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journal.pbio.1001268 | 2,012 | p53 Regulates Cell Cycle and MicroRNAs to Promote Differentiation of Human Embryonic Stem Cells | Embryonic stem cells ( ESCs ) have an unlimited potential to proliferate ( self-renewal ) and the ability to generate and differentiate into most cell types ( pluripotency ) 1 , 2 ., The undifferentiated ESC state is regulated by a network of transcription factors , e . g . , OCT4 , SOX2 , NANOG , and KLF4 , and epigenetic modifiers , which promote expression of ESC-specific genes and suppress differentiation 3–7 ., Exogenous introduction of transcription factors such as OCT4 , SOX2 , NANOG , and KLF4 into murine or human adult cells induces pluripotency by reprogramming these cells into induced pluripotent stem cells ( iPSCs ) , which are functionally and phenotypically similar to ESCs 8 , 9 ., The ability of ESCs to self-renew and maintain pluripotency is linked to the ability of these cells to remain in a proliferative state ., ESCs progress through an abbreviated cell cycle , leading to rapid cell division 10–12 , characterized by a truncated G1 phase , elevated expression of G1-associated cyclins , active cyclin-dependent kinases ( CDKs ) , and low levels of inhibitory cell cycle proteins p21 , p27 , and p57 13 ., During differentiation to embryoid bodies , mouse ESCs ( mESCs ) accumulate in G1 and exhibit a cell cycle lengthened from 8–10 h to more than 16 h , as observed in adult cells 14 ., In the creation of iPSCs , multiple studies show more efficient reprogramming of cells with dysfunctional ARF/p53 pathways and increased cellular proliferation , shortening of G1 , and lack of cell cycle checkpoints 15–20 ., Additional studies identified several small non-coding RNAs that play roles in cell cycle regulation and control of ESC status 21 ., MicroRNAs ( miRNAs ) are small , non-coding RNAs of 21–23 nucleotides in length that regulate gene expression , generally at a post-transcriptional level 22 ., Specific miRNAs regulate self-renewal , pluripotency , and mESC stability 23 , 24 , and several are differentially expressed in human ESCs ( hESCs ) 25 , 26 ., Here , we connect both regulatory arms , e . g . , cell cycle progression and transcription of miRNAs , to tumor suppressor p53 in regulated differentiation of hESCs ., Exposure of hESCs to differentiating conditions signals an acetylation switch to stabilize p53 protein ., Activation of p53 elongates the G1 phase of the cell cycle by p21 induction , and increases miR-34a and miR-145 , which target specific stem cell factors for repression ., These functions of p53 are direct , as ectopically expressed p53 binds these chromatin targets and causes spontaneous differentiation without retinoic acid ( RA ) addition ., The combined effects of p53 not only antagonize self-renewal and pluripotency but also have an active role in promoting differentiation of hESCs ., In vitro differentiation of hESCs ( WA09 cells ) , induced by addition of RA and withdrawal of fibroblast growth factor ( FGF ) , is marked by a steady decline in levels of proteins and RNAs associated with pluripotency and self-renewal , e . g . , NANOG , OCT4 , SOX2 , and KLF4 , and increased expression of endodermal marker GATA4 , AFP , ectodermal marker PAX6 , and neural progenitor gene Nestin ( Figures 1A–1C and S1A ) , as previously described 27 ., In parallel , p53 protein levels increase significantly but transiently ( Figure 1B and 1C ) without increased TP53 transcription ( Figure 1D ) ., In response to RA , induced p53 is nuclearly localized in differentiating cells ( Figures 1E , 1F , and S1B ) , which are identifiable by the loss of homogeneity and elongated nuclei seen in highly p53-expressing cells ( Figure 1E ) ., As one of several examples where hESCs differ from mESCs 28 , 29 , p53 is expressed at low levels and nuclearly enriched in hESCs prior to induction ( Figures 1B , 1E , and S1C ) ., Transient induction of p53 protein levels during RA-mediated differentiation was also observed in WA01 hESCs ( Figure S1D and S1E ) , and in BMP4-mediated differentiation of hESCs ( data not shown ) ., Stress activation of p53 is primarily attributed to post-translational modification of p53 and increased protein stability 30 ., During RA-mediated differentiation of hESCs , p53 gained acetylation at residue lysine 373 ( p53K373; Figures 2A , 2B , and S2A ) , and DNA-damage-associated modifications , such as phosphorylation of p53S15 or p53S46 , were not observed ( Figure S2A ) ., p53K373 is a known substrate of histone acetyltransferase CBP/p300 31 , and treatment of differentiating hESCs with CBP/p300 inhibitor circumin 32 led to loss of p53K373ac and p53 stability during differentiation ( Figure 2D ) ., Increased p53K373ac occurred in parallel with reduced levels of SIRT1 , at days 1–3 of RA treatment , suggesting that a pool of p53 escapes deacetylation by SIRT1 ( Figure 2C ) ., NAD+-dependent histone deacetylase SIRT1 is down-regulated during differentiation of hESCs , as described previously 33; however , SIRT1 protein levels and p53 interaction recover after differentiation of hESCs ( day 4 , Figure 2C ) , as p53 and p53K373ac are restored to low levels ( Figures 1 and 2A ) ., Addition of an inhibitor of SIRT1 activity , nicotinamide 34 , on day 4 of RA treatment , maintained p53K373ac ( Figure 2D ) ., These results suggest that an active acetylation/deacetylation switch regulates p53 during differentiation of hESCs ., Pluripotent hESCs have low p53 levels ( Figures 1 and S1C ) , similar to somatic cells , where p53 levels are regulated by E3-ubiquitin ligases , ubiquitin modification , and proteasomal degradation 35 , 36 ., HDM2 , which is embryonicly lethal when deleted ( as mdm2 ) in mice 37 , and TRIM24 , a negative regulator of p53 identified in mESCs 38 , are associated with p53 in pluripotent hESCs but dissociate after RA addition ( Figure 2E ) ., Ubiquitin-modified p53 species are detectable at 0–24 h of RA treatment in the presence of inhibitors of the proteasome , MG132 ( lanes 2 and 4 , Figures 2F and S2B ) and lactacystin ( Figure S2C ) ., After RA treatment , ubiquitin-modified p53 species decreased in abundance with time of differentiation: compare ( lanes 5 and 6 , Figures 2F , S2B , and S2C ) ., As a positive control for regulated loss of ubiquitin-modified p53 39 , hESCs were treated in parallel with the DNA-damaging agent adriamycin ( Adr ) ( Figure 2F , lane3 , and Figure S2B and S2C ) ., Together , gain of acetylation and loss of ubiquitination transiently increased p53 stability during hESC differentiation ., DNA damage of mESCs , where p53 is expressed at high levels and is primarily cytoplasmic , leads to repression of NANOG and spontaneous differentiation into other cell types , which undergo p53-dependent apoptosis 28 , 40 , 41 ., However , a previous report shows that , unlike mESCs , exposure of hESCs to DNA damage induces p53-dependent cell cycle arrest rather than differentiation 42 ., In order to assess functions of p53 during differentiation of hESCs we performed flow cytometry analysis of the cell cycle in hESCs at time points of exposure to RA , and compared control and p53-depleted hESCs ( Figure 3 ) ., We efficiently depleted p53 , and other targets , with pools of small interfering RNA ( siRNA ) and a modified siRNA transfection protocol ( see Materials Methods for details ) that had an average 60% transfection efficiency and that produced an up to 80% reduction in RNA expression ( Figure S3 ) ., Flow cytometry showed that 60% of pluripotent hESCs are in S phase , and approximately 10% of hESCs are in G1 ( time\u200a=\u200a0 ) , consistent with a rapid cell cycle ( 15–16 h ) due to truncation of G1 13 ( Figures 3A and S4A ) ., During differentiation , hESCs spend increased time in G1 , slowing down cell cycle over time with RA treatment: at day 4 there is a 3-fold increase in cells in G1 ( Figure 3A ) ., The accumulation of hESCs in G1 continued during differentiation; after 10 d of RA treatment , hESCs attained a cell cycle profile more similar to that of differentiated cells ( human foreskin fibroblasts ) , with more than 60% of cells in G1 ( Figure S4B ) ., When hESCs were depleted of p53 by siRNA and exposed to RA , the accumulation of cells in G1 was attenuated , indicating that p53 plays an integral role in the process ( Figures 3A and S4A ) ., The accumulation of hESCs in G1 during differentiation stands in contrast to DNA damage , which led to a p53-dependent arrest at the G2–M transition and apoptosis with exposure to damage-inducing levels of Adr ( Figure S4C–S4E ) ., Cell cycle arrest in G1 phase may be mediated by cyclin-dependent kinase inhibitor p21/WAF1 , a downstream gene target of p53 43 , 44 ., We observed increased expression of CDKN1A ( p21 ) ( Figure 3B ) , which was p53-dependent ( Figure 4C ) , in parallel with accumulation of hESCs in G1 phase during differentiation of both WA09 and WA01 hESCs ( ) ., p53 directly regulates p21 expression , as chromatin immunoprecipitation ( ChIP ) analysis revealed RA-induced enrichment of p53 at the distal p53 response element ( p53RE ) of CDKN1A ( Figure 3C ) ., The importance of p21 in RA-mediated alteration of the hESC cell cycle was shown by siRNA depletion of CDKN1A and loss of hESC accumulation in G1 ( Figure 3A ) ., An RA-mediated elongation of the G1 phase during differentiation of hESCs was marked by a specific increase in unmodified retinoblastoma tumor suppressor protein ( non-phosphorylated RB ) , alongside up-regulated p21 protein ( Figure S4F ) ., Previous studies show that RB is hyper-phosphorylated and inactive in self-renewing , cycling hESCs 11 , 45 ., Interestingly , differentiation-induced p53 did not activate expression of genes GADD45A and BAX , which are associated with apoptosis ( Figure S4G ) ., In contrast , hESCs underwent cell death after exposure to appropriate levels of DNA-damaging agents ( Figure 3D and 3E ) , and showed p53 enrichment on p53REs and increased expression of CDKN1A , MDM2 , BAX , and GADD45A under these conditions ( Figure S4G and S4H ) ., RA-induced p53 and differentiation had little effect on the number of apoptotic hESCs , as shown by Annexin V staining and γ-H2AX levels ( Figure 3D and 3E ) ., One approach we used to assess the progression of RA-mediated differentiation was to stain hESC cultures with alkaline phosphatase ( AP ) at each time point ., As previously reported 46 , differentiation is marked by loss of AP staining and appearance of cells with a flattened cellular morphology ( Figure 4A ) ., Depletion of p53 by siRNA delayed RA-mediated differentiation ( Figure 4A and 4B; see also Figure 5E ) , as more than 60% of hESCs remained undifferentiated after 3 d of RA treatment ( AP-stained colonies quantified in Figure 4F ) ., Additionally , with siRNA of p53 , pluripotency markers NANOG and OCT4 maintained expression and there was no induction of p21 , as compared to cells transfected with non-target siRNA ( siControl ) ( Figure 4B and 4C ) ., These results were confirmed by flow cytometry analysis of hESCs stained with OCT4 and SSEA4 , which revealed no reduction in OCT4 staining in cells depleted of p53 compared to only 64% cells positive for OCT4 in siControl hESCs , 3 d after RA treatment ( Figure 4D ) ., Up-regulation of p53 is transient during differentiation of hESCs , as auto-regulation of negative regulators , HDM2 and TRIM24 , is triggered by p53 ( Figure S5 ) ., We transiently transfected pools of siRNA specific for either HDM2 or TRIM24 , and achieved ∼70% reduction in levels of gene expression in each case ( Figures S3 and S5 ) ., Depletion of HDM2 and TRIM24 by siRNA in untreated hESCs increased p53 protein levels ( Figure S5A ) , increased p21 RNA and protein , and decreased OCT4 and NANOG expression ( Figure S5B ) ., Depletion of either HDM2 or TRIM24 led to spontaneous differentiation ( approximately 50% differentiated colonies ) ( Figure 4E and 4F ) , as well as a 4-fold increase in the number of hESCs in G1 phase ( Figure 4H ) ., Flow cytometry revealed a significant reduction in OCT4-positive cells ( Figure 4G ) , which correlates with siRNA-mediated depletion of HDM2 and TRIM24 ( Figure S3 ) ., Spontaneous differentiation of hESCs , with increased expression of p53 and p21 , occurred even under cell culture conditions where pluripotency is normally maintained and without RA treatment ( Figures 4E–4H and S5 ) ., This is in sharp contrast to hESCs depleted of p21 , which exhibited significantly delayed differentiation ( Figure 4A , bottom panel , and Figure 4F ) , no change in percent of cells positively stained for OCT4 ( Figure 4D ) , and no increase in cells residing in G1 ( Figure 3A ) ., Expression of pluripotency markers under these conditions further supported the links between p53 , p21 , and differentiation ( Figure S5B ) ., We established a system in hESCs where expression of p53 is controlled in a dose-dependent manner without addition of RA ., Lentiviral constructs , which express wild-type or mutant p53 with an IRES-GFP reporter under control of a tetracycline ( doxycycline Dox ) –responsive promoter , were introduced into hESCs and selected for stable integration ., Cell lines with regulated expression of wild-type p53 ( p53WT ) or p53 mutated within its DNA-binding domain ( p53R175H and p53R175P ) were positive for both OCT4 and SSEA4 stem cell markers , as assessed by flow cytometry analysis , in the absence of Dox ( Figure S6A ) and exhibited Dox-regulated expression of p53 ( Figure 5 ) ., In the absence of RA , Dox-induced expression of p53WT led to loss of OCT4 expression ., In contrast , expression of a mutated form of p53 , incapable of binding to DNA and regulating p53 target gene expression ( p53175H ) , did not correlate with decreased OCT4 even though p53R175H protein levels are higher than those of p53WT ( Figure 5A ) ., Interestingly , Dox induction of a mutated form of p53 , p53R175P , known to induce cell cycle arrest but not apoptosis in mouse models 47 , 48 led to loss of OCT4 expression , although the decrease was smaller than the significant reduction induced by p53WT ( Figure 5A and 5C ) ., The dichotomy between responses of hESCs exposed to p53R175H versus p53R175P supports functions of p53 in the activation of cell cycle arrest , without apoptosis , during differentiation of human cells ., Further analysis of differentiation driven by conditionally regulated p53 , in the absence of RA , showed that p53 target genes , CDKN1A and HDM2 , were activated over a time course of Dox exposure ( Figures 5C and S6B ) ., In parallel , pluripotency marker gene expression of KLF4 , NANOG , and OCT4 was significantly reduced with length of exposure to Dox ( Figures 5C and S6C ) ., Dox treatment led to a ∼4- to 5-fold induction in exogenous wild-type and mutant p53 RNA , with an insignificant change in endogenous TP53 levels ( Figures 5C and S6B ) ., Differentiation occurred as marked by a gain in AFP expression and flattened cell morphology , with loss of AP staining in hESCs expressing p53WT or p53R175P but not p53R175H ( Figure 5B and 5C ) ., Ectopic expression of p53WT and p53R175P led to an increase of cells in G1 , similar to induction by RA treatment; however , p53R175H did not affect the cell cycle profiles of hESCs ( Figure 5D ) ., Induction of p53WT in hESCs led to expression of endoderm markers GATA4 and AFP , as well as ectoderm marker PAX6 ( Figure 5F and 5G ) ., However , these cells did not express mesodermal marker Brachyury ( Figure 5F and 5G ) ., Differentiation is specific to p53 , as depletion of Dox-induced p53 by siRNA , added at t\u200a=\u200a0 , rescues OCT4 protein expression ( Figure 5E ) ., Similarly , after siRNA-mediated depletion of p21 in hESCs over-expressing p53 , OCT4 protein levels were rescued , further confirming p21 as a mediator of p53-induced differentiation of hESCs , whether induced by exogenous p53WT or by RA treatment ( Figure 5E ) ., Interestingly , Dox-induced p53 is not acetylated at K373 , as is readily detectable when RA is used to induce equivalent levels of endogenous p53 and differentiation of hESCs ( Figures 2 and 5H ) ., This finding suggests that ectopic induction of p53 circumvents K373 acetylation , which promotes release of endogenous p53 from negative regulatory proteins during RA-induced differentiation ( Figure 2 ) ., Taken together , these results support a view of p53 as a critical regulator of hESC differentiation , capable of acting in the absence of RA and other stimuli that may be induced by RA treatment ., The role of cell cycle regulators in this process is underscored by expression of mutated forms of p53 that specifically regulate genes that lead to G1 arrest but are unable to regulate apoptosis in mouse models 47 ., To understand the mechanism underlying p53-mediated differentiation of hESCs , we performed high-throughput ChIP sequencing analysis of hESCs incubated with RA ( unpublished data ) ., Putative p53 targets included miRNAs; among these , we focused on miR-34a and miR-145 as likely significant in the p53-mediated regulation of hESCs ., In somatic cells , miR-34a acts in a feed-forward loop of p53 control ., In response to stress stimuli , p53 is activated and induces expression of miR-34a , which in turn represses negative p53 regulator SIRT1 to augment p53 activation 49 , 50 , and CyclinD1 and CDK6 to support cell cycle arrest 51 ., SIRT1 deacetylates p53 , which decreases the ability of p53 to bind DNA and regulate gene expression 52 , as we also show in pluripotent hESCs ( Figure 2 ) ., Regulation of miR-34a and its downstream targets in ESCs has not been previously reported , to our knowledge ., In contrast , a role for miR-145 in differentiation of hESCs is known , where miR-145 acts by negatively regulating levels of pluripotency genes , OCT4 , SOX2 , and KLF4 53 ., miR-145 is known to be a p53 target in somatic cells 54; however , the mechanisms that lead to miR-145 up-regulation during differentiation of hESCs have not been defined ., In response to RA treatment and differentiation of hESCs , miR-34a and miR-145 were significantly up-regulated in a p53-dependent manner ( Figure 6A and 6B ) , an induction which also occurs with a DNA-damaging agent , Adr ( Figure S7A ) ., RA treatment led to a time-dependent enrichment of p53 at predicted p53REs of both miR-34a and miR-145 ( Figure 6C ) , in parallel with the transient activation of p53 ., Interestingly , p53 enrichment on two identified p53REs of miR-145 exhibited distinct patterns during differentiation compared to DNA damage: p53 accumulation occurred at both p53REs but was stronger on the proximal p53RE ( p53RE2 ) during differentiation and on the distal p53RE ( p53RE1 ) after DNA damage ( Figures 6C and S7B ) ., Introduction of small inhibitory oligonucleotides to counter expression of targeted miRNAs ( anti-miRNAs ) , anti-miR-34a and anti-miR-145 , resulted in ∼80% miRNA depletion ( Figure S6C ) , and had specific effects on expression of stem cell factors: inhibition of miR-34a led to increased expression of OCT4 , KLF4 , LIN28A , and SOX2 proteins , and to a lesser extent SIRT1 ( Figure 6E ) , as well as SOX2 and SIRT1 RNA ( Figure 6D ) ., Inhibition of miR-145 induced protein levels of OCT4 , SOX2 , and KLF4 , as well as increased RNA expression of SOX2 and KLF4 ( Figure 6D and 6E ) ., Quantitative determination of OCT4/SSEA4-positive cells by flow cytometry analysis revealed that hESCs could differentiate with RA after inhibition of miR-34a but not in the presence of anti-miR-145 ( Figure 6F ) ., Depletion of both miRNAs significantly delayed differentiation of hESCs , as ∼97% of hESCs remained OCT4-positive 3 d after RA treatment ( Figure 6F ) , indicating the significance of these miRNAs during differentiation ., Depletion of miR-145 also significantly affected accumulation of hESCs in G1 after RA treatment ( Figure S7D ) ., miR-145 targets c-Myc 54 , which is known to repress p21 55; thus , miR-145 represses pluripotency factors and likely contributes to regulation of the hESC cell cycle by decreasing c-Myc and indirectly activating p21 during differentiation ., In silico analysis by TargetScan 56 , PicTar 57 , miRanda 58 , and miRBase 59 of genes potentially regulated by miR-34a and miR-145 identified several genes significant to ESC biology ( Figure S7E ) ., Pluripotency genes targeted by mir-145 are known 53; additionally , we found that mir-34a has predicted target sites within the 3′ UTRs of KLF4 and LIN28A , which are conserved across species ( Figure 6G ) ., To investigate whether KLF4 and LIN28A are directly targeted by miR-34a , we engineered luciferase reporters that have either the wild-type 3′ UTRs of these genes , or mutated 3′ UTRs with a 4-bp mutation in the predicted target sites ., The luciferase reporters were cotransfected with miRNA precursor ( pre-miRNAs ) mimics , which are processed into mature miRNAs in HEK293 cells ., A scrambled precursor with no homology to the human genome was used as a control ., The pre-miRNA mimic of miR-34a ( pre-miR-34a ) significantly reduced the luciferase activity of the wild-type LIN28A reporter ( ∼30% ) , compared to the scrambled precursor control ( two-tailed Students t test; Figure 6H ) , and did not alter activity of mutated reporters ( Figure 6H ) ., Repression was specific to LIN28A , as there was no significant effect of pre-miR-34a transfection on the KLF4 reporter ., These results suggest that miR-34a directly targets sites within the LIN28A 3′ UTR ., Taken together , p53-activated miRNAs decrease expression of major stem cell factors to oppose self-renewal of hESCs , as well as inhibiting SIRT1 , a negative regulator of p53 ., Thus , p53-mediated regulation of miRNAs reinforces and expands the direct effects of p53 in regulation of the cell cycle during differentiation of hESCs ., TP53 is mutated in more than half of all human cancers , and maintains genomic stability in somatic cells , primarily as a stress-responsive transcription regulator of genes that control cell cycle arrest and apoptosis 60 , 61 ., Functions of p53 in cellular metabolism , homeostasis , and development are less understood , but are increasingly appreciated 62 , 63 ., In adult stem cells , p53 negatively regulates proliferation and self-renewal of neural stem cells and hematopoietic stem cells to maintain their quiescent state 64 , 65 ., A role for p53 in ESC modulation was first suggested by a report that p53 directly represses Nanog in mESCs 40 ., Likewise , p53 functions in apoptosis and differentiation of hESCs were previously reported , but no clear mechanisms were revealed 66 , 67 ., Here , we show that p53 actively promotes differentiation of hESCs and does so by mechanisms distinct from direct regulation of NANOG transcription ( Figure 7 ) ., In contrast to in mESCs , human p53 is localized in the nucleus of hESCs at a low concentration and in a deacetylated state ., In response to differentiation signals , SIRT1 is down-regulated 33 , allowing p53 to be acetylated at Lys373 , a target of CBP/p300 ., Acetylation of p53 activates its functions as a transcription factor 68 , and relieves p53 from HDM2- and TRIM24 ( shown here ) – mediated ubiquitination and degradation 34 ., The importance of p53 concentration and its regulation was shown by the significant levels of differentiation that occur either in response to siRNA-mediated depletion of MDM2 and TRIM24 or by ectopic expression of p53 ., In these cases , differentiation occurs in the absence of RA and in medium that normally maintains stem cells as such ., Comparison of cell cycle profiles during RA-mediated differentiation and DNA damage conditions highlights the diverse roles played by p53 to restrict cell division and initiate either differentiation or DNA repair , respectively ., Differentiation-activated p53 binds to the p53REs of downstream gene target CDKN1A to promote a G1 block that effectively elongates G1 and lengthens the cell cycle of hESCs , with minimal induction of apoptosis ., The pattern of post-translational modifications of p53 and the accumulation of hESCs in G1 induced by RA are in contrast to arrest of Adr-treated hESCs at G2–M of cell cycle in a classical , p53-mediated DNA damage response 60 ., We find that p53 , transiently activated during differentiation , regulates cell cycle but does not induce significant apoptosis ., Although selectivity of p53 in activating arrest of cell cycle versus apoptosis remains incompletely understood 30 , our findings for hESC differentiation recapitulate the specificity previously shown in mouse models that express specific point mutants of p53 48 , 69 and suggest that these mechanisms are highly conserved ., Differentiation of hESCs is significantly delayed when TP53 and/or CDKN1A levels are reduced , as shown by AP staining , cell cycle analysis , and expression of markers of pluripotency in hESCs transfected with siRNAs ., Recently , Dox-inducible exogenous expression of p21 in hESCs was shown to induce cell cycle arrest and massive hESC differentiation 70 , further supporting that induced levels of p21 and control of cell cycle are required for hESCs to differentiate ., A number of p53 downstream target genes have been extensively studied , especially in transformed cells , and non-coding RNAs regulated by p53 are now being identified 71 ., Roles of miRNAs during hESC differentiation or reprogramming of somatic cells have recently been reported 24 , 53 , 72–76 , with specific signatures of miRNAs shown in distinct stem cell states of pluripotency regulated by p53 77 ., A known p53 target , miR-34a , was shown to increase p53 activation by repressing SIRT1 in cultured cells 49 but was not previously linked to hESCs ., miR-145 was discovered to be a direct repressor of pluripotency factors , but was not shown to be regulated by p53 in stem cells 53 ., Here we show that p53 activates miR-34a and miR-145 expression during RA-mediated differentiation of hESCs ., Expression of these miRNAs , directly induced by chromatin interaction of p53 at p53REs , impacts a network of target transcripts that control pluripotency , either directly or indirectly ., Further , during preparation of this manuscript , Choi et al . 78 showed that miR-34a provides a barrier to somatic cell reprogramming ., This study offers further support for our finding that miR-34a antagonizes pluripotency of hESCs and has pro-differentiation functions in stem cell biology ., We found that miR-145 has a more significant role in differentiation of hESCs , with miR-34a acting to augment its functions ., However , since DNA damage induced miRNAs miR-34a and miR-145 but did not promote accumulation of hESCs in G1 and differentiation of hESCs , as seen with RA treatment or ectopic p53 expression , it is clear the miRNAs alone are insufficient to induce differentiation of hESCs ., We showed that p53 activation is transient during differentiation of hESCs; thus , activation of miRNAs that repress stem cell factor expression broadens the impact of p53 activation and may prevent “backsliding” to pluripotency , once p53 returns to its normally low concentration during differentiation to a committed state ., Recently , the creation of TP53−/− hESCs by homologous recombination showed that loss of p53 promotes pluripotency , a role for p53 conserved in both murine and human ESCs 79 ., However , TP53−/− hESCs contribute to all three germ layers during teratoma formation in SCID mice , perhaps because of compensation by structurally similar members of the p53 family , p63 and p73 ., Deletion of all p63 and p73 isoforms in mice reveals critical roles in development and differentiation 80 , 81 ., p63 and p73 can bind to canonical p53 DNA-binding sites and regulate transcription from p53-responsive promoters , in the presence or absence of p53 itself 82–84 ., Compensation may be incomplete , as p53-null mice exhibit some developmental anomalies , such as a high percentage of exencephaly in females , and specific genes exhibit altered p53-regulated gene expression during development 85–87 ., The establishment of elongated Gap-phase timing in stem cells , more similar to that in somatic cells , was previously proposed as a requirement for reception of differentiation signaling 11 ., Our studies show that p53 is integral in this process and actively promotes differentiation of hESCs , in the absence of cellular stress or DNA damage ., The collective effects of p53 activation elongate the G1 phase and antagonize pluripotency by induction of miR-34a and miR-145 ( Figure 7 ) ., Importantly , activation of p53 during differentiation of hESCs is transient , allowing later stages of growth and differentiation , while p53-induced miRNAs regulate a network of genes that bolster forward progression to differentiate ., How these findings in ESCs may be relevant in adult and tumor stem cells—where they may be channeled toward therapeutic applications to restructure cell cycle and regulate a network of miRNAs—is an important area for further study ., hESCs ( WA09 and WA01 ) were obtained from National Stem Cell Bank and cultured according to the protocol from WiCell Research Institute ., Briefly , WA09 cells were maintained in hESC culture medium on γ-irradiated mouse embryonic fibroblasts ( MEFs ) prepared using WiCell instructions ., hESCs ranging from passage number 32–38 were used for all of our experiments ., hESC complete culture medium is composed of DMEM/F12 supplemented with 20% knockout serum replacement , 1 mM L-glutamine , 1% nonessential amino acids , 4 ng/ml human FGF2 ( all from Invitrogen ) , and 0 . 1 mM 2-mercaptoethanol ( Sigma ) ., The medium was changed daily , and cells were passaged every 4–6 d with 1 mg/ml Collagen IV ( Invitrogen ) ., For differentiation studies hESCs were cultured in differentiation medium ( hESC medium without FGF ) containing 1 µM RA for 5 d , with fresh medium change daily ., hESCs were also maintained as feeder-free cultures on hESC qualified Matrigel ( BD Biosciences ) in mTeSR1 medium ( Stemcell Technologies ) and MEF conditioned medium ( CM ) ., Passage 32 hESCs were grown on mTeSR1 medium for five passages ., hESCs were cultured on Matrigel following manufacturers instructions and received fresh mTeSR1 medium daily , and cells were passaged every 4–6 d with 1 mg/ml Dispase ( Stemcell Technologies ) ., For differentiating hESCs cultured on mTeSR1 , 1 µM RA was added to homemade MEF CM ( without additional FGF ) ., CM was prepared in our facility by culturing γ-irradiated MEFs in complete hESC culture medium for 24 h , collected daily , filtered , and freezed at −20°C ., FGF was added to CM before use to a final concentration of 10 ng/ml to culture the cells grown on Matrigel under pluripotent conditions ., hESC colonies were grown on Matrigel in six-well plates ., siRNA targeting human TP53 , CDKN1A ( p21 ) , HDM2 , TRIM24 ( Table S1 ) and non-target ( control ) were purchased from Dharmacon and anti-miRNA oligonucleotides targeting human miR-34a , miR-145 , and miR-nonspecific were purchased from Applied Biosystems ., 75 nM siRNA or 75 nM anti-miRNA oligonucleotides were transfected twice into cells using Lipofectamine2000 ( Invitrogen ) transfection reagent according to manufacturers protocol within a period of 5 d ., The first siRNA transfection was performed 24 h after splitting the cells , followed by medium change 6 h post-transfection ., 36 h after the first siRNA transfection , cells were cultured in differentiation medium ( −FGF , +RA ) for 3 d and harvested to analyze protein , RNA , AP , or cell cycle status ., Cells were transfected one more time with siRNA on the beginning of day 2 of RA treatment to maintain the knockdown efficiency ., To determine the transfection efficiency these siRNAs were cotransfected with siGLO Green ( FAM ) , also purchased from Dharmacon ., Twenty-four hours post-transfection cells were either visualized by microscopy or subjected to flow cytometry analysis to determine the percent of cells transfected ., See Figure S3 for percent transfection and knockdown efficiencies of siRNAs ., In case of anti-miRNA transfections , cells were harvested 24 h post-transfection f | Introduction, Results, Discussion, Materials and Methods | Multiple studies show that tumor suppressor p53 is a barrier to dedifferentiation; whether this is strictly due to repression of proliferation remains a subject of debate ., Here , we show that p53 plays an active role in promoting differentiation of human embryonic stem cells ( hESCs ) and opposing self-renewal by regulation of specific target genes and microRNAs ., In contrast to mouse embryonic stem cells , p53 in hESCs is maintained at low levels in the nucleus , albeit in a deacetylated , inactive state ., In response to retinoic acid , CBP/p300 acetylates p53 at lysine 373 , which leads to dissociation from E3-ubiquitin ligases HDM2 and TRIM24 ., Stabilized p53 binds CDKN1A to establish a G1 phase of cell cycle without activation of cell death pathways ., In parallel , p53 activates expression of miR-34a and miR-145 , which in turn repress stem cell factors OCT4 , KLF4 , LIN28A , and SOX2 and prevent backsliding to pluripotency ., Induction of p53 levels is a key step: RNA-interference-mediated knockdown of p53 delays differentiation , whereas depletion of negative regulators of p53 or ectopic expression of p53 yields spontaneous differentiation of hESCs , independently of retinoic acid ., Ectopic expression of p53R175H , a mutated form of p53 that does not bind DNA or regulate transcription , failed to induce differentiation ., These studies underscore the importance of a p53-regulated network in determining the human stem cell state . | Most cell types in an organism are generated from embryonic stem cells ( ESCs ) , which are able to proliferate an unlimited number of times and have the potential to produce any kind of cell of that organism ( this ability is called pluripotency ) ., In order to maintain these properties , ESCs have to remain in a proliferate state , which is achieved by the collaboration of several factors ., Expressing combinations of these factors in differentiated cells can result in ESC-like qualities; these induced pluripotent stem cells ( iPSCs ) can then function like ESCs ., Previous studies suggested that p53 , generally known for its roles in maintaining genomic integrity by regulating cell cycle and cell death pathways , also acts as a barrier to reprogramming adult cells during the creation of iPSCs; whether this is strictly due to repression of proliferation remains a subject of debate ., Here , we show that p53 plays a significant role in actively promoting differentiation of human ESCs ( hESCs ) ., We find that , prior to differentiation , p53 is expressed at very low levels in hESCs , held in check by two negative regulators , HDM2 and TRIM24 , that trigger p53 degradation ., Upon induction of differentiation , lysine 373 of p53 is acetylated , and this disrupts the existing interactions with negative regulators , thus allowing stabilization and activation of p53 ., Active p53 in turn promotes expression of the cell cycle regulator p21 to slow down the hESC cell cycle; cells in the gap ( G1 ) phase of the cell cycle accumulate , preventing division ., In parallel , p53 activates specific microRNAs , miR-34a and miR-145 , that inhibit the expression of several stem cell factors and prevent differentiated cells from backsliding to pluripotency ., Our results highlight a novel function of p53 in determining the human stem cell state . | developmental biology, biology | null |
journal.pcbi.1006071 | 2,018 | Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds | As we begin to lose the arms race against microbial infections , it is important that we develop new treatment strategies as a complement or alternative to antibiotics ., In this paper , we use mathematical modelling to explain and predict the effects of a novel anti-adhesion therapy in the treatment of infected burn wounds , with the aim of improving treatment outcome ., Each year , millions of lives are saved through the use of antibiotics to combat bacterial infections ., However , sustained use of any given antibiotic leads to the clinical emergence of drug-resistant strains ., Since the discovery of penicillin , many new classes of antibiotics have been identified , allowing clinicians to switch between antibiotics if resistance emerges either within an individual patient or within a patient population 1 ., Over time , strains have emerged which exhibit resistance to multiple classes of antibiotics ( multi-drug resistance ) and reports of bacterial infections which are resistant to all known antibiotics ( pan-resistant ) are becoming increasingly common ., At present , a reported 700 , 000 individuals worldwide die each year due to antimicrobial resistance and this figure is predicted to rise to 10 million per year by 2050 unless steps are taken to combat this threat 2 ., While resistant strains continue to evolve , our ability to develop new classes of antibiotics is diminishing , the rate of antibiotic discovery having slowed significantly since its ‘Golden Era’ in the 1940s–1960s 1 , 3 ., It is therefore vital that we develop alternative treatment strategies to replace or complement antibiotics 4 , 5 ., One potential way forward is through the use of anti-virulence treatments ., Whereas antibiotics either kill bacteria ( bactericidal ) or inhibit their growth ( bacteriostatic ) , anti-virulence treatments interfere with a pathogen’s ability to cause damage and disease in the host 6 ., As such , they are likely to exert a smaller selective pressure upon a bacterial community , reducing the chances that resistance will develop ( though opinions vary over the extent to which they may be resistance-proof , see 7 , 8 ) ., Anti-virulence treatments take a number of forms including those which target or inhibit toxin activity , adhesion , toxin secretion , virulence gene expression and inter-bacterial signalling 9–11 ., In this paper , we consider a form of anti-adhesion treatment consisting of polystyrene microbeads coupled to a protein known as multivalent adhesion molecule ( MAM ) 7 ( see also 12 and other papers from their group for alternative anti-adhesion treatments that operate by blocking pilus assembly or function ) ., MAM7 is anchored in the outer membrane of many Gram-negative bacteria , where it is responsible for initiating attachment of bacteria to host cells 13 , 14 ., When applied to an infection site , MAM7-coated beads ( henceforth , inhibitors ) act as a bacteriomimetic , competitively inhibiting the infectious agent from binding to host cells ., This prevents bacteria from deploying those virulence mechanisms for which cell-to-cell contact is required and renders them more susceptible to natural or artificial physical clearance ., Given that inhibitors must bind to host cells before bacteria in order to block them from binding , it is unclear whether their application may ever be expanded from prevention ( prophylaxis ) to the treatment of established infections ( therapy ) ., Bacterial infection is a major cause of mortality in patients with burn wounds , where it is responsible for up to 75% of deaths in cases where severe burns are sustained to more than 40% of the body surface area 15 ., Burn wounds are commonly infected by Pseudomonas aeruginosa 15–18 , an opportunistic Gram-negative bacterium; the infection often being hospital-acquired ( nosocomial ) 15 , 19 ., Current treatment of such infections involves use of topical and systemic antibiotics , and regular debridement ( mechanical wound cleaning ) ., Debridement is either achieved through regular wound cleansing with a cloth , or through application of negative pressure devices ( negative pressure wound therapy , NPWT ) in which fluid is drawn out of the wound , either continuously or intermittently , using a pump , attached to a foam dressing covering the wound 20 ., Some studies have shown NPWT to be effective in reducing the bacterial burden 21; however , this result is not consistent across all studies 20 , 22 ., In earlier work we have shown , using an experimental model for P . aeruginosa burn wound infections in the rat , that treatment with inhibitors can significantly reduce the bacterial burden in the wound without impeding wound closure 23 ( see Experimental set-up for more details ) ., In vitro studies have also demonstrated the efficacy of inhibitor treatment in reducing cytotoxicity 9 , 24 and have shown that inhibitors do not interfere with host cell functions critical to wound healing 25 ., A number of mathematical modelling studies have considered the use of anti-virulence treatments to combat bacterial infections ., The majority of these studies focus upon anti-quorum sensing treatments ( see , for example , 26–33 ) ., An exception to this rule; the model in 34 is of particular relevance to the present work ., This ordinary differential equation ( ODE ) model considers a general anti-virulence treatment , which operates by enhancing innate immunity in bacterial clearance ., The model predicts that , when used in isolation , anti-virulence treatment is unlikely to eliminate a bacterial infection ., However , the model predicts that , when combined with antibiotics , anti-virulence treatments could eliminate bacteria , provided antibiotic and anti-virulence treatments are applied in staggered doses ., Other modelling work has considered the bacterial invasion of burn wounds and the resultant tissue damage 30 , 35–37 , the influence of bacterial infection upon the healing of burn wounds 38 and the effects of ambient gas plasma treatment in this context 39 ., Each of these models is formulated as a system of partial differential equations in one or two spatial dimensions ., Models have also been developed to describe microbial adhesion to surfaces , for example , 40 developed an ODE model for the competitive colonisation of the gut wall by host and invader strains of Escherichia coli ., Lastly , 41 have developed an individual-based model to describe the colonisation of a generic surface by phenotypically heterogeneous bacteria , in which bacteria may migrate between the surface and a liquid medium ., In this paper , we construct a mathematical model , formulated as a system of ODEs , to describe the population dynamics and treatment of a bacterial infection within a burn wound ., Basing our mathematical model upon Huebinger et al . ’s 23 experiments , we use it to explain the empirical results and to predict the effects of various treatment regimes , involving inhibitor dosing and debridement , with the aim of improving efficacy ., A particular strength of this study is that we consider multiple parameter sets , twelve in total , each of which provides a good fit to the experimental data ., Classifying these sets into four qualitatively different cases , we consider the long-term effects of each treatment strategy , predicting the conditions under which treatment will eliminate the bacterial burden across all four cases ., In this section we provide a simple description of the experimental set-up which forms the basis for our mathematical model ., The experimental work was published previously in 23 , wherein a more detailed description can be found ., We consider a burn wound infection model in the Sprague-Dawley rat ( see Fig 1 ) ., Rats were anaesthetised and the portion of each rat which was to be burned was shaved ., Rats were then immersed in 100°C water for 12s resulting in full-thickness cutaneous burns to 40% of the body surface area , in a region spanning the back and upper sides of the body ., We label the time at which the burn is administered as day −2 ., Rats were then resuscitated and given the appropriate pain control for the remainder of the experiment ., On day 0 , two days after the burn was administered , a section of eschar ( dead ) tissue , approximately 4 × 4 cm in area , was surgically excised ., Next , 5 × 106 CFU ( colony-forming units ) of multidrug-resistant P . aeruginosa were applied to the excised region , followed by suspensions containing either 3 × 108 inhibitor or control beads ( without a MAM7 coating ) in saline ., Identical inhibitor and control treatments were repeated every 24 hours for days 1–5 post infection; however , since a scab ( i . e . a layer of solidified exudate ) forms over the excision by day 1 , treatments administered on or after day 1 are unlikely to enter the ( liquid ) exudate ., Rats were euthanized after the experiment , on day 6 ., A bioluminescent , multidrug-resistant P . aeruginosa isolate , Xen5 , was chosen , such that the bacterial burden and their spatial distribution across the wound could be detected ., An IVIS Spectrum in vivo imaging system ( Perkin Elmer ) was used to record bacterial luminescence on days 1–6 post infection , from which the total flux ( photons sec−1 ) was calculated using MetaMorph software ( Molecular Devices ) to integrate over the pixels ., The total number of bacteria in CFU was then calculated using the conversion factor 4 × 108 photons sec−1 ↔ 5 × 106 CFU , which was determined by measuring the luminescence of suspensions which contained an experimentally determined number of bacterial colony-forming units ., 13 experiments were conducted using inhibitor and 11 using control beads ., Two of the control bead experiments were discounted because the exposure setting used was too high to prevent the image from saturating ., This may imply that the mean bacteria population size over time calculated for the control bead scenario slightly underestimates the true mean value ., The experimental results are summarised in Fig 2 ., These results raise two important questions: In what follows , we formulate a mathematical model of the burn wound infection experiment described above to help us answer these questions ., The burn wound is assumed to consist of two physical compartments: the host cells , and a fluid compartment exuded by the host cells and ( hence ) known as the exudate ., The host cells and the overlying exudate extend to the perimeter of the burn wound beneath the necrotic tissue , while the exudate is exposed to the air at the excision ( see Fig 3A ) ., The area of the burn wound , Ar ( cm2 ) , remains essentially fixed during the experiment; however , the exudate height , h ( cm ) , and volume , V ( cm3 ) , are elevated for a short period following the application of bacteria and inhibitors to the excision at the beginning of day 0 ( see Fig 1A ) ., This excess fluid is lost rapidly via run-off ( down the sides of the rat ) , evaporation and absorption ( into the host cells ) ., Since the timescale over which the height and volume are elevated ( on the order of minutes ) is small compared to the timescale of the experiment ( on the order of days ) , we neglect this variation and assume a fixed height and volume throughout the course of the experiment ., Both bacteria and inhibitors can exist in one of two states; either free in the exudate or bound to the host cells ( we note that inhibitors do not bind to bacteria ) ., It is assumed that the system is well-mixed since any given bacteria ( or inhibitor ) has an equal chance of interacting with any given binding site and since all other processes ( growth , clearance , phagocytosis and unbinding ) are thought not to depend upon their spatial location ., This allows us to forgo an explicit spatial component and so to construct an ODE model for the evolution of the free bacteria density , BF ( t ) ( cells cm−3 ) , bound bacteria density , BB ( t ) ( cells cm−2 ) , free inhibitor concentration , AF ( t ) ( inhibitors cm−3 ) , and bound inhibitor concentration , AB ( t ) ( inhibitors cm−2 ) , over time , t ( hr ) ., It is assumed that the total binding site density on the host cells , consisting of both free and occupied sites , is conserved , such that the free binding site density E ( t ) = Einit − ϕBacBB ( t ) − ϕAAB ( t ) ( sites cm−2 ) , where Einit ( sites cm−2 ) is the initial density of free binding sites , and ϕBac ( sites cell−1 ) and ϕA ( sites inhibitor−1 ) are the number of binding sites occupied by a bacterium or an inhibitor respectively ., The model is summarised in Fig 3B and described by the following governing equations, d B F d t= r F B F ( 1 - B F K F ) ︸ logistic growth + ( 1 - η ( E ) ) H ( K B - B B ) r B h B B ( 1 - B B K B ) ︸ daughter cells freed from host cells upon division - α B a c A r B F E ︸ binding to host cells + β B a c h B B ︸ unbindingfromhostcells - ψ B a c ( t ) B F ︸ natural clearance , ( 1 ), d B B d t= ( 1 + ( η ( E ) - 1 ) H ( K B - B B ) ) r B B B ( 1 - B B K B ) ︸ logistic growth ( a proportion , η , remain attached ) + α B a c V B F E ︸ binding to host cells - β B a c B B ︸ unbindingfromhostcells - δ B B B ︸ phagocytosis , ( 2 ), d A F d t= - α A A r A F E ︸ bindingtohostcells + β A h A B ︸ unbindingfromhostcells - ψ A ( t ) A F ︸ naturalclearance , ( 3 ), d A B d t= α A V A F E ︸ bindingtohostcells - β A A B ︸ unbindingfromhostcells , ( 4 ), where parameter values are given in Tables 1 and 2 , and Table A in S2 Supporting Information ., See Parameter fitting and S1 Supporting Information for details on how the parameters were obtained ., Note that we consider multiple parameter sets , each of which provides a good fit to the data ., Both free and bound bacteria are assumed to grow logistically with respective intrinsic growth rates rF ( hr−1 ) and rB ( hr−1 ) , and carrying capacities KF ( cells cm−3 ) and KB ( cells cm−2 ) ., We interpret the carrying capacities to represent the maximum number of bacteria that can be sustained by available nutrients and the situation in which BF ( t ) = KF , or BB ( t ) = KB , to be one in which the rate of bacterial division is negligible ( see 42 , 43 ) ., The burn wound exudate contains glucose and other nutrients and has been shown to be capable of supporting a proliferating population of P . aeruginosa 44 , 45 ., We note that , in general , KB ≠ Einit/ϕBac , such that the number of bacteria that can be nourished on the host cells is not equal to the number that can bind to the host cells ., For all of the parameter sets considered in this paper , KB < Einit/ϕBac ( see Tables 1 and 2 , and Table A in S2 Supporting Information ) ., It is assumed that bacteria and inhibitors bind to and unbind from the host cells in accordance with the law of mass action , with respective binding rates αBac ( hr−1 sites−1 ) and αA ( hr−1 sites−1 ) , and unbinding rates βBac ( hr−1 ) and βA ( hr−1 ) ., Examination of histological sections through the burn wound shows that neutrophils are present within and at the surface of the host cells , but not within the exudate 23 ., Administration of a burn wound causes neutrophils to be fully activated such that no further neutrophils are recruited in response to the bacterial infection 23 , 46 ( in contrast to 34 ) ., Therefore , the immune response can be captured by the exponential decay of bound bacteria with rate δB ( hr−1 ) , where δB accounts for the density of neutrophils ., It is assumed that inhibitor degradation , if it occurs , is sufficiently gradual that it can be neglected ., Several of the terms in Eqs 1–4 contain h , Ar or V as a factor in order to ensure dimensional consistency ., These constants could have been combined with other parameters , but we retain them in the interests of clarity ., A proportion of the daughter cells of bound bacteria , 0 ≤ η ( E ( t ) ) ≤ 1 ( dimensionless ) , remain bound to the surface , while the remaining fraction , 1 − η ( E ( t ) ) , enter the exudate ., Daughter cells may not bind immediately either because the long axis of the parent cell is angled away from the host cell surface upon division or due to a lack of free binding sites on the neighbouring host cell surface ., The proportion that remains bound , η ( E ( t ) ) , depends upon the density of free binding sites , E ( t ) , such that a larger fraction of the daughter cells remain bound when more binding sites are available ., We capture this dependence using a Hill function with constant γ ( sites cm−2 ) and Hill coefficient n ( dimensionless ) as follows, η ( E ) = η m a x E n γ n + E n , ( 5 ), where ηmax ( dimensionless ) is the maximum proportion of daughter cells which may remain bound to the surface ., If the density of bound cells , BB ( t ) , exceeds the bound carrying capacity , KB , then the bound logistic growth term becomes a death term ., In this case , the loss of bacteria is confined to the bound compartment and does not affect the free compartment ., This is achieved through the use of a Heaviside step function , H ( KB − BB ( t ) ) , in Eqs 1 and 2 , where, H ( x ) ≔ { 0 if x < 0 , 1 if x ≥ 0 ., ( 6 ) The rates of clearance of bacteria and inhibitors , ψBac ( t ) ( hr−1 ) and ψA ( t ) ( hr−1 ) , vary with time , such that clearance occurs at a constant rate for the first 24 hours and then stops after this point due to the formation of a scab over the excision ., Thus , clearance occurs at rates, ψ B a c ( t ) = ψ ˜ B a c H ( 24 - t ) and ψ A ( t ) = ψ ˜ A H ( 24 - t ) , ( 7 ), where ψ ˜ B a c ( hr−1 ) and ψ ˜ A ( hr−1 ) are the constant rates of clearance in the first 24 hours , and H is a Heaviside step function , as defined in Eq 6 ., We choose the time t = 0 ( hr ) to correspond to the point at which bacteria and inhibitors are applied to the burn wound following the excision ., Bacteria and inhibitors are present only in the free compartment initially , not having had the opportunity to bind to the host cells , such that, B F ( 0 ) = B F i n i t , B B ( 0 ) = 0 , A F ( 0 ) = A F i n i t , A B ( 0 ) = 0 , ( 8 ), where B F i n i t and A F i n i t are constants ., See Tables 1 , 2 and Table A in S2 Supporting Information for parameter values ., The parameters in Table 1 and Table A in S2 Supporting Information were fitted to the experimental data ( see Parameter fitting and S1 Supporting Information for details ) , while those in Table 2 were either measured , calculated or estimated ., The area of each burn wound was determined from images , such as those in Fig 1 , using the MetaMorph software , while the height of the fluid layer was measured to be 1 mm ., As described in Experimental set-up , the initial density of bacteria and the initial concentration of inhibitor are known ., The volume of the exudate is calculated as the product of the wound area and the height of the exudate ., We know that there are about 1 . 5 × 105 host cells per cm2 and that approximately 17 inhibitors may bind per host cell 9 ., Taking the product of these two values gives us the initial density of free binding sites , Einit ., We define a binding site to consist of the number of host cell binding receptors occupied by an inhibitor , such that an inhibitor occupies a single site and hence ϕA = 1 sites inhibitor−1 ., Inhibitors have been designed to occupy the same number of host cell binding receptors as a bacterium ., Therefore , we also have that ϕBac = 1 sites cell−1 ., We note that while an inhibitor occupies the same number of binding sites as a bacterium , a rod-shaped P . aeruginosa cell ( which we have measured to be approximately 1μm×3μm ) covers up to three times the host cell surface area as a spherical ( 1 μm diameter 23 ) inhibitor without occupying any more sites ., Simulations were found to be insensitive to the value of the Hill coefficient , n; therefore , we set it to unity for simplicity ., We leave our equations in dimensional form so as to make them easier to interpret biologically and since non-dimensionalisation would not reduce the number of fitted parameters ( although it does reduce the total number of parameters ) ., In addition to the untreated/control ( A F i n i t = 0 ) and single inhibitor dose ( A F i n i t > 0 ) scenarios based upon Huebinger et al . ’s 23 experiments ( see Experimental set-up ) , we consider a further six theoretical scenarios , five of which include either regular or continuous debridement ( see Table 3 ) ., Since inhibitors operate by blocking bacteria from binding to the wound host cells , it is intuitive that this may result in the majority of bacteria occupying the free compartment ., Thus , any treatment , such as debridement , which removes the exudate , could clear the free compartment of bacteria—and with them , inhibitors—significantly reducing the total population size of bacteria when combined with an inhibitor treatment ., ( Bound bacteria and inhibitors are left mostly intact by debridement . ), Regular debridement consists of a series of discrete instantaneous debridement events , while continuous debridement consists of a sustained , high level of clearance ( ψBac = ψA = 1000 hr−1 ) and may be thought of as the limiting case of regular debridement in which the time between debridement events tends to zero ., While it may not be possible to maintain such a high rate of clearance in practice , this clearance rate is chosen to determine the theoretical best-case-scenario were such a treatment to be applied ., Following a discrete debridement event , it is assumed that the fluid compartment is restored on the timescale of a few minutes , such that the volume fluctuation can be neglected ., For each of the treatment strategies , Eqs 1–8 were solved using the Matlab routine ode15s , a variable-step , variable-order solver based upon numerical differentiation formulas ., The untreated and single inhibitor dose scenarios are those considered in the experiments and are described above and in Experimental set-up ., The key difference between the numerical simulations ( see Numerical solutions ) and the experiments is that the simulations extend beyond the time frame of the experiments ., In the regular inhibitor dose scenario ( and the regular inhibitor dose with regular debridement scenario ) , the repeat doses of inhibitors are identical to the initial dose , A F i n i t ., The second dose is not applied until 48 hr for consistency with the treatments involving debridement ( see below ) ., As noted in Experimental set-up , a scab forms over the wound after the first 24 hr , ending clearance and preventing further inhibitor doses from reaching the exudate ., Thus , in practice , inhibitor doses could not be repeated without removing the scab and incurring further clearance at levels similar to those in the first 24 hr ., However , since we are interested in the theoretical effect of repeated inhibitor doses independent of clearance , we neglect further clearance effects in this case ., Were we to include additional clearance upon re-treatment with inhibitor , treatment efficacy would be improved ., In the scenarios involving regular debridement , clearance is re-established for the first 24 hours after each debridement event , with rates given in Table 1 and Table A in S2 Supporting Information , to account for leakage due to the loss of the scab upon debridement ., The first debridement event is chosen to occur at t = 48 hr , rather than some earlier time , so as to give the inhibitors time to bind to the host cells ., We present two sets of sensitivity analyses ., The first set ( presented in Case A–Case D ) shows the effect of a tenfold increase or decrease in each of the 13 fitted parameters , rF , rB , KF , KB , αBac , βBac , δB , ηmax , γ , ψ ˜ B a c , αA , βA and ψ ˜ A , on the total number of bacteria either at steady-state ( treatment scenarios 1 and 2 ) or at t = 90 days = 2160 hr ( treatment scenarios 3–8; Figs O–V in S2 Supporting Information ) ., We truncate the simulations for the latter treatment scenarios at 90 days since simulating treatments with regular inhibitor doses or regular debridement is computationally expensive and those involving continuous debridement are close to steady-state by this time ., We note that the total population size of bacteria oscillates in those treatments that involve regular debridement , undergoing a sharp drop upon each debridement event ., We plot the value of BT ( t ) at the peak of the oscillation at t = 90 days ( directly prior to debridement ) , since we consider that it is by the maximum number of bacteria that the efficacy of a treatment should be judged ., In the second set of sensitivity analyses ( presented in Inhibitor sensitivity analysis ) , we consider the effect of varying the binding and unbinding rates of inhibitors , αA and βA , in the space {10−12 , 10−11 , … , 1} × {10−12 , 10−11 , … , 1} upon the total number of bacteria at 4 weeks ( = 672 hr ) post infection in the 5 scenarios that involve inhibitor treatment ( Figs W , Y , AA , AC and AE in S2 Supporting Information ) ., We also consider the effect of increasing all inhibitor doses by 10 fold from 6 . 12 × 107 inhibitors cm−3 ( the standard value ) to 6 . 12 × 108 inhibitors cm−3 ( Figs X , Z , AB , AD and AF in S2 Supporting Information ) ., A combination of Markov Chain Monte Carlo ( MCMC ) and frequentist methods were used to fit the model given by Eqs 1–8 to the mean of the experimental data in the untreated and single inhibitor dose scenarios ., Unfortunately , we have insufficient data to generate informative posterior distributions using the MCMC method; however , we are able to identify a number of good fits ( twelve parameters sets are presented here ) and to classify these into four general cases—A , B , C and D—based upon their qualitative behaviour ( see Results ) ., By considering a range of valid parameter sets , rather than a single good fit , we are able to gain a more comprehensive understanding of the model behaviour ., The fitting procedures used differ between parameter sets and are summarised in Table A in S1 Supporting Information ., See S1 Supporting Information for more details ., We confirmed these fits with a nonlinear mixed-effects model using the Matlab routine sbiofitmixed , with parameter sets 1–12 as initial guesses ., Model fits are compared against the experimental data in Fig 4 and Figs A and B in S2 Supporting Information , where parameter sets 2 ( Case A ) , 3 ( Case B ) , 8 ( Case C ) and 12 ( Case D ) are presented in Fig 4 ., Since the experimental data does not distinguish between free and bound bacteria , we compare it against the simulated total number of bacteria , BT ( t ) = VBF ( t ) + ArBB ( t ) ., The model achieves a good fit to the experimental mean in all cases , remaining mostly within the shaded region denoting the standard error of the mean ., Steady-state analyses of Eqs 1–6 , in the absence of clearance , with and without a single dose of inhibitors were performed using Maple ., Clearance was neglected since leakage of fluid from the wound only occurs in the first 24 hours ., It was found that the system has two physically realistic steady-states in both the untreated and single inhibitor dose scenarios for all 12 parameter sets ., In each case the first steady-state , at which bacteria are absent , is unstable , while the second steady-state , at which both free and bound bacteria are present , is stable ( see S3 Supporting Information for more details ) ., By characterising the stability of the system in this way , we can be sure that we are not overlooking any potential stable steady-state solutions in the time-dependent simulations below ., Having explored the behaviour of the system at steady-state , we consider the full time-dependent problem ( Eqs 1–8 ) ., We begin by making a few general comments , before taking Cases A–D in turn ., Further details can be found in S4 Supporting Information ., In each case we present results to show the evolution in the total number of bacteria , BT ( t ) = VBF ( t ) + ArBB ( t ) ( Fig 5 , and Fig D in S2 Supporting Information ) , the total numbers of free and bound bacteria , B ^ F ( t ) = V B F ( t ) and B ^ B ( t ) = A r B B ( t ) , free and bound inhibitors , A ^ F ( t ) = V A F ( t ) and A ^ B ( t ) = A r A B ( t ) , and free binding sites , E ^ ( t ) = A r E ( t ) ( Fig 6 , and Figs E and F in S2 Supporting Information ) , and of the individual terms in Eqs 1–4 ( Figs G–L in S2 Supporting Information ) in the untreated and single inhibitor dose scenarios ., We also present results to show the evolution in the total number of bacteria in the treatment scenarios involving regular inhibitor doses and regular or continuous debridement ( Fig 7 , and Figs M and N in S2 Supporting Information ) ., Lastly , we present a sensitivity analysis showing the effects of a tenfold increase or decrease in each of the 13 fitted parameters ( Figs O–V in S2 Supporting Information , see Sensitivity analyses for details ) ., In the remainder of this paper , we distinguish between rate constants , e . g . , αBac and δB , and the rate at which processes occur , e . g . , αBacArBFE and δBBB , the former being distinguished from the latter by the use of the word ‘constant’ ., We also distinguish between the intrinsic growth rate , e . g . , rF , and the rate of logistic growth , e . g . , rFBF ( 1 − BF/KF ) ., The time-dependent results are summarised in Table 4 ., Treatments are most effective in Case A , some of them eliminating the bacterial burden completely ., Most treatment scenarios are also effective in Case B . Surprisingly , treatment with inhibitors can actually increase the bacterial burden in Case C , although some treatments are still effective , while in Case D all treatments are ineffective , the bacterial burden settling to its untreated steady-state in all scenarios ., When effective , treatment with inhibitors may reduce the total bacterial burden in two ways ., Firstly , inhibitors may reduce the number of bound bacteria through competition for binding sites ., The second way , which may follow as a consequence of the first , is by reducing the rate of production of daughter cells by bound bacteria ., We note that the maximum proportion of bound daughter cells to enter the bound compartment , ηmax , ranges between O ( 10−10 ) and O ( 10−2 ) , across the 12 parameter sets considered ( see Table 1 , and Table A in S2 Supporting Information ) ., Therefore , the majority of bound daughter cells enter the exudate in all cases ( though , once there , they will not continue to divide if the free carrying capacity is exceeded ) ., This insight is not intuitively obvious , demonstrating the benefit of mathematical modelling ., As bacteria gain increasing resistance to antibiotics it is vital that we develop alternative treatment strategies ., Anti-virulence treatments—specifically MAM7-coupled beads , which operate by competitively inhibiting the binding of bacteria to host cells—present a promising complement or alternative to antibiotics ., Opinion as to the likely efficacy of such treatments is mixed , with some suggesting that their utility may be limited to preventing the initiation of a bacterial infection ( prophylaxis ) as opposed to treating a pre-existing infection ( therapy ) 6 , 10 , 47 , 48 ., In this paper we have used mathematical models to help us interpret the results of an experimental model , involving the inhibitor treatment of a burn wound infected by P . aeruginosa in the rat ( see Experimental set-up ) ., Our models allow us to predict the conditions under which treatment with inhibitors will be effective and to explore ways in which inhibitor dosing could be augmented to improve efficacy ., Mathematical models were fitted to experimental data using a combination of MCMC and frequentist techniques ( Parameter fitting ) ., A number of close fits were obtained ( 12 parameter sets are explored here ) and classified into four qualitatively different cases ( A–D ) ., Given the significant qualitative , and not merely quantitative , differences in predicted treatment outcomes between Cases A–D , this work highlights the importance of considering a range of | Introduction, Materials and methods, Results, Discussion | As the development of new classes of antibiotics slows , bacterial resistance to existing antibiotics is becoming an increasing problem ., A potential solution is to develop treatment strategies with an alternative mode of action ., We consider one such strategy: anti-adhesion therapy ., Whereas antibiotics act directly upon bacteria , either killing them or inhibiting their growth , anti-adhesion therapy impedes the binding of bacteria to host cells ., This prevents bacteria from deploying their arsenal of virulence mechanisms , while simultaneously rendering them more susceptible to natural and artificial clearance ., In this paper , we consider a particular form of anti-adhesion therapy , involving biomimetic multivalent adhesion molecule 7 coupled polystyrene microbeads , which competitively inhibit the binding of bacteria to host cells ., We develop a mathematical model , formulated as a system of ordinary differential equations , to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat ., Benchmarking our model against in vivo data from an ongoing experimental programme , we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies , with the aim of improving treatment outcome ., The model consists of two physical compartments: the host cells and the exudate ., It is found that , when effective in reducing the bacterial burden , inhibitor treatment operates both by preventing bacteria from binding to the host cells and by reducing the flux of daughter cells from the host cells into the exudate ., Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however , when combined with regular or continuous debridement of the exudate , elimination is theoretically possible ., Lastly , we present ways to improve therapeutic efficacy , as predicted by our mathematical model . | Humankind is engaged in an arms race; one we are in danger of losing ., Since the development and application of the first antibiotics , resistant strains of bacteria have steadily emerged ., As the rate of discovery of new antibiotics slows , the threat increases ., At present , 700 , 000 individuals globally die each year due to antimicrobial resistance and this number is predicted to rise to 10 million per year by 2050 unless fresh action is taken ., It is important , therefore , that we explore alternative treatment strategies to replace or complement traditional antimicrobials ., Here we use mathematical models to explain and predict the effects of a novel anti-adhesion therapy applied to infected burn wounds ., This theoretically resistance-proof therapy operates by impeding bacteria from binding to host cells by blocking the host cell binding sites ., This prevents bacteria from accessing nutrients and renders them susceptible to artificial clearance ., Fitting our model to experimental data , we identify a number of valid parameter sets , and predict the conditions under which treatment will be effective for each set ., These predictions are experimentally testable , and could be used to guide the development and application of anti-adhesion treatments in a clinical setting . | cell binding, antimicrobials, cell physiology, medicine and health sciences, viral transmission and infection, dose prediction methods, drugs, microbiology, mathematical models, bacterial diseases, antibiotic resistance, pharmaceutics, antibiotics, pharmacology, adjustment of dosage at steady state, research and analysis methods, infectious diseases, antimicrobial resistance, mathematical and statistical techniques, host cells, cell biology, virology, microbial control, biology and life sciences | null |
journal.pcbi.1001018 | 2,010 | Axial and Radial Forces of Cross-Bridges Depend on Lattice Spacing | Radial forces are the same order of magnitude as axial forces in contracting muscles 1–3 ., These forces , along with axial force acting in the direction of muscle contraction , depend on myofilament lattice spacing 4 , 5 ., At the same time , structural information about myosin cross-bridges suggests that they generate force by applying torque to a lever arm 6–8 ., This lever arm generates the strain accompanying the power stroke via a change in the rest angle at which the lever is attached to S1 region 8 , 9 ., This change in angle occurs at the converter region , a flexible area in myosin S1 which acts as a torsional spring ., These phenomena may be related: the radial forces a cross-bridge creates are results of the lever arm geometry ( as suggested by Schoenberg 10 ) ., Existing theoretical and computational models of cross-bridge force generation at the level of the half-sarcomere assume that force is generated by a simple extensional linear spring oriented parallel to the long axis of the myofilaments ( Figure 1A ) ., This assumption has persisted from the earliest fundamental models of muscle contraction to more elaborate and spatially explicit models 11–15 ., These single-spring models yielded insight into the processes that regulate production of force in the direction of contraction , parallel to the long axis of the myofilaments ., However , these prior models of muscle contraction have paid less attention to radial forces and the effects of changes in filament lattice spacing ., As a result , geometries of the single spring cross-bridge models have changed little while kinetic schemes governing transitions between conformational states have increased in complexity 11 , 12 , 16 , 17 ., To analyze the radial forces that occur during muscle contraction , a different cross-bridge geometry is needed: a geometry that produces both forces aligned with and forces orthogonal to the long axis of the myofilaments ., A lever arm of several springs can: ( 1 ) simulate the deformations a cross-bridge undergoes as it generates force through the power stroke , ( 2 ) provide a geometry which is practical for use in cross-bridge models , and ( 3 ) account for both axial and radial forces 9 ., Here we detail two models of cross-bridges that use multiple springs to replicate the lever arm mechanism and capture its biologically relevant effects ( Figure 1B–C ) ., Both models are affected by changes in lattice spacing as well as axial offset from binding sites along the thin filament , and both account for the radial component of force produced during the power stroke ., The first model ( referred to as the 4sXB model ) simulates the cross-bridge as a system of four linearly elastic springs arranged in a geometry based upon the structure of the S1 and S2 regions of myosin II ( Figure 1C ) ., Our second model ( referred to as the 2sXB model ) consists of two linearly elastic springs and provides greater computational efficiency than the 4sXB model while replicating many of the more complex models behaviors ( Figure 1B ) ., A prior two spring cross-bridge model was proposed by Schoenberg ( 1980 ) , with the S2 arm represented as an extensional spring and the S2-S1 junction as a torsional spring 10 , 18 ., Both the 4sXB model and the 2sXB model use a three-state model of cross-bridge cycling kinetics , consisting of an unbound state , a low-force pre-power stroke state , and a force-producing post-power stroke state ., The kinetics of transition from one state to another in our models are similar to those used previously but are generalized for use in two dimensions; our kinetics calculate transition probabilities using the free energy landscape of the cross-bridges instead of the offset of the cross-bridge head ( Figure 1D and Figure S1 ) 12 , 14 , 16 , 19 ., We compare the 4sXB and 2sXB models to a single spring model of the cross-bridge ( referred to as the 1sXB model ) , similar to those used previously ., We quantify both the axial and the radial forces of our two cross-bridge models ., Additionally , we show how changes in lattice spacing and axial offset affect kinetics and forces in our multiple-spring models ., At rest lattice spacing , the free energies and kinetics of the of the single- and multi-spring cross-bridge models are largely similar , as seen in Figure 2 ( where the 1sXB values used are calculated as in Figure 10 of Tanner et al . ( 2007 ) 14 ) ., These properties share a common base that is intentionally conserved , where possible , between the multiple-spring and single-spring cross-bridges 16 ., The free energies of the multi-spring cross-bridges are a result of both extensional springs that are at an angle to the thick filament and torsional springs sensitive to the angle they make with the thick filament ., As the multi-spring cross-bridges move in the axial direction , their angles to the thick filament backbone change ., This angle dependence skews the free energies of the multi-spring cross-bridges from the symmetric hyperbola of the 1sXB ( Figure 2A ) ., The two-dimensional diffusion-based binding probability function that governs the multi-spring cross-bridges ( as described in the binding rate calculation section ) causes the likely binding areas to occupy a greater range of axial positions than those of the single-spring cross-bridge ( Figure 2B ) 20 , 21 ., Multi-spring cross-bridges are thus less likely than the 1sXB model to bind near their rest position , but are more likely to bind than the 1sXB at greater offsets from their rest position ., This flattening and spreading of the binding probability function is a result of the extra degrees of freedom of motion in the two-dimensional models ., The power stroke rate constants of the multi-spring cross-bridges are the same as those of the single-spring cross-bridge , with energy-dependent terms using the sum of the free energy of every spring comprising a cross-bridge ( Figure 2C ) ., The detachment rate constant of the 1sXB explicitly relies on cross-bridge head position as well as energy ., This position dependence was removed in adapting the 1sXB models detachment rate constant for the multi-spring cross-bridges ., The detachment rate constant thus loses the intentional asymmetry that the position term provided and retains only the asymmetry created by the spring geometries of the 2sXB and 4sXB models ( Figure 2D ) ., The rate of detachment and the other cross-bridge kinetic rate constants remain close to those of the 1sXB , even though the kinetics of the multi-spring cross-bridges are based not on axial position but on the free energy of the cross-bridge in multiple dimensions ., The axial offset of a cross-bridge property is the axial distance from the point where the cross-bridge attaches to the thick filament to the point where the cross-bridge property reaches an extreme value or inflection point ., These axial offsets are depicted in Figure 3 and Figure S2 where , for example , the axial offset of the 2sXB attachment rate constant at 34 nm is approximately 12 nm ., As lattice spacing increases , the axial offsets of most multi-spring cross-bridge kinetic rates and free energies grows smaller ., This relationship is shown in Figures 3A and B and Figure S2 A and B , where the axial offset of the 4sXB or 2sXB models lowest energy point is more than 3 nm greater at a lattice spacing of 32 nm than at a lattice spacing of 38 nm ., The positions where cross-bridges are most likely to bind shift to smaller axial offsets at larger lattice spacings , decreasing how extended a cross-bridge is likely to be upon binding ( Figures 3C–D and Figure S2C–D ) ., Similarly , as lattice spacing increases , decreases in the axial offset of the power stroke rate constant inflection point cause the size of the power stroke to change with lattice spacing ( Figures 3E–F and Figure S2E–F ) ., The 4sXB models rate of detachment is the only cross-bridge property whose axial offset is predominately invariant with changes in lattice spacing ( Figure 3G and Figure S2G ) ., This exception is explained by the largely radially aligned post-power stroke orientation of , the 4sXB models final spring ., Combined , these effects reduce the axial force a cross-bridge generates at larger lattice spacings with implications for the sarcomere length dependence of force production and relaxation ., These multi-spring cross-bridge models are the first to be capable of reproducing these lattice spacing dependent effects on force production and kinetics ., The number of cross-bridges in a force generating state depends on lattice spacing ., At any axial location , as lattice spacing diverges from its 34 nm rest value , the rate of attachment decreases while the rate of detachment increases ( Figure 3C–D and 3G–H ) ., These kinetic rate constants change with lattice spacing because they depend on the difference in free energy between the unbound state and the pre- or post-power stroke state , a difference which increases with lattice spacing ., This increase in energy makes a cross-bridge increasingly likely to transition to the unbound state and remain there ( Figure 3C–D and 3G–H ) ., An example of the decrease in the likelihood of a cross-bridge remaining bound can be seen in the 4sXB model , where the slowest rate of detachment is 20/sec at a lattice spacing of 34 nm but rises to 260/sec at 38 nm ( Figure 3G ) ., As a result of these changes , individual cross-bridges spend less time in a bound state and are less likely to generate force as lattice spacing diverges from its rest value ., The axial and radial forces at a given axial offset correlate with lattice spacing ( Figures 4 and 5 ) ., When lattice spacing is compressed , more expansive radial forces and smaller axial forces are produced ., When lattice spacing is expanded , more compressive radial forces and larger axial forces are produced ., An example of increased forces with increased lattice spacing is seen in the 4sXB model which , at a 10 nm axial offset , produces half the radial and half the axial force at 35 nm as it does at 38 nm ( Figure 5A–B ) ., Similarly with the 2sXB model at a 12 nm axial offset , a lattice spacing of 35 nm produces two thirds of the axial and radial forces as does a lattice spacing of 38 nm ( Figure 5C–D ) ., At large lattice spacings , this greater force per cross-bridge competes with the decreased probability a cross-bridge will bind and generate force , an interaction that requires a model of the half-sarcomere using multi-spring cross-bridges to fully evaluate 22 ., The force landscapes of Figure 5 also show that no lattice spacing is free of radial force at all axial offsets ., The radial force produced by a cross-bridge , even at rest lattice spacing , increases in magnitude as the cross-bridge tip moves away from its unstrained axial offset ., The step size of both multi-spring models varies with lattice spacing ( Figure 6 ) ., We define step size at a given lattice spacing as the axial distance between the pre- and post-power stroke positions of the myosin head ., Put another way , step size at one lattice spacing is the distance from the axial offset with the lowest free energy in the pre-power stroke state , to the axial offset with the least amount of energy in the post-power stroke state ., Both models have a peak step size at a relatively uncompressed lattice spacing , with decreasing step size as lattice spacing diverges from that value ., The 4sXB model has a maximum step size of 5 . 0nm near 34nm lattice spacing and the 2sXB model has a maximum step size of 6 . 1nm near 36nm lattice spacing ., The radial and axial components of force , produced by a 4sXB model or 2sXB model moved from its rest position to an axial offset , are of the same order of magnitude ( Figures 2E–F and 4A–D ) ., The values of the axial and radial forces produced by the multiple-spring cross-bridge models at rest lattice spacing are compared to those produced by the single-spring cross-bridge model in Figure 2E–F ., The relative values of the radial and axial forces are visualized as the angles of the force vectors in Figure 4A–D ., Axial locations and lattice spacings with balanced axial and radial forces produce force vectors which are neither vertical nor horizontal , but in some intermediate orientation ., Most axial and radial offsets are populated by such vectors , particularly regions a cross-bridge would be most likely to occupy ( unlikely regions are not shown in the vector plots ) ., The few regions dominated by one force , notably some small offset positions in the 2sXB model ( Figure 4D ) , are dominated by radial forces ., This presence of large radial forces suggests that , in all but the least strained locations at the smallest axial offsets , radial forces will be present in magnitudes comparable to those of axial forces ., The lattice spacing of the filaments around an attached multi-spring cross-bridge determine the energy landscape of the cross-bridge and thus the force it can generate ., The forces and strains a cross-bridge produces at most axial offsets grow more positive as lattice spacing increases ( Figure 4E–H ) ., While this increased cross-bridge strain translates into greater axial and radial force per post-power stroke cross-bridge , the probability that these cross-bridges will bind decreases as lattice spacing increases ( Figure 3C–D ) ., The decrease in attachment rate constants at extreme lattice spacings , while power stroke rate constants remain unchanged ( Figure 3E–F ) , suggests lattice spacing influences muscle fiber force generation by altering the rate of cross-bridge attachment rather than the power stroke rate 22 ., Spatially explicit effects in the compliant sarcomere , such as cross-bridge induced realignment of binding sites , may act to balance the decreased binding and increased detachment at larger lattice spacings ., The energies , kinetics , and forces generated by the 2sXB model are subject to the same governing trends as those of the 4sXB model , and can be made similar by deliberate parameter choice ( Table 1 and Figures 2 , 3 , 4 , and 5 ) ., That the 2sXB model can replicate the results of the 4sXB model indicates two things: first , the 2sXB can be used in place of the 4sXB in larger simulations , enabling work that would otherwise require prohibitive resources , and second , a feature shared between our two models is responsible for the interesting properties of our simulations , the use of a lever arm which undergoes an angle change to generate force ., While the energies , binding rate constants , and power stroke rate constants of the multi-spring cross-bridges are almost identical , there are some smaller differences between the two models ., The rate constant of detachment is rotated by approximately 20 between the two systems due to differences in the way the post-power stroke position is achieved ( Figure 3 ) ., The 4sXB model and the 2sXB model generate somewhat different forces; the axial force produced by each model increases with lattice spacing , but that produced by the 4sXB does so more steeply ( Figure 5A , C ) ., In a reversal of this pattern , the 2sXB models radial force is more dependent on lattice spacing ( Figure 5B , D ) ., In each of these cases , the forces generated by both multi-spring cross-bridges are subject to the same trend ., The close agreement between the forces and other properties of the two cross-bridge representations supports the position that the key feature of our multi-spring models is the use of a lever arm to generate force , rather than a factor unique to the 4sXB model , such as the simulation of interaction between the lever arm and the S2 domain ., Substituting the 2sXB model for the 4sXB model reduces the runtime of a simulation by two orders of magnitude and puts multi-spring cross-bridge simulations of the half-sarcomere within reach ., The geometries of the multi-spring models require a change in step size accompany a change in lattice spacing ., This is because , while the length of the lever arm changes as lattice spacing varies , the pre- and post-power stroke angles do not ., Step size varies more in the 4sXB model as the 4sXB models spring configuration causes the pre- and post-power stroke free energies to differ more than in the 2sXB model ., As the detachment rate constant is a product of the post-power stroke free energy , the greater rotation in the 4sXBs post-power stroke free energy , relative to that of the 2sXB model , can be seen in Figure 3 G–H ., Experimental measurements of step size vary , and it has been postulated that this is due to more than experimental error , but to our knowledge these results are the first prediction of a step size that varies with lattice spacing 24 ., Experimental confirmation of these predictions is not possible with current literature: existing in vivo measurements of step size are from isolated myosin preparations which are unable to simulate a change in muscle lattice spacing 25 , 26 ., While our single cross-bridge models lack the predictive power of a multi-filament model , the dependence of step size on lattice spacing offers insight into unloaded shortening velocity ., Maximum unloaded shortening velocity is commonly interpreted as a function of both myosins step size and drag from attached post-power stroke cross-bridges 27 ., A decrease in unloaded shortening velocity is observed when lattice spacing is compressed via dextran 28 , 29 ., This slower unloaded shortening is supported by the multi-spring models: their step size exhibits a similar decrease as lattice spacing shrinks ( Figure 6 ) ., However , a moderate increase in the rate of detachment at highly compressed lattice spacings , seen in Figure 3 G–H , may balance smaller steps sizes ., This increased detachment rate is due to the greater post-power stroke strain that is present with greater radial displacement of the cross-bridge ., Changes in modeled detachment rates and step size are both likely to be needed , along with changes in filament overlap , to explain the complicated dependence of unloaded shortening velocity on sarcomere length 30 ., The 4sXB and the 2sXB produce radial forces of the same order of magnitude as the axial forces generated by a cross-bridge ., These forces range between 10% and 50% of the axial force at the least strained axial and radial offsets where a cross-bridge is most likely to enter the post-power stroke state ( Figure 4 ) ., Muscle fibers display these radial forces by resisting width changes as osmotic pressure is applied 1 ., Direct measurement of lattice spacing by X-ray diffraction has confirmed fiber width estimates of radial force 31 ., Checchi et al . ( 1990 ) 2 observed large radial forces by examining lattice spacing during redevelopment of tension following length changes ., A spatially explicit model , even one using multiple thick and thin filaments arranged in a lattice , is insensitive to lattice spacing if it uses a version of the 1sXB model ., Embedding multi-spring cross-bridges in a multi-filament model allows the simulation of radial force regulation in a lattice of thick and thin filaments ., The inclusion of radial forces in a multi-filament model permits examination of previously unavailable kinds of cooperativity , ones where radial force can be transmitted through the backbone lattice to affect the kinetics of other cross-bridges ., Radial force is a potential regulator of lattice spacing and of sensitivity as lattice spacing and sarcomere length vary 3 ., A multi-filament model using the 4sXB or 2sXB can simulate the interaction of radial force generated by a cross-bridge with radial forces provided by other mechanisms , e . g . titin or electrostatic repulsion 3 , 22 , 32 ., Thus multi-spring cross-bridges make it possible to evaluate the influence of these radial forces , posited to be regulators of lattice spacing , and processes which may depend on lattice spacing or myosin head to thin filament distance , such as the Frank-Starling mechanism; something not possible with a 1sXB model 33 ., In future studies , these models will permit the investigation of radial forces and lattice spacing in multi-filament models , and will allow us to examine disease states that alter myosin compliance ., The inclusion of radial forces and lattice spacing in half-sarcomere models will illuminate regulatory mechanisms of shortening velocity and length-dependent axial force generation ., Other efforts may use existing studies of how disease-related mutations alter myosin compliance to produce disease state mimicking cross-bridge models 34 ., Multi-filament simulations using these altered cross-bridge models have the potential to explain how symptoms of disease states such as hypertrophic cardiomyopathy arise from myosin-level changes ., To describe the kinetics we use a simplified three-state model of the cross-bridge cycle originally described by Pate and Cooke ( 1989 ) 16 and modified by Tanner et al . ( 2007 ) 14 ., This relatively simple scheme directly links the cross-bridges kinetics and mechanics; the three kinetic states are directly comparable to the myosin configurations described in Houdusse ( 2000 ) 36 ., The kinetic rates are independent of the number of springs used in a model cross-bridge , allowing the 4sXB and the 2sXB models to use the same system ., The three states represented in the kinetic scheme are ( 1 ) an unbound state: Myosin- ( 2 ) a loosely-bound state:Actin-Myosin- and ( 3 ) a force-generating post-power stroke state: Actin-Myosin-ADP ( Figure 1D ) ., These kinetics replicate those of a generic cross-bridge , and are aimed at reproducing properties shared between cardiac , skeletal , and insect myosin types ., The kinetics of both the 4sXB and the 2sXB models are strain dependent and are essentially transforms of the free energy landscapes experienced by the cross-bridges in their different states ., These free energies are a function of the distortion necessary to move the point representing the simulated myosin heads tip to the proposed binding site ., Examples of these free energy landscapes are shown in Figure 3A and B , with cuts through them at the rest lattice spacing visible in Figure 2A ., As the free energies of the cross-bridges are functions of their spring rest values and stiffnesses , changing the geometry and stiffness of the springs used by the model also changes the kinetics of the model ., The binding probabilities of both the 4sXB and the 2sXB models are determined by Monte-Carlo simulations of their diffusion as a result of being perturbed by Boltzmann-derived energy distributions 21 ., After a new head location is found , a binding probability is calculated which decreases exponentially with distance from the potential binding site ., This probability is tested against a random number from a uniform distribution to determine if binding occurs in our chosen time step of 1 ms . | Introduction, Results, Discussion, Models | Nearly all mechanochemical models of the cross-bridge treat myosin as a simple linear spring arranged parallel to the contractile filaments ., These single-spring models cannot account for the radial force that muscle generates ( orthogonal to the long axis of the myofilaments ) or the effects of changes in filament lattice spacing ., We describe a more complex myosin cross-bridge model that uses multiple springs to replicate myosins force-generating power stroke and account for the effects of lattice spacing and radial force ., The four springs which comprise this model ( the 4sXB ) correspond to the mechanically relevant portions of myosins structure ., As occurs in vivo , the 4sXBs state-transition kinetics and force-production dynamics vary with lattice spacing ., Additionally , we describe a simpler two-spring cross-bridge ( 2sXB ) model which produces results similar to those of the 4sXB model ., Unlike the 4sXB model , the 2sXB model requires no iterative techniques , making it more computationally efficient ., The rate at which both multi-spring cross-bridges bind and generate force decreases as lattice spacing grows ., The axial force generated by each cross-bridge as it undergoes a power stroke increases as lattice spacing grows ., The radial force that a cross-bridge produces as it undergoes a power stroke varies from expansive to compressive as lattice spacing increases ., Importantly , these results mirror those for intact , contracting muscle force production . | The molecular motor myosin drives the contraction of muscle , but doesnt just produce force in the axis of shortening ., Models of muscle contraction have primarily treated myosin as a simple spring oriented parallel to its direction of movement ., This assumption does not allow prediction of the relationship between the forces produced and the spacing between contractile filaments or of radial forces , perpendicular to the axis of shortening , all of which are observed during muscle contraction ., We develop an alternative model , still computationally efficient enough to be used in simulations of the sarcomere , that incorporates both extensional and torsional ( angle dependent , like those found in a watch ) springs ., Our model captures much of the spacing-dependent kinetics and forces that are missing from single-spring models of the cross-bridge . | biophysics/theory and simulation, computational biology | null |
journal.pcbi.1006975 | 2,019 | A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP | Brain-Derived Neurotrophic Factor ( BDNF ) is a member of the protein family of mammalian neurotrophins , further comprising nerve growth factor , neurotrophin 3 and neurotrophin 4/5 ., Neurotrophins are well known across the animal kingdom to support survival , ontogenetic development , differentiation , and stability of neurons in the entire nervous system1 , 2 ., In the mature nervous system , BDNF , in particular , serves additional roles by regulating functional and structural synaptic plasticity ( reviewed e . g . in 1 , 3–5 ) ., In recent years , a wealth of data has been accumulated on the many roles of BDNF in regulating synaptic plasticity at glutamatergic and GABAergic synapses , e . g . in hippocampus , neocortex , amygdala , and cerebellum , unraveling signaling pathways of unprecedented complexity 1 , 6–10 ., However , despite this well-established role of BDNF as a central activity-dependent mediator ( i . e . switching on biochemical pathways that induce and maintain enhanced synaptic transmission 11–13 ) and modulator ( i . e . , facilitating synaptic changes that are mediated by other signaling pathways 3 , 14–17 ) of synaptic plasticity , the interplay between intra- and extracellular signaling pathways 18 , 19 that regulate and fine-tune BDNF-dependent synaptic changes is not well understood ., The overall picture is rather complex ., BDNF consists of a protein homodimer that is generated exclusively in glutamatergic neurons from two identical peptide chains held together by noncovalent interactions ., The precursor protein , pre-proBDNF , is sequestered into the endoplasmic reticulum , where the pre-sequence is cleaved off , yielding proBDNF ., Intracellularly , proBDNF can be cleaved ( by protein convertases , PCs and furin ) into mature BDNF ( mBDNF ) and BDNF pro-peptide ., All three BDNF species are thought to be assembled into secretory vesicles that are transported to the plasma membrane in soma , dendrites , and axons , where they release their content via Ca2+-dependent exocytosis 20 ., Following secretion , remaining proBDNF can be cleaved by extracellular proteases ( e . g . plasmin and matrix metalloproteinases ) ., This is an important functional step since at this point it is determined whether mBDNF or proBDNF dependent signaling cascades are activated at a synapse ., Because proBDNF and mBDNF activate signaling cascades that partially antagonize each other , the importance of knowing the exact identity of released BDNF can hardly be overestimated ., While mBDNF preferentially binds to the tyrosine-kinase receptor B ( TrkB ) and , among other functions , supports LTP , proBDNF preferably docks to the p75 receptor , which mediates long-term depression ( LTD ) 8 ., The complexity of BDNF control over neuronal growth , plasticity , and modulation , makes it difficult to carry out experimental studies to fully understand BDNF-dependent processes ., Computational modeling can significantly help to untangle the interplay of these processes but , despite the widespread implications of BDNF signaling in structural and functional neuromodulation during normal and pathological physiological conditions , a biologically realistic model of how BDNF signaling instructs these changes is still missing ., Except for a very recent example of a model of a positive BDNF feedback loop , to take into account experiments on inhibitory avoidance training 21 , to the best of our knowledge there are no published models available that address BDNF-dependent pathways ., In this paper , we set out to investigate BDNF-dependent synaptic mechanisms , by implementing the first kinetic model of the central BDNF-dependent subcellular pathways underlying spike timing-dependent Long-Term Potentiation ( t-LTP ) at hippocampal synapses ., For this purpose , we focused on TrkB-dependent processes at hippocampal Schaffer collateral to CA1 pyramidal cell synapses , for which extensive experimental work is available that can be used to constrain the parameter values 11 , 13 ., We show that the model can capture the main experimental findings by using a minimal set of subcellular pathways , with which we can make specific predictions on how to enhance LTP induction in such a way to rescue or improve cognitive functions under pathological conditions ., As a reference for our model , we considered the data from 11 ., In the paper , the authors described t-LTP elicited in hippocampal pyramidal CA1 neurons by repeatedly pairing , with different delays ( Δt ) , a single stimulation of the Schaffer Collaterals with one , two or four postsynaptic action potentials elicited at 200 Hz ., These induction protocols are hereof designated 1:1 , 1:2 , and 1:4 t-LTP ., The paper highlights important properties of the mechanisms underlying t-LTP ., Their main results are summarized in Fig 1A , where the EPSP slope recorded in whole-cell patch clamp mode is plotted as a function of time ., On average , the expression of t-LTP was relatively delayed , and it took approximately 30 min to reach its maximum expression ( Fig 1A , data reproduced from Fig . 1B of 11 ) ., The increase in synaptic strength after both 1:1 and 1:4 t-LTP was graded with time ., Assuming that an individual synapse switches to a potentiated state following an all-or-none change 22 , 23 , this progressive increase in the overall t-LTP observed at the soma suggests a distribution of transition times for different spines , driven by the time course of the processes underlying t-LTP induction and expression ., For both protocols , a Δt>15 ms did not result in a significant t-LTP ( Fig 1B ) , being consistent with other experimental findings 24 , 25 ., As commonly expected , only short positive delays between pre- and postsynaptic stimulation are efficient to produce timing-dependent LTP , while longer delays reduce t-LTP magnitudes ., In the experimental study that forms the basis of our model 11 a significant reduction of t-LTP was observed with positive time delays between 15–25 ms . It should be stressed that there was a rather large variability in the overall potentiation ( i . e . in time course and magnitude ) observed in recordings from individual cells , as demonstrated by the six typical cases of recording from different cells reported in Fig 1C ., As will be discussed later , this finding is important for a better understanding of the interplay among the different processes underlying the induction of plasticity at each synaptic contact ., Additional properties of t-LTP are summarized in Table 1 and suggest that , in all cases , t-LTP induction was found to be postsynaptic and NMDA receptor-dependent ., Instead , expression was found to be pre-synaptic for the 70x 1:1 protocol , post-synaptic for the 25x 1:4 protocol , and mixed for the 50x 1:2 protocol ., The pre- or post-synaptic expression of t-LTP was experimentally determined by, i ) analyzing synaptic responses to short latency ( 50 ms ) paired pulses inducing pre-synaptic short term plasticity ( i . e . paired-pulse facilitation ) ,, ii ) by infusing an inhibitor of AMPA receptor insertion into the postsynaptic membrane via the recording pipette solution ,, iii ) by testing the AMPA/NMDAR current ratio , and, iv ) by using analysis of the coefficient of variation of EPSPs pre- vs . post LTP induction ( see Fig . 2 in 11 ) ., E . g . pre-synaptic 1:1 t-LTP changes the glutamate release probability of release and therefore changes the temporal dynamics of short-term plasticity ., Conversely , the post-synaptically expressed 1:4 t-LTP does not change short-term plasticity , but rather changes postsynaptic AMPA/NMDAR current ratio and depends on incorporation of new AMPA receptors into the postsynaptic membrane ( all respective data shown in Fig . 2 of 11 ) ., Of note , the 1:1 t-LTP protocol was composed of 1 EPSP paired with 1 backpropagating action potential ( bAP ) , whereas the 1:4 t-LTP protocol was composed of 1 EPSP paired with 4 ( instead of 1 ) bAPs ., Thus the 1:1 t-LTP protocol can be considered as being included ( i . e . being a part of ) in the 1:4 t-LTP protocol ., One might thus expect that the mechanisms triggered by the 1:1 t-LTP protocol should also be activated by the 1:4 t-LTP protocol , but this was not experimentally observed 11 ., Other experimental suggestions that could be used to further constrain the model implementation:, ( i ) An increase in postsynaptic intracellular calcium , Ca2+i , was necessary to initiate a complex chain of biochemical reactions leading to the vesicular release of BDNF 3; this process has stochastic dynamics that are ~10 times slower than glutamate release which resulted in a large variability of the time course of postsynaptic BDNF release with respect to the triggering event of a transient Ca2+i elevation 26 ., ( ii ) The postsynaptic BDNF release could last from a few seconds up to approximately 300 s ( 20 , supplementary Fig . 5 in 11 ) ., ( iii ) There is no 1:1 ( i . e . pre-synaptic ) t-LTP expressed following 1:4 t-LTP stimulation 11; this result may imply the existence of an additional mechanism , triggered by the 1:4 t-LTP protocol which is able to block the induction of 1:1 t-LTP ., In summary , these experimental observations form a useful set of properties that give specific indications on what the model must be able to reproduce to be considered a reasonable representation of the many biochemical pathways that can be involved ., In agreement with experimental suggestions 22 , 23 , the model was based on the assumption that any given individual synaptic contact , following the appropriate conditioning protocol , will change its state in an all-or-none manner ., This was an important point to consider in comparing model and experimental findings since experimental recordings are customarily carried out from the soma , whereas the stimulation most likely involved an unknown number of synapses located in a relatively wide range of distances from the soma ., The progressive increase in synaptic potentiation over time may thus be the result of an ensemble dynamics where different synapses undergo potentiation at different times ., Unless explicitly stated otherwise , in discussing the model implementation we will always refer to individual synapses ., The biochemical pathways that we considered for this work are schematically represented in Fig 2A , and it is based on the hypothesis that distinct biochemical pathways are activated by different levels of intracellular Ca2+ in the postsynaptic compartment 27 , 28 ., In our model , there were three different Ca2+i thresholds , θ1 , θ2 , and θ3 ., Ca2+ entry can independently occur through NMDA receptor or voltage-gated Ca2+ channels , both explicitly included in our model and known to drive postsynaptic BDNF secretion 29 ., A transient Ca2+i increase above each threshold activated one or many pathways in the spine head ., The Ca2+i range below θ1 corresponded to the non-plastic regime , i . e . any combination of pre- and/or post-synaptic input did not alter the current state of the pre- and/or post-synaptic mechanisms ., Above θ1 , it activated the 1:1 t-LTP signaling cascade ( Fig 2A , blue boxes ) , which released a yet to be identified retrograde messenger ( RM ) ., This release activated presynaptic processes ( RM proc and presyn proc in Fig 2A ) resulting in a persistent increase in stimulus induced presynaptic glutamate release ( release ) , in agreement with the change in the paired-pulse ratio observed experimentally 11 ., Experimental findings suggest that neither nitric oxide ( NO ) 11 nor endocannabinoids were involved as RM ., The fusion of postsynaptic BDNF vesicles was activated by a larger and more long-lasting Ca2+ transient ( Ca2+i>θ2 ) , which may be obtained with the 70x 1:4 t-LTP protocol ( Fig 2A , dark pink blocks ) ., The largest Ca2+ transients ( Ca2+i>θ3 ) activated biochemical reactions blocking RM production ., The rationale for this choice was that experimental recordings clearly show that the 1:4 t-LTP protocol did not induce presynaptic LTP 11 ., For this to happen , there must be an activity-dependent ( postsynaptic ) process blocking the biochemical pathways leading to presynaptic LTP ., In the model , we made the simple assumption that this process could be a Ca2+-dependent block of the retrograde messenger release , occurring for a Ca2+i threshold ( θ3 ) that is higher than the one for LTP induction ., Other model behaviors were not affected by this assumption , and this scheme left open the possibility , for spines in which Ca2+i reaches an intermediate concentration ( θ2<Ca2+i< θ3 ) , to account for a t-LTP with mixed pre- and post-synaptic mechanisms of expression that is obtained with a 1:2 t-LTP protocol 11 ., The complete set of kinetic equations implementing the model ( introduced in the next paragraph ) were included into the membrane equation for each of the 18 explicitly modeled spines ( see Methods ) ., To roughly take into account the local dendritic temporal integration process , 12 of the 18 spines were distributed on one oblique dendrite ( Fig 2B ) , whereas the remaining 6 spines were distributed on a different dendrite ( see blue bracket in Fig 2B ) and had different values for the θi ( see Table 2 ) ., The presynaptic mechanisms specific for this work were added to the phenomenological model discussed in 31 , and described by the following set of equations:, dxdt=zτrec−USE⋅x⋅δ ( t−tspike ), ( 1 ), dydt=−yτin+USE⋅x⋅δ ( t−tspike ), ( 2 ), z=1−x−y, ( 3 ), where δ ( t ) is a delta function , tspike was the time of arrival of a spike at the pre-synaptic terminal , the variables x , y , and z are the fraction of resources in the recovered , active , and inactive states , respectively , and USE was proportional to the glutamate released by each synaptic stimulation ., They reproduced the stereotypical synaptic response dynamics between pyramidal neurons under physiological conditions ., The values for the presynaptic parameters were those used in Ref ., 31 , with USE0 = 0 . 1 , τrec = 0 . 8 sec , and τin = 3 ms . This presynaptic mechanism has been previously shown to reproduce experimental findings on the normalization of temporal summation of synaptic inputs targeting distal or proximal dendrites of CA1 pyramidal neurons 32 ., USE was additionally modulated by retrograde messenger-dependent pathways described by the following equations:, dRMdt=αRM⋅ ( RM−RMinf ) +αCRM⋅S ( cai , θ1 , σ1 ) ⋅1−S ( cai , θ3 , σ3 ) −αRMp⋅ ( RM−RMinf ) ⋅S ( cai , θRM , σRM ), ( 4 ), dRMpdt=αRMp⋅ ( RM−RMinf ) ⋅S ( cai , θRM , σRM ) −αpp⋅RMp, ( 5 ), dppdt=αpp⋅RMp, ( 6 ), USE=USE0⋅ ( 1+αRMpU⋅S ( pp , θU , σU ) ) ,, ( 7 ), where RMp and pp were presynaptic processes activated in cascade by RM accumulation in the synaptic cleft ( “RM proc” and “presyn proc” in Fig 2A ) , and S ( i , j , k ) =11+e ( j−i ) /k is the typical sigmoidal logistic function ubiquitously observed in biological systems 33 ., Our hypothesis is that the activation of these mechanisms follows a dose-response curve ., These processes are usually implemented with a sigmoid or a Hill function ., Although the latter can be more easily related to the biomolecular pathways it represents , it also implies a significantly higher computational cost ( for NEURON running on a PC we verified a 35% difference in CPU time ) ., This occurs because of the internal representation of the computational algorithms used to calculate an exp ( in a sigmoid function ) or a power ( in the Hill function ) on any given computer ., Since we plan to use this model on a large-scale network , we have preferred to implement these curves with a sigmoid function ., The cai was the intracellular calcium concentration Ca2+i ., Note that pp does not have a decay term ., This ensures that a potentiated synapse does not spontaneously fall back to its non-potentiated state ., It is technically possible to continue to present the induction protocol for infinite time yielding to RM release and consequent infinite growth of pp ., However , this exploratory modeling work does not consider this remote possibility ., Post-synaptic mechanisms are activated by different levels of Ca2+i , with the instantaneous Ca2+ dynamics determined by the complex interaction between AMPAR and NMDAR conductances , voltage-gated Ca2+ channels , and all other active and passive membrane properties ., All the equations regulating the instantaneous Ca2+ dynamics were taken from a previously published CA1 neuron model 30 ( ModelDB a . n . 55035 ) ., We reported here only the equations of the new mechanisms introduced in this work , and directly related to the synaptic transmission pathways using Ca2+i as an input ( see Methods for detail on how to access the full model ) ., The fusion of BDNF-containing vesicle with the spine head membrane is a complex process , possibly involving several biochemical pathways for which there are not enough experimental constraints to build a detailed kinetic scheme ., For this reason , we implemented the effective action of these pathways using two mechanisms , accounting for the dependency of BDNF vesicle fusion probability and delay with respect to the STDP induction protocol ., The first mechanism is implemented with an empirical variable , which we called intracellular signaling ( is ) ., It is based on the experimental findings 11 suggesting that the fusion of BDNF-containing vesicles occurs only for conditioning protocols consisting of at least 25 induction stimuli repeated at a frequency close to 0 . 5 Hz , while no fusion was achieved in response to test stimulations at 0 . 05 Hz ., In the model , this was obtained by increasing is by a fixed amount every time Ca2+i crossed the θ2 threshold , and decreasing it with a time constant of 8 s ., The fusion was allowed to occur only for is>0 . 15 ., With the second mechanism , we took into account the experimental findings ( 20 , supplementary Fig . 5 in 11 ) showing that the fusion of an individual vesicle containing BDNF , when activated , is a stochastic process occurring over a relatively long time window ., We modeled all the involved processes by assuming that the fusion process happened with probability pf ( defined for Ca2+i>θ2 ) , and delay df , calculated as:, pf={Cai−θ2Cai_max−θ2 ( is>0 . 15 ) 0 ( is≤0 . 15 ), ( 8 ), df=300⋅ ( 1−Cai−θ2Cai_max−θ2 ) ⋅rand0 , 1 ,, ( 9 ), and assuming that, Fused_vescicles=f ( pf , df ), ( 10 ), The function f ( pf , df ) keeps track of how many vesicles , in each synapse , have fused with the plasmatic membrane and were in the process of releasing BDNF ., For each synapse , this function is increased by 1 with probability pf after a time interval df from the instant at which Ca2+i crosses the θ2 threshold ., The function is updated , asynchronously for each vesicle , every 1 ms of simulated time , theoretically leading to a minimum interval of 1 ms between the start of a new vesicular release , with a maximum number of available vesicles in each synapse set at 200 , consistent with experimental data 34 ., The function decreases by 1 ( with a minimum value of zero ) every time a vesicle has been fused for 30min ., Random numbers from a uniform distribution in the interval 0–1 were used to choose the values for df , and pf; it should be stressed that this choice should not be considered as parametric randomization but , rather , as a way to introduce into the model an intrinsic stochastic behaviour ., During the time the Ca2+i remains above the θ2 threshold the process leading to the release of a quantal amount of BDNF is active ., In this time window , the fusion process of individual vesicles is initiated with probability pf and results in an actual fusion starting at a random time df ( up to 300 sec ) from activation , in agreement with experimental observations ( 20 , supplementary Fig . 5 in 11 ) ., This also means that for Ca2+i remaining for a prolonged time above threshold , more fusion processes are started ., Once a vesicle has fused with the membrane , it continuously releases a fraction of the stored mBDNF and proBDNF for some time ., The experimental evidence for this process is indirect , and it suggests a lower and an upper bound for the overall process: the release lasts for at least 5 min 29 , but the overall LTP induction proceeds for approximately 30 min 11 ., We made the somewhat simplifying and minimal assumption that the BDNF release lasts for 30 min ., However , if this assumption would be invalidated by new experimental data , for example with longer experimental recordings of the BDNF release from single vesicles , the model could be straightforwardly revised by including an additional variable activated by a short BDNF release and slowly decaying over a period of 30 min ., In any case , it is important to stress that in order to be consistent with the available experimental findings , the process modulating the magnitude of induced LTP must have a time course of approximately 30 min ., The ratio between mBDNF and proBDNF inside these vesicles is unknown ., Indirect experimental evidence 35 , 36 indicate for the mBDNF:proBDNF proportion a value in the range of 10% to 90% ., This ratio also depends on the pH inside the vesicle 20 , 37 ., Since in our mouse brain slices we detected ~66% mBDNF vs . ~33% proBDNF in cell lysates , we used a 70%:30% proportion of mBDNF and proBDNF , respectively ., In the Golgi apparatus and in BDNF-containing vesicles proBDNF can be cleaved by protein convertases ( PC ) into mBDNF and BDNF pro-peptide ., Following the release , remaining proBDNF can be cleaved by plasmin or matrixmetallo proteinases 20 ., To empirically model extrasynaptic diffusion and reuptake 38 , 39 , mBDNF , proBDNF , and PC were all assumed to decay at a constant rate αdiff ., The overall level of mBDNF present in the synaptic cleft determined the extent of TrkB receptor activation , Through a chain of postsynaptic processes represented by postsyn in Fig 2A , TrkB induces t-LTP by increasing the AMPA receptor conductance 11 ., We implemented these processes as:, dproBDNFdt=αfuse⋅0 . 3⋅Fused_vesicles⋅v_BDNF−αPC⋅PC⋅proBDNF−αdiff⋅proBDNF, ( 11 ), dmBDNFdt=αfuse⋅0 . 7⋅Fused_vesicles⋅v_BDNF+αPC⋅PC⋅proBDNF−αdiff⋅mBDNF, ( 12 ), dPCdt=αfuse⋅Fused_vesicles⋅v_PC−αdiff⋅PC, ( 13 ), TrkB=mBDNF⋅S ( mBDNF , θTrkB , σTrkB ), ( 14 ), dpostdt=αpost⋅TrkB, ( 15 ), gAMPA=gmax⋅1+αAMPA⋅S ( post , θAMPA , σAMPA ) ,, ( 16 ), where gAMPA is the peak AMPA conductance , gmax its maximum value before LTP , and post represents the long-term effects of TrkB-dependent processes on the overall AMPA conductance ., Note that post does not have a decay term ., This ensures that a potentiated synapse does not spontaneously fall back to its non-potentiated state ., It is technically possible to continue to present the induction protocol for infinite time yielding to BDNF release and consequent infinite growth of post ., However , this exploratory modeling work does not consider this remote possibility ., The overall model was too complex to attempt an automatic fitting procedure , especially considering that there were not enough clear experimental constraints to reduce the number of free parameters ., For this reason , the parameters were set in two steps: 1 ) for each block shown in Fig 2A , an initial estimate for the involved parameters was obtained by presenting inputs that mimic the signals that could be generated in the full model , and manually adjusting the values to obtain what we considered a reasonable output signal; 2 ) test simulations of the full model were carried out with all spines placed on the dendrites ., In this latter step , which can take into account the non-linear interaction between a spine and a backpropagating action potential , the parameters were further adjusted in such a way to result in an overall LTP level consistent with the experimental findings shown in Fig 1A ., It is important to stress that the key point in this paper was not to explore the parameter space or to find their best values but to study if , how , and to what extent , the proposed scheme was able to take into account the basic experimental findings on BDNF-dependent spike-time-dependent LTP ., As mentioned when discussing Fig 2 , we explicitly modeled eighteen independent spines , each containing the mechanisms described above with the parameters reported in Table 2 ., To introduce the physiological variability of the biochemical pathway dynamics in the model , the αpp value in each synapse was drawn from a random uniform distribution ., The number of synapses was not important for the scope of the paper ., We found it a convenient number to illustrate and demonstrate that the overall effect measured at the soma was the result of a number of independent synapses ., The key point here is that , as we will discuss later , the experimental findings cannot be reproduced by modeling a single synapse or a group of identical synapses ., It should also be noted that there are many sources of noise that could affect the model behaviour ., For example , random background synaptic activity could jitter the interaction between the elicited EPSPs and the bAPs ., However , due to the large number of stimuli repetitions and the slow processes that they activate , this contributed to the overall behaviour in a way similar to the random localisation of the spines ., The same would be with variability in the morphological and/or electrophysiological spine parameters ., The table shows only the model parameters introduced in this work ., All model files and the Python scripts used to run the simulations described in the paper are available for public download under the ModelDB section of the Senselab database ( http://senselab . med . yale . edu , a . n . HYPERLINK http://modeldb . yale . edu/244412 244412 ) ., In summary , we have introduced a biophysical model of spike timing-dependent LTP at the Schaffer collateral synapse s of hippocampal CA1 pyramidal neurons ., The model took explicitly into account , for the first time , several experimental findings on the BDNF-dependent biochemical pathways ., In Fig 3A , we plotted the membrane potential at a spine head during a conditioning stimulus in which a synaptic activation ( arrow ) was paired with a bAP elicited with a Δt = +5 or +50 ms . The same time course was typically observed at all synapses ., Note that for Δt = +50 ms ( Fig 3A , thin grey trace ) the synaptic activation and the bAP could be considered as completely separate events ., In this case , the maximum voltage deflection observed in the spine head was approximately 22 mV during the EPSP alone and 17 mV for the bAP ., With a Δt = +5 ms , the two events overlapped and summed nonlinearly , with a maximum deflection of 66 mV ., The nonlinear summation of an EPSP paired with a properly timed bAP has been experimentally observed 40 , and in our model was a key factor in inducing LTP ., It can be explained by considering that the depolarization caused by the synaptic activation has the effect of inactivating the KA channels , allowing a bAP arriving within a relatively narrow time window to better propagate in the dendrite and the spine ., The resulting depolarization released the NMDA receptor Mg-block and allowed a supralinear Ca2+ influx ., The Ca2+i time course , recorded in the 12 spines distributed along one of the oblique dendritic branches during a synchronous activation of all synapses , is shown in Fig 3B ( colored lines correspond to different spines ) ., For comparison , we also plotted Ca2+i in a single spine for Δt = +50 ms ( grey trace ) ., In all 12 spines of the dendritic segment shown at higher magnification in Fig 2B , the Ca2+i transiently raised above the θ1 threshold for a Δt = +5 ms , whereas none of the spines in the other branch ( compare blue bracket in Fig 2B ) reached the θ1 threshold ( remaining coloured transients in Fig 3B ) ., The Ca2+i transient was significantly different among spines ., This occurred because the back-propagation of an AP depends on the local dendritic properties ., Since the RM release is proportional to the amount of Ca2+i above the θ1 threshold , the spines with larger Ca2+i transients ( e . g . bright green and red traces in Fig 3B ) were able to accumulate in a shorter time the amount of RM required to activate the pre-synaptic mechanisms of plasticity ., This resulted in t-LTP induction ( in terms of an increase in the glutamate release ) earlier than in other spines ( Fig 3C dark green and brown traces ) ., Spines for which there was a higher release of RM were potentiated earlier and with a faster transition ( bright green trace ) ; spines with lower RM release switched to a potentiated state later and with a slower transition ( e . g . dark green trace ) ., Two spines did not release a sufficient amount of RM to trigger potentiation ( Fig 3B cyan and thick grey ) ., In only one spine the Ca2+i transients crossed also the θ2 threshold triggering the postsynaptic potentiation mechanisms with a time course depending on TrkB activation ( Fig 3D bright green trace ) ., In agreement with the experiments ( Fig 1A ) , it took around 25 min after the induction protocol to switch all synapses to a potentiated state ., In the model , we assumed that this could be caused by the slow time constants of the biochemical pathways involved with the retrograde messenger ( see αpp in Table 2 ) ., As expected , since the 1:1 t-LTP protocol was in general not able to generate enough Ca2+ entry to cross the θ2 threshold , the AMPA conductance , which was modulated by the post-synaptic plasticity mechanisms , did not increase for all but one of the synapses ( Fig 3D ) ., Taken together these results suggest that , in order to be consistent with the experimental findings , it was necessary to make the physiologically reasonable assumption that the RM-dependent mechanisms needed to generate a different response at each synaptic location , which is a physiologically plausible condition ., This was an important issue that is usually not considered in implementing subcellular models for synaptic transmission ., Alternatively , it is possible that long time constants in downstream processes ( not explicitly modeled here ) , such as the incorporation of new glutamate-containing vesicles into the readily releasable pool , are responsible for the approximately 25 min delay in completing the induction of synaptic potentiation 41 , 42 ., Pairing one synaptic stimulation with four bAPs ( Fig 4A ) resulted in Ca2+i transients spanning a range covering all thresholds ( Fig 4B both panels ) ., For 2 of the 12 spines in one branch , the Ca2+i transient crossed only the θ1 threshold , resulting in a pre-synaptic t-LTP induction ( Fig 4C , cyan and thick grey traces ) ., In other 2 synapses ( Fig 4B left panel , light brown and dark green traces ) it was θ2<Ca2+i<θ3 ., This indicated the activation of both pre- and post-synaptic mechanisms ., For the other 10 synapses , the Ca2+i crossed also the θ3 threshold , eliciting the activation of all the post-synaptic ( but not presynaptic ) pathways , with a consequent long-term potentiation of the AMPA peak conductance ( Fig 4D ) ., For the 6 spines in the other dendritic branch , Ca2+i crossed the θ3 threshold in all spines , but only two spines were potentiated ( Fig 4D , yellow and magenta traces ) , while the other 4 spines were not because they did not release and accumulate enough BDNF in the cleft to activate the downstream signaling cascade ., This behaviour allowed us to point out a suggestion of our model that will turn out to be extremely important later: BDNF release was necessary but not sufficient to trigger postsynaptic t-LTP ., The model suggested that BDNF must accumulate in the synaptic cleft up to an amount sufficient to activate TrkB receptors , i . e . the release must be sufficiently frequent and strong ., During the 25 stimulus repetitions , this condition was achieved for only two of the 6 spines ( Fig 4D , yellow and | Introduction, Results, Discussion, Methods | Across the mammalian nervous system , neurotrophins control synaptic plasticity , neuromodulation , and neuronal growth ., The neurotrophin Brain-Derived Neurotrophic Factor ( BDNF ) is known to promote structural and functional synaptic plasticity in the hippocampus , the cerebral cortex , and many other brain areas ., In recent years , a wealth of data has been accumulated revealing the paramount importance of BDNF for neuronal function ., BDNF signaling gives rise to multiple complex signaling pathways that mediate neuronal survival and differentiation during development , and formation of new memories ., These different roles of BDNF for neuronal function have essential consequences if BDNF signaling in the brain is reduced ., Thus , BDNF knock-out mice or mice that are deficient in BDNF receptor signaling via TrkB and p75 receptors show deficits in neuronal development , synaptic plasticity , and memory formation ., Accordingly , BDNF signaling dysfunctions are associated with many neurological and neurodegenerative conditions including Alzheimer’s and Huntington’s disease ., However , despite the widespread implications of BDNF-dependent signaling in synaptic plasticity in healthy and pathological conditions , the interplay of the involved different biochemical pathways at the synaptic level remained mostly unknown ., In this paper , we investigated the role of BDNF/TrkB signaling in spike-timing dependent plasticity ( STDP ) in rodent hippocampus CA1 pyramidal cells , by implementing the first subcellular model of BDNF regulated , spike timing-dependent long-term potentiation ( t-LTP ) ., The model is based on previously published experimental findings on STDP and accounts for the observed magnitude , time course , stimulation pattern and BDNF-dependence of t-LTP ., It allows interpreting the main experimental findings concerning specific biomolecular processes , and it can be expanded to take into account more detailed biochemical reactions ., The results point out a few predictions on how to enhance LTP induction in such a way to rescue or improve cognitive functions under pathological conditions . | Storing memory traces in the brain is essential for learning and memory formation , and it occurs through synaptic plasticity processes ., Timing-dependent Long-Term Potentiation ( t-LTP ) is a physiologically relevant type of synaptic plasticity that results from the repeated sequential firing of action potentials ( APs ) in pre- and postsynaptic neurons ., T-LTP is observed during learning in vivo and is a cellular correlate of memory formation ., T-LTP can be elicited by different patterns of combined pre- and postsynaptic activity that recruit distinct synaptic growth processes underlying t-LTP ., The protein Brain-Derived Neurotrophic Factor ( BDNF ) is released at synapses and mediates synaptic plasticity in response to specific patterns of t-LTP stimulation in the theta frequency band , while other patterns mediate BDNF-independent t-LTP ., Here , we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP ( 70 repeats of pairing 1 presynaptic with 1 postsynaptic AP ) and BDNF-dependent 1:4 t-LTP ( 25 repeats of pairing 1 presynaptic with 4 postsynaptic APs ) ., The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover ., Since BDNF levels are decreased in demented patients , understanding the function of BDNF in memory processes is important to counteract neurodegenerative diseases . | medicine and health sciences, action potentials, neurochemistry, vesicles, nervous system, membrane potential, electrophysiology, neuroscience, synaptic plasticity, neurotransmitters, cellular structures and organelles, neuronal dendrites, excitatory postsynaptic potentials, developmental neuroscience, animal cells, glutamate, biochemistry, cellular neuroscience, anatomy, synapses, cell biology, physiology, neurons, biology and life sciences, cellular types, neurophysiology | null |
