish , and medaka possessed two copies ( note that the medaka TRPV1/2b gene was excluded from the phylogenetic tree in Figure 1 since it has large deletions in the central region which reduces the resolution of the phylogenetic tree; Figure 1 and Figure 2A ) . The TRPV1/2a and TRPV1/2b genes in medaka and torafugu were located in different genomic regions in which syntenic relationships were preserved around TRPV1/2s ( Figure 2A ) . The syntenic relationship also existed in the genomic regions around the TRPV1-TRPV3 genes among vertebrate species ( Figure 2B ) . In platypus , western clawed frog , and chicken , the TRPV1 and TRPV3 genes were located adjacently , and TRPV2 was positioned several genes away from TRPV1 and TRPV3 . In humans , the TRPV1 and TRPV3 genes were also located adjacently although the TRPV2 gene was distantly located in the same chromosome [5] . The teleost fish TRPV1/2 gene was located in a position corresponding to the terrestrial vertebrate TRPV1 gene ( Figure 2B ) . Although a syntenic relationship can be observed around the TRPV1-TRPV3 genes among vertebrate species , the genes corresponding to the terrestrial vertebrate TRPV3 and TRPV2 were not found in the teleost fish genome sequences ( Figure 2B ) . In the course of phylogenetic analysis , we found several novel TRPV genes that have not previously been described . We found one novel gene from platypus that formed a sister group to a cluster of vertebrate TRPV1-TRPV4 genes , but was located outside of them ( tentatively named TRPV7 ) ( Figure 1 ) . The TRPV7 gene was flanked by the TRPV1 and TRPV3 genes in the platypus genome sequence ( Figure 2B ) . We could not find a corresponding gene in the other vertebrate species examined , including human , mouse , dog , cow , opossum , chicken , western clawed frog , medaka , and zebrafish . The predicted amino acid sequence of platypus TRPV7 possessed the putative ankyrin repeat and six transmembrane domains that are highly conserved among TRP channels . TRPV7 showed 44 . 2% , 42 . 7% , and 44 . 0% amino acid sequence similarity to platypus TRPV1 , TRPV2 , and TRPV3 , respectively , in the central conserved regions ( from ankyrin repeat domain 1 to the TRP domain; Figure 3A ) . In addition to platypus TRPV7 , we also found three genes that are closely related to the vertebrate TRPV5 and TRPV6 genes ( Figure 1 ) . Two of them , from platypus and western clawed frog , formed a monophyletic cluster ( tentatively named TRPV8 ) . Platypus possessed one additional gene that clustered together with the TRPV8 genes ( tentatively named TRPV9 ) . The western clawed frog also possessed a TRPV6 gene that formed a sister group with the African clawed frog TRPV6 , although the western clawed frog TRPV6 gene has a large portion that has not been sequenced yet , thus it was excluded from the phylogenetic tree shown in Figure 1 . Detailed comparative analyses from this and previous studies [5] raised the possibility that TRPV3 of the western clawed frog is diversified from that of mammals . However , as mentioned above , the predicted TRPV3 gene lacks the N- and C-terminal portions as they could not be annotated bioinformatically from the genome sequence database of the western clawed frog . We performed RT-PCR , 3′- and 5′-RACE using total RNA extracted from the toe of the western clawed frog to sequence the cDNA of western clawed frog TRPV3 from the 5′- to 3′-UTRs to obtain the full length coding sequence . We obtained a 2819-bp cDNA fragment ( AB588024 ) which had a 2319-bp open reading frame ( 773 amino acid residues ) starting near the 5′ end in the 2nd exon and ending in the last exon ( Figure S1 ) . Comparison of this cDNA sequence with the genome sequence database of the western clawed frog revealed that the nucleotide sequence corresponding to the second exon ( 114 bp ) did not exist in the database . To confirm the result obtained by 5′-RACE , the cDNA fragment spanning exons 1 to 6 was amplified by RT-PCR . We obtained an approximately 650-bp DNA fragment that contained the second exon ( Text S1 ) . We further amplified and sequenced the genomic regions containing the second exon of the TRPV3 gene ( AB588025 ) and confirmed that the second exon was located within the genomic portions that had yet to be sequenced by the genome sequence project ( Text S1 and Figure S2 ) . Therefore , the existence of the second exon was not an artifact . Comparison of the amino acid sequences of western clawed frog TRPV3 with those of other terrestrial vertebrate orthologs revealed that it possesses conserved motifs such as four ankyrin repeat domains and six transmembrane domains ( Figure S3 ) . The amino acid sequences in the central portion of TRPV3 were relatively conserved among the tetrapod species examined . In contrast , amino acid sequences in the N- and C-terminal regions of western clawed frog TRPV3 were highly divergent from those of amniote TRPV3s although the corresponding regions of TRPV3 among amniotes were relatively well conserved ( Figure 3B , 3C and Figure S3 ) . In both terminal regions , a large number of amino acid substitutions as well as many gaps existed between western clawed frog and amniotes TRPV3s . The exon-intron structure of the TRPV3 gene of the western clawed frog was highly similar to those of other vertebrate TRPV3 genes ( Figure 3D ) . Highly divergent regions in western clawed frog TRPV3 spanned across several exons although exon boundaries were conserved among the vertebrate species compared ( Figure 3 ) . This suggests that the divergence of the terminal regions of TRPV3 was not the result of modification of gene structure; rather , the divergence can be attributed to the accumulation of considerable amino acid substitutions . We next examined the ion channel properties of TRPV3 in the western clawed frog by expressing it in oocytes of the African clawed frog ( Xenopus laevis ) from which we recorded ionic currents using a two-electrode voltage-clamp method . As the mammalian TRPV3 channel is activated by temperatures >31-39°C [6]-[8] , we first asked whether the TRPV3 channel of the western clawed frog is also activated by heat . Heat stimuli , however , did not induce any response in the oocytes injected with western clawed frog complementary RNA ( cRNA; Figure 4A ) . Instead , surprisingly , cold stimulations induced large currents in the oocytes injected with western clawed frog TRPV3 cRNA ( Figure 4A and 4B ) , but not in water-injected oocytes ( Figure 4C ) . The currents induced by cold temperatures were also observed without prior heat stimulation ( Figure 4B ) . The cold-induced currents were desensitized rapidly during the first cold stimulation and were considerably smaller during the second cold stimulation ( Figure 4A ) . This property is different from that of mammalian TRPV3 which becomes sensitized with repeated heat stimulations [6] , [8] . The average temperature threshold for activation was 16 . 35±0 . 51°C ( n = 14 ) when analyzed with Arrhenius plots ( Figure 4D ) . We next examined the pharmacological properties of western clawed frog TRPV3 currents . The oocytes expressing western clawed frog TRPV3 also responded to 2-APB , a known agonist of mammalian TRPV3 [13] , [14] , in a dose-dependent manner ( Figure 4A , Figure 5A and 5B ) . In human , dog , and chicken , histidine residues at position 426 in TRPV3 are reported to be involved in 2-APB sensitivity [12] . The corresponding residue of western clawed frog TRPV3 was also histidine as reported previously [12] ( Figure S3 ) . We also confirmed that western clawed frog TRPV3 responded to 2-APB ( Figure 5A and 5B ) . The 2-APB current tended to be sensitized when short-period stimulations ( 20 seconds ) were repeatedly applied to the oocytes expressing western clawed frog TRPV3 ( Figure S4A ) . This observation is similar to that of mammalian TRPV3 , which showed sensitization upon heat , camphor , and 2-APB [6] , [8] , [9] , [13] . In mouse TRPV3 , a synergistic effect has been reported for temperature and 2-APB stimuli [13] , [14] . Thus , temperature effects on 2-APB stimulation in TRPV3 of the western clawed frog were examined . Unexpectedly , cold stimulations suppressed 2-APB currents ( Figure S4B ) , while heat stimulations showed potentiation effects ( Figure S4C ) , implying that the activation mechanisms may be different between the cold and 2-APB responses for western clawed frog TRPV3 . Ruthenium red , a broad TRP channel antagonist [4] , [6]–[8] , inhibited cold-induced currents in a reversible manner ( Figure 5C ) and also inhibited 2-APB-induced currents ( Figure 5D ) in oocytes expressing western clawed frog TRPV3 . Moreover , the currents induced by both 2-APB and cold temperatures showed an outwardly-rectifying current-voltage relationship with slightly negative reversal potentials ( –12 . 35±3 . 24 mV , n = 4; and –9 . 33±2 . 03 mV , n = 4 for cold- and 2-APB-induced currents , respectively; Figure 5E ) . These results indicate that TRPV3 of the western clawed frog is a nonselective cation channel with a property similar to that of mammalian TRPV3 [6]–[8] , [14] . On the other hand , western clawed frog TRPV3 did not respond to camphor ( 8 mM ) , eucalyptol ( 10 mM; Figure 5F and 5G , Left ) , menthol ( 2 mM ) , vanillin ( 10 mM ) , and eugenol ( 2 mM ) ( Figure S4D–S4F ) , well known activators of mammalian TRPV3 ( Figure 5F and 5G , Right ) [9] , [18] , [19] . These observations suggest that while western clawed frog TRPV3 shares some electrophysiological properties with mammalian TRPV3 , it also possesses distinct properties , which may be related to its opposite temperature sensitivity from mammalian TRPV3 . To compare the expression profiles of TRPV3 between mammals and western clawed frog , the tissue distribution of TRPV3 mRNAs in the western clawed frog was examined by semi-quantitative RT-PCR . TRPV3 mRNAs of the western clawed frog were expressed in skin from various parts of its body , toes of both fore and hind limbs , as well as testis ( Figure 6 ) . TRPV3 mRNAs were not detected in the gastrointestinal tract , peripheral nerve or brain where expression has been reported in mammals [6]–[8] .
In the present study , we performed comprehensive phylogenetic analysis on genes belonging to the TRPV subfamily from various kinds of vertebrate species ( Figure 1 ) . As previously reported [5] , the TRPV3 and TRPV4 genes diverged earlier than the timing of the gene duplication between the TRPV1 and TRPV2 genes . Given that the teleost fish genes were included in the TRPV1/2 cluster , TRPV3 and TRPV4 genes emerged , at the latest , in the common ancestor of teleost fishes and terrestrial vertebrates . Whether the gene duplication of TRPV1 and TRPV2 occurred before or after the divergence of teleost fishes and terrestrial vertebrates is unclear since statistical support for the branch connecting the terrestrial vertebrate TRPV1 and teleost fish TRPV1/2 genes had a moderate value . In the teleost fishes , the TRPV1/2 genes showed copy number variation . Only one copy of the TRPV1/2 genes was found for stickleback and zebrafish , while two copies were found for medaka , torafugu , and spotted green pufferfish ( Figure 1 and Figure 2A ) . Since teleost fish TRPV1/2 genes were clustered together with 99% bootstrap value , the gene duplication events producing teleost fish TRPV1/2s and vertebrate TRPV1 and TRPV2 must have independently occurred in each lineage . Around the genomic region encompassing the two paralogous TRPV1/2 genes , syntenic relationships are preserved ( Figure 2A ) , suggesting that the two copies of the TRPV1/2 genes were produced by the whole genome duplication that occurred in the common ancestor of teleost fishes [20] . One copy has subsequently been lost in the stickleback and zebrafish lineages . In contrast , given that the TRPV1-TRPV3 genes are closely located in the terrestrial vertebrate genome , they were produced by tandem gene duplications . In the course of phylogenetic analysis , we found four novel TRPV genes that had yet to be reported ( Figure 1 ) . Three of these novel genes clustered with the TRPV5 and TRPV6 genes - two genes were from platypus ( TRPV8 and TRPV9 ) and one from the western clawed frog ( TRPV8 ) . That these three genes split from the TRPV5/6 cluster suggests that they emerged in the common ancestor of teleost fish and terrestrial vertebrates . We also found another novel TRPV gene ( TRPV7 ) in platypus that formed a monophyletic cluster with the TRPV1-TRPV4 clusters ( Figure 1 ) . Since TRPV7 was located outside of the vertebrate TRPV1-TRPV4 genes in the phylogenetic tree , we expected that other vertebrate species also possess orthologous genes . Our search for the orthologs to platypus TRPV7 in several vertebrate genome sequences , however , failed to find any orthologous genes . One of the explanations for this observation is that the TRPV7 gene may have emerged in the common ancestor of teleost fish and terrestrial vertebrates , and has been lost in most of the lineages . However , this scenario is unlikely because we have to assume independent gene loss events in the lineages leading to the different vertebrate classes . Another explanation is that TRPV7 was specifically produced in the lineage leading to platypus and a large amount of amino acid substitutions have subsequently been accumulated . The fact that TRPV7 was located between the TRPV1 and TRPV3 genes in the genome sequence of platypus suggests that TRPV7 was produced from either TRPV1 or TRPV3 , or from both genes ( Figure 2B ) . At present , it is unclear if platypus TRPV7 is a functional gene; further characterization of TRPV7 will prove interesting for future study since phylogenetically it is closely related to the TRPV1-TRPV4 genes that code for temperature sensitive channels . In dog , cow , and horse , we found one copy each of the TRPV1-TRPV6 genes , as has been found for human and rodents ( Figure 1 ) . Chicken and green anole possessed one copy each of the TRPV1-4 genes . Western clawed frog possessed one copy each of the TRPV1-3 genes and possessed six copies of TRPV4 genes as reported previously [5] . Copy numbers also varied for TRPV1/2 genes among teleost fishes and for TRPV5-9 genes among vertebrate species . In addition , TRPV3 has been lost in the teleost fishes ( Figure 1 and Figure 2B ) . In conclusion , the repertoires of the TRPV gene subfamily in vertebrates are essentially conserved , but gene duplication and loss events that occurred in specific lineages resulted in copy number variation; which potentially contributed to adaptation in the respective species . In the present study , we attempted to identify thermoTRPs that have changed their functional properties within specific evolutionary lineages as these thermoTRPs must have been involved in adaptation to thermal environments . In some cases , the functional shift of thermoTRPs was accompanied by diversifications of amino acid sequences . To identify these changes , we performed detailed comparative analyses of mammalian thermoTRP homologs utilizing the genome sequence database of various vertebrate species . In the first phase of our study , we comprehensively collected thermoTRP homologs from various vertebrate species and conducted comparative analyses ( Figure 1 and Figure 2 ) [5] . These analyses showed that the N- and C-terminal regions of the western clawed frog were missing from the predicted gene in the genome sequence database . A search for the homologous sequences to mammalian TRPV3 terminal regions in the genome sequence database of the western clawed frog failed to detect such regions . Thus we predicted that both terminal regions of western clawed frog TRPV3 are different those of from mammalian orthologs . To elucidate the amino acid sequences of western clawed frog TRPV3 , the cDNA sequence was determined and the deduced amino acid sequence was compared to those of other vertebrate TRPV3s . As expected , the N- and C-terminal regions of TRPV3 in the western clawed frog were highly diversified from those regions of TRPV3 in other terrestrial vertebrate species , although the central portions were relatively conserved among all terrestrial vertebrates examined ( Figure 3B , 3C , and Figure S3 ) . Characterization of western clawed frog TRPV3 channel properties revealed striking differences from those of mammalian TRPV3 channels . The TRPV3 channel of the western clawed frog was not activated by chemical compounds that are known to activate the mammalian TRPV3 channel ( Figure 5F and 5G ) [9] , [18] , [19] . Furthermore , the TRPV3 channel of the western clawed frog was activated by cold temperatures whereas the mammalian TRPV3 channels has been reported to be activated by warm temperatures ( Figure 4 ) [6]–[8] . Thus through a combination of interdisciplinary approaches including bioinformatics , molecular evolution , molecular biology , and electrophysiology , we were able to successfully identify a thermoTRP that has undergone a functional shift during the course of vertebrate evolution . Opposite temperature sensitivities among orthologs have been reported in other thermoTRPs . For instance , the TRPA1 channels are activated by warm temperatures in several snake and insect species [21] , [22] , while it is activated by cold temperatures in mouse , although cold activation of mouse TRPA1 is the subject of some debate [23]–[27] . In the present study , we clearly demonstrate that the TRPV3 gene of the western clawed frog and mammals are orthologous genes by showing a monophyletic relationship among them ( Figure 1 ) as well as conserved syntenic relationships of the genes flanking the TRPV3 genes among terrestrial vertebrate species ( Figure 2B ) . These results indicate that TRPV3 channels have acquired opposite temperature sensitivities during the course of terrestrial vertebrate evolution . This , in turn , indicates that the temperature sensitivity of thermoTRPs is not always stable but can dynamically change , even reveres in some cases , during the course of evolution . The molecular determinants for the difference in temperature sensitivities are not clear at present , but several lines of evidence suggest the N- and C-terminal regions as candidate domains . First , amino acid sequences of TRPV3 channels in these regions are highly divergent between mammals and the western clawed frog ( Figure 3B , 3C , and Figure S3 ) . Second , the C-terminal regions of thermoTRP channels have been reported to be involved in the modulations of temperature sensitivities . It has been shown that the swapping of the C-terminal regions between TRPV1 and TRPM8 channels of rat results in an exchange of temperature sensitivities [28] . Furthermore , it has also been reported that gradual truncations of the C-terminal regions of rat TRPV1 channels gradually shift temperature thresholds for activation [29] . Additionally , in the case of TRPV2 , the N- and C-terminal regions are reported to play crucial roles in heat sensitivity in rodents [30] . Lastly , a chimeric mutant of the TRPV3 channel ( in which the N- and C-terminal regions of mouse TRPV3 were fused to the central portion of TRPV3 from the western clawed frog ) exhibited warm temperature activation when expressed in cultured mammalian cells ( HEK293T ) [11] . This chimeric mutant was also reported to be activated by camphor , thus the N- and C-terminal regions are likely to be involved in both chemical and temperature sensitivity of the TRPV3 channel . Future detailed study using chimeric mutants of the TRPV3 channel will be necessary to understand the molecular basis for the differences in temperature as well as chemical sensitivities of TRPV3 channels between mammals and the western clawed frog . What is the physiological role of TRPV3 in the western clawed frog ? TRPV3 was mainly expressed in the skin in the western clawed frog similar to the expression pattern of mammalian TRPV3 ( Figure 6 ) . In mammals , it has been proposed that thermal stimuli perceived by the skin are transmitted to peripheral nerves [3] , [4] , [31] , [32] . Therefore , TRPV3 of the western clawed frog is likely to be involved in sensing temperatures at the body surface . The western clawed frog is a fully aquatic anuran that inhabits tropical areas , and its optimal ambient temperature range is 22–28°C [33] , [34] . Temperatures below 18–20°C have detrimental effects [35] , [36] , and here we show that the temperature threshold for activation of the TRPV3 channel is slightly below its temperature limit ( about 16°C ) ( Figure 4D ) . Thus , the physiological role of the TRPV3 channel is likely to detect noxious cold temperatures in the western clawed frog . It is expected that the physiological importance of warm temperature perception is higher in homeothermic vertebrates than for ectothermic vertebrates . TRPV3 channels serve crucial roles in homeothermic mammals by detecting innocuous temperatures near the body temperatures such as in mouse [9] , while it serves as a sensor to detect noxious cold temperatures in ectothermic vertebrates such as the western clawed frog ( Figure 4 ) . Similar observations have been reported for TRPM8 channels that act as cold temperature receptors . Activation temperatures of TRPM8 channels of the western and African clawed frog are much lower than those of rat and chicken TRPM8 channels [37] . Since body temperatures of frogs are lower than mammals and birds , it has been interpreted that the shift in temperature sensitivity of TRPM8 channels reflects the differences in the physiological requirements of body temperatures between homeothermic and ectothermic vertebrates . Characterization of TRPV3 channels from more diverse amniote species including both homeothermic and ectothermic animals will provide new insights into the functional evolution of temperature receptors related to homeothermy in vertebrates . In conclusion , detailed comparative analyses on the TRPV subfamily performed in the present study identified novel TRPV genes that have not been reported previously ( Figure 1 ) and also elucidated the flexible nature of TRPV3 in vertebrate evolution ( Figure 7 ) . The TRPV3 gene emerged in the common ancestor of teleost fishes and terrestrial vertebrates but has subsequently been lost in teleost fish lineages . Terrestrial vertebrates retained the TRPV3 channel , however , the western clawed frog and mammals acquired opposite temperature sensitivity to detect environmental temperatures suitable for their respective species . Thus the results of the present study reveal that thermoTRPs can dynamically change channel properties to adapt to different thermal environments during the course of evolution .
The TRPV1-5 genes from human , mouse , rat , chicken , western clawed frog , zebrafish , and torafugu were previously collected [5] . For the present study we collected TRPV homologous genes from several mammalian species , green anole , western clawed frog , and teleost fishes utilizing the orthologue prediction based on the draft genome sequence database published by Ensembl ( http://www . ensembl . org/index . html ) . Multiple sequence alignments were performed using the CLUSTAL W algorithm [38] , with minor manual adjustments . Evolutionary distances between the amino acid sequences were calculated using the central conserved portions containing the ankyrin repeat and transmembrane domains ( 381 residues ) by applying the JTT model [16] after all alignment gap sites were eliminated . The phylogenetic tree was then reconstructed using the minimum-evolution method [17] . The statistical confidence of each branch in the phylogenetic tree was estimated by the bootstrap method with 1 , 000 replications [15] . All of the above analyses were performed using MEGA4 software [39] . The TRPV genes and species used for phylogenetic reconstruction are listed in Table S1 . All procedures involving the care and use of animals were approved by the National Institute for Physiological Sciences . Western clawed frogs ( Xenopus tropicalis ) were kindly provided by the National Bio-resource Project ( NBRP ) of the Ministry of Education , Science , Sports and Culture of Japan . The western clawed frog strain used was the Yasuda line [34] . Using total RNA extracted from the toe of a fore-limb of an adult female western clawed frog as the template , a cDNA fragment spanning the 5′- to 3′-UTR of the TRPV3 gene was amplified by RT-PCR and 5′- to 3′-RACE . A DNA fragment containing the western clawed frog TRPV3 gene was amplified by RT-PCR and cloned into the pGEMHE vector . The PCR primers used are listed in Table S2 . The mouse TRPV3 gene that was cloned into the pcDNA3 vector ( Invitrogen ) was the kind gift of Mike Caterina ( Johns Hopkins , Baltimore , USA ) and was subcloned into pOX+ vector . The TRPV3 channel of the western clawed frog was heterologously expressed in oocytes of the African clawed frog Xenopus laevis , and ionic currents were recorded using the two-electrode voltage-clamp method . Western clawed frog TRPV3 cRNA was injected into defolliculated oocytes and ionic currents were recorded 1–4 days post-injection . The oocytes were voltage-clamped at −60 mV . All chemical compounds were diluted into ND96 bath solution and applied to the oocytes by perfusion . For thermal stimulations , heated or cold ND96 bath solutions were applied by perfusion . The current-voltage relationship was obtained using 200 ms voltage-ramp pluses from −100 to +100 mV applied every 1 . 5 seconds . The data values are expressed as mean ± SEM . Total RNA was extracted from skin from various parts of the body , toes of the fore- and hind-limbs , thigh skeletal muscle , fat body , gastrointestinal tract , lung , liver , kidney , heart , testis , ovary , peripheral nerve , and brain of male and female adult western clawed frogs . A 149-bp and a 206-bp cDNA fragment containing the TRPV3 and EF-1α ( as internal control ) genes , respectively , were amplified by RT-PCR . Details of the Materials and Methods are described in Text S1 . | Evolution of temperature perception is crucial for adaptation to thermal environments; however , this process is poorly understood . Here we investigated the evolution of the vertebrate TRPV subfamily which contains several mammalian temperature receptors . We identified several novel TRPV genes that have not been found previously and discovered evolutionary flexibility of the TRPV3 gene during vertebrate evolution . TRPV3 channels perceive warm temperature and serve as sensors to detect ambient temperatures near the body temperature of homeothermic animals such as mammals . To examine the functional evolution of TRPV3 channels in vertebrate evolution , we cloned the gene from the western clawed frog and found that its N- and C-terminal regions were highly diversified from those of other terrestrial vertebrate TRPV3 channels . Characterization of the channel properties of western clawed frog TRPV3 revealed that it was not activated by heat stimuli , but instead was activated by cold stimuli . Temperature thresholds for activation were about 16 °C , slightly below the lower temperature limit for the western clawed frog . Thus , the western clawed frog and mammals acquired opposite temperature sensitivity of TRPV3 channels to detect environmental temperatures suitable for their respective species , indicating that temperature receptors can dynamically change properties to adapt to thermal environments during evolution . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"signal",
"transduction",
"molecular",
"cell",
"biology",
"molecular",
"biology",
"genetics",
"genetics",
"and",
"genomics",
"biology",
"evolutionary",
"biology",
"comparative",
"genomics",
"neuroscience",
"evolutionary",
"processes",
"sensory",
"perception",
"gene",
"function"
]
| 2011 | Evolution of Vertebrate Transient Receptor Potential Vanilloid 3 Channels: Opposite Temperature Sensitivity between Mammals and Western Clawed Frogs |
Affinity maturation and class switching of antibodies requires activation-induced cytidine deaminase ( AID ) -dependent hypermutation of Ig V ( D ) J rearrangements and Ig S regions , respectively , in activated B cells . AID deaminates deoxycytidine bases in Ig genes , converting them into deoxyuridines . In V ( D ) J regions , subsequent excision of the deaminated bases by uracil-DNA glycosylase , or by mismatch repair , leads to further point mutation or gene conversion , depending on the species . In Ig S regions , nicking at the abasic sites produced by AID and uracil-DNA glycosylases results in staggered double-strand breaks , whose repair by nonhomologous end joining mediates Ig class switching . We have tested whether nonhomologous end joining also plays a role in V ( D ) J hypermutation using chicken DT40 cells deficient for Ku70 or the DNA-dependent protein kinase catalytic subunit ( DNA-PKcs ) . Inactivation of the Ku70 or DNA-PKcs genes in DT40 cells elevated the rate of AID-induced gene conversion as much as 5-fold . Furthermore , DNA-PKcs-deficiency appeared to reduce point mutation . The data provide strong evidence that double-strand DNA ends capable of recruiting the DNA-dependent protein kinase complex are important intermediates in Ig V gene conversion .
In humans and mice , primary antibody ( Ig ) diversity is produced by V ( D ) J recombination , which is dependent on the RAG-1 and −2 proteins [1] . Over a lifetime , primary repertoires are largely re-shaped by the processes of Ig somatic hypermutation ( SHM ) and class switching [2] , independent processes which occur in B cells activated by infection or immunization . SHM and class switching absolutely depend on a mutator protein , activation-induced cytidine deaminase ( AID or AICD ) , whose expression is restricted to activated B cells [3 , 4] . In humans and mice , Ig SHM predominantly involves point mutation of rearranged Variable ( V ) gene segments and the immediately downstream intron sequences , leaving the Constant region ( C ) gene segments largely unaffected [5 , 6] . In some species , including chickens , SHM of rearranged V genes also involves intra-chromosomal gene conversion with related pseudo- ( Ψ ) V genes , in preference to point mutation [7] . A minority ( 5%–10% ) of AID-induced mutations in Ig V ( D ) J genes in all species are small deletions and insertions , which might be due to nonhomologous DNA end joining ( NHEJ ) and template slippage during translesion synthesis [8–11] . Although class switching also involves AID-induced point mutation , now targeted to the Switch ( S ) regions located upstream of each C region gene in the IgH locus [6 , 12–14] , its salient outcome is recombination between S regions via NHEJ and the concomitant deletion of kilobase regions of DNA [1] . There is now compelling evidence that AID represents a previously unrecognized class of DNA-editing enzymes vital for both antibody diversification and direct destruction of viral DNA [15] . AID deaminates deoxycytidine ( dC ) bases in targeted Ig gene regions , converting the targeted bases to deoxyuridine ( dU ) , and thus directly causes transition mutations of dC/dG ( deoxycytidine/deoxyguanosine ) base pairs to dA/dT ( deoxyadenosine/deoxythymidine ) base pairs [10] . Excision of AID-deaminated bases by uracil-DNA glycosylase ( UNG ) or by mismatch repair leads to further mutation via translesion DNA repair [10 , 16–23] . In chicken Ig V genes , excision of AID-induced dU bases by UNG mostly leads to homology-directed gene conversion with ΨV genes by a process independent of translesion DNA repair , rather than to point mutation [21 , 24 , 25] . In yeast and vertebrate cell models , gene conversion is stimulated by the induction of a double-strand break ( DSB ) , which produces the requisite free 3′-ends [26 , 27] . However , this does not imply that DSBs are obligatory for gene conversion because free 3′-ends are also generated during DNA replication . It is clear that the combined attack of Ig S regions by AID and UNG results in DSBs , which are required for class switching [28 , 29] , but there is no a priori reason to expect a role for DSBs in AID/UNG-induced point mutation or gene conversion . On the contrary , nicking at AID/UNG-induced abasic sites could even prevent mutation , promoting faithful Ig V gene conversion with sister chromatids ( in S-phase ) or faithful base excision repair ( in G1-phase ) instead [11 , 30] . Attempts to directly demonstrate AID-dependent DSBs in mutating Ig V genes by ligation-mediated PCR have produced mixed results [31–35] . This is probably because DNA extracted from mutating cells carries a high background of breaks caused by , for instance , normal DNA replication , apoptosis , and even mechanical damage during DNA extraction . Although the frequency of staggered double-strand DNA ( dsDNA ) ends detected in the VDJH-rearrangement of human CL-01 cells is increased by AID overexpression , there is no convincing evidence that physiological expression of AID causes Ig V region DSBs [35] . Since NHEJ plays a role in repair of DSBs in all phases of the cell cycle [36] , we tested whether NHEJ influenced Ig V hypermutation in DT40 B cells . The V ( D ) J-rearranged heavy- and light-chain genes in chicken DT40 cells mutate constitutively by both dC/dG point mutation and gene conversion , in an AID- and UNG-dependent manner , although the mismatch-repair-mediated dA/dT mutation pathway is essentially inactive in these cells [37 , 38] . We show that reducing the efficiency of NHEJ in DT40 cells by inactivating either the Ku70 or DNA-PKcs genes increases the rate of gene conversion with ΨV genes , implicating DNA breaks in the mechanism of AID-induced gene conversion .
Ku70 , Ku86 , and the DNA-dependent protein kinase catalytic subunit ( DNA-PKcs ) form the heterotrimeric protein DNA-dependent protein kinase ( DNA-PK ) , which is primarily responsible for processing dsDNA ends in G1-phase vertebrate cells , protecting them from inappropriate homologous recombination , and promoting rapid , usually faithful end re-joining by DNA ligase IV [1] . Deficiency for Ku70 or DNA-PKcs was previously reported to have no effect on sIg loss in DT40 cells [39] , but the possibility that Ig V gene conversion might involve DSBs prompted us to re-assess whether sIg gain was affected by NHEJ . In DT40 cells , sIg fluctuation is complicated by the fact that the Ig V gene rearrangements mutate by both point mutation and gene conversion . The donor ΨV genes have varying homology to the acceptor V ( D ) J genes , but in the Igλ locus ( and probably also the IgH locus ) they usually code for nearly complete reading frames with only a few ΨVλ genes carrying premature stop codons [7] . Ig V gene conversion tracts frequently cover many codons [7] . Gene conversion is therefore a more efficient way to repair premature stop codons than is single base point mutation , because any gene conversions initiated near a deleterious mutation are biased toward repairing it . This is particularly true in the DT40-CL18 subline where sIg loss in the founder cell was due to a single base frame shift in the VJλ gene [40] . We can therefore infer that the rate of sIg gain in lines derived from CL18 cells is essentially an indirect measure of the Ig V gene conversion rate . The DNA-PKcs- and Ku70-knockouts were originally generated in DT40 cells carrying the canonical CL18 VJλ frame shift [41] , which we confirmed by DNA sequencing ( unpublished data ) . We found that deficiency for either Ku70 or DNA-PKcs increased sIg gain relative to control CL18 cells at the 0 . 001 significance level ( Figure 1 ) . To our surprise , DNA-PKcs-deficiency had more of an effect on sIg gain than Ku70-deficiency: a repeat experiment where clones were cultured for 24 d , rather than 50 d , confirmed the reproducibility of these results ( Figure 1B ) . This demonstrated that the power of sIg fluctuation analyses to detect small differences in Ig V mutation rates depends on the use of a large number of clones , rather than on the duration allowed for mutations to accumulate—a conclusion consistent with mathematical modeling of DT40 sIg fluctuation [42] . Similar phenotypes in two independent knockouts acting in the same DNA repair pathway ( NHEJ ) made it unlikely that the observed increases in sIg gain were artifacts due to unknown additional mutations . Nor were the increases due to preferential outgrowth of sIg+ve cells in the NHEJ-deficient cultures , because the cloning efficiency of sIg−ve and sIg+ve NHEJ-deficient cells was the same ( unpublished data ) . In our cultures , the doubling times for CL18 , DNA-PKcs−/−/− , and Ku70−/−DT40 cells in log-phase growth were 11 . 2 h , 11 . 6 h , and 12 . 0 h , respectively . Using these doubling times in mathematical modeling [42] of our fluctuation data suggested that the mean rate of sIg gain in DNA-PKcs- and Ku70-deficient cultures was 5 . 0× and 2 . 9× that of control cells , respectively ( Table 1 ) . VJλ rearrangements PCR-amplified from random Ku70- and DNA-PKcs-deficient DT40 clones carried more Ig Vλ gene conversions than those derived from control CL18 clones grown in parallel , while no gene conversions were detected in DNA amplified from control AID−/−cells ( Figure 2 and Table 2 ) . The relative increases in Ig V gene conversion detected by sequencing were not large ( Table 2 ) , but this was probably due to sampling error . The effective sampling rate of the sIg fluctuation assay is much higher than that of DNA sequencing because the mutagenic gene conversion rate of DT40 cells is fairly low . We chose not to overexpress AID as a way of counteracting this problem because variation in AID overexpression could have greatly increased data variance , and because mutation by overexpressed AID may not reflect physiological AID-induced mutation . The sequence data were consistent with the statistically highly significant increases in the rates of sIg gain calculated in Table 1 . Furthermore , sequencing of 93 VJλ-rearrangements from CL18 , DNA-PKcs−/−/− , and Ku70−/− clones , which started from sIg+ve cells ( part of the dataset shown in Table 2 ) , confirmed that sIg gain in these cells always involved a gene conversion that removed the canonical VJλ frame shift ( unpublished data ) . Overall , the sequence data confirmed that both DNA-PKcs- or Ku70-deficiency increased Ig V gene conversion . In addition to the obvious increase in sIg gain , we were able to measure a small and reproducible decrease in sIg loss in DNA-PKcs-deficient cells , but not Ku70-deficient cells ( Figure 1 and Table 1 ) . Point mutation is far more likely to produce deleterious amino acid changes than it is to repair them . Thus , sIg loss should be more sensitive than sIg gain to changes in point mutation rates . This is illustrated by cells deficient for any of the Rad51-paralogs [39] , such as XRCC3−/− cells , which were included in one of our sIg fluctuation assays as a control ( Figure 1A ) . XRCC3-deficiency caused little change in the rate of sIg gain in DT40 cells but had a dramatic effect on the rate of sIg loss ( Figure 1A ) . Thus , the inverse changes in sIg gain and sIg loss in DNA-PKcs-deficient cells ( Figure 1; Table 1 ) suggested that loss of the DNA-PKcs protein both promoted gene conversion and inhibited point mutation in DT40 cells . The data from XRCC3−/− cells also demonstrated the ability of our mathematical modeling [42] to estimate changes in mutation rate . The 21× increase in the rate of sIg loss estimated for XRCC3−/−cells ( Table 1 ) corresponds well to their rate of point mutation determined by sequencing [43] . Reliable point mutation data were not collected in our initial sequencing of VJλ sequences because point mutations occurred in a control AID−/− dataset ( unpublished data ) and were therefore largely due to errors introduced by the “BioXact” polymerase mix used for PCR . However , sequencing of an additional 40–47 clones amplified with “Phusion” DNA polymerase ( Finnzyme ) yielded five , three , and zero point mutations in VJλ genes amplified from CL18 , Ku70−/− , and DNA-PKcs−/−/− cells , respectively ( Table 3 ) . Combined with the data summarized in Table 1 , the sequence data indicate that increased gene conversion in DNA-PKcs-deficient DT40 cells is accompanied by reduced point mutation . Intriguingly , point mutation was either not reduced by Ku70-deficiency or the reduction was too small to be measured in our experiments . Increased Ig V gene conversion in DNA-PK-deficient DT40 cells implies competition between DNA-PK and homology-directed repair ( HDR ) factors for access to hypermutating Ig V genes in wild-type DT40 cells . How might DNA-PK be recruited to mutating Ig V genes ? The generation of DSBs in Ig V genes is the most obvious mechanism , although we cannot rule out the possibility that DNA-PK or its subunits play roles in Ig V mutation independent of NHEJ . AID could generate staggered Ig V DSBs in any phase of the cell cycle if two AID-bearing complexes attacked both strands of a V gene and thus recruited UNG and an abasic site-endonuclease or -lyase activity ( Figure 3A ) . However , this scenario is unlikely to be a major cause of DSBs in DT40 cells because the rate of attack of the DT40 VJλ gene by AID ( as revealed in UNG- and ΨV-deficient DT40 cells [25 , 38 , 44] ) is not high enough for AID-induced cleavage of both strands of an Ig V gene to occur very frequently . Alternatively , dsDNA ends ( “pseudo”-DSBs ) could also be produced during S-phase if a replication fork encountered a single-strand AID-induced nick ( Figure 3B ) . In both Figure 3A and 3B , resection of dsDNA ends would produce 3′-extensions capable of initiating gene conversion with ΨV genes; dsDNA ends with both 5′- and 3′-extensions have been detected by LM-PCR in rearranged Ig V genes in human B cells expressing AID [35] . Our data provide indirect , less artifact-prone confirmation that physiological expression of AID does indeed generate dsDNA ends in Ig V genes . Di Noia et al . [11] have recently argued that AID-induced dC/dG point mutation need not involve DNA breaks . Indeed , in Rad51 paralog-deficient cells , AID/UNG-induced abasic sites must be diverted from gene conversion to point mutation prior to excision of the abasic site , otherwise no lesion would be present to recruit translesion bypass and transversion point mutation [19 , 39 , 45] . This is illustrated in Figure 3A and 3B , where only intermediates 3 and 4 can divert to translesion bypass . We were able to envisage a scenario where NHEJ could inhibit Ig V conversion independently of nicking at abasic sites , but the scenario required nicks between Okazaki fragments to persist in template ΨV genes for some time after the replication fork had passed ( Figure 3C ) . This requirement is more likely to be met in chicken B cells than in human or mouse B cells ( which do not undergo AID-induced gene conversion ) because the V genes are much closer together in chicken B cells , and furthermore , is consistent with the preferential use of closer ΨV genes as gene conversion donors [7] . However , we think scenario B in Figure 3 is more likely than scenario C , because it is clear that the combined activity of AID and UNG recruits DNA nicking to Ig S regions participating in switching [28] . Thus , it is reasonable to expect the same in Ig V regions . Nonetheless , there is no data available to rule out scenario C in Figure 3 yet . We conclude that inhibition of mutagenic gene conversion by DNA-PK strongly implicates dsDNA ends as frequent , even obligatory precursors of Ig V gene conversion . The production of a dsDNA end by any of the scenarios shown in Figure 3 provides two 3′ DNA ends that can simultaneously prime strand invasion into an upstream ΨV gene . Trimming of mismatched 3′-ends ( which frequently occurs when nonidentical sequences participate in HDR [46] ) after simultaneous strand-invasion provides a simple mechanism by which both strands of the ΨV gene are copied into the acceptor V ( D ) J gene ( Figure 3 ) . A good candidate for the nicking enzyme required for models A or B in Figure 3 is the abasic site-lyase activity of MRE11/RAD50 [47] . In contrast to inactivation of DNA-PK , the inactivation of Rad51 paralogs causes a dramatic increase in point mutation in DT40 cells [39] . Thus , the ability of NHEJ to compete with Ig V gene conversion does not , at first glance , appear to be comparable to the ability of HDR to inhibit translesion bypass . However , this is probably because the majority of Ig V gene conversions induced by AID are non-mutagenic: using ΨV genes , which have regions of identity to the 3′ acceptor VJ gene or the sister chromatid , as repair templates . Thus , any increase in Ig V gene conversion increases the rate of faithful gene conversion as much as it increases mutagenic gene conversion . In fact , it is only when gene conversion is inhibited that the rate of attack of Ig V genes in DT40 cells by AID is “unmasked” as being much higher than the mutation rate of wild-type DT40 cells would suggest [25 , 39 , 44 , 45] . A 2- to 5-fold increase in mutagenic gene conversion in the absence of DNA-PK therefore implies that DNA-PK in fact blocks Ig Vλ gene conversion most of the time . Gene conversion-mediated repair of I-Sce I-induced DSBs is elevated much more by Ku70-deficiency than it is by DNA-PKcs-deficiency [41] , probably because Ku70 directly competes with the gene conversion machinery for access to dsDNA ends , while DNA-PKcs does not . This contrasts with AID-induced Ig V gene conversion , where DNA-PKcs appears to be more inhibitory than Ku70 ( Tables 1 and 2 ) . Perhaps some of the inhibitory activity of DNA-PKcs is independent of Ku70 . Wu et al . showed that DNA-PKcs , and not Ku , associates with AID in a DNA-dependent manner [48] . It is unclear whether the reported association between AID and DNA-PKcs was physiological because it was enhanced by addition of exogenous DNA [48] . Nonetheless , one can speculate that in wild-type cells simultaneous recruitment of AID to both DNA strands of a V gene could directly promote DNA-PKcs dimerization , partially inhibiting access by UNG or HDR factors to the deaminated site , and thus inhibiting gene conversion whilst promoting point mutation ( Figure 3A ) . The scid mutation , which is generally considered to essentially inactivate DNA-PKcs , has no detectable effect on Ig V hypermutation in mouse Peyer's patch B cells [49] . However , this finding needs to be interpreted cautiously . Ig class switching is only reduced 50%–60% by the mouse scid mutation [13] , in contrast to the almost complete abrogation of switching in mouse DNA-PKcs−/− cells [50] , proving that the scid form of DNA-PKcs is surprisingly functional in some NHEJ reactions . With the exception of gene conversion , the mechanism of AID-induced mutation so far appears to be very similar in mouse , human , and chicken B cells , so it is reasonable to predict that the DNA-PK complex is recruited to mutating Ig V genes by physiological AID-activity in human and mouse Ig V genes . Our data support the possibility that a subset of human oncogenic translocations that involve Ig V genes are a consequence of hypermutation , rather than V ( D ) J-recombination , occurring via a DSB-induced mechanism similar to “switch” translocation [9] .
All chemicals were supplied by Sigma-Aldrich ( http://www . sigmaaldrich . com ) , unless otherwise stated . Complete culture medium was RPMI , supplemented with 10% fetal bovine serum ( lot 092K2300 ) , 1% chicken serum ( IMVS , http://www . imvs . sa . gov . au ) , benzyl penicillin ( 0 . 06 g/l ) , and streptomycin sulphate ( 0 . 1 g/l ) . The DT40 subline CL18 and AID−/− , Ku70−/− , DNA-PKcs−/−/− , and XRCC3−/− DT40 lines produced by gene-targeting have been described before [40 , 41 , 51 , 52] . Aliquots ( ∼106 cells ) of DT40 cells were stained with saturating amounts of FITC-conjugated anti-chicken IgM Ab ( clone M-1 , Southern Biotechnology Associates , http://southernbiotech . com ) at 4 °C for 30 min in sterile PBS containing 0 . 5% ( w/v ) BSA and 0 . 02% ( w/v ) sodium azide ( PBA ) . Using a FACSVantage DIVA sorter ( Becton Dickinson , http://www . bd . com ) , sIg+ve or sIg−ve cells were pre-sorted to enrich the rarer population and allowed to expand in culture for several days . Direct sequencing of VJλ PCR products amplified from sIg−ve DNA-PKcs−/−/− and Ku70−/−DT40 cells after pre-sorting confirmed that the overwhelming majority of sIg−ve cells in these lines carried the canonical CL18 frame shift ( unpublished data ) . PCR amplification across the DNA-PKcs and Ku70 exons deleted by gene targeting was also performed to confirm correct genotypes for the cells recovered from pre-sorting ( unpublished data ) . Following expansion in culture , pre-sorted cells were sorted again for single sIg+ve or sIg−ve cells into either 384-well ( experiment A ) or 96-well ( experiment B ) tissue culture plates , containing complete culture medium plus Primocin antibiotic ( InvivoGen , http://www . invivogen . com ) . 8 d after sorting , random clones were transferred to individual wells in 96-well ( experiment A ) , or 24-well ( experiment B ) plates . Thereafter , when most clones had reached a density of 1 to 2 × 106 cells per ml ( generally at 2- to 3-d intervals ) , 1/20th of each culture was transferred to a fresh well containing 0 . 2 ml ( experiment A ) or 1 . 0 ml ( experiment B ) fresh medium until analysis on day 50 ( experiment A ) or day 24 ( experiment B ) . For FACS , ∼80% of each culture was harvested into 1-ml tubes , pelleted ( 500g , 5 min ) , and stained with FITC-conjugated anti-chicken IgM Ab in a 50-μl volume of ice-cold PBA . After washing with ice-cold PBA , cells were fixed with 2% paraformaldehyde in PBS . Clones of the same genotype and sIg phenotype were grown side-by-side in the multi-well plates and Ab-stained for FACS-analysis in tubes arrayed in a similar format . This ensured that any cross-contamination that might occur between wells during culture or Ab-staining would most likely only occur between clones of the same genotype and starting phenotype and would thus cause minimal distortion of the results . After fixing the Ab-staining , samples were randomized ( http://www . random . org ) prior to collection of the FACS data using a FACScan machine ( Becton Dickinson ) . The frequency of sIgM+ or sIgM− cells in each sample was then determined blind using FlowJo software ( Tree Star Incorporated , http://www . treestar . com ) and the gating strategy of Arakawa et al . [37] . Data on a median of 3 . 6 × 104 viable cells ( defined by forward- and side-light scatters ) were collected for each sample . The experiments were designed to ensure that differences in sIg fluctuation rates between groups did not arise because of fluorescent antibody detachment over time , variations in machine parameters over the course of data collection , or because of human bias in data analysis . The frequencies of sIg-gain and sIg-loss were estimated using the formula shown in Appendix 2 of [42] . Let f be the median proportion of cells that gained sIgM in sIgM− clones . Let b be the median proportion of cells that lost sIgM in sIgM+ clones . Let g be the number of generations in the experiment , calculated from the cell line's doubling time . Let r = b/f . Let w = [ ( ln ( g ) – ln ( f + b ) ) /ln ( 2 ) ] – 1 . Let s = 2 × ( 1 − eln ( 1 −b − f ) / ( g − w ) ) . Then the estimates , Φ and β of the rates of sIg-gain and sIg-loss , respectively , are Φ = s/ ( r + 1 ) and β = s – Φ . Genomic DNA was extracted from multiple random clones cultured for 50 d ( i . e . , experiment B ) . The VJλ exon and 3′-flanking sequences were PCR-amplified with published primers [39] using “BioXact Short” DNA polymerase mix ( Bioline , http://www . bioline . com ) or “Phusion” DNA polymerase ( Finnzymes Oy , http://www . finnzymes . fi ) and cloned into plasmids . To minimize the acquisition of redundant sequences , only a few plasmids derived from each clone were sequenced , as indicated in Tables 1–3 .
The Ensembl ( http://www . ensembl . org ) accession numbers for the genes discussed in this paper are AID ( ENSBTAG00000018849 ) , DNA-PKcs ( ENSGALG00000012914 ) , Ku70 ( ENSGALG00000011932 ) , and XRCC3 ( ENSGALG00000011533 ) . | To generate highly specific antibodies in response to an immune challenge , the antibody genes in activated B cells mutate at a very high rate over a period of several days . The enzyme that initiates antibody gene mutation is activation-induced cytidine deaminase ( AID ) , the first protein recognized to directly edit DNA genomes in vivo . AID induces point mutation of antibody V genes in all vertebrates , as well as transfer of short sequences from nonfunctional donor V genes to functional acceptor V genes ( “gene conversion” ) in birds and some other species . Whether or not the mechanism of AID-induced V gene mutation and gene conversion involves double-strand DNA breaks is controversial and potentially important because double-strand DNA breaks are known to promote cancer-associated gene translocations . We used genetic inactivation of a double-strand break repair protein ( DNA-dependent protein kinase ) in a chicken B cell line to indirectly test whether AID induces double-strand breaks in the antibody V genes . We conclude that physiological expression of AID causes the formation of double-strand DNA ends in antibody V genes , which appear to be prevented from participating in homologous recombination if they recruit DNA-dependent protein kinase . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
]
| [
"in",
"vitro",
"immunology",
"molecular",
"biology"
]
| 2007 | DNA-Dependent Protein Kinase Inhibits AID-Induced Antibody Gene Conversion |
The soil-dwelling saprophyte bacterium Burkholderia pseudomallei is the cause of melioidosis , a severe disease of humans and animals in southeast Asia and northern Australia . Despite the detection of B . pseudomallei in various soil and water samples from endemic areas , the environmental habitat of B . pseudomallei remains unclear . We performed a large survey in the Darwin area in tropical Australia and screened 809 soil samples for the presence of these bacteria . B . pseudomallei were detected by using a recently developed and validated protocol involving soil DNA extraction and real-time PCR targeting the B . pseudomallei–specific Type III Secretion System TTS1 gene cluster . Statistical analyses such as multivariable cluster logistic regression and principal component analysis were performed to assess the association of B . pseudomallei with environmental factors . The combination of factors describing the habitat of B . pseudomallei differed between undisturbed sites and environmentally manipulated areas . At undisturbed sites , the occurrence of B . pseudomallei was found to be significantly associated with areas rich in grasses , whereas at environmentally disturbed sites , B . pseudomallei was associated with the presence of livestock animals , lower soil pH and different combinations of soil texture and colour . This study contributes to the elucidation of environmental factors influencing the occurrence of B . pseudomallei and raises concerns that B . pseudomallei may spread due to changes in land use .
Burkholderia pseudomallei is a Gram-negative bacterium whose main habitat is in moist tropical soil between latitudes 20° N and 20° S [1] . B . pseudomallei is not only a soil saprophyte but also a human and animal pathogen causing the severe disease melioidosis [1] , [2] . Clinical manifestations range from subclinical infection to localized abscess formation , pneumonia and systemic sepsis with mortality rates up to 90% [3] , [4] . A large proportion of melioidosis patients have host predisposing factors such as diabetes , renal disease and alcoholism [5] . The bacteria are mainly transmitted by exposure to contaminated wet soil and surface water and the mode of infection is predominantly percutaneous inoculation , with inhalation and ingestion also reported [6] . Melioidosis is an endemic disease in southeast Asia and tropical Australia . In northeastern Thailand , B . pseudomallei accounts for up to 20% of community-acquired septicemia [1] and in Royal Darwin Hospital in northern Australia , melioidosis has been the most common cause of fatal community-acquired bacteremic pneumonia [7] . Despite increased awareness of melioidosis being an emerging disease [6] , [8]–[10] , not much is known about the habitat of B . pseudomallei . Studies have shown that proliferation of B . pseudomallei is dependent on high water content of the soil and B . pseudomallei has been isolated from muddy , moist and clay-rich soil and pooled surface water [11]–[13] . B . pseudomallei has been detected in unchlorinated water supplies [14] , [15] and there is a clear positive association between monsoonal rain or extreme weather events and incidence of melioidosis [6] , [16] . Environmental studies have shown an association with irrigated cultivated areas such as rice paddies in Thailand [17]–[19] ( with corresponding high rates of disease in rice farmers [1] , [20] ) and anecdotal reports tell of B . pseudomallei positive , irrigated sports fields ( own observation , [19] , [21] ) . Although this may suggest an association with land use , it is unclear whether this may represent a bias in sampling and exposure as a systematic survey has not been performed . In order to explore the habitat of B . pseudomallei in the tropical Top End of Australia and the influence of environmental manipulations upon its occurrence , we performed a large survey on B . pseudomallei occurrence in the soil of the Darwin rural area . Previously , we have developed and validated a molecular tool to detect B . pseudomallei in soil [22] . This method was based on soil DNA extraction and real-time PCR and proved to be faster and more sensitive than the gold standard culture , while still being specific [22] . By using this tool , we screened more than 800 soil samples from rural Darwin for the presence of B . pseudomallei . With multivariable analyses we discovered new associations between the occurrence of B . pseudomallei and environmental factors .
In the dry season 2006 ( July to October , “dry 06” ) , 499 soil samples were collected at a depth of 30 cm from 141 soil sampling sites within a 50 km radius of Darwin ( 12° S ) in the Top End of the Northern Territory in Australia . The Top End mainly consists of tropical savanna [23] and wetland ecosystems [24] . Sites were randomly chosen using the “Random Point Generator” extension for ArcView 3 . 2 . with the following restrictions: The Darwin rural region was subdivided into nine rural areas ( average area 51 km2 , SD 12 km2 ) and within each area , sites were distributed between undisturbed and environmentally manipulated sites . The latter consisted of residential or farming properties with livestock ( horses , cattle , pigs or chickens ) or fruit farming ( predominantly mango farms ) . Four samples were collected per undisturbed or farm site and two samples per residential property . In the wet season 2007 ( March to April , “wet 07” ) , 74 of the 141 sites were visited again and another 256 soil samples were collected at a depth of 30 cm ( see Table 1 ) . These 74 sites consisted of all accessible , previously positive sites ( 30 of 38 previously positive sites ) and 44 controls from the same rural areas . Sites were matched for level of environmental manipulation and waterlogging . In the dry season 2007 ( July to September , “dry 07” ) , 30 sites were visited again of which 17 were previously positive following the same scheme as above and 54 soil samples were collected . Soil was collected into sterile 50 mL specimen containers containing 5 ml of dH2O and auger and spade were cleaned with 70% ethanol between soil collections . Soil sampling sites were mapped and various environmental factors were recorded on site such as distance to next stream , vegetation class , presence of roots in soil , presence of animals ( livestock , dogs or native animals such as wallabies; the latter was declared positive if droppings were sighted in close proximity of the sampling site ) . Soil water status and soil texture were determined using the “Australian Soil and Land Survey” Field Handbook [25] and by following a common soil texture flowchart ( http://www . h2ou . com/h2twss96 . htm ) . Soil color was interpreted using the Munsell Soil Color Chart as described previously [22] . DNA was extracted from 20 g of soil as previously described [22] . While 20 g of most soils roughly equaled 20 mL of volume , a few soils showed large mass variations . We therefore set a lower and upper volume limit for all soil samples of 15 to 25 mL to avoid large variations in volume . In brief , starting with an enrichment step incubating 20 g of soil in 20 mL of selective modified Ashdown's broth [26] for 39 hours shaking at 37°C , 1 mL of CaCO3 saturated water was added and the sample was centrifuged twice . The soil pellet was processed for DNA extraction using a modified protocol of the ultraclean soil DNA isolation kit ( MoBio Laboratories , USA ) . Modifications included the addition of 0 . 8 mg of aurintricarboxylic acid ( ATA ) and 20 µL of proteinase K ( 20 mg / mL ) . DNA was purified further with the QIAamp DNA Micro Kit ( Qiagen , Hilden , Germany ) and eluted in 50 µL of 10 mM Tris HCl , 0 . 5 mM EDTA , pH 9 . 0 . B . pseudomallei DNA was detected by real-time PCR using a Rotor-Gene 2000 ( Corbett Research , Australia ) targeting a 115 bp stretch of the B . pseudomallei specific orf2 of type III secretion system ( TTS1 ) as described in [22] . Briefly , 4 µL of DNA were amplified in duplicates in 25 µL volumes . The probe was at a final concentration of 256 nM and labelled with FAM and a black hole quencher ( Biosearch Techonologies ) . Supplementary reagents included 0 . 25 U Uracil DNA Glycosylase ( Invitrogen ) , dUTPs and nonacetylated BSA at final 400 ng / µL . Non-Template Controls ( NTC ) were added to each run and no amplifications were detected . In order to check for PCR inhibitors , 0 . 3 pg of an inhibitor control plasmid [22] were amplified alone and in parallel spiked with 4 µL of sample DNA . In each PCR run , the plasmid was also used as standard positive control in a dilution series in duplicates and at final concentrations of 4 . 4 ng / mL , 217 pg / mL , and 11 pg / mL . Ct values had an average of 33 . 2 with a 95% confidence interval ( 95% CI ) of 32 . 3–34 . 1 . Statistical analysis was carried out using Stata ( Intercooled Stata , version 8 . 2 , USA ) . For univariate analysis , Fisher's Exact test and Mann-Whitney U test were used . For multivariable analysis , odds ratios were calculated in stepwise multivariable logistic regression analyses clustered by site . The specification of the models was assessed using a link test . All tests were 2-tailed and considered significant if P values were less than 0 . 05 . Autocorrelation was assessed by calculating Geary's c statistic , which tests the null hypothesis of global spatial independence ( indicated by values of around 1 ) , in bands of 0 . 05 degrees ( 5 . 6 km ) up to 0 . 20 degrees ( 22 . 2 km ) . We performed all further analysis assuming spatial independence where there was no evidence of significant autocorrelation . One-way Analysis of Similarities ( ANOSIM ) is a non-parametric permutation procedure ( 999 permutations ) and was used to test the null hypothesis of no difference in the composition of environmental variables between two groups of soil samples ( for instance , B . pseudomallei positive versus negative soil samples ) ; it was based on a resemblance matrix of Euclidean distances between soil samples with short distances indicating high similarities between the composition of environmental variables of two soil samples ( normalized data ) . Similarity Percentage Breakdown ( SIMPER ) analysis was used to evaluate the main environmental factors which were responsible for the observed clustering of samples ( also using a Euclidean Distance matrix with normalized data ) . Principal Component Analysis ( PCA ) proved useful to visualize the dataset based on the combination of environmental factors describing the soil samples . The normalized dataset was projected onto a 2-dimensional ordination with the axes maximizing the variance of the data . The axes are a linear combination of environmental factors and the vectors reflect the coefficients of these factors . ANOSIM , SIMPER and PCA analyses were performed using Primer 6 . 1 . 9 ( Primer-E Ltd . , UK ) .
809 soil samples collected at a depth of 30 cm were screened for B . pseudomallei by using a previously developed and validated soil DNA extraction and real-time PCR protocol [22] . Screening resulted in a total of 107 B . pseudomallei positive samples from 48 sites ( see Table 1 , 2 and Figure 1 ) . In the dry season 2006 , soil samples of 21% ( 11 / 53 ) of undisturbed and 31% ( 27 / 88 ) of environmentally disturbed sites such as farms or residential properties tested positive for B . pseudomallei ( Fisher's Exact , P = 0 . 242 ) . Various environmental factors such as soil texture , soil colour , soil moisture , vegetation class , presence of animals and distance to a stream were recorded for all soil samples . In a multivariable logistic regression analysis , significant risk factors for the presence of B . pseudomallei were close proximity to a stream ( Odds Ratio OR 2 . 5 , 95% CI 1 . 3–4 . 9 ) , moist soil ( OR 2 . 6 , 95% CI 1 . 6–4 . 2 ) , the presence of animals ( OR 2 . 4 , 95% CI 1 . 3–4 . 6 ) , as well as roots-rich soil ( OR 1 . 8 , 95% CI 1 . 1–3 . 0 ) and red brown - grey soil ( OR 3 . 4 , 95% CI 1 . 4–8 . 0; OR 2 . 8 , 95% CI 1 . 1–6 . 9 ) ( see also Table 3 ) . In a Principal Component Analysis ( PCA ) , the composition of environmental factors was compared between all B . pseudomallei positive soil samples and a separate clustering was evident for soil samples collected at undisturbed sites as compared to environmentally manipulated sites ( see Figure 2 ) . This was also confirmed by a non-parametric permutation procedure called Analysis of Similarities ( ANOSIM ) ( P = 0 . 001 ) . The vectors in Figure 2 show that B . pseudomallei positive undisturbed sites exceed disturbed sites in waterlogged and roots-rich soil , ( open ) forests such as found along creeks whereas environmentally disturbed B . pseudomallei positive sites had a higher proportion of animal resting places , red brown soil , clay loam or single trees such as found on mango farms and paddocks . This was consistent with a Similarity Percentage Breakdown ( SIMPER ) analysis which evaluates the main environmental factors responsible for the observed clustering and showed a similar contribution of factors to the clustering as PCA . Both SIMPER and PCA analysis also revealed that B . pseudomallei positive native sites exceeded positive disturbed sites in moist soil . Indeed , significantly more B . pseudomallei positive soil samples were classified as dry if from disturbed areas ( 37% dry ( 29/78 ) ) , especially from non-irrigated disturbed sites ( 56% dry ( 13/24 ) ) as compared to undisturbed sites ( 17% dry ( 10/58 ) ) ( Fisher's Exact , P = 0 . 013 and P = 0 . 001 for non-irrigated sites ) . Of the 24 B . pseudomallei positive soil samples from non-irrigated disturbed sites , 16 ( 67% ) were collected in close proximity to or within pens , paddocks or kennels with livestock , chickens or dogs . The combination of environmental factors describing B . pseudomallei positive undisturbed sites is a clear-cut subset of factors describing all samples collected at undisturbed sites ( ANOSIM , P = 0 . 003 ) . For soil samples collected at undisturbed sites , SIMPER analysis showed that more than 50% of the observed clustering of B . pseudomallei positive samples versus negative samples was due to a higher percentage of positive soil samples being moist , red brown or roots-rich and having been collected at grass-rich sites and in forests ( the latter mainly along creeks ) . This distinct environmental factor composition of positive versus negative samples was used to explain different B . pseudomallei prevalence rates in different areas of rural Darwin . For instance , one area showed a significantly lower B . pseudomallei occurrence compared with another area ( 1 . 6% versus 21 . 9% , Fisher's Exact , P<0 . 001 ) . This matched with significantly more sandy soil samples ( 34 . 7% versus 12 . 9% , Fisher's Exact , P<0 . 001 ) and less sampling sites rich in spear grass ( 0% versus 12 . 9% , Fisher's Exact , P<0 . 001 ) in the area of lower B . pseudomallei occurrence . In order to assess whether the B . pseudomallei status of undisturbed sites clustered together , an autocorrelation analysis was performed . Using both positive ( n = 23 ) and negative ( n = 187 ) points , there was some evidence of negative autocorrelation in the 0–0 . 05 degree ( 5 . 6 km ) band ( Geary's c 1 . 17 , p = 0 . 015 ) indicating weak evidence of a negative correlation between positives and negatives within this band . A positive autocorrelation was detected in the 0 . 05–0 . 10 degree ( 5 . 6–11 . 1 km ) band ( Geary's c 0 . 88 , p = 0 . 034 ) which matches the above finding of different B . pseudomallei prevalence rates in different areas of rural Darwin . Because the magnitude of autocorrelation was weak and inconsistent between bands , we performed further analysis assuming spatial independence . No autocorrelation was detected for environmentally disturbed areas and no specific factor combination was evident for B . pseudomallei positive environmentally disturbed areas apart from the presence of livestock and pets , impeding the prediction of B . pseudomallei occurrence at environmentally manipulated sites .
The major finding of this study is that two sets of environmental factors describe the habitat of B . pseudomallei in the Top End . One set refers to the habitat of B . pseudomallei at undisturbed sites whereas the other one characterises environmentally manipulated sites . At undisturbed sites , B . pseudomallei was frequently found along creeks in highly vegetated areas . B . pseudomallei positive sites were often in close proximity to annual spear grass ( such as Sorghum spp . ) and grasses in riparian zones . Some of these grasses are known to have an extensive root system reaching down to the ground water to survive in the dry season [27] , which would be a favorable feature for the survival of water dependent B . pseudomallei in the dry season . In the wet season , we also observed a strong increase of B . pseudomallei load at sites rich in spear grass ( data not shown ) , which coincides with the time when annual grasses flourish involving a high increase of fine root mass [28] . Data on B . pseudomallei load was obtained by an approximate semi-quantification method measuring soil B . pseudomallei load from standard curves generated in soil inoculation experiments [22] and the inclusion of an internal plasmid into the soil DNA extraction as additional efficiency control . However , this quantification data should be interpreted with caution because of only moderate reproducibility . Work is ongoing in validating these preliminary results and determining their significance . Current data suggest that B . pseudomallei might be associated with roots of some of these grasses . This would not be surprising as many relatives of B . pseudomallei such as those of the B . cepacia genomovars are closely associated with the rhizosphere i . e . with the soil immediately surrounding the roots of plants [29]–[31] . Our current results do not allow any conclusions on which grass species in particular were associated with B . pseudomallei . This will be addressed in future field and in vitro studies . B . pseudomallei could also be associated with arbuscular mycorrhizal fungi ( AMF ) which are symbionts of many plants and also live in the rhizosphere . B . pseudomallei has been shown in vitro to be able to colonize spores of AMF such as Gigaspora decipiens [32] . At environmentally manipulated sites , highest B . pseudomallei counts were retrieved from paddocks , pens and kennels holding horses , pigs , chickens or dogs and cats ( data not shown ) . We also found significantly more B . pseudomallei positive soil samples which were dry in environmentally manipulated areas as opposed to undisturbed sites . This suggests that other factors make up for the reduced water supply while the observed high B . pseudomallei counts may indicate superior growth conditions for B . pseudomallei at some disturbed sites . The observed strong association of B . pseudomallei with the presence of animals raises the question whether these animals were infected with B . pseudomallei and acted as an amplification stage for these bacteria . We cannot rule out this possibility . However B . pseudomallei is highly pathogenic for most farm animals and asymptomatic carriage has generally only been reported in pigs [2] . Thus , more likely explanations are digging or foraging activities of animals increasing soil aeration [33] and water infiltration [34] or increased access to organic material and nitrogen derived from animal waste . All this could contribute to the growth of the preferentially aerobic saprophyte , together with soil acidification processes which are a by-product of nitrification processes [35]–[37] . We observed a significantly lower pH of B . pseudomallei positive soils . This was mainly evident in garden soil where the pH was generally in a more neutral range than for other soils studied . In contrast , the pH range of undisturbed soil overlapped the pH of most B . pseudomallei positive soils . Less B . pseudomallei positive soil samples were retrieved from residential properties in the wet season as opposed to the dry season , which was in stark contrast to undisturbed sites where B . pseudomallei prevalence increased in the wet . We hypothesize that on residential properties , B . pseudomallei might be spread by irrigation systems of gardens which are only operated in the dry season . Up to 33% of water bores of rural residential properties are B . pseudomallei positive ( Mark Mayo , manuscript in preparation ) and irrigation systems are fed by these bores . Hence , not only might irrigation of gardens and cultivated areas improve surface conditions for the survival of B . pseudomallei , but irrigation systems themselves might also actively pump bacteria to the surface . Our data clearly indicate that environmental perturbations have an influence upon the occurrence of B . pseudomallei . Land use management such as agriculture has been shown to have a large effect on soil bacteria and their community structure [38] . Bacterial diversity was shown to decrease on arable land [39] and a shift to Burkholderia spp . was evident after a change from forest to pasture vegetation in one study [40] . An increase of B . pseudomallei load on farm properties could lead to melioidosis outbreaks in livestock such as goats , sheep or pigs which have been reported [2] including in non-endemic areas [41] . Further soil perturbations such as those caused by construction and soil excavation work have been associated with a melioidosis outbreak in Western Australia in the dry season [42] . Also extreme natural events such as monsoonal heavy rains , cyclones or tsunamis have a large impact upon landscapes and soil and such events have been reported to be associated with an increase of melioidosis incidence [16] , [43]–[45] . Sporadic flooding has also unmasked melioidosis in areas such as temperate southern Queensland , despite this region being considerably south of the melioidosis endemic belt in tropical Australia [46] . Our data also suggest that in other endemic regions of the world such as Thailand , agricultural practices like rice farming may favor the growth of B . pseudomallei and might contribute to the strong association of B . pseudomallei with rice fields observed in southeast Asia [17]–[19] . However other factors including different soil , vegetation and climate , the nature of soil disturbance and the interaction with different environmental microbes including the closely related B . thailandensis , limit the generalizability . Further studies are therefore required in other regions with different environmental conditions . Whereas clay was only significantly associated with B . pseudomallei in combination with the soil color red brown ( indicating oxidized iron ) , a strong correlation between clay loam and B . pseudomallei was evident . Clay loam is roughly an equal mixture of clay , silt and sand [25] . While clay provides excellent water and nutrient retention abilities due to its large surface area and chemical activity , clay loam is less dense than clay , less prone to waterlogging and provides better aeration for e . g . plant root development which is why , clay loam is often regarded as a good garden soil . Therefore , it was no surprise to find a significant association with clay loam and roots-rich soil . However , clay loam is not a typical soil of the Top End and was mainly found on environmentally manipulated sites such as farms or gardens . Common soil types in rural Darwin are kandosols which are well drained , gravelly , yellow or red massive earths often overlaying weathered , iron-rich material [47] . Along drainage lines and creeks , hydrosols are common , which are seasonally wet , sandy massive earths . A widespread topsoil of the Top End is sandy loam over sandy clay loam subsoil and light to medium clay at depth . In the last two decades , there has been a substantial increase of human activities in the Darwin rural area where this study was undertaken . In particular , small scale horticulture and farming have expanded on the many rural land blocks . This has not only increased the number of people being exposed to B . pseudomallei but B . pseudomallei itself could potentially be spreading along with the ongoing land management changes . There are also on-going changes in landscape ecology in northern Australia inflicted by changed fire regimes which led to an increase of some annual grasses [48] and the introduction of invasive plant species such as Andropogon gayanus Kunth ( Gamba grass ) . These changes have a large impact on native grasses , soil moisture and soil nitrogen cycles [49] , [50] which could further prove advantageous to the survival of B . pseudomallei such as the potential spread of grasses associated with B . pseudomallei or the persistence of annual soil wetting caused by invasive wetland grasses [51] . In summary , we have described a combination of environmental factors that are strongly associated with the presence of B . pseudomallei in the tropical Top End of Australia and we provide evidence that changes in land use influence the occurrence of B . pseudomallei . Therefore , melioidosis and B . pseudomallei might not only be an emerging infectious disease due to improved recognition and diagnostic techniques [8] , but they might indeed be spreading in and beyond endemic locations because of complex environmental disturbances and changed landscape ecology . | Melioidosis is a severe disease affecting humans and animals in the tropics . It is caused by the bacterium Burkholderia pseudomallei , which lives in tropical soil and especially occurs in southeast Asia and northern Australia . Despite the recognition that melioidosis is an emerging infectious disease , little is known about the habitat of B . pseudomallei in the environment . We performed a survey in the Darwin area in tropical Australia , screening 809 soil samples for the presence of these bacteria using molecular methods . We found that environmental factors describing the habitat of these bacteria differed between environmentally undisturbed and disturbed sites . At undisturbed sites , B . pseudomallei was primarily found in close proximity to streams and in grass- and roots-rich areas . In disturbed soil , B . pseudomallei was associated with the presence of animals , farming or irrigation . Highest B . pseudomallei counts were retrieved from paddocks , pens and kennels holding livestock and dogs . This study contributes to the elucidation of the habitat of B . pseudomallei in northern Australia . It also raises concerns that B . pseudomallei may spread due to changes in land management . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"infectious",
"diseases/bacterial",
"infections",
"ecology/environmental",
"microbiology",
"microbiology/medical",
"microbiology",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases"
]
| 2009 | Landscape Changes Influence the Occurrence of the Melioidosis Bacterium Burkholderia pseudomallei in Soil in Northern Australia |
The JAK/STAT pathway is a key signaling pathway in the regulation of development and immunity in metazoans . In contrast to the multiple combinatorial JAK/STAT pathways in mammals , only one canonical JAK/STAT pathway exists in Drosophila . It is activated by three secreted proteins of the Unpaired family ( Upd ) : Upd1 , Upd2 and Upd3 . Although many studies have established a link between JAK/STAT activation and tissue damage , the mode of activation and the precise function of this pathway in the Drosophila systemic immune response remain unclear . In this study , we used mutations in upd2 and upd3 to investigate the role of the JAK/STAT pathway in the systemic immune response . Our study shows that haemocytes express the three upd genes and that injury markedly induces the expression of upd3 by the JNK pathway in haemocytes , which in turn activates the JAK/STAT pathway in the fat body and the gut . Surprisingly , release of Upd3 from haemocytes upon injury can remotely stimulate stem cell proliferation and the expression of Drosomycin-like genes in the intestine . Our results also suggest that a certain level of intestinal epithelium renewal is required for optimal survival to septic injury . While haemocyte-derived Upd promotes intestinal stem cell activation and survival upon septic injury , haemocytes are dispensable for epithelium renewal upon oral bacterial infection . Our study also indicates that intestinal epithelium renewal is sensitive to insults from both the lumen and the haemocoel . It also reveals that release of Upds by haemocytes coordinates the wound-healing program in multiple tissues , including the gut , an organ whose integrity is critical to fly survival .
Innate immunity provides the first line of defense against invading pathogens . This response is initiated by host pattern recognition receptors ( PRRs ) , which sense specific and conserved motifs called microbe associated molecular patterns ( MAMPs ) found in microbes , but not in the host . MAMPs include lipopolysaccharide , peptidoglycan , lipoproteins , and CpG motifs of DNA [1] . Upon the recognition of MAMPs , PRRs activate signaling cascades that trigger the expression of immune effectors and regulators . Over the years , many PRRs , their ligands and the downstream signaling cascade components have been identified [2] . Metazoans have also acquired the capacity to recognize signals called damage-associated molecular patterns ( DAMPs ) . DAMPs are associated with tissue damage and wounding , and activate specific pathways involved in tissue repair or inflammation [3] . As most infections are associated with tissue damage , signaling pathways induced by both DAMPs and MAMPs participate in a coordinated manner to mount an effective host defense response . Several DAMPs have been identified in mammals , including cellular components such as ATP , uric acid , nucleic acids and actin [4] . However , the pathways and effector mechanisms that are activated by DAMPs and how these pathways are integrated with other facets of the innate immune response are poorly understood . The JAK/STAT signaling pathway plays a pivotal role during development and immunity in both mammals and insects [5 , 6] . It is activated upon binding of a secreted ligand to a receptor , leading to the recruitment of the Janus kinase ( JAK ) , and the subsequent activation of the transcription factor STAT ( Signal Transducer and Activator of Transcription ) . STAT , which is activated through phosphorylation , then translocates into the nucleus where it regulates the expression of target genes . While the genome of mammals encodes multiple combinatorial JAK/STAT pathways , only one canonical pathway is found in Drosophila . The Drosophila JAK/STAT pathway was originally identified through its role in embryonic segmentation [7] . It has three main cellular components: the receptor Domeless , the JAK Hopscotch , and the transcription factor STAT92E [8] , and is activated by three secreted proteins of the Unpaired ( Upd ) family–Upd1 , Upd2 and Upd3 [9] . Several studies have revealed a role of JAK/STAT in the insect systemic immune response . The first evidence for a role of the JAK/STAT pathway in insect immunity was the observation that bacterial infection triggers the phosphorylation and translocation of STAT92E in the fat body of the mosquito Anopheles gambiae [10] . Subsequent gene expression profiling studies identified a subset of Drosophila immune-responsive genes that are regulated by JAK/STAT upon injection of bacteria in the body cavity ( referred to as a septic injury ) . This includes the genes encoding a complement-like protein Tep2 , the cytokine Diedel , a JAK/STAT negative regulator Socs36E , and Turandots [11–16] . Turandots are Drosophila-specific genes of unknown function that are induced under various stress conditions , especially septic injury [17] . Studies done in larvae have shown that septic injury induces the release of the cytokine Upd3 from haemocytes , leading to the activation of the JAK/STAT pathway in the fat body [18] . In addition to its role in the humoral response , the JAK/STAT pathway also plays a pivotal role in Drosophila haematopoiesis . Constitutive activation of the pathway in haemocytes leads to a 5–20 fold increase in plasmatocyte numbers in larvae [19] . Many of the plasmatocytes differentiate into large flat cells called lamellocytes , which play a role in the encapsulation and destruction of aberrant self-tissue [20] . The JAK/STAT pathway also participates in the control of tumors or aberrant self in flies . The Upd3 ligand is expressed either at a wound site or by tissues with damaged basement membrane due to tumor outgrowth [20 , 21] . Circulating Upd3 then triggers JAK/STAT activation in both haemocytes and fat body , resulting , among others , in an increased number of haemocytes , which contribute to wound healing or tumor reduction [20] . Although many studies have suggested a link between JAK/STAT activation and tissue repair , the mode of activation and the precise function of this pathway in Drosophila systemic immunity remain unclear . In this article , we analyze the role of the JAK/STAT pathway in the resistance to wounding and septic injury in Drosophila . By using previously generated upd2 and upd3 mutants [22] , we show that this pathway contributes to fly resistance to wounding and bacterial infection . Our study reveals that haemocytes express the three Upds and that injury induces the production of Upd3 , which then activates the JAK/STAT pathway in the fat body and in the gut . One of the most surprising findings of the study is that Upds released from haemocytes can remotely stimulate intestinal stem cell proliferation . Thus , we uncover an unexpected interaction between haemocytes and intestinal stem cell proliferation that contributes to fly survival after injury .