journal.pcbi.1006458 | 2,018 | Assessment of mutation probabilities of KRAS G12 missense mutants and their long-timescale dynamics by atomistic molecular simulations and Markov state modeling | The small GTPase protein KRAS is a signal-transducing protein , which binds GDP in its inactive state and GTP in its active state 1 ., The gene KRAS is frequently mutated in various human cancers ., The mutation is most often , in about 86% of the cases 2 , found at G12 ., In fact , every missense mutation at G12 ( G12X ) is oncogenic ., The oncogenic properties associated with KRAS G12X mutation are characterized by the deficiency of the intrinsic GTPase activity and the insensitivity for GTPase-activating proteins ( GAPs ) 3 , 4 ., These alterations lead to increased KRAS signaling , as there is more active GTP-bound protein present ., Still , the mutant KRAS undergoes GDP–GTP cycling 5 ., The basis of the specific G12X mutation frequencies has remained unclear , except for the G12C transversion mutation ( c . 34G>T ) associated with smoking in lung cancer 6 , 7 ., An interesting discrepancy among KRAS G12X mutants is observed in their intrinsic GTPase activity 8 ., The G12A mutation exhibits the most hindered intrinsic hydrolysis ( ~1% compared to the wild-type ) , whereas the G12C mutation displays the least hindered activity ( ~72% ) ., All G12X mutants , however , show insensitivity to GAPs that accelerate hydrolysis 8 ., Importantly , not only RAS G12X mutants exhibit a discrepancy in GTP hydrolysis , but they also give rise to differences in the preferred signaling pathway ( in terms of effector protein binding ) 9 , 10 ., This behavior was first observed in NSCLC cell lines 9 , where KRAS G12D showed activation of PI3K and MEK signaling , while G12C and G12V mutants exhibited activated RalGDS-pathway and diminished growth factor-dependent Akt activation ., Furthermore , an NMR study revealed different binding preferences for mutant HRAS G12V compared to wild-type HRAS , with various effector proteins 10 ., Here , HRAS G12V showed reduced interactions with Raf and enhanced binding with RalGDS ., However , given that the non-hydrolysable GTP-analog GNP was used in the study , the difference is not due to impaired hydrolysis ., Similarly with HRAS , KRAS G12X mutants exhibit reduced affinity to Raf compared to wild-type 8 ., The G12D , G12R , and G12V mutants display highly reduced affinity to Raf , while the affinity of G12A is only moderately reduced ., Interestingly , the affinity of the G12C mutant is similar to that of wild-type ., To bind RAS , the effector proteins use a ubiquitin ( UB ) -like fold: a RAS-binding domain ( RBD ) or a RAS-association domain ( RA ) 11 , 12 ., While KRAS has not been co-crystallized with any of its effector proteins , distinct effector proteins have been resolved in complex with HRAS: RalGDS ( PDB ID: 4G0N ) 13 , Raf-1 ( PDB ID: 1LFD ) 14 , PI3Kγ ( PDB ID: 1HE8 ) 15 , PLCε ( PDB ID: 2CL5 ) 16 , RASSF5 ( PDB ID: 3DDC ) 17 and AF-6 ( PDB ID: 6AMB ) 18 ., These effector proteins bind to HRAS on top of its switch regions: switch-I ( residues 30–40 ) and switch-II ( residues 58–72 ) , and the binding conformation of HRAS is almost identical in all of the complexes ( S1A Fig ) ., Given this , and since the G12X mutation is far from the binding interface ( S1B Fig ) , Smith and Ikura 10 proposed that the discrepancies in the effector protein binding profiles of the mutants are due to altered switch dynamics ., Overall , switch-I displays highly dynamic characteristics manifested as two different states when GTP is bound to RAS , and the distribution between these states is altered in mutants 19–22 ., Given that the switch regions in HRAS and KRAS are identical ( S1C Fig ) , their expected binding mode to their effectors is alike ., A model of KRAS in complex with A-Raf-RBD tethered to a lipid-bilayer nanodisc suggested by NMR data agrees with this binding mode 23 ., At the cellular level , the isoform specificity to effector proteins is primarily determined via membrane interactions 24 , but the differences among RAS isoforms’ absolute effector protein binding affinities rise from allosteric effects 25 ., It was observed that even a single point mutation in RAS ( Q61L ) has long-range effects on dynamics and alters effector protein interactions 13 ., Previous molecular dynamics ( MD ) simulation studies of KRAS at microsecond timescales have mainly focused on the dynamical differences between the three wild-type RAS isoforms ( HRAS , KRAS , NRAS ) 26 , differences among selected KRAS and HRAS mutants 27 , 28 , the role of the hypervariable region ( HVR ) 29 , KRAS’s membrane association or orientation 30–32 , and KRAS oligomerization on the membrane 33 ., The total simulation times of these studies were in the range of 1–8 μs , which is reasonable but likely not sufficient to unravel long-time dynamics associated with slow conformational changes ., More importantly , there is a lack of comprehensive atomistic MD simulations of all KRAS G12X mutants with extensive simulation times , allowing a reliable analysis for the differences in structure and dynamical behavior between the wild-type and the mutants , especially in the effector protein binding interface ., What is the underlying cause for the broad range of different G12X mutations ?, How do these distinctly different mutations manifest themselves in the structure , dynamics , and function of KRAS ?, This knowledge is crucial to understand KRAS oncogenesis and to develop future therapies targeting mutant KRAS harboring tumors ., Therefore , in the present study we first assessed to what extent G12X mutation frequencies are explained by mutation probability ., Intriguingly , an outstanding mutational bias emerged from the data ., We next employed state-of-the-art atomistic MD simulations ( total simulation time 170 μs ) to study the dynamical behavior of KRAS with its natural ligands ( GDP , GTP ) bound , both in the wild-type KRAS and with all existing oncogenic G12X mutations ., The results provided compelling evidence that mutations alter the dynamics of KRAS , that the alteration is mutation specific , displays allosteric characteristics , and that the alteration is manifested especially in the effector protein-binding interface ., Furthermore , our data suggest that the observed mutational bias and the oncogenic properties of the individual KRAS G12X mutants are caused , at least in part , by mutation-specific altered dynamics ., First , to perceive up-to-date data of KRAS G12X missense mutation frequencies , we compiled data from the Catalogue of Somatic Mutations in Cancer 2 ., A total of 32 , 654 tumor samples identified with a KRAS G12X missense mutation were found from the database ., For our analysis , we included only tissues that exhibited these mutations >10% ., This status is displayed in eight tissue types , which in total comprised 31 , 251 positive samples ( 95% of all KRAS G12X mutations in the database ) ., The large intestine ( 18 , 174 ) , the lung ( 5 , 640 ) , and the pancreas ( 5 , 528 ) were observed to have numerous positive samples , whereas the biliary tract , the endometrium , the ovary , the peritoneum , and the small intestine comprised altogether only 2 , 085 positive samples ., A point mutation in KRAS G12X may result in one of six possible missense mutations ( Fig 1J ) ., However , instead of being evenly distributed , these specific mutations display considerable variation ( Fig 1A ) ., Overall , G12D ( 42% ) , G12V ( 28% ) , and G12C ( 14% ) mutations are very common , whereas G12A , G12R , and G12S are less popular ., When the relative fractions of these mutations are considered in different tissues , they are readily observed to vary significantly ( Fig 1B–1I ) 2 ., For instance , the G12R mutation is observed in the pancreas with a probability of 13% , while in the small intestine it appears in less than 2% of the cases ., The predominating mutations are G12D and G12V , the lung being an exception with G12C standing as the most abundant mutation ., In a G12X missense mutation , the guanine ( G ) base in c . 34G or c . 35G is substituted to adenine ( A ) , cytosine ( C ) , or thymine ( T ) ( Fig 1J ) ., This base-substitution type exhibits variation ( Fig 1K ) ., G>A and G>T mutations ( 47 . 4% and 42 . 1% , respectively ) occur very often , while the G>C mutation ( 10 . 5% ) takes place more seldom ., These occurrences display some variation in different tissues ., Particularly the lung differs from other tissues with a higher G>T fraction and a diminished proportion of G>A mutations ( P < 0 . 001 ) ., Meanwhile , in the pancreas the probability of the G>C mutation is increased ( P < 0 . 001 ) ., Moreover , as all of the G12X mutations occur in the first or the second guanine of the codon ( c . 34G , c . 35G ) ( Fig 1J ) , we ascertained if there is a mutational bias between these positions ., Interestingly , 76 . 6% of the G12X mutations are c . 35G>X mutations ( G12A , G12D , G12V ) and only 23 . 4% are c . 34G>X mutations ( G12C , G12R , G12S ) ( Fig 1L ) ., In fact , all tissues , except for the lung ( 55 . 3% ) , display 77–90% of c . 35G>X mutations ., The positional mutation preference for c . 35G>X seems to be the highest with a G>A mutation ( >7x ) , whereas G>C or G>T exhibit nearly twofold preference , 1 . 75- and 2 . 03-fold , respectively ( Fig 1M ) ., A few exceptions in the c . 35G>X preference , however , appear in specific tissues ., In the pancreas , the G>C mutation occurs nine times more often in c . 34 than in c . 35 ( Fig 1O ) ., As for the G>T mutations , the lung is the only tissue where c . 34 is preferred ( >1 . 5x ) ( Fig 1P ) ., All tissues , interestingly , exhibit over fivefold c . 35 preference in G>A mutations ( Fig 1N ) ., Above all , the pancreas ( >28x ) , the peritoneum ( >37x ) and the ovary ( >41x ) exhibit the most prominent preference for the G>A mutation in c . 35 ., We evaluated how random the occurrences of the specific G12X mutations are ., To this end , we used the transition:transversion mutation ratio as a figure of merit , and compared this figure to a value of 2 . 3 , which is the ratio for missense mutations observed in large-scale genomic analyses 34 , 35 ., If the mutations would take place randomly , G12D and G12S mutations should be the most abundant mutations as they are transition mutations ( S2 Fig ) ., G12D mutation is consistent with this view , as it occurs very often in all tissues ., Meanwhile , G12S is not consistent with this behavior at all , as it occurs in tumors , perhaps surprisingly , very rarely ., Also , regardless of the tissue type , the G12V mutation is overexpressed compared to values expected based on the assumption of random occurrences ., Concluding , the mutations’ probabilities of occurrences are not consistent with a transition:transversion mutation ratio based on a random process ., Since local DNA-sequences have clearly a major influence on the mutation probability , a sequence-dependent basis for the observed non-random mutations may exist ., The TGGT sequence lacks a typical hotspot mutation region , such as a CpG site 36 ., However , an adjacent GG region is a susceptible site for a mutation 37 , 38 ., The oxidation of guanine by endogenous reactive oxygen species may also result in DNA mutation 39 ., Both guanines , the 5’G and the 3’G in a GGT-sequence , are found to act as sites for frequent one-electron oxidation reactions , and they exhibit only a minor difference ( 0 . 05 eV ) in their vertical ionization potential 37 ., The oxidation can further transform guanine to 7 , 8-dihydro-8-oxoguanine ( 8oxoG ) , which promotes especially the G>T transversion mutation 40 , and the G>T mutations take place on a regular basis ( Fig 1K ) ., Interestingly , studies of DNA-adduct formation by exogenous agents have resulted in adduct formation only in c . 34G , and not in c . 35G 41 , 42 ., Finally , cigarette smoking promotes G12C mutations exhibited regularly in the lung tissue ( Fig 1D and 1K ) 7 ., Concluding , there are several potential mechanisms able to alter the mutation profile of guanine , thereby leading to the data we discussed above ., To understand how G12X mutations affect KRAS functionality , we conducted a total of 170 μs ( 85 x 2 μs ) atomistic MD simulations of wild-type KRAS and all G12X missense mutants , with GDP and GTP ., Each individual system was replicated five to ten times starting from different initial conditions ( S1 Table ) ., In the simulations , we observed no differences in the dynamics of the residue 12 ( or in its vicinity ) , which appeared to be extremely stable ., In contrast , the switch regions ( switch-I and switch-II ) exhibited highly dynamic behavior demonstrated by the root-mean-square fluctuation ( RMSF ) analysis , which revealed major fluctuations in the protein in these regions ( S3 and S4 Figs ) ., Nevertheless , there were no evident differences between the different systems , as generally the individual replicas displayed variation as much as the different systems ., Only with GDP , the G12A and the G12S display a different RMSF profile in the switch-I region ., To gain better insight into the protein dynamics , we conducted principal component analysis ( PCA ) 43 with an objective to find the most significant large-scale motions of KRAS ., PCA revealed that the greatest dynamic movements in the protein occur in the switch regions ( Fig 2 , S5A Fig ) ., Furthermore , the most significant principal components 1–3 ( PC1-3 , see S5B Fig for contributions ) highlight that there are strong differences between GDP-bound and GTP-bound systems ., PC1 of the GTP-bound systems displayed movement only in the switch regions , whereas PC1 of the GDP-bound systems exhibited additional movement also in the α3-helix ., PC2 of GDP-bound KRAS revealed the movement in the switch regions and also extensive motion in the α3-helix , the hairpin loop between the β2- and β3-sheets , and the P-loop ( see S1D Fig ) ., PC2 of GTP-bound KRAS in turn brought out the movements observed in GDP-bound systems , and further also the motion in the α1- and α4-helices , and in the turn near the SAK-motif ., These observations indicate that the key to resolve the changes in protein dynamics is the γ-phosphate ., Notably , the α4-helix motion is only observed with GTP bound systems ( PC2 ) ., This observation is in agreement with the experimental results by Mazhab-Jafari et al . 23 ., They observed that the GDP-bound KRAS drives the protein in the “exposed” configuration on the membrane , where the α4-helix is located in close proximity to the membrane ( PDB ID: 2MSC ) ., This would indicate that the dynamical stability of the helix is important for this state ., In order to ascertain dissimilarities between the different systems , we next generated score plots for the principal components PC1-3 ( Fig 3 , S5C and S5D Fig ) ., The results highlight dissimilarities between the wild-type KRAS and the mutants , as well as between GDP- and GTP-bound proteins ., Interestingly , in all of these systems , only the G12R and G12S mutants with GDP appear to reside in the closed switch-I conformation , whereas all other systems eluded this conformation ., Even more interestingly , both of these mutants evaded this conformation when they were bound to GTP ., The fully open conformation of switch-I appears to be more accessible to the systems , especially with the G12D mutant with GDP ., Moreover , wild-type in both GDP- and GTP-bound systems seems to have a unique state with a high-scoring value ( +3 and +4 ) in PC1 and a low-scoring value ( -2 and -0 . 5 ) in PC2 ., Taken together , the results show for all the mutants that the profile of their large-scale motions differs from wild-type regardless of the bound ligand , and that the profile is also unique to each mutant ., We extended the analysis by carrying out PCA for each system to illustrate the differences in their dynamics ( S6–S9 Figs ) ., The individual PCA analyses highlight not just variation in the switch region movements among the systems , but they also show that specific systems display more dynamical behavior in the α3-helix , hairpin loop between the β2- and β3-sheets , the α4-helix , the loop between β5-sheet and α4-helix , and in the SAK-motif ( residues 145–147 ) regions ., For example , in GTP-bound systems only the G12A , G12D , G12R , and G12V mutants exhibited movement in the α4-helix in their PC1 or PC2 ., Interestingly , these are also the systems that exhibit clearly diminished Raf affinity 8 ., Also , the G12R mutant with GTP displayed notably reduced movement in the switch-II region in both PC1 and PC2 ., Even though there are no additional direct interactions from the mutated side-chains of G12X , a mutation in this position inflicts a change to the dynamics in the distant sites of KRAS that were highlighted by the PCA analysis ., To investigate this , and to identify possible interaction network routes in KRAS , we conducted an interaction network analysis 44 ., Interestingly , we identified a hydrophobic hub network in KRAS that indeed connects the distant sites in the structure and is able to convey these effects in an allosteric way ( Fig 4 ) ., Therefore , a change in KRAS dynamics in one of the hydrophobic hubs could traverse through this network even to the distant sites ., This hub network is comprised of 11 hubs: V14 , M72 , F78 , L79 , F90 , I100 , V114 , A146 , A155 , F156 and L159 ., One of these hubs , V14 , is located in the P-loop , in the close proximity of G12X ., This hub interaction network is highly distorted in G12A and G12S mutants ( S10 and S11 Figs ) ., The distortion in these mutants is further not ligand dependent ., For example , in the V14 hub , the G12A and G12S mutants lack the interaction to A81 ( <2% vs . wild-type 26 . 9% and 39 . 7% , with GDP and GTP , respectively ) , and also display highly diminished interaction to A11 compared to the wild-type KRAS ( S10A Fig ) ., From the V114 hub , these mutants lack interactions to A155 and L79 , but instead have a strengthened interaction to I142 and a totally new interaction to L113 , which is not displayed by other systems ( S11A Fig ) ., From the hub A146 , they lack the interactions to A18 and L19 ( S11B Fig ) ., From the hub A155 , both lack the interactions to V114 , L79 , and I142 , but instead they have an elevated interaction to F156 , and the G12A has an additional interaction to V152 ( S11C Fig ) ., In contrast to G12A and G12S , the other mutants ( G12C , G12D , G12R , and G12V ) seem to follow more closely the wild-type’s interaction patterns ., However , selected interactions are shifted even with these mutants , although not that extensively as observed with G12A and G12S ., For instance , in the hub M72 located in the switch-II region the interaction patterns are shifted with G12C , G12R , and G12V ( S10B Fig ) ., Interestingly , the GTP-bound G12D mutant displays almost identical interactions with the wild-type ., We also noticed that the frequency of the salt-bridge between the residues D154–R161 was altered in different systems ( S11F Fig ) ., Both of these residues are located in the α5-helix , in the close proximity of three hydrophobic hubs: A155 , F156 and L159 ( Fig 4 ) ., With the wild-type KRAS this salt-bridge is more stable with GDP ( 69 . 1% ) than with GTP ( 46 . 4% ) ., Meanwhile , again with the G12A and G12S mutants this salt-bridge is highly distorted ( 4 . 5%–20 . 6% ) , regardless of the bound ligand ., As discussed above , the PCA and the interaction network analysis suggest that the protein dynamics is altered among the systems , yet in some obscure manner ., To gain better insight into the origin of these differences in wild-type and mutant GTP-bound systems ( active KRAS ) , we analyzed the simulation data by constructing Markov state models ( MSMs ) 45 , 46 to explore the long-time statistical conformational dynamics of KRAS ., The goal here was to identify the clusters of highly identical protein conformational states , here called metastable states , and to explore how the conformations of the wild-type and the mutant proteins are distributed between these metastable states ., For the analysis , we selected the wild-type KRAS together with the most abundant mutants G12D and G12V , and the intriguing G12R mutant , which displays a highly variable distribution in the different tissues ( Fig 1 ) ., The MSM analysis identified seven metastable states represented schematically in Fig 5 ., Overall , all systems populate frequently two of the states: the states VI and VII ( 77–87% ) ., The less populated metastable states I-IV are specifically represented among the systems ., The metastable state I is quite unique for G12R ( 6% ) and the state III for G12V ( 6% ) , whereas the other systems are mostly absent from these two states ., The metastable state IV is only present in wild-type ( 3% ) and in the G12V mutant ( 3% ) ., The moderately populated metastable state V , where switch-II appears in a fixed conformation , is rarely observed with the G12V mutant ( 1% ) , whereas it is similarly represented among the other mutants and the wild-type ( 12–16% ) ., In fact , the switch-II conformation appears to be closed in the effector protein complexes ( S1A Fig ) ., However , none of the observed metastable states corresponds to this specific switch-II binding end-point conformation ., The states can be further divided in four groups based on their switch conformations ( Table 1 ) ., The states I and V as combined form the first group , where switch-I appears to be in a fully open conformation and switch-II in a fixed conformation ., This group is frequently occupied by G12R ( 24% ) , whereas it is mostly absent from G12V ( 1% ) ., The states VII and VI form individually the second and the third groups , in respective order ., In the state VII , switch-II is in a fixed conformation and switch-I is more closed compared to the first group ., This group is more frequently populated by G12R ., In the state VI , switch-I is open and switch-II is in a mixed conformation between the fixed and perpendicular conformations ., This state is clearly less populated by G12R compared to the other systems ., The fourth group , where the switches appear in a perpendicular conformation , is frequent with G12V ( 12% ) , whereas the other mutants rarely visit this state ( 1% ) ., Of all the mutants , G12D displays the most similar metastable state population distribution compared to wild-type ( Fig 5 , Table 1 ) ., This is most evident in the most populated states ( states IV , VI , and VII ) , yet G12D also differs from wild-type in the less populated states ( I–IV ) ., In contrast , the conformations of G12R are clearly shifted towards the fixed switch-II states , whereas G12V is shifted away from these states towards the perpendicular states ., The results suggest that for G12V the shift among the states is due to the mutant’s lipophilic character , which may cause changes in solvent organization preventing specific switch-II conformations ., Finally , it is exceptional that while wild-type does not populate the metastable states I and III at all , there are mutants ( G12R , G12V ) whose population in these states is significant ( about 6% ) ., This summarizes the main message: the conformation distribution of KRAS mutants includes conformations not occupied by wild-type , and these conformations are also mutation specific ., Although frequently observed in cancer , not only is the basis for the specific frequencies of KRAS G12X mutations poorly understood 47 , but also the effects of these specific mutations on a molecular scale are not clear ., To the best of our knowledge , this is the first study to assess KRAS G12X mutation probabilities , and to understand how they are associated with the observed mutation frequencies ., Generally , the mutation frequencies have an explanatory basis ., For instance , chemical characteristics of c . 35G explain the enrichment of the G12V mutations by oxidation ., However , complex mutation distributions are displayed by the tissues , and we conclude that some of the observed frequencies cannot be explained simply by the mutation probability ., For example , there is no clear explanation why , on average , a mutation occurs five times more probably in , e . g . , c . 35G than in c . 34G ., One plausible explanation is that the 3D-environment in the DNA-sequence may aid the c . 35G mutations to evade DNA-repair mechanisms ., In fact , the structures of DNA in complex with N-glycosylase/DNA lyase ( OGG1 ) , which is a base-excision repair enzyme for 8oxoG , exist in a catalytically active form for 8oxoG that is adjacent to guanine only in the 5’-position in -AGGT- sequences ( S12 Fig ) 40 , 48–51 ., Correspondingly , this 5’G position in the KRAS sequence ( -TGGT- ) represents the c . 34G position , thus suggesting that the c . 34G position is more susceptible for DNA-repair ., Nevertheless , this observation holds true only for the G>T transversion mutation ., For the other mutations and their repair mechanisms , the positional bias needs to be investigated , especially for the G>A transition mutation , which holds the strongest bias in favor of c . 35G mutations ( >5x in all tissues ) ., Furthermore , exceptions or an enhanced preference in c . 35G for specific mutations in particular tissues were observed ., For instance , it seems that either the advantage for G12D or the disadvantage for G12S , or both , exists in the pancreas , given that there is a 28-fold preference for G>A mutations for c . 35G over c . 34G ., Similarly , the G12R mutation displays an advantage in the pancreas , while G12A is perhaps disadvantageous , given that there is a 9-fold preference for c . 34G over c . 35G in the mutation probability in the G>C mutations ., Altogether , these data suggest that specific mutations are advantageous or disadvantageous depending on the cellular and tissue environments ., Therefore , we hypothesize that the biochemical and biophysical differences among mutants , resulting in signal-transducing differences , may explain , at least in part , the observed mutational bias ., To gain insight into these observed discrepancies among the mutants on a molecular level , we carried out a comprehensive all-atom MD simulation study of all KRAS G12X missense mutants ., We found that mutations have a profound effect on the dynamics of KRAS ., In particular , we observed that the switches are highly dynamic ., This conformational flexibility revealed through atomistic simulations is consistent with 31P-NMR spectroscopy studies of RAS proteins 21 , 52 , while the published KRAS crystal structures do not unlock this behavior ., Even in our extensive analysis of the long-timescale simulation data the differences in the dynamics were not readily visible ., This is not surprising given that even though the binding affinity of a specific mutant toward an effector protein is increased or diminished , the ability to bind still exists 8 ., This suggests that the changes in protein dynamics are quite subtle and difficult to quantify ., In our work , we unlocked this issue through the analysis of the simulation data using PCA , interaction network analysis and MSMs that indeed revealed the differences , not only between the wild-type and the mutants , but also between the mutants ., In order to capture the subtle differences among the mutants , we kept our MD simulation systems realistic but sufficiently simple , enabling the extended simulation times close to 200 μs in total ., Even though the HVR and the cell membrane were absent from our simulations , we recognize that these elements have a substantial influence on KRAS dynamics 53 , 54 and signaling 55 ., We therefore cannot deduce whether some of the observed mutational effects attenuate or amplify through these factors ., However , effects related to the cell membrane remain to be explored in follow-up studies ., Importantly , we identified the hydrophobic hub interaction network that is able to convey the shifts in KRAS dynamics throughout the whole structure in an allosteric manner ., The crystal structures of KRAS G12X mutants display only minor differences , but the lack of structural differences does not exclude the allosteric effect of the mutation 56 ., The shift in the dynamics by G12X is able to occur via the closest hydrophobic hub V14 ., As the wild-type KRAS has a flexible glycine residue in this position , a G12X mutation alters the dynamics of the neighboring residue A11 or the whole P-loop ( including both A11 and V14 ) ., As this hydrophobic hub V14 is connected to a hydrophobic network , a local alteration in KRAS dynamics can be conveyed via the hydrophobic network to the other remote structural regions in KRAS in an allosteric manner ., Supporting the fact that V14 is an important hub in the KRAS hydrophobic interaction network , a mutation in this position , V14I , is found to be one of the responsible mutations for the Noonan syndrome 57 , 58 and may also predispose tumor development 59 ., Whereas the V14I mutation does not change the GTPase activity of KRAS , it displays similar affinity to RAF1 , as does also the G12V mutant 60 ., Therefore , a mutation that has an influence in the dynamics of these hydrophobic hub interactions may have a dramatic influence in overall KRAS dynamics and thereby KRAS signaling ., The most altered interaction pattern within the hydrophobic interaction network in all the hubs is observed with the G12A and G12S mutants ., Surprisingly , the other mutations ( G12C , G12D , G12R and G12V ) are not radically different compared to the wild-type , although some alterations in the hub interactions are evident ., However , even though a mutant , such as G12D , displays the same interaction frequencies as the wild-type , the characteristics of the interactions may still differ , as the exact characteristics of these interactions , unfortunately , cannot be derived from this analysis , only their frequencies ., To highlight that the alteration in KRAS dynamics is also present with the mutants that display a minor shift in the hydrophobic hub interaction network compared to the wild-type , is the observed variability in the distribution among the metastable states of exceptional importance ., The MSM confirmed the indirect effect of the mutation on the switch-region protein conformations and dynamics ., As for MSMs one needs to have extended simulation data , we focused on the most important KRAS G12X mutants ( G12D , G12R and G12V ) ., In crystal structures these dynamic metastable states are not observed ., This is due to the fact that in the structures the switch regions are disordered , if there are no crystal contacts to the switches ., Based on the MSMs , the G12D mutant follows the dynamics of the wild-type more closely than of the G12R and G12V mutants ., Intriguingly , this is in line with the findings of the interaction network analysis , where the G12D displayed the most similar profile with the wild-type ., In particular , our MD results show that the effects of KRAS G12X mutants are mutation-specific , and suggest that the observed changes in protein conformations and dynamics may alter protein activity 61 ., We consider that the difference in the mutant dynamics , for instance the G12V dynamics with its inability to reach the metastable states I and V ( Fig 5 ) , may reflect the differences observed in the RAS effector protein binding 8 , 10 ., In fact , simple protein complexes assemble generally via a single pathway 62 , and the observed metastable states may correspond to the first steps in the effector protein binding process ., These states may be important for specific effector protein binding and pathway activation ., However , based on the simulation data we were unable to distinguish if a putative effector protein ( s ) or a particular signaling pathway ( s ) is related to a specific metastable state ., It needs to be clarified , if these states act as intermediate steps in the KRAS–effector protein association and play a role in the macromolecular recognition process , effecting the ass | Introduction, Results, Discussion, Materials and methods | A mutated KRAS protein is frequently observed in human cancers ., Traditionally , the oncogenic properties of KRAS missense mutants at position 12 ( G12X ) have been considered as equal ., Here , by assessing the probabilities of occurrence of all KRAS G12X mutations and KRAS dynamics we show that this assumption does not hold true ., Instead , our findings revealed an outstanding mutational bias ., We conducted a thorough mutational analysis of KRAS G12X mutations and assessed to what extent the observed mutation frequencies follow a random distribution ., Unique tissue-specific frequencies are displayed with specific mutations , especially with G12R , which cannot be explained by random probabilities ., To clarify the underlying causes for the nonrandom probabilities , we conducted extensive atomistic molecular dynamics simulations ( 170 μs ) to study the differences of G12X mutations on a molecular level ., The simulations revealed an allosteric hydrophobic signaling network in KRAS , and that protein dynamics is altered among the G12X mutants and as such differs from the wild-type and is mutation-specific ., The shift in long-timescale conformational dynamics was confirmed with Markov state modeling ., A G12X mutation was found to modify KRAS dynamics in an allosteric way , which is especially manifested in the switch regions that are responsible for the effector protein binding ., The findings provide a basis to understand better the oncogenic properties of KRAS G12X mutants and the consequences of the observed nonrandom frequencies of specific G12X mutations . | The oncogene KRAS is frequently mutated in various cancers ., When the amino acid glycine 12 is mutated , KRAS protein acquires oncogenic properties that result in tumor cell-growth and cancer progression ., These mutations prevail especially in the pancreatic ductal adenocarcinoma , which is a cancer with an exceptionally dismal prognosis ., To date , there is a limited understanding of the different mutations at the position 12 , also regarding whether the different mutations would have different consequences ., These discrepancies could have major implications for the future drug therapies targeting KRAS mutant harboring tumors ., In this study , we made a critical assessment of the observed frequency of KRAS G12X mutations and the underlying causes for these frequencies ., We also assessed KRAS G12X mutant discrepancies on an atomistic level by utilizing state-of-the-art molecular dynamics simulations ., We found that the dynamics of the mutants does not only differ from the wild-type protein , but there is also a profound difference among the different mutants ., These results emphasize that the different KRAS G12X mutations are not equal , and thereby they suggest that the future research related to mutant KRAS biology should account for these observations . | medicine and health sciences, crystal structure, markov models, condensed matter physics, multivariate analysis, mutation, mathematics, statistics (mathematics), mutation databases, crystallography, research and analysis methods, solid state physics, mathematical and statistical techniques, principal component analysis, biological databases, salt bridges, chemistry, exocrine glands, probability theory, physics, biochemistry, biochemical simulations, point mutation, electrochemistry, anatomy, database and informatics methods, pancreas, genetics, endocrine system, biology and life sciences, physical sciences, computational biology, statistical methods | null |
journal.pcbi.1006721 | 2,019 | 16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses | Recent advances in high-throughput sequencing techniques have dramatically increased the availability of microbiome sequencing data , allowing investigators to identify genomic differences among microbes , as well as characterize microbiome community structure in situ ., A microbiome is defined as the collection of microorganisms ( bacterial , archaeal , eukaryotic , and viral ) that inhabit an environment ., Recent work has shown that there exists reciprocal interplay between microbiome and environment , such that the configuration of microbes is often influenced by shifts in environmental state ( such as disease in human hosts 1–3 , chemical alterations in soil 4 , 5 , or oceanic temperature changes 6 ) ., Conversely , the environmental state may be impacted by particular microbial profiles 7–10 ., Identifying and characterizing important microbial profiles often entails sequencing collections of heterogeneous , fragmented genomic material , which act as a proxy for the microbiome’s configuration in situ ., Sequencing these fragments yields short strings of nucleotides ( “sequence reads” ) with no easily discernible clues to determine from which microbe they originated ., Still , the order of nucleotides within each sequence provides enough information for approaches that utilize sequence alignment or sequence denoising algorithms , along with taxonomic reference databases , to quantify the abundance of sequences belonging to different microbes at different taxonomic levels ( e . g . , genus ) , which in turn can be associated with environmental factors 11 , 12 ., Thus , a given sample is represented as a vector of hundreds , often thousands , taxonomic counts ( nonnegative integers ) , where the taxa have been traditionally termed Operational Taxonomic Units ( OTUs ) ., Clustering sequences into OTUs that approximate species has relied on the 97% similarity heuristic , which has been widely criticized as a poor threshold for hypervariable regions and as not being biologically meaningful 13–16 and can vary from lineage to lineage 17 ., Moreover , in Callahan et al . 14 , the authors argue that even though clustering OTUs helps diminish the influence of Illumina sequencing errors , it nevertheless discards many of the subtle differences between sequences ., Other work has shown that reference gene methods poorly reflect community diversity because they depend on the database used in the analysis 18–20 Given this , alternative approaches to alignment-based similarity , especially those that can take into account subfeatures and their context , warrant exploration ., An alternative approach to aligning or denoising nucleotide sequences is to represent the nucleotide sequences numerically ., One can then search for similarities among these numeric features ., In addition , these numeric representations are more suitable for machine learning algorithms ., Two examples of such approaches are one-hot-encoding and k-mer ( n-gram ) counting 21 , 22 ., With one-hot-encoding ( also referred to as generating binary indicator sequences 23 ) , each sequence is binarized—that is , each nucleotide ( ACGT ) is represented as a unit vector of length four , with a value of one indicating the presence of a particular nucleotide ., k-mer counting , on the other hand , counts the frequency of all possible substrings of length k in a sequence ., A larger k yields a higher dimensional , more sparse representation of the sequence since there are more possible k-mers ( 4k ) ( “the curse of dimensionality” 21 , 24 ) , but a smaller k is unlikely to capture much of the nucleotide-to-nucleotide sequential variation among sequences 25 ., With the large GreenGenes database 26 , there are over 2 million sequences comprising over 2 . 5 billion basepairs , and therefore using k-mers up to 15 ( 415 features ) reduces the basepairs to a lower dimensional representation ., However , sample classification usually uses OTUs which are on the order of tens of thousands ., Thus , if k-mers are to be used and lower dimensionality is desired , a representation that further reduces the dimensions of the k-mers is needed ., Either set of engineered features ( one-hot-encodings or k-mer frequencies ) can be used in various machine learning algorithms to characterize the sequences in some way 27–30 ., Still , one-hot-encoding or k-mer frequencies , when k is large , yield sparse , high-dimensional features that often present difficulties during training 31 ., In addition , neither approach encodes the relative ordering of the k-mers 21 , 32 ., A more suitable representation of nucleotide sequences involves first encoding ( “embedding” ) each sequence into a dense , numeric vector space via the use of word embedding algorithms such as word2vec 27 ., Word embeddings are commonly used for natural language processing 27 , 33–36 ., Various architectures exist , but their objective is generally the same: capture semantic and lexical information of each word based on that word’s context—i . e . , its neighboring set of words ., Each word is represented in a vector space of predefined length , where semantically similar words are placed near one another ., Thus , k-mer representations of sequences could be embedded in such a way that their context is preserved ( the position of k-mers relative to their neighbors ) , and they become suitable for down-stream machine learning approaches ., Recent work has successfully embedded short , variable-length DNA k-mers 21 , as well as protein sequences for down-stream tasks such as protein structure prediction 37 ., Ng 21 showed that the cosine similarity between embedded k-mers is positively correlated with Needleman-Wunsch scores obtained via global sequence alignment ., In addition , he showed that vector arithmetic of two k-mer embeddings is analogous to concatenating their nucleotide sequences ., This finding is consistent with work demonstrating the ability of vector arithmetic to solve word analogies , such as “King is to Queen , as Man is to _____” 38 ., Thus , here we explore word embeddings as a means to represent 16S rRNA amplicon sequences , obtained from microbiome samples , as dense , low-dimensional features that preserve k-mer context ( i . e . , leverage the relative position of k-mers to their neighbors ) ., We use Skip-Gram word2vec to perform the initial k-mer embedding ., Then , we leverage an existing sentence embedding technique 39 to embed individual nucleotide sequences or sets of sequences ( e . g . , all sequences belonging to a given sample ) from k-mer embeddings ., This sentence-embedding technique interestingly does not explicitly encode word order; yet , it has shown to outperform competing methods such as recurrent neural networks in textual similarity tasks 39 ., The sentence embedding procedure is simple , but effective , consisting of down-weighting the embeddings for high frequency k-mers , averaging the k-mer embeddings that constitute a given sequence or set of sequences ( forming a sequence or set embedding ) , and then subtracting the projection of the sequence/set embedding to its first principal component ( “common component removal” , which , per 39 , we refer to as “denoising” ) ., Representing nucleotide sequences in vector space provides multiple benefits that may prove valuable in characterizing a microbiome: ( 1 ) the embeddings are dense , continuous , and relatively low-dimensional ( compared to using k-mer frequencies , for example ) , making them suitable for various down-stream machine learning tasks; ( 2 ) they leverage k-mer context , yielding potentially superior feature representations compared to k-mer frequencies; ( 3 ) once trained , the k-mer-to-embedding mapping vectors can be stored and used to embed any set of 16S rRNA amplicon sequences; ( 4 ) the embedding model can be trained with data that are independent of the query sequences of interest , such that the training procedure can leverage a significant amount of unlabeled data that would otherwise go unused; ( 5 ) feature extraction is performed at the sequence level , enabling one to detect relationships between sample-level information ( e . g . , soil quality ) and all sequences belonging to a given sample and then traceback to determine not only which sequences are key , but also which k-mers; and ( 6 ) once important k-mers are identified , because the embedding initially takes place at the k-mer level , the k-mer contextual information is available , which may indicate the neighborhood noteworthy k-mers occupy ., In this work , we prove that the embedding space performs well at classifying samples , predicting the correct sample class ( e . g . , body site ) given the embedding of all its sequences ., Moreover , because these embeddings are encoded from k-mer embeddings , their classification performance helps justify the use of k-mer embeddings as input in more complex architectures such as deep neural networks ., We show that the embedding space provides a set of meaningful features that capture sample-level ( sample class ) , taxonomic-level ( sequence , read ) , and sequence-level ( k-mer , k-mer context ) characteristics , which not only justifies the use of the embedding space for supervised tasks such as classification , but also justifies its use for unsupervised feature extraction , to capture meaningful signal for the exploratory phase of a given analysis ., Lastly , we illustrate approaches that may help disentangle what the embedding learned from the data , in the context of microbiome information , such as taxonomy and sample information ., We began by evaluating the performance of sequence embeddings ., DNA alignment combined with clustering 11 or sequencing denoising algorithms 12 are readily capable of identifying sequence-level differences between genera ., We consequently aimed to discern if genus-level differences were in fact detectable in the sequence embedding vector space ., Genus level resolution is not only relevant to characterizing a microbial community; it also would suggest that the vector space is capable of capturing subtle , but important sequence differences among taxa ., These differences may be critical in characterizing data at the sample level , particularly during classification , where its desired to discern how the configuration of microbes comprising a sample ( i . e . , all its sequences ) is influenced by sample-level information such as soil quality ., The k-mer embedding space was obtained by training Skip-Gram word2vec on 2 , 262 , 986 full-length 16S rRNA amplicon sequences from the GreenGenes reference database ( Fig B in S1 Appendix ) ., An independent set of 16 , 699 16S rRNA sequences from the KEGG REST server 40 were obtained as our test dataset ., ( We will refer to these sequences as “KEGG 16S sequences” henceforth . ) 14 , 520 contained k-mers that intersected with the training set and thus were sequence-embedded using the sentence embedding approach by 39 ., Briefly , for each sequence of length N , its N − k + 1 k-mers were embedded into vectors of length d ., With these k-mer embeddings , we calculated a weighted average by summing across all k-mer embeddings ( element-wise ) , down-weighting high-frequency k-mers , and dividing by the total number of k-mers ( N − k + 1 ) ., The sequence embedding was then obtained be subtracting its projection to its first principal component ( denoising ) ., Next we aimed to try our embedding approach on empirical data ., We obtained sequencing data of microbiota from three body sites sequenced by the American Gut project 18 ., After preprocessing , 11 , 341 samples from each of three body sites ( fecal , skin , oral ) were embedded ., Unlike the KEGG 16S sequences described above , the fact that these are reads ( and not full-length 16S rRNA sequences ) presents a new challenge in that they are significantly shorter , spanning only 125 nucleotides per read; thus , each read is composed of , at most , 116 k-mers for a 10-mer embedding ., The k-mer , sequence , and sample embedding spaces are shown in Fig, 5 . There was clear grouping among samples from body sites ( Fig 5C ) and phyla for sample and sequence embeddings , respectively ( Fig 5B ) ., For the k-mer embedding , discerning any meaningful patterns is more difficult since the embedding simply encodes contextual information for each k-mer ., We were interested in the relative position of single nucleotide differences for a given k-mer ., In Fig 5A , we mark the position of every 10-mer present that differs from AAAAAAAAAA by one nucleotide ., As expected , despite subtle differences among the 10-mers , their relative position in the embedding space is broad , suggesting that each 10-mer’s context significantly influenced its embedding ., As a baseline , we visualized the t-SNE projection of each sample’s vector of k-mer frequencies ( with k = 6 ) ( Fig 6 ) ., We choose k = 6 based on classification results shown in Table 2 , which indicated that the best performing k using k-mer frequencies was k = 6 . Note that 10-mers were too computational expensive for t-SNE ., Fig 6 shows that using k-mer features , one can separate body sites fairly well ., There is no substantial difference compared with Fig 5 , which indicts that the embedding method does not result in much information loss ., Here we have applied word and sentence embedding approaches to generate k-mer , sequence , and sample embeddings of 16S rRNA amplicon sequencing data ., We present our results at a time when deep neural network approaches are readily being applied to genomic sequencing data 62 ., Although these approaches have been utilized to a lesser extent in microbiome research , increased use is likely inevitable as sequencing data becomes more available ., Thus , obtaining meaningful numeric representations of microbiome sequences that does not suffer from the curse of dimensionality and can act as input to various machine learning architectures is necessary ., Our work demonstrates that sequence and sample embeddings are dense , lower-dimensional representations that preserve relevant information about the sequencing data such as k-mer context , sequence taxonomy , and sample class ., We have shown that these representations are meaningful by helping to classify taxa and samples and hence the embedding space can be exploited as a form of feature extraction for the exploratory phase of a given analysis ., The sequence embedding space performs well compared to common approaches such as clustering and alignment , and the use of sample embeddings for classification seemingly results in little-to-no performance loss compared to the traditional approach of using OTU abundances ., Because the sample embeddings are encoded from k-mer embeddings , its classification performance justifies further inquiry as pretrained input for more complex machine learning architectures such as deep neural networks ., In addition , future work should aim at elucidating the effects of different training datasets and obtaining a better understanding of the feature representations at the k-mer level ., Word2vec represents words as continuous vectors based on the frequency of pair co-occurrence in a context window of fixed length ., We can understand it as mapping individual words ( k-mers in our application ) to points in a continuous , higher-dimensional space , such that words with similar semantic meaning are closer to one another ., We obtained 1 , 262 , 986 full length 16S rRNA amplicon sequences from the GreenGenes database 63 ., Full length sequences were used to ensure that our embeddings were region agnostic , in that the embedding would be trained on context windows found in any variable or conserved region throughout the 16S rRNA gene , permitting query sequences to be embedded irrespective to the region of the gene they spanned ., For each sequence of length N , we generated all possible subsequences ( N − k + 1 ) of length k ( k-mers ) , In natural language processing terms , these k-mers were treated as words belonging to a corpus , and the set of all unique k-mers comprise the corpus’s vocabulary ., k-mers with degenerate bases ( bases other than ACGT ) were removed ., Four training sets were created for k-mer lengths of 4 ( 4-mers ) , 6 ( 6-mers ) , 8 ( 8-mers ) , 10 ( 10-mers ) , 12 ( 12-mers ) and 15 ( 15-mers ) ., k-mer embeddings were trained using gensim’s Skip-Gram word2vec implementation over 5 epochs 64 ., k-mers occurring fewer than 100 times were removed ., We varied the parameters to generate 48 model parameterizations ., Using 6-mers and 10-mers , we varied the dimensionality of the embedding ( 64 , 128 , 256 ) ; the threshold in which high frequency k-mers were down-sampled ( 0 . 0001 , 0 . 000001 ) ; the number of negative samples ( 10 , 20 ) ; and the width of the context window ( 20 , 50 ) ., Other parameters were set to their default values ., However , the model could not finish training on 8-mers , 12-mers , and 15-mers training sets within 7 days , whereas the model could finish training using other training sets within 5 days ., Limited by our computational resources , we removed 8-mers and 12-mers from our analyses ., In the future work , one could use other embedding implementations such as GLoVe 65 ., In GLoVe , the model is trained on word-to-word co-occurrence matrix which is sparse and much smaller than the total number of words in the corpus ., Therefore , the training iterations are much faster ., k-mer based 16S rRNA embedding for the host phenotype prediction has already been presented in 66 ., In addition , k-mer profiles of samples have been shown to outperform OTU profiles in body site classification tasks 67 ., k-mer frequencies are therefore a natural baseline for the embedding approach to be compared ., Here , we created a k-mer frequency table for our experimental datasets ., For the k-mer frequency table for American Gut dataset , each row is a sample and each column represent the frequency of a k-mer across all reads for that sample ., For the k-mer frequency table for KEGG dataset , each row is a taxon and each column represent the frequency of a k-mer in the corresponding DNA sequence in KEGG dataset ., These tables can then be processed and learned by other down-stream analysis tools ., 16 , 399 full length 16S rRNA amplicon sequences were obtained from the KEGG REST server ( 9/2017 ) ., 188 , 484 , 747 16S rRNA amplicon reads from the American Gut project ( ERP012803 , 02/21/2017 ) underwent quality trimming and filtering ., Sequences were trimmed at positions 10 and 135 based on visualizing the quality score of sampled sequences as a function of base position 12 ., Then , sequences were truncated at positions with quality scores less than or equal to 2 ., Truncated sequences with total expected errors greater than 2 were removed ., Some American Gut samples were contaminated by bacterial blooming during shipment ., Contaminated sequences were removed using the protocol provided in the American Gut documentation ( 02-filter_sequences_for_blooms . md ) ., Only sequences from fecal , hand , head , and tongue body sites were kept ., Head and hand were merged into a “skin” category ., Any remaining samples with fewer than 10 , 000 total reads were removed ., Closed-reference OTU picking was performed with QIIME using SortMeRNA against GreenGenes v13 . 5 at 97% sequence identity 11 ., Library size was normalized via clr , where the normalized vector of abundances x s * for sample s was obtained by x s * = log ( x s + 1 ) / g s , where gs is the geometric mean for sample s ,, g s = ( ∏ x ∈ s x ) 1 | s | , ( 1 ), and x is the unnormalized abundance of a single OTU ., To generate a sequence embedding , the weighted embeddings of all k-mers m belonging to sequence r were summed and then normalized by the total number of k-mers Mr in sequence r ., Each k-mer was weighted based on its frequency within the query set of sequences ( sequences to be embedded , but not the sequences initially used for training ) ., Note this down-weighting is distinct from the down-weighting used during training , which down-weighted k-mers based on their frequency in the training ( GreenGenes ) sequences ., Thus , we have, ε r ( r a w ) = 1 M r ∑ m ∈ r ε m a a + f m , ( 2 ), where εm is the d-dimensional embedding for k-mer m , fm is the frequency of k-mer m across the entire set of query sequences to be embedded , a is the parameter to control the degree in which k-mer m is down-weighted , and Mr is the total number of k-mers embedded into sequence r ( i . e . , the total number of k-mers belonging to sequence r that were also present in the training set and thus have embeddings ) ., The resulting raw sequence embedding was then denoised by removing its projection to its first principle component ,, ε r = ε r ( r a w ) - ε r ( r a w ) ν ν T , ( 3 ), where ν is the first principle component obtained via singular value decomposition ., For sample , cluster , or body site embeddings , the process was instead applied to all k-mers belonging to all sequences from a specific sample , all sequences from a specific cluster , or all sequences from all samples from a specific body site , respectively ., Note that k-mers with degenerate bases were removed ( bases other than ACGT ) ; thus , some sequences received no embedding due to no k-mers intersecting with the training k-mer embeddings ., For an embedding A , its cosine similarity with respect to embedding B is defined as, cosim ( A , B ) = ∑ i A i B i ∑ i A i 2 ∑ i B i 2 ., ( 4 ) For visual exploratory analysis of the embedding space , American Gut sample embeddings were reduced to 2 dimensions via t-SNE ., 21 samples ( 7 samples each from fecal , tongue , and skin ) were randomly chosen to lessen the computational burden ., Principal component analysis was not performed beforehand to further reduce the dimensionality of the embedding space , as typically done ., This is because the embedding space is already a lower-dimensional representation of the original input feature space ., Centering and scaling was also not performed ., Perplexity was set to 50 and t-SNE was run for 1000 iterations ., sequence embeddings of the 14 , 520 KEGG 16S sequences were explored in the same manner ., For the American gut data , because the number of total sequences was large ( 11 , 838 , 849 ) , and for 10-mer embeddings in general , t-SNE was impractical in terms of time and memory requirements ., Thus , to project American Gut sequence and GreenGenes 10-mer embeddings to 2-dimensions , we performed independent component analysis ., The KEGG sequences were clustered using VSEARCH ., Sequences with pairwise identity ( as defined above ) with its centroid below 0 . 8 were omitted from their respective cluster ., We embedded the consensus sequence of each cluster ( a consensus embedding ) , as well as all sequences belonging to that cluster ( a cluster embedding ) ., Then , the pairwise cosine similarities between all consensus and cluster embeddings were calculated ., After generating sequence embeddings of the American Gut data , we randomly sampled approximately 1 , 000 , 000 sequence embeddings across all body sites ( tongue , skin , gut ) and used K-means 43 to cluster them into 1000 clusters ( 1000 pseudo-OTUs ) ., We then obtained the centroids of these clusters ., sequences from each sample were classified into the closest centroid/cluster ., Finally , we quantified the number of sequences that were classified into each cluster and the abundance of each pseudo-OTU ., American Gut samples with body site ( fecal , skin , tongue ) labels were split into 90/10 training/testing sets containing 7526 and 835 samples , respectively ., The training set was composed of 6729 , 282 , and 497 fecal , skin , and tongue samples , whereas the testing set consisted of 749 , 31 , and 54 samples , respectively ., We performed multinomial classification using the lasso classifier with sample embeddings , clr-transformed OTUs , their top-256 principal components , or clr-transformed pseudo-OTUs as features ., For training , we performed 10-fold cross validation to select the optimal value of the regularization parameter λ ., We evaluated performance using the held-out testing set in terms of balanced accuracy , which adjusts for class imbalance by averaging the three accuracies for each individual body site:, Acc * = 1 3 ∑ b s p ( y t r u e ( b s ) = y p r e d ( b s ) ) , ( 5 ), where ytrue is the true label and ypred is the predicated label for only samples from body site bs ., Taxonomic assignment for both the KEGG and American Gut 16S rRNA amplicon sequences was conducted using the RDP naïve Bayes classifier 68 implemented in QIIME ., We obtained the sparse set of lasso regression weights estimated when we performed body site classification using sample embedding ( described above ) ., Each body site ( skin , fecal , tongue ) had its own vector of regression coefficients ., To obtain k-mer activations , we calculated the outer product between all k-mer activations and the regression coefficients: αk = εk ⊗ β , where , for a corpus of M k-mers , αk is an M × J matrix of k-mer activations for J body sites , εk is an M × d matrix of k-mer embeddings , and β is a d × J matrix of regression coefficients ., Each column in αk was ranked , and the top-1000 ( arbitrary ) k-mer activations for each body site were selected ., For each set of 1000 k-mers , we identified which corresponding American Gut reads ( from samples of a particular body site ) contained these k-mers and randomly sampled 25 , 000 of k-mer-containing sequences from each body site ( to ease the computational burden in the subsequent alignment step ) ., We performed multiple alignment 69 for each set of 25 , 000 sequences with relatively strict gap penalties to prevent exceedingly large alignments ( -25 and -10 gap opening and extension penalties , respectively ) ., We finally mapped the position of the high-ranking k-mers to the alignments ., The position is the range in which the k-mer spans in the multiple alignment , including the presence of gaps ., We calculated sequence activations for each body site in a similar manner as described above ., To obtain sequence activations , we calculated the outer product between all sequence embeddings and the sparse set of lasso regression coefficients: αr = εr ⊗ β , where , for a corpus of R sequences , αr is an R × J matrix of sequence activations for J body sites , εr is an R × d matrix of sequence embeddings , and β is a d × J matrix of regression coefficients ., Then , for a given sample , we summed each sequence activation αr to obtain a cumulative sum for all sequence activations through sequence activation αr . | Introduction, Results and discussion, Materials and methods | Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure in situ ., We explore using word and sentence embedding approaches for nucleotide sequences since they may be a suitable numerical representation for downstream machine learning applications ( especially deep learning ) ., This work involves first encoding ( “embedding” ) each sequence into a dense , low-dimensional , numeric vector space ., Here , we use Skip-Gram word2vec to embed k-mers , obtained from 16S rRNA amplicon surveys , and then leverage an existing sentence embedding technique to embed all sequences belonging to specific body sites or samples ., We demonstrate that these representations are meaningful , and hence the embedding space can be exploited as a form of feature extraction for exploratory analysis ., We show that sequence embeddings preserve relevant information about the sequencing data such as k-mer context , sequence taxonomy , and sample class ., Specifically , the sequence embedding space resolved differences among phyla , as well as differences among genera within the same family ., Distances between sequence embeddings had similar qualities to distances between alignment identities , and embedding multiple sequences can be thought of as generating a consensus sequence ., In addition , embeddings are versatile features that can be used for many downstream tasks , such as taxonomic and sample classification ., Using sample embeddings for body site classification resulted in negligible performance loss compared to using OTU abundance data , and clustering embeddings yielded high fidelity species clusters ., Lastly , the k-mer embedding space captured distinct k-mer profiles that mapped to specific regions of the 16S rRNA gene and corresponded with particular body sites ., Together , our results show that embedding sequences results in meaningful representations that can be used for exploratory analyses or for downstream machine learning applications that require numeric data ., Moreover , because the embeddings are trained in an unsupervised manner , unlabeled data can be embedded and used to bolster supervised machine learning tasks . | Improvements in the way genomes are sequenced have led to an abundance of microbiome data ., With the right approaches , researchers use these data to thoroughly characterize how microbes interact with each other and their host , but sequencing data is of a form ( sequences of letters ) not ideal for many data analysis approaches ., We therefore present an approach to transform sequencing data into arrays of numbers that can capture interesting qualities of the data at the sub-sequence , full-sequence , and sample levels ., This allows us to measure the importance of certain microbial sequences with respect to the type of microbe and the condition of the host ., Also , representing sequences in this way improves our ability to use other complicated modeling approaches ., Using microbiome data from human samples , we show that our numeric representations captured differences between various types of microbes , as well as differences in the body site location from which the samples were collected . | sequencing techniques, taxonomy, split-decomposition method, multiple alignment calculation, data management, molecular biology techniques, cellular structures and organelles, research and analysis methods, sequence analysis, computer and information sciences, sequence alignment, bioinformatics, biological databases, molecular biology, nucleotide sequencing, ribosomes, biochemistry, rna, dna sequence analysis, sequence databases, ribosomal rna, cell biology, computational techniques, nucleic acids, database and informatics methods, biology and life sciences, non-coding rna | null |
journal.pcbi.1003964 | 2,014 | Glutamate Mediated Astrocytic Filtering of Neuronal Activity | Evidence obtained during the last few years inaugurated the notion that astrocytes may play a role in information processing in the brain ., The tripartite synapse concept ( presynaptic , postsynaptic , astrocyte ) 1 , 2 is gaining acceptance , and is progressively replacing the historical bipartite synaptic view ( restricted to presynaptic and postsynaptic ) ., This concept builds on expansive experimental evidence which shows that astrocytes display a form of excitability based on elevations of their intracellular Ca2+ concentrations ( Ca2+i ) in response to synaptically released neurotransmitters 3–8 ., As astrocytes form non-overlapping domains , and can cover areas containing hundreds of dendrites ( up to 140 , 000 synapses per a single astrocyte ) 9 , astrocytes are well positioned to propagate neuronal information to neighboring synapses , thus bypassing or enforcing the neuron-neuron pathway 1 , 10–12 ., The study of astrocyte-neuron communication is complicated by its bidirectional nature ., Glutamate transmission is a typical example: just like neurons , astrocytes express efficient glutamate transporters that clear glutamate from the synaptic cleft and glutamate receptors ( metabotropic ) ; they also release glutamate in a process that may be similar to neurons 1 , 13 ., This suggests that neurons can transmit glutamate signals to astrocytes ( feedforward communication ) and vice-versa ( feedback ) ., Whether astrocytic glutamate release is regulated and able to transmit feedback signals from astrocytes to neurons in physiological conditions is still hotly debated 14–16 ., On the contrary , the existence of the forward signaling , whereby neurons transmit glutamate-mediated signals to astrocytes , is well established 1 , 17 , 18 ., Experimental evidence generally supports the idea that astrocytes are more than simple passive ( linear ) read-out of the synaptic activity but process it in an integrated and complex fashion , encoding the input neuron activity as a nonlinear response in their Ca2+ dynamics 13 , 19 ., However , how exactly astrocytes integrate and process synaptic information is still unclear ., Thus , additional studies are needed to reveal the complex nature of neuron-to-astrocyte communication and the properties of astrocytic Ca2+i signals evoked by synaptic activity ., In this study we focus on the feedforward glutamate signaling ( from neurons to astrocytes ) and show that neuron-to-astrocyte signaling includes a form of complex encoding and information processing , which was classically considered to be exclusively within the domain of neurons ., To explore neuronal modulation of astrocytic activity at a network level we used neuron-glia mixed cultures ., We applied direct electrical stimulation with micro electrodes to selectively activate neurons while optically reading neuronal and astrocytic activation using a calcium imaging technique 20 ., In particular , our setup features the possibility to independently set the frequency and the amount of neuronal stimulation ., This property allows to unambiguously question which parameter of the neuronal stimulation ( frequency or amount ) is important for the astrocyte response ., We show that astrocyte response is a nonlinear function of neuronal stimulation frequency , with an onset that varied from one astrocyte to the other between 1 and 10 Hz ., This response was abolished by the application of metabotropic glutamate receptor antagonists , thus demonstrating that this signal is mediated by glutamate ., Using a realistic biophysical model of glutamate-based intracellular calcium signaling in astrocytes , we suggest that the observed stepwise response is due to the supralinear dynamics of intracellular IP3 and that the heterogeneity of the responses may be due to the heterogeneity of the astrocyte-to-astrocyte couplings via gap junction channels ., The results presented in this work thus indicate the existence of a rate dependent encoding process underlying neuro-glia pathway ., Dissociated cortical cultures were prepared from surgically removed cortices of E18 Sprague Dawley rat embryos ., The cortical tissue was digested by 0 . 065% trypsin ( Biological Industries , Beit Haemek , 03-046-1 ) in phosphate-buffered saline ( Beit Haemek , 02-023-1 ) for 15 min , followed by mechanical dissociation by trituration ., Cells were re-suspended in a modified essential medium ( MEM ) without phenol red nor glutamine ( Gibco , 21200-046 ) , complemented with 5% horse serum ( Beith Haemek 04-004-1 ) , 5 mg⋅ml−1 gentamycin ( Beith Haemek 03-035-1 ) , 50 µM glutamine ( Beith Haemek 03-020-1 ) and 0 . 02 mM glucose ( BDH101174Y ) ., Cells were then plated on multielectrode arrays ( MEAs ) ( 500/30iR-Ti or HD 30/10iR-ITO , by Multi Channel Systems ) coated by poly-D-lysine ( PDL , Sigma , catalog no . p-7889 ) , at density of 3500–4500 mm−2 ( that is ∼2×106 cells per culture ) ., Cultures were maintained at 37°C with 5% CO2 at 95% of humidity ., The growth medium was partially replaced every 3–4 days ( approximately 30% ) ., Suppression of synapse efficacy was obtained by adding ∼1 µM b-cyano-7-nitroquinoxaline-2 , 3-dione ( CNQX ) which is a α-amino-3-hydroxy-5-methyl-4-isoxazelepropionic acid receptor ( AMPAR ) antagonist , and ∼3 µM ( 2R ) -amino-5-phosphonovaleric acid ( APV ) which is a N-methyl-D-aspartate receptor ( NMDAR ) antagonist to the recording medium ., This approach was shown to reduce functional connectivity in neuronal cultures 21 ., Neuronal action potentials were blocked by 1 . 5 µM of the sodium channel blocker tetrodotoxin ( TTX ) ., Inhibition of astrocytic metabotropic glutamate receptors mGluR5 and mGluR1 was achieved by adding to the bath 25 µM 6-Methyl-2- ( phenylethynyl ) pyridine hydrochloride ( MPEP ) and 50 µM ( S ) - ( + ) -a-amino-4-carboxy-2-methylbenzeneacetic acid ( LY367385 ) respectively 22 ., All chemicals were purchased from Sigma-Aldrich ., Cultures were washed twice in phosphate buffered solution ( PBS ) , then fixed by 4% paraformaldehyde ( Merck ) solution for 10 min , and left in PBS before staining ., To perform immunocytochemical staining , fixed cultures were washed three times with PBS ( 10 min/wash ) and permeabilized by 0 . 5% triton X-100 ( Sigma-Aldrich ) in PBS for 10 min ., Cultures were then blocked with 2% BSA , 10% normal donkey serum and 0 . 5% triton X-100 solution in PBS for 1 hr at room temperature and incubated overnight at 4°C with primary antibodies GFAP ( 1∶400 Sigma-Aldrich ) and NeuN ( 1∶200 , Millipore ) ., Cultures were further washed by PBS ( 3 times , 10 min/wash ) and incubated for 1 hr at room temperature with the appropriate secondary antibodies: Alexa fluor 488 goat anti rabbit IgG ( 1∶400 , Jackson ) for the detection of GFAP , and Cy-3 donkey anti-mouse IgG ( 1∶700 , Jackson ) for NeuN ., Finally , after another wash by PBS ( 3 times , 10 min/wash ) , cultures were mounted with aqueous DAPI-containing medium ( VECTASHIELD Mounting Medium with DAPI , Vector Laboratories , H-1200 ) ., Rectangular and biphasic 400 µs-long current pulses of 25–35 µA were applied to cell cultures by an extracellular multi-electrode array ( MEA ) , using 30 µm diameter electrodes and a dedicated 4-channel stimulus generator ( STG 2004 , Multi Channel Systems ) ., Note that for the study of cellular safety and efficacy of electrical stimulation , we express below the stimulus in units of charge density ( mC/cm2 ) , rather than current ( µA ) , in order to normalize the electrode diameter and duration of stimulation pulses , thus enabling generic measures and facilitating comparisons ., Ca2+ imaging was performed in open air environment , and accordingly culture medium was replaced by buffered-ACSF medium ( 10 mM HEPES , 4 mM KCl , 2 mM CaCl2 , 1 mM MgCl2 , 139 mM NaCl , 10 mM D-glucose , adjusted with sucrose to an osmolarity of 325 mOsm , and with NaOH to a pH of 7 . 4 ) ., Cultures were washed three times to remove traces of incubation medium and incubated in ACSF with 3 µM Oregon-Green BAPTA-I ( Invitrogen 06807 , one vial with 6 . 7 µL Anhydrous-DMSO for stock of 6 mM ) and same volume of Pluronic acid F-127 ( Biotium 59000 , stock 10% w/v after mixing 1 g vial in 10 ml distilled deionized water ) for 30 min ., Following incubation , cultures were washed again and kept in ACSF ., During recordings , cultures were kept at 37°C ., Time lapse data were taken with an Olympus upright microscope ( BX51WI ) fitted with an EMCCD camera ( Andor Ixon-885 ) and a ×20 water immersion objective ( Olympus , UMPLFLN 20XW NA 0 . 5 ) ., This setup allowed the visualization of cells residing on top of non-transparent electrodes ., Fluorescent excitation was delivered by a 120 W mercury lamp ( EXFO x-cite 120PC ) coupled with a dichroic mirror with a filter to match the dye spectrum ( Chroma T495LP ) ., Camera control utilized Andor propriety SOLIS software ., Time-lapse recordings were performed at 2×2 binning mode for resolution of 500×502 and 51 . 948 frames per second ., Time lapse sequences were collected via a dedicated 12 bit Andor data acquisition card installed on a personal computer , spooled to a high capacity hard drive and stored as uncompressed multi-page TIFF file libraries ., The effect of bleaching was very moderate and addressed by using normalization of fluorescence values ( ΔF/F0 ) ., A scheme illustrating our experimental setup is given as S1 Fig . In cell cultures at the culture stage observed in this paper ( DIV 14–27 ) , neural activity typically organizes as periodic synchronized bursting events in which most neurons fire once or several times within a short time interval ( population bursts ) 23–25 ., The amplitude and extension of these bursts of neural activity are so large that they eclipse the subtler crosstalk between neurons and glial cells ., To overcome this issue and focus on the neuron-glia interactions , we used synaptic blockers APV and CNQX ( see “Pharmacology” ) which suppress the spontaneous bursting events ., Each Ca2+ imaging session typically consisted in the observation of 3–4 different fields of view per each neuron-astrocyte culture dish ., Ca2+ imaging data was stored as uncompressed TIFF library , where pixel values represented fluorescence intensity ., Boundaries of astrocyte somata were semi-automatically segmented from the time-averaged Ca2+ image using a custom code implemented in MATLAB ( The MathWorks Inc . , Natick , Massachussetts , USA ) , followed by manual adjustments ., Ca2+ variations in the astrocyte cell bodies were estimated as normalized changes of fluorescence signal from baseline ( ΔF/F0 ) ., Local baseline fluorescence ( F0 ) was evaluated from the histograms of the signal within a running time window ., Time windows without cellular activity were best fit by a single Gaussian ( due to white noise ) , whereas those with cellular activity were best fit by two Gaussians ( due to white noise and activity ) ., For display purposes , the signal was smoothed by convolution with a 50-data point-large Savitsky-Golay filter of polynomial degree 7 26 , 27 ., Neuronal signals were distinguished from astrocytic signals based on typical dynamic time scales , and physiological properties of their calcium signals 28 ., Neurons were characterized by fast variations in Ca2+ activity during spike onset , whereas astrocytes exhibited slowly varying signals ( S2 Fig . ) ., Neuronal activation probability was calculated over several stimulations at different amplitudes ., The probability is defined for each neuron as the portion of times it responded by an action potential to an electrical stimulation at a specific amplitude ., We neglected neurons with no response throughout the whole stimulation range ., The stimulation threshold was defined for each neuron as the amplitude which activated it with 0 . 35 probability ., For astrocytes , single-cell responsiveness to electrical stimulation at a given frequency was estimated by the sum of fluorescence values ΔF/F0 for all time windows at which electrical stimulation was applied to a given astrocyte , and normalized by the length of the time window ., The spontaneous responsiveness of the astrocyte , calculated as the fluorescence values ΔF/F0 recorded in absence of stimulation , was then subtracted , to eliminate the contribution of spontaneous activity to the measured responsiveness ., In other words , the responsiveness r was computed as ( 1 ) where t0 is the start of the stimulation , Tstim its duration , and t1 and Tspont are the same parameters but in the absence of stimulation ., Note that r has no dimension and will be given below in arbitrary units ( AU ) ., An astrocyte was considered to be stimulated by neuronal activity when its recorded Ca2+ response was highly correlated in time with the applied electrical stimulation ., Population responsiveness to electrical stimulation at a given frequency was defined as the average of the single-cell responsiveness of all the stimulated astrocytes in a given experiment ., Single-cell and population responsiveness as a function of the frequency of electrical stimulation were generally sigmoid in shape , with an exponential rising phase ., To compute the onset frequency , that is the smallest frequency of the electrical stimulation that triggered a detectable astrocyte response , the responsiveness ( at the single-cell or population level , as indicated in the text or captions ) was fitted by a four-parameter logistic function: ( 2 ) where A is the minimum plateau , B is the maximum plateau , C is the 50% point , and τ controls the maximum slope ., In cases where the average responsiveness did not reach a plateau for the largest applied frequency , the sigmoid fit was poor , and therefore we interpolated the results to the exponential growth part of the function ., In all cases , the onset frequency was defined using the fitted function as the frequency for which the astrocyte responsiveness was 10% of its maximum value ., Error was quantified according to the fits 95% confidence level ., We used this definition of onset frequency for both experimental data and their simulated counterparts since they were associated to a small number of different stimulation frequencies ., Fitting a sigmoid function thus allowed us to interpolate between these few points and estimate the onset frequency ., For specific simulations that were independent from experimental data , we were no longer limited by the restricted number of data points and we thus computed the exact onset frequency as the stimulation frequency corresponding to the inflection point of the responsiveness curve ., Astrocytic intracellular Ca2+ traces form a very non-stationary signal ., Therefore , to quantify astrocyte-oscillating frequencies during electrical stimulation , we applied time-frequency analysis ., Wavelet decomposition 29–32 was applied on each Ca2+ trace ., Compared to the more classical methods based on short-term Fourier transforms 33 , wavelet decomposition provides a better tradeoff between time and frequency resolution and does not require setting a specific sliding window length ., We used the Morlet wavelet basis , which is composed of a complex exponential ( carrier ) multiplied by a Gaussian window ( envelope ) as its shape resembled that of astrocytic Ca2+ signals ., Signal pseudo-frequencies in Hz were calculated according to the scale parameter using the wavelet center frequency , defined as the highest amplitude in the Fourier transform of the Morlet function ., In order to extract only the significant astrocyte oscillations , we restricted the time-frequency plane to those time points that were within the electrical stimulation windows and which had at least one transform coefficient greater than 70% of the largest transform coefficient ., For each astrocyte and each time point in this restricted time-frequency plane , we extracted the representative frequency of this astrocyte at this time point as the frequency that showed the largest transform coefficient at that time point ., We then built for each astrocyte the distribution of its representative frequencies ., Finally , we defined the maximal oscillating frequency of an astrocyte as the 95 percentile of the distribution of its representative frequencies ( we could not use the maximum of the distribution because of potential artifacts from the wavelet transform ) ., Intracellular Ca2+ dynamics in the cytoplasm of astrocytes in response to glutamatergic neuronal stimulation can be described by the G-ChI model that we previously developed and studied , and provides a realistic description of Ca2+ dynamics in an isolated astrocyte 34 ., This model neglects possible spatial non-homogeneities of the intracellular distribution of chemical species , and the intricate and complex shape of astrocytes , thus simplifying model astrocytes as perfectly stirred cells with spherical shapes ., The G-ChI model considers both Ca2+ regulation by IP3-dependent calcium-induced calcium released ( CICR ) , as well as IP3 dynamics resulting from PLCδ and PLCβ-mediated production and degradation both by IP3 3-kinase ( 3K ) and inositol polyphosphate 5-phosphatase ( 5P ) ., Fig . 1 illustrates the main processes involved in the dynamics , as well as the associated rates or fluxes ., The Ca2+i dynamics for each astrocyte of the network are described by three coupled nonlinear ordinary differential equations: ( 3 ) ( 4 ) ( 5 ) where the variables Ci , hi , Ii , Gi respectively denote the cell-averaged cytosolic Ca2+ concentration , the fraction of activable IP3R channels on the endoplasmic reticulum ( ER ) membrane , the cell-averaged cytosolic IP3 concentration , and the glutamate concentration released by the presynaptic neuron in the extracellular space ., In eq ., ( 5 ) , a indicates whether astrocyte i is directly activated by a synapse ( in which case a\u200a=\u200a1 ) or not ( a\u200a=\u200a0 ) ., The additional term in equation ( 5 ) sums IP3 flows from/to any astrocyte j that is directly connected by gap junction channels ( GJC-coupled ) to astrocyte i , i . e . with the set of astrocytes that are GJC-coupled to i 35 ., See below for the modeling of astrocyte coupling via gap-junctions ., Model parameters ( S1 Table ) were set as to display frequency modulation ( FM ) encoding ( 35 ) in order to match the shape of experimentally observed Ca2+ signals ., Time-dependent parameters were then adjusted so that the maximum oscillation frequency of model astrocytes matched the maximum oscillation frequency ( ∼0 . 2 Hz ) measured in our experiments ., Additionally , since we did not witness intercellular Ca2+ waves in the experiments , we lowered the IP3 production rate of PLCδ so as to minimize intercellular waves in the model as well ., As hinted by the presence of the coupling term in equation ( 5 ) , our model astrocytes were coupled to each other with gap-junction channels ( GJC ) ., For each computer simulation , we defined the coupling topology of the model astrocyte network on the basis of a corresponding cell culture , thus defining one-to-one correspondence between experiments and computer simulations ., Cell cultures we composed of three types of cells: astrocytes , neurons , and unclassified cells ( cells which did not show any Ca2+ activity ) ., Since all these cells were constrained in a two-dimensional space and astrocytes are known to occupy separate anatomical domains 9 , we assumed that GJC couplings were restricted to nearby astrocytes ., For example , when two astrocytes were spatially separated by a neuron or an unclassified cell , we did not couple these two astrocytes in the model ., To determine the precise coupling topology , cell positions were defined from the reference experiment as the center of mass of the cell bodies ( retrieved as described above ) ., The anatomical region of each cell was then established by computing the Voronoi diagram of the cells ., For each cell , the Voronoi diagram actually associates the cell to a 2D region around it that is such that all points inside this region are closer to the center of its associated cell than to any other cell center ., An illustration of a Voronoi diagram is given in grey ( Fig . 2B ) with its associated cell culture ( Fig . 2A ) ., All astrocyte pairs whose anatomical regions shared a border were GJC coupled in the model ., As an illustration , Fig . 2C displays an immunostaining image together with its associated Voronoi tessellation ( light gray lines ) ; astrocyte A shares its anatomical region boundaries with 7 other astrocytes ( B1 through B7 ) ., Assuming that all of its neighbors are correctly characterized as astrocytes ( and not unclassified cells ) , A will be GJC linked to all its neighbors in the reconstructed network ., Note that the non-astrocyte cells ( and the astrocytes that were not classified as such because they did not display any Ca2+ activity ) were not used subsequently in the model ., In terms of cell-averaged concentrations , the exchange of IP3 between two cells can be regarded as a multiscale phenomenon that depends on many factors , including astrocyte morphology and physiology , GJC location and permeability to IP3 36 ., To account for these factors , IP3 exchange between two GJC-coupled astrocytes i and j was assumed nonlinear ., To model this nonlinearity , we chose to use a sigmoid function of the IP3 gradient between the two cells , according to 35 , 37: ( 6 ) Where represents the threshold IP3 gradient for effective intercellular exchange , i . e . the minimal IP3 gradient for which Jij>0; ωI sets stiffness of this sigmoid function and F quantifies the strength of coupling between these two cells ., Our computer model distinguishes two astrocyte populations: directly activated astrocytes , that receive direct glutamate stimulation from a neuronal process ( for which a\u200a=\u200a1 in eq ., 5 ) and indirectly activated astrocytes ( a\u200a=\u200a0 in eq ., 5 ) whose Ca2+ transients are triggered by a GJC-coupled neighbor astrocyte ., For each astrocyte in the experiments and for each electrical stimulation frequency , we computed the time delay between the stimulation start and the initiation of the astrocyte Ca2+ response ( defined as the smallest time after the stimulation start for which the normalized fluorescence goes over 60% of its maximum value ) ., Every astrocyte showing in the associated experiment a time delay between stimulation start and initiation of the response of less than 1 . 5 s was defined as a stimulated astrocyte in the computer model ., We chose this 1 . 5 s threshold in order to make sure that the Ca2+ responses of the stimulated astrocytes are due to neuronal stimulation and not to GJC coupled astrocytes ., Indeed , even for the highest stimulation frequencies , astrocytes responded to the electrical stimulation in less than 1 . 5 s in the model whereas the typical delay needed to transmit a Ca2+ signal from one astrocyte to another via GJC coupling was 2s ., Each stimulated astrocyte of the computer model was stimulated using the Tsodyks and Markram model 38 , 39 for the average synaptic release in response to a sequence s ( t ) of action potentials ( see S1 Text ) ., The resulting model reads: ( 7 ) ( 8 ) ( 9 ) where the product r ( t ) of the two synaptic variables u and x , that is , represents the fraction of synaptic glutamate released upon an action potential of the sequence s and Gi ( t ) is the amount of glutamate in the synapse at time t ., We assumed that each electrical pulse delivered by MEAs to the network triggered one action potential in each stimulating neuron , so that s ( t ) in the above equation actually represented the sequence of electrical stimulation of the MEA ., Even in in vitro cultures , astrocytes extend processes that contact neurons 40 ., However , as we cannot determine the number of synapses that contact a given stimulated astrocyte , we considered the above equations as a description of an equivalent synapse , accounting for all synapses enwrapped by one astrocyte ., Model parameters for all simulations are reported in S1 Table ., To validate our electrical stimulation conditions we first mapped the efficacy of MEA stimulations relative to the triggering of Ca2+ transients ., To define a proper stimulation charge density range , we performed an activation safety mapping ( S2C Fig . ) ., Based on these experiments , we defined a maximal stimulation range of ., The stimulation efficacy was explored by mapping the location of activated neurons relative to the stimulating electrode ., Fig . 3A shows neuronal activation maps at three different stimulation amplitudes ( response probability is color coded ) ., This figure shows that the distance between a neuron soma and the stimulation electrode is not highly correlated to the amplitude of the stimulation needed to activate it ., Indeed , some of the neurons located far from the electrode are activated by lower stimulation amplitudes than neurons located close to the electrode ., This suggests that activation is transmitted over long distances by the neuronal processes ., This effect is further illustrated in Fig . 3B which shows the increase in the number of activated cells with increased charge density averaged over three cultures and eight electrodes ., Activation ratio reaches a saturation level , suggesting that electrical stimulation activates processes at the electrode vicinity ., Fig . 3C shows activation maps depicting the response to stimulations applied at two different electrodes ( highlighted ) ., Each electrode activated a unique set of neurons and the neuron population that responded to both electrodes was very small ., This confirms that the distance between neuron and electrode is not a dominant factor determining response probability ., Altogether these results indicate that in our MEA setup , electrical neuronal activation is dominated by the activation of neuronal processes rather than by that of the soma and is highly non-localized 44 , 45 ., We next explored astrocytic activation in response to neuronal activity ., To maximize neuronal activation we used nine stimulating electrodes and applied a stimulation protocol consisting of 30 s current pulse trains at frequencies ranging from 0 . 2 to 70 Hz ., At low stimulation frequencies ( <1 Hz ) , single spike patterns are apparent in neuronal Ca2+ traces ( Fig . 4A , red traces ) ., For higher frequencies however , the neuronal response saturates due to slow dynamics and high affinity of the Ca2+ dye ., Fig . 4A also displays the Ca2+ responses of selected astrocytes ( green curves ) ., Strong changes in astrocytic Ca2+ are observed in response to stimulated neuronal activity ., The central result in this work is that the astrocytic responses are highly dependent on neuronal activity frequency: globally , astrocyte tends to respond only when the stimulation frequency becomes large enough ., A similar effect was observed in 9 experiments , from 7 different cultures ., It should be noted that , whatever the stimulation frequency , the responsive astrocytes were uniformly distributed around the stimulating electrode ., Since the astrocyte response does not depend on the distance to the electrode , the possibility of direct electrical stimulation , from the electrode to the astrocyte , is unlikely ( S4C-S4E Fig . ) ., To further dismiss the hypothesis that astrocytic response is due to direct electrical stimulation , rather than a result of stimulation-triggered neuronal activity , we conducted control experiments where sodium channel blockers ( TTX ) were applied to the cultures ., Application of TTX eliminated neuronal activity , as expected , but also abolished the nontrivial astrocytic activity ( S5 Fig . ) ., Such a disappearance of astrocyte response in the presence of TTX strengthens the notion that astrocytes are not directly activated by the electrodes but by the neuronal activity resulting from electrode stimulation ., A candidate molecule to support the above-described neuron-astrocyte communication is glutamate 5 , 46–50 ., To explore the role of glutamate as the biological transmitter underlying the neuron-astrocyte activity , we applied mGluR1 and mGluR5 antagonists ( LY367385 and MPEP respectively ) ., Fig . 4B shows that in those conditions , the neuronal Ca2+ traces are essentially similar and faithfully follows electrical stimulation ., However astrocytic activity is completely abolished as a result of blocking mGluRs ., These experiments yield two important conclusions ., First , in our experimental conditions , calcium dynamics in the neuron somata does not appear to be significantly dependent on mGluR group I receptors ., More importantly , these experiments indicate that the neuron-astrocyte communication evidenced here is mediated by glutamate activation of astrocytic mGluR group I receptors ., To further test the above indication that stimulation frequency is indeed the significant parameter affecting astrocyte response , we applied an alternative stimulation protocol , in which we varied the frequency of the electrical stimulation but kept the number of stimulations constant ( the stimulation duration was thus inversely related to its frequency ) ., In addition , the order with which the stimulation frequencies were applied to the MEA was chosen at random , so as to avoid artifactual responses such as cell fatigue or dye poisoning ., The goal of this alternative stimulation protocol was to distinguish an astrocyte response that would depend on the stimulation frequency from a response that depends on the number of neuronal spikes in an accumulative manner ., Fig . 5A shows that astrocytic calcium activity in response to neuronal stimulation is indeed frequency dependent since in response to this alternative protocol , the astrocyte still tend to respond only to the largest stimulation frequencies ., Furthermore , as with the stimulation protocol of Fig . 4 , the application of mGluR1 and mGluR5 antagonists abolished astrocyte calcium activity in response to neuronal activation ( Fig . 5B ) ., To quantify the above results , we computed the single-cell responsiveness of an astrocyte as the increase of the calcium signal of this astrocyte that is specifically triggered by the stimulation ( see Methods ) ., Fig . 5C displays the distributions of the single-cell responsivenesses of all the activated astrocytes in the experiment of Fig . 5A ., For low stimulation frequencies , the single-cell responsivenesses are essentially peaked around a very low mean value ., When the stimulation frequency increases , the distributions of single-cell responsivenesses get much broader , thus revealing increasing cell-to-cell variations in the response , but the average value of the distribution increases rapidly ., Fig . 5D shows the evolution of the average value of the single-cell responsivenesses distribution ( referred to as the population average , see Methods ) as a function of the stimulation frequency ( each data point on the figure shows the population average of a single experiment ) ., In control conditions ( empty grey circles ) , the population responsiveness is very low below a frequency threshold and increases rapidly above this threshold ., This sigmoid-like response thus defines an onset frequency that varies from one astrocyte to the other in an experiment and between experiments , as a result of network and intrinsic astrocyte parameters ., The variability of the onset frequency will be further explored below using a computer model ., The average responsiveness over the experiences ( black circles in Fig . 5D , total of n\u200a=\u200a284 cells ) displays a sigmoid shape ( dashed line ) with an onset frequency of Hz ( 95% confidence level ) ., Fig . 5D also shows the population responsiveness in the presence of mGluR group I antagonists ( n\u200a=\u200a239 ) ( empty red squares ) ., In agreement with the traces in panel B , blocking mGluR receptors suppresses population responsiveness throughout the frequency range ., Linear fit of the average population responsiveness ( full blue squares ) yields a roughly zero s | Introduction, Methods, Results, Discussion | Neuron-astrocyte communication is an important regulatory mechanism in various brain functions but its complexity and role are yet to be fully understood ., In particular , the temporal pattern of astrocyte response to neuronal firing has not been fully characterized ., Here , we used neuron-astrocyte cultures on multi-electrode arrays coupled to Ca2+ imaging and explored the range of neuronal stimulation frequencies while keeping constant the amount of stimulation ., Our results reveal that astrocytes specifically respond to the frequency of neuronal stimulation by intracellular Ca2+ transients , with a clear onset of astrocytic activation at neuron firing rates around 3-5 Hz ., The cell-to-cell heterogeneity of the astrocyte Ca2+ response was however large and increasing with stimulation frequency ., Astrocytic activation by neurons was abolished with antagonists of type I metabotropic glutamate receptor , validating the glutamate-dependence of this neuron-to-astrocyte pathway ., Using a realistic biophysical model of glutamate-based intracellular calcium signaling in astrocytes , we suggest that the stepwise response is due to the supralinear dynamics of intracellular IP3 and that the heterogeneity of the responses may be due to the heterogeneity of the astrocyte-to-astrocyte couplings via gap junction channels ., Therefore our results present astrocyte intracellular Ca2+ activity as a nonlinear integrator of glutamate-dependent neuronal activity . | Over the past 20 years , astrocytes , a type of brain cells that were considerably disregarded , have gradually been found to actually display remarkable properties ., In contrast with neurons , which communicate through changes in their membrane potential , astrocytes communicate as networks through propagated changes in their internal calcium concentration ., While still hotly debated , recent findings even indicate that astrocytic activity could influence neuronal activity in several ways , including the modulation of synaptic plasticity between neurons ., How exactly these astrocytes react to neuronal activity as individual cells and as a network is however still unclear ., In this work , we address this question by using neuron-astrocyte cell cultures that we stimulate with multi-electrode arrays in conjunction with a computational model of neuron-astrocyte communication ., Our results show that astrocytes respond to neuronal activity in a frequency-dependent manner through glutamate signaling ., We also propose that the heterogeneity of astrocyte response time and oscillation frequencies that emerges from our experiments could be linked to their network organization ., Since astrocytes are known to communicate with neurons by releasing signaling molecules upon activation , our findings , by shedding new light on astrocyte responses to neuronal activity , help uncover the complex mechanisms underlying neuron-astrocyte cross-talk . | cell biology, animal cells, computational neuroscience, astrocytes, biology and life sciences, cellular types, computational biology, glial cells, biophysics, macroglial cells, biophysical simulations | null |
journal.pgen.1004370 | 2,014 | A Mutation in the FAM83G Gene in Dogs with Hereditary Footpad Hyperkeratosis (HFH) | The skin and most notably its outermost layer , the epidermis , forms an essential barrier against the environment ., The soles of the feet and the palms of the hands are covered by the specially structured palmoplantar epidermis , which has to bear the strongest mechanical forces of the entire skin ., Epidermolytic palmoplantar keratoderma ( EPPK ) is an inherited disorder characterized by abnormal thickening of the palmoplantar epidermis ., It is typically caused by dominant variants in the KRT9 gene encoding keratin 9 , a type I intermediate filament specifically expressed in the suprabasal layer of the palmoplantar epidermis 1 , 2 ., Most human EPPK patients are heterozygous for dominant KRT9 variants 1 ., However , homozygous Krt9 deficient mice show a very similar phenotype 2 ., Related human genodermatoses which may involve the palmoplantar epidermis , but are not exclusively restricted to palms and soles are caused by variants in KRT1 3 , KRT10 4 , KRT16 5–7 , and AQP5 encoding the water channel aquaporin 5 8 ., Many genetic defects in the keratin genes themselves have been characterized in keratinizing disorders and provided first insights into the function of specific keratins in the various epithelia ., However , much less is known about other molecules that interact with the keratins and are potentially involved in posttranslational modifications of keratins , or other regulatory mechanisms ensuring the mechanical stability of the epidermis 9 , 10 ., Spontaneous animal mutants with genodermatoses or other heritable phenotypes of the skin provide an opportunity to identify further components of the complex molecular machinery required to maintain skin function ., Due to their special population structure purebred dogs are particularly well suited for genetic analyses 11 ., Successful examples for the utilization of dog genetics in skin research include the identification of genes involved in the determination of hair characteristics 12 , ectodermal development 13 , one form of ichthyosis 14 , congenital keratoconjunctivitis sicca and ichthyosiform dermatosis 15 , the excessive skin folding in Chinese Shar Pei dogs 16 , and hereditary nasal parakeratosis 17 ., Hereditary footpad hyperkeratosis ( HFH , also known as digital hyperkeratosis ( DH ) or “corny feet” ) is a specific form of an orthokeratotic palmoplantar hyperkeratosis , which has been originally described in Irish Terriers 18 ., HFH has also been observed in other related dog breeds , such as e . g . the Kromfohrländer , a young German dog breed founded in 1945 ., HFH initially leads to thickened and hardened footpads , which can be recognized in juvenile dogs starting at an age of 18 to 24 weeks ., The inelastic pad surface subsequently develops cracks and fissures , which predispose affected dogs to secondary infections ., If not properly managed , HFH can thus lead to considerable pain and lameness in affected dogs ., HFH is inherited as a monogenic autosomal recessive trait 18 ., A previous candidate gene study was not successful in identifying the causative gene 19 ., In this study we used genome-wide association studies ( GWAS ) in independent Kromfohrländer and Irish Terrier cohorts and whole genome re-sequencing ( WGS ) to identify the causative genetic lesion for HFH in both breeds ., We collected samples from 13 HFH affected Kromfohrländer and 29 controls and genotyped them with the 173k SNP chip ., After removing 95 , 759 markers , which had low call rates ( <90% ) , were non-informative ( MAF <0 . 05 ) , or showed a strong deviation from Hardy-Weinberg equilibrium in the controls ( p<10−5 ) , we retained 77 , 903 markers for the final genome-wide allelic association study ., Three best-associated SNPs in the GWAS had identical raw p-values of 1 . 0×10−13 ( Figure 1A ) ., The corrected p-value after 100 , 000 permutations was <10−5 ., The 159 best-associated SNPs with raw p-values of less than 1×10−4 were all located on chromosome 5 ., The genomic inflation factor in this analysis was 1 . 40 ., We also performed a GWAS in an independent cohort of Irish Terriers to replicate our findings ., For the replication we had 10 cases , 21 controls , and 82 , 671 markers ., In the Irish Terrier cohort HFH was also strongly associated with the same region on chromosome 5 with a raw p-value of 6 . 9×10−10 ( Figure 1B ) ., The genomic inflation factor in the Irish Terrier analysis was 1 . 32 ., As both cohorts showed considerable population stratification we repeated the analyses with a mixed model that corrects for this confounding effect ., The same markers as in the initial analyses showed the strongest associations ., Subsequently , we applied a homozygosity mapping approach to fine-map the region containing the HFH mutation ., We hypothesized that the affected dogs most likely were inbred to one single founder animal ., In this scenario , the affected individuals were expected to be identical by descent ( IBD ) for the causative mutation and flanking chromosomal segments ., We analyzed the 23 combined cases for extended regions of homozygosity with simultaneous allele sharing ., In the associated interval on CFA 5 , all 23 affected dogs were homozygous and shared identical alleles over 36 consecutive SNP markers ., We concluded that the causative mutation should be located in the 611 kb critical interval between the closest heterozygous markers on either side of the homozygous segment ( CFA5: 40 , 521 , 040–41 , 131 , 739 CanFam 3 . 1 assembly; Figure 1C ) ., A total of 13 genes and loci are annotated in the critical interval on CFA 5 ( Figure 1D ) ., In order to obtain a comprehensive overview of all variants in the critical interval we sequenced the whole genome of one affected Kromfohrländer ., We collected nearly 293 million paired-end reads from a shotgun fragment library corresponding to 23 . 5× coverage of the genome ., We called SNPs and indel variants with respect to the reference genome of a presumably non-affected Boxer ., Across the entire genome , we detected ∼6 . 8 million variants of which ∼3 . 5 million were homozygous ( Table 1 ) ., Within the critical interval there were 1 , 314 variants of which 16 were predicted to be non-synonymous ., We further compared the genotypes of the affected Kromfohrländer with 46 dog genomes of various breeds that had been sequenced in the course of other ongoing studies ( Table S1 ) ., We hypothesized that the mutant allele at the causative variant should be completely absent from all other dog breeds in our sample set ., After this filtering step only two private variants remained in the critical interval and only one of them was predicted to be non-synonymous , FAM83G:c . 155G>C or Chr5:41 , 055 , 619G>C ., We confirmed this variant by Sanger sequencing ( Figure 2 ) and genotyped it in 43 Kromfohrländer , 194 Irish Terriers , and 288 dogs of other breeds ., It was perfectly associated with the HFH phenotype ( Table 2 , Table S2 ) ., The FAM83G:c . 155G>C variant represents a missense mutation in the FAM83G gene , encoding the family with sequence similarity 83 , member G . The variant changes an arginine codon to a proline codon ( p . R52P ) ., SIFT , Polyphen-2 , and PMUT predict that this non-conservative amino acid exchange affects protein function 20–22 ., The arginine at position 52 is perfectly conserved across all known eutherian FAM83G orthologs ( Figure 3 ) ., We confirmed by immunofluorescence that FAM83G is strongly expressed in footpad epidermis , but not the underlying dermis ( Figure S1 ) ., Hyperkeratosis of the foot pads is noticed by the owners of both breeds at 4–5 months of age and involves all footpads ., With time horny protrusions appear on the rims of the footpads and the pad surface becomes hard and develops cracks ( Figure 4A ) ., Affected animals avoid walking on irregular surfaces and may go lame ., The nails of affected dogs are very hard and seem to grow faster ., We noticed a duller , less wiry , softer coat on an affected Kromfohrländer ( Figure 4C ) ., Similar clinical symptoms were noted on 5 HFH affected Irish Terriers ., We also performed histopathological examinations of palmoplantar and normal epidermis ., We did not observe any obvious changes in the normal epidermis from an HFH affected Kromfohrländer ( data not shown ) ., A paw pad biopsy from an affected Kromfohrländer revealed a moderate to severe palmoplantar epidermal hyperplasia associated with papillated epidermal protrusions to the outside ., The differentiation of the dermal keratinocytes was morphologically normal ., The palmoplantar epidermis was covered by abundant compact orthokeratotic keratin ( Figure 5 ) ., In this study we identified a missense variant of FAM83G as candidate causative genetic defect for HFH in two related dog breeds ., We cannot formally rule out the possibility that another , potentially non-coding regulatory variant , in absolute linkage disequilibrium ( LD ) with the FAM83G:c . 155G>C variant is the actual causative variant ., However , in our genome re-sequencing data , there is only one other variant in complete LD with the FAM83G variant ., This variant is an intergenic SNP more than 15 kb away from the next annotated transcript and thus unlikely to be functionally important ., It also has to be considered that our variant detection relied on short read mapping to an imperfect reference genome ., Thus , we will have missed variants , which are located in genome segments that are not contained in the reference genome , such as gap regions ., We may also have missed non-synonymous variants in genes that are not or not correctly annotated in the dog reference genome ., While acknowledging the limitations of the currently available technologies our genome re-sequencing data taken together with the precise genetic mapping and the strictly recessive mode of inheritance of HFH , which suggests a complete loss-of-function allele , very strongly support the causality of FAM83G:c . 155G>C ., FAM83G is hardly characterized so far ., It has recently been shown that a partial deletion of the Fam83g gene causes the phenotype of the wooly ( wly ) mouse mutant 23 ., Wooly mice macroscopically display a rough or matted appearance of their coat ., Similar to our findings in dogs and in spite of this clearly visible macroscopic phenotype , microscopic examination did not reveal any consistent anomaly in any of the four murine hair types nor any consistent changes in skin histology 23 , 24 ., According to our knowledge the foot pads of wooly mice have not been studied in detail ., The FAM83 protein family consists of 8 known members FAM83A – FAM83H ., Apart from the single report on murine Fam83g , a physiological in vivo function has so far only been discussed for FAM83H 15 , 23 , 25 ., Heterozygous nonsense variants in the last exon of the FAM83H gene have been reported in human patients with autosomal dominant hypocalcified ameliogenesis imperfecta , a disorder of enamel formation during tooth development 25 ., However , a FAM83H frameshift variant in the last exon was shown to cause the autosomal recessive congenital keratoconjunctivitis sicca and ichthyosiform dermatosis ( CKCSID ) , also called “dry eye curly coat syndrome” , in the Cavalier King Charles Spaniel dog breed 15 ., The apparent discrepancy in the reported phenotypes of human and dog patients with FAM83H variants might be due to the specific nature of the involved variants and calls for further investigations ., In this context , it is interesting to note that the FAM83H mutant Cavalier King Charles Spaniels have a defect that shares several phenotypic features with the FAM83G mutant Kromfohrländer and Irish Terriers , such as an altered coat texture , altered nails and palmoplantar hyperkeratosis ., The FAM83 members share a conserved protein domain of about 300 amino acids at their N-terminus ( Pfam DUF1669 ) , which shows homology to the phospholipase D catalytic domain ., However , as critical catalytic histidine residues are lacking , it is unlikely that this domain actually has a phospholipase activity in the members of the FAM83 family 23 ., FAM83G is highly conserved in eutherians ( placental mammals ) ., In more distantly related vertebrate species the predicted FAM83G orthologs show a drastically reduced overall homology of the amino acid sequence , which might indicate that FAM83G acquired a new function during eutherian or possibly mammalian evolution ., It is tempting to speculate that this new function is related to the evolution of hair and a specialized palmoplantar epidermis in mammals ., In conclusion , we have identified a missense variant in FAM83G as most likely causative for HFH in dogs ., This provides a first indication of a physiological function of this particular gene in maintaining the integrity of the palmoplantar epidermis ., Together with previous data from mice our data also confirm that this gene has an additional role in hair morphology ., All animal experiments were performed according to the local regulations ., The dogs in this study were examined with the consent of their owners ., The study was approved by the “Cantonal Committee For Animal Experiments” ( Canton of Bern; permit 23/10 ) ., We used 13 HFH cases and 30 controls from the Kromfohrländer breed ., The phenotype information was extracted from a database that is maintained by the breeding club and based on reports from dogs owners and evaluations by the breeding committees of the club ., One of the 13 Kromfohrländer cases was additionally examined by a board certified veterinary dermatologist ( PR; Figure 4 ) and the clinical diagnosis was confirmed by the histhopathological analysis of a biopsy from the footpad , which was evaluated by a board certified veterinary pathologist ( MMW; Figure 5 ) ., In the Irish Terrier breed we initially started our analysis with 26 reported cases and 171 controls ., In the Irish Terriers we also primarily relied on phenotypes as reported by the owners ., We initially selected 13 owner-reported cases and 21 owner-reported controls for the GWAS and homozygosity mapping ., During this analysis we realized that 3 of the owner-reported cases did not carry the disease-associated haplotype ., This prompted us to carefully re-evaluate the phenotypes of all Irish Terrier cases ., It then turned out that the 3 suspicious dogs had only lesions on one to three feet , whereas all other Irish Terrier cases had lesions on the footpads of all four feet ., We therefore used a refined phenotype classification , which required reported lesions on all four feet for HFH cases ., Based on this new phenotype classification we assumed the 3 Irish Terriers that did not have lesions on all four feet to represent phenocopies and excluded them from all further analyses ., We thus ended up with 23 Irish Terrier HFH cases and 171 controls for the final analysis ., We also used additional DNA samples from other breeds that were collected for various research projects at the Institute of Genetics of the University of Bern ., For these other samples a non-affected phenotype was assumed as HFH is supposedly confined to the Kromfohrländer and Irish Terrier breeds ., We isolated genomic DNA from EDTA blood samples with the Nucleon Bacc2 kit ( GE Healthcare ) and from cheek swabs with the NucleoSpin 96 Tissue DNA Kit ( Macherey-Nagel ) ., Genotyping was done on illumina canine_HD chips by GeneSeek/Neogen ( Kromfohrländer , 173 , 662 SNPs called ) or the Centre National de Génotypage , Evry , France ( Irish Terriers , 174 , 376 SNPs called ) ., Genotypes were stored in a BC/Gene database version 3 . 5 ( BC/Platforms ) ., We used PLINK v1 . 07 26 to perform genome-wide association analyses ( GWAS ) ., In the Kromfohrländer analysis all 13 cases and 29 controls had call rates >90% ., We removed 3 , 303 markers with call rates <90% from the analysis ., We also removed 94 , 714 markers with minor allele frequency ( MAF ) <5% and 30 markers strongly deviating from Hardy-Weinberg equilibrium in the controls ( p<10−5 ) ., The final Kromfohrländer dataset consisted of 42 dogs and 77 , 903 SNPs ., In the Irish Terrier cohort with 10 cases and 21 controls , all dogs had call rates >90% ., We removed 5 , 723 markers with call rates <90% from the analysis ., We also removed 89 , 179 markers with MAF <10% and 366 markers strongly deviating from Hardy-Weinberg equilibrium in the controls ( p<10−5 ) ., The final Irish Terrier dataset consisted of 31 dogs and 82 , 671 SNPs ., We performed an allelic association study and determined an empirical significance threshold by performing 100 , 000 permutations of each dataset with arbitrarily assigned phenotypes ., As both datasets showed considerable population stratification , we also analyzed the data using GenABEL and a mixed model approach 27 ., This procedure corrects for cryptic relatedness by using the genomic kinship estimated from the marker data as covariable in the model ., In both cohorts the same markers showed the highest association regardless whether the simple PLINK analysis or the mixed model GenABEL analysis was performed ., With the correction for population stratification the genomic inflation factors were reduced from 1 . 40 to 1 . 07 in Kromfohrländer and from 1 . 32 to 1 . 02 in Irish Terriers ., The corrected p-values ( Pc1df ) for the best associated markers were then 4 . 7×10−8 in Kromfohrländer and 4 . 2×10−6 in Irish Terriers ., We also used PLINK to search for extended intervals of homozygosity with shared alleles ., The final critical interval was defined by visual inspection of all SNP chip genotypes on chromosome 5 for the 13 Kromfohrländer and 10 Irish Terrier cases in an Excel-file ., We used the dog CanFam 3 . 1 assembly for all analyses ., All numbering within the canine FAM83G gene corresponds to the accessions XM_003434636 . 2 ( mRNA ) and XP_003434684 . 1 ( protein ) ., We analyzed the functional effects of variants in silico with SIFT , Polyphen-2 und PMUT 20–22 ., We prepared a fragment library with 300 bp insert size and collected 293 , 647 , 193 illumina HiSeq2500 paired-end reads ( 2×100 bp ) or roughly 23 . 5× coverage ., We mapped the reads to the dog reference genome using the Burrows-Wheeler Aligner ( BWA ) version 0 . 5 . 9-r16 28 with default settings and obtained 551 , 317 , 870 uniquely mapping reads ., After sorting the mapped reads by the coordinates of the sequence and merging the 2 lanes of data with Picard tools , we labeled the PCR duplicates also with Picard tools ( http://sourceforge . net/projects/picard/ ) ., We used the Genome Analysis Tool Kit ( GATK version v2 . 3-6 , 29 ) to perform local realignment and to produce a cleaned BAM file ., Variant calls were then made with the unified genotyper module of GATK ., Variant data for each sample were obtained in variant call format ( version 4 . 0 ) as raw calls for all samples and sites flagged using the variant filtration module of GATK ., Variant calls that failed to pass the following filters were labeled accordingly in the call set:, ( i ) Hard to Validate MQ0 ≥4 & ( ( MQ0/ ( 1 . 0 * DP ) ) >0 . 1 ) ;, ( ii ) strand bias ( low Quality scores ) QUAL <30 . 0 || ( Quality by depth ) QD <5 . 0 || ( homopolymer runs ) HRun >5 || ( strand bias ) SB >0 . 00;, ( iii ) SNP cluster window size 10 ., The snpEFF software 30 together with the CanFam 3 . 1 annotation was used to predict the functional effects of detected variants ., We considered the following snpEFF categories of variants as non-synonymous: NON_SYNONYMOUS_CODING , CODON_DELETION , CODON_INSERTION , CODON_CHANGE_PLUS_CODON_DELETION , CODON_CHANGE_PLUS_CODON_INSERTION , FRAME_SHIFT , EXON_DELETED , START_GAINED , START_LOST , STOP_GAINED , STOP_LOST , SPLICE_SITE_ACCEPTOR , SPLICE_SITE_DONOR ., The critical interval contained 610 , 700 bp and 15 , 160 coding nucleotides , respectively ., In our re-sequencing data , we had ≥4× coverage on 566 , 516 bp of the critical interval ( 93% ) and on all 15 , 160 coding bases ., Additionally , we searched for structural variations ( deletions , insertions , inversions ) within the critical interval using the software SVDetect 31 ., SVDetect calls intrachromosomal and interchromosomal rearrangements from discordant , quality pre-filtered read pairs ., As per the authors suggestion SVDetect software was set to detect rearrangements with 3 or more supporting read pairs using 2 times standard deviation of the insert size as threshold for both deletions and duplications ., This analysis identified 15 rearrangements between 100 and 450 bp in size in the critical interval ., Most of these variants were within repeats and none of them affected an exon of the annotated genes in the critical interval ., The sequence data of the affected Kromfohrländer were deposited in the short read archive of the European Nucleotide Archive ( ENA ) under accession PRJEB6076 ., We used Sanger sequencing to confirm the illumina sequencing results and to perform targeted genotyping for selected variants ., For these experiments we amplified PCR products using AmpliTaqGold360Mastermix ( Applied Biosystems ) ., PCR products were directly sequenced on an ABI 3730 capillary sequencer ( Applied Biosystems ) after treatment with exonuclease I and shrimp alkaline phosphatase ., We analyzed the Sanger sequence data with Sequencher 5 . 1 ( GeneCodes ) . | Introduction, Results, Discussion, Materials and Methods | Hereditary footpad hyperkeratosis ( HFH ) represents a palmoplantar hyperkeratosis , which is inherited as a monogenic autosomal recessive trait in several dog breeds , such as e . g . Kromfohrländer and Irish Terriers ., We performed genome-wide association studies ( GWAS ) in both breeds ., In Kromfohrländer we obtained a single strong association signal on chromosome 5 ( praw\u200a=\u200a1 . 0×10−13 ) using 13 HFH cases and 29 controls ., The association signal replicated in an independent cohort of Irish Terriers with 10 cases and 21 controls ( praw\u200a=\u200a6 . 9×10−10 ) ., The analysis of shared haplotypes among the combined Kromfohrländer and Irish Terrier cases defined a critical interval of 611 kb with 13 predicted genes ., We re-sequenced the genome of one affected Kromfohrländer at 23 . 5× coverage ., The comparison of the sequence data with 46 genomes of non-affected dogs from other breeds revealed a single private non-synonymous variant in the critical interval with respect to the reference genome assembly ., The variant is a missense variant ( c . 155G>C ) in the FAM83G gene encoding a protein with largely unknown function ., It is predicted to change an evolutionary conserved arginine into a proline residue ( p . R52P ) ., We genotyped this variant in a larger cohort of dogs and found perfect association with the HFH phenotype ., We further studied the clinical and histopathological alterations in the epidermis in vivo ., Affected dogs show a moderate to severe orthokeratotic hyperplasia of the palmoplantar epidermis ., Thus , our data provide the first evidence that FAM83G has an essential role for maintaining the integrity of the palmoplantar epidermis . | The palms and soles of mammals are covered by the palmoplantar epidermis , which has to bear immense mechanical forces and has therefore a special composition in comparison to the epidermis on regular skin ., We studied a Mendelian disease in dogs , termed hereditary footpad hyperkeratosis ( HFH ) ., HFH affected dogs develop deep fissures in the paw pads , which are the consequence of a pathological thickening of the outermost layer of the epidermis ., We mapped the disease causing genetic variant in the Kromfohrländer and Irish Terrier breeds to a 611 kb interval on chromosome 5 ., HFH affected Kromfohrländer and Irish Terriers shared the same haplotype indicating descent from a common founder ., We re-sequenced the genome of an affected dog and compared it to genome sequences of 46 control dogs ., The HFH affected dog had only one private non-synonymous variant in the critical interval , a missense variant of the FAM83G gene ., We genotyped this variant in more than 500 dogs and found perfect association with the HFH phenotype ., Our data very strongly suggest that the FAM83G variant is causative for HFH ., FAM83G is a protein with unknown biochemical function ., Our study thus provides the first link between this protein and the palmoplantar epidermis . | sequencing techniques, genome-wide association studies, dermatology, medicine and health sciences, animal welfare, animal genetics, animal breeding, genome sequencing, genome analysis, molecular biology techniques, animal management, dermatologic pathology, veterinary science, veterinary medicine, hair and nail diseases, molecular biology, agriculture, genetics, biology and life sciences, genomics, genetics of disease, computational biology | null |
journal.pcbi.1006184 | 2,018 | Interplay of multiple pathways and activity-dependent rules in STDP | Synaptic plasticity , one of the paramount biological mechanism supporting learning and memory in the brain , has been the object of a wide literature spanning from experimental works 1–3 to computational investigations 4–6 ., In 1949 , Donald Hebb’s pioneering work postulated that long-term modifications of the synaptic efficacy can be induced in response to patterns of activity of the pre- and postsynaptic neurons 7 ., Since then , many experimental studies have confirmed and extended Hebb’s postulate and have highlighted the complexity of the signaling pathways and their neuromodulation leading to synaptic efficacy changes in response to pre- and postsynaptic activity patterns 1 , 2 , 8 , 9 ., Numerous mathematical models were also developed to emulate this diversity and infer their computational capabilities 4–6 ., Spike-timing-dependent plasticity ( STDP ) is a biological phenomenon of activity-dependent change in synaptic connectivity that is viewed as a synaptic Hebbian learning rule ., STDP has been widely studied in the last two decades and experimentally observed at many synapses in various forms , and those were classified depending on the manner in which they implement Hebb’s postulate 8 , 9 ., STDP is assessed experimentally through repetitive paired activations of the pre- and postsynaptic sites with a prescribed timing that is denoted in this paper Δt ., By convention , we consider Δt < 0 when the postsynaptic stimulation occurs before the paired presynaptic one ( post-pre pairings ) , and Δt > 0 when the presynaptic stimulation occurs before the postsynaptic one ( pre-post pairings ) ., Classically , the same paired stimulation is repeated between 80 and 150 times at a constant frequency ( between 0 . 1 and 5 Hz ) 8–10 ., In many cases , these pairing patterns induce long-term plasticity exhibiting various polarities ( increase or decrease of the synaptic weight as a function of the sign of Δt ) or magnitudes ., In the vast majority , the expression of STDP is restricted to a narrow interval of values for Δt; thus , when pre- and postsynaptic activities are separated by a large Δt ( |Δt| > 50 ms in most of the cases ) , no long-term plasticity is observed 11 , 12 ., In this study , we term Hebbian STDP the plasticities whereby sequences of presentations of a presynaptic spike followed by a postsynaptic spike lead to Long-Term Potentiation ( LTP ) when repeated a specific number of times ( denoted NPairings ) at a certain frequency , whereas reverse sequences induce Long-Term Depression ( LTD ) ., Hebbian STDP was reported in various structures such as the hippocampus 11 , 13–15 , the cerebral cortex 12 , 16–19 and the striatum 20–23 ., Conversely , we will term here ( bidirectional ) anti-Hebbian STDP , the forms of STDP exhibiting a reverse polarity when compared to the aforementioned Hebbian STDP: causal pre-post pairings induce LTD and anti-causal post-pre pairings induce LTP ., Bidirectional anti-Hebbian STDP was also observed , for instance in the striatum 24–28 , in the somatosensory cortex 29 or in the cerebellum-like structure of the electrical fish 30 ., Unidirectional anti-Hebbian STDP , i . e . LTD for both post-pre and pre-post pairings , is another main form of STDP observed in the neocortex 31 , 32 , the dorsal cochlear nucleus 33 , the cerebellum 34 , 35 and the hippocampus 36 ., We underline that different definitions of ( anti- ) Hebbian STDP were used in the literature; the present study follows the terminology of early experimental studies 11 , 12 , or Figure 2 of the review 8 , but differs , e . g . , from the definitions used in 37 ., These plasticities were shown to be dependent upon the parameters of the stimulation beyond spike-timing: for instance , varying the frequency at which pairings are presented or the total number of pairings , presenting distinct spike patterns ( triplets , single spike , theta bursts , … ) 17 , 38–41 or changing neuromodulatory tones 21 may lead to distinct forms of STDP ., Despite the existence of multiples forms of STDP 8 , 9 , all of them have in common the crucial role played by the calcium transients in the pre- and postsynaptic compartments for the induction and maintenance of plasticity ., Postsynaptic calcium influxes through NDMA receptors ( NMDAR ) and voltage-sensitive calcium channels have been demonstrated to be key factors governing plasticity expression and polarity 10 ., Regarding Hebbian plasticity , calcium-dependent mechanisms act as coincidence detectors , essential to implement any type of STDP ., In addition , distinct signaling pathways appear to be involved , namely, ( i ) calcium triggering downstream cascades modulating calcium/calmodulin-dependent kinase II ( CaMKII ) 42 which ultimately regulates the gene expression and/or, ( ii ) the endocannabinoid ( eCB ) system , whose synthesis and release is calcium-dependent , acting retrogradely on the presynaptic element 43–45 ., Importantly , both of these pathways are able to trigger LTP or LTD depending on the spatio-temporal kinetics of the calcium 19 , 40 , 46 ., Calcium dynamics thus constitute a key factor in synaptic plasticity induction and in selecting plasticity forms ., Accordingly , numerous mathematical models were based on calcium transients and described various forms of STDP 5 ., In particular , Graupner and Brunel 47 proposed simple calcium-based models able to account for a wide range of experimental observations on synaptic plasticity ., However , while calcium-based models succeed in reproducing the results of the “classical” STDP ( ∼ 100 pairings ) , they do not take into account the dynamics of the establishment of plasticity and the variety of timescales involved in plasticity induction ., Indeed , in computational neuroscience , it is implicitly admitted that the synapse gradually amplifies synaptic changes as the number of stimulus presentation increases to reach the final plasticity profiles ., However , plasticity occurs at vastly distinct timescales and protocols based on one hundred trials ( i . e . , pairings ) , as classically performed in STDP experiments , only reveal an extreme steady state outcome ., Actually , a dozen of trials can be sufficient to induce plasticity , if not less in the case of associative memory 39 , 40 , 48 , 49 ., Importantly , it was recently shown that depending on the number of pairings , STDP on the cortex-to-striatum synapses ( cortico-striatal STDP ) exhibits three distinct forms of plasticity: an NMDAR-mediated LTP and an eCB-mediated LTD for 100 post-pre and pre-post pairings , respectively 20 , 21 , 24 , 25 , 27 , and an eCB-mediated LTP for 5-15 post-pre pairings 39 , 40 ., Note that at cortico-striatal synapses , GABA operates as a Hebbian/anti-Hebbian switch 27 , 28 and that without the blockade of GABA transmission , an anti-Hebbian STDP is induced as observed in vivo 26 ., These phenomena were reproduced in a biophysical model of the cortico-striatal synapse accounting for receptors activation dynamics ( 36 equations and 150 parameters ) 40 ., However , no simple phenomenological model reproduces these phenomena , and in particular models of plasticity based on the calcium hypothesis fail to reproduce such complex dynamical emergence of plasticity ., Here , we propose a new model built upon the calcium hypothesis and taking into account the existence of multiple signaling pathways at a given synapse that may be activated at distinct calcium levels ., We instantiate one model to fit the data from cortico-striatal STDP 39 , 40 , and show that the model accurately reproduces the experiments on the dependence of STDP on both the number and frequency of pairings ., We use this model to predict the response of the system as the stimulus frequency and number of presentations are varied , and extend the model to show how triplet rules will depend on the number of stimulus presentations ., Our model goes beyond the case of the cortico-striatal synapse with two signaling pathways , and we further explore the diversity and limited range of dynamical plasticity establishments that can be unfolded from classical Hebbian STDP ., In the face of this diversity , we further propose experimentally implementable protocols to differentiate those scenarii ., This study thus sheds a new light on the interplay of multiple signaling pathways at single synapses and how this multiplicity endows the synapse with the capacity of encoding multiple STDP profiles depending on the number and frequency of stimulus presentation , and argues that experiments based on stereotypical stimulus presentations are not sufficient to finely account for the complexity of plasticity , even in widely studied synapses ., We developed a general calcium-based model of the synapse allowing to take into account the presence of multiple pathways and past activity in the establishment of plasticity ., Our developments build upon the Graupner and Brunel model 47 , and extend it by, ( i ) introducing multiple plasticity pathways , and, ( ii ) taking into account the fact that receptor activation thresholds may depend on past activity ., We provide here the details of the models and the emergent changes in synaptic weight , as well as a theoretical formula thereof ., The model we built in the previous section is general and is thus able to reproduce a variety of synapses and plasticity mechanisms relying on calcium dynamics ., We study in this section the case of STDP at the cortico-striatal synapse , which was studied experimentally with varying Npairings 39 , 40 ., In these contributions , it was shown that STDP at the cortico-striatal synapse relied both on NMDAR and endocannabinoid pathways ( see Fig 1 ( a ) ) , and that synaptic changes after paired pre- and postsynaptic spikes not only depended on the timing between the pre- and postsynaptic spikes , but also varied with the number and the frequency of the pairings presented ., Namely , it was shown that for pre-post pairings ( 0 < Δt < +40 ms ) , an eCB-LTD progressively appeared as the number of pairings was increased , while for post-pre pairings ( −30 ms < Δt < 0 ms ) , a biphasic STDP emerged with an eCB-LTP for a low number of pairings ( 5 − 15 pairings ) , an absence of plasticity between 25 and 50 pairings , leaving room for NMDAR-LTP at higher numbers of pairings ( ≥ 75 pairings ) ., A schematic representation of the biological pathways involved is provided in Fig 1 ( a ) together with the biophysical mechanisms and proteins cascades ( described in more detail in 40 ) ., A minimal model of cortico-striatal plasticity thus requires taking into account two different and independent calcium-dependent pathways ( P = 2 ) , an eCB-dependent mechanism ( α = e ) which induces both LTP or LTD depending on the specific timing Δt of the pairings , and an NMDAR-dependent ( α = n ) associated to LTP only ., This yields to the following system of stochastic differential equations ( see schematic diagram in Fig 1 ( b ) ) :, { τdρedt=−ρe ( 1−ρe ) ( ρ*−ρe ) +γep ( 1−ρe ) Θ c ( t ) −θep ( c˜t ) −γedρeΘ c ( t ) −θed ( c˜t ) +Noisee ( t ) τdρndt=−ρn ( 1−ρn ) ( ρ*−ρn ) +γnp ( 1−ρn ) Θ c ( t ) −θnp ( c˜t ) +Noisen ( t ) ( 6 ) The complete synapse model is made of N ≫ 1 independent pairs ( ρ e i , ρ n i ) i = 1 ⋯ N satisfying Eq ( 6 ) , and a synaptic change deduced from the proportion of synapses that switch from being potentiated to depressed or reciprocally , through the sigmoidal map H of Eq ( 4 ) ., As described above , in response to pre- and postsynaptic spike-timing ( Δt = tpost − tpre ) , the calcium dynamics c undergoes jumps followed by exponential relaxation as described in Eq ( 2 ) , activating eCB-LTP , eCB-LTD and NMDAR-LTP as soon as c exceeds specific LTP or LTD thresholds ( see Fig 1 ( c ) , where the LTP and LTD thresholds are represented by the green and red lines respectively ) with both thresholds taken from the adjusted parameters of Table 1 for the eCB pathway ., When only one presynaptic spike ( thus without postsynaptic spike ) is evoked , the calcium concentration amplitude exceeds the LTP threshold for a short amount of time , and remains below the level of LTD induction: repeating this protocol does not lead to significant plasticity ., For a pre-post stimulation ( Δt > 0 ) , the summation of the pre- and postsynaptic calcium spikes triggers both LTP and LTD ., The same is valid for Δt < 0 , but the relative time spent above the LTP and LTD thresholds would be significantly different depending on Δt , underlining the importance of the timing and order between the spikes in the resulting plasticity: in the example depicted in Fig 1 ( c ) and parameters γ α x from Table 1 , a pre-post stimulation yields LTD whereas a post-pre stimulation yields LTP , consistent with anti-Hebbian STDP at cortico-striatal synapses ex vivo 27 , 28 in the absence of GABAA receptor antagonist and in vivo 26 ., STDP at the cortico-striatal synapse , studied in the previous section , provides a realistic example of plasticity with multiple pathways ., Our model , relying on only two equations and a small number of biologically interpretable parameters emulating NMDAR- and eCB-dependent pathways , reproduces all the phenomena reported at the cortico-striatal synapse , and allowed to draw predictions on plasticity for more complex stimuli such as triplet rules ., The present model is however much more general than the case of the cortico-striatal synapse: it can indeed emulate synapses with more than two signaling pathways with arbitrary independent plasticity rules , and thus allows unraveling the dynamics of plasticity expression in a variety of synapses with distinct plasticity ., Interestingly , while being quite versatile , the repertoire of behaviors that can be reproduced given a fixed number of pathways remains limited , and the model thus also provides predictions on the minimal number of pathways involved given a plasticity profile ., Indeed , a single pathway shall induce a monotonic establishment of plasticity if there is no inactivation of the pathway , whereas situations with two pathways can lead to four changes of plasticity ( LTP and LTD inactivation for each of the two pathways ) , possibly with periods of non-significant synaptic changes ., More generally , plasticity with P pathways may lead to up to 2P changes of monotonicity , possibly interspersed with periods of non-significant plasticity ., We investigate in the next sections a few possible scenarii relying on at most two signaling pathways that could lead to Hebbian or anti-Hebbian plasticity and suggest experiments that could distinguish distinct situations ., Synaptic plasticity is a complex phenomenon relying on the activation of a number of receptors and signaling pathways 3 , 10 ., A substantial difficulty for experimentalists is to characterize plasticity in the large variety of possible situations occurring in vivo ., To reduce this complexity , a protocol designed to reveal plasticity consists in considering changes in synaptic transmission after the reiterated presentation of a fixed spike pattern a large number of times ( on the order of one hundred ) and at a slow rate ., From these experiments , it remains complex to decipher the multiple signaling pathways involved in the expression of plasticity , and their complex interplay , particularly for low numbers of stimulus presentations or for various pairing frequencies ., To disentangle the distinctive role of multiple pathways , we developed and studied a phenomenological model of the evolution of synaptic weights and tested its responses in distinct situations ., The model relies on calcium transients triggered by the spiking activity of neurons on both sides of the synapses , and is built upon previous theoretical works ( see 47 and references therein ) ., When plasticity ( LTP and LTD ) relies on multiple signaling pathways 10 , the timescales at which these mechanisms activate and inactivate upon repetitive stimulation can lead to a variety of behaviors as a function of the number and of the frequency of pairings , which cannot be inferred from experiments where those are fixed ., Our model proposes a general and minimal framework to integrate multiple signaling pathways and their dependences upon repetitive stimulations ., We have instantiated this model with two specific pathways , NMDAR- and eCB-dependent , that was inspired by experiments at cortico-striatal synapse showing variations of the emergent plasticity upon variation of the number of pairings 39 , 40 ., Our model reduces to two stochastic equations Eq ( 6 ) and a small number of parameters , and accurately reproduced the data obtained in that experimental contribution ., To our knowledge , this model is the most parsimonious model reproducing STDP experimental results , yet many models of the class that we introduced can be proposed , including for instance NMDAR-LTD or pathways activated by distinct molecules ., We also used the model to predict the response of the system when the number of stimulations , the pairing frequency or the number of spikes , are varied ., This led us to draw predictions on the modifications of STDP profiles when the frequency of stimulus presentations was varied ., Eventually , we have made new predictions on the dependence of triplet rules upon the number of stimulus presentations , and showed that complex non-monotonic STDP profiles emerge with up to three distinct phases ., Our model goes beyond the particular case of the cortico-striatal synapse for which data was available , and we pursued our investigations by considering distinct mechanisms that could underlie another type of plasticity , symmetric anti-Hebbian LTD ( with LTD for pre-post and post-pre pairings ) ., In this case , we investigated three distinct possible scenarii involving up to two distinct pathways , and showed that unexpected phenomena may arise upon variations of the number and frequency of pairings , and in particular the emergence of an LTP at 100 pairings for high frequencies ., Overall , these results highlight the fact that electrophysiological experiments at a fixed frequency and a prescribed number of pairings may not be sufficient to extrapolate to other situations with smaller numbers of pairings or presentation frequencies ., To our knowledge , the present model is the first to take into account distinct signaling mechanisms involved in plasticity in a simple and compact framework ., The simplicity of the present model allows to envision the implementation of this type of synapse at the level of a neural network , opening the way to theoretical studies of information processing capacity of networks endowed with complex activity-dependent plasticity rules ., In addition to the development of a framework integrating multiple pathways , one of the main novelties of this model compared to other calcium-based models is that we have explicitly incorporated activity-dependent thresholds allowing to recover the response of plasticity mechanisms on the past activity of cells ., In the present model , we simply assumed that this history-dependence is parameterized by a cumulative calcium concentration ., Explicitly incorporating this dependence allows taking into account in the model multifarious experimental facts including finiteness of the calcium pool in the postsynaptic compartment , desensitization of synaptic-receptors and homeostasic mechanisms 63 ., The present model proposing that this dependence on past activity relies on cumulative calcium constitutes a first step , and could be refined in several directions , for instance incorporating a slow decay of past-activity dependence with time ( considering integrated calcium spikes with an exponentially decaying kernel for instance ) , moving averages in the flavor of sliding thresholds in the classical Bienenstock-Cooper-Munro ( BCM ) rule 56 , 57 ., In our case , the average activity of the neuron would be simply modeled by postsynaptic calcium concentration ( a reasonable proxy of neural activity ) , or with more refined models involving distinct molecular species and their timescales ., Despite a good qualitative agreement and an improved accuracy on the dynamics of the expression of plasticity , we found that our model shows a slight mismatch in the timescales at which plasticity emerges: first , although experiments at cortico-striatal synapses show a significant plasticity arising as early as 5 pairings and reaching a maximum at 10 pairings , we did not find in the model significant plasticity at 5 pairings and the maximal plasticity occurred after a slightly larger number of 12 pairings ., Moreover , a unidirectional LTP in the cortico-striatal plasticity at 100 pairings was observed experimentally when the frequency of pairing presentations reached 4 Hz , while the model reproduces this phenomenon slightly above 30 Hz ., We believe that this slower response of the present model relies on the bistable nature of the model , following 47 ., This bistability makes the system quite rigid and resistant to rapid changes , and a direct perspective would be to implement a more flexible model dropping the bistable model but conserving the long-term stability of macroscopic synaptic strength ensured by the bistable potential ., The present model would be also used in future works focusing on the implementation of the cortico-striatal STDP in large stochastic neural networks , with several classes of interneurons , aimed at understanding the possible role of implementing distinct cortico-striatal plasticity , in particular LTP , arising at various timescales and their possible role in information processing in striatum ., All in all , the present model suggests to reconsider a current widely admitted implicit hypothesis in models , and questions the usual view of STDP in models that consider a fixed curve solely dependent on the spike timing ( Δt ) ., Indeed , in most neural network models with STDP , it is considered that synaptic coefficients are progressively incremented depending on spike timings and according to toy-models of STDP ( e . g . , double-exponential curves ) ., This is implemented in various manners , including additive or multiplicative changes depending on all spike pairings or on the nearest-spike ( see e . g . 64 ) ., At the level of networks , a number of stochastic models were developed to study the influence of STDP as a synaptic plasticity rule ( see the review 4 ) ., In particular , early works showed the influence of classical Hebbian and asymmetric STDP in the dynamics of neuronal networks 65–67 ., The role of STDP-based rules in the emergence of structures in recurrent neural networks was also studied in a series of papers , highlighting for instance a possible key impact on the self-organization of microcircuits 68–70 ., More recently , the distribution of synaptic weights and its stability in randomly stimulated networks with different triplet rules has been extensively studied 37 ., The activity-dependent rule we proposed here , reproducing variable synaptic changes as a function of the number of stimulations , may lead to significant changes in the resulting connectivity and dynamics of neural networks ., Our model offers an avenue to revaluate the possible modifications of the resulting dynamics emphasizing the role of timescales in these systems 71 ., Simulations were performed with a custom code implemented in Python 2 . 7 or 3 . 5 , developed within the Spyder environment of the Anaconda suite ( Anaconda Software Distribution . Computer software . Vers . 2-2 . 4 . 0 . Continuum Analytics , Nov . 2015 . Web . <https://continuum . io> ) ., The main modules used numpy , matplotlib , math , scipy ., Elementary simulations of the model were run on a Macbook Pro ( Intel Core i5 processor and 16 RAM ) and more demanding simulations were executed on the Inria Paris-Rocquencourt computer cluster ., Figures and plots were realized using matplotlib module of Python and Illustrator/Photoshop of the Adobe series ., Unless specified otherwise , we used the parameter values listed in Table 1 ., These parameters were optimized starting from initial guesses chosen for consistency of the model with the data using the extensive analysis of 47 , Fig 2 ., For adjusting our parameters to the cortico-striatal plasticity , we used a global optimization algorithm , the differential-evolution function from scipy . optimize module to obtain qualitative fits ., Simulations of the model were realized either from the theoretical expressions computed , or with numerical simulations of the system of stochastic equations Eq ( 6 ) ., We used temporal discretization using an Euler scheme on t = −1…101 s with Niter steps ( see Table 1 ) and run the simulation for a set of N = 1000 individual efficacies ., To compute the change in macroscopic synaptic strength for the different pairings , we run the simulation for NPairings = 100 pairings and store the results for all the pairings during the STDP protocol ., Therefore , for each fixed Δt , the results obtained for different pairings are not independent , which has the interest of uncovering the evolution of one given synapse , and has no impact on the global outcome of the simulations as can be seen when compared with analytical results ., Except for Fig 3 where the analytical mean-field solutions are represented , all the figures show the numerical simulations ., For Fig 6 , we have reproduced 30 independent simulations in parallel to obtain the statistical means and standard deviations depicted ., All heatmaps used a logarithmic color bar to represent changes in synaptic strength ., The classification in mono- , bi- or tri-phasic regimes in Fig 7 ( c ) was done through a visual inspection of the STDP curves associated to each of the 20 Δt1 and Δt2 ., The model we studied is nonlinear , and as such , it was complex to derive the explicit form of the probability distribution of the solutions ., Following the approach proposed in the Appendix of 47 , we derived the probability distribution of the solution of an approximate model valid when the system remains in the linear part of the cubic bistable term ., The model involves a linear Ornstein-Uhlenbeck with deterministic time-dependent coefficients α ( t ) and β ( t ) that we computed as follow ., The solution of linear stochastic differential equations of type 72 ( with B a standard Brownian motion ) :, d ρ ( t ) = ( α ( t ) ρ ( t ) + β ( t ) ) d t + σ ( t ) d B ( t ) ( 7 ), with initial condition ρ0 can be easily expressed in closed form as:, ρ ( t ) = ρ 0 exp ( ∫ 0 t α ( s ) d s ) + ∫ 0 t exp ( ∫ u t α ( s ) d s ) β ( u ) d u + ∫ 0 t exp ( ∫ u t α ( s ) d s ) σ ( u ) d B ( u ) ., ( 8 ), As indicated in the main text , the synaptic change is obtained using a sigmoid transform of the proportion U of synapses that , after the protocol , have crossed upwards the threshold value ρ* , over the proportion D of those that crossed downwards ., Since the above described Ornstein-Uhlenbeck process is Gaussian , these probabilities are fully characterized by the mean and single-time variance functions of ρ , which have the following expressions:, E ρ ( t ) | ρ 0 = ρ 0 exp ( ∫ 0 t α ( s ) d s ) + ∫ 0 t exp ( ∫ u t α ( s ) d s ) β ( u ) d u ( 9 ), and, Var ρ ( t ) | ρ 0 = ∫ 0 t exp ( 2 ∫ u t α ( s ) d s ) σ 2 ( u ) d u ( 10 ) We thus derive the time-varying coefficients α and β arising in the approximated model , for the eCB pathway ( the other can be dealt with in the same way ) ., These are computed describing the time spent above the various thresholds of the model ., We denote by η i x the average time spent above the threshold θ i x: this quantity only depends on the calcium dynamics can be easily computed analytically for each given a pairing protocol ., Similarly , we define te = T ne the time at which the eCB-LTP is inactivated at the cortico-striatal synapse , with T being the duration between two pairings and ne the pairing number at which eCB-LTP is first inactivated ., Denoting Γ i x = γ i x η i x , we have the following compact formulae for the coefficients of the Ornstein-Uhlenbeck processes αe , βe and σe:, α e ( t ) = { - Γ e d + Γ e p τ = - 1 τ 1 if t < t e - Γ e d τ = - 1 τ 2 if t e < t ( 11 ) β e ( t ) = { Γ e p τ = ρ ˜ 1 τ 1 si t < t e 0 si t e < t ( 12 ) σ e ( t ) = { η e d + η e p τ σ = σ 1 τ 1 si t < t e η e d τ σ = σ 2 τ 2 si t e < t ( 13 ) Because of the simple , piecewise constant form of the coefficients , we have , for deterministic initial conditions:, E ρ e ( t ) | ρ e ( 0 ) = { ρ e ( 0 ) exp ( - t τ 1 ) + ρ ˜ 1 ( 1 - exp ( - t τ 1 ) ) if t < t e ρ e ( 0 ) exp ( - t - t e τ 2 - t e τ 1 ) + ρ ˜ 1 exp ( - t - t e τ 2 ) ( 1 - exp ( - t e τ 1 ) ) if t e < t ( 14 ), and, Var ρ e = { σ 1 2 ( 1 - exp ( - 2 t τ 1 ) ) t < t e σ 1 2 exp ( - 2 t - t e τ 2 ) ( 1 - exp ( - 2 t e τ 1 ) ) + σ 2 2 ( 1 - exp ( - 2 t - t e τ 2 ) ) if t e < t ( 15 ) The probability that an initially depressed synapse becomes potentiated is thus given by:, U e ( n T ) = P ( ρ e > ρ * | ρ e ( 0 ) = 0 ) = 1 2 ( 1 + erf ( − ρ * − E ρ e | ρ e ( 0 ) = 0 ( n T ) Var ρ e ( n T ) ) ) , ( 16 ), and the probability of an initially potentiated synapse to become depressed by:, D e ( n T ) = P ( ρ e < ρ * | ρ e ( 0 ) = 1 ) = 1 2 ( 1 − erf ( − ρ * − E ρ e | ρ e ( 0 ) = 1 ( n T ) Var ρ e ( n T ) ) ) ., ( 17 ), allowing directly to obtain the change in synaptic weight associated as H ( U e ( n T ) D e ( n T ) ) ., A comparison of the Ornstein-Uhlenbeck approximation with the numerical simulations of the nonlinear system is provided in Fig 3 ( a ) and S2 ( a ) Fig , showing a good agreement for the parameter set chosen ., The data used to fit and validate our results were previously published in 39 , 40 ., We refer to these papers for more specific information on the experimental protocol . | Introduction, Results, Discussion, Methods | Hebbian plasticity describes a basic mechanism for synaptic plasticity whereby synaptic weights evolve depending on the relative timing of paired activity of the pre- and postsynaptic neurons ., Spike-timing-dependent plasticity ( STDP ) constitutes a central experimental and theoretical synaptic Hebbian learning rule ., Various mechanisms , mostly calcium-based , account for the induction and maintenance of STDP ., Classically STDP is assumed to gradually emerge in a monotonic way as the number of pairings increases ., However , non-monotonic STDP accounting for fast associative learning led us to challenge this monotonicity hypothesis and explore how the existence of multiple plasticity pathways affects the dynamical establishment of plasticity ., To account for distinct forms of STDP emerging from increasing numbers of pairings and the variety of signaling pathways involved , we developed a general class of simple mathematical models of plasticity based on calcium transients and accommodating various calcium-based plasticity mechanisms ., These mechanisms can either compete or cooperate for the establishment of long-term potentiation ( LTP ) and depression ( LTD ) , that emerge depending on past calcium activity ., Our model reproduces accurately the striatal STDP that involves endocannabinoid and NMDAR signaling pathways ., Moreover , we predict how stimulus frequency alters plasticity , and how triplet rules are affected by the number of pairings ., We further investigate the general model with an arbitrary number of pathways and show that depending on those pathways and their properties , a variety of plasticities may emerge upon variation of the number and/or the frequency of pairings , even when the outcome after large numbers of pairings is identical ., These findings , built upon a biologically realistic example and generalized to other applications , argue that in order to fully describe synaptic plasticity it is not sufficient to record STDP curves at fixed pairing numbers and frequencies ., In fact , considering the whole spectrum of activity-dependent parameters could have a great impact on the description of plasticity , and a better understanding of the engram . | The brain’s capacity to treat information , learn and store memory relies on synaptic connectivity patterns , which are altered through synaptic plasticity mechanisms ., Experimentally , such plasticities were evidenced through protocols involving numerous repetitive stimulations of a given synapse , and were shown to be supported by multiple pathways ., Using a simple biologically grounded mathematical model , we show how activation timescales and inactivation levels of each pathway interact and alter plasticity in an intricate manner as stimuli are presented ., Building upon data from the synapse between cortex and striatum , we show that synaptic changes may revert or re-emerge as stimuli are presented , and predict specific responses to changes in stimulus frequency or to distinct simulation patterns ., Our general model shows that a given plasticity profile emerging in response to a repetitive stimulation protocol can unfold into various scenarii upon variations of the number of stimulus presentations or patterns , which tightly depends on the underlying activated pathways ., Altogether , these results argue that in order to better understand learning and memory , single plasticity responses obtained through intensive stimulations do not reveal the complexity of the responses for smaller number of presentations , which may have a strong impact in fast learning of stimuli with low numbers of presentations . | medicine and health sciences, action potentials, neural networks, nervous system, membrane potential, electrophysiology, neuroscience, simulation and modeling, synaptic plasticity, calcium signaling, neuronal plasticity, research and analysis methods, developmental neuroscience, computer and information sciences, animal cells, signal transduction, cellular neuroscience, anatomy, synapses, cell biology, physiology, neurons, biology and life sciences, cellular types, cell signaling, neurophysiology | null |
journal.ppat.1003746 | 2,013 | The Cytotoxic Necrotizing Factor of Yersinia pseudotuberculosis (CNFY) Enhances Inflammation and Yop Delivery during Infection by Activation of Rho GTPases | Enteropathogenic Yersinia species such as Y . enterocolitica and Y . pseudotuberculosis initially infect the terminal ileum and colonize the Peyers patches ( PPs ) within several hours of infections ., Bacteria are subsequently transported to the mesenteric lymph nodes ( MLNs ) and can also spread systemically to reach liver and spleen via the bloodstream ., The infections typically result in enteritis , enterocolitis and mesenteric lymphadenitis where the infected tissues show formation of microabscesses or granuloma-like lesions with central necrosis 1 ., Enteropathogenic yersiniae have been shown to secrete exotoxins and/or inject effector proteins by specialized secretion machineries to manipulate host cell functions , including cytoskeletal rearrangements , to prevent immune responses and to establish a successful infection ., They encode a type III secretion system ( T3SS ) on a 70 kb virulence-associated plasmid ( pYV ) that is essential for their defense against the host immune system 2–4 ., The Yersinia T3SS has been shown to form a syringe-like apparatus with a thin needle-like surface exposed projection 5 ., It is used to insert a translocation channel ( composed of YopB and YopD ) within the host membrane to inject the effector proteins YopE , YopH , YopJ/YopP , YopK/YopQ , YopM , YopO/YpkA , and YopT into the cells cytoplasm ., Yops target different cell signaling molecules and processes , in particular cytokine production and actin dynamics , often resulting in the inhibition of phagocytosis 6 ., YopH is a tyrosine phosphatase that dephosphorylates proteins of the focal adhesion complex 7–10 ., The effectors YopE , YopT and YopO/YpkA manipulate the regulation of Rho GTPases , which control the formation of lamellipodia , filopodia and stress fibers 2 , 11 ., YopJ/YopP promotes cell death of macrophages by inactivating the counterregulators of the Toll-like receptor 4-triggered apoptotic pathway , the mitogen-activated protein kinase kinases ( MEKs ) and the inhibitor κB kinase β ( IKK β ) 12–16 ., YopM forms a complex with RSK and PRK kinase isoforms , traffics to the nucleus , and is important for Yersinia to persist in liver and spleen with a contextual decrease of several proinflammatory cytokines , including IL-1β , IL-12 , IL-18 , interferon γ , and TNF-α , and depletion of NK cells 17–21 ., The effector YopK/YopQ seems to play a role in orchestrating the translocation of effector proteins by modulating the ratio of the pore-forming proteins YopB and YopD ., This appears to prevent unintended Yop delivery and neutrophil death , which would enhance the inflammatory response possibly favoring the host 22–24 ., Insertion of the YopB/D translocation channel allows Yop delivery while maintain the host cell membrane intact ., The YopB/D complex results in activation of Rho GTPases , actin polymerization and pore-formation ., However , pore formation is usually prevented by the GTPase-downregulating function of YopE and YopT ., Yet , expression of constitutively active forms of Rac1 and RhoA leads to a loss of membrane integrity and results in increased pore formation even when YopE and YopT are expressed 25 ., In addition , signaling pathways triggered by high affinity-binding of the main Yersinia adhesins YadA and InvA to β1 integrin receptors and YopB/D signaling were shown to induce activity of Rho GTPases and actin polymerization which are crucial for efficient translocation of the Yop effectors 26 ., Another Yersinia factor shown to activate the small GTPase RhoA is the cytotoxic necrotizing factor-Y ( CNFY ) 27 , 28 ., CNFY is prevalent in some Y . pseudotuberculosis isolates , e . g . the widely used Y . pseudotuberculosis strain YPIII ., All these strains belong to the serogroup III , but other isolates of this serogroup do not express CNFY and contain deletions within the corresponding cnfY gene 27 ., On the amino acid level , CNFY is highly similar ( >68% ) to the CNF toxins found mainly in E . coli strains isolated from patients and domestic animals with extraintestinal infections ( CNF1-3 ) 29 , 30 ., CNF1 is the best-characterized toxin of this class of bacterial toxins and is transferred to host cells through outer membrane vesicles ( OMVs ) 31–34 ., The CNF1 toxin is a single-chain A-B toxin with an N-terminal delivery domain including subdomains for receptor binding , pore formation and proteolytic cleavage , and a C-terminal deamidase domain 35 , 36 ., Internalization of the toxin into target cells occurs through receptor-mediated endocytosis , which appears to be independent of clathrin and lipid rafts ( sphingolipid/cholesterol rich microdomains ) 37 , 38 ., After uptake , the 55 kDa C-terminal deamidase domain is autocatalytically cleaved off in the late endosome , and delivered into the cytoplasm in a pH-dependent manner 39 ., CNF1 deamidates Gln-61/-63 of RhoA , Rac1 and Cdc42 to Glu-61/-63 resulting in Rho GTPases with a blocked GTP hydrolase activity ., Deamidated Rho GTPases induces polymerization of F-actin at focal contacts , increase cell-matrix adhesion , and promote formation of stress fibers , lamellipodia and filopodia , which led to the classification as ‘constitutively active’ 40–45 ., Cytoskeletal rearrangements attributed to CNF lead to multinucleated cells due to inhibited cytokinesis with ongoing cell cycle progression 46 ., Additionally , CNF1 has been reported to, ( i ) induce phagocytosis in epithelial cells and reduce CR3-mediated phagocytosis in monocytes 47 , 48 ,, ( ii ) promote bacterial cell entry 49 ,, ( iii ) decrease the barrier function of intestinal tight junctions 40 , 50 ,, ( iv ) decrease transmigration of polymorphonuclear leukocytes across a T84 monolayer 51 , and, ( v ) induce apoptosis of bladder cells 52 ., The overall amino acid sequence of CNFY of Y . pseudotuberculosis is very similar to CNF1 ., However , CNFY is not recognized by neutralizing antibodies against CNF1 27 ., Moreover , CNFY seems to bind to different cell receptors and preferentially deamidates RhoA ( over Rac1 and Cdc42 ) in cultured epithelial cells 28 , 38 ., Although CNFY and certain Yop effectors alter the cytoskeleton by affecting the activity of the Rho GTPases , little is known about the interplay , cooperation and joint role of these toxins in the pathogenic lifestyle of Y . pseudotuberculosis ., Here , we provide evidence that CNFY is an important virulence factor of Y . pseudotuberculosis YPIII ., CNFY is shown to enhance Yop protein delivery , which is crucial for pathogenicity ., Furthermore , the toxin was found to induce inflammatory responses and increase the severity of a Yersinia infection ., Since many Y . pseudotuberculosis isolates as well as Y . pestis contain deletions within the cnfY gene 27 , we first tested whether the intact cnfY toxin gene in the Y . pseudotuberculosis wild-type strain YPIII is expressed and induced under virulence-relevant growth conditions ., A cnfY-lacZ transcriptional fusion was only slightly expressed when Y . pseudotuberculosis was grown at 25°C , but its expression was strongly induced at 37°C and reached its maximum during stationary phase ( Fig . S1A ) ., High cnfY transcription was generally observed in complex media , in particular BHI , whereas only low expression levels were detected in all tested minimal media ( Fig . S1B , data not shown ) ., In summary , cnfY is predominantly expressed at 37°C in a nutrient rich environment , resembling conditions found in the mammalian intestinal tract ., This result prompted us to test expression of the toxin during infection ., BALB/c mice were orally infected with 2×108 bacteria of the Y . pseudotuberculosis wild-type strain YPIII expressing a cnfY-luxCDABE fusion , and the bioluminescent signal was monitored in the mice for six days using an in vivo imaging system ., Only very low luciferase activity was measured in the bacterial culture before infection ( data not shown ) and in the intestinal tract directly after oral ingestion ( 1 h , Fig . 1 ) ., However , a very strong bioluminescent signal of the cnfY-luxCDABE fusion was detectable during the entire following course of the infection ., The most intensive signals were detected two days post infection in the intestine and associated lymphoid tissues ( Fig . 1 ) ., No light emission was monitored in mice infected with bacteria carrying the promoterless luxCDABE operon in the identical expression system ( data not shown ) ., In order to study cnfY expression in the individual infected tissues , we used a set of established fluorescent fusion vectors for in vivo expression analysis ., To do so , Y . pseudotuberculosis YPIII harboring a plasmid-encoded constitutive PgapA::dsred2 reporter construct and a compatible PcnfY::gfpmut3 . 1 fusion was used to infect BALB/c mice ., Five days post infection , the small intestine , caecum , colon , PPs , MLNs , spleen and liver were isolated and cryosections were prepared ., The bacteria in the tissues were visualized by monitoring dsRed2 , and then tested for PcnfY::gfpmut3 . 1 ., As shown in Fig . 2 , the PcnfY::gfpmut3 . 1 fusion was expressed in all tested organs ., In summary , a temperature shift to 37°C , but most likely no tissue-specific signals are required to induce toxin expression in infected tissues ., Absence of a functional toxin gene in other Y . pseudotuberculosis clinical isolates , may suggest that CNFY only adds another potential virulence factor to the variety of effector proteins and toxins that are produced by this pathogen ., However , high expression of cnfY during the entire course of an infection also indicates that presence of this toxin may enhance the pathogenicity of Y . pseudotuberculosis ., To first assess the impact of CNFY on pathogenesis , the potential of the Y . pseudotuberculosis wild-type strain YPIII and the isogenic cnfY-deficient strain to cause lethal infections was compared ., BALB/c mice were orally infected with 2×109 bacteria of the cnfY mutant ( YP147 ) and the wild-type strain ( YPIII ) harboring the empty vector ( pJNS11 ) or a cnfY-encoding plasmid ( pJNS10 ) ., Survival and weight of the mice were monitored over two weeks and date of death was recorded ( Fig . 3 , S2 ) ., Mice infected with YPIII showed signs of the infection , e . g . weight loss , piloerection and lethargy , and succumbed to infection between day four and day six ., Strikingly , none of the mice infected with YP147 developed severe disease symptoms and all mice were still alive 14 days post infection ., Monitoring of body weight demonstrated that also mice infected with the cnfY knock-out strain YP147 showed a slight reduction in weight , but they recovered quickly and regained weight ( Fig . S2 ) ., Presence of the cnfY-encoding low-copy number plasmids reverted the avirulent phenotype of the cnfY mutant and reduced the average day of death of the wild-type strain YPIII by one day , most likely due to the overexpression of the toxin ., The Y . pseudotuberculosis YPIII isolate , unlike other Y . pseudotuberculosis strains , is unable to replicate in murine macrophages due to a defective allele of phoP 53 ., To exclude that CNFY influence on virulence is only visible in a phoP-deficient derivative with an overall lower pathogenicity , the inability to grow in macrophages was complemented by an exchange of the allele against the phoP ORF from Y . pseudotuberculosis IP32953 ., However , when mice were challenged with 2×109 CFU of the equivalent phoP+ strains , 100% of the mice infected with the CNFY-positive strain died during the observation period , while 80% of the mice infected with the isogenic cnfY-deficient strain survived and regained weight ( Fig . S3 ) ., To gain a deeper insight into the differences in the infection process of CNFY-positive and -negative strains , we determined the number of bacteria that colonized the small intestine , caecum , PPs , MLNs , liver and spleen of BALB/c mice at different time points after oral infection with 2×108 bacteria ( Fig . 4 ) ., Comparable amounts of wild-type ( YPIII ) and the mutant strains ( YP147 ) were recovered from PPs and caecum during infection , and only a very small increase of bacterial counts was observed with the cnfY mutant in the small intestine at days 5–7 post infection ( Fig . 4 ) ., However , significantly reduced numbers of YP147 were recovered from MLNs and spleen ( Fig . 4 ) ., The number of cnfY-positive and -negative bacteria in these organs was almost identical up to day three post infection , but the cnfY mutant was eliminated very rapidly later during the infection ., At day seven , none or only few mutants were recovered from MLNs and spleen , whereas 108–109 bacteria of the wild-type strain were recovered per gram of both organs ., The effect was less pronounced in the liver , but the strongly reduced number of mutant bacteria relative to the wild-type bacteria six and seven days after infection clearly indicated that the presence of CNFY is also advantageous for the colonization of the liver ( Fig . 4 ) ., This demonstrated that loss of CNFY , resulting in avirulence of Y . pseudotuberculosis YPIII , is reflected by a fast elimination of the bacteria from MLNs , liver and spleen ., Within the first week after infection with wild-type strain YPIII the size of the spleen and liver decreased two-fold , whereby changes of the organ size were first visible at day three post infection ( Fig . S4A , B ) ., In contrast , infection with the isogenic cnfY mutant strain YP147 had no effect on the size of the liver and induced a considerable increase of the size of the spleen ., In addition , mice infected with wild-type strain YPIII had significantly shorter intestines ( 30% ) at day six and seven post infection than mice infected with the cnfY mutant ( Fig . S4C ) ., The shortening of the intestine is a sign of marked intestinal inflammation ., This indicated that CNFY not only affects colonization of systemic organs , but has also a strong influence on the hosts inflammatory response against the bacterial infection ., Histopathological examination of the infected host tissues demonstrated marked differences of the overall inflammatory reaction , which was stronger in YPIII-infected animals , especially in the small intestine and spleen compared to YP147-infected mice ., In the intestine , inflammation was most prominent in the ileum and caecum in both groups ., However , in YPIII-infected mice inflammation was diffuse affecting the entire ileum at day six ( Fig . 5A , upper panel , 5B middle panel ) ., In YP147-infected mice inflammation was locally restricted to multifocal lesions characterized by the presence of inflammatory cells from the muscular layer up to the epithelial cells ( Fig . 5A , lower panel , 5B right panel ) ., In these areas inflammation led to epithelial cell hyperplasia ( increased proliferation ) resulting in an increase of the villi length ., However , this lesion is only locally restricted and adjacent tissue remains unaltered ., In addition , inflammation was more generalized in YPIII- compared to YP147-infected organs ., In mice infected with YP147 , no bacterial foci ( diffuse patches of bacteria ) could be detected microscopically in hematoxylin and eosin ( H & E ) stained sections of the spleen at day six post infection , whereas in the majority of YPIII-infected mice bacterial foci were visible in the histological sections ( Fig . 5C ) ., YPIII infections were accompanied by a more severe inflammation of the spleen , where presence of the bacteria resulted in necrotizing spleenitis leading to splenic atrophy with marked depletion of the white pulp ., YPIII caused multifocal necrosis in spleen , whereas in YP147-infected spleens , only mild hyperplasia of the white pulp and increased erythropoiesis were found ( Fig . 5C ) ., Taken together , CNFY has a significant influence on the number of microcolonies in the tissues and leads to a more severe and widespread inflammation in the small intestine , liver and spleen ., Because of the strong influence of CNFY on the colonization of bacteria in MLNs , spleen and liver , it was hypothesized that the toxin might counteract host immune defenses ., To test this hypothesis , we infected BALB/c mice with 2×108 bacteria of the wild-type or the cnfY mutant strain , and immune cell composition in the spleen was analyzed by multi-color flow cytometry three days and six days post infection ., The spleen was chosen since here the most pronounced CNFY-triggered pathological effects had been observed ., Cell suspensions of isolated tissues were prepared and cells were stained with fluorescently labeled antibodies to distinguish neutrophils from macrophages/monocytes , dendritic cells ( DCs ) , natural killer ( NK ) cells , B cells , and T cells ( Fig . S5 ) ., All alterations of immune cell populations seen at day six ( data not shown ) were already visible at day three post infection , when the bacterial load is still similar and the overall health status of YPIII-infected mice is only slightly and not severely reduced as at day six ., A very pronounced variation of the immune cell population between the YPIII- and YP147-infected mice was observed ( Fig . 6 ) ., All types of immune cells were significantly decreased in the spleen three days after infection with YPIII when the spleen started to shrink , but the most severe changes were observed with cells of the innate immune system ., In particular , numbers of macrophages , monocytes and NK cells were significantly reduced; whereas reduction of neutrophils and conventional DCs was less pronounced ., In contrast , no reduction of immune cells was detectable in spleens of YP147-infected mice ( Fig . 6 ) ., In contrast , a significant higher influx of neutrophils and macrophages/monocytes was observed , which is consistent with the rapid clearance of mutant bacteria from the spleen upon triggering of the immune response ., To determine whether CNFY affects the steady-state level of certain cell populations , the population percentage was also compared and further confirmed a significant expansion of neutrophils and macrophages/monocytes in YP147-infected spleens ( Fig . S6 ) ., These and the histopathological data strongly suggest that the CNFY toxin reduces influx and/or causes rapid cell death of invading immune cells in the spleen ., Our infection experiments clearly demonstrated that absence of the CNFY toxin renders the bacteria completely avirulent , resulting in the clearance of the bacteria in MLNs , liver and spleen ., A similar attenuation in mouse models of oral infection was observed, ( i ) when the virulence plasmid , encoding the T3SS and the Yop effectors is cured from Y . pseudotuberculosis YPIII ,, ( ii ) when multiple yop genes were deleted or, ( iii ) when the regulator LcrF that controls expression of the T3SS/Yops is absent 54 , 55 ., Moreover , a significant influx of neutrophils was observed in the spleen of mice infected with a yopM mutant strain of Y . pestis , while the numbers of neutrophils decreased during infection with the parental strain 20 , 21 ., In addition , YopJ translocation has been shown to promote cell death of professional phagocytes 13 , 15 ., This suggested that the CNFY toxin is important for the efficient injection of the Yop effectors into host cells during the infection process ., In fact , recent work by Mejia et al . 26 demonstrated that efficient translocation of the Yop effectors requires Rho activation – a process that has been shown to be stimulated by the CNFY toxin 28 , 38 ., To address whether CNFY-mediated activation of Rho GTPases influences Yop-translocation into professional phagocytes , we first tested the influence of recombinant CNFY toxin on non-activated and PMA-activated macrophages , thus mimicking its effect on unstimulated and stimulated macrophages during infection ., Intoxification of murine macrophages ( J774A . 1 ) led to activation of all three Rho GTPases , RhoA , Cdc42 and Rac1 ( Fig . 7A ) ., CNFY further induced a marked increase in cell size with some giant multinucleated cells ( Fig . 7B ) ., These CNFY effects occurred independently of macrophage stimulation with PMA ., This indicates that CNFY controls actin dynamics in macrophages through deamidation of Rho GTPases ., Since host actin polymerization by Rho activation plays a role in Yop translocation by Y . pseudotuberculosis 26 we also tested the influence of CNFY on Yop delivery ., To do so , we generated Y . pseudotuberculosis strains expressing a YopE-β-lactamase reporter fusion 56 , namely YP173 ( YPIII-ETEM ) , YP174 ( YP101ΔsycS-ETEM ) , and YP217 ( YP147ΔcnfY-ETEM ) , and used these strains to infect host cells treated with the dye CCF4-AM ., CCF4-AM consists of coumarin and fluorescein conjugated by a lactam ring and is modified by cellular esterases , whereby the dye becomes green fluorescent and is trapped inside the cell ., If the β-lactam ring is cleaved by β-lactamase the dye changes its fluorescence from green to blue 57 , 58 ., The green to blue conversion allows identification of host cells in which the YopE-β-lactamase fusion protein has been successfully injected ., We first used this fluorescence-based system to monitor translocation of the chimeric protein into HEp-2 cells , and determined the number of green and blue fluorescent cells by fluorescence microscopy and flow cytometry ., Efficient translocation of YopE-β-lactamase into epithelial cells was observed upon infection with YP173 ( YPIII-ETEM ) , but not with the secretion-deficient control strain YP174 ( YP101ΔyscS-ETEM ) ( Fig . 8A , B ) ., YopE-β-lactamase translocation by the cnfY-deficient strain YP217 ( YP147ΔcnfY-ETEM ) was significantly reduced compared to YP173 ( YPIII-ETEM ) , whereas preincubation of the host cells with CNFY increased translocation of the fusion protein ( Fig . 8A , B ) , indicating that CNFY enhances effector delivery ., Since Y . pseudotuberculosis predominantly injects the Yops into professional phagocytes in vivo 59 , we also tested CNFY influence on YopE-β-lactamase translocation into murine macrophages , and found that pretreatment with CNFY also boosts Yop delivery into these phagocytes ( Fig . 8C ) ., Stimulation of Rac1 through YadA and invasin-bound β1-integrins was shown to be essential for Yersinia uptake into epithelial cells 11 , but neither internalization nor activation of Rac1 was required for Yop translocation by Y . pseudotuberculosis into HeLa cells 26 ., This suggested that CNFY-mediated stimulation of Yop delivery into macrophages might preferentially be caused by activation of RhoA ., To validate this assumption , we pretreated macrophages with the Clostridium botulinum C3 toxin , an ADP-ribosylating protein that specifically inhibits RhoA , B and C , or with toxin B from variant Clostridium difficile serotype F strain 1470 ( TcdBF ) , which specifically inhibits Rac but not RhoA/B/C 60 , 61 ., Treatment with the toxins induced actin cytoskeleton rearrangements and cell morphology changes , but had no effect on the viability of the macrophages and the number of associated bacteria ( data not shown ) ., As shown in Fig . 8C , the RhoA/B/C inhibitor reduced the percentage of blue macrophages significantly , whereas the Rac inhibitor had no influence on YopE-β-lactamase translocation ., These findings indicated that the CNFY toxin enhances Yop delivery into murine macrophages , and in particular activation of RhoA seems to play a role in the processes that stimulate Yop translocation into these professional phagocytes ., It has been reported that translocated effector YopE of Y . pseudotuberculosis YPIII is a GTPase-activating protein ( GAP ) for Rac1 and RhoA and this function appears important to regulate Yop translocation and modulate host defenses crucial for virulence 62–65 ., This raised the question how YopE and CNFY contribute to RhoA-GTP and Rac1-GTP levels and Yop translocation ., To address this , we analyzed RhoA and Rac1 activation and Yop translocation in the presence and absence of YopE in untreated or CNFY-pretreated murine macrophages ., As shown in Fig . S7 , only low amounts of active Rac1 and RhoA could be detected in uninfected macrophages ., Addition of the wild-type strain YPIII pregrown at 37°C to mimic the situation prior to host cell contact induced activation of RhoA and Rac1 ., Absence of YopE resulted in a small additional increase in RhoA-GTP , but had no or only a slight influence on Rac1-GTP levels ., Furthermore , it had no or only a very small stimulatory effect on the translocation of YopD and YopH without or after pretreatment of the macrophages with CNFY ( Fig . S7 ) ., This indicates that under these conditions intracellular YopE is unable to efficiently counteract CNFY-mediated RhoA/Rac1 activation and reduce Yop translocation into murine macrophages ., We next analyzed whether the CNFY toxin affects Yop translocation into host cells in the original tissue environment ., MLNs were harvested from uninfected mice and filtered to disrupt the tissue architecture and generate single-cell suspensions ., Single cell suspensions were infected with a multiplicity of infection ( MOI ) of 10 , incubated with CCF4-AM , and then analyzed by flow cytometry ., As shown in Fig . S8 , significantly higher numbers of blue cells with translocated YopE-β-lactamase were measured after infection with YPIII , indicating that Yop delivery into host cells can be enhanced by the toxin through activation of Rho GTPases ., It has been previously reported that Y . pseudotuberculosis selectively targets Yops to professional phagocytes in the PPs , MLNs and spleen during the oral route of infection 59 ., To analyze whether the CNFY toxin also affects YopE-β-lactamase delivery in the course of an infection , we orally infected mice with 2×109 bacteria YP173 and the isogenic cnfY mutant strain YP217 ., The T3SS-deficient yscS mutant , encoding the YopE-β-lactamase , and YPIII without the fusion were used as negative controls ., At day three post infection mice were sacrificed , the PPs , MLNs , and spleen were harvested , and the translocation of Yops into various immune cell subsets was analyzed by flow cytometry ( Fig . S9 ) ., Following infection with the YopE-β-lactamase expressing wild-type strain 4 . 5% of all living cells within PPs were affected by Yop translocation ., In contrast , only 1 . 5% of all living cells in the PPs contained the fusion protein after infection with the cnfY-deficient strain ( Fig . S10 ) ., Yop translocation efficiency was still significantly reduced in tissues infected with the cnfY-deficient strain when the percentage of translocated blue cells was normalized to the bacterial load of the tissue/organ ( Fig . 9 ) ., This excludes that lower bacterial numbers account for this effect , but it also assumes that bacteria are infecting different cell types at the same MOI , which is unknown ., Yop delivery was also significantly lower in the absence of the CNFY toxin in MLNs and spleen in which the total number of targeted cells was reduced compared to PPs ( Fig . 9 , S10 ) ., We further determined whether CNFY-mediated stimulation of Yop translocation affected specific immune cells more frequently than others ., Translocation of YopE-β-lactamase into each immune cell type was compared in MLNs and spleen from mice infected with YP173 ( YPIII-ETEM ) or the cnfY mutant derivative YP217 ( YP147-ETEM ) ( Fig . 9B , S10 ) ., In general , all immune cells analyzed were targeted by Y . pseudotuberculosis ., However , Yop-injected neutrophils were significantly enriched in the MLNs and the spleen , indicating that this cell population is preferentially targeted in the tissues ., In addition , DCs , NK cells and macrophages were well represented in the blue population , while B and T cells remained underrepresented ( Fig . 9B , S10 ) ., This is in full agreement with previous studies demonstrating that translocated YopH of Y . pseudotuberculosis strain IP2666 is enriched in neutrophils , macrophages and DCs in MLNs and spleen 59 ., We further found that the apparent enhanced targeting to professional phagocytes , in particular neutrophils , macrophages and DCs and to a smaller extent also translocation into B and T cells was reduced in the absence of CNFY in the MLNs and spleen three days post infection ( Fig . 9B , S10 ) ., Yop translocation into NK cells was also somewhat reduced in the spleen ., Since Y . pseudotuberculosis induces host cell death 66 , which may be reflected in the strong reduction of professional phagocytes in the spleen ( Fig . 6 ) , the actual amount of Yop translocation in this organ is probably underestimated ., Taken together , these results demonstrate that the CNFY toxin plays a critical role during the infection , facilitating targeting of Yops to host immune cells , in particular professional phagocytes ., Many bacterial toxins and translocated effector proteins target Rho GTPases , which control crucial eukaryotic signal transduction pathways involved in the organization of the cell cytoskeleton , cell cycle progression , genetic information processing , and host defense processes to promote invasion , survival and replication of pathogens within their hosts 29 , 67 ., In this study we investigated the Rho-activating cytotoxic necrotizing factor CNFY of Yersinia ., Although much progress has been made unravelling the molecular mechanism of this toxin , the functional consequences for host-pathogen interaction and pathogenesis were largely unknown ., Using a murine model for gastrointestinal tract infections we provide evidence that this Rho-activating protein is crucial for virulence of the naturally toxin-expressing Y . pseudotuberculosis strain YPIII ., The importance of CNFY for pathogenesis was first established by the analysis of the expression and the role of the toxin during the infection of mice ., We show that cnfY is strongly expressed in all infected tissues during pathogenesis in mice , and is crucial for virulence , in particular for the dissemination of the bacteria into the MLNs , spleen and liver ., Histological analysis and immune cell composition of the infected tissues suggest that CNFY contributes significantly to the acute characteristics of the inflammatory response and host tissue damage during infection ., Histo-pathologic evaluation underlines the finding that CNFY induces apoptosis , as focal necrosis was not seen in YP147-infected animals ., Cell death leads to atrophy of the spleen in YPIII-infected mice ., Moreover , a restriction of the inflammation to small foci could be observed in the intestine of YP147-infected animals , whereas the entire ileum was affected by a diffuse inflammation in YPIII-infected animals , explaining the shortening of the intestine ., Hyperplasia of the white pulp seen in YP147-infected mice displays the immune response triggered by the infection ., The infection is restricted to small foci in the intestine and is reversible , whereas the infection in YPIII infected animals is generalized and most probably leads to death by endotoxiemia ., This inflammatory necrotizing phenotype is reminiscent of earlier studies analyzing the effect of CNF1 of E . coli using subcutaneous injections as well as animal models of urinary tract and prostatitis infection 31 , 68 , 69 ., Infections of the gastrointestinal tract by enteropathogenic Yersiniae lead to a biphasic inflammatory process in which bacterial adhesion and transmigration through the intestinal epithelial layer triggers an initial antibacterial defense response with little inflammation , e . g . expression of IL-8 by epithelial cells , which is followed by an acute infiltration and activation of neutrophils , cytokine production and tissue necrosis 70 ., First recognition of Y . pseudotuberculosis occurs through contact of the bacterial LPS with TLR4 on naïve host macrophages and this leads to proinflammatory cytokine production through activation of MAPK and NF-kB ., However , translocation of YopJ inhibits activation of MAPK and NF-κB and induces an apoptotic signaling pathway | Introduction, Results, Discussion, Material and Methods | Some isolates of Yersinia pseudotuberculosis produce the cytotoxic necrotizing factor ( CNFY ) , but the functional consequences of this toxin for host-pathogen interactions during the infection are unknown ., In the present study we show that CNFY has a strong influence on virulence ., We demonstrate that the CNFY toxin is thermo-regulated and highly expressed in all colonized lymphatic tissues and organs of orally infected mice ., Most strikingly , we found that a cnfY knock-out variant of a naturally toxin-expressing Y . pseudotuberculosis isolate is strongly impaired in its ability to disseminate into the mesenteric lymph nodes , liver and spleen , and has fully lost its lethality ., The CNFY toxin contributes significantly to the induction of acute inflammatory responses and to the formation of necrotic areas in infected tissues ., The analysis of the host immune response demonstrated that presence of CNFY leads to a strong reduction of professional phagocytes and natural killer cells in particular in the spleen , whereas loss of the toxin allows efficient tissue infiltration of these immune cells and rapid killing of the pathogen ., Addition of purified CNFY triggers formation of actin-rich membrane ruffles and filopodia , which correlates with the activation of the Rho GTPases , RhoA , Rac1 and Cdc42 ., The analysis of type III effector delivery into epithelial and immune cells in vitro and during the course of the infection further demonstrated that CNFY enhances the Yop translocation process and supports a role for the toxin in the suppression of the antibacterial host response ., In summary , we highlight the importance of CNFY for pathogenicity by showing that this toxin modulates inflammatory responses , protects the bacteria from attacks of innate immune effectors and enhances the severity of a Yersinia infection . | Various toxins and effector proteins of bacterial pathogens have been found to manipulate eukaryotic cell machineries to promote persistence and proliferation within their hosts ., Many of these virulence factors target small Rho GTPases , but their role in pathogenesis is often unknown ., Here , we addressed the expression and functional consequences of the CNFY toxin found in some isolates of Y . pseudotuberculosis ., We found that CNFY besides modulating the cell cytoskeleton by activation of the GTPases RhoA , Rac1 and Cdc42 , contributes to increased inflammation and tissue damage ., Moreover , CNFY increases the ability of Yersinia to prevent the attack of the immune system , by enhancing the delivery of antiphagocytic and cytotoxic effectors into professional phagocytes ., Our findings provide the first insights into the multi-functional action and severe consequences of the CNFY toxin on the inflammatory response and disease-associated tissue damage during the natural course of the infection . | null | null |
journal.pcbi.1006267 | 2,019 | Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model | The locus coeruleus ( LC ) is the major source of noradrenaline ( NA ) in the brain , projecting to most territories from the frontal cortex to the distal spinal cord ., Changes in LC firing have been associated with behavioural changes , most notably the switch from ‘exploiting’ to ‘exploring’ the environment , and the facilitation of appropriate responses to salient stimuli 1 , 2 ., Tonic LC activity is correlated with global levels of arousal and behavioural flexibility , where firing rates increase with rising levels of alertness 1 ., At the extreme , high rates of tonic firing have been causally related to behavioural variability and stochastic decision making 3 ., This ‘tonic mode’ has previously been modelled as a response to factors such as declining utility in a task 4 or ‘unexpected uncertainties’ 5 , triggering behavioural variability and a switch from ‘exploiting’ a known resource to ‘exploring’ for a new resource ., The LC also fires in short , high frequency bursts ., Such phasic activity occurs in animals in response to behaviourally relevant salient stimuli 1 , 6–8 ., This phasic response has been described as a ‘network interrupt’ or ‘reset’ , which facilitates a shift to shorter-term behavioural planning 9 , 10 ., Activating stimuli are those which have an established behavioural significance; for instance , signalling the location of food or the presence of a predator ., They may also include stimuli that are highly unexpected 1 , 11–although the phasic response will habituate rapidly to novelty alone in the absence of behavioural salience 12 ., A series of studies has provided evidence of further nuance to phasic LC responses ., Similar to the well-known dopaminergic response , as an animal learns a cue-reward relationship , phasic LC responses will transfer from temporal alignment with an unconditioned stimuli ( US ) to a predictive , conditioned stimuli ( CS+ ) 13 ., Additionally , rarer stimuli , or those predicting a large reward , elicit a stronger LC response 6 , 8 ., In contrast if predictive cues are delivered consecutively , the size of the response appears to decrease 6 ., The rich array of factors affecting the nature of the phasic response suggests that LC activation is linked to both facilitation of behavioural response and to internal representations of uncertainties and probabilities ., Despite the increasing body of knowledge about the impact of the LC on behaviour , a comprehensive computational account remains elusive–in contrast to the more developed account of other neuromodulators; most notably dopamine , which has been interpreted as a signal of reward prediction error ., In particular , existing modelling approaches have generally tackled the tonic and phasic firing responses of the LC as separate modes with distinct functional significance , triggered by different circumstances 4 , 5 , 9 , 10 ., Here , we propose that a critical computational role of the LC-NA system is to react to high level ‘state-action’ prediction errors upstream of the LC and cause appropriate flexibility in belief updating via feedback projections to cortex ., In brief , our account of noradrenergic activity is based on the fact that the degree of belief updating reflects volatility in the environment and can therefore inform the optimal rate of evidence accumulation and plasticity ., The ‘state-action’ prediction error considered in this work is the ‘Bayesian surprise’ or change in probabilistic beliefs before and after observing some outcome ., We develop these ideas as neural correlates of discrete updates and action planning under the formalism of Active Inference ( AI ) ., AI offers an effective mathematical framework for such modelling , unifying inferences on states and action planning and providing a detailed description of beliefs at each step of a behavioural task 14–17 ., In taking this formal approach , our description of the LC is integrated into a general theory of the brain function and uses constructs that underwrite the normal cycle of perceptual inference and action selection ., This contrasts with previous LC modelling approaches , which have invoked the separate monitoring of statistical quantities ( such as unexpected uncertainty ) outside of the action selection cycle 4 , 5 ., In the following we apply AI to simulate the updating of beliefs about states of the world–and actions–as a synthetic agent engages with two scenarios ( a Go/No-go task with reversal and an ‘explore/exploit’ task ) that elicit archetypal LC responses ., Using this approach , we show that the ‘state-action prediction error’ offers an effective predictor of LC firing over both long ( tonic ) and short ( phasic ) timescales , without the need to invoke switches between distinct modes ., Furthermore , we described how the signal may be broadcast back to cortex to affect appropriate updates to internal models of the environment ., This links the error via the LC to model flexibility–bringing two key concepts of the LC together: ‘explore-exploit’ and ‘network reset’ ., It also produces behavioural changes that agree with experimental knowledge of animal behaviours under noradrenergic manipulation ., Finally , the simulations produce realistic LC firing patterns that could , in principle , be used to model empirical responses ., Active Inference is a theory of behaviour that has previously been mapped to putative neural implementations 14 ., The basic premise of AI is that to stay in states compatible with survival , an agent must create and update a generative model of the world 14 , 18 , 19 ., To do this effectively the agent represents the true structure of the world with an internal model that is a good approximation of how its sensations are generated ., ( Note that in this paper , we often use the term ‘model’ to refer to the agent’s beliefs about states and actions in the world . Technically , these beliefs are posterior probability distributions , which require a generative model to exist ) ., The generative model encompasses a set of discrete states and transition patterns that probabilistically capture all the agent’s beliefs about the world and likely outcomes under different actions ., The model is formulated as a Partially Observable Markov Decision Process ( POMDP ) , under which the agent must infer its current state , make predictions about the outcome of actions in the future and make postdictions about the landscape it has just traversed ., In this context the word ‘state’ refers to a combination of features relevant to the agent , including its location and the cognitive context of that location; i . e . , states of the world that matter for its behaviour ., To optimise this model , the agent constantly seeks to minimise variational free energy ., This free energy is a mathematical proxy for the difference between the agent’s generative model and a ‘perfect’ or ‘true’ model of the world , and thus must be continually updated for the agent to survive ., Estimates of the free energy can be obtained over time by comparing predictions from the generative model with the results of actions in the real world , for instance , by checking whether an action produces the expected sensory feedback ., Using this information from the real world , the agent minimises free energy in two ways: by adjusting the parameters of the generative model itself , and by picking actions that it believes will be associated with the lowest free energy ., This allows the agent to both optimise the model and change its action plans ., Updating proceeds in cycles , with each round of model updates accompanied by predictions that are then checked by selecting and executing an action–in turn allowing a new round of updates ( Fig 1 ) ., This framework means that each round of updates combines perceptual inference with action selection ., Mathematically , this takes the form of a series of iterative updates to parameters that are repeated until convergence ., Specifically , each new observation from the environment enables posterior beliefs about states to be updated via iterating expressions that minimise free energy ., Similar iterative updates are then applied to posterior beliefs about competing policies and precision parameters ( step 4 of Fig 2 ) ., Finally , the updated beliefs are used to select an action which in turn generates a new observation ( step 5 of Fig 2 ) ., It is this machinery that we will map to LC/NA firing and function ., A derivation of the Active Inference framework is provided in Appendix 1; S1 Fig shows hierarchical dependencies within the model ., Appendix 2 also gives an overview of the implementation in code ., There are two more subtleties that should be noted in this brief description ., Firstly , the requirement to minimise free energy in action selection means that actions are driven by twin goals–the future attainment of states that the agent holds valuable ( utility ) , as well as the attainment of information when performing an action ( epistemic value ) ., Formally , these describe the path integral of free energy expected under competing policies ( see 14 and Appendix 1 ) ., Thus , agents that act to minimise free energy will end up where they hoped to , while resolving uncertainty about their environment ., If policies do not differ in their ability to resolve uncertainty ( i . e . no policy will harvest more information ) then utility will drive policy selection ., It has already been established that this particular cost function explores and exploits in a predictable and mathematically well-defined manner , depending on the relative utility of outcomes and on the uncertainty with which the agent views its environment 15–17 , 20 ., The second important component is the timespan covered by inferences ., The agent continually updates its understanding of the past , the present and the future ., This means that observations in the present can be used to update inferences on states that occurred in the past–in this way , past events continue to be useful for belief updating long after they occurred ., This is just a formalisation of our ability to postdict ( e . g . , “I started in this context , even if I didnt know at the time” ) ., Equally , the agent’s knowledge of the world is used to form predictions at future times ( e . g . , “These are the outcomes I expect under this policy” ) ., The agent not only attempts to use events that have already happened to minimise free energy , but also tries to select actions and inferences which it believes will minimise free energy of future observations ., As outlined in Figs 1 and 2 , the generative model comprises probability distributions over states , sequences of actions , precision ( confidence in predictions ) and observations ., At each time step , the agent updates its beliefs about these probability distributions over states , actions and precision by minimising free energy ., Once all updates have been completed the agent combines all of its inferences to produce a Bayesian Model Average ( BMA ) of states under possible actions ., This can be considered as a summary of everything the agent knows about its place in the world–an overall ‘map’ of the states it believes it occupied in the past , the state it occupies now and the states it believes it will occupy in the future ., The distribution implicitly includes action planning that is informed by inferences about events in the past ., The Bayesian Model Average is then used by the agent to select an action , as described in Fig 2 . The action causes a transition to a new state , which generates an observation from the environment ., Any large change in the state-action heatmap between time steps represents a state-action prediction error ., These errors indicate that the agent’s beliefs about its past and future states have changed substantially after receiving a fresh observation ., Such prediction errors indicate that the agent’s model of the world–including its plan for actions–must change ., This may either be because an unexpected stimulus has occurred , requiring an abrupt change in behaviour , or because observations over longer timescales are consistently demonstrating that key components of the model ( for example , the observation likelihood ( A ) and state transition ( B ) matrices ) are no longer fit for purpose ., Crucially , errors originating from both situations are reflected in the state-action prediction error ., We propose that they are a driver of LC activity ., The BMA is estimated for each time point within the task ( indexed by τ ) and takes the form of a weighted sum over state probabilities ( states are weighted by the probability of each policy predicting that state at the given time ) ., To estimate the state-action prediction error during a task , we take the Kullback-Leibler divergence between Bayesian Model Average ( BMA ) distributions at successive time steps ., Mathematically , this reflects the degree of belief updating induced by each new observation ., It is often known as a relative entropy , information gain or Bayesian surprise ., The following expressions describe the BMA ( upper equation ) and the state-action prediction error ( lower ) :, Sτ=∑pπp . sτp, SAPE ( t ) =∑τDKL ( Sτt ) || ( Sτt−1 ) , Here , Sτ is the BMA over states for time τ within the task , while sτp is the vector of probabilities for states at time τ under policy p , which has a probability πp ., In the expression for state-action prediction error , superscripts ( either t or t − 1 ) refer to the time at which the estimate is calculated ., Prediction errors over shorter timescales ( i . e . between actions , during the iterative cycle of belief updating ) are an integral feature of AI ., The state-action prediction error , in contrast , represents a global error: it is expressed over the timescale of a behavioural epoch as a response to the outcome of belief updating that facilitates action selection ., In the implementation , the state-action prediction error is calculated immediately after the BMA over states , as shown in Fig 3 . Why might it be useful for the LC to respond to state-action prediction errors ?, We suggest that one important function is that such errors require a specific modulation of distributed cortical activity encoding representations of the structure of the environment , particularly in frontal cortex ., This modulation would boost the flexibility of internal representations ( where our matrices would be formed by connected cell assemblies in frontal cortex ) and increase their responsiveness to recent observations ., In vivo , this may be mediated by the release of noradrenaline from LC projections to the frontal cortex occurring in response to state-action prediction errors ., The need for flexible model updating is directly relevant to a related challenge for Active Inference models; namely , the rate at which the agent’s experience is assimilated into its model ., Addressing this issue provides a pathway for modelling the effect of LC activation and closes the feedback loop between brainstem and cortex ., So what computational role does NA have in facilitating adaptive flexibility ?, Under AI , the agent’s model of the world is encoded by a set of probability distributions that keep track of the mappings between states and outcomes , and between states occupied at sequential time points ., These mappings are encoded by Dirichlet distributions , the parameters of which are incremented with each instance of a particular mapping the agent experiences ( as shown in step 6 , Fig 2 and Appendix 1 ) 14 , 20 ., However , difficulties arise when environmental contingencies change , because the gradual accumulation of concentration parameters is essentially unlimited ., Accumulated experience can come to dominate the agent’s model , with new information having little effect on the agent’s decisions ., This occurs because the generative model does not allow for fluctuations in probability transitions , i . e . environmental volatility ., This issue can be finessed by adding a volatility or decay factor ( α ) , which effectively endows the generative model with the capacity to ‘forget’ experiences in the past that are not relevant if environmental contingencies change ., This introduces a modification to the update equations shown in Fig 2 , of the form:, oldupdate:d=d+S1, newupdate:d=d+S1− ( d−1 ) α, Where d and d are the updated and existing beliefs respectively ., In the original update , the d vector ( which describes the agent’s prior about its state at t = 1 ) is simply incremented by adding the agent’s beliefs about the state it occupied at t = 1 ., This update is then applied after each trial ., In the new version , the same increment occurs , but with a ‘decay’ of the values in d that is controlled by α ., The same modification is made to the updates for a and b ( the updated forms are given explicitly in Appendix 1 ) ., In the context of reversal learning , this is not a trivial adjustment but a crucial addition to the generative model which enables AI agents to adapt flexibly ., However , the level at which to set the decay term poses a further challenge: if the decay is too big , the model is too flexible and will be dominated by its most recent experiences ( as all the other terms will have decayed ) ., If the decay is too small concentration parameters may accumulate too slowly , rendering the model too stable ., There are several ways one can optimise this ‘forgetting’ in volatility models ., One could equip the Markov decision process with a further hierarchical level modelling fluctuations from trial to trial–as in the hierarchical Gaussian filter 21 ., A simpler ( and biologically parsimonious ) solution is to link the decay factor to recent values of state-action prediction error via the LC ., In other words , equip the agent with the prior that if belief updating is greater than expected , environmental contingencies have become more volatile ., This produces flexibility in model learning when state-action prediction error is high ( low α ) but maintains model stability when state-action prediction error is low ( high α ) ., We have modelled this feedback using a simple logistic function to convert the error into a value for α:, α=αmin+αmax1+ek ( SAPE−m ), where SAPE is the state-action prediction error seen during the trial ( in tasks with more than one prediction error per trial , the maximum error is used ) , k is a gradient and m is a mean ( i . e . , expected ) value ., In all simulations presented below , αmin = 2 , αmax = 32 , k = 8 , and m was set one standard deviation above the mean error value encountered in 100 trials of each task with α = 16 ., Under this scheme , a brief but large state-action prediction error ‘boosts’ the impact of a recent experience upon the agent’s model of the world ., This occurs by temporarily increasing the attrition of existing , experience dependent parameters encoding environmental contingencies ., Crucially , this causes recent actions and observations to have a greater effect on the Dirichlet distributions than they would otherwise ., If errors then decrease , the model stabilises again ., However , if actions consistently produce large state-action prediction errors then the underlying model parameters will gradually lose their structure–equivalent to the flattening of probability distributions that form the agent’s model—leading to greater variability in action selection ., This ‘flat’ model does not need to track volatility separately: we instead incorporate LC/NA directly into the decision-making loop ., General MATLAB code implementing Active Inference can be found at https://www . fil . ion . ucl . ac . uk/spm/software/spm12/ ( in the folder toolbox/DEM ) ., Code from this toolbox was modified to perform the simulations described in this paper; examples are available at https://github . com/AnnaCSales/ActiveInference ., We now turn to simulations in which the state-action prediction error is linked–via LC activity—to the model decay parameter ., When this link is introduced there are improvements in performance in the simulations of both the Go/no-go and explore/exploit tasks ( Figs 7 and 8 ) ., In the explore/exploit task , the dynamic modulation of model building allows state-action prediction errors to reduce more quickly when the rat is settled into the ‘exploit’ mode of harvesting a reward in a reliable location , promoting model stability ., When the reward is no longer available , errors mount and the increase in model decay causes the agent to make more explorative choices ., This contrasts with the same task simulated with fixed values of α ( Fig 7 ( A ) and 7 ( B ) ) : when the model is hyper-flexible ( α = 2 ) , the agent often switches behavioural strategy after a single failed trial; when the model is inflexible , the agent takes a large number of trials to visit a new location ( α = 2 ) ., The highly flexible agent performs well when the structure of the environment is volatile ( Fig 7C ) ., In this context , even small errors may reasonably indicate that the underlying rules of the task have changed , and the agent’s rapid shifts in strategy yields rewards ., Over multiple simulations the flexible agent obtains a significantly higher total reward than the inflexible agent ( averages over 50 simulations of 150 trials each with rules as shown in Fig 7 ) ., Conversely , the inflexible agent obtains significantly more overall reward when the environment is more stable ( 5 ( d ) ) ., Fig 7 ( C ) and 7 ( D ) also show the performance of an agent with a dynamic α with a value determined by the state-action prediction error , ranging between α = 2 and α = 32 ., This agent performs as well as , or better than , the fixed α agents in both contexts , responding with a rapid changes to its model ( and resulting behaviour ) when errors are high for a sustained period , but stabilising when errors decrease ., This agent also outperforms both fixed α agents when the arm locations change after random intervals ( Fig 7 ( D ) ) ., A full plot of state-action prediction errors , simulated LC firing and behavioural output for the explore/exploit task with the flexible α agent is also shown in S2 Fig . The Go/No-Go task was also simulated during a reversal of cue meanings ( Fig 8 ) ., As expected , the well-trained agent begins the session by showing a phasic response in state-action prediction error / LC firing in response to the ‘Go’ cue ( cue 1 ) ., At trial 35 , the meaning of the two cues switches so that the ‘Go’ context is predicted by cue 2 ., At the reversal , state-action prediction errors cannot be resolved and LC firing switches to a higher tonic level ., During this period , model updating–and behaviour—becomes more flexible and the new rules of the task are learnt ., Eventually the high levels of tonic activity fall away and phasic responses to the new ‘Go’ cue re-emerge; coupled with a lower level of tonic activity ., This mirrors the pattern of LC firing recorded in monkeys during the same task 22 ., Note that after the reversal , phasic responses emerge initially in response to the reward itself , then to both the cue and reward , and finally only in response to the cue ., Over multiple trials of the reversal , only the agent with a flexible α linked to state-action prediction error is able to learn the new contingencies and return to optimum performance levels ( Fig 8 ( E ) , for which the reversal was repeated 50 times ) ., The characteristics of the state-action prediction error for this agent were then examined in more detail ( Fig 8 ( F ) and 8 ( G ) ) ., 2000 trials were run of the go/no-go task in which the cue meanings were held constant ( no reversal ) , and the agent started each trial with ‘well-trained’ priors obtained through 750 trials of training , as above ., We find that the size of the state-action prediction error–the proposed input into the LC—changes in ways that are consistent with experimentally reported LC activation ( see discussion below ) ., As shown in Fig 8 , the size of the phasic peak in state-action prediction error is larger for rarer ‘go’ stimuli ( Fig 8 ( G ) , in which ‘G’ = go cues , ‘NG’ = no go cues ) ., When the probability of the ‘go’ cue is held fixed , the resulting peak increases as the reward becomes more valuable to the agent ( represented by the value held in the agent’s c matrix , Fig 8 ( F ) ) ., Finally , when consecutive ‘go’ trials occur , the second peak is reduced in size ( mean reduction of 12 . 9%±1 . 4% , see S3 Fig ) ., As expected , when the ‘go’ cues are rare ( e . g . p ( g ) = 10% ) the state-action prediction error response to the ‘go’ cue is significantly larger than the response to the ‘no-go’ cue ., Interestingly , this is still true when the cues occur with equal probability ( p ( go ) = 50% ) , and when the ‘go’ cue is slightly more probable ( p ( go ) = 55% , Fig 8G ) ., Many of these response characteristics are also present in agents with a fixed α and are an inherent feature of the state-action prediction error ( see S3 Fig ) ., However only the agent with flexible α displays both the correct profile of prediction error responses and the ability to learn the reversal of contingencies shown above ., A full plot of state-action prediction errors , simulated LC firing and behavioural output for the static go/no-go task for the flexible α agent is shown in S2 Fig ., In previous Active Inference literature the calculation of Bayesian Model Averages has been mapped to the dorsal prefrontal cortex 14 ., This is one of the frontal regions known to send projections to LC 29 , 30 and is a candidate for the calculation of state-action prediction error ( although we accept that without further experimental work such anatomical attributions are largely speculative ) ., Experimental evidence for a neural representation of a distinct prediction error based on states , rather than rewards , has also been found in dorsal regions of the frontal cortex in a human MRI study 31 ., Turning to the LC-prefrontal connections and the modulation of model updating , converging experimental evidence suggests that working models of the environment are reflected by ACC activity ., Activity in the ACC has been shown to correlate to many factors relevant to the maintenance of a generative model , including reward magnitude and probability ( for review see 32 ) , estimation of the value of action sequences and subsequent prediction errors 33 , 34 and the value of switching behavioural strategies 35 ., Marked changes in activity in ACC have been observed at times thought to coincide with significant model updating and occur in parallel with explorative behaviour–an event that has been directly linked to increased input from locus coeruleus 3 , 36 ., Similarly , a direct ACC/ LC connection has also been found in response to task conflicts 37 ., ACC activity is also correlated with learning rate during times of volatility , such that when the statistics of the environment change , more recent observations are weighted more heavily in preference to historical information 38 ., This evidence provides a solid foundation for the hypothesis that the LC modulates learning rate by governing model updating via ACC ., Specifically , we propose that the release of noradrenaline would cause a temporary increase in the susceptibility of model-holding networks to new information ., At a cellular level , this would lead to NA effectively breaking and reshaping connections amongst cell assemblies ., In vitro investigation of the cellular effects of noradrenaline provides support for this idea , indicating that noradrenaline may suppress intrinsic connectivity of cortical neurons , causing a relative enhancement of afferent input 1 , 39 , 40 ., Sara 41 and Harley 42 also suggest that LC spiking synchronises oscillations at theta and gamma frequencies , allowing effective transfer of information between brain regions during periods of LC activity ., This may allow enhanced updating of existing models with more recent observations ., A role for the LC in prioritising recent observations during times of environmental volatility has been explicitly suggested experimentally 43 and is supported by evidence regarding the critical role of LC activation in reversal learning , e . g . 44 ., We note that if the LC is indeed responding to state-action prediction errors , model updating is likely not the only functionality it has ., For instance , LC activation has been experimentally linked to the potentiation of memory formation 41 , 45 , 46 , analgesic effects 47 , 48 and changes to sensory perception for stimuli occurring at the time of LC activation 1 , 49 , 50 ., These are all reasonable responses to a large state-action prediction error: the increase in gain on sensory input may ensure that salient stimuli are more easily detectable in the future , whilst enhanced formation of memory might ensure that mappings between salient stimuli and states are remembered over longer timeframes ., Similarly , the temporary suppression of pain may facilitate urgent physical responses to important stimuli ( for instance , allowing action in response to a stimulus indicating the presence of a predator ) ., The possibility that the LC has the capacity to provide a differentiated response to state-action prediction error is supported by recent work indicating that existence of distinct subunits with preferred targets producing different functional effects 48 , 51–53 ., The ideas described above are not a radical departure from existing models of LC function–but use the theory of active inference to integrate similar concepts into a general theory of brain function , without invoking the need for monitoring of ad-hoc statistical quantities ., The adaptive gain theory proposed by Aston Jones and Cohen 4 proposes that the LC responds to ongoing assessments of utility in OFC and ACC by altering the global ‘gain’ of the brain ( the responsivity of individual units ) ., Phasic activation produces a widespread increase in gain which enables a more efficient behavioural response following a task-related decision; however , when the utility of a task decreases , the LC switches to a tonic mode which favours task disengagement and a switch from ‘exploit’ to ‘explore’ ., The mechanism we have described reproduces many elements of the adaptive gain theory , with the important exception that different LC firing patterns promoting explorative or exploitative behaviour are an emergent property of the model rather than a dichotomy imposed by design ., Since the probability assigned to individual policies is explicitly dependent on their utility ( in combination with their epistemic value ) a large state-action prediction error will ultimately reflect changes in the availability of policies which lead to high utility outcomes ., This may be a positive change , as is the case when a cue indicates that a ‘Go’ policy will secure a reward , or a negative change , when rewards are no longer available in the explore/exploit task ., This link is demonstrated in Fig 6 for the explore/exploit task , where increases in state-action prediction error / LC firing occur in tandom with abrupt changes in the agents assessment of a given policys utility ., Both the LC response , and the underlying cause ( state-action prediction error ) , show a shift between ‘phasic’ and ‘tonic’ modes ( although it is entirely possible that coupling mechanisms within the LC also act to exaggerate the shift and cause the LC to fire in a more starkly bi-modal fashion , as suggested by computational modelling of the LC 4 , 54 ) ., As described above , a short prediction error will act to heighten the response to a salient cue over the short term , whilst a large , sustained prediction error–occurring in parallel with declining utility in a task–will act to make behaviour more exploratory ., Yu and Dayan have proposed an alternative model where tonic noradrenaline is a signal of ‘unexpected uncertainty’ , when large changes in environment produce observations which strongly violate expectations | Introduction, Models, Results, Discussion | The locus coeruleus ( LC ) in the pons is the major source of noradrenaline ( NA ) in the brain ., Two modes of LC firing have been associated with distinct cognitive states: changes in tonic rates of firing are correlated with global levels of arousal and behavioural flexibility , whilst phasic LC responses are evoked by salient stimuli ., Here , we unify these two modes of firing by modelling the response of the LC as a correlate of a prediction error when inferring states for action planning under Active Inference ( AI ) ., We simulate a classic Go/No-go reward learning task and a three-arm ‘explore/exploit’ task and show that , if LC activity is considered to reflect the magnitude of high level ‘state-action’ prediction errors , then both tonic and phasic modes of firing are emergent features of belief updating ., We also demonstrate that when contingencies change , AI agents can update their internal models more quickly by feeding back this state-action prediction error–reflected in LC firing and noradrenaline release–to optimise learning rate , enabling large adjustments over short timescales ., We propose that such prediction errors are mediated by cortico-LC connections , whilst ascending input from LC to cortex modulates belief updating in anterior cingulate cortex ( ACC ) ., In short , we characterise the LC/ NA system within a general theory of brain function ., In doing so , we show that contrasting , behaviour-dependent firing patterns are an emergent property of the LC that translates state-action prediction errors into an optimal balance between plasticity and stability . | The brain uses sensory information to build internal models and make predictions about the world ., When errors of prediction occur , models must be updated to ensure desired outcomes are still achieved ., Neuromodulator chemicals provide a possible pathway for triggering such changes in brain state ., One such neuromodulator , noradrenaline , originates predominantly from a cluster of neurons in the brainstem—the locus coeruleus ( LC ) —and plays a key role in behaviour , for instance , in determining the balance between exploiting or exploring the environment ., Here we use Active Inference ( AI ) , a mathematical model of perception and action , to formally describe LC function ., We propose that LC activity is triggered by errors in prediction and that the subsequent release of noradrenaline alters the rate of learning about the environment ., Biologically , this describes an LC-cortex feedback loop promoting behavioural flexibility in times of uncertainty ., We model LC output as a simulated animal performs two tasks known to elicit archetypal responses ., We find that experimentally observed ‘phasic’ and ‘tonic’ patterns of LC activity emerge naturally , and that modulation of learning rates improves task performance ., This provides a simple , unified computational account of noradrenergic computational function within a general model of behaviour . | learning, medicine and health sciences, decision making, brain, social sciences, neuroscience, learning and memory, simulation and modeling, cognitive psychology, systems science, mathematics, probability distribution, cognition, thermodynamics, research and analysis methods, frontal lobe, computer and information sciences, behavior, agent-based modeling, locus coeruleus, probability theory, free energy, physics, psychology, anatomy, biology and life sciences, physical sciences, cognitive science, cerebral cortex | null |