The Drosophila genome contains three unpaired genes that encode ligands capable of activating the JAK/STAT pathway receptor Domeless . Since upd1 null mutants are embryonic lethal [8] , we focused our attention on the role of upd2 and upd3 in the systemic immune response [22] . To assess their role in the antimicrobial response , we monitored the survival rate of flies lacking upd2 and/or upd3 after systemic infection with either the Gram-negative bacterium Erwinia carotovora carotovora 15 ( Ecc15 ) or the Gram-positive bacterium Enterococcus faecalis . Fig 1A shows that upd2Δ , upd3Δ and upd2 , 3Δ flies were significantly more susceptible than wild-type flies to septic injury with Ecc15 . Compared to Imd deficient mutants , which usually die within 48h from the same challenge , the susceptibility of the upd mutants was mild with about 50% mortality after 10 days . Fig 1B shows that only upd2 , 3Δ flies displayed a higher susceptibility to septic injury with E . faecalis . The absence of a differentiated survival phenotype for the upd2Δ or upd3Δ single mutants could be a result of the faster killing rate induced by this bacterium ( Fig 1B ) . The Toll and Imd pathways are two NF-kB signaling pathways that contribute to resistance to septic injury by regulating many immune genes , notably those encoding antimicrobial peptides [23] . One possibility is that loss of upd2 and upd3 disrupts one or both of these NF-kB pathways , resulting in lower survival to septic injury . To test this hypothesis , we monitored the expression of the antimicrobial peptide genes Diptericin ( Dpt ) and Drosomycin ( Drs ) as readouts of the Imd and Toll pathways , respectively , in upd mutants . Both antimicrobial peptide genes remain fully inducible in upd2Δ , upd3Δ and upd2 , 3Δ mutants ( S1A and S1B Fig ) . This indicates that Upd ligands contribute to host survival to wounding and septic injury in a Toll and Imd independent manner . At this stage , it was unclear whether the higher susceptibility of upd mutants to septic injury was caused by the presence of the bacterium itself or by the wounding associated with this mode of infection . To distinguish between the two , we monitored the survival rates of upd deficient flies to sterile wounding and indeed , upd2 , 3Δ and to a lesser extent the single mutant flies showed an enhanced susceptibility to a clean wound ( Fig 1C ) . We conclude that upd2 , 3Δ flies are more susceptible to injury and that the presence of bacteria in the wound simply accelerates the mortality rate in both wild-type and upd2 , 3Δ flies . Resistance and resilience mechanisms both contribute to the survival to bacterial infection , where resistance involves the activation of the immune responses to eliminate pathogens , whereas resilience encompasses the capacity to survive infection without eliminating the pathogen [24] . We observed that the number of E . faecalis in flies both 10 h and 50 h after infection ( when flies start to die ) was roughly similar in the upd2 , 3Δ flies and in wild-type ( Fig 1D ) . Hence , resistance mechanisms were unaffected in the upd2 , 3Δ flies in response to bacterial infection . In the following step , we investigated how Upd ligands contribute to survival by restricting our analysis to two wounding protocols , clean or septic injury with Ecc15 ( referred to below as septic injury ) . Previous studies have shown that upd1 , upd2 and upd3 are induced in various intestinal cell types upon oral infection to stimulate stem cell proliferation and differentiation . Upd3 has also been shown to be induced in haemocytes of third instar larvae upon septic injury [15 , 22 , 25] . To investigate the spatial expression pattern of upds in adults , we monitored their expression in the three major immuno-responsive tissues , fat body , gut and haemocytes . In the haemocytes , upd1 and upd2 are expressed , but their expression is not consistently altered upon septic injury ( Fig 2A and 2B ) . In contrast , the transcription of upd3 is significantly up-regulated in haemocytes 2h post-infection ( Fig 2C ) . In the gut , all three upds are expressed at basal levels . However , the expression of upd2 was up-regulated at 2h following septic injury ( Fig 2B ) . In contrast , the fat body did not significantly up-regulate any of these three genes ( Fig 2A–2C ) . We next investigated whether Upds are required in adult haemocytes for optimal survival to wounding . For this , we knocked down individually each upd gene in haemocytes using an in vivo RNAi approach with the haemocyte driver hmlΔGAL4 [26 , 27] and monitored the survival of flies to wounding as well as to septic injury . We discovered that knockdown of either upd2 or upd3 in haemocytes increased the susceptibility of flies to wounding , while only the knockdown of upd3 resulted in higher susceptibility to septic injury with Ecc15 ( Fig 2D and 2E ) . To reinforce our conclusion , we performed survival to clean and septic injury in flies that express both the upd2 and upd3 RNAi using another GAL4 driver , peroxidasin ( pxn ) GAL4 ( S1C Fig ) . While hmlΔGAL4 is a haemocyte specific driver [26 , 27] , pxnGAL4 is expressed in haemocytes and weakly expressed in the fat body [28] . Simultaneous silencing of upd2 and upd3 with pxnGAL4 markedly increased susceptibility to both clean and septic injury ( S1C Fig ) . These experiments support the notion that the release of Upds by haemocytes promotes host survival to clean and septic injury . In agreement with this , flies lacking most of their haemocytes due to the expression of the pro-apoptotic factor Bax in the plasmatocyte lineage ( genotype: hmlΔGAL4 > UAS-Bax ) were susceptible to both wounding and septic injury showing similar survival kinetics ( S1D Fig ) . Collectively , these results provide strong evidence that the production of Upd2 and Upd3 ligands by haemocytes is crucial for fly survival to wounding and septic infection . The precise function of the JAK/STAT pathway in the systemic immune response is still poorly defined . The pathway regulates a small subset of immune inducible genes , notably genes encoding stress peptides of the Turandot family , the opsonin Tep2 as well as Socs36E , a negative regulator of the JAK/STAT pathway [10–12] . Having shown that Upds are expressed in haemocytes and that some of them are required for resistance to infection , we next determined the impact of upd2 , 3Δ deficiencies on JAK/STAT transcriptional activity . As expected , we observed a reduced expression of the JAK/STAT target genes Socs36E , TotA and TotM in upd2Δ , upd3Δ and upd2 , 3Δ deficient flies upon septic injury ( Fig 3A , 3C and 3E ) . Next we wanted to explore where the JAK/STAT pathway was activated in wild-type flies upon septic injury , by monitoring the expression of TotM and Socs36E in three immune compartments: the gut , the carcass ( comprising of the fat body ) and haemocytes . Surprisingly , we observed that Socs36E was strongly induced in the gut and to a lower extent in the carcass upon septic injury ( Fig 3B ) . TotA and TotM were induced mostly in the fat body , consistent with a previous study on Drosophila larvae [15] ( Fig 3D and 3F ) . To further characterize JAK/STAT pathway activity in vivo , we used flies carrying the reporter gene 10XSTAT92E-eGFP , which drives GFP expression under the control of ten STAT92E binding sites [29] . Since the same 10XSTAT92E-eGFP transgene is induced in larval fat body upon septic injury ( S2A Fig ) and since a previous study has already shown that the transcription factor STAT is activated in the fat body of adult flies after septic injury [15] , we would have expected the same in adults . However , we cannot exclude that a specific feature of the adult fat body hinders an accurate observation of GFP activity in this tissue . Interestingly , both clean and septic injury induces a significant expression of 10XSTAT92E-eGFP in the gut ( Fig 4A ) . Altogether , our data indicate that Upd2 and Upd3 contribute to the activation of the JAK/STAT pathway following septic injury , and reveal that the gut is one of the main sites of JAK/STAT activation upon septic injury in adults . The JAK/STAT pathway has two main functions in the intestine . The first one is to promote epithelium renewal by stimulating the activity of intestinal stem cells and their differentiation into new enterocytes to rebuild the gut . The JAK/STAT pathway is activated through the release of Upd2 and Upd3 by stressed or damaged enterocytes , establishing a compensatory homeostatic loop [23 , 30 , 31] . Interestingly , the JAK/STAT pathway is also activated in the visceral muscles that surround the epithelium , regulating the production of the epidermal growth factor Vein , which stimulates the EGF-Receptor pathway in intestinal stem cells [30 , 32] . The second function of the JAK/STAT pathway is to regulate a subset of peptides in the anterior part of the gut , with homology to the antimicrobial peptide Drosomycin: Drsl2 , Drsl3 and Drl4 . The expression of these Drosomycin-like peptides also depends on the JAK/STAT ligands Upd2 and Upd3 , which are released by the intestinal epithelium following damage associated with oral bacterial infection [22] . Our observation that clean and septic injury induces STAT activity in the gut suggests that a body wound can remotely stimulate stem cell proliferation and an anti-microbial response in the intestine . To investigate this , we further analyzed the site of 10XSTAT92E-eGFP expression in the midgut of challenged adult flies . We noticed that the STAT reporter gene is induced both in the epithelium and in the visceral muscle that surrounds the epithelium ( Figs 4A , 4B and S2B ) . In the epithelium , 10XSTAT92E-eGFP signals are restricted to progenitor cells ( stem cells and enteroblasts ) in unchallenged flies , but the signal expands to enteroblasts and enterocytes after wounding and septic injury ( Fig 4B ) . An expansion of 10XSTAT92E-eGFP is suggestive of an increased epithelium renewal rate and is known to occur when flies are fed with infectious bacteria or corrosive agents , two conditions that stimulate stem cell activity [33 , 34] . Supporting the hypothesis of increased epithelium turnover , the gene encoding the EGF ligand Vein is induced in the visceral muscles surrounding the gut following wounding and septic injury ( Figs 4C and S4A ) . Intestinal epithelium renewal can easily be monitored in Drosophila by counting the number of dividing stem cells along the midgut using an anti-phosphohistone H3 ( anti-PH3 ) antibody . Guts derived from flies collected after clean and septic injury exhibit an increased level of PH3 counts ( Fig 4C–4E ) . Importantly , the midgut PH3 counts were strongly reduced , but not abolished , in upd2 , 3Δ flies upon both clean and septic injury ( Fig 4E for females and S3A Fig for males ) . We next investigated whether body injury can trigger the induction of Drosomycin-like peptides in the gut . Drosomycin-like 3 ( Drsl3 ) is indeed induced in the anterior part of the gut 6 h after septic injury ( S4B Fig ) . In all these experiments we noticed that septic injury triggers a stronger response than a clean injury , indicating that the presence of bacteria increases the activation of JAK/STAT in the gut . Taken together , our study uncovers a signaling pathway linking wound healing in the thorax to activation of stem cell activity and Drosomycin-like expression in the intestine . It also implies that Upds are required as a link between the wound and intestinal stem cell proliferation . The results above show that JAK/STAT activity can be induced in the gut upon wounding and that this activation requires Upd2 and Upd3 . However , they do not inform us in which tissue Upds are expressed . The increased stem cell proliferation and Drs-like induction upon body injury could be a secondary consequence of damage to the gut , which is known to produce unpaired ligands . This hypothesis is supported by a recent paper by Takeishi and colleagues , which showed that wounding induces apoptosis in the gut , which in turn activates the stem cell division pathway [21] . The authors postulated the existence of a ‘lethal’ factor that is released in the haemolymph upon wounding , which is responsible for intestinal damage . This prompted us to investigate whether clean or septic injury induces cell death in the gut that could explain the increase of STAT activity and epithelium renewal we observed . For this , we monitored apoptosis in the intestine of flies 6 h septic injury and 16 h after oral infection using an anti-caspase 3 staining and inspect caspase activation by monitoring the cleavage of DCP-1 . We did not observe any increase of caspase 3 signal or cleavage of DCP-1 in flies following septic injury ( S5A and S5B Fig ) . As expected , we observed a marked increase of apoptosis in the intestine of flies collected 16 h after oral infection with Ecc15 , confirming the validity of the two apoptotic assays [30] . In light of these findings , we hypothesized that it might be rather the release of Upd ligands from circulating haemocytes that contributes to intestinal regeneration . To test this notion , we monitored stem cell activity upon septic injury in flies lacking haemocytes due to the hmlΔGal4 driven expression of Bax in plasmatocytes . Flies lacking haemocytes have a significantly lower intestinal mitotic index after septic injury as compared to their wild-type counterparts ( Fig 5A ) . This strongly suggests that haemocytes do contribute to the signal that activates stem cell proliferation upon systemic wounding . We then tested whether over-expression of Upd ligands in haemocytes was sufficient to activate intestinal stem cell proliferation . Over-expression of upd2 or upd3 but not upd1 using the haemocyte specific driver hmlΔGal4 , stimulates an increased stem cell activity in the gut in the absence of wounding ( Fig 5B ) . In contrast , we confirmed that over-expression of each of upd2 , upd3 and upd1 ( but a lower extent ) in enterocytes ( genotype: MyoTS-Gal4> UAS-upd ) stimulates stem cell proliferation in agreement with previous studies [32] . The observation that the expression of upd2 and upd3 but not upd1 in haemocytes can remotely activate stem cells is consistent with in vivo and in vitro data , showing that Upds have distinct diffusing properties and biological activities as suggested by previous studies [22 , 35 , 36] . We next tested whether the expression of Upds in haemocytes is sufficient to stimulate intestinal stem cell proliferation in the absence of any intestinal source of Upd2 and Upd3 . Consistent with our hypothesis that haemocytes remotely activate intestinal stem cells , we observed that the over-expression of either upd2 or upd3 by haemocytes can stimulate intestinal stem cell proliferation in upd2 , 3Δ flies ( Fig 5C ) . In this experiment , we noticed that over-expression of Upd2 , but not of Upd3 , led to the accumulation of small nucleate cells in the gut of upd2 , 3Δ flies , suggesting a differentiation defect ( S4C Fig ) . This indicates that while hemolymphatic production of Upd2 or Upd3 can trigger gut stem cell proliferation , only Upd3 ensured proper epithelium renewal with differentiation of the proliferating stem cells , pointing to a specific requirement of Upd2 to ensure proper enterocyte differentiation . Finally , to confirm that Upds derived from haemocytes play a role in septic injury induced intestinal stem cell proliferation , we used an in vivo RNAi approach to silence upd1 , upd2 or upd3 in haemocytes using the hmlΔGal4 driver , and monitored stem cell activity after septic injury . Fig 5D shows that reduction of upd2 or upd3 in haemocytes ( but not upd1 ) resulted in decreased PH3 counts in the gut 8 h after a septic injury , compared to wild-type . We conclude that production of Upd2 and Upd3 by haemocytes upon systemic wounding is necessary for JAK/STAT activation in the gut . This uncovers a new mechanism where haemocytes can remotely activate intestinal stem cell activity . Previous studies have established a link between JNK activity and the expression of Upds in the intestine [25 , 31 , 37] . We therefore investigated a possible role of the JNK pathway in the regulation of Upds in haemocytes upon septic injury . Interestingly , RT-qPCR analysis revealed that the JNK pathway is activated in both haemocytes and gut upon septic injury , as illustrated by the induction of puckered , a reporter gene of JNK pathway activity [38] ( Fig 6A ) . We then monitored upd2 and upd3 expression and intestinal mitotic activity in flies expressing a dominant-negative form of the Drosophila JNK Basket in haemocytes [39] . Inactivation of JNK signaling reduces both haemocyte expression of upd3 and intestinal PH3 counts upon septic injury , revealing that the stress-pathway JNK regulates upd gene expression in haemocytes ( Fig 6B and 6C ) . Many studies have shown that dysfunction of the Drosophila intestine impacts both aging and resistance to stress in flies [40–42] . Along this line , Takeishi et al . have shown that a viable mutation in the caspase activator Apaf-1/dark , dpf-1K1 , which leads to defects in gut epithelial renewal , results in lethality to wounding [21] . Based on these and our results , we hypothesized that the susceptibility of upd2Δ , upd3Δ and upd2 , 3Δ mutants to systemic wounding could be linked to an impairment in gut epithelium renewal . Recently , a method named the ‘Smurf assay’ has been developed to probe gut integrity . In this assay , flies are fed on food colored with a non-toxic water-soluble dye , and gut integrity is estimated by measuring the diffusion of the blue dye from the gut lumen to the hemocoel [41 , 42] . ‘Smurf’ flies , which show systemic diffusion of blue dye due to intestinal barrier dysfunction , exhibit a lower life expectancy . We used this assay to assess gut barrier integrity of upd2 , 3Δ flies . Upd2 , 3Δ flies display a higher proportion of ‘Smurf’ flies over time upon wounding and septic injury ( Fig 6D and 6E ) . The kinetics of ‘Smurf’ occurrence correlates with the survival to clean and septic injury . A higher proportion of ‘Smurf’ flies over time was also observed in hmlΔGal4 > UAS-Bax haemocyte-less flies compared to wild-type ( S3B Fig ) . Hence , an accelerated loss of gut integrity in upd2 , 3Δ flies is likely underlying their enhanced susceptibility to injury . To further test this hypothesis , we knocked down the JAK/STAT pathway specifically in the gut progenitor cells using esgtsGAL4 , and monitored survival to septic injury . In these experiments , a dominant-negative form of the receptor Domeless ( DomelessDN ) or a STAT RNAi construct ( STAT-IR ) were expressed in 3 day old adults using the thermo-sensitive driver esgtsGAL4 . Flies with reduced JAK/STAT activity in progenitor cells failed to survive clean and septic injury ( Figs 6F and S4D ) . Thus , our results identify intestinal epithelium renewal as a critical event for survival to septic injury . Our study is not the first to identify a role for haemocytes in intestinal epithelium renewal . Ayyaz et al . ( 2015 ) have recently shown that adult haemocytes are required for proper stem cell proliferation upon ingestion of paraquat and oral bacterial infection via the release of the Dpp ligand [43] . They also reported some haemocytes are recruited to the intestine upon stresses such as oral bacterial infection . This study does not preclude that haemocytes could also be a source of Upds in those conditions . To test this , we first investigated whether haemocytes respond to oral bacterial infection by expressing upds . Upd3 was slightly induced in haemocytes upon oral infection with Ecc15 , but the level of increase did not reach statistical significance ( Fig 7A–7C ) . The relative gene expression level of upd3 over RpL32 indicates that upd3 expression after oral infection in haemocytes was much weaker as compared to septic injury with Ecc15 ( Fig 2C ) . This indicates that haemocytes are more reactive to systemic infection than oral infection . We next monitored intestinal stem cell proliferation in the midgut of flies expressing upd3 RNAi in haemocytes , or of flies lacking nearly all haemocytes due to the expression of pro-apoptotic genes Bax ( see above ) or hid . Neither the absence of plasmatocytes , nor the silencing of upd3 in haemocytes affected the gut mitotic index in response to oral bacterial infection ( Figs 7D , 7E and S6B ) . Use of an HmlΔ-Gal4 , UAS-GFP reporter strain reveals an aggregation of haemocytes close to the midgut at the level of the loop ( S6A Fig , as described by Ayyaz et al . [43] ) . Nevertheless , we did not observe any obvious change in haemocytes number around the gut upon septic injury or oral bacterial infection with Ecc15 . We thus conclude that the intestinal repair response to oral bacterial infection can occur in absence of haemocytes .
Although it is recognized that three signaling pathways in Drosophila , Toll , Imd , and JAK/STAT , mediate the bulk of the transcriptional response to septic injury , functional studies on the JAK/STAT pathway have been hampered by the fact that loss-of-function mutations in genes encoding components of this pathway cause severe developmental defects . In this study , we have used viable mutations in upd2 and upd3 and tissue-specific inactivation of JAK/STAT components to assess their functions in systemic immunity . We demonstrate that Upd-JAK/STAT signaling is required to resist wounding and septic injury in adults . The upd2 , 3Δ deficient flies show a mild but clear susceptibility ten days after wounding . This is consistent with the common view that this pathway responds to damage , while Toll and Imd pathways are more responsive to microbial infection . Thus , the Upd-JAK/STAT axis should be seen as an integral part of the systemic wound response . Agaisse et al . 2003 initially reported that JAK/STAT activation in the larval fat body is mediated by a ligand , Upd3 , which is secreted by haemocytes [15] . Our data confirm and extend this finding , showing that the production of Upd3 by haemocytes activates JAK/STAT signaling in the adult fat body ( See model Fig 8 ) . It also shows that haemocytes produce two other upds , Upd1 and Upd2 , whose expression is less sensitive to septic injury . However , the observation that haemocytes contribute to intestinal epithelium renewal in response to septic injury was unexpected . Takeishi et al . ( 2013 ) were the first to establish a link between systemic wounding and intestinal homeostasis . They showed that body wounds remotely control caspase activity in enterocytes , which in turn activates the intestinal regeneration pathway in the gut [21] . However , the authors of this paper did not identify the link between body injury and the induction of apoptosis in the gut , they postulated the existence of a haemolymph ‘lethal’ factor that contributes to intestinal damage . Our study demonstrates that the wounding of the cuticle can activate the production of Upds by haemocytes , and that these secreted ligands are sufficient to induce stem cell proliferation in the intestine . Our observation that Upds released by haemocytes can remotely stimulate stem cell activity indicates that epithelium renewal is influenced by insults from both the luminal ( microbial infection , ingested toxins ) and the hemolymphatic compartments ( septic injury ) . Along the same line , it is well known that gut dysfunction and subsequent loss of appetite in humans commonly occurs following surgery or major injury [44] , although the molecular mechanisms of this process are poorly understood . The reason why septic injury stimulates intestinal stem cells remains enigmatic . Takeishi et al . ( 2013 ) and our study suggest that the intestine is an organ sensitive to systemic wounding . In agreement with this notion , the ‘Smurf’ assay reveals an increased intestinal permeability in upd2 , 3Δ flies upon septic injury . Stem cell proliferation is still activated , albeit at a much lower rate , in upd2 , 3Δ and in haemocyte-depleted flies . This suggests that additional factors beyond Upds , can stimulate intestinal stem cell proliferation . A first hypothesis would be that septic injury with a needle directly damages the intestine [21] . We do not favor this notion because the intestine did not appear damaged upon septic injury as assessed by phalloidin staining of the entire gut ( S2B1 and S2B4 Fig ) and did not show an increase of apoptosis at early time points ( S5A and S5B Fig ) . A second hypothesis is that wounding of the cuticle indirectly alters gut integrity . For instance , reactive oxygen species ( ROS ) induced during the systemic wounding reaction could damage the intestinal basement membrane . Thus , the production of Upds by haemocytes could be a way to stimulate intestinal repair as an anticipatory tissue repair mechanism . The role of the JAK-STAT pathway in the systemic wound response is not limited to the fat body and the gut . The JAK/STAT also plays a major role in larval haemocytes where it contributes to haemocyte recruitment or the differentiation into lamellocytes [19 , 20] . A recent study has shown that infestation by the parasitoid wasp Leptopilina boulardi triggers the release of Upd2 and Upd3 from haemocytes , which in turn activate the JAK/STAT pathway in the larval somatic muscles [45] . Collectively , haemocyte-derived Upds stimulate a broad array of responses in multiple tissues , and future studies should decipher how each organ contributes to the systemic wound response . Blocking the JAK/STAT pathway in the gut makes flies susceptible to a simple injury , suggesting that proper gut function is essential for recovery from systemic injury . Therefore , it cannot be excluded that several roles initially attributed to the JAK/STAT pathway are linked to its ability to turn on epithelium renewal in the intestine . For instance , the involvement of the JAK/STAT pathway in the resistance to Drosophila C Virus and cricket paralysis viruses in Drosophila or its implication in the defense against Plasmodium in Anopheles [46–49] could be linked to a defect in epithelium renewal . Our study reveals that haemocytes are dispensable to epithelium renewal upon oral infection with Ecc15 . Thus , haemocytes promote epithelium repair in response to a systemic threat but are less important in the defense against oral bacterial infection . Our results diverge from those of a recent study , which pointed to a contribution of adult haemocytes to stem cell proliferation , via their release of the Dpp ligand upon ingestion of paraquat or oral bacterial infection [43] . Consistent with the work of Ayyaz et al . , ( 2015 ) , we did observe aggregation of haemocytes close to the midgut at the level of the loop . Nevertheless , we did not observe any obvious recruitment of haemocytes to the gut upon septic injury or oral bacterial infection with Ecc15 . The continuous presence of haemocytes in close proximity to the midgut could allow a local enrichment of the diffusible JAK/STAT ligands upon septic injury [43] . The presence of haemocytes in close proximity to many tissues including gut and imaginal discs [20 , 50] underlines their function in tissue homeostasis as local tissue repairers . Our study delineates a pathway in which damages associated with injury induce the release of Upd3 by haemocytes to activate JAK/STAT in the intestine both in the visceral muscles and stem cells ( Fig 8 ) . We further show that the JNK pathway regulates the expression of upd3 as previously described for the gut [25 , 31 , 41] . This is in line with a former study describing a role for the JNK pathway component Mekk1 in the expression of the JAK/STAT target genes of the Turandot family , although the tissue where Mekk1 is required had not been identified [18] . Another recent study already implicated the JNK pathway for the regulation of Upd3 production in haemocytes in response to the ingestion of a high-lipid diet [51] . The observation that septic injury triggers upd3 expression in haemocytes more effectively than clean injury suggests that the Imd and Toll pathways , which sense microbial determinants , could promote the induction of upds in haemocytes possibly through a convergence on the JNK pathway . Systemic wounding induces a complex series of events that includes the entry of oxygen , rupture of basement membranes , ROS burst , production of melanin , as well as aggregation of clotting factors and haemocytes around the wound [52 , 53] . Wounding also leads to the release of cytoplasmic material from dead cells such as cytoplasmic actin , which functions as a DAMP in mammals [54] . A major challenge is now to identify the underlying mechanism that senses damage and activates the pathway . Previous studies have shown that H2O2 is a primary local signal responsible for the homing of haemocytes to the wound site in embryos [28 , 55] . An attractive hypothesis is that ROS may be the signal activating the JNK pathway in the haemocytes close to the wound site . Alternatively , it cannot be excluded that haemocytes harbor a receptor capable of recognizing a component normally restricted to the cytoplasm of living cells and unable to cross the basement membrane . This would explain why they are activated only by the concomitant destruction of both the basement membrane and the underlying epithelium . Our study emphasizes the central role of haemocytes via the production of Upds in an integrated systemic wound response . Future studies should now address how damage is sensed by haemocytes and how the JAK/STAT pathway targets genes contributes to repair programs and host survival .
CantonS ( CanS ) and w1118 flies were used as wild-type controls . The following fly lines were used in this study are upd2Δ , upd3Δ and upd2 , 3Δ [22]; w;UAS-upd1-IR ( VDRC # 3282 ) , w;UAS-upd2-IR ( NIG # 5988 R1-3 ) w;UAS-upd3-IR [15] , hmlΔGal4 , UAS-GFP [26] , w;;UAS-DomeDN [56] , yw;vein-lacZ ( P1719 ) , FRT82B/TM6B [32] , w , UAS-upd1 [36] , w , UAS-upd2 [33] , w , UAS-upd3 [31] , w;Gal80TS/CyO;howGal424B [30] , Myo1AGal4;Gal80TS [25] , w;10XSTAT-GFP [28] , UAS-BskDN [39] , UAS-hid [57] . The F1 progeny of hmlΔGal4 , UASGFP females crossed with UAS-Bax males were used to produce haemocyte defective flies , as described in [58] . RNAi flies carrying a GAL4 construct and UAS-IR construct were raised at 18°C during their larval and pupal development , and then shifted to 29°C for 8 days to activate the UAS-IR . All stocks were reared on standard fly medium comprising of 6% cornmeal , 6% yeast , 0 . 62% agar , 0 . 1% fruit juice , that was supplemented with 10 . 6g/L moldex and 4 . 9ml/L propionic acid and were maintained at 25°C on a 12 h light/ 12 h dark-cycle . Unless otherwise stated , experiments are done in male flies . Erwinia carotovora carotovora 15 ( Ecc15 ) is a Gram-negative bacterium described in [59] , while Enterococcus faecalis ( E . faecalis ) and Microccus luteus ( M . luteus ) are both Gram-positive bacteria [14] . Ecc15 and M . luteus were cultured overnight in LB medium at 29°C , and E . faecalis was cultured at 25°C . For bacterial counts , w1118 and upd2 , 3Δ male flies were infected with E . faecalis , and the number of bacteria was determined as follows at 10 h and 50 h post-infection [60] . Flies were surface sterilized in 95% ethanol for 1 min , and then 5 flies were homogenized using a Precellys 24 instrument ( Bertin Technologies , France ) , and then five 1/10 serial dilutions were made and plated on LB culture medium by spotting the serial in duplicates and incubated overnight at 25°C . Colonies were counted from spots containing more than 10 colonies . The experiment was repeated three times . For septic injury and natural infection experiments , Drosophila 3–4 days old adults were used . For septic injury , male flies were pricked in the thorax with a needle dipped into a concentrated culture of Ecc15 ( OD600 ~200 ) , M . luteus ( OD600 ~200 ) and E . faecalis ( OD600 ~10 ) . After septic injury , flies were incubated at 29°C for Ecc15 and M . luteus infection and at 25°C for E . faecalis infection . For oral infection , batches of 20 adult female flies were starved for 2 h at 29°C in an empty vial before being transferred to a fly vial with an infection solution . The infection solution consisted of an equal volume of 100X concentrated pellet from an overnight culture of Ecc15 ( OD600 = 200 ) with a solution of 5% sucrose ( 1:1 ) which was deposited on a filter disk that completely covered the surface of standard fly medium [59] . Flies were incubated for one day at 29°C on the contaminated filter , after which they were transferred to fresh vials containing standard medium without living yeast . In survival experiments , flies were maintained on medium without fresh yeast following infection and survivors were counted daily . 10–15 adult male flies or 20 dissected guts ( including crop , midgut and hindgut but without Malpighian tubules ) , or 15 carcasses ( that comprise mostly fat body ) were collected in 500 μl of Trizol ( Invitrogen ) . For collecting haemocytes , 20 individuals were placed on a 30 μM filter of an empty Mobicol spin column ( MOBITEC ) , then covered with glass beads and centrifuged for 20 minutes at 4°C , 10’000 r . p . m . This was done twice for a total of 40 adult male flies , and then the haemolymph recovered was pooled and collected in 300 μl of Trizol . Total RNA was extracted according to the manufacturer’s instructions . Quality of the RNA was determined using a NanoDrop ND-1000 spectrophotometer . The purified RNA was then treated with DNAse ( Ambion ) according to the manufacturer’s instructions . The quantity of RNA was determined using the NanoDrop and then 1 μg of RNA was used to generate cDNA using SuperScript II ( Invitrogen , Carlsbad , California , United States ) . RT-qPCR was performed using dsDNA dye SYBR Green I ( Roche Diagnostics , Basel , Switzerland ) . Expression values were normalized to RpL32 . Primer sequences used are provided in S1 Table . For immunofluorescence , guts from 3 to 5 day old females were dissected in 1X PBS , fixed for 20 minutes in PBS , 0 . 1% Tween 20 ( PBT ) , and 4% paraformaldehyde; then stained with primary antibody [1/1000 mouse anti-GFP ( Roche ) ; 1/500 rabbit anti-PH3 ( Upstate/Millipore ) ]; 1/200 rabbit anti-Caspase 3 activity ( ThermoFischer ) ; 1/200 rabbit anti-cleaved DCP1 ( Cell Signaling Technology ) in PBT + 2% BSA] . Secondary staining was performed with Alexa594 anti-rabbit and Alexa488 anti-mouse antibodies ( Invitrogen ) . Visceral muscles were stained using 1/500 Phalloidin-Rhodamine or 1/500 Phalloidin-FITC ( Life Technologies ) . DNA was stained with 1/15000 dilution of 4’ , 6- diamidino-2-phenylindole DAPI ( Sigma ) . The stained gut tissue was mounted in the antifading agent Citifluor AF1 ( Citifluor Ltd . ) . The mitotic index was determined by counting the number of PH3 positive cells along the midgut with Axioplot imager ( Zeiss ) . For determination of PH3 counts , at least 10 guts were counted per condition in each experiment and data was pooled from 3 independent experiments . The mean number of mitoses per midgut and S . E . M . are shown for each genotype or treatment in the graphs . Each experiment was repeated independently a minimum of three times ( unless otherwise indicated ) , error bars represent the standard error of the mean of replicate experiments ( unless otherwise indicated ) . Statistical significance was determined using Student’s t test , Mann-Whitney U test or log–rank test on GraphPad Prism , and P values of <0 . 05 = * , <0 . 01 = ** and <0 . 001 = *** for the t test and Mann-Whitney test while P <0 . 0001 = *** for log-rank test were considered significant . | Innate immunity acts as the primary line of defense to overcome invading organisms . This response starts through sensing of microbe-specific molecules such as lipopolysaccharide and peptidoglycan by host receptors . Metazoans can also recognize signals that are associated with tissue damage and wounding , to then activate specific pathways involved in tissue repair or inflammation . Previous studies have suggested a link between JAK/STAT activation and tissue repair in Drosophila , but the mode of activation and the precise function of this pathway remain unclear . In this article , we analyze the role of the JAK/STAT pathway in the resistance to wounding in Drosophila . We show that this pathway contributes to fly resistance to wounding and bacterial infection . Mechanistically , injury induces the production of secreted molecules called Unpaireds by blood cells , which then activate the JAK/STAT pathway in the fat body ( an organ equivalent to the vertebrate liver ) and in the gut . One of the most surprising findings of our study is that Unpaireds released from blood cells can remotely stimulate intestinal stem cell proliferation . Thus , we uncover an unexpected interaction between circulating blood cells and intestinal stem cell proliferation that contributes to fly survival after injury . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"animals",
"animal",
"models",
"c-jun",
"n-terminal",
"kinase",
"signaling",
"cascade",
"bacterial",
"diseases",
"drosophila",
"melanogaster",
"model",
"organisms",
"stem",
"cells",
"enterococcus",
"bacteria",
"drosophila",
"bacterial",
"pathogens",
"digestive",
"system",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"lipids",
"animal",
"cells",
"fats",
"medical",
"microbiology",
"microbial",
"pathogens",
"biological",
"tissue",
"enterococcus",
"faecalis",
"insects",
"arthropoda",
"gastrointestinal",
"tract",
"biochemistry",
"signal",
"transduction",
"anatomy",
"cell",
"biology",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"cell",
"signaling",
"organisms",
"signaling",
"cascades"
]
| 2016 | Remote Control of Intestinal Stem Cell Activity by Haemocytes in Drosophila |
In human cells and in Saccharomyces cerevisiae , BLAP75/Rmi1 acts together with BLM/Sgs1 and TopoIIIα/Top3 to maintain genome stability by limiting crossover ( CO ) formation in favour of NCO events , probably through the dissolution of double Holliday junction intermediates ( dHJ ) . So far , very limited data is available on the involvement of these complexes in meiotic DNA repair . In this paper , we present the first meiotic study of a member of the BLAP75 family through characterisation of the Arabidopsis thaliana homologue . In A . thaliana blap75 mutants , meiotic recombination is initiated , and recombination progresses until the formation of bivalent-like structures , even in the absence of ZMM proteins . However , chromosome fragmentation can be detected as soon as metaphase I and is drastic at anaphase I , while no second meiotic division is observed . Using genetic and imunolocalisation studies , we showed that these defects reflect a role of A . thaliana BLAP75 in meiotic double-strand break ( DSB ) repair—that it acts after the invasion step mediated by RAD51 and associated proteins and that it is necessary to repair meiotic DSBs onto sister chromatids as well as onto the homologous chromosome . In conclusion , our results show for the first time that BLAP75/Rmi1 is a key protein of the meiotic homologous recombination machinery . In A . thaliana , we found that this protein is dispensable for homologous chromosome recognition and synapsis but necessary for the repair of meiotic DSBs . Furthermore , in the absence of BLAP75 , bivalent formation can happen even in the absence of ZMM proteins , showing that in blap75 mutants , recombination intermediates exist that are stable enough to form bivalent structures , even when ZMM are absent .
From a diploid mother cell , meiosis generates four haploid products from which gametes differentiate . This ploidy reduction is a direct consequence of two rounds of chromosomal segregation ( meiosis I and meiosis II ) following a single S phase . The first meiotic division separates homologous chromosomes from each other while meiosis II separates sister chromatids . Recombination is one of the key events in meiosis . It gives rise to crossovers ( COs ) , which are essential for the correct segregation of homologous chromosomes during meiosis I , ensuring the association of homologous chromosomes into bivalents . Meiotic recombination can also lead to gene conversion not associated with COs ( NCOs ) , events that are probably much more frequent than COs at least in plants and mammals [1] . The current model for meiotic recombination [2] , [3] proposes that it is initiated by the programmed formation of DNA double-strand breaks ( DSBs ) , which are then resected to generate 3′ single stranded DNA molecules that drive DNA repair onto the homologous chromosome by invading an intact homologous chromosome . DNA strand exchange results in the formation of joint molecules . These joint molecules either dissociate enabling the broken chromosome to rejoin through synthesis-dependent strand annealing ( SDSA ) [3]–[5] , or form stable D-loops which proceed through the capture of the second processed DNA end to produce a double Holliday junction intermediate ( dHJ ) . The dHJ is then resolved by an unknown resolvase to generate COs products . This CO pathway is under the control of a set of genes which includes the ZMM family ( for Zip1 , Zip2 , Zip3 , Zip4 , Mer3 and Msh4/Msh5 ) . Another CO pathway , that does not proceed through dHJ formation , also coexists in most species and is under the control of the Mus81/Mms4 endonuclease [6] . In somatic cells , homologous recombination ( HR ) is also used to repair DNA DSBs that arise either from damage or from stalled replication forks . In this context , contrary to what happens during meiotic HR , repair is mostly directed towards the sister chromatids rather than the homologous chromosome . Furthermore , COs are generally prevented in favour of NCO events , probably by preferential involvement of the SDSA repair pathway and to dissolve dHJ to generate NCO events . The eukaryotic homologues of the highly conserved RecQ helicase family are known to be particularly crucial components of regulation mechanisms against CO formation . The Bloom protein ( BLM , one of the five human RecQ helicases ) was shown to disrupt D-loop intermediates in vitro [7] , to dissolve dHJ [8] , [9] and to disrupt the Rad51 presynaptic filament [9] , [10] . In vivo , the antiCO effect of BLM/Sgs1 helicase was demonstrated by the fact that yeast sgs1 mutants as well as Bloom's syndrome patients or BLM-deficient mice have elevated rates of mitotic recombination ( either reciprocal sister chromatid exchanges ( SCE ) or increased frequency of exchange between homologous chromosomes ) [11]–[14] . In plants at least seven RECQ-like genes were identified [15] and functional analyses showed that A . thaliana RECQ4A is likely to be the functional homologue of BLM [16] , [17] . It was also shown to partially suppress the embryo-lethality of A . thaliana top3α and to be lethal in conjunction with the A . thaliana mus81A mutation [18] . The human protein BLAP75 ( for Bloom Associated Protein of 75 kD ) was recently identified [19] , [20] as a 75 kD protein which co-purified in diverse Bloom ( BLM ) -containing complexes from HeLa cells . It was proposed to form the structural core of all BLM complexes with BLM and TopoIIIα ( the human topoisomerase 3α ) [19] . A BLAP75 homologue was described in yeast ( Rmi1/Nce4 ) and the conservation of the BTB complex ( BLM-TopoIII-BLAP75 ) was demonstrated in S . cerevisiae , where it is known as the RTR ( RecQ helicase-Top3-Rmi1 ) complex [21] , [22] . Recent biochemical studies showed that the BTB/RTR complex plays a crucial role in the dissolution of dHJ to produce NCOs [9] , [23] , [24] . The proposed mode of action is that BLM/Sgs1 decatenates dHJs to form a hemicatenane substrate for the topoisomerase 3 . BLAP75/Rmi1 would be necessary for the loading and stability of the complex . It strongly enhances BLM-TopoIIIα dependant dHJ dissolution in vitro [9] , [21] , [24]–[26] . It is also possible that this complex works on other HR substrates such as Rad51 presynaptic filaments or D loops [10] , [27] . Therefore , the BTB/RTR complexes are proposed to act at different levels of the HR process to limit CO formation in favour of NCO events . Limited data is available on the involvement of BTB/RTR complexes in meiotic DNA repair . In S . cerevisiae , sgs1Δ top3Δ , and rmiΔ mutants show reduced sporulation and decreased spore viability [11] , [12] , [22] , [28] , [29] . For sgs1 , the phenotype was correlated with meiosis I nondisjunction and precocious sister segregation [11] , [28] but , unlike the situation in somatic cells , in most cases no increase in meiotic recombination was detected [11] , [28] , [30] , [31] . Nevertheless , Sgs1 was shown to prevent CO maturation in zmm mutants , and could suppress sister chromatid dHJ formation during meiotic recombination [30] , 31 . In mouse spermatocytes , the BLM protein was shown to colocalise with the recombination proteins RPA , RAD51/DMC1 and MSH4 [32]–[34] , but BLM disruption in mouse has no effect on meiotic CO rates [14] . This is not the case for the Drosophila melanogaster , Caenorhabditis elegans and Schizosaccharomyces pombe BLM orthologues , for which depletion is associated with a decrease in CO rates [35]–[37] . In this study we show that the Arabidopsis BLAP75/Rmi1 homologue is absolutely required for meiotic DSB repair onto homologous chromosome or sister chromatid . We also provide evidence that , in the absence of A . thaliana BLAP75 , recombination is initiated and progresses until the formation of recombination intermediates that allow the formation of bivalents even in the absence of ZMM .
In a screen for A . thaliana T-DNA ( Agrobacterium tumefaciens transferred DNA ) insertions that generate meiotic mutants , we isolated a mutant ( line FCN288 ) disrupted in the A . thaliana predicted open reading frame , At5g63540 ( see Materials and Methods ) , annotated as a protein of unknown function in TAIR ( http://arabidopsis . org/ ) . Another insertion line in At5g63540 available in the public databases ( http://signal . salk . edu/ ) , SALK_093589 , was obtained ( Figure 1A ) and showed the same meiotic phenotype as FCN288 ( Figure 1A ) . Genetic tests confirmed that these two mutations were allelic ( see Materials & Methods ) , demonstrating that disruption of At5g63540 is responsible for the mutant phenotype observed in both lines . Interestingly , other insertion lines which we investigated ( Salk_005449 , Salk_054053 , Salk_054062 and Salk_094387 , Figure 1A ) that contained a T-DNA insertion in the 3′ region of At5g63540 did not show any detectable phenotype ( not shown ) . According to the T-DNA insertion sites , these mutant lines are expected to produce a truncated protein , suggesting that the C-terminal part of the protein is not necessary for its function . The At5g63540 cDNA encodes a 644-amino acid ( aa ) protein ( Figure 1B ) . Database searches using the BLASTP program ( Blosum 45 ) for proteins similar to that encoded by At5g63540 revealed the existence of a conserved domain ( from aa 101 to 194 , e value 6×10−20 ) [38] annotated as a domain of unknown function ( DUF1767 , pfam08585 ) but found in the N-terminus of the nucleic acid binding domain of several protein families , represented mainly by the mammalian BLAP75 proteins and showing weak homology with an OB-fold domain [19] . When BLAST searches against the non-redundant database with the At5g63540 protein sequence were carried out , the highest scores ( outside the plant kingdom ) were obtained with several sequences similar to the protein BLAP75/Rmi1 , including similarities outside the DUF1467 region ( Figure 1B ) . Alignment of these proteins revealed two conserved domains: one spanning from aa 101 to aa 294 ( DUF1467 , Figure 1B ) and another one from aa 484 to aa 627 . Recent biochemical studies performed on the human BLAP75 protein showed that aa 151 to aa 211 ( contained in DUF1467 , and corresponding to aa 219 to aa 279 on At5g63540 ) are necessary for the interaction of BLAP75 with BLM and TopoIIIα . One conserved lysine ( K166 , corresponding to K235 in At5g63540 , Figure 1B ) is absolutely necessary not only for interaction with BLM-TopoIIIα but also for enhancing dHJ dissolution and HJ dissociation [25] . These authors also identified a single strand DNA binding activity domain lying in the C terminus of the human protein [25] . It corresponds to the second conserved domain of this protein family which is found in plant BLAP75 ( Figure 1B ) . The S . cerevisiae Rmi1 protein is much shorter than higher eukaryotic BLAP75 , containing only the N-terminal region [22] and when it was used to query the A . thaliana non-redundant accessions using PSI-BLAST ( Blosum 45 ) , no hits were obtained . When the same search was carried out using Homo sapiens BLAP75 , however , we picked up At5g63540 after the first round of iteration ( e value = 8e-14 , Identities = 54/187 ( 28% ) , Positives = 93/187 ( 49% ) ) . We therefore called this new A . thaliana gene BLAP75/RMI1 . The insertional line FCN288 ( accession Ws ) was named blap75-1 , and the Salk_093589 line ( Col-0 accession ) blap75-2 . This BLAST search also resulted in a high score with the A . thaliana At5g19950 gene ( e value = 1e-11 , Identities = 32/78 ( 41% ) , Positives = 47/78 ( 60% ) ) . However , the GABI-Kat Line 679A11 with an insertion in At5g19950 did not display a vegetative or reproductive phenotype . Reverse-transcriptase PCR ( RT-PCR ) studies showed that BLAP75 was expressed at low levels in roots and flower buds but not in leaves ( see Figure S1 ) . The two blap75 mutants displayed the same phenotype: normal vegetative growth but short siliques ( Figure 2 ) suggesting fertility defects . Indeed , the mean seed number per silique of both blap75 mutants was extremely low ( 0 . 03 for blap75-1 and 0 . 0006 for blap75-2 , counted on 1 , 000 siliques ) whereas the average is 63 and 71 seeds per silique for Ws ( blap75-1 accession ) and Col-0 ( blap75-2 accession ) , respectively ( n = 50 ) . We examined the reproductive development of these mutants and found that blap75 sterility is due to abortion of male and female gametophytes ( data not shown ) . No differences were seen between wild-type and mutant plants when the early stages of microsporogenesis were compared , with round pollen mother cells ( PMCs ) found within the anther locules ( Figure 3A–C ) . In wild-type anthers , these cells underwent two meiotic divisions to produce a characteristic microspore tetrad ( Figure 3D ) . Meiosis products were also detected in mutant plants but these lacked the regular tetrahedral structure , and either single , double or multiple cell products were observed ( Figure 3E , F ) . blap75 mutants produced a majority of dyads ( 55% of the cells counted for blap75-1 ( n = 348 ) , and 73% of the cells for blap75-2 ( n = 316 ) ) suggesting that the meiotic program is disrupted in blap75 mutants . We therefore investigated male meiosis by staining PMC chromosome spreads with 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Wild-type A . thaliana meiosis has been described in detail in [39] , and the major stages are summarised in Figure 4 ( A–H ) . During prophase I , meiotic chromosomes condense , recombine , and undergo synapsis , resulting in the formation of five bivalents , each consisting of two homologous chromosomes attached to each other by sister chromatid cohesion and chiasmata , which become visible at diakinesis ( Figure 4C , arrow heads ) . Synapsis ( the close association of two chromosomes via the synaptonemal complex ( SC ) ) begins at zygotene and is complete by pachytene ( Figure 4B ) , by which point the SC has polymerised along the whole length of the bivalents . At metaphase I , the five bivalents are easily distinguishable ( Figure 4D ) . During anaphase I , each chromosome separates from its homologue , leading to the formation of dyads corresponding to two pools of five chromosomes ( Figure 4E–F ) . The second meiotic division then separates the sister chromatids , generating four pools of five chromosomes ( Figure 4G–H ) , which give rise to a tetrad of four microspores ( Figure 3D ) . In A . thaliana blap75 mutants , the early stages of meiosis could not be distinguished from wild type: chromosomes condensed and synapsis of the homologous chromosomes proceeded normally ( shown for blap75-1 allele in Figure 4 I–J ) . To confirm that no synapsis defects could be detected in blap75 mutants we performed immunolocalization studies by double-labelling wild-type and mutant PMCs with anti-ZYP1 ( a major component of the central element of the SC , [40] ) and anti-ASY1 ( a protein associated with the axial element of the SC , [41] ) antibodies . We could not detect any difference in mutant compared to wild-type cells ( Figure 4Q–T ) , either in the progression ( Figure 4Q and 4S ) or completion of synapsis ( Figure 4R and 4T ) . However , chromosomal abnormalities appeared later at prophase , when condensing bivalents could be recognised ( diakinesis , Figure 4K ) . At this stage in wild type , the five bivalents can be identified , each of them composed of a pair of homologous chromosomes connected one to the other where COs have occurred ( some of these chiasmata are shown by arrowheads in Figure 4C ) . In blap75 mutants , chromosome arms are visible but it was impossible to distinguish a chromosome arm from its homologue as if the two were intimately linked ( compare 3K to 3C ) . At metaphase I , abnormalities were even more obvious , with a range of phenotypes illustrated in Figure 4L to 4O . In the majority of the metaphase I cells ( 86% n = 76 for blap75-1 and 89% n = 107 for blap75-2 ) chromosomes did appear to be associated in bivalent-like structures because five entities can be recognised ( Figure 4M–O ) . Nevertheless , their shape is very unusual , often showing ( 52% of the metaphase for each allele ) bubble-like extensions ( Figure 4L , M arrows ) that sometimes seem to connect the bivalents together , leading to the whole set of chromosomes having a rattle-like structure ( Figure 4N ) . In the remaining cells , the bivalent-like entities displayed a very unusual compact appearance ( Figure 4O ) , and we never observed the five typical bivalents observed during wild-type metaphase ( Figure 4D ) . Next , anaphase I proceeded and led to dramatic chromosome fragmentation ( Figure 4P ) . Nevertheless , chromosome migration occurred and was followed by de-condensation of the various DNA pools produced after anaphase I . Typical telophase I could be recognised ( data not shown ) but meiotic division appeared to stop at this stage , and we could never identify a second meiotic division in any of the two blap75 mutants . When we analysed female meiosis in blap75 mutants we observed the same defects as for male meiosis ( Figure S2 ) . In order to understand the nature of the metaphase structures observed in A . thaliana blap75 mutants at metaphase I , we performed fluorescent in situ hybridization ( FISH ) analyses on PMCs with diverse probes . Firstly , we used a probe corresponding to the A . thaliana centromere repeat sequences ( Figure 5A–D ) . This probe allows the very clear positioning of the ten A . thaliana centromeres , which were observed in wild-type and in most blap75 cells , as expected , grouped in two pools of five , pointing toward the two spindle poles ( Figure 5A–C ) . It also showed that contrary to what occurs in wild type , the chromosome arms are floating on the metaphase plate ( Figure 5B–C , arrows ) , explaining the rattle-like structures seen in Figure 4N . When probed with the centromeric repeat , the “compact” blap75 bivalents shown in Figure 4O ( here in Figure 5D ) appeared to have the same structure as in 5B and 5C with two centromeres directed towards opposite directions and two chromosome arms floating , except that the whole structure is more condensed ( compare Figure 5D to Figure 5B , C ) . In some cases however , more than two centromere signals could be observed ( asterisks , Figure 5D ) , suggesting that these entities underwent premature sister centromere uncoupling . We also carried out FISH experiments using probe mixes designed to specifically label pairs of chromosomes: a 45S rDNA probe together with a cocktail of chromosome 4 BACs , shown in Figure 5E to L; a mixture of 45S and 5S rDNA repeats , shown in Figure 5 M to 5Q; and a mixture of chromosome 1 BACs shown in Figure 5R to U . These combinations allowed the clear identification of either chromosomes 2 and 4 ( Figure 5E–L ) , chromosomes 2 , 4 and 5 ( Figure 5M–Q ) or chromosomes 1 ( Figure 5R–U ) . Labelling of a blap75-1 pachytene cell ( Figure 5G , 5T ) showed that the multiple BAC probes were correctly positioned along the chromosome arms , demonstrating that in blap75 , synapsis is occurring between homologous chromosomes . When metaphase I PMCs were probed , we found that homologous chromosomes were associated together in bivalent-like structures ( Figure 5H , J , O , P , Q ) as in wild-type cells ( Figure 5E , M or R ) . We also observed that in many cases chromosome arms are much less compact than in wild type ( compare Figures 5H , J , Q to Figure 5E , M ) , float around , and sometimes appeared connected to each other ( Figure 5H ) . Furthermore we observed very frequent evidences of chromosomal fragmentation ( Figure 5 arrowheads ) . Therefore , we can conclude from these results that the structures observed at metaphase I in blap75 mutants are bivalent-like in the sense that they connect homologous chromosomes from pachytene to anaphase I . Nevertheless , the architecture is highly perturbed with chromosome arms floating on the metaphase plate and numerous evidence of chromosome breakages as early as metaphase I/anaphase I transition . Meiotic recombination is initiated by the formation of DNA DSBs that are catalysed by Spo11 in budding yeast and in all other eukaryotes studied to date [42] . In A . thaliana , the disruption of SPO11-1 or SPO11-2 induces a typical asynaptic phenotype ( Figure 6A–C ) associated with a dramatic decrease in meiotic recombination , leading to the formation of achiasmatic univalents , which is correlated with an absence of meiotic DSBs [43] , [44] . In order to understand if the meiotic chromosomal defects observed in blap75 mutants were dependent upon DSB formation , we generated spo11-1blap75 double mutants ( Figure S3 ) . These plants showed a typical spo11-1 phenotype: synapsis failed to engage ( Figure 6D ) , there was an absence of bivalents ( Figure 6E ) and lack of chromosome fragmentation at anaphase I ( Figure 6F ) or II ( not shown ) . Therefore , blap75 bivalent-like structures as well as blap75 fragmentation are dependent upon meiotic DSB formation . Next , we analysed the nuclear distribution of the DMC1 protein , which is an essential component of the recombination machinery ( Figure S3 ) . Its appearance on meiotic chromosomes during prophase is thought to mark the sites of recombination repair . To follow DMC1 focus formation throughout meiosis , co-immunolocalisation was performed with antibodies that recognise the meiotic protein ASY1 . Detailed analysis of DMC1 progression in wild-type Arabidopsis meiotic prophase was described in [45] . DMC1 foci appear at late leptotene/early zygotene reaching an average of 240 foci per nucleus ( 239 +/− 74 n = 49 ) and disappear by pachytene [45] . DMC1 foci had similar characteristics in blap75-1 male meiocytes , with an average of 235+/−68 per zygotene nuclei ( n = 60 ) ( Figure 7 ) . Therefore , early DSB repair events do not appear to be disrupted in blap75 mutants . In order to obtain more precise information concerning the function and position of A . thaliana BLAP75 in the DSB repair steps , we also generated the rad51blap75 and mnd1blap75 double mutants . The Rad51 protein is a recombinase that is loaded on single-stranded DNA generated after DSB processing and mediates the search for homology and invasion of an intact homologous DNA molecule [46] . The Mnd1 protein is another key actor of the strand invasion step , stimulating the activity of Dmc1 and/or Rad51 [47] . In A . thaliana the two mutants , rad51 and mnd1 , show drastic meiotic defects that can be summarised by an absence of synapsis , the formation at metaphase I of a mass of entangled chromosomes linked together by chromosomes bridges and prominent chromosome fragmentation at anaphase I [48]–[51] ( shown on Figure 6G–I for rad51 ) . Nevertheless , these abnormalities do not prevent meiosis II from occurring , and a second round of chromosomal segregation is observed , leading to the formation of very abnormal meiotic products ( not shown ) . The phenotype of rad51blap75 and mnd1blap75 double mutants could not be distinguished from that of the rad51 or mnd1 single mutants ( shown for rad51blap75 in Figure 6J–L ) , suggesting that A . thaliana BLAP75 acts after RAD51 and MND1 in the DSB repair cascade . The situation in the A . thaliana dmc1 mutant is very different because even if meiotic DSBs are formed in this background , they are completely repaired , probably using the sister chromatid as a template [52]–[55] leading to a typical asynaptic phenotype ( Figure 6M–O ) . Therefore , we wondered whether A . thaliana BLAP75 is involved in this repair pathway and we analysed the phenotype of the blap75dmc1 double mutant . In this background we observed a cumulative effect of the two mutations . Firstly , no trace of synapsis was observed during prophase ( not shown ) as is the case in dmc1 ( Figure 6M ) . Then , at metaphase/anaphase I , chromosomes with very altered morphology , showing fragmentation and chromosome bridges , were observed ( Figure 6P , Q ) . This fragmentation was even more spectacular while anaphase I proceeded , but second division figures were observed ( Figure 6R ) , contrary to the situation in the A . thaliana blap75 single mutant . Another striking difference between both genotypes was the absence in the double mutant of any bivalent-like structures at metaphase I . Therefore it appears that in the absence of BLAP75 , the repair of meiotic DSBs onto sister chromatids is altered . Furthermore , bivalent-like structures formed in blap75 mutants are dependant upon DMC1 function . In order to understand the nature of the association between homologous chromosomes existing in blap75 mutants , we analysed the involvement of two ZMM proteins: MSH5 and MER3 ( Figure S3 ) . Both were previously shown to be involved in the maturation of class I COs , which represent 85% of the total CO number in A . thaliana . Their mutation has no effect on early meiosis events but results in a highly pronounced decrease in CO formation ( 85% of the wild-type level for A . thaliana msh5 and 76% for A . thaliana mer3 ) leading to a large number of achiasmatic univalents at metaphase I ( [56] , [57] , shown for msh5 in Figure 8A–C ) . When we analysed blap75msh5 and blap75mer3 double mutants , we could not detect any difference between them and the single blap75 ( Compare Figure 8 D–I to Figure 4J , M and P ) . Therefore , we can conclude that the formation of stable associations between homologous chromosomes observed in blap75 mutants does not require ZMM proteins .
In human cells and S . cerevisiae , BLAP75/Rmi1 , BLM/Sgs1 and TopoIIIãTop3 were demonstrated to interact [19]–[22] , to form one or several complexes involved in maintaining genome stability [27] . In yeast , Rmi1 and Top3 appear to act in the same pathway downstream of Sgs1 since most of the defects exhibited by top3 and rmi1 mutants are suppressed by mutation of SGS1 [22] , [58] , [59] . These data led to the hypothesis that the yeast RecQ helicase activity ( Sgs1 ) produces toxic DNA structures that are removed by the combined action of Top3 and Rmi1 . In A . thaliana , the existence of this complex has not yet been shown , but the results of several recent studies provide evidence for its conservation . Firstly , the A . thaliana recq4A-4 mutation suppresses ( at least partially ) the lethality of the A . thaliana top3α-1 mutation [16] . Secondly , A . thaliana blap75/rmi1 mutants as well as recq4A-4 and the leaky top3α -2 show hypersensitivity to the same DNA damaging agents as well as increased rates of somatic homologous recombination [60] . Therefore , the existence of a plant BTB/RTR complex composed of A . thaliana RECQ4A , TOP3α and BLAP75/RMI1 that would be involved in vegetative cell cycle surveillance , is very likely . However , its function during meiosis is less clear . Our data together with the those of [60] clearly show that two members of the plant BTB/RTR complex ( BLAP75/RMI1 and TOP3α ) are involved in meiotic recombination where they are likely to act in the same pathway . In A . thaliana , the topoisomerase 3α protein is essential for somatic development [16] making its function during meiosis difficult to investigate . Nevertheless , partial suppression of the top3α -1 somatic phenotype by the recq4a mutation , together with analysis of a leaky top3α-2 allele made it possible to show that blap75/rmi1 , top3α-2 and recq4A-4top3α-1 have the same meiotic defects [60] suggesting that TOP3α and BLAP75/RMI1 act together during meiosis . Several findings , however , suggest that the last member of the complex , RECQ4A , may not be involved in meiosis . Firstly , its disruption does not impair meiosis [16] , [17] , [60] . Secondly , recq4A-4 suppresses ( at least partially ) the somatic phenotype of the top3α-1 mutation [16] , but not the meiotic phenotype [60] . This suggests that if Arabidopsis BLAP75 and TOP3α do act together with a meiotic helicase , it is probably not RECQ4A . By characterising the A . thaliana homologue of BLAP75/RMI1 , we could report one of the first studies of the role played by a member of the BTB/RTR complex in meiosis . The BLAP75/Rmi1 proteins share a N-terminal domain containing a putative OB ( oligonucleotide/oligosaccharide binding ) -fold which is responsible for the single stranded DNA binding activities of proteins such as RPA or BRCA2 [61] . Nevertheless , to date , a DNA binding capacity was not associated with this region in the BLAP75/Rmi1 protein family . However it was recently shown , in vitro , to be necessary for H . sapiens BLAP75 to form a complex with BLM and hTopoIIIα and to activate the dissolution activity of this complex . The C-terminal region of BLAP75 proteins is specific to higher eukaryotes: it is not found in S . cerevisiae Rmi1 which is much shorter than the vertebrate or plant BLAP75 . We found that mutations that disrupt this C terminal region of the A . thaliana BLAP75 do not lead to any detectable phenotype , at least at the reproductive level . Since in the H . sapiens BLAP75 , the C-terminal domain was recently shown to bind to single stranded DNA in vitro [25] , it suggests that , in A . thaliana , the single strand DNA binding capacity of BLAP75 is dispensable for its meiotic function . In vitro studies comparing the activity of a truncated BLAP75 protein containing only the N-terminal region and the full length protein might help understand the function of this conserved higher eukaryote C-terminus extension . Our study of A . thaliana blap75/rmi1 insertional mutants showed that this gene is crucial for meiosis . Disruption of A . thaliana BLAP75 led to drastic chromosome fragmentation at anaphase I and to an absence of the second meiotic division . Inhibition of meiosis II is either indirect and due to the strong chromosomal fragmentation observed at meiosis I or A . thaliana BLAP75 is directly involved in meiosis II induction , but further studies are needed to decipher the precise explanation . Our study revealed that A . thaliana BLAP75 is involved in meiotic recombination . Firstly , we showed that the A . thaliana blap75 meiotic phenotype depends on SPO11-1 and therefore on DSB formation . Thus it is likely that the DNA fragmentation observed in blap75 mutants reflects DSB repair defects . Secondly , we showed that A . thaliana BLAP75 is not necessary for homologue recognition and synapsis . Even if we cannot exclude subtle effects ( timing differences for example ) , major perturbations to homologous recognition , association and synapsis can be ruled out: all pachytene stages appeared perfectly normal in terms of synapsis ( observed by DAPI spreads as well as ZYP1 immunolabelling ) and homology ( according to FISH results ) . Therefore , homologue recognition and synapsis occur normally in the absence of A . thaliana BLAP75 . Even in the absence of A . thaliana BLAP75 , homologous bivalents are formed and can be observed from diakinesis to metaphase I . We observed that these bivalents are formed independently of two A . thaliana ZMM proteins ( MSH5 and MER3 ) , that are known to be necessary for all ( MSH5 ) or most ( MER3 ) Class I CO formation ( see Introduction , Figure S3 , and [56] , [57] , [62] ) . The biochemical function of ZMM is still poorly understood , but data obtained in yeast support the idea that these proteins allow the formation of stable SEI intermediates , committing these to the Class I pathway [63] , [64] . Thus these proteins act just after the invasion step by the Rad51-Dmc1-coated single stranded DNA , to stabilise the newly formed heteroduplex in order to direct repair toward dHJ intermediates and CO formation . In the absence of these proteins , a stable heteroduplex cannot occur . Since homologous bivalents are formed in blap75mer3 or blap75msh5 , we conclude that in blap75 mutants , recombination intermediates exist that are stable enough to form bivalent structures , even when ZMM are absent . Such intermediates could be , for example , the complex joint molecules corresponding to several interconnected DNA duplexes that have been observed in yeast sgs1 mutants [30] . Our findings are also in agreement with data from [30] , [31] who detected an anti-CO effect of Sgs1 in zmm mutants . It was suggested that yeast Zmm protect recombination intermediates from dissolution by Sgs1 . When Zmm are removed , the anti-CO activity of Sgs1 occurs and recombination intermediates do not produce COs but are repaired onto the sister chromatid , explaining the decrease in COs observed in these backgrounds . However , when both Zmm and Sgs1 are removed , CO recombination intermediates are formed and the CO level is close to that of wild type [30] , [31] . In the case of A . thaliana , the removal of both ZMM and BTB/RTR complex activity ( blap75 mutants ) , did not restore CO formation ( since normal bivalents were not observed ) , but recombination nevertheless progressed , allowing the formation of stable bivalent-like structures . Mutations affecting the BTB/RTR complexes lead to genomic instability at mitosis and meiosis . During mitosis , these mutations provoke a characteristic “hyper-rec” phenotype showing that this complex acts to suppress CO formation , likely acting at different steps of the recombination process , one of the most documented steps being the dissolution of dHJ intermediates toward NCO events [9] , [23] , [24] , [26] , [65] . This complex could also be involved in repressing recombination by acting on earlier intermediates [27] , by destabilising the Rad51 filament ( as was shown in vitro for BLM protein [10] ) , or by disrupting D-loop intermediates ( shown also for the BLM protein , [7] ) . Biochemical studies have shown that BLAP75/Rmi1 has affinity for a number of DNA structures , with a preference for HJ [21] . The role of BLAP75/Rmi1 in the BTB/RTR complex would be to promote BLM-dependent dissolution of homologous recombination intermediates by recruiting TopoIIIα [9] , [23] , [24] , [26] . Mutations affecting any member of the BTB/RTR complex also affect meiosis , but are not accompanied by a “hyper-rec” phenotype ( see Introduction ) . Studies we performed on the A . thaliana BLAP75 homologue showed that abnormalities in blap75 mutant meiosis appear at diakinesis . Homologous bivalents were formed but showed very abnormal structures , with no visible chiasmata but intimately linked homologous chromosomes arms . The recombinases Rad51 and Dmc1 act very early during meiotic recombination ( Figure S3 ) . They are loaded onto 3′ single strand DNA generated at DSB sites and are thought to play a crucial role in the search for homologous intact DNA duplexes . Mnd1 , together with its partner Hop2 , was shown to stabilise the Rad51 presynaptic filament and to promote D-loop formation [66] . We analysed the phenotypes of the rad51blap75 and mnd1blap75 double mutants and found that we could not distinguished them from single rad51 or mnd1 , showing that the blap75 phenotype depends not only on SPO11-1 ( as discussed earlier ) , but also RAD51 and MND1 . We also found that DMC1 focus number was identical in blap75 and in wild type . The Dmc1 protein is a meiotic specific RecA homologue that plays a crucial role in driving meiotic DNA repair towards the homologous chromosome instead of the sister chromatid . If BLAP75 was involved in destabilizing early recombination intermediates , one might expect to see a difference in DMC1 focus formation . Since this difference was not observed , it suggests that BLAP75 is not involved in destabilising early recombination intermediates . Therefore , A . thaliana BLAP75/RMI1 is a protein necessary for the repair of meiotic DSBs that acts after the invasion step mediated by RAD51 and associated proteins . We also showed that BLAP75 is necessary for repair onto sister chromatids , since DSB repair in the dmc1 background is perturbed in the absence of BLAP75 . Therefore , taken together all these data suggest that BLAP75 , probably along with TOP3α , fulfils a key function in meiotic recombination by processing ( dissolving ) recombination intermediates , which are dependent upon RAD51 , MND1 and DMC1 and generated during meiotic DSB repair on either homologous chromosomes or sister chromatids .
The blap75-1 mutant ( FCN288 line ) was obtained from the Versailles Arabidopsis T-DNA transformant collection [67] . Mutant screening was performed as described in [68] . The blap75-2 mutant , line Salk_093689 , was obtained from the collection of T-DNA mutants at the Salk Institute Genomic Analysis Laboratory ( SIGnAL , http://signal . salk . edu/cgi-bin/tdnaexpress ) [69] and provided by the Nottingham Arabidopsis Stock Centre ( NASC ) ( http://nasc . nott . ac . uk ) as well as lines Salk_005449 , Salk_054062 , Salk_054053 and Salk_094387 . The spo11-1 allele used is spo11-1-2 described in [44] , [54] . The dmc1 , rad51-1 , msh5 , and mer3 mutants were described in [48] , [52] , [56] , [57] , [70] . Isolation of blap75-1: the FCN288 line segregated 3∶1 for the meiotic mutation ( revealing the presence of a single recessive mutation ) and 3∶1 for kanamycin resistance ( one of the T-DNA markers ) . After crossing to wild type , linkage between the T-DNA insert and the meiotic phenotype was confirmed as described in [44] . We tested for allelism between the blap75-1 and blap75-2 mutations by crossing two heterozygous plants blap75-1+/− and blap75-2+/− . Among the F1 plants , one fourth was sterile and carried each of the mutant alleles . Double mutants were obtained by crossing plants heterozygous for each mutation . The resulting hybrids were self-pollinated . We used PCR screening to select the sterile plants in the F2 progeny homozygous for both mutations . The full length cDNA sequence for At5g63540 was obtained from NCBI ( http://www . ncbi . nlm . nih . gov/entrez/ ) with the accession number AY954880 and checked by RT-PCR amplification . The left border genomic sequence flanking the blap75-1 T-DNA insert was amplified by thermal asymmetric interlaced PCR ( TAIL PCR ) according to [71] , with the modifications described in [44] . Subsequent sequencing revealed that the insertion was at nt 696 in the AY954880 sequence . The right border could not be amplified because of a complex insertion of two T-DNAs in tandem . The T-DNA insertion led to a deletion of the 5′ region of At5g63540 ( since no amplification product could be detected with primers P6 ( GGAGCCCGTCTAGAAGTCGACAACGA ) and P10 ( GCTCACTGACTCCGACGGAT ) or P3 ( ACGAAGAAGAAGAAGATGAAACTGG ) and P1R ( TGAGTGGGCAGCCAATGTTAAC ) , however we checked that the gene located 5′ to At5g63540 ( At5g63550 ) was not affected . The left border of blap75-2 was amplified with primers LbSalk2 and P3 and subsequently sequenced , showing that the T-DNA was inserted in At5g63540 ( nt 272 of sequence AY954880 ) . The right border could not be amplified , and we observed that the T-DNA insertion induced a deletion of AY954880 3′ region ( at least from nt 272 to nt 900 of AY954880 ) . The spo11-1-2 mutation was identified using a CAPS marker . PCR amplification was performed with primers MG52 ( GGATCGGGCCTAAAAGCCAACG ) and MG96 ( CTTTGAATGCTGATGGATGCATGTAGTAG ) and subsequently cleaved with AseI . The digestion generates two 500 bp fragments for the mutant allele only . The blap75-1 T-DNA left border was amplified with primers P2 ( GCAGCTAGAGTTGCTCTGGTTG ) and LbBAR2 ( CGTGTGCCAGGTGCCCACGGAATAG ) . The wild-type blap75-1 allele was amplified with primers P2 and P7 ( GCTGGTCCGTTTGTTCTGCAG ) . blap75-2 T-DNA left border was amplified by PCR with primers P6 and primer LbSalk2 ( GCTTTCTTCCCTTCCTTTCTC ) . The wild-type allele of blap75-2 was PCR amplified with primers P6 and P1R . Protein sequence similarity searches were performed at the National Centre for Biotechnology Information ( http://www . ncbi . nlm . nih . gov/BLAST ) and the Arabidopsis Information Resource ( TAIR , http://www . arabidopsis . org/Blast ) , using BLOSUM45 matrix and default parameters . Sequence analyses were performed with BioEdit software ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) . The anti-ASY1 polyclonal antibody has been described in [41] . It was used at a dilution of 1∶500 . The anti-ZYP1 polyclonal antibody was described by [72] . It was used at a dilution of 1∶500 . The anti-DMC1 antibody was described in [45] and the purified serum was used at 1∶20 . Comparison of early stages of microsporogenesis and the development of PMCs was carried out as described in [44] . Preparation of prophase stage spreads for immunocytology was performed according to [41] with the modifications described in [73] . The fluorescence in situ hybridization ( FISH ) was performed according to [74] . The A . thaliana 180 bp pericentromeric tandem repeat ( pAL1 , [75] ) and pTa71 , a 9 kb clone containing 18S-5 , 8S-25S Triticum aestivum rDNA [76] were labelled by biotin nick translation mix according the manufacturer's instruction ( Roche ) and detected by Avidin-Texas Red and goat anti-avidin-biotin antibodies ( Vector laboratories ) . A 3 . 5 kb fragment of 5S A . thaliana rDNA ( pCT4 . 2 , [77] ) and seven BACs ( F25I24 , F25E4 , F28A21 , F17L22 , F10M23 , F4I10 , F22I13 ) from the long arm of chromosome 4 ( http://www . arabidopsis . org/servlets/mapper ) were labelled by digoxigenin nick translation mix according the manufacturer's instruction ( Roche ) and were detected by mouse anti-digoxigenin antibodies ( Roche ) , rabbit anti-mouse FITC and goat anti-rabbit Alexa-488 antibodies ( Molecular Probes ) . To label the chromosome 1 arm , ten BACs ( F6F3 , F24B9 , F14N23 , F13K23 , F19K19 , F14010 , F26F24 , F17L21 , F12K21 , F26G16 ) were labelled alternately with digoxigenin-dUTP and biotin-dUTP and detected as described above . All observations were made using a Leica DMRXA2 microscope; photographs were taken using a CoolSNAP HQ camera driven by Open-LAB 4 . 0 . 4 software; all images were further processed with OpenLAB4 . 0 . 4 or AdobePhotoshop 7 . 0 . | Recombination is a process by which cells can repair DNA damage . Such repair can either be crossovers ( CO ) , in which DNA molecules are submitted to major exchanges , or non-crossover ( NCO ) events . Eukaryotic cells have developed several mechanisms to maintain genome stability during vegetative development by limiting the occurrence of CO events in favour of NCO . BLAP75/Rmi1 , BLM/Sgs1 , and TopoIIIα/Top3 act together in a complex ( BTB/RTR ) known to be a crucial component of regulation mechanisms against CO formation . However , CO/NCO regulation is thought to be very different during meiosis since homologous chromosomes ( paternal and maternal ) overcome at least one CO/pair . In this study , we investigate the role of the BTB/RTR complex during meiotic recombination through the analysis of a function of one of its members: the A . thaliana homologue of BLAP75/Rmi1 . We show for the first time that BLAP75/Rmi1 is also a key protein of the meiotic homologous recombination machinery . In Arabidopsis , we found that this protein is dispensable for homologous chromosome recognition and synapsis , but necessary for the repair of meiotic double-strand breaks . Furthermore , in the absence of BLAP75 , bivalent formation can happen even in the absence of CO . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"molecular",
"biology/recombination",
"molecular",
"biology/chromosome",
"structure",
"genetics",
"and",
"genomics/chromosome",
"biology",
"cell",
"biology/plant",
"genetics",
"and",
"gene",
"expression",
"molecular",
"biology/dna",
"repair"
]
| 2008 | The Arabidopsis BLAP75/Rmi1 Homologue Plays Crucial Roles in Meiotic Double-Strand Break Repair |
Lepidopterans ( butterflies and moths ) are a rich and diverse order of insects , which , despite their economic impact and unusual biological properties , are relatively underrepresented in terms of genomic resources . The genome of the silkworm Bombyx mori has been fully sequenced , but comparative lepidopteran genomics has been hampered by the scarcity of information for other species . This is especially striking for butterflies , even though they have diverse and derived phenotypes ( such as color vision and wing color patterns ) and are considered prime models for the evolutionary and developmental analysis of ecologically relevant , complex traits . We focus on Bicyclus anynana butterflies , a laboratory system for studying the diversification of novelties and serially repeated traits . With a panel of 12 small families and a biphasic mapping approach , we first assigned 508 expressed genes to segregation groups and then ordered 297 of them within individual linkage groups . We also coarsely mapped seven color pattern loci . This is the richest gene-based map available for any butterfly species and allowed for a broad-coverage analysis of synteny with the lepidopteran reference genome . Based on 462 pairs of mapped orthologous markers in Bi . anynana and Bo . mori , we observed strong conservation of gene assignment to chromosomes , but also evidence for numerous large- and small-scale chromosomal rearrangements . With gene collections growing for a variety of target organisms , the ability to place those genes in their proper genomic context is paramount . Methods to map expressed genes and to compare maps with relevant model systems are crucial to extend genomic-level analysis outside classical model species . Maps with gene-based markers are useful for comparative genomics and to resolve mapped genomic regions to a tractable number of candidate genes , especially if there is synteny with related model species . This is discussed in relation to the identification of the loci contributing to color pattern evolution in butterflies .