journal.pcbi.1005909 | 2,017 | Molecular recognition and packing frustration in a helical protein | Molecular recognition is the basis of biological function ., For different parts of the same molecule or different molecules to recognize one another , a target set of interactions need to be favored while other potential interactions are disfavored ., Biomolecules accomplish these simultaneous tasks via the heterogeneous interactions encoded by their sequences ., For proteins , such energetic heterogeneity is enabled but also constrained by a finite alphabet of twenty amino acids ., Thus the degree to which non-target interactions can be avoided through evolutionary optimization is limited 1 , 2 ., Conflicting favorable interactions , referred to as frustration , are often present in biological systems ., From a physical standpoint , it is almost certain that some of the frustration is a manifestation of the fundamental molecular constraint on adaptation , although under certain circumstances frustration can be exploited to serve biological function 3 , 4 ., Protein folding entails intra-molecular recognition ., Early simulations suggested that nonnative contacts can be common during folding 5 ., This predicted behavior applies particularly to models embodying a simple notion of hydrophobicity as the main driving force 6 , 7 ., Experimentally , however , protein folding is thermodynamically cooperative 7 , 8 ., Folding of many single-domain proteins does not encounter much frustration from nonnative interactions in the form of kinetic traps 9 ., Celebrated by the consistency principle 10 and the principle of minimal frustration 11 , these empirical trends have inspired Gō-like modeling , wherein native-centric interactions are used in lieu of a physics-based transferable potential 12–14 ., Extensions of this approach allow nonnative interactions to be treated as perturbations in a largely native-centric framework 15–17 ., The success of these models poses a fundamental challenge to our physical understanding as to why , rather non-intuitively , natural proteins are so apt at avoiding nonnative interactions ., Solvation effects must be an important part of the answer 18 , as has been evident from the fact that coarse-grained protein models incorporating rudimentary desolvation barriers exhibit less frustration and higher folding cooperativity than models lacking desolvation barriers 7 , 19 , 20 ., More recently , and most notably , folding of several small proteins has been achieved in molecular dynamics studies with explicit water 21 , 22 ., Nonnative contacts are not significantly populated within sections of the simulated trajectories identified as folding transition paths 23 though they do impede conformational diffusion 24 ., These advances suggest that certain important aspects of protein physics are captured by current atomic force fields , although they still need to be improved to reproduce the high degrees of folding cooperativity observed experimentally 22 , 25–28 ., In this context , it is instructive to ascertain how atomic force fields , as they stand , disfavor nonnative interactions , so as to help decipher molecular recognition mechanisms in real proteins ., We take a step toward this goal by comparing the stabilities of native and nonnative configurations of fully formed helices from a natural protein ., By construction , this approach covers only a fraction of all possible nonnative configurations and therefore only provides , albeit not unimportantly , a lower bound on the full extent of frustration ., Nonetheless , because of its focus on tractable systems , we obtain a wealth of reliable simulation data from which physical insights are gleaned ., We do so by applying explicit-water molecular dynamics simulations to compute potentials of mean force ( PMFs ) between various helices 29 of the E . coli colicin immunity protein Im9 30 ., Im9 is a small single-domain protein that undergoes two-state-like folding 31 , 32 to a native structure with four helices packed around a hydrophobic core 33 ., Its folding mechanism and that of its homolog Im7 have been extensively characterized experimentally 30–40 and theoretically 41–46 ., Of particular relevance to our study are experimental Φ-value analyses suggesting that the Im9 folding transition state has a partially formed hydrophobic core stabilized by interactions between helix 1 ( H1 ) and helix 4 ( H4 ) , whereas helix 3 ( H3 ) adopts its native conformation only after the rate-limiting step of folding 32 ., These experimental inferences have since been rationalized by simulations showing that H1 and H4 are formed whereas about one half of helix 2 ( H2 ) remains unstructured in the Im9 transition state 41 , and that , unlike Im7 , there is no significant kinetic trap along the Im9 folding pathway 45 , 46 ., Building on these advances , our systematic PMF analysis provides a hitherto unknown perspective on these hallmarks of Im9 folding ., Notably , we found significant packing frustration between H1 and H2 , viz . , a nonnative packing orientation can achieve a lower free energy than that afforded by the native packing of these two helices in isolation ., Superficially , this simulation result seems at odds with experiments indicating little frustration in Im9 folding ., On closer examination , however , our discovery provides an unexpected rationalization for experiments indicating that folding is initiated by the more stabilizing H1-H4 interactions rather than by H1-H2 packing ., Because the H1-H2 packing frustration can be circumvented by following such a kinetic order , our finding suggests that the Im9 folding pathway might have evolved to avoid a potential H1-H2 kinetic trap ., This example underscores that the inner workings of molecular recognition can be rather subtle and deserves further exploration , as will be elaborated below ., We begin by investigating the free energy landscape for the association of H1 with H2 , a packing interaction that accounts for the largest two-helix interface in the native state of Im9 , burying 5 . 3 nm2 or 17% of the total surface area of H1 and H2 ., Throughout this study , surface areas of helical bundles are computed as the solvent-accessible surface areas of the given bundles in isolation , irrespective of the solvent exposure of the configurations in the complete Im9 folded structure ., Using an enhanced sampling technique known as umbrella sampling with virtual replica exchange ( US-VREX , see Methods ) for restrained helical configurations at systematically varied target packing angles , we compute PMFs for H1-H2 association in the absence of their intervening loop ( the H1→H2 system in Fig 1B and Table 1 ) ., The PMFs are determined for the native orientation as well as for nonnative orientations and nonnative crossing angles entailed by the imposed rotational preferences ( Methods and S1 Text ) ., Our technique allows these simulations to converge rapidly ( S1 Fig ) ., Each PMF is then integrated over a free-energy basin to provide a binding free energy , ΔGbind , for a specific inter-helix geometry ., Unexpectedly , H1-H2 association is favored by a 20–30° positive rotation of H1 against H2 ., Binding in this nonnative orientation is 10–12 kJ/mol more stable than that in the native orientation ( black circles in Fig 2A and Table 2 ) , a free energy difference equivalent to a ~50-fold increase in bound population ( S1 Text ) ., In contrast , the binding free energy profiles for rotating H2 against H1 ( Fig 2A , red squares ) or changing the H1-H2 crossing angle ( Fig 2A , blue triangles ) indicate that the state corresponding to native packing ( 0° angle in Fig 2A ) is situated well within the basin of lowest free energy with respect to these degrees of freedom , although a ≤50° positive change in H1-H2 crossing or a ≤20° negative rotation of H2 against H1 would leave the system approximately iso-energetic with the native packing ( Fig 2A ) ., As mentioned , these binding energies are computed from PMFs such as those in Fig 2B and S2 Fig . A broader view of the orientation-dependent H1-H2 packing free energy landscape can be seen in Fig 2C ., Instead of fixing either H1 or H2 in its native orientation ( as in Fig 2A ) , Fig 2C provides the relative favorability of packing orientations resulting from simultaneous rotations of H1 and H2 ., This two-dimensional PMF is generated by combining sampling data for H1 and H2 rotations under harmonic biasing potentials ( S1 Text ) ., It is clear from this two-dimensional landscape that native packing ( H1 , H2 ) rotations equal ( 0° , 0° ) is less favored than the free energy minimum at ( +19° , +4° ) ., Indeed , this minimum is situated in a rather broad basin encompassing many nonnative orientations with simultaneous H1 rotation from approximately +5° to +25° and H2 rotation from approximately ‒3° to +15° that are energetically more favorable than the native H1-H2 orientation ( 0° , 0° ) ., Fig 2C reveals further that there exists another basin of favorable nonnative H1-H2 packing for which both helices rotate by approximately ‒20° ., In short , our systematic analysis in Fig 2 demonstrates unequivocally that packing frustration exists in Im9 , in that when H1 and H2 are considered in isolation , nonnative packing is favored over native packing ., To assess the prospect that intervening loop residues may provide additional guidance for native packing of H1 against H2 , we also simulate this helix-loop-helix as a single chain ( H1LH2 system; Table 1 ) ., Because the covalent connection of H1 to H2 is incompatible with the large helical separations used in our importance sampling , we study the H1LH2 system without inter-helical distance bias in simulations initiated in either the native state or one of 20 different nonnative orientations in which H1 or H2 is rotated by ±10–50° ., Because the actual rotations sampled during simulations are close to those targeted by the restraining potentials ( S4 Fig ) , we do not distinguish between target and actual rotations hereafter ., Although these simulations do not converge to a single conformational distribution , they show broad sampling of H1 rotation with a stable or metastable state near +20° rotation of H1 , even when simulation is initiated at the native packing angle ( S6 Fig ) ., To explore how the H1-H2 packing frustration might be overcome in Im9 folding , we next investigate the impact of the rest of the protein on the packing between H1 and H2 by computing binding free energies for the association of H1 and H2 not in isolation but in the presence of additional protein fragments involving the other two helices H3 and H4 as well as loop and terminal residues ., The conformations of the loop and terminal residues in our simulations are restrained to those in the Im9 PDB structure ., We first consider the association of H1 with a bundle comprising helices 2 , 3 , and 4 connected by their intervening loops and extending to the proteins C-terminus ( H1→H2LH3LH4C; Table 1 ) ., Interestingly , for this system , native packing is found to be 13 ± 3 kJ/mol more favorable than the nonnative packing resulting from a +30° rotation of H1 ( Table 2 ) ., The very fact that a nonnative rotation of H1 is substantially favored in H1→H2 ( Fig 2A and Table 2 ) but disfavored in H1→H2LH3LH4C ( Table 2 ) demonstrates clearly that some components of the H2LH3LH4C bundle besides H2 are crucial for overcoming the H1-H2 packing frustration and guiding H1 to pack natively ., Furthermore , because native packing is favored in H1→H2LH3LH4C despite the residues N-terminal to H1 ( including a short 3–10 helix ) being excluded in this model system , these N-terminal residues are likely not necessary for ensuring native packing of H1 against the rest of the Im9 protein ., We now dissect the H2LH3LH4C bundle to ascertain the contributions from different parts of this bundle to native H1 packing ., To this end , binding free energies for the association of H1 with a variety of subsets of H2LH3LH4C are computed ., We first consider a bundle comprising helices 2 and 4 ( H1→H2/H4; Table 1 ) ., Somewhat surprisingly , native packing in the H1→H2/H4 system is disfavored by as much as 22 ± 1 kJ/mol when compared against nonnative packing with H1 rotated by +30° , even more than the corresponding nonnative preference of 10 ± 1 kJ/mol for H1→H2 ( Table 2 ) ., This observation implies that H4 by itself is not promoting H1-H2 native packing and therefore H3 , loops , and/or the C-terminus must be responsible for driving native packing of H1 with H2LH3LH4C ., Indeed , when compared against H2/H4 , the presence of these other elements in H2LH3LH4C results in a 26 ± 1 kJ/mol preference for native H1 packing and a 9 ± 3 kJ/mol discrimination against nonnative H1 packing with a +30° rotation ( Table 3 ) ., To better pinpoint the role of H3 in this intra-molecular recognition process , we compute binding free energies for the association of H1 and a bundle comprising helices 2 , 3 and 4 but without the intervening loops and the C-terminus ( H1→H2/H3/H4; Fig 1D and Table 1 ) ., For this model system , native packing is less favorable than +30° rotation of H1 by 11 ± 3 kJ/mol ( Table 2 ) ., Nonetheless , in comparison to H1→H2/H4 , the inclusion of H3 favors native packing more than it favors nonnative packing with a +30° rotation of H1 ( Table 2 ) ., This observation indicates that H3 is capable of correcting part of the nonnative tendencies of H1 imparted by its interactions with a bundle comprising only of H2 and H4; but H3 is insufficient to ensure native packing in the absence of the connecting loops and/or the C-terminus ., To explore whether inclusion of residues neighboring H4 may alter its effect on H1-H2 packing , we consider three residues immediately N-terminal to H4 ( Asp62 , Ser63 , and Pro64 ) ., These residues are chosen because they are known to associate directly with H1 in the NMR structure 47 and thus they may contribute positively to native intra-molecular recognition ., Consistent with this expectation , once these three residues are included , the H1-binding free energies in the resulting H1→H2/NH4 system ( Table 1 ) for native packing and nonnative +30° rotation of H1 become essentially energetically equivalent ( ΔΔGbind = 2 ± 6 kJ/mol; Table 2 ) ., Inasmuch as promoting native H1-binding is concerned , this represents a significant improvement over H1→H2/H4 that favors the +30°-rotated nonnative packing by 22 ± 1 kJ/mol ( Table 2 ) ., Indeed , in the context of H1→H2/H4 , addition of these N-terminal flanking residues assists native packing by 31 ± 5 kJ/mol , much more than the 7 ± 3 kJ/mol increase in stability they also impart on the nonnative packing of H1 with a +30° rotation ( Table 3 ) ., These numbers underscore the important role of Asp62 , Ser63 , and Pro64 in discriminating against nonnative packing of H1 ., Another set of helix-flanking residues that may assist native packing in Im9 is its C-terminus ., Such an effect is expected because a +30° rotation of H1 would likely place its constituent residue Phe15 into a steric clash with the C-terminal residue Phe83 ( S7 Fig ) and thus existence of the C-terminus should discriminate against such a rotation of H1 ., To evaluate this hypothesis , we compute H1-binding free energies with a bundle comprising H2 and H4 as well as the proteins C-terminus ( H1→H2/H4C; Table 1 ) ., Similar to the addition of Asp62 , Ser63 , and Pro64 N-terminal to H4 in H2/NH4 bundle , inclusion of the C-terminus in H2/H4C eliminates the strong nonnative bias in H1→H2/H4 , resulting in essentially no discrimination between the native orientation and a +30° rotation of H1 ( ΔΔGbind = 1 ± 3 kJ/mol; Table 2 ) ., Relative to H1→H2/H4 , addition of the C-terminus not only favors native packing by 6 ± 2 kJ/mol but also directly disfavors +30° rotation of H1 by 17 ± 2 kJ/mol ( Table 3 ) ., The latter penalization of nonnative packing ( which does not occur in H1→H2/NH4 ) is consistent with the aforementioned steric consideration ( S7 Fig ) ., Interestingly , the native-promoting effects of N- and C-terminal extensions to H4 are essentially additive ., When both extensions are added to H4 , the H2/NH4C system ( Table 1 ) is sufficient to favor native packing of H1 by 14 ± 6 kJ/mol over the nonnative packing with +30° rotation of H1 ( Table 2 ) ., After analyzing systems involving H2 , we now turn to the intra-molecular recognition between H1 and H4 without involving H2 ., Native H1-H4 packing constitutes the second largest two-helix interface in the Im9 folded structure , burying 3 . 7 nm2 which amounts to 13% of the sum of individual surface areas of H1 and H4 ., PMFs for helices 1 and 4 in isolation ( H1→H4; Fig 1C and Table 1 ) are computed in the native orientation as well as nonnative orientations resulting from rotations of H1 or H4 ., When H1 is rotated while H4 is fixed , native packing is favored ( Fig 3A , black circles ) ; however , when H4 is rotated with H1 fixed , a +30° nonnative rotation of H4 leads to 5 ± 1 kJ/mol stabilization ( decrease in ΔGbind ) relative to native ( red squares in Fig 3A and Table 2 ) ., Distance-dependent PMFs for the native orientation and ±30° rotations of H4 are shown in Fig 3B , indicating that the favored nonnative packing at +30° is attained at an H1-H4 separation slightly larger than native by about 0 . 1 nm ., The two-dimensional PMF ( Fig 3C ) as a function of H1 and H4 rotation angles shows further that native H1-H4 packing ( 0° , 0° ) is situated at the periphery of a broad basin of favored orientations centered roughly around ( +10° , +10° ) ., The same two-dimensional landscape suggests that H1 rotations of ≥ +50° or ≤ ‒50° can also be favored with little or no H4 rotation ., We noted earlier that a 3-residue N-terminal extension to H4 directly contacts H1 in the native state and that the inclusion of these residues assisted the native packing of H1 against a bundle comprising helices H2 and H4 ., Consistent with that observation , these three residues—Asp62 , Ser63 , and Pro64—likewise assist the native packing of H1 against H4 , viz . , their inclusion in the H1→NH4 system ( Table 1 ) makes native packing ( ΔGbind = ‒44 ± 1 kJ/mol ) significantly more favorable than the nonnative packing with a +30° rotation of H4 ( ΔGbind = ‒21 ± 2 kJ/mol ) while still favoring native orientation of H1 ( Table 2 ) ., We conclude from these results that helices H1 and H4 are nearly capable of associating in native-like conformations by themselves in isolation; and that they can certainly achieve native packing with the assistance from the 3-residue N-terminal extension to H4 ., These results suggest that Im9 residues 12–23 and 62–78 may serve as major components of a native-like folding nucleus ., To better understand the driving force for nonnative H1-H2 packing , the potential energies between specific pairs of amino acid residues on the H1-H2 interface in the native orientation are compared against those in the nonnative orientation with a +30° H1 rotation ., We make this comparison for helix-helix center of mass distance di0 = 1 . 10 nm in both the native and non-native configurations , wherefore each pair of helices in question is in close spatial contact ( Fig 4 ) ., The analysis indicates a prominent role by the more favorable Lennard-Jones interactions between interfacial residue pairs Glu14-Met43 , Leu18-Phe40 , and Ile22-Phe40 in favoring the nonnative packing , whereas electrostatic interactions between these residue pairs are of similar strengths for the native and nonnative packing orientations ., In contrast , the interaction between Ile22 and Leu33 favors native packing , but its effect is more than compensated by the aforementioned multiple residue-residue interactions that drive nonnative packing such that a +30° rotation of H1 is favored over the native orientation for H1-H2 packing in isolation ., It is noteworthy , however , that while these residue-residue energetic effects can be significant individually ( Fig 4 ) and collectively ( Table 2 ) , they are not accompanied by obvious , drastic structural changes at the level of residue-residue contacts ., When contacts between residues on different helices at a helix-packing interface are identified by a commonly used proximity threshold , contact probabilities between the helices are seen to remain essentially unchanged upon a +30° H1 native-to-nonnative rotation in both the H1→H2 and H1→ H2LH3LH4C systems ( S8 Fig ) ., Seeking physical reasons for favoring native packing in H1→ H2LH3LH4C but not in H1→H2 , we compare the potential energies of these systems in the native and the +30° H1-rotated nonnative configurations ( Fig 5 ) ., When potential energies are analyzed by the molecular species involved in the interactions , for H1→H2 , solvent-protein ( solvent-helix ) interactions are more unfavorable with nonnative rotation of H1 by +30° , but this effect is overwhelmed by larger , favorable changes in solvent-solvent and intra- and inter-helix interactions ( Fig 5A ) ., More specifically , this nonnative H1 rotation favors inter-helix Lennard-Jones interactions ( as exemplified by the three residue pairs circled in red in Fig 4A ) as well as intra-helix and solvent-solvent electrostatic interactions ( Fig 5A ) , netting an overall favorable ( more negative ) potential energy for the nonnative orientation ( Fig 5A , “sum” ) ., In contrast , the corresponding analysis for H1→H2LH3LH4C yields a set of average potential energies that favors the native state overall ( Fig 5B , native “sum” more negative than nonnative ) ., This potential energy ( enthalpic ) trend is consistent with the above PMF/binding free energy prediction that the native orientation is favored for H1→H2LH3LH4C ( Table 2 ) , though entropic effects may make additional contribution to the stability of native packing of H1 against H2LH3LH4C ( see below ) ., Because nonnative +30° H1 rotation has opposite effects on intra-H2 ( Fig 5A ) versus intra-H2LH3LH4C ( Fig 5B ) Coulomb energies , one of the reasons for disfavoring nonnative +30° H1 rotation in H1→H2LH3LH4C is that this rotation of H1 induces energetic strain within H2LH3LH4C , resulting in a destabilizing increase in intra-H2LH3LH4C Coulomb energy collectively , whereas the same +30° H1 rotation leads to an overall stabilizing decrease in intra-H2 Coulomb energy ., The atomic basis of this difference remains to be analyzed ., To gain further insight into the differential effects of H2 and H2LH3LH4C on the favorability of the native orientation upon H1 binding , we resolve the distance-dependent H1→H2 and H1→H2LH3LH4C PMFs ( Fig 6A and 6B , respectively ) into their enthalpic ( Fig 6C and 6D ) and entropic ( Fig 6E and 6F ) components ., Since the backbones of the helical elements in our simulation systems are restrained to be essentially rigid , the entropic contributions computed here originate almost exclusively from the water solvent and sidechain degrees of freedom , whereas contributions from mainchain conformational entropy are negligible in comparison ., Despite sampling uncertainties , several likely trends can be quite clearly discerned: For H1→H2 , the lower PMF ( ΔG ) minimum for the nonnative orientation ( Fig 6A ) is driven by enthalpy ( lower ΔH minimum for +30° H1 rotation than for native in Fig 6C ) ., This effect is partially , but not completely , compensated by the entropic component of the free energy , ‒TΔG ., The latter is seen favoring native packing in Fig 6E ( red curve below blue curve at distance marked by vertical blue dashed line ) , although the differences are largely within error bars ., Entropy has a similar effect on H1→H2LH3LH4C in stabilizing native packing ( Fig 6F ) ., In this case however , unlike H1→H2 , enthalpy is also favorable ( though only slightly ) to the native state ( Fig 6D , see also Fig 5B ) , thus the entropic and enthalpic effects reinforce each other , yielding a ΔG favorable to native packing for H1→H2LH3LH4C ( Fig 6B ) ., It should be noted that the trends of entropic stabilization seen here in Fig 6 are similar to those exhibited by a pair of poly-alanine or poly-leucine helices 29 ., In both cases , the entropic trends are likely manifestations of the well-recognized solvent-entropic origin of hydrophobic interactions at ambient temperatures ., Every helix-helix association in Fig 6 entails an enthalpic barrier at separation ≈1 . 5 nm ( Fig 6C and 6D ) ., As implied by the absence of PMF barriers at these positions ( Fig 6A and 6B ) , the enthalpic barriers here are compensated by a larger decrease in entropic free energy at the same positions ( Fig 6E and 6F ) ., Further examples of enthalpic barriers and entropic compensations are provided in S2 Fig . These results are consistent with burial of hydrophobic surfaces being concomitant with increase in solvent ( water ) entropy at room temperature and the idea that enthalpic barriers to protein folding 20 , 29 , 49 , 50 may arise largely from steric dewetting 29 ., Because steric dewetting creates voids ( between the approaching helices in the present cases; S9 Fig ) , it leads to volume barriers 29 such as those seen in Fig 6G and 6H ., As has been discussed , such volume barriers probably amount to part of the activation volume of protein folding 51 , 52 ., For the systems studied in Fig 6 , it is not surprising that the enthalpic and volume barriers are higher for H1→H2LH3LH4C than for H1→H2 because the former binding process buries a significantly larger protein surface area ., Therefore , we expect a larger transient void volume between the approaching helices before close packing is achieved for H1→H2LH3LH4C than for H1→H2 ., It is interesting to note that , perhaps because void volumes are largely a consequence of geometry and less of energetics , the volume barrier heights in Fig 6G and 6H are essentially insensitive to the difference between native and nonnative packing ., To recapitulate , we have conducted a systematic analysis of the relative stability of native versus nonnative packing of helices in the Im9 protein as a means to address the physical basis of biomolecular recognition ., These results are summarized schematically in Fig 7: Relative to native packing , three nonnative configurations ( H1→H2 , H1→H2/H4 , and H1→H2/H3/H4 , each with H1 rotated ) are significantly stabilized whereas one other nonnative packing orientation ( H1→H4 with H4 rotated ) is mildly stabilized ., Other Im9 systems that we have simulated either favor the native configuration or essentially do not discriminate between native and nonnative packing ., As emphasized at the outset , our method is designed to characterize packing frustration of constrained , locally native protein substructures by varying the orientation between interacting substructures that are rigid by construction , viz . , the secondary structure ( main-chain conformation ) of each of the helices is essentially fixed ., It follows that while our substantial computational effort has succeeded in gaining structurally and energetically high-resolution information about frustration that is novel and complementary to that obtained from our previous coarse-grained chain model study of Im7 and Im9 45 , the present investigation—unlike our coarse-grained modeling 45—cannot by itself address certain general questions regarding folding pathways such as the viability of nucleation-condensation mechanisms 53 because backbone conformational freedom is not treated ., For the same reason , the present method does not tackle frustration involving disordered , flexible main-chain segments that may adopt locally nonnative conformations ., A notable example in this regard is the second helix of Im7 ., Among the four respective helices in Im7 and Im9 , the amino acid sequence of the second helix varies the most between the homologs 30 ., The second Im7 helix has been identified as a part of the protein which is disordered and participates in nonnative interactions that stabilize a kinetically trapped folding intermediate during the process of non-two-state folding of Im7 45 ., However , revealingly , the significant role of a disordered H2 in frustrating Im7 folding is not reflected by its behavior as an ordered helix: Unlike the H1→H2 system of Im9 , the H1→H2 system of Im7 exhibits no favorable nonnative packing ( S10 Fig ) ., This finding underscores the importance of disordered conformations to frustration in globular protein folding , an effect that the present analysis has not addressed ., From a broader perspective , such effects have to be even more critical for molecular recognition among intrinsically disordered proteins 54 , 55 ., Notwithstanding aforementioned limitations of the present approach , several important lessons can already be learned from our extensive computational investigation ., First , a majority of the helical systems that we consider favor native packing , indicating that the Im9 amino acid sequence encodes a sufficiently strong native bias such that the native structure can be recognized by the folding protein ., Second , frustration exists , manifested most notably by—but not necessarily limited to—the significantly stabilized nonnative H1-H2 packing ., Although the conformational space accessible to an 86-residue polypeptide is vast compared to what is accessible via contemporary simulation and thus our ability to identify all possible sources of frustration is limited , the systematic approach taken in the present study does pinpoint one class of frustrated configurations ., Third , the native fold is favored overall despite frustration , at least within the class of configurations we tested , because nonnative H1-H2 packing is destabilized when other parts of the protein , especially H4 and its flanking residues , are involved in the interaction ., A logical inference from our results is that favorable nonnative interactions can be largely suppressed during Im9 folding by favoring trajectories that assemble H1 and H2 not in isolation but only in the presence of H4 plus flanking residues ., Such preference would help avoid kinetic traps to facilitate known two-state folding behaviors of Im9 32 , 36 ., This expectation is consistent with the Im9 folding mechanism deduced from experimental phi-values ( ΦF ) by Radford and coworkers , who determined that residues in H2 have the lowest ΦF-values among H1 , H2 , and H4; but ΦF-values are higher for the hydrophobic residues in H1 and H4 ., This and other findings led them to conclude that the H1-H4 interface “is the most structured region in the transition state ensemble” , and that the native configuration of H1 , H2 , and H4 is partially formed in the transition state whereas H3 is formed after the rate-limiting step 32 ., Since our simulation results also suggest that H1-H2 interactions should be weaker than those between H1-H4 to minimize kinetic trapping , our data offer a physical rationale as to why the Im9 folding pathways might have evolved ., A general theoretical formalism due to Wolynes and coworkers provides quantitative estimates of local frustration 3 , 42 , 56 ., Of relevance here is their configurational frustration index , which quantifies the likelihood of a pair of residues that are in contact in a protein’s native structure to be engaged in favorable nonnative interactions in alternate conformations ., Their web-based “Protein Frustratometer” algorithm 56 predicts a high configurational frustration region in Im9 encompassing residues 25–38 , which overlaps substantially with H2 ( residues 30–44 , Table 1 ) ., In contrast , H1 and H4 are predicted to be situated in lower configurational frustration regions on average ( S11 Fig ) ., These predictions are consistent with , and therefore lend further support to the aforementioned perspective emerging from our simulation results ., It is noteworthy , however , that the Frustratometer-computed configurational frustration Fc of Im7 is not noticeably higher on average than that of Im9 ( S11 Fig ) , notwithstanding the fact that folding is significantly more frustrated for Im7 than for Im9 experimentally 30–40 ., In particular , while the predicted frustration of H4 is higher for Im7 than for Im9 ( which is c | Introduction, Results, Discussion, Methods | Biomolecular recognition entails attractive forces for the functional native states and discrimination against potential nonnative interactions that favor alternate stable configurations ., The challenge posed by the competition of nonnative stabilization against native-centric forces is conceptualized as frustration ., Experiment indicates that frustration is often minimal in evolved biological systems although nonnative possibilities are intuitively abundant ., Much of the physical basis of minimal frustration in protein folding thus remains to be elucidated ., Here we make progress by studying the colicin immunity protein Im9 ., To assess the energetic favorability of nonnative versus native interactions , we compute free energies of association of various combinations of the four helices in Im9 ( referred to as H1 , H2 , H3 , and H4 ) by extensive explicit-water molecular dynamics simulations ( total simulated time > 300 μs ) , focusing primarily on the pairs with the largest native contact surfaces , H1-H2 and H1-H4 ., Frustration is detected in H1-H2 packing in that a nonnative packing orientation is significantly stabilized relative to native , whereas such a prominent nonnative effect is not observed for H1-H4 packing ., However , in contrast to the favored nonnative H1-H2 packing in isolation , the native H1-H2 packing orientation is stabilized by H3 and loop residues surrounding H4 ., Taken together , these results showcase the contextual nature of molecular recognition , and suggest further that nonnative effects in H1-H2 packing may be largely avoided by the experimentally inferred Im9 folding transition state with native packing most developed at the H1-H4 rather than the H1-H2 interface . | Biomolecules need to recognize one another with high specificity: promoting “native” functional intermolecular binding events while avoiding detrimental “nonnative” bound configurations; i . e . , “frustration”—the tendency for nonnative interactions—has to be minimized ., Folding of globular proteins entails a similar discrimination ., To gain physical insight , we computed the binding affinities of helical structures of the protein Im9 in various native or nonnative configurations by atomic simulations , discovering that partial packing of the Im9 core is frustrated ., This frustration is overcome when the entire core of the protein is assembled , consistent with experiment indicating no significant kinetic trapping in Im9 folding ., Our systematic analysis thus reveals a subtle , contextual aspect of biomolecular recognition and provides a general approach to characterize folding frustration . | classical mechanics, protein interactions, molecular dynamics, potential energy, protein folding, protein structure, thermodynamics, reaction dynamics, physical chemistry, proteins, chemistry, molecular biology, free energy, physics, biochemistry, biochemical simulations, transition state, biology and life sciences, physical sciences, computational chemistry, computational biology, macromolecular structure analysis | null |
journal.pcbi.1003788 | 2,014 | Defining the Estimated Core Genome of Bacterial Populations Using a Bayesian Decision Model | The advent of next-generation sequencing ( NGS ) has greatly increased the number of bacterial genomes sequenced and made available for study in public databases such as GenBank , the Sequence Read Archive and European Nucleotide Archive ( ENA ) 1–3 ., Increasing computational power allows for comparative genomics studies involving hundreds or even thousands of sequences , but large scale computational resources are not available to all researchers ., Developing methods for analysing large datasets that capitalise on the computational power of modern desktop computers will make comparative genomics analyses much more accessible to the wider research community , allowing this vast quantity of data to be analysed more extensively ., A bacterial species can be defined by its pan-genome , which consists of a core genome conventionally defined as those genes present in all isolates , and an accessory genome , which includes the genes absent from one or more isolates or unique to a given isolate ( note that we use the term “gene” here to refer to a putative protein-coding sequence ) 4 ., Identifying the core complement of genes in a bacterial species is often the first step in population genomics studies and the core genome can be defined in different ways ., The most conservative and most frequently employed method is to only include genes present in 100% of isolates within the study population; however , this presents problems related to both biological sampling and the sequencing technology ., Any collection of isolates is a subset of the entire population for the species of interest , and if the subset of isolates has limited genetic diversity then the number of “core” genes shared by all isolates in that sample will be higher than in a dataset which is genetically more diverse ., This is not necessarily a problem , unless the intention is to extrapolate the findings to the wider bacterial population ., Another biological limitation to using a 100% cut-off for inclusion in the core genome is that there may be rare variant strains which are missing genes that would otherwise be considered core genes ., These variant strains may survive long enough to be sampled , potentially skewing the analyses ., More generally , the size of the core genome is dependent on the size of the data set , with the core genome decreasing in size as more genomes are added to the analysis 4 ., A large proportion of the bacterial genome sequences available at the time of writing are produced using next-generation sequencing platforms such as Illumina or Roche 454 , so that even high-quality assemblies remain as incomplete or “draft” genomes ., This is acceptable for most studies , but analyses of these genomes may exclude a gene from a list of core genes simply because it contains a sequence gap or is otherwise incomplete at that locus in the assembly of one or a few genomes ., This assumes that the sequences being compared are all full-length: if an analysis accepts less than full-length coding sequences then gaps may not be an issue , but there will be other challenges with using incomplete sequence data , e . g . calculating pairwise distance ( p-distance ) measures ., If a definition of core genes as those found complete in all isolates in the dataset is too conservative , then the problem becomes that of determining an acceptable limit to the number of isolates missing any particular gene ., One approach is to plot a frequency distribution that indicates how many genes are present in all isolates , or are missing in one or more isolates within the study population ( Figure S1 ) ., For some bacterial species , there is a reasonably clear delineation between genes present in a large proportion of the study population versus those that are infrequent or rare , but for other bacterial species it is not clear ., Rather than make an arbitrary decision , we developed a statistical model for estimating the core genome that can be applied to different bacterial species by formalising the decision in the language of probability ., The aim was to develop a Bayesian decision model to identify the genes found in what we will call the “estimated core genome” and apply this decision model to several large bacterial genome datasets ., We described the nucleotide sequence diversity for each gene in the estimated core genome and considered how core genome sequence diversity varied across unrelated bacterial species ., Finally , we depicted the data in a way that allowed us to explore the sequence data in greater detail and generate testable hypotheses about the estimated core genome ., The five bacterial species chosen for inclusion in this study were disease-causing organisms responsible for a large proportion of the global bacterial disease burden: Streptococcus pneumoniae ( respiratory disease , the most important cause of infectious disease mortality ) ; Campylobacter jejuni ( gastrointestinal disease ) ; Neisseria meningitidis ( meningitis ) ; Staphylococcus aureus ( skin and soft tissue infections ) ; and Helicobacter pylori ( gastrointestinal ulcers ) ., In total , 2096 genomes were analysed across the 5 different bacterial species ( Table 1 and Datasets S1 ) ., A phylogenetic network for each dataset was derived using Neighbor-Net 5 as part of the initial Genome Comparator 6 analyses ( see Methods for a description of the Genome Comparator program ) ; these diagrams demonstrated the overall diversity of the genomes in each study dataset ( Figure 1 ) ., The S . pneumoniae ( pneumococcal ) dataset consisted of 336 genomes for isolates of 39 different serotypes collected over 90 years ( 1916–2008 ) from at least 32 countries around the world ., The isolates were recovered from individuals of a wide range of ages , including isolates from patients with disease and isolates recovered from healthy individuals ., The multilocus sequence typing ( MLST ) data revealed 163 sequence types ( STs ) , which could be clustered into 74 different clonal complexes ( CCs ) indicative of isolates with shared ancestry ( Tables 1 and S1 ) ., The largest genome dataset analysed was that of C . jejuni ( N\u200a=\u200a601 genomes ) ., Isolates were recovered from human stool samples collected from patients in Oxfordshire , United Kingdom ( UK ) with gastroenteritis during 2011 ., 134 STs from 29 CCs were characterised in this collection , which was representative of the broader C . jejuni population genetic diversity 7 , 8 ., The N . meningitidis ( meningococcal ) dataset was comprised of 518 genomes and these isolates were collected nearly exclusively from patients residing in England , Wales and Northern Ireland in the 2010/11 epidemiological year , apart from 4 historical isolates from Norway ( 1976 ) , The Gambia and UK ( 1983 ) and UK ( 1986 ) ., The 2010/11 genomes are part of the Meningitis Research Foundation Meningococcus Genome Library , which contains genomes from all culture-confirmed cases of meningococcal disease submitted to the Meningococcal Reference Unit in 2010/11 and 2011/12 ., Isolates of seven serogroups were included , mostly serogroup B ( n\u200a=\u200a394 ) , Y ( n\u200a=\u200a74 ) and W-135 ( n\u200a=\u200a27 ) ., 198 STs were represented by the isolates and the STs clustered into 24 CCs ., Culture-confirmed cases of meningococcal disease are largely representative of the England and Wales disease-causing N . meningitidis population as described previously 9 ., The S . aureus dataset was large ( N\u200a=\u200a534 genomes ) but genetically less diverse ( 25 STs and 11 CCs; Tables 1 and S1 ) than other datasets , since the analyses were restricted to methicillin-resistant S . aureus ( MRSA ) only ., Most of the publicly-available genomes that are already published are of a limited number of CCs , predominantly the MRSA CCs that are epidemiologically the most important ., The MRSA isolates were recovered from patients in 27 countries , although 39% of isolates were recovered in the UK ., The H . pylori dataset included 107 genomes and 82% of the collection was from the USA , Canada or Japan ., Only limited additional metadata were available for these isolates ., The size of the reference genomes used in the Genome Comparator analyses for each dataset varied from 1 . 6 to 2 . 8 Mb , and the total number of genes in each reference genome ranged from 1566 to 2547 ( Table 2 ) ., There were small numbers of unique loci , i . e . genes found only in the reference genome and/or present in only one genome: S . pneumoniae ( n\u200a=\u200a6 ) ; C . jejuni ( n\u200a=\u200a6 ) ; N . meningitidis ( n\u200a=\u200a7 ) ; S . aureus ( n\u200a=\u200a4 ) ; and H . pylori ( 45 ) ., An initial BLASTN criteria of 70% identity was chosen , which allowed for the identification of variable sequences among conserved gene classes 10 and avoided bias towards reference-specific sequences , and 100% sequence alignment , which means that coding sequences occurring at the ends of contigs or with gaps were therefore not included ., Lowering the BLASTN criteria to 70% identity and 90% alignment increases the number of genes in the estimated core , as partial gene sequences will be detected and included ( Table S2 ) , which may be more suitable for other user-specific analyses but is not ideal for the calculation of p-distances to estimate sequence diversity ., The smallest estimated core genomes were those of MRSA and H . pylori ( 242 and 244 genes , respectively; Table, 2 ) and the S . pneumoniae , C . jejuni and N . meningitidis core genomes were similar in size , ranging from 744 to 866 genes ., The percentage of genomes within a dataset that possessed each estimated core gene ranged from ≥99 . 1% to ≥99 . 8% ., The number of putative paralogues identified in the initial Genome Comparator analyses varied from 1 in C . jejuni to 40 among the S . pneumoniae genomes , and these genes were removed from further analyses ( lists of putative paralogues for each species are provided in Table S3 ) ., If putative paralogues had not been removed , one would have been included in each of the estimated core genomes of S . pneumoniae and N . meningitidis ., Within each genome dataset , median p-distance values were calculated for each of the estimated core genes and the estimated probability density function was plotted for each bacterial species ( Figure 2 ) ., The estimated probability density function plotted a smoothed histogram of the median p-distances vs . the estimated probability ( relative frequency ) of each p-distance value ., The shape of the graphs for S . pneumoniae and C . jejuni were similar , showing a large peak of very small p-distance values , i . e . highly conserved genes , but these were entirely different from the graphs depicting the data for the other bacterial species ., Each graph is an indication of the overall sequence diversity of the set of estimated core genes for that particular genome dataset ., The median p-distance value for each estimated core gene was then plotted against its position in the reference genome and illustrated as a circular bacterial chromosome ( Figure 3 ) ., The length of each line indicates the median p-distance value for that gene ., The estimated core genes were distributed around each genome and accessory regions in the reference genomes ( e . g . ICE elements or phage genes ) , were observed as gaps where no core genes clustered ., Estimated core genes with p-distances above 0 but less than the 95th percentile ( blue lines ) and those above the 95th percentile ( red lines ) stood out in a pattern on each genome diagram and allowed for an evaluation of specific genes and gene clusters in the genome , as demonstrated below ., Table S4 lists all the estimated core genes and p-distances for each bacterial species ., All genes with a p-distance value greater than the 95th percentile for each bacterial species , and the Cluster of Orthologous Groups ( COG ) functional category for each of those genes , are listed in Table S5 ., The field of bacterial genomics is advancing rapidly and it is now possible to generate enormous quantities of sequence data ( albeit currently incomplete ) at a low cost; therefore , it is also essential to find and develop suitable , widely accessible and inexpensive methods of processing and analysing these data in order to maximise the utility and benefits of whole genome sequence data ., This model formalises the estimation of the core bacterial genome as a Bayesian decision problem and the resulting outputs reveal many areas for further exploration of the bacterial core genome ., Complete lists of the bacterial genome data included in this study , with accession numbers and available metadata ( obtained by consulting the relevant published papers or websites ) are listed in Datasets S1 ., Publicly-available whole genome sequence data for H . pylori ( N\u200a=\u200a107 genomes ) and S . aureus ( N\u200a=\u200a534 genomes ) were collected in two ways: 1 ) raw sequence trace files from the ENA were downloaded via an in-house genome assembly pipeline , assembled using Velvet 28 and uploaded to the rMLST BIGSdb database 6 , 29; and 2 ) finished reference genomes for each species were downloaded from GenBank and uploaded to the rMLST BIGSdb database ., Only genomes that were already published in the scientific literature were included in our analyses ., By comparison to MRSA genomes , few methicillin-susceptible S . aureus ( MSSA ) genomes are currently available ( 27 MSSA genomes were available at the time of our analysis , 18 of which were ST398 ) and thus we restricted the analyses to MRSA genomes only ., Approximately 1000 S . aureus genomes were publicly available and published at the time , and we aimed to select a diverse dataset of ∼600 genomes such that the final MRSA dataset was similar in size to the C . jejuni and N . meningitidis datasets ., Many of the available S . aureus genomes were ST239 or ST22 , thus selection proceeded as follows:, i ) any non-ST239/ST22 genomes were automatically included;, ii ) among the 456 ST239/ST22 genomes available , 167 ST239 and 186 ST22 were selected ( duplicates or re-sequenced genomes were removed ) ; and, iii ) any genomes that were MSSA or an unknown ST ( n\u200a=\u200a54 ) were subsequently removed ., The Global Historical S . pneumoniae dataset ( N\u200a=\u200a336 genomes ) included 85 assembled genomes from our previously published study 30; sequences for 25 published genomes 31 downloaded from the ENA and assembled using Velvet; 134 genomes downloaded from GenBank; and 92 isolates sequenced and assembled as described in Protocol S1 ., Raw sequence data for the 616 pneumococcal genomes comprising the comparison Massachusetts data set 19 were downloaded from the ENA , assembled and uploaded to the rMLST BIGSdb database as described above ., Data for N . meningitidis were collected largely as part of the Meningitis Research Foundation Meningococcus Genome Library database ( MRF GL; N\u200a=\u200a514 genomes ) plus 4 additional historical isolates were included 32 ., The C . jejuni isolates included in this study ( N\u200a=\u200a601 genomes ) , all of human origin , were collected at the John Radcliffe Hospital in Oxford and form part of the Oxfordshire Human Surveillance collection 7 ., Sequence data for C . jejuni and N . meningitidis can be found on the PubMLST 33 and rMLST BIGSdb 34 databases ., STs were assigned to genomes either by retrieving the ST information from previously published papers or by extracting the sequences corresponding to the MLST loci and looking up the ST on the MLST website ( S . pneumoniae and S . aureus ) 35 ., STs and CCs for N . meningitidis and C . jejuni were extracted from the relevant BIGSdb databases ., All other CCs were defined using goeBURST 36 and the species-specific MLST databases downloaded from the MLST website ., When goeBurst could not resolve the group founder , the group was assigned to ‘CC NoneX’ where X is the ST with the lowest numerical value in the group ., When a lack of closely-related STs meant that a CC could not be assigned , such genomes were named ‘SingletonX’ where X corresponds to the isolate ST . Table S1 provides a summary of the STs and CCs included in this study for each bacterial species apart from H . pylori ., Although an MLST scheme is available for H . pylori , the high genetic diversity of the species means that virtually every new strain has new alleles and new STs , making the interpretation of such data difficult and thus we have not defined STs and CCs for H . pylori ., Genome Comparator is a component of the BIGSdb genome analysis database and software suite 7; BIGSdb facilitates whole genome analysis based on the allelic variation of individual genes ., The BIGSdb Genome Comparator tool allows whole genome sequence data for one or more genomes to be compared against an annotated reference genome ., The BLASTN parameters selected used a cut-off of 70% identity over a 100% alignment with a word size of 15 ., Potential paralogues were removed from the analyses by identifying which of the coding loci were found in more than one copy in any query genome and excluding these sequences from any further analyses ., For each coding locus in the reference genome , ClustalW 37 sequence alignments were generated for all of the query genomes containing that particular sequence ., Neighbor-Net diagrams were also created by Genome Comparator as part of its standard analysis and figures were created using SplitsTree 38 ., For each of the collections of bacterial genomes , we have chosen a reference genome , consisting of a set of genes ., Each gene is considered independently during the analysis ., Let be the number of isolates under consideration ., For each isolate , , let if the -th gene is present in isolate or zero if it is not present ., Then , for the -th gene , is the number of times the gene is found in isolates ., We model as a sequence of binomial random variables with the probability parameter ., Letting denote a probability density , the probability of observing the -th gene times in isolates given the model parameter is: ( 1 ) The above equation , viewed as a function of , is the binomial likelihood function ., We specify , for the parameter , a prior probability density ., Then , using Bayes rule , we can compute the posterior density of conditional on the observed frequency: ( 2 ) If we assume the prior density to be a beta density with parameters then we can combine the prior with the binomial likelihood ( 1 ) and use Bayes rule ( 2 ) to find that the posterior density is also a beta distribution with parameters 39 ., The parameters of the prior are related to the posterior parameters by and ., The posterior density represents our uncertainty of the parameter ., If the density has a greater value near then we are inclined to believe that the gene is in the core genome ., In light of this observation we introduce the following decision rule for each gene: ( 3 ) The set of genes not rejected according to ( 3 ) can be defined as the estimated core genome ., The selection of a prior amounts to specifying our prior belief of whether a gene is or is not in the core genome ., We might also adopt the belief that we are equally unsure of whether a gene is present in the core or not ., Priors that reflect this type of belief are known as near ignorance priors 40 ., Rather than selecting a near ignorance prior we argue that a prior should be selected to reflect the nature of the decision process ., Prior to analysing any of the isolates we have no reason to believe that any of is or is not in the core ., As each strain is analysed we accumulate evidence to suggest that the -th gene is not a core gene ., To reflect this process we adopt the prior belief that every gene is a core gene and then attempt to falsify this statement using the decision rule ., Formally , this corresponds to selecting as our prior a beta density with parameters ., Custom Perl scripts were used to split the merged sequence alignment files generated by Genome Comparator into separate sets of nucleotide sequence alignment files by estimated core gene , and then the nucleotide distance between each pair of sequences for each estimated core gene was calculated ., We counted the number of sites at which the nucleotides differed between each pair of genes ., We let be the proportion of sites that differed between isolate and isolate for the -th gene in the estimated core ., Then for each gene the matrix of pairwise evolutionary distances was calculated using the Jukes-Cantor model 41 where the entry of the matrix was given by: ( 4 ) The median p-distance value was then calculated as a summary statistic for each estimated core gene: ( 5 ) For each species we collected the median distances for each gene in the estimated core ., This information was plotted against the genome position of each gene ( relative to the position of that locus in the reference genome ) and depicted in a circular diagram created using Circos 42 ., In addition , the estimated probability density function of the median pairwise evolutionary distances for each species , or for individual genes within a species , was plotted using ksdensity ( Kernel smoothing function estimate ) 43 ., Finally , the COG functional groups of the genes with p-distance values greater than the 95th percentile were determined using eggNOG 44 ., The computationally intensive part of the analysis is the Genome Comparator run ( because it creates sequence alignments for every gene ) , but this runs via a publicly-available web interface on a cluster of servers hosted at the University of Oxford ., Output files are stored for one week on the server ., The Bayesian model for estimating the number of core genes , the calculation of p-distance values for all reference genes , creation of the genome diagrams ( Figure 3 ) and the generation of estimated probability graphs ( Figure 4 ) can be implemented using freely available scripts written in the open source R software package 45 ., Along with wrapper scripts that prepare the Genome Comparator outputs and a detailed manual , the relevant code is available at: https://sourceforge . net/projects/bayesianestimatedcoregenome/ ., As a frame of reference , the Genome Comparator analysis of the largest dataset , C . jejuni ( 601 genomes ) took 90 hours to run , including all sequence alignments and generation of the Neighbor-Net diagram , but the subsequent steps took approximately 2 hours and can be run on any modestly-powered computer ., For case studies 2 and 3 the names of the core genes for each of the datasets included in the comparisons were compared to each other using the VLOOKUP function in Microsoft Excel , which matches cells containing the same text ( the same reference genomes were used for each dataset so the gene names were the same ) in order to generate numbers of shared and unique genes ., Functional groups for each of the sets of unique genes were assigned using eggNOG . | Introduction, Results, Discussion, Materials and Methods | The bacterial core genome is of intense interest and the volume of whole genome sequence data in the public domain available to investigate it has increased dramatically ., The aim of our study was to develop a model to estimate the bacterial core genome from next-generation whole genome sequencing data and use this model to identify novel genes associated with important biological functions ., Five bacterial datasets were analysed , comprising 2096 genomes in total ., We developed a Bayesian decision model to estimate the number of core genes , calculated pairwise evolutionary distances ( p-distances ) based on nucleotide sequence diversity , and plotted the median p-distance for each core gene relative to its genome location ., We designed visually-informative genome diagrams to depict areas of interest in genomes ., Case studies demonstrated how the model could identify areas for further study , e . g . 25% of the core genes with higher sequence diversity in the Campylobacter jejuni and Neisseria meningitidis genomes encoded hypothetical proteins ., The core gene with the highest p-distance value in C . jejuni was annotated in the reference genome as a putative hydrolase , but further work revealed that it shared sequence homology with beta-lactamase/metallo-beta-lactamases ( enzymes that provide resistance to a range of broad-spectrum antibiotics ) and thioredoxin reductase genes ( which reduce oxidative stress and are essential for DNA replication ) in other C . jejuni genomes ., Our Bayesian model of estimating the core genome is principled , easy to use and can be applied to large genome datasets ., This study also highlighted the lack of knowledge currently available for many core genes in bacterial genomes of significant global public health importance . | Whole genome sequencing has revolutionised the study of pathogenic microorganisms ., It has also become so affordable that hundreds of samples can reasonably be sequenced in an individual project , creating a wealth of data ., Estimating the bacterial core genome – traditionally defined as those genes present in all genomes – is an important initial step in population genomics analyses ., We developed a simple statistical model to estimate the number of core genes in a bacterial genome dataset , calculated pairwise evolutionary distances ( p-distances ) based on differences among nucleotide sequences , and plotted the median p-distance for each core gene relative to its genome location ., Low p-distance values indicate highly-conserved genes; high values suggest genes under selection and/or undergoing recombination ., The genome diagrams depict areas of interest in genomes that can be explored in further detail ., Using our method , we analysed five bacterial species comprising a total of 2096 genomes ., This revealed new information related to antibiotic resistance and virulence for two bacterial species and demonstrated that the function of many core genes in bacteria is still unknown ., Our model provides a highly-accessible , publicly-available tool to use on the vast quantities of genome sequence data now available . | bacteriology, bacterial diseases, infectious diseases, medicine and health sciences, genetics, biology and life sciences, microbiology, genomics, microbial genomics | null |
journal.pcbi.1004460 | 2,015 | Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response | Prolonged inflammation is a recognized contributor to a multitude of pathological conditions , including cardiovascular , metabolic , and neurodegenerative diseases , as well as chronic injuries 1 , 2 ., Timely resolution of inflammation is essential for tissue homeostasis ., Inflammation resolution , previously believed to be a passive , self-regulatory process , is now known to be actively modulated by several different classes of endogenous molecular mediators , such as anti-inflammatory cytokines and growth factors interleukin-10 ( IL-10 ) and transforming growth factor-β ( TGF-β ) , oxygenated lipid mediators ( lipoxins , resolvins , protectins , and maresins ) , and protease inhibitors 2–5 ., Ongoing research efforts , including pharmacological animal model research and clinical trials , are focused on novel pro-resolution therapies for a variety of inflammatory conditions 6 , 7 ., There is a clear , documented need for new approaches to develop resolution-centric therapeutic interventions 2 to supplement or replace the currently used anti-inflammatory therapies , which are only modestly effective 8–10 ., The concentrations of molecular and cellular components of the inflammatory process are typically characterized by single-peak temporal trajectories reflecting a distinct period of activation followed by resolution 2 , 5 , 11 , 12 ( Fig 1 ) ., The quantitative properties of these trajectories vary as a result of differences in inflammatory conditions and scenarios ., Recently , four quantitative indices namely , peak height ( Ψmax ) , activation time ( Tact ) , resolution interval ( Ri ) , and resolution plateau ( Rp ) ( Fig 1 ) were introduced as informative measures to analyze the quantitative patterns characterizing temporal inflammatory trajectories 1 , 13 ., For a given molecular species or cell type , the Ψmax is defined as the maximum value of the corresponding temporal trajectory ., Tact is the time after inflammation initiation required for the temporal trajectory to reach the Ψmax level ., Ri is the time difference between Tact and the time it takes to reach 50% of Ψmax ., Rp is the trajectory level at the end of the considered time period , expressed as a percentage of the Ψmax value; it therefore reflects residual inflammation ., Based on the aspects of the temporal trajectories that they represent , the indices can be divided into amount indices ( Ψmax and Rp ) and timing indices ( Tact and Ri ) 14 , 15 ., Additionally , the area under the curve ( AUC ) of a kinetic trajectory has been recently introduced as a metric to quantitatively assess cumulative tissue damage under various inflammatory scenarios 16 ., Experimental studies have demonstrated the utility of the inflammation indices in distinguishing between the time courses for normal and pathological inflammation , as well as in establishing the quantitative efficacy of external interventions 1 , 3 , 17 ., For example , in a mouse peritonitis model , it was shown that the macrophage Ψmax , Tact , Ri , and the neutrophil Tact were noticeably higher ( specifically , higher by 2-fold , 3-fold , 2-fold , and 12-fold , respectively ) in the chronic inflammatory scenario compared to the acute inflammatory scenario 17 ., In the same study , several drugs ( specifically , ibuprofen , resolvin E1 , a prostaglandin D2 receptor 1 agonist , dexamethasone , rolipram , and azithromycin ) were evaluated for their inflammation resolution efficacy by quantifying their regulation of these inflammation indices in the chronic inflammation scenario ., Another peritonitis mouse model study investigated the efficacy of drug-filled nanoparticles in inflammation resolution ., The drug efficacy was established based on its ability to reduce the neutrophil Ψmax and Tact by ~1 . 5-fold 3 ., Yet , systematic experimental characterization of the patterns and mutual relationships for the inflammation indices is challenging due to the immense diversity of possible inflammatory scenarios ., This diversity hampers our understanding of the global control of the indices by specific molecular mechanisms , and our understanding of the modulation of the indices by therapeutic interventions ., Computational modeling offers a possibility to complement experimental investigations and address this complex problem using an integrated approach ., Computational modeling can be used to “screen” thousands of inflammatory scenarios in a systematic way and thereby guide the generation of focused , mechanistic , experimentally testable hypotheses 11 , 16 , 18–21 ., Previous works have demonstrated the utility of mathematical models in the study of inflammation in specific disease scenarios and in the identification of crucial inflammatory mechanisms 11 , 12 , 16 , 20–32 ., For example , computational models have been used to predict the efficacy of cytokine regulation therapies in chronic inflammatory diseases 24 , 27 , 33 ., However , quantitative characterization of the inflammatory response of different cell types and molecular mediators in terms of indices , as well as a rigorous analysis of their mechanistic determinants , have not been carried out in previous modeling efforts ., In the present study , we used our recently developed quantitative kinetic model of acute and chronic inflammation in wounds to generate hypotheses regarding mechanistic control of the inflammation indices 11 ., Our model could capture the behavior of inflammatory cell type and molecular mediator kinetics in acute and chronic inflammation initiated by both infection and injury 17 , 34 , 35 ., In our simulations , the chronic inflammatory scenarios were characterized by higher concentrations ( Ψmax ) and delayed resolution timing ( Ri ) for key inflammatory components in comparison with acute inflammatory scenarios ., Using this model , we identified essential inflammation-driving mechanisms ( specifically , macrophage influx and efflux rates ) and informative indicators ( i . e . , IL-6 , TGF-β , and PDGF ) of chronic inflammation ., Our findings regarding the mechanistic regulation of inflammation by macrophage fluxes are supported by experimental studies where wound macrophage levels ( regulated by macrophage fluxes ) controlled the timing of wound healing 36–39 and the quality of wound scarring 40 ., Furthermore , our modeling predictions characterizing IL-6 as an informative indicator of pathological inflammation are consistent with a recent clinical wound study 41 ., Here , we use this model to elucidate the functional relationships between specific molecular/cellular processes and inflammation indices during normal and pathological inflammation ., The goal of our study was to identify specific mechanistic determinants that can be targeted to modulate the index values during abnormal ( delayed ) inflammation and drive them toward a desired outcome ., We used two complementary analyses ( namely , sensitivity and correlation analysis ) and identified such targetable mechanisms for regulating the inflammatory indices in the model ., Furthermore , we wanted to test the effectiveness of cytokine inhibition as an intervention strategy to regulate the inflammation indices ., For this purpose , we extended the model to represent cytokine inhibition kinetics for three cytokines ( chosen based on our predictions regarding targetable mechanisms ) ., We used this extended model to study the efficacy of individual/combined cytokine inhibition in the regulation of the inflammation indices for neutrophils and macrophages ., Our modeling results indicate that , for the majority of the model output variables representing inflammatory cell types and molecular mediators , the amount indices Ψmax and Rp were robustly regulated by the macrophage influx and efflux rate , respectively ., In contrast , for the timing indices ( i . e . , Tact and Ri ) , such a robust functional dependence on single model parameters was not detected in the sensitivity analysis ., Yet , the timing indices for the inflammatory components were strongly correlated with the platelet degradation rate ., Moreover , strong correlations between the timing indices and certain mechanistic processes existed , but only under specific inflammatory situations representing chronic inflammation ., Our inflammatory mediator inhibition modeling suggested that during an abnormal ( delayed ) inflammatory response , TNF-α and TGF-β inhibition strongly shifted the macrophage Ψmax and Rp indices toward inflammation resolution , whereas CXCL8 inhibition regulated the neutrophil Ri and could nearly restore this index to its normal ( i . e . , acute-inflammation ) value ., Notably , combined inhibition of TNF-α and CXCL8 resulted in improved restoration of normal neutrophil dynamics compared with the inhibition of these two targets acting independently ., Comparisons with available experimental data provided validation for our TNF-α inhibitor modeling results ., To investigate the regulation of the inflammation indices via changes in the model parameters , we performed a local sensitivity analysis for the model in 10 , 000 inflammatory scenarios ( simulations ) , as described in the Materials and Methods Section ., We regarded the regulation of an inflammation index ( computed for a given model output variable ) by a specific parameter as robust , if the sensitivity sij ( Eq 1 , defined in the Materials and Methods Section ) of the index with respect to this parameter was the highest ( or second highest , third highest , etc . ) across all parameters in the majority of the 10 , 000 simulations ., In the figures and tables , for the sake of brevity , we identify each of the model’s 69 parameters using its assigned number in the model preceded by the prefix “P# . ”, We use these designations when referring to the figures and tables in the text and , in addition , provide descriptive names for the parameters ( see Table 1 for a full list of the model parameters ) ., The amount indices , Ψmax and Rp , for the majority of the model output variables were robustly regulated by the parameters representing the rate of macrophage influx ( P#7 ) into the inflamed area and the rate of their efflux ( P#10 ) , respectively ., Table 2 shows the first , second , and third most influential ( based on the ranking of the corresponding sensitivities ) model parameters for the inflammation indices for six specific model output variables ., These variables ( namely , TNF-α , IL-1β , IL-6 , IL-10 , total neutrophils , and total macrophages ) are the ones that typically demonstrate abnormal characteristics during pathological inflammation 17 , 34 , 36 , 42 ., By definition , Ψmax and Rp characterize the inflammation intensity at its peak and during its resolution ( Fig 1 ) ., Therefore , these results are consistent with , and complement , our earlier findings identifying macrophage influx ( P#7 ) and efflux ( P#10 ) rates as the main regulators of kinetic trajectories during the initial and final phases of inflammation , respectively 11 ., In addition to the well-pronounced regulation by macrophage flux rates , our local sensitivity analysis identified the TNF-α degradation rate ( P#19 ) and the neutrophil influx rate ( P#3 ) as robust regulators of the Ψmax for the TNF-α and the neutrophil ( Ntot ) model output variables , respectively ( Table 2 ) ., In contrast to the robust regulation of the amount indices , no model variables had their timing indices ( i . e . , Tact and Ri ) robustly regulated by a model parameter ., Indeed , the largest sensitivity values for Tact and Ri corresponded to different parameters depending on the specific simulation ( i . e . , on the specific model parameter set ) ., For all of the16 variables , the model parameter effecting the strongest Tact regulation in the largest fraction of the 10 , 000 inflammatory scenarios was the platelet degradation rate ( P#1 ) ( Table 2 ) ., Yet , this largest fraction ( which we call the “robustness fraction” ) was too small ( ~18–46% ) to consider this regulation robust ., For the Ri , the parameter effecting the strongest regulation of this index in the largest fraction of scenarios was macrophage efflux rate ( P#10 ) for 10 out of the 16 model output variables ., However , the corresponding robustness fraction for it was even smaller ( ~15–35% ) ., These results suggest that robust control of the timing indices may require a sophisticated strategy involving simultaneous modulation of multiple inflammatory mechanisms ., To gain additional insights into the regulation of the inflammation indices , we calculated the correlation coefficients ( CCs ) and the associated p-values between the inflammation indices of each model output variable and each model parameter ( see Materials and Methods ) ., The CC values ranged between −1 to +1 reflecting a negative or a positive index-parameter correlation , respectively ., The signs of the CCs for the four indices are shown in S2 Fig . Absolute index–parameter correlations above 0 . 5 were considered strong 43 , 44 ., Among the correlations identified as strong , only correlations with p ≤ 0 . 05 were considered statistically significant ., For the amount indices , we detected a strong positive correlation between inflammation peak height ( i . e . , Ψmax ) and macrophage influx rate ( P#7 ) for 10 model output variables , including macrophages , IL-6 , and IL-10 ( Fig 2a and 2b ) ., Furthermore , we found a strong positive correlation between the Ψmax for neutrophils ( Ntot ) and neutrophil influx rate ( P#3 ) ( Fig 2a and 2b ) , which was consistent with our sensitivity analysis results ( Table 2 ) ., This result suggests that the inflammation indices for the neutrophil inflammatory response trajectory may depend on the degradation rates of CXCL8 ( P#31 ) and TGF-β ( P#13 ) , which are the two main neutrophil chemoattractants in our model 11 ., For the other amount index , i . e . , Rp , only one parameter namely , macrophage efflux rate ( P#10 ) showed a strong ( negative ) correlation with a large number ( namely , 13 ) of model output variables ( Fig 2c and 2d ) ., These findings were consistent with our sensitivity analysis results regarding the Ψmax and Rp regulation by macrophage influx and efflux rates ( Table 2 ) ., For the timing indices , the correlation analysis highlighted a strong negative correlation between TGF-β degradation rate ( P#13 ) and the Tact of four model variables , including neutrophils ( Ntot ) and IL-6 ( Fig 3a and 3b ) ., Moreover , we detected a strong negative correlation between the platelet degradation rate ( P#1 ) and inflammation activation time ( i . e . , the Tact index ) for 9 model variables ( Fig 3a and 3b ) ., However , this relationship simply demonstrates the connection between Tact and the strength and persistence of inflammation-initiating stimuli , reflected in our model by the presence of platelets in the wound ., Additionally , the Ri for the majority of the model variables , including macrophages and IL-6 ( results not shown ) , exhibited a strong negative correlation with macrophage efflux rate ( P#10 ) , which could be expected based on our sensitivity analysis results ( Table 2 ) ., Yet , our correlation analysis did not detect any other strong correlations for the timing indices ., The limited number of detected strong correlations prompted us to hypothesize that a larger number of strong correlations between the timing indices and the model parameters could be detected in a smaller set of simulations reflecting specific conditions , such as chronic inflammation ., To test this , we divided the 10 , 000 simulations into two subsets , “acute” and “chronic” ( see Materials and Methods ) ., Then , for only the “chronic” subset , we performed the correlation analysis between each of the timing indices ( i . e . , Tact and Ri ) for the 16 model output variables and the 69 model parameters ., As hypothesized , in this analysis many model parameters emerged as strongly correlated with Tact ( 18 parameters ) and Ri ( 9 parameters ) , for different model variables ( Fig 3c and 3d , respectively ) ., In subsequent analyses , we specifically focused on TGF-β and CXCL8 degradation rates ( P#13 and P#31 , respectively ) , because they were strongly correlated with several key model outputs and those correlations were statistically significant ., In summary , the correlation analysis provided us with candidate mechanisms for directly regulating both of the amount indices ( via neutrophil influx rate and the macrophage flux rates ) and the timing indices ( via the TGF-β and CXCL8 degradation rates ) of the model output variables ., The goal of model parameter randomization in our 10 , 000 simulations was to account for possible biological variability in inflammation scenarios 11 ., While wider variation ranges for the parameters may allow for a fuller representation of this variability , such random , simultaneous , uncorrelated variations may also introduce excess noise that could mask the biologically relevant patterns we want to detect ., An informative analysis should therefore utilize parameter ranges sufficiently wide to represent variation and yet narrow enough to define the vicinity of our carefully chosen default parameter set , which had been derived directly from experimental data and represented the expected “typical” injury-triggered inflammation scenario 11 ., To assess the impact of large parameter deviations , we performed the sensitivity and correlation analysis for an additional set of 40 , 000 simulations ., In these simulations , the parameters were randomly and uniformly sampled from a 9-fold range ( i . e . , 3-fold down and 3-fold up ) around the default parameter values ., In the sensitivity analysis , the introduction of these larger parameter deviations reduced by 5–30% ( results not shown ) the robustness of the Ψmax regulation by macrophage influx rate for the six different outputs shown in Table 2 ., Similarly , the robustness of the Rp regulation by macrophage efflux rate was reduced by 1–14% ( results not shown ) in comparison with the results shown in Table 2 ., However , in the correlation analysis , the major trends i . e . , the strong correlation between the Ψmax and macrophage influx rate and the strong correlation between the Rp and macrophage efflux rate were preserved , while other strong correlations ( Figs 2 and, 3 ) were not detected in the case of large parameter deviations ( S3 Fig ) ., Thus , it appears that some of the detected biological patterns are particularly strong in the fewfold vicinity of our default parameter set , which reflects their dependence on specific type of inflammatory scenario ., The results of our sensitivity and correlation analyses ( Table 2 and Figs 2 and, 3 ) suggested that specific inflammation indices of the model variables can be considerably impacted by the following five model parameters: macrophage influx and efflux rates and the TNF-α , CXCL8 , and TGF-β degradation rates ., We therefore wanted to investigate whether modulation of these parameters during chronic inflammation could result in ( at least , partial ) restoration of the normal ( i . e . , acute-inflammation ) values of the respective inflammation indices ., We addressed this question for all of the five parameters except macrophage influx rate , because we used its modulation to induce chronic inflammation in the model ( see caption for Fig 4 ) ., Using the same protocol as implemented in our previous modeling study ( see Figure 5 in 11 ) , we simulated a chronic inflammatory scenario ( Fig 4a and 4b , red line ) ., Here , we specifically focused on the kinetic trajectories for neutrophils and macrophages , because their kinetic behavior is well studied and is known to be disrupted during delayed ( or chronic ) inflammation 34 , 45 ., To investigate the regulation of neutrophil and macrophage inflammatory trajectories , we repeated the chronic inflammation simulations upon modifying the values of the parameters mentioned above , as follows ., In the chronic inflammation simulation for neutrophil regulation , we implemented two distinct parameter modification strategies ., In one strategy , we increased the CXCL8 degradation rate , which caused the Ri for the neutrophils to decrease ( Fig 4a , solid black line and green values in the table ) compared to the chronic scenario with no modification ( Fig 4a , red line ) ., In the other strategy , we increased the TGF-β degradation rate , which caused both Tact and Ψmax for the neutrophils to decrease ( Fig 4a , dashed black line , green values in the table ) compared to the chronic scenario with no modification ( Fig 4a , red line ) ., Similarly , for macrophage regulation , we introduced two distinct parameter modification strategies into the chronic inflammation simulation ., In the first strategy , we increased the TNF-α degradation rate , which caused the Ψmax for the macrophage variable to decrease ( Fig 4b , solid black line and green values in the table ) compared to the chronic scenario with no modification ( Fig 4b , red line ) ., In the second strategy , we increased the macrophage efflux rate , which caused the Tact , Ri , and Rp for the macrophage variable to decrease ( Fig 4b , dashed black line and green values in the table ) compared to the chronic scenario with no modification ( Fig 4b , red line ) ., The observed decrease in the inflammation index values suggested that the considered parameter modifications can partially restore acute inflammatory kinetics , i . e . , can bring the inflammation index values for a chronic inflammatory scenario closer to those for an acute inflammatory scenario ., The analyses described above were supplemented with modulation of other parameters ., This was done to illustrate that not all model parameters exhibiting strong correlations with certain model variables could effectively regulate their indices ., For example , the IL-10 production rate parameter ( P#26 ) was strongly and negatively correlated with the macrophage Tact ( Fig 3c ) , and the parameter for TNF-α inhibition by TGF-β ( P#55 ) had a strong and negative correlation with the neutrophil ( Ntot ) Ri ( Fig 3d ) ., However , increasing those two parameters did not result in a significant modulation of the respective neutrophil or macrophage indices ( Fig 4a and 4b , dotted lines ) ., The model parameters that effected weak inflammatory index regulation or exhibited low-confidence correlations with the inflammatory indices were not selected for further analysis ., Because the TGF-β , CXCL8 , and TNF-α degradation rates represent inherent biochemical properties of the respective molecular mediators , they cannot be easily changed in vivo ., We therefore wanted to test whether a mechanistically distinct process , such as cytokine inhibition , could be used to obtain functionally similar outcomes ., For this purpose , we extended our model 11 to include the kinetics of inhibitors for TGF-β , CXCL8 , and TNF-α and performed inhibition kinetics simulations for chronic inflammatory scenarios ( Figs 5 and 6 and S1 ) ., We derived the values for the association ( kon ) and dissociation ( koff ) rate constants ( Eq 2 , defined in the Materials and Methods Section ) for each mediator inhibitor from literature data ( S1 Table ) ., In our simulations , CXCL8 inhibition primarily regulated inflammation timing ( specifically , the Ri index for the neutrophil variable; Figs 5a and 6a , black lines ) , whereas TNF-α and TGF-β inhibition primarily regulated inflammation intensity ( specifically , the Ψmax and Rp indices for both neutrophil and macrophage variables ) ( Figs 5b and 5e and 6b and 6e; S1a and S1d Figs , black lines ) ., For each inhibitor , we performed simulations for three different inhibitor concentrations ., We used inhibitor concentrations of 10 nM , 100 nM , and 500 nM for both the TNF-α and CXCL8 inhibitors ., For the TGF-β inhibitor , we used the concentrations of 1 nM , 20 nM , and 200 nM ., We found that the inhibitors for different targets are most effective within concentration ranges that can be vastly different ( Figs 5 and S1 ) ., Indeed , the CXCL8 and TNF-α inhibitors were most effective in restoring ( at least , partially ) their target indices when the inhibitors were added at concentrations ≥100 nM ( Fig 5a , 5b and 5e ) , while TGF-β inhibition was effective for inhibitor concentrations in the range ~1–10 nM ( S1 Fig ) ., These results attest to the efficacy of the simple strategy involving the inhibitor addition at time 0 ., To validate our modeling predictions regarding TNF-α inhibition , we compared our simulations with the experimental data generated for H1N1 virus-induced lung inflammation regulated by the TNF-α inhibitor etanercept 46 ., These data characterized three scenarios:, 1 ) control ( i . e . , no infection and , therefore , no inflammation ) ,, 2 ) inflammation with infection without added TNF-α inhibitor , and, 3 ) inflammation with infection and with added 200 nM TNF-α inhibitor ., From these data , we calculated the ratios of the total neutrophil and macrophage concentrations for the inflammation scenario without the TNF-α inhibitor to the corresponding concentrations for the inflammation scenario with added TNF-α inhibitor ., We compared these ratios with the respective ratios calculated from our model simulation of injury-induced chronic inflammation ., There was a reasonable agreement between the simulation-derived and experiment-derived ratios ( Table 3 ) ., Note that , in our analysis , we did not attempt to model the nonzero neutrophil and macrophage concentrations detected for the experimental control scenarios , because in our model , which represents extravascular space , the concentrations of neutrophils and macrophages in the absence of inflammation are zero ., In contrast , the control experiments measured the cell concentrations in entire lung lobes , which included cells present in the vasculature , resulting in nonzero neutrophil and macrophage concentrations even in the absence of inflammation ., We chose this particular experimental study for the validation because TNF-α is a key pro-inflammatory cytokine that is secreted by inflammatory cells in most inflammatory scenarios , and whose expression and regulatory functions are largely independent of the specific inflammation-inducing stimuli ( e . g . , viral load , LPS 47 , and wounding 39 ) ., Thus , despite the difference in the inflammation-inducing stimulus between the experimental study and our modeling study ( viral loading in lungs vs . injury , respectively ) , the reasonable agreement of the neutrophil and macrophage kinetics between the two studies suggests that our model adequately captured TNF-α inhibition ., To test whether the timing of inhibitor administration can impact the index restoration outcomes , we performed simulations in which the inhibitors were added at three different time points ( i . e . , 24 , 48 , and 72 h ) during chronic inflammation ., We chose 24 h as the earliest intervention time point because neutrophils are the first blood leukocytes to arrive at the inflammation site , and 24 h approximately corresponds to the neutrophil peak time for acute inflammatory response 35 , 48 ., Macrophages peak at 48 h , which motivated our choice of the second intervention time ., For all the simulations , the added CXCL8 and TNF-α inhibitor concentrations equaled 200 nM ( selected based on the observed effective range >100 nM , Fig 5a , 5b and 5e ) ., In the case of CXCL8 , the inhibitor added at 24 h was more effective in reducing the Ri for the neutrophil variable than the inhibitor added at other time points ( Fig 6a , dotted black line ) ., For TNF-α , the inhibitor added at 24 h provided a more complete restoration of the Ψmax for both neutrophils and macrophages to its acute-inflammation value than the TNF-α inhibitor added at later time points ( Fig 6b and 6e , dotted black lines ) ., A nearly identical effect on the neutrophil Ri and macrophage Ψmax was observed when the CXCL8 and TNF-α inhibitors , respectively , were added at 48 h ( Fig 6a and 6e , dashed black lines nearly overlapped the dotted black lines ) ., Mediator addition at 72 h showed the least degree of restoration in the neutrophil Ri and macrophage Ψmax among the three inhibitor addition times analyzed ( Fig 6a and 6e , black solid lines ) ., However , this observed effect of mediator inhibition on the inflammation indices was not monotonic ., Specifically , when the CXCL8 and TNF-α inhibitors were added at 36 h ( results not shown ) , the degree of restoration in the neutrophil Ri and macrophage Ψmax values , respectively , was less than that when the mediator inhibitors were added at the 48 h time point ., Experimental data from cytokine inhibition studies 49 , 50 support the possibility of this type of non-monotonic behavior , which may be due to the complex nonlinear functional dependencies at work in the system ., In summary , the CXCL8 and TNF-α inhibitors were characterized by similar optimal-efficiency concentration ranges and preferred administration timing regimens , but distinct preferentially regulated inflammation indices ., Among the three time points considered , mediator inhibition was most effective when introduced during peak neutrophil response ( i . e . , at 24 h ) ., For TGF-β , however , the inhibition outcomes were more complicated ( see S1 Fig and S1 Text ) ., Because the neutrophil is the primary microbicidal cell type that produces powerful cytotoxic molecules that can potentially destroy the surrounding healthy tissue , ( at least partial ) restoration of the “normal” ( i . e . , acute-inflammation ) timing and intensity of the neutrophil surge is essential for the resolution of chronic inflammation 51 ., Based on the preferential regulatory action of individual CXCL8 and TNF-α inhibitors on the total neutrophil Ri and Ψmax , respectively ( Fig 5a and 5b ) , we hypothesized that simultaneous inhibition of these two mediators in chronic inflammation might induce simultaneous restoration of the normal values for both of these indices ., We tested this hypothesis by simulating the effect of adding both CXCL8 and TNF-α inhibitors at time 0 ., Each of the two inhibitors was added at a concentration of 200 nM ., As hypothesized , the combined inhibition resulted in simultaneous restoration of both the Ri and Ψmax of the total neutrophil trajectory in a chronic inflammation simulation ( Fig 5c , solid black line ) ., Similarly to the action of the CXCL8 inhibitor ( Fig 6a ) , the combined inhibition of CXCL8 and TNF-α was most effective at 24 h ( Fig 6c and 6f , dotted black lines ) ., These results suggest that “cocktails” of therapeutic agents with different targets may provide strategies of improved efficacy for simultaneous control of both timing and intensity of inflammation after wounding ., Timely resolution of inflammation following an injury , infection , or disease is essential for the maintenance of healthy tissue 2 , 52 ., Despite ongoing research and development efforts , current anti-inflammatory therapies are only modestly effective and have significant negative side effects 9 , 53 , 54 ., This work was motivated by the need to identify molecular mechanisms that could serve as targets for intervention strategies intended to modulate chronic inflammatory responses ., Chronic inflammation may have distinct phenotypic manifestations depending on the inflammatory condition , e . g . , increased apoptotic neutrophil levels in diabetic ulcers 36 , or increased levels and prolonged presence of classically activated macrophages in ischemic wounds 45 , or prolonged oscillations in the levels of inflammatory cells and cytokines 55 ., Here , we focused on chronic inflammation characterized by heightened levels and/or delayed resolution timing for kinetic trajectories describing accumulation and depletion of the inflammatory cell types and molecular mediators in comparison with acute inflammation 2 ., In our simulations ( both acute and chronic ) , the kinetic trajectories of all the inflammatory components return to their baseline levels fo | Introduction, Results, Discussion, Materials and Methods | Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue ., Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators , whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets ., Here , we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets ., This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios ., We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios ., We identified three molecular mediators − tumor necrosis factor-α ( TNF-α ) , transforming growth factor-β ( TGF-β ) , and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation ., Modulation of macrophage flux , as well as of the abundance of TNF-α , TGF-β , and CXCL8 , may improve the resolution of chronic inflammation . | A recent approach to quantitatively characterize the timing and intensity of the inflammatory response relies on the use of four quantities termed inflammation indices ., The values of the inflammation indices may reflect the differences between normal and pathological inflammation , and may be used to gauge the effects of therapeutic interventions aimed to control inflammation ., Yet , the specific inflammatory mechanisms that can be targeted to selectively control these indices remain unknown ., Here , we developed and applied a computational strategy to identify potential target mechanisms to regulate such indices ., We used our recently developed model of local inflammation to simulate thousands of inflammatory scenarios ., We then subjected the corresponding inflammation index values to sensitivity and correlation analysis ., We found that the inflammation indices may be significantly influenced by the macrophage influx and efflux rates , as well as by the degradation rates of three specific molecular mediators ., These results suggested that the indices can be effectively regulated by individual or combined inhibition of those molecular mediators , which we confirmed by computational experiments ., Taken together , our results highlight possible targets of therapeutic intervention that can be used to control both the timing and the intensity of the inflammatory response . | null | null |
journal.pgen.1005484 | 2,015 | RAB-10-Dependent Membrane Transport Is Required for Dendrite Arborization | Dendrites and axons are two distinct functional and morphological domains of neurons ., Due to the complexity and heterogeneity in the morphology of dendrites , it has been challenging to study the development of dendrites in comparison with the more uniform , simply structured thread-like axons ., Previous studies have shown that dendritic growth relies heavily on the secretory pathway and endosomal function1 , 2 ., In Drosophila , loss-of-function mutations in genes encoding the small GTPases Rab1 and Sar1 , which are key regulators of ER-to-Golgi vesicular transport 3 , severely reduce the growth of dendrites ., Notably , loss of Rab1 and Sar1 do not diminish axon outgrowth , suggesting that the mechanisms underlying the extension of dendrites and axon are distinct 1 ., Furthermore , Golgi outposts , which are primarily found in dendrites but not axons , play an important role in supplying membranes for dendritic branching and growth 1 ., These experiments suggest that membrane components generated in the ER and trafficked to the Golgi are essential for dendritic growth ., In addition , Rab5 and Rab11-dependent endocytic membrane trafficking has also been implicated in dendrite morphogenesis 2 , 4 ., The molecular mechanisms that deliver membranes from the Golgi , Golgi-outposts and endosomes to the dendritic plasma membrane , however , are unclear ., To identify the membrane trafficking mechanisms that support dendrite branching and growth , we use the C . elegans multi-dendritic PVD neurons as a model ., The PVD neurons exist as a pair , PVDL and PVDR , and they function to detect harsh mechanical forces and cold temperatures 5–7 ., Each PVD neuron sits on one side of the animal and has a single axon that extends to the ventral nerve cord , as well as a highly branched dendritic arbor that covers most of the body , except for the neck and head 8 ., Recently , the transmembrane leucine-rich repeat protein DMA-1 was identified as a PVD dendritic receptor ., DMA-1 recognizes skin-derived pre-patterned cues that promote dendrite stabilization and branching 9–11 ., In addition , the claudin-like transmembrane protein HPO-30 also promotes dendrite stabilization 12 ., Both DMA-1 and HPO-30 are dendrite specific proteins that are rarely observed in axons ., The mechanisms that regulate their sorting and trafficking to the dendritic membranes are still unknown 9 , 12 ., The small GTPase Rab10 , which is an ortholog to the yeast Sec4p protein that controls post-Golgi vesicle trafficking , has been shown to mediate polarized membrane addition during axonal growth in mammals ., In axons Rab10 is activated by the mammalian ortholog of the Drosophila gene Lethal giant larvae , Lgl1 ., The Lgl1 protein dissociates the Rab10-GDI complex 13 ., Activated Rab10 then interacts with multiple effector proteins to direct distinct steps of axonal membrane addition ., These include an initial interaction with myosin Vb ( MYO5B ) , which controls the biogenesis of post-Golgi Rab10 carriers 14 ., Rab10 then binds with c-Jun N-terminal kinase-interacting protein 1 ( JIP1 ) to facilitate anterograde transport of Rab10 cargos 15 ., Finally , Rab10 binds myristoylated alanine-rich C-kinase substrate ( MARCKS ) ., The Rab10-MARCKS interaction allows the docking and fusion of Rab10 vesicles with the axonal plasma membrane 16 ., Unlike extensive studies on the role of Rab10 in axonal growth , it is unclear whether Rab10 is required for dendrite arborization during development ., Several studies have shown association between the conserved exocyst complex and Rab10 GTPases 17 ., The exocyst complex is composed of eight subunits ., In C . elegans these are encoded by the genes sec-3 , sec-5 , sec-6 , sec-8 , sec-10 , sec-15 , exoc-7 and exoc-8 18 ., The exocyst complex functions as the effector of the yeast Rab10 ortholog Sec4p , and facilitates tethering , docking and fusion of secretory vesicles during bud formation 17 ., The exocyst complex also associates with Rab10 in renal epithelial cells and may mediate membrane transport to the primary cilium 19 ., Both Rab10 and the exocyst complex are further required for the exocytic transport of the glucose transporter Glut4 20 , 21 ., However , whether Rab10 and the exocyst complex function together during other processes , such as dendritic growth and branching , is not clear ., Here , we report that loss of the C . elegans rab-10 gene reduces dendritic arborization of the PVD neuron ., We show that RAB-10 functions cell-autonomously , and localizes to the Golgi and the early endosomes in the PVD neurons ., Further , we find that deficiencies in rab-10 and the exocyst subunits cause accumulation of the dendritic membrane proteins DMA-1 and HPO-30 within intracellular vesicles ., We also show that Rab10 and the exocyst complex are required for dendrite arborization in Drosophila , and dendritic spine formation in mammalian neurons ., Together , these data suggest that Rab10 and the exocyst complex play a conserved role in controlling Golgi-to-plasma membrane and/or endosome-to plasma membrane trafficking required for dendrite morphogenesis ., To identify potential regulators of membrane trafficking during dendritic branching and growth of the multi-dendritic PVD neuron , a candidate-based genetic screen was performed ., We crossed animals harboring mutations in genes encoding proteins important for various membrane trafficking pathways with a PVD neuron-specific fluorescent marker ( F49H12 . 4>gfp or ser2prom3>gfp ) ., The morphology of PVD dendritic arbors was then examined ., We found that two putative null alleles of rab-10 presented severely abnormal PVD dendrite morphology ( Table 1 and Fig 1A–1E ) ., In the proximal region ( including the middle and tail areas ) , rab-10 ( ok1494 ) and rab-10 ( dx2 ) mutant animals contained far fewer dendritic branches compared to wild-type ., For example , a count of secondary dendrites within a 100μm long region along the primary dendrite anterior to the PVD cell body in rab-10 ( ok1494 ) and rab-10 ( dx2 ) animals revealed an average of 1 . 1±0 . 3 and 2 . 8±0 . 7 secondary dendrites ., In comparison , wild-type animals within this region contained 11 . 1±0 . 5 secondary dendrites ( Fig 1F and 1G ) ., Tertiary and quaternary branches were even more affected and were essentially absent in the proximal region of rab-10 ( ok1494 ) and rab-10 ( dx2 ) animals ( Fig 1F , 1H and 1I ) ., Interestingly , in the distal area of the PVD , the dendritic branching and growth were minimally affected by loss of rab-10 , indicating that a rab-10-independent mechanism mediates distal dendritic branching ( Fig 1A–1E , 1G–1I ) ., The growth of the primary dendrite and axon of the PVD appeared normal in rab-10 deficient animals ( Fig 1A–1E , and S1 Fig ) ., rab-10 was also required for the dendritic arborization of the FLP neuron , which covers the head region and has a similar morphology and function as the PVD neuron ( S2 Fig ) 8 ., However , rab-10 was dispensable for the growth of unbranched dendrites of OLL , AWB , and AWC neurons , suggesting a specific role of rab-10 in mediating the growth of dendritic branches in multi-dendritic neurons ( S2 Fig ) ., Together , these data indicate that RAB-10 is required for the elaboration of branched dendrites in C . elegans ., To determine if RAB-10 activity is required within the PVD , we tagged full length RAB-10 at its N-terminus with GFP and expressed this construct under a PVD-specific promoter ( ser2prom3 ) in rab-10 ( ok194 ) animals ., GFP::RAB-10 expressed from multicopy extrachromosomal arrays in the PVD neuron fully rescued the morphology of the PVD dendritic arbor in most animals in two independent lines ( Fig 2A–2C ) , indicating that RAB-10 functions within the PVD to promote proximal dendritic arborization ., As a GTPase , RAB-10 cycles between the GDP-bound inactive form and GTP-bound active form ., To test whether its function in promoting dendrite branching and growth requires GTPase activity , we expressed both GDP-locked ( T23N ) and GTP-locked ( Q68L ) forms of RAB-10 and examined their rescuing ability ., Dominant-negative RAB-10 ( T23N ) not only failed to rescue the proximal PVD defects in rab-10 ( ok1494 ) mutants , but also disrupted the distal dendrite arbor in wild-type animals ( Fig 2C , S3 Fig ) ., In contrast , constitutively active RAB-10 ( Q68L ) fully rescued the PVD dendrite morphogenesis defects in rab-10 ( ok1494 ) mutant animals ( Fig 2C ) ., Over-expressing constitutively active RAB-10 ( Q68L ) did not cause over-growth of dendrites in PVD neuron ( S4 Fig ) , suggesting that other factors limit dendrite growth and patterning ., We conclude that RAB-10 functions cell autonomously in the PVD neuron and requires GTPase activity to promote PVD dendrite morphogenesis ., RAB-10 orthologs are known to regulate Golgi to plasma membrane vesicle trafficking as well as endocytic recycling events 22–26 ., To determine if RAB-10 localizes with either Golgi or endosomes in the PVD neuron we tagged full length RAB-10 with GFPnovo2 , a mutant form of GFP that is brighter ., We generated a single copy insertion line using miniMos method to minimize potential ectopic localization of RAB-10 protein induced by over-expression 27 , 28 ., Similar to the multicopy line , we crossed this line into the rab-10 null mutant and found that it fully rescued the PVD dendrite arborization defect ( S5 Fig ) ., Many intracellular vesicles were labeled by GFP::RAB-10 in the PVD dendrites ., Notably , there was a strong correlation between GFP::RAB-10 localization and mCherry::FAPP1-PH ( Golgi reporter ) and a strong correlation of GFP::RAB-10 and mCherry::RAB-5 ( early endosome reporter ) ( Fig 2D and 2E ) 29 ., These observations are consistent with previous studies showing RAB-10 localization to the Golgi and early endosomes in the C . elegans intestinal cells 24 ., Supporting a role in mediating vesicular trafficking , time-lapse recordings revealed that GFP::RAB-10 labeled vesicles moved bi-directionally along dendrites , consistent with localization to transport vesicles ( S1 Movie ) ., Together , these data suggest that RAB-10 might mediate PVD dendrite outgrowth by regulating Golgi-to-membrane trafficking or endosomal membrane recycling events ., To attempt to determine whether the PVD dendrite morphogenesis defect in rab-10 mutants might be due to a role for RAB-10 in Golgi-to-plasma membrane trafficking , endocytic recycling , or both , we perturbed each pathway and tested whether it altered PVD dendritic arborization ., Consistent with previous studies in Drosophila and rat hippocampus neurons implicating ER-to-Golgi trafficking in dendritic growth , over-expressing dominant-negative RAB-1 ( a key regulator of ER-to-Golgi trafficking ) caused dramatically reduced branching in the PVD ( Table 1 , and S6 Fig ) 1 ., In contrast , genetic loss of rme-1 or chat-1 , and over-expression of dominant-negative RAB-5 or RAB-11 . 1 ( all key regulators of endocytic recycling ) 30–33 , had no effect of PVD dendrite morphology ( Table 1 ) ., Together , these data suggest a role for RAB-10 in mediating Golgi-to-plasma membrane trafficking that is important for dendrite morphogenesis ., Notably , however , we cannot rule the possibility that RAB-10 regulates endosomal recycling independent of rme-1 , rab-5 or rab-11 . 1 ., Thus , RAB-10 might also regulate an endosome-to-plasma membrane transport pathway independent of RME-1 , RAB-5 , RAB-11 . 1 function in the PVD neuron that is required for dendritic arborization ., Rab GTPases are molecular switches that exert their functions by recruiting and releasing specific effectors 34 ., We next sought to identify possible effectors that function with RAB-10 in mediating the branching and growth of PVD dendrites ., We first examined EHBP-1 , an Eps 15 domain binding protein that acts as an effector of RAB-10 in endocytic recycling and secretory pathways 26 ., Mutant animals of ehbp-1 , however , had normal PVD dendritic arbors ( Table 1 ) ., Importantly , we cannot fully exclude the possibility that the PVD dendrite development was rescued by maternally loaded ehbp-1 mRNA or EHBP-1 protein , since we can only examine ehbp-1 homozygous animals derived from heterozygous mothers ., We next tested members of the exocyst complex , an established effector of yeast Sec4p with which RAB-10 shares high homology 35 ., Notably , loss of the exocyst subunit exoc-8 in viable null mutant animals or loss of sec-8 in the mutant progeny of heterozygous sec-8 mutant mothers , caused a PVD dendrite morphogenesis defect in the proximal but not in the distal region ( Table 1 , and Fig 3A–3F ) 36 ., Further , the growth of the primary dendrite and the axon was normal ( Fig 3A–3C , S1 Fig ) ., This phenotype was similar to rab-10 mutants , although the growth of secondary dendrites was only mildly affected in exoc-8 and sec-8 mutant worms ( Fig 3A–3F ) ., Expression of exoc-8 cDNA under the PVD-specific promoter ( ser2prom3 ) fully rescued the dendritic arborization defect in exoc-8 ( ok2523 ) mutant worms , indicating that the exocyst complex functions in the PVD to promote dendritic arborization ( Fig 3G ) ., Homozygous mutants of two other exocyst components , sec-5 and sec-10 , derived from the heterozygous mothers did not show any obvious PVD dendrite defect ( Table 1 ) ., To test whether this was due to maternal rescue , we used a newly developed targeted protein degradation system to specifically remove the SEC-5 protein from the PVD 37 ., This system takes advantage of cell type specific expression of the E3 ubiquitin ligase substrate-recognition subunit ZIF-1 , which recognizes proteins tagged with the 36 amino acid ZF1 zinc-finger domain ., To selectively remove SEC-5 in the PVD neuron , we drove ZIF-1 in PVD using the ser2prom3 promoter in a sec-5::zf1::yfp knock-in strain ( sec-5 ( xn51 ) ) —a strain where both copies of the endogenous sec-5 genes are tagged with zf1 ( Fig 4A ) 37 ., Confirming a role for the exocyst complex in promoting dendritic arborization , depleting ZF1::YFP tagged SEC-5 in PVD resulted in PVD dendrite arborization defects ( Fig 4C–4E ) ., In some of these animals , both distal and proximal regions lacked menorah structures , suggesting that sec-5 might be important for the growth of both proximal and distal dendrites ( Fig 4D ) ., Control animals expressing ser2prom3>ZIF-1 transgene alone and control animals expressing ZF1::YFP tagged SEC-5 alone had normal PVD dendrite morphology ( Fig 4B and 4E ) ., We conclude that the exocyst complex plays a cell-autonomous role in promoting dendritic arborization and that maternal contributions of some of exocyst components contribute to this function ., Further , based on the more severe PVD phenotype after ZF-1 tag-directed loss of sec-5 versus the exoc-8 null mutant , our results also suggest that exocyst components have different requirements ( perhaps reflecting their differential necessity for exocyst activity ) during PVD dendrite morphogenesis ., Yeast Sec4p and the exocyst complex function together to promote docking and possible fusion of post-Golgi vesicles 17 ., Thus , we hypothesized that the PVD dendritic arborization defects in rab-10 , exoc-8 and sec-8 mutants might in part be due to the failure of docking of post-Golgi vesicles ., To test this , we built fluorescent reporters for the dendritic membrane proteins , DMA-1 and HPO-30 , transmembrane proteins expressed in PVD neurons that function to mediate dendritic branching and stabilization 9–12 ., Consistent with previous studies , DMA-1::GFP and HPO-30::GFP were localized in the dendritic membranes , and in some intracellular vesicles in wild-type animals ( Fig 5A–5C , and S7 Fig ) 9 , 12 ., These two transgene reporters likely represent endogenous protein localization , as the transgenes rescued the PVD dendrite arborization defects of dma-1 ( tm5159 ) and hpo-30 ( ok2147 ) mutants ( S5 Fig ) ., Consistent with a role in docking and fusion of vesicles , loss of rab-10 and exoc-8 caused severe accumulation of DMA-1::GFP containing vesicles within the growing PVD dendrites ( Fig 5D–5I ) ., We quantified the number of vesicular units ( which might be a single vesicle or a vesicle cluster ) in three areas—the distal , middle and proximal regions ., We found that wild-type animals contained on average 29 . 6±1 . 9 vesicular units in a 76 . 8μm x 76 . 8 μm area from the middle region of the PVD ., In contrast rab-10 ( ok1494 ) and exoc-8 ( ok2523 ) mutant worms contained a dramatic two-to-three fold increase in vesicular units—65 . 6±2 . 8 and 87 . 8±6 . 8 , respectively ( Fig 5J ) ., Furthermore , in wild-type animals the DMA-1::GFP containing vesicles mainly localized to the primary dendrites , and rarely appeared in the higher-order dendrites ( including the secondary , tertiary and quaternary dendrites; Fig 5A–5C ) ., In contrast , in rab-10 ( ok1494 ) and exoc-8 ( ok2523 ) mutant worms , numerous vesicles appeared in the higher-order dendrites ( Fig 5D–5I and S8 Fig ) ., The presence of numerous DMA-1::GFP intracellular vesicles in rab-10 and exoc-8 mutants suggested that delivery of DMA-1 to the membrane might be reduced ., To test this idea , the fluorescence intensity of DMA-1::GFP at the surface ( which we presume is predominantly plasma membrane localization ) of the primary dendrites was determined ., In the distal region of the dendritic arbor , the intensity of DMA-1::GFP at the primary dendrite in rab-10 ( ok1494 ) and exoc-8 ( ok2523 ) mutant worms was decreased by 63 . 9% and 47 . 8% compared with wild-type animals ( Fig 5K ) ., In the rab-10 mutants , reduction of dma-1 by RNAi-mediated knock-down further suppressed distal dendritic arborization , suggesting that the reduced levels of DMA-1 on the surface of distal dendrites is sufficient to promote dendritic stabilization and branching ( S9 Fig ) ., In the proximal region , the decrease in DMA-1 was more dramatic ., The intensity of DMA-1::GFP at the surface of the primary dendrite in rab-10 ( ok1494 ) and exoc-8 ( ok2523 ) mutant worms was decreased by 89 . 5% and 90 . 3% in the middle regions , and 94 . 4% and 94 . 8% in the tail regions compared to wild-type animals ( Fig 5K ) ., HPO-30::GFP showed similar vesicular accumulation and decreased dendrite surface localization in rab-10 ( ok1494 ) and exoc-8 ( ok2523 ) mutant animals ( S7 Fig ) ., These results offer compelling evidence that rab-10 and exocyst activity are required for the vesicular delivery of DMA-1 and HPO-3 to the dendritic membrane ., The similar phenotypes after loss of exocyst components and rab-10 , as well as functions of these molecules in yeast and vertebrates in delivering vesicles to the plasma membrane 17 , 19 , 35 , 38 , 39 led us to examine whether RAB-10 and exocyst components coexist in vesicles in the PVD dendrites ., EXOC-8::GFP and mCherry::RAB-10 strongly colocalized on intracellular vesicles ( Fig 6A ) , indicating that the exocyst might function together with rab-10 to mediate vesicle delivery ., Next , we examined the subcellular localization of RAB-10 in animals harboring mutations in the exocyst components exoc-8 and sec-8 ., Loss of these exocyst components caused a dramatic accumulation of RAB-10 labeled vesicles in the PVD dendrites ( Fig 6B ) ., Wild-type animals contained 39 . 8±2 . 6 GFP::RAB-10 vesicular units in a 88 . 1μm x 88 . 1 μm area from the middle region of the PVD neuron ., Worms with mutations in exoc-8 ( ok2523 ) and sec-8 ( ok2187 ) contained over three fold more GFP::RAB-10 vesicular unites—129 . 7±7 . 5 and 127 . 6±2 . 7 , respectively ( Fig 6C ) ., To determine whether the accumulated vesicles containing GFP::RAB-10 were the same population observed in exoc-8 ( ok2523 ) mutants carrying DMA-1::GFP or HPO-30::GFP , we expressed DMA-1::GFP or HPO-30::GFP and mCherry::RAB-10 in exoc-8 ( ok2523 ) mutant animals ., Confirming these are the same vesicle population , most of the mCherry::RAB-10 labeled vesicles also contained DMA-1::GFP or HPO-30::GFP ( Fig 6D and 6E ) ., To test whether RAB-10 functions to recruit EXOC-8 onto vesicles , the EXOC-8::GFP reporter was crossed into the rab-10 loss-of-function mutant ., EXOC-8::GFP , however still localized to vesicles in PVD neurons in rab-10 mutants ( S10 Fig ) ., Taken together , these results suggest the exocyst complex promotes fusion of RAB-10 carriers within the PVD neuron to facilitate dendritic growth and stabilization , but that the exocyst complex is recruited to these vesicles in a RAB-10 independent manner ., To investigate whether Rab10 and exocyst complex-mediated dendritic membrane transport is required for the dendrite morphogenesis in other organisms , we examined Drosophila class IV dendritic arborization neurons and cultured rat hippocampal neurons after loss of rab10 and exocyst components ., In Drosophila , RNAi mediated knock-down of rab10 and exo84 significantly reduced dendrite branching and growth ( S11 Fig ) ., Compared to animals treated with control RNAi , rab10 and exo84 RNAi targeted loss resulted in a 20% decrease in both the number of total end points and the total dendritic arbor ( S11 Fig ) ., ShRNA mediated knock-down of rab10 , sec8 and exoc84 did not alter the total dendrite length in cultured rat embryonic hippocampal neurons , but did lead to a dramatic 67% reduction in the density of dendritic spines at 21 days in vitro ( DIV ) compared to control shRNA treated neurons ( S12 Fig ) ., Collectively , we conclude that rab10 and the exocyst complex have important and likely conserved roles during dendrite morphogenesis ., Since some higher-order dendrites in the PVD still formed in rab-10 null alleles , we hypothesized that another Rab protein might regulate dendritic branching ., To test this idea , we examined the function of the RAB-10 related GTPase RAB-8 40 ., Notably , homozygous viable rab-8 ( tm2526 ) null mutant animals showed normal PVD morphology ( Fig 7A ) ., This suggested that if RAB-8 functions in the PVD , it might act redundantly with RAB-10 ., To test this idea , we first attempted to create animals with null mutations in both rab-8 and rab-10 ., The rab-8 and rab-10 double mutant animals , however , were sterile , which made it challenging to determine PVD morphology 26 ., Interestingly , we found that expression of a dominant-negative RAB-8 ( T22N ) in the PVD neuron caused a severe dendrite morphogenesis defect ( S3 Fig ) ., In 15% ( n = 40 ) and 12% ( n = 50 ) of transgenic animals ( two independent lines ) harboring the transgene of dominant negative RAB-8 , the dendritic growth in both distal and proximal regions was dramatically reduced ( S3 Fig ) ., These results suggest that the dominant negative RAB-8 might act to block the function of both the RAB-8 and RAB-10 proteins ( perhaps through inhibition of a common guanine nucleotide exchange factor ) and that RAB-8 may function redundantly with RAB-10 to promote PVD morphogenesis ., To more directly test the idea that RAB-10 and RAB-8 function together in regulating PVD morphogenesis , we took advantage of a newly developed CRIPSR/Cas9-mediated conditional knock-out method and specifically disrupted the function of the rab-10 gene in the PVD neuron and other descendants of the seam cell lineage by restricting Cas9 endonuclease expression using nhr-81 promoter ( Pnhr-81>Cas9 ) 41 , 42 ., In three separate lines , approximately 5–10% of animals ( 11 . 6% ( 35/303 ) , 5 . 1% ( 4/78 ) and 4 . 9% ( 3/61 ) ) targeted with conditional PVD knock-out of rab-10 ( which we refer to as rab-10 ( cKO ) ) generated a PVD phenotype ., Animals displaying a PVD dendrite arborization defect , showed a similar phenotype as that of rab-10 null mutants ( Fig 7B ) ., These results suggest that the CRIPSR/Cas9-mediated conditional knock-out only disrupts the rab-10 gene in the PVD lineage in a small percentage of transgenic animals , but that when it does target rab-10 , it completely perturbs rab-10 function ., We crossed the most penetrant rab-10 ( cKO ) line into the rab-8 deletion mutant ., We hypothesized that if rab-8 functions redundantly with rab-10 , it should enhance the rab-10 ( cKO ) PVD phenotype ., From 302 animals that carried the Pnhr-81>Cas9 and PU6>rab-10-sgRNAs transgene , 27 animals ( 8 . 9% ) showed severely defective PVD dendritic arbors ., We measured the total number of secondary dendrites , and found that the rab-10 ( cKO ) rab-8 ( tm2526 ) animals had fewer secondary dendrites than rab-10 ( cKO ) alone ( Fig 7E ) ., Further , we observed that 55 . 6% ( 15/27 ) of rab-10 ( cKO ) rab-8 ( tm2526 ) animals had truncated posterior primary dendrites , which was rarely observed in rab-10 ( cKO ) or rab-8 ( tm2526 ) strains ( 2 . 8% ( 1/35 ) and 0% ( 0/25 ) , respectively ) ( Fig 7F ) ., Taken together these results suggest that the related GTPases RAB-8 and RAB-10 function redundantly to promote dendritic morphogenesis in the PVD neuron ., Previous work in Drosophila dendritic arborization neurons identified rab1 , sec23 and sar1 , three genes that mediate ER-to-Golgi traffic , as essential regulators of membrane addition during dendritic growth 1 ., This study also demonstrated that laser ablation of Golgi-outposts within the dendrites reduced dendrite arborization ., Together this work strongly implicated ER-to-Golgi trafficking as well as Golgi outposts as essential for dendrite morphogenesis ., These findings left open the question , however , of how membrane trafficking from the Golgi to the dendritic plasma membrane is regulated ., Through a candidate-based genetic screen and analysis of the C . elegans multidendritic PVD neuron we have identified the small GTPase Rab10 as a post-Golgi regulator of vesicle trafficking required for dendrite morphogenesis ., Rab10 is known to regulate both Golgi-to-plasma membrane vesicle trafficking and endocytic recycling 13 , 22 , 24–26 , 43–45 ., Our data suggest that the C . elegans RAB-10 protein primarily promotes post-Golgi trafficking in the PVD neuron to facilitate dendritic growth and branching ., We did not observe any obvious PVD dendrite morphogenesis defects after inhibiting numerous important mediators of endocytic recycling ., This includes rme-1 and chat-1 mutant animals and animals carrying transgenes for dominant-negative rab-5 or rab-11 . 1 GTPases 30 , 31 ., In contrast , animals expressing a dominant-negative rab-1 , which regulates ER-to-Golgi trafficking , showed dramatically reduced PVD dendrite branching and growth ., Furthermore , animals harboring exoc-8 and sec-8 mutations , which encode two subunits of the exocyst complex that function to dock secretory vesicles onto fusion sites on the plasma membrane , led to PVD dendrite morphogenesis defects similar to rab-10 17 ., These data strongly implicate a function for the RAB-10 protein in mediating membrane trafficking from the secretory pathway that is crucial for dendritic branching in the PVD ., Since we cannot rule out the possibility that RAB-10 might also regulate recycling of endosomes in a RME-1/RAB-5/ RAB-11 . 1-independent manner , we suggest it is possible that RAB-10 might also contribute to PVD morphogenesis by regulating endocytic trafficking ( see Fig 8 ) ., Although loss of rab-10 resulted in a dramatic reduction in dendrite arborization in the PVD and FLP neurons , the absence of rab-10 function did not affect the growth of the PVD primary dendrites or the unbranched dendrites of the OLL , AWB and AWC neuron ., This might reflect a heavy reliance on post-Golgi trafficking in the PVD and FLP neurons to supply the dramatic expansion in membrane required to form highly branched dendrites ., In support of this idea , a recent study in Drosophila revealed that Rab10 and exocyst-mediated membrane trafficking is crucial for the elaborate branching of tracheal terminal cells 44 ., It is also possible that rab-10 may transport cargoes that are necessary for the branching and growth of higher-order dendrites ., Consistent with this notion , loss of rab-10 severely affected the dendritic transport of DMA-1 and HPO-30 , two dendritic specific transmembrane proteins that are required for the branching and stabilization of higher-order dendrites ., The exocyst complex is a well-characterized effector of yeast Sec4p , which shares high sequence homology with RAB-10 26 ., In yeast the exocyst complex functions to target Sec4p secretory vesicles to sites of exocytosis 35 ., Suggesting a similar mechanism in the PVD dendrite , we found that loss of the exocyst components exoc-8 and sec-8 caused a dendrite morphogenesis defect that was similar to rab-10 mutants ., In animals carrying mutations in these genes , GFP tagged DMA-1 and HPO-30 were sequestered in intracellular vesicles and the dendrite surface expression was greatly reduced ., The similarity of the phenotypes suggests that the exocyst complex functions as an effector of RAB-10 to mediate trafficking of secretory vesicles to the dendritic plasma membrane ., This idea is further supported by the colocalization of RAB-10 and EXOC-8 on intracellular vesicles in the PVD dendrites ., Notably , compared to the PVD dendrite morphogenesis defects observed in the rab-10 mutants , the dendrite phenotypes in exoc-8 and sec-8 mutants were not as severe ., This could indicate that another mechanism mediates tethering , docking or fusion of RAB-10 cargo vesicles to the dendritic plasma membrane ., However , it is likely that the exoc-8 and sec-8 mutant animals examined were not nulls for exocyst function ., While the deletion allele of exoc-8 ( ok2523 ) is thought to completely remove exoc-8 function 36 , it does not lead to lethality , which is associated with loss of all other exocyst subunits ., These observations suggest that loss of exoc-8 does not completely eliminate exocyst function 46 ., Further , the sec-8 ( ok2187 ) homozygotes were obtained from heterozygous mothers that likely provided maternally loaded sec-8 mRNA or SEC-8 protein ., Consistent with this idea , the removal of the exocyst component SEC-5 through ZF-1 tag-mediated degradation often resulted in a complete loss of dendritic branching ., We thus suggest that the exocyst complex is the primary effector of RAB-10 ., Further , given that loss of SEC-5 lead to a more severe dendrite arborization defect than loss of RAB-10 , it is likely the exocyst complex has functions outside of regulating RAB-10 vesicle trafficking ( possibly RAB-8 vesicle trafficking , see below ) ., In rab-10 mutants , the growth and branching of more distal dendrites was minimally affected , suggesting that a rab-10 independent mechanism exists ., Notably , expressing dominant negative RAB-10 in PVD caused both distal and proximal dendrite morphogenesis defects ., A possible explanation is that dominant negative RAB-10 competes for RAB activators , such as a guanine nucleotide exchange factor ( GEF ) , that interferes with another RAB protein that is essential for distal dendrites ., A strong candidate for this other RAB protein is RAB-8 ., Although loss of rab-8 had no obvious effect on PVD dendrite morphogenesis , expression of a dominant negative RAB-8 in the PVD caused similar dendritic phenotype to the dominant negative RAB-10 ., rab-8 is related to rab-10 , and rab-8 and rab-10 function redundantly in mediating secretion in C . elegans germ cells 26 ., Using a newly developed CRIPSR/Cas9-mediated conditional knock-out method , we found that conditional knockout of rab-10 in a rab-8 null mutant showed stronger dendrite arborization defect than loss of rab-10 alone ., These observations offer compelling evidence that RAB-10 and RAB-8 function redundantly to regulate PVD dendritic arborization ., RAB-10 and the exocyst complex are evolutionarily conserved from C . elegans to humans ., We observed that knock-down of rab10 or exo84 caused a significant reduction of the dendrite arbor in the Drosophila class IV dendritic arborization neuron ., Knock-down of rab10 , exoc84 or sec8 had no effect on the total length of the dendrites in rat cultured hippocampal neurons , but the number of den | Introduction, Results, Discussion, Materials and Methods | Formation of elaborately branched dendrites is necessary for the proper input and connectivity of many sensory neurons ., Previous studies have revealed that dendritic growth relies heavily on ER-to-Golgi transport , Golgi outposts and endocytic recycling ., How new membrane and associated cargo is delivered from the secretory and endosomal compartments to sites of active dendritic growth , however , remains unknown ., Using a candidate-based genetic screen in C . elegans , we have identified the small GTPase RAB-10 as a key regulator of membrane trafficking during dendrite morphogenesis ., Loss of rab-10 severely reduced proximal dendritic arborization in the multi-dendritic PVD neuron ., RAB-10 acts cell-autonomously in the PVD neuron and localizes to the Golgi and early endosomes ., Loss of function mutations of the exocyst complex components exoc-8 and sec-8 , which regulate tethering , docking and fusion of transport vesicles at the plasma membrane , also caused proximal dendritic arborization defects and led to the accumulation of intracellular RAB-10 vesicles ., In rab-10 and exoc-8 mutants , the trans-membrane proteins DMA-1 and HPO-30 , which promote PVD dendrite stabilization and branching , no longer localized strongly to the proximal dendritic membranes and instead were sequestered within intracellular vesicles ., Together these results suggest a crucial role for the Rab10 GTPase and the exocyst complex in controlling membrane transport from the secretory and/or endosomal compartments that is required for dendritic growth . | Dendrites are cellular extensions from neurons that gather information from other neurons or cues from the external environment to convey to the nervous system of an organism ., Dendrites are often extensively branched , raising the question of how neurons supply plasma membrane and dendrite specific proteins from the source of synthesis inside the cell to developing dendrites ., We have examined membrane trafficking in the PVD neuron in the nematode worm C . elegans to investigate how new membrane and dendrite proteins are trafficked ., The PVD neuron is easy to visualize and has remarkably long and widely branched dendrites positioned along the skin of the worm , which transmits information about harsh touch and cold temperature to the nervous system ., We have discovered that a key organizer of vesicle trafficking , the RAB-10 protein , localizes to membrane vesicles and is required to traffic these vesicles that contain plasma membrane and dendrite proteins to the growing PVD dendrite ., Further , our work revealed that a complex of proteins , termed the exocyst , that helps fuse membrane vesicles at the plasma membrane , localizes with RAB-10 and is required for dendrite branching ., Together , our work has revealed a novel mechanism for how neurons build dendrites that could be used to help repair damaged neurons in human diseases and during aging . | null | null |