With the need for a wider sampling of biological diversity [1]–[3] , the availability of tools for large-scale genetic and genomic analysis is rapidly being extended beyond a handful of classical model systems . Gene collections are growing for various species and with them , the need for methods to assign genes to genetic maps and to assess synteny with relevant sequenced genomes . Gene-based linkage maps are invaluable in the search for the loci that contribute to phenotypic evolution; they are more easily transferable and comparable between species than anonymous markers , and facilitate resolution of mapped genomic regions to candidate genes , also via comparisons of maps or gene functions between species . The Lepidoptera ( butterflies and moths ) are a diverse order of insects with an abundance of species , including many agricultural pests , and one of two species of domesticated insects . Lepidopterans have some unusual genetic properties , such as holocentric chromosomes , heterogametic females , and male-restricted meiotic recombination , whose underlying mechanisms and consequences for genome evolution remain to be fully explored . However , lepidopteran species are relatively under-represented in terms of genomic resources with little available outside the model silkworm Bombyx mori [4] . Comparative genomics among lepidopterans and a detailed comparative analysis of the B . mori genome have been hampered by the relative scarcity of relevant genomic information . Dipterans , the closest insect lineage with available sequenced genomes , diverged from lepidopterans more than 200 MYA , and there is relatively little genomic information within the Lepidoptera . This is especially striking for butterfly species ( derived from moths some 100 MYA ) , despite much interest in their diverse , derived , and ecologically-relevant wing patterns . Color patterns on butterfly wings include some compelling examples of adaptation and have regained interest in evolutionary developmental biology's quest to understand the mechanistic basis of phenotypic variation [5]–[7] . A number of candidate genes well described in relation to wing development in Drosophila melanogaster have been implicated in formation ( reviewed in [5] , [8] ) and variation [9]–[11] of wing patterns in butterflies . Despite the success of the Drosophila-based candidate gene approach , it is clear that a more unbiased approach will be necessary . For example , for those cases where there are no obvious candidate genes [12] , and because it is conceivable , if not likely , that genes other than those described for a derived model system will be relevant for traits that are restricted to a lineage diverged more than 200 MYA . For this reason , there have been a number of recent efforts to push forward butterfly genomics [13] , [14] , including construction of large EST collections [15]–[17] , and genetic linkage maps [18]–[21] for a few target species . The latter are , however , largely or exclusively composed of anonymous markers , limiting broad-coverage comparative analysis of gene co-segregation and order across species . Recent studies based on a limited number of pairs of mapped orthologous markers have proposed conservation of syntenic blocks and gene order between B . mori and Manduca sexta moths and/or Heliconius melpomene butterflies [22]–[25] . Extending this type of analysis to many more pairs of mapped orthologs will be crucial to exploring the use of B . mori as a pan-lepidopteran genomics reference , and to allow integration of genomics information now accumulating for different species of butterflies [14] . Bicyclus anynana is probably the closest to a butterfly equivalent of a “lab rat” . This species was introduced to captivity some two decades ago and it has since been the focus of studies on the evolution and development of wing patterns and other phenotypes [26] . Two key processes in morphological evolution are captured on the wings of these butterflies; diversification of evolutionary novelties ( as are the scale-based color patterns of butterflies [27] ) , and of serially-repeated structures ( as are the eyespots of many Nymphalids [28] ) . Laboratory populations of B . anynana have been used to examine the genetic , developmental , and physiological basis of phenotypic variation [29] , and have provided the material for identification of anonymous [30] and expressed gene-based [15] , [31] markers . Here , we describe a study that genetically maps SNPs in a large number of ESTs to B . anynana chromosomes . We use a mapping panel composed of a number of small families to maximize the number of mapped markers and produce the densest gene-based map available to date for any butterfly species . This map includes a number of color pattern loci defined by spontaneous Mendelian mutations and enabled a large-scale analysis of synteny with the lepidopteran reference species . The usefulness of gene-based linkage maps and comparative analysis of chromosomal composition is illustrated in relation to the identification of color pattern loci .
We selected 768 SNPs in expressed genes to genotype in a mapping panel composed of 288 individuals from 12 F2 families ( Table S1 ) . These markers correspond to 745 SNPs in 744 UniGene contigs ( marker name starting with BaC ) and 23 SNPs in 14 selected candidate genes ( marker name starting with BaG ) . The contigs were identified from the assembly of over 100 , 000 EST reads , and the candidate genes were selected based on their developmental roles ( see Methods ) . We selected a single SNP for most genes , with the exception of 5 genes whose potential role in development warranted extra effort . Seventy percent ( 533 of 768 ) of the target SNPs converted into good assays , defined as those with 90% of the panel individuals being genotyped and with a minor allele frequency greater than 5% . For these SNPs ( Table S2 ) , each of the 12 families had an average of 60 SNPs that were informative in females only ( ranging from a maximum of 84 to a minimum of 43 ) , 63 SNPs that were informative in males only ( ranging from 81 to 44 ) , and 141 SNPs that were doubly-informative ( ranging from 155 to 119 ) . On average , each of those SNPs was male-informative only in 1 . 4 families , female-informative-only in 1 . 3 families and both male and female informative in 3 . 2 families . Upon visual inspection of the genotypes for the 533 markers ( Table S2 ) , we identified 513 markers with autosomal segregation patterns , 9 with segregation patterns consistent with sex linkage , and 11 with several Mendelian inconsistencies which were excluded from further analysis . Absence of recombination in lepidopteran females can be exploited to construct genetic linkage maps via “biphasic mapping” [32]; marker pairs that are female fully-informative are used to initially assign markers to segregation groups , and male informative markers are then used to order markers within those groups . We used this strategy and CRIMAP software for pedigree analysis [33] and were able to assign 508 SNPs to 28 B . anynana linkage groups ( Table 1; Figures 1–4 ) , possibly corresponding to the 27 autosomes and Z sex chromosome of this species [20] . We were able to map 10 of our 14 candidate genes ( BaG markers ) : cubitus interruptus ( ci ) , Ecdysone receptor ( EcR ) , engrailed ( en ) , APC-like ( Apc ) , naked cuticle ( nkd ) , cinnamon ( cin ) , Henna ( Hn ) , echinus ( ec ) , Catalase ( Cat ) , and Heat-shock protein 70 ( Hsp70 ) . Failure to map the other four candidate genes was due to a failed assay ( split ends , spen ) , Mendelian inconsistencies ( scabrous , sca ) , or the SNP being fixed in the mapping panel ( wingless , wg and groucho , gro ) . We also attempted to map nine Mendelizing mutations affecting body or larval coloration which were segregating in some of the 12 full-sib mapping families ( Table 2 ) . Two of the visible markers could not be mapped ( LOD score not significant ) , and the other seven were assigned to six LGs . Mapped markers typically had poorly resolved map positions , often corresponding to the entire length of the chromosome ( Figures 1–4 , Table 2 ) . Among the mapped visible mutants , two are particularly worrisome: 1 ) the Spotty mutation for which a 2 LOD support interval included positions at either end , but excluded the middle region of LG10 , and 2 ) the Goldeneye mutation which mapped to LG28 whose validity we are uncertain of ( see below ) . Poor mapping resolution for the visible markers likely reflects the fact that: 1 ) any given mutation was typically only segregating in 1–4 families ( Table 2 ) , 2 ) in the case of non co-dominant mutations a fraction of the segregants needed to be scored as “missing” which resulted in further loss of resolution , and 3 ) the mutations may not be 100% penetrant . Nonetheless , the mapping of these mutations to chromosomes is a very valuable first step towards efforts to clone the corresponding loci . Fine mapping efforts need now only employ markers in the same linkage groups . With 508 markers in expressed genes and seven visible mutants , this is the densest non-anonymous marker map ever reported for a butterfly species . Up until now , the most anchor loci mapped in this group was 101 for Heliconius melpomene ( cf . [23] ) , another Nymphalid . For all the SNPs assigned to a given segregation group we used male informative markers to build a map for that group ( Figures 1–4 ) . For 297 of the 508 gene-based markers , we were able to assign a position in the corresponding LG ( hereafter , “ordered markers”; Table S3 ) . The remaining 211 markers were not assigned to a unique position , but their position was typically narrowed to two or three intervals ( hereafter , “unordered markers”; Table S4 ) . LGs had on average 10 . 6 ordered and 7 . 5 unordered markers with standard deviations of 5 . 5 and 4 . 6 respectively ( Table 1 ) . Three linkage groups ( LG24 , LG27 , and LG28 ) consisted only of single ordered markers at the tips and zero to two extra , unordered markers . In addition , despite a total of 24 markers assigned to LG13 , this LG only had markers placed at the tips . The reasons for poor marker resolution in LG13 are unknown . Our total estimated map length , based on LG “male-based” distance between terminal markers , was 1642 . 2 cM , with individual LG length varying between ∼14 cM ( LG14 ) and ∼122 cM ( LG19 ) ( Table 1 ) . This map length is well within that estimated for different butterfly species ( 1430 cM–2542 cM , [18] , [19] , [21] ) and close to that estimated for B . anynana based largely on AFLP markers ( 1354 cM or 1873 cM depending on the mapping software used cf . [20] ) . However , because of the probable non-zero distance between terminal markers and chromosome ends , the “male distance” between terminal markers can be an underestimate of actual LG lengths . Our dataset allows for two types of quality control of map assignments . First , estimates of map distance in females ( which should be zero since they do not have recombination ) is a measure of potential map expansions due to errors . About 26% ( 70 of 269 ) of the “female distances” between neighbor ordered markers were greater than 0 cM ( Table 1 ) . The average distance for the non-zero distances was 3 . 0 cM , and included 11 distances greater than 5 cM , and 4 greater than 10 cM ( Table S3 ) . The total female map is 212 . 4 cM implying a map expansion due to genotyping errors and/or the mapping algorithm of ∼12 . 9% . The extent of this expansion varies greatly between LGs ( Table 1 ) ; while for most , the expansion is lower than 10% , for LG12 it reaches 55% ( due mainly to a single terminal marker; see Table S3 ) . Secondly , male recombinational distance between multiple SNPs at the same gene measures error in map position assignments . We have two genes where multiple markers have been ordered , Apc ( LG6 ) and EcR ( LG10 ) . For Apc two of the three ordered markers overlap and the third maps at a distance of 5 . 4 cM from them , while for EcR all three markers map to positions within 2 . 6 cM from each other ( Figures 1–4 ) . The average maximum distance between ordered non-overlapping markers at the same locus is 4 cM , and the average distance of the four possible distances between consecutive markers ( 0 , 5 . 4 , 1 . 9 , 0 . 7 ) is 2 cM . Some of this error is certainly associated with genotyping errors , but it may also result from our mapping approach which attempts to integrate marker information over several families ( see below ) . In any case , this analysis suggests that distances smaller than ∼5 cM might not be well resolved in our map . Our method was designed to maximize the number of gene-based markers assigned to linkage groups with minimum de novo SNP identification . This approach involved: 1 ) focusing on SNPs identified in EST collections ( thus , in expressed genes ) and for which the minor allele was seen at least twice ( thus making it less likely that SNPs are cDNA-related errors; [31] ) , 2 ) using a mapping panel composed of a number of small families rather than one large one ( maximizing the number of mapped markers at the expense of their mapping resolution; see below ) and CRI-MAP software for pedigree analysis , 3 ) using Illumina GoldenGate genotyping technology ( without any per SNP assay optimization ) , and 4 ) following a biphasic linkage mapping method [32] which takes advantage of the fact that there is no recombination in lepidopteran females . We chose to use a mapping panel made up of a number of small families rather than the more typical single large family . With this strategy we maximize the chance of assigning any given SNP to a LG ( as this only requires having one female informative family ) , and , once assigned to a linkage group , we maximize the chance of identifying at least a second family in which that SNP is ( also ) male informative . Of the 508 mapped markers , 11 corresponding to nine Z-linked loci and to two autosomal markers ( BaC645 on LG2 and BaC4454 on LG11; cf . Methods ) were not female-informative ( i . e . heterozygous ) in any family . Similarly , only three SNPs were not male informative in any family ( Table S2 ) . A panel derived from several independent parental pairs , additionally allows for estimates of population SNP frequencies , which will be useful in future mapping experiments . The disadvantages of this strategy are noticeable in terms of mapping resolution when a marker is male informative only in a single ( or few ) families and because of the need to integrate marker information across families . The majority of the SNPs ( 264 of 508 ) were male-informative ( including both SNPs informative only in males and those doubly-informative ) in at least five families ( corresponding to at least 120 individuals in the mapping panel ) , and three SNPs were informative for a maximum of ten families ( 240 individuals ) . In downstream uses of this map ( e . g . , for mapping QTLs or visible mutants ) , we will be able to choose from mapped , intermediate-frequency , informative SNPs , to design assays for larger mapping panels derived from a smaller number of founders . For this , having a large number of gene-based markers ( even if mapped with limited resolution ) and knowledge of SNP frequency is more useful than having a very accurate map of sparse markers ( which may not be informative in another context ) . A recent very high density SNP map for Bombyx mori [34] combined with a new assembly ( unpublished ) of the whole-genome sequence of this species [35] , 36 with larger average scaffold sizes , may be used as a pan-Lepidoptera reference . Using blastn , we assigned 1711 of the 1755 mapped SNPs in the silkworm B . mori [34] to the recent genome-sequencing scaffolds [37] ( Table S5 ) . The mapped markers aligned to 185 of the 645 different scaffolds , consistent with the highly skewed distribution of scaffold lengths . Of the 185 scaffolds to which markers mapped 29% , 10% , 6% , and 6% had one through four mapped markers , respectively . On the other hand , ∼90% of the mapped markers were assigned to only 91 scaffolds having more than four markers each , implying that the bulk of the current B . mori genome assembly is contained in 91 large scaffolds . As a check on the quality of the current assembly , we looked for scaffolds with more than four mapped markers in which at least one marker mapped to a different linkage group than the remainder . We observed seven scaffolds ( 7 . 7% ) with material coming from two chromosomes and two additional scaffolds ( 2 . 2% ) with material derived from three chromosomes , suggesting that there are errors with the current assembly . A visual inspection suggests that those assembly errors tend to be associated with the very ends of scaffolds . So , although the fraction of large scaffold with such errors is significant , very little of the assembly is affected . We next fitted local regressions for each scaffold that allowed for predictions of genetic positions ( cM ) given a physical position ( bp ) on the scaffold ( see Methods ) . The B . mori map thus generated was the basis for the comparative analysis with our B . anynana gene-based map . The current B . mori map consists of over 1650 SNPs covering 1413 cM [34] . With 28 chromosome pairs , B . mori has the largest chromosome number of all insect genomes sequenced to date . Previous studies of deep synteny across insects showed that divergent gene order correlates with divergent protein sequence [38] , and that there is more conservation of syntenic groups between B . mori and the coleopteran Tribolium castaneum than between B . mori and the hymenopteran Apis mellifera [34] . Both these orders have presumably split from a common ancestor with lepidopterans earlier than dipterans did . However , analysis of synteny blocks with sequenced representatives of the Diptera is hampered by the large difference in chromosome number; typically around 30 pairs in most lepidopteran species [39] and between three and six pairs for the various sequenced dipteran ( mosquito and Drosophila ) species . Among lepidopterans , and despite the available phylogenetic framework for comparative analysis ( e . g . [40]–[44] ) , relatively sparse genomic resources have resulted in very few attempts to examine synteny . Previous studies compared synteny blocks between moths and Heliconius butterflies based on a modest number of mapped orthologous pairs ( maximum 72 with many “unordered” [23] ) . Here , in a comparison between B . mori and B . anynana genetic maps , we increased this number by more than seven times , with a large fraction of our markers being “ordered” . This type of analysis , hopefully extending also to representatives of the microlepidoptera ( all lepidopterans examined to date are macrolepidopterans ) , will be crucial to put the gene collections and genetic maps , growing for a variety of butterfly species , into phylogenomic context . We used blast to identify orthologs of the gene-based markers in B . anynana ( Neolepidoptera; Papilionoidea; Nymphalidae; Satyrinae ) mapped in other lepidopteran species: the butterfly Heliconius melpomene ( Neolepidoptera; Papilionoidea; Nymphalidae; Heliconiinae ) , and the silworm Bombyx mori ( Neolepidoptera; Bombycoidea; Bombycidae; Bombycinae ) . Of the 508 B . anynana markers , 29 ( 18 ordered and 11 unordered ) had an ortholog among the 101 anchor loci mapped in H . melpomene [23] , and 462 ( 269 ordered and 193 unordered ) could be assigned to a mapped B . mori scaffold . Of the remaining 46 mapped B . anynana markers ( blue in Figures 1–4 ) , 20 had orthologs in B . mori scaffolds which we could not assign to a B . mori LG and 26 did not have significant sequence similarity with any B . mori scaffold . Despite the ca . 100 MY that separate butterflies and moths [23] , [42] , there is much conservation of the grouping of genes in linkage groups ( Figures 1–5 ) . Our numbering of B . anynana LGs reflects homology with B . mori with the exception of B . anynana LG28 , which has only two markers and none with orthologs mapping to B . mori LG28 ( Figures 1–4 ) . Of the 462 pairs of mapped orthologous markers in the two species , 425 ( ∼92% ) are found in orthologous LGs ( Figure 5 ) . The 37 orthologous genes found on non-orthologous LGs , include 17 that are associated with three large chromosomal rearrangements ( involving LG2 and LG24 , LG16 and LG23 , and LG20 and LG28 ) , and 20 which are potential single gene transpositions . The latter may also include blast false positive ( even though only five cases had e-values higher than 1 . 0e-20; Figure 5 ) , blasts to pseudo- or duplicate genes , or mapping errors ( e . g . , markers mapping to non-syntenic linkage groups that are isolated at the tips of chromosomes are especially suspicious ) . Both B . mori and B . anynana have 28 pairs of chromosomes , while basal lepidopterans have 31 pairs [45] and different species of butterflies and moths have very variable numbers [39] , [45]–[47] . The instances where individual B . anynana LGs are made up of syntenic blocks from different B . mori LGs suggest that the two lineages have undergone independent karyotype reductions , via non-homologous chromosomal fusions . A previous study compared linkage group assignment for 72 orthologous pairs of markers available for another Nymphalid butterfly ( Heliconius melpomene ) and the reference lepidopteran ( Bombyx mori ) and concluded that extensive synteny existed [22] , [23] . Some striking differences , however , are apparent between the genome-wide analysis of macro-synteny for B . mori and B . anynana ( this paper ) and that for B . mori and H . melpomene [23] . First , the comparison between H . melpomene and B . mori did not detect any of the chromosomal rearrangements we document ( Figures 1–4 ) . This may be because these rearrangements are not present in Heliconius , or because they could not be detected given the relatively small number of mapped orthologs pairs in H . melpomene and B . mori . Thus , it remains unclear to what extent the rearrangements we see in B . anynana are characteristic of Nymphalid butterflies or more lineage-restricted . Second , we see no evidence of the six chromosomal fusions proposed to distinguish the H . melpomene and B . mori genetic maps [23] . This probably reflects the fact that Heliconius butterflies have a lower chromosome number ( 21 pairs instead of the 28 pairs in both B . anynana and B . mori ) , and must thus have undergone further , or independent , chromosomal fusions relative to B . anynana . It is , however , noteworthy that the proposed fusions separating H . melpomene and B . mori are based on few pairs of mapped orthologous markers ( mostly 1–3 pairs [23] ) and our analysis shows that single marker transpositions do occur ( Figures 1–5 ) . Figures 1–4 illustrate synteny between B . anynana and B . mori orthologous markers: both in terms of the grouping of markers in LGs ( see also Figure 5 ) , and in terms of conservation of gene order along individual LGs . For most LGs with multiple ordered markers , we have evidence for some reordering of genes which suggests multiple inversions separating B . anynana and B . mori . From the 23 B . anynana LGs with greater than three ordered and non-overlapping markers ( i . e . excluding multiple markers mapping to the same genetic position ) , with a mapped B . mori ortholog on a syntenic block , only LG10 and LG21 have fully conserved marker order ( Figures 1–4 ) . For the remaining LGs , we see evidence of order rearrangements ranging from one ( e . g . LG9 , LG18 ) to multiple ( e . g . LG17 , LG19 ) markers whose relative position in B . anynana differs from that in B . mori . Where the marker order inferred for B . anynana differed from that of the orthologous markers in B . mori , we compared the log10 likelihoods of the two ( Table S6 ) . Of the 24 comparisons made ( complete LGs or LG fragments with homology to different B . mori LGs ) , inferred marker order in B . anynana was at least twice as likely than B . mori order in 20 cases ( and at least 630 times more likely for 18 of the comparisons ) . For the four situations where the B . mori order was better supported than the one originally inferred for B . anynana ( LG2 , LG6 , LG10 , and LG17 ) , and for LG13 ( which had many but very poorly resolved markers and where the original inferred order was only ∼2 times better than that of B . mori ) , we used the B . mori order as a starting point in CRI-MAP and further improved it ( see Methods ) . In all cases except LG10 , and the LG2 segment homologous to B . mori LG2 , the final order was different from that in B . mori . The difference between the log10 likelihoods for the final inferred marker order in B . anynana and that in B . mori ranges between 1 . 1 for LG13 ( i . e . the inferred B . anynana order is ∼13 times more likely than that in B . mori ) and 34 . 8 for LG11 ( i . e . inferred order ∼10∧34 times better ) ( Table S6 ) . Because de novo map construction using CRI-MAP uses a “hill climbing” algorithm to maximize marker order likelihood , the map order arrived at is dependent on the particular subset of markers used to initiate a build . This explains why the build corresponding to some B . anynana LGs reached a local maximum that could be improved upon by using the B . mori gene order as seed . Note that marker mapping was further improved by re-adding to the map markers with no mapped B . mori ortholog and by re-assessing unordered markers in those LGs . The mapping information in all Tables and Figures corresponds to the final CRI-MAP builds . Our data suggest that previous conclusions of highly conserved gene order between H . melpomene and B . mori [23]–[25] may have been over-stated , perhaps as a result of the limited number of markers examined ( maximum of 10 ordered orthologous pairs in one syntenic block [25] ) . Future work adding extra markers and improving marker mapping resolution in B . anynana , and extending comparative analysis to additional species will be crucial to rigorously quantify the extent of inversions separating different lepidopteran lineages . Unfortunately , the number of shared ordered markers in the H . melpomene and B . anynana maps prevents evaluating the consistency of gene order within Nymphalid butterflies . We have a single B . anynana LG ( LG15 ) with greater than two ordered markers with mapped orthologs in H . melpomene . However , of those four markers , only one has a resolved genetic map position in H . melpomene [23] making impossible the assessment of conservation of gene order . Previous studies that analyzed order of more than three ordered H . melpomene - B . mori marker pairs were much more localized than the study presented here . They either focused on one individual chromosome ( and reported on four perfectly aligned markers [24] ) , or on a BAC-level scale ( and reported on nine of ten aligned markers [25] ) . However , conservation of gene order for small collections of orthologous markers can occur by chance alone ( e . g . , four perfectly aligned markers occur by chance ∼10% of the time ) , and comparisons of marker order at the level of single BACs can only infer conservation at sub-centimorgan scales . Here , we extended the analysis of gene order to many more markers in many more linkage groups and alert for the fact that , even though we have syntenic blocks and broad conservation of gene order ( see , for example , LG10 ) , we also have clear evidence of multiple rearrangements ( see , for example , LG19 ) . These intra-chromosomal rearrangements do not mean that B . mori cannot serve as a pan-macrolepidopteran reference , but they do argue that marker order is likely conserved over tens of centimorgans as opposed to entire linkage groups . Our observations are remarkably similar to the emerging consensus view in the Drosophila clade ( including species diverged some 40 MYA ) , that the assignment of genes to Mullerian elements is highly conserved but gene order within those elements is variable [48] , [49] . It will be interesting to look both more widely ( across species from different families ) and also more narrowly ( across multiple species within some selected genera ) in the Lepidoptera . It is still unclear how the relatively numerous and relatively small ( in insect terms ) chromosomes in this diverse group have evolved and what the role of the holocentric chromosome structure and male-restricted recombination has been in the process . Aside from enabling analysis of macro- and micro-synteny , gene-based maps are of great value in studies attempting to map genes that contribute to phenotypic variation because they greatly facilitate the resolution of mapped genomic regions into a tractable number of candidate genes . This is not only because the mapping analysis itself can exclude candidate genes ( namely , those in non-implicated LGs ) , and identify candidate genes among available markers , but also because conservation of gene grouping and gene order in related species with dense linkage maps might allow identification of extra candidate genes within the implicated genomic regions . For example , the B . mori ortholog to the pigmentation gene black localizes to a B . mori scaffold ( nscaf2986 in [37] ) mapping to the Chocolate-containing region of B . anynana LG7 . This makes black an interesting candidate gene for the Chocolate larval phenotype ( Figure 6I ) . With the exception of Bigeye and Chocolate ( and the more dubiously mapped Spotty; see above ) , at present we have only mapped our collection of B anynana visible mutants to entire linkage groups ( Table 2 ) . While this renders identification of individual candidate genes premature , our analysis enables us to clearly identify “anti-candidates” . For example , from a developmental point of view , the gene engrailed would be a good candidate for several of our Mendelian mutations . The expression of engrailed is regulated in relation to different stages of eyespot development [50] , and to changes in eyespot size [29] , color-composition [50] and number [51] , [52] . The involvement of engrailed in eyespot formation and also in embryonic development [27] makes it a potential candidate gene for mutations such as Goldeneye and Bigeye which affect both embryonic viability and eyespot morphology ( Figure 6 ) . However , none of the mapped visible markers maps to the engrailed-containing LG2 , and hence none can be alleles at this locus . This , of course , does not mean that the engrailed locus cannot contribute to complex naturally occurring segregating variation or other laboratory mutations affecting wing patterns . Future studies trying to refine the location of each of our mapped color pattern loci will need only to concentrate on markers throughout single LGs , greatly reducing the genotyping effort . Another exciting aspect of having color pattern loci in gene-based maps of different lepidopteran species is the possibility to investigate to what extent color pattern diversification in different lineages has a similar genetic basis . Recent studies have shown that color pattern loci contributing to race variation map to homologous genomic regions in different Heliconius species [12] , [53] . Whether these loci play a role in color pattern variation outside Heliconius and to what extent color pattern diversification has repeatedly recruited the same loci in different lineages are interesting questions in evolutionary ( developmental ) biology . We looked for H . melpomene and B . mori color pattern loci mapping to orthologous LGs to those where we mapped visible markers in B . anynana ( Table 3 ) . Particularly interesting is the case of the B . anynana Bigeye and 067 spontaneous mutations , both affecting eyespot size ( Figure 6 , Table 2 ) . We mapped these to LG17 , which , based on comparisons to B . mori , we know is orthologous to H . melpomene LG15 ( Table 3 ) . This is the linkage group carrying the color pattern loci above-mentioned which have been implicated in the race-divergence in three different Heliconius species [6] , [12] , [23] . Also , the Band mutant with lighter background coloration on the distal section of the wings maps to LG4 whose Heliconius ortholog carries a white/yellow color switch locus [10] . In the future , emerging comparative maps in Heliconius and Bicyclus can be exploited to accelerate the dissection of the genetic basis of wing pattern variation in butterflies; potentially aided by patterns of conserved microsynteny detected for “developmental genes” in insect genomes [54] . With gene collections growing for a variety of species , so is the need for methods that enable the mapping of markers in those genes and comparisons with genetic maps of relevant reference species . These maps will aid in the genetic dissection of phenotypic variation in non-model systems , enable analysis of synteny and genome evolution , and facilitate future sequence-assembly efforts . Here , we report on the mapping of 508 markers in expressed genes and seven color pattern loci in an emerging butterfly model system . We used our map to compare gene grouping and gene order with the lepidopteran reference genome . Based on 462 pairs of orthologous markers mapped in Bicyclus anynana and Bombyx mori , we show that there is extensive conservation of syntenic blocks and gene order but not as much as had been previously suggested . We illustrate how gene-based maps and synteny with relevant species in relation to dissecting the genetic basis of wing pattern variation .
We used different Bicyclus anynana laboratory populations to establish a mapping panel of 288 individuals from 12 families . These were all F2 families composed of a F1 mother and father , and 22 offspring ( typically 11 females and 11 males ) . The F2 families were obtained by using single-pairs of P grand-parents that were either from “outbred” , or 1–3 generation inbred ( i . e . , single brother-sister mating pairs ) populations . DNA from thorax or head of freshly frozen butterflies ( killed in liquid nitrogen and stored at −80°C until processed ) was extracted using the QIAGEN tissue kit following manufacturer's recommendations . Genomic DNA was checked for quality and yield on agarose gel and NanoDrop spectrophotometer . From each of the 288 individuals in the mapping panel , 1 . 7 µg of genomic DNA in 100 µl of QIAGEN kit elution buffer was dried down ( SpeedVac ) , re-suspended in 20 µl water , and sent to Southern California Genotyping Consortium - Illumina Genotyping Core Laboratory at UCLA [55] . We selected 768 SNPs to genotype using the Illumina Golden Gate platform [56] . The 768 target SNPs were identified in 759 expressed B . anynana genes ( Table S1 ) . These correspond to 744 contigs resulting from the assembly of an on-going , large-scale EST project ( sequencing of the new ∼91 , 000 ESTs ( GenBank GE654128–GE745563 ) , assembly of those together with the previously published collection of ∼10 , 000 ESTs [15] and 13 genes available on GenBank nr database , and discovery and characterization of SNPs will be described elsewhere ) , and 14 candidate genes identified in previous sequencing efforts ( including [15] , [29] , [57] ) . The contig-derived markers ( name starting with BaC ) correspond to SNPs with a minor allele count of two or greater identified in CAP3 alignments of at least 4 EST reads . We identified ∼1 , 200 contigs with at least one such “double-hit” SNP and chose the 745 target SNPs based on criteria listed below . The candidate genes ( marker designation starting with BaG ) were selected based on their potential role in wing color patterns or other phenotypes of interest . The genes ci , EcR , en , and wg , as well as others from the Wingless signaling pathway , Apc , gro , nkd , and spen , are presumably involved in butterfly wing pattern formation [8] , [58] . The genes cin and Hn are involved in pigmentation . Other candidate genes represent various key biological processes , such as wing disc development ( sca ) , programmed cell death ( ec ) , lifespan ( Cat ) , and stress response ( Hsp70 ) . We attempted to choose only one high quality SNP for each gene but , in the case of a minority of putative B . anynana homologs of developmental candidate genes , we designed two or more assays . These were: two SNPs in the pigmentation gene yellow ( BaC4163 ) , in ci ( BaG15 ) , en ( BaG21 ) , and nkd ( BaG24 and BaG25 ) ; and four SNPs in Apc ( BaG14 and BaG16 ) , and EcR ( BaG19 and BaG20 ) . Many criteria went into choosing the target SNPs , including: the estimated frequency of the SNP ( preference given to SNPs with high frequency of the minor allele ) , absence of secondary polymorphisms in the ∼100 bp up- and down-stream of it , the contig annotation ( preference given to markers in genes with sequence similarity to genes in public databases ) , and score for Illumina “type-ability” . Sequences associated with the 768 markers we attempted to genotype are available in Genbank's sequence or EST archive; accession numbers in Table S1 . A large fraction of the SNPs assayed converted into working assays and ∼75% had a call rate of greater than 95% . The poorest 15% of SNP assays had a call rate of 0% , whereas the best 80% had a minimum call rate of 89% . The individuals in our genotyping panel consistently generated good data; a 95% Confidence Interval on the number of called SNPs over individuals was 626 to 644 with the poorest and second poorest individuals yielding 527 and 600 called SNPs , respectively . Consistent with this narrow confidence interval , we did not consider removing any individuals from the study because of poor quality DNA . The vast majority of SNPs were ascertained from an EST project so the observation that 15–25% of the attempted SNPs did not convert to a useful assay was not unexpected . Reasons for failure to convert include factors such as: SNPs having a low allele frequency in the mapping panel , some SNPs being falsely identified due to over assembly problems , introns resulting in non-functioning Golden-Gate assays , and errors in flanking regions that the Golden-Gate oligonucleotides anneal to [31] . We chose to focus solely on SNPs for which greater than 90% of the genotyped individuals were “called” and whose minor allele frequency over all called individuals was greater than 5% . These criteria resulted in a set of 533 “converting” SNPs ( ∼70% of assays attempted ) . Most SNPs not meeting our inclusion criteria were very clearly failed assays , so either relaxing or increasing the stringency for a SNP's inclusion did not greatly change the number of SNPs in further analyses . We visually examined the dataset for clear genotyping errors that resulted in a SNP showing a pattern of inheritance inconsistent with Mendelian expectations ( Table S2 ) . SNPs fell into three categories: 1 ) inheritance that was sex linked ( nine SNPs ) , 2 ) several Mendelian inconsistencies ( eleven SNPs ) , or 3 ) no or a handful of Mendelian inconsistencies ( 513 SNPs ) . Sex-linked SNPs were duly noted as they were treated differently in subsequent steps , and SNPs showing several Mendelian inconsistencies ( possibly genotyping mistakes , duplicated genes , gene families ) were excluded from further consideration . For the SNPs with no or a small number of Mendelian inconsistencies , we manually changed the genotypes of those inconsistent individuals to missing . In the majority of cases this meant discarding the genotype of 1–2 of the 22 full-sib offspring in a family , but in a minority of cases the most parsimonious change involved discarding a parental genotype . This set of 513 hand-annotated putative autosomal SNPs plus the nine sex-linked SNPs were used in all subsequent mapping analysis . We used marker-pairs that were female fully-informative ( e . g . , dad = aabb & mom = AaBb ) to initially assign markers to segregation groups . For all possible pairs of SNPs in the 513 putative autosomal SNPs , we calculated a LOD score summarizing the evidence for complete linkage ( LOG10[L ( data;r = 0 ) /L ( data;r = 0 . 5 ) ] ) . For any given pair of SNPs that LOD score could be missing ( if that pair of SNPs was never female fully-informative across the 12 families ) or summarize linkage information from 1 to 12 female fully-informative families . We then grouped SNPs connected by LOD scores of greater than eleven . As a result , a SNP could be assigned to a segregation group despite not having a LOD score of greater than 11 with all the SNPs in that group . Unpublished simulations suggested that this algorithm rarely results in “over-clustering” . At our CRIMAP inclusion LOD score of 11 we identified 27 segregation groups , with the smallest number of markers in any given group being three , the largest 28 and the mean 13 . 6 SNPs . Increasing the LOD score for inclusion to values as high as 16 resulted in fewer SNPs assigned to segregation groups , and never split a segregation group identified at an inclusion value of 11 into two . Whereas decreasing the LOD score resulted in the merging of segregation groups ( and fewer than 27 clusters ) . For all the SNPs assigned to a given segregation group we used CRIMAP [33] to build a map for that group . As a result of our having 12 full-sib families , in many instances in which there existed a female informative SNP in one family , at least one other family displayed a: 1 ) male fully-informative SNP-pair ( e . g . , dad = AaBb & mom = aabb ) , 2 ) a male semi-informative SNP-pair ( e . g . , dad = AaBb & mom = Aabb ) , or 3 ) a doubly-informative SNP-pair ( e . g . , dad = AaBb & mom = AaBb ) . CRIMAP was designed for integrating such information in complex human pedigree data [33] . We wrote scripts to take the genotyping data for all the SNPs within a segregation group , irrespective of inheritance patterns , and create input files for CRIMAP . We then used the “build” option of CRIMAP to make a consensus map for each segregation group using default parameters , with the exception of lowering the PUK_LIKE_TOL from 3 . 0 to 1 . 0 . We built a sex-chromosome map using CRIMAP by simply encoding the “second-allele” in each female as a “9” ( i . e . , an allele not present ) . We manually inspected the resulting maps . In cases where the two “ordered-loci” used to initialize the Expectation Maximization algorithm underlying CRIMAP were loosely linked we re-ran the build option with a different set of random starting loci . In other cases where we observed SNPs that were completely linked in males we re-ran the “build” using the “hap_sys” option for those SNPs . We then used the “flips4” option on the ordered loci to confirm that our maps had the highest possible likelihood , creating a new order when necessary , and re-running the “build” and “flips4” analysis until the order stabilized . We then used the “two-point” option in CRIMAP in an attempt to assign to segregation groups the remaining 146 putative autosomal markers , not initially assigned . This resulted in our being able to: 1 ) assign 134 markers to pre-existing segregation groups , 2 ) merge two pairs of pre-existing linkage groups in single groups , and 3 ) identify two novel small linkage groups ( one having three and the other four SNPs ) . Typically , added markers displayed a high LOD score for linkage with several members of a pre-existing linkage group and below background LOD scores with members of any other group . The few SNPs that could not be assigned to any segregation group were typically only informative in a single family and/or showed segregation patterns that were unlikely under Mendelian inheritance . Based on the newly defined segregation groups and starting with the markers ordered in the previous round , we carried out another round of “builds” , followed by another round of “flips4” , and iterating until we achieved an ordering for which the “flips4” command could no longer identify orders with higher likelihoods . Details about the mapping of the ordered and unordered markers are found in Tables S3 and S4 , respectively . A total of nine Mendelizing visible mutants affecting adult or larval coloration were segregating in six of the twelve full-sibs families used for mapping ( Table 2 ) . All offspring of these families were phenotyped and 22 were selected so that each phenotypic class was represented in approximately similar numbers in the mapping panel . Consequently , segregation patterns of the visible mutants in the mapping families do not follow Mendelian ratios . To assign each visible marker to a linkage group , we used the “two-point” option of CRIMAP . This allowed us to assign seven of the nine mutant genes to linkage groups . Despite attempts with lower LOD threshold scores and/or examining only a subset of families we were unable to assign the other two mutants , comet and Missing , to linkage groups . For the seven mutants mapping to linkage groups we used the “all” option of CRIMAP separately for each mutant and its respective linkage group in an attempt to localize that mutation within a linkage group . For all genes in the B . anynana map , we used blastn and tblastx analysis ( e-score cut-off value of 1 . 0e-05 ) against the scaffolds from the B . mori genome assembly ( May 1 , 2008; only the “nscaf” fasta entries from [37] ) , and against the mapped anchor loci in Heliconius melpomene [23] . Two genes in our collection ( ci and en ) did not have a significant direct blast hit to any of the target H . melpomene markers ( “na” notation in marker name in Figures 1–4 ) . However , we were able to identify orthologous pairs based on annotation available for both species via blast analysis to collections from other species . For the contigs with a B . mori ortholog , we used custom prediction algorithms ( see below ) to estimate its position in the B . mori map . Details of the blast analysis with B . mori and H . melpomene can be found in Tables S3 and S4 for the B . anynana ordered and unordered markers , respectively . We downloaded the new collection of B . mori scaffolds and used blastn to query all the mapped B . mori SNPs from ( “DE” accessions from [34] ) against the collection [37] ( Table S5 ) . We then wished to develop a prediction equation for every scaffold , that when given a base position on that scaffold would return the map position associated with that base position . Such a prediction equation would allow us to estimate a B . mori map position for any B . mori gene . For B . mori scaffolds having greater than four mapped markers this predictor is simply the slope and intercept obtained from a linear regression of map position on base position ( of the midpoint of the highest scoring blast hit ) . For B . mori scaffolds with one to four markers this predictor is simply the average position of the markers mapping to that scaffold . For scaffolds with no mapped markers the predictor is undefined . This heuristic seemed reasonable , as a large fraction of the genome is contained in scaffolds with more than four mapped markers , and scaffolds harboring four or fewer markers are typically small enough that returning a single map position for the midpoint of that scaffold is acceptable . During this annotation effort we discovered a small number of B . mori scaffolds with termini mapping to different chromosomes , we assumed these are mis-assembly errors and removed these sections of scaffold from further consideration . We wished to ask if within linkage group , inferred marker orders in B . anynana were different from those in B . mori . To do this we used the “fixed” option of CRIMAP to compare the inferred ( non-haplotype system ) order in B . anynana to that in B . mori for the subset of markers having orthologs . This analysis allowed us to obtain log10 likelihoods for both orders , and identify instances where the B . mori order was more highly supported ( Table S6; see Text S1 for an explanation of the contents of all supplementary tables ) . In those cases we used the B . mori order as a new starting point , incorporated any observed haplotype systems , and reran the “flips” analysis to look for iterative improvements over the B . mori order . In cases where the “flips” analysis improved upon the B . mori order we obtained a log10 likelihood indicating the support for this new order over the B . mori order . We then used the order resulting from the “flips” analysis as a seed for an additional “build” run ( to possibly place additional unordered markers and/or previously ordered markers without a B . mori ortholog ) . This final build went though additional “flips” rounds and then “fixed” was run on the final order to obtain the map displayed in Figures 1–4 . We used the MapChart software [59] to build a graphical representation of the B . anynana genetic map , and of synteny between B . anynana and B . mori chromosomes ( Figures 1–4 ) . The map produced by MapChart was further processed to include unordered markers and visible mutations . B . mori markers were named with the corresponding B . anynana marker name , B . mori scaffold number , and blast e-score ( see legend to Figures 1–4 ) . For the graphical display of the synteny analysis , we multiplied estimated map positions of B . mori markers by a factor of two so as to facilitate visualization of homologies with the otherwise relatively condensed B . mori LGs . For markers with an estimated position of less than 0 cM , that marker's position was set as 0 cM and the positions of other markers on same linkage group were adjusted accordingly . | Butterflies and moths ( called the Lepidoptera ) are a large and diverse group of insects that has long captured the attention of biologists and laymen . The colorful patterns on the wings of butterflies , in particular , offer an ideal system to investigate which genes and developmental mechanisms contribute to evolutionary diversification . Genetic analyses that try to find the position of genes along chromosomes are invaluable for such efforts , also because they allow researchers to compare chromosome content between species . Here , we report on a study which built a gene-based map for the chromosomes of a butterfly “lab rat” and identified chromosomes carrying color pattern genes . We compare our map to that of the reference lepidopteran species , the silkworm . Despite these species having diverged some 100 million years ago , there is much conservation in terms of which genes are found together in chromosomes and even how genes are ordered within chromosomes . However , because we were able to compare positioning of many more genes than had ever been reported before for this group , we also found evidence of several large- and small-scale chromosomal rearrangements . We discuss the advantages of gene-based maps in understanding the genetic basis of color pattern evolution . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
]
| [
"genetics",
"and",
"genomics/gene",
"discovery",
"genetics",
"and",
"genomics/comparative",
"genomics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"evolutionary",
"biology/genomics",
"evolutionary",
"biology/developmental",
"evolution"
]
| 2009 | A Gene-Based Linkage Map for Bicyclus anynana Butterflies Allows for a Comprehensive Analysis of Synteny with the Lepidopteran Reference Genome |
It is an assumption of large , population-based datasets that samples are annotated accurately whether they correspond to known relationships or unrelated individuals . These annotations are key for a broad range of genetics applications . While many methods are available to assess relatedness that involve estimates of identity-by-descent ( IBD ) and/or identity-by-state ( IBS ) allele-sharing proportions , we developed a novel approach that estimates IBD0 , 1 , and 2 based on observed IBS within windows . When combined with genome-wide IBS information , it provides an intuitive and practical graphical approach with the capacity to analyze datasets with thousands of samples without prior information about relatedness between individuals or haplotypes . We applied the method to a commonly used Human Variation Panel consisting of 400 nominally unrelated individuals . Surprisingly , we identified identical , parent-child , and full-sibling relationships and reconstructed pedigrees . In two instances non-sibling pairs of individuals in these pedigrees had unexpected IBD2 levels , as well as multiple regions of homozygosity , implying inbreeding . This combined method allowed us to distinguish related individuals from those having atypical heterozygosity rates and determine which individuals were outliers with respect to their designated population . Additionally , it becomes increasingly difficult to identify distant relatedness using genome-wide IBS methods alone . However , our IBD method further identified distant relatedness between individuals within populations , supported by the presence of megabase-scale regions lacking IBS0 across individual chromosomes . We benchmarked our approach against the hidden Markov model of a leading software package ( PLINK ) , showing improved calling of distantly related individuals , and we validated it using a known pedigree from a clinical study . The application of this approach could improve genome-wide association , linkage , heterozygosity , and other population genomics studies that rely on SNP genotype data .
Single nucleotide polymorphism ( SNP ) genotyping is used to delineate the extent and nature of chromosomal variation , examine population genetic structure , and find loci that contribute to disease . SNPs are used as proxies for the unobserved sequence variants in the surrounding DNA , allowing measurement of the flow of genetic material through populations [1] . There are important limitations for using SNPs to identify causal disease variants . Genome-wide association studies ( GWAS ) rely on representative sampling of a subset of individuals from a population . Therefore , calculations testing the association between alleles , the frequency of alleles in the population , and the contribution of alleles to a phenotype must use estimates of the population allele frequency based on the representative sampling . These estimates of allele frequencies are sensitive to inflation or deflation when the genotyping data are derived from individuals with unreported familial relationships or with admixed ancestry ( potentially leading to population stratification ) . For any given pair of individuals with genotype information , identity-by-state ( IBS ) can be observed at a given locus with three possible outcomes: the individuals have two different alleles ( IBS0 ) or they share one ( IBS1 ) or two ( IBS2 ) alleles in common . For example , a pair of individuals with genotypes AA and BB are IBS0 at this locus whereas a pair with AA and AB are IBS1 . Two individuals who share 1 or 2 alleles IBS at a given locus may have inherited the shared allele ( s ) from a recent common ancestor , in which these allele ( s ) are identical-by-descent ( IBD ) . IBD approaches have been applied to linkage mapping [2] in which segments of IBD are detected with informative SNPs . IBD regions tend to be small between pairs of individuals derived from a given population that are not closely related , primarily because their last common ancestor was many generations ago . As such , GWAS are predicated upon the detection of regions of IBD when stratifying by phenotype . In this study we demonstrate an approach that combines our IBD method with IBS information to estimate the relatedness between individuals in pedigrees and/or in large population-based studies . There are two main aspects of this work . First , we introduce plots based in part on methods suggested by Lee [3] and Rosenberg [4] to analyze a subset of informative IBS observations . We implement Lee's mathematical approach to characterizing genetic relatedness based on the ratio of concordant heterozygotes ( i . e . AB/AB genotype calls ) divided by the sum of concordant heterozygotes plus discordant homozygotes ( e . g . AB/AB plus AA/BB ) [3] . This metric represents the x-axis in several of our figures below . Additionally , we use IBS as the basis for a metric that graphically distinguishes relatedness consistent with earlier work [4] . Second , we introduce a method to calculate IBD in comparisons between two individuals , providing highly accurate estimates of Cotterman coefficients of relatedness ( K0 , K1 , and K2 are our estimates of Cotterman coefficients k0 , k1 , k2 ) [5] . Combining these IBS and IBD approaches , our analyses simultaneously reveal previously unknown familial relationships and population substructure in large-scale SNP data . Notably , our method applies to pedigrees but does not rely on prior knowledge of relationships or ethnicity . While other exploratory techniques such as principal components analysis ( PCA ) of genotype data can indicate outliers , the nature of such relationships is not explicitly described . In contrast , our method is useful to define relationships . We observed differences within and between populations ( and pedigrees ) due to multiple factors including familial relationships , autosomal heterozygosity rate , chromosomal anomalies , and population admixture . We analyzed data from the Coriell Institute's National Institute of General Medical Sciences ( NIGMS ) Human Genetic Cell Repository Human Variation Panel ( referred to as the Human Variation Panel ) , an extensively used data source , and found undocumented familial relationships . Our IBD method uses an overlapping window approach ( see Methods ) and is comparable to that of PLINK [6] , which employs a hidden Markov model to infer underlying IBD in chromosomal segments based on observed IBS states . Similar to PLINK's HMM , we analyze SNPs in a genome-wide fashion to detect patterns of IBS0 , IBS1 , and a subset of IBS2 , and further infer regions of IBD sharing that are estimates of Cotterman coefficients of relatedness k0 , k1 , and k2 . These IBD estimates are not reliant on prior sample annotation or haplotype data . Our approach , however , reports fewer false positives ( defined as individuals who are unrelated based on IBS sharing , but who are called as related ) relative to PLINK's HMM . Other methods for inferring IBD relatedness using SNP data include GERMLINE , BEAGLE IBD , and fastIBD [7] , [8] , [9] . These are based on identifying shared haplotypes and rely on haplotype maps of the human genome [10] , [11] , [12] , [13] . These programs allow for a robust detection of shared haplotypes for regions as small as 2cM ( ∼2 Mb ) . Other methods for estimating kinship coefficients , paternity indices and other relationship indices in the forensic and genetic literature do not rely on haplotype data . For example , EMMAX ( efficient mixed-model association eXpedited ) addresses kinship and population stratification using a variance components approach [14] . Related individuals that share , on average , longer stretches of IBD ( 10 Mb for example ) are identified with very high confidence levels [8] . The approach we introduce is robust in detecting shared segments between individuals of recent ancestry . It provides accurate IBD estimates allowing for improved inference of relationships .
The Human Variation Panel consists of four populations ( individuals of African-American ancestry [AA] , Caucasian ancestry [CAU] , Han Chinese ancestry [CHI] , and Mexican-American ancestry [MEX]; n = 100 per group ) . All samples from these individuals were submitted to the NIGMS repository with annotation indicating they were unrelated . Using autosomal SNP genotype data ( n = 872 , 242 SNPs ) from these samples , we analyzed all pairwise IBS relationships in each population group ( n = 19 , 800 comparisons ) . For the four within-population comparisons we generated a plot ( referred to as an IBS2* plot ) having x-axis values referred to as IBS2*_ratio and based on the ratio of IBS2*/ ( IBS0 + IBS2* ) , suggested by Lee [3] . We plotted y-axis values termed percent informative SNPs and consisting of the sum of ( IBS0 + IBS2* ) divided by all IBS counts ( IBS0+IBS1+IBS2; Figure 1A ) . For each population we observed a major cluster of data points having IBS2*_ratio values near 0 . 66–0 . 67; these values were expected to form a normal distribution centered at 2/3 for unrelated individuals ( see Methods ) . We implemented a two-sided statistical test of the null hypothesis that a given pairwise comparison does not have an IBS2*_ratio value either significantly >2/3 ( indicating familial relatedness ) or <2/3 ( indicating different allele frequencies between the compared samples accounted for by phenomena such as population admixture or reduced heterozygosity due to stretches of homozygosity [lacking AB calls]; see Methods ) . We used a Z-test ( as suggested by Lee [3] ) , and measured the p-value for every pairwise comparison . We observed that p-values ≤0 . 000025 ( including a Bonferroni correction for 19 , 800 tests ) were found in comparisons greater than 0 . 672 and less than 0 . 661 suggesting a very narrow range of IBS2*_ratio values for which the null hypothesis was not rejected . We note that an IBS2*_ratio value greater than 0 . 70 was used empirically to highlight potential pairwise comparisons suspected to be related . Given that the Human Variation Panel had no previously annotated familial relationships or replicate samples , we expected no IBS2*_ratio values >2/3 ( e . g . 0 . 70 ) . Surprisingly , we observed 25 data points with values >0 . 70 that potentially corresponded to familial relationships ( Table 1 includes a subset of 16 of these pairwise comparisons for which we obtained evidence of familial relationships , as discussed below; 6 other relationships in the table with IBS2*_ratio values <0 . 70 are described below ) . The CAU group included a pair of identical samples ( Figure 1A arrow , corresponding to NA17255/NA17263 ) . The IBS2*_ratio value was near 1 . 0 for this pairwise comparison , as expected for identical samples that lack essentially all IBS0 calls . This relationship is supported by plotting IBS for each chromosomal position across all autosomes using SNPduo software [15] , a program that performs pairwise comparisons of SNP genotype data and plots IBS ( as well as genotypes ) for one chromosome or the entire genome . This revealed a predominant pattern of IBS2 as shown for chromosome 2 ( Figure 2A ) . Typical of other genetically identical samples analyzed with low genotyping error rates , these two individuals shared only 11 IBS0 calls and 6 , 410 IBS1 calls in contrast to 838 , 898 IBS2 calls from autosomal loci . The samples were annotated by the Human Genetic Cell Repository as a 6 year-old boy ( NA17255 ) and a 26 year-old female ( NA17263 ) . The two samples were likely to be technical replicates for the 6 year-old boy based on a lack of AB calls on the X chromosome ( data not shown ) . Putative parent-child relationships ( Figure 1A ) also had IBS2*_ratio values near 1 . 0 ( n = 6 , IBS2*_ratio value range 0 . 998–0 . 999 ) with essentially no IBS0 observations as expected for annotated parent-child relationships . In contrast to replicate samples , parent-child relationships were also characterized by extensive IBS1 sharing ( Figure 2B ) . We note that X chromosome SNPs were excluded for all comparisons because parent-child relationships involving father and son , having hemizygous genotypes interpreted as biallelic AA or BB calls , result in IBS0 that skew those IBS2*_ratio values lower to ∼0 . 95–0 . 97 . The Y chromosome and mitochondrial SNPs were also excluded . The y-axis ( percent informative SNPs ) of the IBS2* plot ( Figure 1A ) provided a useful separation of replicate samples from parent-child samples ( both of which have IBS2*_ratios near 1 because they lack IBS0 calls ) . Since replicates have mostly IBS2 calls ( including IBS2* ) , the percent informative SNPs for these samples was extremely close to their averaged heterozygosity rate . This distinguishes identical samples from parent-child pairs: for identical samples , every genotype comparison aligns to itself , and the equation for the percent informative SNPs reduces to IBS2* divided by the total number of SNPs . IBS2* reflects the number of AB calls in that sample that aligned with other AB's since IBS1 is unexpected ( i . e . heterozygosity rate with variation due to genotype errors ) . Inferred sibling comparisons ( n = 5 ) were evident with IBS2*_ratio values ranging from 0 . 92 to 0 . 95 . We defined this group ( boxed in Figure 1A ) as siblings because data points for all annotated sibling relationships from other datasets were located there . For all potential sibling pairs that we identified there were typical patterns of allele sharing with ( 1 ) blocks of IBS0 , IBS1 , and IBS2 that indicated unshared regions of IBD0 , ( 2 ) blocks of IBS1 and IBS2 that indicated shared regions of IBD1 , and ( 3 ) blocks of IBS2 that indicated IBD2 sharing . An example is shown in Figure 2C . A presumptive second-degree relationship had an IBS2*_ratio value of 0 . 82 ( Figure 1A , arrow 3; Figure 2D ) , separable from potential third-degree relationships ( Figure 1A , arrows 1–2; Figure 2E ) and unrelated individuals . IBS2* plot x-axis ( IBS2*_ratio ) values less than 2/3 reflected differences in heterozygosity values in pairwise comparisons either within or between geographic groups . For example , CAU individual NA17251 in comparison with other CAU individuals had a sum of heterozygosity of 56 . 7 +/- 0 . 01% in contrast to other CAU comparisons having values of 59 . 7 +/- 0 . 1% ( Figure 1B , arrow ) . Among the MEX population , 9 individuals had pairwise heterozygosity sums that were outliers ( Figure 1B , region 4 ) . These low values were due to extended regions of homozygosity in these individuals ( e . g . Figure 2D; see genotype calls of NA17656 ) and will be discussed in detail below . Regions of homozygosity in one ( or both ) individuals decreased the amount of IBS2* calls ( i . e . instead of potential AB/AB observations , we instead observed AB/AA or AB/BB ) and increased the amount of IBS0 calls ( e . g . instead of potential AB/AA observations , we instead observed AA/AA or BB/AA ) , thus reducing the IBS2*_ratio value . For 9 of the 25 comparisons with IBS2*_ratio values greater than 0 . 70 ( but less than 0 . 714 ) , chromosomal IBS analysis using SNPduo failed to reveal any regions lacking IBS0 calls that would imply the presence of IBD1 . Additionally , these samples represented all CAU within-group comparisons and had among the highest pairwise sums of heterozygosity rates ( Figure 1B , arrow 3 ) . An increasing heterozygosity rate provides more opportunities for an AB genotype in one individual to align with an AB in a second individual to produce higher IBS2* levels and decreased IBS0 levels ( resulting in a higher IBS2*_ratio ) . This led us to refine the interpretation of the alternate hypothesis of the IBS statistic: values above 2/3 ( e . g . 0 . 70 ) are attributable either to relatedness for any level of heterozygosity or high heterozygosity rates in one or both individuals relative to their population levels ( Figure 1B ) . These IBS2*_ratio values were consistent with those of distantly related individuals . We have observed empirically that these values may mimic relationships between individuals up to and including first cousins ( or similar 1/8th relationships ) . Comparisons between individuals from different Human Variation Panel geographic groups were all expected to represent pairs of unrelated individuals having IBS2*_ratio measurements <2/3 . Consistent with this expectation , there were no IBS2*_ratio values >0 . 69 ( n = 60 , 000 pairwise comparisons; Figure 1C ) . Pairwise comparisons that centered around 2/3 ( or that were slightly greater ) were inferred to have similar allele frequencies ( e . g . MEX and CAU ) or had one or both members of the pair with high heterozygosity rates ( data not shown ) . This similarity could also reflect more recent shared ancestry than other between-group comparisons . The five data points with the highest IBS2*_ratio values ( 0 . 685 to 0 . 692; Figure 1C , arrow 3 ) corresponded to pairwise comparisons between individuals with the highest summed heterozygosity rates ( NA17709 compared to NA17275 , NA17283 , NA17294 , NA17295 , and NA17298; each pair had >61% summed heterozygosity ) . IBS can be used to infer IBD , which is further useful in defining relationships between individuals . PLINK software incorporates a method of moments approach using a hidden Markov model ( HMM ) to infer IBD from IBS data [6] . We developed an alternative approach ( see Methods ) to define IBD . We generated IBS2* plots of the Human Variation Panel within-group dataset in which the y-axis included IBD0 , 1 or 2 estimates of Cotterman coefficients of relatedness k0 , k1 , k2 . These were generated by our method ( K0 , K1 , K2 ) or PLINK's HMM ( Z0 , Z1 , Z2 using the notation provided by Purcell et al . [6] ) . We used PLINK to measure Z0 , Z1 , and Z2 using standard quality control measures in two different ways ( see Methods ) that resulted in the removal of 22 out of 400 samples ( Table S1 ) in the first analysis but kept all of the samples for the second one . We divided our analysis into two sections . The first dealt with recently related individuals ( i . e . those that were 1/4th related or more ) and had an IBS2*_ratio >0 . 80 ( Figure 3A–3F ) . The second focused on the remaining pairwise comparisons that had IBS2*_ratio values <0 . 76 ( Figure 4A–4D ) . For the 13 relationships that were previously inferred to be second-degree related or more based on the IBS2*_ratio , our method revealed expected estimates of Cotterman coefficients of relationship . For identical samples that are expected to have a K2 of 1 . 0 , our method estimated zero IBD1 ( Figure 3A; see arrow ) and 100% IBD2 sharing ( Figure 3B; see arrow ) . Several samples were removed by quality control procedures ( see Methods ) so that the first analysis by PLINK's HMM could not assign a Z1 or a Z2 estimate ( Figure 3C , 3D ) : one of the identical samples ( NA17255 ) , NA17626 ( putative parent-child relationship with NA17624 ) , and NA17687 ( putative parent-child relationship with inferred siblings NA17686 and NA17644 ) . In the second analysis , in which no samples were removed , PLINK's HMM gave Z1 estimates of ∼0 . 02 ( Figure 3E; see arrow ) and Z2 estimates of ∼0 . 98 ( Figure 3F; see arrow ) for the identical samples . Both K1 and Z1 IBD1 estimates for putative parent-child relationships were 1 . 0 , as expected ( Figure 3A , 3C; see arrow ) . Notably , the Z1 given by PLINK's HMM was not at 1 . 0 for the parent-child relationships that were affected by the inclusion of low genotyping rate in one individual with Z1 estimates below 1 ( Figure 3E ) and Z2 estimates as high as 0 . 16 ( Figure 3F ) . IBD1 estimates ( K1 , Figure 3A; Z1 , Figure 3C , 3E ) were comparable for full sibling relationships which have an expected IBD1 coefficient of 0 . 5 . Also , IBD2 estimates for our method ( Figure 3B ) and PLINK's HMM ( Figure 3D , 3F ) were centered on the expected coefficient of 0 . 25 . PLINK's HMM estimated the putative second-degree relationship ( NA17655/NA17656 ) as having a Z2 value of ∼ 0 . 18 ( Figure 3D , 3F; see arrow ) with an IBD1 estimate of 0 . 42 ( Figure 3C , 3E; see arrow ) . In contrast we estimated the second-degree relationship to have a K1 of 0 . 50 ( Figure 3A; see arrow ) , consistent with an expected value for putative second degree relatives , and a K2 of 0 . 002 ( Figure 3B; see arrow ) , which was slightly higher than the expected coefficient of zero . SNPduo analysis provided evidence for IBD2 presence in the putative second-degree relationship with 10Mb on chromosome 10 ( data not shown , but similar to the amount of IBD2 shown in Figure 2B for a putative parent-child relationship ) . However , IBS analysis using SNPduo did not indicate any other IBD2 sharing that would explain the high Z2 estimate of 0 . 18 . This Z2 level approached the expected coefficient of IBD2 for siblings . We speculate that such a Z2 value could have been mistakenly interpreted as being associated with a full sibling relationship based on PLINK analysis alone . For the great majority of pairwise comparisons , for which IBS2*_ratio values were centered on 2/3 , we expected to observe IBD0 but little ( if any ) IBD1 or IBD2 . Our method revealed extensive IBD0 ( as measured by K0; data not shown ) and very limited amounts of IBD1 and IBD2 , as expected for unrelated individuals ( Figure 4A , 4B ) . Based on IBS and IBD analyses , distantly related samples included MEX pairs NA17673/NA17680 and NA17454/NA17459 ( Figure 4A; arrows 1 and 2 ) ; CAU pairs NA17203/NA17257 and NA17299/NA17289 ( arrows 3 and 4 ) ; and CHI pairs NA17785/NA17794 ( arrow 5 ) . These five relationships were supported by visual inspection of chromosomal IBS ( e . g . NA17673/NA17680 comparison in Figure 2E ) . In our initial analyses using IBS measurements ( Figure 1A ) , 25 comparisons had IBS2*_ratio values greater than 0 . 70 , suggesting genetic relatedness . For 16 comparisons we independently inferred relatedness with K1 values from our IBD test . For nine comparisons that had IBS2*_ratio values greater than 0 . 70 but were due to high heterozygosity , our IBD test did not indicate K1 sharing ( Figure 4A , arrow 6 represents an example in CAU individuals NA17275/NA17296; also see Figure 1B , arrow 3 showing the high heterozygosity of this pair ) . Thus , the addition of K1 information supported a model in which elevated IBS2*_ratio values may be attributed to atypical heterozygosity rather than familial relatedness . PLINK's HMM reported dramatically more IBD1 for comparisons having IBS2*_ratio values approaching 2/3 and above ( Figure 4C; see region 1 ) . This could be because PLINK's HMM analyzes whole-genome IBS when calculating IBD probabilities [6] . In contrast , our method reports more samples with low K1 levels ( up to 0 . 03 ) presumably due to our windowed approach . Notably , PLINK's HMM estimates of IBD1 were comparable to ours for several pairwise comparisons having elevated K1 values ( Figure 4A , 4C arrows 1–3 ) , but did not provide IBD1 estimates for several other comparisons ( Figure 4A , 4C arrows 4 and 5 ) . Also , a Z1 of ∼0 . 14 was assigned to a pair of individuals ( NA17275/NA17296; Figure 4C , arrow 6 ) for whom our K1 estimate was very low ( ∼0 . 01; Figure 4A , arrow 6 ) . We note that this corresponds to one of the CAU comparisons that had high heterozygosity , giving it a high IBS2*_ratio value . Thus we infer that the PLINK Z1 result was a false positive . Our method revealed little IBD2 ( Figure 4B ) as expected for comparisons involving few related individuals . However , PLINK's HMM reported a large set of high IBD2 estimates centered at 2/3 ( Figure 4D ) . Among the comparisons with the highest IBD2 estimates with Z2 values from ∼0 . 05 to 0 . 2 were those MEX outliers that had low heterozygosity due to contiguous regions of homozygosity ( Figure 4D region 2 ) . Since visual analysis using SNPduo did not provide evidence for relatedness such as the occurrence of SNPduo blocks lacking IBS0 or lacking IBS0 and IBS1 ( indicating IBD1 and IBD2 , respectively; data not shown ) , it is likely that most of these samples with high Z1 and Z2 estimates represented false positives . We note that IBD estimates provided by PLINK's HMM analysis with all samples included were almost identical to those provided when samples that had low genotyping call rates were removed . Therefore , we only present Z1 and Z2 estimates by PLINK's HMM analysis that included all samples . To further characterize IBD relationships , we calculated IBS2* and IBD metrics for the Human Variation Panel between-group comparisons , which serve as a gold standard for authentically unrelated individuals . These plots again indicated very high levels of K0 ( as expected; data not shown ) . The K1 and K2 estimates were very low ( data not shown ) . PLINK is not designed for the analysis of IBD involving members of different populations [6] , [16] . The IBD method we introduce lacked apparent false positive results that occurred using PLINK's HMM for the determination of distant genetic relatedness . The 21 comparisons ( not including identical samples ) that we identified as related were among those having the 22 highest K1 values . The most distantly related pair that we present as related ( NA17632/NA17695 ) had a K1 value of 0 . 0323 , while an unrelated pair had a comparable K1 value ( 0 . 0326 ) . This is slighter higher than a theoretical 1/64th relationship ( with a K1 of 0 . 03125 ) that appears to be the limit of detecting relatedness inferred by our IBD method . Some comparisons lacked an IBS2*_ratio greater than 0 . 70 . Based on our IBS and IBD analyses we identified previously unannotated familial relationships ( Table 1 ) and reconstructed 14 pedigrees ( Figure S1 ) . The CAU group included a pair of identical samples , a sibling pair , and two distant relatives ( <3rd degree ) . The CHI group included a father/mother/son trio and a pair of distant relatives ( <3rd degree ) . In the MEX group we identified a mother and her two children; four siblings; two mother/daughter pairs; a 2nd degree relationship; two 3rd degree relationships; and three distant relationships ( <3rd degree ) . After our analyses revealed unexpected familial relationships , Coriell provided nine additional samples for consideration as substitutes . None of these were closely related to each other or to any samples in the original panel ( data not shown ) . We validated the combined IBS and IBD approach by analyzing data from individuals with known familial relationships and compared it to PLINK's HMM . We used genotype data from a pedigree in a study of metachondromatosis [17] . This pedigree ( see Figure S2 ) included known relationships from parent-child to first cousins that were twice-removed ( e . g . a grandchild of individual X that is compared to individual X's first cousin ) . We analyzed IBS and IBD values in pairwise relationships ( Table 2 ) , and generated plots annotated by proportion IBD1 ( K1 , Figure 5A; Z1 , Figure 5C ) . K1 and Z1 values increased comparably as a function of an increasing IBS2*_ratio . This was consistent with a decrease in IBS0 calls and an increase in the percent of the genome that was shared . Our K1 estimate was comparable to the Z1 estimate of PLINK's HMM for most relationships . However , Z1estimates were zero for some relationships with an expected coefficient of relatedness ≤0 . 0625 ( Figure 5C; see arrow ) . In contrast , we obtained low K1 values ( Figure 5A; see arrow ) . Both our K2 and PLINK's Z2 accurately estimated percent IBD2 shared for siblings around the expected value of 0 . 25 ( Figure 5B , 5D ) .
As the number of genome-wide association studies , SNP datasets , and meta-analyses increase , proper characterization of familial relationships and underlying population structure increases in importance . As an example , Pemberton et al . recently identified a series of unexpected relationships in phase 3 HapMap data [18] . It is expected that the greater the number of individuals in a study , the greater the power to detect variants of small to moderate effect . However , as the size of these studies increases , it is also more likely that related individuals could be introduced or that substantial population substructure develops . Methods to appropriately identify these underlying confounders are key . In this study we applied visualization and analysis of autosomal IBS and IBD measurements to geographic populations . We introduced a metric based on informative IBS combinations of IBS0 and a subset of IBS2 , termed IBS2* , in which AB genotypes are aligned in two individuals at a given chromosomal position . These IBS analyses were complemented by an IBD method that revealed the extent of genome sharing in unannotated related ( or known related ) pairs of individuals . The main significance of these analyses is that ( 1 ) we identified first , second and third degree relationships that were unexpected ( for the Human Variation Panel ) or consistent with prior annotation ( for the validation dataset ) ; ( 2 ) we identified population substructure for the geographic groups; ( 3 ) we identified individuals who accounted for dramatic variations in the population substructure; ( 4 ) knowledge of these individuals may inform future studies that use these datasets; and ( 5 ) these combined methods do not require prior information about allele frequencies , ethnic background , or haplotype structure . Our approach is scalable to the study of datasets of any size . In terms of our IBS approach , Rosenberg [4] applied a closely related approach to an HGDP-CEPH Human Genome Diversity Cell Line Panel and identified close familial relationships from a set of 1 , 066 samples , while Lee [3] provided a theoretical basis for the method . Some of these ideas have been implemented in PLINK [6] which also provides IBD estimates and has a pairwise concordance test that is also derived from Lee's method . We note that our IBD method called fewer potentially related individuals for whom we could detect no shared alleles on a chromosome-by-chromosome basis , but who had atypical heterozygosity levels with unusually high Z2 estimates and low Z0 estimates . PLINK's HMM results for Z2 estimates for the metachondromatosis pedigree more closely matched expected coefficients ( and our K2 estimates ) , possibly due to a smaller dataset or better annotation . Allele-sharing methods based on IBS metrics have been widely used [19] . Applications include assessment of population stratification [20] , detection of outliers , analysis of pairwise relationships between individuals [21] , and linkage analysis [2] . One common approach to visualizing large SNP data sets is principal components analysis ( PCA ) , a technique to reduce the dimensionality of data [22] . Examples include studies of the Han Chinese [23] , Europeans [24] , [25] , [26] , [27] , Ashkenazi Jews [28] , West Africans and African Americans [29] , Asians [30] , [31] , and Indians [32] . PCA allows outlier data points to be identified , and it often results in graphic representation of SNP data that correspond to geographic maps of the populations under study . McVean [33] has shown that the locations of samples in PCA space from genome-wide data can be predicted based on the average coalescent time for pairs of samples . However , the nature of the outliers cannot be assessed ( e . g . the occurrence of familial relationships ) , and it represents an exploratory data analysis approach that is not readily amenable to hypothesis testing of the separation of clusters or of their internal cohesion . Studies based on PCA and the related approach of multidimensional scaling have yielded insights into fundamental population genetics studies such as population stratification or admixture [34] , [35] , [36] and variation in recombination rate [37] . Plots of mean versus standard deviation of IBS values , such as those by Abecasis and colleagues with Graphical Representation of Relatedness ( GRR ) [38] and by us [15] , are comparable to PCA plots in their ability to represent clusters showing familial relationships , population stratification , or other types of separation . IBS2* plots are even more useful because they provide an objective criterion for defining any pairs of samples as unrelated ( IBS2*_ratio value = 2/3 ) , more related than expected by chance ( IBS2*_ratio values >2/3 ) , or less related than expected by chance ( IBS2*_ratio values <2/3 ) . The method we introduce is useful for population studies involving even thousands of samples . However , it is also relevant to studies of even a single pedigree . For example , IBS2* plots can be used to confirm reported familial relationships ( an essential requirement for successful linkage studies ) and to explore the genetic relatedness of individuals who are nominally unrelated but could have more relatedness than expected ( e . g . having regions of autozygosity ) or less relatedness than expected ( e . g . having different ethnic backgrounds ) . Apparent genetic relatedness between two individuals could have two independent explanations: shared ancestry ( e . g . the two are third cousins ) or membership in geographic or ethnic groups that have varying population allele frequencies . The combined IBS and IBD method allowed us to visualize and determine relatedness in the context of either ( or both ) explanations . The dimension of IBS2*/ ( IBS0 + IBS2* ) reveals relatedness in a manner that is largely independent of population allele frequencies . Our analyses indicated that atypical heterozygosity levels can lead to high IBS2*_ratio values ( e . g . 0 . 70–0 . 75 ) . Such cases do not necessarily imply familial relatedness and are characterized by two features: ( 1 ) relatedness between a given individual and large numbers of others in the population , beyond what is observed in typical pedigrees , and ( 2 ) a lack of IBD1 regions on a chromosome-by-chromosome basis , confirming that the individual with atypical heterozygosity is not related to others despite the deviation from a 2/3 IBS2*_ratio value . While IBS variability can be attributed to familial relatedness and/or to population allele frequencies , another source of variability is the SNP selection process which Clark et al . have shown is subject to ascertainment bias [39] . SNPs detected , annotated , or targeted with a focus on any specific population ( s ) may not capture the full genetic diversity of other populations . This potential bias provides additional motivation for the introduction of IBS and IBD methodology .
The genotypes of 400 individuals from the NIGMS Human Genetic Cell Repository obtained on the Affymetrix Genome-Wide Human SNP Array 6 . 0 using the Birdseed algorithm were obtained from the Coriell Cell Repositories ( accessed June 06 , 2008 ) . These collections ( n = 100 each ) were from AA ( HD100AA ) , CAU ( HD100CAU ) , CHI ( HD100CHI; each individual had all four grandparents born in Taiwan , China , or Hong Kong ) and MEX ( HD100MEX; each individual had either three or four grandparents born in Mexico ) . In all cases , these individuals were reported to be unrelated and apparently healthy . The data set is available from dbGaP ( study accession phs000211 . v1 . p1 ) . Only autosomal data were used for analysis . For a validation dataset , we obtained SNP genotype data from a published study that included 12 individuals of ‘known’ relationship [17] . The expected coefficients of relatedness ranged from 1/2 ( parent-child and sibling ) to 1/32 ( first cousins that were twice-removed ) and zero ( unrelated ) . There were 66 pairwise comparisons involving all individuals in the pedigree . We measured autosomal IBS values using the freely available , cross-platform SNPduo++ software ( v1 . 02 ) which measures IBS2* from pairwise relationships as well as measurements of IBS0 , IBS1 , and IBS2 [15] ( available for download [40] ) . The output was imported into Partek Genomics Suite ( GS ) software ( Partek Inc . , St . Louis , MO ) for visualization and analysis . We also implemented the measurements of IBS and IBS2* statistics within Partek GS software v6 . 4 that allows easier import of SNP data , measurement of IBS values , and plotting functions . As suggested by Lee [3] , all loci having two A alleles and two B alleles represent informative IBS observations that can distinguish between related and unrelated pairs of individuals . IBS0 is here defined as the total number of observations in which two discordant homozygotes are present ( e . g . AA/BB ) while IBS2* results when two concordant heterozygotes are compared ( i . e . AB/AB ) between any pair of individuals . The null hypothesis , which assumes that these are unrelated individuals , is that these two individuals have four unrelated alleles , while the alternative hypothesis is that they do not . We consider alleles A and B having frequencies p and q , where pi+qi = 1 denotes the allele frequency for the ith informative locus , with informative markers i = 1 , 2 , … m ( for m<n total genotyped SNPs ) . Conditional probabilities for concordance under the null hypothesis H0 ( assuming Hardy-Weinberg equilibrium of alleles at any one locus ) are given by Lee as follows [3]: ( 1 ) A notable feature of this approach is that the probabilities are expected to be independent of the population allele frequencies for each SNP and should reduce to 2/3 based on the genome-wide sums of IBS2* and IBS0 . The test statistic ( T1 ) , variance , and statistic Z1 are given in [3] . We estimated probability values based on the Z1 statistic . Results based on an exact binomial test for p = 2/3 were quantitatively and qualitatively similar ( data not shown ) . Lee [3] gave conditional concordance probabilities for the two alternative hypotheses in which a ratio greater than 2/3 implied relatedness , and less than 2/3 implied the two individuals are from different populations . We introduced an IBS2* plot y-axis based on the frequency of informative SNPs as follows: ( 2 ) In contrast to the x-axis ( IBS2*_ratio ) , the allele frequencies which are represented by p and q do not cancel out . Values for pairwise comparisons displayed on the y-axis ( percent informative SNPs ) are positively correlated with heterozygosity rates ( shown in Figure 1B ) . The more AB genotypes that are within a population , the more likely to align AB calls when comparing two individuals . This corresponds to a higher IBS2* count which increases the percent informative SNPs ( y-axis ) . We empirically noticed that IBS0 levels have a slight effect on the height of the y-axis but contribute more to the placement on the x-axis ( i . e . IBS2*_ratio ) . This not only applies to the comparison of unrelated individuals , but is true for other relationships such as full siblings and parent-child as well . We implemented a method for IBD estimation . We present a simplified graphical overview in Figure S3 . For each pair of individuals , we removed all concordant homozygous SNPs ( i . e . AA/AA , BB/BB ) throughout all autosomes resulting in an average of 423 , 328 SNPs per pairwise comparison . Note that loci having NCs ( no calls ) in either sample were ignored . We restricted our calculations to windows of 300 SNPs that iteratively overlapped along each chromosome . Within each window , we included IBS0 ( e . g . AA/BB ) and IBS2* ( i . e . AB/AB ) SNPs for estimating IBD0/ not IBD0 , for which there was an average of 134 , 880 SNPs with an average genomic length ( for the Human Variation Panel ) of 6 . 67 Mb per pairwise comparison . Note that for "not IBD0" states the symbol corresponds to "not" . For determining whether IBD0 states were IBD1 or IBD2 , IBS1 ( e . g . AA/AB ) and IBS2* ( i . e . AB/AB ) SNPs were used with an average of 377 , 360 that corresponded to a genomic length of 2 . 38 Mb per pairwise comparison . We employed a series of window sizes , using 300 as a default size based on empirical observation that it reduced background noise while yielding expected values of IBD . We note that increasing the window size over 600 decreased the estimation of expected IBD values because the boundaries between the different IBD states were not as easily defined . This window size is user-selectable . For each window , we calculated a likelihood of each IBD state given the observed IBS values . For example , we expected to observe an IBS0 ratio ( i . e . IBS0/ ( IBS2* + IBS0 ) of 1/3 for IBD0 states , and an IBS0 ratio of 0 for IBD0 states that share 1 or 2 alleles IBD . The samples were assumed to be drawn from the same population with unknown allele frequencies . We express the likelihood of observing a given IBS frequency vector as the marginalization across all IBD states ( P ( S ) ) , and we define D0 , D1 , D2 , as IBD0 , IBD1 , IBD2 respectively . ( 3 ) ( 4 ) We assume prior IBD probabilities of P ( D0 ) = P ( D1 ) = P ( D2 ) = 1/3 , P ( D1 | D0 ) = P ( D2 | D0 ) = 1/2 . We note that any prior could be used within the algorithm , but without any reason to believe there is a specific relationship , the non-informative prior is a simple choice , and each IBD state is equally as likely to occur . An observation error rate is also incorporated into the likelihood model . This is fixed for all of the analyses . The probabilities used to compute likelihoods given an IBS call at a SNP for each IBD state assignment are in Table 3 for SNP specific values p and q . To estimate P ( S | D0 ) , we limit our likelihood calculations to only the IBS0 and IBS2* calls . The observed frequencies of these two IBS states are independent of allele frequency at each SNP when conditioned on an observed IBS0 or IBS2 call , with P ( observed S0 | D0 , observed S0 and S2 ) = 1/3 . We use the following distribution assumptions to create the likelihood of observing the IBS0 frequency . The error ( set by default at 0 . 01 ) reflects the genotyping error . ( 5 ) ( 6 ) From this , we have P ( S | D0 ) and P ( S | D0 ) , and we compute posterior IBD0 probability of the region . ( 7 ) ( 8 ) We use this estimate of P ( D0 | S ) as our estimated IBD0 probability . The next step is to divide P ( D0 | S ) into estimates of P ( D1 | S ) and P ( D2 | S ) . We use the following distribution assumption to approximate the expected frequency of IBS1 calls ( i . e . IBS1/[IBS1 + IBS2*] ) . The parameter c is discussed below . The error rate is set at 0 . 01 . ( 9 ) ( 10 ) Using these distributions , we can provide estimates for P ( S | D1 ) and P ( S | D2 ) . Since all the three terms of P ( S ) above are estimated after specifying a prior probability , we can compute the three IBD probabilities , P ( D0 | S ) , P ( D1 | S ) , and P ( D2 | S ) using Bayes' rule . We estimate the K coefficients using the estimates of P ( D0 | S ) , P ( D1 | S ) , P ( D2 | S ) for each window , w , spanning genome length lw . Pw denotes a probability value across a given window . ( 11 ) ( 12 ) ( 13 ) The windowed approach used observed IBS to identify regions that were IBD0 or IBD0 . In estimating regions of IBD0 or IBD0 the allele frequencies cancelled out ( similar to equation [1] ) . A further step of our algorithm is to distinguish the set of regions that are IBD1 and IBD2 ( i . e . the IBD0 regions ) . It is necessary to account for allele frequencies in these regions . We present a justification for the c parameter ( equation 9 ) which is used for distinguishing IBD1 and IBD2 for regions that are IBD0 . For each SNP , the proportion of observing an IBS1 event given that an IBS1 or IBS2* event was observed in a region of IBD1 can be defined as a function , f , taking the allele frequency p , as defined in Table 3 . ( 14 ) We can integrate over the allele probability function , P ( p ) , to calculate an expected f ( p ) . ( 15 ) We can specify P ( p ) to represent any prior belief about the allele frequency of those SNPs that have observed variation . We used a one-step empirical Bayes' estimate of P ( p ) to suggest a practical value . Assume that an event O occurs when a SNP has observed variation in the two comparison samples . ( 16 ) We define P ( O | p ) in a region of IBD1 as P ( O | p ) = 5+p ( 1-p ) . This is the complement of the chance to observe identical homozygous genotypes in a region of IBD1 . An initial estimate of P ( p ) is specified as a uniform prior over p . This initial uniform prior is used to compute a posterior P ( p | O ) , which is then used as the empirical prior to compute Pe ( p | O ) . Using Pe ( p | O ) in place of P ( p ) in the calculation of c above results in the expectation c = . 518 . The value of c should be chosen greater than the error and less than or equal to than the maximum allowable value of 2/3 . The assumed binomial model considers all SNPs to be drawn with the same allele frequency which is not accurate , as the binomial parameter p varies across SNPs as a function of allele frequency . The binomial model described above was chosen for its simplicity and the experimental insensitivity to choice of c across a wide range of reasonable values . Future work may use the beta binomial distribution to better account for the distribution of allele frequencies . We tested a range of c values and observed consistent results for K1 and K2 estimates given c≥0 . 25 ( data not shown ) . The IBD algorithm ( called kcoeff ) is available as an executable at the authors' website [40] , as well as the source code . For the Human Variation Panel , we applied PLINK's HMM , given by the "--genome" option , to each group independently ( e . g . 100 Mex individuals ) because a homogeneous population is recommended [6] , [16] . Each group of 100 had the following quality control measures: ( 1 ) individuals with ≤98% genotype call rate were removed; ( 2 ) SNPs with ≤99% genotype call rate were removed; ( 3 ) SNPs with a failure of Hardy-Weinberg equilibrium with a p≤0 . 0001 were removed; ( 4 ) SNPs with a minor-allele frequency ( MAF ) ≤0 . 01 were removed . Results using these quality control criteria are shown in Figure 3C , 3D and Figure 4C , 4D . We also ran PLINK's HMM without removing individuals that had ≤98% genotype call rate as shown in Figure 3E , 3F . We summarized the effects of these quality control measures for each of the four Human Variation Panel populations ( Table S2 ) . PLINK's HMM analysis for the validation dataset was run with incomplete pedigree information since data for both parents , which must be specified in a tped file , were not available . Only one trio was specified . We analyzed the 12 family members' data in PLINK using the same quality control measures as above , and no samples were excluded based on low genotyping rate . 7 SNPs were removed due to ≤99% genotype call rate , while 87 , 234 were removed due to MAF ≤0 . 01 leaving 450 , 924 SNPs for analysis . | High-density microarrays measuring single nucleotide polymorphisms ( SNPs ) provide information about the genotypes across many loci . SNP genotypes observed for any two individuals can be compared in terms of identity-by-state ( IBS ) , in which two individuals are observed to have 0 , 1 , or 2 alleles in common at a given locus , across a chromosomal region , or throughout the genome . These alleles may be shared identical-by-descent ( IBD ) in which 0 , 1 , or 2 alleles are inherited from a recent common ancestor , or they may be identical by chance because the allele is frequent in the population . The expected proportion of genome sharing between two individuals varies as a function of their genetic relatedness . We introduce a method to estimate IBD that can be used to analyze relatedness in pedigrees or in large-scale population studies with thousands of individuals . This can be combined with observed IBS to distinguish a variety of types of relatedness , providing theoretically justified results that are graphed in a manner that is straightforward to interpret . The methods we introduce are relevant to a variety of SNP applications including linkage and association studies and population genomics studies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"haplotypes",
"chromosomal",
"inheritance",
"heredity",
"genetics",
"population",
"genetics",
"biology",
"human",
"genetics",
"genetics",
"and",
"genomics"
]
| 2011 | Inference of Relationships in Population Data Using Identity-by-Descent and Identity-by-State |
The fungal pathogen Candida glabrata has risen from an innocuous commensal to a major human pathogen that causes life-threatening infections with an associated mortality rate of up to 50% . The dramatic rise in the number of immunocompromised individuals from HIV infection , tuberculosis , and as a result of immunosuppressive regimens in cancer treatment and transplant interventions have created a new and hitherto unchartered niche for the proliferation of C . glabrata . Iron acquisition is a known microbial virulence determinant and human diseases of iron overload have been found to correlate with increased bacterial burden . Given that more than 2 billion people worldwide suffer from iron deficiency and that iron overload is one of the most common single-gene inherited diseases , it is important to understand whether host iron status may influence C . glabrata infectious disease progression . Here we identify Sit1 as the sole siderophore-iron transporter in C . glabrata and demonstrate that siderophore-mediated iron acquisition is critical for enhancing C . glabrata survival to the microbicidal activities of macrophages . Within the Sit1 transporter , we identify a conserved extracellular SIderophore Transporter Domain ( SITD ) that is critical for siderophore-mediated ability of C . glabrata to resist macrophage killing . Using macrophage models of human iron overload disease , we demonstrate that C . glabrata senses altered iron levels within the phagosomal compartment . Moreover , Sit1 functions as a determinant for C . glabrata to survive macrophage killing in a manner that is dependent on macrophage iron status . These studies suggest that host iron status is a modifier of infectious disease that modulates the dependence on distinct mechanisms of microbial Fe acquisition .
Candida glabrata has emerged as an opportunistic fungal pathogen that causes life-threatening infectious disease in humans [1] . The dramatic rise in the number of immunocompromised individuals due to HIV infection and tuberculosis , and as a result of immunosuppressive regimens in cancer treatment and transplant interventions , have provided fertile ground for unchecked C . glabrata proliferation . While Candida species now account for over 10% of all bloodstream infections [2] , the poor susceptibility of C . glabrata to antifungal therapeutics is in great part responsible for the high mortality rate of up to 50% associated with C . glabrata candidemia [3] , [4] . The limited knowledge of virulence factors that contribute to the pathogenesis of C . glabrata demands insights into the biology of this opportunistic pathogen that contribute towards the successful colonization of the mammalian host . Iron ( Fe ) is an essential metal for virtually all organisms . The ability of Fe to cycle between the reduced ferrous ( Fe2+ ) and oxidized ferric ( Fe3+ ) forms endows it with redox versatility that is utilized in both catalysis and structural biology . However , Fe2+ also has the potential to generate damaging reactive oxygen species ( ROS ) via Fenton/Haber Weiss chemistry [5] and , as a consequence , organisms have evolved sophisticated homeostatic mechanisms to tightly regulate the acquisition , utilization , storage and mobilization of Fe [6] , [7] . Eukaryotic Fe homeostasis has best been described in the budding yeast , Saccharomyces cerevisiae that possesses two high-affinity mechanisms of Fe acquisition [7] . In an aerobic environment , Fe is oxidized and largely insoluble [8] and one mechanism relies on the reduction of environmental Fe sources by cell surface reductases and transport through a ferroxidase/permease complex composed of the Fet3 and Ftr1 proteins , respectively . The activity of the Fet3 Cu-dependent ferroxidase is essential for Fe transport through Ftr1 and , thus , reductive high-affinity Fe assimilation is dependent on adequate cellular Cu bioavailability . Alternatively , S . cerevisiae expresses cell surface siderophore transporters . Synthesized and secreted by most bacteria and fungi as well as some higher plants , siderophores are low molecular weight organic chelators with high affinity for Fe3+ ( binding constants between 1023 M and 1049 M ) . Given the low Fe bioavailability in an aqueous environment ( free Fe3+ concentration below 10−9 M at pH 7 ) and the further strict active withholding of Fe within mammalian hosts ( free serum Fe concentration of approximately 10−24 M ) [9] , siderophores represent a powerful and widespread mechanism for microbial Fe scavenging . This is underscored by the fact that bacteria and fungi have evolved transporters that allow them to utilize siderophores they themselves do not produce ( xenosiderophores ) and several fungi , such as Candida species , and bacteria do not possess the ability to synthesize siderophores yet express transporters for xenosiderophores [7] . Although the mechanisms of Fe homeostasis in Candida albicans , similar to most fungi , differ significantly from that of S . cerevisiae and C . glabrata [10] , iron-regulated CaSit1 shares high homology with S . cerevisiae siderophore transporters and its deletion compromises utilization of fungal ferrichrome-type hydroxamate siderophores . CaSit1 has been shown to be required for the invasion and penetration of human epithelial cells in an in vitro model of oral candidiasis but was not required for murine systemic candida dissemination [11] , possibly reflecting the sterile environment of the serum in which siderophore concentrations are predicted to be negligible . The absence of an identifiable heme receptor in C . glabrata present in Candida albicans [12] , suggests that C . glabrata may rely predominantly on the solubilization of the circulating exchangeable Fe pool to meet its requirements for Fe . Given that mammals are host to a formidable number of microbial species , the aggressive exploitation of local siderophore reservoirs that allows for high-affinity Fe uptake from virtually any host ligand is likely to play a critical role in microbial virulence . Mammalian cellular immunity relies heavily on the action of macrophages , cells that also play a critical role in systemic Fe homeostasis [13] . A key mechanism through which macrophages mediate microbial containment is through the localized reduction of serum transferrin-bound Fe , decreased Fe export and the depletion of Fe from the macrophage phagosome , an intracellular compartment that surrounds ingested microorganims . These macrophage activities promote the killing of microorganisms by depriving them of Fe essential not only for cellular biochemistry but also for incorporation into microbial ROS-detoxifying enzymes , such as catalase or superoxide dismutase , that are critical to resistance to high levels of oxidative stress generated in the phagosome [14] . Emerging evidence for increased infections in patients with Fe overload disease highlights the strict requirement for tight host Fe restriction in microbial containment [15] . Hereditary hemochromatosis ( HH ) is the most common single-gene disorder affecting Caucasian individuals of European descent [16] . Mutations associated with HH are found in the gene encoding the Fe exporter , ferroportin ( Fpn ) , in HAMP , encoding the peptide hormone hepcidin and negative regulator of Fpn , as well as in genes encoding regulators of HAMP expression . Hepcidin expression and release from the liver leads to targeted Fpn degradation at a systemic level resulting in a block in dietary Fe uptake through enterocytes and a block in Fe release from macrophage and hepatocyte stores [17] . Although several of the mutations that underlie HH lead to similar outcomes of pathophysiological Fe overload in parenchymal organs , the clinical presentations differ with regard to macrophage Fe levels which are low when hepcidin levels are low or fails to be recognized by Fpn , and high when hepcidin levels increase or Fpn Fe-export activity is compromised . Given the critical role of macrophages in immunity , the dominant effects of Fpn activity on macrophage Fe status may be predicted to impinge on their immuno-protective functions . Here we describe the C . glabrata Sit1 siderophore transporter , its regulation under Fe deficiency and substrate siderophore specificity . In infection assays using both mouse and human macrophage cell lines we observe a strong Sit1-mediated , siderophore-dependent increase in C . glabrata survival that is blunted with increased macrophage Fe levels . Genetic manipulation of macrophage Fe status to parallel that which is observed in human diseases of Fe overload revealed an inverse correlation between macrophage Fe and C . glabrata dependence on siderophore-Fe as indicated by yeast survival rates as well as by SIT1 expression in the macrophage phagosome . Consistent with this , infection of primary macrophages derived from a mouse model of Ferroportin disease in which macrophage Fe levels are chronically elevated demonstrated enhanced C . glabrata survival that is independent of Sit1-mediated siderophore utilization . These observations are consistent with reports of greater microbial burden in patients with Fe overload disease [15] , [18] , [19] , [20] , [21] and predict that mutations affecting macrophage Fe levels may not only compromise microbial containment , but may also dictate the dependence of microbes on distinct mechanisms of Fe uptake .
Fe acquisition is a critical microbial virulence determinant due to the Fe-restricted nature of the mammalian host . The great majority of body Fe is compartmentalized within red blood cells bound as heme to hemoglobin . Several microorganisms , including C . albicans , can access this rich Fe source by secreting hemolytic factors that results in red blood cell lysis [22] . Heme can then be taken up , often by means of a dedicated receptor ( such as Rbt5 and Rbt51 in C . albicans [12] ) and the porphyrin ring hydrolysed by intracellular heme oxidases [23] . To test whether red blood cells represent an efficient source of Fe for C . glabrata , we grew the wild type strain as well as that of C . albicans and S . cerevisiae , on sheep blood agar plates containing 5% whole blood . Formation of a hemolytic halo surrounding yeast colonies was evaluated after 48–72 hours growth at 30 °C or 37 °C with 5% CO2 . Unlike the robust growth and associated hemolytic activity observed for C . albicans , C . glabrata and S . cerevisiae displayed very weak hemolysis when grown on blood sheep agar ( Figure 1A ) . Furthermore , whereas C . albicans was able to efficiently utilize heme as a source of Fe at sub-micromolar hemin concentrations , C . glabrata required 100-fold higher hemin concentrations to achieve the same level of growth . In the same way , higher hemin concentrations were required for a slower growth of C . glabrata due to hemin toxicity when compared to C . albicans ( Figure 1B ) . The observed differences in growth when heme is the sole Fe source may due to the absence of an identifiable heme receptor in the C . glabrata genome that is present in C . albicans . To address this possibility , C . albicans and C . glabrata were cultured in Fe-deficient medium supplemented or not with 20 µM zinc protoporphyrin ( ZPP ) for 3 hours . In contrast to heme , which is non-fluorescent due to the quenching of the intrinsic porphyrin fluorescence by Fe , ZPP fluoresces and its cellular uptake can be evaluated by in vivo microscopy . Figure 1C shows that in contrast to C . albicans that showed intense intracellular fluorescence , this was not observed for C . glabrata , the intracellular fluorescence of which did not rise above background as determined by comparison to the signal obtained by growth under Fe deficiency alone . This suggests that C . glabrata is not capable of taking up heme . Thus , given its weak hemolytic activity and inability to efficiently import heme , we predict that C . glabrata relies primarily on circulating host Fe sources to satisfy its requirements for Fe . Analysis of the Candida glabrata genome [24] predicts the existence of orthologues to the Saccharomyces cerevisiae high affinity reductive Fe assimilation pathway as well as a single open reading frame , CAGL0E04092g , encoding a predicted membrane protein with 14 transmembrane domains and a high degree of identity ( 72% ) to the siderophore-Fe transporter , Arn1 , that we have designated SIT1 ( Siderophore Iron Transporter 1 ) ( Figure 2A and Figure S1A ) . Computational topological analyses of Sit1 identified sequence signatures characteristic of members of the Major Facilitator Superfamily of transporters ( data not shown ) . In addition , Sit1 orthologues predicted to exist across the ascomycetes , and to lesser extent basidiomycetes , share high sequence identity within the carboxyl terminus of the protein . Protein BLAST analysis [25] of a 51 amino acid sequence comprising the last extra-cellular loop ( from amino acid residue 531 to 581 ) exclusively returned known siderophore transporters and uncharacterized proteins predicted to encode fungal siderophore transporters ( data not shown ) . This SIderophore Transporter Domain ( SITD ) is conserved in many pathogenic fungi ( Figure 2A ) . To investigate the effects of Fe availability on SIT1 expression , C . glabrata was grown under conditions of Fe-repletion or Fe-deficiency . Figure 2B shows that steady state SIT1 mRNA levels are elevated under Fe deficiency and that this elevation is sustained throughout the time-course of the experiment over 6 hours . To analyze the levels of Sit1 protein as a function of Fe availability , a Flag epitope was introduced at the carboxyl terminus of SIT1 and the fusion gene , encoding a functional protein , was integrated at the endogenous SIT1 genomic locus ( Figure S1B ) . Similar to what was observed for SIT1 mRNA , Sit1Flag fusion protein expression is low under Fe repletion but robustly accumulates under conditions of Fe deficiency ( Figure 2C ) . Siderophore production is common among most microorganisms and is a major mechanism of Fe solubilization and acquisition . The very high Fe-binding constants observed for siderophores of fungal origin ( approximately 1030 M at pH 7 ) are several orders of magnitude higher than that of the Fe2+ chelator BPS , which inhibits the Fet3/Ftr1 pathway of Fe2+ uptake . We reasoned that if a siderophore biosynthetic pathway exists in this microorganism , C . glabrata would exhibit growth in the presence of BPS . As shown in Figure 2D , growth of the wild type strain was severely compromised in Fe-deficient medium chelated with 100 µM BPS . This is consistent with a predicted lack of siderophore biosynthetic machinery in C . glabrata based on the absence of identifiable orthologues to the gene encoding ornithine-N5-oxygenase that catalyses the first committed step in fungal siderophore biosynthesis ( the DNA and protein sequences of the Aspergillus nidulans ornithine-N5-oxygenase SidA were used to perform the BLAST analyses ) [26] . To address the role of the Sit1 transporter in xenosiderophore utilization , a mutant lacking the SIT1 gene , as well as the reconstituted strain in which the wild type SIT1 gene was integrated in the sit1Δ strain at the endogenous genomic locus , were generated and evaluated for growth in Fe-deficient medium alone or in the presence of several structurally distinct fungal and bacterial siderophore substrates . Supplementation of Fe-deficient media with the fungal hydroxamate-type siderophores ferrichrome , ferrirubin or coprogen promoted robust growth of the wild type and SIT1-reconstituted strains ( Figure 2D ) . In contrast , the sit1Δ mutant strain was unable to use siderophore-Fe , strongly supporting the notion that Sit1 is a siderophore transporter capable of utilizing these xenosiderophores . Neither the ester-linked siderophore triacylfusarine C , nor the bacterially-derived siderophores enterobactin , salmochelin , yersiniabactin or desferrioxamine were able to complement the growth of C . glabrata under iron deficiency ( Figure S1C ) suggesting that these siderophores are either not transported by Sit1 or C . glabrata lacks a mechanism of releasing the Fe from these siderophores intracellularly . The subcellular localization of Sit1 was evaluated using the Sit1Flag and Sit1mCherry fusion proteins . Previous studies in S . cerevisiae have shown that during Fe deficiency the Arn1 siderophore transporter is not localized to the plasma membrane but rather to intracellular endosomal vesicles [27] . Trafficking of Arn1 to the plasma membrane is a substrate-dependent process that occurs in a dose-dependent manner [28] , [29] . In contrast to this , indirect immunofluorescence as well as live microscopy of C . glabata Sit1 revealed strong accumulation of the Sit1 protein at the plasma membrane under Fe deficiency in the absence of siderophore substrate and this accumulation was largely unaltered by the addition of ferrichrome ( Figure 2E and Figure S1D ) . High-affinity elemental Fe uptake requires the activity of the Fet3/Ftr1 ferroxidase/permease complex , with the protein complex assembled in the ER and 4 Cu ions loaded onto Fet3 within the Golgi . Under Cu deficiency , apo-Fet3-Ftr1 is assembled and targeted to the plasma membrane , but the absence of ferroxidase activity abolishes high-affinity Fe2+ transport through Ftr1 [30] . As such , elemental Fe uptake is a copper-dependent mechanism . In contrast , siderophore transporters are not known to require Cu for activity and would be predicted to be functional under low Cu conditions . Figure 2F shows that siderophore utilization is functional under Cu deficiency as imposed by growth in the presence of the Cu chelator BCS suggesting that Fe-siderophore transport is likely to be critical as the sole mechanism of high affinity Fe acquisition under conditions of Cu deficiency . The ability of C . glabrata to exploit local xenosiderophore reservoirs in the absence of an endogenous siderophore biosynthetic pathway suggests a selective advantage imparted by this mechanism of Fe uptake that may impact on the survival of this fungal pathogen . Fe acquisition is challenging within the mammalian host , in which Fe is tightly bound to proteins and ligands in extracellular and intracellular environments . The further active depletion of Fe from the phagosomal compartment amplifies the microbicidal potency of macrophages as microorganisms defend themselves from ROS under conditions that are expected to compromise ROS-detoxifying enzyme activity . We tested whether Fe deficiency would increase the susceptibility with which C . glabrata resists macrophage killing and if this might be circumvented by exposure of C . glabrata to substrate siderophore . Using the mouse macrophage-like cell line , J774A . 1 , isogenic wild type , sit1Δ and sit1Δ::SIT1 C . glabrata strains were grown under Fe limiting conditions and briefly incubated in the absence or presence of ferrichrome prior to co-culture with activated macrophages ( Figure 3A ) . C . glabrata cells were recovered by cell lysis and survival was evaluated by quantitating colony forming units ( CFU ) and comparing percentage yeast survival relative to the wild type strain grown under Fe deficiency ( represented by a dashed line ) . Figure S2A shows that when C . glabrata cells were grown under Fe deficiency prior to macrophage infection , differences were not observed in the survival of the three strains to macrophage killing . However , growth of both the wild type and reconstituted sit1Δ::SIT1 strains in the presence of ferrichrome significantly increased survival when compared to the sit1Δ strain , indicative of enhanced C . glabrata ability to survive macrophage killing as a result of siderophore-Fe utilization ( Figure 3B , left columns ) . This suggests that Sit1-mediated ferrichrome utilization provides a survival advantage to phagocytosed C . glabrata cells that is not observed in the strain that cannot internalize this Fe source . To address whether the decreased survival of the sit1Δ strain could be attributed to altered levels of phagocytosis of the mutant yeast cells rather than decreased Fe satiety , CFU were monitored 2 hours post-infection and no significant differences between the wild type , sit1Δ and sit1Δ::SIT1 strains were observed in the presence or absence of ferrichrome , strongly suggesting equally efficient phagocytosis of the three yeast strains by macrophages ( Figure S2A ) . To test whether the Sit1-dependent increase in C . glabrata survival in the presence of siderophore was a consequence of macrophage Fe status , macrophages were Fe-loaded prior to infection with the C . glabrata strains and the levels of ferritin , the primary intracellular Fe storage protein , were evaluated by immunoblotting . As shown in Figure S2B , activated macrophages cultured under Fe supplementation ( lane 4 ) showed higher ferritin protein levels than those cultured in normal medium ( lane 2 ) . Under these conditions of exogenous Fe loading , differences in survival were not observed between the wild type , sit1Δ and sit1Δ::SIT1 yeast strains grown in the presence or absence of ferrichrome ( Figure 3B , right columns ) . Furthermore , significantly higher CFU were recovered from Fe-loaded macrophages under all conditions tested when compared to infection of macrophages growing in the absence of Fe supplementation , suggesting that elevated Fe compromises the ability of macrophages to contain C . glabrata proliferation within the phagosome . To ascertain whether Fe-loading compromises macrophage activation and the ability to mount an inflammatory response , secreted levels of TNF-alpha , an acute phase inflammatory cytokine secreted by activated macrophages , were assayed and no significant changes were observed between that secreted from activated Fe-loaded macrophages and activated macrophages cultured in standard growth media ( Figure S2C ) . Despite the fact that the overall mechanisms of innate and adaptive immunity are relatively conserved between mice and humans , there are striking immunological differences between the two species [31] . In particular , there is some controversy over whether human macrophages express functional inducible nitric oxide synthase ( iNOS ) . This enzyme catalyzes the production of NO from L-arginine that , in the oxidative environment of the phagosome , reacts with superoxide leading to the generation of peroxynitrite , a powerful oxidant used by macrophages and neutrophils in microbial killing during the oxidative burst . Given this and other differences in mouse and human macrophage immunobiology , we asked whether Sit1-mediated siderophore utilization enhanced C . glabrata survival to killing by human macrophages . To test this , we used the U937 cell line derived from a hystiocystic lymphoma whose differentiation and activation have been previously described [32] , [33] . Upon differentiation into mature macrophages , U937 cells were infected with the wild type , sit1Δ and sit1Δ::SIT1 C . glabrata strains grown under Fe deficiency , with or without a brief exposure to ferrichrome , and yeast CFU quantitated after 18 , 24 and 36 hours of co-culture . Figure 3C shows that when exposed to ferrichrome , the wild type and SIT1 reconstituted strains displayed enhanced survival at all time points in contrast to the sit1Δ mutant strain that is unable to transport the siderophore . Pre-loading U937 cells with Fe by culturing in the presence of 20 µM FAC promoted the growth of C . glabrata and abrogated the requirement for Sit1-mediated siderophore utlization in enhanced C . glabrata survival ( Figure 3D ) . These results recapitulate the observations made in the J744A . 1 mouse macrophage cell line and suggest that Sit1-mediated siderophore utilization protects C . glabrata against the intracellular microbicidal activities of macrophages . Given the generation of ROS in the phagosome of activated macrophages ( Figure S2D ) , indicative of higher levels of oxidative stress , we hypothesize that siderophore-Fe plays a role in providing the Fe required for DNA replication and cellular proliferation as well as for defence against oxidative stress . Moreover , these results indicate that macrophage Fe-withholding from phagocytosed microorganisms is fundamental for the containment of microbial growth and also impacts on the mechanisms available for microbial iron uptake , imposing a strict dependence of C . glabrata on the availability and utilization of xenosiderophores via Sit1 . Based on the effect of macrophage Fe loading on C . glabrata Sit1-mediated siderophore-dependent survival , we asked whether mutations that alter macrophage Fe homeostasis might similarly modulate the dependence of C . glabrata on siderophore-Fe acquisition . The sole mammalian Fe exporter Fpn is expressed on the surface of enterocytes , macrophages , hepatocytes and placental cells where it plays a fundamental role in systemic Fe homeostasis . Type IV hemochromatosis , or Ferroportin disease , is an autosomal-dominant condition that arises from missense mutations in Fpn that compromise regulated Fe export activity . Two often contrasting clinical presentations observed in patients result from distinct mutations in Fpn that either abolish the hepcidin binding site or compromise Fe export , leading to differential effects on macrophage Fe levels [16] , [34] , [35] , [36] . We modulated macrophage Fe status by stably expressing a cDNA encoding the FPNC326Y hepcidin-insensitive disease mutation , associated with low macrophage Fe levels , or the FPNH32R allele identified in the flatiron mouse that fails to localize to the plasma membrane , leading to Fe-overload . Using ferritin protein levels as a biomarker of macrophage Fe loading , macrophages expressing the FPNH32R transgene were chronically iron-loaded in the absence of hepcidin when compared to macrophages expressing the wild type gene due to the loss in ability to export cellular Fe ( Figure 4A , lanes 1 and 3 ) . Incubation with hepcidin increased ferritin levels in cells expressing wild type FPN , as would be predicted from Fpn internalization and degradation , resulting in increased cellular Fe accumulation ( Figure 4A , lane 2 ) . In contrast , hepcidin had only a mild effect on FPNH32R-expressing macrophages ( Figure 4A , lane 4 ) . Given that Fpn functions as a dimer [37] , this modest increase in ferritin levels may reflect the contribution of hepcidin-sensitive endogenous wild type homodimers that correctly localize to the plasma membrane . In contrast , macrophages expressing the FPNC326Y transgene show lower than wild type ferritin levels when grown under Fe-replete conditions in the presence of hepcidin , consistent with a chronic Fe efflux under conditions that induce Fpn turnover of the wild type protein ( Figure 4B , lanes 2 and 4 ) . Indirect immunofluorescence in macrophages expressing the wild type and mutant FPN transgenes was performed to monitor protein localization . Both the wild type and FpnC326Y-expressing cells , but not cells expressing FpnH32R , show plasma membrane ferroportin accumulation in the absence of hepcidin ( Figure S3A ) . Exposure to hepcidin leads to a predominantly intracellular localization of the wild type protein , whereas the hepcidin-insensitive FpnC326Y protein remains at the cell surface . Using these macrophages with altered Fe homeostasis , we ascertained whether genetically programmed differences in macrophage Fe levels impinge on the dependence of C . glabrata on siderophore-Fe for resistance to macrophage killing activities . Macrophages stably expressing the wild type and mutant forms of FPN were infected with wild type , sit1Δ and the SIT1-reconstituted C . glabrata strains that had been grown under Fe deficiency and exposed or not to ferrichrome . As shown in Figure 4C , wild type and SIT1-reconstituted C . glabrata yeast cells , but not sit1Δ cells , showed enhanced survival to macrophages expressing wild type Fpn only when grown in the presence of ferrichrome prior to infection , as interpreted from the increased number of CFU when compared to the wild type C . glabrata strain grown in the absence of FC ( dashed line set at 100 ) . No differences were observed in the survival of the three C . glabrata strains grown in the absence of ferrichrome supplementation ( data not shown ) . This recapitulates the results obtained in non-transfected J774A . 1 mouse macrophages and human differentiated U937 cells in the absence of Fe supplementation ( Figure 3B , left columns ) . In contrast , siderophore-dependent differences in C . glabrata survival were not observed between the yeast strains recovered from the chronically Fe-loaded macrophages expressing the FPNH32R transgene , supporting our previous results using exogenously Fe-loaded macrophages ( Figure 3B , right columns ) . This is consistent with the high levels of labile intracellular Fe in FPNH32R macrophages that elevate the abundance of ferritin and might be inefficiently withheld from C . glabrata . In contrast , C . glabrata recovered from FPNC326Y macrophages cultured in the presence of hepcidin exhibit increased dependence on siderophore-Fe utilization for survival when compared to those recovered from macrophages expressing wild type Fpn ( Figure 4D ) . This is consistent with the refractility of FpnC326Y to hepcidin-stimulated degradation and Fe levels in these macrophages that are lower than wild type . To ascertain whether Fe overload or deficiency might compromise the ability of macrophages to mount an inflammatory response , the levels of TNF-alpha were quantified from the culture medium of activated macrophages expressing the FPN , FPNC326Y and FPNH32R transgenes and no difference was found between cell lines ( Figure S3B ) . Taken together , these data suggest that elevated macrophage Fe accumulation increases Fe accessibility to internalized C . glabrata . This ameliorates the strict requirement for Sit1-mediated siderophore utilization observed in macrophages with limited intracellular Fe . The results presented here suggest that Sit1-dependent siderophore utilization enhances C . glabrata survival to activated macrophages in a manner that is dependent on macrophage labile Fe status . SIT1 characterization demonstrated that expression is responsive to environmental Fe bioavailability , showing low levels of expression under Fe repletion and robust mRNA and protein accumulation under Fe deficiency ( Figure 2B and 2C ) . Using a reporter strain expressing a SIT1mCherry fusion gene from the endogenous SIT1 locus , we ascertained whether internalized C . glabrata cells sense differences in genetically-programmed macrophage Fe status . As shown in Figure 5A by live microscopy , C . glabrata cells phagocytosed by macrophages expressing the FPNC326Y transgene showed enhanced Sit1mCherry accumulation when compared to yeast internalized by macrophages that were Fe-loaded by virtue of FpnH32R expression , suggesting that C . glabrata cells experienced different degrees of Fe deficiency in these macrophages . Fluorescein-conjugated dextran beads were included in the infection medium to allow for a clearer visualization of intra- versus extra-cellular yeast . To quantitatively evaluate the influence of macrophage Fe status on the Fe satiety of C . glabrata , SIT1 transcript levels were monitored in yeast cells recovered from macrophages by semi-quantitative RT-PCR . Figure 5B shows that cells recovered from FPNH32R-expressing macrophages exhibited lower levels of the SIT1 transcript when compared to cells recovered from FPNC326Y-expressing macrophages two and four hours post-infection , consistent with an increased Fe satiety within the phagosome . These results support the hypothesis that chronic perturbations in macrophage Fe levels translate into altered microbial Fe availability that may impact on the mechanisms of microbial Fe uptake . Given the effects of macrophage Fe status on the dependence of C . glabrata siderophore utilization using well-established macrophage cell lines , we ascertained whether this could be recapitulated in an ex-vivo infection of primary mouse macrophages . Bone marrow cells were recovered from the femurs of control mice and of the flatiron ( ffe ) mouse model of human Ferroportin Disease , which harbors the FPNH32R mutation [36] . Cells were differentiated into mature macrophages in culture and endogenous Fpn expression stimulated by incubating cells in 10 µM ferric ammonium citrate for 2 days prior to activation and infection with C . glabrata ( Figure 6A ) . Consistent with the results obtained using the J774A . 1 and U937 cell lines , when grown in the presence of ferrichrome , C . glabrata wild type and SIT1-reconstituted cells showed enhanced survival to the killing activities of wild type primary macrophages when compared to the sit1Δ strain ( Figure 6B ) . However , siderophore-dependent survival was significantly diminished in infected ffe macrophages , supporting our previous observations that increased macrophage labile Fe pools promote the growth of C . glabrata independently of siderophore utilization . Computational analysis of the Sit1 SITD ( Figure 2A ) revealed a high degree of conservation among orthologous siderophore transporters in a large number of fungal species , including several animal and plant pathogens , a representative number of which are shown in Figure S4 . To begin to evaluate a potential role for the SITD in Sit1-mediated siderophore utilization in C . glabrata , the predicted extracellular topological orientation of the carboxyl-terminal loop between transmembrane domains 13 and 14 was first evaluated experimentally . Sit1 was tagged with a 2XFLAG epitope within the carboxyl-terminal loop between residues G542 and D543 ( Figure S5A ) . Cells expressing this tagged Sit1 protein , or the empty vector , were cultured under Fe deficient conditions and the accessibility of the anti-Flag antibody for its cognate epitope evaluated in non-permeabilized cells by indirect immunofluorescence . As shown in Figure S5B , plasma membrane accumulation of the Flag antibody was observed in non-permeabilized cells expressing carboxyl-loop-Flag Sit1 . This immunofluorescence signal was absent in cells expressing vector alone and is consistent with an orientation of this loop region , and therefore the SITD , toward the extracellular face of the plasma membrane . To test whether the Sit1 SITD may be of functional importance , we mutated the conserved residue , Y575 to alanine ( Figure S4 ) , and tested whether this might compromise Sit1-mediated ferrichrome utilization . A strain carrying the SIT1Y575A allele integrated at the endogenous genomic locus ( Figure S5C ) was strongly impaired in its ability to use ferrichrome as an Fe source ( Figure 7A ) despite exhibiting levels of protein expression ( Figure S5D ) and plasma membrane localization ( Figure 7B and Figure S5E ) comparable to the wild type strain . The observed decrease in the ability of the Sit1Y575A mutant to utilize ferrichrome prompted us to evaluate the ability of this strain to survive macrophage killing when grown in the presence of this siderophore . In contrast to wild type C . glabrata cells , neither the sit1Δ nor the strain bearing the Sit1Y575A substitution in Sit1 showed the Sit1-mediated siderophore-dependent increase in the ability to survive macrophage killing , suggesting that although this mutation does not lead to a complete loss of Sit1 function ( see Figure 7A ) , the remaining activity is insufficient to meet the cellular demand for Fe within the phagosome ( Figure 7C ) .
Candida glabrata has risen from relative obscurity as a relevant opportunistic human fungal pathogen to an increasing cause of serious infectious disease particularly in immunocompromised individuals . Yet , the significant phylogenetic relatedness of C . glabrata to Saccharomyces cerevisiae argues for some fundamental differences in the biology of this microorganism , either through the functions of the as yet poorly studied 400 genes unique to Candida glabrata and/or through the enhancement or acquisition of novel functions of shared genes . Our results underscore a role for siderophore utilization in the successful existence of C . glabrata as a human commensal as well as during pathogenesis , primarily with respect to survival to phagocytosis by mammalian macrophages . Furthermore , our studies support a pivotal contribution for unregulated host Fe acquisition in the outcome of fungal infection . The severe Fe restriction imposed by mammalian hosts as an antimicrobial strategy dictates that Fe acquisition is a bona fide virulence determinant . The existence of clinical correlations between elevated Fe bioavailability and increased microbial burden extend beyond diseases of Fe overload for which the higher susceptibility to bacterial infection , notably to Vibrio vulnificus , has been described [15] , [18] . In the same way , transfusion-dependent thalassemia patients present with increased susceptibility to widespread microbial infection that correlates with the degree of Fe overload [19] . Treatment of HH and thalassemia patients is further complicated by the fact that the sole FDA-approved Fe chelator desferrioxamine , a naturally occurring bacterial siderophore , effectively further solubilises Fe in a form that is readily utilized by a diversity of bacterial and fungal species , thus elevating the potential for infectious disease in these patients . Individuals with malignant hematological disorders , such as acute myeloid leukemia , who typically present with very high serum Fe , serum ferritin and transferrin saturation , are known to sustain increased mycoses , especially by Candida and Aspergilli [38] , [39] . Furthermore , clinical evidence also indicates that Fe overload is an independent risk factor for post-transplant infection [20] , [21] and correlates with poor prognosis and in patients infected with HIV , a virus known to depend on and manipulate target cellular Fe accumulation [40] . What then are the implications of Fe overload diseases to the susceptibility to fungal infections ? Previous studies support the notion that optimal immunity operates within a range of physiological Fe concentrations [41] . This is ensured in part through Fpn activity , mobilizing dietary and systemic Fe , and the regulatory action of hepcidin . Hereditary Hemochromatosis presents with different genetic etiologies that impart distinct effects on macrophage Fe levels . Using mutations associated with Ferroportin Disease , an autosomal dominant disease in humans , we provide evidence that C . glabrata dependence on the siderophore-Fe uptake machinery is inversely correlated to labile macrophage Fe levels and that Fe overloaded macrophages are markedly less efficient at killing and containing C . glabrata . We infer that this results from the inability of Fe-laden macrophages to withhold this essential metal from internalized C . glabrata cells given the decreased dependence of C . glabrata on siderophore-Fe , decreased SIT1 expression indicative of increased Fe satiety , increased C . glabrata fitness and the increased levels of host cell ferritin expression . Previous studies have shown that activated macrophages accumulate Cu within the phagosome [42] suggesting perhaps that inefficiently withheld macrophage Fe can be mobilized through reductive Fe uptake and supporting a host-dependent modulation of high-affinity mechanisms of Fe assimilation . This notion of a host modulatory action over the mechanisms utilized for microbial Fe acquisition is further evidenced in a study by Lesuisse [43] that compared the effects of exposure of C . albicans to either fetal bovine serum or synthetic Fe-deficient medium supplemented with distinct Fe sources . The results obtained showed that reductive Fe uptake from ferric citrate was downregulated in the presence of serum , as evidenced by decreased Fe uptake and reduced ferrireductase activity , whereas that from ferrichrome-type siderophores was elevated . In contrast to Fe-loaded macrophages , the low macrophage Fe levels associated with FpnC326Y expression impose Fe deficiency on phagocytosed C . glabrata cells , as demonstrated by elevated SIT1 expression and further supported by the increased dependence on ferrichrome and Sit1 for proliferation . As a result of chronic Fe-deficiency , hepcidin-insensitive macrophages are expected to represent a formidable challenge for microbial proliferation . The FPNC326Y disease-associated mutation results in a similar clinical outcome to that observed in patients carrying HFE mutations present in approximately 80% of HH patients and exhibiting an allelic frequency of over 10% in Caucasians of European descent [13] . Given the high prevalence of HFE mutations , it has been postulated that this mutation was positively selected for conferring protection during outbreaks of infections caused by intracellular pathogens such as Chlamydia and Yersinia [44] , [45] highlighting the intimate association between host Fe status and infectious disease . Whilst siderophore utilization is also undoubtedly critical to microbial colonization of healthy immunocompetent individuals in its state of commensalism , our study prompts an as yet unresolved question in the field regarding the origin of xenosiderophore substrates that are utilized by non-siderophore producers such as Candida and Cryptococcus species . A recent study by Ghannoum et al . characterizing the mycobiome of the oral cavity of healthy individuals identified in this host milieu a complex and dynamic profile of fungal species , the number of which parallels that observed for bacteria [46] . Among the fungi found to colonize the oral cavity of humans are several siderophore-producers that include Aspergillus and Fusarium species known to copiously produce siderophores that include coprogens and ferrichromes , both substrates for Sit1-facilitated utilization in C . glabrata . In particular , Aspergillus fumigatus , one of the most prevalent air-borne human pathogens , synthesizes chemically distinct siderophores for the solubilization of extracellular Fe as well as for its safe intracellular storage [47] . In fact , siderophore utilization by this saprophytic fungus has been shown to be required for virulence , in contrast to reductive Fe assimilation [48] , and the conidia of a siderophore-deficient mutant exhibit decreased levels of stored Fe and higher sensitivity to oxidative stress [47] . Given the ubiquitous nature of siderophore production and the mixed nature of the mammalian microbiome localized siderophore reservoirs are likely available to C . glabrata and other non-siderophore producers . In particular , it is tempting to speculate that the higher microbial burden associated with immunocompromised patients generates higher concentrations of microbial siderophores that might tip the balance towards the transition from commensalism to pathological C . glabrata proliferation and subsequent infectious disease . Given the inability of C . glabrata to efficiently use heme , Sit1-dependent siderophore utilization represents a likely mechanism for high-affinity Fe uptake under conditions of Cu insufficiency , conditions under which elemental high affinity Fe uptake is compromised . This suggests that siderophore utilization might contribute toward the virulence of C . glabrata in both the Fe- and Cu-restricted mammalian host [49] . Sit1 is syntenic to its orthologous gene in S . cerevisiae , Arn1 , with which it shares high sequence identity that translates into similar siderophore substrate specificity , which includes ferrichromes and coprogen . Yet , Sit1 and Arn1 differ in their subcellular localization . The routing of trans-Golgi Arn1 away from the plasma membrane and to the vacuolar protein-sorting pathway requires the monomeric clathrin adaptor , Gga2 [50] . Given the existence of a putative orthologue for Gga2 in C . glabrata , it is unclear whether differences in transporter localization reflect distinct protein-protein interactions or another mechanism of siderophore transport altogether - pore versus receptor , for instance . Interestingly , C . albicans Sit1 as well as S . cerevisiae Enb1 [7] , also localize predominantly to the plasma membrane in either the absence or presence of substrate siderophore [51] . It is tempting to speculate that under the pressure of harsh competition for Fe and local xenosiderophore reservoirs , C . glabrata and C . albicans have evolved to become more competitive for siderophore binding by expressing the transporter constitutively at the cell surface . Further studies are required to elucidate the mechanism of siderophore recognition and uptake by Sit1 as well as its intracellular fate , an aspect of siderophore utilization that is currently poorly understood in fungi .
C . glabrata strains , BG2 and BG14 ( ura3::KAN ) , were a gift from Dr . Brendan Cormack at the Johns Hopkins School of Medicine , Maryland . Growth under Fe-replete conditions was achieved by supplementing media with 5 µM ferrous ammonium sulfate . Fe-deficient media contained 100 µM bathophenantroline disulphonate ( BPS ) . For spot assays , cells were grown in synthetic complete medium ( SC ) to mid-logarithmic phase and serial dilutions were spotted on SC or SC supplemented with 100 µM BPS or 150 µM bathocuproine sulfonate ( BCS ) with or without the indicated siderophores . Ferrichrome , desferrioxamine , hemin and zinc protophorphyrin were purchased from Sigma Aldrich , St . Louis , MO; ferrirubin , coprogen , enterobactin , salmochelin , triacylfusarine C and yersiniabactin were purchased from EMC Microcollections , Tuebingen , Germany . Alexa Fluor-conjugated dextran was purchased from Invitrogen , Carlsbad , CA . For macrophage infection experiments , yeast cells cultured in low Fe medium for 5 hours were exposed or not to ferrichrome [10 µM] 30–35 minutes and the yeast cultures were washed three times with PBS prior to infection . The BG14 strain was used to generate the sit1Δ strain by the PRODIGE PCR-based approach [52] . The SIT1FLAG knock-in cassette was created by overlap PCR of the SIT1 ORF and 964 bp upstream promoter was used to generate the in-frame SIT1FLAG-fusion DNA subcloned upstream of the nourseothricin resistance ( NATR ) cassette with a further 400 bp of the SIT1 3'UTR cloned downstream of NATR . The sit1Δ::SIT1mCherry strain was generated by targeted integration of a SIT1mCherryNATR cassette cloned into pBM46 . Generation of SIT1 tagged with two FLAG epitopes between residues G542 and D543 within the carboxyl-terminal loop was generated by overlap PCR . The Sit1Y575A mutation was generated by site-directed mutagenesis ( QuikChange Site-Directed Mutagenesis Kit , Stratagene , La Jolla , CA ) and either integrated at the endogenous genomic locus or expressed episomally . For C . glabrata transformation , overnight cultures were transformed with 5 µg DNA using the Frozen-EZ Yeast Transformation II Kit ( Zymo Research , Orange , CA ) according to the manufacturers instructions . Transformants were selected by growth on YPD media containing 200 µg/ml nourseothricin ( Werner BioAgents , Jena , Germany ) at 30 °C for 2–4 days . Cells were cultured in SC containing ferrous ammonium sulfate [5 µM] in the absence or presence of BPS [80 µM] and samples harvested at the indicated time points and processed for RNA and protein extraction . Total RNA was extracted using the hot acid phenol method [53] . PCR-amplified probes were gel-purified and radiolabeled with alpha-P32-dCTP ( GE Healthcare , Piscataway , NJ ) using Random Primed DNA Labeling Kit ( Roche Applied Science , Indianapolis , IN ) . Semi-quantitative PCR was performed on cDNA generated by reverse transcription of isolated total RNA ( Superscript III first strand; Invitrogen , Carlsbad , CA ) using primers specific for SIT1 and ACT1 . SIT1 expression levels were normalized against those of ACT1 . Total protein was extracted in ice-cold lysis buffer ( Tris 25 mM , pH7 . 5 , NaCl 150 mM , 1 mM EDTA ) supplemented with protease inhibitors ( Roche complete tablet; Roche Applied Science , Indianapolis , IN ) using the Triton X-100/glass bead method and total protein quantified using BCA reagent ( Pierce , Rockford , IL ) . Sit1Flag was detected using an HRP-conjugated α-Flag antibody ( Sigma Aldrich , St . Louis , MO ) . An alpha-Pgk1 antibody was used as loading control . The sit1Δ::SIT1mCherry cells were visualized with a Zeiss Axio Image widefield fluorescence microscope . In vivo visualization of macrophages infected with C . glabrata strain sit1Δ::SIT1mCherry was performed using a Zeiss Axio Observer Z1 fluorescence microscope . Indirect immunofluorescence of Fpn was observed and captured using a motorized Zeiss Axio Observer Z1 . Cells were fixed in methanol at −20 °C for 5 minutes , incubated with Fpn antibody ( a gift from Drs . Jerry Kaplan and Ivana de Domenico , University of Utah ) and incubated with a secondary fluorochrome-conjugated antibody ( Alexa-fluor 488; Molecular Probes , Invitrogen , Carlsbad , CA ) . J774A . 1 macrophages seeded at 2×105 cells/ml were activated with 1 µg/ml LPS and 5 ng/ml LPS for 3 hours and 2 µM 2′ , 7 dichlorofluorescein diacetate ( DCFH-DA ) added during the last 15 minutes . Fluorescence was monitored 450 nm excitation and 530 nm emission using a fluorescence plate reader ( Perkin-Elmer 1420 Victor 3 ) . The mouse macrophage cell line J774A . 1 was maintained in Dulbecco's Modified Eagles Medium 4 . 5 g/L D-glucose ( Gibco , Invitrogen , Carlsbad , CA ) , 10% FBS , 100U penicillin and 100 mg/ml streptomycin , at 37 °C and 5% CO2 . For iron loading experiments , J774A . 1 macrophages were cultured in 20 µM ferric ammonium citrate for 2 days and washed 3 times with warm DMEM prior to infection with C . glabrata . Macrophages seeded at 2×105 cells/ml overnight were activated with IFN-gamma [5 ng/ml] ( Sigma Aldrich , St . Louis , MO ) and LPS [1 µg/ml] ( Sigma Aldrich , St . Louis , MO ) 3 hours prior to infection with C . glabrata strains at a multiplicity of infection of 1:4 ( macrophage:yeast ) . At the indicated time points infected macrophages were lysed with water , and dilutions of the lysates plated on YPD . Colony forming units ( CFU ) were counted after growth at 30 °C for 2 days . U937 cells were maintained in RPMI-1640 medium ( Gibco , Invitrogen , Carlsbad , CA ) , supplemented with 2 mM glutamine , 10% FBS , 100U penicillin and 100 mg/ml streptomycin , at 37 °C and 5% CO2 . Differentiation of these pro-monocytic cells into mature macrophages was induced by the addition of 10 nM phorbol myristate acetate ( PMA ) to 1×106 cells/ml for 48 hours . Cells were washed 3 times with warm culture medium and incubated for a further 48 hours at 37 °C and 5% CO2 . LPS ( 1 µg/ml ) was added to the macrophages 3 hours prior to infection with C . glabrata ( MOI of 1:1 ) and yeast cell recovery and CFU counting performed as described above . For primary macrophage infection assays , mouse femurs were kindly supplied to us by Drs . Jerry Kaplan and Ivana De Domenico , at the University of Utah . Primary bone marrow cells were harvested by flushing the femurs of 3–5 month-old female control ( C3H ) or flatiron ( ffe ) mice and macrophage differentiation induced by culturing in RPMI 1640 containing 20% FBS , 20 µM ß-mercaptoethanol , 2 mM L-glutamine , 100U penicillin and 100 mg/ml streptomycin and GM-CSF [3 ng/ml] . Macrophages were seeded for infection assays at 2×105 cells/ml . For the generation of stable cell lines expressing wild type and mutant forms of FPN , full-length mouse FPN cDNA ( TrueORF , OriGene , Rockville , MD ) was used as a template to introduce the H32R and C326Y mutations through site-directed mutagenesis . FPN , FPNH32R and FPNC326Y plasmid DNA were stably transfected into J774A . 1 macrophages using Lipofectamine reagent ( Invitrogen , Carlsbad , CA ) . Total protein was extracted in lysis buffer as described above supplemented with 0 . 1% SDS and a phosphatase inhibitor cocktail ( HALT , Pierce , Rockford , IL ) . Antibodies against ferritin and α-tubulin were purchased from Abcam ( Cambridge , MA ) . IL-6 and TNF-alpha secretion was measured by enzyme-linked immunosorbent assay ( ELISA ) ( R&D Systems , Minneapolis , MN ) . Fluorescence was measured using a SpectraMax Plus luminometer ( Molecular Devices , Sunnyvale , CA ) . The SIT1 DNA , protein sequence and functional description were deposited in GenBank with the Accession Number HQ734814 . | Candida glabrata is a major human pathogen due to its low susceptibility to conventional antifungal drugs and the dramatic increase in the number of immunocompromised individuals suffering from HIV AIDS , cancer , and diabetes . Iron overload is one of the most common genetically inherited diseases and reports suggest increased susceptibility of these patients to bacterial infection . The ability of microorganisms to obtain iron from their environment is a major determinant in their fitness and hence in their ability to cause infectious disease . Here we demonstrate that the siderophore iron carrier is critical for C . glabrata survival after ingestion by mouse and human macrophage immune effector cells . Through the generation of macrophage models of human iron overload disease we demonstrate that ingested C . glabrata cells sense altered macrophage iron levels , and that the Sit1 siderophore-iron transporter functions as a critical determinant in the ability of C . glabrata to survive macrophage killing in a manner that is dependent on macrophage iron status . Our results reveal a role for siderophore-iron as a source of iron during C . glabrata infection , suggest additional therapeutic intervention strategies , and support a pivotal contribution for a common human iron overload disease in the mechanisms used for Fe acquisition in C . glabrata . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"infectious",
"diseases/nosocomial",
"and",
"healthcare-associated",
"infections",
"microbiology/immunity",
"to",
"infections",
"infectious",
"diseases/fungal",
"infections",
"microbiology/innate",
"immunity",
"cell",
"biology/microbial",
"growth",
"and",
"development",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/medical",
"microbiology"
]
| 2011 | Host Iron Withholding Demands Siderophore Utilization for Candida glabrata to Survive Macrophage Killing |
Pulmonary infections are a major global cause of morbidity , exacerbated by an increasing threat from antibiotic-resistant pathogens . In this context , therapeutic interventions aimed at protectively modulating host responses , to enhance defence against infection , take on ever greater significance . Pseudomonas aeruginosa is an important multidrug-resistant , opportunistic respiratory pathogen , the clearance of which can be enhanced in vivo by the innate immune modulatory properties of antimicrobial host defence peptides from the cathelicidin family , including human LL-37 . Initially described primarily as bactericidal agents , cathelicidins are now recognised as multifunctional antimicrobial immunomodulators , modifying host responses to pathogens , but the key mechanisms involved in these protective functions are not yet defined . We demonstrate that P . aeruginosa infection of airway epithelial cells promotes extensive infected cell internalisation of LL-37 , in a manner that is dependent upon epithelial cell interaction with live bacteria , but does not require bacterial Type 3 Secretion System ( T3SS ) . Internalised LL-37 acts as a second signal to induce inflammasome activation in airway epithelial cells , which , in contrast to myeloid cells , are relatively unresponsive to P . aeruginosa . We demonstrate that this is mechanistically dependent upon cathepsin B release , and NLRP3-dependent activation of caspase 1 . These result in LL-37-mediated release of IL-1β and IL-18 in a manner that is synergistic with P . aeruginosa infection , and can induce caspase 1-dependent death of infected epithelial cells , and promote neutrophil chemotaxis . We propose that cathelicidin can therefore act as a second signal , required by P . aeruginosa infected epithelial cells to promote an inflammasome-mediated altruistic cell death of infection-compromised epithelial cells and act as a “fire alarm” to enhance rapid escalation of protective inflammatory responses to an uncontrolled infection . Understanding this novel modulatory role for cathelicidins , has the potential to inform development of novel therapeutic strategies to antibiotic-resistant pathogens , harnessing innate immunity as a complementation or alternative to current interventions .
Pulmonary diseases caused by bacterial or viral infections are a common cause of morbidity and account for 1 in 5 deaths in the UK [1] . There is an increasing global threat from antibiotic-resistant bacterial infections and newly emerging viral infections . In this context , therapeutic interventions aimed at protectively modulating host responses , to enhance defence against infection , take on greater significance as alternative or complementary future approaches . An important component of first line defences against such infections is the innate immune response of airway epithelial cells , which constitute the principal barrier first encountered by respiratory pathogens . These innate epithelial cell responses include the secretion of antimicrobial host defence peptides ( HDP ) [2 , 3] , and the release of chemokines and cytokines to help orchestrate the response of other immune effector cells . HDP are produced by innate immune effector cells in response to infection , damage and inflammation [4] Understanding their effect on epithelial cell responses to infection is important in elucidating the potential of HDP as targets for development of future antimicrobial interventions . HDP are evolutionarily conserved short cationic peptides with direct and indirect antimicrobial activity , produced by a wide range of cells , including neutrophils , macrophages and epithelial cells . In addition to their well-characterised microbicidal activity against bacteria , viruses and fungi [4 , 5] , HDP can have a wide range of immunomodulatory and inflammomodulatory properties [4 , 6] . The cathelicidin family of HDP , including the sole human cathelicidin LL-37/h-CAP18 , has a particularly broad range of modulatory properties and has been shown to be critical to host defence against infections in vivo in a wide range of systems [7] , including bacterial and viral pulmonary infections [8–10] . Cathelicidin can promote clearance of bacterial lung infection in vivo specifically by inflammomodulatory mechanisms , in the absence of susceptibility of the pathogen to direct peptide-mediated killing [8] . However , the mechanisms employed to alter the nature and/or efficacy of host responses to bacterial lung infection in vivo remain unclear . HDP , including cathelicidin , have been shown to modulate innate pattern recognition receptor ( PRR ) responses in host cells , including airway epithelial cells [11–13] . This raises the possibility that HDP-mediated changes to the earliest responses after epithelial cell detection of infection could dramatically alter the effectiveness of the early host defence and outcome of infection . PRRs can function extra- and intracellularly , and include Toll-like Receptors ( TLR ) , NOD-like receptor ( NLR ) , C-type lectin receptors and RIG-I receptors [14 , 15] . These receptors can recognise highly conserved pathogen associated molecular patterns ( PAMPs ) to initiate innate responses to infectious non-self threats , and are critical to effective host defence against infection . In addition to their capacity to modulate TLR signaling , cathelicidins have also been suggested to activate inflammasomes [16 , 17]; cytoplasmic multi-protein sensing platforms formed by some NLRs to respond to PAMPs [18] . Inflammasome activation can lead to activation of the cysteine protease caspase 1 , processing and release of pro-inflammatory cytokines IL-1β and IL-18 , and causing a pro-inflammatory form of cell death , termed “pyroptosis” in macrophages . These responses would be expected to promote an inflammatory response , but their significance in airway epithelial cells is not well understood . Mechanisms that promote an early and/or enhanced inflammatory response to infection have the potential to stimulate early pathogen clearance and resolution , when appropriately regulated . Our previous studies demonstrated that cathelicidin , whether infection-induced endogenous peptide , or early therapeutically administered peptide , promoted murine pulmonary clearance of Pseudomonas aeruginosa [8] . This was the result of cathelicidin-mediated upregulation of early neutrophil recruitment and generation of an enhanced pro-inflammatory response to infection . However , the mechanisms underlying initiation of enhanced inflammomodulatory clearance of this multi-resistant , opportunistic respiratory pathogen remained unknown . We have also demonstrated that cathelicidin modified airway epithelial cell responses to P . aeruginosa by inducing cell death of infected cells [19] , potentially removing a safe niche for these pathogens in the lung . Here we focus on determining the mechanisms that underpin cathelicidin-mediated modulation of airway epithelial cell responses to P . aeruginosa infection . We show that P . aeruginosa-infected airway epithelial cells preferentially internalise high levels of LL-37 , resulting in lysosomal leakage and cathepsin B-mediated , NLRP3-dependent , activation of caspase-1 . These epithelial cells release IL-1β and IL-18 and promote neutrophil influx . This represents a novel mechanism , contrasting significantly with processes in myeloid cells , by which a host defence peptide can provide the second signal required by infection-compromised epithelial cells to promote an inflammasome-mediated protective inflammatory response .
We previously demonstrated that therapeutic airway administration of the human cathelicidin LL-37 , in a murine model of pulmonary infection with P . aeruginosa strain PAO1 , promoted pathogen clearance [8] . This enhanced host defence occurred in the absence of any microbicidal effect of LL-37 in the infected lung , by enhancing neutrophil infiltration . Similarly , induction of endogenous murine cathelicidin was essential for an optimal neutrophil response in this pulmonary infection model , and for effective clearance [8] . Importantly , this cathelicidin-mediated protective pro-inflammatory response only occurred in the presence of the bacteria , demonstrating an interaction between infection and HDP-mediated modulation of host defence . However , no cathelicidin-mediated differences in the pulmonary cytokine responses to infection assessed in that study were found . Pulmonary infection with P . aeruginosa in this murine model leads to extensive exposure of the airway epithelium to this pathogen . P . aeruginosa can bind and invade ( or be internalized by ) airway epithelial cells , and induce inflammatory responses [20–23] . Our previous demonstration that LL-37 can modulate the response of airway epithelial cells to P . aeruginosa infection [19] therefore prompted us to investigate whether these cells could be responsible for enhanced neutrophil recruitment . An in vitro assay was established to analyse the neutrophil chemotactic properties of conditioned media , containing soluble mediators released by primary human bronchial epithelial ( NHBE ) cells that had been treated for 3 hours with LL-37 and P . aeruginosa strain PAO1 or controls . This timepoint was selected to examine the early responses of epithelial cells interacting with an invading pathogen , well in advance of any epithelial cell death , and because significant LL-37-enhanced neutrophil influx into the Pseudomonas aeruginosa-infected mouse lung had been observed to be well established by 6 hours after infection with LL-37 exposure in vivo , implying an early change in signalling responses [8] . Peripheral blood neutrophils , isolated from healthy human donors , were allowed to migrate through a ChemoTx migration chamber towards conditioned media and subsequently quantitated . Significantly more neutrophils migrated towards conditioned media from PAO1-infected NHBE cells exposed to LL-37 , than to media from infected cells exposed to a scrambled control LL-37 peptide ( Fig 1A and 1B ) . This was also significantly greater than the responses to media conditioned by NHBE cells exposed to PAO1 or LL-37 alone ( which were not significantly above background control level ) and greater than the sum of these conditions , demonstrating a synergistic effect compatible with our in vivo observations [8] . These data demonstrate that LL-37 can modulate airway epithelial cell responses to P . aeruginosa infection to induce mediators capable of promoting neutrophil migration to the site of infection . To evaluate the differences between conditioned media from PAO1-infected NHBE cells treated with LL-37 and those only infected , or only treated with LL-37 ( or exposed to scrambled LL-37 ) , protein microarrays and confirmatory ELISAs were conducted . Whereas no significant difference was seen between the different samples in the levels of IL-8 by ELISA ( Fig 2A ) , or levels of CXCL1/KC , IL-1α , IL-1ra , MIF , or Serpin E1 in protein microarrays ( S1 Table ) , both IL-1β and IL-18 were induced by 3 hours of treatment with LL-37 , and secreted at higher levels in LL-37-treated infected cells than in cells treated with LL-37 or PAO1 alone ( Fig 2B and 2C ) . Induction of IL-1β was significantly higher in response to combined stimuli , and greater than the sum of either stimuli individually , with PAO1 alone unable to induce an IL-1β response from these cells . Scrambled LL-37 had no effects , demonstrating the specificity of the LL-37 peptide . These data demonstrate that LL-37 exposure can promote the release of the pro-inflammatory cytokines IL-1β and IL-18 by airway epithelial cells during P . aeruginosa infection , with the potential to induce cellular inflammatory host responses . Previous studies have demonstrated that cathelicidin can promote the release of IL-1β in myeloid cells , in which IL-1β transcription has been induced by pre-treatment with LPS [16 , 17] . To confirm these previous observations in our systems , primary human blood-derived monocytes were isolated , pretreated with LPS , and exposed for 3 hours to LL-37 or ATP ( as a positive control ) . As previously described , LL-37 significantly induced the release of IL-1β , although it was a log less effective than ATP ( Fig 3A and 3B ) . However , in contrast to the previous proposal that LL-37 might act directly as an agonist of P2X7 receptor ( P2X7R ) [16] , inhibition of P2X7R had no significant impact on LL-37-mediated induction of IL-1β ( Fig 3C ) . This was in contrast to significant inhibition of ATP-mediated IL-1β release . To confirm this observation , LPS pre-treated peritoneal macrophages from wild type or P2X7R-deficient mice were exposed to LL-37 ( Fig 3D and 3E ) . Exposure to LL-37 induced IL-1β release from both wild type cells and P2X7R-deficient cells , which ( as expected ) did not respond to ATP . These data confirm the capacity of LL-37 to promote IL-1β by myeloid cells , but indicate a P2X7R-independent mechanism . Having confirmed the capacity of LL-37 to induce myeloid cell production of IL-1β , we then evaluated the responses to P . aeruginosa PAO1 and LL-37 in myeloid cells , as a parallel to the conditions applied in the airway epithelial cell experiments . Primary human blood monocyte-derived macrophages ( MDM ) , pre-treated with LPS , expressed IL-1β in response to positive control treatment with ATP , as expected ( Fig 3F ) , although at lower levels than primary monocytes ( Fig 3A ) . Concomitant addition of LL-37 did not further enhance this response . Further treatments were conducted in the absence of LPS pre-treatment , to model the response to live bacteria . Under these conditions , 3 hours after PAO1 infection alone , but not LL-37 alone , exposure had promoted a significant release of IL-1β ( Fig 3F ) . This was not enhanced by the concomitant addition of LL-37 . Assessment of caspase-1 activation , as the driver of IL-1β release , was then undertaken using a live cell fluorescent probe ( FLICA ) . Caspase-1 activation in MDM mirrored the release of IL-1β ( Fig 3G ) , with ~35% of LPS pre-treated cells showing activated caspase-1 after 3 hours exposure to ATP , and ~70% of cells activating caspase-1 in response to a 3 hour infection with live PAO1 . Responses were not affected by the presence of LL-37 in these cells . P . aeruginosa infection is known to activate the inflammasome in macrophages via NLRC4 detection of flagellin and the type-3 secretion system inner-rod protein [24–26] . Involvement of Pseudomonas with NLRP3 is less widely reported , but it has been shown to cause activation of NLRP3 to trigger autophagy in macrophages [27] , and was shown to activate NLRP3 via mitochondrial perturbation in airway epithelial cells expressing mutant CFTR [28] . More recently , NLRP3 activation was also described in macrophages during longer term ( 16 hour ) infection by Pseudomonas via the action of guanylate binding proteins [29] . Thus , to evaluate whether MDM and NHBE cell types both had the required components to facilitate equivalent responses , Q-RTPCR was performed to examine transcription of the genes encoding NLRC4 , NLRP3 and caspase-1 and -4 ( Fig 3H ) . Whereas NLRP3 and caspase-1 and -4 were expressed at equivalent levels , no expression of NLRC4 was detected in NHBE cells , in contrast to MDM . NLRC4 was also undetectable in the bronchial epithelial cell line 16HBE14o- ( S1 Fig ) . This absence of NLRC4 expression in bronchial epithelial cells may explain the minimal IL-1β responses of these cells to P . aeruginosa alone ( Fig 2B ) . Taken together these data suggest that macrophages may be maximally stimulated by infection with P . aeruginosa , but that airway epithelial cells may need an additional signal to respond . This suggested that the differential in vivo host responses to pulmonary infection with P . aeruginosa in the presence and absence of cathelicidin is more likely related to epithelial cell responses . Therefore , subsequent studies focused on epithelial cells . The FLICA probe was next used to confirm that the pattern of caspase-1 activation in NHBE cells recapitulated the IL-1β responses . Whereas treatment with LL-37 alone , for 3 hours , caused activation of caspase-1 in a small ( ~7% ) proportion of NHBE cells , P . aeruginosa PAO1 had a very limited effect ( Fig 4A ) , in agreement with previous studies on lung epithelial responses to Pseudomonas [30] . However , in the presence of LL-37 , a 3 hour infection with PAO1 led to an increase in caspase-1 activated NHBE cells ( to ~16%; Fig 4A ) and 16HBE14o- cells ( S2 Fig ) , significantly greater than to LL-37 or PAO1 alone , with the same synergistic pattern observed as for IL-1β release . These responses were a striking contrast to the responses of MDMs ( Fig 3G ) , and suggested the possibility of differential ability to respond to these stimuli . In further contrast to MDM ( Fig 3G ) , LPS pre-treated primary airway epithelial cells did not significantly activate caspase-1 in response to ATP ( Fig 4A ) . However , interestingly , LL-37 also potentiated the responses to ATP in these cells , in manner similar to that observed for P . aeruginosa . These data suggest that cathelicidin may potentiate inflammasome activation in response to stimuli that are suboptimal activators in airway epithelial cells . Having previously showed that , over a longer time frame , LL-37 preferentially induced cell death in cells infected with P . aeruginosa PAO1 , in the 16HBE14o- airway epithelial cell line [19] , we re-examined this model in light of our new data . Significant LL-37-mediated enhancement of cell death in PAO1-infected cells , assessed by TUNEL staining at 6 hours after infection , also showed a synergistic pattern ( Fig 4B ) . LL-37 treatment with PAO1 infection induced significantly greater cell death ( ~30% ) than in cells treated with LL-37 alone , or those infected without cathelicidin . This could be blocked by the caspase-1 inhibitor , YVAD-CHO , reducing cell death to the level observed in LL-37-only treated cells . Having determined that the synergistic component of this epithelial cell death was capase-1 dependent , a live cell fluorescent probe was used to quantify activation of the apoptosis-inducing caspases-3 and -7 , preceding cell death . Low level caspase 3/7 activation was induced in response to stimuli ( Fig 4C ) . However , in contrast to the induction of cell death , the proportion of cells in which these caspases were activated was equivalent in cells irrespective of whether they had received 3 hours exposure to LL-37 or PAO1 alone , or LL-37 exposure concomitant with PAO1 infection ( Fig 4C ) . Using live cell fluorescent probes to evaluate both caspase-1 and caspase-3/7 in the same cells , showed that activation of these caspases was mutually exclusive in cells , with classical condensed morphology seen in apoptotic caspase-3/7 positive cells , and intense caspase-1 foci seen in FLICA positive cells , but no double positive cells seen ( Fig 4D ) . These data demonstrate that cathelicidin-mediated capase-1 activation underpins the LL-37-induced death of P . aeruginosa-infected cells that we have previously observed [19] . To determine the mechanism by which LL-37 could activate caspase-1 in airway epithelial cells , previously identified potential receptors for LL-37 were examined . Neither inhibition of FPRL1 ( Fig 5A ) , nor inhibition of P2X7R ( Fig 5B ) , by pre-incubation with WRW4 and KN-62 respectively , were able to inhibit LL-37-mediated activation of caspase-1 during PAO1 infection . The latter data are compatible with our evidence for a P2X7R-independent mechanism of LL-37-mediated IL-1β release in MDM . These findings suggested that modulation of airway epithelial cell caspase-1 activation is mediated by mechanisms other than the classically described LL-37 receptors . To further characterise the nature of this interaction , the requirement for bacterial secreted factors and/or live bacteria was examined . Substitution of heat-killed PAO1 , or bacterial supernatant , for live PAO1 , did not replicate the LL-37-enhanced caspase-1 activation ( Fig 5C ) . Although , levels of caspase-1 activation in these cells increased in the presence of LL-37 , this was no greater than the response to the LL-37 alone control . However , live PAO1 genetically modified to delete the T3SS operon [31] , which renders this Pseudomonas strain incapable of invading the cell , retained this function . These data indicate that secreted factors are not sufficient , and that components of the bacterial T3SS are not required , but detection of live P . aeruginosa by the epithelial cells is sufficient for synergistic activation of capase-1 by LL-37 . Pseudomonas can attach to epithelial cell surfaces and can invade ( or be internalized by ) epithelial cells [21 , 23 , 32 , 33] . Timelapse imaging demonstrated cell-associated aggregates of bacteria rapidly accumulated in infected epithelial cell cultures ( Fig 5D; S3 Fig ) . Subsequent addition of TAMRA-labelled LL-37 to these cultures , 30 min after infection ( time “0” ) , showed the peptide steadily accumulating inside cells associated with large P . aeruginosa aggregates ( white arrows ) . Using confocal microscopy of live GFP-labelled PAO1 , entry of bacteria into the epithelial cells was observed ( Fig 5E , white arrow heads ) with others accumulating on the surface . Extensive accumulation of TAMRA-LL-37 throughout the cytoplasm ( white arrows; Fig 5D ) only occurred in cells associated with PAO1 aggregates , and those cells were found to have ~4-fold greater level of associated PAO1 aggregates than cells in which LL-37 had not accumulated ( S4 Fig ) , demonstrating an association between magnitude of infection and high level accumulation of intracellular LL-37 . We separately confirmed that the PAO1-infected epithelial cells were not dead prior to addition of LL-37 , using the aqueous live cell marker Sytox Green . Sytox Green remained excluded from cells at one hour after infection , at which point TAMRA-LL-37 was added . Subsets of cells were then observed to initially accumulate TAMRA-LL-37 at their membrane , followed by extensive LL-37 uptake into the cytoplasm and Sytox Green positivity ( S5 Fig ) . This may not indicate the onset of cell death , as LL-37 has been shown to enable entry of various materials ( including nucleic acid stains and labelled DNA ) across the membrane of live cells [16 , 34] . However , these experiments demonstrate that the data are not simply LL-37 accumulating non-specifically in dead cells , subsequent to any early bacterially-induced cytotoxicity . To examine the consequence of this , GFP-PAO1-infected , live NHBE cells , exposed to TAMRA-LL-37 , were also stained for caspase-1 activation , using a biotin-labelled caspase-1 inhibitor YVAD , followed by brilliant violet-labelled streptavidin ( Fig 5E ) . Confocal imaging showed that caspase-1 activation occurred in the cells with the most bacterial infection and uptake of LL-37 . Taken together , these data demonstrate that LL-37 preferentially accumulates inside airway epithelial cells most compromised by infection with live bacteria , and leads to activation of caspase-1 by a P2X7R and FPRL-1 independent mechanism . To investigate the mechanism by which intracellular LL-37 activated caspase-1 in airway epithelial cells , we examined the importance of NLRP3 , previously implicated in LL-37-mediated inflammasome activation in neutrophils [17] . The addition of the NLRP3-inhibiting , potassium ion channel blocker Tetraethylammonium chloride ( TEA ) , to PAO1-infected , LL-37-treated NHBE cultures was found to significantly inhibit peptide-mediated activation of caspase-1 in infected cells ( Fig 6A ) . This was reduced to levels similar to that observed with exposure to LL-37 alone , suggesting a role for NLRP3 in the synergistic induction phenotype . To test the involvement of NLRP3 directly , NLRP3 expression was genetically modified in the 16HBE14o- cell line using both siRNA knock-down ( Fig 6B ) and CRISPR-mediated knock-out ( Fig 6C and 6D ) airway epithelial models . NLRP3 siRNA transfection in 16HBE14o- cells resulted in significantly lower levels of caspase-1 activation in PAO1-infected LL-37-treated cells ( Fig 6B ) . To definitively confirm these data , knock-out 16HBE14o- cell lines were generated by CRISPR/Cas9 . Two separate mutant lines , B9 and G6 ( both confirmed as single base pair insertions causing a non-sense frameshift ) were unable to significantly upregulate caspase-1 activation above background stimulation levels upon LL-37 exposure of PAO1-infected cells ( Fig 6C ) . These NLRP3 knock-out cells were shown by western blot to have complete absence of NLRP3 protein ( Fig 6D ) . These data demonstrate that NLRP3 is required for inflammasome-mediated synergistic activation of caspase-1 by uptake of LL-37 into P . aeruginosa infected airway epithelial cells . LL-37 is known to interact with cell membranes; permeabilising bacterial outer membranes [35 , 36] and the membranes of apoptotic mammalian cells [37 , 38] . LL-37 has also been shown to traffic into human airway epithelial cells [39] , and specifically to lysosomes and endosomes in THP-1 cells [40] . The release of enzymes cathepsin B and cathepsin D from compromised lysosomal compartments have been shown to induce inflammasome activation [41 , 42] . Thus , the extent to which LL-37 could induce lysosomal disruption in infected airway epithelial cells was examined . Loading of NHBE cells with fluorescently labelled 10 kDa Dextran resulted in punctate accumulation of fluorescent signal in lysosomes ( Fig 7A ) . Subsequent infection of these cells with PAO1 , and exposure to TAMRA-labelled LL-37 , resulted in dextran leakage from the lysosomes of a subset of epithelial cells , evidenced by marked diffusion of the fluorescent signal throughout the cell cytoplasm ( Fig 7A , white arrows ) . Visualisation of the TAMRA-labelled LL-37 localisation , demonstrated that the cells with lysosomal leakage were those cells preferentially internalising LL-37 during infection ( Fig 7A; red cells ) . Where cells did not internalise LL-37 , the punctate lysosomal location of the dextran remained intact . Quantification of the percentage of epithelial cells with lysosomal leakage ( Fig 7B ) , demonstrated that this was significantly greater in infected cells exposed to LL-37 , when compared to LL-37 alone or PAO1 infection alone , recapitulating the pattern for caspase-1 activation . To evaluate the significance of leakage of lysosomal cathepsin B and D , inhibitors CA-074-Me [43] ) and Pepstatin A [44] were used . Whereas inhibition of cathepsin B significantly inhibited the synergistic induction of caspase-1 activation in PAO1-infected primary NHBE cells ( Fig 7C ) and 16HBE14o- cell ( S6 Fig ) exposed to LL-37 , inhibition of cathepsin D did not . Taken together , these data demonstrate that intracellular LL-37 in PAO1-infected airway epithelial cells , leads to NLRP3 inflammasome-mediated capase-1 activation via induction of lysosomal leakage and the release of cathepsin B . To determine the physiological relevance of lysosomal dependent activation of caspase-1 in these cells , the cathepsin B inhibitor was applied to the initial neutrophil migration assay detailed in Fig 1 . NHBE cells were pre-incubated with CA-074-Me cathepsin B inhibitor , before infection with PAO1 and concomitant exposure to LL-37 , after which conditioned media was generated and filtered . Inhibition of cathepsin B significantly reduced the level of IL-1β release by LL-37-treated infected cells , to the level of those treated with LL-37 alone ( Fig 7D ) . Application of these conditioned media to the primary human neutrophil migration assay , demonstrated that cathepsin B inhibition blocked LL-37-promoted production of neutrophil chemotactic factors by PAO1-infected airway epithelial cells . ( Fig 7E ) . These data demonstrate that the enhanced neutrophil chemotactic capacity of airway epithelial cell conditioned media , results from LL-37-mediated activation of the NLRP3 inflammasome and caspase-1 in P . aeruginosa-infected cells , operating via a mechanism of lysosomal leakage of cathepsin B ( Fig 8 ) .