journal.pcbi.1000986 | 2,010 | Chromosome Driven Spatial Patterning of Proteins in Bacteria | A variety of molecular mechanisms have been identified for localizing proteins in bacteria cells ., The emergence of spontaneous patterns from instabilities arising from the reactions of diffusing proteins 1–3 and protein polymerization dynamics 4 , 5 have been shown to play a role in the patterning of the Min system that regulates cell division 6 , 7 ., The periodic patterning of protein clusters involved in bacterial chemotaxis is due to the growth of protein domains from purely stochastic nucleation 8 , 9 ., In many bacteria , proteins that form scaffolds at both poles serve as anchoring points for other localizing proteins and the tethering of the chromosome ., Models have shown that membrane curvature can act as a mechanism for generating such polar localization 10 , 11 and is indeed responsible for the patterning of the scaffolding protein DivIVA 12 , 13 ., In all of the above mechanisms , the patterns result from protein-protein interactions and from interactions with the cellular membrane ., Recent experiments on the polar localized scaffolding protein , PopZ , in Caulobacter Crescentus show that the presence of the chromosome may also play a key organizing role in positioning protein scaffolds at the poles independent of interactions with the membrane 14 , 15 ., Other recent experimental work has shown that aggregating misfolded protein in the bacteria Escherechia coli is also preferentially localized to the poles , in particular to the older mother cells pole 16 , thereby preventing the daughter cell from inheriting potentially deleterious misfolded protein ., In C . crescentus the scaffolding protein PopZ forms domains that occupy the cytoplasmic space at the two poles 14 , 15 ., In Fig . 1a a schematic of the dynamic patterning of PopZ over the course of the cell cycle is shown ., At the beginning of the cell cycle of C . crescentus , PopZ exists at only one pole , inherited from the previous division ., After division it begins to assemble and form at the other pole , leading to a bipolar pattern in the dividing cell ., However , PopZ can also display a variety of subcellular localizations , not just bipolar , depending on both the amount of PopZ in the cell and on the cellular shape ., Amazingly , such patterning can also be reproduced by expressing PopZ in Escherichia coli that possesses no popZ homologue ., Similar patterning was observed for misfolded proteins in E . coli , from unipolar to bipolar localization , with the occasional localization midcell 16 ., In these experiments reporter proteins that unfold in response to temperature increases were observed to aggregate at the poles under misfolding conditions ., The misfolded protein was consistently found to go to chromosome free regions ., When the temperature was lowered and the protein refolded , the domains would fall apart and the protein would return to being diffuse within the cell ., What mechanisms could lead to the diversity of observed patterns of aggregating proteins with bacterial cells , such as PopZ or misfolded protein ?, Cell curvature has been suggested as a mechanism for sorting proteins to the poles via attraction to lipid domains that prefer negative curvature ., Experiments on spherical cells showed that PopZ can show diffuse 14 or localized patterning 15 , with the latter arguing against curvature mediated mechanisms ., Experiments on non-dividing cells that possess multiple chromosomes showed that PopZ is patterned not only at the poles but also in regions between chromosomes 15 ., For the situation of misfolded proteins , it is unlikely that the aggregates are able to sense membrane curvature ., As shown , nucleoid occlusion gives a reasonable explanation of the aggregation of misfolded protein in E . coli ., These experiments have led to the hypothesis that the presence of chromosome free regions along with protein self-association could be a potential mechanism driving localization 15 , 16 ., This hypothesis requires no active mechanism driving sorting; localization would arise as a result of the entropic forces exerted by the chromosome on the protein and the energy gained from growing protein domains ., Prior work on chromosome segregation has shown that purely entropic forces can be sufficient to drive DNA separation within cells without the need to invoke active transport 17 ., In 16 a mathematical model of the patterning process using Langevin dynamics for the protein and a mean-field treatment of the nucleoid gave observed patterns ., Using a complementary approach we explore a simple biophysical model of the above localization mechanism and whether it may play a role in protein patterning ., In this article we provide a model along with simulations that show that protein multimerization in chromosome free regions can be a sufficient mechanism for polar localization ., Fig . 1b summarizes the model ., In the cell the chromosome packs into the nucleoid that occupies a significant fraction of the cellular volume , however because of its condensed structure it spends less time exploring the cell poles ., Because of this , proteins that multimerize into larger structures , such as PopZ or misfolded protein , will have an entropic force that will naturally sort them to the poles ., The proteins ability to nucleate and grow into domains depends upon its density at the poles and also the pressure exerted by the nucleoid on the free volumes at the poles ., For fixed nucleoid volume fractions , the protein remains diffuse at low concentrations ., At higher concentrations it potentially can nucleate and grow at only one pole and at yet higher concentrations it becomes possible to localize and grow domains at both poles ., The model predicts that no other mechanisms besides the formation of chromosome free regions and protein multimerization are required ., Using this model we can reproduce all the observed patterns observed for PopZ in C . crescentus and misfolded protein in E . coli and suggest new experiments that would help to test the model further ., Here we consider the patterning of aggregating proteins in cells possessing a cylindrical geometry with length and where the ends are capped with hemispheres of diameter ., We take the aspect ratio of the cell to be that of an elongating dividing cell , such that ., The effects of changing the cellular size and volume fraction of DNA will be presented below ., For the bacterial chromosome , we used a volume fraction for DNA of , which is consistent with the estimate for E . coli from 18 and has been used in other models for bacterial chromosomes 17 ., We put attractive interactions between the DNA beads to condense them into a nucleoid ( see the figure caption and Materials and Methods for parameter values ) ., In Fig . 2, ( a ) we show typical protein patterns at different concentrations of aggregating protein ., In these simulations , the protein bead diameter is that of the DNA beads ., The effect of changing the protein bead size is discussed below ., We find that as the volume fraction of protein is changed , different patterns of localization emerge ., For low protein concentrations , , it remains diffuse , mixed throughout the cellular volume ( see Fig . 2, ( a ) top panel , and Fig . 3b ( black ) ) ., The proteins probability density as a function of cell length , , for the diffuse/gas pattern is shown in Fig . 3, ( a ) , and it can be seen that it is roughly uniform with position ., This is also true for the radial distribution of protein shown in Fig . S1, ( b ) , where there is only a slight increase in protein density on the cells periphery where the chromosomal density is less as shown in Fig . S1, ( a ) ., As the protein concentration increases , , its density becomes sufficient at one pole to seed the formation of a protein domain at one end ( Fig ., 2, ( a ) middle panel , and Fig . 3a for a representative spatial density of the unipolar phase ) ., Formation of this domain at either pole occurs with equal likelihood ( see Fig . 3, ( b ) ) since the chance that the chromosome will create a free volume for seed formation is equal for both sides ., The overall likelihood is for the unipolar phase at these concentrations , and only rarely does the diffuse state occur ., Finally as the protein levels increase further , , sufficient density builds up to seed the formation of a protein domain at the other pole ( see Fig . 2a , bottom panel and Fig . 3b ) ., The localization of protein at the poles is driven partly by the entropy gained by the DNA polymer by forcing protein to the poles and the favorable energy gained from protein multimerization ., Formation of domains in locations other than the poles is influenced by factors that we now discuss ., The volume fraction of DNA within the cell affects the types of patterns that can be generated and at what protein concentrations they occur ., This effect is particularly relevant since there is a difference between the volume fractions of DNA between E . coli and C . crescentus due to the fact that their genomes are roughly the same size yet C . crescentus has a smaller cellular volume ., To separate the effect of DNA volume fraction from cell size , we decided to increase the volume fraction of DNA within the cell from to leaving the cell geometry fixed ., The results are shown in Fig . 3c for the frequencies of the different patterns as a function of protein concentration ., Because of the increased density of chromosome the onset of bipolar patterning is now more abrupt compared to situation with less DNA ., Observing the unipolar phase under such conditions becomes less likely as the bipolar phase is favored ., Also , there is a small chance of seeing persistent domains at locations other than at the poles ( blue curve Fig . 3c ) ., Patterning in wild type C . crescentus , favors bipolar patterning of PopZ , whereas the unipolar pattern occurs more frequently in E . coli ., These results would also predict a potential change from unipolar patterning to bipolar patterning under osmotic shock that would cause the cell size to shrink , thereby increasing the DNA volume fraction 20 , 21 ., To further test the effect of DNA and its polymer structure in the model , we also consider the possibility of breaking the DNA into smaller fragments ., In Fig . 2 ( d ) , we show the results for protein localization where the DNA has been broken into 10 fragments and for protein concentrations that previously generated bipolar patterns ., Now the protein forms a single extended domain , that from one simulation to the next occurs in different locations ., Thus the volume fraction of DNA and it being a connected polymer plays an important role in the localization of the self-associating protein in our model ., We have also modeled the nucleoid using just a self-avoiding DNA polymer confined within a smaller cylindrical volume and find that the similar progression of patterns is found ., We find that we need to confine the chromosome to a cylinder whose diameter is of the cells diameter , and that for a purely self-avoiding polymer , the volume fraction of DNA that can lead to patterns that emerge over short simulation times is ., This decrease in DNA volume fraction occurs since the effective excluded volume effect is larger than for the case when there were attractive condensing interactions between DNA beads ., Thus it seems that the key mechanism is that there be sufficient chromosome free space to produce a protein density that can seed domain formation , and that different nucleoid models only differ slightly in how they generate this mechanism ., Next we examined the effect of scaling cell size for the situation where unipolar patterns were favored ., From the simulations discussed above , using a DNA volume fraction of and a protein volume fraction of yielded a unipolar in of simulations for cells with a diameter of ., Keeping an aspect of ratio of three we changed the cell radius and computed the probability of observing the various patterns ( see Fig . S2a ) ., Only a marginal change in the propensity to form the unipolar pattern was seen , with smaller cells stabilizing the pattern , while in larger cells the diffuse state becomes slightly more likely ., Also in the smaller cell geometry it is more likely to see domains formed not just at the poles , and we suspect that these are metastable states ., Thus scaling cell size , keeping volume fractions constant for the two components has only a marginal affect on the propensities of patterns ., However , reducing the size of the protein diameter from to leads to bipolar pattern formation for protein levels that favored unipolar behavior at larger protein size ( Fig ., S2, ( c ) ) ., Smaller protein multimers have an easier time to find the chromosome free regions at both poles , allowing both poles to form domains equally ., Making the protein diameter nearly the same size as the DNA pushes the balance in the opposite direction , favoring the diffuse phase ., Thus the size of the intermediate protein aggregates has a significant influence on what patterns are possible ., In our model , the interaction between protein subunits is governed by a single energy , , which controls the phase behavior of the protein 22 ., In Fig . 2 ( b , c ) we show the effects of either increasing or decreasing ., For weak protein interactions , and a concentration that previously led to unipolar localization , we now find that protein returns to being diffuse throughout the cell ., This represents the situation of the vast majority of proteins inside a cell that do not possess multimerizing interactions that allow for isotropic domain growth , and so remain diffuse throughout the cellular volume ., This was seen in the experiments on misfolded proteins in E . coli when the properly folded protein did not localize whereas it did upon unfolding ., For strong protein interactions , , we see the proteins condense into droplets on the periphery of the cell ( see Fig . 2c ) ., In Fig . S2b , we show the change in the frequency of various patterns by increasing ., Now the bipolar pattern becomes the most likely , and there are a significant number of times that multi-domain patterns are observed compared to when the interaction was weaker ., At these stronger interaction energies , the diffuse pattern is no longer prevalent as the protein always condenses into clusters ., Experimental time-lapse images do not show lots of protein domains; rather show rapid turnover of clusters before the final equilibrium pattern stabilizes ., We next consider the situation where the aspect ratio of the cell is changed ., First we consider the situation of a growing cell , where through the replication of DNA , the production of protein and by dilution as the cell grows , the volume fractions of both protein and DNA remain fixed ., The DNA volume fraction is and the protein volume fraction is chosen to be that was found to favor unipolar patterning in a cell with an aspect ratio of three ., For smaller aspect ratios , Fig . 4 ( a-top ) , the unipolar pattern is favored ( see Fig . 4c ) ., As the cell continues to elongate , the unipolar pattern persists , until eventually the bipolar pattern becomes the most likely at larger aspect ratios ( Fig ., 4 ( a-bottom ) ) ., Lastly we consider changing the aspect ratio by stretching the cell ., In Fig . 3 ( b-left ) we show a representative unipolar pattern that emerged for an aspect ratio of three ., When the cell is elongated to an aspect ratio of 3 . 5 , with the same initial total DNA and protein amounts , we find that the diffuse state becomes the most likely , ( see Fig . 4c and Fig . 4 ( b-right ) ., Thus we would predict that changing the cellular geometry changes the density of protein at the poles , which is a crucial factor for stable domain growth in our model ., We explore other cellular geometries further in the next two sections ., The above simulations were performed with fixed protein concentrations , allowing the system to come to equilibrium from an initially random spatial distribution of protein within the cell interior ., We explored the effect of initial conditions by allowing the system to come to equilibrium and then we changed the amount of protein in the cell ., In particular this allows us to address whether a cell starting with a protein domain at one pole will continue to grow only at that pole or will a bipolar pattern ultimately emerge as more protein is added ?, We see the system transition from diffuse to unipolar and then to bipolar localization as the protein concentration is increased from one initial condition to the next ., For the diffuse to unipolar transition , unsurprisingly the unipolar pattern emerged with the same frequency as found above using random initial conditions ., For the unipolar to bipolar transition , we found that the bipolar pattern was favored although at slightly less frequency than the situation when the initial protein distribution was random ( compared to for random initial conditions ) ., Thus under appropriate DNA and protein concentrations it is possible for one polar domain to appear first , and the 2nd pole to form upon addition of more protein ., In the case of C . crescentus that already has a PopZ domain at one of its poles , in a newly divided cell where the PopZ concentration is likely at levels to satisfy bipolar domain formation , we predict that instead of the preexisting domain continuing to grow , a 2nd polar domain of PopZ will form ., The same would also be true for misfolded protein , where if the concentration of misfolded protein is large enough , another aggregate will begin forming at the new pole ., Adding more protein to a preexisting bipolar pattern caused the polar domains to grow further , similar to what was seen in PopZ overexpression experiments ., In cylindrical cells , where different curvatures of the cell membrane exist , it was speculated that proteins may localize in part due to interactions with biomolecules that sort to the poles because of curvature ., Experiments on spherical protoplasts and cells treated with A22 that destabilizes the cytoskeleton leading to spheroid cells showed that these curvature effects may not play a significant part in PopZ localization 15 ., In such cells , PopZ was found to be diffuse 14 , 15 , unipolar 15 , and occasionally bi/multipolar 15 ., Our own simulations involve no specific membrane interactions and yet show patterns of localization of protein , consistent with localization being independent of curvature ., In Fig . 5 we show our results on spherical cellular geometries ., In these simulations we use the same total cellular volume as was used for the cylindrical cells shown in Fig . 2 , the same DNA volume fraction of ., We consider the situation where the nucleoid is condensed using attractive interactions between the DNA beads or decondensed when the attraction between DNA beads is turned off ., Interestingly , for protein concentrations that previously had a tendency to form unipolar patterns ( ) we find only diffuse patterning ., At higher protein levels , a single domain is favored , occasionally with multiple smaller protein domains ( blue fraction in Fig . 5, ( b ) ) ., For higher protein levels , bipolar patterns become more frequent , opposing each other , and pushing the chromosome into a lobed like structure ( see Fig . 5, ( c ) ) ., For spheroid cells generated using the drug A22 that disrupts the cytoskeleton , it has been suggested that this may serve to destabilize the nucleoid , allowing the chromosome to more fully explore the cells volume 17 ., When we turn off the condensing interaction between DNA beads , leaving just the self-avoidance interaction , and allowing the polymer to explore the full volume of the cell , we do not find a significant affect on the frequencies of the various patterns ., There is a slight tendency to favor multiple domains , which has the effect at lower concentrations to keep the system in the diffuse state ., But the effects seem marginal ., These results potentially help to explain the observed differences in PopZ localization from two different experiments utilizing A22 to form spheroid cells 14 , 15 ., For cylindrical E . coli cells that favored unipolar spot formation , treatment of A22 leading to spheroid cell geometries showed diffuse behavior 14 , consistent with our findings above ., We speculate that for the experiment that generated spherical cells 15 that there may have been more time for PopZ to accumulate to levels that admit domain formation ., Quantification of protein levels within cells would help to clarify the observed differences to see if it is consistent with our predictions ., Experiments on mutants that form filamentous cells possessing multiple chromosomes show that PopZ and misfolded protein not only forms domains at the poles but also at the interchromosomal boundaries 15 , 16 ., We performed simulations on cells possessing multiple chromosomes and of variable length to see how the protein patterns would change as a function of the length of the cell and the number of chromosomes ., The results are shown in Fig . 6 and Fig . 7 ., In cells possessing two chromosomes and that are less than two full cell lengths; for the unipolar pattern is favored ( Fig . 7a ) ., For cells that are longer than two full cell lengths , the bipolar pattern becomes favored for the same concentration of protein ( see Fig . 6a ( top-right ) and Fig . 7b ) ., At higher levels of protein , the unipolar pattern is unfavored , and protein domain formation between chromosomes becomes possible ( Fig ., 6, ( a ) , bottom panel ) ., In long filamentous cells , all chromosome free regions can become occupied , and this pattern is favored at higher protein concentrations ( see Fig . 6a ( bottom-right ) and Fig . 7b ) ., We also simulated cells possessing three chromosomes that now allow for the possibility of two interchromosomal regions ( Fig . 6b ) ., In experiments on long cells , not every interchromosomal band was occupied 15 ., We find similar behavior , attributing different banding patterns to the concentration of protein ( see Fig . 7c ) ., In particular , at certain protein concentrations we find it possible to pattern both poles and one inter-chromosomal boundary ( Fig ., 6 ( b-middle ) ) ., At yet higher protein concentrations , again all chromosome free regions can become occupied by a protein aggregate ., Thus patterns of PopZ in longer cells can be interpreted in the light of a model that only relies on the generation of chromosome free regions and protein multimerization ., Recent work has shown that nucleoid occlusion may be sufficient to drive protein aggregation at the poles 15 , 16 ., In this paper we have explored a simple biophysical model for how the presence of the nucleoid in addition to multimerizing interactions between proteins such as PopZ or misfolded proteins can localize the protein domains to the poles and interchromosomal regions ., Other potential mechanisms , such as membrane curvature may indeed play a role but are not required ., As has been pointed out for PopZ 15 and misfolded protein 16 the spontaneous organization of a protein to the poles depending on concentration has a number of biologically attractive outcomes ., In particular , the model showed that under appropriate cell geometries and DNA concentration , it is possible for the pattern to transition from diffuse , to unipolar to bipolar with increasing protein concentration ., Breaking the spatial symmetry provides the opportunity to differentially pattern the polar regions ., Thus there is no requirement for any prior history to differentiate the poles as the breaking of spatial symmetry due to the formation of the unipolar pattern can occur spontaneously ., The modeling presented here may help to interpret some of the recent experimental findings ., In particular , recent experimental work has shown that by treating cells with the drug A22 that destabilize the cytoskeleton through action on the cytoskeletal protein MreB , producing spheroidal cells , can lead to either diffuse 14 or localized PopZ 15 ., Our model would offer a resolution to these results , suggesting that the observations are consistent with the systems having differing PopZ levels - diffuse at lower concentrations and localized at higher ., Further experimental characterization of PopZ levels is required to determine whether a difference in total amounts could account for the difference in observed patterns ., Another connection to experiment is with respect to the cell cycle and the effect of initial conditions on the emergent protein patterns ., For a cell with a bipolar pattern , upon division two unipolar cells result , yet protein levels should be at the same concentration ., We found that cells that start with a unipolar initial condition , but with concentrations that admit the formation of a bipolar pattern , do indeed have the bipolar pattern emerging as the most frequent ., Overexpression experiments of PopZ showed continued growth of both polar domains , and our results are consistent with these findings , in that protein that is added to a bipolar initial condition favors continued growth at both poles ., Experiments on the aggregation of misfolded protein in E . coli showed that when the protein was allowed to refold , the domains disappeared and the protein went back to being diffuse within the cell ., We found this when we lowered/turned off the attraction between protein monomers ., We also found that patterns could be destabilized via mechanical manipulation of the cells ., Doing such experiments on the misfolded protein in E . coli system seems like a reasonable test ., Such experiments on PopZ may be hindered by potential domain stabilizing interactions with membrane bound protein like SpmX that is known to interact with PopZ ., Such interactions could help tether PopZ to the membrane thereby stabilizing growing domains ., Our results also may help to provide some insight into the differences in patterning observed between C . crescentus and E . coli ., E . coli cells are larger than C . crescentus yielding a lower volume fraction of DNA given that their genomes are roughly the same size ., In experimental work , unipolar patterns were more often seen in the ectopic expression of PopZ in E . coli , and were also observed when protein misfolding was induced ., Under wild-type or overexpression conditions of PopZ in C . crescentus bipolar patterning was favored ., Our own results show that for increased DNA volume fractions , there is an abrupt transition from the diffuse pattern to bipolar pattern as the protein concentration is increased , with the unipolar pattern only rarely occurring ., Thus the model would predict that the increased volume fraction in C . crescentus favors it forming bipolar patterns whereas similar PopZ levels in E . coli would favor unipolar patterns ., These results also suggest new experiments using osmotic shock to change cell size thereby changing the volume fraction of DNA ., For E . coli cells with a unipolar pattern , the model would predict that shrinking cell size , thereby increasing DNA volume fraction would favor bipolar patterns ., Although our model is simple the observed protein fractions that are seen to lead to patterning in experiment ( volume fraction for misfolded protein , corresponding to 1000s of proteins ) are consistent with the values seen in our model ., In our model , patterns emerge when there are several hundred protein beads , where each bead represents an aggregate of 6–15 proteins , thus yielding total protein amounts in the thousands ., Experimental data on PopZ suggest that there are likely 1000s of PopZ proteins in the cell ., The ratio of bead size between DNA and the protein multimer had a strong influence on the pattern and we expect that scaling of this ratio should lead to similar pattern formation ., Using a sphere to represent a segment of DNA likely overestimates its excluded volume , hence using cylindrical segments with a smaller crossectional area would admit using smaller bead sizes for the protein multimers , yet with similar patterns emerging ., Besides changing the volume fraction of DNA , another suggestion for an experiment would be to damage the DNA such that it is fragmented within the cell ., We predict that breaking the chromosome into fragments should be sufficient to destroy the polar patterning and that protein localization should then occur at random positions within the cell volume ., Deforming the cells is also predicted to have a significant effect on protein patterning; elongating the cell is predicted to destabilize domains ., It is also predicted that for a fixed protein and DNA volume fraction that as the aspect ratio grows there should be an abrupt change with bipolar patterns being favoured for aspect ratios ., Careful control of the concentration of aggregating protein in the cell and monitoring the resulting patterns as it grows should show provide a test of these predictions ., Despite its simplicity , having only three molecular parameters and the cellular geometry , the model has rich behavior ., It has connections to cluster growth models in phase separating systems 23 and extending such theory to include the physics of the confined DNA polymer 24 is forthcoming ., The simulations presented here have been at equilibrium , showing the most likely low energy conformations ., A dynamical treatment , taking into account diffusion and reaction kinetics as was done in 16 will provide insight into the time-scale of formation of the domains and how this relates to the domain kinetics seen in experiment ., This will be addressed in future work ., In summary , recent experiments on the polar localization of aggregating proteins suggest that patterning is driven by protein self-association in regions free of DNA ., We have shown that a model based on such a mechanism is indeed sufficient to produce all the variety of observed patterns ., Its simplicity is attractive as it requires no active components; the patterns spontaneously emerge via a competition between the entropy of the chromosome and the energetic gain of forming a protein domain ., The cell is modeled as a closed volume of either, i ) cylindrical geometry with a cylindrical region of length capped by hemispheres of diameter , or, ii ) a sphere with radius ., Inside the cellular volume there is the chromosome and diffusing beads representing protein multimers ., The chromosome is modeled as a circular string of tethered self-avoiding beads ., The diameter of each bead is given by and the number of beads making up the chromosome , is calculated from its volume fraction , such that where is the volume of the cell , and the volume of a DNA bead ., All length scales in the system are expressed in terms of the bead size of DNA , which is taken to be where nm is the persistence length of DNA ., For a cell geometry of and , and a DNA volume fraction of , this gives a chromosome consisting of 204 beads ., The largest chromosome modeled was for a geometry of and giving 292 beads making up the chromosome ., Protein multimers are modeled as beads with diameter , and their number is given by , where is the volume fraction of protein ., For and a cell with and an aspect ratio of 3 . 0 , the amount of protein multimers in the cell ranges from 164 for to 328 for ., For the larger cell geometries there are protein multimers in the cell ., With respect to energetic interactions , for the beads making up the chromosome , they are tethered together using the following potential between neighboring beads , and , ( 1 ) where is the tethering strength and sets the length scale of the potential ., We take that keeps the beads on the DNA chain from stretching much beyond a bead-to-bead distance of ., We use a Lennard-Jones potential to model the interactions between the various types of beads in the system , given by ( 2 ) where is the distance between | Introduction, Results, Discussion, Model | The spatial patterning of proteins in bacteria plays an important role in many processes , from cell division to chemotaxis ., In the asymmetrically dividing bacteria Caulobacter crescentus , a scaffolding protein , PopZ , localizes to both poles and aids the differential patterning of proteins between mother and daughter cells during division ., Polar patterning of misfolded proteins in Escherechia coli has also been shown , and likely plays an important role in cellular ageing ., Recent experiments on both of the above systems suggest that the presence of chromosome free regions along with protein multimerization may be a mechanism for driving the polar localization of proteins ., We have developed a simple physical model for protein localization using only these two driving mechanisms ., Our model reproduces all the observed patterns of PopZ and misfolded protein localization - from diffuse , unipolar , and bipolar patterns and can also account for the observed patterns in a variety of mutants ., The model also suggests new experiments to further test the role of the chromosome in driving protein patterning , and whether such a mechanism is responsible for helping to drive the differentiation of the cell poles . | A key process in biology is the self-assembly of biomolecules into highly organized structures ., This spontaneous assembly can give rise to complex spatial patterns that help give spatial order to the cellular environment ., In many bacteria , the patterning of proteins to the cell poles allows the bacteria to differentiate one end of the cell from the other ., What mechanisms can lead to the spontaneous organization of proteins to the cell poles ?, Prior work has shown that such patterning can emerge from interactions between proteins and the cell membrane ., In this paper we use computational modeling to show that a novel patterning mechanism involving only the presence of the bacterial chromosome and a self-associating protein is sufficient to generate polar patterning in bacteria ., This model explains recent experiments on polar patterning in C . crescentus and misfolded protein aggregation in E . coli and provides predictions about how this mechanism could spontaneously lead to asymmetric patterning of the poles . | cell biology, biophysics/theory and simulation, computational biology | null |
journal.pgen.1007391 | 2,018 | Prickle is phosphorylated by Nemo and targeted for degradation to maintain Prickle/Spiny-legs isoform balance during planar cell polarity establishment | Planar cell polarity ( PCP ) instructs tissue patterning in a wide range of organisms from Drosophila to humans , through input into cellular orientation across tissues , individual cell fate decisions , and the coordinated movement of groups of cells 1–8 ., In the Drosophila eye , Frizzled core PCP signaling coordinates the cell fate decisions of individual photoreceptors , and their subsequent collective movements during ommatidial rotation , via asymmetric localization of two distinct membrane-bound complexes on opposite sides of a cell 1–7 , 9 ., The two core Fz/PCP pathway complexes comprise of Frizzled/Dishevelled/Diego ( Fz/Dsh/Dgo ) in one complex , and Van Gogh/Prickle ( Vang/Pk ) ( Vang , also known as Strabismus/Stbm ) in the other ., These complexes are localized to opposite sides of the cell and stabilized intercellularly via the atypical cadherin Flamingo ( Fmi ) associating with both complexes 1–7 ., Each component is highly conserved between Drosophila and vertebrates , and mutation of PCP genes in humans is linked to a range of diseases from spina bifida to polycystic kidney disease and epilepsy 10 ., Feedback between the two complexes is essential to reinforce Wnt-induced cellular orientation bias 11–13 into coordinated tissue-wide polarity ., Positive intercellular interactions between transmembrane factors , Fz , Vang and Fmi , relay positional information and negative intracellular interactions between cytosolic factors Pk and Dsh/Dgo enhance asymmetry on a cellular level 1 , 3 , 5 , 7 , 14 ., In mammals there are four prickle genes and although there is only one prickle gene in Drosophila the range of Prickle functions are performed by distinct isoforms 15 , Prickle ( Pk ) , Spiny-legs ( Pk-Sple ) and PrickleM ., Pk and Pk-Sple are the two functionally relevant isoforms during establishment of PCP ., The balance between the two isoforms is tissue specific: Pk-Sple is the ‘major’ isoform in eyes and legs , and Pk the ‘major’ isoform in wings 15–17 ., The precise balance has functional significance since the two isoforms can antagonize each other and/or the other’s function , although the underlying mechanism is not well understood 15–17 ., Recent work has shown that this isoform balance is regulated transcriptionally at the tissue level in the wing , where Pk mRNA is present at 10-15-fold higher levels than Pk-Sple mRNA 17 ., However , it is unclear how the balance is maintained in the eye , because it cannot be explained by transcriptional regulation: Pk mRNA is actually expressed at slightly higher levels than Pk-Sple mRNA 17 , even though Pk-Sple is the major functional isoform here ., When the Pk/Pk-Sple balance is perturbed it causes PCP defects , for example by overexpression of one isoform or isoform-specific alleles such as pkpk1 , in which expression of only the Pk isoform is lost and Pk-Sple expression is unchanged 15–17 ., Recent reports demonstrate that imbalance between the specific Pk and Pk-Sple isoforms causes seizures in Drosophila and , moreover , they suggest that disrupting PRICKLE genes underlies cases of epilepsy in humans 18 , 19 ., The Drosophila eye is a compound eye with ~800 individual ommatidia , each containing eight photoreceptor neurons ( R1-8 ) arranged as a trapezoid 20–22 ( also Suppl S2 Fig ) ., Chirality of the trapezoid is determined by positioning of the R3/R4 pair , whose fate is specified by Fz/PCP signaling ., Fz activity is higher at the dorso-ventral midline ( equator ) of developing eye discs 20 ., Consequently , for each R3/R4 pair the cell closer to the midline exhibits increased Fz activity , adopts the R3 fate , and signals to its neighbor to induce it as R4 ., Ommatidial preclusters then undergo a 90° rotation that is coordinated across the field by PCP activity , resulting in a line of symmetry around the equator ., Disruption of PCP signaling causes chirality defects , whereby the R3/R4 fate decision becomes uncoupled from positional information or fails to be resolved 20 ., PCP defects also involve misregulation of ommatidial rotation ( OR ) such that OR is no longer coordinated across the tissue 20–22 ., Here , we report a new function for Nemo ( Nmo ) kinase , a classic ‘OR’ gene 23 , 24 , and demonstrate its role in regulating levels of the Pk isoform of pk via direct phosphorylation of Pk and its targeting for proteasomal degradation ., Nmo is required in specific cells , the R4 cells , where the Pk isoform needs to be suppressed in the eye , and also in PCP mediated leg patterning , where Pk is also the minor functional isoform ., Our results establish a new regulatory mechanism of PCP factors with tissue- and cell-specific regulation of core PCP protein degradation being coupled to PCP-mediated cell fate induction and function ., In order to better understand how Nmo acts as a PCP effector in the eye , we performed a genome-wide , gel-shift based screen for novel Nmo kinase substrates , similarly to our recent studies identifying novel dROK and Hpo substrates 25 , 26 ., In brief , pooled cDNA clones were in vitro translated and labeled with 35S-Methionine , and then incubated with purified Nmo in the presence of unlabeled ATP ., Reduced mobility on Anderson gels was used as the criterion to select candidate substrates ., Surprisingly , one of the candidates identified in this screen was Prickle , an ‘upstream’ core component of PCP complexes ., Nmo , but not dRok or Hpo kinases , was able to induce a band shift of Prickle ( Fig 1A ) , as well as the positive control Pan/dTCF 27 , but not the negative control , Mbs ( Fig 1A and Suppl . S1 Fig ) ., Band shift assays of cell culture extracts confirmed that Nmo kinase activity reduces the mobility of Prickle ( Suppl . S1 Fig ) ., Using purified Nmo kinase we performed in vitro kinase assays and determined that Nmo directly phosphorylated the common region of Pk ., The two main Prickle isoforms required for PCP , Pk and Pk-Sple , are largely identical except for an extended Pk-Sple N-terminal region ( outlined in Fig 1B ) ., To identify the Nmo phosphorylation site ( s ) within the common Pk sequence , we designed a series of deletion constructs ( outlined in Fig 1B ) with the Vang C-term included as a negative control 24 ., Through comparison of the phosphorylation of these constructs in in vitro kinase assays , the phosphorylation sites were mapped to a middle region of the common Pk sequences ( fragment M , Fig 1B and 1C ) ., Nmo did not phosphorylate the Sple N-terminus , PET and LIM domains , or the region within the Pk C-terminus required for binding to Vang ( contained within fragment C1 28 ) ., Our previous studies had suggested a model whereby Vang recruited Nmo to the membrane in ‘mature’ ommatidial clusters , where it acted as an effector and phosphorylated β-catenin to promote cluster rotation 24 ., Prickle being a Nmo substrate raised the possibility that Nmo had an earlier role in regulating the PCP complexes themselves ., Although the zebrafish nmo homolog , nemo-like kinase had been known to genetically interact with the Wnt/PCP pathway during the coordinated movements of convergent extension in the embryo 29 , our result is the first indication that nmo directly affects the core PCP factors during the establishment of PCP itself ., Given that Nmo phosphorylated a region that is shared between the two prickle isoforms , Pk and Pk-Sple , we wanted to determine whether one or both isoforms were functionally affected in vivo ., We performed genetic interaction assays with nmo mutants and isoform specific alleles , pkpk1 and pksple1 ( loss of function , LOF ) or over-expressed individual Pk and Pk-Sple isoforms ( gain of function , GOF ) ., We first examined the interaction between nmo and pksple1 in the eye ., Wild-type and nmoP hypomorphs show wild-type ommatidial chirality , in addition to the previously-described underrotation in nmo mutant clusters ( Fig 2A , 2B and 2M ) 23 , 24 , 30 ., pksple1 clusters adopt almost random chirality , whereby the R3/R4 fate decision is resolved , but the dorsal vs ventral chiral arrangements are intermixed , termed ‘chirality flips’ , and R3/R4 fate is uncoupled from dorso-ventral positioning ( Fig 2C and 2M ) ., However , in the pksple1; nmoP double mutant , a significant proportion of achiral , symmetrical clusters were observed; a phenotype rarely observed in either single mutant ( Fig 2D and 2M , note over 20-fold increase in symmetrical clusters—see Suppl . S2A Fig for schematic of photoreceptor arrangement ) ., We confirmed the achiral nature of the clusters by immunostaining larval eye discs using the mδ-LacZ construct , which in wild-type specifically labels the R4 precursor 31 , 32 ( Fig 2E and 2F and Suppl . S2B Fig for overview ) ., Clusters with two negative or two positive cells can often be seen in the double mutant , but not wild-type , where there is a regularly-spaced array of one β-gal positive cell per cluster ( Fig 2E and 2F ) ., Such symmetrical clusters are considered the strongest PCP defect as they completely fail to resolve the R3/R4 fates , indicating an inability to establish PCP-mediated cell fate differences at all 20 , 22 , 33 ., Interestingly , symmetrical clusters were also observed in nmo , fz double mutant clones 34 , but this was neither commented upon nor followed up on in that study ., To further define the genetic interaction between pksple1 and nmoP , we analyzed the PCP phenotype in the tarsal region of the leg ., pksple1 mutants display supernumerary tarsal joints with altered bristle polarity ( Fig 2G , 2I and 2N ) 15 ., This results in spiny-looking legs , giving the allele its name ., Compared to pksple1 single mutants , joint number significantly increased in pksple1; nmoP double mutants ( Fig 2I , 2J and 2N ) ., As the only isoform expressed in pksple1 adults is Pk , we confirmed the genetic interaction in a Pk GOF assay ., Consistent with the notion that eye patterning is very sensitive to Pk isoform levels , even the low level overexpression of Pk ( via direct act-Pk , without Gal4-associated amplification ) is sufficient to unsettle the balance between Pk and Pk-Sple and cause defects: act-EGFP-Pk animals 35 displayed predominantly ‘flips’ in the eye ( Fig 2M and Suppl . S2G Fig ) ., The chirality defects in act-EGFP-Pk animals were dominantly enhanced by nmoDB/+ ( Fig 2M and Suppl . S2G and S2H Fig ) ., As act-EGFP-Pk is expressed at low levels throughout the animal , we also analyzed the leg phenotype ., Consistent with the eye results , loss of nmo function enhanced the ectopic joint phenotype associated with act-EGFP-Pk ( Fig 2K , 2L and 2N; note that like the eye the PCP leg patterning is also sensitive to the Pk/Pk-Sple balance ) ., In contrast , we did not observe PCP defects in wings of pksple1; nmoP mutants or act-EGFP-Pk and act-EGFP-Pk/nmoDB animals; all wings displayed a wild-type appearance ( Suppl . S3 Fig , note Pk is the major isoform in the wing ) ., Examining whether there was an interaction between pkpk1 ( where Pk-Sple is the isoform expressed ) and nmo in the eye , we detected no chirality defects ( Suppl . S2C , S2D and S2I Fig , note that pkpk1; nmo double mutant ommatidia displayed only the expected nmo rotation phenotypes ) ., Moreover , in GOF scenarios with overexpressed Pk-Sple , no interaction between nmo and Pk-Sple was observed in either tissue studied ( Suppl . S4 Fig ) , although act-EGFP-Sple wings did have a strong PCP phenotype 17 , 35 ( Suppl . S4C–S4E Fig ) ., Importantly , in a pk null background ( pkpk-sple13 , with neither Pk or Pk-Sple isoforms expressed ) , we did not detect an increase in symmetrical cluster formation in pk-; nmo double mutant ommatidia ( Suppl . S2E , S2F and S2I Fig ) ., The double mutant phenotype resembles that of pk null eyes , further reinforcing the notion that Nmo acts specifically on the Pk isoform , and thus if both isoforms are absent ( as with the null allele ) Nmo has no PCP substrate upon which to act ., Collectively , the above data suggest that Nmo phosphorylates the Pk isoform of the pk gene , which has functional consequences in eyes and legs; tissues where both isoforms are expressed and Pk-Sple is the major protein isoform ., Given the importance of the Pk/Pk-Sple balance , Nmo might be required to repress the Pk isoform in these tissues ., The genetic interaction between pksple1 and nmoP being similar to that between Pk over-expression and nmo LOF ( Fig 2M and 2N ) suggests that the phenotype of the pksple1; nmoP double mutants results from increased Pk activity and/or amount ., We therefore proceeded to examine the effect of Nmo phosphorylation on Pk activity ., The sequence of the Nmo target fragment of Pk , region M ( Fig 1B and 1C ) , contains two clusters of 4 potential MAPK phosphorylation sites ( Fig 3A; Nmo is a member of the MAPK family ) ., All 4 serine/threonine residues in each cluster were mutated to alanine to create phospho-mutant cluster 1 , cluster 2 , or a construct with all 8 sites mutated ( mut1+2; Fig 3A ) ., These fragments were tested in in vitro kinase assays , which revealed that mutations of either cluster alone had little effect; but when both clusters were mutated phosphorylation was markedly reduced ( Fig 3A ) ., These data identify two clusters of MAPK consensus sites within the common M fragment of Pk as direct phosphorylation targets of Nmo ., To investigate the effect of these Nmo phosphorylation sites in vivo , we generated transgenic flies of either wild-type myc-Pk , or “phospho-mutant” myc-Pk ( PkWT and PkMut1&2 , where both clusters of Nmo phosphorylation sites were mutated to alanine ) ( Fig 3A and Methods ) ., Pk overexpression in both cells of the R3/R4 precursor pair during PCP signaling ( under sevenless-Gal4 control: sev>Pk; 28 , 36 ) produced a phenotype with rotation and chirality defects ( Fig 3B and 3D; chirality defects were mainly ‘flips’ , although some symmetrical clusters were observed ) ., Comparing Gal4-driven expression of wild-type Pk and phospho-mutant Pk ( Pkmut1+2 ) the phospho-mutant displayed more severe phenotypes with an increase in chirality defects and particularly symmetrical clusters ( Fig 3B and 3D; both transgenic constructs were inserted in the same genomic att-site and are thus transcriptionally expressed at equal levels; Methods ) ., The above data mimic the effect of nmo loss-of-function on wt-Pk ( Fig 2M ) and below , corroborating the notion that the phosphorylation event causes a post-transcriptional reduction in Pk activity/levels ., We modulated levels of Nmo and assessed the effects in the sev-driven Pk GOF assay in the eye ., We used either nmo loss-of-function alleles ( nmoP and nmoDB alleles: Fig 3F and 3G , hypomorphic and null , respectively ) or Nmo co-overexpression ( Fig 3H ) 24 ., There was a dose-dependent effect of loss of nmo function on the sev>Pk phenotype ., Chirality defects increased in nmo heterozygotes with an increased number of symmetrical clusters in particular ( Fig 3E–3G and 3I ) ., Conversely , Nmo co-overexpression with Pk suppressed the sev>Pk phenotype , markedly reducing the number of symmetrical clusters ( Fig 3E , 3H and 3I ) ., Consistently with this , increasing the levels of Pk in a pksple1 background causes a synergistic increase in symmetrical clusters , similarly to Nmo LOF ( Suppl . S5B and S5C Fig ) ., These data are consistent with a hypothesis that Nmo function is required to limit Pk activity or levels ., Consistent with our earlier results , we did not detect an effect of nmo LOF on the Pk-Sple isoform overexpression phenotype ( Suppl . S4C–S4I Fig ) or differences in the activity of wild-type Pk-Sple compared to to Pk-SpleMut1&2 ( sev-SpleWT vs sev-SpleMut1&2 , the equivalent mutations to PkMut1&2; Suppl . S5E and S5F Fig ) ., Moreover , mutation of the Nmo sites in Pk-Sple , does not affect the ability of the Pk-Sple isoform to rescue the chirality defects present in the pk null mutant ( Suppl . S5G Fig ) ., We did not observe a difference in the activity levels of wild-type and phospho-mutant Pk in the wing either ( nub>PkWT and >PkMut1&2; Suppl . S5D Fig ) ., Collectively , these results support a model in which Nmo acts primarily on Pk to maintain a tissue-specific balance of Pk/Pk-Sple activity in the eye and leg , where both are expressed with Pk-Sple being the major isoform ., Therefore , post-translational regulation of Pk acts in the eye , in addition to the transcriptional control of isoform expression previously described for the wing 17 ., Although the Nmo phosphorylation sites are shared between all pk isoforms , we have no evidence to suggest that the Pk-Sple isoform is affected ., The Pk and Pk-Sple isoforms are known to form different protein complexes 37 , potentially explaining this disparity ., It remains possible that Nmo does also phosphorylate Pk-Sple , but that Pk-Sple phenotypes are not affected by Nmo , potentially because the Pk-Sple specific N-terminus interferes with phosphorylation and/or masks the biological read-out ., Based on these results we would predict Nmo to be required in the polar R4 precursor to limit Pk activity , because the pk gene ( the Pk-Sple isoform ) is required in the polar cell to establish PCP complexes and direct proper cell fate 28 ., Genetic mosaic analysis is highly useful to determine which cell of the R3/R4 precursor pair requires an individual core PCP gene 14 , 28 , 34 , 38 , 39 ., As nmo mutants show frequent chirality defects only in a pksple1 background rather than wild-type , we performed mosaic analysis in the pksple1 genetic background ., Specifically we induced clones of nmo- cells in pksple1 eyes and analyzed R3/R4 pairs that were bisected by the clonal boundary; one cell of the R3/R4 pair was nmo+ and the other nmo- ( Methods ) ., Clones of nmo- mutant cells were marked by absence of pigment ( Fig 4A and 4B shows schematic and example image ) ., If nmo were specifically required only in one cell of the pair , for instance the R4 precursor , then we would expect to see more clusters developing with wild-type chirality when nmo function is removed from the other cell , R3 , than from R4 ., Strikingly , when nmo function was only removed from R4 , ommatidia displaying wild-type chirality were markedly reduced ( to 42% ) , very similar to the fully double mutant pksple1; nmoP eyes ( Fig 4C and 4D’ ) ., In contrast , when only R3 was nmo- , the proportion of wild-type clusters was 68% , very similar to pksple1 single mutant eyes ( Fig 4C and 4D” ) ., These data indicate that this pk-associated nmo function is specifically required in the R4 cell ., This was confirmed when the chirality defect ratios were compared with the pksple1 single and pksple1; nmoP double mutants ( Fig 4C , 4D”’ and 4D‘”’ ) ., Thus the mosaic analyses suggested that for chirality establishment and core PCP function , it is the presence of Nmo in R4 that is required ., The combined mosaic analyses of nmo ( this work ) and our previous study of pk 28 demonstrate that Nmo is required in the same cell as the pk gene to reduce the activity/levels of Pk in order for functional PCP complexes to be established ., Compare this result to the genetic requirement of nmo in ommatidial rotation , when it functions as an effector of PCP , and is required in all R-cells and even cone cells 24 , 40 ., Together these results demonstrate a spatially- and temporally-distinct role for nmo in regulating Pk in core PCP complex establishment in R4 , and then subsequently acting as an effector downstream of the core PCP complexes throughout the ommatidial cluster to regulate ommatidial rotation ., One possibility of how Nmo could limit Pk activity is to regulate the levels of the Pk isoform , thereby preventing excess Pk disrupting the Pk/Pk-Sple isoform balance ., To examine nmo loss-of-function effects on Pk levels , we performed Western blots with larval eye disc lysates ., We first compared the levels of wild-type Pk and PkMut1&2 that were expressed under actin-Gal4 control ., Compared to wild-type myc-Pk , mutation of the Nmo phosphorylation sites resulted in an increased protein level , as quantified as signal ratio of myc:gamma tubulin antibodies ( Fig 5A and Suppl . S6D and S6H Fig ) ., We then compared levels of act-EGFP-Pk in either wild-type or nmoDB/+ backgrounds ( Fig 5B and Suppl . S6B and S6I Fig ) ., nmo loss-of-function resulted in an increase in EGFP-Pk levels ( Fig 5B ) , similarly to mutation of Nmo phosphorylation sites ( Fig 5A ) ., As nmo is only expressed in a stripe posteriorly to the furrow 41 , and its effect on Pk is specifically required during R3/R4 specification , there is a sizeable amount of act-EGFP-Pk that is unaffected , explaining the subtle increase , which is nevertheless significant ., Conversely and as a specificity control , we did not detect a similar change in EGFP-Sple levels in a nmoDB/+ background , confirming that nmo acts specifically on the Pk isoform ( Fig 5B and Suppl . S6A–S6C Fig ) ., These data are consistent with Nmo functioning to maintain lower levels of the Pk isoform , thus limiting the Pk/Pk-Sple ratio ., Given the increase in PkMut1&2 , which is expressed from a transgene lacking endogenous 3’ UTR , under control of Gal4/UAS system , this also indicated that Nmo regulates Pk at the post-translational level ., Degradation by the proteasome could be a means to regulate Pk levels ., We examined this hypothesis by first co-expressing a dominant negative ( DN ) form of the proteasome 20S β2 subunit , Prosbeta2 42–44 along with Pk under sev-Gal4 control and analyzing the adult phenotype ., DNProsbeta2 expression synergized with the Pk GOF phenotype to enhance chirality defects , particularly symmetrical clusters ( Fig 5C , 5D and 5G ) ., In a complementary approach , we used the milder act-EGFP-Pk phenotype and examined the effect of another DN proteasome component—this time β6 ( Prosbeta6 ) 42–44 , under the control of GMR-Gal4 , which is expressed in all post-mitotic , differentiating cells in the eye ., We again saw an increase in chirality defects , and of symmetrical clusters in particular ( Fig 5E–5G ) ., Moreover in this scenario , we also saw an increase in act-EGFP-Pk protein levels ( Fig 5H and Suppl . S6E Fig ) ., In both cases , the control animals , with mildly reduced proteasome function alone , did not induce chirality defects ( Suppl . S6F and S6G Fig ) ., It has been suggested that Pk levels are constitutively regulated by the Cullin1/ SkpA/Supernumary limbs ( Slmb ) SCF E3-ubiquitin ligase complex in Drosophila wings 45 , 46 ., Consequently , we reasoned that the SCF complex might be operating in the eye to regulate Pk levels through ubiquitination and promoting subsequent proteasomal degradation of the Pk isoform ., To test this hypothesis , we reduced the activity of the Cul1/SkpA/Slmb complex in the eye in the Pk GOF assay ., We used RNAi to knockdown components of the complex temporally during establishment of PCP in the eye ., Co-expressing the respective RNAi constructs , enhanced the Pk gain-of-function effects ( Fig 6A–6E and Suppl . S7D Fig; in control animals , the SCF LOF alone did not cause chirality defects; Suppl . S7A–S7C Fig ) ., As with nmo LOF alleles , knockdown of each SCF component caused an increase in chirality defects , and symmetrical clusters in particular , in the Pk GOF scenario ., Knockdown of slmb also caused a severe loss of photoreceptors , as well as many symmetrical clusters ( Suppl . S7D Fig ) and therefore the effect was confirmed by analyzing sev>Pk in a slmb00295/+ background ( Fig 6D ) ., For SkpAIR , although there was a reproducible , mild enhancement of the phenotype , there was also loss of photoreceptors and of tissue integrity ., Repeating the experiment at higher temperature to increase the knockdown , resulted in lethality ( sev-Gal4 includes the sev enhancer coupled to a heat shock promoter ) , so we were limited in terms of the temperature range in which we could work ., We next examined the effect of reduction in SCF complex function on Pk protein levels ., We examined act-EGFP-Pk levels in a slmb00295/+ background ., Similarly to nmoDB/+ and GMR> DNProsbeta6 , we saw an increase in EGFP-Pk protein levels in the slmb LOF background ( Fig 6F and Suppl . S7E and S7F Fig ) ., Taken all together , our data suggest that the increase in chirality defects and symmetrical clusters in the Nmo-Pk phosphorylation context is a result of an altered Pk/Pk-Sple isoform balance , which is caused by reduced Cul1-SkpA-Slmb-mediated proteasomal targeting of Pk ., Previous studies have linked Nmo phosphorylation of a substrate to ubiquitination by the SCF complex and the proteasome; in mammalian cells , phosphorylation by Nemo-like kinase ( NLK ) acting downstream of TGF-β activated kinase ( TAK ) induces ubiquitination and proteasomal degradation of c-myb 47 , 48 ., To investigate whether a dTAK-Nmo-protein degradation link is conserved and acts in the Nmo-Pk and PCP-signaling context , we tested for potential effects of the dTAK179 allele 49 on the sev>Pk phenotype ., The chirality defects induced by sev>Pk were indeed enhanced in dTAK179/+ heterozygous females ( Fig 7A and 7B ) , suggesting that dTAK functions upstream of Nmo to limit Pk activity during establishment of PCP ., In the TAK1-NLK-SCF complex axis , phosphorylation by Homeodomain Interacting Protein Kinase 2 ( HIPK2 ) also occurs and promotes substrate degradation 47 , 48 ., We investigated whether the Drosophila Hipk homologue was also involved in regulating Pk ., Scanning the Pk sequence for potential Hipk consensus sites 50 we detected a putative site in the C-terminus of the protein ( Fig 7G ) ., Furthermore , in the Pk GOF assay we noted that knockdown of Hipk enhanced the PCP phenotype , similarly to knockdown of Nmo ( Fig 7C–7F ) ., Together these data suggest that Nmo acts with dTAK and Hipk to phosphorylate Pk and recruit the SCF complex , promoting proteasomal degradation of Pk to maintain the Pk/Pk-Sple balance ., In our systematic , genome-wide screen to identify Nmo substrates during its role in ommatidial rotation , we identified Pk as an unexpected bona fide target ., Our functional studies then established a role for Nmo kinase during PCP establishment in addition to its known role during the subsequent rotation process ., The phosphorylation of the Pk isoform serves as a way to limit the activity of the minor isoform in tissues where Pk-Sple is the major functional isoform ., It was previously documented that the isoform balance is regulated at the transcriptional level within the wing , but it was unclear how Pk-Sple was able to act as the ‘major’ isoform in eyes , given that Pk mRNA was even expressed at higher levels 17 ., Here we define a novel cell-specific requirement for post-translational regulation of Pk through phosphorylation and associated proteasomal targeting within the R4 cell ., Our results point to a new paradigm of PCP modulation in which spatially-dependent regulation of core PCP protein degradation is required for robust PCP-cell fate coupling ., Although it has been well documented that the balance of Pk isoforms is important 15 , 17 , it has remained unclear as to how such a balance would be maintained and/or reinforced post-translationally ., Importantly , the Pk and Pk-Sple isoforms can form different protein complexes and localize to different sites within wing cells 37 ., In particular , the correct presence of either Pk or Pk-Sple appears critical in the context of coupling the orientations of the core PCP complex alignments with the Fat/Ds-system polarity orientation 17 , 37 , 51 ., While in the eye Fz-core PCP and Fat/Ds orientation is anti-parallel , the two systems are aligned in a parallel manner in the wing for example 51 , 52 ., These opposing alignments correlate with differential requirements of either the Pk ( wing ) or Pk-Sple ( eye ) isoforms , and hence it is critical to maintain the correct levels of the individual isoforms ., Our data suggest that Nmo-mediated phosphorylation of the Pk isoform participates in this context ., Nmo phosphorylation of Pk is required in a tissue and cell-specific manner to maintain low levels of the Pk isoform and favor Pk-Sple in contexts where this is the ‘major’ isoform ., Our mosaic analysis demonstrates that Nmo is required in the same photoreceptor cell as Pk-Sple and inhibits Pk function ., It has been shown previously that the Cul1/SkpA/Slmb complex regulates overall Pk levels throughout the wing 45 , 46 , but in this case Pk is the major isoform and optimal Pk levels are required to prevent interference with core PCP complex function , particularly the internalization of transmembrane PCP components ., The Cul1/SkpA/Slmb complex appears to play a maintenance role in the wing in preventing Pk hyperactivity throughout the tissue ., Interestingly in this case , Pk-Sple accumulated in Cul1 LOF clones , similarly to Pk 45 ., In contrast , our results show that the same machinery operates in a spatially restricted and isoform-specific manner in the eye ., How might this specificity be achieved ?, One possibility emerges from comparison with mammalian studies 47 , 48 ., In this case , Wnt1 ligand acts upstream of TAK1-NLK-HIPK and subsequent SCF/proteasomal degradation of c-myb ., In the case of the developing ommatidia , we observed a requirement for nmo in the R4 cell ., This is the polar cell of the R3/R4 pair and the one that is closer to the Wingless/dWnt4 ligand sources at the dorsal and ventral poles of the imaginal disc 13 , 53 ., This raises the possibility that Wingless/dWnt4 are acting upstream of dTAK and Nmo in regulating Pk levels ., The involvement of Hipk may be complicated by its pleiotropic roles in regulating Wingless and Hedgehog signaling during eye development , in part through phosphorylation of Slmb itself 54 ., This molecular circuitry governing Pk isoform degradation adds another layer of regulation to the intricate feedback mechanisms within a cell to increase robustness of position-based cell fate decisions and helps to coordinate tissue-wide patterning events ., Our previous work demonstrated that Vang recruits Nmo to PCP complexes 24 ., Based on our current study , this raises the possibility that when Pk , the minor isoform , is recruited into PCP complexes instead of Pk-Sple , Nmo serves a ‘gatekeeping’ role and phosphorylates Pk , targeting it for degradation ., The importance of appropriate levels of Pk ubiqutination and degradation is underlined by a recent study that identified USP9X , a de-ubiqutinase , as a regulator of PRICKLE-mediated seizures in mammals 55 ., In zebrafish , nemo-like kinase ( nlk ) genetically interacts with non-canonical wnt11 during convergent extension 29 , suggesting that Nemo/Nlk regulation of Prickle in PCP patterning processes is conserved ., Together , these studies and our work highlight the importance of regulating the balance of Prickle family proteins during embryonic development and adult homeostasis to prevent disease ., The genetic tools and alleles used in this study are listed here along with their Flybase ID ( www . flybase . org ) : w1118 ( FBal0018186 ) ; pkpk1 ( FBal0013838 ) ; pksple1 ( FBal0016024 ) ; pkpk-sple13 ( FBal0060943 ) ; nmoP ( FBti0003251 ) ; eyFLP ( FBti0015982 ) ; pw+ , FRT80B ( FBst0001940 ) ; dTAK179 ( FBst0026275 ) ; UbxFLP ( FBti0150346 ) ; actGal4 ( FBst0003954 ) ; white RNAi ( FBst0031088 ) Cul1 RNAi ( FBst0029520 ) ; SkpA RNAi ( FBst0028974 ) ; slmb RNAi ( FBst0031056 ) ; slmb00295 ( FBst0011493 ) ; nmo RNAi ( FBst0025793 ) ; Hipk RNAi ( FBst0035363 ) ., act-EGFP-Pk and act-FRT-STOP-FRT-EGFP-Sple flies , nmoDB , mδ-lacZ , and GMR>DNProsbeta6 ( also termed GMR>Dts5 ) flies were generous gifts from David Strutt ( University of Sheffield , UK ) , Esther Verheyen ( Simon Fraser University , Canada ) , Sarah Bray ( University of Cambridge , UK ) , and Hermann Steller ( The Rockefeller University , USA ) , respectively ., sevGal4 , UAS-Pk; sevGal4 , UAS-Sple 14 , 28; sev-Sple-WT 14 , 28; UAS-Nmo , 24 , nubGal4 25 ., UAS-DNProsβ2 and UAS-DNProsβ6 ( also called Dts5 ) 4 | Introduction, Results and discussion, Materials and methods | Planar cell polarity ( PCP ) instructs tissue patterning in a wide range of organisms from fruit flies to humans ., PCP signaling coordinates cell behavior across tissues and is integrated by cells to couple cell fate identity with position in a developing tissue ., In the fly eye , PCP signaling is required for the specification of R3 and R4 photoreceptors based upon their positioning relative to the dorso-ventral axis ., The ‘core’ PCP pathway involves the asymmetric localization of two distinct membrane-bound complexes , one containing Frizzled ( Fz , required in R3 ) and the other Van Gogh ( Vang , required in R4 ) ., Inhibitory interactions between the cytosolic components of each complex reinforce asymmetric localization ., Prickle ( Pk ) and Spiny-legs ( Pk-Sple ) are two antagonistic isoforms of the prickle ( pk ) gene and are cytoplasmic components of the Vang complex ., The balance between their levels is critical for tissue patterning , with Pk-Sple being the major functional isoform in the eye ., Here we uncover a post-translational role for Nemo kinase in limiting the amount of the minor isoform Pk ., We identified Pk as a Nemo substrate in a genome-wide in vitro band-shift screen ., In vivo , nemo genetically interacts with pkpk but not pksple and enhances PCP defects in the eye and leg ., Nemo phosphorylation limits Pk levels and is required specifically in the R4 photoreceptor like the major isoform , Pk-Sple ., Genetic interaction and biochemical data suggest that Nemo phosphorylation of Pk leads to its proteasomal degradation via the Cullin1/SkpA/Slmb complex ., dTAK and Homeodomain interacting protein kinase ( Hipk ) may also act together with Nemo to target Pk for degradation , consistent with similar observations in mammalian studies ., Our results therefore demonstrate a mechanism to maintain low levels of the minor Pk isoform , allowing PCP complexes to form correctly and specify cell fate . | For functional tissues to form , individual cells must correctly orient themselves and function appropriately for their particular location in the body ., The Planar Cell Polarity ( PCP ) complexes transmit one set of spatial cues by acting as signposts to mark direction across an epithelial layer ., PCP signals can direct and coordinate cell differentiation , the behavior of groups of cells , or the orientation of individual cellular protrusions , depending on the tissue ., PCP signals act as a polarization relay with two different complexes being positioned on opposite sides of each cell ., This pattern of polarity is transmitted to neighboring cells and so extends across the tissue ., In the fly eye , PCP signals control the differentiation of a pair of photoreceptors , R3 and R4 , where the cell that is positioned closer to the dorso-ventral midline becomes R3 ., An excess of the PCP protein Prickle prevents the proper assembly of PCP complexes in the eye and so alters R3/R4 fate ., Here we show that Nemo kinase is required in the R4 cell to phosphorylate Prickle and promote its degradation by the proteasome ., Maintenance of low Prickle levels allows proper formation of PCP complexes , cell fate specification , and eye development . | phosphorylation, medicine and health sciences, rna interference, social sciences, cloning, neuroscience, pigments, materials science, molecular biology techniques, epigenetics, eyes, research and analysis methods, animal cells, proteins, genetic interference, gene expression, materials by attribute, sensory receptors, head, molecular biology, proteasomes, biochemistry, signal transduction, rna, cellular neuroscience, psychology, protein complexes, anatomy, post-translational modification, cell biology, phenotypes, nucleic acids, neurons, genetics, photoreceptors, biology and life sciences, ocular system, cellular types, afferent neurons, sensory perception, physical sciences | null |
journal.pbio.1000090 | 2,009 | Target Genes of the MADS Transcription Factor SEPALLATA3: Integration of Developmental and Hormonal Pathways in the Arabidopsis Flower | In contrast to animals , most developmental processes of plants occur postembryonically and integrate a variety of internal and environmental cues ., Modularity of plant development is based on the ability of plants to maintain pools of undifferentiated stem cells throughout the life cycle of the plant ., Stem cells near the tip of the growing shoot are located in the shoot apical meristem , where different types of plant organs , such as vegetative leaves or floral organs , can be initiated from the flanks of the meristem ., Leaves and floral organs are “variations of a theme”: they arise via modifications of a common basic genetic program 1 ., Which type of organ is produced by the meristem depends on the developmental phase of the plant ., Initially , leaves are produced during the early vegetative phase of the plant , followed by the transition to reproductive phase , which triggers the transformation of vegetative shoot meristems to inflorescence and floral meristems , giving rise to flowers and floral organs , respectively ., Thus , change in the identity of plant organs is initiated by reprogramming within the meristems 2 ., Plant developmental biologists have identified a number of key regulatory genes that trigger changes in meristem and organ identity , many of them encoding transcription factors , chromatin remodeling factors , or other signaling molecules like microRNAs ( miRNAs ) ., One family of transcription factors that is important in this process is the MADS-box gene family 3 ., MADS-box genes play crucial roles in the switches from vegetative to inflorescence and finally to floral meristems ., These latter meristems give rise to flowers and floral organs , respectively 4 ., Developmental transitions and organ differentiation require global changes in gene expression ., The genome of the model flowering plant Arabidopsis thaliana is roughly 20-fold smaller than the human genome; still , it encodes about 27 , 000 protein-coding genes , which is more than found for humans ( http://www . arabidopsis . org; 5 ) ., One of the most challenging current questions is how developmental control genes trigger global changes in gene expression during the multiple phase transitions and in organ identity determination , starting from a small pool of undifferentiated cells ., In the present study , we focus on the MADS-box transcription factor SEPALLATA3 ( SEP3 ) ., SEP3 is a member of the SEP subfamily of MADS-box genes , whose members have nearly redundant functions in the specification of floral meristem identity and in the identity of all types of floral organs: sepals , petals , stamens , and carpels ., Triple mutants impaired in SEP1–3 function have flowers with floral organs converted into sepals and display a loss of determinacy in the center of the flower 6 ., This phenotype masks the involvement of the SEP genes in processes occurring later in development , e . g . , the formation of the ovules as has been shown by Favaro et al . ( 2003 ) 7 ., The SEP3 protein appears to be the central player , since it is part of at least a dozen different MADS domain dimer complexes 8 and it is expressed throughout flower development , from the floral meristem to fully developed floral organs 9 ., This suggests that SEP3 is a multifunctional protein controlling a plethora of developmental processes ., According to the current model of flower development , the SEP3 protein is proposed to mediate the higher-order protein complex formation between MADS-domain proteins with more specific floral organ identity functions 10 ., Furthermore , it may provide the transcriptional activation potential to the floral homeotic protein complexes 10 ., More-recent evidence suggests that the SEP3 protein may also recruit transcriptional corepressors , demonstrating that it can modulate the function of the plant protein complexes in a broader sense , depending on the availability of cofactors 11 ., However , evidence for higher-order complex formation between MADS-domain proteins comes mostly from protein interaction studies in heterologous systems and genetic data , and there is no indication for the relevance of these interactions in target-gene recognition in planta so far ., Another question is how different MADS-domain protein complexes achieve functional specificity , since the in vitro DNA-binding characteristics of MADS-domain proteins appeared rather similar , and the short DNA sequence motifs supposedly bound by MADS-domain proteins are very abundant in the Arabidopsis genome 12 ., In order to characterize the mode of action and general downstream pathways of floral homeotic genes , we generated genome-wide DNA-binding profiles of SEP3 in its native context ., Chromatin immunoprecipitation ( ChIP ) followed by ultrahigh-throughput Solexa ( Illumina ) sequencing ( ChIP-SEQ ) has been shown recently to be a powerful tool to obtain genome-wide DNA-binding patterns of transcription factors 13 , 14 ., The large numbers of short individual sequence reads produced by novel instruments facilitate the digital quantification of DNA sequences that are present in a sample ., An alternative method comprises the combination of ChIP and whole-genome microarrays ( ChIP-CHIP ) to map the genomic DNA regions enriched in the immunoprecipitated sample 15 , 16 ., These genomic tiling arrays are available for Arabidopsis and have been used to map binding sites for plant transcription factors 17 ., We compared the targets of SEP3 in wild-type and the floral homeotic agamous ( ag ) mutant background ., In the ag mutant , stamens are replaced by petals , and instead of the carpels in the fourth whorl , a new mutant flower is formed 18 ., Accordingly , the analysis of this mutant should reveal SEP3 target genes specifying petal development , whereas targets that are specific to stamens and carpels should be absent ., We further studied the function of SEP3 in the regulation of downstream pathways by analyzing the effects of a dominant repressor version of SEP3 in plants ., The genome-wide identification of direct target genes of SEP3 provides a framework for a hierarchical transcriptional network underlying the formation of floral organs ., SEP3 binds to thousands of genomic regions containing the consensus binding sites for MADS-domain proteins , but it also acts as part of regulatory modules with other transcription factors ., These modules link floral homeotic gene functions with organ growth ., Our analysis identified multiple links between SEP3 and hormonal pathways , and in particular auxin signaling ., Auxin signaling is crucial for the outgrowth and development of lateral organs , and its role in flower development has been suggested previously based on mutant phenotypes 19–21 ., Our ChIP-SEQ data and the phenotypes of plants that repress direct SEP3 targets in a dominant-negative fashion suggest cooperation of SEP3 and genes in the auxin pathway in the regulation of floral organ growth and differentiation ., To identify genomic regions bound in vivo by SEPALLATA3 ( SEP3 ) , we used ChIP followed by deep sequencing by ChIP-SEQ 14 , 15 ., In a parallel experiment , the ChIP was followed by whole-genome tiling array hybridizations to identify enriched regions ( ChIP-CHIP ) ., For the ChIP experiments , we used inflorescences including inflorescence meristems and floral buds of stage 1–12 from Arabidopsis wild-type and agamous ( ag-1 ) mutant plants ., Protein–DNA complexes were immunoprecipitated using a peptide antibody specific to SEP3 ., As a negative control , we performed ChIP-SEQ using the same antibody on sep3-1 mutant plants ., Western blot analysis revealed that the SEP3 antibody reacts exclusively with the SEP3 protein ( Figure S1 ) ., From the ChIP-SEQ experiments , we obtained between 3 to 7 . 5 million approximately 35-bp sequence reads after one to three independent rounds of sequencing for each sample , of which 30%–40% were uniquely mapped on the Arabidopsis nuclear genome ( Table S1 ) ., The uniquely mapped reads were extended to 300 bp in order to recover the average original DNA fragments that were subjected to sequencing in a similar fashion as described by Robertson et al . ( 2007 ) 14 ., This allows positioning of the maximum of enrichment present in the samples at high resolution ., The number of mapped reads was counted for every nucleotide position ( defined as “number of hits” ) , and for each strand independently ., For some genomic positions , we observed that the number of hits was only due to reads with identical sequence ., Although , it is expected that some reads will have an identical sequence , it is also expected that a true peak of enrichment should be represented by several reads with partly overlapping but different sequences ., In order to avoid any artifact due to identical sequence reads , we included the requirement that the number of hits at each genomic position should be supported by reads mapped in both DNA strands ., To test for enrichment at each nucleotide position in the sample compared to the control , we used a score based on the Poisson distribution , as it is commonly used for statistical modeling of tag counts 22 ., For each genomic region representing a candidate peak , the maximum score value was used to test the significance of the peak ( defined as “peak score” ) ., We used the false discovery rate ( FDR ) to control the error rate of our testing procedure ., The number of significant peaks for the ChIP-SEQ datasets is given in Table 1 ., Notably , SEP3 binds to thousands of regions in the Arabidopsis genome ., At FDR <0 . 001 , we found 4 , 282 significantly enriched regions for the SEP3 in wild-type plants , and 2 , 828 regions in the ag mutant ., Thus , at this level of significance and given our data , SEP3 seems to bind a reduced number of regions in the ag mutant compared to wild-type plants ., We used biological replicates and comparison of ChIP-SEQ and ChIP-CHIP data in order to evaluate the reproducibility of the generated genome-wide binding profiles of SEP3 ., To simplify the comparison of ChIP-SEQ replicates , the average score in nonoverlapping 5 , 000-bp windows for each replicate was calculated ., A high correlation between the two different sequencing rounds and biological replicates was found ( Figure S2E and S2G ) ., Peaks positions and ranks from independent biological replicates overlap strongly ( Figure S2H ) ., Since more sequences were produced for replicate 1 , we focused in our further bioinformatic analysis on this replicate ., Comparison of the ChIP-CHIP and the ChIP-SEQ experiments reveals a good agreement between the results of the two methods , as reflected in the large overlap in peak positions and similar ranking of the peaks ( Figure S2 ) ., We were also interested in comparing the positional resolution of the two platforms ., The average width of the most strongly significant ChIP-SEQ peaks is around 800 bp ., In contrast , the peaks with an equivalent ChIP-CHIP rank have a width of approximately 1 , 300 bp ( Figure S2 ) ., This larger window size for the ChIP-CHIP peaks compared to ChIP-SEQ peaks results in a lower positional resolution , which is particularly problematic in regions with multiple binding sites that are close to each other ., cis-Regulatory elements controlling gene expression are preferentially found in the promoters of target genes ., However , there are also numerous examples of important regulatory sequences in introns , particularly near the 5′ end of genes 23 ., A typical example is the second intron in the MADS-box gene AG , which is bound by multiple factors 24–26 ., AG , on its turn , binds to the downstream region of the SPOROCYTLESS gene , demonstrating that cis-regulatory elements are not exclusively located in the upstream or intragenic regions of plant genes 27 ., We determined the position of the putative binding sites relative to the nearest gene based on our ChIP-SEQ dataset ., As evident from our ChIP-SEQ experiments , most in vivo binding sites of SEP3 are close to or within protein-coding genes and only about 6% to 8% of all peaks ( FDR <0 . 001 ) are not located within 3 kb upstream to 1 kb downstream of any genomic locus ( wild-type and ag datasets ) ., A total of 3 , 475 genes are targeted by SEP3 in wild type , whereas 2 , 424 genes are putative targets in the ag mutant at FDR <0 . 001 ( Tables S2 and S3 ) ., In agreement with its role as transcriptional regulator , DNA-binding sites of SEP3 are predominantly located in the upstream region of genes ( Figure S3; Table S3 ) ., Notably , we found the highest enrichment of SEP3 binding sites in a region spanning a few hundred base pairs directly upstream of the annotated transcriptional start of genes ( Figure S3 ) ., Surprisingly , binding sites are also enriched in the downstream region of genes , located just downstream of the 3′ UTR , although this enrichment is clearly less pronounced than the one found near the transcriptional start site of genes ., Within genes , peaks are preferentially located in introns and UTR regions ( Figure S3 and Table S3 ) ., It is known from in vitro–binding studies that MADS-domain proteins bind to specific DNA elements called CArG boxes ( reviewed in 12 ) ., Most well known is the serum response element ( SRE or SRF ) -type CArG box , which has the consensus CCA/T6GG ., A related sequence motif is the MEF2-type CArG box , which has the general consensus CA/T8G but is usually more strictly defined as CTAA/T4TAG ., Plant MADS-domain proteins often show relatively broad DNA-binding preferences , recognizing SRF− and MEF2− , as well as intermediate motifs 28 , 29 ., CArG boxes are frequently found in the Arabidopsis genome , so the presence of this motif alone is not sufficient to predict targets of MADS-box transcription factors 12 ., Given the large number of MADS-box transcription factor genes present in the Arabidopsis genome and the capacity to form an even larger number of heterodimeric transcription factor complexes 8 , and considering their divergent functions in plant development , it is important to understand how different MADS-domain proteins ( and protein complexes ) achieve target-gene specificity ., Also , the relevance of the formation of higher-order protein complexes in DNA binding , associated with the binding to more than one CArG box , has not been demonstrated in planta yet ., We considered the 1 , 001 bp surrounding the peak maximum score position , defined as peak area , for the characterization of transcription factor binding sites ., The peak areas were searched for the presence of different types of CArG boxes ., Our results show that the SRF , MEF2 , and intermediate types are enriched in the genomic regions bound by SEP3 in vivo ( Figure 1A ) ., In agreement with a true enrichment of CArG boxes in genomic regions bound by SEP3 , we found that the “background” frequency of CArG boxes of type CCA/T6GG in promoter regions ( −1 , 000 bp upstream ) was approximately 7% , whereas in our ChIP-SEQ data at FDR = 0 . 001 , the frequency is approximately 12 . 5%; and in more strongly bound regions , the frequency increases to more than 20% ., SRF and intermediate types of CArG boxes showed considerably more enrichment than MEF2-type CArG boxes , suggesting that they are more frequently bound by SEP3 or SEP3-containing protein complexes in planta ., The CArG boxes are usually positioned in the center of the peak ( peak maximum score position ) ( Figure 1B ) ., The strong preference of CArG boxes for the center of the peak demonstrates the high positional resolution of ChIP-SEQ experiments combined with our method of peak detection ., In order to further characterize the features of binding sites recognized by SEP3 and its complex partners in planta , we used the ChIP-SEQ peak score as a measure of affinity for binding sites within enriched genomic regions as implemented in the MatrixREDUCE software 30 ., We found that the obtained consensus models tend to be relatively flexible in most nucleotide positions ( Figure 1 ) ., This suggests that the in vivo binding data reflect a mix of affinities of different homo- and heterodimeric SEP3 protein complexes ., Since MADS-domain proteins bind as dimers to a CArG box , CArG boxes can be considered as composite elements , with each half-site contacted by a different monomer with possibly different binding preferences ., We estimated the frequencies of all possible half-sites for the CArG boxes of the general consensus CCA/T6GG and CCA/T7G in the ChIP-SEQ data ., Using a binomial test , we found that three out of eight possible half-sites for the consensus CCA/T6GG were significantly overrepresented in the SEP3 ChIP-SEQ data , and four out of 24 for the consensus CCA/T7G ( Table S4 ) ., Not all possible combinations of the most frequent half-sites are represented among the most strongly overrepresented core CArG boxes ( Table S4 ) , suggesting dependencies between the half-sites ., The most frequently represented sequence of type CCA/T6GG , which is CCAAAAATGG , is in fact the same or highly similar to the consensus sequences that were identified by MatrixREDUCE ( Figure 1C ) ., Based on the combinations of half-site sequences found in the ChIP-SEQ data , we measured the dependencies between single nucleotides using a chi-square test ., Here , we found that strong dependencies exist between nucleotides within the A/T-rich core of the CArG box ( Table S5 ) ., Surprisingly , we also identified dependencies for the nucleotides surrounding the core CArG box , which is in line with experimental evidence suggesting that sites surrounding the core consensus are contacted by MADS-domain proteins and may contribute to DNA-binding specificity as well 31–33 ., The dependencies between nucleotide positions in functional CArG boxes could ( at least partly ) explain why only 7 . 7% and 5 . 7% of all CArG boxes perfectly matching the consensus CCA/T6GG and CA/T7GG , respectively , are bound in vivo by SEP3 ., According to the “floral quartet” model 34 , higher-order MADS-domain protein complexes bind to two CArG box–like DNA sequences at short distance from each other ., Thus , we would expect an enrichment of ChIP-SEQ peaks with regulatory modules consisting of two CArG box elements ., In order to identify regulatory modules composed of more than one binding site , we used the Explain software 35 ., The module with the highest fitness score ( 0 . 748 on a scale of 0 to 1 ) was composed of a single pair of CArG boxes separated by a DNA stretch varying between 10 to 200 bp in length ., This finding supports the idea that MADS-domain proteins act in complexes composed of two dimers that bind to two adjacent binding sites , as has been predicted by the floral quartet model ., Next , we were interested whether CArG boxes within ChIP-SEQ peaks have preferred distances from each other ., To test this , we compared the distribution of distances of CArG boxes within peaks to “background distributions” obtained from random sets of Arabidopsis promoters or randomized sequences ., As shown in Figure 2 , there is a preference for close distances , with the strongest preference around 42–43 bp ( which corresponds to four helical turns of the DNA ) ., Aside from these relatively short distances , the frequent occurrence of multiple peaks in the same genomic regions open the possibility that MADS-domain protein complexes can also bridge and bend larger DNA stretches as was suggested in the floral quartet model 34 ., In addition to our targeted screen for MADS-domain binding sites , we used MatrixREDUCE and MEME 36 in order to identify DNA sequence motifs that are abundant in the regions bound by SEP3 ., Using these tools , we recovered motifs corresponding to CArG boxes , but interestingly , we also detected sequence motifs potentially bound by non-MADS transcription factors ., A motif that was identified using these programs has the consensus sequence CACGTG ., This motif has been named “G-box” in the literature and represents a DNA-binding site for bHLH and bZIP transcription factors 37 ., We found that this motif is indeed overrepresented in the genomic regions bound by SEP3 ( Figure 3A ) ., Similar to CArG boxes , the G-box motif is enriched in the center of the peak ( Figure 3B ) ., A second motif that was found using these programs has strong similarity to the DNA-binding consensus of TCP transcription factors with the general consensus motif CCNGGG 38 ., We analyzed whether this TCP DNA binding consensus was overrepresented in the regions bound by SEP3 ., Indeed , it appeared to be enriched with increasing peak score threshold , and it is enriched in the center of the peak ( Figure 3A and 3B ) ., Next , we tested systematically for enrichment of known DNA-binding consensus sequences of transcription factors using information from the Transfac and AGRIS databases ., In total , these databases contain information for 105 ( Transfac ) and 72 ( AGRIS ) DNA-binding consensus sequences of plant transcription factors ., In addition to confirming the enrichment of MADS- , TCP , and bHLH/bZIP binding sites , we found that also ARF , C2H2 ( ID1 ) DNA recognition motifs , and a bHLH ( MYC ) DNA-binding site similar to the G-box were enriched with increasing peak score threshold , and located in the center of the ChIP-SEQ peaks ( Figures 3 and S4 ) ., The ChIP-SEQ experiments were done with samples from wild-type plants and the ag mutant ., We were interested in the overlap of DNA-binding sites in these two samples , which could point to target genes involved in the formation of perianth organs ., There is clear preference for overlapping genomic positions of SEP3 ChIP-SEQ peak maximum positions in wild type and ag mutant ( Figure 4A ) ., The overlap of potential SEP3 targets in wild type and ag mutant is also evident from genes that are targeted by SEP3 ( Figure 4B ) ., Whereas the number of peaks in ag is only approximately 65% of the number of peaks in wild type at FDR = 0 . 001 , the overlap in affected target genes is almost 70% at the same FDR level ( Figure 4B ) ., Thus , individual peaks are more likely affected by loss of AG than target genes ., With increasing significance level , targets of SEP3 in wild type and ag mutant overlap progressively more ( Figure 4B ) , as do individual peaks ., Highly enriched target genes are usually common to wild type and ag mutant ( overlap >90% ) ., According to these results , only a small fraction of strongly enriched direct target genes is specific to the SEP3 complexes specifying stamen and carpel development ( Table S2 ) ., These genes may represent candidate genes determining the specific morphologies of stamens and carpels downstream of the floral homeotic genes ., In the ChIP-SEQ approach to identify potential SEP3 targets , different floral tissues corresponding to different developmental stages were used ., In order to evaluate the relevance of DNA-binding events in the regulation of the genes corresponding to the ChIP-SEQ peaks , we used comprehensive gene expression array data that are publicly available ., Mainly , the collection of AtGenExpress experiments provides information about timing of gene expression and changes in different floral homeotic mutants 39 ., We found that about 45% of all genes with significantly enriched peaks in the SEP3 ChIP-SEQ experiment ( FDR <0 . 001 ) were differentially expressed at very young developmental stages in at least one of the homeotic mutants ( lfy-12 , ap1-15 , ap2-6 , ap3-6 , ag-12 ) ( Figure S5 ) ., This fraction was higher than the overall genome-wide fraction of differentially expressed genes in these mutants ( 29% ) ., Forty-five percent ( 903/2022 ) of the genes with ChIP-SEQ peaks in the ag mutant are differentially expressed in the ag-12 mutant compared to wild type ( up to floral stage 12; 28% ( 5 , 927/21 , 039 ) in the total dataset ) ., Considering SEP3 binding sites in genes that are differentially expressed during development , we found the strongest enrichment for genes that change expression in the meristem during the earliest stages of floral development ( FDR <0 . 001 , p-value 4 . 3e−40 , binomial test ) ., About 63% of the potential targets of SEP3 are differentially expressed at any stage of reproductive development starting from floral transition to flowers of stage 12 ( Figure S5 ) ., In total , 72% of the potential SEP3 targets are differentially expressed during flower development or in any of the homeotic mutants ., Although the differential expression can also be due to indirect effects , the data suggest that the majority of potential direct SEP3 targets may also be regulated by SEP3 ., We also found an enrichment in frequency of genes that are correlated in expression with SEP3 expression with increasing peak score threshold in ChIP-SEQ ( Figure S5 ) ., The fraction of genes with ChIP-SEQ peaks that are positively coexpressed with SEP3 is clearly higher than that of negatively regulated genes , supporting the idea that SEP3 acts mostly as a transcriptional activator ., Genetic and gene expression experiments suggest that the SEP genes are required for the up-regulation of floral homeotic genes , and that this up-regulation is crucial for the establishment of the identities of the different floral organs ., However , until now , it has not been demonstrated whether this regulation is direct ., We analyzed the binding profiles for the genomic loci corresponding to the floral homeotic genes and found that SEP3 binds to nearly all of these loci ( Figure 5 ) ., Only SEEDSTICK ( STK ) and CAULIFLOWER ( CAL ) do not have significantly enriched regions ., In most cases , the peaks are located in the promoters of the respective genes ., In case of the APETALA1 ( AP1 ) , APETALA3 ( AP3 ) , SEP1 , and SEP2 loci , there are also peaks in the 5′ UTR , whereas a SEP3 binding site is present in the second intron of AGAMOUS ( AG ) ., Of all homeotic genes , the genomic regulatory sequences controlling the expression of AG and AP3 are best characterized ., The spatial expression pattern of AP3 is driven by regulatory elements within approximately 500 bp upstream of the transcriptional start ., CArG boxes in this part of the promoter are important for the positive as well as negative regulation of AP3 40 ., Our ChIP-SEQ results demonstrate that SEP3 binds to the genomic region comprising positively and negatively acting CArG boxes in the AP3 promoter , strongly suggesting a direct molecular link between the binding of SEP3 and the regulation of AP3 ., Most regulatory sequences controlling the expression of AG are located in its second , 4-kb large intron 24 , 25 ., Consistent with this observation , we identified a peak of enrichment of SEP3 in this intron ., More specifically , the peak marks a CArG box in the 3′ activation domain located in this intron ., The 3′ activation domain functions in the up-regulation of AG in stage 3 floral meristems and is also responsible for maintenance of AG expression in developing carpels 24 ., This CArG box was also found to be bound by AG itself in previous experiments 26 ., Interestingly , we identified a second peak of enrichment in the upstream region of AG ., Consistent with the idea that an AG/SEP heterodimer is responsible for the positive autoregulation of AG 26 , the heights of the peaks in the AG locus are reduced in the ag mutant compared to wild type ( Figure 5 ) ., The regulatory sequences controlling the expression of the SEP1–4 genes are still not well characterized ., Our ChIP-SEQ results , however , strongly suggest autoregulation of the redundantly acting SEP MADS-box genes ., All floral MADS-box genes that are targeted by SEP3 in wild type , are also targeted in the ag mutant , although there is some variation in the heights or presence of individual peaks ( e . g . , AP1 , SEP2 , SHP1; see Figure 5 ) ., This raises the possibility that different SEP3 complexes may have different affinities to individual binding sites ., In order to characterize the regulatory effects of SEP3 on MADS-box genes that are potential direct targets , we analyzed the expression of these MADS-box genes upon SEP3 induction using a constitutively expressed translational fusion of SEP3 to the rat glucocorticoid receptor hormone binding domain ( GR ) ., For this , seedlings expressing the 35S:SEP3-GR construct were treated with dexamethasone ( DEX ) for 8 h , 1 d , or 10 d , and the expression of MADS-box genes was determined by real-time reverse transcriptase ( RT ) -PCR ., The relative expression levels in comparison with nontreated plants is shown in Figure 6 ., Our results reveal that SEP3 is indeed able to activate the expression of other floral homeotic genes as suggested previously 6 ., SEP3 itself is most strongly up-regulated , demonstrating a strong autoregulatory feedback loop ., Although some of the tested genes show an early response to SEP3 induction , others are regulated only after prolonged SEP3 induction , suggesting that SEP3 alone is not sufficient to regulate these genes , but needs to interact with partner proteins that are encoded by the induced MADS-box genes ., In particular , AP3 , AG , and AP1 are strongly activated by SEP3 ., These three genes correspond to the three major classes of floral homeotic genes according to the classical ABC model: class A ( AP1 ) , class B ( AP3 , together with PI ) , and class C ( AG ) ., Their gene products also represent major protein interaction partners of SEP3 , suggesting that later induced targets of SEP3 are targets of the corresponding SEP3-containing protein complexes ., Thus , SEP3 can activate the flower developmental program by enhancing the expression of its interaction partners as one of its first steps ., Induction of the ABC classes of genes is sufficient to form the flower , which explains the very early flowering and the terminal-flower phenotype that we observed upon SEP3 induction ., Whereas flower-specific genes are mostly activated , MADS-box genes that are involved in the floral transition ( AGL24 , SOC1 , and SVP ) tend to be down-regulated by SEP3 ., Together with the fact that SEP3 binds to the promoters of these genes , our results suggest that SEP3 is involved in the down-regulation of these genes during early flower development , possibly as part of protein complexes together with other flower-specific MADS-domain proteins , such as AP1 ., It has been a long-standing question in plant developmental biology whether floral homeotic genes act directly on the structural or metabolic genes that create the final morphology of floral organs , or whether they act via intermediate regulators , i . e . , other transcription factors , which in turn regulate subsets of targets conferring the final organ shape and function ., To answer this question , we investigated the enrichment of gene ontology ( GO ) terms 41 among genes that are closest to the peaks as a function of their ChIP-SEQ peak score ., In terms of molecular function , genes encoding transcription factors are clearly the most enriched group of genes ( GO:0030528; p-value 3 . 62e−19 ) ., When dissecting gene functions according to biological processes , there is a clear enrichment for genes involved in development , in response to hormonal stimuli , and in lipid biosynthesis ., Figure 7 presents the top-five most enriched specific GO terms ., The SEP3 targets in the GO category “lipid biosynthetic process” include genes involved in hormone biosynthesis ( terpenoid and steroid pathways ) , as well as in sterol and wax synthesis ., Next , we were interested in whether some transcription factor families were more frequently represented among potential direct SEP3 target genes than others ., The results shown in Table 2 reveal that 15 transcription factor families were significantly overrepresented among SEP3 targets ., Interestingly , we found overrepresentation of families for which we also found enrichment of their DNA-binding sites in the ChIP-SEQ data: bHLH , TCP , and ARF families are overrepresented in both the target and the binding site datasets , which points | Introduction, Results, Discussion, Materials and Methods | The molecular mechanisms by which floral homeotic genes act as major developmental switches to specify the identity of floral organs are still largely unknown ., Floral homeotic genes encode transcription factors of the MADS-box family , which are supposed to assemble in a combinatorial fashion into organ-specific multimeric protein complexes ., Major mediators of protein interactions are MADS-domain proteins of the SEPALLATA subfamily , which play a crucial role in the development of all types of floral organs ., In order to characterize the roles of the SEPALLATA3 transcription factor complexes at the molecular level , we analyzed genome-wide the direct targets of SEPALLATA3 ., We used chromatin immunoprecipitation followed by ultrahigh-throughput sequencing or hybridization to whole-genome tiling arrays to obtain genome-wide DNA-binding patterns of SEPALLATA3 ., The results demonstrate that SEPALLATA3 binds to thousands of sites in the genome ., Most potential target sites that were strongly bound in wild-type inflorescences are also bound in the floral homeotic agamous mutant , which displays only the perianth organs , sepals , and petals ., Characterization of the target genes shows that SEPALLATA3 integrates and modulates different growth-related and hormonal pathways in a combinatorial fashion with other MADS-box proteins and possibly with non-MADS transcription factors ., In particular , the results suggest multiple links between SEPALLATA3 and auxin signaling pathways ., Our gene expression analyses link the genomic binding site data with the phenotype of plants expressing a dominant repressor version of SEPALLATA3 , suggesting that it modulates auxin response to facilitate floral organ outgrowth and morphogenesis ., Furthermore , the binding of the SEPALLATA3 protein to cis-regulatory elements of other MADS-box genes and expression analyses reveal that this protein is a key component in the regulatory transcriptional network underlying the formation of floral organs . | Most regulatory genes encode transcription factors , which modulate gene expression by binding to regulatory sequences of their target genes ., In plants in particular , which genes are directly controlled by these transcription factors , and the molecular mechanisms of target gene recognition in vivo , are still largely unexplored ., One of the best-understood developmental processes in plants is flower development ., In different combinations , transcription factors of the MADS-box family control the identities of the different types of floral organs: sepals , petals , stamens , and carpels ., Here , we present the first genome-wide analysis of binding sites of a MADS-box transcription factor in plants ., We show that the MADS-domain protein SEPALLATA3 ( SEP3 ) binds to the regulatory regions of thousands of potential target genes , many of which are also transcription factors ., We provide insight into mechanisms of DNA recognition by SEP3 , and suggest roles for other transcription factor families in SEP3 target gene regulation ., In addition to effects on genes involved in floral organ identity , our data suggest that SEP3 binds to , and modulates , the transcription of target genes involved in hormonal signaling pathways . | plant biology | The key floral regulator SEPALLATA3 binds to the promoters of a large number of potential direct target genes to integrate different growth-related and hormonal pathways in flower development. |