Pseudomonas aeruginosa is an important , opportunistic , multidrug-resistant human pathogen . It is associated with a wide range of serious acute and chronic infections , including ventilator-associated pneumonia and sepsis syndromes , and is the predominant pulmonary infection leading to fatal deterioration of lung function in patients with CF [45] . New interventional approaches for the treatment of P . aeruginosa are urgently needed in the context of the increasing global threat of antimicrobial resistance , and may be informed by a greater knowledge of effective innate host defence . Cathelicidins are a vital , non-redundant antimicrobial host defence peptide component of innate host defence , necessary for in vivo protection against infections of the lung , skin , intestinal tract , urinary tract and eye ( reviewed in [7] ) . We have previously demonstrated that innate antimicrobial host defence peptides of the cathelicidin family can enhance the clearance of pulmonary P . aeruginosa in vivo by amplifying the protective neutrophilic inflammatory response [8] . However , the mechanism by which cathelicidin promotes protective pulmonary inflammation in the context of an infectious threat , but not when delivered to the quiescent lung , remained unclear . Here we describe a novel mechanism to promote innate host defence against P . aeruginosa infection , mediated by a modulatory function of the cathelicidin LL-37 upon infected airway epithelial cells . Unless rapidly cleared by professional phagocytes , P . aeruginosa entering the lung will predominantly encounter airway epithelial cells , to which this pathogen can adhere and invade , or be internalised; a process that could be a pathogenic mechanism to avoid host-mediated killing , or a host defence mechanism [21 , 23 , 32 , 33 , 46–48] . Under permissive conditions , this can create a safe niche for survival and intracellular proliferation of the bacteria in compromised epithelial cells [47] . However , the release of danger signals , coupled with altruistic cell death to remove a small number of infected cells which have internalised multiple organisms , could protect the host [19 , 48 , 49] . In contrast to professional phagocytes , a relatively quiescent epithelium is desirable , unless the initial host defence has been overwhelmed and the epithelial barrier is compromised by infection . In this context , a requirement for multiple signals to licence significant epithelial cell induction of a robust neutrophilic response in the lung may be a necessary protective safeguard . We demonstrate that P . aeruginosa infection of airway epithelial cells in vitro does not activate the inflammasome without an additional signal , perhaps due to an absence of NLRC4 expression . However , infection in the presence of cathelicidin promotes caspase-1 activation , release of IL-1β and IL-18 , and the influx of neutrophils . Whether the key source of this cathelicidin is activated local macrophages or epithelial cells ( with potential responses to cathelicidin produced by the infected epithelial cell itself , or by its neighbours ) , or degranulating neutrophils entering the site in an escalating inflammatory response in the lung , remains to be determined . An additional limitation of this study was the use of submerged airway epithelial cell cultures . While both undifferentiated primary cells and an immortalised cell line showed the same responses to stimuli , we cannot exclude the possibility that cells differentiated at air-liquid interface could respond differently . Regardless , the synergistic activity of infection and peptide exposure in these cells was found to be mechanistically dependent upon interaction of the epithelial cells with live P . aeruginosa , but not dependent upon T3SS , and to be dependent upon cathelicidin-mediated release of cathepsin B from lysosomes or phagolysosome in infected cells , inducing activation of the NLPR3 inflammasome . The process by which infection increased the uptake of LL-37 into those compromised epithelial cells , remains the focus of ongoing work . In this regard , the significance of Pseudomonas-induced ceramide-enriched lipid platforms , will be of interest; having critical roles in pathogen internalisation , NF-κB activation and IL-1β transcription in response to the infection [46 , 50] . Once inside the cell , LL-37 has been shown to traffic to lysosomes and endosomes in THP-1 and epithelial cells [39 , 40] , is known to localise in and cross cellular membranes , and can induce pore formation in apoptotic cell membranes [37 , 38] . Release of cathepsin B and cathepsin D from compromised lysosomal compartments has been shown to induce the activation of inflammasomes [41] . In addition , the cathepsin B inhibitor CA-074-Me has been shown to inhibit activation of the NLRP3 inflammasome [43] . In our studies , the effects of LL-37 were blocked by a cathepsin B inhibitor , with no impact of cathepsin D inhibition . Although inflammasome activation was required for the synergistic danger response observed in infected , peptide-treated cells , parallel effects of cathelicidin may also contribute to this process . In that regard , it is interesting to note that LL-37 also potentiated airway epithelial cell activation of caspase-1 in response to ATP , to which , in contrast to myeloid cells , airway epithelial cells were largely unresponsive . Given this apparent capacity to potentiate suboptimal inflammasome-activating signals in airway epithelial cells , the impact upon infection with other , clinical isolates of P . aeruginosa , and potentially also infections with other bacteria and/or viruses , will be of great future interest . Regardless , this synergistic activation of inflammasome-mediated neutrophilic inflammation is compatible with our in vivo observations; in which endogenous cathelicidin was required for maximal neutrophil-mediated inflammation , and therapeutic delivery of LL-37 enhanced neutrophil responses to P . aeruginosa infection [8] . It is also worth noting that while LL-37 alone was capable of inducing low level NLRP3-independent caspase-1 activation in our epithelial cell studies ( Fig 6 ) , this was not sufficient to promote significant neutrophil chemotaxis ( Fig 1 ) . Similarly , delivery of LL-37 alone ( in the absence of infection ) to the murine lung did not induce pulmonary neutrophil influx in vivo [8] . Our new in vitro data now justify future mechanistic in vivo research to examine the impact of cathelicidin on release of IL-1β and IL-18 responses in murine models of pulmonary P . aeruginosa infection , to determine the factors modified by induction of these cytokines to act as the direct chemokines for neutrophils in our in vitro and in vivo models , and the effect of NLRP3 deficiency on the efficacy of therapeutic administration of cathelicidin . Inflammasomes have been widely investigated in immune effector cells , particularly macrophages , but are less well studied in epithelial cells . Nevertheless , recent research has shown the biological importance of epithelial cell inflammasome-mediated responses against pathogens ( summarised in [51] ) . Of particular significance , studies in the gut have shown that inflammasome activation and pyroptosis are important in host defence against Salmonella infection , helping to clear pathogens by removing an intra-cellular replication niche [52] . Although LL-37-mediated cell death in our P . aeruginosa infected airway epithelial cells was caspase-1 dependent , the role of non-canonical pathways and gasdermin-D in this system remain to be determined . It is therefore unclear whether this cell death should be described as a form of epithelial cell pyroptosis . Furthermore , the relative significance of this , versus upregulated neutrophil responses , to the cathelicidin-enhanced protection against pulmonary P . aeruginosa in vivo remains to dissected in future studies of lung infection models . Evidence suggests that inflammasome activation can have significant consequences of pulmonary diseases [53 , 54] , but the relative impacts of acute , potentially essential , protective activation , versus inappropriate , potentially damaging , chronic activation remains to be fully elucidated . Despite this , it is clear that early innate immune responses of airway epithelial cells constitute an important component of first line of defense against respiratory disease , and , compatible with our data , lung epithelial cells can secrete IL-1β and IL-18 to induce the inflammatory response [51] . Although not of the same magnitude as myeloid cells in our studies , the combined effect of epithelial cell cytokine production at sites of infection may be pivotal . Furthermore , in addition to its capacity to directly stimulate the production of chemokines by peripheral blood monocytes and lung epithelial cells [55 , 56] , LL-37 can also synergistically enhance IL-1β-mediated production of cytokines and chemokines [57] . Similarly to the properties described in our manuscript , this potentiation was observed using concentrations of cathelicidin observed in the inflamed the human lung in vivo [58] , suggesting a mechanism for further enhancement of the impact of the cathelicidin-mediated induction of IL-1β described herein , in the context of pulmonary inflammation . In conclusion , we therefore propose that cathelicidin-mediated NLRP3 inflammasome activation in infected airway epithelial cells represents activation of a “fire-alarm”; triggered by escalating pulmonary inflammation to an overwhelming threat that necessitates epithelial-generated enhanced neutrophilic inflammation and potentially the altruistic death and removal of compromised infected epithelial cells . This represents a novel modulatory function of an important innate antimicrobial host defence peptide , with the potential to open opportunities for the development of HDP-based therapeutics ( or inducers ) that can combine microbicidal activity with immunomodulatory / “innate immune adjuvant” function . Such approaches aim to harness the most effective , evolutionary-tested successes of the innate immune system as alternative or complementary approaches to our conventional therapies , avoiding issues of rapid generation of resistance by promoting pathogen clearance indirectly by modulatory properties of the HDP . This “immunomodulatory antimicrobials” approach has the potential to revolutionise our strategies to infectious diseases in a manner that could parallel the recent successes seen in cancer immunotherapy , and is of high significance in the context of the threat of antibiotic-resistance .
For leukocytes isolated from healthy volunteer blood , informed written consent was obtained from all subjects , AMREC reference no . 15-HV-013 . All animal experiments were carried out in accordance with the United Kingdom Animals ( Scientific Procedures ) Act 1986 and approved by the Home Office and the local Animal Ethical Review Group , University of Manchester , project license number 403076 . LL-37 ( LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES; MW 4493 . 33 ) and scrambled LL-37 control peptide ( ScrLL-37 ) ( RSLEGTDRFPFVRLKNSRKLEFKDIKGIKREQFVKIL ) were custom synthesised by Almac ( East Lothian , Scotland ) using Fmoc solid phase synthesis and reversed phase HPLC purification . Peptide identity was confirmed by electrospray mass spectrometry , purity ( >95% area ) by RP-HPLC and net peptide content determined by amino acid analysis . Lyophilised peptides were reconstituted in endotoxin free water at 5 mg/ml stock concentration , determined to be endotoxin-free using a Limulus Amebocyte Lysate Chromogenic Endotoxin Quantitation Kit ( Thermo Scientific , UK ) , and stored at -20 °C . Peptide functionality was confirmed by assessing anti-endotoxic activity . IL-1beta antibody was from R&D systems and used at 100 ng/ml , anti-NLRP3/NALP3 antibody was used at 1:500 dilution , from Caltag Medsystems ( Buckingham , U . K . ; Cat . No . AG-20B-0014-C100 ) ; anti-Beta-Actin antibody was used at 1:2000 , from Sigma Aldrich ( Merck group , Darmstadt , Germany; Cat . No . A1978 ) . Caspase 1 inhibitor YVAD-CHO and the Formyl Peptide Receptor-Like 1 antagonist WRW4 were from Calbiochem ( Merck group , Darmstadt , Germany ) . Cathepsin B inhibitor CA-074-Me was from Enzo Life Sciences ( Exeter , U . K . ) . Cathepsin D inhibitor Pepstatin A , Tetraethylammonium chloride ( TEA ) , the purinergic receptor P2X7R inhibitor KN62 , and Staurosporine were from Sigma Aldrich . Normal Human Bronchial Epithelial ( NHBE ) primary cells ( Lonza , Basel , Switzerland ) from two donors were purchased , cultured and maintained in BEBM media with supplements ( Lonza ) and used throughout . No significant donor-dependent variation was observed . 16HBE14o- cell line ( transformed human bronchial epithelial; a kind gift from Dieter Gruenert at the University of California San Francisco ) was maintained in DMEM containing 10% FBS , 2 mM L-glutamine and 1% Penicillin/Streptomycin ( Pen/Strep ) . Cells were incubated in a 37°C incubator with humidified atmosphere of 5% CO2 , and grown on flasks or chamber slides previously coated with a solution of Collagen ( Cultrex , Trevigen , Gaithersburg , U . S . A . ; 0 . 05 mg/ml ) and Fibronectin ( Sigma-Aldrich; 0 . 1 mg/ml ) syringe-filtered through a 0 . 22 μm filter . Transfection of epithelial cells was performed using Lipofectamine 2000 ( Invitrogen , Carlsbad , U . S . A . ) by following the manufacturers’ instructions . For transient transfection , cells were transfected 48–72 hours prior to use . Transfection of control siRNA and NLRP3 siRNA cells were performed using Lipofectamine 2000 reagent by following the manufacturers’ instructions . Cells were transfected 48–72 hours prior to use . P . aeruginosa strain PAO1 was a gift from J . R . W . Govan ( University of Edinburgh ) . GFP-PAO1 was a gift from T . Tolker Nielsen ( University of Copenhagen ) , and PAO1-dT3SS was a gift from E . Gulbins ( University Duisburg-Essen , Germany ) . Peripheral blood neutrophils and monocytes were isolated from healthy human volunteers via dextran sedimentation and Percoll discontinuous gradients as described [59] . Neutrophils were used immediately in ChemoTx ( Neuro Probe Inc . , Gaithersburg , U . S . A . ) migration assay plates , at a concentration of 5 x 104 in PBS on the upper side of the membrane as per manufacturer’s instructions , and allowed to migrate towards conditioned BEBM media from NHBE cells . Conditioned media was made by treating NHBE cells for 3 hours with LL-37 , scrambled LL-37 or Pseudomonas aeruginosa PAO1 , either alone or in combination ( all as detailed in Fig 1 ) , removing cell supernatant and filtering through a 0 . 22 μm-filter . Cells were allowed to migrate for 1 hour at 37˚C , and migrated cells in the lower well were then stained with 1 μM Calcein AM for 15 minutes before visualization/quantitation on an ‘EVOS fl’ fluorescence inverted microscope ( Fisher Scientific , Loughborough , U . K . ) . Peripheral blood mononuclear cells were incubated at 4 x 106/mL in IMDM ( PAA Laboratories , Somerset , UK ) at 37°C , 5% CO2 , for 1 hour . Non-adherent cells were removed and adherent monocytes were either used immediately , or cultured for 6 days in IMDM with 10% autologous serum to generate monocyte-derived macrophages before treatment . Cells were either primed with LPS ( E . coli 0111:B4 Ultrapure , Invivogen ( Toulouse , France ) ; 10 ng/ml ) for 3 hours prior to addition of 5mM ATP ( Sigma Aldrich ) as required , or treated with LL-37 or ScrLL-37 ( 20–50 μg/ml ) or PAO1 at a Multiplicity of Infection ( MOI ) of 10:1 as described in the text . Cells were then used for preparation of RNA by RNeasy kit ( Qiagen , Manchester , U . K . ) , and cell supernatant used for cytokine analysis by ELISA as described below . Macrophages were prepared from adult male C57BL/6 ( WT ) mice ( Harlan ) and P2X7R KO mice [60] as described previously [61] . In brief , the peritoneal cavity was lavaged with RPMI 1640 media ( Sigma ) . Cells were collected by centrifugation ( 250 x g , 5 minutes ) and plated in 24-well plates at a density of 5 x 105 cells/well in RPMI 1640 media ( Sigma ) supplemented with 5% FBS ( PAA Laboratories ) , 100 units/ml penicillin , and 100 μg/ml streptomycin ( Sigma ) . Cells were cultured overnight ( 37 °C , 5% CO2 ) before non-attached cells were removed by a media change . Cells were incubated with LPS ( 1 μg/ml for 2 hours ) before treatment with LL-37 and ATP as indicated . 16HBE14o- or NHBE cells were grown overnight at a seeding concentration of 0 . 8 x 105 per well of a collagen/fibronectin-coated chamber slide . Prior to treatments below , cells were washed twice in PBS ( Gibco ) and media was replaced with 250 μl serum-free BEBM ( Lonza; for NHBE cells ) or DMEM ( Gibco , Thermo Fisher UK; for 16HBEo- cells ) and returned to 37˚C . PAO1 bacteria were grown in Luria Bertani ( LB ) Broth overnight at 37˚C with shaking to achieve a stationary-phase suspension . Before use , bacterial suspensions were diluted 1:5 in fresh LB broth and were returned to incubate at 37˚C for a further 2 hours to reach log phase . Bacterial cultures were centrifuged at 1885 x g for 10 minutes , and bacterial supernatant was removed and filter-sterilised through a 0 . 22 μM filter for use as required in the text . PAO1 were re-suspended in PBS , and diluted to an optical density ( OD ) 600 of 0 . 1 , equating to 108 bacteria/ml . If required , bacteria were then heat killed by incubation at 70˚C for 30 minutes . PAO1 were then added to cells in serum-free media at 10:1 MOI . LL-37 or control scrambled peptide was added at the concentrations indicated in the text . If required , cells were pre-incubated for 1 hour at 37˚C with inhibitors as indicated in the text , prior to addition of PAO1/peptide . Cells and PAO1 were then returned to 37˚C for 3 hours . Caspase 1 and Caspase 3/7 activity in live cells was assessed using FLICA and Magic Red fluorescent probes respectively ( ImmunoChemistry Technologies , Bio-Rad AbD Serotec , Kidlington , U . K . ) , as per manufacturer’s instructions , in an incubator at 37˚C for 1 hour , with the addition of 1 μg/ml Hoechst ( Life Technologies ) for the final 30 minutes . Cells were then viewed using an EVOS fl inverted fluorescence microscope ( Thermo Fisher U . K . ) and analysed using ImageJ software . Hoechst staining was used to calculate total cell number per field , and Caspase 1 or 3/7 positivity of cells was manually assessed and counted for each field . TUNEL staining was performed with In Situ Cell Death Detection Kit ( Roche Applied Science , West Sussex , UK ) , according to manufacturer’s instructions , 6 hours after treatments . NHBE cells seeded onto chamber slides were loaded for 3 hours with 20 μM Alexa 488 Dextran , 10kDa ( Life Technologies ) at 37˚C in a humidified incubator , prior to treatment with media only , 20 μg/ml LL-37 ( or TAMRA-labeled LL-37 ) , PAO1 at 10:1 MOI , or PAO1 + LL-37 for a further 3 hours . Cells were then imaged live using a Leica SP5 confocal microscope . Acquired images were subsequently analysed for evidence of lysomal leakage , indicated by a diffusion of the green fluorescent signal throughout the cell cytoplasm , compared to bright punctate fluorescence in media-only control conditions . Quantitation of leakage was performed by expressing the number of cells displaying diffused green fluorescence as a percentage of the total number of cells in that field , across a minimum of 5 fields per experiment . Cells were lysed with MPER lysis buffer ( Life Technologies ) , centrifuged at 17 , 740 x g at 4 ˚C for 10 minutes , and protein concentration determined by BCA assay . Equalized samples were loaded onto 4–12% pre-cast polyacrylamide gel ( Novex NuPage , Life Technologies ) , transferred to a Nitrocellulose membrane ( Life Technologies ) , blocked for an hour with 5% skimmed milk dissolved in TBS + 0 . 05% Tween-20 ( TBS-Tw ) , and then incubated with primary antibodies as indicated in the text in blocking solution overnight at 4˚C . After washing in TBS-Tw , membranes were incubated with appropriate secondary HRP-labelled antibodies ( DAKO , Agilent , Santa Clara , U . S . A . ) in blocking solution , treated with ECL Prime substrate ( Sigma-Aldrich ) in accordance with manufacturer’s instructions , and imaged on CL-Xposure film ( Thermo-Scientific ) . Cells ( MDM , NHBE or 16HBEo- ) were plated at 1 . 5 x 106 cells in 1 ml complete media in a 6-well plate and were then incubated overnight at 37˚C . Cells were infected for 3 hours with PAO1 and LL-37 , as per Caspase 1 activity section above . PAO1 was used at 10:1 MOI , LL-37 peptide was used at 20 μg/ml . Supernatants were collected and stored at -20 ˚C until use . The concentration of IL-1β , IL-18 and IL-8 in cell supernatants was measured by ELISA kit ( eBioscience , Thermo Fisher U . K . ) , according to the manufacturer’s instructions . RNA was made from treated cells using RNeasy mini kit ( Qiagen ) , according to manufacturer’s instructions , and DNase treated with RQ1 DNase ( Promega , Wisonsin , U . S . A . ) for 30 min at 37˚C . cDNA was prepared using TaqMan reverse transcriptase kit ( Life Technologies ) . Quantitative Real Time PCR was performed on a StepOne Real Time PCR machine ( Life Technologies ) , using Gene Expression Mastermix and TaqMan gene expression assays for Caspase 1 ( Assay ID Hs00354836_m1 ) , Caspase 4 ( Assay ID Hs01031951_m1 ) , NLRP3 ( Assay ID Hs00918082_m1 ) , NLRC4 ( Assay ID Hs00892666_m1 ) and 18S ( Assay ID 4319413E-1403063 ) ( all Life Technologies ) , as indicated in the text . Two target sites for NLRP3 gRNA were designed according to the Zhang laboratory online CRISPR designing tool [62] . Target sequences were cloned into vector pSpCas9 ( BB ) -2A-GFP ( PX458 ) , a gift from Feng Zhang ( Addgene plasmid # 48138; http://n2t . net/addgene:48138; RRID:Addgene_48138 ) , and transfected into 16HBE14o- cells with Lipofectamine 2000 ( Invitrogen ) . Cells were flow sorted using a BD Biosciences FACS Diva 8 . 0 . 1 into single cells per well of a 96-well plate , gating for live single cells and GFP-positivity . Isolated GFP-positive cells were expanded , and tested for mutation by PCR and sequencing across the target area . Two clones , B9 and G6 , were identified as having single base pair insertions at the target site causing a non-sense mutation that abolished NLRP3 protein production , as demonstrated by Western blotting . For microscopy , other than in Caspase 1 FLICA and neutrophil migration experiments described above , images were acquired on a Leica SP5 confocal microscope , with 63x oil objective . Cells were grown on Nunc LabTekII chamber coverslides ( Sigma Aldrich ) , pre-coated with Collagen and Fibronectin , overnight at 37˚C . Cells were imaged live after incubation with GFP-PAO1 , TAMRA-LL-37 ( Almac ) , Biotin-YVAD-CMK ( Cambridge Bioscience , Cambridge , U . K . ) and Brilliant Violet 421 Streptavidin ( BioLegend , San Diego , U . S . A . ) , FLICA Caspase 1 probe or MagicRed Caspase 3/7 probe ( both ImmunoChemistry Technologies ) or Alexa 488 10kDa Dextran ( Cat . No . D-22910 , Life Technologies ) , as indicated in the text . Images were acquired using Leica Application Suite software , and subsequently processed ( cropping/brightness-contrast only ) in Adobe Photoshop CS5 . 1 software . Statistical analysis was performed using the GraphPad PRISM statistical package ( GraphPad software , La Jolla , USA ) by 2-way ANOVA with Bonferroni Multiple Comparison Post-test ( Figs 1B , 2A–2C , 3A , 3F , 3G , 4A–4C , 5A–5C , 6A–6C and 7B–7E , and Supporting S3 and S6 Figs ) , 1-way ANOVA with Bonferroni Post-test ( Fig 3D and 3E ) , or unpaired t-test ( Figs 3C and 4B between last 2 columns , 4C between first 2 columns ) , as stated in the respective figure legends . p-values below 0 . 05 were considered significant . Figures show mean +/- SEM . | Lung infections are a common cause of death worldwide . As the threat of antibiotic-resistance becomes realised , new approaches are needed to treat disease-causing bacteria , such as multidrug-resistant Pseudomonas aeruginosa . Treatments that could enhance the body’s most effective natural defences can overcome antibiotic-resistance issues and/or complement existing therapies . Antimicrobial Host Defense Peptides , such as human LL-37 , can kill microbes , but also have vital roles in clearing lung infections by modifying naturally-occurring defenses . Understanding these “immunomodulatory” activities is key to harnessing their potential . Airway lining cells have important barrier roles , but , if infected , these compromised cells must be removed and signal danger , to prevent harmful bacteria establishing a protected site and proliferating . We show that when Pseudomonas aeruginosa infects these cells , LL-37 can provide a second signal , acting like a fire alarm , instructing the compromised cells to signal the danger , to recruit host defence cells to the site , and to activate their own altruistic death . We demonstrate the mechanism of this process , from bacterial and host cell perspectives , occurring by activation of an intracellular sensing platform ( the NLRP3 inflammasome ) with a novel synergy between the infection and the impact of LL-37 , with implications for innovative new approaches to treat multi-drug resistant infections . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| []
| 2019 | Cathelicidin is a “fire alarm”, generating protective NLRP3-dependent airway epithelial cell inflammatory responses during infection with Pseudomonas aeruginosa |
Repurposing existing proteins for new cellular functions is recognized as a main mechanism of evolutionary innovation , but its role in organelle evolution is unclear . Here , we explore the mechanisms that led to the evolution of the centrosome , an ancestral eukaryotic organelle that expanded its functional repertoire through the course of evolution . We developed a refined sequence alignment technique that is more sensitive to coiled coil proteins , which are abundant in the centrosome . For proteins with high coiled-coil content , our algorithm identified 17% more reciprocal best hits than BLAST . Analyzing 108 eukaryotic genomes , we traced the evolutionary history of centrosome proteins . In order to assess how these proteins formed the centrosome and adopted new functions , we computationally emulated evolution by iteratively removing the most recently evolved proteins from the centrosomal protein interaction network . Coiled-coil proteins that first appeared in the animal–fungi ancestor act as scaffolds and recruit ancestral eukaryotic proteins such as kinases and phosphatases to the centrosome . This process created a signaling hub that is crucial for multicellular development . Our results demonstrate how ancient proteins can be co-opted to different cellular localizations , thereby becoming involved in novel functions .
The transition from unicellularity to multicellularity occurred independently in many eukaryotic lineages [1] . Compared to other multicellular organisms , animals stand out with respect to the high number of cell types [1] , [2] , the complexity of body plans , and the necessity of cell migration for development [3] . The evolution of these traits in animals was facilitated by the properties of the cell membrane , cell motility and cell division: Animals retained the ancestral modes of cell motility ( amoeboid and flagellar motility ) and a soft cell membrane . In plants and fungi , a rigid cell wall evolved , restricting cell motility . However , we know little about how the organelles required for cell division , motility and organization evolved with increasing complexity of animals . The cytoskeleton is a key player behind cell organization , motility and division [4] , [5] . One of the coordinators of the cytoskeleton is the microtubule-organizing center ( MTOC ) . In most animals and many other eukaryotes , the basal body or centrosome is the MTOC . Basal bodies are ancestral to eukaryotes and are composed of paired centrioles [6] . In animals , the centrosome is composed of the centrioles and the surrounding pericentriolar material ( PCM ) . The centrosome acts as a signaling hub [7] , [8] , coordinating many functions of multicellular organisms , for example cell migration or maintenance of cell orientation during division [9]–[11] . Fungi and slime molds independently evolved spindle pole bodies , while a great diversity of acentriolar MTOC exists for plants [12] . The expansion and loss of functions of the centrosome throughout evolution can thus be traced in the different eukaryotic lineages . Recognized mechanisms for the evolution of novel functions include the expansion of gene families through duplication , the emergence of coordinated regulation , and de novo gene birth [13]–[16] . Another mechanism is the rewiring of molecular signaling networks , thereby utilizing existing molecular components of the cell in new contexts [17] , [18] . It is , however , unclear which mechanisms are involved in the evolution of whole organelles . We used the centrosome to study the interplay between macroscopic and cellular evolution: The animal centrosome has an extended PCM compared to other species , but its core dates back to the last eukaryotic common ancestor [6] . Previous studies , which focused on the evolution of centrioles , have indicated that many components of the animal centrosome first appeared in animals [19] , [20] . However , many of these apparently novel proteins are coiled-coil proteins . Helices that form coiled coils have a regular , repeating pattern of hydrophobic , charged , and hydrophilic amino acids [21] . This so-called heptad repeat of seven residues causes traditional sequence alignment algorithms to overestimate the significance of the observed sequence similarity , leading to incorrectly predicted homologous proteins . In other words , apparently similar proteins can obscure the actual homologous protein . Previous studies have therefore masked coiled-coil sequences from similarity searches [22] , which increases the risk of missing true orthologs . Thus , a complete survey of the evolutionary history of centrosomal proteins needs to be based on a refined alignment of coiled-coil proteins . We have developed a novel method that takes the restricted space of possible substitutions into account , thereby greatly reducing the amount of false positives . Our method distinguishes between coiled-coil and “normal” regions and treats residues in different positions on the heptad repeat differently . We performed an all-against-all alignment for proteins from 108 eukaryotic species to predict orthologs [23] . Combining our predictions with those based on BLAST searches , we created a dataset of protein families that can be used to pinpoint the establishment of a protein family during evolution , similar to phylostratigraphy [24] . We correlated the appearance of protein families , protein networks , and functions to infer important contributors to the evolution of the animal centrosome ( see Fig . S1 for an overview ) .
A recent addition to BLAST is composition-based adjustment of substitution matrices [25] . This approach modifies the substitution matrices by adjusting the substitution scores to reflect the amino acid composition observed in the query and database proteins , while keeping the matrices' entropy constant . Proteins with biased sequence composition occur in certain protein families or even in whole organisms with AT- or GC-rich genomes . To some extent , compositional matrix adjustment can also account for the biased composition of coiled-coil proteins , e . g . by observing the abundance of hydrophilic residues and decreasing the substitution scores . Nonetheless , compositional matrix adjustment does not take into account the regular repeat structure of coiled-coil proteins , and it also can not deal with the different compositional biases found within and outside coiled-coil domains . There have been various approaches to create specialized substitution matrices for parts of proteins with different compositions , for example for trans-membrane proteins [26] or to distinguish hydrophobic and non-hydrophobic regions [27] . Initially , we also created specialized substitution matrices for coiled-coil and non-coiled-coil regions of the proteins . While this approach outperformed the standard BLOSUM matrix ( data not shown ) , it did not perform better than BLAST with compositional matrix adjustment . We therefore developed two algorithms that take the sequence properties of the coiled-coil structure into account . In both algorithms , coiled coils are predicted using MultiCoil2 [28] using a cutoff probability of 0 . 8 . In the first algorithm , the pair of proteins to be aligned is divided into coiled-coil and non-coiled-coil subsequences . These subsequences are then used to perform compositional matrix adjustment . Then , a full Smith-Waterman-Gotoh alignment is performed . The substitution matrix is chosen according to the coiled-coil status of the considered residues ( Fig . 1 , see Methods for details ) . In the second algorithm , the coiled-coil domains are further sub-divided into three parts: the hydrophobic interface , the charged intermediate residues and the hydrophilic outside . In order to compare different implementations against each other , we have adopted a benchmarking scheme based on the manually annotated KOGs ( eukaryotic orthologous groups ) and a separate set of S . pombe–S . cerevisiae homologs [29] , [30] . To simulate proteins with a high fraction of coiled-coils , we took the coiled-coil proteins from these two datasets and created artificial proteins by excising predicted coiled-coil domains together with a linker of variable length . The subsequences ( i . e . , all instances of linker–coiled-coil–linker ) are concatenated and used for the alignment . We first used the KOG database to set parameter choices for our alignment algorithms ( Fig . S2 ) . Then , we compared the performance of our algorithms to several BLAST options: standard BLAST , standard BLAST with full Smith-Waterman alignment ( not optimized and therefore very slow ) , PSI-BLAST , and , for reference , BLAST without compositional matrix adjustment and ungapped BLAST ( which also employs a fixed substitution matrix ) . The results from the yeast dataset ( Fig . 2 ) are consistent with those from the KOG dataset ( Fig . S3 ) : CCAlign and CCAlignX perform better than the other methods . For example , over the yeast benchmark set with linker length 50 and a 5% FDR , CCAlign correctly identifies 67 . 7% of all possible reciprocal best hits , CCAlignX identifies 68 . 4% and BLAST 61 . 1% . BLAST can also be run with a complete Smith-Waterman algorithm that has not been optimized for speed and can thus not be used for large-scale applications . With this configuration , BLAST identifies 62 . 3% of all possible reciprocal best hits . PSI-BLAST performs much worse , identifying only 48 . 8% ( at three iterations , and a correspondingly increased runtime ) . Building position-specific scoring matrix ( PSSMs ) , the hallmark of the iterative approach taken by PSI-BLAST , is partly incompatible with compositional matrix adjustment . The PSSMs pick up on the strong sequence signal of the coiled-coil domains , and therefore detect many false hits . In essence , PSI-BLAST violates the adage “when you find yourself in a hole , stop digging” by iteratively building a profile to detect coiled-coils , but not sequence similarity that is due to homology . Outside the benchmark set , we compared the performance of BLAST , CCAlign and CCAlignX on the complete set of proteins in our dataset of 108 species . For proteins with at least 20% of their residues in coiled-coils , CCAlign detected 11 . 1% more reciprocal best hits than BLAST ( CCAlignX: 11 . 3% , bitscore cutoff: 30 ) . A large part of this improvement is due to the full Smith-Waterman alignment done even for non-coiled-coil proteins , where performance increased by 10 . 7% for CCAlign ( CCAlignX: 10 . 5% ) . The impact of the adjusted substitution matrices becomes more apparent for proteins with higher coiled-coil content: For proteins with at least 50% coiled-coil residues , performance increased by 13 . 1% for CCAlign and 13 . 5% for CCAlignX . The peak performance increase is reached at 17 . 3% for both methods at coiled-coil contents of at least 86% and 81% , respectively . We used CCAlign , CCAlignX and BLAST to perform all-against-all alignments for proteins from 108 eukaryotic species . Combining evidence from all three alignments , we predicted homologs for all proteins ( see Methods ) regardless of coiled-coil content or centrosome localization , yielding a database of orthologous proteins that can be accessed at http://projects . biotec . tu-dresden . de/orthologs/ . To validate our predictions , we searched the literature for homologs of centrosomal proteins that have previously been uncovered by manual investigation of individual proteins . We confirmed , for example , the homology between CDK5RAP2 ( CEP215 , fly: cnn ) and the S . pombe proteins mto1 and pcp1 [31] , [32] , or the occurrence of homologs of DISC1 in plants [33] . For many other proteins ( see Table S1 ) , we found homologs beyond what has been shown in previous small- or large-scale studies . For example , our approach identified homologs of AKAP9 , PCNT and PCM1 in fungi . ( See Dataset S1 for multiple sequence alignments . ) We found homologs of the C . elegans protein spd-5 in filarial nematodes ( e . g . Brugia malayi ) and Ascaris suum . Spd-5 is essential for centrosome formation in C . elegans , but had previously only been reported in Caenorhabditis species . A recent study uncovered two novel subunits of the Arabidopsis thaliana augmin complex , AUG7 and AUG8 , and reported these two proteins to be unique to plants [34] . Based on more species and on a more suitable alignment method , we could show that the human augmin subunits HAUS7 and HAUS8 are in fact homologous to AUG7 and AUG8 , respectively ( Fig . 3 ) . Overall , we found exactly 1000 protein families that are centrosome-related in any species ( see Tables S2 and S3 for an overview ) . Of these , 897 protein families also occur in human , 610 of which are known to be of centrosomal localization in humans or other mammals ( Fig . 4 ) . In each protein family , we can now check the species distribution and for example find the species that is most distantly related to human . Thus , we found that most centrosomal protein families are more ancient than other human proteins: 72% of all centrosomal proteins first appeared before the fungi–animal ( opisthokont ) ancestor ( Fig . 5 ) , compared to 46% for all human protein families . For further analysis on the evolution of centrosome functions , we divided proteins into categories based on their known function in human ( see Methods , Fig . 5 ) . Of the proteins without annotation , we designated proteins with at least 20% of their residues in coiled-coils as “coiled-coil proteins . ” In other words , coiled-coil proteins that have an annotated function ( e . g . motor proteins ) were grouped with corresponding functional class . Note that the choice of the coiled-coil threshold does not affect the outcome of the network analyses , as explained below . For proteins that occur in mammals , we determined their evolutionary age by looking for the most distantly related species . Thus , a protein also found in Ciona is chordate-specific , while a protein also found in chytrid fungi is opisthokont-specific . Our analysis revealed that coiled-coil proteins are on average significantly younger than most centrosome proteins , whereas kinases , and phosphatases are older ( Fig . 5 ) . For example , 86% of kinases and phosphatase families first appeared before the opisthokont ancestor , compared to only 56% for coiled-coil proteins . Many coiled-coil proteins thus evolved earlier than previously thought , but are still younger than other centrosomal proteins . Interestingly , 76% of all centrosomal kinase families have been shown to be involved in multicellular organismal development , compared to 55% of all kinases . We found similar patterns for other functional categories ( Fig . S4 ) . Thus , centrosome-associated kinases and other regulatory proteins ( which are often ancient ) are enriched for functions related to multi-cellularity . In the ( unicellular ) eukaryote ancestor , kinases cannot have had these functions , and therefore must have acquired them later through other mechanisms . The novel functions are , for example , reflected in an increased PCM size ( Table S4 ) . To gain insight into the mechanisms by which ancient proteins were recruited to centrosomes , we developed a strategy for simulating the changes in the protein interaction network of the centrosome during evolution . We first assembled the centrosome's protein–protein interaction network , to identify those interactions that contribute to the structural backbone . This network was then used to emulate the course of evolution by iteratively removing the most recently evolved proteins . Using this method , we generated an approximation for the structure of the interaction network at different stages of evolution . In particular , we tested how many of the remaining proteins lost or changed their mode of recruitment to the centrosome . We do not have enough data on the basal body of the eukaryote ancestor to quantify the impact of protein losses . However , the evident increase in complexity and size from the basal body to the animal centrosome make it likely that the gain of proteins played a much larger role than the loss . The impact of the loss of proteins is , however , apparent both in fungi and in plants . In these lineages , the basal body became obsolete , leading to the loss of many centriole proteins . We first extracted the interaction network from the STRING database [35] , using interactions derived from experimental evidence and text-mining ( see Methods ) . This network contains both direct and indirect interactions and represents the functional interactions of centrosome proteins , even if there is not enough detailed structural data for the complete centrosome . The evolutionary and structural core of the centrosome is the centriole , serving as a seed for the formation of the PCM . In particular , two conserved proteins , which were already present in the eukaryote ancestor , are important for centriole formation: SASS6 serves as a template for the barrel-shaped centriole [36] . SAS-4 , the C . elegans ortholog of CENPJ , controls centrosome size [37] . Its Drosophila ortholog , sas-4 , has been shown to recruit cytoplasmic complexes of PCM proteins [38] . These PCM proteins can then in turn recruit other centrosomal proteins , forming a protein–protein interaction network that is dominated by a dense core of regulatory proteins , kinases , phosphatases , and their substrates ( Fig . S5 ) . In the periphery of this signaling hub , ciliary proteins , the gamma-tubulin ring complex and the augmin complex are situated in less connected areas of the network . The distance in the network between a given protein and the centriole can be calculated as the number of steps along the shortest path between the protein and the centriolar proteins SASS6 and CENPJ . We simplified the analysis by using proteins of the structural backbone as intermediate nodes ( i . e . only coiled-coil and uncategorized proteins , see Methods and Fig . 6a ) . These proteins are likely to mediate interactions of kinases and other proteins with the PCM . To emulate the evolution of the centrosome , we iteratively removed the most recently evolved proteins from the network ( see Methods , Fig . 6b ) . Our analysis showed that in the complete interaction network , 71% of all proteins were reachable within three steps from the centriole . This fraction stayed virtually constant when chordate- and animal-specific proteins were removed . However , when opisthokont-specific proteins were removed , only 41% of all proteins remained reachable within three steps . We ascertained the significance by shuffling the proteins' evolutionary origin 10 , 000 times . Proteins were divided into five bins according to their coiled-coil content and evolutionary age was shuffled within each of these bins to control for possible biases in the detected ages of proteins . Indeed , we found that the actual change in the fraction of proteins within three steps of the centriole is highly significant ( p = 0 . 007 ) . This means that when coiled-coil proteins that first occurred in opisthokonts are removed , older proteins that had been connected to the centriole by these coiled-coil proteins lose their “main connection” to the centriole . Thus , the number of steps between the centriole and these proteins increases . No further change was observed when only proteins present in the eukaryote ancestor were considered . Thus , structural backbone proteins that evolved prior to , or shortly after , the last common ancestor of fungi and animals are crucial for the formation of the interaction network of the centrosome . In fact , acentriolar MTOCs in mouse oocytes and Drosophila mutants still contain PCM coiled-coil proteins like PCNT and Cnn [39] , [40] . We further distinguished the evolution of the PCM compared to a reduced network of basal body , cilium and centriole proteins ( Fig . S6 ) . The influence of removing coiled-coil proteins on the basal body network is much smaller , consistent with the observation that the PCM is a more recent development . We evaluated the robustness of the model by testing the impact of removing other protein categories , and found that coiled-coil proteins are unique in their effect on the network ( see Suppl . Text and Table S5 ) and that changes in the thresholds for the STRING network and the coiled-coil content do not affect the conclusions ( Fig . S7 ) . The findings presented above rely on the accuracy of the predicted evolutionary age . In order to evaluate the sensitivity of our conclusions on the improved alignment method , we repeated the above analysis using standard BLAST . Whereas most results remained qualitatively similar , coiled-coil proteins were predicted to be older: using our specialized alignment procedure 44% of the coiled-coil proteins were opisthokont-specific or younger , compared to 41% with BLAST ( Fig . S8a ) . Just using BLAST may overestimate the homology between proteins due to high sequence similarity in coiled-coil regions , which leads to an elevated grouping of distant proteins in joint families . When emulating the evolution of the centrosome ( Fig . S8b ) , the change when removing opisthokont-specific proteins was not significant ( Suppl . Table S5 ) , but the removal of coiled-coil proteins still has the strongest effect on the network . Removing pre-opisthokont proteins ( i . e . keeping only universal proteins ) , however , led to a significant change ( p = 0 . 029 for coiled-coil and uncategorized proteins , compared to p = 0 . 017 using all three alignment methods ) . Our work showed that coiled-coil proteins are in fact older than previously thought . Taken to the extreme , one could also postulate that all coiled-coil proteins occurred in the eukaryote ancestor . To further corroborate the robustness of our findings we conducted additional tests that are independent of the evolutionary age of protein families: we assessed the importance of nodes in the network according to the number of shortest paths that pass through the nodes ( Text S1 , Fig . S9 and Table S6 ) . This test exclusively depends on the topology of the network . Proteins with the largest number of shortest paths passing through them were designated as bottlenecks ( cut-off: top 5% or 20 shortest paths ) . We had to control for the influence of hubs , i . e . proteins with very many interaction partners , which are more likely to be part in shortest paths ( cut-off: top 5% or 39 edges ) . Among the proteins that are not hubs , coiled-coil proteins have the greatest enrichment among bottlenecks ( P = 0 . 11 , one-sided Fisher's exact test ) . When we constructed a network where edges leading to hubs receive a larger distance score ( i . e . are less likely to be part of a shortest path , see Suppl . Text ) , we again find that coiled-coil proteins have the strongest enrichment of bottlenecks ( P = 0 . 03 ) . We furthermore assessed the validity of our model's evolutionary explanations in a framework formulated by Scriven [41] ( see Text S1 and Fig . S10 ) . Based on these observations , we divided centrosome proteins into three classes ( Fig . 7a ) according to their change in network distance upon removal of proteins that first occur in opisthokonts: core proteins ( that keep their distance to the centrioles , e . g . AURKA , polo-like kinases and the HAUS complex ) , peripheral proteins ( whose distance increases , e . g . CEP290 , DISC1 and the BBSome ) , and novel proteins ( that first occur in opisthokonts proteins , e . g . PCNT , AKAP9 and PCM1 ) . Although this classification is only a rough representation of the order of recruitment , we found significant functional differences when testing the main functions carried out by the centrosome ( Fig . 7b ) . Universal functions such as cell cycle and division have a significantly higher fraction of core proteins . In contrast , processes that have become more important for animals compared to their unicellular ancestors are carried out by a lower fraction of core proteins . For example , signaling proteins are enriched ( p = 0 . 07 , using Fisher's exact test ) in the periphery , underlining that the centrosome became increasingly important as a signaling hub at the transition to multi-cellularity . Thus , the core centrosome reflects the ancestral functions related to individual cells , whereas the novel and expansion proteins are involved in newer functions related to multi-cellularity . An exemplar member of the periphery is the kinase GSK3B , a member of a large family of signaling proteins [42] . In S . pombe , it is involved in cytokinesis and bipolar cell growth [43] , [44] , while in S . cerevisiae it has been implicated in stress response [45] . It takes part in cell differentiation in Dictyostelium [23] , [46] . In animals , the protein localizes to the centrosome and takes part in many developmental processes , for example neural development: It targets centrosomal proteins such as ninein and the asymmetric inheritance of the centrosome with the mother centriole may be a mechanism of regulating neuronal differentiation [47] .
In this work , we have extended the space of known homologs of centrosomal proteins over previous studies , finding that proteins that were previously thought to be restricted to animals first occurred earlier in evolution . Nonetheless , the fast divergence of coiled-coil proteins leads to gaps in the matrix of homologs ( e . g . within nematodes and insects , see Fig . 4 ) . In the future , comparative structural approaches might make it possible to bridge these gaps , although high-throughput expression of centrosomal proteins is difficult [48] . While our predictions showed that many centrosomal coiled-coil proteins are older than previously thought , future method development , and structural and functional assays may further increase age estimates of these proteins . However , as shown above , the role of coiled-coil proteins on the evolution of the centrosome interaction network could also be demonstrated without assumptions on the age of the proteins . Coiled-coil proteins at the centrosome have long been recognized to be part of a “centromatrix” or centrosomal matrix [49]–[52] . Indeed , many previous studies have shown that centrosomal coiled-coil proteins function as scaffolds for the recruitment of other proteins ( Table S1 ) . This is a general trend: In the Gene Ontology , 44 human proteins are annotated as protein complex scaffolds , 12 of which have coiled-coil sections . This fraction of 27% is a significant enrichment over the background rate of coiled-coil proteins , which is 12% of all human proteins ( p-value: 0 . 005 using a one-sided Fisher's exact test ) . Hence , proteins with a high fraction of residues in coiled-coils are more likely to be scaffold proteins than proteins without coiled-coil residues . Here , we were able to show that many centrosomal coiled-coil proteins indeed act as scaffold proteins , providing a mechanism for earlier observations . For example , based on the analysis of only five animal species and the non-centrosomal budding yeast as an out-group , Nido et al . observed an increase in coiled-coil content and disorder in centrosomal proteins towards mammals [53] . They linked this increase in coiled-coil content to the ability of these proteins to change their physical properties upon post-translational modification . Consistent with these findings , we discovered an increased fraction of residues in disordered regions for opisthokont-specific proteins . When comparing core and peripheral proteins ( Fig . 7a ) , we found no change in disorder for coiled-coil proteins . However , the fraction of residues in disordered regions is increased in the core for regulatory proteins ( p-value 0 . 054 , two-sided Kolmogorov-Smirnov test ) and for uncategorized proteins ( p-value 0 . 01 ) , but not for the other functional categories . In general , centrosomal proteins have higher disorder content than non-centrosomal proteins [53] . The further division among centrosomal proteins that we observe is consistent with our finding that coiled-coil proteins facilitated the evolution of the centrosome by acting as scaffolds that recruit ancient proteins for novel functions: Peripheral proteins may have been recruited to the centrosome more recently , and are thus more similar in their disorder content to non-centrosomal proteins . It was possible for us to quantify the impact of scaffolds on the evolution of the centrosome because of its organization: it has a small proteinaceous core ( the centriole ) that is used by the cell to control centrosome number and localization . Other non-membrane-bounded organelles are recruited by DNA ( e . g . kinetochores and nucleoli ) or not controlled in number ( e . g . P granules ) . In the case of membrane-bounded organelles , membranes provide large surfaces for the organization of protein complexes . Thus , additional modes of recruitment of proteins may have acted in those organelles . Nonetheless , we found that coiled-coil proteins are also significantly more novel than other proteins in the case of kinetochores and the Golgi apparatus ( Fig . S11 ) . Thus , the recruitment of molecular functions through coiled-coil scaffolds may not be restricted to the centrosome .
There are three elements that distinguish our approach to previous algorithms: ( 1 ) Scoring matrices are adjusted to take the coiled coils' amino acid composition into account . ( 2 ) Scores from different positions in the heptad repeat are weighted . ( 3 ) A correct alignment of the heptad repeat between the aligned proteins is rewarded ( for the second algorithm only ) . In the first algorithm ( “CCAlign” ) , proteins are divided into coiled-coil and non-coiled-coil sections . The sequences of these two classes are concatenated separately , yielding two artificial sequences per protein . To align a pair of proteins , composition-adjusted substitution matrices are calculated for the pair of coiled-coil sections , for the pair of non-coiled-coil sections , and for the complete proteins . The calculation uses BLAST's matrix adjustment algorithm , made accessible by a modified version that does not perform alignments , but only computes and returns the adjusted matrix ( Fig . 8 ) . For alignment , we use a modified Smith-Waterman-Gotoh algorithm [54] , [55] , based on the open-source implementation JAligner . In a Smith-Waterman alignment of two proteins , all possible pairs of residues are considered . In traditional algorithms , the same substitution matrix ( e . g . BLOSUM62 ) is used for all pairs of residues . For this algorithm , the substitution matrix is chosen according to the status of the pair of residues under consideration: the coiled-coil substitution matrix is used when both residues are in a coiled coil . The non-coiled-coil matrix is used if none of the residues is in a coiled coil . In the mixed case , the substitution matrix based on the full-length proteins is used . Intuitively , the registers of the heptad repeat should contain varying amounts of phylogenetic signal , i . e . be more or less informative with regard to the potential homology of two proteins . To estimate this , we extracted coiled-coil residues from the Blocks database [56] , a set of highly conserved sequences that has been used to generate the BLOSUM substitution matrices . We derived sub-databases that correspond to either single registers of the heptad repeat , or groups of registers . Using the entropy of the substitution matrices as a proxy for phylogenetic signal , we observe that the hydrophobic interface residues are more informative ( entropy 0 . 45 ) than the intermediate residues ( 0 . 32 ) and the hydrophilic outside ( 0 . 28 ) . However , all positions of the heptad repeat are less informative than the background ( BLOSUM62 , 0 . 70 ) . We benchmarked different weighting schemes for the register-specific phylogenetic signal , with two degrees of freedom: ( 1 ) Entropies can be calculated for groups of registers ( a/d , e/g , b/c/f ) or for individual registers . ( 2 ) The entropies can be normalized by the entropy of BLOSUM62 , or by the median coiled-coil entropy . Out of these schemes , normalizing group entropies with the median entropy proved to be most successful in the benchmark scheme ( see below ) . Mathematically , the algorithm for determining the score for a pair of residues from the proteins sequences can be described in this way:For the second algorithm ( “CCAlignX” ) , the coiled-coil residues are further subdivided by their position in the heptad repeat: the hydrophobic interface ( a , d ) , the hydrophilic outside ( b , c , f ) and the intermediate residues ( e , g ) . Based on these groups , additional substitution matrices are computed . When two coiled-coil residues are considered for alignment , the matrix that corresponds to the register of the more confident MultiCoil2 prediction is used . For this algorithm , benchmarking indicates that adding another scoring mechanism yields better results: When the predicted coiled-coil registers overlap , an additional bonus score is awarded to the residue pair under consideration . The approach of dividing proteins into regions of different evolutionary constraints could be applicable to other classes of proteins that contain regions with different evolutionary pressures and different amino acid compositions , like trans-membrane proteins . Software implementing the algorithms mentioned above is available from https://bitbucket . org/mkuhn/blast-matrix and https://bitbucket . org/mkuhn/ccalign . Genomes were gathered by extending eukaryotic genomes in the STRING 9 database [35] with a number of other genomes , yielding a total of 108 genomes ( see Fig . S12 for a phylogenetic tree [57] , [58] ) . For nematode genomes of interest where only nucleotide sequences were available , genes were predicted with Maker [59] . If a genome was predicted to have more than 50 , 000 genes , the genes were aligned against the UniProt database ( using the metazoan UniRef90 dataset ) . All genes were then sorted by the bitscore of their top hit . Genes were retained if they were among top 50 , 000 hits or had a bitscore greater than 50 . An all-against-all protein alignment was performed in multiple steps: First , a speed-optimized Smith-Waterman alignment was computed using ParAlign [60] . Hits from the first step were then re-aligned using the coiled-coil aware alignment algorithms . ( For non-coiled-coil proteins , this step adds compositional matrix adjustment . ) As an optimization , only the top 50 hits of each protein in each other species were determined by the re-alignment . In addition to the coiled-coil aware alignments , the complete all-against-all alignment was performed with BLAST . Thus , three sets of alignments have been calculated: CCAlign , CCAlignX and BLAST . For each set , groups of homologous proteins are predicted using the eggNOG pipeline [23] . In order to trace common ancestry with more sensitivity , we modified the eggNOG pipeline to allow for more merging of similar groups in the last stages of the pipeline: The eggNOG pipeline first searches for triangles of proteins in different species that are reciprocal best hits ( RBH ) of each other , and adds other RBHs to these seed groups . Then , through several iterations , orthologous groups are joined ( when they have many RBHs between each other ) and split ( when the set of proteins becomes too diverse ) . We have added a final step that used the diagnostic output of the last merging step , namely the set of OG pairs and their merging score . Applying a threshold for the score produced filtered set of OG pairs . When two OGs contained overlapping sets of species ( and thus proteins that may be paralogous ) , we used a more stringent threshold to avoid merging paralogs . The filtered set of OG pairs was then converted into a graph . In decreasing order of scores , connected OGs were then combined into clusters . As a precaution to avoid indefinite growth of clusters , we imposed a restriction on the diameter of the cluster: for each pair of OGs in the cluster , the maximum allowed distance is four edges ( i . e . there can be up to three OGs in between ) . With these modifications , we can detect more distant homologs , even in the case of greater sequence divergence . As can be seen in Fig . 3 , orthologous groups connect proteins through intermediate proteins . Within the original eggNOG pipeline , alignment positions are checked to avoid connecting non-homologous proteins through shared domains [61] . In the final merging step that we have added , the stringent cutoff to prevent merging of paralogs also serves as a precaution against such false positives . In our manual inspection of alignments , including those for HAUS7/8 ( Fig . 3 ) , we always found shared conserved regions between all orthologs except when additional truncated copies of the protein occurred in certain species along with the full-length protein . To reduce false positives , predictions from BLAST , CCAlign and CCAlignX were then combined using a voting scheme: if at least two of the three methods agree that two proteins are homologous , then they are accepted to be homologs in the combined prediction ( Fig . S13a ) . In some cases , however , individual proteins caused spurious links between unrelated groups of homologous proteins ( Fig . S13b ) . To avoid these links , we determined the proteins' betweenness centrality for all groups of homologs ( using the NetworkX package for Python ) . Proteins that generate spurious links have a high betweenness centrality , as many shortest paths between other proteins pass through them . These proteins are tentatively removed from the combined groups of homologs . If a link mediated by these proteins was spurious , then the group of homologs disintegrates into sub-groups . If the link was valid , then it will be backed up by other links , and the group does not disintegrate . The newly formed sub-groups are checked for spurious links in turn . Known centrosome proteins were extracted from a variety of sources: Gene Ontology ( GO ) annotations [62] , the MiCroKit database [63] , and proteomic screens in mammals , Giardia lambia and Chlamydomonas reinhardtii [64]–[68] . Proteins were assigned to categories based on their GO annotation , InterPro domains [69] , Enzyme Commission numbers [70] , and limited manual annotation . Motors are assigned based on InterPro domains ( dynein , kinesin , myosin ) . GO annotations are used for these classes: kinases ( protein kinase activity ) , phosphatases ( phosphoprotein phosphatase activity ) , cytoskeletal proteins ( structural constituent of cytoskeleton ) , scaffolds ( protein complex scaffold ) , regulators ( regulation of signal transduction , regulation of protein modification process ) and transcription factors ( sequence-specific DNA binding transcription factor activity ) . As there were only seven transcription factors known to localize to the centrosome , we added these to the regulatory proteins . Proteins that have been assigned an Enzyme Commission number are assigned as enzymes . Lastly , proteins with at least 20% coiled-coil residues are also assigned as scaffolds . Thirty-five percent of centrosome proteins do not fit any of these categories and are designated as “other” proteins . The order in this paragraph reflects the priority of assignment of functions , e . g . ROCK1 ( a kinase with a coiled-coil domain ) has been annotated as a kinase , not a scaffold . A protein interaction network was extracted from the STRING 9 database using a confidence cutoff of 0 . 5 . Only the “experiments” and “text-mining” channels were included . In particular , edges from the “database” channel were not included , as some manually annotated pathway databases contain the centrosome as one very large ( unstructured ) complex , which is undesirable for the present analysis . In order to find traces of the expansion of the centrosome and its development into a signaling hub , we analyzed the role of scaffold proteins and their interactions with regulatory proteins in more detail . Only a subset of the interactions in the network belong to the structural backbone . For example , protein interactions involving kinases , phosphatases and regulatory proteins are likely to be transient interactions , whereas interactions mediated by scaffold and uncategorized proteins are more likely permanent physical interactions with higher specificity . This is also reflected by the number of interaction partners: scaffolds and uncategorized proteins have fewer interaction partners than other classes of proteins ( Fig . S9 ) . To capture the majority of permanent interactions , we designate scaffolds and uncategorized proteins as the structural backbone of the PCM . To determine shortest paths within the protein interaction network , scaffold and uncategorized proteins were used as backbone nodes . Computationally , the network was represented as a directed graph , with directed edges going out from backbone nodes . Thus , non-backbone proteins such as kinases are sinks , i . e . they have only incoming edges . The NetworkX package for Python was then used . We used DISOPRED2 [71] for predicting disordered regions . When a residue was predicted to be both in a coiled-coil domain and in a disordered region , we treated the residue as being not disordered . | The centrosome helps cells to divide , and is important for the development of animals . It has its evolutionary origins in the basal body , which was present in the last common ancestor of all eukaryotes . Here , we study how the evolution of novel proteins helped the formation of the centrosome . Coiled-coil proteins are important for the function of the centrosome . But , they have repeating patterns that can confuse existing methods for finding related proteins . We refined these methods by adjusting for the special properties of the coiled-coil regions . This enabled us to find more distant relatives of centrosomal proteins . We then tested how novel proteins affect the protein interaction network of the centrosome . We did this by removing the most novel proteins step by step . At each stage , we observed how the remaining proteins are connected to the centriole , the core of the centrosome . We found that coiled-coil proteins that first occurred in the ancestor of fungi and animals help to recruit older proteins . By being recruited to the centrosome , these older proteins acquired new functions . We thus now have a clearer picture of how the centrosome became such an important part of animal cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"sequencing",
"techniques",
"sequence",
"analysis",
"biochemistry",
"organismal",
"evolution",
"evolutionary",
"modeling",
"animal",
"evolution",
"biology",
"and",
"life",
"sciences",
"molecular",
"biology",
"techniques",
"proteomics",
"computational",
"biology",
"evolutionary",
"biology",
"molecular",
"biology"
]
| 2014 | Coiled-Coil Proteins Facilitated the Functional Expansion of the Centrosome |
Cationic amino acid transporters ( CAT ) are important regulators of NOS2 and ARG1 activity because they regulate L-arginine availability . However , their role in the development of Th1/Th2 effector functions following infection has not been investigated . Here we dissect the function of CAT2 by studying two infectious disease models characterized by the development of polarized Th1 or Th2-type responses . We show that CAT2−/− mice are significantly more susceptible to the Th1-inducing pathogen Toxoplasma gondii . Although T . gondii infected CAT2−/− mice developed stronger IFN-γ responses , nitric oxide ( NO ) production was significantly impaired , which contributed to their enhanced susceptibility . In contrast , CAT2−/− mice infected with the Th2-inducing pathogen Schistosoma mansoni displayed no change in susceptibility to infection , although they succumbed to schistosomiasis at an accelerated rate . Granuloma formation and fibrosis , pathological features regulated by Th2 cytokines , were also exacerbated even though their Th2 response was reduced . Finally , while IL-13 blockade was highly efficacious in wild-type mice , the development of fibrosis in CAT2−/− mice was largely IL-13-independent . Instead , the exacerbated pathology was associated with increased arginase activity in fibroblasts and alternatively activated macrophages , both in vitro and in vivo . Thus , by controlling NOS2 and arginase activity , CAT2 functions as a potent regulator of immunity .
Tissue macrophages comprise a heterogeneous population of cells , recently separated into three major categories based on their unique functional capabilities . The TH2 cytokines IL-4 and IL-13 trigger a characteristic ‘alternative’ state of activation in macrophages that is distinct from the ‘classical’ TH1-type activation by IFN-γ or deactivation phenotype associated with IL-10 and TGF-β [1] . In contrast to classically activated macrophages ( CAMø ) , which regulate cellular immunity to intracellular pathogens , alternatively-activated macrophages ( AAMø ) are associated with chronic helminth infections and allergic disease [2] , [3] , [4] . AAMø's participate in humoral immune responses , facilitate clearance and presentation of antigens , and regulate the important process of tissue repair [1] , [5] . In the murine model of schistosomiasis , mice chronically infected with Schistosoma mansoni develop severe liver pathology characterized by the formation of eosinophil-rich granulomas and fibrosis , which leads to portal hypertension , bleeding from collateral vessels , and ultimately death [6] . As with many helminth infections the immune response to S . mansoni is TH2-biased [7] . Consequently , AAMø's are the major macrophage subpopulation observed in schistosomiasis [8] , [9] , with recent studies suggesting their development is critical to the long-term survival of the infected host [10] . Although the exact role of AAMø's in inflammation and fibrosis remains unclear , numerous studies including our own have suggested they are important regulators of wound healing . This hypothesis is based on the observation that AAMø's express a number of genes known to be involved in cell proliferation and collagen synthesis , most prominent being the enzyme arginase-1 ( Arg-1 ) [2] , [9] , [11] , [12] , [13] , [14] . In contrast to iNOS , a widely investigated enzyme critically involved in many aspects of host immunity [15] , much less is known about the role of arginase in infectious disease models [16] . Although it is known that arginases can antagonize NO synthesis by competing for L-arginine [17] , [18] , [19] , the inducible Arg-1 isoform is believed to regulate other important functions as well . One of the major products of arginase is L-ornithine , a precursor in the production of polyamines and proline , which control cell proliferation and collagen production , respectively [16] , [19] . It is thought that extracellular L-ornithine and L-proline , secreted from arginase expressing cells ( AAMø's ) , are transported into fibroblasts , where they subsequently become incorporated into collagen [20] . Therefore , Arg-1 expressing cells have been hypothesized to be critical regulators of fibrosis . Thus , a better understanding of the mechanisms regulating Arg-1 activity could reveal novel strategies to control fibroproliferative diseases . Since extracellular L-arginine is required for sustained NO and L-ornithine production [21] , mechanisms controlling L-arginine transport may critically regulate iNOS and Arg-1 activity . Among the transport systems that facilitate L-arginine uptake , system y+ is considered to be the major L-arginine transporter in most cells and tissues [22] . Encoded by the solute carrier 7a1-3 ( Slc7a1-3 ) family of genes , y+ is a Na+-independent high affinity amino acid transport system . CAT2 is the most dynamically regulated of the three transporters , with CAT1 operating as the product of a constitutively expressed “housekeeping” gene and CAT3 expressed primarily in the brain [23] , [24] . Several pro-inflammatory mediators including LPS can regulate the expression of CAT2; thus , it likely functions as the key L-arginine transporter during inflammatory responses . Recent studies with CAT2-deficient mice showed sustained NO production in macrophages is dependent on CAT2 [25] . Thus , it appears to be the essential L-arginine transporter in macrophages . However , while CAT2 has been studied in the context of iNOS activity [25] , no studies have addressed its role in the regulation of Arg-1 activity following infection . To elucidate the function of the Slc7a2 gene in vivo , we infected CAT2−/− mice with either Schistosoma mansoni or Toxoplasma gondii; pathogens that induce highly polarized Th2 and Th1 responses , respectively [7] . Strikingly , following infection with S . mansoni , CAT2−/− mice developed granulomas that were 3- to 4-times larger than WT and hepatic fibrosis ( a feature of severe disease ) was significantly exacerbated in chronically infected mice [26] , [27] , [28] , [29] , indicating a general worsening of Th2-associated pathologies in the absence of CAT2 . The CAT2−/− mice were also more susceptible to T . gondii infection , demonstrating that CAT2 is critical for the development of protective Th1-dependent immunity . Thus , these studies identify CAT2 as a powerful regulator of TH1 and TH2 effector responses , which may have major implications for a variety of infectious diseases .
To determine whether CAT2 plays a regulatory role during an acute Th2 response , we exploited the S . mansoni pulmonary granuloma model [30] . In this model , schistosome eggs are delivered to the lungs of mice via tail vein injection . The eggs are deposited in the pulmonary vasculature where they induce an eosinophil rich , CD4+ Th2 cell-dependent granulomatous response [7] . Wild-type ( WT ) and CAT2−/− mice were sensitized and challenged with 5000 live S . mansoni eggs and on day 4 and 7 post-challenge , animals were sacrificed and the effects of CAT2 deficiency were examined microscopically in the lung . Although there were no significant differences in granuloma size or composition on day 4 , the CAT2−/− mice displayed an average 37% increase in granuloma size on day 7 ( Fig . 1A ) , the peak of the granulomatous response [31] . The increase in peak granuloma size was also associated with significant picrosirius red staining of histological sections ( Fig . 1B ) , providing evidence of increased fibrosis in the CAT2−/− lung . To explore the role of CAT2 during a chronic Th2-driven inflammatory response , WT and CAT2−/− mice were infected with S . mansoni cercariae and the granulomatous response was examined in the liver 8 , 12 , and 24 weeks post-infection . As expected , peak granuloma size was observed at the acute time point ( wk 8 ) , with subsequent down-modulation in granuloma formation in chronically infected ( wk 12–24 ) animals ( Fig . 2A ) . When the responses in WT and CAT2−/− mice were compared , however , it was clear that the CAT2−/− mice developed granulomas 2- to 3-times larger than WT at all time points ( Fig . 2A ) . There was also a small but significant increase in tissue eosinophils on wk 12 ( Fig . 2B ) and a consistent increase in mast cells in the CAT2−/− granulomas ( Fig . 2C ) . The representative photomicrographs shown in panels 2D ( WT ) and 2E ( CAT2−/− ) illustrate the exacerbated inflammatory response in the CAT2−/− liver . When infected with a high dose of parasites , the CAT2−/− mice also succumbed significantly faster than WT animals ( Fig . 2F ) . However , at low doses , the survival of CAT2−/− was not significantly different from WT through at least 24 wk of infection ( not shown ) . Liver fibrosis is the primary cause of chronic morbidity in S . mansoni infections [32] . To determine whether CAT2 regulates tissue fibrogenesis , liver tissue was taken at various time points post-infection and collagen content was measured as hydroxyproline [33] . Although both groups developed significant fibrosis , hydroxyproline levels were markedly increased in the CAT2−/− livers , particularly at the chronic time points ( Fig . 3A , 3B ) . Collagen deposition was also examined histologically with Masson's trichrome ( Figs . 3C and 3D ) and picrosirius red stains ( not shown ) , and thick bands of collagen were seen throughout the livers of the infected CAT2−/− mice . In contrast , collagen deposition was primarily in areas surrounding the granulomas in WT animals . Serum AST ( SGOT ) and ALT ( SGPT ) levels were similarly increased in both groups following infection ( Fig . 4A , 4B ) , indicating there was no evidence of significant egg-induced hepatotoxicity in the CAT2−/− animals . In fact , AST/ALT levels were slightly reduced in the CAT2−/− mice at the 8 wk time point . However , the CAT2−/− mice displayed significant hepatomegaly , particularly at the acute and early chronic time points ( Fig . 4C ) . There was also marked splenomegaly in the absence of CAT2 ( data not shown ) . Thus , in contrast to the enhanced liver toxicity observed in IL-4−/− , IL-4Rα−/− , and LysMCreIL-4R−/− mice [10] , [29] , [34] , CAT2−/− mice developed significant liver fibrosis , portal hypertension , and collateral vessels , which are features of severe hepatosplenic disease . Importantly , the increased pathological responses in the CAT2−/− mice were not attributed to differences in parasite burden since similar numbers of eggs and paired adult parasites were found in the tissues of both groups at all time points ( Table S1 ) . Granuloma formation and fibrosis are tightly controlled by the egg-induced Th2 response [5] , [7] . Therefore , to determine whether local or systemic changes in Th2 cytokine production were responsible for the severe pathological reactions in CAT2−/− mice , granuloma-associated lymphocytes were isolated from the livers of individual mice ( wk 8 ) and IL-5 , IL-13 , and IFN-γ production was assayed by intracellular cytokine staining ( ICS ) . Surprisingly , despite displaying a significant increase in pathology , the frequency of IL-5 and IL-13-producing CD4+ T cells was markedly reduced in the livers of infected CAT2−/− mice ( Fig . 5A ) . IL-13 production was also reduced in the non-CD4+ T cell population ( Fig . 5B ) . The reduction in type-2 cytokines did not result from an increased type-1 response because the frequency of IFN-γ producing cells ( CD4+ and CD4− cells ) was also reduced in the CAT2−/− livers , but not to the same magnitude as the type-2 cytokine producing cells . We also isolated RNA from the liver and examined IFN-γ , IL-5 , IL-13 , IL-4 , and IL-10 mRNA responses by real-time PCR at 8 , 12 , and 24 wk ( Fig . 5C ) . As expected [35] , there was a marked increase in IL-4 , IL-5 , IL-10 , IL-13 , and IFN-γ mRNA in the livers of infected WT mice . However , consistent with the ICS results , IL-13 mRNA levels were significantly reduced in the CAT2−/− mice at all time points post-infection . Similar results were seen with IL-4 , although IL-5 mRNA was only slightly reduced in the knockout mice . Also consistent with the ICS studies , IFN-γ mRNA expression was reduced in the CAT2−/− liver , but only significantly at the 8 wk time point . In contrast to the other cytokines , IL-10 mRNA levels increased to a similar extent in both groups at all time points post-infection . Together , these results indicate that CAT2 expression ensures maximal development of Th2 cytokine responses in vivo . To explore mechanisms by which CAT2 regulates Th2 response development in vivo , we investigated whether the proliferation of cytokine-producing cells was affected by CAT2 deficiency . Purified lymphocytes isolated from the granulomatous livers ( Fig . 6A ) and mesenteric lymph nodes ( Fig . 6B ) were CFSE-labeled and stimulated polyclonally with ConA for 72 hr . Following stimulation , cells were assayed by intracellular cytokine staining for IFN-γ and IL-13 , as markers of Th1 and Th2 effector cells , respectively . In the liver ( Fig 6A ) , there was significant proliferation without additional ConA stimulation , indicating the presence of a large population of antigen-activated T cells in the granulomatous tissues of both WT and CAT2−/− mice . 34 . 7% of the lymphocytes in the unstimulated WT group were also producing IL-13 , which increased to 45 . 1% after Con A stimulation . The majority of the cytokine producing cells were also proliferating ( 20 . 6% before and 33 . 1% after Con A stimulation ) , indicating the presence of a large population of effector Th2 cells in the infected WT liver . In contrast , only 21 . 1% of the CAT2−/− lymphocytes were producing IL-13 and no increase was observed after Con A stimulation . The CAT2−/− IL-13-producing cells were also proliferating at a much slower pace ( 8 . 5% before and 10 . 3% after ConA stimulation ) . Similar results were seen for IFN-γ ( right panels ) , although in general there were more IL-13 than IFN-γ producers in the liver . The number of proliferating IFN-γ producing cells in WT liver was 24% , which decreased to less than 5% in CAT2−/− mice ( Con A stimulated ) , demonstrating that both the proliferative and cytokine producing capabilities of granuloma-associated lymphocytes were diminished in the absence of CAT2 . As expected , the frequency of cytokine producing cells was much lower in the MLN ( Fig . 6B ) . Moreover , although the frequency of cytokine-producing cells increased following Con A treatment , there were no significant differences between the two groups , suggesting that the impaired cytokine and proliferative responses of CAT2−/− mice were restricted to the granulomatous tissues . In addition to Th1/Th2 cytokines , we also examined whether FoxP3 , IL-17 , and TGF-β1 expression were altered in the infected CAT2−/− mice . In contrast to the marked effect observed on Th2 cytokine expression , however , granuloma-associated CD4+ T cells from CAT2−/− and WT mice displayed similar IL-17 , FoxP3 , and TGF-β1 responses . In fact , the Th17 response was weak when compared with the Th2 cytokine response . For example , at 9 wk post-infection , the percentage of CD4+ T cells that were IL-13 positive was 17 . 1% and 11 . 8% in WT and CAT2−/− mice , respectively , while only 0 . 19% and 0 . 2% were IL-17 positive . Although we observed significant FoxP3 expression in the liver , the responses in WT and CAT2−/− were again nearly identical , with 6 . 87% of WT and 6 . 19% of CAT2−/− CD4+ T cells expressing FoxP3 . There were also no significant difference in TGF-β1 mRNA expression in the livers of infected WT and CAT2−/− mice ( not shown ) . Numerous studies have demonstrated that granuloma formation and hepatic fibrosis are dependent on Th2 cytokines [26] , [29] , [36]; therefore , it was surprising to find a markedly reduced Th2 cytokine response in the granulomatous tissues of the CAT2−/− mice , since immunopathology increased significantly in these animals . Because Arg-1 and iNOS activities are regulated by the availability of L-arginine [17] and alternatively-activated macrophages play an important role in the pathogenesis of schistosomiasis [9] , [10] , we examined whether CAT2 deficiency was regulating the function of alternatively- ( AA ) or classically- ( CL ) -activated macrophages . CAT2 mRNA levels were increased 20- to 40-fold in both classically and alternatively activated macrophages , suggesting that CAT2 activity is not restricted to a Th1- or Th2-polarized response ( Fig . 7A ) . CAT2 mRNA levels were also increased over 10-fold in the granulomatous tissues of infected mice ( data not shown ) . Interestingly , however , when nitric oxide and urea levels ( a quantitative measure of arginase activity ) were measured , the NO producing ability of macrophages was decreased in the absence of CAT2 , regardless of the activation stimuli used ( Fig . 7B ) . In marked contrast , urea production was significantly increased in alternatively-activated CAT2−/− macrophages ( Fig . 7C ) . IL-4 , IL-13 , IL-21 , and GM-CSF have all been shown to increase arginase activity in macrophages [13] , [37] , [38] . Interestingly , the CAT2−/− macrophages displayed enhanced arginase activity with nearly every stimulus examined ( Fig . 7D ) . Finally , there were also significantly more macrophages in the CAT2−/− granulomas ( Fig 7E ) , suggesting that the increase in granuloma size was due in part to the increased recruitment of macrophages to the liver . Next we investigated fibroblast activity . For these studies , primary fibroblasts were generated from lung tissue and in initial studies , the production of NO and urea was compared in WT and CAT2−/− fibroblasts following classical or alternative activation . In contrast to classically activated macrophages , where NO expression was only partly CAT2 dependent ( Fig . 7B ) , production of NO by CAT2−/− fibroblasts was almost entirely dependent on CAT2 activity ( Fig . 8A ) . Nevertheless , the amount of NO produced by CL-activated fibroblasts was nearly ten-fold lower than macrophages plated at the same density ( Figs 7B and 8A ) . Unlike macrophages , in which IFN-γ/LPS was strictly required for iNOS activity and IL-4/IL-13 for arginase activity ( Fig . 7B and 7C ) , we observed significant spontaneous arginase activity in primary fibroblasts . Indeed , unstimulated WT fibroblasts ( Fig . 8B ) produced nearly the same amount of urea as alternatively-activated WT macrophages ( Fig . 7C ) . There was also no evidence of enhanced arginase activity in fibroblasts following stimulation with IL-4 , IL-13 , IL-21 , or GM-CSF ( Fig . 8B ) . Most striking however , was the 4- to 5-fold increase in arginase activity in the CAT2−/− fibroblasts . The CAT2−/− fibroblasts also proliferated more rapidly , both spontaneously and in response to FGF stimulation ( Fig . 8C ) . In addition , production of IL-6 , a key cytokine in fibroblast proliferation and activation was also increased in the CAT2−/− fibroblasts , both at baseline and in response to IL-4/IL-13 stimulation ( Fig . 8D ) . Consistent with these in vitro observations , we detected significantly more fibroblasts in CAT2−/− liver granulomas at both 8 and 12 wk post-infection ( Fig . 8E ) . Finally , to provide evidence that alternative activation was increased in vivo , we injected WT and CAT2−/− mice intravenously with 5000 viable S . mansoni eggs and examined the expression of Arg1 and Retlna ( RELM-α/Fizz1 ) mRNA in the lung at 4 and 7 days post-injection . As shown in Figure 8F , both genes associated with alternative activation were significantly upregulated in the CAT2−/− lung . Finally , we also stained liver sections from infected mice with antibodies to Arg1 , alpha smooth muscle actin ( α-SMA ) , and F4/80 , to characterize the pattern of Arg1 expression in vivo . Consistent with the enhanced fibroblast activity observed in vitro , the CAT2−/− granulomas showed much greater staining for α-SMA , a marker of activated myofibroblasts . They also displayed much stronger staining for Arg1 and the overlay ( purple staining ) suggested that the majority of Arg1 was associated with myofibroblasts , with lesser staining observed in macrophages ( Fig . 9 ) . The IL-13 receptor alpha 2 functions as a decoy receptor for IL-13 [39] , and studies conducted with IL-13Rα2−/− mice demonstrated that the decoy receptor inhibits the development of hepatic fibrosis in schistosomiasis [40] , [41] . Because fibroblasts are believed to be the key producers of sIL-13Rα2 and fibroblast function was altered in the absence of CAT2 ( Fig . 8 ) , we measured the circulating levels of IL-13Rα2 in infected CAT2−/− mice , since changes in IL-13Rα2 expression might be contributing to their exacerbated IL-13-associated pathologies . Surprisingly however , we found either similar , or at some time points , increased levels of sIL-13Rα2 in the infected CAT2−/− mice ( Fig . 8G ) . Thus , despite displaying decreased IL-13 responses ( Figs . 5–6 ) and increased IL-13 decoy receptor levels ( Fig . 8G ) , the CAT2−/− mice developed an exacerbated fibrotic response . Next , to determine whether the severe liver pathology in the infected CAT2−/− mice was in fact dependent on IL-13 , we infected WT and CAT2−/− mice with S . mansoni and inhibited IL-13 with a neutralizing mAb . As expected [26] , IL-13 blockade significantly decreased fibrosis in WT mice ( Fig . 10A ) without affecting the overall magnitude of the granulomatous response ( Fig . 10B ) . Surprisingly however , IL-13 blockade was completely ineffective in CAT2−/− mice ( Fig . 10A ) , suggesting that their fibrotic response was independent of IL-13 activity . Importantly , similar numbers of eggs and paired adult parasites were found in the tissues of all groups ( Table S2 ) . In a final series of experiments , we investigated whether CAT2 is required for the development of Th1-dependent immunity , since NO production was impaired in CAT2−/− macrophages ( Fig . 7B ) , as well as in fibroblasts ( Fig . 8A ) . In these studies , the Toxoplasma gondii model was used , since resistance is known to be mediated by an IFN-γ and NO-dependent mechanism [42] . Initially , we examined whether susceptibility was altered in the CAT2−/− mice by monitoring host survival following infection with T . gondii . IFN-γ−/− and NOS2−/− mice were included as controls . As expected , WT mice were much more resistant than either IFN-γ−/− and NOS2−/− mice , with approximately 50% of the WT animals surviving through day 50 ( Fig . 11A ) . In contrast , 100% of the IFN-γ−/− and NOS2−/− mice succumbed between days 7–10 post-infection , while CAT2−/− animals displayed an intermediate phenotype , with 100% mortality observed by day 42 . We also infected WT and CAT2−/− mice and isolated peritoneal exudate cells ( PECs ) on day 7 to quantify the number of infected cells and to examine IFN-γ and NO responses ex vivo . Consistent with their enhanced susceptibility , the percentage of infected cells increased in the CAT2−/− mice ( Fig . 11B ) . This was also associated with a significant increase in IFN-γ production , both at baseline and following stimulation with soluble T . gondii antigen ( STAG ) . Nevertheless , despite displaying much stronger IFN-γ responses , production of NO was markedly decreased in the CAT2−/− PECs , which likely explains their enhanced susceptibility .
Previous studies have suggested that CAT2 controls NOS2 and arginase activity by regulating arginine transport into cells [43] , however the relative importance of CAT2 in the development of Th1 and Th2 effector functions was not investigated . Here , we examined the role of CAT2 encoded by the Slc7a2 gene in vivo by studying two well-established infectious disease models , characterized by the development of either protective Th1- or pathogenic Th2-type immune responses [5] , [42] . We found that CAT2-deficient mice were significantly more susceptible to the Th1-inducing pathogen T . gondii . The increased susceptibility was attributed to the attenuated NO response , which led to uncontrolled parasite replication . When CAT2−/− mice were challenged with the Th2-inducing pathogen S . mansoni , the animals developed significantly worse Th2-associated pathology , despite displaying weaker Th2 responses . Importantly , the pathological changes in the CAT2−/− mice were associated with increased arginase activity in fibroblasts and alternatively activated macrophages . These results reveal an essential role for CAT2 in the development of Th1 immunity . However , they also suggest that CAT2 functions as a potent negative regulator of Th2-associated pathology , most likely by limiting arginase activity in important effector cells like fibroblasts and macrophages . NO production by iNOS contributes to normal cellular processes , resistance to intracellular pathogens , and pathophysiological conditions [23] , [44] . MacLeod and colleagues found that CAT2 is induced coordinately with iNOS in numerous cell types and studies conducted with CAT2-deficient cells demonstrated that arginine uptake via CAT2 is required for sustained NO production in macrophages [25] and to a lesser extent in astrocytes [45] . However , NO synthesis in fibroblasts was only partially dependent on CAT2 , suggesting that other compensating transporters can provide arginine for iNOS-mediated NO synthesis [46] . Thus , the dependence on CAT2-mediated L-arginine transport for NO production appears to vary in different cell types . Moreover , the relative importance of CAT2 in the development of NO-dependent immunity in vivo was previously unknown . To evaluate the function of CAT2 in vivo , we infected CAT2−/− mice with the intracellular pathogen T . gondii . Resistance to T . gondii is mediated by an IFN-γ and NOS2-dependent mechanism [42] . Therefore , we compared CAT2−/− mice with IFN-γ- and NOS2-deficient animals , since they are known to rapidly succumb to T . gondii infection . Interestingly , despite developing a significantly stronger IFN-γ response ( due to the higher parasite burdens ) , the CAT2−/− mice were much more susceptible to T . gondii , with all animals succumbing within 6 weeks . The increased susceptibility was associated with a markedly attenuated NO response , suggesting that CAT2 is critically important to the development of Th1-associated immunity . However , the fact that NOS2−/− mice died earlier than the CAT2−/− animals suggests that NO synthesis in vivo is only partly dependent on CAT2 . This was consistent with the reduced but not completely ablated NO responses of CAT2−/− peritoneal exudate cells . Since NOS2 and Arg-1 both require L-arginine as a substrate [1] , we hypothesized that CAT2 might also regulate important Th2 effector functions . Recent in vitro studies with bone marrow-derived macrophages demonstrated that CAT2 is induced by both Th1- and Th2-type stimuli [43] , [47] , which was consistent with our observations . Moreover , studies conducted with macrophages showed that L-arginine transport is significantly impaired in the absence of CAT2 , regardless of the stimuli used to activate the cells [47] . Thus , it was suggested that CAT2 regulates both the classical and alternative activation of macrophages [43] . Because Th2-driven alternative macrophage activation plays a critical role in the pathogenesis of schistosomiasis [9] , [10] , we investigated the function of the CAT2 gene in the murine model of schistosomiasis . Strikingly , although CAT2 deficiency did not affect the establishment of S . mansoni infection , Th2-associated pathology in the liver was exacerbated and the animals died at a significantly accelerated rate when compared with WT mice . Indeed , granuloma size increased more than 3-fold and development of hepatic fibrosis was exacerbated . Similar results were also obtained with the S . mansoni pulmonary granuloma model . Because granuloma formation and fibrosis are driven by the Th2 cytokine response [7] , [10] , [26] , [27] , [29] , [35] , [36] , [48] , we initially examined whether CAT2−/− mice were developing stronger Th2 responses . Unexpectedly , despite displaying a significant increase in Th2-associated pathology [6] , the frequency of cytokine-producing CD4+ Th2 cells was markedly reduced in the livers of the infected CAT2−/− mice ( Fig . 5A ) . The granuloma associated CD4+ Th2 lymphocytes also proliferated less when restimulated in vitro . Together , these results indicate that CAT2 is required for the maximal development of Th2 responses . Thus , the severe pathological changes in the CAT2−/− mice were paradoxically associated with reduced rather than enhanced Th2 cytokine production . Previous studies with IL-4Rα−/− and some IL-4-deficient mice demonstrated that development of the Th2 response is critical for survival in schistosomiasis , especially during the early stages of infection [10] , [27] , [49] , [50] . In addition , recent studies with macrophage/neutrophil-specific IL-4Rα-deficient mice suggested that the development of alternatively activated macrophages , in particular , is critically important for host survival . [10] . Despite developing significantly weaker Th2 responses , however , the CAT2−/− mice showed no signs of increased susceptibility to S . mansoni when infected with a low dose of parasites , with all of the knockout animals successfully establishing chronic infections . Moreover , in contrast to infected IL-4Rα and LysM ( Cre ) IL-4Rα ( −/flox ) animals [10] , the CAT2−/− mice did not default to a Th1-type immune response . They also displayed no evidence of significant hepatoxicity as determined by their serum AST/ALT responses . In fact , liver enzymes were slightly reduced in the CAT2−/− mice when compared with infected WT animals . These data , when combined with the histological findings discussed above , suggest that alternative macrophage activation is not significantly impaired in the infected CAT2−/− mice . In fact , evidence was obtained both in vitro and in vivo that alternative activation increased in the absence of CAT2 . To investigate this hypothesis further , we stimulated WT and CAT2−/− bone marrow-derived macrophages with cytokines that are known to promote alternative macrophage activation including , IL-4 , IL-13 , IL-21 , and GM-CSF [13] , [37] , [38] and examined the induction of arginase activity , a key feature of AAMøs [1] , [51] , [52] . As expected , the Th2-associated cytokines triggered significant arginase activity in WT macrophages . However , the macrophages generated from CAT2−/− mice consistently displayed a markedly exaggerated response . These data demonstrate that CAT2 functions as a negative regulator of arginase activity in macrophages , which may in part explain their exacerbated fibrotic response . Thus , although it was recently suggested that CAT2 could regulate both the classical and alternative activation of macrophages [43] , our combined in vitro and in vivo data indicate that the primary role of CAT2 is to optimize NO production in classically-activated macrophages , while limiting arginase activity in alternatively-activated cells . The maintenance of arginine transport by the constitutive arginine transporter CAT1 could explain the preservation of arginase activity in the CAT2−/− mice . Although alternatively-activated macrophages are believed to be important regulators of wound healing and fibrosis [1] , [5] , fibroblasts are the primary collagen secreting cells . While it was previously shown that CAT2 has only minimal effect on NO production in classically-activated fibroblasts [46] , the effects of CAT2-deficiency on arg1 activity was not examined . To determine whether CAT2 regulates fibroblast activation , we generated primary lung fibroblasts from WT and CAT2−/− mice and stimulated the cells with various Th1 or Th2-type stimuli . Surprisingly , although classically-activated fibroblasts were in general less potent producers of NO than macrophages , we observed a significant ( >75% ) reduction in NO production in CAT2−/− fibroblasts . In combination with earlier studies focused on embryonic fibroblasts that reported a minimal ( <20% ) effect on NO production [46] , our data suggest the dependence on CAT2 for NO synthesis varies in different fibroblast subpopulations . We also examined the effects of CAT2 deficiency on arginase activity . In contrast to macrophages , however , where arginase activity was strictly dependent on Th2 cytokine stimulation , fibroblasts displayed no significant cytokine inducible arginase response , even when stimulated with an optimal combination of Th2-type cytokines [37] , [38] . Nevertheless , when arginase activity in WT and CAT2−/− fibroblasts was compared , the CAT2−/− fibroblasts exhibited much greater arginase activity at baseline . The fibroblasts from CAT2−/− mice also proliferated faster and produced significantly more of the autocrine growth factor IL-6 [53] , both before and after stimulation with IL-4 and IL-13 [54] , [55] , [56] . Thus , the enhanced arginase activity in CAT2−/− fibroblasts and AAMø's likely contributed to their exacerbated inflammatory and fibrotic responses following infection with S . mansoni . The increased arginase response may also explain the suppressed CD4+ Th2 cell responses in the granulomatous tissues . Because T cells , macrophages , and fibroblasts all compete for arginine , the increased arginase activity in CAT2−/− macrophages and fibroblasts may have reduced arginine levels in the granulomatous tissues , resulting in the local suppression of CD4+ T cell responses , as has been postulated recently in related studies [17] , [57] , [58] . In contrast to classically activated macrophages , AAMø's are also known to be inefficient stimulators of T cell proliferation [59] , with F4/80+ alternatively activated macrophages functioning as potent inhibitors of antigen-specific CD4+ T cell proliferative responses in vivo [58] . Fibroblasts are also important producers of the soluble IL-13Rα2 [40] , [60] , which can function as a decoy receptor for IL-13 and was recently shown to inhibit the development of fibrosis in schistosomiasis [39] , [40] , [41] . Since CAT2 fibroblasts displayed an unusual activated phenotype , we examined whether production of the sIL-13Rα2 was also altered in the CAT2−/− mice . Decreased production of the sIL-13Rα2 could provide a simple and straightforward explanation for their exacerbated IL-13-driven pathological responses . Surprisingly , the exact opposite was observed . Indeed , serum levels of sIL-13Rα2 were either the same or slightly increased in the infected CAT2−/− mice . Thus , we questioned whether the development of fibrosis in CAT2−/− mice was in fact dependent on IL-13 . To formally address this question , we performed a series of studies with neutralizing antibodies to IL-13 [61] . Strikingly , although IL-13 blockade had a highly significant anti-fibrotic effect in WT mice , CAT2−/− mice were unresponsive . These observations , when combined with the reduced IL-13 and enhanced IL-13Rα2 responses , suggest that the development of fibrosis in CAT2−/− mice is to a large extent IL-13-independent . When viewed together , the data point to fibroblasts and AAMø's as the key mediators of the exacerbated pathological response , since arginase activity was increased in the CAT2−/− cells . In the case of fibroblasts , the enhanced arginase response also appeared to be independent of Th2 cytokine stimulation . Arg1 and α-SMA expression also colocalized in the granulomatous livers and both proteins were expressed at much higher levels in the infected CAT2−/− mice , confirming that there were more activated myofibroblasts . However , there was no evidence of spontaneous liver fibrosis in uninfected CAT2−/− mice , suggesting that a chronic inflammatory stimulus or some type of tissue damage was needed to initiate the fibroproliferative response . Nevertheless , a recent study found that CAT2-deficient mice are susceptible to the development of spontaneous inflammation in the lung [62] . The same group also showed that CAT2 expression is linked with the development of asthma [63] . Thus , mucosal tissues , which are repeatedly exposed to irritants , may be particularly sensitive to changes in CAT2 activity . As such , CAT2 may be involved in the regulation of a wide variety of diseases that are normally associated with chronic Th2 responses . In conclusion , these studies demonstrate for the first time that CAT2 is critically important for the development of IFN-γ/NO-dependent immunity to the intracellular protozoan pathogen T . gondii . In addition , by inhibiting arginase activity in fibroblasts and alternatively-activated macrophages , CAT2 functions as a powerful negative regulator of type-2 cytokine-driven pathology . Thus , these findings may have major implications for a wide variety of infectious and inflammatory diseases .
Female C57BL/6 were obtained from Taconic Farms ( Germantown , NY ) [64] . Breeding pairs of C57BL/6 CAT2−/− mice were obtained from the UCSD Cancer Center ( La Jolla , CA ) [25] . Mice were housed under specific pathogen-free conditions at the National Institutes of Health in an American Association for the Accreditation of Laboratory Animal Care approved facility . The NIAID animal care and use committee approved all experimental procedures . S . mansoni eggs were extracted from the livers of infected mice ( Biomedical Research Institute , Rockville , MD ) as previously described [35] . For the induction of secondary granulomas , mice were sensitized intraperitoneally ( i . p . ) with 5000 live eggs , and then challenged with 5 , 000 live eggs i . v [31] . In the infection experiments , mice were infected percutaneously via the tail with 30–35 cercariae of a Puerto-Rican Strain of S . mansoni ( NMRI ) obtained from infected Biomphalaria glabrata snails ( Biomedical Research Institute ) . Soluble egg Antigen ( SEA ) was obtained from purified and homogenized S . mansoni eggs [33] . All animals underwent perfusion at the time of sacrifice so that worm and tissue egg burdens could be determined [33] . 20 cysts of the avirulent ME49 strain were inoculated i . p into C57BL/6 , CAT2−/− , NOS2−/− ( Taconic ) , and IFN-γ−/− ( Taconic ) mice for morbundity studies . In some studies , mice were sacrificed day 7 post-inoculation and PECs were harvested and set up in culture for 24 and 48 hours in media and with soluble tachyzoite Ag ( Stag ) , which was prepared as described [65] . The sizes of pulmonary and hepatic granulomas were determined on histological sections that were stained with Wright's Giemsa stain ( Histopath of America , Clinton , MD ) . Approximately 30 granulomas per mouse were included in all analyses . A skilled pathologist evaluated the percentages of eosinophils , mast cells , and other types of cells in the same sections . The number of schistosome eggs in the liver and the gut and the collagen content of the liver , as measured by hydroxyproline levels , were determined as previously described [33] . Specifically , hepatic collagen was measure as hydroxyproline by the technique of Bergman and Loxley [66] . The increase in hepatic hydroxyproline was positively related to egg numbers in all experiments and hepatic collagen is reported as the increase above normal liver collagen in µmoles per 10 , 000 eggs; ( infected liver collagen – normal liver collagen ) /liver eggs × 10−4 or µmoles per worm pair . At late chronic time points , fibrosis is reported as total liver collagen per liver . The same individual scored all histological features and had no knowledge of the experimental design . Mesenteric lymph nodes ( MLN ) and about 200 mg of granulomatous liver tissue was disrupted into single cell suspension by grinding through a 100 µm nylon mesh . The WBCs from liver cells were separated on a 34% percoll gradient ( 350 g for 20 min ) ( Fluka ) . MLN and Liver WBCs were treated with 2 ml of ACK lysis buffer ( Quality Biological ) for 2 min . Purified leukocytes were stained with 5 mM CFSE ( Molecular Probes ) for 5 min at RT . Excess CFSE was quenched by washing the cells in RPMI supplemented with 10% FBS . 3×106 cells were cultured in 24 well plates and were either left unstimulated or stimulated with 1 µg/ml of Con A for 72 hours . ( note , the WBCs separated from the liver contain live S . mansoni eggs and therefore all liver leukocyte cultures are exposed to soluble egg antigens as well ) . ICC: Liver leukocytes either freshly isolated ( ex vivo ) or restimulated for 72 hrs were stimulated with PMA ( 10 ng/ml ) , Ionomycin ( 1 µg/ml ) and BFA ( 10 µg/ml ) ( Sigma ) for 3 hrs . Cells were surface stained for CD4 PE-Cy5 ( BD Biosciences ) , fixed in 2% formaldehyde for 20 min at RT , permeabolized with 0 . 1% saponin buffer ( Sigma ) and stained for IFN-γ ( APC or FITC ) , IL-5 APC ( BD Biosciences ) and IL-13 PE ( Centocor ) and acquired with FACS Calibur® . Data were analyzed in Flowjo® V8 . Lung and liver tissue samples were placed individually in 500 µl of RNAlater and frozen at −20°C ( Ambion ) . Samples were removed from RNAlater and placed in 500 µl TRIzol reagent ( Invitrogen ) to purify RNA . Total RNA was further purified using RNeasy Mini Kit from Qiagen ( Qiagen Sciences ) . RNA ( 1 µg ) was reverse-transcribed using Superscript II ( Invitrogen , Carlsbad , CA ) and quantification of transcripts was performed using Applied Biosystems ( Foster City , CA ) pre-designed gene expression assays for IFN-γ , IL-5 , IL-13 , IL-4 , and IL-10 . Each Taqman assay was run in duplicate . For each sample 5 ul of a 1∶30 dilution of cDNA reaction cocktail in a 20 ul final volume TaqMan reaction was used for each assay . Reaction preparation and thermal cycling were carried out following the manufacturer's protocol with a modification of increasing qPCR cycles to 50 . Assay samples were normalized to HPRT expression and compared to uninfected controls according to comparative CT method ( Applied Biosystems ) . Bone marrow was recovered from female C57BL/6 and CAT2−/− mice and cultured in Petri dishes ( 100 × 15 mm ) containing supplemented DMEM media ( 20% L929 conditioned medium ) for a period of 6 days . After six days cells were harvested and seeded at a concentration of 5 × 105 cells/well in 24 well plates containing supplemented DMEM media ( 10% FBS , 2 mM L-glutamine , 100 U/mL penicillin , and 100 ug/mL streptomycin ) . Cells were stimulated for 16 hr with combinations of IL-4 , IL-13 , and GMCSF ( 20 ng/ml ) , IFNγ ( 100 U/mL ) , or LPS ( 100 ug/mL ) ( Peprotech ) . In some cases , the cells were pretreated with IL-21 ( R&D ) for a period of 6 hr . Supernatants were collected for NO analysis and cells were lysed for arginase activity and RNA isolations . Real-time RT-PCR was performed on an ABI PRISM 7900HT Sequence Detection System ( Applied Biosystems ) . Relative quantities of mRNA was determined using SYBR Green PCR Master Mix ( Applied Biosystems ) and by the comparative threshold cycle method . In this method , mRNA levels for each sample were normalized to hypoxanthine guanine phosphoribosyl transferase ( HPRT ) mRNA levels and then expressed as a relative increase or decrease compared with levels in media only controls . Primers were designed using Primer Express software ( version 2 . 0; Applied Biosystems ) . Primers for CAT2-F CTC CTG GGT GCT CTG AAC CA and CAT2-R CTT CTC CCC TCC CGT TGA AC . Whole lungs were harvested in cRPMI supplemented with 10% FBS ( Hyclone ) , 2 mM- L-Glutamine , 100 µg/ml penicillin–streptomycin ( Gibco ) , 50 uM ß-mercaptoethanol ( Sigma ) , minced into small pieces , and exposed to Collagenase D ( 1 mg/ml ) ( Roche ) and 4 U/ml DNase I ( Sigma ) for 40 mins at 37°C with shaking . Tissues were disrupted by straining through a 100 micron nylon mesh ( BD Falcon ) . The single cell suspensions were plated in Iscove's Modified Dulbecco's Medium with 2 mM L-glutamine , 5% FCS , 25 mM HEPES , 100 µg/mL Streptomycin , 100 U/mL Penicillin , 50 uM 2-ME . ( 3 lungs plated on 3–100 × 15 mm Petri dishes ) . 50% of media was changed on day 7 and cells were recovered on day 14 by adding 4 mL of HyQtase ( Hyclone ) reagent for 20 min and rigorously pipetting repeatedly to remove cells . Cells were then cultured at 5 × 105 cells/well in 24 well plates . After activation with cytokines , supernatants were collected for NO analysis and IL-6 determination . Other cells were lysed to determine arginase activity or cultured for fibroblast proliferation . IL-13Ra2 levels were determined by ELISA as previously described [41] . The concentration of IL-13Rα2 in the sample was determined from a serial-fold diluted standard of rmIL-13Rα2 Fc/chimera ( R & D Systems ) . The sensitivity of the assay was approx . 98 pg/ml . IL-6 levels were measured using murine IL-6 DuoSet ELISA Development System ( R&D Systems ) according to the manufacturer's protocol . IFN-γ was assayed by sandwich ELISA , as previously described [41] , and quantitated by comparison with standard curves generated with rIFN-γ ( provided by Genentech , San Francisco , CA , and Genetics Institute , Cambridge , MA , respectively ) . The concentration of nitrite in supernatants of primary lung fibroblasts and bone-marrow derived macrophages stimulated in vitro was determined spectrophotometrically by using the Griess reagent . Supernatants were collected after 16 hours , mixed 1/1 with Griess reagent , and absorbance measured at 543 nm using a SpectraMax 190 ( Molecular Devices , Sunnyvale , CA ) . The nitrite concentration was determined using sodium nitrite as standard . In the arginase assays , cells were plated at 5 × 105 per well in 96 well tissue culture plates and stimulated with combinations of IL-4 , IL-13 , and IL-21 ( 20 ng/mL ) . IL-21 was added 6 hours prior to IL-4 or IL-13 stimulation . Following stimulation , cells were washed with PBS and lysed with 0 . 1% TritonX-100 containing protease inhibitor ( Roche ) . Lysates were transferred into a 96 well PCR plate and incubated with 10 mM MnCl2 and 50 mM Tris HCl ( pH 7 . 5 ) to activate enzyme for 10 min at 55°C . After enzyme activation , 25 µl of lysate was removed and added to 25 µl 0 . 5 M arginine ( pH 9 . 7 ) in a new PCR plate and incubated for 1–2 hours at 37 C . 5 µl of each sample was added in duplicate to a 96 well ELISA plate along with 5 µl of each standard , diluted in same assay conditions , starting at 100 mg/dL . Urea determination reagent from BioAssay Systems Quantichrome Urea Assay Kit was used according to the manufacturer's protocol . C57BL/6 ( 10/group ) mice were infected percutaneously via the tail with 30–35 S . mansoni cercariae . Beginning on wk 5 post-infection , mice were treated with either mouse anti-IL-13 mAb [61] or GL113 control antibody ( Harlan Bioproducts ) . Each mouse received one 0 . 5 mg dose/wk via i . p . injection on wk 5 , 6 , 7 and 8 . Mice were sacrificed on wk 9 post-infection . Acute tachyzoite growth was assessed using cytocentrifuge smears of peritoneal cells as previously described [65] . Differential analyses , including assessment of intracellular T . gondii infection , were performed on 700 or more cells per animal . Single-cell suspensions were prepared from peritoneal exudates and washed in CRPMI . Peritoneal cells were cultured at 2 . 5 × 106 cells/mL in 200 µl of RPMI 1640 supplemented with 10% FBS , penicillin ( 100 U/ml ) , streptomycin ( 100 mg/ml ) , L-glutamine ( 2 mM ) , HEPES ( 10 mM ) , and 2-ME ( Sigma ) in the presence or absence of soluble tachyzoite Ag ( 5 µg/ml ) . Supernatants were harvested 24 and 48 hr later for determination of levels of IFN-γ and NO . WT and CAT2−/− primary lung fibroblasts ( 1 × 105/well ) were plated in Iscove's Modified Dulbecco's Media in 96 well flat bottom plates ( BD Falcon ) for 16 hr at 37°C . 1 µCi/well of [3H]thymidine ( Amersham ) was added for 24–72 hrs . Cells were frozen at −20 C and were later collected onto a glass fiber filter pads ( LKB Wallac , Turku , Finland ) using a 96-well harvester ( Tomtec , Orange , CT ) . Scintillation cocktail was ( XSC/9200; LKB Wallac ) added , and radioactivity determined on a liquid scintillation counter ( Betaplate model 1205 , LKB Wallac ) . Some cells were stimulated with FGFβ at 50 ng/ml ( Invitrogen ) . Each sample was set up in triplicate . 8 um liver sections were taken from WT and CAT2−/− 10 wk S . mansoni infected livers . Sections were fixed in cold acetone for 10 minutes and stored at −20°C . Slides were washed 3× in DPBS and incubated with F4/80 conjugated with Alexa 488 ( Caltag Clone CI:A3-1 ) , Alpha Smooth Muscle Actin ( Sigma-Aldrich Clone 1A4 ) conjugated with Texas Red , and Arginase1 ( Santa Cruz Biotechnology Clone V-20 ) conjugated with Alexa 647 . Images were collected on a Leica SP5 confocal microscope ( Leica Microsystems , Exton , PA USA ) using a 20× oil immersion objective NA 0 . 70 . Fluorochromes were excited using an Argon laser for Alexa 488 , an Orange Helium-Neon laser for Alexa 594 and a Red Helium-Neon laser for Alexa 647 . To avoid possible crosstalk the wavelengths were collected separately and later merged . Images were processed using Leica LAS-AF software ( version 1 . 7 . 0 build 1111 ) . Hepatic fibrosis ( adjusted for egg number ) decreases with increasing intensity of infection ( worm pairs ) . Therefore , these variables were compared by analysis of covariance , using the logarithm of total liver eggs as the covariate and the logarithm of hydroxyproline content per egg . Variables that did not change with infection intensity were compared by one-way ANOVA or Student's t test [33] . Changes in cytokine mRNA expression and granuloma size were evaluated using ANOVA . Differences were considered significant when p<0 . 05 . | Recent studies conducted with amino transporter Slc7a2-deficient mice ( CAT2 ) demonstrated that NOS2 activity in macrophages is regulated by CAT2 . NOS2 , which synthesizes nitric oxide , regulates numerous important activities , including resistance to infectious organisms , tumor development , and autoimmune diseases . It also competes with the enzyme Arginase-1 ( Arg1 ) for the common substrate L-arginine . However , the role CAT2 in the regulation of Arg1 activity has not been previously examined . Therefore , we infected CAT2-deficient mice with the helminth parasite Schistosoma mansoni or with the protozoan pathogen Toxoplasma gondii , two organisms that trigger highly divergent host immune responses . Strikingly , following infection with S . mansoni , CAT2−/− mice developed parasite egg–induced lesions in the liver that were 3 to 4 times larger than wild type and hepatic fibrosis ( a feature of severe schistosomiasis ) was exacerbated , indicating a general worsening of disease in the absence of CAT2 . The CAT2−/− mice were also more susceptible to T . gondii infection , demonstrating that CAT2 is critical for the development of protective cell-mediated immunity . Thus , these studies identify CAT2 as a powerful regulator of host immune responses , which may have major implications for a variety of infectious , inflammatory , and autoimmune diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
]
| [
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"gastroenterology",
"and",
"hepatology/hepatology",
"immunology/immune",
"response",
"infectious",
"diseases/helminth",
"infections",
"infectious",
"diseases/protozoal",
"infections",
"immunology/immunity",
"to",
"infections"
]
| 2008 | Cationic Amino Acid Transporter-2 Regulates Immunity by Modulating Arginase Activity |
FAS-associated factor-1 ( FAF1 ) is a component of the death-inducing signaling complex involved in Fas-mediated apoptosis . It regulates NF-κB activity , ubiquitination , and proteasomal degradation . Here , we found that FAF1 positively regulates the type I interferon pathway . FAF1gt/gt mice , which deficient in FAF1 , and FAF1 knockdown immune cells were highly susceptible to RNA virus infection and showed low levels of inflammatory cytokines and type I interferon ( IFN ) production . FAF1 was bound competitively to NLRX1 and positively regulated type I IFN signaling by interfering with the interaction between NLRX1 and MAVS , thereby freeing MAVS to bind RIG-I , which switched on the MAVS-RIG-I-mediated antiviral signaling cascade . These results highlight a critical role of FAF1 in antiviral responses against RNA virus infection .
FAS-associated factor 1 ( FAF1 ) was originally identified as a member of the FAS death-inducing signaling complex [1] . FAF1 harbors several protein interaction domains , including FAS-interacting domains ( FID ) , a death effector domain-interacting domain ( DEDID ) , and multi-ubiquitin-related domains , which interact with ubiquitinated target proteins and regulate their proteolysis [2] . Although FAF1 initially demonstrated to have Fas induced apoptotic potential [3] , it also has diverse biological functions such as regulation of NF-κB signaling , chaperone activity and proteosomal degradation by ubiquitination . [2 , 4–7] . Early recognition of invading viruses by host cells is critical to antiviral innate immunity . Invading viruses trigger type I interferon-mediated antiviral responses and induce production of effector proteins that inhibit completion of the virus cycle and virus dissemination in vivo [8–12] . Germline-encoded pattern recognition receptors ( PRRs ) within the innate immune system sense signature molecules expressed by pathogens , known as pathogen-associated molecular patterns ( PAMPs ) . To date , PRRs are classified into three families: retinoic acid inducible gene ( RIG ) -I-like receptors ( RLRs ) , Toll-like receptors ( TLR ) , and the nucleotide oligomerization domain ( NOD ) and leucine-rich repeat and pyrin domain-containing ( NLRP ) proteins [8 , 13] . RLRs such as RIG-I and melanoma differentiation-associated gene-5 ( MDA-5 ) are important molecules that detect viral RNA in the cytosol . In uninfected cells , RIG-I exists in an auto-repressed conformation in which the caspase activation and recruitment domains ( CARDs ) are not available for binding to induce downstream signal transduction [14] . Upon recognition of viruses , particularly RNA viruses , RIG-I is activated and undergoes self-dimerization and structural modifications that permit CARD-CARD interactions with the downstream adapter molecule , mitochondrial antiviral signaling protein ( MAVS; also known as IPS-1 , VISA , and Cardif ) [15–20] . Then it activates type I interferon responses via downstream signaling molecules TBK1/IKKi and IRF3 , and NF-κB activation via IKK , to elicit inflammatory responses [21–26] . However , interferon- or NF-κB-mediated immune responses need to be tightly regulated to maintain host immune homeostasis , otherwise the uncontrolled immune response can be deleterious , or even fatal , to the host [27–32] . Hence , molecules involved in regulating interferon-mediated innate immune response are the subject of much research . Indeed , mechanisms that regulate RIG-I-mediated antiviral signaling , which is tightly controlled by a series of positive and negative regulators , have been reported [13 , 33 , 34] . Among these , NLRX1 , a member of the nucleotide-binding domain and leucine-rich-repeat-containing ( NLR ) protein family , resides on the outer mitochondrial membrane and interfere CARD-CARD interactions between MAVS and RIG-I to negatively regulate antiviral interferon signaling [35–38] . However , during virus infection , the mechanism which controls type I interferon ( IFN ) signaling via modulating the MAVS and NLRX1 interaction , needs to be investigated more in detail . Here , we show that FAF1 is a positive regulator of the NF-κB and type I interferon signaling pathways during RNA virus infection . FAF1 competitively binds to NLRX1 , thereby disrupting its interaction with MAVS and ultimately amplifying the downstream antiviral immune response .
To examine the biological function of FAF1 , we performed experiments using FAF1+/+ and FAF1gt/gt mice after confirmed by genotyping ( S1 Fig , panels A-B-C ) . First , mice were infected with the of vesicular stomatitis virus ( VSV ) Indiana strain ( VSV-Indiana ) via tail-vein injection and their survival was monitored to determine susceptibility to viral infection ( Fig 1 , panel A ) . Knockdown of FAF1 rendered mice significantly more susceptible to lethal VSV infection . A plaque assay and quantitative real-time polymerase chain reaction ( qRT-PCR ) was conducted to measure the amount of VSV in spleen , lung , liver , and brain tissues at 24 hr and 6 days post-infection ( hpi and dpi ) ( Fig 1 , panels B-C and S1 Fig , panel D ) . Organs from FAF1gt/gt mice contained higher amount of virus than those from FAF1+/+ mice . This suggests that the virus replicates more actively in FAF1gt/gt mice than in FAF1+/+ mice , resulting increased mortality . Additionally , serum samples were collected at different time points after mice were infected with green fluorescent protein ( GFP ) -tagged VSV ( VSV-GFP ) ( Fig 1 , panels D-E ) or treated with Poly ( I:C ) ( S1 Fig , panel E ) . The serum of FAF1gt/gt mice contained more replicating virus and lower levels of IFN-β and IL-6 than that of FAF1+/+ mice , indicating that knockdown of FAF1 suppresses cytokine secretion upon virus infection . Moreover , peripheral blood mononuclear cells ( PBMCs ) from both groups of mice injected with VSV-GFP via tail-vein were collected and measured to check mRNA encoding IFN-related genes expression at 24 hpi ( Fig 1 , panel F ) . PBMCs from FAF1gt/gt mice expressed lower levels of mRNA encoding IFN-related genes than those from FAF1+/+ mice . These results provide in vivo evidence that FAF1 knockdown affects type I IFN mediated signaling and antiviral immunity . BMDMs were isolated from the bone marrow of FAF1+/+ and FAF1gt/gt mice and infected with VSV-GFP or GFP tagged H1N1 influenza virus ( A/PR8/8/34; PR8-GFP ) . Virus replication was higher in BMDMs of FAF1gt/gt than in those of FAF1+/+ mice at 12 and 24 hpi ( Fig 2 , panel A ) . To determine the reason for the increased viral replication in BMDMs of FAF1gt/gt mice , IL-6 and IFN-β levels were analyzed after 12 and 24 hr of VSV-GFP and PR8-GFP infection or Poly ( I:C ) treatment ( Fig 2 , panel B ) . BMDMs of FAF1gt/gt mice produced less IL-6 and IFN-β than BMDMs of FAF1+/+ mice . Next , BMDCs and PBMCs were isolated from FAF1+/+ and FAF1gt/gt mice , and stimulated with VSV-GFP , PR8-GFP or Poly ( I:C ) . Virus titers and cytokine secretion were then compared ( S2 Fig ) . BMDCs and PBMCs isolated from FAF1gt/gt mice harbored greater amounts of virus and secreted lower levels of cytokines than BMDCs and PBMCs of FAF1+/+ mice . These data suggest that immune cells within the BMDMs , BMDCs , and PBMCs populations from FAF1gt/gt mice show inhibited type I IFN signaling , which facilitates viral replication . To find out whether FAF1 has a similar effect after infection with a DNA virus , BMDMs were isolated from FAF1+/+ and FAF1gt/gt mice and infected with GFP tagged Herpes Simplex virus 1 ( HSV-GFP ) ( S2 Fig , panels C-D ) . There was no difference in the observed levels of cytokine secretion or virus replication between BMDMs from the two groups of mice . This confirms that FAF1 has no role in DNA virus-stimulated type I IFN signaling . Taken together , these data suggest that FAF1 positively regulates type I IFN signaling in response to infection by RNA viruses . To examine the effect of FAF1 on virus replication in vitro , we prepared FAF1 knockdown MEFs from FAF1gt/gt mice . FAF1 knockdown was confirmed by immunoblot analysis ( S3 Fig , panel A ) . Virus titers and cytokine levels were measured at 12 and 24 hpi with VSV-GFP , PR8-GFP ( Fig 3 , panels A-B ) or GFP tagged New castle disease virus ( NDV-GFP ) ( S3 Fig , panels B-C ) . The amount of GFP expressed by cells following viral infection was examined by fluorescence microscopy and quantitated using a fluorescence modulator . FAF1 knockdown MEFs showed increased GFP expression . The virus titer was also higher in FAF1 knockdown MEFs than in wild-type ( WT ) MEFs ( Fig 3 , panel A and S3 Fig , panel B ) . Supernatants from FAF1 knockdown MEFs contained less IL-6 , IFN-α , and IFN-β than those from WT MEFs ( Fig 3 , panel B and S5 Fig , panel C ) . Moreover , supernatants from FAF1 knockdown cells contained lower levels of cytokines than those from WT cells after stimulation with Poly ( I:C ) or 5’ppp-dsRNA ( Fig 3 , panel C ) . Taken together , these data suggest that knockdown of FAF1 inhibited the immune responses by reducing IFN secretion in response to viral infection , thereby facilitating virus replication . Additionally , FAF1-reconstituted MEFs were prepared and expression of FAF1 was confirmed by immunoblotting ( S3 Fig , panel D ) . Virus titers and cytokine levels in FAF1 knockdown MEFs and FAF1-reconstituted MEFs were compared after virus infection ( S3 Fig , panels E-F-G ) . FAF1-reconstituted cells showed reduced viral replication and higher cytokine secretion than FAF1 knockdown MEFs , demonstrating that reconstitution of FAF1 restores induction of type I IFN signaling . To exclude the possibility that positive regulation of type I IFN signaling by FAF1 is a cell type-specific phenomenon , knockdown FAF1 murine macrophage cell line was prepared by infecting a lentivirus harboring FAF1 shRNA ( small hairpin RNAs ) or transfecting FAF1 siRNA ( small interfering RNA ) to RAW264 . 7 . First , reduced FAF1 expression was confirmed by immunoblot analysis ( S4 Fig , panel A ) . Viral titers and cytokine levels were evaluated after VSV-GFP or PR8-GFP infection ( Fig 4 , panels A-B and S4 Fig , panels B-C ) and treatment with Poly ( I:C ) or 5’ppp-dsRNA ( Fig 4 , panel C ) . Consistent with our previous results , viral titers were higher and cytokine levels were lower in both shRNA and siRNA FAF1 knockdown RAW264 . 7 cells than in control ( scramble ) cells . Additionally , THP-1 cells ( a human immune cell line ) were transfected with siRNA targeting FAF1 , and virus replication and cytokine levels were measured after virus infection ( S4 Fig , panels D-E-F-G ) . The results were similar to those for FAF1 knockdown RAW264 . 7 cells . To confirm these results , we generated stable FAF1 overexpressing RAW264 . 7 cells and overexpression was confirmed by immunoblot analysis ( S5 Fig , panel A ) . FAF1-overexpressing RAW264 . 7 cells infected with PR8-GFP , VSV-GFP ( Fig 4 , panels D-E ) or NDV-GFP ( S5 Fig , panels B-C ) showed lower levels of viral replication and higher levels of IL-6 , IFN-β , and IFN-α production than control RAW264 . 7 cells . Treatment with Poly ( I:C ) or 5’ppp-dsRNA yielded consistent results with the virus infection experiments ( S5 Fig , panel D ) . Additionally , to find out whether FAF1 has no effect to DNA virus infection in RAW264 . 7 cells , similar to HSV infection in BMDMs , GFP tagged adenovirus ( Adeno-GFP ) were infected to control and FAF1 knockdown ( S6 Fig , panel A ) or overexpressing ( S6 Fig , panel B ) RAW264 . 7 cells . Accordance with the results of HSV-GFP in BMDMs , Adeno-GFP experiment also showed no difference in virus replication and cytokine secretion levels between control and FAF1 knockdown or overexpressing cells . Taken together , these results suggest that , irrespective of the cell type , FAF1 positively regulates type I IFN secretion upon RNA virus infection , and not upon DNA virus infection . Moreover , to confirm whether enhanced VSV-GFP replication in FAF1 knockdown RAW264 . 7 cells and MEFs was due to repressed IFN secretion by knockdown of FAF1 and not due to intrinsic block to replication of RNA viruses , we infected VSV-GFP to FAF1 knockdown RAW264 . 7 cells and MEFs in the presence of an anti-IFNAR blocking antibody ( IFNAR Ab ) and determined VSV-GFP replication level ( S7 Fig , panels A-B ) . As shown in the results , IFNAR Ab treated control cells showed almost two to three times higher virus replication level compared with non-treated control cells due to the IFNAR blocking effect of IFNAR Ab . On the contrary , in FAF1 knockdown cells , virus replication levels slightly enhanced after treatment of IFNAR Ab , which indicated that IFNAR Ab could not exhibit IFNAR blocking effect prominently in FAF1 knockdown RAW264 . 7 cells , since type I IFN secretion was already suppressed by knockdown of FAF1 . These results suggests that enhanced or reduced virus replication depends on knockdown or overexpression of FAF1 due to the regulation of type I IFN secretion by FAF1 . Furthermore , from our results of Poly ( I:C ) and 5’ppp-dsRNA stimulation studies , we could anticipate that FAF1 regulates type I IFN signaling through RIG-I-MAVS signaling pathway , as those stimulants induce type I IFN signaling pathway by activating RIG-I . To investigate whether FAF1 regulates type I IFN secretion not through TLR7 and TLR9 , we also stimulated the TLR7 and TLR9 by their agonists , imiquimod and ODN2395 , respectively to FAF1 knockdown RAW264 . 7 cells ( S7 Fig , panel C ) . According to data , the IL-6 and IFN-β secretion levels of control and FAF1 knockdown RAW264 . 7 cells were similar , which indicate that FAF1 regulates type I IFN signaling through RIG-I mediated pathway , and not via the TLR7 and TLR9 mediated pathway . To further examine the effects of FAF1 on the antiviral signaling cascade , we next examined virus-induced phosphorylation of IRF3 , p65 , STAT1 , p38 , and TBK1 . Cells were stimulated with PR8-GFP and samples were collected at indicated time points . Whole cell lysates ( WCL ) were prepared and analyzed by immunoblotting ( Fig 5 , panels A-B and S8 Fig , panel A ) . First , scramble and FAF1 knockdown RAW264 . 7 cells were infected with PR8-GFP , and phosphorylation levels of the indicated proteins were examined ( Fig 5 , panel A ) . Protein phosphorylation in both scramble and FAF1 knockdown RAW264 . 7 cells was initiated at 8 hpi , and increased until 16 hpi , however , at later time points the levels of protein phosphorylation detected in FAF1 knockdown cells were lower than those in scramble RAW264 . 7 cells . By contrast , FAF1-overexpressing RAW264 . 7 cells showed higher levels of phosphorylation at early time points than control cells ( Fig 5 , panel B ) . These results provide strong evidence that FAF1 activates the type I IFN signaling pathway . In addition , we examined phosphorylation of target proteins in FAF1 knockdown and FAF1-reconstituted MEFs after PR8-GFP infection ( S8 Fig , panel A ) . The results showed that higher phosphorylation of these signaling proteins occurred at early time points in FAF1-reconstituted MEFs than in FAF1 knockdown MEFs . mRNA encoding IFN-related gene expressions were measured to determine whether type I IFN-related gene transcriptions were affected by FAF1 protein knockdown and reconstitution ( Fig 5 , panels C-D and S8 Fig , B-C ) . Lower mRNA expression levels were observed in FAF1 knockdown MEFs than WT MEFs ( Fig 5 , panel C ) , and significantly higher levels were noted in FAF1-reconstituted MEFs compared to FAF1 knockdown MEFs ( S8 Fig , panel B ) . Furthermore , we examined the expression of mRNA encoding IFN-related genes in BMDMs and PBMCs isolated from FAF1+/+ mice and FAF1gt/gt mice after infection with PR8-GFP or VSV-GFP ( Fig 5 , panel D and S8 Fig , panel C ) . Consistent with our previous findings , low levels of IFN-related gene transcription was observed in BMDMs and PBMCs of FAF1gt/gt mice . To examine FAF1 expression levels in response to viral infection , the levels of FAF1 mRNA were measured in BMDMs , RAW264 . 7 , THP-1 , HEK293T , HeLa and A549 cells after PR8-GFP infection . As shown in S9 Fig , panel A , FAF1 mRNA expression levels were increased after viral infection , however , this increase varied according to cell type . Results from these experiments led us to postulate that FAF1 is a positive regulator of the type I IFN signaling pathway . Previous studies in our laboratory focused on identifying binding partners for NLRX1 . A large scale pull-down assay using HEK293T cells overexpressing the GST-tagged N-terminal domain ( amino acids ( aa ) 1–225 ) of NLRX1 followed by mass spectrometry analysis identified that FAF1 is a binding candidate for NLRX1 ( Fig 6 , panel A ) . Immunoprecipitation of GST-tagged NLRX1 followed by immunoblotting with an anti-FAF1 antibody showed that NLRX1 interacted with FAF1 ( Fig 6 , panel B ) . Additionally , V5-tagged FAF1 was pull-down from HEK293T and RAW264 . 7 cell lysates , and NLRX1 was visualized by immunoblotting with an NLRX1 antibody ( Fig 6 , panel C ) . To further confirm whether FAF1 directly interacts with NLRX1 , in vitro binding assay was performed using purified GST tagged FAF1 ( Fig 6 , panel D ) . Incubation of GST tagged FAF1 with recombinant His tagged NLRX1 followed by immunoblotting with anti-His antibody showed the thick band corresponding to the NLRX1 protein size , which indicates direct binding between FAF1 and NLRX1 . Moreover , as shown in Fig 6 , panel E , confocal microscopic visualization of overexpressed V5-tagged FAF1 and Flag-tagged NLRX1 in HEK293T cells or overexpressed V5-tagged FAF1 and endogenous NLRX1 in FAF1-reconstituted MEFs showed overlapping of NLRX1 and FAF1 spots , confirming their co-localization . To examine endogenous protein binding upon virus infection , FAF1 was immunoprecipitated from PR8-GFP or H1N1-infected HEK293T or RAW264 . 7 cells using an anti-FAF1 antibody , followed by immunoblotting with an anti-NLRX1 antibody , and band corresponds to NLRX1 was detected ( Fig 6 , panel F ) . Similar results were obtained from BMDMs of FAF1+/+ mice after infection of PR8-GFP or VSV-GFP ( Fig 6 , panel G ) . These results suggested that FAF1 interacts with NLRX1 . To further elucidate the interaction between FAF1 and NLRX1 , the NLRX1 domains responsible for the interaction with FAF1 were analyzed using GST-labeled NLRX1 domain constructs ( Fig 7 , panel A ) . This experiment revealed that amino acids ( aa ) 1–327 of NLRX1 are required for the interaction with FAF1 . Moreover , we found that the FAF1 binding site within NLRX1 overlapped with the binding site for MAVS ( aa 75–556 ) ( Fig 7 , panel B ) . For further confirmation , another two NLRX1 fragments were constructed ( aa 556–975 and 75–975 ) to check whether FAF1 can bind with MAVS binding region of NLRX1 ( S10 Fig , panel A ) . FAF1 bound to aa 75–975 of NLRX1 , however , did not bind to aa 556–975 of NLRX1 . This indicates that FAF1 binds with the aa 75–556 region of NLRX1 which binds with MAVS . Based on these findings , we hypothesized that FAF1 and MAVS compete for binding to NLRX1 . To test this , a competition assay was performed by transfecting HEK293T cells with V5-tagged FAF1 in a dose-dependent manner ( Fig 7 , panel C ) . The results confirmed reduced binding between MAVS and NLRX1 , meanwhile increased binding between FAF1 and NLRX1 . Thus , FAF1 inhibits the interaction between NLRX1 and MAVS by binding to NLRX1 competitively . To investigate time-dependent changes in the interaction between FAF1 and NLRX1 after virus infection , RAW264 . 7 cells and BMDMs were infected with H1N1 or PR8-GFP and then harvested at different time intervals . Immunoprecipitation using an anti-NLRX1 antibody , followed by immunoblotting with an anti-FAF1 antibody , revealed that FAF1 bound to NLRX1 in RAW264 . 7 cells and BMDMs at early time points ( 4 and 2 hpi , respectively ) ( Fig 7 , panel D ) . Additionally , the interaction between NLRX1 and FAF1 or NLRX1 and MAVS were examined in infected cells ( Fig 7 , panel E ) . The interaction between NLRX1 and MAVS in non-infected cells was markedly reduced after viral infection in a time-dependent manner , importantly , there was a corresponding increase in FAF1-NLRX1 binding . These results suggest that FAF1 interacts with NLRX1 and inhibits binding between MAVS and NLRX1 . For further confirmation of this mechanism , we checked whether knockdown of NLRX1 abolishes the antiviral effect of FAF1 on type I IFN signaling . FAF1 and NLRX1 was knockdown in HEK293T cells using FAF1 and NLRX1 specific siRNA ( S11 Fig , panel A ) , and then VSV-GFP was infected to the cells . First , we confirmed antiviral effect of FAF1 in HEK293T cells , as shown in S11 Fig , panel B-C . In knockdown of NLRX1 , increased cytokine secretion and reduced VSV-GFP replication were observed . Interestingly , knockdown and overexpression of FAF1 had no effect to virus replication and cytokine secretion levels in NLRX1 knockdown condition . These results correlated with our proposed mechanism that FAF1 regulates NLRX1 mediated type I IFN signaling upon virus infection by interacting with NLRX1 . Indeed , FAF1 competes with MAVS for binding to NLRX1 , leading to disassociation of NLRX1 from MAVS , MAVS is then free to interact with RIG-I and initiate type I IFN signaling .
MAVS is the key adaptor protein for RLR-mediated signaling [15 , 39] . The RLR signaling pathway is initiated by recognition of distinct species of viral RNA by RIG-I or MDA5 , and activated RLRs bind MAVS via CARD-mediated interactions [19] . MAVS then recruits downstream signaling molecules and , eventually , induces production of type I IFNs and proinflammatory cytokines [20 , 40] . Although activation of MAVS plays a role in inducing type I IFN to limit virus spread , it must be tightly modulated to prevent excessive cellular immune responses that may have a detrimental effect on the host [28 , 41] . After viral infection , MAVS is regulated negatively or positively by different mechanisms , including mitochondrial dynamics [42 , 43] , post-translational modifications [44 , 45] , or protein-protein interactions [35 , 46 , 47] . With respect to protein-protein interactions , the first protein to be identified as a negative regulator of MAVS was the nucleotide-binding domain and leucine-rich repeat containing family member , NLRX1 [35–38] . Although conflicting results have been reported with regard to the function of NLRX1 as a negative regulator of RLR-mediated antiviral signaling [48–50] , it is believed that NLRX1 associates with MAVS on the mitochondrial membrane to inhibit antiviral signaling by interrupting virus-induced RLR-MAVS interactions [35–37 , 51] . However , the mechanism that regulates NLRX1 during virus infection remains poorly characterized . FAF1 , a member of the ubiquitin regulatory X ( UBX ) family , potentially interacts with diverse proteins and functions as a negative and/or positive regulator in variety of biological possesses , including apoptosis [1 , 3] , tumor growth [2 , 4–7] , protein degradation [2 , 6 , 52] and chaperone activity [53] . Here , we provide several lines of evidence showing that FAF1 is a positive regulator that modulates the type I interferon signaling pathway in response to RNA virus infection . First , FAF1gt/gt mice were more susceptible to infection by VSV as they were permissive to high rates of virus replication and mounted weak antiviral immune responses . Second , knockdown of endogenous FAF1 in immune cells or MEFs from FAF1gt/gt mice reduced RNA virus-induced IFN-β and proinflammatory cytokines production and increased viral replication . Third , overexpression of FAF1 in immune cells or MEFs promoted RLR-mediated antiviral response against RNA virus infection but not DNA virus infection . Fourth , FAF1 interacts with NLRX1 in response to RNA virus infection or RLR stimulation , and aa 1–327 of NLRX1 are responsible for the interaction between FAF1 and NLRX1 . Finally , FAF1 interacts with NLRX1 at the early time points after RNA virus infection; this interaction inhibits binding of MAVS to NLRX1 , which in turn switches on RIG-I mediated antiviral immune responses . Taken together , these findings indicate that FAF1 is a crucial regulator that induces the antiviral innate immune responses against RNA virus infection . Recently , Song et . al . , reported that FAF1 negatively regulates virus-induced IFN-β signaling and the antiviral response by inhibiting the translocation of active phosphorylated IRF3 from the cytosol to the nucleus [54] . However , this result contradicts that presented herein , and we clearly demonstrated that FAF1 acts as a positive regulator of the type I IFN signaling pathway during RNA virus infection . In particular , we identified the physiological role of FAF1 in innate immune responses against viral infection in FAF1+/+ and FAF1gt/gt mice . FAF1gt/gt mice were more susceptible to infection with VSV than FAF1+/+ mice , resulting high mortality in FAF1gt/gt mice due to a high viral load in the organs . After virus infection or Poly ( I:C ) stimulation , FAF1gt/gt mice showed lower levels of cytokine production ( IL-6 and IFN-β ) than FAF1+/+ mice , which strongly supporting an impaired antiviral immune response , especially with respect to type I IFN signaling . We also evaluated antiviral responses in several cell types . BMDMs , BMDCs , and PBMCs isolated from FAF1gt/gt mice , as well as FAF1 knockdown RAW264 . 7 cells and MEFs showed higher viral replication and lower IL-6 and IFN-β production , suggesting reduced antiviral and inflammatory responses due to suppression of FAF1 . However , reconstitution of FAF1 in FAF1 knockdown MEFs restored the antiviral function of FAF1 by recovering production of these cytokines . Consistent with this , overexpression of FAF1 in RAW264 . 7 cells resulted enhanced antiviral responses . These results strongly support the involvement of FAF1 as a positive regulator of RNA virus-mediated type I IFN signaling . Here , we also examined the phosphorylation of IRF3 ( the main integral component of the type I IFN response ) , p65 ( a subunit of NF-κB ) , STAT1 , p38 , and TBK1 after the induction of IFN responses by PR8-GFP . Together with this , significantly decreased type I IFN , ISGs , and antiviral mRNA transcript levels were observed in BMDMs and PBMCs of FAF1gt/gt mice compared with those in FAF1+/+ mice after infection with VSV and PR8 . Interestingly , our large scale co-immunoprecipitation data demonstrated that FAF1 is one of the binding partners for NLRX1 ( Fig 6 , panel A ) , which negatively regulates type I IFN signaling by modulating the interaction between RIG-I and MAVS [35–38] . We confirmed that FAF1 binds to and co-localizes with NLRX1 . Moreover , we performed domain studies to better understand the mechanism underlying the interaction between FAF1 and NLRX1 , and to identify which domain of NLRX1 binds to FAF1 . The results showed that the MAVS binding site within NLRX1 ( aa 75–556 ) [35] overlaps with the binding site for FAF1 ( aa 1–327 ) . Hence , we postulate that the mechanism by which FAF1 positively regulates IFN signaling probably operates through FAF1-mediated disassociation of NLRX1 from MAVS . This supports our time course binding studies , which showed that FAF1 binds to NLRX1 at early time points ( 2 and/or 4 hpi ) after virus infection . We also examined the time course binding of NLRX1 and MAVS and compared it with that of NLRX1 and FAF1 after the virus infection . Similar to previous studies showing constitutive interaction between RIG-I and MAVS in NLRX1-deficient cells [38] , we found that NLRX1 bound to MAVS in the absence of virus infection . This suggests that under normal conditions NLRX1 blocks the interaction between MAVS and RIG-I . However , after virus infection , FAF1 appears to displace MAVS from NLRX1 by competitive binding to NLRX1 . Thus , we postulate that FAF1 stimulates type I IFN signaling pathway by sequestering NLRX1 from the RIG-I-MAVS-mediated pathway . Furthermore , we confirmed that knockdown of NLRX1 abolishes the FAF1 mediated effects on type I IFN signaling , which supports our proposed mechanism . Nevertheless , several controversial reports are related with NLRX1 [48–50] , indicating more complicate mechanisms might involve on the role of NLRX1 on MAVS-dependent antiviral responses that unexplored yet , and FAF1 could be one of the key participant molecule in this mechanism which needs to be elucidated in future . Recently , Guo et al . demonstrated that NLRX1 is a negative regulator of host innate immune responses to DNA viruses by sequestering the DNA-sensing adaptor , STING , from TANK-binding kinase 1 ( TBK1 ) [55] . However , in this study , we found no differences in cytokine secretion levels or viral replication levels after HSV infection , suggesting that FAF1 may not modulate type I IFN production via STING-mediated sequestration of NLRX1 upon DNA virus infection . Hence , the present data indicate that FAF1 targets NLRX1 to regulate type I IFN production upon infection with RNA virus only . Moreover , the upstream signaling molecule that activates FAF1 after RNA virus infection and the reason that FAF1 only regulates NLRX1 upon RNA virus invasion remains unclear . In summary , we showed that FAF1gt/gt mice are highly susceptible to RNA virus infection and show defective innate immune responses both in vitro and in vivo . Upon RNA virus infection , FAF1 binds competitively to NLRX1 , thereby preventing it from binding to MAVS; this frees MAVS to interact with RIG-I and switch on the antiviral signaling cascade . These results suggest a plausible and novel mechanism by which FAF1 positively regulates type I IFN signaling and increases our understanding of the molecules that control type I IFN signaling and antiviral immune responses .
All animal experiments were managed in strict accordance with the Guide for the Care and Use of Laboratory Animals ( National Research Council , 2011 ) and performed in BSL-2 and BSL-3 laboratory facilities with the approval of the Institutional Animal Care and Use Committee of Bioleaders Corporation ( Reference number BLS-ABSL-14-009 ) . C57BL/6 FAF1+/+ and FAF1gt/gt mice were kindly provided by Dr . Eunhee Kim ( Department of Biology , Chungnam National University , Korea ) [56] . Mice ( 6–7 weeks of age ) were infected with vesicular stomatitis virus ( VSV ) Indiana strain ( VSV-Indiana; 2 × 108 pfu ( plaque forming unit ) per mouse ) or green fluorescent protein ( GFP ) -tagged VSV ( VSV-GFP; 2 × 108 pfu per mouse ) via tail vein injection . Mice infected with VSV-Indiana were observed daily to measure the mortality until 12 dpi . Organs and sera of mice infected with VSV-Indiana or VSV-GFP were collected at indicated time points to measure virus titers by plaque assays and qRT-PCR as described below , and levels of mouse IFN-β ( PBL interferon source ) and mouse IL-6 ( BD biosciences ) were measured by ELISA . Poly ( I:C ) ( Invivogen; 200 mg per mouse ) was injected intravenously via tail vein , and the sera were collected to measure mouse IFN-β and IL-6 levels by ELISA at indicated time points . Peripheral blood mononuclear cells ( PBMCs ) were isolated from whole pheripheral blood of FAF1+/+ and FAF1gt/gt mice infected with VSV-GFP ( 4 × 108 pfu per mouse ) via tail-vein injection at 24 hpi as described below . Total RNAs from PBMCs were extracted and were used for qRT-PCR analysis as described below . The femurs and tibias were isolated from euthanized C57BL/6 mice ( 4–6 weeks of age ) aseptically . After removing muscles , the bones were flushed with Dulbecco’s Modified Eagle’s medium ( DMEM , Gibco ) using syringe ( 26G × ½ needle ) to extrude bone marrow at least 3 times . After centrifugation of bone marrow , pellet was re-suspended with 1 . 0 ml of ammonium-chloride-potassium ( ACK ) lysis buffer ( Gibco ) to lyse the red blood cells , and supernatant was aspirated from the white cell pellet after centrifugation . Cells were cultured with DMEM supplemented with 10% heat-inactivated fetal bovine serum ( FBS , Gibco ) and 1% antibiotic-antimycotic ( Gibco ) ( 10% FBS DMEM ) and 10 ng/ml granulocyte-macrophage colony-stimulating factor ( GM-CSF ) for 5 days to obtain Bone Marrow-Derived Macrophages ( BMDMs ) . Additionally , cells were cultured for 6 days by adding 100 ng/ml IL-4 ( Invivogen ) to the above media to prepare Bone Marrow-Derived Dendritic Cells ( BMDCs ) . Whole peripheral blood obtained from mice was diluted with roswell park memorial institute ( RPMI ) 1640 medium ( Gibco ) and PBMCs were isolated by Histopaue-1077 ( Sigma ) . Isolated PBMCs were washed 3 times and cultured in 10% FBS and 1% antibiotic-antimycotic included RPMI1640 medium ( 10% FBS RPMI ) . FAF1 knock-down murine embryonic fibroblasts ( MEFs ) provided by Dr . Eunhee Kim ( Department of Biology , Chungnam National University , Korea ) [56] , mouse leukaemic monocyte macrophage ( RAW264 . 7; ATCC TIB-71 ) , human embryonic kidney 293 ( HEK293T; ATCC CRL-11268 ) , human epithelial cervix adenocarcinoma ( HeLa; ATCC CCL-2 ) and adenocarcinomic human alveolar basal epithelial ( A549; ATCC CCL-185 ) cell line were grown and maintained in 10% FBS DMEM at 37°C and 5% CO2 . Human acute monocytic leukemia ( THP-1; ATCC TIB-202 ) cell line was grown and maintained 10% FBS RPMI . FAF1 tagged with V5 expression plasmid ( pIRES-FAF1-V5 ) was constructed by inserting the FAF1 complete ORF which was amplified from pFLAG-CMV-2/hFAF1 plasmid [6] to the pIRES-V5 vector between AflII and EcoRI site . NLRX1 inserted to pIRES plasmid was kindly donated by Dr . Jae U . Jung ( Department of Molecular Microbiology and Immunology , University of Southern California , USA ) , and GST tagged NLRX1 full ( aa 975 ) and 6 fragments ( aa 1–156 , 157- . 327 , 386–674 , 675–975 , 556–975 and 75–975 ) were constructed by cloning into the pEBG vector between BamHI and NotI site . For stable overexpressing cell line preparation , pIRES-V5 vector or pIRES-FAF1-V5 was transfected to RAW264 . 7 and HEK293T with Lipofectamine 2000 ( Invitrogen ) according to manufacturer’s protocol . Cells stably expressing pIRES-V5 and pIRES-FAF1-V5 were selected with 2 μg/ml puromycin ( Gibco ) containing 10% FBS DMEM for 2 weeks . VSV-GFP , GFP tagged Herpes Simplex virus 1 ( HSV-GFP ) and Adenovirus ( Adeno-GFP ) were propagated in Ceropithecus aethiops epithelial kidney ( Vero; ATCC CCL-81 ) cells . GFP tagged H1N1 influenza virus ( A/PR8/8/34; PR8-GFP ) and Newcastle disease virus ( NDV-GFP ) were propagated in embryonated chicken eggs . Culture medium was replaced by DMEM supplemented with 1% FBS right before virus infection , and the viruses were added into the medium with indicated MOI . After 2 hr incubation , extracellular virus was removed and replace with 10% FBS DMEM or RPMI . Poly ( I:C ) was transfected with Lipofectamine 2000 into MEFs or treated to RAW264 . 7 cells . 5’- triphosphate double-stranded RNA ( 5’ppp-dsRNA , Invivogen ) was transfected into both cell lines with Lipofectamine RNAiMAX ( Invitrogen ) . Imiquimod ( Invitrogen ) and ODN2395 ( Invitrogen ) were treated to RAW264 . 7 cells . Oligonucleotide sequences of FAF1-specific shRNA cloned into the pGIPZ lentiviral vector expressing GFP was purchased at Open Biosystems . ( http://www . openbiosystems . com ) . Lentiviruses were produced using transient transfection of packaging plasmids ( psPAX2 and pMD2 . VSV-G purchased from Addgene ) into HEK293T cells using Lipofectamine 2000 . Media supernatant containing the virus particles were collected after 72 hr , filtered ( 0 . 45 μm filter , Millipore ) and infected to the RAW264 . 7 cells with 8μg/ml polybrene ( Sigma ) . Culture medium was replaced after the transduction process ( after 12hr ) with fresh puromycine-containing medium every 2 days until resistant colonies could identified . Similarly , control cells were prepared by infecting lentivirus which was produced with pGIPZ lentiviral vector expressing GFP . To knockdown the FAF1 or NLRX1 gene expression , siRNA oligonucleotide duplexes for targeting mouse FAF1 ( si-mFAF1-S 5’-UGUUUCCCUGGGACCAUCU-3’ and si-mFAF1-AS 5’-AGAUGGUCCCAGGGAAACA-3’ ) , human FAF1 ( si-hFAF1-S 5’-CAGUAGAUGAGUUAAUGAU-3’ and si-hFAF1-AS 5’-AUCAUUAACUCAUCUACUG-3’ ) or human NLRX1 ( si-hNLRX1-S 5’-GAGGAGGACUACUACAACGAU-3’ and si-hNLRX1-AS 5’- AUCGUUGUAGUAGUCCUCCUC-3’ ) was transfected to cells ( si-mFAF1; RAW264 . 7 cells , si-hFAF1; THP-1 and HEK293T cells and si-hNLRX1; HEK293T cells ) using Lipofectamine RNAiMAX according to the manufactures protocol . To measure virus titer , supernatant of homogenized organs ( VSV-Indiana ) , cells ( VSV-GFP , NDV-GFP and HSV-GFP ) or freezed-thawed cells ( PR8-GFP and Adeno-GFP ) which collected at indicated time points were serially 10-fold diluted and inoculated to Vero cells in 1% FBS containing media . After incubation for 2 hr at 37°C , cells were overlaid with DMEM containing 1% agarose ( Sigma ) . Cultures were incubated at 37°C , 5% CO2 for 48 hr , plaques were visualized with crystal violet . Virus titer was calculated using the number of plaques and the dilution factor . GFP expression levels were measured using a fluorescence modulator ( GloMax-Multi detection system; Promega ) to digitize . ELISA was used to detect the production of pro-inflammatory cytokines and type I interferon from cells . After infection , treatment and transfection of stimulants , cell supernatant was collected and analyzed cytokine production levels . Mouse IFN-α ( PBL interferon source ) , mouse IFN-β ( PBL interferon source ) , mouse IL-6 ( BD biosciences ) and mouse TNF-α ( BD biosciences ) , human IFN-β ( PBL interferon source ) and human IL-6 ( BD biosciences ) were used for analysis according to manufacturer’s protocol . At 48 hr post-transfection of indicated plasmids , cells were harvested and lysed with radio-immunoprecipitation assay ( RIPA ) lysis buffer ( 50 mM Tris-HCl , 150 mM NaCl , 0 . 5% sodium deoxycholate , 1% IGEPAL , 1 mM NaF , 1 mM Na3VO4 ) supplemented with protease inhibitor cocktail and phosphatase inhibitor cocktail ( Sigma ) and sonicated to prepare the whole cell lysate ( WCL ) . WCL were precleared with Sepherose 6B ( GE Healthcare Life Science ) at 4°C at least for 2 hr . Precleared lysates were incubated with 50% slurry of glutathione-conjugated Sepharose ( GST ) beads ( Amersham Biosciences ) for GST pull-down , and for immunoprecipitation of anti-V5 and NLRX1 , lysates were incubated with anti-V5 or NLRX1 antibody ( 1 . 0 μg/ml ) for 12 hr and then protein A/G plus agarose beads ( Santacruz ) were added . Immunoprecipitates were collected by centrifugation , washed with lysis buffer in different washing conditions . Additionally , WCL in control , FAF1 knockdown and overexpressing RAW264 . 7 and FAF1 knockdown and reconstituted MEFs infected with PR8-GFP during indicated time points were subjected to immunoblotting to analyze protein phosphorylation levels using respective antibodies . For all the immunoblot analysis , samples were separated by SDS-PAGE and transferred onto a PVDF membrane ( Bio-rad ) using Trans-Blot semi dry transfer cell ( Bio-rad ) . Membranes were blocked for 1 hr in tris-buffered saline containing 0 . 05% tween 20 ( TBST ) containing 5% bovine-serum albumin ( BSA ) . After overnight-incubation at 4°C with antibodies , membranes were washed with TBST . Membranes were incubated at room temperature with 1:3000 dilutions of horseradish peroxidase-conjugated secondary antibodies . Membranes were developed with western blotting detection reagents ( GE healthcare , ECL select Western Blotting Detection Reagent ) . The antibodies used in this study were as follows: anti-GST ( Santacuze , #SC-138 ) , anti-V5 ( Invitrogen , #46–0705 ) , or anti-IRF3 ( Abcam , #ab25950 ) , anti-phospho-IRF3 ( Ser 396 ) ( Cell signaling , #4947 ) , anti-NF-κB p65 ( Cell signaling , #4764 ) , anti-phospho-NF-κB p65 ( Ser536 ) ( Cell signaling , #3031 ) , anti-STAT1 ( Cell signaling , #9175 ) , anti-phospho-STAT1 ( Cell signaling , #9167 ) , anti-phospho-p38 ( Cell signaling #9216 ) , phospho-TBK1 ( Cell signaling #5483 ) , anti-NLRX1 ( Proteintech , #17215-1-AP ) and anti-His ( Santacuze , #SC-1803 ) antibodies . The anti-FAF1 monoclonal antibody was provided by Dr . Eun-hee Kim ( Department of Biology , Chungnam National University , Korea ) . The anti-interferon-α/β receptor ( IFNAR ) ( 25 μg/ml; Leinco Technologies ) was pre-incubated in RAW264 . 7 cells and MEFs for 1 hr before VSV-GFP infection to block IFNAR . Total RNA was isolated from cells and tissues from the organs using RNeasy Mini Kit ( Qiagen ) and cDNA synthesis was performed using ReverTra Ace kit ( TOYOBO ) . cDNAs were then quantified with gene specific primer pairs using QuantiTect SYBR Green PCR kit ( Qiagen ) on a Rotor-Gene Q ( Qiagen ) and relative expression of mRNA was normalized to GAPDH mRNA expression using delta-delta CT method . Gene specific primer pairs were referred in Table 1 . The cells were seeded into collagen-coated chamber slides ( LabTek , Nunc ) , 1 day prior to the experiments . Following day , the cultured cells were washed with phosphate buffer saline ( PBS ) and fixed with 4% paraformaldehyde for 20 min , then permeabilized through incubation for 20 min with 100% methanol at -20°C . The fixed cells were first incubated with 2% FBS diluted in PBS for 1 hr to block non-specific binding of antibodies . V5 and NLRX1 were detected through incubation with the primary antibodies ( 1:100 diluted in 2% BSA ) for 12 hr at 4°C . After 3 times PBS containing 0 . 05% tween 20 ( PBST ) washing , the secondary antibodies ( 1:100 diluted in 2% BSA; Alexa 488 goat anti-rabbit IgG ( Invitrogen ) , Cy3-conjugated donkey anti-mouse IgG ( The Jackson Laboratory ) were added and the cells were incubated for 1 hr at room temperature . Three times PBST washing followed by 10 min incubation with 1 μg/ml DAPI ( Sigma-Aldrich ) containing 0 . 01% RNase A , the nuclei were visualized , and then the slides were mounted with mounting solution ( VECTOR ) to check under fluorescence microscopy . Images were acquired from Nikon C2 Plus confocal microscope ( Nikon ) consisting of a Nikon Eclipse Ti inverted microscope with a confocal scanning system ( Nikon ) in conjunction with C-HGFIE precentered fiber illuminator ( Nikon ) . FITC and TRITC fluorescence was detected using the 488 nm and 561 nm laser line of a Sapphire driver unit ( Coherent ) , respectively , and DAPI fluorescence was detected using 405 nm laser line of a CUBE laser system ( Coherent ) . The image data were analyzed using NIS-Elements microscope imaging software program ( Nikon ) . HEK293T cells were transfected with an empty GST vector ( GST ) or with the GST-NLRX1-N-terminal region containing vector ( aa 1–225; GST-NLRX1-N ) . Cells were harvested after 48 hr and after cell lysis , proteins in the cell lysates were immunoprecipitated with GST beads and separated by 4–15% Nu-PAGE gels ( Invitrogen ) , followed by silver staining [57] . Protein bands present exclusively in GST-NLRX1-N lane were excised from the gel and identified by mass spectrometry . GST and GST tagged FAF1 ( GST-FAF1 ) were expressed and purified using GST beads . The purified GST-FAF1 was incubated with recombinant His tagged NLRX1 ( NovoPro ) in binding buffer ( 50 mM Tris-HCl , 150 mM NaCl , 1% IGEPAL and protease inhibitors ) at 4°C for 3 hr with gentle rocking . After centrifugation , collected beads were washed five times with binding buffer , and bound proteins were subjected to SDS-PAGE followed by immunoblotting with GST and His antibodies . Statistical analysis was performed using GraphPad Prism software version 6 for Windows ( GraphPad Software ) . All the data were from at least of two independent experiments and data are shown as mean ± SEM . The means values of all the in vitro experiments were compared by Student’s t test . Log Rank test and Mann-Whitney test was subjected for in vivo survival data analysis . Comparisons between multiple time points were analyzed by one way analysis of variance ( ANOVA ) . In all experiments , p values of less than 0 . 05 were considered statistically significant . *p<0 . 05 , **p<0 . 01 and ***p<0 . 001 | Type I interferon-mediated antiviral response is critical for controlling virus infections . However , interferon-mediated immune responses need to be tightly regulated to maintain host immune homeostasis . Recently , molecules involved in regulating interferon-mediated innate immune response are the subject of much research . Among these , the first protein to be identified as a negative regulator of MAVS was the nucleotide-binding domain and leucine-rich repeat containing family member , NLRX1 . NLRX1 associates with MAVS to inhibit antiviral signaling by interrupting virus-induced RLR-MAVS interactions . Interestingly , we found that FAF1 interacts with NLRX1 in response to RNA virus infection and this interaction inhibits binding of MAVS to NLRX1 , which in turn switches on RIG-I mediated antiviral immune responses . As results , we showed that FAF1gt/gt mice , which deficient in FAF1 , and FAF1 knockdown immune cells were highly susceptible to RNA virus infection and showed low levels of inflammatory cytokines and type I interferon ( IFN ) production . Our findings suggest that FAF1 is a crucial regulator that induces the antiviral innate immune responses against RNA virus infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| [
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"immune",
"physiology",
"cytokines",
"antiviral",
"immune",
"response",
"immunology",
"microbiology",
"light",
"microscopy",
"viruses",
"developmental",
"biology",
"rna",
"viruses",
"immunoprecipitation",
"microscopy",
"molecular",
"development",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"proteins",
"fluorescence",
"microscopy",
"immunoassays",
"viral",
"replication",
"precipitation",
"techniques",
"immune",
"response",
"immune",
"system",
"biochemistry",
"virology",
"physiology",
"interferons",
"biology",
"and",
"life",
"sciences",
"organisms"
]
| 2017 | FAS-associated factor-1 positively regulates type I interferon response to RNA virus infection by targeting NLRX1 |
Mucociliary clearance is one of the major lines of defense of the human respiratory system . The mucus layer coating the airways is constantly moved along and out of the lung by the activity of motile cilia , expelling at the same time particles trapped in it . The efficiency of the cilia motion can experimentally be assessed by measuring the velocity of micro-beads traveling through the fluid surrounding the cilia . Here we present a mathematical model of the fluid flow and of the micro-beads motion . The coordinated movement of the ciliated edge is represented as a continuous envelope imposing a periodic moving velocity boundary condition on the surrounding fluid . Vanishing velocity and vanishing shear stress boundary conditions are applied to the fluid at a finite distance above the ciliated edge . The flow field is expanded in powers of the amplitude of the individual cilium movement . It is found that the continuous component of the horizontal velocity at the ciliated edge generates a 2D fluid velocity field with a parabolic profile in the vertical direction , in agreement with the experimental measurements . Conversely , we show than this model can be used to extract microscopic properties of the cilia motion by extrapolating the micro-bead velocity measurement at the ciliated edge . Finally , we derive from these measurements a scalar index providing a direct assessment of the cilia beating efficiency . This index can easily be measured in patients without any modification of the current clinical procedures .