journal.pcbi.1005138 | 2,016 | Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling | Our ability to adapt to a constantly changing environment is thought to depend on the dynamic and flexible organization of intrinsic brain networks 1 , 2 ., Characterizing the temporal dynamics of interactions between distributed brain regions is fundamental to our understanding of human brain organization and its development 2–8 ., However , most of our current knowledge of functional brain organization in adults and children is based on investigations of time-independent functional coupling ., Progress in the field has been impeded by both a lack of appropriate computational techniques to investigate brain dynamics as well as an inadequate focus on core brain systems involved in higher-order cognition 3 , 4 , 9 , 10 ., In particular , progress has been limited by weak analytical models for identifying time-varying brain states , and their occurrence rates and mean lifetimes , for quantifying transition probabilities between brain states , and for characterizing the dynamic evolution of functional connectivity patterns over time 9–11 ., Here we overcome limitations of extant methods by developing and applying novel computational techniques for characterizing dynamic functional interactions between distributed brain regions and address two key neuroscientific goals ., The first scientific goal of our study was to investigate the dynamic functional connectivity of the salience network ( SN ) , the central-executive network ( CEN ) and the default mode network ( DMN ) , three core neurocognitive systems that play a central role in cognitive and affective information processing 1 , 12 ., Our second scientific goal was to characterize the maturation of the dynamic functional connectivity of the SN , CEN and DMN between childhood and adulthood in order to address important gaps in the literature regarding the nature of dynamic cross-network interactions over development and the question of how brain systems become more flexible during the period between childhood and adulthood ., The SN is a limbic-paralimbic network anchored in the anterior insula and dorsal anterior cingulate cortex with prominent subcortical nodes in affective and reward processing regions including the amygdala and ventral striatum 13 , 14 ., The SN plays an important role in orienting attention to behaviorally and emotionally salient and rewarding stimuli and facilitating goal-directed behavior 12 , 14–16 ., The fronto-parietal CEN is anchored in the dorsolateral prefrontal cortex and supramarginal gyrus and is critical for actively maintaining and manipulating information in working memory 17 , 18 ., The DMN is anchored in the posterior cingulate cortex , medial prefrontal cortex , medial temporal lobe , and angular gyrus 19–21 and is involved in self-referential mental activity and autobiographical memory 22 ., In adults , task-based fMRI studies have consistently demonstrated that SN , CEN and DMN nodes are involved in a wide range of cognitive tasks , with the strength of their responses increasing or decreasing proportionately with task demands 12 , 23 , 24 ., Analysis of causal interactions between these networks has also shown that high-level attention and cognitive control processes rely on dynamic interactions between these three core neurocognitive networks 16 , 25–27 ., Thus , far from operating independently , these three brain networks , which have only been probed using static time-invariant connectivity analysis , must form transient dynamic functional networks ( DFNs ) allowing for flexible within- and cross-network interactions ., While the SN , CEN and DMN can be reliably identified in most individuals using static network analysis of rs-fMRI data 26 , 28 , progress in characterizing their dynamic temporal properties has been limited by currently available computational tools and procedures ., Most current studies of dynamic brain connectivity use a sliding window approach 29 , 30 , which is problematic because of arbitrary parameters such as window size , which can lead to erroneous estimates of dynamic connectivity 7 , 9 , 11 ., Furthermore , extant methods do not provide information about the occurrence and lifetimes of individual dynamic brain states , transition probabilities between network states or unique dynamic network configurations associated with each brain connectivity state ., To address these weaknesses , we developed a novel variational Bayesian hidden Markov model ( VB-HMM ) 31 to uncover time-varying functional connectivity ., HMM uses a state-space approach to model multivariate non-stationary time series data 32 , 33 and cluster them into distinct states , each with a different covariance matrix reflecting the functional connectivity between specific brain regions ., Importantly , VB-HMM automatically prunes redundant states , retaining only those that significantly contribute to the underlying dynamics of the fMRI data , and provides the posterior distribution of parameters rather than point estimates of maximum likelihood-based methods ., We then used VB-HMM to characterize dynamic functional interactions between the SN , CEN and DMN to address our two neuroscientific goals ., VB-HMM allowed us to examine for the first time several important metrics of brain dynamics: the number of distinct brain states , their occupancy rates and mean lifetimes , and switching probabilities between brain states and DFNs ., Crucially , VB-HMM enabled us to investigate the temporal dynamics and evolution of states where the SN , DMN and CEN are fully segregated from each other , and states where they interact with each other ., We hypothesized that segregation of the SN , DMN and CEN would constitute a dominant state with high occupancy rates and mean lifetimes ., We further hypothesized that states with high occupancy rates would be temporally stable and marked by a higher probability of switching within the state compared to switching across states ., We use sub-second resting-state fMRI ( rs-fMRI ) datasets acquired as part of the Human Connectome Project ( HCP ) ( http://www . humanconnectome . org ) and demonstrate the robustness of our findings across two independent cohorts of healthy adults ., Next , we used VB-HMM and insights from our analyses of the adult brain to characterize the maturation of dynamic functional networks and connectivity associated with the SN , DMN and CEN between childhood and adulthood ., Flexible and dynamic cross-network functional interactions are essential for mature brain function 5 , 34 , yet little is known about the nature of dynamic organization and time-varying connectivity in children relative to adults ., Studies using static connectivity analyses suggest that functional brain networks undergo significant reconfiguration from childhood to adulthood , with analysis of time-averaged whole-brain connectivity patterns suggesting prominent increases as well as decreases in connectivity between childhood and adulthood ., In a previous study we showed that time-averaged connectivity within key nodes of the SN and DMN as well as their inter-network interactions is weaker in children relative to adults 28 ., Recent reports suggest that time-varying connectivity between distributed brain areas changes significantly with age , with greater temporal variability of connection strengths in children compared to adults34 ., Based on these observations , we hypothesized that compared to adults , children would show immature and less flexible patterns of dynamic connectivity between the SN , CEN and DMN ., Crucially , VB-HMM allowed us to , for the first time , probe developmental changes in dynamic networks properties including the occurrence rates and mean lifetimes of distinct brain states , such as those in which the SN , CEN and DMN are fully segregated from each other with decreased switching probabilities ., This study was approved by the Stanford University Institutional Review Board ., Written informed consent was obtained from all the subjects ., We first describe a novel VB-HMM framework we developed for characterizing dynamic brain networks in human fMRI data ., In the following sections , we represent matrices by using uppercase letters while scalars and vectors are represented using lowercase letters ., Let Y={{yts}t=1T}s=1S be the observed voxel time series , where T is the number of time samples and S is the number of subjects ., yts is an M dimensional time sample at time t for subject s , where M is the number of brain regions or nodes of the dynamic functional network under investigation ., Let Z={{zts}t=1T}s=1S be the underlying hidden/latent discrete states , where zts is the state label at time t for subject s ., Let Z be a first order Markov chain , with stationary transition ( A ) and initial distributions ( π ) defined as:, p ( zts=k|zt−1s=j ) =Ajk, ( 1 ), p ( z1s=k ) =πk, ( 2 ), where 0≤Ajk≤1 , ∑k=1KAjk=1 , and πk≥0 , ∑k=1Kπk=1 ., We assume the probability of the observation yts given its state zts=k to be a multivariate normal distribution with parameters mean μk and covariance Σk:, p ( yts|zts=k ) =N ( μk , Σk ), ( 3 ), Here we assume that the number of possible states K is not known a priori ., Each state k has M μk and an M x M Σk ., Let Φ = {π , A , Θ} ( where Θ={μk , Σk}k=1K ) be the unknown parameters of the HMM model ., Using the factorization property 35 of the Bayesian network shown in Fig 1A , the joint probability distribution of the observations , hidden states , and parameters can be written as, p ( Y , Z , Φ ) =∏s=1Sp ( z1s|π ) ∏t=2Tsp ( zts|zt−1s , A ) ∏t=1Tsp ( ( yts|zts , Θ ) P ( Φ ), ( 4 ), In maximum likelihood methods , the parameters Φ of the model are assumed to be unknown deterministic quantities , whereas in the Bayesian approach they are treated as random variables with prior probability distributions ., Here we assume that conjugate priors 35 for Φ and Z are defined as in 31 with the goal of estimating the joint posterior distribution p ( Z , Φ|Y ) of the hidden states and parameters ., Estimating this posterior distribution is analytically intractable but inference methods , such as sampling or variational methods , can instead be used 31 , 35 ., Here , to estimate p ( Z , Φ|Y ) , we use a variational Bayesian ( VB ) method 31 , which not only provides an elegant analytical approximation to the required posterior distribution but is also computationally faster than sampling approaches ., Let q ( Z , Φ|Y ) be any arbitrary probability distribution and p ( Z , Φ|Y ) be the true posterior probability distribution ., Then the log of the marginal distribution of observations Y can be written as, log\u2061P ( Y ) =F ( q ) +KL ( q||p ), ( 5 ), where F ( q ) is known as the negative free energy and KL ( q||p ) is the Kullback-Leibler ( KL ) divergence between the approximate and true posterior ., These quantities are given by, F ( q ) =∫dZdΦq ( Z , Φ|Y ) log\u2061p ( Y , Z , Φ ) q ( Z , Φ|Y ), ( 6 ), KL ( q||p ) =−∫dZdΦq ( Z , Φ|Y ) log\u2061p ( Z , Φ|Y ) q ( Z , Φ|Y ), ( 7 ), Since KL ( q||p ) is nonnegative , F ( q ) serves as the strict lower bound on log P ( Y ) ., F ( q ) and log P ( Y ) are equal if and only if the approximate posterior q ( Z , Φ|Y ) is equal to the true posterior p ( Z , Φ|Y ) for which KL ( q||p ) = 0 ., The goal of VB approximation is to find the approximate posterior for which the lower bound F ( q ) is maximized ., We make a mean field approximation on the approximate posterior 31 wherein it factorizes as, q ( Z , Φ|Y ) =q ( Z , A , Θ , π|Y ) =q ( Z|Y ) q ( π|Y ) q ( A|Y ) q ( Θ|Y ), ( 8 ), The functional forms of these factors are defined by the priors on the parameters and the likelihood of the data ., We assume conjugate priors for the priors , which results in elegant analytical approximations to the required posterior distributions of the Eq ( 8 ) ., Accordingly , the conjugate prior for π and rows of A is the Dirichlet ( Dir ) distribution , while the prior over the parameters of the Gaussian distribution Θ is the Normal-Wishart ( NW ) distribution ., We further assume that the prior distribution over Φ factorizes as, P ( Φ ) =p ( π ) p ( A ) p ( Θ ), ( 9 ), The forms of the Dirichlet and Normal-Wishart distributions are defined in 31 ., We provide the values of the hyper-parameters of these distributions in the Appendix ., Since we define conjugate priors on the model parameters , q ( π|Y ) and q ( A|Y ) follow multinomial distributions and q ( Θ|Y ) follows the Normal-Wishart distribution 31 ., The update equations for the posterior parameters are provided in the Supplementary Material ., The posterior distribution of the hidden states can be estimated using an efficient forward-backward method similar to the Baum-Welch algorithm for ML-HMM 33 , 35 ., Furthermore , our VB-HMM estimates the parameters of Normal-Wishart distribution for each state ., VB-HMM therefore discovers states for which the parameters of the Normal-Wishart distributions are distinct for each state ., A new state will be discovered if either mean or covariance or both are different in that state with respect to other states ., In task-based fMRI studies it is important to discover states with both mean and covariance differences ., However , in resting-state fMRI studies , as in the current study , differences in absolute signal levels are not relevant and states are based solely on changes in covariance over time ., This can be accomplished elegantly in our Bayesian framework using the hyperparameter λk in the joint Normal-Wishart distribution ., A non-informative prior value ( say , λk = 0 . 001 ) allows the data to determine the joint posterior distributions for the mean and covariance ., However , setting it to a very high value ( λk = 1000 ) biases the posterior to the prior mean which is 0 in our case ( equation S . 10 ) ., This ensures that our states are discovered only by the changes in covariance/inverse covariance in each state ., Similar to the expectation maximization algorithm for ML-HMM , the posterior distributions for the latent and model parameters are iteratively updated in VB-HMM as follows: We iterate steps, ( b ) and, ( c ) until the fractional lower bound F ( q ) between two consecutive thresholds is below a set threshold value of tol = 10−3 ., We initialize the states using the K-means algorithm with the number of clusters/states K set to a high value ( K = 25 ) ., The sparsity property of VB-HMM prunes away unwanted clusters/states in the model ., Like ML-HMM , VB-HMM provides suboptimal estimates of the posterior distributions , and these estimates are sensitive to the initial estimates of states using K-means initialization ., To account for this , we repeat VB-HMM with 100 different random initializations and choose the solution for which the lower bound F ( q ) is maximum ., We validated VB-HMM using three different simulation models; the details of each are provided in the Supplementary Materials ., Briefly , in Simulation-1 , we created datasets with two nodes and two hidden states ., The hidden states were constructed using a typical block design with two conditions ( or states ) : “OFF” and “ON” as shown in S2A Fig . We simulated observations with two nodes where the nodes are negatively correlated in the “OFF” state and uncorrelated in the “ON” state ., In Simulation-2 , we simulated data with six nodes and two hidden states using the HMM generative model given by Eqs 1–3 ., In this case , the two hidden states were constructed using a specified state transition matrix A and six nodes/ROIs with observations drawn from a zero-mean multivariate Gaussian distribution and state specific covariance matrices ( S3A Fig ) ., Simulation-3 also consisted of six nodes and two hidden states ., Here , however , the first three nodes/ROIs were correlated in the first half ( 116 samples ) of the experiment ( state 1 ) while the other three ROIs were correlated in the second half ( 116 samples ) of the experiment ( state 2 ) ( S4A Fig ) ., Five datasets were simulated ( akin to a group size of five subjects in fMRI studies ) for each simulation type ., We first validated VB-HMM using computer-simulated datasets generated from three different simulation models ., Here we briefly summarize the results from these simulations; details are in the Supplementary Materials ., For all three simulations , we applied VB-HMM with the number of hidden states ( K ) initialized to 25 and used VB-HMM to automatically determine the optimal number of states from the data ., S2 Fig shows the actual states , the estimated posterior probabilities and the Viterbi decoded states for Simulation-1 ., Among the 25 states , the occupancy rates of 18 states are zero suggesting that VB-HMM penalizes redundant states ., Further analysis suggests that among the seven with non-zero occupancy rates , four states together constitute 98% of the total occupancy rate and these states match the underlying true states in terms of their associated estimated Pearson correlation matrices and their occurrences with respect to their respective true states ., Similarly , 21 out of the 25 states in Simulation-2 had zero occupancy rates ( S3 Fig ) ., The top two most dominant states comprise 98% of the total occupancy rate and are well matched with the temporal occurrence of the underlying actual states ., Lastly , Simulation-3 yielded 21 out of 25 states with an occupancy rate of zero ( S4 Fig ) ., Of the four states with non-zero occupancy rates , the top two account for 99 . 2% of the total occupancy rate and match the true states used to generate the data ., These simulations demonstrate that VB-HMM can accurately discover the optimal number of states and the underlying dynamic connectivity across different models of simulated data ., We applied VB-HMM on rs-fMRI data to uncover dynamic functional interactions between the SN , CEN and DMN in two cohorts of HCP data ., Our first goal here was to identify dynamic brain states and their associated functional networks ., We computed the occupancy rates and mean lifetimes of each state as well as the switching probabilities between states ., A particular theoretical focus was on the occurrence of brain states in which the three networks were disconnected from each other ., We conducted separate analyses on Cohorts 1 and 2 and investigated the robustness and consistency of our key findings across the two cohorts ., To characterize the connectivity patterns associated with each functional state , we used a community detection algorithm on the estimated partial correlations in each state and examined the functional connectivity between ROIs ., Below we describe the salient features of the dynamic functional network structure in each cohort ., Given our focus on the temporal properties of the state in which the SN , CEN , and DMN were disconnected from each other , we combined states with a similar community structure into distinct DFNs ( see S1 Text ) ., We then examined the occupancy rates , mean lifetimes and switching probabilities of these DFNs ., Based on our primary goal of characterizing the network structure associated with segregated SN , CEN and DMN as encapsulated by DFN-1 ( Fig 3B ) and the common patterns of network structure involving DFN-1 and DFN-2 in both cohorts ( see previous sections ) , we next examined state transitions between these networks ., In each cohort , network structures corresponding to all other functional states were combined together into a mixed DFN-M ., As in previous sections , these analyses were conducted separately in the two cohorts with the aim of elucidating replicable findings ., We next used VB-HMM to characterize the maturation of dynamic functional interactions between the SN , CEN and DMN in a Stanford cohort of IQ- and gender-matched adults and children ., We used the same analytic procedures as described above on data from adults and children and then compared dynamic network properties between the two groups ., To investigate whether DFN occupancy rates and mean lifetimes differ between children and adults , we focused on DFN-1 and DFN-2 , the two dominant DFNs with identical community structures in adults and children that together account for about 77% occupancy rates in both groups ., Network configurations corresponding to all other functional states were combined into DFN-M ., The mean lifetimes , but not the occupancy rates , of all three DFNs were significantly greater in children compared to adults ( p < 0 . 05 , FDR corrected ) ( Fig 6A and 6B ) ., These findings indicate that children tend to persist longer in the same DFN than adults , as illustrated by the time evolution of the three DFNs ( Fig 5A and 5F ) ., Below we further investigate this pattern of developmental differences in terms of transition probabilities between DFNs ., To further investigate whether children tend to stay in one DFN configuration longer than adults , we computed transition probabilities in children and adults and compared them between the groups ., The probability of within-DFN transitions was not significantly different between the two groups ( p > 0 . 05 , FDR corrected ) ., However , transition probabilities to the fully disconnected SN-CEN-DMN configuration ( DFN-1 ) from both connected network configurations ( DFN-2 and DFN-M ) were significantly higher in adults compared to children ( p < 0 . 05 , FDR corrected ) ( Fig 6D ) ., In contrast , children showed a higher probability of switching between the two connected network configurations ( p < 0 . 05 , FDR corrected ) ., These findings demonstrate that , compared to children , adults switch back more frequently to DFN-1 , in which the SN , DMN and CEN are completely segregated from each other ., Finally , to investigate how dynamic functional connectivity matures with age we compared the strength of DFN connectivity assessed using within- and cross-network links as described above ., In this analysis , we further excluded participants with DFN connectivity beyond 3 standard deviations from their specific group or for whom both DFNs were not present ., After exclusion , our sample consisted of 22 adults and 16 children ., We found a significant three-way interaction between DFN ( DFN-1 vs . DFN-2 ) , link type ( within- vs . cross-network ) , and participant groups ( children vs . adults ) ( F1 , 36 = 10 . 99 , p = 0 . 002 ) ( Fig 6C ) , such that DFN-1 and DFN-2 configurations differed in connection strength by link type in adults ( F1 , 21 = 119 . 5 , p < 0 . 001 ) but not in children ( F1 , 15 = 0 . 491 , p = 0 . 494 ) ., These results demonstrate that DFN connectivity is weaker and less differentiated in children relative to adults ., The main scientific aims of our study were to ( 1 ) investigate the temporal properties of dynamic functional connectivity between the SN , CEN and DMN , three core neurocognitive networks implicated in a wide range of goal directed behaviors 12 , 15 , 16 , 26 , 48 , 49 , and ( 2 ) investigate how the temporal properties of dynamic functional connectivity between these core networks change from childhood to adulthood ., To accomplish this , we first developed a novel Bayesian HMM ( VB-HMM ) model for quantifying dynamic changes in functional connectivity ., A variational Bayes approach for estimating latent states and unknown HMM model parameters allowed us to overcome weaknesses associated with conventional methods and to investigate dynamic changes in intrinsic functional connectivity between three networks , which have previously only been investigated using static network analysis ., VB-HMM allowed us to quantify the temporal evolution of distinct brain states and probe the dynamic functional organization of the SN , CEN and DMN in an analytically rigorous manner ., Contrary to previous observations based on static time-averaged connectivity analysis 20 , 50 , we found that temporal coupling between the SN , CEN and DMN varies considerably over time and that these networks exist in a completely segregated state only intermittently with relatively short mean lifetimes ., VB-HMM also revealed immature and inflexible dynamic interactions between the SN , CEN and DMN characterized by higher mean lifetimes in individual states and reduced transition probability between states , in children relative to adults ., VB-HMM is a novel machine learning approach for identifying dynamic changes in functional brain connectivity ., VB-HMM has several advantages over existing methods 6 , 9 , 29 , 51 , 52:, ( i ) the automated estimation of latent states and their temporal evolution;, ( ii ) estimation of posterior probabilities of latent states and model parameters;, ( iii ) selection of models based on a trade-off between the model complexity and fit of the data , thereby reducing overfitting;, ( iv ) use of sparsity constraints resulting in pruning of weak states without having to specify the number of states a priori; and, ( v ) a generative model that has the potential to provide a more mechanistic understanding of human brain dynamics ., Our approach also overcomes weaknesses of existing HMM methods that are based on a maximum likelihood estimation approach and require a priori specification of the number of hidden states ., Furthermore , in contrast to conventional HMM methods , VB-HMM can discover dynamic changes in states based on signal mean or covariance or both ., This flexibility can be useful in uncovering latent brain dynamics during cognitive task processing , where states typically differ in both signal mean and covariance , as well as rs-fMRI , where states are better characterized by changes in covariance rather than mean signal levels ., In applications to rs-fMRI , as in the present study , this is accomplished in VB-HMM by setting the prior hyperparameter value λk = 1000 for each state k ., This choice forces the posterior mean values for each state ( μk ) close to prior mean ( which is zero ) ( Equation S . 10 ) and ensure that states are characterized by differences in the covariance matrices ( Σk ) , but not the mean ( μk ) ., Another advantage of our Bayesian approach is that the covariance ( or inverse covariance ) estimates are regularized and the extent of regularization is determined by the data ( Eqs S11–S . 13 ) ., This regularization ensures that the covariance matrices are full rank and therefore invertible to estimate partial correlations ., Such regularized estimation is not possible with maximum likelihood approaches ., Our simulations using three different simulation models demonstrate that VB-HMM can accurately discover the number of states , their temporal evolution , the transition probabilities between states and dynamic connectivity patterns associated with each state ( see S1 Text for details ) ., We next used VB-HMM to characterize the temporal evolution of dynamic brain states in two independent cohorts of adult participants from the HCP ., VB-HMM identified multiple stable states in both cohorts of participants ., The observation that the number of states is strictly greater than one is consistent with previous results demonstrating that the rs-fMRI time series is not stationary29 , 53 ., Importantly , VB-HMM identified similar patterns of stable brain states in both cohorts and provided reliable and replicable estimates of occupancy rates , mean lifetimes , and state transition probabilities associated with each brain state ., Although VB-HMM identified 16–19 states in both adult cohorts , only three states had occupancy rates greater than 10% ( Fig 2C and 2G ) , and these states demonstrated the highest mean lifetimes ., However , even these dominant states had short mean lifetimes ranging from 7–10 s , demonstrating that brain states are temporally persistent over durations far shorter than the length of a typical rs-fMRI scan session ., These features were observed in both adult cohorts , demonstrating the robustness of our findings ., Furthermore , analysis of the state transition probability indicated that each state had the highest probability of transitioning to itself rather than other states ( Fig 2D and 2H ) , suggesting that temporal stability of individual states does occur ., Taken together , these results demonstrate the existence of dynamic , yet stable , brain states in rs-fMRI and identify distinct connectivity patterns associated with each state ., We suggest that this balance of temporal stability and dynamic connectivity is a fundamental principle of brain organization ., By construction , VB-HMM states are characterized by distinct patterns of inter-node connectivity ( Figs 2 and 3 ) ., To test specific hypotheses related to the dynamic interactions between the SN , CEN and DMN and interpret the neurobiological relevance of connectivity profiles , we identified dynamic functional connectivity profiles associated with the three previously known static networks ., To accomplish this we applied modularity-based community detection algorithms 36 on the functional connectivity matrix estimated by VB-HMM for each state ( Fig 1B ) ., This analysis revealed that , in some cases , states with non-identical connectivity matrices had similar overall community structures ( S5 , S6 , S8 and S9 Figs ) ., For example , multiple states ( S5 and S6 Figs ) demonstrated a pattern in which the SN , CEN and DMN formed separate , segregated communities , reminiscent of the static functional networks previously identified by independent components analysis 50 ., We next combined states with identical community structures into dynamic functional networks ( DFNs ) and examined the temporal properties of segregated and non-segregated DFNs as well as the dynamic interactions between key nodes of the SN , CEN and DMN ., The SN , CEN and DMN formed separate communities and were segregated from each other ( DFN-1 in Fig 3 ) approximately 31% of the time ( 31% and 27% in Cohorts 1 and 2 , respectively ) ., In this case , all three networks maintained their within-network connectivity structure–AI and ACC nodes of the SN were connected with each other , PMC and VMPFC nodes of the DMN were connected with each other , and DLPFC and PPC nodes of the CEN were connected with each other ., Crucially , VB-HMM also revealed that this DFN had a mean lifetime of about 7–10 s ( 8 . 3 s and 8 . 8 s in Cohorts 1 and 2 , respectively ) ( Fig 3C and 3G ) ., These findings suggest that although this particular DFN configuration is a prominent feature of SN , CEN and DMN organization , it has a relatively short lifetime ., The second dominant DFN identified by VB-HMM had a community structure in which the CEN and DMN were interconnected in one community , while the SN nodes remained segregated from the CEN and DMN , forming an independent network ( DFN-2 in Fig 3 ) ., This DFN configuration had occurrence rates of 36% and 18% in Cohorts 1 and 2 , respectively ( Fig 3 ) ., The remaining states had distinct DFN configurations ( S5 and S6 Figs ) , with varying levels of cross-network interactions , but their occurrence rates were lower and not consistent across the two cohorts ., Previous work from our lab 12 39 and recent work by other labs 54 , 55 has indicated that the SN plays a critical role in switching between the DMN and the CEN ., Our results suggest that this switching is transient ( i . e . doesn’t persist for a long time ) and may occur not very frequently ., Finally , analysis of the switching probability between DFNs revealed that each DFN had a high probability ( 0 . 91 in Cohort 1 and 0 . 93 in Cohort 2 ) of making self-transitions ( Fig 3D and 3H ) ., Thus , as with individual brain states , the two dominant DFN configurations ( DFN-1 and DFN-2 in Fig 3 ) were stable over time but persistent only for short time intervals ., Taken together , these findings identify key features of dynamic functional interactions associated with the SN , CEN and DMN and confirm that the static segregated networks previously identified using independent component analysis occur only about 30% of the time ., The organization of brain networks in adults is shaped by years of development , learning and brain plasticity 5 ., Previous stud | Introduction, Materials and Methods, Results, Discussion | Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis ., Here we develop a novel Variational Bayesian Hidden Markov Model ( VB-HMM ) to investigate dynamic temporal properties of interactions between salience ( SN ) , default mode ( DMN ) , and central executive ( CEN ) networks—three brain systems that play a critical role in human cognition ., In contrast to conventional models , VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN , CEN , and DMN ., Furthermore , the three “static” networks occurred in a segregated state only intermittently ., Findings were replicated in two adult cohorts from the Human Connectome Project ., VB-HMM further revealed immature dynamic interactions between SN , CEN , and DMN in children , characterized by higher mean lifetimes in individual states , reduced switching probability between states and less differentiated connectivity across states ., Our computational techniques provide new insights into human brain network dynamics and its maturation with development . | Characterizing the temporal dynamics of functional interactions between distributed brain regions is of fundamental importance for understanding human brain organization and its development ., Progress in the field has been hampered both by a lack of strong computational techniques to investigate brain dynamics and an inadequate focus on core brain systems involved in higher-order cognition ., Here we address these gaps by developing a novel variational Bayesian Hidden Markov Model ( VB-HMM ) that uncovers non-stationary dynamical functional networks in human fMRI data ., In two cohorts of adults , VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between the salience ( SN ) , default mode ( DMN ) , and central executive ( CEN ) networks—three brain systems critical for higher-order cognition ., In children , relative to adults , VB-HMM revealed immature dynamic interactions between SN , CEN , and DMN , characterized by higher mean lifetimes in individual states , reduced switching probability between states and less differentiated connectivity across states ., Our findings suggest that the flexibility of switching between distinct brain states is weaker in childhood , and they provide a novel framework for modeling immature brain network organization in children ., More generally , the approach used here may prove useful to the investigation of dynamic brain organization in neurodevelopmental and psychiatric disorders . | children, medicine and health sciences, diagnostic radiology, functional magnetic resonance imaging, markov models, neural networks, applied mathematics, random variables, neuroscience, covariance, magnetic resonance imaging, algorithms, simulation and modeling, age groups, adults, probability distribution, mathematics, brain mapping, neuroimaging, families, research and analysis methods, computer and information sciences, imaging techniques, hidden markov models, probability theory, people and places, radiology and imaging, diagnostic medicine, population groupings, biology and life sciences, physical sciences | null |
journal.pcbi.1006412 | 2,018 | Maintaining maximal metabolic flux by gene expression control | We will first introduce the control problem that a cell faces ., We consider a well-understood example: the regulation of galactose metabolism in yeast ( Figs 1A and 2B ) ., We aim to characterise the dynamics of a controlling gene circuit that always maximises the steady-state flux per unit invested enzyme in this pathway ( the specific flux ) upon an environmental change , such as in the extracellular galactose concentration ., The controlling gene network has to distribute a finite amount of biosynthetic resources for enzyme synthesis over the four pathway enzymes to maximise the steady state pathway flux ., Depending on the external galactose concentration , less or more enzymatic resources should be invested in the galactose import reaction ., This leaves a correspondingly smaller or larger pool of enzymatic resources for the remaining pathway reactions ., An increase in Galout will cause an increase in Galin , which is therefore indicative for the external change ., Galin can thus act as a signal for the adjustment of enzyme concentrations of the pathway: the transporter concentration should decrease and the others should increase ., In yeast , Galin plays the role of metabolic sensor 27 ., It relays information to the GAL operon by binding to gal3p , a regulatory protein that can activate transcription factors , such as gal80 and gal4 ., The key question is how the concentration of Galin should influence the gene network in order to steer the galactose pathway to maximal specific flux ., We refer to the relation between the steady-state concentrations of the metabolic sensor ( Galin ) and the metabolic enzymes as the input-output relation of the gene circuit ., qORAC specifies this relation for robust maximisation of specific pathway flux ., Whether a gene circuit with realistic biochemical kinetics can be found that can implement this input-output relation then still needs to be determined ., Since the gene network for the galactose pathway in yeast is known , the optimal input-output relation may be found by fitting parameters in this network , which we achieved in an earlier paper 28 ., In the current paper , however , we show that the problem of finding optimal input-output relations for a given metabolic pathway has a general solution , applicable to all examples shown in Fig 2A–2D ., This indicates that cells can implement qORAC using simple regulating circuits ., The qORAC theory starts with the dynamics of the intracellular metabolite concentrations xI = ( x1 , … , xn ) of a metabolic network ,, x I ˙ = N v ( x I ; x E ) - μ x I ., ( 1 ), Here , N is the stoichiometry matrix , v ( xI; xE ) is the vector of reaction rates , xE are fixed external concentrations , and μ is the cellular growth rate ., It is generally assumed that the dilution rate of concentrations by growth , −μxI , is negligible for metabolism ., We take the same view here , and consider, x I ˙ = N v ( x I ; x E ) ., ( 2 ), The qORAC framework couples this metabolic pathway to enzyme dynamics , by choosing, e ˙ = E ( x S ) - μ e ., ( 3 ), Since enzyme dynamics occur at time scales of similar order as the growth rate , the dilution by growth cannot be neglected this time ., Throughout the paper , the growth rate is a predefined parameter , and not part of the optimisation problem ( see the Discussion for more information ) ., E ( xS ) denote the enzyme synthesis rates for all the different enzymes involved in the pathway ., These functions may only depend on internal sensor metabolite concentrations , as explained in the Introduction ., The task is to define these functions in such a way that the combined dynamical metabolic-enzyme system converges to a steady state in which flux through the pathway is maximal ., We aim to maximise a steady-state specific flux vr/eT through the network where vr is some chosen output flux ( e . g . in mM/hr ) and eT ( e . g . in grams ) is total amount of invested enzyme ., The optimisation problem we study is, max x I , e { v r e T | N v = 0 , ∑ j e j = e T } , ( 4 ), with ej as the concentration of enzyme j ., Thus , we wish to maximise a given output flux vr per unit of total invested enzyme eT of a metabolic network at steady state ., The optimisation problem stated in Eq ( 4 ) is equivalent to minimising the amount of enzyme necessary to sustain a given steady-state flux vr at rate Vr ,, min x I , e { ∑ j e j v r | N v = 0 , v r = V r } ., ( 5 ), A crucial observation is now that since reaction functions generally are of the form vj = ejfj ( xI; xE ) 31 , we may prescribe vr = 1 ., After all , if we can solve that problem then we can solve it for vr = Vr as well by multiplying all the enzyme concentrations by Vr , because the specific flux vr/eT remains the same ., Hence , we simplify ( 5 ) to, min x I , e { ∑ j e j | N v = 0 , v r = 1 } ., ( 6 ), The relation vj = ejfj ( xI; xE ) may also be used to write ej = vj/fj ( xI; xE ) and rewrite ( 6 ) to, min x I { ∑ j v j f j ( x I ; x E ) | N v = 0 , v r = 1 } ., ( 7 ), Observe that the enzyme concentration vector e has disappeared from the problem ., ( Note also that this optimisation is not a stoichiometric-model optimisation , such as flux balance analysis 32 . The qORAC method takes into account the kinetics of the metabolic enzymes and the metabolite concentrations are the variables in this approach . The outcome of qORAC is the definition of a self-optimising dynamical system; this has nothing to do with the optimisation associated with stoichiometric modelling . ), It has recently been shown that the flux profiles that solve ( 7 ) ( and therefore also the equivalent original problem ( 4 ) ) are always subnetworks with a particularly simple structure , called Elementary Flux Modes ( EFMs; 30 , 29 ) ., Such EFMs are one-degree-of-freedom flux vectors satisfying Nv = 0 that cannot be simplified further by deleting reactions without violating the steady state assumption 33 , 34 ., A given EFM is thus characterised by λ ( V1 , … , Vm ) , where λ is a free parameter and the flux vector ( V1 , … , Vm ) is fixed ., If we want to optimise specific flux within a given EFM with flux vector ( V1 … , Vm ) , we still need to find a vector xI for, min x I { ∑ j λ V j f j ( x I ; x E ) } ., ( 8 ), This motivates the introduction of the objective function O ( x I ) : = ∑ j λ V j f j ( x I ; x E ) , ( 9 ), which is to be minimised , for given external concentrations xE , by suitably choosing internal concentrations xI ., This function is convex for pathways with many kinds of reaction kinetics 11 , and in the Supporting Information ( SI ) we show that it is in fact strictly convex , for an even larger class of rate laws ., Hence , the optimum is uniquely specified by the external concentrations xE ., Note that the objective function has a lower value if the values of fj ( xI; xE ) are higher ., Maximising specific flux may thus be reinterpreted as maximising the values of all fj’s simultaneously ., These fj are closely associated to the saturation levels of enzyme j with its reactants ( and effectors ) ., This optimisation can be done by making as little enzyme as possible , so that the enzymes are used at their maximal capacity ., If we find the vector x I o which minimises O ( xI ) , then we can infer the corresponding optimal enzyme concentrations eo by setting, e j o = λ V j f j ( x I o ; x E ) ., ( 10 ), It is clear that we may choose λ = 1 in O ( xI ) : having found the minimiser of O ( xI ) for λ = 1 , we have found it for all λ: the corresponding enzyme levels e j o just scale with λ ., In hindsight , we may also for instance normalise the enzyme concentrations such that they sum to a given total concentration eT ., At this stage , the optimal enzyme concentrations that maximise the specific flux at steady state are still defined in terms of external concentrations xE: for each choice of xE , the objective function ( 9 ) needs to be minimised to find x I o , and subsequently eo needs to be calculated ., In order to characterise gene regulatory networks that produce the right concentrations of enzymes in steady state , robustly with respect to changes in external concentrations but without direct knowledge of those changes , we need to understand the defining characteristics of optimal solutions ., Steady-state optimisers x I o are minima of O ( xI ) , and are dependent on ( i . e . , parameterised by ) xE ., So , x I o is a ( in fact , the ) critical point of O ( xI ) = O ( x1 , … , xn ) , satisfying the optimality relations 0 = ∂ O ∂ x i = ∂ ∂ x i ∑ j V j f j ( x I ; x E ) , i = 1 , … , n ., ( 11 ), So instead of minimizing O ( x ) for given external conditions xE , we could solve ( 11 ) by prescribing xE and solving for the remaining variables , the internal concentrations xI ., However , the gene network does not have access to xE ., Eq ( 11 ) should be solved with knowledge of the current sensor concentrations only ., We therefore solve ( 11 ) by prescribing a subset of the internal metabolite concentrations , sensor values xS , and solving for all remaining concentrations , namely all other internal concentrations , but now also the ( unknown ) external concentrations ., The solution is denoted by ξ = ( ξI , ξE ) , and is the estimated optimal concentration vector , under the assumption of steady state and optimality of the sensor values ., In short , we call ξ the optimum as predicted by the sensors ., Here , ξE are the external concentrations for which the current sensor values would have been optimal if the pathway had been in steady state ., The part of ξI corresponding to sensor metabolites , ξS , of course coincides with the real concentrations xS , by construction ., Since ξ is defined by xS , we denote it by ξ ( xS ) ., To solution of ∂O ( x ) /∂xi = 0 for different sensor values is well-defined mathematically if the Implicit Function Theorem ( IFT ) holds ( see SI for a more detailed exposition ) ., In essence , this means that it is then possible to calculate the optimal allocation by varying the sensors appropriately ., The sensors are able to “track” the optima ., Any choice of sensor metabolites for which the IFT holds is a candidate for the proposed adaptive control ., An immediate consequence of the IFT is that the number of sensor metabolite concentrations must equal the number of changing external metabolite concentrations to which the system needs to be robust ., This makes intuitive sense: to track changes ( and hence achieve robustness ) in N parameters , the gene network should be influenced by ( at least ) N ( independent ) internal sensors ., Examples of parameters are environmental nutrient concentrations , temperature , pH and toxin concentrations ., With ξ ( xS ) , we can define corresponding predicted optimal enzyme levels , analogous to ( 10 ) , by setting, e j o = V j f j ( ξ ( x S ) ) ., ( 12 ), At these enzyme concentrations , the pathway is either in steady state or not ., If not , the metabolic concentrations are still changing , including the sensor concentrations ., Hence , the predicted optimal enzyme levels also change ., This argument indicates that the only steady state of the metabolic network steered in this fashion is the optimal one ., In the SI we prove that an EFM metabolic pathway with added qORAC control has a unique steady state , the optimum ., The proof is fully worked out for linear chains of enzymatic reactions ( Theorem 3 in SI ) , but the techniques of the proof extend to a much larger class of pathways ., All one needs to require is that for each choice of enzyme concentrations , the metabolic pathway has a unique steady state ( a common enough assumption ) , and that the sensors are a few reaction steps away from the external concentrations ( which makes intuitive sense ) ., This result therefore ensures that when the qORAC-controlled pathway has reached a steady state , it necessarily must be optimal ., We now finish by implementing the enzyme synthesis rate functions Ej in, e ˙ j = E j - μ e j ., By setting, E j = μ V j f j ( ξ ( x S ) ) , ( 13 ), we have ensured that at steady state the enzyme levels are optimal ., The complete construction is termed qORAC , and is summarised in Definition 1 ., A fully-worked out example for the small pathway shown in Figs 3 and S2 is specified in Example 1 ., Definition 1 ( qORAC ) : The following differential-algebraic system of equations implements Specific Flux ( q ) Optimisation by Robust Adaptive Control ( qORAC ) through an EFM with flux vector ( V1 , … , Vm ) in a cell culture growing at fixed growth rate μ ., Let I be the index set of internal metabolite concentrations , E the index set of external concentrations , and S the index set of sensor concentrations ., Let furthermore O ( x I ) = ∑ j = 1 m V j / f j ( x I ; x E ) be the objective function ., Then we consider for i ∈ I , and j = 1 , … , m ,, x ˙ i= ∑ j = 1 m N i j v j = ∑ j = 1 m N i j e j f j ( x I ; x E ) , ( 14 ), e ˙ j= E j ( x S ) - μ e j , ( 15 ), E j ( x S ) = μ V j / f j ( ξ ( x S ) ) ∑ l = 1 m V l / f l ( ξ ( x S ) ) , ( 16 ), where ξ ( xS ) = ( ξI ( xS ) , ξE ( xS ) ) is the predicted optimum , and is the ( time-dependent ) solution of, ξ S= x S , ( 17 ), ∂ O ∂ ξ i ( ξ ) = 0 ., ( 18 ), The rescaling of Ej ( xS ) in ( 16 ) by the sum of all the inverses of 1/fj implies that total enzyme concentration is chosen to be equal to 1 ., Other rescalings give identical results , up to the chosen scaling factor ., The choice above , however , is particularly useful , since it ensures positive synthesis rates both for positive and negative metabolic rates through the pathway , and it ensures that it is well-defined also at thermodynamic equilibrium ( see SI for details ) ., Example 1: qORAC for a simple pathway The example qORAC-controlled metabolic pathway from Figs 4 and S2 is specified by the following set of equations for the metabolite concentrations x = ( x1 , … , x4 ) = ( C , C′ , N′ , C3N2 ) ., Note that x1 = C is an external concentration which may change value periodically , as shown in Fig 4 ., x˙1=0 , x˙2=v1−v3 , x˙3=v2−v3 , x˙4=v3−v4 ,, where vi = eifi ( x ) , i = 1 , … , 4 , and the kinetics functions fi ( x ) are defined by, f 1 ( x ) = 0 ., 6 x 1 - 0 ., 75 x 2 ( 0 . 2 x 1 + 1 . 0 ) ( 0 . 33 x 2 + 1 . 0 ) , f 2 ( x ) = N - 3 ., 0 x 3 ( 0 . 5 x 3 + 1 ) ( N + 1 ) , f 3 ( x ) = 0 ., 2 x 2 x 3 - 0 ., 17 x 4 ( 0 . 2 x 3 / 5 + 1 . 0 ) ( 0 . 33 x 2 + 0 . 17 x 4 + 1 . 0 ) , f 4 ( x ) = x 4 - 0 ., 0025 0 ., 33 x 4 + 1 ., 0025 ., The objective function is given by O ( x ) = 1 f 1 ( x ) + ⋯ + 1 f 4 ( x ) ., The enzyme dynamics are given e ˙ j = E j ( x 2 ) - e j , j = 1 , … 4 , where, E j ( x 2 ) = 1 / f j ( ξ ( x 2 ) ) ∑ k 1 / f k ( ξ ( x 2 ) ) , j = 1 , … , 4, and the predicted optimum ξ ( x2 ) is defined by, { ξ 2 = x 2 , ∂ O ∂ ξ 2 ( ξ 1 , … , ξ 4 ) = ∂ O ∂ ξ 3 ( ξ 1 , … , ξ 4 ) = ∂ O ∂ ξ 4 ( ξ 1 , … , ξ 4 ) = 0 ., A toy metabolic network , with two external parameters and one output flux , is shown in Fig 4 ( see Box 2 for the mathematical implementation ) ., In this example , only the external C concentration is allowed to vary , so one internal sensor metabolite is required ., Upon changes in this external concentration , the sensor concentration changes , causing changes in enzyme synthesis , which finally result in adaptation to the new optimum ., The optimal enzyme synthesis relations of the gene network are also shown ., They are simple curves , suggesting that small gene circuits are sufficient for optimal steering of this pathway ., To illustrate the general applicability of qORAC , consider the complicated branched example network in Fig 5 . It has two inputs and two outputs and two allosteric interactions; by employing four sensors , it can be made robust to changes in all four external concentrations ., The qORAC framework is able to start from nearly any initial condition ., As an extreme example , with no enzymes present , and only the sensor concentration and no other internal metabolite , the qORAC-controlled pathway still steers to optimum ( S1 Fig ) ., Similarly , if the sensor concentrations are ‘wrong’ , such that they predict a metabolic flow in the opposite direction to the one dictated by external concentrations , the combined controlled system nevertheless converges to the correct optimum ( S2 Fig ) ., The qORAC control does not guarantee that a metabolic pathway is actually steered towards the optimum ., In an example in which one of the periodically changing parameters is a Km parameter of a rate law , the choice of sensors matters critically ( Figs 6 and S3 ) ., With one choice , the system robustly steers to the optimal specific flux steady state , but with another choice it does not ., In both cases , the technical requirements to use the internal metabolites as sensors are met ., In each of the pathways shown in Fig 2A–2D , the sensor metabolite ( s ) and transcription factor ( s ) have been identified ., Specifying the kinetics for each enzymatic step in the pathway now directly gives the corresponding objective function ( 9 ) and the qORAC framework can be set up ., The case of galactose uptake ( Fig 2B ) in yeast has been studied theoretically in detail by 28 , including fitting the parameters of the well-characterised GAL gene network to approximate optimal input-output relations ., Recent experimental evidence moreover shows that yeast cells are indeed able to tune the levels of these enzymes to optimise growth rate ( 9; Fig 1A ) ., Experimental evidence is accumulating that suggests that cells can tune their enzyme resources to maximise growth rate 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 10 ., We addressed whether cells growing at a fixed rate can tune limited enzyme resources to steer metabolism to optimal flux states , given only limited information about the current metabolic state of the cell in the form of sensor-metabolite concentrations ., We demanded robustness of optimality in the face of environmental changes ., We logically derived the qORAC framework , which implements such control for Elementary Flux Modes , the minimal steady state pathways that maximise specific flux 29 , 30 ., Maximisation of specific fluxes is a requirement for maximisation of the specific growth rate of cells ., We use the term Specific Flux ( q ) Optimisation by Robust Adaptive Control ( qORAC ) to describe the regulatory mechanism that we study ., ‘Robust’ signifies that attaining optimal states is independent of ( environmental ) parameter values—the system is robust to them ., ‘Adaptive’ means that the control system steers the metabolic system to optimality without direct knowledge of external changes , contrary to the more widely studied problem of ‘optimal control’ , in which the steering mechanism works using external changes as inputs to the controller 35 ., It is important to note that the growth rate itself is not optimised in our approach ., Maximising steady state growth rate rather than specific flux requires a fundamentally different approach ., The modelling framework should be extended to Metabolite-Enzyme models in which enzymes are made from precursors 36 , 37 ., In such models , the growth rate features quadratically rather than linearly , in the resulting steady state and optimality equations ., EFMs therefore no longer apply , and the objective function O ( x ) is also absent ., Our approach is therefore more suitable to isolated pathways then to all of metabolism ., For such smaller pathways , it is more reasonable to assume that there is a fixed amount of enzyme resources to distribute , and that the cellular growth rate is considered constant ., Recent work does suggest , however , that the objective function O ( x ) studied here in fact matters to cells also on a more global metabolic level 11 ., An important finding of our work is that the number of sensor metabolites must be ( at least ) equal to the number of parameters for which the metabolic pathway is robustly optimal ., In other words , if the metabolic pathway always achieves states of maximal specific flux , regardless of the values of three ( independently changing ) environmental parameters , such as , for example , osmolarity , temperature and some nutrient concentration , then the number of sensors is expected to be three ., This is a general result that follows from the associated mathematics of this control problem ., Finding the sensors experimentally is difficult , and the number of known sensors is still quite small ., However , it is telling that the whole of central carbon metabolism in E . coli seems to be controlled by just three sensors , FBP , cAMP and F1P 25 ., The identity of suitable sensors does not follow immediately from the optimisation problem ., In general , one needs to make sure that the Implicit Function Theorem applies to the optimum Eq ( 11 ) , and this is not a trivial matter ., However , a different argument shows that sensors near the beginning or ends of the pathway would work in most cases ., The reason is that for all metabolites in between a set of fixed concentrations , their optimal value is uniquely determined by minimising the corresponding optimisation problem ( i . e . finding the minimum of a suitable objective function O ( x; xS ) with x the set of metabolites between the sensors xS ) ., The remaining variables , including the external concentrations , then need to be determined using the optimum Eq ( 11 ) ., This is easiest ( it involves the smallest number of equations and unknowns to solve for ) when sensors are close to the external metabolites ., Also from a biological standpoint this makes sense: such sensors obviously provide the most information of any change in external concentrations ., An important question is whether the adaptive control can be achieved by molecular circuits , given our understanding of biochemical kinetics and molecular interactions ., The explicit example from galactose metabolism in yeast 28 gives hope that this might be true in general ., If the necessary gene network is small , then the optimal circuit is likely also evolvable ., We cannot give definite answers about this , but the computational analyses of different networks , of which some are shown in this paper , indicate that qORAC-controlled networks show remarkably simple dynamics and input-output relations ., One would expect that biochemical systems are capable of evolving those , and that synthetic biologists are capable of designing them ., The parameterisation of the optimising circuit is completely determined by the kinetics and the wiring of the metabolic pathway that it controls , since the objective function ( 9 ) contains only this information ., This interdependence between the controller and the controlled is sometimes called the ‘internal model principle’ in engineering 26 which roughly states that the control system should have knowledge of the dynamic behaviour of the system in order to be able to control it ., Additional control mechanisms may then prevent for instance undesired oscillations or slow responses ., The internal model principle , applied to metabolic pathway control , suggests a new perspective on the larger problem of understanding metabolic regulation ., The theory presented here indicates that knowledge of the metabolic pathway , including properties of catalysing enzymes , is sufficient to understand how this pathway needs to be controlled to maximise flux ., It is not necessary to know the controlling regulatory pathway in advance ., This offers hope for situations in which this circuit has not been characterised yet , or for which it needs to be designed synthetically ., Technological advances have spurred recent interest in studying control properties of gene regulatory networks in cellular metabolism ., One line of work involves characterising a particular gene control system and studying its theoretical properties ., Examples are the perfect adaptation in the chemotaxis network in E . coli 38 , 39 , the robustness properties of the heat-shock response system 40 and of the circadian clock 41 ., Several authors have considered dynamic optimisation of resources in pathways from a mostly computational perspective , e . g . to minimise the time of adaptive response 42 , deFBA 43 , and for other objectives than maximal specific flux , such as detecting equilibrium regimes of pathways 44 , robustness to flux perturbations 45 , and noise propagation 46 ., In many studies , the control is not adaptive , but optimal; the objective is then usually to maximise the long term production of biomass 47 , 42 , 48 , e . g . ., The approach taken here differs principally from most previous works in the following respect ., The objective ( maximal specific flux ) is defined in advance , and the optimal input-output relations are characterised later ., The framework is also analytic rather than computational: the input-output relations are obtained by solving the optimum equations ( 11 ) for the pathway , rather than by using a numerical optimisation routine ., The latter is impossible , since this would require knowing the external concentrations ., A few recent papers have used adaptive controls similar to ours ., So-called Flux Control Regulation ( FCR; 49 ) comes closest , and uses the same type of adaptive control as proposed in qORAC ., FCR also explicitly relies on making estimates at each time point under the assumption of steady state ., When the system is in fact in steady state , it has reached the desired objective ., The principle difference between FCR and qORAC lies in the objective ., The input-output relations in FCR come from measurements and ensure steady state properties only ., qORAC , however , solves a steady state optimisation problem , and constructs input-output relations directly from the kinetic rate laws of the metabolic pathway itself ., Another recent example of a coarse-grained model of cellular physiology including gene expression control can be found in 50 ., Two other examples using adaptive control are from the context of optimal ribosomal allocation to maximise the growth rate in E . coli ., The free amino acid concentration acts as a sensor to ppGpp , which downstream influences gene expression ., Two models have been proposed that are based on optimal synthesis of ribosomes so as to maximise growth rate 15 , 16 ., The input-output relations used in these models are not derived from kinetic properties as in qORAC , but are designed by hand to approximate maximal growth rates in different conditions ., The choice of sensors sometimes matters for the control to steer the pathway to optimum ( Figs 6 and S4 ) ., This example already indicates that , although the qORAC control follows logically from the design objective , it is not easy to decide which intermediate metabolites make it controllable ., We cannot expect completely general mathematical theorems ., Apparently , some choices of sensors do work , and others do not , for the same pathway , using the same initial conditions ., A second , mathematical reason why one cannot expect convergence to optimal states is that if time would be reversed , the control would remain the same , but dynamics would be reversed ., The control is based on steady state properties of the system , and these do not change upon time reversal ., qORAC has direct applications in synthetic biology ., To achieve maximal production rates in a biotechnological-product producing pathway requires a controller that qORAC provides ., The only ingredient to design such a controller are the enzymatic rate laws in the pathway ., qORAC then immediately makes predictions about the optimal enzyme synthesis rates , as a function of one or more intermediate metabolites ., As the synthetic biology field advances , synthetic circuits with the required input-output relationships for the constituent enzymes of the pathway can be designed and built ., qORAC therefore does not only contribute to the general understanding of steering mechanisms to optimal states , but provides direct operational relevance for microbiology , synthetic biology and biotechnological applications . | Introduction, Methods, Results, Discussion | One of the marvels of biology is the phenotypic plasticity of microorganisms ., It allows them to maintain high growth rates across conditions ., Studies suggest that cells can express metabolic enzymes at tuned concentrations through adjustment of gene expression ., The associated transcription factors are often regulated by intracellular metabolites ., Here we study metabolite-mediated regulation of metabolic-gene expression that maximises metabolic fluxes across conditions ., We developed an adaptive control theory , qORAC ( for ‘Specific Flux ( q ) Optimization by Robust Adaptive Control’ ) , and illustrate it with several examples of metabolic pathways ., The key feature of the theory is that it does not require knowledge of the regulatory network , only of the metabolic part ., We derive that maximal metabolic flux can be maintained in the face of varying N environmental parameters only if the number of transcription-factor binding metabolites is at least equal to N . The controlling circuits appear to require simple biochemical kinetics ., We conclude that microorganisms likely can achieve maximal rates in metabolic pathways , in the face of environmental changes . | To attain high growth rates , microorganisms need to sustain high activities of metabolic reactions ., Since the catalysing enzymes are in finite supply , cells need to carefully tune their concentrations ., When conditions change , cells need to adjust those concentrations ., How cells maintain high metabolism rates across conditions by way of gene regulatory mechanisms and whether they can maximise metabolic activity is far from clear ., Here we present a general theory that solves this metabolic control problem , which we have called qORAC for specific flux ( q ) Optimisation by Robust Adaptive Control ., It considers that external changes are sensed by internal “sensor” metabolites that bind to transcription factors in order to regulate enzyme-synthesis rates ., We show that such a combined system of metabolism and its gene network can self-optimise its metabolic activity across conditions ., We present the mathematical conditions for the required adaptive control for robust system-steering to optimal states across conditions ., We provide explicit examples of such self-optimising coupled metabolism and gene network systems ., We prove that a cell can be robust to changes in K parameters , e . g . external conditions , if at least K internal metabolite concentrations act transcription-factor binding sensors ., We find that the optimal relation of the enzyme synthesis rates of self-optimising systems and the concentration of the sensor metabolites can generally be implemented by basic biochemistry ., Our results indicate how cells are able to maintain maximal reaction rates , even in changing conditions . | chemical compounds, enzymes, metabolic networks, enzymology, carbohydrates, galactose, organic compounds, optimization, mathematics, metabolites, network analysis, enzyme metabolism, enzyme chemistry, computer and information sciences, proteins, metabolic pathways, chemistry, biochemistry, organic chemistry, gene regulatory networks, genetics, monosaccharides, biology and life sciences, physical sciences, computational biology, metabolism | null |
journal.pcbi.1000675 | 2,010 | A Dynamic Model of Interactions of Ca2+, Calmodulin, and Catalytic Subunits of Ca2+/Calmodulin-Dependent Protein Kinase II | Calcium ( Ca2+ ) is a critical second messenger in the brain ., For example , it has long been recognized that Ca2+ influx through N-methyl-D-aspartate ( NMDA ) receptors initiates changes at synapses that enable us to form memories and to learn ., Transient influx of Ca2+ through NMDA receptors triggers activation of complex protein signaling networks that regulate changes in synaptic efficacy including long-term potentiation ( LTP ) and long-term depression ( LDP ) 1 , 2 ., Calmodulin ( CaM ) , a small protein ( 18 kDal ) with four Ca2+ binding sites , is a molecular detector of influxes of Ca2+ across the synaptic membrane ., It is ubiquitous in all cells including neurons 3–5 , and it regulates proteins in postsynaptic spines of excitatory neurons 6 ., When Ca2+ enters the spine , it binds to CaM and to other Ca2+-binding proteins ., As Ca2+ binds to CaM , the Ca2+/CaM complex can then bind to and regulate its enzyme targets , many of which are immobilized in the “postsynaptic density” ( PSD ) , a scaffold for signaling molecules attached to the postsynaptic membrane 7 ., The relative rates of binding and affinities of the target enzymes for Ca2+/CaM are believed to determine their level of activity in a sensitive and selective fashion ., Among the prominent CaM targets in the spine is Ca2+/CaM-dependent protein kinase II ( CaMKII ) 8 , which plays a central role in initiating persistent synaptic changes 9 ., It is required for normal LTP; transgenic mice lacking the major neuronal subtype of CaMKII show defective LTP and are deficient in spatial learning and memory 10 , 11 ., Thus , understanding the kinetics of interactions of Ca2+ , CaM , and CaMKII can provide important insight into the initiation of mechanisms of synaptic plasticity ., The structure and Ca2+ binding properties of CaM have been extensively characterized 12 ., It comprises two pairs of Ca2+-binding EF-hand domains located at the N-and C-termini , respectively , separated by a flexible linker region ( Figure 1 , 13–15 ) ., The pairs of EF-hands at the N and C termini have substantially different Ca2+ binding kinetics; however , both pairs bind Ca2+ ions cooperatively 16 , 17 ., The interactions of Ca2+-bound CaM with its targets are kinetically complex ., CaMs affinity for many of its target proteins is increased upon Ca2+ binding and its affinity for Ca2+ is enhanced upon binding of its target proteins 18 ., Dissociation of the N-terminal bound Ca2+ ions from CaM often precedes dissociation of CaM from its target peptides 19 ., When this is the case , the dissociation rate of the peptide from the Ca2+-bound C-terminal domain of CaM ( CaM-2C ) strongly influences the overall dissociation rate of the peptide from CaM ., The kinetics of Ca2+ binding to CaM are likely to be particularly important in determining the outcome of Ca2+ signaling in neuronal spines because Ca2+ triggers biochemical events in the spine during a period when the Ca2+ concentration is fluctuating rapidly 20 ., Furthermore , in conditions of limiting Ca2+ , including basal Ca2+ or during relatively low amplitude Ca2+ transients , very few of the free CaM molecules will have four bound Ca2+ ions ., Yet physiological responses to small increases in Ca2+ are observed 21 , suggesting that CaM with fewer than 4 bound Ca2+ ions participates in initiating these responses ., CaMKII is a large holoenzyme comprising 12 homologous catalytic subunits held together by association of their carboxyl terminal domains 8 , 22 ., Each of the subunits contains a single CaM binding domain ., Binding of Ca2+/CaM to this domain releases inhibition of the active site and stimulates catalytic activity 23 ., Soon thereafter , a specific site within the catalytic subunit is autophosphorylated; the autophosphorylation event stabilizes the active conformation resulting in Ca2+-independent catalytic activity 24 ., Recently , we showed that CaM with two Ca2+ ions bound to its C-terminal sites , binds to CaMKII and activates autophosphorylation , though at a ten-fold lower catalytic rate than fully loaded CaM ( CaM with 4 Ca2+ bound ) 25 ., Thus , a kinetic model that describes Ca2+ binding to each of the individual CaM termini , as well as binding of Ca2+ to the CaM/CaMKII complex is important for a complete description of the activation dynamics of CaMKII in spines ., Furthermore , a model that accounts for the activity of CaM/CaMKII with less than 4 bound Ca2+ , is necessary to understand the extent of activation of CaMKII at relatively low and/or fluctuating Ca2+ concentrations such as occur in the spine cytosol ., Here we present two kinetic models of activation of monomeric subunits of CaMKII which include binding of Ca2+ to free CaM and to CaM bound to individual ( ie . monomeric ) CaMKII subunits ., The model of monomeric CaMKII ( mCaMKII ) allows us to examine the significance of the dynamics of Ca2+ and CaM binding independent of the effects of cooperativity of binding of CaM between subunits within the holoenzyme 26 ., Both models include the different kinetics of Ca2+/CaM binding at the N and C termini , and the thermodynamic stabilization of Ca2+-binding when CaM is bound to a target protein 18 ., The first model is a complete model of binding of Ca2+ to the two CaM termini , including 9 Ca2+/CaM states and their interactions with mCaMKII ., It differs from a recently published allosteric model 27 in which the Ca2+ binding rates depend explicitly on whether CaM is in one of two abstracted ensemble conformational states , R or T . Most of the required kinetic rates in our model are well constrained by previous experimental studies; however , a few have not been measured directly ., In these cases , we used the principle of microscopic reversibility and fitting of existing experimental data to derive reasonable ranges of values for the kinetic rates ., The second model is a coarse-grained model that is motivated by experiments showing high cooperativity of binding between Ca2+ ions at each terminus 16 ., Binding of the second Ca2+ to each terminus of CaM is assumed to be rapid; thus , binding of pairs of Ca2+ to each terminus is treated as a single event ., The resulting model includes 4 Ca2+/CaM states and their interactions with mCaMKII ., We created computer simulations based on each of these two models and explored their behavior under commonly used experimental concentrations of Ca2+ , CaM , and mCaMKII , and under conditions that are closer to those believed to exist in synaptic spines ., We determined a range of initial conditions under which the results of the coarse grained , pair-binding model are indistinguishable from those of the complete model , and a range under which the two deviate significantly ., We show that Ca2+/CaM species with fewer than four bound Ca2+ predominate under many conditions that are believed to prevail in spines , and can sometimes completely determine the level of autophosphorylation ., We find that activation of mCaMKII is highest at a particular frequency of Ca2+ fluctuations ., The frequency that gives highest activation depends on the ratio of the time interval between Ca2+ transients and the rates of Ca2+ binding to the N and C termini of CaM , as well as on the the width of the Ca2+ transients ., Finally , we performed global variation and sensitivity analyses to determine which parameters most affect the levels of autophosphorylation at particular times and under various conditions ., We use these analyses to help infer the kinetic pathways through which autophosphorylation of CaMKII is likely to occur and to identify parameters whose refinement by direct measurement will be most important for the accuracy of predictions from our models ., The models presented here are a first step in a larger project to build kinetic simulations of activation of the CaMKII holoenzyme in the context of physiologically realistic models of Ca2+ fluctuations in postsynaptic spines 25 , 28 ., In addition , the models provide a framework in which to study activation of other Ca2+/CaM dependent enzymes , including the CaM-dependent protein kinases ( CaMKI , CaMKIV , CaMKK , myosin light chain kinase ) , phosphatases ( calcineurin ) and others ( adenylate cyclase , neuronal nitric oxide synthase , etc ) ., Detailed kinetic analysis of these interactions are critical for understanding the molecular mechanisms that underlie synaptic plasticity because the events that determine whether a synapse undergoes LTP or LTD are determined under non-equilibrium conditions , when the Ca2+ concentration is fluctuating ., Such analyses may also be useful for understanding Ca2+/CaM signaling in other tissues such as cardiac myocytes and cells of the immune system ., We constructed a detailed model ( Model 1 ) and a coarse-grained model ( Model 2 ) , both of which describe the kinetics of reversible binding of Ca2+ ions to free CaM and to the resulting intermediate Ca2+/CaM complexes ., The models also describe reversible binding of Ca2+ to the Ca2+/CaM complexes after they have bound to individual subunits of CaMKII ( mCaMKII ) ., Finally , they describe the kinetics of irreversible autophosphorylation of mCaMKII , which is triggered by binding of Ca2+/CaM ., Many of the simulations were carried out with concentrations of Ca2+ , CaM , and CaMKII that approximate those in postsynaptic spines of excitatory neurons in the forebrain ., The concentration ranges of CaM , CaMKII , and Ca2+ in spines were estimated from previous biochemical studies as follows ., The average protein concentration in rat brain was taken as 100 mg/ml ( equivalent to ∼10% by weight , see 31 ) ., CaMKII is an unusually abundant enzyme in the forebrain; its concentration is 2% of total protein by weight in the hippocampus and 1% in the rest of the forebrain as measured by quantitative immunoblot 32 ., Therefore , its average concentration in the hippocampus is ∼2 mg/ml ., CaMKII is found almost entirely in excitatory neurons which account for approximately half of forebrain weight , the rest consisting of inhibitory neurons , glial cells , blood vessels , and other minor cell types ., Thus , the average concentration of CaMKII in excitatory neurons is ∼4 mg/ml ., Given that the molecular weight of individual CaMKII subunits is ∼56 kDa , the average concentration of CaMKII catalytic subunits in the hippocampus is ∼74 µM ., In the rest of the forebrain , the average concentration is ∼37 µM ., Several studies have shown that CaMKII is usually more concentrated in the heads of spines than in dendritic shafts e . g . 33 and is highly concentrated in the postsynaptic density fraction 34 ., On the other hand , CaMKII appears to move into or out of spines in response to synaptic activation 35 , 36 and can associate with proteins in or near the PSD 7 ., Thus , in our simulations , we explore the effect of concentrations of CaMKII subunits from 40 to 200 µM on the rate of autophosphorylation ., When studying other variables , we set the concentration of mCaMKII at 80 µM ., The concentration of CaM in bovine and rat brain varies from ∼17 µM in the hippocampus 3 to ∼26 µM in the cerebral cortex and whole brain 3 , 4 ., If CaM in the particulate fraction is included , the estimated concentration in brain rises to ∼33 µM 3 ., In our simulations , we use concentrations of CaM from 20 to 40 µM ., The concentration of Ca2+ in postsynaptic spines varies dramatically ., Basal concentrations from 50 to 500 nM have been reported; whereas , in the immediate vicinity of activated NMDA receptors in the PSD , the transient concentration can rise to tens of µM 28 , 37–39 ., Here , we explore the dynamics of autophosphorylation of CaMKII through a large range of physiological Ca2+ concentrations and in response to trains of brief calcium transients ( msec duration ) , similar to those thought to occur in neurons ., We also examine autophosphorylation at steady-state concentrations of Ca2+ ranging from 0 . 5 to 250 µM , which mimic experimental conditions ., In all simulations , the concentration of Mg2+-ATP is assumed to be saturating , as it would be in the cell ., Reaction networks were entered into Mathematica 40 with the Xcellerator package 41 which translated the networks into systems of ODEs based on the law of mass action ., Numerical integration was performed in Mathematica 40 ., This method assumes well-mixed conditions and thus only approximates the situation in the cytosol ., Ca2+ spikes were simulated as exponential functions ; where and set the spike height and half width , respectively; centers the spike at a location relative to the last spike , depending upon the input frequency; and t is time ., This function was used as a fixed boundary condition representing the free Ca2+ concentration ., Thus , total Ca2+ was not conserved over the sum of the driving function and the Ca2+ bound to various molecular species ., This algorithm simulates a neuronal environment in which Ca2+ enters the cytosol through voltage and ligand-gated channels and is then rapidly sequestered or removed ., Mathematica packages implementing the models are available from the authors ., We used sensitivity analyses to determine which parameters of Model 1 ( Table S1 ) produce the most variation in the predicted autophosphorylation of mCaMKII ., We assembled random sets of input parameters , sampled over the range of experimental values for each parameter , using Latin Hypercube sampling 42–44 , as described in Text S1 ., The values were taken from Table S1 , and from the range of estimates of physiological concentrations of Ca2+ , CaM , and mCaMKII ( above ) ., We then calculated output of the model for each set of randomized parameters every 0 . 05 s for a 2 s simulation ., In one set of calculations , we used a series of fixed concentrations of Ca2+ to examine how the importance of individual parameters varies at different Ca2+ concentrations ., The contribution of variations in each input parameter to variation in the output was quantified by calculating the partial rank correlation coefficient ( PRCC ) 42–45 , reviewed in 46 , as described in 47 ., The PRCC quantifies the correlation of values of each individual parameter with the output , when the linear effects of the other parameters on output are removed ., A perfect positive correlation gives a PRCC of 1; whereas , a perfect negative correlation gives a PRCC of −1 ., Details are described in Text S1 ., Model 1 was used to predict the time evolution of binding of Ca2+ to CaM and of Ca2+/CaM to mCaMKII after a rapid increase in concentration of Ca2+ ., In particular , we examined the time evolution when the concentrations of Ca2+ or CaM are not high enough to saturate binding to mCaMKII; conditions that are likely to prevail in postsynaptic spines during activation of NMDARs 7 ., Figure 7 shows the predicted time evolution of all CaM species ( 0–4 bound Ca2+ ions ) when 10 µM Ca2+ was introduced at time zero into a system containing 30 µM CaM and 80 µM mCaMKII ., We picked these values because they are within the likely physiological ranges of concentrations in a spine during activation of NMDARs ., Changes in the species of free CaM ( Figure 7A ) , CaM bound to mCaMKII ( Figure 7B ) , and CaM bound to autophosphorylated mCaMKII ( Figure 7C ) were plotted during one sec of simulation ., Ca2+ bound rapidly to the N-terminus of free CaM within the first few msecs after addition , resulting in peaks in the concentrations of CaM1N and CaM2N ( Figure 7A; brown and pink ) ., Because Ca2+ also dissociates rapidly from these sites , the concentrations decayed within the first 200 msec to a relatively low equilibrium value ., In contrast , Ca2+ bound more slowly to the C-terminus of CaM ( blue and purple ) , but free CaM1C ( blue ) reached a relatively high equilibrium concentration because Ca2+ has a higher affinity for the C-terminal sites ., The equilibrium concentration of free CaM2C remained low because this species ( purple ) binds very rapidly to mCaMKII ( 9 . 2 µM−1 sec−1 , see Table S1 ) ., Thus , by 50 msecs , K•CaM2C was the most abundant K•CaM species in the simulation ( Figure 7B , purple ) ., We have shown experimentally that CaM2C and CaM2N support autophosphorylation upon binding to mCaMKII , although at a rate 10 to 20-fold lower than CaM4 25 ., Because of the faster autophosphorylation rate of CaM4 , the most abundant autophosphorylated mCaMKII species throughout most of the simulation was pK•CaM4 ( Figure 7C , red ) ., Nevertheless , under the conditions of this simulation in which the concentrations of Ca2+ and CaM are limiting , pK•CaM2C became the dominant species by the final 200 msecs ( Figure 7C , purple ) ., To test whether the actual binding of mCaMKII to CaM species with less than 4 bound Ca2+ ions influenced the final extent of autophosphorylation under these conditions , we constrained Model 1 such that only CaM4 could bind directly to mCaMKII ., Thus , we set reaction rates to zero for all the vertical yellow arrows in Figure 2C , except the on and off rates for binding of CaM4 to mCaMKII to form K•CaM4 ., We altered the model for autophosphorylation such that only K•CaM4 could be autophosphorylated ., However , we continued to allow K•CaM species with less than four bound Ca2+ ions to carry out autophosphorylation of K•CaM4 as follows:Thus , we continued to allow dissociation reactions in which K•CaM4 loses Ca2+ ions ., However , as in the complete Model 1 , we assumed that after autophosphorylation , pK•CaM4 did not lose either CaM or its bound Ca2+ during a one sec reaction ., Figure 8 shows the time evolution of all CaM species predicted by this limited model under the same conditions as in Figure 7 ., The time evolution of free CaM species ( Figure 8A ) was similar to that in Figure 7A , although free CaM with less than 4 bound Ca2+ ions reached higher equilibrium concentrations , presumably because they could not bind to mCaMKII ., A larger divergence between the full and limited Models is evident in Figure 8B ., The total concentration of CaM species bound to mCaMKII after one sec was reduced from ∼3 µM in Figure 7B to less than 1 µM in Figure 8B ( note difference in scales of the ordinates ) ., Conversely , the concentration of K•CaM4 ( Figure 8B , red ) was elevated relative to the other K•CaM species presumably because the nonsaturated CaM species could not bind directly to mCaMKII , leaving more of them to bind Ca2+ and be “promoted” to free CaM4 , after which they could bind to mCaMKII ., The total concentration of all K•CaM species with fewer than 4 bound Ca2+ was considerably reduced compared to Figure 7B because the only kinetic pathway by which these species could be formed was via loss of Ca2+ from K•CaM4 ., The only phosphorylated species was pK•CaM4 ( Figure 8C ) , as dictated by the design of the limited Model 1 ., The most interesting result was that the equilibrium concentration of pK•CaM4 species in the limited model was only ∼25% of the level reached in the full model ( ∼0 . 02 in Figure 7C vs . ∼0 . 005 in Figure 8C; note difference in scale of the ordinates ) ., This result means that , when concentrations of Ca2+ and CaM are limiting , the most important pathway toward formation of pK•CaM4 in Model 1 is via binding of Ca2+ to partially filled K•CaM species prior to autophosphorylation ., Thus , even if autophosphorylation of K•CaM2C and K•CaM2N could not occur , these partially filled CaM species would assume an important kinetic role in the autophosphorylation reaction , presumably because binding of CaM to the kinase target enhances the affinity of CaM for Ca2+ ., This kinetic pathway may have general significance for signaling through CaM because theoretical considerations suggest that different targets of Ca2+/CaM have different abilities to stabilize Ca2+/CaM species , depending on the structures of their CaM binding sites and surrounding residues ., The kinetic role of stabilization of sub-saturated Ca2+/CaM species by targets may significantly influence the outcome of regulatory events initiated by Ca2+ transients in vivo; and these outcomes may not be accurately predicted by the behavior of the enzyme targets at saturating , steady-state Ca2+/CaM concentrations in a test tube ., We compared predictions of Model 1 to two other models of interactions of Ca2+ , CaM , and CaMKII: Model 2 , a coarse-grained 4 state model derived from Model 1 ( Figure 5 ) and the “Empirical” Model , a 2 state model in which only CaM4 can bind to CaMKII ., The Empirical Model includes a version of the Adair-Klotz equation which represents the relation between Ca2+ concentration and levels of CaM4 48 , and assumes cooperativity of binding of the four Ca2+ ions to CaM ., This empirical model is similar to other 2-state models that have been used in studies of CaMKII 49 , 50 ., The initial concentrations of free CaM and mCaMKII were set to 30 µM and 80 µM , respectively , as in Figures 7 and 8 , and ratios of the output of these two models to that of Model 1 were calculated , varying Ca2+ from 1 to 500 µM , and time from 0 to 60 sec ( Figure 9 ) ., The output of Model 2 differs considerably from Model 1 at physiological concentrations of Ca2+ ( 1 to 30 µM ) ., This result means that Model 1 is required to obtain the most acccurate estimates of binding of Ca2+ to CaM and CaMKII under the conditions that prevail in a spine; the simpler Model 2 can be used when accuracy within a factor of 2 is adequate ., In contrast , the differences between the empirical model and Model 1 in the same physiological range of Ca2+ are much greater ., For example , at 10 µM Ca2+ , the empirical model predicts ∼100 fold higher autophosphorylation after 1 sec than does Model 1; whereas , Model 2 predicts ∼1 . 12 fold higher autophosphorylation than does Model 1 ., This result means that the empirical model , and by inference other 2 state models , do not accurately predict Ca2+/CaM dynamics at concentrations of Ca2+ , CaM , and CaMKII present in vivo ., Thus , in order to achieve the highest accuracy in predictions of CaMKII activity in a spine , it is necessary to include the kinetic details of binding among Ca2+ , CaM , and CaMKII ., Previous investigators have studied the dependence of activation of CaMKII on the frequency of rapid series of Ca2+ transients such as occur inside a cell during signaling ., One experimental study demonstrated a frequency dependence by applying brief pulses ( 80 ms to 1 s at 0 . 1 to 10 Hz ) of fully saturated Ca2+/CaM ( 500 µM Ca2+ , 0 . 1 to 0 . 4 µM CaM ) to immobilized CaMKII and then measuring the resulting Ca2+-independent catalytic activity 51 ., Autophosphorylation was dependent on frequency between ∼0 . 5 and 4 Hz for 100 ms pulses , and between ∼2 and 10 Hz for 80 ms pulses ., The authors theorized that the frequency dependence arises from the requirement that two CaMs must bind to two adjacent kinase subunits in a holoenzyme to initiate autophosphorylation 29 ., Thus , if the off rate for dissociation of Ca2+/CaM from a single subunit is significantly slower than the inter-stimulus interval of the Ca2+ transients , some Ca2+/CaM will remain bound individual subunits and contribute to activation of autophosphorylation during the next transient stimulus ., The theoretical model of Kubota and Bower , which included the empirical model described in Figure 9A for association of Ca2+ , CaM , and CaMKII , also supported this same mechanism 48 ., We found that Model 1 predicts an additional mechanism for frequency dependence in which the kinetics of Ca2+ binding to the C terminus of CaM in the K•CaM complex give rise to frequency dependence of autophosphorylation in the 1 to 8 Hz range ., Figure 10 shows plots of summed autophosphorylation after 30 Ca2+ pulses , as a function of frequency of the pulses ., Figure 10A illustrates pulses of width 20 ms; Figures 10B and C , pulses of 100 ms . The three curves in each figure were generated with three different values of ; default ( median of range in Table S1 , blue ) , default divided by 10 ( magenta ) , and default times 10 ( yellow ) ., The default value produces 2-fold variation in autophosphorylation from 0 . 5 to 4 Hz for 20 ms pulses of height 10 µM , no frequency dependence for 100 ms pulses of 100 µM , and a 3-fold variation from 0 . 5 to 7 Hz for 100 ms pulses of height 2 µM ., Faster values of decrease the range and magnitude of frequency dependence; whereas , slower values increase the range of the frequency dependence ., To determine whether this form of frequency dependence requires that two CaMs must bind to two kinase monomers to initiate autophosphorylation , Model 1 was altered to permit zero-order autophosphorylation ( that is autophosphorylation without the requirement for monomer-monomer interactions ) ., The modified model showed similar frequency dependence ( data not shown ) , indicating that the requirement for two CaMs binding to two monomers does not play a large role in this mechanism of frequency dependence ., To explore the mechanism further , we examined how the frequency of Ca2+ pulses affects the accumulation of CaM species during each pulse ., Ten and 1 sec of 20 ms pulses of 10 µM Ca2+ were simulated at 0 . 5 and 7 Hz , respectively ., At 0 . 5 Hz ( Figure 11A ) , K•CaM2C and K•CaM4 formed by a single pulse dissociated completely before the next pulse began ., Thus , there was no interaction between the species formed from one pulse to the next , and no frequency dependence of autophosphorylation ., In contrast , at 7 Hz , K•CaM2C was entirely converted to K•CaM4 during each pulse , but some of the K•CaM4 dissociated into K•CaM2C during the inter-pulse interval ., The additional K•CaM2C was converted to K•CaM4 during the next pulse ., Thus , the concentrations of K•CaM2C and K•CaM4 increased significantly with each pulse , resulting in a slightly higher level of autophosphorylation after the same number of pulses at 7 Hz , compared to 0 . 5 Hz ., This small increase translates into a 2-fold increase in autophosphorylation for 30 pulses at 7 Hz compared to 30 pulses at 0 . 5 Hz ( Figure 10A ) ., We performed sensitivity analyses , as described under Methods , to identify which parameters most influence the outcome of Model 1 ., The analyses were carried out in two different ways ., We first examined the importance of each input parameter based on the range of the estimated experimental uncertainty in its measurement , as listed in Table S1 ., For this analysis , parameters were varied over the full range of values in Table S1 ., Values of parameters that do not have ranges , or for which the range is unknown , were varied 4-fold with the value in Table S1 taken as the mean ., We next determined the importance of each parameter without using the estimated range of experimental uncertainty ., For that analysis , we assumed that the mean values are accurate estimates of the real mean ., Parameters were varied 2 . 5-fold around the mean values in Table S1 ., This second analysis measured the influence of the relative magnitude of each parameter and its position in the model rather than the limitations of experimental estimates of individual parameters ., We used PRCC values ( calculated as described under Methods ) to describe the relative importance of each parameter for predicting the level of autophosphorylation ., Not surprisingly , we found that autophosphorylation is highly sensitive to changes in Ca2+ concentration when all parameters are varied globally ( Table 1 ) ., Because Ca2+ signaling in vivo often occurs over a period of a few hundred msecs or less , we examined which parameters most influence autophosphorylation levels at different times during a reaction ., We calculated PRCCs for time series under three different regimes of Ca2+ concentration; low ( 1–5 µM ) , medium ( 10–50 µM ) , and high ( 50 to 250 µM ) ., The low regime encompasses the range believed to arise in and near the PSD during low frequency stimulation of NMDA-receptors 28 , 52 ., The medium regime encompasses the concentrations believed to occur in the PSD during strong stimulation of NMDA receptors 28 , 52 ., Concentrations above 100 µM likely do not occur under normal physiological conditions , but are frequently used in enzymatic experiments in the laboratory ., As expected , the importance of specific binding parameters varies considerably with time and among the three Ca2+ regimes ., Table 1 lists the parameters having a PRCC value either above 0 . 3 or below −0 . 3 , indicating a strong correlation ( positive or negative , respectively ) with the output value ., The concentration of CaMKII subunits was an important determinant at low Ca2+; whereas the concentration of CaM assumed more importance at higher Ca2+ and longer times ., The KD for the interaction between two CaMKII subunits with bound CaM was a strong determinant of the output at all Ca2+ concentrations and times ( Table 1 ) ., Of the 47 individual rate constants , 14 had a significant PRCC value in at least one of the regimes ., In low Ca2+ , 6 rate constants at 0 . 1 sec , and 5 at 1 and 2 secs , had significant PRCCs; in medium Ca2+ , 4 at 0 . 1 sec , and 8 at 1 and 2 secs , had significant PRCCs; and in high Ca2+ , only the intrinsic rate of autophosphorylation had a significant PRCC ., At lower Ca2+ concentrations and shorter times , the most important rate constants are those for formation of K•CaM species with fewer than 4 bound Ca2+; and the autophosphorylation rate constants , and ., When the narrower range of parameters is used in the calculations ( Table S3 ) , and replace ., Thus , the ability of K•CaM complexes with few bound Ca2+ ions to support autophosphorylation is critical at low Ca2+ ., At medium Ca2+ , K•CaM4 has a strong influence on autophosphorylation at all times because its autophosphorylation rate constant ( ) is 10 times higher than that of K•CaM2C ( ) ., The rate constants for binding of Ca2+ to K•CaM at the N terminus ( , , , ) have a strong influence , reflecting the fact that K•CaM2C reaches a higher concentration than K•CaM2N after the first 100 msecs because of the higher affinity of the C-terminus for Ca2+ ., Thus , the rate of conversion of K•CaM2C to K•CaM4 by binding of Ca2+ to the N-terminus of CaM is critical ., In the high Ca2+ regime , which represents the usual well-mixed experimental conditions , Ca2+ concentration , , CaM concentration , and the KD for monomer-monomer association are the determining parameters ., Figure 12 A and B illustrate that the importance of some of the parameters varies dramatically with Ca2+ concentration during a 1 s reaction ., Interestingly , the concentration of CaM is inversely correlated with autophosphorylation between ∼20 and ∼100 µM Ca2+ ., Partially bound Ca2+/CaM species are more prevalent than fully bound CaM4 at these Ca2+ concentrations ., Thus , higher CaM concentrations may result in less autophosphorylation because binding to extra CaM reduces the amount of Ca2+ available for binding to K•CaM species ., Figures 12 B and C also illustrate the differing importance of the intrinsic autophosphorylation rate constants for K•CaM2C and K•CaM4 ( and , respectively ) as the Ca2+ concentration rises ., Below ∼25 µM Ca2+ , the two species have approximately equal influence on autophosphorylation ., However , above 100 µM Ca2+ , K•CaM4 and its autophosphorylation rate constant dominate ., The differences between the PRCC curves for autophosphorylation rate constants in Figures 12 C and D reflect the influence of K•CaM species with odd numbers of bound Ca2+ ( , , , ) , which have very high experimental uncertainty ., For example , note how the influence of decreases when the range of uncertainty is narrowed in Figure 12D ., The experimental range of values for in Figure 12C ( Table S1 ) spanned the measured value for ( 0 . 079 s−1 ) to that for ( 1 . 25 s−1 ) , a range of 16-fold ., In contrast , the narrower range of values ( Figure 12D ) spanned 0 . 38 s−1 to . 95 s−1 , a range of 2 . 5 fold around the mean of 0 . 66 s−1 ., The importance of , which has a smaller expe | Introduction, Methods, Results, Discussion | During the acquisition of memories , influx of Ca2+ into the postsynaptic spine through the pores of activated N-methyl-d-aspartate-type glutamate receptors triggers processes that change the strength of excitatory synapses ., The pattern of Ca2+ influx during the first few seconds of activity is interpreted within the Ca2+-dependent signaling network such that synaptic strength is eventually either potentiated or depressed ., Many of the critical signaling enzymes that control synaptic plasticity , including Ca2+/calmodulin-dependent protein kinase II ( CaMKII ) , are regulated by calmodulin , a small protein that can bind up to 4 Ca2+ ions ., As a first step toward clarifying how the Ca2+-signaling network decides between potentiation or depression , we have created a kinetic model of the interactions of Ca2+ , calmodulin , and CaMKII that represents our best understanding of the dynamics of these interactions under conditions that resemble those in a postsynaptic spine ., We constrained parameters of the model from data in the literature , or from our own measurements , and then predicted time courses of activation and autophosphorylation of CaMKII under a variety of conditions ., Simulations showed that species of calmodulin with fewer than four bound Ca2+ play a significant role in activation of CaMKII in the physiological regime , supporting the notion that processing of Ca2+ signals in a spine involves competition among target enzymes for binding to unsaturated species of CaM in an environment in which the concentration of Ca2+ is fluctuating rapidly ., Indeed , we showed that dependence of activation on the frequency of Ca2+ transients arises from the kinetics of interaction of fluctuating Ca2+ with calmodulin/CaMKII complexes ., We used parameter sensitivity analysis to identify which parameters will be most beneficial to measure more carefully to improve the accuracy of predictions ., This model provides a quantitative base from which to build more complex dynamic models of postsynaptic signal transduction during learning . | Networks of neurons in the brain are connected together by specialized signaling devices called synapses ., One way an active neuron relays its activity to other neurons is by releasing small amounts of chemical transmitters from its presynaptic terminals which induce electrical activity in postsynaptic neurons connected to it ., Memories are formed when synapses in the network encoding the memory change their strength in order to stabilize the network ., The decision whether or not a synapse becomes potentiated is controlled by delicate variations in the amount of Ca2+ ions that flow across the membrane at the postsynaptic site , and by the pattern of influx over time ., The mechanisms of activation of regulatory enzymes that decode this Ca2+ signal have been extensively studied under laboratory conditions which are different from the conditions encountered inside a neuron ., Therefore , we created a dynamic model of activation of one enzyme that is critical for learning by Ca2+ ., The model allows us to simulate activation of the enzyme within a biochemical milieu similar to what it will encounter at the postsynaptic site ., It predicts unexpected behaviors of the enzyme in vivo and provides a framework for quantitative exploration of complex mechanisms of synaptic plasticity . | biochemistry/cell signaling and trafficking structures, cell biology/neuronal signaling mechanisms | null |
journal.pgen.1000151 | 2,008 | Off-Target Effects of Psychoactive Drugs Revealed by Genome-Wide Assays in Yeast | Neuropsychiatric disorders will effect 25% of all individuals at some point in their lives , with devastating social and economic consequences 1 ., This constellation of diseases encompasses schizophrenia , depression , age-related memory and cognition decline , and the degeneration of neuromuscular function ., Most prescribed psychoactive drugs are thought to primarily target neurotransmission pathways in the central nervous system , and thereby cause changes in perception , mood , consciousness , and behavior ., Many of these therapeutics have been developed using in vitro assays and , as such , may have other unknown targets and unanticipated cellular effects in vivo ., For example , side effects of antipsychotic drugs include tremors , hypotension , impotence , lethargy , and seizures 2 ., In an effort to improve efficacy and to reduce side effects , new generations of drugs have been developed; among these are the so-called atypical antipsychotics such as clozapine ., While clozapine is linked to a reduced risk of neuromuscular side effects , it is associated with new side effects such as life-threatening agranulocytosis in up to 1% of patients 3 , and , less frequently , fatal myocarditis 4–6 ., As such , the therapeutic benefit of this and other new atypical drugs remains open to debate ., For example , a comprehensive meta-regression analysis that compared both typical and atypical drugs concluded that atypical antipsychotics were neither more effective nor better tolerated than conventional agents 7 ., Other classes of psychoactive drugs , such as the antidepressants , also cause numerous undesirable side effects and the broad usage of these medications have been questioned 8 ., Surrogate genetics is an effective approach to interrogate heterologous gene function or drug mechanism of action using simpler model organisms 9 , 10 ., The budding yeast Saccharomyces cerevisae has previously been used to help elucidate the basis of some psychiatric disorders 11–18 ., For example , the expression in yeast of mutant and wildtype forms of the Huntingtons disease gene revealed important factors regulating the toxicity of protein aggregates 11 , 15 , 19 , and a genome-wide suppressor screen in yeast uncovered kynurenine 3-monooxygenase as a potential new therapeutic target for the treatment of Huntingtons disease 13 ., In other studies , expression in yeast of the alpha-synclein gene associated with Parkinsons disease yielded a network of interacting genes that modulate cellular toxicity 11 , 15 , 19 ., Recently , the genome-wide collection of yeast gene deletion strains has been used to generate genetic profiles of drug sensitivity and resistance 20–26 ., These profiles have uncovered unexpected mechanisms of action for well-known drugs , such as for the anti-metabolite 5-fluorouracil in perturbation of rRNA processing 22 , 25 and for the anti-cancer agent tamoxifen in calcium homeostasis 26 ., To better understand potential off-target effects of FDA-approved psychoactive drugs and their analogs , we profiled 214 psychoactive compounds in quantitative wildtype yeast growth assays and generated genome-wide deletion sensitivity profiles for the 81 drugs that caused overt growth defects ., The sensitivity profiles for 49 of these drugs were overrepresented for core cellular functions such as chromatin organization , establishment of cell polarity , and membrane organization and biogenesis ., Our results provide a rational foundation for personalized drug approaches and for understanding unwanted side effects in clinically important psychoactive agents ., To ask if psychoactive compounds can inhibit wildtype budding yeast growth , we challenged yeast with 76 high-purity psychoactives representing 16 ligand categories that encompass a broad spectrum of treatments for neurological disorders ( see Figure 1 for workflow and Table S1 for drug information ) ., Despite the fact that yeast lacks the established neuronal targets of these compounds , 17/76 ( 22% ) drugs inhibited the growth of wildtype yeast ( when tested at 200 µM ) and are hereafter referred to as “bioactive” ., This observation shows that in addition to their reported targets , many of these compounds also have secondary mechanisms of action ., In fact , over half of the 16 tested ligand classes included compounds that were bioactive ( Figure 2A ) ., Among these , serotonin uptake inhibitors were most effective; four of five tested molecules in this class inhibited yeast growth ( Figure 2A ) ., Because our assay depends on growth inhibition in order to observe any effects on specific deletion strains , we proceeded with the 17 bioactive compounds and determined a drug dose that inhibited wildtype growth by ∼15% ( Figure 2B , Table S1 ) ., In our previous genome-wide studies this level of inhibition best captured the ability to identify the known drug target while minimizing the number of generally sensitive strains 22 , 23 ., Applying this drug dose , we subjected the bioactive compounds to genome-wide parallel fitness profiling ., In this technique , pools of deletion strains are grown competitively for several generations in the presence of a sub-lethal concentration of drug , and genomic DNA is extracted ., After PCR-amplification of the unique molecular barcodes incorporated into each gene deletion cassette , the relative role of each gene for growth in the presence of drug is determined by hybridization of the PCR products to a DNA microarray carrying the barcode complements 27–29 ., The relative abundance of sequence tags in the drug experiments is compared to control experiments and fitness ratios and z-scores are calculated ( see Materials and Methods ) ., We used two pools of diploid strains:, i ) heterozygous deletion strains deleted for one copy of the essential genes ( 1158 strains ) , which often identifies compound targets through HaploInsufficiency Profiling ( HIP ) 22 , 30 , and, ii ) homozygous deletion strains deleted for both copies of non-essential genes ( 4768 strains ) ; this HOmozygous Profiling ( HOP ) assay identifies genes that buffer the drug target pathway 24 ., Using this combination of the HIP and HOP assays we found that only a few deletion strains ( ∼5 ) exhibited significant sensitivity to most of the 17 bioactive compounds ( Figure 2C ) ., In contrast , several deletion strains ( ∼50 ) were scored as sensitive for the α1-adrenoceptor antagonist SR 59230A and the three selective serotonin re-uptake inhibitors fluoxetine ( Prozac ) , clomipramine , and fluvoxamine ( Figure 2C ) ., Given this unexpected potency of the serotonergic drugs in our yeast assays , we extended our investigation to encompass pharmacologically related agents and screened two commercially available drug libraries encompassing 95 serotonergic and 55 dopaminergic compounds ., These drug libraries contained the four FDA-approved serotonergics sertraline ( Zoloft ) , fluoxetine ( Prozac ) , paroxetine ( Paxil ) , and cyproheptadine ( Periactin ) , and the four FDA-approved dopaminergics bromocriptine ( Parlodel ) , clozapine ( Clozaril ) , haloperidol ( Haldol ) , and pimozide ( Orap ) ., Based on our initial results , we anticipated a high rate of bioactivity on yeast for these two drug classes ., Indeed , 66/150 ( 44% ) of the serotonergic and dopaminergic drugs were bioactive , a significant difference compared to the 22% of the initially screened drugs that represented the 16 different ligand sets ( p<10−7 ) ., The high prevalence of bioactivity in yeast prompted us to ask if any particular psychoactive drug attribute correlated with the ability of these compounds to inhibit wildtype yeast growth ., We first performed structural clustering of all ∼220 screened psychoactive compounds using chemical fingerprints in Pipeline Pilot ( Accelyrs , San Diego ) ., As more than half of the resulting clusters contained both active and inactive drugs , chemical structure was not predictive of drug action on wildtype yeast growth for this selection of compounds ( data not shown ) ., We next asked if any physiochemical properties , as predicted from the structures , were linked to drug activity ., The parameters we tested included the number of H-bond donors and acceptors , molecular weight , and hydrophobicity as measured by AlogP ( the octanol-water partition coefficient ) ., These measures are important descriptors used in the empirical parameter set known as Lipinskis Rule of Five 31 ., In addition to the Lipinski descriptors , we tested six other parameters relevant to drug activity: van der Waals surface area , molecular surface area , molecular solubility , logD ( the octanol-water distribution coefficient; a combination of logP and pKa ) , number of rings and number of rotatable bonds ., Principal component analysis revealed that a partition coefficient of AlogP>3 was best able to predict drug activity ( p<4 . 9e-13 , for details see Materials and Methods ) as shown in Figure 3 ., A molecular weight of >260g/mole was also indicative of an active compound ( p<3 . 4e-05 , Figure 3 ) ., If there is a correlation between human side-effects and conserved cellular pathways scored using our surrogate yeast system , it is possible that an additional study could help predict such effects based on structural features ., To systematically interrogate compound mechanisms of action , we subjected the 66 bioactive serotonergic and dopaminergic compounds to genome-wide fitness assays using the approach described above ( Figure 1 ) ., Combined with the initial set of 17 bioactive drugs , we screened a total of 81 unique drugs ( two drugs occurred in duplicate in the chemical libraries ) , eight of which are used therapeutically ( Table 1 ) ., Fitness ratios and z-scores for all deletion strains are provided in Tables S2 and S3 , respectively ( raw data are available at ArrayExpress , EMBL-EBI , accession number E-MTAB-34 ) ., The genome-wide fitness profiles were reproducible as the average correlation coefficient for the five replicated compounds was 0 . 83 , which is similar to the average correlation coefficient of 0 . 72 reported in a previous large-scale fitness study 23 ., As an unbiased control , we calculated the average correlation coefficient between all possible random drug pairs in our assay ., As expected , this value ( 0 . 44 ) was lower than the average correlation coefficient for duplicates , but well above the previously noted average correlation of zero for unrelated compounds ( Maureen Hillenmeyer , unpublished data ) ., In agreement with this , two-dimensional hierarchical clustering 32 did not separate the dopaminergic and serotonergic profiles into two distinct groups , but clearly separated drugs from these two classes from most other compounds profiled ( Figure 4 ) ., Further indicating the general similarities between dopaminergic and serotonergic drugs in our yeast screen , 25% of the significantly sensitive strains ( r>2 , z>3 , see Materials and Methods ) scored in both drug categories ( Table S4 ) ., To ask which cellular functions and pathways were required for resistance to the tested drugs , we performed functional enrichment tests using Gene Ontology ( GO ) annotations specifically focusing on sensitive strains in the, i ) essential heterozygous ,, ii ) homozygous or, iii ) both collections ( see Materials and Methods ) ., 32 drug sensitivity profiles were not enriched for any GO Process but the remaining 49 profiled drugs ( 60 . 5% ) interfered with 106 different processes ( multiple-testing corrected p-value<0 . 0001 , Table S5 ) ., For visual clarity , we collapsed these 106 processes down to 22 ( Table S6 ) ., The drug sensitivity profiles obtained with the combined set of heterozygous and homozygous strains were enriched for the highest number of condensed GO processes ( 119 processes , purple color in Figure 5 ) , while 12 processes were uniquely enriched among sensitive homozygous deletion strains ( blue color in Figure 5 ) ., These processes likely reflect drug detoxification mechanisms ( e . g . “vesicle transport” and “response to drug” ) or other processes required for resistance to compound by an unknown mechanism ( e . g . “amino acid biosynthesis and metabolism” ) ., Two processes were uniquely scored for essential genes ( red color in Figure 5 ) and are further discussed below ., Investigating the general nature of our enrichment profiles , we found that the most frequently enriched processes across all drugs and genetic backgrounds were vesicle transport , protein localization , and telomere biology ( Figure 5 ) ., Genes functioning in cell morphogenesis , establishment of cell polarity , cell cycle , amino acid biosynthesis , chromatin organization , RNA metabolism , and membrane organization were also needed for resistance to several ( >5 ) of the psychoactive drugs ., A few GO Processes were unique to a single drug: protein glycosylation ( A77636 ) , methylation ( SB 216641 ) , cell wall organization and biogenesis ( GR 127935 ) , and membrane lipid metabolic process ( pimozide ) ., In the subsequent sections we focus on the analysis of the FDA-approved drugs and summarize the most notable enrichments for these drugs in Table 2 ., First , we discuss identified buffering pathways and drug detoxification mechanisms ., Next , we concentrate on potential new drug targets identified for the therapeutically used psychoactive drugs ., Vesicle transport was the most commonly overrepresented process among genes required for resistance to psychoactive drugs ( Figure 5 ) suggesting that uncompromised vesicle transport function is a general requirement for psychoactive drug detoxification ., The enrichment of cellular transport genes was especially pronounced in response to clozapine treatment , where 9 of the 10 most required genes belonged to this category ( Table 3 ) ., Protein sorting and localization accounted for the second most frequently enriched process ( Figure 5 ) ., Deletion of vesicle trafficking and protein localization genes often resulted in very severe phenotypes ( bright yellow in Figure 4 ) ., Gene products with protein localization roles include those involved in selecting cargo proteins for endosome-to-Golgi retrieval ( e . g . Vps29 ) , and those involved in sorting proteins in the vacuole ( e . g . Pep8 ) ., Interestingly , the fitness profiles obtained with certain vesicle transport and protein localization deletions clustered with those obtained with strains deleted for genes functioning in actin filament organization/stabilization ( arc18Δ , tpm1Δ , vrp1Δ , ) , mRNA degradation ( lsm1Δ ) , and stabilization of membrane amino acid transporters ( npr1Δ ) ( Figure 4 , left text panel ) ., A second , large group of strains mainly deleted for genes functioning in vesicle transport and protein localization exhibited similar phenotypes across the 81 drugs as ckb1Δ and ckb2Δ , which are deleted for genes functioning in regulation of transcription and mitotic cell cycle ( Figure 4 , right panel ) ., Most of the drug sensitivity profiles were enriched for both protein localization and telomere biology ( Figure 5 ) ., The apparent “linking” of these enrichments could be attributed to genes that are , in fact , involved in both these processes ., Examples of such genes function in the three Endosomal Sorting Complexes Required for Transport , more specifically in ESCRT I ( VPS28 , STP22 ) , in ESCRT II ( SNF8 and VPS25 ) , and in ESCRT III ( SNF7 ) ., These genes are , in addition , associated with telomere defects 33 , 34 ., Because the more recently developed atypical antipsychotic drugs are still associated with side effects and their benefits are currently debated , we compared the phenotypic profiles of the atypical antipsychotic clozapine to two traditional antipsychotics , reasoning that if atypical drugs are more specific , they would exhibit fewer off-target effects in yeast ., In contrast to this expectation , the atypical antipsychotic clozapine exhibited a similar number of significantly sensitive ( r>2 , z>3 , see Materials and Methods ) deletion strains ( 26 ) as the typical antipsychotic drugs pimozide ( 29 ) and haloperidol ( 20 ) ., Comparing the fitness profiles of clozapine with the typical antipsychotics pimozide and haloperidol , we found that each drug was associated with unique functional enrichment profiles: clozapine for telomere biology and protein localization , pimozide for membrane lipid metabolic processes , and haloperidol for aromatic amino acid biosynthesis and metabolism ( Figure 5 ) ., In contrast , vesicle transport was enriched in all three drug sensitivity profiles ., The more detailed GO processes behind the condensed process vesicle transport were vesicle-mediated transport for all three drugs and , in addition , secretory pathway , secretion , post-Golgi vesicle-mediated transport and Golgi vesicle transport for haloperidol and clozapine ( Tables S5 and S6 ) ., The distinct fitness profiles are consistent with the structural differences that exist between these drugs ( Figure S1 ) ., For example , clozapine has substructures ( piperazine and diazepine ) that do not exist in pimozide and haloperidol , and haloperidol contains two benzene rings while pimozide has three ., Compared to the other investigated therapeutics , the fitness profile in the anti-Parkinson drug bromocriptine pointed to a single potential off-target mechanism of action for this drug ., The only overrepresented function among sensitive strains was amino acid biosynthesis and metabolism ( Figure 5 ) and the most sensitive strains were deleted for the aromatic biosynthesis genes TRP3 , TRP4 , TRP1 , ARO1 , TRP2 , and ARO2 ., In addition to bromocriptine , six other dopaminergic drugs also interfered with amino acid biosynthesis and metabolism ( Figure 5 ) ., The sensitivity profiles of all these seven drugs shared the enrichment for the detailed GO process aromatic compound metabolic process ( Tables S5 and S6 ) due to the sensitive phenotype of 13 strains in total ., Among them , strains deleted for TRP1 , TRP2 , TRP3 , TRP4 , TRP5 , ARO2 , and ARO3 were scored in all 7 drugs and strains deleted for ARO1 and ARO7 in 6 drugs ., Besides the notable enrichment for genes involved in aromatic compound metabolism , the sensitivity of strains missing other genes also contributed to the observed GO process enrichment ., Such genes included the folic acid ( vitamin B9 ) biosynthesis gene FOL2 , the panthothenate ( vitamin B5 , precursor of coenzyme A ) biosynthesis gene FMS1 , and the protein kinase GCN2 , which induces amino acid biosynthesis genes in yeast in response to starvation and , in addition , restricts intake of diet lacking essential amino acids in rats 35 ., The sensitivity profile of the typical antipsychotic pimozide showed a unique enrichment for membrane lipid metabolic processes not seen for any of the other 80 profiled drugs ( Figure 5 ) ., In pimozide , the MCD4-deletion strain had the strongest phenotype and was 21-fold depleted compared to the control ( Table 3 ) ., MCD4 is highly conserved among eukaryotes and functions in glycosyl-phosphatidylinositol ( GPI ) anchor synthesis ., Because MCD4 is an essential gene , it may represent an additional , clinically relevant drug target for pimozide ., The inositol-lipid-mediated signaling gene PIK1 and the spingholipid-mediated signaling gene YPK1 were also among the ten most required genes for resistance to pimozide ( Table 3 ) ., They clustered with a group of other strains deleted for genes involved in lipid biology ( Figure 4 ) , such as the de novo lipid synthesis genes PAH1 and SUR4 ., Eight drugs , among them the antidepressant fluoxetine , were enriched for the condensed term establishment of cell polarity ( purple or blue color in Figure 5 ) ., In total , 51 genes were assigned to the detailed GO process establishment and/or maintenance of cell polarity and caused a sensitive phenotype when deleted ( Tables S5 and S6 ) ., Many of these genes scored in the majority of the drugs , for example all four members ( CKA1 , CKA2 , CKB1 , and CKB2 ) of the casein kinase II-holoenzyme complex , and TPM1 , the major isoform of tropomyosin which directs polarized cell growth and organelle distribution ., For the seven drugs where the enrichment for establishment and/or maintenance of cell polarity was scored using sensitive homozygous and essential heterozygous strains ( purple color in Figure 5 ) , six essential members ( EXO70 , SEC3 , SEC6 , SEC8 , SEC10 and SEC15 ) of the exocyst complex , which determines where secretory vesicles dock and fuse , were scored in all drugs except fluoxetine ., Drug targets are often encoded by essential genes , thus essential genes scored in our assay may represent important additional targets of psychoactive compounds that may be useful in the development of therapeutics for other applications ., In a given heterozygous strain , the reduced gene copy number of a potential drug target leads to a reduced level of the corresponding protein ., When this strain is grown in the presence of a drug targeting the heterozygous locus , the result is a further decrease in “functional” dosage due to the drug binding to the protein target ., If this protein is important for growth , the result will be drug sensitivity 22 ., In our functional enrichment tests , two processes were uniquely overrepresented among sensitive essential genes ( red color in Figure 5 ) : mitotic and meiotic cell cycle for fluorophenyl-methoxytropane and chromatin organization for cyproheptadine ., Examples of targeted essential genes in cyproheptadine treatment include chromatin-remodeling genes ( ARP4 , ARP7 , ARP9 ) , genes in the multisubunit ( NuA4 ) histone acetyltransferase complex ( EPL1 , ESA1 , SWC4 ) , and RSC4 and RSC6 in the RSC Chromatin remodeling complex ., Although not revealed as a functional enrichment among sensitive strains deleted for essential genes , most of the other FDA-approved drugs also have potential secondary drug targets as infered by the presence of essential genes among the ten most required genes for drug resistance ( Table 3 ) ., As judged by the high number of sensitive strains deleted for essential genes in paroxetine treatment ( 10 strains ) and sertraline treatment ( 9 strains ) , these selective serotonin re-uptake inhibitors are particularly rich in potential secondary drug targets ., Essential genes required for resistance to the FDA-approved drugs include those involved in RNA processing , transcription and translation , genes functioning in the protein folding chaperonin complex , and the chromatin-remodeling/DNA repair gene ARP4 ( bold in Table 3 ) ., Deletion of ARP4 resulted in some of the most sensitive phenotypes when cells were treated with cyproheptadine , sertraline , or with haloperidol ( Table 3 ) ., ARP4 has a close human homolog , ACTL6B , which encodes a subunit of the BAF ( BRG1/brm-associated factor ) complex in mammals , functionally related to the SWI/SNF complex in S . cerevisiae ., The SWI/SNF complex is thought to facilitate transcriptional activation by antagonizing chromatin-mediated transcriptional repression 36 ., Another example of an essential gene required for drug resistance in several FDA-approved drugs is GSP1 , which functions in RNA-processing ( Table 3 ) ., The mammalian homolog of Gsp1 , Ran ( BlastP E-value<E-261 ) is , as in yeast , a nuclear GTP-binding protein ., Interestingly , the fitness profile of the ARP4-deleted strain was very similar to the strains deleted for the cytosolic chaperonin subunits CCT5 , CCT8 and TCP1 ( Figure 4 ) ., The chaperonin complex is involved in protein folding ( primarily of actin and tubulin ) and cytoskeleton organization 37 ., In our fitness assays , seven of eight CCT-strains scored as significantly sensitive in many of the probed psychoactive drugs ., Some ( CCT3 , CCT4 , CCT7 and CCT8 ) were even among the top-ten required genes for resistance to cyproheptadine , fluoxetine , paroxetine , and sertraline ( Table 3 ) ., Furthermore , several deletion strains with uncharacterized functions had similar fitness profiles as the chaperonins CCT5 , CCT8 and TCP1 ( Figure 4 ) ., Among them were TVP23 and YIP5 which both localize to the late Golgi , YEL048C which is synthetic lethal with GCS1 ( involved in ER to Golgi transport ) , APM2 ( homologous to medium chain of mammalian clathrin-associated protein complex involved in vesicle transport ) and SWH1 ( similar to mammalian oxysterol-binding protein , localized to Golgi and nucleus-vacuole junction ) ., To test if our findings in yeast might reflect drug action in human cells , we looked at the proportion of scored genes with human homologs ., Among the strains significantly sensitive to at least one psychoactive compound , 58 . 4% were deleted for a gene with a close human homolog ( BlastP E-value<E-6 ) , as compared to 45 . 0% for all analyzed deletion mutants regardless of whether they had a fitness defect or not ., To test if strains deleted for genes involved in core cellular processes are more sensitive in general , we compared our results obtained with the 81 psychoactive compounds to 81 randomly chosen chemically diverse compounds ( see Materials and Methods ) ., We found that a similar proportion of genes with close human homologs ( 59 . 7% ) were scored for strains that were significantly sensitive to at least one of these diverse chemicals ., Despite this similarity in proportion of sensitive strains with human homologs in the two datasets , conserved genes were scored much more frequently ( in >10% of the compounds ) in the psychoactive drug set than in the random drug set ., In fact , considering only genes deleted in frequently scored strains , 64 . 1% of the psychoactive drugs had close human homologs ( BlastP E-value<E-6 ) while the corresponding proportion for the structurally diverse drug set was significantly ( p<0 . 006 ) lower ( 45 . 4% ) and similar as the fraction of human homologs for multi-drug resistance genes ( 47 . 1% ) in a recently published study 23 ., This difference points to a significant enrichment of frequently scored sensitive strains with human homologs for the psychoactive drugs ., Among the strains sensitive to the highest number of psychoactive compounds , seven of eight had close human homologs: NEO1 , SAC1 , PIK1 , VPS29 , PEP8 , ARP4 and VPS35 ., The majority of these genes are involved in vesicle transport , which was the most frequently enriched function among strains sensitive to psychoactive drugs ., Thus , the specific psychoactive drug detoxification mechanisms identified in yeast are likely to be of importance in humans treated with psychoactives ., Many psychoactive drugs are associated with adverse secondary effects in humans yet the mechanisms that underlie these off-target effects are poorly understood ., To address mechanisms of drug action in a systematic manner , we profiled the genome-wide collection of budding yeast deletion strains for sensitivity to a broad spectrum of psychoactive compounds , of which dopaminergic and serotonergic drugs had a high bioactivity ., Among 214 tested compounds , we uncovered 81 drugs that conferred a measurable growth defect on wildtype yeast ., An appropriate dose of these active compounds was applied to the pooled heterozygous and homozygous yeast deletion sets to identify genes whose function is required for optimal growth in the presence of drug ., Fifteen percent of all yeast strains ( deleted for non-dubious ORFs ) exhibited significant sensitivity ( r>2 , z>3 ) to these 81 psychoactive compounds and more than half of the drugs interacted with core cellular functions ., Several clinically important drugs , such as fluoxetine , cyproheptadine , and clozapine were linked to diverse cellular processes ., This observation may explain both the diversity of side effects observed in human patients and the therapeutic variability associated with these drugs ., That is , polymorphisms in any of the conserved processes affected by a given drug are a likely source of the individual variation in response to drug ., For instance , the response to the frequently prescribed antipsychotic clozapine is highly variable between individuals as the same dose can have markedly different efficacy and/or side effects in different patients 38 ., Genes functioning in vesicle transport , protein localization , telomere biology , and catabolic processes were required for clozapine resistance in yeast ., In another example , fluoxetine is associated with side effects such as seizures , nausea , sleepiness , anxiety , and serious allergic reactions ., This antidepressant affects numerous cellular processes including establishment of cell polarity , protein localization , and cytoskeleton organization and biogenesis ., Given the limited number of FDA-approved drugs within the set of 81 compounds analyzed here and the overlapping side effects associated with these drugs , it is not yet possible to correlate any single side effect to a particular perturbed pathway ., The most frequently scored sensitivity for the 81 profiled antipsychotic drugs was due to loss of secretory pathway function , likely indicating the importance of vesicle transport ( e . g . to the vacuole ) for drug detoxification ., The lysosome ( the mammalian vacuole equivalent ) is known as the major site of degradation of both exogenous and endogenous molecules ., For FDA-approved drugs , the requirement for vesicle transport genes was reflected in the frequent sensitivity of the neo1 deletion strain as the most sensitive strain in six FDA-approved drugs ., Neo1 is an essential , highly conserved type 4 P-type ATPase involved in intracellular membrane- and protein-trafficking ., Members of this family of P-type ATPases are implicated in the translocation of phospholipids from the outer to the inner leaflet of membrane bilayers ., Our data suggested that interference with membrane structure and transport through inhibition of Neo1 is an additional , unwanted mechanism of action for clozapine , cyproheptadine , fluoxetine , paroxetine , sertraline and haloperidol , and their drug analogs ., The importance in humans of functional 4 P-type ATPases is well documented as hereditary cholestasis , caused by defects in biliary epithelial transporters , has been directly linked to mutations in a 4 P-type ATPase gene 39 ., In addition to the frequently observed requirement for uncompromised vesicle transport for drug detoxification , several drug sensitivity profiles were enriched for more specific processes ., Within the FDA-approved drug group , the antidepressant paroxetine was unique in targeting RNA processing genes , pimozide interfered with membrane lipid metabolic processes , cyproheptadine preferentially targeted essential genes with chromatin remodelling functions , and fluoxetine interfered with establishment of cell polarity ., Furthermore , seven dopaminergic compounds including the anti-Parkinson drug bromocriptine resulted in sensitivity of strains deleted in aromatic amino acid biosynthetic genes ., This sensitivity may be a result of that dopaminergic drugs block aromatic amino acid uptake in yeast , requiring yeast to activate the corresponding biosynthetic pathways ., Given the fact that aromatic amino acids are precursors to dopamine and serotonin , this was an interesting observation suggesting that the levels of intracellular precursors may be important in the response to certain psychoactive drugs ., Interestingly , interference with members of the chaperonin complex resulted in some of the most severe phenotypes ., Seven of eight CCT-strains scored as significantly sensitive in several psychoactive drugs , among them CCT5 ., The human homolog of this gene is associated with hereditary neuropathy 40 ., Although it is unclear how mutated CCT5 causes this disease , it has been postulated that its mutation leads to accumulation of misfolded cytoskeletal proteins , leading to defective assembly of actin into microfilaments resulting in neuronal apoptosis 41 ., In our yeast screens , CCT5 was needed for resistance to eight different compounds ( cyproheptadine | Introduction, Results, Discussion, Materials and Methods | To better understand off-target effects of widely prescribed psychoactive drugs , we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system ., Because the known human targets of these drugs do not exist in yeast , we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner ., Among 214 tested , documented psychoactive drugs , we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling ., Many of these drugs had a propensity to affect multiple cellular functions ., The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion , protein folding , RNA processing , and chromatin structure ., Interestingly , fluoxetine ( Prozac ) interfered with establishment of cell polarity , cyproheptadine ( Periactin ) targeted essential genes with chromatin-remodeling roles , while paroxetine ( Paxil ) interfered with essential RNA metabolism genes , suggesting potential secondary drug targets ., We also found that the more recently developed atypical antipsychotic clozapine ( Clozaril ) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol ( Haldol ) and pimozide ( Orap ) ., Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes . | Neuropsychiatric disorders such as depression and psychosis affect one-quarter of all individuals during their lifetime , and despite efforts to improve the selectivity of psychoactive drugs , all are associated with side effects ., Drug efficacy and tolerance are known to be linked to an individuals genetic profile , but little is known about the nature of this correlation due , in part , to the current emphasis on screening compounds against targets in vitro ., Here we present a comprehensive , genome-wide effort to understand drug effects on the cellular level using an unbiased genome-wide assay to determine the importance of every yeast gene for tolerance to 81 psychoactive drugs ., We found that these medications perturbed many evolutionarily conserved genes and cellular pathways , such as those required for vesicle transport , establishment of cell polarity , and chromosome biology ., The 500 , 000 drug–gene measurements obtained in this study increase our understanding of the mechanism of action of psychoactive drugs ., Specifically , this study provides a framework to assess the next generation of psychoactive agents and to guide personalized medicine approaches that associate genotype and phenotype . | genetics and genomics/genomics, genetics and genomics/gene function, genetics and genomics, genetics and genomics/functional genomics | null |