Mucociliary clearance is one of the major defense mechanisms of the respiratory airway system . The mucus layer coating the epithelial surface of the airways filters the inhaled air by trapping potentially harmful material ( fungi , bacteria and other particles ) [1–4] . This mucus layer is continuously carried away and out of the airways by the activity of motile cilia . Neighboring cilia beat in an organized manner with a small phase lag , their tips creating an undulating surface on top of the cilia layer which deforms in a wave-like fashion called the metachronal wave [5–7] . The beat pattern of an individual cilium displays a two-stroke effective-recovery motion [8] . During the effective stroke , cilia beat forwards and engage with the mucous layer , propelling it forward . In contrast , during the recovery stroke , they return to their initial position in the underlying periciliary fluid , minimizing thereby the drag on the mucus in the opposite direction ( Fig 1 , left ) . This asymmetry in the beat pattern is responsible for a net fluid flow in the direction of the effective stroke . In the airways , each mature ciliated cell may be covered with up to 200 cilia , with a surface density around 5–8 cilia/μm2 [6 , 9] . A cilium , approximately 6 μm long and of diameter around 0 . 2 μm , beats 12 to 15 times per second , resulting in a velocity of the mucus layer of several mm/min [10] . This work intends to build a mathematical model of the experiments described in a companion paper [11] , and to derive from this model an efficient and reliable method to assess the cilia beating efficiency in a clinical setting . In the experiments , ciliated cell clusters issued from nasal brushing are immersed in a cell survival medium devoid of mucus , pushing forward the medium as they beat . One has to stress here that the absence of mucus is an important ingredient of the experimental setting and the developed mathematical model . Polystyrene micro-beads are then used as massless tracers to visualize and quantify the fluid velocity field around the cilia . The theoretical and numerical work presented here therefore aim at a quantitative modeling of the ciliary beating , then of the fluid flow generated by it . In the literature , two main types of ciliary beating models can be found: discrete-cilia models and volume-force models [12] . In discrete-cilia model , each cilium is modeled as a discrete body and its shape is parametrized along its stroke period [1 , 13 , 14] . Discrete-cilia models are themselves divided into two types: in the prescribed beating models , the cilium motion is imposed as an input to the simulation [15]; in the couple-internal mechanics/fluid-structure interaction models [16 , 17] , cilia motions originate from the coupling between the internal structure of cilia and the external viscous forces . In contrast , in volume-force models cilia are modeled through a phenomenological continuous force distribution , varying in space and time as the cilia beat [18–20] . In this second type of model , the envelope modeling approach accounts for the formation of metachronal waves above the cilia layer [21] . The cilia tips are seen from the fluid as a “wavy wall” , hereby ignoring the details of the sub-layer dynamics [22 , 23] ( see Fig 1 , right ) . Many studies have addressed experimentally [24–26] and numerically [27–31] the effect of fluid visco-elasticity on transport and locomotion . We want to stress here that in our model , no finite layer of viscous mucus sits on top of the cilia , since they are surrounded by a semi-infinite layer of watery fluid . In the following , we first compute the wave envelope boundary condition from the cilia motions , based on the work of Ross [22] . We then derive the non linear equations for a fluid flow periodic in the direction of the metachronal wave . These equations are expanded in ε , which is the ratio of the cilium amplitude to the fluid layer thickness , then solved using a Fourier decomposition . The steady contribution to the flow field in the vertical direction above the cilia is shown to exhibit a parabolic profile , to a very good approximation . We finally show that measuring microbead velocities as a function of the distance to the ciliated edge enables us to compute a scalar index which accounts for the transfer of momentum between the cilia and the fluid , and therefore assesses the cilia beating efficiency .
Each individual cilium is assumed to undergo a periodic motion in which its tip follows an elliptic trajectory , see Fig 1 , left . Taking the limit of a continuous cilia distribution , the cilia array is simplified as an undulating surface that covers the cilia layer , ignoring the details of the sub-layer dynamics ( Fig 1 , right ) [22 , 23] . The x* axis is chosen parallel to the ciliated edge , each ciliary tip being located around y* = 0 on average ( the ‘*’ notation stands for dimensioned quantities , and will be removed once we switch to dimensionless quantities ) . The tip of a cilium located at the horizontal coordinate ξ* is assumed to follow a periodic elliptic trajectory centered in ( ξ* , 0 ) during each elementary beat ( Fig 2 ) . At time t* , the tip coordinates ( X w * , Y w * ) are {X w * = ξ * - a cos ( ω t * ) Y w * = β a sin ( ω t * ) ( 1 ) where β is the ellipse eccentricity , 2a is its major axis in the x* direction , and 2βa its minor axis in the y* direction . For β > 0 , the tip orbits clockwise , while for β < 0 , the tip orbits counterclockwise . To reproduce the backwards traveling metachronal wave of wavelength λ , we introduce in the periodic motion of each cilium a phase shift 2 π ξ * λ which linearly depends on the cilium position: {X w * = ξ * - a cos ( ω t * + 2 π ξ * λ ) Y w * = β a sin ( ω t * + 2 π ξ * λ ) ( 2 ) The corresponding wave frequency is f = 2πω and its speed is c = fλ . By setting the space and time units respectively to be h and ω−1 , dimensionless parameters ε and k , and variables ( Xw , Yw ) are introduced: ε = a h , k = 2 π h λ , X w = X w * h , Y w = Y w * h , x = x * h , y = y * h , ξ = ξ * h , t = ω t * ( 3 ) The motion equations of each tip are thus rewritten in a dimensionless form: {X w ( ξ , t ) = ξ - ε cos ( k ξ + t ) Y w ( ξ , t ) = β ε sin ( k ξ + t ) ( 4 ) In the following , we assume that the ciliary beating amplitude is much smaller than the film thickness , i . e . ε ≪ 1 . In the limit of continuously distributed cilia along the x axis , the envelope of their tip motions forms a continuous boundary that generates a forcing on the fluid layer above the cilia . Starting from Eq 4 , we now express the position of a particle of this envelope ( or wall ) in the Eulerian frame of reference ( x , y , t ) . A tip located at position x at time t corresponds to a cilium centered in ( ξ , 0 ) such that: x = ξ - ε cos ( k ξ + t ) ( 5 ) This equation shows that ( x − ξ ) is of order ε . Therefore the vertical location of the cilia wall yw ( x , t ) , which is also the y-coordinate of the corresponding cilium , Yw ( ξ , t ) , can be developed around ξ = x using a Taylor expansion: y w ( x , t ) = Y w ( ξ , t ) = Y w ( x , t ) + ( ξ - x ) ∂ Y w ∂ ξ | ξ = x + ( ξ - x ) 2 2 ∂ 2 Y w ∂ ξ 2 | ξ = x + ⋯ ( 6 ) Since both Yw and ( ξ − x ) are first order in ε ( Eqs 4 and 5 ) , the expansion to the second order in ε of yw is y w ( x , t ) = ε β sin ( k x + t ) + ε 2 β k cos 2 ( k x + t ) ( 7 ) Now that we have determined the location of the ciliated wall at time t , we can derive its velocity . The horizontal component of the wall velocity is obtained as the time derivative of the Lagrangian velocity of the tip at location x at time t , while its vertical component is calculated from the time derivative of the vertical coordinate of the wall at position x and time t: { uw ( x , t ) = ( ∂Xw∂t ) ξ=εsin ( kξ+t ) vw ( x , t ) =∂yw∂t=εβcos ( kx+t ) −ε2βksin ( 2 ( kx+t ) ) ( 8 ) The value of ξ to insert in the above horizontal velocity is given by Eq 5 . Expanding the horizontal component of the wall velocity in Taylor series to the second order in ε yields u w ( x , t ) = ∂ X w ∂ t | ξ = x + ( ξ - x ) ∂ u w ∂ ξ | ξ = x + … = ε sin ( k x + t ) + [ ε cos ( k x + t ) ] [ ε k cos ( k x + t ) ] = ε sin ( k x + t ) + ε 2 k cos 2 ( k x + t ) ( 9 ) In summary , at location x in the Eulerian frame , the second order expansion in ε of the vertical position yw and of the velocity vector ( uw , vw ) of the ciliated wall are: y w ( x , t ) = ε β sin θ + ε 2 β k 2 ( 1 + cos ( 2 θ ) ) ( 10 ) u w ( x , t ) = ε sin θ + ε 2 k 2 ( 1 + cos ( 2 θ ) ) ( 11 ) v w ( x , t ) = ε β cos θ - ε 2 β k sin ( 2 θ ) ( 12 ) where θ = kx + t is the local phase of the metachronal wave . We now introduce the first and second orders of the ε-expansion of the wall velocity , called U → w , 1 = ( u w , 1 , v w , 1 ) and U → w , 2 = ( u w , 2 , v w , 2 ) , respectively , defined such that: U → w = ε U → w , 1 + ε 2 2 U → w , 2 ( 13 ) In summary , the first and second orders in ε of the location and velocities of the cilia wall are: {y w , 1 ( θ ) = β sin θ = β 2 i e i θ - β 2 i e - i θ y w , 2 ( θ ) = β k ( 1 + cos ( 2 θ ) ) = β k + β k 2 e 2 i θ + β k 2 e - 2 i θ u w , 1 ( θ ) = sin θ = 1 2 i e i θ - 1 2 i e - i θ u w , 2 ( θ ) = k ( 1 + cos ( 2 θ ) ) = k + k 2 e 2 i θ + k 2 e - 2 i θ v w , 1 ( θ ) = β cos θ = β 2 e i θ + β 2 e - i θ v w , 2 ( θ ) = - 2 β k sin ( 2 θ ) = i β k e 2 i θ - i β k e - 2 i θ ( 14 ) We can observe therefore that all velocity terms at the ciliated wall are oscillatory , except one steady contribution to the horizontal velocity at the second order in ε that appears in uw , 2 . This contribution is proportional to k and will be the origin of the steady horizontal motion of the fluid above the cilia . We now compute the oscillatory flow field of a fluid of density ρ and viscosity μ in the channel comprised between y = yw ( x , t ) and y = h in the vertical direction . Due to the periodic nature of the forcing from the wall , we will consider periodic solution in the x direction . Given the normalization chosen in Eq 3 , the normalization factors for velocity and pressure are hω and μω , respectively . Hence , the dimensionless Navier-Stokes equation reads α 2 ( ∂ U → ∂ t + ( U → · ∇ → ) U → ) = - ∇ → p + Δ U → , ( 15 ) where U → = ( u , v ) is the dimensionless velocity field , p is the dimensionless pressure , and α is the Womersley number defined by α 2 = ρ h 2 ω μ . In the limit α → 0 , one recovers the classical stationary Stokes equation . In our case , typical values for the channel thickness and beating frequency are h = 50 μm and f = 10 Hz . In water ( ρ = 1 g . cm−3 and μ = 1 cP ) , the above expression leads to a Womersley number α ≈ 0 . 4 , which means α2 of the order of 0 . 1 . Since the flow is two-dimensional and incompressible , a natural formulation of the problem is obtained by introducing the stream function ψ such that: u = ∂ ψ ∂ y and v = - ∂ ψ ∂ x ( 23 ) Such a solution automatically fulfills the continuity equation div ( U → ) = 0 . The dimensionless Navier-Stokes equation now reads: {α 2 ( ψ y t + ψ y ψ y x - ψ x ψ y y ) = - p x + ψ y x x + ψ y y y α 2 ( - ψ x t + ψ y ψ x x + ψ x ψ x y ) = - p y - ψ x x x - ψ x y y ( 24 ) We derive a Partial Differential Equation ( PDE ) in ψ only by taking the y-derivative of the first equation and subtracting it to it the x-derivative of the second equation: α 2 {ψ x x t + ψ y y t + ψ y ψ y y x - ψ x ψ y y y + ψ y ψ x x x - ψ x ψ x x y} = ψ y y y y + 2 ψ x x ψ y y + ψ x x x x ( 25 ) This equation can finally be rewritten in the following compact form: α 2 {Δ ψ t + ψ y Δ ψ x - ψ x Δ ψ y} = Δ 2 ψ ( 26 ) Once the oscillatory velocity field U → = ( u , v ) is computed , the trajectories and the crossing times of micro-beads in this field are calculated by solving their equation of motion: m d U → b * d t * = F → drag , ( 67 ) where m is the individual mass of the micro-bead , U → b * its velocity , and F → drag is the drag force applied on the bead by the fluid flow . Assuming a spherical shape for the bead and small speed differences between the bead and the fluid , the drag force takes the Stokes expression F → drag = - 6 π R μ ( U → b * - U → * ) , ( 68 ) where R stands for the bead diameter . Using the previously defined space and time units h and ω−1 , the adimensioned version of Eq 67 reads: d U → b d t = U → - U → b S t k with S t k = m ω 6 π R μ ( 69 ) Stk is the bead Stokes number , which characterizes the effective inertia of the bead in the fluid flow . For spherical particles of radius R and density ρb , this number also reads: S t k = 4 3 π R 3 ρ b ω 6 π R μ = 2 9 R 2 ρ b ω μ ( 70 ) The micro-beads are about 4 . 5 μm diameter , and made out of polystyrene of density ρb of order 1 g . cm−3 . At 10 Hz in water , the corresponding Stokes number is about 10−4 , which means that the micro-beads can be considered as massless tracers . Their velocity can be assumed to be permanently equal to the fluid velocity at the same location . For each micro-bead entering the simulation window at x = 0 and a given altitude y0 , the effective speed is computed as: where τ ( y0 ) is the crossing time of the micro-bead entering at ( 0 , y0 ) , and T y 0 is the trajectory followed by this micro-bead . During each elementary step of this trajectory , the infinitesimal duration is d t = d s / ‖ U → ( s ) ‖ , U → being the fluid velocity at curvilinear abscissa s of the trajectory . The effective speed V eff corresponds to the quantity measured in our experiments .
The fluid velocity field is solved using the Fourier transform decomposition exposed earlier . This field is periodic both in space and time . Fig 3A and 3B displays the horizontal and vertical components of the fluid velocity , respectively , while Fig 3D presents the micro-bead trajectories in this fluid , simulated by numerically solving Eq 69 . One can observe that the beads follow slightly wavy trajectories when close to the cilia wall , but that these trajectories become almost perfect straight lines when moving away from the wall farther than a cilia length . This means that the transit time of a micro-bead across the simulation window is dominated by the steady part of the flow field . Fig 4A displays the various contributions to the flow profile in the y-direction for typical values of the input parameters CBF , CBA , λ , ϕ , and h . One observes that the total bead velocity ( blue line ) is essentially dominated by the steady parabolic contribution ( black line ) determined by the coefficients A2 , B2 , and C2 in Eq 57 . The oscillatory part coming from the first order in ε ( green line ) brings a significant contribution only very close to the ciliated edge , and the exponential contribution in the steady part is negligible everywhere . Consequently , measuring the bead velocity as function of the distance to the ciliated edge essentially amounts to measuring the steady parabolic profile of the flow . Fie 4B shows the excellent agreement between the model and the actual measurements performed on different ciliated edges ( here 3 samples from 3 different subjects are presented ) . The horizontal component of the steady pressure gradient p x ( s ) ( see Eq 48 ) , applies a force on the fluid directed negatively along Ox . This force is exactly compensated by the force exerted by the cilia on the fluid , proportional to the dimensionless shear stress ∂u/∂y ( see S1 File for the detailed force balance between the cilia and the fluid ) . The dimensionless steady force applied by the cilia to a volume fluid of length 2π/k ( the dimensionless wavelength ) in the Ox direction and dimensionless thickness H/h in the Oz direction thus reads: F w ( s ) = - H h 2 π k ∂ u ( s ) ∂ y | ( y = 0 ) = - λ H h 2 ∂ u ( s ) ∂ y | ( y = 0 ) ( 72 ) Using the parabolic velocity profile u ( s ) ( y ) = u ( 0 ) ( y − 1 ) 2 found in Eq 62 , and putting back dimensional quantities finally leads to the expression of the local shear stress τw applied to the fluid by the cilia: τ w = ( μ ω h 2 ) F w ( s ) λ H = μ ω h 2 λ H λ H h 2 2 u ( 0 ) = 2 μ U 0 h ( 73 ) where U0 is the velocity extrapolated at y = 0 from the measurements of microbead velocities above the cilia . This shear stress characterizes the momentum transfer between cilia and the surrounding fluid . Since h is also directly measured by fitting the microbead velocity profile with a parabolic profile , it means that τw can be directly deduced using this microbead tracking technique . Consequently , we propose this shear stress as an index for assessing the efficiency of the ciliary beating . One has to stress that we do not intend to reproduce in this model the in vivo condition . The shear stress measured using this micro-bead velocity technique is not assumed to be identical to the shear stress experienced by mucus in the pulmonary airways . It is only a way to assess of the ability of the ciliated edge to transfer momentum into the surrounding fluid , thus defining a usable clinical index . Finally , the extrapolated velocity at the wall can be compared to the one predicted from Eq 64: U 0 = h ω u ( 0 ) = h ω a 2 2 h 2 ( 1 + 2 β - β 2 1 + 2 ϕ ) 2 π h λ = π a 2 ω λ ( 1 + 2 β - β 2 1 + 2 ϕ * h ) ( 74 ) The cilium beating amplitude a ( CBF ) , the the cilium beating frequency ω/2π ( CBF ) , and the metachronal wavelength λ are measured directly by microscopic measurements on the cilia , while h is extracted from the parabolic fitting of the microbead velocity profile . In the approximation of a flat beating ( β = 0 ) , this set of measurements therefore provides a way to assess ϕ* , the equivalent sliding length introduced in the boundary condition of Eq 18 .
The model proposed in this article relies on several assumptions , hence has a few limitations that we examine now . First , the motion of each individual cilium tip is assumed to be elliptic . Although this is a generally accepted hypothesis , microscopic imaging of the actual motion of the cilium shows a more complicated trajectory [34] . In particular , the path followed by the tip on the backward trajectory seems to be closer to the forward path than for an elliptic motion . However , we have seen that the net steady motion of the fluid is essentially generated by the metachronal wave . This discrepancy of the trajectory should be accounted for through a change of the parameter β representing the ellipse eccentricity . One also has to stress that , since our model reproduces an experimental setup in which cilia are surrounded only by water , one might expect a different motion from the one achieved in in vivo situations where cilia are beating in a periciliary liquid while their tips are entering the bottom of the mucus layer . The model also assumes an exact synchronization of the individual cilia motions , generating a metachronal wave of constant velocity . This implies that the phase shift between two cilia is a linear function of the distance separating them along the direction of the metachronal wave propagation . Ex vivo experiments carried out in [11] show that this hypothesis is satisfactorily verified at the observational scale of the experiment , i . e . , an edge of a cluster containing a few ciliated cells . A deviation from the linear phase shift would disrupt the translational periodicity of the system and alter our solution based on a Fourier decomposition along the horizontal axis . The fluid velocity at the cilia wall computed in our model can thus be considered as an “ideal velocity” , reached for a perfect metachronal wave . Any measured discrepancy with respect to this ideal velocity could be therefore be attributed either to an imperfect momentum transfer ( through the effective slip length ϕ ) , or to a perturbed metachronal wave . In the model , the ciliated edge is supposed to be flat . This assumption is generally valid in real experiments ( see [11] ) . Cases in which cell clusters appear to have a rough , curved , or disrupted ciliated edge are removed from the measurement procedure . An important assumption lies in the fact that the fluid surrounding the cilia is stagnant at a distance h above the edge . The stagnation of the fluid above the cilia is observed experimentally , and originates from the external environment of the measured cell cluster . Various explanations can account for it: it can be due to the presence of other cell clusters at a few hundred microns distance which create a complex fluid flow in the entire system , or by the friction of the fluid on the upper and lower slides , or the appearance of boundary layers . Determining this value would require modeling the full 3D geometry of the system whose geometry is not easily accessible by microscopic observation . The distance h therefore is treated as the only fitting parameter of the model ( the sliding length ϕ being calculated from the measured cilia density ) . Finally , the bead velocities are found in the model to be almost always parallel to the cilia wall . In real experiments , a vertical component ( i . e . , in the direction perpendicular to the wall ) might appear , although much smaller than the horizontal component . This is in particular the case when the ciliated edge is not strictly flat , or when the observation window is such that the ciliated edge is on one side of the window and the bead passing on the other side of the same window . The micro-beads are in this situation not set into motion only by the considered ciliated edge , and external influence originating from outside the observation window interfere , contributing to create a more complex fluid flow pattern . Such situations should be discarded from the analysis , either through eye examination or by an automatized procedure . We have developed a mathematical model of micro-bead velocity in a fluid set into motion by the periodic beating of a ciliated edge . The cilia wall is represented as a continuous envelope whose motion is calculated from the coordinated movement of all cilia . The boundary condition imposed by the effective wall induces a motion of the fluid above the wall . This motion has two components: one oscillatory , at the spatial and temporal periodicity of the metachronal wave and the cilia beating , respectively , and one steady , oriented along the direction of the wall . The oscillatory component vanishes at a distance of the order of the metachronal wavelength . The steady component extends on a much longer distance and exhibits a parabolic profile in the direction perpendicular to the wall . The parameters governing this profile are the fluid viscosity , the ciliary beating frequency ( CBF ) , the ciliary beating amplitude ( CBA ) , the metachronal wavelength ( λ ) , the cilia density ( through the sliding length ϕ* ) , and the distance from the wall to the region of stagnant fluid ( h ) . Polystyrene micro-beads immersed in the fluid act as massless tracers , allowing the measurement of the local fluid velocity , hence the measurement of the velocity profile . All aforementioned parameters can be determined through local measurement by high speed video-microscopy , except for the distance h which remains the only fitting parameter of the model extracted from the velocity profile . The velocity extrapolated from the profile at the wall is found to be linearly related to the shear stress exerted by the cilia on the fluid . This shear stress is proposed as a new index for assessing the efficiency of the ciliary beating . One has to stress that this index can be measured from nasal brushing in the clinical setting without any modification of the current clinical procedures . Preliminary tests ( see [11] ) have already shown that this index has the potential to be a powerful screening test , able to distinguish patients suffering from various alterations of the cilia beating such Primary Ciliary Dyskinesia ( PCD ) . | Mucociliary clearance is the first line of defense mechanisms of the human airways . The mucus transporting debris , particles , microorganisms and pollutants is carried away by the coordinated motion of cilia beating at the surface of the airway epithelium . We present here a mathematical and numerical model aiming at defining a global index for assessing the efficiency of this beating . Numerical simulations show that the bead velocity parallel to the wall varies according a parabolic profile with the distance to the wall . The velocity extrapolated at the wall is demonstrated to be a measurement of the momentum transfer between cilia and the surrounding fluid . This model allows us to interpret experimental measurements performed in a companion article and to propose a universal index characterizing the beating efficiency , which can be extracted in the current clinical setting . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
]
| [
"velocity",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"classical",
"mechanics",
"fluid",
"mechanics",
"mechanical",
"stress",
"cellular",
"structures",
"and",
"organelles",
"mucus",
"flow",
"field",
"fluid",
"dynamics",
"continuum",
"mechanics",
"fluid",
"flow",
"physics",
"shear",
"stresses",
"cell",
"biology",
"anatomy",
"cilia",
"momentum",
"physiology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"motion"
]
| 2017 | A new index for characterizing micro-bead motion in a flow induced by ciliary beating: Part II, modeling |
Trypanosoma cruzi infection is associated with severe T cell unresponsiveness to antigens and mitogens and is characterized by decreased IL-2 synthesis . In addition , the acquisition of the anergic phenotype is correlated with upregulation of “gene related to anergy in lymphocytes” ( GRAIL ) protein in CD4 T cells . We therefore sought to examine the role of GRAIL in CD4 T cell proliferation during T . cruzi infection . Balb/c mice were infected intraperitoneally with 500 blood-derived trypomastigotes of Tulahuen strain , and spleen cells from control non-infected or infected animals were obtained . CD4 T cell proliferation was assessed by CFSE staining , and the expression of GRAIL in splenic T cells was measured by real-time PCR , flow cytometry and Western blot . We found increased GRAIL expression at the early stages of infection , coinciding with the peak of parasitemia , with these findings correlating with impaired proliferation and poor IL-2 and IFN-γ secretion in response to plate-bound antibodies . In addition , we showed that the expression of GRAIL E3-ubiquitin ligase in CD4 T cells during the acute phase of infection was complemented by a high expression of inhibitory receptors such as PD-1 and CTLA-4 . We demonstrated that GRAIL expression during infection was modulated by the mammalian target of the rapamycin ( mTOR ) pathway , since addition of IL-2 or CTLA-4 blockade in splenocytes from mice 21 days post infection led to a reduction in GRAIL expression . Furthermore , addition of IL-2 was able to activate the mTOR pathway , inducing Otubain-1 expression , which mediated GRAIL degradation and improved T cell proliferation . We hypothesize that GRAIL expression induced by the parasite may be maintained by the increased expression of inhibitory molecules , which blocked mTOR activation and IL-2 secretion . Consequently , the GRAIL regulator Otubain-1 was not expressed and GRAIL maintained the brake on T cell proliferation . Our findings reveal a novel association between increased GRAIL expression and impaired CD4 T cell proliferation during Trypanosoma cruzi infection .
Chagas disease , caused by the intracellular protozoa Trypanosoma cruzi , is one of the major human health problems in Latin America . It evolves from an acute to a chronic phase , where subjects may be clinically asymptomatic or show progressive heart disease and leads to an end-stage dilated cardiomyopathy in 20–30% of infected individuals . It is estimated that approximately 4 million chagasic individuals have developed heart disease , making Chagas disease the most frequent cause of infectious cardiomyopathy in the world [1 , 2] . The immune control of T . cruzi is complex , requiring the generation of a substantial antibody response and the activation of both CD4 and CD8 T cell responses . Even in cases in which these responses are sufficiently stimulated to be able to control the acute infection , T . cruzi is not completely eradicated , but instead persists in infected hosts for decades [3] . T . cruzi employs a variety of strategies to evade the immune system and remain in the infected host . The main method involves the inhibition of specific T-cell responses , and consequently , can favor the establishment of chronic infections [4 , 5 , 6 , 7 , 8] . Related to this , a number of both host-dependent and parasite-induced mechanisms have been previously shown to affect immune regulation [9 , 10] . Moreover , T cells from infected hosts are largely unresponsive to antigens and mitogens , resulting in reduced IL-2 synthesis [8] . IL-2 production initiates proliferation , effector functions , and clonal expansion via IL-2 receptor ( IL-2R ) -mediated signaling [11] . In the absence of a robust activation initiated by TCR and CD28 signaling , CD4 T cells fail to proliferate or to produce IL-2 and enter a state of unresponsiveness following immunogenic stimulation , referred to as “anergy” [11 , 12] . In the case of CD4 T cells , the development of anergy depends on the alteration of the expression of several genes [11 , 12 , 13] . Post-translational modification of proteins via ubiquitination also plays an essential role in the regulatory mechanism of CD4 T cell anergy [14 , 15] . GRAIL , also known as ring finger protein-128 ( RNF-128 ) , has been identified as a novel E3 ubiquitin-protein ligase that induces and maintains anergy in CD4 T cells [16 , 17 , 18] . It has been shown that GRAIL expression could be correlated with the inhibition of CD4 T cell proliferation and antigen-induced IL-2 transcription by disrupting the T cell stimulatory signaling [19] . In support of this observation , T cells from GRAIL knock-out mice were shown to be defective in anergy induction both in vitro and in vivo [17 , 20] . In particular , GRAIL ( -/- ) T cells hyperproliferated [17 , 21] and produced more cytokines [17] compared with wild type ( WT ) cells in response to TCR stimulation alone in vitro or with concomitant anti-CD28 costimulation . It has also been recently demonstrated that GRAIL , by mediating TCR-CD3 degradation , regulated naive T cell tolerance induction [20] . Furthermore , several investigations have shown how GRAIL interacts with T-cells , antigen presenting cell ( APC ) receptors and cytoskeletal proteins , thereby promoting their degradation [22 , 23 , 24 , 25] . GRAIL expression is regulated by Otubain-1 ( Otub-1 ) [26] , which is a member of deubiquitinating enzymes with the capability to cleave proteins at the ubiquitin-protein bond by using its cysteine protease domain [27] . It has been shown that Otub-1 is expressed and GRAIL degraded when naive CD4 T cells are productively activated to undergo proliferation [19] . Moreover , the loss of GRAIL was mechanistically controlled through a pathway involving CD28 co-stimulation , IL-2 production and IL-2R signaling , and ultimately , mTOR-dependent translation of select mRNAs . Blocking the mTOR by using CTLA-4-Ig , anti-IL-2 or rapamycin prevented Otub-1 protein expression and maintained GRAIL expression , which inhibited T cell proliferation [19] . Although the function of GRAIL in CD4 T cells has been studied extensively for the development of tolerance [17 , 20] , with its participation having been demonstrated in the development of autoimmune diseases [20] , only recently has its role been studied during T cell dysfunction in the course of infections [28 , 29 , 30 , 31] . Thus , the aim of this work was to search for a novel link between GRAIL and CD4 T cell unresponsiveness in the context of abnormalities of T cell proliferation observed during Trypanosoma cruzi infection . Our results provide evidence demonstrating that CD4 T cells from T . cruzi infected mice exhibited an increase in GRAIL expression during the acute phase of infection , which was correlated with defects in proliferation and immune responsiveness . In addition , we showed that high expression of CTLA-4 and low levels of IL-2 prevented mTOR activation and Otub-1 protein expression , and maintained GRAIL expression , which inhibited T cell proliferation during the acute phase of the infection . Our results therefore indicate that GRAIL is an important player in CD4 T cell anergy during the acute phase of Trypanosoma cruzi infection .
All the animal experiments were examined by the Institutional Experimentation Animal Committee , from Facultad de Ciencias Químicas , Univesidad Nacional de Córdoba , which approved the experimental procedures ( authorization no . 2016–209 ) . This committee follows the guidelines for animal care of “Guide to the care and use of experimental animals” ( Canadian Council on Animal Care , 1993 ) and of “Institutional Animal Care and Use Comittee Guidebook” ( ARENA/OLAW IACUC Guidebook , Nacional Institutes of Health , 2002 ) . BALB/c mice obtained from the Comisión Nacional de Energía Atómica ( CNEA; Buenos Aires , Argentina ) were inbred and housed according to institutional guidelines . BALB/c mice , when 6–8 weeks old , were intraperitoneally infected with 1x106 blood-derived T . cruzi trypomastigote forms from Tulahuén strain , which was maintained through intraperitoneal inoculation every 11 days [32] . Female BALB/c mice were infected intraperitoneally with 500 blood-derived T . cruzi trypomastigote forms diluted in saline solution . After different days post infection ( p . i . ) , these mice were sacrificed by CO2 asphyxiation and spleens were extracted . Non-infected animals were processed in parallel . Trypomastigotes of the Tulahuen and Y strains were obtained from the extracellular medium of infected monolayers of Vero or NIH3T3 cells , respectively , and were collected by centrifugation at 4400 rpm for 10 min and resuspended in RPMI medium containing 10% FCS . Parasites were counted using a Neubauer chamber and used for in vitro infection experiments as described below . Spleen cells were obtained from control or infected animals by homogenizing the organs in a cell strainer . Then , the spleen cell suspensions were centrifuged ( 1500 rpm , 5 min , 4°C ) and the pellets treated with RBC lysis buffer ( GIBCO ) . These cells were subsequently resuspended in complete RPMI medium containing 10% fetal bovine serum ( FBS , PAA laboratories ) , L-glutamine ( 2 mM , GIBCO ) and gentamicin ( 40 g/ml ) , and the isolated cell suspensions were passed through a 50-mm nylon mesh ( BD Falcon ) for cell culture , flow cytometry or cell isolation . Finally , the T cells were isolated from spleens using a CD4+ T cell isolation kit , according to the manufacturer’s instructions ( Miltenyi Biotec ) , with the average purity found to be 95–98% . HEK-293 cells ( ATCC ) were maintained in Dulbecco's modified Eagle's medium ( Gibco ) supplemented with 10% fetal bovine serum ( FBS , PAA laboratories ) , L-glutamine ( 2 mM , GIBCO ) and gentamicin ( 40 g/ml ) , and these cells were grown at 37°C under 5% CO2 . To examine PD-1 , CTLA-4 and GRAIL expression , splenocytes or CD4 T cells from control and infected animals or in vitro infected CD4 T cells were washed with saline solution 2% FBS and incubated with anti-mouse CD32/CD16 antibody for 20 minutes at 4°C to block Fc receptors . Then , cells were incubated with APC labeled anti-CD4 , PercP labeled anti-CD3 ( BD Pharmingen ) , and with PE-labeled anti-PD-1 or anti-CTLA-4 ( BD Pharmingen ) for 20 min at 4°C . For the assessment of intracellular GRAIL expression , cells were first stained with FITC-CD3 and APC-CD4 antibodies , and then fixed and permeabilized with Citofix/Citoperm ( BD Biosciences , ) for 30 min followed by reacting with rabbit anti-GRAIL primary Ab ( Abcam ) for 45 min . After washing , cells were stained for 20 min with PE–anti-rabbit IgG ( Biolegend ) . Finally , cells were washed twice with saline solution of 2% FBS , and stored at 4°C in the dark until analysis using a FACS flow cytometer ( FACS Canto II , BD Biosciences ) . The results were processed using Flow Jo software ( version 7 . 6 . 2 ) . Cytokines were measured in culture supernatants using a capture enzyme-linked immunosorbent assay ( ELISA ) . IFN-γ ( eBioscience ) and IL-2 ( Biolegend ) were used as paired monoclonal antibodies in combination with recombinant cytokine standards . All assays were performed according to the manufacturer’s guidelines . CD4 T cells from control and infected animals were washed and lysed for 30 min at 4°C in RIPA buffer [1% Triton X-100 ( v/v ) , 0 . 5% sodium deoxicolate ( p/v ) , 0 . 1% sodium dodecyl sulfate ( SDS ) ] containing a protease inhibitor cocktail ( Roche ) , and the cell debris was spun down at 13 , 000 g for 15 min . Precipitates were removed , and aliquots of the cell lysates were diluted in SDS sample buffer , boiled at 100°C for 3 min , spun down , and applied to precast 10% acrylamide Tris-glycine gels at 40 μg protein/lane and run at 150 V for 1 h . Samples were transferred to nitrocellulose membranes ( BioRad ) at 100 V for 1 h , and these membranes were probed using rabbit anti-mouse GRAIL ( Santa Cruz Biotechnology ) , anti-mouse Otub-1 or anti-mouse p-4EBP1 ( Cell Signaling Technology ) followed by peroxidase conjugated anti-rabbit antibody ( Sigma Chemical Co . ) , before being visualized using enhanced chemiluminescence ( Pierce ) for detection . The protein loading was evaluated by actin expression . RNA was extracted from splenocytes from infected or control animals by the Trizol reagent ( Invitrogen ) and reverse-transcribed into cDNA by using Revert Aid First Strand cDNA Synthesis ( Fermentas ) . Transcripts were quantified by real-time quantitative PCR on an ABI Prism 7500 sequence detector ( Applied Biosystems ) with predesigned TaqMan gene expression assays and reagents ( Applied Biosystems ) , according to the manufacturer´s instructions . Probes with the following Applied Biosystems assay identification numbers were used: Cblb , Mn01343092 . m1; Rnf128 , Mn00480990 . m1; Rn18s , Mn03928990 . g1 . For each sample , mRNA abundance was normalized to the amount of 18S RNA and expressed as arbitrary units . CD4 T cell proliferation was measured using the cell division tracking dye carboxyfluorescein diacetate succinimidyl ester ( CFSE ) ( Molecular Probes , Eugene , OR ) . Spleen CD4 T cells isolated from infected or control animals were stained with CFSE dye at 5 μM concentrations . Cells were incubated at 37°C for 10 min , and then the reaction was stopped by adding 10 ml of RPMI medium containing 10% FBS . After washing , cells were resuspended in warm RPMI complete medium before being plated in anti-CD3/CD28 Abs ( 1 μg/mL of each Abs ) -coated plates . After 72 h of incubation , cells were stained with PerCP/Cy5 . 5-CD4 Ab and acquired for FACS analysis . Unstimulated CFSE-labeled cells served as a non-dividing control . Data analysis was performed using a FACS flow cytometer ( FACS Canto II , BD Biosciences ) with FlowJo software ( version 7 . 6 . 2 ) , by setting a gate on the live cells to side-scatter versus forward-scatter dot plots and determining the expression of the CFSE . CD4+ T cells were isolated from the spleen of control or infected animals and then cultured in complete RPMI with or without rmIL-2 ( R&D Systems ) at a concentration of 20 ng/mL for 3 days for the cell proliferation assays , or for 1 day for the assessment of GRAIL intracellular expression . Splenocytes were isolated at 21 days p . i . and then cultured in complete RPMI medium in the presence of CTLA-4 blocking antibody or control antibody ( 10 μg/ml , eBioscience ) . Then , intracellular GRAIL expression was evaluated 48 h later by flow cytometry . Statistical analyses were performed using the student’s t-test . Values of p < 0 . 05 were considered to be statistically significant .
To analyze the proliferative efficacy of the CD4 T cells , they were first isolated from the spleen of control and T . cruzi infected mice at different time points . Then , cells were stained with CFSE , stimulated with anti-CD3/CD28 , and proliferation was analyzed 72 h later . CD4 T cells isolated from the spleen of infected animals showed a considerable decrease in their proliferation during the acute phase of infection compared to CD4 T cells from control animals ( Fig 1A and 1B ) . However , proliferation of CD4 T cells from infected animals was recovered later on in the infection . In addition , stimulated CD4 T cells from infected animals produced less IFN-γ and IL-2 at an early time point of infection ( 21 p . i . ) compared to control CD4 T cells ( Fig 1C and 1D ) . These results coincided with the peak of parasitemia ( Fig 1E ) . Because CTLA-4 and PD-1 are known to inhibit T cell function , we examined the expression levels of these molecules in cells from the spleens of control and infected animals . It has been shown that T . cruzi is able to modulate the expression levels of the negative coreceptor PD-1 in several immune cells [33] . However , in that study , different mice and parasite strains were used . Thus , to evaluate in our experimental model if T . cruzi infection upregulates PD-1 and CTLA-4 expression in CD4 T cells , flow cytometry was performed on spleen cells at several time points after infection , and the percentage of CD4+ T cells expressing PD-1 or CTLA-4 on the surface was determined , as shown in Fig 2A and 2C , with representative dot plots displayed in Fig 2B and 2D . We found that the infection led to an increase in the expression of PD-1 and CTLA-4 in spleen CD4 T cells from infected mice at 21 days p . i . compared to control cells ( Fig 2A and 2C ) . In addition , we found that expression levels of CTLA-4 fell to normal levels at day 42 p . i . , and PD-1 expression also decreased significantly ( Fig 2B and 2D ) . Considering that GRAIL is an inducer of impaired CD4 T cell proliferation during in vitro and in vivo tolerance [16 , 17] , as well as being involved in CD4 T cell dysfunction during infection [28 , 29 , 30] , we aimed to assess its expression in spleen cells from control and infected animals by real time PCR . We found a significant upregulation of its mRNA levels in splenocytes from infected mice , which were strongest at the earliest time point post-infection ( Fig 3A ) . However , we did not observe upregulation of Cbl-b , which is another E3-Ubiquitin ligase shown to be involved in regulating T cell functions ( Fig 3B ) [34 , 35 , 36] . To confirm GRAIL expression in CD4 T cells , splenocytes were labeled with anti-CD4 and anti-CD3 antibodies , and then GRAIL expression was evaluated by intracellular labeling and analyzed by flow cytometry . A considerable upregulation of GRAIL protein was found in splenic CD4 T cells isolated from animals at 21 days p . i . , compared to splenic CD4 T cells from control uninfected animals . However , GRAIL expression decreased in cells from animals at 42 days p . i . ( Fig 3C ) . GRAIL expression in the HEK 293 cell line was used as a positive control ( Fig 3C ) . Next , to test if GRAIL upregulation in CD4 T cells is a general phenomenon of T . cruzi infection or is dependent on the strain used , we evaluated GRAIL expression by FACS in CD4 T cells cultured in vitro with two different T . cruzi strains . We observed that both T . cruzi strains induced GRAIL expression at similar levels ( Fig 3D ) . Thus , our results showed that GRAIL expression is induced during acute phase of infection and correlates with the peak of parasitemia and with CD4 T cell hiporesponsiveness . In addition , we observed that GRAIL expression is induced directly by different parasite strains . It has previously been shown that Otub-1 is expressed and GRAIL is degraded when naive CD4 T cells are productively activated to undergo proliferation [19] . In addition , Lin et al . demonstrated that the loss of GRAIL is mechanistically controlled through a pathway involving CD28 costimulation , IL-2 production and IL-2R signaling , and ultimately , by mTOR-dependent translation of select mRNA . Interference of this pathway using CTLA-4-Ig , anti-IL-2 , or rapamycin prevents Otub-1 protein expression and maintains GRAIL expression , which inhibits T cell proliferation [19] . These findings implicate Otub-1 and GRAIL as important components governing T cell unresponsiveness . Given that we observed a reduced IL-2 production and increased CTLA-4 and GRAIL expression in CD4 T cells from the acute phase of T . cruzi infected mice , we evaluated GRAIL as well as Otub-1 expression and mTOR activation in CD4 T cells from T . cruzi infected mice at different time points after infection . An increased GRAIL expression was observed during infection , which was stronger at the acute phase of infection ( Fig 4A ) as shown also by real time PCR and FACS ( Fig 3A and 3C ) , while GRAIL expression dropped at 42 days p . i . ( Fig 4A ) . In addition , Otub-1 expression was not evident early on , although it increased as the infection progressed , with a peak occurring at day 36 p . i . Finally , GRAIL expression was not observed at 42 days p . i . while Otub-1 protein expression was observed ( Fig 4A ) , showing that GRAIL downregulation happened later on in the infection and depended on Otub-1 expression . As CD4 T cells require CD28 costimulation and IL-2R signaling to modulate GRAIL expression , we reasoned that the mTOR pathway might also control Otub-1 and GRAIL expression . On examining mTOR activity , we did not observe phosphorylation of 4EBP1 during the acute phase of infection although it was detected later on ( Fig 4A ) , thereby allowing Otub-1 expression and GRAIL degradation . This effect could has been related to CTLA-4 expression ( Fig 2A ) since it has previously been shown that CTLA4-Ig treatment blocks CD28 costimulation and the resultant IL-2 expression . This in turn inhibits the mTOR-dependent translation of mRNA transcripts , including Otub-1 , thus maintaining GRAIL expression and inhibiting CD4 T cell proliferation [19] . To test this hypothesis , we performed experiments by culturing splenocytes from infected mice at 21 days p . i . with blocking anti-CTLA-4 or control antibody and GRAIL intracellular expression was evaluated 48 h later by FACS . We observed a reduction in GRAIL expression in cells from infected mice cultured with anti-CTLA-4 compared to cells treated with control antibody ( Fig 4B ) . This might indicate that the absence of co-stimulation during the acute phase of infection due to increased expression of inhibitory molecules , ( Fig 2 ) such as CTLA-4 , which may allow GRAIL expression to be maintained via a blockade of mTOR activation . Taking into account that GRAIL expression during T . cruzi infection might be regulated by Otub-1 and that this depends on mTOR and IL-2 signalling , we hypothesized that the addition of exogenous IL-2 to CD4 T cells at 21 days p . i . may compensate for either diminished or delayed IL-2 production . We performed in vitro experiments by comparing CD4 T cell proliferation in control and in animals at 21 days p . i . after treatment with rmIL-2 . CD4 T cells were stimulated with anti-CD3/CD28 ligands in the presence or absence of rmIL-2 for 3 days and then cell proliferation was assessed . It was found that CD4 T cells from control as well as 21 days p . i . animals had an increase in proliferation when treated with rmIL-2 together with the TCR stimulatory ligands ( Fig 4C ) . In addition , we also evaluated GRAIL and Otub-1 expression as well as the phosphorylation of 4EBP1 in CD4 T cells with or without rmIL-2 . In agreement with the increase in CD4 T cell proliferation from 21 days p . i . , we found an increase in 4EBP1 phosphorylation and Otub-1 expression and a reduction in GRAIL expression ( Fig 4D and 4E ) , indicating this E3 ubiquitin ligase to be a new player in T cell hyporesponsiveness during the acute phase of T . cruzi infection .
Several alterations of the immune response have been described in Chagas disease . Early investigations suggested that infection with T . cruzi was associated in both humans and mice with a severe T cell unresponsiveness to mitogens and antigens during the acute phase of the disease [37 , 38] . This immunosuppression was thought to facilitate the dissemination and establishment of the parasite in the infected host [9 , 39] , which was ascribed to various mechanisms [5 , 8 , 40 , 41 , 42 , 43 , 44] . However , it has been widely demonstrated that the most affected cytokine in acute T . cruzi infection is IL-2 , an important growth factor for T lymphocytes that is suppressed in several lymphoid organs such as thymus , mesenteric lymph nodes and spleen [45] . Many authors have shown that T . cruzi glycoproteins induce T cell anergy [37] , cell cycle arrest [46] or inhibit T cell activation [5 , 6 , 8] by affecting IL-2 secretion or IL-2 receptor expression . In the present work , we found that CD4 T cells from acute T . cruzi infections in mice produced low levels of IL-2 when stimulated with anti-CD3/anti-CD28 , and also had less capacity to proliferate , which could be related to the increase in GRAIL expression . In fact , the high expression of this gene alone is enough to convert a naïve CD4 T cell into an anergic phenotype [18] . Therefore , it is possible that T cell hyporesponsiveness caused by T . cruzi antigens such as mucins [8 , 37 , 46] , and characterized by decreased IL-2 synthesis , might be mediated by GRAIL since expression of this E3 Ubiquitin Ligase correlates with the peak of the parasitemia . However , this still remains to be tested . Related to this , we have observed that GRAIL expression is induced directly by different T . cruzi strains . It has been shown that parasite components such as mucins are able to inhibit early events in T cell activation and induce T cell anergy . These parasite components bind to L-selectin and inhibit different activation pathways that lead to inhibition of IL-2 secretion and T cell proliferation [8] . In addition , another work reported that parasite-derived mucins bind to Siglec-E ( CD33 ) and inhibit mitogenic responses in CD4 T cells by inducing a cell cycle regulator that blocks the cell cycle [46] . It has also been shown that mannose-capped lipoarabinomannan ( LAM ) from Mycobacterium tuberculosis can inhibit CD4 T cell activation by downregulating the phosphorylation of key proximal TCR signaling molecules , which facilitates induction of anergy-related genes , and results in long-term CD4 T cell dysfunction [30] . In another study , engaging the TLR7 expressed on CD4 T cells resulted in complete anergy by inducing intracellular calcium flux , with the activation of an anergic gene-expression program being dependent on the transcription factor NFATc2 . Then , T cell unresponsiveness was reversed by knockdown of TLR7 and restored the responsiveness of HIV-1+ CD4+ T cells in vitro [47] . Thus , it is possible that parasite derived factors might contact different receptors/molecules on T cells and induce T cell hiporesponsiveness directly by increasing the expression of anergy factors . We hypothesize that the high levels of parasites occurring during the acute phase of infection may induce GRAIL expression on T cells by a mechanism that we have not yet explored . However , additional studies are needed to understand how T . cruzi directly induces GRAIL expression on CD4 T cells . Expression of the E3-Ubiquitin Ligase has been linked to CD4 T cell hyporesponsiveness during sepsis [29] and chronic murine schistosomiasis [31] . In addition , it has been reported to be induced by tegumental antigens from Fasciola hepatica and [28] LAM from Mycobacterium tuberculosis in CD4 T cells [30] . In fact , the T cell anergy observed during these infections is characterized by a lack of cytokine responses and reduced proliferative activity , which can be reversed by the addition of IL-2 and results in a reduction of GRAIL expression [29 , 30] . Although it has previously been shown that IL-2 is able to reverse the human T cell anergy induced by T . cruzi mucins [37] , this is the first work that links the ability of IL-2 to reverse T cell hyporesponsiveness during T . cruzi infection to GRAIL regulation . During the acute phase of infection , we observed an increased expression of GRAIL with low Otub-1 and mTOR expression and activation in CD4 T cells . As Otub-1 is controlled by the Akt-mTOR pathway and is a negative regulator of the GRAIL function [19 , 25] , this suggests that T . cruzi infection may disrupt the Akt-mTOR pathway resulting in Otub-1 downregulation , which in turn may induce GRAIL . In agreement with this , Sande et al . observed a downregulation of Otub-1 in LAM-treated T cells [30] . The absence of co-stimulation due to increased expression of inhibitory molecules such as CTLA-4 interferes with Otub-1 translation , with GRAIL expression being maintained via a blockade of the activation of the mTOR pathway [25] . In addition , most studies concerning T cell anergy have established that it results from TCR stimulation in an inhibitory environment , involving increased co-inhibition , decreased co-stimulation , or TCR engagement with a weak agonist peptide [48] . However , more recently , the role of mTOR and other related metabolic sensors and regulators has emerged as being of particular importance and has broadened our view of anergy-inducing signals [49 , 50] . Here , we observed differences in the expression of the co-inhibitory receptors PD-1 and CTLA-4 between CD4 T cells from acute and chronic phases of infection . An increased expression of CTLA-4 during the acute phase of infection may block mTOR activation , thus preventing protein translation , ( including Otub-1 ) and leading to the maintenance of GRAIL expression with reduced T cell proliferation and cytokine production . Regarding this , we observed that CTLA-4 blockade in splenocytes from infected mice resulted in a reduction in GRAIL expression . Additionally , during several infections the expression of inhibitory molecules and E3 Ubiquitin ligases has been previously shown to be upregulated and to induce T cell hyporesponsiveness [51 , 52] , with blocking CTLA-4 or neutralizing TGF-β during lymphatic filariasis restoring the ability to mount Th1/Th2 responses to live parasites and reversing the induction of anergy-inducing factors [51] . Furthermore , dendritic cells activated by tegumental antigens from Fasciola hepatica suppress T cells in vitro by inducing GRAIL and CTLA-4 expression [28] . Although the expression of inhibitory molecules has previously been observed during Chagas disease and in T . cruzi experimental infection [32 , 33 , 53] , this is the first study that has demonstrated that CD4 T cell hyporesponsiveness may be caused by a combinatory effect of inhibitory molecules and GRAIL expression . Considering the above results , we speculate that early upon infection parasite derived factors might contact receptors/molecules to induce directly GRAIL expression and T cell anergy . Later on , expression of inhibitory receptors such as CTLA-4 prevented Otub-1 protein expression and maintained GRAIL expression , which inhibited T cell proliferation . However , we have shown that GRAIL expression is reduced in T cells from acute infected mice cultured in the presence of CTLA-4 blocking antibodies or rm-IL-2 . Therefore , these results may indicate that GRAIL expression during T . cruzi infection could be induced directly by the parasite and sustained by expression of inhibitory molecules . GRAIL is not only expressed in naive T cells , but also in effector T cell subsets and controls their activation . Kriegel et al . reported that GRAIL-knockout Th1 effector CD4 T cells overproduce IFN-γ [17] . Related to this , we observed that CD4 T cells from acute infected mice , where GRAIL expression is increased , produced lower levels of IFN-γ than CD4 T cells from the later stages of T . cruzi infected mice . This is consistent with a recent report by Nunes et al . demonstrating that in vivo administration of T . cruzi mucin during murine experimental infection with T . cruzi parasites resulted in a lower number of splenic IFN-γ producing CD4 T cells [46] , with these effects being accompanied by a greater susceptibility to infection , as shown by the higher levels of parasitemia [46] . CD3 is a known target of GRAIL , with the upregulation of GRAIL in T cells leading to degradation of CD3 [20] . In the present study , a lower CD3 expression in T cells was observed during the acute phase of infection that correlated with increased GRAIL expression ( S1 Fig ) . Although additional experiments are needed to corroborate CD3-GRAIL interaction , it has been reported that immunosuppression during T . cruzi infection is due to defective T-Cell Receptor-CD3 functioning [54] and might be related to a lower expression of the CD3 molecule caused by degradation . In summary , we have established a novel link between T cell hyporesponsiveness during T . cruzi infection and the expression and regulation of GRAIL in CD4 T cells . It is now important to extend these studies to evaluate additional GRAIL targets in T . cruzi-anergized T cells . In addition , it is necessary to determine if GRAIL can be detected in T cells from infected patients , and whether its expression can be induced directly in a dose dependent manner by purified antigens of T . cruzi . Finally , our data provide novel insights into why , despite the large immune cell activation by a wide variety of T . cruzi antigens , CD4 T cells may not respond optimally to their cognate antigens . In addition , T cell immune evasion strategies likely contribute to the host’s inability to eliminate T . cruzi and consequently permit survival and persistence of the parasite in the host . | Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and is endemic in Central and South America , where it affects about 10 million people . In addition , migration has led to the disease being established in non-endemic countries . Infection involves an acute stage that evolves to a chronic stage where infected individuals may or may not show clinical symptoms or suffer progressive heart disease . The relevance of T cells in the control of T . cruzi infection has been demonstrated in human infection and in experimental models . However , the T . cruzi parasite employs different strategies to downregulate the T cell function . These mechanisms can act at the initial time of T cell activation , leading to a state of anergy where lymphocytes do not respond . However , the molecular components that regulate this process during T . cruzi infection are not well understood . Our findings demonstrate for the first time that this T cell hyporesponsiveness could be linked to an increased expression of GRAIL . We propose that GRAIL expression induced by the parasite could be maintained by increased expression of inhibitory molecules , which blocked mTOR activation and IL-2 secretion . GRAIL could then play a key role in downregulating T cell functions by allowing the parasites to establish the chronic disease . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"blood",
"cells",
"t",
"helper",
"cells",
"flow",
"cytometry",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"spleen",
"immunology",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"protozoans",
"anergy",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"spectrum",
"analysis",
"techniques",
"white",
"blood",
"cells",
"animal",
"cells",
"proteins",
"t",
"cells",
"spectrophotometry",
"trypanosoma",
"cruzi",
"cytophotometry",
"t",
"cell",
"receptors",
"biochemistry",
"trypanosoma",
"signal",
"transduction",
"cell",
"biology",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"immune",
"receptors",
"organisms"
]
| 2017 | GRAIL and Otubain-1 are Related to T Cell Hyporesponsiveness during Trypanosoma cruzi Infection |
There is a global need for cost-effective and environmentally friendly tools for control of mosquitoes and mosquito-borne diseases . One potential way to achieve this is to combine already available tools to gain synergistic effects to reduce vector mosquito populations . Another possible way to improve mosquito control is to extend the active period of a given control agent , enabling less frequent applications and consequently , more efficient and longer lasting vector population suppression . We investigated the potential of biodegradable wax emulsions to improve the performance of semiochemical attractants for gravid female culicine vectors of disease , as well as to achieve more effective control of their aquatic larval offspring . As an attractant for gravid females , we selected acetoxy hexadecanolide ( AHD ) , the Culex oviposition pheromone . As toxicant for mosquito larvae , we chose the biological larvicides Bacillus thuringiensis israelensis ( Bti ) and Bacillus sphaericus ( Bs ) . These attractant and larvicidal agents were incorporated , separately and in combination , into a biodegradable wax emulsion , a commercially available product called SPLAT ( Specialized Pheromone & Lure Application Technology ) and SPLATbac , which contains 8 . 33% Bti and 8 . 33% Bs . Wax emulsions were applied to water surfaces as buoyant pellets of 20 mg each . Dose-mortality analyses of Culex quinquefasciatus Say larvae demonstrated that a single 20 mg pellet of a 10−1 dilution of SPLATbac in a larval tray containing 1 L of water caused 100% mortality of neonate ( 1st instar ) larvae for at least five weeks after application . Mortality of 3rd instar larvae remained equally high with SPLATbac dilutions down to 10−2 for over two weeks post application . Subsequently , AHD was added to SPLAT ( emulsion only , without Bs or Bti ) to attract gravid females ( SPLATahd ) , or together with biological larvicides to attract ovipositing females and kill emerging larvae ( SPLATbacAHD , 10−1 dilution ) in both laboratory and semi-field settings . The formulations containing AHD , irrespective of presence of larvicides , were strongly preferred as an oviposition substrate by gravid female mosquitoes over controls for more than two weeks post application . Experiments conducted under semi-field settings ( large screened greenhouse , emulating field conditions ) confirmed the results obtained in the laboratory . The combination of attractant and larvicidal agents in a single formulation resulted in a substantial increase in larval mosquito mortality when compared to formulations containing the larvicide agents alone . Collectively , our data demonstrate the potential for the effective use of wax emulsions as slow release matrices for mosquito attractants and control agents . The results indicate that the combination of an oviposition attractant with larvicides could synergize the control of mosquito disease vectors , specifically Cx . quinquefasciatus , a nuisance pest and circumtropical vector of lymphatic filariasis and encephalitis .
Mosquitoes transmit several serious pathogens in the tropics . Mosquito population control interventions are therefore frequently implemented to keep the diseases at bay , which are caused by those pathogens . The most targeted mosquito species include members of the mosquito genera Anopheles ( vectors of malaria , [1] ) , Aedes ( vectors of dengue , yellow fever virus , chikugunya , Zika , Rift Valley fever and many other arboviruses , [2 , 3] ) and Culex ( vectors of lymphatic filariasis and West Nile virus , [4 , 5] ) . Control measures often target adults and may take the form of indoor residual spraying [1 , 6 , 7] , space spraying [3 , 8] and insecticide treated materials such as curtains and bed nets [9–13] . However , adult mosquitoes often adapt strategies to evade control through the development of behavioural and physiological resistance , such as reducing endophilic and endophagic behaviour , changes in diel biting rhythms [14] , and developing insecticide avoidance [15] . Another set of mosquito population control techniques targets the aquatic stages . Aquatic life stages are generally more vulnerable to control measures , because they are restricted to the water body in which they hatch . The oldest , and still among the most efficient larval control methods is source reduction , a technique consisting of the eradication of mosquito larval breeding sites or steps taken to render them inaccessible , so that breeding is prevented [16] . Since full source control is unfeasible in many situations—most obviously in wetlands , irrigated rice cultivation , and regions with high precipitation rates—chemical insecticides , such as DDT , organophosphates , and methoprene [17] , have been used as larvicides for mosquito control for many decades . Concerns over the negative ecological/environmental impacts attending the use of chemical insecticides have driven the search for environmentally safer alternatives , including Bacillus thuringiensis israelensis ( Bti ) and Bacillus sphaericus ( Bs ) , entomopathogenic fungi , and natural enemies like nematodes , copepods , and fish [18–21] . Of these , Bti and Bs are among the most eco-friendly and efficient larvicides used to suppress mosquito populations and to reduce disease transmission , especially by Culex and Anopheles mosquitoes [2 , 4 , 18 , 22–25] . However , the use of Bacillus larvicides is restricted by the economic constraints of application on wide spread ephemeral water bodies , their rapid inactivation through UV radiation [26–28] and sedimentation in the soil and leaf litter . This poor residual activity requires frequent reapplication for Bti and Bs larvicides [29–32] , hampering intervention and efficacy [32–34] . Formulations that remain buoyant and do not sediment to the ground of water bodies could potentially increase the effectiveness of Bti and Bs at least by an order of magnitude as shown for Anopheles [35] . However , this possibility has been underresearched and underutilized , perhaps in part because of the lack of a suitable technique to achieve the required buoyancy [36] . In addition to increasing the longevity of Bti and Bs and the buoyancy of their carrier material , the potential of these control agents could be further enhanced through increasing the contact rate with the target organisms . By attracting mosquitoes to or arrestment of mosquitoes at the control agents , commonly called attract and kill , synergy in control can be expected [37–39] . If the attractant used is species specific , it will further improve the environmental friendliness of the intervention . For odorants to act in the desired manner , however , it is critical to deliver them at the most effective concentrations and ratios . Attractants can become unattractive or even repellent to the target insects if release rates are too high or when they are applied at improper ratios [40–44] . For several mosquito species , there is accumulating evidence that odorants play a significant role in the selection of oviposition sites by gravid females [45] . One example of such an odorant is the oviposition pheromone of Cx . quinquefasciatus ( - ) - ( 5R , 6S ) -6-acetoxy-5-hexadecanolide ( henceforth abbreviated to AHD ) , which is released from droplets on the top of floating egg rafts , and which induces conspecific females to lay their eggs nearby [37 , 46–51] . AHD is frequently referred to as “Mosquito Oviposition Pheromone , ” or MOP , though this is inaccurate and can be misleading , since only species of the genus Culex are known to produce and use AHD in their communication system . Application of AHD for purposes of vector mosquito control is limited by the same factors as Bti/Bs application: AHD rapidly precipitates , diffuses , or breaks down after application onto the water surface layer . Thus , the efficacy of both Bti and Bs control agents and the AHD attractant could be greatly improved by their incorporation into a buoyant , slow-release carrier material . Wax emulsion matrices [52 , 53] offer such potential . Focusing primarily on a vector of lymphatic filariasis , Cx . quinquefasciatus , our research investigated 1 ) if wax emulsions that contain control agents such as Bti and Bs induce mortality in a dose , larval stage and time-since-application dependent manner in mosquito larvae 2 ) if wax matrices incorporating the Culex oviposition pheromone ( AHD ) attract gravid females in a species-specific , dose and time-since-application dependent manner , and finally , 3 ) whether some synergistic effect can be observed when combining the killing agents ( Bti/Bs ) with the attractant ( AHD ) under both laboratory and field-resembling conditions .
For laboratory assays , Cx . quinquefasciatus Say 1823 ( Thai strain; obtained from the London School of Hygiene and Tropical Medicine ) , Aedes aegypti ( Linnaeus 1762; Rockefeller strain obtained via Wageningen University from Bayer AG Monheim ) , Anopheles arabiensis ( Patton 1905; Dongola strain; obtained from the International Atomic Energy Agency , Vienna , Austria ) and Anopheles gambiae ( Giles 1902; Kisumu strain; a strain brought from Kenya Medical Research Institute and colonized at the National Institute for Medical Research , Amani Research Centre since early 1982 ) were maintained in a controlled environment ( 27±1°C , 65±5% relative humidity ( RH ) , and at a 12 h:12 h light: dark cycle ) . Larvae were reared in distilled water-filled plastic trays ( 20 cm × 30 cm × 10 cm ) in groups of <500 ( Culex , Aedes ) or <250 ( Anopheles ) per tray and fed on fish food once a day ( SuperVit— 8 Mix—Tropical and Tetramin , one tea spoon tip per day and tray ) . For semi-field assays with Cx . quinquefasciatus , we used a strain established by the Tropical Pesticide Research Institute ( TPRI ) , Tanzania . Adults were kept in cages ( 30 cm × 30 cm × 30 cm ) with ad libitum access to a 12% sucrose solution . To enable reproduction , female mosquitoes were fed on sheep blood ( Håtunalab , Bro , Sweden ) via a membrane feeding system ( Discovery labs , Accrington , UK ) or , partially , on a human arm ( DLP Schorkopf for Anopheles ) . The participation of humans in blood-feeding mosquitoes during routine colony maintenance was approved and monitored by the Central Ethical Review Board in Sweden . For semi-field assays in Tanzania , Cx . quinquefasciatus ( TPRI strain , Tanzania ) were blood fed on rabbits according to Standard Operating Procedures approved by the Medical Research Coordinating Committee of the National Institute for Medical Research ( NIMR , Tanzania; research permit NIMR/HQ/R . 8a/Vol . IX/1584 ) . Rabbits were kept following European Community guidelines and standards ( http://www . dantes . info/Tools&Methods/Othertools/Docs/86 . 609 . EEC . pdf ) . Other semifield mosquito rearing conditions were the same as described for the lab assays above . For all experiments , we used the commercial matrix SPLAT™ ( Specialized Pheromone and Lure Application Technology , ISCA Technologies , Riverside , CA , USA ) , which is modified from similar matrices used by Atterholt et al . [52 , 53] . The waxes and oils used in SPLAT are biodegradable [54 , 55] and therefore SPLAT is suitable for sustainable pest management . Of the different varieties of SPLAT available from its manufacturers , we used SPLATblank and SPLATbac . SPLATblank did not contain attractants or control agents , whereas SPLATbac contained 83 . 3g B . thuringiensis Berliner 1915 subsp . israeliensis de Barjac 1980 ( serovar H14 , henceforth Bti ) and 83 . 3g B . sphaericus Meyer and Neide 1905 ( serotype H5a5b , strain 2362 , henceforth Bs ) per kilogram of formulation . SPLAT has a putty-like consistency and is usually applied directly onto substrates . However , we observed during the beginning of the present study that freshly made dollops applied directly on water partly dissolve and spread like a film across the water surface . To create cohesive , buoyant dollops that slowly release both attractants and control agents , we first dried the dollops for 5 days prior to application , so that they kept their shape and floated on the water surface ( henceforth SPLAT pellets , see below ) . Blood-fed females in their first gonotrophic cycle ( 6 to 24 hours post blood meal ) were transferred to 30 cm × 30 cm × 30 cm cages ( BugDorm , Megaview , Taiwan ) more than 24 h prior the start of the experiments with ad libitum access to a 12% sucrose solution placed in the centre . Single or multiple females of either Cx . quinquefasciatus , Ae aegypti , An . arabiensis or An . gambiae ( as specified ) were given a dual choice between two 250 mL disposable cups each containing 50 mL distilled water , containing either SPLATblank or SPLATahd . Disposable cups were conventional paper or plastic cups , which we only used once and then discarded . For each set of oviposition choice experiments we used the same type and brand of 250 mL cups . Oviposition experiments with Aedes included a filter paper disc ( diameter 9 cm ) on the inner wall of each oviposition cup , as Aedes’ prefers to deposit eggs on moist edges of water bodies rather than directly onto the water . The position of cups was alternated between experiments to minimize positional bias . Test conditions were the same as rearing conditions . Eggs were counted after each experimental night . The longevity of SPLATahd in inducing oviposition in Cx . quinquefasciatus was tested by using dollops 1 , 5 , 10 , 20 , and 40 days after application in water . Methods otherwise followed those mentioned above . Experiments were carried out in screened greenhouses ( so-called mosquito spheres ) in the field located at the National Institute for Medical Research , Amani Research Centre ( 0510'220"S , 3846'733"E ) , in Muheza , Tanzania [58] . The mosquito spheres ( 12 . 2 m long , 8 . 2 m wide , and approximately 5 m high ) contained a traditional mud hut and natural vegetation , including different grass , flower , and shrub species and three small trees no taller than 2 . 4 m . To better mimic the appearance of natural oviposition sites , we used locally made clay bowls ( ~ 0 . 2 m in diameter and 0 . 09 m high ) and positioned them so that the top bowl margins were level with the surrounding ground . We filled the bowls with soil and water from a nearby natural Culex oviposition site . The positions of the 32 bowls within the sphere were approximately equidistant from each other . We applied either SPLATbacAHD ( treatment ) or SPLATblank ( control ) in the morning preceding the experiment . Treatments and controls were alternated within and between experiments to minimize positional bias . A cage containing between 150 to 300 gravid Cx . quinquefasciatus was placed in the hut ( “indoors” ) and opened in the early evening so that the females could leave the cage . In the morning after the experiment , each clay bowl was checked for egg rafts and the number noted . Because the number of total egg rafts laid in each of the six experimental nights differed , we used the proportion of egg rafts laid in treatment vs . control for comparison rather than the total number of egg rafts in each bowl . Results were discarded when rainfall led to overflowing of water in any of the clay bowls . After counting , egg rafts collected from control ( SPLAT ) and test formulations ( SPLATbac or SPLATbacAHD ) bowls were transferred to two separate 5 L plastic buckets , respectively . Only one SPLAT or SPLATbac pellet was allowed to remain per L of above mentioned oviposition site material in those buckets . The number of emerging larvae from the sphere assays was estimated in decadic steps ( nearest to 10 , 20 , 30 etc… ) and mortality was observed for a period of 72 h post emergence . For oviposition choice results , the non-normally distributed percentages of total number of eggs or egg rafts generally required non-parametric statistical approaches , especially when data did not meet the assumptions of equal variance ( Spearman rank correlation p>0 . 05 ) and normal distribution ( Shapiro-Wilk test p>0 . 05 ) . For comparison of proportions within the same experiment , we used Wilcoxon signed rank tests , and Mann-Whitney U tests between two separate oviposition experiments . When multiple data sets were compared , we used Kruskal-Wallis ANOVAs , followed by Dunn’s pairwise comparison after finding significant differences , unless assumptions for parametric ANOVA were met . For cases meeting the latter criteria , we calculated conventional ANOVAs , followed by Tukey’s pairwise comparisons . For pairwise comparisons in longevity studies of AHD , we chose the less conservative post-hoc Fishers’ LSD . This was chosen under the assumption of a decline of pheromone release over time ( Atterholt et al . 1998 , 1999 ) and following pilot studies strongly indicating a gradual decrease of oviposition attraction over time . Box plots in this paper show the range from 1st to 3rd quartile with median ( bold line ) and arithmetic mean ( dotted line ) plus 5/95% percentiles ( whiskers ) . As statistical software package , we used Sigmastat 4 . 0 ( Systat Software Inc . ) .
In the laboratory , SPLATbac caused 100% mortality of Cx . quinquefasciatus larvae within 48 h after application of undiluted SPLATbac in all tested amounts ( 20 mg L-1 , 8 mg L-1 , 1 mg L-1 per litre ) , irrespective of larval instar ( 1st instar: N = 6 x 60 larvae; or 3rd instar: N = 5 x 30 larvae ) . As 100% mortality does not permit the detection of efficiency and longevity thresholds of SPLATbac , we henceforth used dilutions of SPLATbac ( 10−1 to 10−4 ) . SPLATbac significantly reduced the developmental success from egg or 3rd larval instar to adult in a concentration dependent manner ( Fig 1; Kruskal-Wallis ANOVA on Ranks; hatching eggs: Hdf = 4 = 17 . 55 , p = 0 . 002; N[each concentration] = 5; late instar: Hdf = 4 = 21 . 489 , p < 0 . 001; N[each concentration] = 5 ) . Compared to the control ( SPLATblank ) , we observed significantly elevated mortality rates following exposure to SPLATbac treatments in recently emerged 1st instar larvae ( all SPLATbac dilutions ) and 3rd instar larvae ( SPLATbac dilutions down to 10−3 ) . SPLATbac induced 100% mortality in all larvae before they reached adulthood at dilutions of 100−10−2 for 1st instar , and at dilutions of 100−10−1 in 3rd instar larvae . Under field resembling conditions ( mosquito spheres ) the relative proportion of Cx . quinquefasciatus eggs laid in SPLATbacAHD containing bowls ( 16 of 32 ) compared to control bowls ( remaining 16 bowls containing SPLATblank ) was comparable to that observed under laboratory conditions ( Fig 6A ) . A significantly higher proportion of egg rafts ( mean difference = 37 . 6% , Mann-Whitney U = 0; p = 0 . 002 , r = 0 . 83; NSPLATbacAHD = 6 , NSPLATbac = 6 ) compared to the control ( SPLATblank containing bowls ) was laid in the SPLATbacAHD containing bowls ( mean difference to paired control = 70 . 9% ±19 . 5SD; Wilcoxon signed rank WN = 6 pairs = 21 , p = 0 . 031 , median = 85 . 1% , Q1 = 76 . 9% , Q3 = 93 . 8%; r = 0 . 64; 1100 females and 185 egg rafts in total ) than in SPLATbac containing bowls ( mean difference to paired control = 4 . 3% ±8 . 1SD; Wilcoxon signed rank WN = 6 pairs = 15 , p = 0 . 156 , median = 45 . 8% , Q1 = 44 . 9% , Q3 = 52 . 8%; 1150 females and 187 egg rafts in total ) . The number of egg rafts found in each clay bowl ( 32 bowls per experiment ) ranged from zero to seven ( Fig 6B and 6C ) . Between trials , clay bowls were shifted between treatment and control . Across all bowls , the average number of egg rafts did not differ between experiments ( Kruskal-Wallis Hdf = 31 = 24 . 435 , p = 0 . 792 ) . Similarly , odd and even numbered bowls did not differ in the average number of egg rafts ( difference between odd and even numbered clay bowls = 0 . 572%; t-test tdf = 30 = 0 . 88 , p = 0 . 381 ) . Positional bias therefore did not appear to account for the “clumped” raft distribution ( Fig 6B ) .
Our results demonstrate that SPLATbac slowly released Bti and Bs into the water in proportions sufficient to kill-off aquatic life stages of mosquitoes , at least under the small water body conditions evaluated in these studies . The varieties of Bti and Bs used in the present study were as effective in combination as described in previous studies ( e . g . [29 , 62 , 72 , 73] ) . A combination of Bti and Bs not only substantially improves larvicidal efficacy against Culex , but also suppresses the selection process that results in the building up of larvicide resistance [74 , 75] . Our study determined that 17 μg per SPLAT pellet ( 20 mg ) for each larvicide ( Bti/Bs ) to be sufficient to kill off all early and late instar larvae ( 1st instar and 3rd instar ) prior reaching adulthood in one litre of water . We did not determine the release rates of Bti and Bs into water by the different SPLATbac formulations , but the biological longevity demonstrates that the release is relatively slow . It should be noted that the longevity of the formulation was assessed only under laboratory conditions . Considering that stability of SPLAT is well established under field conditions for numerous other semiochemicals and active ingredients [76–79] , we anticipate that SPLATbac longevity would be similarly prolonged in the field . It is well known that young mosquito larvae are much more susceptible to Bti and Bs than later instars [36] . In our study , the differential susceptibility may have been further augmented by the longer exposure period of 1st instar larvae compared to 3rd instar larvae . Our results demonstrate that mosquito oviposition attractants in wax emulsions can modulate the behaviour of gravid females and be used in surveillance and control . By exploiting the chemosensory system of the target species e . g . by utilizing species-specific attractants or pheromones , the sensitivity of surveillance and the efficacy of control can be greatly enhanced . Here we show that gravid Cx . quinquefasciatus consistently preferred bodies of water treated with SPLAT containing small amounts ( 0 . 4 μg/mg ) of Culex oviposition pheromone ( AHD ) , irrespective of the presence of Bti and Bs . Since the presence of AHD did not reduce SPLATbac induced mortality , our results show that attractants and control agents can be successfully combined into a single formulation without negatively impacting the efficacy of either component . The buoyancy of our SPLAT pellets facilitates the emission of volatiles from the wax matrix into the air , which otherwise would risk entrapment in water , depending on the properties ( e . g . polarity , vapour pressure ) . Application on a floating structure appears critical for AHD to be able to attract mosquitoes [39 , 48 , 51 , 81–86] . None of the other species tested ( Ae . aegypti , An . gambiae , An . arabiensis ) preferred to oviposit into AHD treated formulations . This is similar to findings of Hwang et al . [82] on An . quadrimaculatus and Ae . aegypti . Although attraction of multiple vector species would have been advantageous , it is encouraging to note that the opposite effect—a repellent or deterrent effect of AHD in Aedes or Anopheles—was not observed , either . Interspecific interactions , such as reported in An . gambiae [87] are clearly not mediated by AHD . It is therefore advantageous that AHD does not deter other important vector species from ovipositing . Further studies should investigate whether other mosquito oviposition attractants for Culex or other mosquito genera influence attraction of AHD and vice versa . Our semi-field experiments demonstrated that AHD synergized mortality caused by Bti and Bs . Because female mosquitoes preferred to oviposit in bowls with AHD the overall larval survival rate within these spheres was reduced to one third compared to spheres where we applied SPLATbac without AHD ( Fig 7 ) . This synergistic effect by SPLATbacAHD needs further evaluation under full field/natural conditions [31 , 36 , 63 , 68 , 71 , 85] . A thorough evaluation should take into account factors that could override the preference for larval breeding sites containing AHD ( e . g . presence of food , predators or competitors; [45 , 89–92] ) , as well as factors that directly affect the attraction to AHD , such as the range of attraction by AHD ( not assessed here ) , and the Culex population density ( density of other AHD-emitting egg rafts ) . Such factors will determine to what extent the addition of AHD can increase the cost-effectiveness of SPLATbac . | Traditionally , a key intervention in mosquito control is the use of insecticides against the adult stage . However , various factors limit the long-term use of these control methods , including the development of insecticide resistance , changes in mosquito biting behaviour , and concerns regarding potential negative impacts of insecticides on the environment . There is therefore a need for alternative management strategies , such as those that target aquatic life stages of mosquitoes . The objective of this study was to investigate the potential of biodegradable wax emulsions such as SPLAT for use in attracting gravid females and control of aquatic stages of culicine vectors . Culex mosquito oviposition pheromone ( acetoxy hexadecanolide , AHD ) was selected as an attractant , and Bacillus thuringiensis israelensis ( Bti ) and Bacillus sphaericus ( Bs ) were used as control agents . Buoyant 20 mg pellets , created by drying SPLAT dollops prior to application , were applied to water surfaces . Dose-mortality analyses of Cx . quinquefasciatus larvae demonstrated that one single pellet caused 100% mortality of first instar larvae for at least five weeks post application . Mortality of 3rd instar larvae remained equally high even at 10−2 dilutions for over two weeks post application . In addition , AHD was embedded in SPLAT to either attract gravid females ( SPLATahd ) or to first attract gravid females to oviposit and then to kill the resulting larval offspring ( SPLATbacAHD , 10−1 dilution ) in both laboratory and semi-field settings . The wax matrix containing AHD , with or without Bti and Bs , was strongly preferred as an oviposition substrate over controls for over two weeks post application . Both laboratory and semi-field experiments showed a marked increase in larval mortality effects when a semiochemical attractant and larvicides were combined , compared to matrices containing larvicides alone . These findings indicate the potential for using wax emulsions such as SPLAT as a slow release matrix for mosquito attractants and control agents; and that the combination could synergize the control of Cx . quinquefasciatus . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"death",
"rates",
"larvicides",
"invertebrates",
"emulsions",
"medicine",
"and",
"health",
"sciences",
"demography",
"colloids",
"animals",
"reproductive",
"physiology",
"developmental",
"biology",
"waxes",
"materials",
"science",
"pest",
"control",
"population",
"biology",
"insect",
"vectors",
"materials",
"by",
"structure",
"agrochemicals",
"epidemiology",
"life",
"cycles",
"disease",
"vectors",
"insects",
"agriculture",
"arthropoda",
"pesticides",
"people",
"and",
"places",
"biochemistry",
"population",
"metrics",
"mosquitoes",
"organic",
"materials",
"physiology",
"oviposition",
"biology",
"and",
"life",
"sciences",
"pheromones",
"physical",
"sciences",
"mixtures",
"larvae",
"organisms"
]
| 2016 | Combining Attractants and Larvicides in Biodegradable Matrices for Sustainable Mosquito Vector Control |
Histamine is an important chemical messenger that regulates multiple physiological processes in both vertebrate and invertebrate animals . Even so , how glial cells and neurons recycle histamine remains to be elucidated . Drosophila photoreceptor neurons use histamine as a neurotransmitter , and the released histamine is recycled through neighboring glia , where it is conjugated to β-alanine to form carcinine . However , how carcinine is then returned to the photoreceptor remains unclear . In an mRNA-seq screen for photoreceptor cell-enriched transporters , we identified CG9317 , an SLC22 transporter family protein , and named it CarT ( Carcinine Transporter ) . S2 cells that express CarT are able to take up carcinine in vitro . In the compound eye , CarT is exclusively localized to photoreceptor terminals . Null mutations of cart alter the content of histamine and its metabolites . Moreover , null cart mutants are defective in photoreceptor synaptic transmission and lack phototaxis . These findings reveal that CarT is required for histamine recycling at histaminergic photoreceptors and provide evidence for a CarT-dependent neurotransmitter trafficking pathway between glial cells and photoreceptor terminals .