journal.pcbi.1006827 | 2,019 | Optimizing the depth and the direction of prospective planning using information values | When confronted with several choices , we need to have an evaluation of how good each option is ., Each choice has some immediate consequences , but also takes us into a new state where new choices emerge , and so on ., Think of chess as an example ., One intuitive way to solve a sequential decision-making problem like chess is to prospectively think into the future ., This idea , known as model-based planning in the reinforcement learning literature 2 , expands a mental decision-tree by simulating a number of future action sequences ., Although this method is accurate ( in terms of statistical efficiency ) , evaluating deep trees is computationally expensive ( in terms of time , working memory , metabolic energy , etc . ) ., In chess , for example , it is impossible even for the best supercomputers to expand the tree of all possible strategies up to the end of the game ., Therefore , several solutions have been provided in the artificial intelligence literature for how to approximate the values of choices without expanding a search tree to its fullest extent 3 or how to make the best use of limited computational resources to plan better 4 ., To avoid the costs of planning altogether , a drastic alternative is to rely on heuristic methods that evaluate choices without any tree expansion ., For example , a chess player can evaluate a chess position , without investigating the possibility of that position leading to a win or lose , by simply counting up the values of their pieces—a common heuristic utilized by novice players ., Another example of approximate evaluation techniques , widely used in both natural and artificial intelligence ., is using habits ., This method , known as model-free reinforcement learning 2 , 5 , simply “caches” the average of previously realized rewards ensued by performing each action , and uses the cached values for evaluating those choices should they come up again in the future ., Although using such heuristics frees cognitive resources from model-based planning , the downside is their inaccuracy ., Habits , for example , take many trials to form , and they are always unreliable in changing environments ., Rather than clinging to one of these extreme solutions ( i . e . , full planning vs . heuristics/habits ) , an intelligent agent can instead combine the two in order to harvest the relative advantages ( i . e . , accuracy vs . affordability ) of both techniques 6–9 ., This , in theory , is achievable by forward planning up to some depth and then exploiting heuristic values as proxies for consequences that may arise in the further future ., That is , when the depth of planning is say d , the agent computes the value of a choice by adding the first d rewards predicted by explicit simulation , to the value of the remaining actions estimated by the heuristic/habitual values ., For example , a chess player could think three steps ahead , and then estimate , heuristically , the strength of the position he could achieve after those three moves ., This integrative approach has been used in artificial intelligence for example for obtaining super-human Go performance 10 ) ., Furthermore , it was shown recently that humans also use this scheme , named plan-until-habit , for integrating planning and habitual processes in a normative way , and that their depth of planning depends on the time-pressure imposed on them 11 ., The plan-until-habit ( or plan-until-heuristic , in general ) scheme aims at mitigating the computational costs of planning by appealing to the habitual system after the planning system has sufficiently expanded the decision-tree ., Obviously , the first questions to be asked in this framework are “in which directions the decision-tree should be expanded ? ” , and “when should the expansion stop ? ” ., In this paper , we present , for the first time , a principled algorithm for optimal tree-expansion in the plan-until-habit framework ., The algorithm is based on a speed/accuracy tradeoff: deeper planning leads to more accurate evaluations , but at the cost of slower decision-making ., As a proof of concept , we show through simulations how this algorithm expands the decision-tree effectively and efficiently in a simulated grid-world environment ., We further show that our algorithm can explain several behavioral patterns in animals and humans , namely the effect of time-pressure on the depth of planning , the effect of reward magnitudes on the direction of planning , and the gradual shift from goal-directed to habitual behavior during training ., The algorithms also provide several predictions testable in animal/human experiments ., From an external-observer viewpoint , the questions to be answered by an agent are of the type “what action should be taken ? ” ., From a metacognitive perspective , however , the agent should first think about how to think ( e . g . , how deep she should plan ) ., In fact , the question she could ask at each step of the planning process is “Should I expand the decision-tree one step further ? ” , and if yes , “In what direction ? ” ., To answer these , assume that the agent has already expanded a tree to a certain extent ( Fig 1A ) ., This means that the agent knows , possibly with some uncertainties , a few next states to be visited upon taking each action , and the immediate rewards associated with each of those transitions ., She can , therefore , sum up the predicted rewards along each trajectory ( i . e . , action-sequence ) and have an estimate of the total rewards to be achieved ., On the top of this “total immediate rewards” , each trajectory ends in a frontier state which represents the edge of the current planning horizon along that trajectory ., The habitual ( or any other heuristic ) values on this frontier state supposedly reflect the total ( discounted ) rewards to be expected from that point on ., Therefore , the sum of “total immediate rewards” and the habitual value of the frontier node provides an estimate of the total expected reward of each trajectory ( Fig 1B ) ., Habitual values , however , can be highly unreliable due to the inflexible nature of habit formation ., For each given trajectory , therefore , the dependence of its estimated total rewards on uncertain habitual values renders the whole estimation uncertain ., If expanding the tree along that trajectory would make value estimation less dependent on habitual values and thus reduce uncertainty , that expansion is worth considering ., In this sense , the critical value to be computed for each trajectory is the “value of uncertainty reduction” ( vur ) ., vur computation for a trajectory should examine whether a new piece of information , possibly providable by a further expansion of the tree along that trajectory , could change agent’s decision about what action to be taken , and how much extra value is expected to be gained by that policy improvement ., vur is , in fact , the expected value of policy improvement-induced rewards , computed over all possible new pieces of information that could be provided by expanding the trajectory one step further ( Fig 1C ) ., Although the agent readily possesses those new pieces of information in her memory ( because she has a model of the environment ) , loading them into working memory and taking them into the value-estimation account is worth doing only if the value of uncertainty reduction is more than its cost ., Here is the general scheme of our algorithm: at each stage of planning , vur is computed for each trajectory on the search tree ( we discuss later that previously-computed vur-values can be reused later under certain conditions ) ., The trajectory with the highest vur is expanded if its vur is bigger than the cost of expansion ., Otherwise , the expansion process is terminated and the agent chooses an action ( e . g . , using soft-max rule ) according to the estimated values derived from the tree ., In this paper , we assume that the cost of expansion simply reflects the opportunity cost of time ., That is , assuming that each expansion takes ϵ time units , the total cost of one expansion is R ¯ ϵ , where R ¯ is the average reward the agent receives in the given environment ., As explained before , the main motivation for expanding the tree is reducing value-estimation uncertainties ., There could be several reasons for why expansion reduces uncertainty ., In many cases , like chess , heuristic estimations become more precise as the game advances ., In general , proximity to goal sometimes makes it easier to evaluate the states ., Another way that expansion reduces uncertainty , which is the focus of our formal model , is through temporal discounting ., By each level of expanding a trajectory , the dependence of its estimated value on the less-reliable habitual system is shifted one step further into the future ., As a simplified example , imagine you are in a maze and you have already thought two steps ahead along a certain trajectory , T1 , of actions , and those two steps will take you to the state s′ ., You can use the MF value , VMF ( s′ ) of that state to compute the total value of the trajectory: V ( T1 ) = r1 + γ ., r2 + γ2 . VMF ( s′ ) , where r1 and r2 are the immediate rewards expected to be received by performing the first and the second actions on the trajectory T1 ., Assuming that the estimates of the immediate rewards have zero uncertainty , and that the MF estimates always have variance σ2 ( i . e . , uncertainty ) ) , the total uncertainty of V ( T1 ) will be ( γ2 . σ ) 2 = γ4 . σ2 ., Now , if you think one step deeper and expect to land in state s′′ after taking the first three steps of trajectory T2 , then V ( T2 ) = r1 + γ ., r2 + γ2 . r3 + γ3 . VMF ( s′′ ) ., Therefore , its variance will be ( γ3 . σ ) 2 = γ6 . σ2 ., This toy example shows that as a natural consequence of temporal discounting , by increasing the depth of planning , the total uncertainty of trajectories decreases , due to the reduced reliance on uncertain MF values ., Therefore , the discount factor is the critical variable that determines the extent of uncertainty reduction by each expansion ., In this paper , we only consider environments where the transition between states via actions are deterministic ( i . e . , deterministic transition function for the Markov decision process; See Methods for how this assumption can be relaxed ) ., Therefore , the expanded tree , at each point , is a deterministic tree ., In order to compute vur , let’s define a strategy in a tree as a combination of actions that an agent can take to reach a leaf in the tree ( see Fig 1 ) , and define a frontier search as the set of all strategies that agent can take in a given tree ( e . g . , the search frontier in Fig 1 is {A1 , A2 , A3 , A4 , A5} ) ., Based on this definitions , as shows in the Methods section , the value of uncertainty reduction for strategy Ai , given the search frontier F , can be written as:, VUR ( A i | F ) = E μ i * max ( μ i * , max A ∈ F - A i E V ( A ) ) ︸ with expansion - max A ∈ F E V ( A ) ︸ without expansion , ( 1 ), where F − Ai is the set F excluding Ai ., According to this equation , computing vur ( Ai|F ) requires μ i * , which is the expected mean of strategy Ai after the potential expansion ., However , this variable can be computed before expansion , by μ i * ∼ N ( μ i , ( 1 - γ 2 ) σ i 2 ) ( see Methods section ) , in which γ is the discount factor , and μi and σ i 2 are respectively the mean and the variance of the MF-value distribution for the last action on Ai ., In other words , vur is computable based on μ i * , the expectation with respect to the predicted value of Ai after expansion , instead of its realized value which is not available before the expansion ( a more general form of the above equation without reliance on the discount factor is presented in the Methods section ) ., The right-hand side of Eq 1 is composed of two parts: the amount of future rewards that are expected to be gained with the expansion of strategy Ai , and the amount expected to be gained without the expansion of Ai ., vur is the difference between these two quantities ., The without-expansion term is simply the value of the best strategy that is currently available to the agent ., In the with-expansion term , the outer ‘max’ operator implies that if after expanding , Ai turns out to be worse than the other available strategies ( F − Ai ) , then the best strategy among the other ones will be taken ., Otherwise , Ai will be taken ., The agent , however , needs to calculate this term before the expansion of Ai and therefore the term is calculated based on the expectation with respect to the predicted value of Ai after expansion ( denoted by μ i * ) instead of its realized value which is not available before the expansion ., It can be shown that in the case of normally distributed MF value functions , Eq 1 has a closed-form solution ( see S1 Text for details ) :, VUR ( A i | F ) = { σ i ϕ ( μ i - μ β σ i ) - μ i - μ β σ i Φ ( - μ i - μ β σ i ) + μ β - μ α if A i is the best strategy σ i ϕ ( μ i - μ α σ i ) - μ i - μ α σ i Φ ( - μ i - μ α σ i ) otherwise ( 2 ), where μi and σi are , respectively , the mean and the standard deviation of strategy Ai ., Furthermore , μα and μβ are the means of the , respectively , first-best and second-best strategies in the currently-expanded tree ., First-best and second-best strategies are the strategies that have the highest and the second-highest mean values ., Finally , ϕ and Φ are , respectively , the probability density and cumulative distribution functions of a standard normal distribution ., A central principle for any meta-control algorithm is that the cost of meta-reasoning ( here , the cost of computing arg maxA VUR ( A|F ) ) should be lower than the cost of expensive reasoning ( here , one-step expansion of the decision-tree ) ., In terms of memory cost , tree-expansion would require loading information about the expanding nodes from the long-term to the working memory ., Furthermore , it would require engaging an additional working memory slot to store such information ., Meta-reasoning , however , has minimal memory cost , since all the variables for computing arg maxA VUR ( A|F ) already exist in the working memory ( i . e . , are in the already-expanded tree ) ., In terms of computational-time cost , we should stress that even though we want to find the strategy with the maximum vur value , this does not necessarily require computing vur’s of all strategies at each time step ., vur ( Ai|F ) only depends on μi , σi and μα ( or μβ ) ., Therefore , vur values can be cached , and reused as long as the aforementioned parameters have not changed ( i . e . , the newly-added strategies are not first- nor second-best strategies ) ., From an algorithmic point of view , computing vur of a given Ai can be viewed as a constant time operation ., Therefore computing arg maxA vur ( A|F ) is in the order of O ( | F | ) in the worst case , where |F| is the cardinality of F ( i . e . , number of items in the search frontier ) ., However , as shown in the appendix , as the tree expands , the expected cost becomes constant ( i . e . , O ( 1 ) ) asymptotically , given that the agent caches previously computed vur values ., This is intuitively becuase as the depth of the tree grows , the uncertainty around the value of the to-be-expanded strategy shrinks ( becuase of the discounting factor ) , which makes it less likely that the strategy ( which is not currently the best strategy ) becomes the best one after expnasion ( or second best strategy ) ., As such , the chances that a new expansion affects previusly computed vur values becomes smaller and smaller as the tree gets deeper ., This rate of decrement is faster than the rate at which new potential strategies are added to the tree as it gets deeper , and therefore overall the number of vur values that need re-computation remains constant as in the limit ., Just as a proof of concept , we would like to see whether our method can be beneficial in a setting in which an agent is combining both MF and MB information for efficient planning ., For this , we first trained an agent in an episodic grid-world environment where she obtains imperfect estimates of state-values by the model-free system ., After training , she utilizes both the MF and the MB systems to use the plan-until-habit scheme , where the MB system is used to construct the tree , and the MF systems is used for estimating the values of state-actions that lie on the frontier of the tree ., We predict that the increased accuracy in model-free estimates , as a result of training , would bias the direction of expanding the tree towards better states ., The agent starts each episode in the center of a 7 × 7 grid and can choose to go up , down , left , or right at each state ., All the transitions are deterministic and are associated with a unit cost ., The bottom right cell is the goal state that concludes the episode ., This state is not associated with any reward , but is implicitly rewarding since it terminates the costly walk in the grid world ., Evidently , the optimal policies are combinations of three right moves and three down moves ., Given the structure of the task , for easier geometric interpretation and without loss of generality , the MF system learns state values , rather than state-action values ., To apply our plan-until-habit pruning algorithm , we require an MF system that learns not just the mean , but also the variance ( i . e . , uncertainty ) over the state values ., In our implementation , the agent estimates the value of a state by generating a number of trajectory samples from the state , similar to the first-visit Monte Carlo method described in 2 , and utilizing the trajectories’ return statistics ., However , instead of estimating the Q-values with Monte Carlo averages , we use independent conjugate normal priors and obtain posterior estimates of Q’s , which are conditioned on the trajectory returns ( see S1 Text ) ., We obtain N trajectory samples starting from each state , such that each sample consists of a trajectory resulting from a fixed uniform random policy that assigns 1 4 probability to each direction {UP , DOWN , LEFT , RIGHT} ., We test our planning model in two different settings ., First , we assume the agent has no experience interacting with the environment ( i . e . , N = 0 ) ., This condition results in the posterior Q-values having large and equal variances ., We compare this with the case where the agent has collected some samples ( i . e . , N = 10 ) , resulting in more accurate estimates of state values ., In both cases , we employ the same pruning mechanism , with a variable number of possible tree expansions ( capturing working-memory limitations; see Discussion section ) selected uniformly from 5 , 25 and γ = 0 . 95 ., As displayed in Fig 2A , in the no-experience condition , the search tree is explored in all directions almost uniformly ., In the second condition , however , the search is directed more towards the goal state as illustrated in Fig 2B ., These results are in line with our intuition that the agent prunes more aggressively as she gathers more experience and thus , is better able to judge what the promising states or actions are ., Behavioral evidence suggests that humans , when planning , curtail any further evaluation of a sequence of actions as soon as they encounter a large punishment on the sequence 12 ., In a behavioral task 12 , subjects were required to plan ahead in order to maximize their income gain ., The environment in the task is composed of six states ., Each state affords two actions , each of which transitions the subject to another state deterministically ., Subjects see their current state on a display and press the ‘U’ or ‘I’ buttons on the keyboard to transition to a different state ., In the first phase of the experiment , subjects learn the deterministic transition structure of the environment ., In the second phase , transitions are associated with specific gains or losses , which are visually cued to make it easier to remember ., At each trial in this stage , subjects are told to take a certain number of actions , varying between 2 and 8 , and collect all the rewards and punishments along their chosen trajectory ., This forces them to think ahead and plan in order to find a relatively profitable trajectory among 22 = 4 to 28 = 256 options ., For example , in the setting described in Fig 3A , 8 possible trajectories resulting from 3 consecutive actions are displayed ., Out of all 12 transitions , 3 of them are associated with a large loss ., The magnitude of this loss is manipulated across trials ( from {−140 , −100 , −70} ) such that for certain losses ( i . e . , −100 and −70 ) , Pavlovian pruning results in suboptimal strategies ., In other words , pruning a strategy that starts with a −100 or −70 loss would result in discarding the most profitable course of actions , since such actions will eventually lead to highly rewarding states ., The results of this experiment show that humans prune infrequently if pruning results in prematurely discarding optimal trajectories ., Conversely , they tend to prune liberally when pruning does not eliminate the optimal trajectories ., That is , they prune more when the loss on a trajectory is so large ( i . e . , −140 ) that cannot be compensated for by future rewards ., We aimed to replicate this task in our simulations ., Because in the first part of the experiments subjects learn the transition and the immediate rewards through repetitive exposure , we assume that the agent ( i . e . , our simulation of a subject ) knows the transition and reward structures ., Since the immediate state-action rewards are visually cued , subjects , after observing their starting state s and their available actions a1 and a2 , presumably incorporate the immediate rewards of those actions into their planning at no cost ., Therefore , we assume that the agent starts the decision tree with two already-expanded actions , with values Q ( ai ) = R ( s , ai ) + γV ( T ( s , ai ) ) , where i ∈ 1 , 2 , and R ( s , a ) and T ( s , a ) are the immediate reward and successor states resulting from taking action a at state s ., As in the previous experiment , we obtain the posterior Q-value distributions of the agent through a training stage ., Similar to the training phase of the original study , we have the simulated agent interact with the environment for 100 episodes , during which she observes transitions and collects reinforcements ., At each trial , the agent is located in a random state and is allowed to make a certain number of moves , which is sampled uniformly from {2 , 3 , 4} ., She selects actions following uniform random policy , and stores the mean cumulative reinforcements collected after taking action a at state s , similar to the first-visit Monte Carlo algorithm 2 ., Those mean values are then used for obtaining the posterior Q-distributions assuming a conjugate normal distribution as in the previous experiment ( see S1 Text ) ., The prior is a normal distribution with mean and standard deviation of 0 and 1000 , respectively ., After the training stage , the agent moves on to the pruning state , where she starts at state s and is asked to mentally expand the planning tree for n ∈ {2 , 4 , 6 , 8 , 10 , 12s} steps ., We record the frequency with which the agent expands the early branch with the large punishment , which we very between −40 and −140 ., Finally , we set γ to 0 . 95 as before ., One critical observation in 12 is that subjects prune more frequently as the magnitude of the punishment increases ., As shown in Fig 4 , our simulation results account for this pattern ., Intuitively , observing a punishment on a trajectory reduces the expected value of the trajectory and thus , reduces the overlap between the value-distribution of that trajectory and that of the best trajectory ., When the punishment is large enough , the overlap becomes very small even if the trajectories have highly uncertain value estimates ., Small overlap is equivalent to low “value of uncertainty resolution” expected from expanding the unpromising trajectory , because there is a very small chance that the new pieces of information will render the unpromising trajectory better than the currently best strategy ., In the simulations , we also vary the maximum number of branches allowed to be expanded , reflecting constraints on the working memory capacity ( see Discussion section ) ., Not surprisingly , as the memory capacity is increased , pruning frequency decreases ( Fig 4 ) ., Another important aspect of the study is that the likelihood of selecting the optimal sequence of actions by the subjects was affected by three factors:, ( i ) subjects were less likely to choose the “Optimal Lookahead” sequence when it contained a large loss ,, ( ii ) this effect became larger as the size of the loss increased , and, ( iii ) the optimal sequence was more likely to be chosen when the tree was shallow ( i . e . , when the subjects were supposed to choose a smaller number of actions ) ., These three effects are shown in the top panel of Fig 5 for the data reported in Huys et . al . 12 ., The bottom panel displays the prediction of our method based on the simulations in the same task ., It can be seen that similar to the actual data , we predict that the subjects will be more successful in picking the optimal sequence when it does not contain a large loss , the tree is shallow and the loss is small ( i . e . , the effect is strongest in the −140 group and the weakest in the −70 group ) ., One notable qualitative mismatch between the top and bottom panels is that , our model assigns a higher probability of choosing optimal sequences for smaller depths than what is shown for the actual data on the top panel ., This is because , in our setting , the agent is very likely to make enough expansions to find the optimal sequence for a tree of depth 2 , as there are only 22 = 4 possible sequences—which can be spanned with a small number of expansions ., The number of expansions are sampled from round ( Gamma ( 4 , 2 ) ) + 1 , where + 1 ensures positivity ., Given this distribution , it is often the case that the agent performs enough expansions to find the optimal ., However , if we look at the top left plot in Fig 5 , we see that the probability of choosing the optimal sequence is low if it contains a large loss—even for depth of, 2 . This might suggest that the subjects do not fully use their “expansion budgets” , if performing expansions do not seem advantageous ., The same could be done in our scheme by stopping expansions altogether if the maximum vur is below a threshold ., However , we refrained from doing so , and instead used a random number of expansions for simplicity , and for limiting the flexibility of the model to prevent overfitting ., Other than this , all other parameters are kept the same as the ones used for generating Fig, 3 . Previously , the punishment-induced pruning discussed here was explained assuming that a Pavlovian system , reflexively evoked by large losses , curtails further evaluation of the corresponding sub-tree 12 , 13 ., In our computational framework , however , this pruning pattern emerges naturally , rather than devising new mechanisms , from a speed-accuracy tradeoff ., Furthermore , the normative nature of our explanation depicts punishment-induced pruning as an adaptive mechanism in the face of cognitive limitations , rather than depicting it an a “maladaptive” Pavlovian response 12 ., Several lines of research have shown a transfer of control over behavior from goal-directed to habitual decision-making during the course of learning 14–17 ., Previous accounts of interaction between MB and MF algorithms 18 , 19 explained this behavior by showing that the MF value estimates become more and more accurate along the course of experiencing a task ., As a result , they eventually become more accurate than MB estimates 18 , or become accurate enough that the extra information that MB planning can provide is not worth the cost of planning 19 ., Therefore , a binary transition from goal-directed to habitual responding occurs in behavior ., Our model also explains the transition , but also suggests that it is gradual , rather than binary ., As MF estimates become more accurate , the variance in strategy values decrease and thus , vur values also decrease monotonically ( see S1 Text for an analytical proof of this effect ) ., This implies that an experienced agent would construct a shallower search tree and hence , spends less time planning compared to an inexperienced agent ., Furthermore , in contrast to the previous accounts that propose ad-hoc 18 or optimal , but with very strong assumptions ( i . e . , MB tree-expansion has an infinite depth ) , 19 models for MB-MF arbitration mechanisms , our proposed model’s optimality is based on more reasonable assumptions ., Our algorithm further predicts that in a plan-until-habit scheme , time-limitation would reduce the depth of planning ., That is , time pressure would monotonically limit the total number of branches to be expanded , pressing the agent to switch to habitual/heuristic values at a shallower depth ., This is due to the fact that every tree-expansion step is assumed to take a certain amount of time , ϵ ., Therefore , our model , for the first time , accounts for recent evidence showing that humans use a plan-until-habit scheme and that time pressure reduces their depth of MB planning 11 , resulting to a relying on habitual responses at a shallower level ., In this experimental study 11 , participants first learned the stationary transition structure of the environment in a three-step task ., They then navigated through the decision tree , in each trial , to reach their desired terminal state ., The rewarding value of the terminal states was non-stationary and changed along the trials , allowing to measure , from participants’ choices , whether or not they use a plan-to-habit scheme; and if they do , what depth of planning they adopt ., The experiment imposed a decision time-limit of either 2000 or 700 milliseconds to two different groups of participants ., While both groups showed a significant behavioral signature of plan-to-habit responding , participants that experienced a shorter time-limitation showed pruning the tree and switching to MF values at shallower levels ., In this section , we qualitatively compare our plan-to-habit pruning algorithm to other methods , such as Monte Carlo tree search ., Finding optimal or near optimal actions requires comparing the expected value of all possible plans that can be taken in the future ., This can be achieved by explicitly expanding a model that represents the underlying structure of the environment , followed by calculating the expected value of each plan ., However , the computational complexity of this process grows exponentially with the depth of search for optimal plans , which makes it infeasible to implement in all but the smallest environments ., Indeed , evidence shows that humans and other animals use alternative ways that have lower computational complexities than explicit search ., Examples are using ‘cached’ values of actions instead of recalculating them at each decision point 18 , or using ‘action chunking’ , in which actions span over multiple future states 23 ., Here , we suggest that such decision-making strategies are not operating independent of the planning processes , but they interact in order to provide a planning process that adapts its extent according to time and cognitive resource and therefore , scales to complex environments ., In particular , the model that we suggest is built upon two bases:, ( i ) the planning process is directed toward the parts of the environment’s model that are most likely to benefit from further deliberation , and, ( ii ) the planning process uses ‘cached’ action values for the unexpanded ( i . e . , pruned ) parts of the tree ., Simulation results showed that the model prunes effectively in a s | Introduction, Results, Discussion, Methods | Evaluating the future consequences of actions is achievable by simulating a mental search tree into the future ., Expanding deep trees , however , is computationally taxing ., Therefore , machines and humans use a plan-until-habit scheme that simulates the environment up to a limited depth and then exploits habitual values as proxies for consequences that may arise in the future ., Two outstanding questions in this scheme are “in which directions the search tree should be expanded ? ” , and “when should the expansion stop ? ” ., Here we propose a principled solution to these questions based on a speed/accuracy tradeoff: deeper expansion in the appropriate directions leads to more accurate planning , but at the cost of slower decision-making ., Our simulation results show how this algorithm expands the search tree effectively and efficiently in a grid-world environment ., We further show that our algorithm can explain several behavioral patterns in animals and humans , namely the effect of time-pressure on the depth of planning , the effect of reward magnitudes on the direction of planning , and the gradual shift from goal-directed to habitual behavior over the course of training ., The algorithm also provides several predictions testable in animal/human experiments . | When faced with several choices in complex environments like chess , thinking about all the potential consequences of each choice , infinitely deep into the future , is simply impossible due to time and cognitive limitations ., An outstanding question is what is the best direction and depth of thinking about the future ?, Here we propose a mathematical algorithm that computes , along the course of planning , the benefit of thinking another step in a given direction into the future , and compares that with the cost of thinking in order to compute the net benefit ., We show that this algorithm is consistent with several behavioral patterns observed in humans and animals , suggesting that they , too , make efficient use of their time and cognitive resources when deciding how deep to think . | decision making, engineering and technology, statistics, applied mathematics, social sciences, neuroscience, learning and memory, simulation and modeling, algorithms, decision analysis, cognitive neuroscience, cognitive psychology, mathematics, animal behavior, management engineering, cognition, memory, zoology, research and analysis methods, decision trees, behavior, mathematical and statistical techniques, monte carlo method, working memory, psychology, biology and life sciences, physical sciences, cognitive science, statistical methods | null |
journal.pcbi.1003871 | 2,014 | Quantifying the Turnover of Transcriptional Subclasses of HIV-1-Infected Cells | High levels of cell-associated HIV-1 RNA can be observed in peripheral blood of patients with undetectable plasma viremia during combination antiretroviral therapy ( cART ) 1–4 ., The various HIV-1 RNA and DNA species that are present during the viral life cycle can serve as biomarkers for basal transcription in viral reservoirs with different properties 5 , 6 ., Gaining a quantitative understanding of the development and turnover of HIV-1-infected subpopulations and viral latency is of particular interest in light of recent efforts in viral eradication strategies 7–10 ., Highly sensitive assays for HIV-1 plasma RNA in patients on cART usually provide bulk measurements of viral activity and cannot distinguish between different infected subpopulations 11 ., In contrast , the study by Fischer et al . 12 combined highly sensitive PCR assays for unspliced ( UsRNA ) and multiply spliced ( MsRNA-tatrev and MsRNA-nef ) HIV-1 RNA species with limiting dilution endpoint analysis of peripheral blood mononuclear cells ( PBMCs ) ., In addition to intracellular RNA transcripts , extracellular virion-enclosed HIV-1 RNA that provides a marker for cells releasing virus particles was also measured ., The study identified four distinct viral transcriptional classes: two overlapping cell classes of high viral transcriptional activity , representative of a virus producing phenotype; and two cell classes that express HIV-1 RNA at low and intermediate levels that match definitions of viral latency 12 , 13 ., Analyzing the decay kinetics of plasma viral load in HIV-1-infected patients on cART using mathematical models has resulted in a detailed understanding of viral replication dynamics in vivo 14–16 ., The plasma viral load typically exhibits three exponential phases during the first year after start of cART ( Figure 1 ) ., Due to the rapid turnover of free virus in blood 17 , the viral decay phases are thought to reflect the contribution of different HIV-1-infected cell populations on viral production ., The first phase with a half-life of 1 to 2 days is attributed to the loss of activated , virus-producing cells 18 , 19 ., The second phase exhibits a half-life of 1 to 4 weeks and is considered to reflect the loss of so-called persistently infected cells with a lower state of activation 20 , 21 ., The third phase decay has a long half-live of 39 weeks suggesting that latently infected cells are a primary candidate for this cellular compartment 22 , 23 , although slow release of virus from the follicular dendritic cell network is another possibility 24 ., Although not shown in Figure 1 , in many patients , after the third phase a final low steady state level of plasma viremia is attained , that has been called a fourth phase 22 ., This phase has also been attributed to release of virus from activated latently infected cells 22 ., Other mathematical models have been developed that stratify the infected cells into additional subpopulations such as non-productively infected cells during the intracellular eclipse phase 25 and defectively infected cells 26 ., Nevertheless , most studies to date are focused on the analysis of viral load and only indirectly allow inferring the kinetics of cellular subpopulations ., Few studies have attempted to characterize the concentration of virus and several infected subpopulations based on data simultaneously 26 ., Fitting mathematical models to multiple quantities of viral replication would result in refined parameter estimates for describing the generation and maintenance of latently infected cells ., In this study , we developed a mathematical model that describes the dynamics of different transcriptionally active subclasses of HIV-1-infected cells and the viral load in peripheral blood ., The model was fitted to previously published data from five chronically HIV-1-infected patients starting cART 12 ., This allowed us to estimate critical parameters of the within-host dynamics of HIV-1 and the turnover of various subpopulations of infected cells ., Finally , we simulated the development of the latently infected cell pool during acute infection , providing useful information for viral eradication strategies ., We first devised a detailed model of the within-host dynamics of HIV-1 that is based on the observations of different subclasses of HIV-1-infected cells in the study by Fischer et al . 12 ., The five subclasses are HIV-1 DNA , low , medium and high HIV-1 RNA expressing and cells that have virion-enclosed HIV-1 RNA associated with them ( also see Methods ) ., These subclasses show distinct decay dynamics in patients on cART ( Figure 2 ) ., The slow decay of the subclass of PBMCs that contains proviral DNA ( DNA ) indicates that this cell population primarily contributes to the third phase decay and likely consists of defectively or latently infected cells to a large extent ., The subclass of cells exhibiting UsRNA only ( Low ) decays slowly and most likely consists mainly of latently infected cells with low basal transcription of HIV-1 ., The cells with medium transcriptional activity ( Mid ) appear to contribute to the second and the third phase viral decay , which is characteristic of persistently and latently infected cells ., The early drop in PBMCs with a higher transcriptional activity ( High ) , which is more pronounced compared to cells with a low and medium transcriptional activity , that is followed by a slower loss of cells is reminiscent of activated , virus-producing and persistently infected cells ., Finally , the PBMCs that have extracellular virion-enclosed HIV-1 RNA associated with them ( Extra ) show a very rapid loss before reaching the limit of detection ., This is expected as they should represent the short-lived population of virus-producing cells 4 that contribute to the first phase of viral decay ., The different subclasses of HIV-1-infected cells clearly overlap and are representative of heterogeneous cell populations ., Furthermore , the life cycle of HIV-1 from infection of a cell to the release of virus particles can be divided into cell populations with different transcriptional activity 27 ., We took both of these important characteristics into account in our model that consists of 12 subpopulations of cells that can be stratified according to their HIV-1 DNA and RNA content ( Figure 3 and Methods ) ., In this model , we defined persistently infected cells ( and ) as long-lived cells that can produce viral particles ., Latently infected cells ( and ) were assumed to transcribe HIV-1 RNA at low or intermediate levels 12 , 13 ., Infected cells that are HIV-1 DNA positive , but HIV-1 RNA negative , were assumed to remain transcriptionally silent during the observation period and considered as defectively infected cells ( ) ., Fitting the mathematical model to the data from five HIV-1-infected patients resulted in a good description of the viral and cellular decay kinetics during cART ( Figure 4 and Text S1 ) ., The individual dynamics of each subpopulation of cells are shown separately ( Figure 5 ) ., The model clearly describes more pronounced decay dynamics in infected cells with increasing transcriptional activity ., Table 1 provides a summary of the geometric means as well as the ranges of the best fit parameter estimates that describe the virus dynamics in each of the five patients ., We found that 1 . 1% ( 0 . 2%–7 . 0% ) of all CD4 T cells can be target cells for infection with HIV-1 ., We also obtained estimates for the average lifespans of target cells ( 61 days , range: 11–528 days ) and latently infected cells ( 33 years , range: 168 days–505 years ) ., While others have estimated the average half-life of latently infected cells to be 6 . 3 months 28 and 44 months 29 , our estimates are less precise due to the much shorter follow-up period after start of cART ., However , the estimated activation rate of latently infected cells ( d , range: d ) that also influences the slope of the third phase decay in plasma HIV-1 RNA is consistent with previous findings 30 ., The parameters , and denote the fractions of cells that end up in a particular subpopulation in a sequential process during the intracellular eclipse phase ., From this , we can calculate the average proportion of newly infected cells that become a certain cell type ( Text S1 ) ., In contrast to another study 26 , we find that only 63 . 4% ( 0 . 2%–7 . 0% ) of infected cells become activated , virus-producing cells ( ) ., A substantial fraction of infected target cells results in defectively ( 14 . 0% ) and persistently infected cells ( 21 . 2% ) ., The proportion of infected cells that become latently infected or die before ending up in one of the subpopulations is small ( 0 . 3% and 1 . 1% , respectively ) ., Note that after activation , latently infected cells can then either become persistently infected or activated , virus-producing cells by moving through cell class ., Transcriptional bursts that increase the level of viral RNA transcription occur on average every 12 . 7 days ( , range: 3 . 5–165 . 2 days ) and 9 . 7 days ( , range: 1 . 5–37 . 0 days ) in latently and persistently infected cells , assuming that bursts last for one day on average ( d ) ., The total number of virus particles produced by a cell during its lifetime , the viral burst size , was estimated at 21′000 virions per cell ( range: 3′500–240′000 virions per cell ) ., Note that we assumed that persistently infected cells in an elevated transcriptional state ( ) produce viral particles at the same rate as activated , virus-producing cells ., However , the duration of virus release is shorter in persistently infected cells as they can revert to a lower transcriptional state ( ) ., The majority of virus particles is produced by activated , virus-producing cells ( 68 . 3% , range: 5 . 6%–98 . 1% ) with the remaining proportion being produced by persistently infected cells ., The high viral burst size suggests that the total number of virus-producing cells in peripheral blood must be small and we indeed found an average of only 25 . 7 cells ml ( range: 7 . 8–143 . 1 cells ml ) in the model during the chronic phase of infection ., The parameters were estimated by fitting the virus dynamics model to data of patients chronically infected with HIV-1 ., Although there are mathematical models that describe acute and chronic HIV infection together 31 , 32 , the virus dynamics during acute infection could differ significantly due to different parameter values and even model structures ., Nevertheless , our model can still be used to simulate the virus dynamics during the acute phase and compare the results to experimental and clinical data ., We used the average of the estimated parameters to simulate early infection with HIV-1 from a small viral inoculum in a hypothetical patient ., We set copy per ml and assumed that the target cells are at steady-state ( ) ., The rapid rise of plasma HIV-1 RNA during the first weeks of infection is followed by the chronic phase at which the virus concentration reaches its set-point level ( Figure 6 ) ., The total pool of latently infected cells ( ) show somewhat different dynamics during acute HIV-1 infection ., A very rapid expansion of latent cells during the viral growth phase is followed by a slower increase into the chronic phase of infection ., From the time of peak viremia ( 22 days ) to the chronic phase ( 1000 days ) , the latently infected cell pool expands 14 . 3-fold from 9 . 8 to 140 . 4 cells per ml ., The expansion of the total number of HIV-1 DNA positive cells from the acute ( 1813 cells per ml ) to the chronic phase ( 7608 cells per ml ) is smaller ( 4 . 2-fold ) ., This is consistent with the 3 . 8-fold difference in the number of HIV-1 DNA copies that were measured in patients that initiated cART during the acute and chronic phase from another study 33 , and see Text S1 ., The time after infection at which latently infected and HIV-1 DNA positive cells reach 50% of their chronic level is 441 and 451 days , respectively ., Altogether , this illustrates the opportunity for eradication strategies during early cART interventions as the pool of HIV-1 infected cells seems to be substantially smaller during acute infection than during chronic infection ., We present the first mathematical model of virus dynamics that groups the different subpopulations of HIV-1-infected cells according to their transcriptional profile ., The model assumes a heterogeneous population of latently and persistently infected cells having occasional transcriptional bursts to increase their level of RNA transcription which is consistent with experimental data from Fischer et al . 12 ., Fitting this model to the unique data of virus transcription levels at the single cell level resulted in new estimates of the HIV-1 dynamics in vivo ., We found that a large fraction of infected cells become either defectively or persistently infected cells ., Furthermore , we found that the viral burst size can be high , between and viral particles per virus-producing cell ., Lastly , we simulated the acute phase of HIV-1 infection in a typical patient ., This illustrated that the latently infected cell pool becomes rapidly established during the first months of acute infection and shows a slow increase during the first years of chronic infection ., Our study is unique in that we fit a mathematical model of HIV-1 within a host to data of the dynamics of different subclasses of infected cells ., This is a substantial step beyond modeling studies that considered free virus in plasma , CD4 T cells and bulk measurements of viral activity only ., The new quantitative insights into the replication dynamics of HIV-1 in vivo that this study provides will be useful for an improved understanding of HIV and the effects of novel treatment strategies ., The measurements of HIV-1-infected cells and the virus concentration were performed in blood only ., In our mathematical model , we thus assume homogeneous mixing of virus and cells throughout the body ., It is important to note , however , that the characteristic decay profile in the study by Fischer et al . 12 could also be a result of differential trafficking of virus particles and HIV-1-infected subpopulations of cells between the blood and lymphoid tissue ., It has also been suggested that the virion clearance rate from the blood corresponds to a virion efflux to other organs where the virus is ultimately cleared 34 ., Furthermore , non-productively infected CD4 T cells could also indirectly die through ‘bystander’ effects 35 , 36 ., Finally , the typical second-phase decay could also result from virus production in infected macrophages 20 or heterogeneity in activation rates of latently infected cells 30 , 37 ., The concept of persistently infected cells has been previously used in mathematical models of HIV-1 dynamics to describe a population of long-lived cells that can contribute to the second-phase decay of virus during cART 20 , 26 ., Since the cellular subclass with medium transcriptional activity ( Mid ) seems to be rather long-lived and strongly characterized by a decay dynamics that could contribute to the second-phase decay of virus , we assumed that the majority of persistently infected cells belong to this class ., This is consistent with the notion that persistently infected cells could be in a lower state of activation 21 ., The contribution of other subpopulations of cells to the subclass Mid is small as the average lifespan of those cells is longer ( ) or shorter ( ) than that of persistently infected cells ., It remains to be determined whether persistently infected cells could indeed release viral particles as a result of an increase in their transcriptional level ., However , the reversion of virus-producing cells into a lower state of activation has been proposed previously 30 ., The data did not allow estimation of both the frequency and duration of transcriptional bursts that lead to the release of virions in persistently infected cells ., We assumed that once persistently infected cells release viral particles , the probability to die through cell lysis is the same as the probability of reversion ., For simplicity , we considered only one type of CD4 target cell whereas HIV-1 can infect activated but also resting CD4 T cells ., Our estimate of the proportion of CD4 T cells that are target cells ( 1 . 1% ) is somewhat lower than the 6 . 5% of CD4 Ki-67 T cells in HIV-1-infected individuals that have been measured previously 38 ., Also , the estimated average lifespan of target cells was longer than what others have estimated for activated cells 39 ., The target cells in the model thus represent a particular subset of CD4 T cells that is smaller than the population of activated cells but has a longer average lifespan ., The longer lifespan of target cells results from the assumption that the death rates of cells during the intracellular eclipse phase ( to ) and persistently infected cells ( ) remain the same after infection , i . e . , are the same as the death rate of uninfected target cells ( ) ., While persistently infected cells are indeed defined as long-lived cells that can produce virus , some studies have suggested that infected cells in the eclipse phase could also be a target of cytotoxic T lymphocyte ( CTL ) killing and experience high death rates 25 , 40–42 ., The early steps of proviral transcription also remain elusive ., It has been suggested that the decay of non-integrated viral DNA in infected cells could render them CD4 target cells again 43–46 ., The kinetics of HIV-1 DNA indeed show a small drop early after start of cART ( Figure 2 and ref . 47 ) ., However , we have excluded this effect for simplicity ., Ultimately , the mechanisms of viral latency in HIV-1 remain a matter of debate 48 ., In our model , we assumed that after proviral insertion some cells fail to increase viral RNA transcription and become latently infected cells ., Latency could also result from infection of resting CD4 T cells or de-activation of activated CD4 T cells ., We have not included the latter two mechanisms in our model as the data would not allow us to distinguish between them ., The complexity of the HIV-1 life cycle and its mathematical representation prevents the identification of a ‘true’ underlying model ., We made several simplifying assumptions in our default model but we also studied a series of alternative models and found that some of those models also fit the data well ( Table S1 in Text S1 ) ., Importantly , the estimates of critical parameters such as the viral burst size , the proportion of CD4 T cells that are target cells , and the fractions of cells that become defectively , latently or persistently infected in the alternative models that fit the data well were very similar to those estimated with the default model ( Table S2 in Text S1 ) ., We were also able to reject some competing hypotheses about the life cycle of HIV-1 ( Table S1 in Text S1 ) ., Removing the intracellular eclipse phase , that contains infected cells at different stages with increasing levels of viral transcription , impairs the model fit ., Assuming that latently or persistently infected cells are homogeneous subpopulations results in a substantially worse fit to the data ., The limited number of data points and patients prevented a more thorough analysis and resulted in substantial uncertainty in estimating the model parameters ., The wide ranges of estimates in Table 1 illustrate that the reported parameter values need to be treated with caution ., We also used the least-squares method to fit the model to the data and did not consider maximum likelihood approaches 49 , values below the limit of detection or nonlinear mixed-effect models 50 ., It remains to be determined how well the parameter estimates that were obtained during the chronic phase of infection represent the situation of acute HIV-1 infection ., It is re-assuring that the simulated virus dynamics of acute infection show a peak around three weeks after infection , which is in agreement with observations in patients 51 , 52 ., Nevertheless , differences in immune activation during acute infection are likely to result in different proportions of cells becoming latent upon infection and different activation rates of latently infected cells ., Hence , our results on the development of the latently infected cell pool during acute infection need to be interpreted with caution ., We found the HIV-1 burst size in vivo to be large , corroborating previous estimates from Chen et al . 53 who found the average burst size in SIV-infected rhesus macaques to be between and ., This is higher than other estimates that were in the range of virions per cell 54 , 55 and suggests that the number of virus-producing cells must be lower than previously anticipated ., Measurements of extracellular virion-enclosed HIV-1 RNA ( ) in the study by Fischer et al . 12 suggest that the number of productively infected cells in peripheral blood is small which is also reflected in our model fits ., In contrast to other studies that assumed the viral production rate in long-lived persistently infected cells to be lower than in activated , virus-producing cells 56 , we considered the viral production rates to be the same in both cell types ., However , in our model persistently infected cells can have occasional transcriptional bursts from to , where they can release virus particles before reverting back to a lower transcriptional state or dying ., Our simulations of the development of different pools of HIV-1-infected cells are in good agreement with observations in patients ., We find that the total number of HIV-1 DNA positive cells rapidly build up during the acute stage of infection ., A very similar expansion was found in a recent study that measured the total number of HIV proviruses in PBMCs during the first weeks of HIV infection 57 ., Also , our predicted ratio of the number of HIV-1 DNA positive cells during acute and chronic infection is in the same range as previously reported 33 , 47 ., The study by Murray et al . 47 further suggested that the level of HIV DNA continuously increases with duration of infection , reaching its 50% level at two years after infection ., This contradicts earlier findings of stable levels of HIV-1 DNA positive PBMCs during the natural course of infection 58 ., Our model predicts that the number of HIV-1 DNA positive PBMCs increases slowly during the first years of chronic infection and reaches its 50% level at 451 days after infection , corroborating the findings by Murray et al . 47 ., An important question that remains is how many of HIV-1 DNA positive cells are latently or defectively infected ., We found that the fraction of cells becoming defectively infected is surprisingly high ., On the one hand , this could be a result of the assumption that HIV-1 DNA positive cells without viral RNA transcription remain silent ., Some of these cells could actually be activated and start to produce UsRNA at low levels , i . e . , become cells of the latent class ., Eriksson et al . 59 measured a 300-fold difference between the number of latently infected cells as measured with a viral outgrowth assay and the total number of HIV-1 DNA positive resting CD4 T cells ., However , Ho et al . 60 showed a substantial fraction of noninduced proviruses in cells that have been stimulated in a viral outgrowth assay are replication-competent ., They found that that the frequency of intact noninduced proviruses was at least 60-fold higher than the frequency of proviruses induced in a viral outgrowth assay ., The median frequency of cells with intact non–induced proviruses per HIV-1 DNA positive resting CD4 T cells was estimated at 3 . 7% 60 ., In our simulation , the fraction of latently infected cells ( ) in all HIV-1 DNA positive cells ( DNA ) is 1 . 8% ( 140 . 4/7608 ) during chronic infection ., The striking correspondence of these numbers suggests that our mathematical model realistically describes the dynamics of the latent reservoir ., Since the subpopulation of is much larger than , the majority of latently infected cells consist of PBMCs that contain solely HIV-1 UsRNA ( Low ) , indicating that this transcriptional subclass is a good marker for viral latency ., This study provides an important step towards a more quantitative understanding of the dynamics of HIV-1 in vivo , in particular of the generation and maintenance of latently infected cells ., A better understanding of the number of latently infected cells during acute infection is crucial for evaluating and predicting the outcome of early treatment and eradication strategies ., Early cART treatment has been suggested to facilitate long-term control of HIV-1 61 and studies have shown that it results in lower viral load levels during chronic infection 62 ., Although the effects on viral load might only be transient 63 , early treatment can prevent the expansion of viral cellular reservoirs in peripheral blood 33 ., More recent strategies aim towards depletion of this reservoir 9 , preferably during acute infection 64 ., Predicting the chances of such eradication strategies critically depends on the ability to accurately quantify the pool of latently infected cells at various time points during HIV-1 infection ., Our study supports the experimental finding that the latent reservoir becomes rapidly established during the first months of infection , and shows that the reservoir represents a significant proportion ( 1% ) of all HIV-1 DNA positive PBMCs during chronic infection ., In addition , our mathematical model realistically describes the dynamics of different HIV-1-infected subpopulations of cells which will be useful for projecting the effects of eradication strategies ., We used previously published data from five chronically HIV-1-infected therapy naive patients that initiated cART using reverse transcriptase and protease inhibitors ( patient numbers: 103 , 104 , 110 , 111 , 112 ) 12 ., Plasma HIV-1 RNA ( copies per ml ) and CD4 T cells ( per µl ) were measured at several time points during the first 48 weeks of cART ., PBMCs were purified at weeks 0 , 2 , 4 , 8 , 12 , 24 and 48 after the start of cART as described in Fischer et al . 65 ., Serial dilution of PBMCs and patient matched PCR quantification of HIV-1 RNA species and DNA was performed as described elsewhere in detail 12 , 13 , 66 , 67 ., The freeze-thaw nuclease digestion method to differentiate between intracellular and virion encapsidated HIV-1 RNA has also been previously described in detail 4 , 33 ., HIV-1 RNA or DNA positive cell fractions measured as cells per 10 PBMCs were converted to number of cells per ml of blood by multiplying with the number of PBMCs per ml ., This ultimately lead to the stratification of cells to five ( partially overlapping ) subclasses 12: For the subclass DNA , we make the assumption that there is only one proviral DNA copy per infected cell 68 ., We devised a new virus dynamics model ( Figure 3 ) which is adapted from previously published models 19 , 25 , 26 , 30 ., The various subpopulations of infected cells were stratified according to their HIV-1 DNA and RNA content ., The model can be described by the following set of ordinary differential equations ( ODEs ) : ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 ) ( 12 ) ( 13 ) CD4 target cells , , are produced at rate and can become infected by virus particles , , at rate ., denotes treatment efficacy , where before the start of antiretroviral therapy ., Newly infected cells move through the intracellular eclipse phase , where denotes the stage of reverse transcription , the stage of proviral integration , and to subsequent stages with increasing transcriptional activity ., After the intracellular eclipse phase , activated , virus-producing cells , , start to release free virus particles with a total viral burst size ., Some of the cells during the intracellular eclipse phase can become defectively infected cells , , latently infected cells , , or persistently infected cells , ., While we assume that defectively infected cells remain transcriptionally silent , both latently and persistently infected cells can exhibit transcriptional bursts that rise their transcriptional profile from Low to Mid and Mid to High , respectively ., Latently infected cells in an elevated transcriptional state can become activated at rate or move back to the lower transcriptional state ., Similarly , persistently infected cells that are highly transcriptionally active can release free virus particles at rate before they revert to a state of lower transcriptional activity or die ., and describe cell death and viral clearance rates , respectively ., Due to the complexity of the full model , we make a number of simplifying assumptions ., First , we assumed several of the cell death rates to be the same: the death rates of virus-producing cells and the death rates of defectively and latently infected cells ., The death rates of infected cells that are not virus-producing and do not solely belong to a resting phenotype , such as defectively and latently infected , were kept the same as the death rate of target cells ( ) ., Second , the viral production rates in both virus-producing cells ( and ) are kept the same , i . e . , ., Note , however , that persistently infected cells ( ) have a lower burst size than activated , virus-producing cells ( ) because they can revert to a non-productive state ( ) ., The default model described above is compared to a number of alternative models with different assumptions of the viral life cycle ( Text S1 ) ., The default model contains 22 parameters of which 10 are fixed to previously used values from the literature or based on assumptions ( Table 1 ) ., The remaining 12 parameters were constrained based on literature values and consensus and we used the geometric mean of the restricted range as starting values when fitting the model to data ., This proved to be a good strategy for estimating the model parameters ., The set of ODEs were solved numerically in the R software environment for statistical computing 69 using the function ode from the package deSolve 70 ., The 12 model variables were initiated with the target cells at their steady-state ( ) , copy per ml , and all other variables being zero ., We assumed that the chronic state of infection is reached after 1000 days ( about three years ) , set 23 and further integrated the system during the time on cART ( 336 days ) ., The concentration of free virus was measured directly but several of the infected cell populations contribute to the different subclasses of PBMCs ( Figure 3 ) : , , , and ., We further assume that target cells , , correspond to a fraction , , of all CD4 T cells ., All 12 parameters ( 11 model parameters and one scaling parameter ) were estimated by fitting the model to the data of each patient individually and minimizing the sum of squared residuals ( SSR ) between the prediction of the model and the data ( taking the natural logarithm ) ., All data points were weighted equally ., However , the higher number of data points for free virus compared to cellular subclasses ( e . g . , ) forced the model to fit the virus concentration better than the other variables ., We used the minimization algorithm by Nelder & Mead 71 that is implemented in the function optim and the parallel package for parallel computation ., The algorithm by Nelder & Mead is very robust in finding local optima ., As a sensitivity analysis , we used different starting values for the parameters and the method SANN that is a variant of simulated annealing ., Simulated annealing usually performs better in finding global optima but is relatively slow ., In both cases , we found the best-fit parameter estimates to be the same or very similar to our default fitting strategy ., Parameter estimates are presented as geometric means including the ranges over all five patients ., Code file | Introduction, Results, Discussion, Materials and Methods | HIV-1-infected cells in peripheral blood can be grouped into different transcriptional subclasses ., Quantifying the turnover of these cellular subclasses can provide important insights into the viral life cycle and the generation and maintenance of latently infected cells ., We used previously published data from five patients chronically infected with HIV-1 that initiated combination antiretroviral therapy ( cART ) ., Patient-matched PCR for unspliced and multiply spliced viral RNAs combined with limiting dilution analysis provided measurements of transcriptional profiles at the single cell level ., Furthermore , measurement of intracellular transcripts and extracellular virion-enclosed HIV-1 RNA allowed us to distinguish productive from non-productive cells ., We developed a mathematical model describing the dynamics of plasma virus and the transcriptional subclasses of HIV-1-infected cells ., Fitting the model to the data allowed us to better understand the phenotype of different transcriptional subclasses and their contribution to the overall turnover of HIV-1 before and during cART ., The average number of virus-producing cells in peripheral blood is small during chronic infection ., We find that a substantial fraction of cells can become defectively infected ., Assuming that the infection is homogenous throughout the body , we estimate an average in vivo viral burst size on the order of 104 virions per cell ., Our study provides novel quantitative insights into the turnover and development of different subclasses of HIV-1-infected cells , and indicates that cells containing solely unspliced viral RNA are a good marker for viral latency ., The model illustrates how the pool of latently infected cells becomes rapidly established during the first months of acute infection and continues to increase slowly during the first years of chronic infection ., Having a detailed understanding of this process will be useful for the evaluation of viral eradication strategies that aim to deplete the latent reservoir of HIV-1 . | Gaining a quantitative understanding of the development and turnover of different HIV-1-infected subpopulations of cells is crucial to improve the outcome of patients on combination antiretroviral therapy ( cART ) ., The population of latently infected cells is of particular interest as they represent the major barrier to a cure of HIV-1 infection ., We developed a mathematical model that describes the dynamics of different transcriptionally active subclasses of HIV-1-infected cells and the viral load in peripheral blood ., The model was fitted to previously published data from five chronically HIV-1-infected patients starting cART ., This allowed us to estimate critical parameters of the within-host dynamics of HIV-1 , such as the the number of virions produced by a single infected cell ., The model further allowed investigation of HIV-1 dynamics during the acute phase ., Computer simulations illustrate that latently infected cells become rapidly established during the first months of acute infection and continue to increase slowly during the first years of chronic infection ., This illustrates the opportunity for strategies that aim to eradicate the virus during early cART as the pool of HIV-1 infected cells is substantially smaller during acute infection than during chronic infection . | immunodeficiency viruses, infectious diseases, medicine and health sciences, diagnostic medicine, medical microbiology, hiv, viral pathogens, hiv clinical manifestations, microbial pathogens, biology and life sciences, microbiology, computational biology, viral diseases | null |
journal.ppat.1004362 | 2,014 | EhCoactosin Stabilizes Actin Filaments in the Protist Parasite Entamoeba histolytica | Human amoebiasis is caused by the protist parasite E . histolytica ., The parasite is highly motile and displays high level of phagocytic activity in the trophozoite stage ., Motility and phagocytosis are essential processes for the survival and invasion of host tissues by the parasite , and largely depends on a highly dynamic actin cytoskeleton ., Moreover , there are other processes , such as phagocytosis that also require dynamic actin filament reorganization ., Molecular mechanisms that regulate actin dynamics in E . histolytica have not been studied in detail ., Preliminary investigations suggest an overall similarity with those described in other eukaryotic cells , but with crucial differences ., For example , a number of calcium-sensing calcium-binding proteins appear to directly regulate actin recruitment and dynamics 1 , 2 , 3 ., Several actin-binding proteins are encoded by the E . histolytica genome and many of these proteins are homologs of those that have been studied in other systems ., Not many of these amebic actin-binding proteins have been characterized ., Understanding structural-functional relationship of these proteins would help to decipher mechanisms of actin dynamics in E . histolytica ., In E . histolytica as well as many other cells , actin dynamics involves both assembly and disassembly of filaments regulated by several actin-binding proteins ., The actin-binding protein coactosin was first identified in Dictyostelium discoidedeum and has been classified as a member of actin depolymerising factor ( ADF ) /cofilin family 4 ., The ADF/cofilin family members are expressed in all eukaryotes studied to date ., The human coactosin-like protein ( HCLP ) binds F-actin and interferes with capping of filaments ., However it does not affect actin polymerisation 5 ., HCLP is also known to bind 5-lipooxygenase 6 ., The binding of members of the ADF/cofilin family to the F-actin results in severing and depolymerisation of F-actin 7 ., However the precise function of this family may vary from actin nucleation to severing depending on the cellular concentration gradient of cofilin 7 ., The E . histolytica genome contains only one copy of the coactosin gene , whose product we refer to as EhCoactosin ., Since the role of EhCoactosin in the actin dynamics of E . histolytica has not been previously investigated , we have carried out structural and functional analyses of this protein and present the results here ., They show that a single conserved ADF homology domain of EhCoactosin is involved in binding F-actin , and that F-actin is stabilized when EhCoactosin is bound ., Moreover , mutation of conserved lysine 75 to alanine does not result in loss of F-actin binding , in contrast to that observed in the case of HCLP , and the binding of this mutant EhCoactosin yields a similar level of F-actin stabilization as does the binding of native EhCoactosin ., But deletion of complete F-loop completely abolishes G-actin binding with loss of F-actin stabilization activity , albeit still binds to F-actin ., We also propose a mechanism for the binding of EhCoactosin to actin based on a structural model obtained by X-ray crystallography ., Overall our results suggest that EhCoactosin displays some features not seen in coactosin from other organisms ., A multiple sequence alignment of EhCoactosin Acc No XP_650926 . 1 from the NCBI database with homologous proteins from different organisms allowed us to identify numerous residues that are conserved in this family of proteins , as well as those unique to EhCoactosin ( Figure 1 ) ., The amebic Coactosin sequence displays 40% similarity with both human and D . discoidedeum CLPs ., Among the conserved residues is a critical lysine at position 75 , known to be involved in F-actin binding 8 ., The binding of EhCoactosin to F-actin was assessed by a sedimentation assay as described previously ( 1 ) ., The full-length wild-type ( WT ) protein binds F-actin , as it was found in the pellet fraction after ultracentrifugation ( Figure 2A ) ., A similar level of F-actin binding was also observed for truncated versions of EhCoactosin where either the N-terminal seven amino acid residues ( EhCoΔN , Figure 2B ) or C-terminal 14 residues ( EhCoΔC , Figure . 2C ) were deleted ., In an attempt to narrow down the specific region involved in actin binding , we deleted the F-loop of EhCoactosin ( from 71–76 amino acids ) and also mutated the critical Lys75 residue ., The F-loop deleted version of EhCoactosin ( ΔF ) retained actin binding property ( Figure 2D ) ., The K75A mutant of EhCoactosin was able to bind F-actin , ( Figure 2E ) , which is in contrast to the complete loss of F-actin binding caused by the same mutation in HCLP 8 ., These observations suggest that F-actin binding by EhCoactosin does not solely depend on F-loop and Lys75 ., G-actin binding was determined by a G-actin sequestering and solid phase assay as described previously 1 ., G-actin sequestering assay uses fluorescently labelled G-actin and when a protein binds the labelled actin its florescence decreases mostly in dose dependent manner ., The WT EhCoactosin shows G-actin sequestering in dose dependent manner ( Figure 2F ) while the EhCoΔF has no G-actin binding activity as seen in Figure 2G ., We also confirmed G-actin binding for other truncated versions of proteins by this assay ., EhCoΔC and EhCoΔN showed dose-dependent G-actin binding but affinity of EhCoΔN was more than EhCoΔC as 10 µM of EhCoΔN was able to sequester same amount of G-actin as 25 µM of EhCoΔC ( Figure S1A and B ) ., EhCoactosin displays specific G-actin binding was also confirmed by binding to a plate coated with G-actin ., The level of binding was 2-fold higher than that of the known G-actin-binding protein EhCaBP1 1 ( Figure S1 ( C ) ) ., While EhCoΔC showed a 33% decrease in binding when compared to the WT protein , EhCoΔN exhibited a 2-fold increase in G-actin binding in comparison to WT ., The homolog pfADF1 , which binds G-actin strongly 9 is positively charged at the N-terminal region compared to EhCoactosin ., The deletion of N-terminal residues in EhCoactosin exposes more positive charges in this region ( Figure S2 ) which , by analogy with pfADF1 , may explain the increased affinity of this mutant for G-actin ., The F-loop deleted ( EhCoΔF ) version exhibited complete loss of G-actin binding which was also observed with G-actin sequestering assay ., The role of EhCoactosin in F-actin stabilization was determined by a pyrene-actin assay where fluorescence of pyrene-labelled F-actin decreases upon depolymerisation ., The assay showed relative stabilization of F-actin by EhCoactosin compared to that by Xenopus cofilin1 ( Xac1 ) ( Figure 3A ) , and the stabilization effect was confirmed by the ability of EhCoactosin to antagonize the F-actin severing activity of Xac1 ( Figure 3B ) 10 ., That is , while addition of Xac1 led to a sharp decrease in fluorescence , indicating its severing effect on F-actin , in the presence of EhCoactosin no decrease in fluorescence was observed and values were similar to that seen with only actin ., The results suggest that EhCoactosin may be protecting F-actin from severing ( Figure 3B ) ., We also checked possibility of interaction between Xac1 and EhCoactosin by pull down assay which may lead to similar results ., We found that Xac1 and EhCoactosin and its mutants do not interact directly with each other ( Figure S3 ) ., EhCoΔC and EhCoΔN showed actin stabilization similar to that of the wild-type protein ( Figure 3C and 3E ) , and a similar stabilization effect was also observed in the case of the K75A mutant ( Figure 3G ) ., Moreover , both truncated versions and K75A mutant of EhCoactosin antagonised Xac1-dependent F-actin severing ( Figure 3D , 3F and 3H ) ., However , EhCoΔN and Xac1 at 2∶1 ratio did show mild F-actin severing ( Figure 3D ) ; the apparent weaker protection conferred by this mutant may be result of its high affinity for G-actin ( Figure S1 ) ., However , the EhCoΔF had lesser F-actin stabilizing property than WT protein ( Figure 3I ) as in presence of the protein F-actin depolymerised to an extent ., Also EhCoΔF was not able to protect F-actin from Xac1 activity ( Figure 3J ) ., These results indicate that F-loop is very essential for stable F and G-actin binding ., The deletion of F-loop results in lower affinity towards F-actin making it accessible for Xac1 activity ., Hence the whole F-loop plays an essential role in stable binding rather than conserved lysine residue at 75th position ., EhCoactosin consists of a central core of β-sheets surrounded by α-helices ., The central core is made up of five strands: β1 ( 26–32 ) , β2 ( 37–44 ) , β3 ( 60–69 ) , and β4 ( 76–85 ) forming antiparallel strands while β5-strand ( 113–117 ) forms parallel strand with β3 and β4 ., The central β-sheets are flanked on both sides by a total of five helices; α1 ( 9–17 ) and α3 ( 92–107 ) are located on the N-terminal side , and α2 ( 48–54 ) , α4 ( 120–122 ) and α5 ( 125–137 ) are located on the C-terminal side ( Figure 7A and B ) ., This arrangement of secondary structural elements is a common structural feature of proteins belonging to the ADF/cofilin family ., EhCoactosin has a long N-terminal end protruding outside with Ser repeats and this signature Ser repeats is expected to bind G-actin as seen in PfADF1 9 , however wild type EhCoactosin binds to F-actin and EhCoΔN shows higher affinity for G-actin , indicating the “Ser” repeats on the N-terminal are not involved in G-actin binding ., The loop connecting strands β3 and β4 , which has a conserved lysine at position 75 , is called the “F-loop” and it is expected to participate in stabilizing and binding to F-actin 11 ., As described in more detail below , the surface of EhCoactosin is highly negatively charged , and this F-loop is part of the negatively charged surface ., The N-terminal end and the F-loop are at two opposite sides of the globular structure ( Figure 7 ) suggesting that EhCoactosin binds G-actin and F-actin in very different ways ., Although there is a general similarity of the overall conformation of EhCoactosin with that of related proteins in other organisms , the surface charge distributions of EhCoactosin is markedly distinctive ., The surfaces of both sides of EhCoactosin are quite negatively charged , although one surface has overall higher level of negative charge as compared to the other surface ., Just a small positively charged surface is found in the α3 and α4 region , as well as is between the β4 and α3 regions , and a hydrophobic pocket is formed between β3 and α5 ( Figure 8A and 8A′ ) ., In contrast , human coactosin-like protein ( HCLP ) is positively charged on one side , while negatively charged on the other , which is a characteristic feature of the ADF/cofilin family ., The F-loop surface , which is negatively charged on both sides in EhCoactosin , is positively charged on one side and hydrophobic on the other in HCLP ( Figure 8B and 8B′ ) ., The surface charge distributions of pfADF1 and pfADF2 also differ from that of EhCoactosin ., For pfADF1 , one side is highly positively charged and the other has a relatively hydrophobic surface ., The N-terminal region of pfADF1 is positively charged relative to that of EhCoactosin 11 ., Also , α1 of pfADF1 has three positively charged residues and is relatively long whereas in EhCoactosin it is relatively small and negatively charged Figure 8C and 8C′ ., The surface of pfADF2 , while more negatively charged than that of pfADF1 , is less negatively charged than that of EhCoactosin ( Figure 8D and 8D′ ) ., Note that the N-terminal regions of EhCoactosin and PfADF2 were also found to be different; while , as indicated above , the former has Ser repeats , the latter does not 12 , 13 ., The overall structure of EhCoactosin is quite similar to that of human coactosin-like protein ( HCLP ) , with an RMSD of 1 . 56 Å and few major differences ., The N-terminal regions of the two proteins do deviate by up to 14 . 7 Å , with that of HCLP bent towards the inside of the structure while in EhCoactosin this N-terminal region is extended ., Also , α1 of HCLP is longer by 3 residues compared to that of EhCoactosin ( Figure 9A ) ., The overall structure of EhCoactosin is also fairly similar to the structures of the two types of ADF proteins of Plasmodium falciparum , pfADF1 and pfADF2 ., Although pfADF1 is functionally different than other ADF/Cofilin proteins , since it binds G-actin 12 and only transiently interacts with F-Actin 12 , its overall structure differs from that of EhCoactosin by an RMSD of just 2 . 0 Å ., Certain structural differences are quite notable: The F-loop is absent in pfADF1; β3 and β4 of EhCoactosin , which are extended towards its F-loop , are shorter in pfADF1; and a long C-terminal α-helix present in EhCoactosin is absent in pfADF1 ., All these observations suggest that the F-loop , β3 , β4 and the C-terminal helix of EhCoactosin could be involved in binding to F-actin ( Figure 9B ) ., Note also that in pfADF1 , the N-terminal end is relatively short , and connected to a short β-sheet , which is a characteristic feature of ADF/cofilin , while in EhCoactosin the N-terminal region is long with characteristic serine repeats , which is thought to participate in G-actin binding ., However , both these proteins bind G-actin and it is difficult to suggest a possible mechanism with this data ., The RMSD between pfADF2 and EhCoactosin is 2 . 13 Å ., pfADF2 binds F-actin as well as G-actin 12 , and in pfADF2 , the F-loop , β3 , β4 , β5 and β6 are similar to those in EhCoactosin ., Moreover , the C-terminal helix , which is missing in pfADF1 , is present in pfADF2 ., This helix is nevertheless longer in EhCoactosin ., These regions are likely to be involved in F-actin binding ( Figure 9C ) ., EhCoactosin directly binds F-actin but the mechanism of preventing depolymerisation is not understood ., The structural differences of EhCoactosin with Coactosins from other organisms may be responsible for the distinct functional properties ., Properties of mutants helped us to model F-actin binding ., Here we have sought to analyse the nature of interactions between actin and EhCoactosin by computational modelling ., We propose different mode of binding of EhCoactosin to G-actin and F-actin to explain the actin binding properties ., Based on the crystal structure of the mouse twinfilin C-terminal ADF homology domain in complex with actin 14 and the recent 9 Å EM model of human Cofilin-2 in complex with actin filaments 15 ( Figure S6A and S6B ) , we built two different models , one for G-actin binding and one for F-actin binding to explain and understand actin binding mechanism of EhCoactosin ., EhCoactosin superimposes well with the cofilin of the cofilin-actin complex filaments 15 ., In the energy-minimized model , EhCoactosin fits well between the subdomain 1 of the actin monomer and the subdomain 2 of the next actin monomer ( Figure 10A ) ., As seen in the model , the N-terminal region of EhCoactosin interacts with subdomain-1 of the actin monomer1 and the C-terminal region of EhCoactosin is placed at the binding interface between two actin molecules ( Figure 10B ) ., The α-3 helix forms extensive contacts with subdomain-1 of the actin monomer-1 whereas the F-loop ( S69-K75 ) interacts with the subdomain-2 of the adjacent actin monomer ( Figure 10C and D ) ., The C-terminal α-5 helix is docked inside the cavity formed by the two actin molecules ., The N-terminal sequence and F-loop region behave like clamps anchoring well within the F-actin structure along the length of the filaments , hence resulting in its stabilization ., This explains the effect of EhCoΔF as the mutation of the F-loop results in loss of F-actin stabilization suggesting F-loop is one side of the clamp interacting with F-actin ., Thus EhCoΔF can bind F-actin but cant stabilize it ., The homology model of the EhCoactosin-F-Actin complex suggests that various regions of the protein , such as the N-terminal sequence , helices α-3 and α-5 and the F-loop play important roles in binding F-actin – and also suggests , in agreement with our mutational studies described above , that no single region or feature of EhCoactosin is indispensible for binding F-actin ., Such is the case for EhCoactosin Lys75 , for example , despite it being conserved and completely responsible for F-actin binding in other systems; EhCoactosin is unique in this regard ., EhCoactosin deletion mutants EhCoΔC as well as EhCoΔN also displayed F-actin binding and stabilization abilities similar to that of the wild type protein ., The model for globular monomeric actin ( G-actin ) binding to EhCoactosin was obtained using the mouse twinflin ADF homology domain in complex with actin ( Figure S4B ) ., Based on the energy minimized model , α3 of EhCoactosin binds the cleft between subdomain 1 & 3 of actin as shown in Figure 11 ., The modelling data suggest that deletion of the N-terminal region and development of positive charge may loosen interaction with a hydrophobic patch on domain 1 of actin ( Figure S2 ) ., Due to this , α3 can enter in the groove between domain 1 and 3 of G-actin ( see G-actin binding model , Figure 11 ) , helping to explain our result described above that EhCoΔN binds G-actin more strongly than does wild-type EhCoactosin ., Interestingly the EhCoΔF abolishes G-actin binding suggesting F-loop deletion might have altered the orientation of α3 and thus loss in G-actin binding ., The protist parasite E . histolytica undergoes extensive pseudopod extension , and displays high level of motility , phagocytosis and macro-pinocytic activities ., These processes are crucial for amebic biology as these are associated with food intake and pathogenesis ., Since actin dynamics drives all of these processes , we have been investigating many molecules that are known to participate in actin dynamics ., Actin-binding proteins , such as those of the ADF/cofilin family , play a major role in actin dynamics ., In the current study , we have investigated structural and functional features of the ADF/cofilin protein EhCoactosin ., Our results indicate EhCoactosin to be both a G- and F-actin-binding protein , and that it stabilizes F-actin by direct binding ., This set of unusual functional feature is due to presence of unique structural motifs not observed in other coactosins or other homologs ., EhCoactosin displays an overall conformational similarity with other ADF/cofilin family members such as HCLP , pfADF1 and pfADF2 , yet also displays distinct differences ( Figure S7A ) ., Some of the features , such as presence of helices α1 and α3 at the N-terminal region as well as the F-loop , which contains conserved Lys75 , are also present in coactosins from other organisms including D . discoideum which are structurally conserved in this family ( Figure S7B ) ., Distinctive features of EhCoactosin include a longer N-terminal sequence and a more negatively charged surface ., As a result of the latter feature , both sides of the F-loop in EhCoactosin is negatively charged while , for example , one side of the F-loop of HCLP is positively charged while the other side is hydrophobic ., The observation that certain features found in EhCoactosin are absent in other coactosins suggests that this molecule in E . histolytica may impart novel functional properties ., Our data clearly show that EhCoactosin is both an F- and G-actin-binding protein in vitro ., It is associated with the actin cortex and co-localises with F-actin during pseudopod formation and erythrophagocytosis ., The presence of EhCoactosin in phagocytic cups is parallel to the F-actin during the phagocytic cup formation ., In vitro functional assays suggest that EhCoactosin is a F-actin stabilizing protein which implies its role in maintaining integrity at the leading edge ., Nearly all coactosins studied previously , including human CLP , have not shown a direct effect on actin polymerisation or depolymerisation , although they can interfere with capping of filaments ., Chick coactosin is an exception which has been shown to be involved in actin polymerisation downstream of Rac signalling and to promote polymerisation 16 ., EhCoactosin is a novel member of the coactosin family with direct effect on F-actin stabilization with F-loop playing important role in binding ., The functional difference between EhCoactosin and other coactosins can be attributed mainly to increased length of the N-terminal part and altered charge distribution ., These distinct properties of EhCoactosin are likely to contribute to its binding of G-actin and stabilization of F-actin ., Deletion of the N-terminal part EhCoactosin , for example , increases the binding affinity for G-actin on the solid phase ., The C-terminal part may also have a role in regulating G-actin binding ., When it is deleted affinity for G-actin decreases but not drastically and this is similar to HCLP where C-terminal does not play significant role in F-actin binding 17 ., Our in silico analysis suggests that one molecule of EhCoactosin binds to two adjacent actin molecules in the filament ., The binding model also suggests that interactions between EhCoactosin and F-actin involve several regions rather than just the F-loop as in other systems ., The N-terminal and F-loop of EhCoactosin function as clamps in F-actin binding and decorate the filament along its length ., The long serine rich N-terminal region plays a role in F-actin binding whereas deletion of which results in F-actin severing activity ., The model and solid phase data suggest that this may be due to high affinity for G-actin displayed by the mutant as a result of uninhibited binding of α3 between subdomain 1 and 3 ., Although the Lys75 residue is needed by HCLP for binding F-actin , it is not required in case of EhCoactosin since the mutant K75A protein has similar experimentally determined F-actin-binding and other properties as does the wild-type protein ., Computational modelling also supports these results as K75A mutant does not show any significant change in binding of EhCoactosin to actin , as K75 is not directly interacting with F-actin ., This implies that binding of EhCoactosin and actin involves interactions other than F-loop and Lys75 residue unlike other homologs ., But complete deletion of F-loop results in loss of F-actin stabilization suggesting F-loop is one side of the clamp interacting with F-actin ., Thus EhCoΔF can bind F-actin but cant stabilize it ., Our experiments have shown that EhCoactosin stabilises F-actin , but we also need to understand the underlying contributions to actin dynamics in E . histolytica since both depolymerization as well as stability of F-actin are required for critical cellular processes ., Many drugs that stabilize F-actin have deleterious effect on processes that require actin dynamics 18 , 19 , and over-expression of EhCoactosin in E . histolytica yields cells that display impaired growth and phagocytosis , presumably due to the proteins stabilization of F-actin ., This consequence of overexpression is not seen with other coactosins and appears to be a unique property of the E . histolytica protein ., E . histolytica is an early branching eukaryote displaying unique biology , and although it shares many of the participants of the cytoskeleton remodelling machinery with metazoan organisms , it also uses a few novel proteins in regulating the actin cytoskeleton 1 , 2 , 3 ., The calcium-binding proteins EhCaBP1 and EhCaBP3 are such examples , and they have been shown to be involved in actin dynamics and phagocytic cup formation 2 , 3 , 20 ., All these studies including present study show that E . histolytica proteins can also undergo functional diversification in order to fulfil its needs , high rate of actin dynamics ., The detailed study of this binding protein will lead to better understanding of the cytoskeletal remodelling in this parasite and also as well evolution of this process in other eukaryotes ., The erythrophagocytosis results indicate in vitro concentration of EhCoactosin above critical level may affect actin remodelling ., Phagocytosis involves both actin polymerisation and depolymerisation which is mediated by several actin-binding proteins ., The high levels of EhCoactosin in cell may promote excess stability of F-actin in vitro by preventing access of actin remodelling protein to F-actin required during the phagocytosis ., Taken together this leads to increased rigidity in actin cytoskeleton which impairs its dynamic remodelling required for processes like motility and phagocytosis ., In conclusion , EhCoactosin is directly involved in F-actin stabilization , which has not been reported earlier ., In vivo EhCoactosin may actively contribute to the maintenance of F-actin during erythrophagocytosis and pseudopod formation ., The interactions between EhCoactosin and F-actin depend on several regions in the protein rather than specific residues such as Lys75 ., The evolutionary basis of development of specific interaction in higher organisms can be understood by studying primitive eukaryotes like E . histolytica ., This study will also lead to better understanding of actin dynamics in this organism and as well as evolution of actin dynamics as a process in organisms ., The coding sequence of coactosin gene ( GenBank accession no . XP_650926 ) was amplified by PCR from genomic DNA of Entamoeba histolytica strain HM1:IMSS using the forward primer 5′-CCGCCATGGCAATGTCTGGATTTGATCTTAG-3′ and the reverse primer 5′-CCGCTCGAGCTTAATTTTAGCAGCGATTTC-3′ ., The EhCoactosin gene was cloned in pET28b ( Novagen ) between Nco1 and Xho1 sites with a C-terminal 6× His tag ., Four constructs were prepared for biochemical experiments: wild-type EhCoactosin ( EhCoWT ) ; an EhCoactosin in which 14 amino acid residues were deleted from the C-terminus because it was predicted to form a loop ( EhCoΔC ) ; another for which 7 residues were deleted from the N terminus ( EhCoΔN ) , F-loop spanning from 71–76 amino acid was also deleted ( EhCoΔF ) and a single site substitution mutant ( K75A ) ., The cloning was confirmed by restriction digestion by Nco1 and Xho1 followed by DNA sequencing ., The CAT gene of the shuttle vector pEhHYG-tetR-O-CAT ( TOC ) was excised using KpnI and BamHI and the EhCoactosin gene was inserted in its place in either the sense or the antisense orientation ., The expression in this vector was tetracycline inducible and expressed sense ( S ) and antisense ( AS ) RNA of the gene in E . histolytica trophozoites ., For the study of co-localization in E . histolytica cells we carried out HA tagging at the N-terminus of EhCoactosin ., The Forward Primer 5′-CGGGGTACCATGTATCC ATATGATGTTC CAGATTATGCTATGTCTGGATTTG-3′ and the reverse primer 5′- GCGGGATCCTTAAGCATAATCTGGAACATCATATGGATAATT TGAGGTGG-3′ were used for HA tagging ., The recombinant plasmid containing the EhCoactosin gene was transformed into E . coli BL21 ( DE3 ) cells ( Novagen ) ., Primary culture was grown overnight in 50 ml LB media from the single colony of transformed BL21 cells supplemented with 50 µg/ml Kanamycin at 37°C ., Secondary culture was grown by inoculating 1% of primary culture in the same media at 37°C until the OD600 reached 1 . 0 ., The culture was induced with 1 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) ( Sigma ) and allowed to grow for another 4 hrs at the same temperature ., Cells were harvested by centrifugation at 6000 rpm for 10 minutes at 4°C ., These cells were stored at −80°C until further processing ., The harvested cells were resuspended and homogenized in resuspension buffer containing 50 mM Tris HCl ( pH 8 . 0 ) , 0 . 1 mM EDTA and 0 . 1 mM DTT ., Resuspended cells were lysed with 3 cycles of flash-freezing in liquid nitrogen and subsequent thawing in water-bath at 37°C ., The lysate was subjected to 5–6 cycles of sonication on ice at 25% amplitude with each pulse of 30 sec and 1 min interval ., The sonicated cell lysate was centrifuged at 13 , 000 rpm for 30 minutes at 4°C ., Supernatant was filtered with Whatman filter paper no . 1 and clear lysate was passed through a Nickel-NTA column ( GE healthcare ) pre-equilibrated with resuspension buffer ., Thereafter , the column was washed with 2 bed volumes of buffer containing 50 mM Tris HCl ( pH 8 . 0 ) , 0 . 1 mM EDTA , 0 . 1 mM DTT and 10 mM imidazole ., The bound protein was eluted with buffer comprising 50 mM Tris-HCl ( pH 8 . 0 ) , 0 . 1 mM EDTA , 0 . 1 mM DTT and 100 mM imidazole ., The purified fractions of protein were concentrated using Centricon filters ( Millipore ) and subjected to gel filtration chromatography on HiLoad Superdex 75G 16/60 column ( GE Healthcare ) pre-equilibrated with buffer containing 50 mM Tris-HCl ( pH 8 . 0 ) , 0 . 5 mM EDTA , 0 . 5 mM DTT and 1 mM sodium azide ., Homogeneity of protein was assessed on 12% SDS-PAGE ( Figure S8 ) ., Peak fractions were concentrated using Centricon filters ( Millipore ) and concentration was estimated with A280 ., Selenomethionine-labelled EhCoactosin was purified under reducing conditions using specific media ( by Molecular Dimensions , United Kingdom ) ., The concentration of selenomethionine was maintained at about 25 mg/litre ., Initially , the primary culture was grown in LB medium overnight ., Cells were then harvested by centrifuging at 4000 rpm for 6 min ., Harvested cells were resuspended in the complete selenomethionine media , and washed once with same media to completely remove any leftover LB medium ., Secondary culture was grown by inoculating 1% of primary culture in the same media at 37°C until the OD600 reached 1 . 0 ., Culture was allowed to grow at 37°C for about 4 hrs after inoculation until OD600 reached 1 . 0 ., Cells were induced with 1 mM IPTG and allowed to grow for another 4 hrs at same temperature ., Cells were harvested at 6 , 500 rpm for 6 min and stored at −80°C for further processing ., Subsequent processing and purification were done by the same method used for native EhCoactosin ., G-actin was purified from rabbit skeletal muscle acetone powder 21 ., Further Actin was labelled with N- ( 1-pyrene ) iodoacetamide ( P-29 , Molecular Probes ) by the protocol described previously 22 for performing the pyrene-actin assay ., Native EhCoactosin protein was crystallized using the hanging drop vapor diffusion method in 24-well Linbro plates against a reservoir solution containing 25–35% PEG 1500 , 100 mM sodium acetate , 0 . 2 mM CaCl2 , 10 mM MgCl2 and 100 mM HEPES , pH 7 . 3–7 . 7 ., Two µl of ∼75 mg/ml protein and 2 µl of reservoir solution were mixed and allowed to equilibrate at 16°C ., The crystals that formed in these drops were flash frozen in a cryoprotectant solution containing additional 5% PEG 400 mixed with mother liquor ., Selenomethionine-labelled protein was prepared and crystallized using similar conditions ., The crystal appeared in condition containing 28–33% PEG 3350 , 100 mM sodium acetate , 0 . 2 mM CaCl2 , 10 mM MgCl2 , 5% isopropanol and 100 mM HEPES pH 7 . 4–7 . 7 ( Figure S9 ) ., The crystals were flash frozen in the same cryo-protectant ., The X-ray data for selenomethionine-substituted crystals were collected at the BM14 synchrotron beamline , ESRF , Grenoble , France at a selenium peak wavelength of 0 . 97860 Å ., Data sets were indexed and scaled using HKL2000 23 ., Anomalous data collected for Se-Met labelled EhCoactosin crystals were used to calculate FA values using the program SHELXC 24 ., Each of the two heavy atoms expected were found using the program SHELXD 24 ., Initial phases were calculated after density modification using SHELXE 25 ., The reflection file was further used in the Autobuild program 25 of the Phenix suite 26 for automated model building ., Then missing residues were traced into the electron density and refined by iterative model building using the COOT graphics package combined with REFMAC5 27 ., HEPES , Na , and water molecules were added by COOT guided by Fo-Fc electron density >3σ ., The final model was validated by the Procheck 28 pr | Introduction, Results, Discussion, Materials and Methods | Entamoeba histolytica is a protist parasite that is the causative agent of amoebiasis , and is a highly motile organism ., The motility is essential for its survival and pathogenesis , and a dynamic actin cytoskeleton is required for this process ., EhCoactosin , an actin-binding protein of the ADF/cofilin family , participates in actin dynamics , and here we report our studies of this protein using both structural and functional approaches ., The X-ray crystal structure of EhCoactosin resembles that of human coactosin-like protein , with major differences in the distribution of surface charges and the orientation of terminal regions ., According to in vitro binding assays , full-length EhCoactosin binds both F- and G-actin ., Instead of acting to depolymerize or severe F-actin , EhCoactosin directly stabilizes the polymer ., When EhCoactosin was visualized in E . histolytica cells using either confocal imaging or total internal reflectance microscopy , it was found to colocalize with F-actin at phagocytic cups ., Over-expression of this protein stabilized F-actin and inhibited the phagocytic process ., EhCoactosin appears to be an unusual type of coactosin involved in E . histolytica actin dynamics . | E . histolytica is an important pathogen and a major cause of morbidity and mortality in developing nations ., High level of motility and phagocytosis is responsible for the parasite invading different tissues of the host ., Phagocytosis and motility depend on highly dynamic actin cytoskeleton of this organism ., The mechanisms of actin dynamics is not well understood in E . histolytica ., Here we report that coactosin like molecule from E . histolytica , EhCoactosin is involved in F-actin stabilization ., The crystal structure obtained for the protein provides explanation for some functional differences observed with respect to the human homologue , such as ability to bind G-actin ., Moreover , computational modelling along with crystal structure helps to explain the F-actin binding and stabilization by wild type protein ., The mutational analysis further suggests that F-actin binding property does not depend on conserved Lys75 residue as observed in Human coactosin like protein ( HCLP ) but other regions present in protein are involved in binding ., Overexpression of this protein in trophozoites leads to stabilization of actin filaments which are not accessible to actin remodelling machinery thereby reducing the growth of parasite due to decreased rate of actin dependent endocytosis ., Overall , EhCoactosin behaves as F-actin stabilizing protein in vitro and it also participates in processes like phagocytosis and pseudopod formation . | biochemistry, cytoskeletal proteins, cell motility, actin filaments, cell biology, proteins, protein structure, structural proteins, biology and life sciences, dynamic actin filaments | null |