Histamine is an important chemical messenger known to be involved in a broad spectrum of biological processes such as inflammation and gastric acid secretion . It is also recognized as an important neurotransmitter [1] . Recycling histamine at histaminergic synapses is a key event both in maintaining synaptic transmission and in terminating histamine’s action on postsynaptic neurons . The Drosophila visual system uses histamine as the neurotransmitter at photoreceptor synapses , and provides a good genetic model for studying histamine , its metabolism and recycling [2] . The compound eye of Drosophila is composed of ~800 ommatidia , each of which contains eight photoreceptor cells . Of the latter , R1-R6 photoreceptors in each ommatidium project axons from the retina to the underlying lamina neuropil , where they are organized into synaptic modules called cartridges . R7/R8 photoreceptors project axons to the second neuropil , the medulla [3–6] . In lamina cartridges , three epithelial glial cells normally envelop six photoreceptor terminals [7] . Although the synthesis of histamine from histidine occurs de novo under the action of histidine decarboxylase ( Hdc ) in photoreceptor cells , recycling of histamine is reported to be the dominant pathway for maintaining the histamine content in photoreceptors [8 , 9] . Both pathways , de novo synthesis and recycling , are required to maintain an adequate content of histamine in photoreceptor cells . Disrupting either pathway affects visual synaptic transmission in Drosophila in the long term [8 , 10] . Upon light stimulation , photoreceptor terminals release histamine as a neurotransmitter , which activates histamine-gated chloride channels ( HisClA ) on large monopolar cells ( LMCs ) in the lamina and hyperpolarizes these postsynaptic neurons [2 , 4 , 11] . After its release , histamine is taken up by lamina glia and conjugated to β-alanine , converting it to carcinine by the N-β-alanyl-dopamine synthase , Ebony , which is expressed in epithelial glia [10 , 12 , 13] . The metabolized histamine conjugate , carcinine , is then transported back into the photoreceptors and hydrolyzed back to histamine by Tan , an N-β-alanyl-dopamine hydrolase[10 , 14] . Despite knowledge of these pathways , little is known about the critical step by which carcinine is transported back to the photoreceptors . It has been proposed that the gene inebriated ( ine ) might encode a carcinine neurotransmitter transporter in photoreceptor cells to take up carcinine from synaptic cleft [15] . However , in this study , we show that Ine fails to function in any clear way in photoreceptor cells . In addition , the cellular location for carcinine uptake , the trafficking route by which it is returned to the photoreceptor cells where the Tan enzyme has to act , and the transporters responsible for carcinine uptake , all remain controversial . Recently , it has been suggested that metabolites of histamine are transported between glia and the cell bodies of photoreceptors through networks of intercellular gap junctions [9] . We identified a photoreceptor cell-enriched neurotransmitter transporter , CarT , which is able to transport carcinine across the membranes of photoreceptors . CarT is predominantly localized to photoreceptor terminals . The cart mutant flies are defective in photoreceptor synaptic transmission , and as a result lack phototaxis . In addition , we found that a human homologue of CarT , Organic Cation Transporter ( OCT2 ) , can also transport carcinine in vitro and is thus able to reverse synaptic transmission defects in cart mutant flies . We therefore propose the presence of a novel pathway for histamine recycling , in which the carcinine transporter CarT efficiently takes up carcinine that is released locally from glial cells lying in close vicinity to photoreceptor terminals .
Given that the histamine/carcinine shuttle in the visual system occurs between photoreceptors and surrounding glia cells [7] , and that the enzyme Tan responsible for hydrolyzing carcinine to release histamine is exclusively expressed in photoreceptor cells , we assumed that the neurotransmitter transporter responsible for taking up carcinine must be enriched in photoreceptor cells . The gene glass ( gl ) gene encodes a zinc finger transcription factor , and glass mutations specifically remove photoreceptor cells , but leave other cell types intact . Mutations of glass specifically remove photoreceptor cells , and thus largely abolish the expression of mRNA transcripts of photoreceptor-enriched genes , such as the gene encoding major rhodopsin neither inactivation nor afterpotential E ( ninaE ) . Expression of ninaE is greatly reduced in the heads of gl3 flies relative to wild-type ( w1118 ) heads ( Fig 1A ) . By comparing mRNAs isolated from wild-type heads with gl3 heads or wild-type bodies , we identified a list of genes that are expressed predominantly in photoreceptor cells . We examined both this RNA-seq data and a DNA microarray data set , which screened for genes expressed predominantly in photoreceptor cells and the compound eyes respectively [16] . This enabled us identify candidate genes that might encode the carcinine transporters . CG9317 and CG3790 are both candidate genes for eye-enriched neurotransmitter transporters . Both proteins share significant amino acid identities with the mammalian solute carrier family 22 ( SLC22 ) family proteins , including the mouse OCT2 and OCT3 ( S1 Fig ) . The expression of CG9317 mRNA was greatly reduced in gl3 fly heads , indicating that CG9317 is expressed predominantly in photoreceptor cells ( Fig 1A and 1B ) . In contrast , the expression levels of the retinal pigment cell marker gene retinol dehydrogenase B ( rdhB ) remain unchanged for both gl3 flies and wild-type flies ( Fig 1A ) [17] . We next conducted in vitro experiments to examine whether CG9317 and CG3790 can transport carcinine . We expressed mCherry-tagged proteins in S2 cells , and used immunolabeling to examine the intracellular signals for histamine or carcinine . Carcinine or histamine was added to the medium to yield final concentrations of 20μM . After three-hour incubations , the intracellular carcinine or histamine signal was examined . No transporter activity for either carcinine or histamine was detected in S2 cells expressing mCherry alone ( Fig 1C and S2A Fig ) . There was no immunosignal for either carcinine or histamine after expressing Ine , which indicates the probability that Ine does not transport either carcinine or histamine under the conditions tested ( Fig 1D and S2B Fig ) . We next examined the candidate carcinine transporters that are highly expressed in eyes , including CG9317 and CG3790 [16] . CG3790 failed to transport either carcinine or histamine ( Fig 1E and S2C Fig ) . We confirmed these results by using a specific rat anti-carcinine antibody from a different source [18] ( S3 Fig ) . In contrast , a clear immunosignal for carcinine but not histamine was detected in cells expressing CG9317 ( Fig 1F and S2D Fig ) . When we expressed histidine decarboxylase ( Hdc ) in S2 cells , immunosignal for histamine was observed , which served as a positive control , validating our in vitro histamine immunolabeling method ( S2E Fig ) . These findings suggest that CG9317 encodes a carcinine transporter , which we therefore named CarT ( Carcinine Transporter ) . To characterize the requirement for CarT in transmitting visual signals , we generated two different null mutations in the cart gene using the CRISPR-associated single-guide RNA system ( Cas9 ) ( Fig 2A ) [19] . We identified fly lines containing these cart1 and cart2 mutations by PCR using genomic primers outside of the deleted regions ( Fig 2A ) . Full-length PCR products were detected in wild-type flies , whereas shorter PCR products were detected in the cart1 and cart2 mutant lines , indicating the disruption of the cart locus in cart1 and cart2 flies ( Fig 2B ) . The cart genomic region in both mutations was sequenced , and 1112 and 2344 bp fragments were deleted in cart1 and cart2 mutants respectively ( S4 Fig ) . As cart mutants were not lethal , so we undertook electroretinogram ( ERG ) recordings directly . ERG recordings are extracellular recordings that measure the summed responses of all retinal cells in response to light . Upon exposure to light , an ERG recording from a wild-type fly contains a sustained depolarizing response from the photoreceptors , and “on” and “off” transients originating from synaptic transmission to the lamina [20] ( Fig 2C ) . Mutations with defective synaptic transmission have obvious reductions in their “on” and “off” transients [6] . As in mutants of genes involved in histamine recycling , ERG transients were not observed in cart1 , cart2 , or cart1/ cart2 mutant flies ( Fig 2C ) . Phototaxis is a visual behavior that requires the integrity of the neuron circuits of the visual system [21] , and defective synaptic transmission of visual signals results in poor phototaxis [22] . Significantly reduced phototaxis was associated with all the cart1 , cart2 , and cart1/cart2 mutations ( Fig 2D ) . To further confirm that the loss of visual synaptic transmission resulted from mutations of the cart locus , we generated a Pcart-cart transgenic fly line expressing the cart cDNA under the control of the cart promoter . The Pcart-cart transgene reversed the loss of “on” and “off” transients and restore phototaxis in the cart1 mutant flies ( Fig 2C and 2D ) . Tan , the hydrolase that deconjugates carcinine and releases histamine , localizes to photoreceptor cells and functions downstream of the transport of carcinine . A carcinine transporter coupled with Tan’s action should therefore be expressed and should function in photoreceptor cells . It has been suggested that ine encodes a putative carcinine neurotransmitter transporter in photoreceptor cells [15] . We used the eyeless-GAL4 UAS-FLP ( EGUF ) /hid technique to generate genetically mosaic flies [23] . The compound eyes of these mosaic flies comprise cells homozygous for a selected mutation , but forming part of an entire mosaic fly that is elsewhere heterozygous for the mutation . Therefore , if Ine functions in the compound eye of Drosophila , eye-specific mutations of ine in ine mosaic flies should mirror at least the same ocular defects in synaptic transmission as those present in the ine mutants . We observed that ERG recordings from wild-type eyes have normal “on” and “off” transients ( Fig 3A and 3A’ ) . Mutations in both the ebony ( e1 ) and the tan ( tan1 ) genes disrupt histamine recycling and this results in the loss of “on” and “off” transients in their ERG recordings ( Fig 3B and 3C ) [10] . As expected , e1 mosaic flies in which all photoreceptors were homozygous mutant for ebony had wild-type “on” and “off” transients . This is because Ebony is not required in the photoreceptors but is required in glial cells lying outside them ( Fig 3B’ ) . As Tan functions in the photoreceptor cells of the compound eye , the tan1 mosaic which lacks tan expression in the photoreceptors displayed reduced “on” and “off” transients ( Fig 3C’ ) . The ERG responses of ine mutants ( ineMI05077 ) contain prominent oscillations superimposed on the sustained depolarizating response and they also have reduced “on” and “off” transients ( Fig 3D ) [15] . The latter phenotype indicates impaired photoreceptor synaptic transmission . However , as with the ebony mutants , heterozygous flies with homozygous ine mutant compound eyes ( ine mosaic flies ) had wild-type ERG responses with normal “on” and “off” transients ( Fig 3D’ ) , indicating that Ine does not function obligatorily in photoreceptor cells . Therefore , it is unlikely that Ine is directly or necessarily responsible for carcinine uptake at the photoreceptor cell membrane , as previously suggested . Its possible role as a transporter elsewhere is not addressed by these experiments . Given that expression of the cart gene is enriched in photoreceptor cells , we assumed that CarT is required in photoreceptor cells for synaptic transmission . As expected , homozygous cart1 mutant eyes lacked “on” and “off” transients despite the heterozygous background elsewhere ( Fig 3E and 3E’ ) . This finding indicates that CarT functions in the compound eyes . Photoreceptor cells and retinal pigment cells are the two major cell types in the compound eye . To confirm the retinal cell type in which CarT functions , we expressed CarT specifically in photoreceptor cells using the ninaE promoter or in retinal pigment cells using the rdhB promoter [17] [24] . Photoreceptor-enriched expression of CarT by PninaE-cart restored both the “on” and “off” transients and phototaxis in cart1 mutant flies , whereas expression of CarT in pigment cells through PrdhB-cart did not ( Fig 3F and 3G ) . These results strongly support the interpretation that CarT functions in photoreceptor cells to maintain synaptic transmission . In addition , we extended these ERG results by phototaxis assays . Wild-type flies displayed positive phototactic behavior , whereas flies that were homozygous mutant for ebony , tan , ine , or cart all displayed poor phototaxis , indicating that these genes are required for visual synaptic transmission ( Fig 3G ) . Consistent with the ERG results , phototaxis was significantly reduced in the mosaic eyes of tan1 and cart1 compared with wild-type flies , whereas phototaxis of both the e1 and the ineMI05077 mosaic flies did not differ from that in wild-type flies ( Fig 3G ) . These results suggest the possibility that CarT rather than Ine functions as a carcinine transporter in photoreceptor cells . Trafficking of carcinine into photoreceptors is a key step in histamine recycling in Drosophila . As we have proposed here that CarT functions as a carcinine transporter acting at the photoreceptor cell membrane , we examined the localization of CarT to photoreceptor cells to evaluate the cellular location of carcinine transport . Since multiple attempts to generate an anti-CarT antibody failed , we eventually generated transgenic flies that expressed mCherry-tagged CarT driven by the cart promoter . Importantly , the Pcart-cart-mcherry transgene completely reversed the loss of “on” and “off” transients in cart mutant flies ( Fig 2C ) . Although CarT was expressed throughout the photoreceptor neurons , the CarT signal was predominantly detected in the lamina layer where it was marked by the Ebony immunosignal , and not appreciably in the region of the retina ( Fig 4A ) . In cross sections at high magnification we observed that CarT was not co-localized with Ebony to the epithelial glial cells ( Fig 4B ) , but rather co-localized with the photoreceptor cell axon marker Tan to both the lamina and medulla neuropils , to which the R1-R6 and R7/R8 photoreceptors project their axons respectively ( Fig 4C and 4D ) . The finding that CarT expression is enriched in photoreceptor terminals is consistent with the assumption that photoreceptor cells take up carcinine mainly from the local synaptic cleft in the lamina , rather than by a long-distance histamine recycling pathway which is mediated by lamina glia and a retinal pigment cell network [9] . However , we cannot exclude the existence of a long-term trafficking pathway for carcinine . Given that the evidence so far suggests that cart acts to transport carcinine into the photoreceptor , where tan then acts to hydrolyze it and release histamine , we next sought to examine whether loss of cart would decrease histamine labeling . We labeled head cross sections from the cart1 mutant and from the w1118 control with anti-histamine antibody . The distribution of histamine signal in cart1 mutant flies relative to their w1118 controls reveals a clear loss of photoreceptor signal ( Fig 5A and 5B ) , compatible with the mutant’s inability to take up carcinine and so liberate histamine . In the enlarged images , it is clear that cart1 mutants showed a dramatic decrease in labeling for histamine in R1-R6 photoreceptor terminals in the lamina , and in R7/R8 photoreceptor terminals in the medulla ( Fig 5C and 5D ) . In contrast to the weak label in R1-R6 photoreceptor terminals in the lamina , a strong label was seen in the underlying marginal glia at the proximal lamina in the cart1 mutant ( Fig 5C and 5D ) [3 , 9 , 25] . The labeling of this region suggests that histamine might be accumulated at an ectopic site in the cart mutant . CarT belongs to the SLC22 protein family and is highly homologous to the mammalian OCT2 protein . We therefore wondered whether heterologous expression of OCT2 in cart mutant flies would restore the synaptic transmission of photoreceptors . OCT2 is known to mediate low affinity transport of some monoamine neurotransmitters [26] . However , it is not known whether OCT2 is able to transport carcinine . We performed in vitro assays to determine whether OCT2 can transport carcinine . After expressing OCT2 in S2 cells , carcinine was taken up by the OCT2-positive cells ( Fig 6A ) . These results indicated that OCT2 can indeed transport carcinine . We next generated a PninaE-oct2 transgene to express OCT2 in photoreceptor cells only , and introduced this transgene into the cart1 mutant background . We found that the expression of human OCT2 in cart1 mutant fly photoreceptor cells fully restored both the “on” and “off” transients and phototaxis in cart1 flies ( Fig 6B–6E ) . These results demonstrated a conserved function for OCTs in both a mammal and Drosophila . We used high-performance liquid chromatography ( HPLC ) to examine the in vivo contents of histamine as well as carcinine and β-alanine , the major metabolites in histamine recycling [18 , 27] . As expected , in the heads of the tan1 mutant flies , which are defective in their capacity to hydrolyze carcinine into histamine and β-alanine , the head contents of both histamine and β-alanine were significantly reduced ( Fig 7A and 7B ) . The lack of carcinine uptake by photoreceptor cells in cart1 mutant flies ultimately depletes carcinine in these cells , which reduces the production of histamine and β-alanine mediated by Tan ( Fig 7A and 7B ) . The reduced head contents of histamine and β-alanine are therefore in agreement with the hypothesis that CarT transports carcinine . In contrast to histamine and β-alanine , the content of carcinine in the tan1 mutant heads was approximately three fold higher than the content in wild-type heads , which we interpret to result from diminished hydrolysis of carcinine in photoreceptor cells ( Fig 7C ) . If the flies were not able to transport carcinine into photoreceptor cells for hydrolysis , there should be a greater amount of carcinine in fly heads . As expected , in cart1 mutants , the head content of carcinine was significantly increased . However , the content of carcinine in the cart1 mutant was not increased to the same extent as in the tan1 mutant flies .
Although histamine is an important neurotransmitter known to regulate multiple physiological processes , the mechanism by which histamine content is regulated in the nervous system still remains to be elucidated . Our study identifies a mechanism and pathway for the uptake of a primary metabolite of histamine , which has hitherto defied analysis in any nervous system . Insofar as histamine is the primary neurotransmitter released by photoreceptors in flies [28] , the ease with which photoreceptor function and anatomy can be assayed has made the compound eye the preferred system to study histamine recycling . In particular the eye lends itself readily to the identification of genes that regulate neurotransmission , by enabling comprehensive genetic screens [23 , 29] . Studies in flies have previously identified a histamine/carcinine recycling pathway that involves two enzymes , Ebony , expressed in the epithelial glia , and Tan , expressed in the photoreceptor cells [13 , 30] . However , the key neurotransmitter transporters required for the histamine/carcinine shuttle pathway have not been identified . Conceptually , the putative carcinine transporter should be functionally coupled to Tan for the uptake of carcinine into photoreceptor cells and its subsequent hydrolysis . For this , both should colocalize to photoreceptors as we have shown in this study . In this study , we identified a new SLC22A family protein CarT and provided evidence that it is functionally coupled with Tan as a photoreceptor cell-enriched carcinine transporter . CarT is predominantly localized to photoreceptor terminals and is able to transport carcinine in vitro . The decrease in head histamine and β-alanine and the increase in head carcinine in cart1 support this hypothesis . The reduction in histamine content in cart1 mutants is ~60% . This amount corresponds rather closely to the reduction in head histamine seen in the mutant sine oculis , which lacks compound eyes and has 28% of the histamine found in the wild-type [27] . The reduction in sine oculis suggests that residual head histamine is not located in the compound eye visual system . In the same way , the reduction in cart1 is not accessible to photoreceptor synaptic transmission . Moreover , mutant ebony , in which head histamine content is reduced by 50% , has an abnormal ERG and phototaxis , corresponding to the strong ERG and phototaxis defects seen in the cart1 mutant . Consistent with these HPLC data , we also found a clear difference in the immunosignal for histamine between cart1 and control fly photoreceptors . The cart head increase in carcinine is not as high as that observed in the head of tan mutants , which raises the question of why the increases in carcinine in tan and cart mutant flies are not similar . This may be because carcinine released in the synaptic cleft in cart1 mutants is removed by other cells , or alternatively it may enter the hemolymph and be excreted . Consistent with the latter , the carcinine content in the abdomen is increased by 43% in cart1 compared with control w1118 flies . We cannot address the carcinine transport by other cells in the lamina , in particular the epithelial and marginal glia , which surround the photoreceptor terminals and which contain carcinine [18] , which we propose must therefore express other carcinine transporters . All of the known plasma membrane neurotransmitter transporters are members of the Solute Carrier ( SLC ) family of proteins [31] . The most extensively studied of these transporters are members of the SLC6 subfamily , a group of Na+/Cl-—dependent transporters for serotonin , dopamine , norepinephrine , GABA and glycine [32] . OCTs , which belong to the SLC22 subfamily , are known to mediate sodium-independent transport of positively charged organic compounds [33] . The expression of OCT2 in neurons has been evaluated previously , but the neuronal function of OCT2 has not been explored sufficiently [26 , 33 , 34] . Carcinine has been identified as a native metabolite related to histamine in multiple tissues in mammals , where it may serve as an antioxidant for scavenging toxic active oxygen species , especially in retinal photoreceptors [35 , 36] . Our findings that OCT2 can transport the inactive histamine metabolite carcinine both in vitro and in vivo suggests a possible new mechanism for OCTs to function in neurotransmitter recycling and cell protection . The histamine/carcinine shuttle pathway plays a dominant role in maintaining an adequate level of histamine in photoreceptors . Evidence for the direct uptake of histamine into photoreceptor cells is lacking , insofar as Ebony is necessary to rescue ERG transients in histamine-fed hdc mutant flies [37] . In addition , in our model S2 cells expressing CarT fails to transport histamine , providing further support for the hypothesis that direct uptake of histamine into the photoreceptor terminals may not occur . Although the enzymatic deconjugation of carcinine to yield histamine has been well established , the route through which carcinine is then trafficked back to the photoreceptor has not been established . It has been suggested that recycling of carcinine to photoreceptor cells involves a long-distance pathway mediated by a gap-junction dependent network of lamina and retinal pigment cells [9] . In our study , we observed that CarT is predominantly localized to the terminals of photoreceptor neurons , rather than to their cell bodies in the retina layer , which suggests that carcinine is transported back to photoreceptor cells mainly from the synaptic cleft in the lamina ( Fig 7D ) . It is also possible that this local pathway works in parallel with the long-distance neurotransmitter recycling pathway . Finally , data from the current study together with previous reports now provide evidence for a more complete histamine/carcinine recycling pathway , one which is critical for maintaining the normal histamine content of neurons ( Fig 7D ) . To complete the model of how histamine is recycled in the fly’s eye ( Fig 7D ) , the remaining question concerns how histamine is transported into the epithelial glia and how carcinine is then transported out of the glia . No specific histamine transporter has been found , in either insects or vertebrates . In insects a mechanism for the fast removal of histamine from the synaptic cleft is essential to maintain the rapid signaling required for insect vision . One transporter may be White [38] , but the problem is that in eukaryotes all known ABC transporters move substrates in the opposite direction i . e . out of the cell . To complete the return path for the carcinine will require us to identify how carcinine is exported out of the epithelial glia . To identify the transporter for this function it will be necessary to identify genes , for example from the expression of mRNA transcripts of genes , such as ebony [12 , 13] , that are enriched in the epithelial glia , in an approach that parallels the one we have adopted here to identify CarT . The transport of β-alanine , the other substrate needed for carcinine synthesis , seems to be of minor importance because this amino acid is present in the head in concentrations greatly exceeding those needed for histamine recycling and it can be also easily synthesized on demand from aspartate or uracil . Answering these questions is necessary to complete the current scheme ( Fig 7D ) for the recycling of histamine , to which our findings now identify CarT as the photoreceptor uptake transporter .
The following stocks were obtained from the Bloomington Stock Center: ( 1 ) 122 , e1; ( 2 ) 130 , tan1; ( 3 ) 38094 , ineMI05077; ( 4 ) 3605 , w1118; and ( 5 ) 24749 , M ( vas-int . Dm ) ZH-2A;M ( 3xP3-RFP . attP ) ZH-86Fb . The ( nos-Cas9 ) attP2 flies were obtained from the lab of Dr . J . Ni at Tsinghua University , Beijing , China . The ey-flp;GMR-hid CL FRT40A/Cyo , ey-flp;FRT42D GMR-hid CL/Cyo , GMR-hid CL FRT19A/FM7;ey-flp , and ey-flp;FRT82B GMR-hid CL /TM3 flies were maintained in the lab of Dr . T . Wang at the National Institute of Biological Sciences , Beijing , China . The cart , CG3790 , ine , and Hdc cDNA sequences were amplified from GH05908 , GH20501 , LP16156 , and LD44381 cDNA clones obtained from DGRC ( Drosophila Genomics Resource Center , Bloomington , IN , USA ) . The oct2 cDNA sequences were amplified from IOH56335 cDNA clones obtained from Ultimate™ ORF clones ( Thermo Fisher Scientific , Waltham , USA ) . Their entire CDS sequences , excluding the stop codon , were subcloned into the pIB-cmcherry vector ( Invitrogen , Carlsbad , USA ) for expression in S2 cells . To construct PninaE-cart , PrdhB-cart , and PninaE-oct2 , the entire coding region of cart and oct2 was amplified from cDNA clones and cloned into the pninaE-attB and prdhB vectors ( both gifts from the lab of Dr . C . Montell at the University of California , Santa Barbara , USA ) [17 , 24 , 39] . To construct Pcart-cart-mcherry , the promoter region ( -2579 to +11 base pairs 5' to the transcription start site ) of the cart gene was amplified from genomic DNA , and cart-mcherry was amplified from pIB-cart-mcherry . These constructs were injected into M ( vas-int . Dm ) ZH-2A;M ( 3xP3-RFP . attP ) ZH-86Fb embryos , and transformants were identified on the basis of eye color . The ( 3xP3-RFP . attP ) locus was removed by crossing with P ( Crey ) flies . The cart1 and cart2 mutations were generated using the Cas9/sgRNA system as described previously [19] . Three recognition sequences of guiding RNA to the cart locus were designed with tools available at the following website http://www . flyrnai . org/crispr2/ ( sgRNA1: AAAACCGCACGGTATGCAGG , sgRNA2: CCTGTCCGGCGTCACTTATC , sgRNA3: TGAGCGTCATGGACACCCAG ) . These were cloned into the U6b-sgRNA-short vector . The pU6-sgRNA1 and pU6-sgRNA2 plasmids were used to generate the cart1 mutant flies , while pU6-sgRNA1 and pU6-sgRNA3 were used to generate the cart2 mutant flies . Plasmids were injected into the embryos of ( nos-Cas9 ) attP2 flies . The F1 progeny were screened by PCR to identify the cart1 and cart2 deletions , using the following primers: pF: 5’-TGTCGCTACAAATCTTAGATCCAA-3' pR: 5’-CCATGTCAGATATTGAGGACAACG-3’ Two glass microelectrodes filled with Ringer’s solution were inserted into small drops of electrode cream ( Sigma , New Jersey , USA ) placed on the surfaces of the compound eye and the thorax . A Newport light projector ( model 765 ) was used for stimulation . The source light intensity was 2000lux , and the light color was orange ( the source light was filtered by FSR-OG550 filter ) . ERG signals were amplified with a Warner electrometer IE-210 and recorded with a MacLab/4 s A/D converter and the clampelx 10 . 2 program ( Warner Instruments , Hamden , USA ) . All recordings were carried out at 23°C . S2 cells were grown in Schneider’s Drosophila medium with 10% Fetal Bovine Serum ( Gibco , Carlsbad , USA ) , and transfected with vigofect reagent ( Vigorous Biotechnology , Beijing , China ) . Carcinine or histamine was added to the medium to yield final concentrations as indicated in the Figure legends . After incubation for 3h , S2 cells were transferred to poly-L-lysine-coated slices , fixed with 4% paraformaldehyde ( for carcinine immunolabeling ) or 4% 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide ( EDAC ) ( for histamine immunolabeling ) for 30min at 25°C , and incubated with rabbit anti-carcinine/histamine ( 1:100 , ImmunoStar , USA ) [9] or rat anti-carcinine antibodies ( 1:100 , raised by Dr . Gabrielle Boulianne , from the lab of Dr . I . A Meinertzhagen ) [18] . Goat anti-rabbit lgG conjugated to Alexa 488 ( 1:500 , Invitrogen , CA ) and goat anti-rat lgG conjugated to Alexa 488 ( 1:500 , Invitrogen , CA ) were used as secondary antibodies , and images were recorded with a Nikon A1-R confocal microscope . Fly heads were fixed with 4% paraformaldehyde for 2h at 4°C or 4% EDAC ( for histamine staining ) , and immersed in 12% glucose overnight at 4°C . The heads were embedded in O . C . T™ compound ( Tissue-Tek , Torrance , USA ) , and 10μm thick cryosections were cut . Immunolabeling was performed on cryosections sections with mouse anti-24B10 ( 1:100 , DSHB , http://dshb . biology . uiowa . edu/ ) , rat anti-RFP ( 1:200 , Chromotek , Martinsried , Germany ) , rabbit anti-Ebony ( 1:200 , lab of Dr . S . Carroll , University of Wisconsin , Madison , USA ) , and anti-Tan ( 1:200 , lab of Dr . B . Hovemann , Ruhr Universität Bochum , Germany ) [30] as primary antibodies . For histamine staining , rabbit anti- histamine ( 1:100 , ImmunoStar , USA ) was used as a primary antibody . The antibody was preadsorbed with carcinine as previously reported [9] . Goat anti-rabbit lgG conjugated to Alexa 488 ( 1:500 , Invitrogen , USA ) , goat anti-rat lgG conjugated to Alexa 568 ( 1:500 , Invitrogen , USA ) and goat anti-mouse lgG conjugated to Alexa 647 ( 1:500 , Jackson ImmunoResearch , USA ) were used as secondary antibodies . The images were recorded with a Nikon A1-R confocal microscope . A transparent glass tube of 20 cm long and 2 . 5 cm in diameter was used in this assay . A white light source ( with a light intensity of 6000lux ) was put at one end of the glass tube , and dark-adapted flies were collected and gently tapped into the other end of the tube . The tube was placed horizontally in the dark , and we counted the number of flies that walked past an 11-cm mark on the tube within 90s after turning the light on . Phototaxis was calculated by dividing the number of flies that walked past the mark as a proportion of the total number of flies . These assays were performed under dark conditions . To quantify the phototactic behaviors of each genotype , three groups of flies were collected for each genotype and three repeats made for each group . Each group contained ≥ 20 flies . Results were expressed as the mean of the mean values for the three groups . Total RNA was prepared from the heads of three-day-old flies using Trizol reagent ( Invitrogen , Carlsbad , USA ) , followed by TURBO DNA-free DNase treatment ( Ambion , Austin , USA ) . Total cDNA was synthesized using an iScript cDNA synthesis kit ( Bio-Rad Laboratories , USA ) . iQ SYBR green supermix was used for the real-time PCR ( Bio-Rad Laboratories , USA ) . Three different samples were collected from each genotype . The primers used for qPCR were as follows: ninaE-fwd , 5’-ACCTGACCTCGTGCGGTATTG-3’ ninaE-rev , 5’-GGAGCGGAGGGACTTGACATT-3’ gpdh-fwd , 5’-GCGTCACCTGAAGATCCCATG-3’ gpdh-rev , 5’-CTTGCCATACTTCTTGTCCGT-3’ rdhB-fwd , 5’-TTGAGGCACTCAGGGATCAAG-3’ rdhB-rev , 5’-CACCACATTCGTGTCGAACAG-3’ cart-fwd , 5’-TACAGCACAAGGGTCTCATCC-3’ cart-rev , 5’-AGACCATCCTAATCACGCTGAG-3’ To measurement the total head contents of histamine , β-alanine , and carcinine , flies were decapitated and their heads collected as previously reported [10] . The heads were then processed and analyzed using HPLC with electrochemical detection , all as previously reported [18 , 27] . Each sample contained ~50 Drosophila heads , and the mean values from five samples were calculated . | Neurotransmitter transporters that remove neurotransmitters and recycle them after their release have particular importance at visual synapses , which must signal at high frequencies and therefore required rapid clearance of neurotransmitters from the synaptic cleft . In this study , we identified a SLC22 family transporter , CarT , in the visual system of Drosophila , which is exclusively located to photoreceptor terminals in the lamina neuropil and is responsible for taking up carcinine , an inactive histamine metabolite , from surrounding glia . Loss of CarT disrupts the regeneration of histamine and blocks neurotransmission at photoreceptor cell synapses . Our work provides direct evidence for a local histamine recycling pathway between glial cells and photoreceptor terminals , and shows that a CarT-dependent histamine/carcinine shuttle pathway is critical for maintaining the normal histamine content of neurons . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| []
| 2015 | Histamine Recycling Is Mediated by CarT, a Carcinine Transporter in Drosophila Photoreceptors |
The countries of West Africa are largely portrayed as cholera endemic , although the dynamics of outbreaks in this region of Africa remain largely unclear . To understand the dynamics of cholera in a major portion of West Africa , we analyzed cholera epidemics from 2009 to 2015 from Benin to Mauritania . We conducted a series of field visits as well as multilocus variable tandem repeat analysis and whole-genome sequencing analysis of V . cholerae isolates throughout the study region . During this period , Ghana accounted for 52% of the reported cases in the entire study region ( coastal countries from Benin to Mauritania ) . From 2009 to 2015 , we found that one major wave of cholera outbreaks spread from Accra in 2011 northwestward to Sierra Leone and Guinea in 2012 . Molecular epidemiology analysis confirmed that the 2011 Ghanaian isolates were related to those that seeded the 2012 epidemics in Guinea and Sierra Leone . Interestingly , we found that many countries deemed “cholera endemic” actually suffered very few outbreaks , with multi-year lulls . This study provides the first cohesive vision of the dynamics of cholera epidemics in a major portion of West Africa . This epidemiological overview shows that from 2009 to 2015 , at least 54% of reported cases concerned populations living in the three urban areas of Accra , Freetown , and Conakry . These findings may serve as a guide to better target cholera prevention and control efforts in the identified cholera hotspots in West Africa .
Seven cholera pandemics have been documented since 1817 [1] . The disease has plagued every continent , spreading along trade routes via both land and sea [1] . Current epidemics are however localized to South Asia , Haiti , and Sub-Saharan Africa [2] . Since the ongoing pandemic first reached West Africa in 1970 [1] , outbreaks have been repeatedly reported throughout the region [2] . However , only a few small-scale studies have investigated the dynamics of recent cholera epidemics in West Africa . Overall , cholera outbreaks throughout the study region have displayed markedly diverse patterns depending on the country . Certain countries such as Benin and Togo have reported cholera cases nearly every year albeit with relatively low incidence [3] , [4] . By contrast , many other countries to the northwest such as Gambia , Senegal , and Mauritania have experienced marked multi-year lulls [5–7] . Many large epidemics erupted on the heels of violent civil conflicts that engendered humanitarian and public health crisis or massive population movement such as that in Senegal in 2004–2006 [7] . The large majority of cases were also reported from large cities following increased rainfall [7–10] . Although many studies were limited to a single outbreak/neighborhood or a short time period , common risk factors were found across the region: crowded living conditions , poor sanitation , and limited access to potable drinking water [3] , [8–11] . Cholera is contracted by consuming food or water contaminated with toxigenic Vibrio cholerae O1 or the derivative Vibrio cholerae O139 [12] . Numerous V . cholerae non-O1 and some O1 serogroups lacking the cholera toxin are autochthonous in seawaters worldwide [13] . V . cholerae non-O1 serogroups have been found associated with a variety of aquatic flora and fauna , notably copepods [14] . In the Bay of Bengal , elevated seawater temperatures , copepod and plankton blooms , and rainfall have been shown to correlate with increased concentrations of V . cholerae in the environment [15–17] . In West Africa , a study has addressed the relationship between climate inter-annual variability and cholera in Nigeria , Benin , Togo , Ghana , and Ivory Coast over a 20-year period . From 1987–1994 , they observed temporospatial synchrony between cholera incidence and rainfall in all countries except Ivory Coast [18] . Cholera has thus been depicted as a waterborne disease driven by ecological factors [12] , [19] . However , despite technological improvements , a perennial aquatic reservoir of cholera-causing V . cholerae O1 has yet to be identified in West Africa [20] . We applied an integrated approach to describe the dynamics of cholera epidemics , including the identification of hotspots , and investigate factors that may influence the disease in several coastal West African countries from Benin to Mauritania . We analyzed weekly cholera outbreak evolution ( at the district or commune level ) throughout the region between 2009 and 2015 and field investigations in Benin , Togo , Ghana , Ivory Coast , Sierra Leone , and Guinea . We performed molecular epidemiology analysis ( MLVA ( Multi-Locus VNTR [Variable Number Tandem Repeat] Analysis ) and whole-genome sequencing ) of V . cholerae isolates from the majority of outbreaks affecting the study region since 2010 . Such molecular epidemiology analysis can supplement epidemiological findings to provide further insight into the relationship between V . cholerae isolates and epidemic populations , identify clusters , establish phylogeny , and track bacterial transmission . We describe our findings country by country , from Benin to Mauritania .
Databases of all suspected cholera cases were collected from the epidemiological units of Benin , Togo , Ghana , and Guinea . National databases comprised weekly case/death numbers at the district level ( Ghana and Togo ) , commune level ( Benin ) , or prefecture level ( Guinea ) since 2009 , according to the WHO cholera case definition ( S1 Text ) . For Ghana ( Accra ) , Togo ( Lomé ) , Benin ( Atlantique and Littoral/Cotonou ) , and Guinea ( Conakry ) , we also analyzed the cholera case line lists , which include data on age , sex , clinical outcome , and residence . We obtained approval from the Ministry of Health ( MoH ) of each country to use these databases for epidemiological , research , and publication purposes . The line lists were anonymized and cleaned prior to analysis . Daily-accumulated rainfall data for the Greater Accra Region ( GAR ) were obtained from satellite estimates ( TRMM_3B42RT_DAILY . 007 ) from NASA ( http://disc . gsfc . nasa . gov/precipitation/tovas ) . For each field visit carried out in Ivory Coast , Ghana , Togo , and Benin , we first organized and coordinated the study with UNICEF and WHO ( for Ivory Coast ) , through whom we were put in contact with national authorities to obtain official access to databases and V . cholerae strains ( when possible ) . Initial field visits involved contact with these local UNICEF and/or WHO offices as well as national public health ( surveillance and laboratory ) authorities . To identify cholera hotspots and sites that may play a role in cholera diffusion , we first analyzed outbreak histograms and mapped weekly cholera cases . Field visits were then performed in the identified locales ( e . g . , sites that were repeatedly affected by cholera outbreaks or sites of an initial outbreak ) . During field visits , and accompanied by local counterparts , we met with local surveillance departments , laboratories ( for information concerning lab-based case confirmation ) , and health facilities . Information was collected concerning how cholera was contracted as well as possible links with other cases . We also evaluated local WASH ( Water , Sanitation , and Hygiene ) conditions . Field visits were performed in Ivory Coast ( 12/2013; SM and RP ) ; Ghana , Togo and Benin ( 11-12/2014; SM , PC , and RP ) ; Guinea and Sierra Leone ( 08-09/2012; SR ) . See S1 Text for additional details concerning the field visit study protocol . The study was approved by the MoH of each country where field visits were carried out . The protocol was further approved by the Ghana Health Service Ethical Review Board according to standard procedures . The remaining countries did not seek ethics approval as epidemic disease surveillance and response is covered by national public health laws as an integral part of the public health mandate of each MoH . All clinical isolates studied were analyzed anonymously . The country maps were generated using QGIS v2·8-Wien with shapefiles from DIVA-GIS ( http://www . diva-gis . org/gdata ) . The shapefile of Accra was generated with QGIS in collaboration with the Ministry of Local Government , Division of Environmental Health , Accra . To design effective public health strategies to combat cholera , it is critical to understand the mechanisms of cholera emergence and diffusion in a region-specific manner . Genetic analysis of responsible strains can supplement epidemiological findings to provide further insight into the relationship between pathogenic strains and epidemic populations [21] . Indeed , isolate genotyping is useful to differentiate between different isolates , identify clusters , establish phylogeny , and track bacterial transmission . Lam et al . [22] have shown that MLVA represents a highly discriminatory technique to distinguish between closely related seventh pandemic isolates . They have also emphasized that the method is best applied for outbreak investigations or to identify the source of an outbreak . Rebaudet et al . have recently demonstrated that MLVA-based analysis of clinical V . cholerae isolates combined with an epidemiological assessment was instrumental in deciphering the origin of the 2012 cholera epidemic in Guinea [10] . All V . cholerae isolates were selected in a manner as to represent both the spatial and temporal evolution of each cholera epidemic in each country sampled . A total of 173 V . cholerae O1 clinical isolates collected throughout Ghana from 2010 to 2014 were provided by the National Public Health Reference Laboratory , Accra . The isolates were sub-cultured and transported in glycerol tubes at ambient temperature to Marseille , France . Aliquots of the culture were directly submitted for DNA extraction . We also analyzed three V . cholerae isolates from Senegal in 2011 following the same procedure . The MLVA results of the Ghanaian isolates were compared with those of previously analyzed strains from Togo ( 35 isolates ) , Guinea ( 37 isolates ) , and Sierra Leone ( 9 isolates ) as previously described [10] , [23] . DNA was extracted using a NucliSENS easyMAG platform ( bioMérieux ) . MLVA of the isolates using an ABI PRISM 3130 Genetic Analyzer ( Applied Biosystems ) was performed using six VNTRs as described previously [23] , and the relationship between the isolates was established using the goeBURST algorithm on PHYLOViZ v1 . 1 ( http://www . phyloviz . net/ ) . We performed a phylogenetic assessment of the core V . cholerae genome of the strains of the third wave of the seventh pandemic [24] based on genome-wide SNPs . Isolates from Togo-2010 ( six ) , Togo-2011 ( six ) , Togo-2012 ( five ) , Ghana-2011 ( one ) , Ghana-2014 ( five ) , and Guinea-2012 ( two ) were also included in the analysis . DNA was sequenced using a HiSeq Illumina System ( Illumina ) and analyzed as described [24] .
From 2009 to 2015 , Benin and Togo accounted for a combined average of 694 reported cholera cases annually ( Table 1 ) . In Benin , the lakeside commune of Sô-Ava , which is directly connected to Nigeria via Lake Nokoué and Yewa River , reported cholera outbreaks every year since 2010 and was often the first and hardest-hit commune . In Benin , Sô-Ava reported 40% of all cases in 2013 and 30 . 4% of all cases in 2014 ( S1 Fig ) . Cotonou , the economic center of Benin , was only affected by limited cholera outbreaks in 2010 , 2011 , and 2013 , in neighborhoods characterized by fishing activity and pronounced population movement ( S1 Fig ) . In neighboring Togo , most outbreaks in Lomé , the capital of Togo , occurred in flood zones ( Lomé D2: Adakpamè , Bè Kpota , Anfamé , and Akodéssewa ) or areas linked with fishing activity and intense population movement ( Lomé D3: Katanga ) ( S2 Fig ) . Meanwhile , the outbreaks in the eastern Lacs Prefecture , Togo ( in Séko ) were associated with people attending traditional animist ceremonies , including those who traveled from Benin or Nigeria , as noted by health facility staff in Séko . Outbreaks in Togo often remain limited ( S2 Fig ) . Ghana accounted for 52 . 4% of all reported cholera cases in coastal countries from Benin to Mauritania , from 2009 to 2015 ( 51 , 333 suspected cases in Ghana / 97 , 887 total suspected cases in the 11 countries ) [2] . Since 2011 , cholera outbreaks have significantly intensified in Accra , the capital of Ghana . The majority ( 73 . 6% ) of cases from 2011 to 2014 were reported in the Greater Accra Region ( GAR ) ( 35 , 985 cases GAR/ 48 , 914 cases Ghana ) ( MoH ) . In 2012 and 2014 , the first confirmed cholera cases were detected in GAR . By contrast , the 2011 epidemic began in late 2010 , during which the first detected cases were in Central Region ( S3 Fig ) . The 2010/2011 epidemic then intensified in GAR in January 2011 , as displayed in Fig 1 . Fig 1 displays the sharp increase in cases at the onset of each epidemic , indicating a rapid early expansion of the bacterium within Accra . Strikingly , from the end of 2012 through mid-2014 , Ghana experienced an 18-month lull in cholera cases , despite typical rainfall . All 20 suspected cholera case samples in 2013 tested negative for V . cholerae . After this significant lull , Ghana experienced the largest epidemic ( 28 , 944 cases in 2014 ) since 1991 [2] . Notably , the strains causing this large outbreak in 2014 were closely related with strains present in Togo in 2010 and 2011 , as demonstrated by the MLVA and phylogeny data described in further detail below . Once outbreaks erupted in Accra , cholera rapidly diffused throughout the majority of the city . This rapid spatial diffusion pattern was observed during the onset of the 2011 , 2012 , and 2014 epidemics . Many Accra neighborhoods were severely affected by cholera each year . However , certain nearby residential areas remained largely cholera-free , despite outbreaks in adjacent neighborhoods ( Fig 2 ) . Once cholera erupted in Accra , outbreaks spread to other districts in Ghana several weeks later . According to health facility staff at the hospital in Ho ( Volta Region ) , the 2014 index case in Ho had recently traveled from Accra , where an outbreak was ongoing at the time ( S4 Fig ) . Field visits revealed that water distribution was often interrupted for several days in many Accra neighborhoods . The majority of water network pipes were visibly damaged and running along the ground through roadside gutters . Residents without access to proper latrine facilities perform open defecation into these gutters . In the Greater Accra Metro Area , access to improved sanitation facilities is limited . Only 5 . 7% of households in Greater Accra have access to latrines that flush into a piped sewer system , while 35 . 1% of Accra households use latrines that flush into a septic tank . Furthermore , Accra households lacking improved sanitation facilities ( 14 . 8% ) are forced to use pan latrines ( buckets that are then dumped into the roadside gutters ) or “flying toilets” ( defecation into a plastic bag which is then literally thrown away ) [25] . We thus hypothesize that ground water and human waste could seep into broken pipes , especially during the frequent water shortages , thereby allowing V . cholerae to enter the water network and spread when water pressure is restored . Increased rainfall markedly exacerbated this effect ( Fig 1 and Fig 2 ) . Additional studies should further investigate the role that the Accra water network plays in cholera outbreaks to confirm this hypothesis . From 2009 to 2010 , Ivory Coast was largely unaffected by cholera ( 37 suspected cases ) [2] . However , an epidemic erupted in Abidjan in January 2011 following the post-election crisis of November 2010 and a collapse in the health and sanitation systems [26] . The 2011 epidemic was responsible for 1 , 261 cases [2] . We found that a new outbreak emerged in May 2012 in Sud Comoé , adjacent to Jomoro District in Ghana , where an outbreak was ongoing . As observed in Ghana , Ivory Coast also experienced a complete lull in cholera in 2013 and early 2014 . Each of the 56 suspected cases in 2013 tested negative for V . cholerae [27] . This lull was interrupted in early October 2014 , when an outbreak occurred in Abidjan with the arrival of ill Ghanaian fishermen ( S5 Fig ) [28] . Liberia has reported 4 , 133 suspected cholera cases from 2009 to 2015 [2] , which includes only 44 cases in 2014 and zero cases in 2015 . However , no cholera-related deaths were reported from 2010 to 2013 , and only two deaths ( 1 , 070 cases ) were reported in 2009 . Following a three-year lull in the incidence of cholera , both Sierra Leone and Guinea experienced a trans-border epidemic in 2012 , with 22 , 932 and 7 , 351 cases , respectively [10] . The two epidemics progressed following a very similar pattern . In Guinea , the outbreak started in February on Kaback Island with a fisherman traveling from Sierra Leone . In Sierra Leone , possible events of V . cholerae importation by fishermen travelling from Liberia and Ghana have been reported [29] . During the rainy season , cholera exploded in the capitals , which recorded over half of the total cases ( Freetown , 52%; Conakry , 64% ) [10] , [29] . As cholera rates declined in Sierra Leone and Guinea , they rose in Guinea-Bissau [29–31] , which also followed a near three-year lull . The country reported 3 , 068 cases in 2012 and 969 cases in 2013 . Eighteen and zero cases were reported in 2014 and 2015 , respectively [2] . The number of cholera cases reported in The Gambia , Senegal , and Mauritania has been very low since 2009 to present . The Gambia has not reported a single suspected cholera case since 2008 . Likewise , Mauritania has not reported cholera cases since 2008 , with the exception of 46 cases in 2011 . Since 2009 , Senegal has reported only 13 suspected cholera cases [2] . We performed MLVA of 255 clinical V . cholerae isolates from Ghana , Togo , Guinea , Sierra Leone , and Senegal . Two environmental isolates from Guinea 2012 were also included ( S1 Table ) . Interestingly , the Minimum Spanning Tree ( MST ) shows that the 2010/2011 isolates from Ghana were related to those that seeded the epidemic in Guinea and Sierra Leone in 2012 . The MST also demonstrates that the 2011 , 2012 , and 2014 epidemics in Ghana were due to three distinct V . cholerae MLVA-type clusters . The three clinical isolates from Senegal in 2011 displayed an identical MLVA type , closely related to strains from Togo in 2011 and 2012 , which may represent imported cases from farther south in West Africa ( Fig 3 ) . When the Ghana and Togo strains were included in the alignment of the seventh pandemic V . cholerae isolates [24] , the Ghana 2011 and closely linked strains from Togo ( 2010 and 2011 ) grouped with the Guinea 2012 strains on the third wave of the current pandemic . By contrast , Ghana 2014 and other Togo strains clustered together in a separate clade , genetically distinct from the Ghana 2011 cluster . Some of the strains from Togo in 2012 clustered together with Ghana strains from 2012 , which were more closely related to the Ghana 2014 strains on the MST ( Fig 4 ) .
From 2009 to 2015 , we found that Accra , the capital of Ghana , reported the highest number of cholera cases during the study period among all cities included in this study . During the period 2009 to 2015 , we found that one major wave of cholera outbreaks spread from Accra in 2011 northwestward to Sierra Leone , Guinea , and likely Guinea-Bissau in 2012 ( S5 Fig ) . Genetic analysis showed that the 2012 isolates from Guinea and Sierra Leone clustered with those collected in Ghana in 2011 . MLVA also demonstrated that the V . cholerae strain responsible for the epidemic in Ghana , which started in 2010 and spilled over into 2011 , was already present in Togo and thus likely shared a common ancestor with strains from Togo in 2010 . The MST and whole-genome sequence results indicate the presence of different V . cholerae populations in Togo , one group that appears to give rise to the 2011 Ghana strains , while the other group was closely related to the strain that triggered the cholera epidemic in Ghana in 2014 . We noted that other cities ( Conakry and Freetown ) also appeared to function as amplifiers of cholera , when cases were present and rainfall increased . Strikingly , we found that many countries deemed cholera endemic in modeling studies [32] actually suffered very few outbreaks , with multi-year lull periods during which no cholera cases were detected . Extended lulls in cholera incidence occurred despite increased rainfall , typical high temperatures , slums , and population exposure to coastal environments . According to the WHO , cholera endemic countries have been defined as countries in which confirmed cholera cases were reported in at least three of the five past years [32] . In fact , some of the countries in our study do not fit the definition of endemic , as at least one case must be confirmed . To improve the current classification methodology , we may consider repeated , lab-confirmed cholera outbreaks in a country to indicate endemicity , keeping in mind that this status is fluid and may change as sanitation and water conditions improve . Our findings and independent reports indicate that the Accra water network may play a role in rapid diffusion of cholera throughout a majority of the city , when cholera cases are present in neighborhoods where sanitary facilities and access to safe water are lacking . A study in Accra ( in Osu Klottey Sub Metro ) has shown that drinking community pipe-borne water ( OR = 2 . 15 ) was associated with cholera in 2012 [33] . Furthermore , a separate study has revealed unsuitable residual chlorine levels and the regular presence of fecal coliform in the Accra network water [34] . During the period 2009 to 2015 , our findings show that one major wave of cholera epidemics spread northwestward from Accra in 2011 to Sierra Leone and Guinea in 2012 . As Ghanaian strains from previous years fail to reappear during subsequent epidemics , we hypothesize that epidemics affecting Accra may likely originate due to imported cases from a nearby cholera hotspot . Neighboring Nigeria represents one of the major cholera foci in the world [35] , with 130 , 007 cases reported from 2009 to 2015 [2] . The country is also a likely source of outbreaks in the Lake Chad Basin [35] . The lull in Ghana during 2013 paralleled a relatively low number of cholera cases reported in Nigeria in 2012 and 2013 . The 2014 Ghanaian epidemic coincided with an epidemic rebound in Nigeria [2] . Furthermore , intense commercial activity via road and boat may represent a major pathway by which cholera is imported from outbreaks in Nigeria to susceptible waterfront communities in Benin . We hypothesize that the detection of closely related strains in Ghana and Togo throughout the study period indicates that this strain may share a common ancestor with strains responsible for persisting outbreaks in a neighboring cholera hotspot such as Nigeria . A phylogenetic analysis of clinical isolates has shown that the current pandemic is characterized by successive global clonal expansion of three waves of closely related serotype O1 El Tor lineages emanating from the Bay of Bengal [24] . A recent study in Science by Weill et al . has analyzed genomic data from 1070 V . cholerae O1 isolates , across 45 African countries and over a 49-year period , to show that past epidemics were attributable to a single expanded lineage . Weill et al . show that seventh cholera pandemic V . cholerae El Tor sub-lineages from Asia were repeatedly introduced into West Africa as well as East and Southern Africa . Epidemic waves then propagated regionally over a period of several years , which correlates with our observations [36] . Their findings strongly suggest that human factors play a much more important role in cholera dynamics and the long-term spread and maintenance of V . cholerae in Africa than environmental factors [36] . Further whole-genome sequence analysis of strains circulating in West Africa would greatly enhance our understanding of V . cholerae transmission pathways in the region . Concerning the study limitations , although the databases used in this study are reliable thanks to cholera surveillance standardization throughout the region ( S1 Text ) , the system does not guarantee detection of every single sporadic or unique imported cholera case . However , cholera cases are quickly detected once an outbreak occurs , especially when cholera-related deaths result . Many countries deemed cholera endemic have experienced several year-long lulls in cholera outbreaks ( when all suspected cholera cases are confirmed negative upon culture ) . Nevertheless , countries considered cholera endemic anticipate outbreaks and perform culture-based tests on samples derived from suspected cholera cases each year . Given the capacity of the countries concerned ( which can vary over time and geographically within a country ) , it is not possible to bacteriologically confirm every suspected case , which could have an effect on the validity of the results . Similar issues concerning surveillance capacity may have affected the completeness of suspected cholera case data collection over time . Nevertheless , our investigations have found that the lack of cholera during a lull period is not due to inadequate surveillance or laboratory incompetence but simply due to the absence of cholera cases in the country . The nonexistence of cholera outbreaks during the recent Ebola crisis in Guinea , Sierra Leone , and Liberia highlights the complete lull in cholera cases despite a heightened disease surveillance system . Furthermore , all isolates analyzed via MLVA were not submitted for whole-genome sequencing due to inadequate shipping conditions , which yielded sufficient DNA for only PCR-based MLVA . V . cholerae isolates from countries such as Ivory Coast , Benin , Guinea-Bissau , and Liberia were not included as the concerned national laboratories did not provide isolates for this investigation . We also acknowledge that our study would be strengthened by sequence-based phylogenic results of all samples assessed via MLVA , and thus efforts are currently underway to complete our panel of V . cholerae isolates . Furthermore , we were not able to include all isolates for sequence analysis , as certain samples lacked sufficient concentrations of DNA for the technique , thus introducing a possible bias due to incomplete selection of samples for whole-genome sequencing . To prevent expansion of cholera outbreaks in the analyzed region of West Africa , epidemiological surveillance should be enhanced in identified vulnerable zones , such as Accra . In vulnerable areas , improved monitoring of the drinking water supply as well as ensuring water quality and proper chlorination would mitigate epidemics and perhaps stop cholera propagation . Based on our findings , we hypothesize that once cases arrive in the urban settings with poor sanitation facilities ( as observed in Accra , Conakry , and Freetown ) , increased rainfall provokes the infiltration of human waste , and therefore toxigenic V . cholerae , into the water network via damaged water pipes , thus promoting a rapid increase in cholera incidence . These findings may serve as a guide to better target cholera prevention and control interventions in the identified cholera hotspots in West Africa . Our study also highlights the value of this type of study combining epidemiological and molecular data to gain insight into the dynamics of cholera , especially in Africa . | We analyzed cholera epidemics from Benin to Mauritania , during 2009 to 2015 , and performed a series of field visits as well as molecular epidemiology analyses of V . cholerae isolates from most recent epidemics throughout West Africa . We found that at least 54% of cases concerned populations living in the three urban areas of Accra , Freetown , and Conakry . Accra , Ghana represented the main cholera hotspot in the entire study region . Our findings indicate that the water network system in Accra may play a role in the rapid diffusion of cholera throughout the city . As observed in Accra , Conakry , and Freetown , once cholera cases arrive in overpopulated urban settings with poor sanitation , increased rainfall facilitated the contamination of unprotected water sources with human waste from cholera patients , thus promoting a rapid increase in cholera incidence . To more efficiently and effectively combat cholera in West Africa , these findings may serve as a guide to better target cholera prevention and control interventions . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"guinea",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"vibrio",
"tropical",
"diseases",
"geographical",
"locations",
"microbiology",
"bacterial",
"diseases",
"vibrio",
"cholerae",
"benin",
"neglected",
"tropical",
"diseases",
"bacteria",
"bacterial",
"pathogens",
"africa",
"togo",
"infectious",
"diseases",
"cholera",
"medical",
"microbiology",
"microbial",
"pathogens",
"sierra",
"leone",
"people",
"and",
"places",
"ghana",
"biology",
"and",
"life",
"sciences",
"organisms"
]
| 2018 | Dynamics of cholera epidemics from Benin to Mauritania |
Forecasting the emergence and spread of influenza viruses is an important public health challenge . Timely and accurate estimates of influenza prevalence , particularly of severe cases requiring hospitalization , can improve control measures to reduce transmission and mortality . Here , we extend a previously published machine learning method for influenza forecasting to integrate multiple diverse data sources , including traditional surveillance data , electronic health records , internet search traffic , and social media activity . Our hierarchical framework uses multi-linear regression to combine forecasts from multiple data sources and greedy optimization with forward selection to sequentially choose the most predictive combinations of data sources . We show that the systematic integration of complementary data sources can substantially improve forecast accuracy over single data sources . When forecasting the Center for Disease Control and Prevention ( CDC ) influenza-like-illness reports ( ILINet ) from week 48 through week 20 , the optimal combination of predictors includes public health surveillance data and commercially available electronic medical records , but neither search engine nor social media data .
Seasonal influenza epidemics annually result in significant global morbidity and mortality [1] , and influenza pandemics can cause catastrophic levels of death , social disruption , and economic loss [2] . Early detection and forecasting of both emergence and peak epidemic activity can inform an effective allocation of resources , surge planning , and public health messaging [1 , 3–5] . Thus , public health and scientific communities have prioritized the development of influenza forecasting technologies [6–11] . There are a growing number and variety of readily available disease-related data sources that may ultimately be integrated into or even replace traditional systems . The Center for Disease Control and Prevention ( CDC ) relies on data from two primary national influenza surveillance systems: ( 1 ) the U . S . World Health Organization ( WHO ) and National Respiratory and Enteric Virus Surveillance System ( NREVSS ) collaborating laboratories ( henceforth , WHO US ) and ( 2 ) the US Outpatient Influenza-like Illness Surveillance Network ( ILINet ) . Recently , Meaningful Use [12] , a CDC led effort , is advancing the expansion of syndromic surveillance systems such as ESSENCE to address a broader set of infectious disease surveillance objectives [13–15] . Novel data sources for outbreak surveillance are also arising outside of public health . Notably , researchers at Google launched the Google Flu Trends service ( GFT ) in 2008 to provide real-time estimates of influenza prevalence based on disease-related search activity [16] . They showed that time series tracking the volumes of influenza-related Google searches closely mirrored influenza data from ILINet . However , it failed to capture the emergence of the 2009 H1N1 pandemic and fell short in subsequent influenza seasons [17–21] , resulting in the termination of the program in August 2015 by the company . Epidemic-related data have also been extracted from not only search engines [22] but also interactive web-based applications ( e . g . , Flu Near You , InfluenzaNet ) [23] and online social platforms such as Twitter ( e . g . , MappyHealth ) [24 , 25] , Facebook [24–29] , and Wikipedia [30] . While most of these data sources contain broad information , epidemic related data is passively mined and filtered . There are , however , a few participatory systems that directly solicit health data from voluntary participants [23] . For example , InfluenzaNet , has over 50 000 volunteers from ten European countries [23] . While many of these sources have been shown , individually , to estimate and predict influenza activity , we have yet to build forecasting models based on systematic comparisons and integration of complementary data . Given the real-time availability of GFT at multiple geographic scales ( from city to continental ) , many of the early forecasting methods used GFT as a test bed . Notably , Shaman et al . [8] pioneers the use of Kalman filters to predict seasonal GFT dynamics from historical GFT and humidity data and Nsoesie et al . [31] couples a simulation optimization method with a network-based epidemiological model to forecast regional influenza peaks . Another study forecasts GFT from a combination of GFT , temperature , and humidity data in a specific metropolitan area ( Baltimore ) , and demonstrates that the integration of multiple data sources can improve forecast accuracy [7] . More recent forecasting efforts have directly targeted CDC ILINet , rather than GFT , using a variety of predictor data sources . Brooks et al . [6] apply a novel simulation-based Bayesian forecasting framework to forecast one season of ILINet from prior ILINet data . Their method first constructs prior distributions of seasonal flu curves by stochastically combining and transforming features of past flu seasons . As a season emerges , it updates the posterior distribution based on real-time observations and uses importance sampling to generate forecasts . Two other studies forecast ILINet from alternative data sources—one evaluates the predictive performance of Google , Twitter , and Wikipedia , individually [32] , and the other considers a multi-linear combination of internet source , digital surveillance , and electronic medical records data [33] . Such data sources vary considerably in both availability and reliability . Some are available in near-real time , whereas others are lagged by days or weeks; some deeply sample geographic or socioeconomic slices of a population , whereas others provide representative but sparse samples of an entire population . In particular , internet and social media data can be misleading , particularly during newsworthy epidemiological events [34–36] , but potentially provide a valuable real-time window into emerging events when combined with validated public health or medical data sources . Optimization allows us to systematically balance such trade-offs and quantify the informational content and complementarity of different categories of data . We argue that , for a given forecasting task , candidate data sources should be evaluated and integrated based on clear performance metrics , which may include , for example , measures of forecast accuracy or precision at one or across multiple time points . Here , we introduce an optimization method for designing robust multi-source epidemic forecasting systems and apply it forecasting seasonal flu in the US . Our framework is intended to be plug-and-play , allowing researchers to evaluate large combinations of data sources with respect to their own forecasting model and performance metrics . In our case study , the candidate data sources include thousands of time series data sources from public health surveillance systems , electronic health records systems ( EHR ) , search engines , and other website and social media applications . Our forecasting model is an extension of the flexible Bayesian machine learning method introduced in [6] , modified to combine multiple predictors . Finally , our objective function considers overall similarity between historical data and out-of-sample forecasts , averaging across 16 recent flu seasons . Unlike recent multi-source forecasting studies ( such as [33] ) , we present a framework to rigorously evaluate much larger sets of candidate data sources both at the national and regional level and select complementary combinations that maximize forecast performance metrics . This approach not only yields more accurate forecasts , but provides quantitative insight into the relative utility of data sources .
We use greedy optimization with forward selection to iteratively identify combinations of predictor data sources that collectively result in the most accurate forecast for a target data source . Our approach consists of three steps , as shown in Fig 1 . First , we individually forecast candidate data sources using an empirical Bayesian framework . Second , we use linear models to combine such individual forecasts into grand forecasts of a target time series . Finally , we build an optimal forecasting system ( i . e . , collection of predictor data sources ) by sequentially adding candidate data sources that most improve the accuracy of historical out-of-sample forecasts of the target . Next sections describe these steps in detail . We use RMSE to evaluate forecasts and thereby select informative combinations of data sources . It measures the difference between predicted and actual time series , as given by RMSE s = 1 n ∑ w = 1 n ( x w - y w ) 2 ( 5 ) where xw and yw denote the observed and predicted values of the target data source , respectively , at week w of the season , for w = {1 , 2 , … , n} . Post selection , we evaluate the quality of the forecasts using two additional metrics that address the timing and magnitude of the epidemic peak . Specifically , the peak week error ( PWE ) of a given season is the absolute difference between predicted and actual peak week , as given by PWE s = | p - p ˜ | ( 6 ) where p and p ˜ denote the weeks during which the observed and predicted time series , respectively , hit their maximum values . The peak magnitude error ( PME ) of a given season is the ratio of the absolute difference between the maximum observed and predicted values of the time series and the maximum observed value , as given by PME s= | h - h ˜ | h ( 7 ) where h and h ˜ denote the maximum values reached by the observed and predicted target time series , respectively .
The best five-source system ( optimized from all available data sources ) consistently produces accurate historical out-of-sample forecasts , as shown in Fig 2 . After observing only the first nine weeks of the flu season , the system is able to predict the remaining 24 weeks of the season with an average RMSE under 1% . The forecasted 95% credible interval contained the historical ILINet value in 87% of all weeks across all 16 forecasts . However , the 2002-2003 and 2003-2004 forecasts capture the peaks but considerably overestimate prevalence towards the ends of the seasons ( 12 weeks out of 24 lie outside the 95% credible interval ) . Excluding these two seasons , 92 . 9% of all historical weeks fall within the forecasted 95% interval . In the system optimized from all national-level data sources except ILINet , accuracy drops to 66% of all historical weeks contained in the credible intervals . ( See S1 Fig for detailed results ) . Although these systems were optimized solely to minimize RMSE , the resulting forecasts perform quite well with respect to predicting the timing and magnitude of the epidemic peak . In over 85% of the seasons , the forecasts predict the peak to occur within two weeks of the actual peak; in over 85% , the predicted height of the peak is within 20% of its actual height . Since the Athena predictors are only available between 2011 and 2014 , they provide no information for the first 13 of the 16 seasons . Consequently , we see a reduction in RMSE for the three most recent forecasts . Performance curves for this optimized system indicate that additional data sources , beyond the five included , are not expected to improve performance considerably , according to our empirical results show in Fig 3 . On their own , ILINet and WHO are the strongest predictors of future ILINet activity . Although the Athena data sources exhibit poor individual performance , they substantially improve forecast accuracy when combined with ILINet and WHO . The hierarchical selection method was thus able to integrate complementary data sources into a multi-source system that is expected to provide more reliable forecasts than single-source systems . This is also true for systems which exclude ILINet and WHO as candidate predictors . ( See S2 Fig for detailed results ) . We also build out-of-sample forecasts of ILINet using ILINet and WHO as predictors , using only ( 1 ) three years ( 2011-2014 ) and ( 2 ) five years of training data ( 2008-2014 ) to build the Bayesian prior distributions . In the original out-of-sample forecasts , we used 15 of the 16 available seasons to build priors for forecasting the remaining season . ( See S3 and S4 Figs for more details ) . Performance increased with the duration of the training data , with average RMSE decreasing from 0 . 69 to 0 . 64 to 0 . 56 as we increase the training period from three to five to fifteen years . However , even the poorest set of forecasts ( based on three years of training ) are decent . In addition , we note that the original experiments selected Athena Health data as highly informative predictors , despite only being available for three years ( 2011-2014 ) .
There are a growing number of powerful methods for forecasting seasonal and pandemic flu ( e . g . [6 , 45] ) . To achieve earlier and more accurate predictions of epidemic emergence , growth , peaks and burden , researchers are developing sophisticated statistical methods–some adapted from mature forecasting sciences like meteorology [8]–and creatively leveraging diverse sources of predictor data . The increasing public availability of disease-related data sources is promising yet daunting , with annually , hundreds of thousands of influenza-related tweets [42] , several millions of page hits on Wikipedia to influenza-related pages [30] , thousands of influenza-related blog posts on Wordpress [40] and hundreds of thousands of hospital and clinic visits . While many studies have demonstrated the promise of surveillance [46] and forecasting from novel data sources [33] , we do not yet have rigorous methods for evaluating the utility of such data or identifying effective combinations of data for particular models and forecasting goals . Over several years , we have developed a general framework for addressing exactly this challenge [20 , 46 , 47] . For any public health surveillance goal , the approach is designed to systematically evaluate up to thousands of candidate data sources and identify complementary combinations of predictors that achieve the stated goal . For example , we have identified optimal zip codes for seasonal flu surveillance and early detection of pandemic flu in Texas [48] , selected informative clinics for dengue surveillance in Puerto Rico [47] , and developed software for optimal selection and integration of surveillance data sources for the Defense Threat Reduction Agency’s ( DTRA’s ) Biosurveillance Ecosystem ( BSVE ) [49] . In this study , we have used this framework to design multi-source surveillance systems for accurate forecasting of seasonal influenza , and , in the process , rigorously assess the performance and complementarity of diverse data sources . To do so , we combined two previously published methods . The first is an empirical Bayes strategy for forecasting seasonal flu from a single data source [6] . Rather than imposing strong assumptions about transmission dynamics , it assumes that the forecasting target ( typically , the currently emerging flu season ) will roughly resemble past seasons in terms of the shape , peak week , peak magnitude , and pace of the epidemic curve . By combining and perturbing these features from prior seasonal data , we simulate distributions of plausible ( hybrid ) flu curves . Then , as a season unfolds , we predict future weeks by extrapolating from variates that most resemble recent activity . To forecast flu ( target ) from multiple data sources ( predictors ) , we make empirical Bayes forecasts of each predictor separately and combine them into a target forecast using a linear model previously fit to historical predictor and target data . The second method is a greedy optimization that sequentially selects a maximally informative set of data sources to achieve a specified goal [47 , 50] . In our case , the candidate providers are a diverse set of public health , commercial health-care , internet query and social media data sources . Our public health goal is accurate forecasting of seasonal flu starting in calendar week 48 . The field has primarily focused on the development of statistical models that predict seasonal dynamics on multiple geopolitical scales , and only secondarily considered the quality of predictor data . Test bed data are often selected based on convenience . Until recently , Google Flu Trends data was free and abundant at multiple scales , and thus a popular choice [7 , 10 , 20 , 31] . A few studies have integrated multiple different types of data and shown that , for short-term forecasting ( one to three weeks ahead ) , the combination of all independent flu predictors performs better than using single source [33] . However , they have not systematically optimized the combination of data sources or quantified their relative contributions to forecast accuracy , as we have done here . Our study confirms that multi-source forecasting can outperform single-source forecasting , but only when complementary sources are identified and systematically integrated . We optimized forecasting models from three classes of data–traditional public health surveillance data , electronic health records ( EHR ) from a data services company , and data aggregated from the influenza-related internet search and social network activity . A priori , each has pros and cons . Official surveillance systems are designed for the purpose of monitoring and predicting flu activity , and thus may provide more accurate and robust signals than the alternatives . However , surveillance data tends to be sparse and time-lagged . Internet source data can be abundant and immediately available , but provides only correlated activity that can be highly susceptible to extrinsic perturbations such as media events and modifications to source websites [34 , 35] . EHR data has the combined advantages of real-time availability and access to multi-dimensional flu data at various geographic scales . However , it is not freely available and may require statistical corrections for sampling biases . Our analyses provide quantitative insights into harnessing these trade-offs for forecasting . First , when data sources are evaluated individually , we find that public health surveillance data yields the most accurate forecasts , followed by EHR data , and internet-source data trailing far behind . Second , optimized combinations of data sources ( with or without ILINet ) provide far better forecasts than any individual data source alone . Third , EHR data are always selected before internet-source data to augment public health data , suggesting that EHR’s provide a more valuable source of complementary information . Forth , when CDC and WHO data are excluded , the optimal EHR and internet-source systems are unable to achieve comparable forecasting performance . Fifth , state-level EHR data improves forecasts significantly more than national-level EHR data . While we believe that these insights are robust , they may reflect specific assumptions of our model , and not apply to other diseases , forecasting methods , or objective functions . First , the superior performance of the public health data source is likely biased by our choice of ILINet as the gold standard forecasting target . If we had instead sought to forecast athenahealth or GFT time series , these data sources may have been selected as their own top predictors . However , we believe that this choice of target is justified , as it is the only data source specifically designed to estimate flu prevalence in the US . Along with WHO it always selected as a top predictor for selected level forecasts . Second , we follow Brooks et al . [6] in assuming uniform distributions for peak height and peak week , constrained by historical observations . This might limit forecasting accuracy for seasons with atypically high , low , early or late peaks . To address this , one could assume distributions that include low probability extreme departures from past seasons . We emphasize that this framework is designed to select optimal combinations of data sources for any combination of predictor data sources , multi-linear forecasting method and objective function . As a case study , we built optimal combinations of data sources for forecasting seasonal flu using a published univariate Bayesian empirical framework ( [6] ) that we extended to forecast with multiple data sources . The optimized systems provide reliable forecasts of the overall seasonal trends and epidemic peak , in most of the 16 historical out-of-sample evaluations . The data-driven selection of informative predictors revealed that public health surveillance data is invaluable for flu forecasting , and that , when rigorously integrated into forecasting models , proprietary electronic health record data can significantly increase accuracy , to a greater degree than freely available internet data . The same optimization framework , forecasting method and RMSE objective function could be readily applied to designing high performing multi-linear forecasting systems for other diseases , for which we have amble historic data , such as Dengue [51–54] and Chikungunya [55] . By modifying the objective function , we can alternatively build systems for forecasting early transmission dynamics or clinical severity of emerging outbreaks . | In the United States , seasonal influenza causes thousands of deaths and hundreds of thousands of hospitalizations . The annual timing and burden of the flu season vary considerably with the severity of the circulating viruses . Epidemic forecasting can inform early and effective countermeasures to limit the human toll of severe seasonal and pandemic influenza . With a growing toolkit of sophisticated statistical methods and the recent explosion of influenza-related data , we can now systematically match models to data to achieve timely and accurate warning as flu epidemics emerge , peak and subside . Here , we introduce a framework for identifying optimal combinations of data sources , and show that public health surveillance data and electronic health records collectively forecast seasonal influenza better than any single data source alone and better than influenza-related search engine and social media data . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
]
| [
"medicine",
"and",
"health",
"sciences",
"influenza",
"sociology",
"social",
"sciences",
"social",
"media",
"mathematics",
"forecasting",
"statistics",
"(mathematics)",
"internet",
"network",
"analysis",
"social",
"networks",
"distribution",
"curves",
"social",
"communication",
"infectious",
"disease",
"control",
"research",
"and",
"analysis",
"methods",
"statistical",
"distributions",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"computer",
"and",
"information",
"sciences",
"epidemiology",
"mathematical",
"and",
"statistical",
"techniques",
"communications",
"probability",
"theory",
"computer",
"networks",
"electronic",
"medical",
"records",
"infectious",
"disease",
"surveillance",
"health",
"informatics",
"database",
"and",
"informatics",
"methods",
"disease",
"surveillance",
"physical",
"sciences",
"viral",
"diseases",
"statistical",
"methods"
]
| 2018 | Optimal multi-source forecasting of seasonal influenza |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.