journal.pgen.1005868 | 2,016 | Gene Network Polymorphism Illuminates Loss and Retention of Novel RNAi Silencing Components in the Cryptococcus Pathogenic Species Complex | Genome reduction is a common adaptation among bacterial pathogens and commensals , and has been hypothesized to occur for a number of reasons , including increased specificity to a host or environmental range , or to increase virulence more directly through loss of an antivirulence gene or gene cluster ., The former case can be explained primarily through loss of genes that play only accessory roles ., These genes can become dispensable as an organism becomes obligately associated with a host , which then acts as an alternative source for these gene products , such as amino acids or metabolic intermediates 1–3 ., In some cases , network polymorphisms can result from loss of one of the components , which then enables additional inactivating mutations to occur in other components of the crippled or disabled pathway , such as loss of the Gal80 repressor in Saccharomyces kudriavzevii 4 ., Genes also can be lost as a result of an “antivirulence” function , as is seen in Shigella and E . coli , where the presence of the lysine decarboxylase cadA interferes with the synthesis of enterotoxins through production of cadaverine 5 ., This model , termed the black hole hypothesis , suggests that gene losses can be the result of active interference with pathogenesis , likely as the result of gain of a new incompatible function ., In either model , understanding the gene network polymorphism can elucidate the biology and evolution of the pathogen , facets that are particularly relevant for new and emerging pathogens ., Cryptococcus deuterogattii , previously C . gattii molecular type VGII 6 , is an emerging human fungal pathogen in the Pacific Northwest ( PNW ) of the United States and southwest Canada 7–9 ., While the sibling species C . neoformans predominantly infects immunocompromised individuals , many of the C . deuterogattii infected patients in the Pacific Northwest outbreak were otherwise healthy ., Both species cause severe pulmonary and central nervous system infections , and are fatal if untreated ., Surprisingly , whole genome sequencing revealed that the C . deuterogattii strain R265 is missing both of the Argonaute genes , essential components of the RNAi-induced silencing complex ( RISC ) 10 , 11 ., Further examination revealed that in addition to the loss of both Argonaute genes , one of the two Dicers and the only RNA-dependent RNA polymerase have also undergone pseudogenization through large sequence losses similar to those of the Argonaute genes 12 ., The loss of critical canonical components of the RNAi pathway raises a number of questions about the origins and biology of the C . deuterogattii species as well as the function of RNAi within the Cryptococcus pathogenic species complex as a whole ., RNA interference ( RNAi ) is a highly conserved mechanism among eukaryotes that facilitates homology-dependent gene silencing ., This transcriptional regulatory strategy was initially observed in Caenorhabditis elegans where exogenously introduced double-stranded RNA ( dsRNA ) triggers silencing of the transcript complementary to the dsRNA sequence 13 ., Since its discovery in C . elegans , numerous species of plants , animals , fungi , and protists have been found to employ similar strategies to either protect their genomes from foreign DNA or to orchestrate gene expression and diverse cellular , developmental , and physiological processes 14–17 ., Repetitive sequences are often found in mobile genetic elements and previous studies found an association between RNAi and transposable elements , which are ubiquitous in eukaryotic organisms ., Transposon activation and movement impairs genome stability and increases the mutational burden of the host ., Therefore , eukaryotes employ different strategies to inhibit and limit transposon expansion ., Arabidopsis thaliana , Drosophila melanogaster , Saccharomyces castellii , Neurospora crassa , and C . elegans all utilize RNAi strategies to control and inhibit transposon expression 18–22 ., C . neoformans also employs an RNAi-related pathway to inhibit transposable elements ., In previous studies , Wang et al . showed that the insertion of a tandem multicopy transgene triggered a homology-dependent gene silencing mechanism during sexual development and termed this process sex-induced silencing ( SIS ) 10 ., This process was identified specifically with a SXI2a-URA5 transgene array inserted into the ura5 locus , resulting in the presence of three functional copies of URA5 and one nonfunctional copy ., During mating , progeny that inherit the array silence the URA5 gene in an RNAi-dependent manner approximately 50% of the time ., In addition , Wang et al . later found that transgene silencing can also occur during vegetative growth , named mitotic-induced silencing ( MIS ) , but at a relatively lower frequency in mitotic ( ~0 . 2% ) compared to meiotic progeny ( ~50% ) 23 ., Further analysis showed that SIS and MIS require the RNAi components Rdp1 ( RNA-dependent RNA polymerase ) , Ago1 ( Argonaute ) , and Dcr1/2 ( dicer-like proteins ) 10 , 23 ., SIS and MIS function to inhibit transposon movement and thus serve as a genome defense mechanism during meiosis and mitosis ., The initial observation of transposon silencing during sexual development was made in the highly virulent C . neoformans lineage ., Later studies found that transgene-related SIS also occurs in C . deneoformans and that the RNAi components are required for transposon silencing during both bisexual and unisexual development 24 ., The lack of the critical Argonaute , Dicer , and RdRp components of the RNAi pathway in C . deuterogattii suggests that the loss of RNAi may represent a gene network polymorphism ., In fact , the RNAi pathway is intermittently conserved and lost across eukaryotes 12 , 25–27 ., In Leishmania and trypanosomes , RNAi losses were previously taken advantage of in order to identify additional , previously unknown components of the RNAi pathway via comparative genomics 16 ., To test the hypothesis that the RNAi pathway represents a gene network polymorphism , we surveyed the genomes of the R265 ( C . deuterogattii ) , WM276 ( C . gattii ) , H99 ( C . neoformans ) , and JEC21 and B-3501A ( C . deneoformans ) strains and found 14 genes missing from C . deuterogattii , including the canonical components of the RNAi pathway RDP1 , AGO1 , and DCR1 ., Here we focus on four of these lost components: ZNF3 , previously identified as a regulator of hyphal development during unisexual and bisexual reproduction 28; CPR2 , a G-protein coupled receptor ( GPCR ) previously studied for its role as an accessory constitutively active pheromone receptor 29; QIP1 , independently identified as an RNAi component via a mass spectrometry approach 30; and FZC28 , a putative transcription factor with no obvious phenotypes in a systematic genome-wide transcription factor deletion study 31 ., Here we demonstrate that the loss of the RNAi components represents a bona fide system polymorphism , with several previously unknown RNAi components lost in C . deuterogattii ., In addition , we show that mutants of these missing genes in C . neoformans fall into two classes: mutants that lose both vegetative silencing and sex-induced silencing , and mutants that are affected only in the frequency of sex-induced silencing ., This suggests that sex-induced silencing may be a specialized , highly induced variant of the vegetative transgene-induced silencing pathway , rather than a separate pathway ., Taken together , our results show that a substantial loss of genes contributing to two related RNAi pathways has occurred in C . deuterogattii ., By using comparative genomics , these gene losses reveal key insights that aid in elucidating the functions of these RNAi-based genome conservation pathways ., The C . deuterogattii lineage ( previously VGII C . gattii ) is responsible for the recent , ongoing outbreak on Vancouver Island and its expansion into the Pacific Northwest of the United States ., Initial analysis of the R265 C . deuterogattii reference genome revealed that both the key canonical RNAi components AGO1 and AGO2 are missing , indicating that the VGII lineage of C . deuterogattii may lack a functional RNAi pathway 10 , 11 ., Upon further examination , we discovered that two of the other canonical components , DCR1 and RDP1 , had both suffered truncations removing key functional domains and are therefore pseudogenes ., Of the known RNAi canonical components , only DCR2 remains intact in C . deuterogattii ( Fig 1A ) 11 , 32–35 ., We hypothesized that this loss of multiple RNAi components may represent a gene network polymorphism where all of the components of a pathway are intact in one species , but have been selectively lost in another closely related species ., We further hypothesized that a whole genome comparison of C . deuterogattii with other related Cryptococcus species would reveal novel components of the RNAi pathway lost in C . deuterogattii but otherwise maintained throughout the pathogenic species complex ., We compared the publicly available reference genomes of JEC21 ( C . deneoformans ) 36 , B-3501A ( C . deneoformans ) 36 , H99 ( C . neoformans ) 37 , and WM276 ( C . gattii ) 11 with R265 ( C . deuterogattii ) 11 to identify otherwise conserved genes that were missing or truncated in the C . deuterogattii lineage ., We found seven conserved genes that were not annotated in R265 and seven others that were dramatically shortened ( over 50% different in length ) as a result of extensive deletions of genomic sequence ( Table 1 ) ., All 14 genes were lost across the entire VGII group , based on 53 publicly available whole genome sequences from C . deuterogattii 32 ., These genome sequences did reveal some diversity in these regions ., Estimation of Tajima’s D in windows across the genome and within the regions left by the deletion events showed a highly negative value for the genome as a whole ( mean of -1 . 122 ) , and a slightly more positive ( mean of -0 . 796 ) , but not statistically significant value ( p = 0 . 0901 ) for the deletion windows ( S1 Fig ) ., We did not identify any transposable elements or repeats that may have mediated the deletion events ., One of the seven missing genes was the previously identified canonical RNAi component AGO1 ., In each case , localized deletions of sequence occurred , encompassing entire ORFs , start codons , and/or functional domains of the candidate genes ( Fig 1B–1H and S2 Fig ) ., Our screen identified two potential transcription factors , FZC27 and FZC48 , and three genes , including GWC1 , GWO1 , and QIP1 , which have been previously identified as participating in the degradation of unspliced mRNA through RNAi 30 ., Two of the 14 missing or truncated genes , CPR2 and ZNF3 , were previously shown to play roles in unisexual and bisexual reproduction , but were not described as having a role in RNAi 17 , 35 ., We chose to focus on four genes as candidates to interrogate for a role in the SIS and MIS RNAi pathways: CPR2 , FZC28 , ZNF3 , and QIP1 ., CPR2 encodes a seven transmembrane domain GPCR closely related phylogenetically to the Ste3 family of pheromone receptors , but it is constitutively active and independent of pheromone ligand binding 29 ., Cpr2 signals via the same G proteins as the pheromone receptor Ste3 , and overexpression of CPR2 can rescue the sterility defect of ste3Δ mutants , although it may bias cells towards unisexual reproduction 29 ., FZC28 is a transcription factor about which very little is known ., It was identified and mutated as part of a genome-wide transcription factor deletion library , and experiments in that study identified no obvious phenotypes 31 ., In previous studies we found that ZNF3 is required for hyphal development during unisexual and bisexual reproduction in C . deneoformans 28 ., Deletion of the gene blocks hyphal development and impairs pheromone expression during mating ., However , it does not play a direct role in the pheromone-signaling cascade ., Surprisingly , microarray expression analysis revealed that deletion of Znf3 increased transposon and transposon-related gene expression during bisexual reproduction 28 ., Znf3 is also somewhat rapidly diverging in amino acid sequence ., While it is found in the Cryptococcus pathogenic species complex and the neighboring sensu stricto ( including C . amylolentus ) and sensu lato groups ( including C . heveanensis ) , the sequence is not well conserved , and it shares only weak homology over a 211 amino acid stretch ( 23% identity and 38% positive ) with the reciprocal best BLAST hit ortholog in Tremella mesenterica ., The encoded protein in Cryptococcus neoformans contains three zinc finger domains , two predicted nuclear localization signals ( NLS ) , and a conserved coiled coil region , often involved in protein-protein interactions , as well as a putative ribonuclease conserved domain indicating that it may be involved in cleavage of RNA ., QIP1 is named for N . crassa QIP , which functions during quelling and MSUD by binding to RISC and stimulating cleavage of the passenger strand of the duplex siRNA 38 ., Moreover , a previous study directly implicated Qip1 in the transcriptional squelching of transposons and the degradation of mRNAs that have poorly spliced non-canonical introns 30 ., Dumesic et al . localized Qip1 in the nucleus and showed that it physically interacts with Rdp1 as part of the Spliceosome-Coupled and Nuclear RNAi ( SCANR ) complex 30 ., Analysis of N . crassa Qip revealed a conserved 3’-5’ exonuclease domain belonging to the DEDDh superfamily of nucleases , showing high similarity to the E . coli DNA polymerase III ε subunit 33 ., Although , the C . neoformans Qip1 protein does not contain any detected conserved functional domains , it exhibits weak similarity to the helical domain of Class IIa histone deacetylases , which may suggest a role different than that of N . crassa Qip ., In previous studies , Wang et al . found that a tandem multicopy insertion of a SXI2a-URA5 transgene triggered silencing of the URA5 gene during bisexual reproduction and vegetative growth in C . neoformans 10 , 23 ., When F1 progeny were isolated from a cross between WT MATα URA5 ( H99α ) and MATa SXI2a-URA5 ( JF289 ) , ~25% were found to be uracil-auxotrophic despite the fact that all of them had intact copies of the URA5 allele ., Further analysis revealed that ~50% of the progeny that inherited the SXI2a-URA5 transgene were uracil auxotrophic ., Recent studies showed that the transgene induced silencing mechanism is activated efficiently during bisexual and unisexual reproduction ( SIS ) and less efficiently during vegetative growth ( MIS ) 23 , 24 ., Deletion of RNA-dependent RNA-polymerase Rdp1 abolished transgene induced silencing during SIS and MIS in both C . neoformans and C . deneoformans ., To investigate the role of the missing genes from R265 in silencing we generated deletion mutants in the JF289a isolate bearing the SXI2a-URA5 transgene ( derived from strain KN99a ) , and the congenic WT H99α strain ., Two independent deletion mutants for each gene were isolated and analyzed ., To determine the silencing efficiency of the mutants during sexual reproduction , unilateral ( one parent is mutant ) and bilateral ( both parents are mutants ) crosses were performed on MS media ., We dissected random F1 spore progeny from each cross and these were tested for growth in the absence of uracil and genotyped for the presence of the SXI2a-URA5 transgene ( Fig 2A and S3 Fig ) ., In unilateral matings with a deletion allele only present in one of the two parents , two meiotic progeny were ura- for qip1Δ ( out of 14 inheriting the array , ~14% ) , none were ura- for znf3Δ ( out of 18 inheriting the array , 0% ) , and three were ura- for cpr2Δ ( out of 22 inheriting the array , ~13 . 6% ) indicating significantly reduced silencing efficiency compared to WT ( Fig 2A and S4 Fig ) ., These results suggest that all three components play a role in RNAi during sexual development ., In contrast , the silencing efficiency of the SXI2a-URA5 transgene in the fzc28Δ , and fzc47Δ unilateral mutant matings was similar to WT ( ~50% ) ( Fig 2C ) ., All of the ura- progeny carry an intact copy of the SXI2a-URA5 transgene , as verified by PCR ., Previous studies showed that bilateral matings of all three canonical RNAi component mutants ( ago1Δ , dcr1Δ , rdp1Δ ) yielded ~20 fold fewer spores , with rdp1Δ mutants also demonstrating disorganized and atypical basidia , but with no effect on the sporulation efficiency of the spores that were produced 10 ., Similarly , although deletion of ZNF3 severely impaired mating in C . deneoformans 28 , hyphal development during bisexual reproduction was similar to WT in C . neoformans znf3Δ mutants , albeit somewhat delayed ., In contrast , in bilateral qip1Δ x qip1Δ mutant crosses we found that spore production was severely impaired and the few spores that were isolated failed to germinate , indicating that Qip1 is required for completion of the sexual cycle and may play a role in meiosis ( S4B Fig ) ., On the other hand , deletion of RDP1 or ZNF3 did not affect sporulation efficiency ., Deletion of ZNF3 in both parents completely abolished silencing , as none of the progeny that inherited the transgene were ura- ( S4A Fig ) ., These results indicate that Znf3 is required for silencing during mating and deletion of the gene causes a severe SIS silencing defect , similar to rdp1Δ ., Silencing of the URA5 gene was also impaired in fzc28Δ and cpr2Δ bilateral matings ( Fig 2C ) ., However , fzc47Δ mutation in both parents did not impair silencing of the URA5 transgene and it was similar to WT , despite a modest increase in silencing rate in a unilateral cross ( Fig 2C and S4 Fig ) ., We then examined the silencing frequency of the SXI2a-URA5 transgene in the mutant strains by measuring spontaneous 5-FOA resistance following mitotic growth in rich media ., The strains bearing the qip1Δ and znf3Δ deletions failed to yield any colonies on 5-FOA media , indicating that these two genes are required for transgene-induced mitotic silencing ( Fig 3 ) ., In contrast , deletion of two transcription factors , FZC28 and FZC47 , obtained from a recently reported systematic transcription factor deletion collection and crossed into the JF289 background 31 , and the GPCR CPR2 , did not alter the mitotic silencing frequency of the SXI2a-URA5 transgene compared to WT ., In conclusion , we found that Znf3 and Qip1 are required for silencing during both MIS and SIS and deletion of the genes generates a phenotype similar to mutation of RDP1 , whose gene product is essential for RNAi function in C . neoformans ., These results suggest that Znf3 and Qip1 are novel regulators or components of the RNAi pathway ., In addition we found that a new transcription factor Fzc28 and the GPCR Cpr2 influence transgene-induced silencing specifically during sexual development , possibly coupling the sexual cycle with the RNAi pathway but likely not acting mechanistically during silencing itself ., In a previous study we found that deletion of Znf3 in C . deneoformans activates transposon expression 28 and here we have shown that it is required for MIS and SIS ., Recent studies revealed that transposable element expression increases during sexual reproduction and the components of the RNAi pathway maintain genome integrity through an efficient transposon silencing mechanism 10 ., Deletion of RDP1 results in centromeric and telomeric retrotransposon overexpression during sexual development in C . neoformans 10 ., We examined the transcript abundance of two transposons , Tcn1 and Tcn2 , in znf3Δ mutant crosses and found that abundance was dramatically increased , similar to rdp1Δ and ago1Δ mutant crosses ( Fig 4A ) ., Deletion of QIP1 also yielded elevated levels of transposon transcript abundance , indicating that Qip1 also plays a major role in transposon quenching during sexual development ( Fig 4A ) ., To further investigate the role of Znf3 in transposon silencing on a genome-wide scale , we performed a comparative transcriptome analysis of znf3Δ x znf3Δ and rdp1Δ x rdp1Δ crosses during sexual development and vegetative growth ., Bilateral crosses of znf3Δ x znf3Δ and rdp1Δ x rdp1Δ mutants were incubated on solid V8 medium ( pH = 5 ) for 24 hours , as well as H99α x JF289a wild type crosses ., RNA was isolated from the mating cultures , transcribed to cDNA , and hybridized to a C . neoformans genome microarray ., Genome-wide expression analysis revealed that among the transcripts with altered expression level , the majority were increased in the znf3Δ mutant cross relative to WT during sexual development , indicating that Znf3 has a repressive role during sexual development ., The few transcripts whose abundance was decreased in the znf3Δ and rdp1Δ crosses are involved in hypoxia , oxidation , ion channels , sugar transport , and possibly sporulation ., During znf3Δ sexual development more than 80 independent microarray tags exhibited a twofold increase in abundance compared with the WT ., Further analysis revealed that the majority of these tags correspond to sequences from hypothetical proteins or align to intergenic regions of the C . neoformans H99 genome ., Alignment to a retrotransposon library 39 showed that almost all of the intergenic probes that were increased in znf3Δ mutants correspond to retrotransposon sequences found in multiple sites in the genome ( S3 Table ) ., We found that these retrotransposons have long terminal repeats ( LTR ) and reside in the centromeric and telomeric regions of the chromosomes ., In addition , most of the upregulated hypothetical proteins in znf3Δ x znf3Δ crosses were found to be RNA and DNA helicases , RNA-dependent DNA polymerases , and other transposon-related proteins ( S3 Table ) ., During vegetative growth fewer transcripts were upregulated in znf3Δ mutants; however , the transcripts that exhibited differential abundance were also involved in transposon expression or activation ., As was observed previously , the Tcn1 , Tcn2 , and Tcn3 elements were increased in znf3Δ × znf3Δ crosses , while their abundance was diminished during znf3Δ vegetative growth but remained significantly higher than the WT ., We compared the transcriptional profile to the rdp1Δ x rdp1Δ mutant cross profile , and the whole genome transcript profiles between the two mutants were highly similar ( Fig 4B ) ., The highly correlated transcript profiles of upregulated genes suggests that Znf3 and Rdp1 have similar functions and may mediate retrotransposon silencing through the same RNAi pathway ., Interestingly , in spite of the loss of RNAi components in C . deuterogattii , transposon copy number does not appear to have dramatically increased in the genome ( S5 Fig ) ., The vast majority of transposable elements are present at substantially lower copy number in C . deuterogattii compared to C . gattii ., However , several classes of transposable elements are present in approximately equal amounts ( TCN3 and TCN6 ) or at even higher levels ( TCN4 and LTR13 ) in C . deuterogattii ( R265 ) than in C . gattii ( WM276 ) , based on a BLAST search using a C . neoformans library 39 ., In previous studies we found that , although Znf3 regulates sexual development , ZNF3 expression remains stable during vegetative growth and mating in C . deneoformans 28 ., In addition , mRNA levels for the RNAi components are relatively similar between mitotic growth and mating based on northern blot analysis; however , their protein abundance was significantly higher during sexual development suggesting that the RNAi components are translationally induced or stabilized during the sexual cycle 10 ., Based on this evidence we hypothesized that ZNF3 and QIP1 expression might also remain the same between the two conditions in C . neoformans ., RNA was isolated during mitotic growth and mating from WT and bilateral mutant crosses and the abundance of their transcripts was analyzed using quantitative RT-PCR ., Unlike the canonical RNAi components , we found that both ZNF3 and QIP1 expression was significantly higher during mating compared to WT ( Fig 5A ) ., This was a surprising result given that the expression of the highly conserved ZNF3 gene in C . deneoformans remains the same and similar to WT during both conditions 28 ., Moreover , Znf3 and Qip1 have similar roles with the RNAi components in SIS and MIS whose expression remains stable ., This indicates that Znf3 and Qip1 expression may have a unique mode of regulation distinct from Rdp1 and Ago1 ., We next assessed whether the RNA abundance during sexual development is correlated with the protein level between the two conditions ., The C-termini of Znf3 and Qip1 were fused with mCherry at the endogenous genomic loci ., MIS and SIS assays were conducted to test if the chimeric proteins retain their functional roles in silencing ., Znf3 tagged with mCherry was completely defective in SIS and MIS , indicating that the mCherry tag interferes with function ., On the other hand , Qip1 tagged with mCherry exhibited wild type levels of silencing during vegetative growth and sexual development ., The protein levels were examined during both conditions and we found that , although the Qip1 protein was present during both vegetative growth and mating , it was significantly more abundant during sexual development , similar to the difference observed in RNA abundance ( Fig 5B ) ., These results indicate that Qip1 , and possibly also Znf3 , have a unique mode of regulation that is possibly distinct from that of other RNAi components ., The MIS and SIS silencing phenotypes of znf3Δ and qip1Δ mutants are very similar to rdp1Δ mutants ., Previous studies have suggested that an unknown RNA-binding factor may govern translational regulation of the transcripts of the RNAi components to result in elevated protein levels specifically during sexual development 10 ., We found that Znf3 bears both zinc fingers and an RNase domain and transcription of the gene is sexually induced ., Considering that Znf3 has a similar phenotype to Rdp1 , it could be involved in the translational regulation of the RNAi components , and the severe loss of silencing phenotype of znf3Δ mutants might be attributable to an absence of these factors ., It is unlikely that Znf3 regulates the transcription of the RNAi components based on microarray expression analysis ., Nevertheless , we performed quantitative RT-PCR in the absence of each of the RNAi components during sexual development ., Surprisingly , we observed a modest increase in the expression of the RNAi components during sexual development compared to vegetative growth ( Fig 5C ) ., In previous studies , northern blot analysis was employed to investigate the expression of these genes during vegetative growth and mating , and the modest 2- to 4-fold increase we observed using RT-PCR was possibly below the level of detection by northern blot ., However , deletion of ZNF3 and QIP1 did not alter the expression of RDP1 or AGO1 , suggesting that Znf3 and Qip1 do not act as transcriptional regulators of the canonical RNAi components or mediate the modest increase in expression we observed in mating conditions We also investigated the expression of ZNF3 and QIP1 in the absence of the canonical RNAi components during sexual development ., Deletion of RDP1 did not affect the ZNF3 transcript levels during sexual development , indicating that RDP1 does not control the expression of this gene ( Fig 5C ) ., The expression of Znf3 was modestly but significantly increased in the ago1Δ mutants , which is the catalytic subunit of the RISC complex ., Interestingly , expression of QIP1 during sexual development decreased to vegetative levels in the absence of RDP1 ., To explore a possible role of Znf3 in the translational regulation of the RNAi components , we deleted ZNF3 and investigated the protein levels of Ago1 and Rdp1 fused with mCherry under the control of the endogenous promoter during sexual development ., We detected a strong protein signal for both Ago1-mCherry and Rdp1-mCherry during sexual development with or without ZNF3 ( Fig 5D ) ., Western blot analysis revealed that deletion of ZNF3 resulted in a modest decrease in the protein abundance of Rdp1 under mating conditions ( Fig 5E ) ., It is possible that this decrease may not have been detectable via direct microscopy of cells expressing the Rdp1-mCherry fusion protein ( Fig 5D ) ., These results indicate that although Znf3 , is not involved in transcriptional regulation of the canonical RNAi components , it could be involved in either translational regulation or in modulating protein stability via a role as a scaffolding protein ., RNAi silencing is a multifunctional pathway and different steps occur at different sites within the cell ., The presence of tandem repeated genes or retrotransposons in the genome induces the transcription of aberrant ssRNA in the nucleus through an unknown mechanism and Rdp1 generates dsRNA from these sequences and evokes the RNAi pathway ., The dsRNA travels to P-bodies , where processing and RNA silencing occurs ., Dcr1/2 and Ago1 , which localize to P-bodies , generate siRNAs that target mRNAs with complementary sequences for degradation 10 ., These findings suggest that additional components of the pathway will localize either to the nucleus or to P-bodies ., Znf3 has two NLS signals , therefore we initially hypothesized that Znf3 might localize to the nucleus where it could act as a transcription factor , or bind and degrade dsRNAs generated by Rdp1 ., To investigate the localization of Znf3 , and because endogenous C-terminal tagging had failed to produce functional protein , the N-terminus of the protein was fused to mCherry , and expressed from the constitutively active GPD1 promoter ., The H99α and JF289a strains were transformed with the mCherry-Znf3 plasmid and evaluated by direct fluorescence microscopy ., Surprisingly , we observed multiple bright foci in the cells indicating that the protein was present in more than one cellular compartment during sexual development ( Fig 6A ) ., To determine this cellular localization , we utilized two established marker components , one for P-bodies and the other for the nucleus ., Dcp1 , found in P-bodies , is responsible for decapping mRNAs during exonucleolytic degradation , while Nop1 is a component of the small subunit processome ( a ribosome assembly intermediate ) complex of the nucleolus 10 , 40 ., GFP-Dcp1 and GFP-Nop1 were expressed from plasmids that were ectopically introduced into the genomes of strains expressing the mCherry-Znf3 protein and localization was observed during vegetative growth and sexual development ., Surprisingly , we found that Znf3 localizes only in the P-bodies during both vegetative growth and sexual reproduction , despite the putative NLS signals ( Fig 6A ) ., These results suggest that Znf3 may participate directly in the RNAi silencing process and it may represent a novel element of the RNAi pathway ., Previous studies found that Qip1 localizes in the nucleus and that it physically interacts with Rdp1 and Ago1 during vegetative growth 30 ., Although Ago1 is primarily localized in P-bodies during mating , where RNA silencing occurs , it has been also reported in the nucleus under vegetative growth conditions 30 ., To further investigate the localization of Qip1 , the protein was fused at the C-terminus with mCherry and expressed from the endogenous QIP1 promoter ., The fluorescent signal was evaluated via microscopy during vegetative growth and sexual development ., Co-localization of Qip1-mCherry with GFP-Dcp1 or GFP-Nop1 revealed surprising results ., During sexual development , where the RNAi pathway is highly induced , Qip1 was localized exclusively in P-bodies , potentially reflecting a role in RNA degradation ( Fig 6B ) ., During vegetative growth we observed Qip1 in association with either the P-bodies or the nucleus ( Fig 6B ) ., These results suggest that Qip1 may interact with Rdp1 in the nucleus during vegetative growth , p | Introduction, Results, Discussion, Material and Methods | RNAi is a ubiquitous pathway that serves central functions throughout eukaryotes , including maintenance of genome stability and repression of transposon expression and movement ., However , a number of organisms have lost their RNAi pathways , including the model yeast Saccharomyces cerevisiae , the maize pathogen Ustilago maydis , the human pathogen Cryptococcus deuterogattii , and some human parasite pathogens , suggesting there may be adaptive benefits associated with both retention and loss of RNAi ., By comparing the RNAi-deficient genome of the Pacific Northwest Outbreak C . deuterogattii strain R265 with the RNAi-proficient genomes of the Cryptococcus pathogenic species complex , we identified a set of conserved genes that were lost in R265 and all other C . deuterogattii isolates examined ., Genetic and molecular analyses reveal several of these lost genes play roles in RNAi pathways ., Four novel components were examined further ., Znf3 ( a zinc finger protein ) and Qip1 ( a homolog of N . crassa Qip ) were found to be essential for RNAi , while Cpr2 ( a constitutive pheromone receptor ) and Fzc28 ( a transcription factor ) are involved in sex-induced but not mitosis-induced silencing ., Our results demonstrate that the mitotic and sex-induced RNAi pathways rely on the same core components , but sex-induced silencing may be a more specific , highly induced variant that involves additional specialized or regulatory components ., Our studies further illustrate how gene network polymorphisms involving known components of key cellular pathways can inform identification of novel elements and suggest that RNAi loss may have been a core event in the speciation of C . deuterogattii and possibly contributed to its pathogenic trajectory . | Genome instability and mutations provoked by transposon movement are counteracted by novel defense mechanisms in organisms as diverse as fungi , plants , and mammals ., In the human fungal pathogen Cryptococcus neoformans , an RNAi silencing pathway operates to defend the genome against mobile elements and transgene repeats ., RNAi silencing pathways are conserved in the Cryptococcus pathogenic species complex and are mediated by canonical RNAi components ., Surprisingly , several of these components are missing from all analyzed C . deuterogattii VGII strains , the molecular type responsible for the North American Pacific Northwest outbreak ., To identify novel components of the RNAi pathways , we surveyed the reference genomes of C . deuterogattii , C . gattii , C . neoformans , and C . deneoformans ., We identified 14 otherwise conserved genes missing in R265 , including the RDP1 , AGO1 , and DCR1 canonical RNAi components , and focused on four potentially novel RNAi components: ZNF3 , QIP1 , CPR2 , and FZC28 ., We found that Znf3 and Qip1 are both required for mitotic- and sex-induced silencing , while Cpr2 and Fzc28 contribute to sex-induced but not mitosis-induced silencing ., Our studies reveal elements of RNAi pathways that operate to defend the genome during sexual development and vegetative growth and illustrate the power of network polymorphisms to illuminate novel components of biological pathways . | biotechnology, cryptococcus neoformans, medicine and health sciences, rna interference, cryptococcus, pathology and laboratory medicine, fungal genetics, gene regulation, pathogens, regulatory proteins, microbiology, dna-binding proteins, developmental biology, fungi, plant science, genetic elements, transcription factors, plant genomics, epigenetics, morphogenesis, fungal pathogens, mycology, genetic interference, proteins, medical microbiology, gene expression, microbial pathogens, plant genetics, comparative genomics, biochemistry, rna, fungal genomics, nucleic acids, sexual differentiation, genetics, transposable elements, biology and life sciences, genomics, mobile genetic elements, plant biotechnology, computational biology, organisms | null |
journal.pbio.1001176 | 2,011 | Mouse PRDM9 DNA-Binding Specificity Determines Sites of Histone H3 Lysine 4 Trimethylation for Initiation of Meiotic Recombination | Meiotic recombination generates reciprocal exchanges between homologous chromosomes ( also called crossovers , COs ) that are essential for proper chromosome segregation during meiosis and are a major source of genome diversity by generating new allele combinations ., COs are not distributed randomly along chromosomes , but are clustered within short intervals ( 1 to 2 kb long in mice and humans ) called hotspots , which result from the preferred initiation of meiotic recombination at specific sites ( reviewed in 1–3 ) ., In mammals , several hotspots were identified with methods allowing direct measurements of recombination frequencies 4–7 , and a human genome-wide map of hotspots , with estimated recombination frequencies , was obtained based on patterns of linkage disequilibrium 8 , 9 ., A major challenge has been to search for specific features of hotspots and to identify factors controlling their location ., While no DNA sequence unambiguously associated with hotspot activity had been found before , the population diversity analysis uncovered a few short sequence motifs , which were overrepresented at CO hotspots 9 ., A further refinement of the analysis revealed that one of them , the partially degenerated 13-mer CCNCCNTNNCCNC , was associated with 41% of 22 , 700 LD-based hotspots identified in the human genome ., Within and around this motif , the most conserved bases showed a 3 bp periodicity , reminiscent of the 3 bp binding unit of C2H2 zinc fingers 10 ., In addition , a chromatin analysis at two mouse hotspots revealed that hotspot activity was correlated with H3K4me3 enrichment at their center 11 ., Interestingly , the Prdm9 gene ( also known as Meisetz ) encodes for a protein with an array of C2H2 zinc fingers , catalyzes the trimethylation of the lysine 4 of histone H3 ( H3K4me3 ) , and is essential for progression through meiotic prophase in mice 12 ., The zinc finger array of the human major isoform of PRDM9 was shown to recognize the 13-mer DNA motif associated with human meiotic recombination hotspots , suggesting that PRDM9 sequence-specific binding to DNA could play a role in specifying the sites of meiotic recombination 10 , 13 , 14 ., This hypothesis was supported by the correlation between variations in the PRDM9 zinc finger array and hotspot usage in mice and humans 13 , 15–17 ., The correlations observed in mice were based on comparisons of hotspot activities in mice carrying different haplotypes over a several Mb region , overlapping the Prdm9 gene ., These regions were named Dsbc1 ( 4 . 6 Mb ) and Rcr1 ( 6 . 3 Mb ) in the two studies where they had been reported 18 , 19 ., Specifically , the presence of the wm7 allele of Dsbc1 ( from Mus musculus molossinus ) correlates with high recombination rate at two hotspots ( Psmb9 and Hlx1 ) and with local H3K4me3 enrichment at the center of these hotspots in spermatocytes 11 , 18 ., Mice with the Dsbc1wm7 allele also show a different genome-wide distribution of COs in comparison to strains carrying the Dsbc1b allele ( for instance , the C57BL/6 hereafter B6 and C57BL/10 B10 strains ) ., Remarkably , the Prdm9b and Prdm9wm7 alleles differ by their number of zinc fingers ( 12 and 11 , respectively ) and by 24 non-synonymous substitutions , which are all , but one , localized in the zinc finger array 13 ., Whether the polymorphisms in the zinc finger array are responsible for these observed effects or whether other loci in the interval defining Dsbc1 could contribute to the control of hotspot distribution remained to be determined ., Here , using transgenic mice , we establish that changing the identity of PRDM9 zinc fingers is sufficient to change hotspot activity , histone H3 lysine 4 trimethylation ( H3K4me3 ) levels at the hotspots tested , and chromosome-wide distribution of COs ., We further demonstrate using in vitro assays that PRDM9 variants bind to DNA sequences located at the center of the hotspots they activate ., Taken together , these results demonstrate that Prdm9 is a master regulator of hotspot localization in mice , through the DNA binding specificity of its zinc finger array ., To demonstrate that the hotspot features of Dsbc1 are due to the identity of the PRDM9 zinc finger array and not to flanking genetic elements , we modified the Prdm9b allele of a B6 Chromosome 17 genomic fragment inserted in a bacterial artificial chromosome ( BAC ) by replacing its zinc finger array with that of the Prdm9wm7 allele ., This modified allele was named Prdm9wm7ZF ., Transgenic mice were produced by micro-injection in fertilized one-cell B6 embryos of the BAC containing the Prdm9wm7ZF allele ( hereafter Tg ( wm7 ) ) or the unmodified Prdm9b allele ( hereafter Tg ( b ) ) as a control ( Table 1 ) ., Prdm9 carried by the transgenes was expressed at a level slightly lower ( Tg ( wm7 ) , strain #43 ) or similar ( Tg ( b ) , strain #75 ) to that of endogenous Prdm9 ( see Figure S1 ) ., We then asked whether the expression of Prdm9wm7ZF was sufficient to recapitulate the Dsbc1wm7 phenotype concerning the recombination rate at the Psmb9 hotspot , the enrichment of H3K4me3 at the Psmb9 and Hlx1 hotspots , and the distribution of COs along one whole chromosome ., First , the recombination rate at Psmb9 was measured by sperm typing in ( B6-Tg ( wm7 ) ×B10 . A ) and in ( B6-Tg ( b ) ×B10 . A ) F1 mice ( Figure 1A and Table S1 ) ., In ( B6-Tg ( wm7 ) ×B10 . A ) F1 mice , COs and non-crossovers ( NCOs ) frequencies were high at the Psmb9 hotspot , like in hybrids with a Dsbc1wm7 allele ( such as the ( RB2×B10 . A ) F1 hybrid , Figure 1A ) ., The RB2 strain carries the Dsbc1wm7 allele , together with the b haplotype at the Psmb9 hotspot like the B6 and B10 strains ( see Material and Methods and Table 1 ) ., Conversely , there was no detectable recombination at Psmb9 in ( B6-Tg ( b ) ×B10 . A ) F1 mice , like in ( B10×B10 . A ) F1 hybrids ., Therefore , expression of the Prdm9wm7ZF allele is sufficient to activate the Psmb9 recombination hotspot ., We then determined the level of H3K4me3 at the Psmb9 and Hlx1 hotspots in spermatocytes from mice carrying Tg ( b ) or Tg ( wm7 ) ., Spermatocytes from ( B6-Tg ( b ) ×B10 . A ) F1 mice did not display any local enrichment for H3K4me3 , similarly to spermatocytes from the recombinationally inactive B6 strain ( Figure 1B , Figure S2 , Tables S2 and S3 ) 11 ., Conversely , H3K4me3 was significantly enriched at the center of both hotspots in spermatocytes from ( B6-Tg ( wm7 ) ×B6 ) F1 mice , similarly to the R209 strain , in which both hotspots are active 11 ., We then compared the chromosome-wide distribution of COs , based on the mapping of MLH1 foci along Chromosome 18 in spermatocytes from mice carrying Tg ( b ) or Tg ( wm7 ) ( Figure 1C ) ., These distributions were significantly different ( Tables S4 and S6 ) as well as the one of B6-Tg ( b ) xB10 . A compared to RB2×B10 . A ( expressing the Prdm9wm7 allele ) and the one of B6-Tg ( wm7 ) xB10 . A compared to B10×B10 . A ( expressing only the Prdm9b allele ) ( Figure S3 , Tables S4 and S5 ) ., In contrast , the distributions of MLH1 foci of the Tg ( b ) and Tg ( wm7 ) transgenic strains were not different from that of strains expressing Prdm9b ( B10×B10 . A ) and Prdm9wm7 ( RB2×B10 . A ) , respectively ( Figure S3 , Table S4 ) ., Therefore , the expression of Prdm9wm7ZF is sufficient to promote a wm7-specific chromosome-wide distribution of COs ., In order to show that these effects are due to a direct interaction between PRDM9 and hotspot DNA sequences , we tested in vitro the binding of different PRDM9 variants to hotspot regions ., We first examined the binding of recombinant His-tagged PRDM9wm7 and PRDM9b to a series of overlapping DNA fragments that covered 1 . 3 kb across the Psmb9 hotspot ., Strikingly , PRDM9wm7 , but not PRDM9b , bound to a single DNA fragment located at the center of this hotspot ( Figure 2A ) ., This 200 bp DNA fragment contains a 31 bp sequence with a partial match ( p\u200a=\u200a2 . 43×10−3 , Figure S4 , Text S1 ) to the predicted PRDM9wm7 binding site ., PRDM9wm7 could also bind to a 61 bp double-stranded oligonucleotide that contained this sequence ( Figure 2B , probe Psmb9TC ) ., Furthermore , in the B10 . MOL-SGR strain , in which this sequence differs by two single nucleotide polymorphisms ( SNPs ) from the one of the B10 strain , recombination initiation rate at Psmb9 is at least 10 times lower than in B10 mice 20 ., In vitro binding assays showed that these two SNPs affected independently the binding of PRDM9wm7 to the double-stranded oligonucleotide ( Figure 2B ) ., Thus , both variation in the zinc finger array of PRDM9 and polymorphisms in the target sequence are involved in the control in trans and in cis of the recombination rate at the Psmb9 hotspot ., Additionally , we examined the binding of PRDM9 to the Hlx1 hotspot on Chromosome 1 , the activity of which depends on the presence of the wm7 or cast haplotype at Dsbc1 ( both haplotypes have the Prdm9wm7 allele 18 , 19 ) and in which the level of H3K4me3 was increased in the presence of Prdm9wm7ZF ( Figure S2 ) ., At Hlx1 , PRDM9wm7 , but not PRDM9b , could bind to a motif localized at the center of the hotspot ( Figure 2C ) ., Interestingly , the B10 and CAST/EiJ ( M . m . castaneus ) strains are polymorphic for that motif , and the distribution of COs across this hotspot in a hybrid carrying one chromosome from each strain indicates that the initiation rate is approximately double on the B10 chromosome than on the CAST chromosome 7 , 11 ., In line with this variation , PRDM9wm7 had a higher affinity for the B10 sequence than for the CAST one ( Figure 2C ) ., The sensitivity to small changes in the PRDM9 target sequence might explain why the recombination rate at hotspots is exquisitely sensitive to either polymorphisms in cis or to subtle changes within the zinc finger array of PRDM9 15 , 21 ., We also examined the reciprocal situation where a hotspot ( the G7c hotspot on Chromosome 17 ) is active in the presence of the b allele of Prdm9 22 ., We determined by sperm typing that the recombination rate at the G7c hotspot was at least 30-fold higher in Prdm9b/b than in Prdm9wm7/wm7 mice ( Table S7 ) ., By examining in vitro the binding of PRDM9 to 10 overlapping DNA fragments covering 2 . 2 kb along the G7c hotspot , we found that PRDM9b bound to a single fragment mapping to the interval with the highest exchange density , whereas no binding of PRDM9wm7 could be detected ( Figure 3 ) ., Taken together , these results demonstrate that PRDM9 recognizes specific DNA sequences that are localized at the center of the three recombination hotspots tested ., Surprisingly , the in vitro binding specificity we detected was not predicted by the C2H2 zinc finger prediction program 23 ., In particular , the Psmb9 and G7c DNA probes showing binding to PRDM9 did not contain any significant match ( with a p value<10−3 ) to the predicted PRDM9 motif , whereas significant matches were predicted in regions where no in vitro binding could be detected ( Figure S4 , Tables S8 and S9 ) ., If PRDM9 is responsible for the H3K4me3 mark that defines initiation sites of meiotic recombination , H3K4me3 enrichment should appear concomitantly with the onset of Prdm9 expression at the time or before meiotic DNA double-strand break ( DSB ) formation 12 ., Therefore , we examined the kinetics of Prdm9 expression and of H3K4me3 at the Psmb9 and Hlx1 hotspots during the first wave of entry into meiosis in testes of prepuberal Prdm9wm7/wm7 mice ., During this wave , B-type spermatogonia enter meiosis at day 8–9 post-partum ( 8–9 dpp ) and reach the leptotene stage of meiotic prophase , when DSBs are generated , at 9–10 dpp 24 ., Spermatocytes then progress through meiotic prophase to reach metaphase I at around 20 dpp 25 ., At 9 dpp , a modest but significant H3K4me3 enrichment was observed that increased at 12 and 15 dpp ( Figure 4A , Figure S5A and Tables S10 and S11 ) ., No H3K4me3 enrichment was detected at 6 dpp , suggesting that this histone post-translational modification is not apposed to recombination hotspots before entry into meiosis ., We then examined by real-time RT-PCR the kinetics of expression of three previously described Prdm9 splicing variants 12 , 26 during the first wave of meiosis ., Full-length Prdm9 , which is the most abundant isoform , and the S1 variant were detected and expressed with similar kinetics , whereas the S2 variant was undetectable ( Figure S5B ) ., Full-length Prdm9 was expressed at a low level at all time points , but increased significantly from 10 dpp ( p<0 . 05 with every previous time point , two-sided Student t test ) ( Figure 4B ) ., Altogether , these findings are consistent with the hypothesis that PRDM9 is responsible for apposing H3K4me3 to recombination hotspots at or before the time of meiotic DSB formation ., Our results provide the first direct demonstration that the identity of the PRDM9 zinc finger array determines hotspot localization in mice through binding of PRDM9 to DNA sequences at hotspots and H3K4me3 enrichment at such regions ., It is remarkable that , at all hotspots tested , the binding of PRDM9 occurs at or very near their center , suggesting a direct or highly localized interaction between PRDM9 activity and DSB formation ., Our in vitro analysis also demonstrates the limitation of in silico prediction of PRDM9 DNA binding specificity , when applied to search for binding sites at individual hotspots ., The complexity of the interaction between the PRDM9 zinc fingers and the DNA is obviously greater than the one analyzed for proteins containing smaller numbers of zinc fingers and used in the prediction algorithms ., Several human hotspots which activity has been shown to depend on PRDM9 do not contain a match to the predicted motif 15 , 27 ., This could be due to the limited power of motif prediction and to additional factors that influence PRDM9 binding and/or its accessibility to its binding sites ., The enrichment for H3K4me3 at active recombination hotspots , which is unambiguously dependent on PRDM9 , is also highly localized and catalyzed very likely by PRDM9 itself ., PRDM9 binding may also lead to the recruitment of additional factors and other chromatin remodelers ., In fact , additional histone post-translational modifications were detected at the Psmb9 hotspot 11 and H3K4me3 is expected not to be sufficient for promoting hotspot activity as it is known to be associated with genomic functional elements that generally are not recombination hotspots ( such as transcriptional promoters ) 28 , 29 ., One should also point out the formal possibility that H3K4Me3 enrichment may not be required for hotspot activity ., Overall , how these hotspot features allow the recruitment of the proteins involved in meiotic DSB formation remains to be understood ( Figure 5 ) ., An additional implication for the close vicinity of PRDM9 binding to the hotspot center is that the PRDM9 binding site has a high probability to be included in gene conversion tracts during meiotic recombination ., This feature is key to account for the drive against the motif observed in humans 14 ., A growing set of data suggests that meiotic recombination occurs mainly at Prdm9-dependent hotspots in mammals 13 , 15 , 18 , 19 ., This view is further supported by a recent genome-wide survey of mouse recombination hotspots , which revealed that 87% of them were overlapping with testis-specific H3K4me3 marks 30 ., Whether alternative pathways for the specification of a subset of initiation sites do exist remains to be determined ., In addition , whether PRDM9 binds to genomic sites not associated to recombination can be envisioned ., Indeed , one unexplained property of PRDM9 is its role in hybrid sterility , where a specific combination of Prdm9 alleles differing in their zinc finger array leads to male-specific sterility , potentially as a result of a change in gene expression 26 ., The Prdm9 gene is well conserved among metazoans , however the domain encoding the zinc finger array experienced an accelerated evolution in several lineages , including rodents and primates 31 , 32 ., This accelerated evolution is restricted to codons responsible for the DNA-binding specificity of PRDM9 zinc fingers , which appear to have been subjected to positive selection 31 , 32 ., Surprisingly PRDM9 appears to have been lost from some lineages in animals 33 , suggesting that alternative pathways may be used for specifying hotspots , such as the one described in the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe where components of the transcription machinery are known to be involved in meiotic DSB formation 34 , 35 ., The mouse strains used in this study are C57BL/6NCrl ( B6 ) , C57BL/10JCrl ( B10 ) , B10 . A-H2a H2-T18a/SgSnJ ( B10 . A ) , B10 . A ( R209 ) ( R209 ) 36 , RB2 , and RJ2 ., The RB2 strain results from backcrossing ( B10×R209 ) F1 with B10 and carries the wm7 haplotype on a Prdm9-containing interval of chromosome 17 18 ., The RJ2 strain is derived from RB2 and carries also the Prdm9wm7 allele , as described in 18 ., The mouse strains are shown on Table 1 with their genotypes at Prdm9 and Psmb9 hotspot ., All experiments were carried out according to CNRS guidelines ., The bacterial artificial chromosome ( BAC ) RP23-159N6 containing an insert derived from C57BL/6J ( Coordinates 15 , 651 , 974–15 , 848 , 091 on Chromosome 17 , NCBI m37 mouse genome assembly ) was obtained from the BACPAC Resource Center at the Childrens Hospital Oakland Research Institute ( Oakland , California , USA ) ., The part of exon 12 encoding the PRDM9 zinc finger array was modified by BAC recombineering 37 , using the primers MsGALKF and MsGALKR for the first step ( Table S12 ) ., GalK was then replaced by the fragment encoding the wm7 zinc finger array , which was generated by PCR amplification of B10 . A ( R209 ) genomic DNA with primers Pr1500U20 and Pr2848L23 ( Table S12 ) , resulting in the BAC RP23-159N6 ( Prdm9wm7ZF ) ., The last Prdm9 exon , which encodes the zinc finger array , has been fully sequenced in both BACs ., Transgenic mice were generated by microinjection of 0 . 5–1 ng/microliter of circular BAC RP23-159N6 ( Prdm9wm7ZF ) Tg ( wm7 ) or RP23-159N6 Tg ( b ) into fertilized one-cell C57BL/6J embryos ., Injected eggs were implanted in pseudopregnant ( C57BL/6J×CBA ) F1 foster mothers ., Transgenic mice were identified by PCR analysis of mouse tail DNA using the primer pairs p3 . 6_1U and p3 . 6_1L , and p3 . 62U and p3 . 62L ., Six pups integrated Tg ( wm7 ) and seven Tg ( b ) ., Four mice with Tg ( wm7 ) and seven with Tg ( b ) showed germ-line transmission ., For Tg ( wm7 ) , one strain ( #43 , which contains two or three copies of the BAC , as determined by Southern blot ) was used for all experiments , and similar results were obtained with another strain for CO measurement at Psmb9 and H3K4me3 enrichment ( not shown ) ., For Tg ( b ) , the distribution of MLH1 foci on Chromosome 18 was analyzed in strain #95 , the recombination rate at Psmb9 was determined in strains #55 and #95 , and H3K4me3 enrichment was measured in strain #75 , which contains four or five copies of the BAC , as determined by Southern blot ., All transgenic mice used in this study were hemizygous for the transgene ., Southwestern blotting assays were performed as described previously 13 , using full-length His-tagged mouse PRDM9wm7 and PRDM9b ., The Prdm9wm7 and Prdm9b coding sequences were cloned as follows: cDNA prepared from C57BL/10Crl and R209 testis RNA was amplified with the primers 1S91U24 and Pr2848L23 ( Table S13 ) using Phusion DNA polymerase ( Finnzymes ) , as recommended by the supplier ., Each PCR product was gel-purified and a second round of amplification was performed with 2 ng of purified product with the primers mPrdm9gwU and mPrdm9gwL ., The products were gel-purified and integrated into the plasmid pDONR201 with BP clonase ( Invitrogen ) ., Then , the inserts containing the coding regions of Prdm9wm7 and Prdm9b were transferred using LR clonase ( Invitrogen ) to the pET15bGtw expression vector , resulting in plasmids encoding N-terminally His-tagged PRDM9wm7 and PRDM9b under the control of the T7 promoter ., The insert sequences were then verified ., For subsequent expression the plasmids were transformed into the BL21 ( DE3 ) E . coli strain ., The probes covering the Psmb9 and G7c hotspots were generated by PCR amplification with XbaI site-tailed primers ( Table S14 ) ., Amplification products were phenol/chloroform purified followed by ethanol precipitation , XbaI-digested , and agarose-gel purified ., The probes containing a motif at the center of the Psmb9 and Hlx1 hotspots were made by annealing complementary oligonucleotides leaving a 3 or 4 bp 5′-overhang at each end ( Table S14 ) ., DNA fragments were labeled by end-filling with alpha-32P dCTP as described previously 13 ., At Psmb9 , COs and NCOs at site 38 were measured in sperm DNA as described 20 ., At G7c , semi-nested PCR was performed to detect the exchanges occurring in an interval overlapping with the genetically identified hotspot center 22 ., PCR amplification was performed as for Psmb9 , with the primers and annealing temperatures listed in Table S15 ., The bias in CO distribution along the Hlx1 hotspot in the ( B10 . A ( R209 ) ×CAST/Eij ) F1 hybrid 11 , which is homozygous for Prdm9wm7 , results in a 68% segregation bias among the CO products that favors the CAST allele at the center of the hotspot ., This segregation distortion indicates that the initiation rate on the B10 chromosome is approximately twice the one on the CAST chromosome in that hybrid ., Chromosome spreads , fluorescent in situ hybridization ( FISH ) , immunofluorescence ( IF ) assays , image acquisition , and statistical tests were performed as described 18 ., Chromosome 18 was identified with a labeled BAC probe ( RP23-101G16 ) , and the following antibodies were used for the immunofluorescence assays: guinea pig anti-SYCP3 serum at 1∶500 dilution and mouse monoclonal anti-MLH1 ( Pharmigen ) at 1∶50 dilution ., Spermatocytes from testes of 3–4 adult mice were enriched by centrifugal elutriation as described 11 ., Native chromatin was prepared from elutriated cells or from whole testis cells of prepuberal mice , immunoprecipitated with an antibody directed against H3K4me3 ( rabbit polyclonal ab8580 , Abcam ) , and immunoprecipitated DNA was quantified using real-time PCR as described 11 ., As a control for the quality of the samples and of the immunoprecipitations , the level of H3K4me3 was measured at the Actin , Sycp1 , and Nestin promoters ., The sequences of the primers and PCR conditions for the studied STSs ( Psmb9-1 , -7 , -8 , -11 , -13 , and -18; Hlx1-1 . 2 , -5 , -6 , -2 . 2 , -3 , and -4; Actin , Nestin , and Sycp1 promoters ) were described previously 11 ., The Mann-Whitney test was used to determine the statistical significance of differences between strains for the data concerning the STSs 7 , 8 , 11 , and 13 ( Psmb9 ) or STSs 5 , 6 , and 2 . 2 ( Hlx1 ) ( Tables S2 and S3 ) or between time points ( Tables S10 and S11 ) ., For determining the kinetics of expression in testes from prepuberal mice , total RNA from one testis from 4 to 18 dpp R209 mice was extracted with the GenElute Mammalian Total RNA Miniprep Kit ( Sigma ) ., Five hundred ng of RNA were reverse-transcribed with SuperscriptIII Reverse Transcriptase ( Invitrogen ) and random 10-mer primers ., Two µl of cDNA at the appropriate dilution ( see Table S16 ) was used for real-time PCR in a 10 µl reaction containing 1× LC480 SYBR Green mix ( Roche ) and 0 . 5 µM of the primers listed in Table S16 , with PCR conditions as described 11 ., The relative amount of each transcript of interest was determined with the 2ΔCp method , using housekeeping genes ( Actin , Gapdh , and Hprt ) as a reference 38 ., For determining the level of Prdm9 RNA in transgenic mice , total RNA was extracted from elutriated cells from adult testes ., The amount of Prdm9 transcript was determined by using the same set of housekeeping genes , plus Spo11 , as references ( Figure S1A ) ., The relative amount of RNA was quantified by using serial dilutions of RNA from a reference sample ( B10 testis elutriated cells ) ., To evaluate the relative amounts of endogenous Prdm9b RNA and Prdm9wm7ZF in B6-Tg ( wm7 ) mice , a 1 . 3 kb interval encompassing the zinc finger array-coding domain was amplified from the cDNA ( primers Pr1500U20 and Pr2848L23 , Table S12 ) and run on an agarose gel in conditions that discriminate both alleles ( amplicon of 1 , 371 bp for Prdm9b , 1 , 287 bp for Prdm9wm7 ) ., The relative amounts of Prdm9b and Prdm9wm7ZF RNAs were compared to a sample resulting from amplifying genomic DNA from a Prdm9b/wm7 mouse , which contains the same amount of both alleles ( Figure S1B ) . | Introduction, Results and Discussion, Materials and Methods | Meiotic recombination generates reciprocal exchanges between homologous chromosomes ( also called crossovers , COs ) that are essential for proper chromosome segregation during meiosis and are a major source of genome diversity by generating new allele combinations ., COs have two striking properties: they occur at specific sites , called hotspots , and these sites evolve rapidly ., In mammals , the Prdm9 gene , which encodes a meiosis-specific histone H3 methyltransferase , has recently been identified as a determinant of CO hotspots ., Here , using transgenic mice , we show that the sole modification of PRDM9 zinc fingers leads to changes in hotspot activity , histone H3 lysine 4 trimethylation ( H3K4me3 ) levels , and chromosome-wide distribution of COs ., We further demonstrate by an in vitro assay that the PRDM9 variant associated with hotspot activity binds specifically to DNA sequences located at the center of the three hotspots tested ., Remarkably , we show that mutations in cis located at hotspot centers and associated with a decrease of hotspot activity affect PRDM9 binding ., Taken together , these results provide the direct demonstration that Prdm9 is a master regulator of hotspot localization through the DNA binding specificity of its zinc finger array and that binding of PRDM9 at hotspots promotes local H3K4me3 enrichment . | Meiosis is the process of cell division that reduces the number of chromosome sets from two to one , so producing gametes for sexual reproduction ., During meiosis in many organisms , there is reciprocal exchange of genetic material between homologous chromosomes by the formation of “crossovers , ” which promote genetic diversity by creating new combinations of gene variants and play an important mechanical role in the segregation of chromosomes ., Crossovers do not occur randomly throughout the genome , but in small regions called hotspots ., Recent work showed that hotspots have specific structural features and that the protein PRDM9 is important in specifying their location ., PRDM9 contains a so-called zinc finger domain that is predicted to bind specific DNA sequences , suggesting that hotspots might be sites where PRDM9 binds ., By using transgenic mice expressing PRDM9 with modified zinc fingers , here we show directly that the nature of the zinc fingers in PRDM9 determines crossover hotspot localization ., We show that PRDM9 binds DNA sequences at the center of hotspots ., Furthermore , we identify DNA sequence polymorphisms that affect its binding and the extent of crossover activity ., Overall , our work shows that PRDM9 , through its zinc finger domain , is a master regulator of hotspot location in the mouse genome . | meiosis, histone modification, epigenetics, dna, chromatin, chromosome biology, biology, molecular biology, cell biology, nucleic acids, genetics, dna recombination, molecular cell biology, genetics and genomics | The nature of the PRDM9 zinc finger domain determines the location of hotspots for meiotic recombination in the genome and promotes local histone H3K4 trimethylation. |
journal.pgen.1003573 | 2,013 | BMS1 Is Mutated in Aplasia Cutis Congenita | Aplasia cutis congenita ( ACC MIM 107600 ) manifests at birth as a localized skin defect that usually heals with a hypertrophic scar ., Most commonly the scalp skin is affected and results in localized alopecia at the site of the defect , but sometimes the defect can extend into deeper structures involving the dura mater or osseous structures ., ACC has to be distinguished clinically from birth trauma or intrauterine herpetic infections ., Most reported cases are sporadic , but autosomal dominant inheritance has been reported as well 1 ., A causative gene mutation for non-syndromic ACC has not been reported so far ., While the majority of patients with aplasia cutis have no other congenital abnormalities , aplasia cutis can occur as part of rare syndromes with a wide spectrum of anomalies ., For example , some patients with Johanson-Blizzard syndrome MIM 243800 have been reported to have aplasia cutis of the scalp skin ., This syndrome is characterized by nasal alar hypoplasia , hypothyroidism , pancreatic achylia and congenital deafness and is caused by a mutation in the UBR1 gene 2 ., Adams-Oliver syndrome ( AOS MIM 100300 ) is characterized by ACC and transverse limb defects , but a wide range of additional congenital anomalies have been reported in patients with AOS , including congenital heart defects ., Recently gain-of-function mutations in the ARHGAP31 gene encoding a Cdc42/Rac1 regulatory protein have been reported in AOS 3 , and two more cases of AOS were reported to harbor homozygous mutations in the DOCK6 gene , encoding a guanidine nucleotide exchange factor that activates Cdc42 and rac1 4 ., A recent report showed mutations in the transcriptional regulator of the Notch pathway , RBPJ , in two small families with AOS 5 ., Furthermore , mutations in the KCTD1 gene were recently reported in patients with Scalp-Ear-Nipple syndrome , which manifests with scalp ACC lesions as well 6 ., However , the majority of individuals with aplasia cutis have no other congenital anomalies , suggesting a distinct pathomechanism between non-syndromic ACC and syndromic cases as in AOS or other syndromes in which aplasia cutis has been described ., Histologically , ACC shows in most cases a complete absence of epidermis at birth suggesting a skin morphogenesis defect ., It has been speculated that a defect in cell proliferation may be underlying ACC that may lead to a delay in skin closure ability late during development at an anatomic site where a steady expansion of the brain structures may require a rapid proliferation of the overlying skin ., However , neither the genetic basis for ACC nor the pathomechanisms that result in the skin formation defects are known ., Identifying the genetic causes for ACC and determining their molecular consequences promises to reveal new mechanisms that govern skin morphogenesis during development ., Here , by combining genome-wide linkage analysis with exome sequencing approaches , a mutation in the ribosomal GTPase BMS1 is identified in autosomal dominant ACC that is associated with a p21-mediated G1/S phase cell cycle transition delay and results in a reduced cell proliferation rate ., In addition , unbiased expression profiling and proteomic analyses in fibroblasts carrying this mutation independently confirm a central role of a p21-mediated G1/S phase cell cycle delay for the ACC phenotype ., In this study , a five-generation family with autosomal dominant inheritance of ACC was identified ( Fig . 1a ) ., In this family the exclusive congenital anomaly is a localized absence of skin at the vertex and occipital area of the scalp that most often healed with a hypertrophic scar ( Fig . 1a ) ., No other congenital anomalies were identified , excluding rare syndromes that can manifest with ACC 2 , 3 , 4 ., Genome-wide linkage analysis revealed a single genomic region with a LOD score>2 on chromosome 10q11 ., The maximal LOD score of 2 . 709 was reached with a sharp peak between marker rs1359280 located at 34 , 877 , 513 bp and marker rs7071514 located at 49 , 518 , 113 bp ( Fig . 1b ) ( an additional affected family member who carried the disease allele was born after the linkage study was completed , which further increased the LOD score ) ., Haplotypes and recombination sites were independently confirmed using microsatellite markers ., Whole-exome sequencing was performed in one affected individual of this family ., Sequencing of coding regions was performed to a mean coverage of 151× to generate 17 . 3 Gb of sequence ., Variants were filtered to exclude homozygous base pair changes , non-coding variants , synonymous variants and all non-synonymous changes that are present in dbSNP129 , the 1000genomes database and the NHLBI Exome Sequencing Project ( ESP ) database ., This genome-wide sequencing approach resulted in the identification of a single non-synonymous heterozygous G>A base change within the linked genomic region ., This sequence change was not identified in 5 , 351 unrelated individuals for which high-quality sequence calls were made at this position in the ESP sequence data , suggesting that the G>A sequence change is not a rare variant but pathogenic in this family with ACC ., Bidirectional Sanger sequencing was performed in all members of this family , showing that none of the unaffected family members had the G>A sequence change , but all affected family members had this sequence change , thus co-segregating with the disease allele ., Finally , sequencing of DNA from 100 geographically and ethnically matched unaffected control individuals did not reveal this mutation as well ., This heterozygous G>A mutation results in an Arg-to-His amino acid change at position 930 ( p . R930H ) of a conserved Arginine within the C-terminal domain of the ribosome assembly GTPase BMS1 , which has previously not been implicated in skin morphogenesis ( Figure 1c–1e ) ., BMS1 is a component of the U3 snoRNA-containing complex that has been shown to be essential in yeast and to be conserved in eukaryotes ., Depletion of BMS1 interferes with pre-ribosomal RNA ( rRNA ) processing at sites A0 , A1 and A2 and the formation of the 40S ribosomal subunit 7 , 8 ., Biochemical analyses demonstrated that BMS1 may function as a GTPase , with its GTPase activity located at the N-terminus of the protein ., In addition to binding to U3 snoRNA , BMS1 binds the endonuclease Rcl1 in a GTP-dependent manner and BMS1 binding is required to recruit Rcl1 to preribosomes ., Thus , it has been suggested that BMS1 functions as a GTP-regulated switch to deliver Rcl1 to preribosomes and has an essential role in the formation of the small ribosomal subunit 9 , 10 ., The C-terminal domain of BMS1 has been proposed to act as a putative intramolecular GTPase-activating protein ( GAP ) domain and is separated from the rest of the protein by a flexible linker region 9 ., However , the in vivo consequences of an impairment of this putative GAP domain for cellular functions have not been defined , and the BMS1 p . R930H mutation in ACC provides the first indication for a role of this domain for overall BMS1 function in vivo ., BMS1 is ubiquitously expressed , including the proliferative developing skin of the scalp that is affected in ACC ( Fig . 1f , 1g ) ., The BMS1 p . R930H mutation within the putative GAP domain does not affect BMS1 expression or its nucleolar localization ( Fig . S1 ) ., Due to the heterozygous nature of the mutation within a regulatory domain of BMS1 with a remaining wild-type allele and the only localized congenital defect in ACC , it would be expected that the p . R930H mutation in BMS1 leads to rather modest abnormalities in pre-rRNA processing and therefore results in a restricted phenotype ., Indeed , pulse-chase labeling experiments with ACCBMS1 ( p . R930H ) fibroblasts ( isolated from an affected member of this family ) and with matched unrelated wild-type fibroblasts , in which labeling of pre-rRNAs was performed with L-methyl-3Hmethionine , showed formation of all pre-rRNAs but with higher levels of persistent 45S pre-rRNA and the pre-rRNA band likely corresponding to the 30S pre-rRNA in ACCBMS1 ( p . R930H ) fibroblasts ( Fig . 2a–2c ) ., Northern blotting experiments using RNA from ACCBMS1 ( p . R930H ) fibroblasts and control fibroblasts with probes for ITS-1 , ETS-1 and ITS-2 ( transcribed spacers ) showed an increase of 45S and 30S pre-rRNAs in mutant cells and a relative decrease of 21S and 18S-E pre-rRNAs ( precursors for 18S rRNA of the small ribosomal subunit ) , while 32S and 12S pre-rRNAs ( precursors for 5 . 8S and 28S rRNAs of the large ribosomal subunit ) were not affected ( Fig . 2d , Fig . S2 ) ., The findings of the pulse-chase labeling and Northern blotting experiments in ACCBMS1 ( p . R930H ) fibroblasts are consistent with a delay in pre-rRNA processing affecting the small ribosomal subunit , as would be predicted based on the reported role of BMS1 for the small ribosomal subunit processome in yeast 7 , 8 ., Similarly , inducible shRNA-mediated stable knockdown of BMS1 to achieve ∼40–50% of BMS1 transcript levels ( to correspond to heterozygous BMS1 cells ) resulted also in a relative increase of 45S and 30S pre-rRNAs , and a relative decrease in 21S and 18S-E pre-rRNAs , while 32S pre-rRNAs were reduced less when compared to the 30S pre-rRNAs ( Fig . 2d and Fig . S2 ) ., These findings suggest that the BMS1 p . R930H mutation results in a reduced activity of BMS1 during the processing of 18S pre-rRNAs ., Mutations in several genes that affect ribosomal function have been described to result in a G1/S phase transition delay that may involve p53-dependent and p53-independent pathways , presumably due to “nucleolar stress” resulting from increased free ribosomal proteins as a consequence of ribosomal processing or assembly abnormalities 11 ., To determine whether ACC fibroblasts that carry the BMS1 p . R930H mutation show a similar cellular response as in some ribosomopathies , these fibroblasts were examined for their proliferation rate and whether a G1/S phase transition delay can be observed ., Skin fibroblasts were isolated from an affected family member through a skin biopsy and expanded in vitro ., Control fibroblasts were obtained from an unrelated unaffected individual , matched for ethnicity , age and anatomic location ., Subconfluent early-passage ACC fibroblasts and control fibroblasts were analyzed for their cell cycle status , cell proliferation rate and cell migration ability ., FACS-based cell cycle analysis showed a G1/S phase transition defect in ACC fibroblasts ( Fig . 3a ) , with a significantly reduced cell proliferation rate ( Fig . 3b ) ., Furthermore , cell migration rate was increased in ACC fibroblasts in an in vitro scratch assay , which is consistent with the observation that cell migration is favored in G1 phase ( Fig . 3c ) 12 ., Thus , similarly as has been observed in some ribosomopathies , ACC fibroblasts with the BMS1 p . R930H mutation show a link between a mutation in a gene involved in ribosomal function and a G1/S phase transition delay that results in a reduced cell proliferation rate ., To determine the molecular changes that are associated with a G1/S phase transition defect and a reduced cell proliferation rate in ACC , unbiased analyses of primary ACCBMS1 ( p . R930H ) fibroblasts compared to control fibroblasts were performed using global gene expression profiling and quantitative comparative proteomic experiments ., Microarray gene expression experiments showed differential expression of 459 genes ( p-value<0 . 05 , FDR<5% ) between ACCBMS1 ( p . R930H ) fibroblasts and cells obtained from an unaffected unrelated individual ( Table S1 ) ., Increased expression of p21 ( CDKN1A ) mRNA in ACC fibroblasts was noticed , which was further confirmed by semiquantitative RT-PCR ( Fig . 4a and 4b ) ., The cyclin-dependent kinase inhibitor p21 mediates a G1/S phase arrest in response to cellular stress and other stimuli , and the increased p21 mRNA levels are consistent with the observed G1/S phase transition delay in ACC fibroblasts ., Western blotting experiments confirmed increased p21 protein levels in ACC fibroblasts compared to control fibroblasts , whereas total p53 protein levels were not significantly increased ( Fig . 4d ) ., There was also no difference in p53Lys342 acetylation and no p53Ser15 phosphorylation was detected in ACC fibroblasts ( data not shown ) ., These findings suggest a role for increased p21 levels in the observed G1/S phase transition defect in ACC fibroblasts ., Serine/arginine-rich splicing factor 3 ( SRSF3 ) was found to be downregulated in ACC fibroblasts ( Fig . 4a and 4b ) , which has recently been reported to promote transcription of G1/S phase checkpoint regulators and silencing of SRSF3 caused a G1/S phase arrest ( Kurokawa et al . , EMBO meeting 2011 abstract ) ., Overexpression of the mutant BMS1 p . R930H in wild-type fibroblasts resulted in increased transcript levels of p21 and decreased SRSF3 transcript levels , as observed in ACCBMS1 ( p . R930H ) fibroblasts ( Fig . 4c ) ., Next , a global quantitative comparative proteomic analysis was performed using iTRAQ-labeling and subsequent MS analysis , comparing control versus ACC early passage subconfluent fibroblasts ., Using stringent parameters for statistical significance , 25 proteins were found to be consistently upregulated in ACC fibroblasts , whereas 18 were downregulated ( Fig . 5a ) ., Downregulated proteins in ACC fibroblasts included several serine/arginine-rich splicing factors ( SRSF1 , SRSF2 , SRSF3 , SRSF7 ) , including SRSF3 that was also downregulated in the microarray transcript profiling experiments ., Other downregulated proteins included heterogenous nuclear ribonucleoproteins ( HNRNPA2B1 , HNRNPH2 , HNRNPA1 ) ., Decreased levels of hnRNPA2B1 in ACC fibroblasts were further confirmed by Western blotting experiments ( Fig . 5c ) ., Importantly , hnRNPA2 knockdown has been shown to result in a p53-independent increase of p21 levels and an inhibition of cell proliferation 13 , as observed here in ACCBMS1 ( p . R930H ) fibroblasts ., Functional enrichment analysis of the proteomic data ranked the category “RNA post-transcriptional modification” as the top-ranked cellular process altered in ACC fibroblasts ( Benjamini-Hochberg multiple test corrected p-value 4 . 72E-06 ) ., Network analysis identified the two most significantly altered networks to include top functions for “RNA-posttranscriptional modification” and “cell cycle” ( Fisher exact test –lg p-value of 70 and 21 respectively ) ( Fig . 5b ) ., Thus , the results of the unbiased global proteomic analysis in ACCBMS1 ( p . R930H ) fibroblasts are consistent with a defect in pre-rRNA processing ., These two top-ranked networks were merged to generate a combined network of the differentially present proteins in ACCBMS1 ( p . R930H ) fibroblasts ( Fig . 6a ) , which were further analyzed for interactions with the differentially expressed transcripts ., This analysis revealed the highest number of interactions to include p21 ( CDKN1A ) and hnRNPA2B1 ( Fig . 6a ) ., Furthermore , interaction analysis among p21 and the entire merged proteomic network revealed a central role of p21 , suggesting that p21 activation is a central determinant of the ACC phenotype ( Fig . 6b ) ., Mutations in genes for structural proteins of the ribosome or in other genes involved in ribosome biogenesis or function have been found in rare congenital diseases termed ribosomopathies , including Diamond Blackfan anemia ( e . g . RPS19 and RPS24 ) , Shwachman-Diamond syndrome ( SBDS ) , X-linked dyskeratosis congenita ( DKC1 ) , cartilage hair hypoplasia ( RMRP ) and Treacher Collins syndrome ( TCOF1 , POLR1C and POLR1D ) 14 , 15 , 16 , 17 , 18 ., However , a disease phenotype in these disorders may not always be a direct consequence of ribosomal dysfunction , but may be due to other disease mechanisms ., For example , in both dyskeratosis congenita and cartilage hair hypoplasia ribonucleoprotein complexes containing telomerase are involved , likely critical to maintain stem cell function ., RMRP , which is mutated in cartilage hair hypoplasia , interacts with TERT ( the catalytic subunit of telomerase ) and forms a ribonucleoprotein complex that has RNA-dependent RNA polymerase activity 19 ., DKC1 , mutated in X-linked dyskeratosis congenita , is associated with small nucleolar RNAs , but also with human telomerase RNA ( TERC ) , and it has been suggested that the disease phenotype in dyskeratosis congenita results from a defect in telomere maintenance 20 ., Thus , although these two diseases have been classified as ribosomopathies , their pathologies may result from cellular dysfunction that is not due to a ribosome biogenesis defect ., Notably , ribosomopathies manifest with very specific clinical features affecting only few organ systems and often resulting in hematologic and craniofacial abnormalities 21 ., These specific phenotypes affect particular cell types , despite the ubiquitous expression of the mutated gene , and illustrate that a mutation in a gene involved in ribosomal function that affects basic cellular pathways can manifest with a selected clinical abnormality ., In this context , it is not surprising that despite the ubiquitous expression of BMS1 , individuals harboring the BMS1 p . R930H mutation in non-syndromic ACC display only a localized skin morphogenesis defect without further systemic anomalies ., A mutation that slows cell proliferation may manifest itself at an anatomic location that requires rapid growth of a tissue compartment , such as the embryonic skin at the vertex area during the rapid expansion of the skull in embryonic development ., As such , it is also not surprising that aplasia cutis can be seen in various diverse syndromes in which cell proliferation rate is affected in addition to other basic cellular pathways that also affect other organ systems and lead to multiple congenital anomalies ., For example , Adams-Oliver syndrome ( AOS ) patients have aplasia cutis and multiple additional congenital anomalies , and skin fibroblasts from AOS families with a mutation in a Cdc42/Rac1 regulatory protein ( ARHGAP31 ) showed an enhanced cell migration rate and a reduced cell proliferation rate in vitro , as seen here in ACCBMS1 ( p . R930H ) fibroblasts 3 ., Several studies have shown that ribosomal gene mutations can lead to “nucleolar stress” and a G1/S phase cell cycle arrest both via p53-dependent and p53-independent mechanisms that are only partially understood 21 , 22 ., It has been proposed that the ribosomal stress response , as a consequence of alterations in ribosomal assembly or processing , results in increased free ribosomal proteins that can either inactivate Mdm2 , resulting in p53 accumulation and p21-mediated cell cycle arrest , or increase p27 levels that ultimately result in a cell cycle arrest 11 ., Consistent with these findings in some ribosomopathies , ACC fibroblasts with the BMS1 p . R930H mutation showed a G1/S phase cell cycle transition defect associated with increased p21-levels that result in a reduced cell proliferation rate ., BMS1 function has been studied mostly in yeast , demonstrating that inducible depletion of BMS1 in yeast results in inhibition of pre-rRNA processing at sites A0 , A1 and A2 affecting the small ribosomal subunit processome 7 , 8 , 9 , 10 , while little is known about the function of BMS1 in vertebrates ., The data presented here are consistent with the findings in yeast and suggest that reduced BMS1 function delays maturation of the 18S rRNA in human cells ., A homozygous mutation in the GTPase domain of BMS1 has recently been reported to result in impaired liver development in zebrafish , while heterozygosity for this mutation does not cause an observable phenotype 23 ., This observation suggests a role for BMS1 and other components of the small subunit processome in liver development , which is further supported by the identification of homozygous mutations in the gene cirhin in North American Indian childhood cirrhosis that is required for proper 18S rRNA maturation 24 , 25 ., The heterozygous BMS1p . R930H mutation in ACC is located in the putative regulatory GAP domain , and therefore is likely to affect BMS1 function to a lesser degree than the homozygous mutation in the GTPase domain of BMS1 in zebrafish ., In this context , it is not surprising that patients with autosomal dominant ACC do not display major developmental liver abnormalities ., Instead , the data presented here show a developmental localized skin formation defect at a site of rapid expansion of the skin due to a heterozygous mutation in the putative GAP domain of BMS1 , which provides the first human mutation for BMS1 ., Through which exact mechanisms this mutation causes a p21-mediated G1/S phase arrest and is correlated with the observed downregulation of hnRNPs and SRSFs remains to be determined in future studies ., The reported observation that hnRNPA2 knockdown results in a p53-independent increase of p21 levels and an inhibition of cell proliferation 13 , similar as observed here in ACCBMS1 ( p . R930H ) fibroblasts , together with the results form the interaction analyses of the proteomic data that show the largest number of interactions to involve p21 and hnRNPA2B1 in ACC fibroblasts , suggest that the reduced protein levels of hnRNPA2B1 likely play a role in ACC pathogenesis ., During embryonic development p21 expression correlates with arrest of cell proliferation and is found in postmitotic cells immediately adjacent to the proliferative compartment , as in the outer embryonic epidermis and particularly in the developing hair follicles 26 , 27 ., Notably , p21 expression is decreased in terminally differentiated cells and overexpression of p21 inhibits late stages of differentiation of keratinocytes and the stem-cell potential of keratinocyte subpopulations 28 , 29 ., Thus , the increased p21 levels in ACC may result in an inhibition of cell proliferation as well as an inhibition of terminal differentiation of the outer epidermis during embryonic development , and explain the observed skin morphogenesis defect in ACC ., Increased p21 levels have also been linked to increased scar formation , which is consistent with the prominent hypertrophic scar formation in patients with ACC , as was also observed in affected members of this family ( Fig . 1a ) 30 ., In summary , the findings in this study show that the BMS1 p . R930H mutation is associated with a downregulation of hnRNPA2B1 ( and other hnRNPs and SRSFs ) and a p21 upregulation , leading to a G1/S cell cycle phase transition delay and an inhibition of cell proliferation ., The data presented here provide a novel link between BMS1 , a p21-mediated cell cycle arrest and skin morphogenesis ., A five-generation family was identified with autosomal dominant inheritance of ACC ., All participants provided written consent , and the Institutional Review Board of Massachusetts General Hospital approved this study ., Genomic DNA was extracted from peripheral blood lymphocytes using the QIAGEN Puregene blood isolation kit ( Qiagen ) ., DNA concentrations were determined using the Picogreen assay ( Life Technologies ) ., Genomic DNA from individuals of this family were prepared , labeled and hybridized to the Affymetrix Genome-Wide Human SNP array 6 . 0 , which features more than 906 , 000 SNPs ., SNP data was analyzed with the Affymetrix Genotyping Console ., Merlin version 1 . 1 . 2 was used for parametric linkage analysis 31 ., Linkage analysis was performed with either 100% or 95% disease penetrance , equal or calculated allele frequencies , and various disease allele frequencies ., The frequency of ACC in the population has been estimated to be about 1∶30 , 000 births ., Independently , microsatellite genotyping was performed using informative markers from A&B Biosciences that spanned chromosome 10 ., About 10 µg of DNA from one affected individual of this family was used for exome capture using the Agilent Sure select 50 Mb kit ., Sequencing was performed on an Illumina HiSeq 2000 sequencer ., Reads were mapped to the UCSC hg19 reference human genome ., Sequence data was analyzed and variants were filtered using the DNAnexus software package ., Bidirectional Sanger sequencing of PCR amplicons from genomic DNA was used to confirm the presence and identity of variants identified via exome sequencing ., Primary fibroblasts were obtained from a family member with ACC ( p . R930H ) through a 4 mm abdominal skin biopsy ., Control fibroblasts were obtained from a skin biopsy of a matched unrelated individual without ACC ( matched for anatomic location , skin phototype , and age ) ., The skin biopsy sample was treated with collagenase I , and subsequently fibroblasts were maintained in culture in DMEM ( Invitrogen ) with 20% fetal calf serum ( Sigma ) and antibiotic/antimycotic ( Invitrogen ) ., For cell immunolabeling experiments , fibroblasts were grown on poly-L-lysine coated glass slides ( BD Biosciences ) and fixed with methanol ., Primary antibodies used were as follows: polyclonal rabbit BMS1 ( Sigma ) , monoclonal mouse BMS1 ( Santa Cruz ) , rabbit polyclonal nucleophosmin ( Invitrogen ) , rabbit polyclonal anti-phospho Histone H3 ( Ser10 ) ( Millipore ) ., Secondary antibodies used were fluorescently labeled Alexa antibodies from Invitrogen ., Nuclei were stained with DAPI ., Mouse embryos at E13 . 5 were fixed in 4% PFA and embedded in OCT and cryosections were used for immunofluorescence experiments ., F-actin labeling was performed with phalloidin-Alexa Fluor 488 conjugates ( Invitrogen ) ., Microscopy was performed with a Zeiss Axiovert microscope ., In vitro mutagenesis was performed according to the manufacturers protocol ( Stratagene ) ., Mutant ( c . 2789G>A ) and wild-type BMS1 cDNA was cloned into the pEGFPN1 ( Clontech ) expression vector , fusing EGFP at the C-terminus of the expressed cDNA ., Primary human wild-type fibroblasts that were used as controls in all experiments ( matched for age- , anatomic location and skin phototype ) were transfected in triplicate with purified plasmid using XtremeGene HP DNA transfection reagent ( Roche ) or nucleofection using the Amaxa human dermal fibroblast nucleofector kit with program U-023 ( Amaxa ) ., Expression and subcellular localization of the expressed EGFP-tagged protein was evaluated on a Zeiss Axiovert fluorescence microscope ., For semiquantitative RT-PCR experiments , transfected fibroblasts ( n\u200a=\u200a3 independent transfections ) were sorted for EGFP 48 hours after transfection using a cell sorter ( MoFlo ) and RNA was immediately isolated after sorting with Trizol reagent ( Invitrogen ) ., Overexpression was confirmed by semiquantitative RT-PCR for BMS1 transcript levels ., All experiments were performed in triplicate ., Cell cycle analysis with propidium iodide was performed according to standard protocols ., Briefly , subconfluent fibroblasts were fixed in 70% ethanol , treated with RNase A and stained with propidium iodide ( Sigma ) and cell cycle status was determined by FACS analysis and subsequent analysis by FlowJo software ( version 9 . 4 . 9 ) using the Watson pragmatic model for determination of cell cycle phases ., Experiments were performed in triplicate for ACC and WT cells ., The CyQUANT fluorescence-based microplate assay was used for quantitation of cell numbers and used according to the manufacturers protocol ( Invitrogen ) ., Binding to cellular nucleic acids was measured by using 485 nm excitation and 530 nm emission filters with a fluorescence microplate reader ., The fluorescence emission of the dye-nucleic acid complexes was then correlated linearly with cell numbers from a dilution series of cells that were collected d2–d7 after cell culture of equal numbers of cells ., For each time point triplicate experiments were performed ., Cell migration assays were performed with WT and ACCBMS1 ( p . R930H ) primary dermal fibroblasts plated on fibronectin-coated 6 cm2 tissue culture dishes ( n\u200a=\u200a3/group ) ., Cells were serum- and growth-supplement- starved for 12 hours before inducing a linear scratch wound with a 1 ml pipette tip ., Cultures were washed twice with PBS , and wound margins were photographed ., The same wound margin fields were photographed after 14 hours , and the scratch areas that were not repopulated by fibroblasts were measured with Zeiss Axiovision software ., Similar findings were obtained when dishes were coated with collagen ., Western blotting was performed with 50 µg of total protein lysate from subconfluent early passage ACC and control fibroblasts ., Protein concentrations were determined by a standard Bradford assay ., NP-40 lysis buffer and complete protease inhibitor cocktail ( Roche ) was used ., NuPage Bis-Tris 4–12% gradient gels were used ., Primary antibodies used were: mouse monoclonal anti-p53 ( clone DO-1 ) ( Becton Dickson ) , rabbit polyclonal anti-β-actin ( Thermo ) , rabbit anti-p21 polyclonal antibody ( Thermo Fisher ) and mouse anti-HNRNPA2B1 ( Millipore ) ., HRP-conjugated secondary antibodies were used from GE ., β-actin loading control was performed on each blot ., Band intensities were quantified using NIH ImageJ software ( NIH , version 1 . 46 ) ., To study the kinetics of pre-rRNA processing pulse-chase labeling with L-methyl-3Hmethionine was performed on early passage subconfluent ACCBMS1 ( p . R930H ) fibroblasts and control fibroblasts as described 32 ., Briefly , cells were cultured on 6-well plates and after being cultured for 15 minutes in methionine-free medium , cells were pulsed with L-methyl-3Hmethionine at a final concentration of 50 µCi/ml for 30 minutes ., 10× methionine medium was used for the chase and total RNA was isolated with Trizol ( Invitrogen ) at 0 , 15 , 45 , 60 and 180 minutes after the end of the pulse ., 1 µg of total RNA was loaded on a 1% formaldehyde-agarose gel and after electrophoresis blotted overnight on a nylon membrane ( Ambion ) ., Signal intensity was increased with the EN3HANCE spray ( PerkinElmer Life ) and treatment with carbon tetrachloride ( Sigma ) as described 32 ., The nylon membrane was exposed to Kodak BioMax MS high-sensitivity radiography film ( Kodak ) ., Early-passage fibroblasts isolated from an affected individual with ACC from this family with the BMS1 mutation p . R930H were used for comparative proteomic analysis ., Fibroblasts , matched for ethnicity and anatomic location , were used as control ., From each individual fibroblasts were used as quadruplicate experimental samples in a 8-plex iTRAQ experiment ., Whole cell lysates were generated using Pressure Cycling technology ., Collected cells were resuspended in RIPA buffer plus Pefablock ( Roche complete tabs , 1 tablet per 10 ml ) and 1 mM DTT ., The cells were then lysed in a Barocyler NEP2320 instrument from Pressure Biosciences , Inc . ( South Easton , MA ) ., Following lysis , proteins were recovered by acetone precipitation ., Acetone precipitated material that resuspended in milliQ water was used as the soluble protein extract ., Protein concentrations were determined using the Bio-Rad Protein Assay with BSA used to generate a standard curve ., The 8-plex iTRAQ assay for multiplexed relative quantitation ( AB SCIEX , Foster City , CA ) was used to determine the protein level differences between control and ACC cells ., Briefly , equal amounts of soluble protein from each fibroblast experimental group ( 100 µg ) was dried in a speed vacuum/centrifuge ., Following the recommended 8-plex iTRAQ protocol , dried proteins were resuspended in 21 µl of 500 mM TEAB ( pH 8 . 5 ) , 0 . 1% SDS ., Proteins were then reduced with 5 mM TCEP by incubation at 55°C for 1 hour ., Reduced disulfide bonds were then blocked by adding a final concentration of 8 mM MMTS and incubating at room temperature for 10 min ., Peptides were generated in each sample by overnight digestion with trypsin ( AB SCIEX ) added at a ratio of 1∶10 ., Samples were then labeled with the 8-plex iTRAQ reagent ., Confirmation | Introduction, Results, Discussion, Materials and Methods | Aplasia cutis congenita ( ACC ) manifests with localized skin defects at birth of unknown cause , mostly affecting the scalp vertex ., Here , genome-wide linkage analysis and exome sequencing was used to identify the causative mutation in autosomal dominant ACC ., A heterozygous Arg-to-His missense mutation ( p . R930H ) in the ribosomal GTPase BMS1 is identified in ACC that is associated with a delay in 18S rRNA maturation , consistent with a role of BMS1 in processing of pre-rRNAs of the small ribosomal subunit ., Mutations that affect ribosomal function can result in a cell cycle defect and ACC skin fibroblasts with the BMS1 p . R930H mutation show a reduced cell proliferation rate due to a p21-mediated G1/S phase transition delay ., Unbiased comparative global transcript and proteomic analyses of ACC fibroblasts with this mutation confirm a central role of increased p21 levels for the ACC phenotype , which are associated with downregulation of heterogenous nuclear ribonucleoproteins ( hnRNPs ) and serine/arginine-rich splicing factors ( SRSFs ) ., Functional enrichment analysis of the proteomic data confirmed a defect in RNA post-transcriptional modification as the top-ranked cellular process altered in ACC fibroblasts ., The data provide a novel link between BMS1 , the cell cycle , and skin morphogenesis . | Elucidating the pathomechanisms in congenital diseases of the skin provides the opportunity to learn what cellular processes are important during embryonic development of the skin structures ., Aplasia cutis congenita ( ACC ) manifests with localized skin defects , most commonly affecting the scalp skin ., Here , global proteomic and transcriptional analyses are combined with genome-wide linkage and exome sequencing approaches to identify the molecular mechanisms involved in ACC ., A mutation in the ribosomal GTPase BMS1 is identified in ACC that affects 18S rRNA maturation ., This mutation is associated with a p21-mediated G1/S phase transition delay during the cell cycle that inhibits cell proliferation ., The findings are consistent with mutations in ribosomal disorders that result in nucleolar stress and a G1/S phase transition delay ., Thus , mutations in BMS1 can affect the formation of a highly proliferative tissue during development , such as the rapidly expanding scalp epidermis . | medicine, dermatology, genetics, biology, human genetics, genetics of disease | null |
journal.pgen.1006106 | 2,016 | Dynamics of Chloroplast Translation during Chloroplast Differentiation in Maize | The evolution of chloroplasts from a cyanobacterial endosymbiont was accompanied by a massive transfer of bacterial genes to the nuclear genome , and by the integration of chloroplast processes into the host’s developmental and physiological programs 1 ., In multicellular plants , chloroplasts differentiate from non-photosynthetic proplastids in concert with the differentiation of meristematic cells into photosynthetic leaf cells ., This transformation is accompanied by a prodigious increase in the abundance of the proteins that make up the photosynthetic apparatus , which contribute more than half of the protein mass in photosynthetic leaf tissue 2 ., Both nuclear and chloroplast genes contribute subunits to the multisubunit complexes that participate in photosynthesis ., The expression of these two physically separated gene sets is coordinated by nucleus-encoded proteins that control chloroplast gene expression , and by signals emanating from chloroplasts that influence nuclear gene expression 1 , 3 ., Beyond these general concepts , however , little is known about the mechanisms that coordinate chloroplast and nuclear gene expression in the context of the proplastid to chloroplast transition ., Furthermore , a thorough description of the dynamics of chloroplast gene expression during this process is currently lacking ., Despite roughly one billion years of evolution , the bacterial ancestry of the chloroplast genome is readily apparent in its gene organization and gene expression mechanisms ., Most chloroplast genes in land plants are grouped into polycistronic transcription units 4 that are transcribed by a bacterial-type RNA polymerase 5 and translated by 70S ribosomes that strongly resemble bacterial ribosomes 6 ., As in bacteria , chloroplast ribosomes bind mRNA at ribosome binding sites near start codons , sometimes with the assistance of a Shine-Dalgarno element 6 ., Superimposed on this ancient scaffold are numerous features that arose post-endosymbiosis 7 ., For example , a phage-type RNA polymerase collaborates with an RNA polymerase of cyanobacterial origin 5 , and chloroplast RNAs are modified by RNA editing , RNA splicing , and other events that are either unusual or absent in bacteria 8 ., Ribosome profiling data from E . coli revealed that the rate of protein output from genes encoding subunits of multisubunit complexes is proportional to subunit stoichiometry , and that proportional synthesis is typically achieved by differences in the translational efficiency of genes residing in the same operon 9 , 10 ., As the majority of chloroplast gene products are components of multisubunit complexes , it is of interest to know whether similar themes apply ., Furthermore , the gene content of polycistronic transcription units in chloroplasts has diverged from that in the cyanobacterial ancestor ., Has “tuned” protein output been maintained in chloroplasts despite this disrupted operon organization ?, If so , what mechanisms achieve this tuning in light of the new gene arrangements and the new features of mRNA metabolism ?, In this work , we used ribosome profiling to address these and other questions of chloroplast gene regulation in the context of the proplastid to chloroplast transition ., For this purpose , we took advantage of the natural developmental gradient of the maize seedling leaf blade , where cells and plastids at increasing stages of photosynthetic differentiation form a developmental gradient from base to tip 11 ., By using the normalized abundance of ribosome footprints as a proxy for rates of protein synthesis , we show that the rate of protein output from many chloroplast genes is tuned to protein stoichiometry , and that tuned protein output is achieved through gene-specific balancing of mRNA abundance with translational efficiency ., This comprehensive analysis revealed developmentally programmed changes in translational efficiencies , which superimpose on programmed changes in mRNA abundance to shift the balance of protein output as chloroplast development proceeds ., We analyzed tissues from the same genetic background and developmental stage as used in previous proteome 2 and nuclear transcriptome 12 , 13 studies of photosynthetic differentiation in maize ., Four leaf sections were harvested from the third leaf to emerge in 9-day old seedlings ( Fig 1A ) : the leaf base ( segment 1 ) , which harbors non-photosynthetic proplastids; 3–4 cm above the base ( segment 4 ) , representing the sink-source transition and a region of active chloroplast biogenesis; 8–9 cm above the base ( segment 9 ) , representing young chloroplasts; and a section near the tip ( segment 14 ) harboring mature bundle sheath and mesophyll chloroplasts 2 , 12 ., The developmental transitions represented by these fractions are illustrated in the immunoblot assays shown in Fig 1B ., The mitochondrial protein Atp6 is most abundant in the two basal sections , subunits of photosynthetic complexes ( AtpB , PetD , PsaD , PsbA , NdhH , RbcL ) are most abundant in the two apical sections , and a chloroplast ribosomal protein ( Rpl2 ) exhibits peak abundance in the two middle sections ., These developmental profiles are consistent with prior proteome data 2 ., To explore the contribution of differential chloroplast gene expression to the distinct proteomes in bundle sheath and mesophyll cells , we also analyzed bundle sheath and mesophyll-enriched fractions from the apical region of seedling leaves ., Standard protocols for the separation of bundle sheath and mesophyll cells involve lengthy incubations that are likely to cause changes in ribosome position ., We used a rapid mechanical fractionation method that minimizes the time between tissue disruption and the generation of ribosome footprints ( see Materials and Methods ) ., Markers for each cell type were enriched 5- to 10-fold in the corresponding fraction ( Fig 1C ) ., This degree of enrichment is comparable to that of the fractions used to define mesophyll and bundle sheath-enriched proteomes in maize 14 ., We modified our previous method for preparing ribosome footprints from maize leaf tissue 15 to reduce the amount of time and tissue required , and to reduce contamination by non-ribosomal ribonucleoprotein particles ( RNPs ) ., In brief , leaf tissue was flash frozen and ground in liquid N2 , thawed in a standard polysome extraction buffer , and treated with Ribonuclease I to liberate monosomes ., Ribosomes were purified by pelleting through a sucrose cushion under conditions that leave chloroplast group II intron RNPs ( ~600 kDa ) 16 in the supernatant ( S1A Fig ) ., RNAs between approximately 20 and 35 nucleotides ( nt ) were gel purified and converted to a sequencing library with a commercial small RNA library kit that has minimal ligation bias 17 ., rRNA contaminants were depleted after first strand cDNA synthesis by hybridization to biotinylated oligonucleotides designed to match abundant contaminants detected in pilot experiments ( S1 Table ) ., Approximately 35 million reads were obtained for each “Ribo-seq” replicate , roughly 50% of which aligned to mRNA ( S2 Table ) ., RNA-seq data was generated from RNA extracted from aliquots of each lysate taken prior to addition of RNAse I . Replicate RNA-seq and Ribo-seq assays showed high reproducibility ( Pearson correlation of >0 . 98 , S2 Fig ) ., Almost all plastid genes were represented by at least 100 reads per replicate in all datasets ( S3 Fig ) ., Several clusters of low abundance reads mapped to small unannotated ORFs , but further investigation is required to evaluate which , if any , of these are the footprints of translating ribosomes ., Ribosomes in the cytosol , mitochondria , and chloroplasts have distinct genetic origins ., Accordingly , the ribosome footprints from each compartment displayed different size distributions ( Fig 2A ) ., The cytosolic ribosome footprints showed a minor peak at 23 nucleotides and a major peak at 31 nucleotides , similar to observations in yeast 18 ., The mitochondrial data showed a major peak at 28–29 nucleotides and a minor peak at 36 nucleotides , similar to the 27 and 33-nt peaks reported for human mitochondria 19 ., The plastid ribosome footprints had a broad size distribution suggestive of two populations , with peaks at approximately 30 and 35 nucleotides ., A similar distribution was observed in pilot experiments involving the gel purification of RNAs up to 40-nt ( S1B Fig ) indicating that the peak at 35-nt was not an artifact of our gel purification strategy ., A broad and bimodal size distribution was also observed for chloroplast ribosome footprints from the single-celled alga Chlamydomonas reinhardtii , albeit with peaks at slightly different positions 20 ., The two prior reports of ribosome footprint size distributions in plants 21 , 22 did not parse the data from the three compartments , but the 31-nucleotide modal size reported in those studies is consistent with our data ., Our data show the 3-nucleotide periodicity expected for ribosome footprints ( Fig 2B and 2C ) ., Interestingly , the degree of periodicity varies with footprint size ( S4 Fig ) ., The reads are largely restricted to open reading frames in the cytosol ( Fig 2C ) and chloroplast ( Fig 2D ) ., Taken together , these results provide strong evidence that the vast majority of the Ribo-seq reads come from bona-fide ribosome footprints ., The placement of ribosome P and A sites with respect to ribosome footprint termini has not been reported for any organellar ribosomes or for cytosolic ribosomes in maize ., A meta analysis of our data showed that the position of the 3’ end of ribosome footprints from initiating and terminating ribosomes in chloroplasts and mitochondria is constant with respect to start and stop codons , respectively , regardless of footprint size; however , the position of the 5’ ends varies with footprint size ( Fig 2E , S4C Fig ) ., Therefore , the positions of the A and P sites in organellar ribosomes can be inferred based on the 3’-ends of their footprints , as is also true for bacterial ribosomes 23 , 24 ., The modal distance between the start of the P site in chloroplast ribosomes and the 3’-ends of chloroplast ribosome footprints is 7 nucleotides ., By contrast , cytosolic ribosome footprints are approximately centered on the P site regardless of footprint size ( S4B Fig ) ., The partitioning of ribosome footprints among the three genetic compartments shifts dramatically during the course of leaf development ( Fig 2G ) ., The contribution of cytosolic translation drops from 99% at the leaf base to 57% in the apical leaf sections due to the increasing contribution of ribosome footprints from chloroplasts ., This shift of cellular resources towards chloroplast translation corresponds with the massive increase in the content of photosynthetic complexes harboring plastid-encoded subunits ( Rubisco , PSII , PSI , cytochrome b6f , ATP synthase , NDH ) ( Fig 1 ) ., Ribosome footprints from mitochondria accounted for a very small fraction of the total at all stages ., However , our protocol was not optimized for the quantitative recovery of mitochondrial ribosomes so these data may not reflect the total mitochondrial ribosome population ., In the discussion below we define the “translational output” of a gene as the abundance of ribosome footprints per kb per million reads mapped to nuclear coding sequences ( RPKM ) , and we use this value to compare rates of protein synthesis among genes on a molar basis ., This is a typical interpretation of Ribo-seq data , and it is based on evidence that the bulk rate of translation elongation on all ORFs is similar under any particular condition , despite the fact that ribosome pausing can lead to the over-representation of ribosomes at specific positions 9 , 25 ., Although this may be an over simplification in some instances , this interpretation of our data produced results that are generally coherent with current understanding of chloroplast biogenesis ( see below ) ., Group II introns interrupt eight protein-coding genes in maize chloroplasts ., These present a challenge for data analysis because the unspliced transcripts make up a substantial fraction of the RNA pool 16 and translation can initiate on unspliced RNAs and terminate within introns 15 ., We therefore calculated translational output based solely on the last exon ( normalized to exon length ) ., Data summaries presented below include RNA-seq data only for that subset of intron-containing genes for which multiple methods of analysis provided consistent values for the abundance of spliced RNA isoforms ( see Materials and Methods ) ., Fig 3 summarizes the abundance of Ribo-seq and RNA-seq reads from protein-coding chloroplast genes in each of the four leaf segments ., To display the low values from Segment 1 , they are replotted with a smaller Y-axis scale in S5 Fig . The abundance of mRNA from genes in the same transcription unit ( Fig 3A and S5A Fig , bracketed arrows ) is typically similar , but the protein output of co-transcribed genes varies considerably ., Translational efficiency ( translational output /mRNA abundance ) varies widely among genes ( Fig 3A and S5A Fig , bottom ) ., The atpH mRNA is the most efficiently translated of any chloroplast mRNA at all four developmental stages , surpassing even psbA , whose product is the most rapidly synthesized protein in photosynthetic tissues 26 ., Prodigious psbA expression results from very high mRNA abundance in combination with a translational efficiency that is comparable to that of other photosystem genes ., When the data are grouped according to gene function , correlations between function and translational output become apparent ( Fig 3B ) ., For example , the translational output of genes encoding subunits of ribosomes and the NDH complex are consistently very low , whereas the translational output of genes encoding subunits of PSI , PSII , the ATP synthase , and the cytochrome b6f complex are consistently much higher ., These trends mirror the abundance of these complexes as inferred from proteome data 27 ., The data for complexes whose subunits are not found in a 1:1 ratio show further that translational output is tuned to subunit stoichiometry ., For example , the chloroplast-encoded subunits of the ATP synthase ( AtpA , AtpB , AtpE , AtpF , AtpH , AtpI ) are found in a 3: 3: 1: 1: 14: 1 molar ratio in the complex 28 , 29 ., The translational output of their genes mirrors this stoichiometry quite well , whereas mRNA abundance does not ( Fig 4A ) ., These genes are distributed between two transcription units ( Fig 4A ) ., A single mRNA encodes AtpB and AtpE , whose rates of synthesis are tuned via differences in translational efficiency ., The atpI-atpH-atpF-atpA primary transcript is processed to yield various smaller isoforms 30 but the abundance of RNA from each gene is nonetheless quite similar ( Fig 4A ) ., The translational output of the atpH gene is boosted relative to that of its neighbors primarily through exceptionally high translational efficiency ( Fig 4A bottom ) ., In a second example , the unequal stoichiometry of subunits of the plastid-encoded RNA polymerase ( PEP ) ( 2 RpoA:1 RpoB:1 RpoC1:1 RpoC2 ) 5 is mirrored by the relative translational output of the corresponding genes ( Fig 4B ) ., In this case , however , tuning occurs primarily at the level of mRNA accumulation ., The plastid-encoded subunits of PSI , PSII , the cytochrome b6f complex , the NDH complex , and chloroplast ribosomes are found in equal numbers in their respective complex ., Genes encoding subunits of each of these complexes are distributed across multiple transcription units , many of which also encode subunits of other complexes ., This gene organization sometimes results in considerable disparity in mRNA level among subunits of the same complex ( Fig 3B bottom ) ., In general , such differences are buffered by opposing changes in translational efficiency , such that translational outputs more closely reflect protein stoichiometry than does mRNA abundance ( see , for example , the NDH complex in S6B Fig ) ., In the case of PSI ( Fig 4C ) , the structural genes ( psaA , psaB , psaC , psaJ , psaI ) exhibit an approximately three-fold range of translational output , but all of these genes vastly out produce two genes encoding PSI assembly factors ( ycf3 and ycf4 ) 31–33 ., The psaI and ycf4 genes are adjacent in the same polycistronic transcription unit ( Fig 4C bottom ) , and their difference in translational output is programmed primarily by a difference in translational efficiency ., The translational output of psbN , which encodes a PSII assembly factor 34 , is likewise much less than that of structural genes for PSII ( Fig 4D ) ., Taken together , this body of data shows that the tuning of translational output to protein stoichiometries is accomplished via trade-offs between mRNA level and translational efficiency , with this balance differing from one gene to the next ., Where mRNA abundance closely matches protein stoichiometry , differences in translational efficiency make only a small contribution ( as observed for rpoA , rpoB , rpoC1 and rpoC2 ) ., Where mRNAs are severely out of balance with protein stoichiometry , differences in translational efficiency compensate ., The translational output of PSII structural genes is well matched , with the notable exception of psbA ( Fig 4D ) , whose output vastly exceeds that of other genes in photosynthetic leaf segments ( segments 9 and 14 ) ., This behavior is consistent with the known properties of the psbA gene product , whose damage and rapid turnover during active photosynthesis is compensated by a high rate of synthesis to support PSII repair 26 ., Setting psbA aside , the relative translational outputs of other genes only approximate the stoichiometries of their products: several-fold differences between relative output and stoichiometry are common among subunits of a particular complex , suggesting that proteolysis of unassembled subunits serves to fine-tune protein stoichiometries ., It is also possible that the calculated translational outputs do not perfectly reflect rates of protein synthesis due to differences in translation elongation rates among mRNAs ., That said , instances in which translational outputs are particularly discordant among subunits of the same complex are worthy of note , as this may reflect physiologically relevant behaviors ., For example , the translational output of ndhK is balanced with other ndh genes in non-photosynthetic leaf segments but ndhK substantially out produces the other ndh genes in mature chloroplasts ( S6B Fig ) ., This behavior is reminiscent of psbA , and suggests that NdhK may be damaged and replaced during active photosynthesis ., To explore the dynamics of chloroplast gene expression during the proplastid to chloroplast transition , we calculated standardized values for translational output , mRNA abundance and translational efficiency such that developmental shifts can be compared despite large differences in signal magnitude ., This analysis shows that the developmental dynamics of translational output varies widely among genes ( Fig 5A top ) ., The standardized values were used as the input for hierarchical clustering , which produced four clusters from the translational output data , four from the mRNA data , and five from the translational efficiency data ( Fig 5B , S7 Fig ) ., The genes in each cluster are identified by color in Fig 5A ., Although the transitions between clusters are not marked by obvious distinctions , the distinct trends defining each cluster are clear in the plots in Fig 5B ., Genes whose translational output and mRNA abundance peak early in development ( segment 4 ) generally encode components of the chloroplast gene expression machinery ( rpl , rps , rpo , matK ) ( Fig 5A and 5C ) ., Most genes encoding components of the photosynthetic apparatus ( psb , psa , atp , pet genes ) have peak mRNA and translational output in young chloroplasts ( segment 9 ) ., A handful of photosynthesis genes either maintain or increase translational output and mRNA in mature chloroplasts ( segment 14 ) ( Fig 5A and 5C ) ., There is considerable similarity among the clusters produced from the translational output and mRNA data ( Fig 5A and 5B ) , implying that programmed changes in mRNA abundance underlie the majority of developmental shifts in translational output ., However , changes in translational efficiency also influence the developmental shifts in translational output ( Fig 5A bottom ) ., In general , ORFs encoding proteins involved in photosynthesis are more efficiently translated later in development and those encoding gene expression factors are more efficiently translated early in development , albeit with numerous exceptions ( Fig 5A bottom , 5C right ) ., Transcription units that encode both photosynthesis and gene expression factors provide revealing examples of distinct translational dynamics ., In the psaA-psaB-rps14 transcription unit , for example , rps14 is found in a translational output cluster with other genes involved in gene expression , whereas psaA and psaB reside in a translational output cluster with other photosynthesis genes ( Fig 5A top ) ., This results from distinct developmental shifts in translational efficiency: the rps14 ORF is translated more efficiently early in development whereas psaA and psaB are more efficiently translated later in development ( Fig 5D ) ., The psaI-ycf4-cemA-petA transcription unit provides a second example ., The translational output of psaI , cemA , and petA show similar developmental dynamics , but ycf4 clusters with different genes due to more efficient translation earlier in development ( Fig 5D ) ., Again , these distinct patterns correlate with function , as psaI and petA encode components of the photosynthetic apparatus , whereas ycf4 encodes an assembly factor for PSI 31 , 32 ., Many polycistronic RNAs in chloroplasts are processed to smaller isoforms ., Although the impact of processing on translational efficiencies remains unclear 35 , 36 , it is plausible that programmed changes in the accumulation of processed isoforms could uncouple the expression of cotranscribed genes during development ., To address this possibility , we used RNA gel blot hybridization to analyze transcripts from two transcription units that include genes whose translational efficiencies exhibit distinct developmental dynamics: psaI-ycf4-cemA-petA and psaA-psaB-rps14 transcription units ( S8 Fig ) ., Processed rps14-specific transcripts accumulate preferentially in immature chloroplasts ( segment 4 ) , correlating with the stage at which rps14 is most efficiently translated ., Analogously , a monocistronic psaI isoform accumulates preferentially in segments 4 and 9 where psaI is most efficiently translated ., Various cause and effect relationships may underlie these correlations , as is discussed below ., In maize and other C4 plants , photosynthesis is partitioned between mesophyll ( M ) and bundle sheath ( BS ) cells ., Three protein complexes that include plastid-encoded subunits accumulate differentially in the two cell types: Rubisco and the NDH complex are enriched in BS cells whereas PSII is enriched in M cells 2 , 14 ., Differential accumulation of several chloroplast mRNAs in the two cell types has been reported 37–41 , but a comprehensive comparison of chloroplast gene expression in BS and M cells has been lacking ., To address this issue we performed RNA-seq and Ribo-seq analyses of BS- and M- enriched leaf fractions ., The translational output of genes encoding subunits of Rubisco , PSII , and the NDH complex ( Fig 6A ) correlated well with the relative abundance of subunits of these complexes in the same sample preparations ( Fig 1C ) , and with quantitative proteome data 2 ., Cell-type specific differences in mRNA accumulation ( Fig 6B ) can account for many of the differences in translational output ( Fig 6A ) , indicating that differences in transcription and/or RNA stability make a strong contribution to preferential gene expression in one cell type or the other ., However , the data suggest that differences in translational efficiency contribute in certain instances ( Fig 6C ) ., Four genes encoding PSII core subunits ( psbA , psbB , psbC , psbD ) provide the most compelling examples , as their translational output is considerably more biased toward M cells than are their mRNA levels ., Organellar RNAs in land plants are often modified by an editing process that converts specific cytidine residues to uridine 42 , 43 ., Some sites are inefficiently edited , which raises the question of whether the translation machinery discriminates between edited and unedited RNAs ., The protein products of several unedited mitochondrial RNAs have been detected in plants 44 , 45 ., We used our Ribo-seq and RNA-seq data to examine this issue for chloroplast RNAs ., Fig 7 summarizes the data for those sites of editing that are represented by at least 100 reads in both the Ribo-seq and RNA-seq data in at least two replicates ( 17 of the 28 edited sites in the maize chloroplast transcriptome ) ., In general , the percent editing was similar in the RNA-seq and Ribo-seq data , implying little discrimination between edited and unedited RNAs by the translation machinery ., There were , however , two major exceptions: rpl2 ( nt 2 ) and ndhA ( nt 563 ) ., In these cases a large fraction of the RNA-seq reads came from unedited RNA , whereas virtually all of the Ribo-seq reads came from edited sites ., These two sites have unusual features that can account for the preferential translation of the edited RNAs ., Editing at the ndhA site is linked to the splicing of the group II intron in the ndhA pre-mRNA: the site is not edited in unspliced transcripts and it is fully edited in spliced transcripts 46–48 ., Failure to edit unspliced RNA is presumably due to the position of the intron between the edited site and the cis-element that specifies it ., Translation that initiates on unspliced ndhA RNA would terminate at an in-frame stop codon within the intron ., Thus , exon 2 is translated only from spliced RNAs , and these are 100% edited ., In the case of rpl2 , the editing event creates an AUG start codon from an ACG precursor; this is the only editing event in maize chloroplasts that creates a canonical start codon ., Although it has been reported that ACG can function as a start codon in chloroplasts 49 , 50 , our data show that this particular ACG is strongly discriminated against by initiating ribosomes ., The fact that the Ribo-seq data show the expected strong bias toward edited rpl2 and ndhA ( 563 ) instills confidence that valid conclusions can be made from our data for other edited sites ., Approximately 40% of the petB and ndhA ( nt 50 ) sequences are unedited in both the RNA-seq and Ribo-seq data , indicating that these unedited sequences give rise to a considerable fraction of the translational output of the corresponding genes ., Editing of the petB site is essential for the function of its gene product ( cytochrome b6 ) 51 ., It seems likely that the product of this unedited RNA is either unstable or selected against during complex assembly , as has also been suggested for the products of two unedited transcripts in mitochondria 52 , 53 ., The remaining sites show almost complete editing in the RNA-seq data and , as expected , in the Ribo-seq data as well ., That said , there is an overall trend toward less representation of unedited sequences in the Ribo-seq data than in the RNA-seq data ., This may simply be a kinetic effect as would be expected if ribosome binding is slow in comparison to editing , such that ribosomes generally translate older ( and therefore more highly edited ) mRNAs ., Ribosome profiling data from bacteria revealed a striking correspondence between the stoichiometry of subunits of multisubunit complexes and their relative rates of synthesis 9 , 10 ., Our results show that the relative translational outputs of chloroplast genes likewise approximate the relative abundance of the gene products ., This tuning is apparent when comparing sets of genes encoding different complexes ( e . g . compare genes encoding the low abundance NDH complex to genes encoding the highly abundant PSI and PSII complexes ) ( Fig 3B ) , and when comparing genes encoding subunits of the same complex ( e . g . the PEP RNA polymerase and the ATP synthase ) ( Fig 4A and 4B ) ., Our calculations of translational output rest on the assumption that the rate of translation elongation on all mRNAs is similar under any particular condition ., This same assumption produced remarkable concordance between protein stoichiometry and inferred translational output in bacteria 9 , 10 ., Although our results show a clear trend toward “proportional synthesis” , they also suggest that the tuning of protein output to stoichiometry is less precise in chloroplasts than it is in bacteria ., Subunits of photosynthetic complexes are subject to proteolysis when their assembly is disrupted 55 , and a similar ( albeit wasteful ) mechanism could contribute to balancing stoichiometries when proteins are synthesized in excess under normal conditions ., That said , instances in which inferred translational outputs are particularly incongruent with protein stoichiometries may reflect physiologically informative behaviors ., The most prominent examples of “over-produced” proteins in our data are PsbA and PsbJ in PSII , PsaC and PsaJ in PSI , NdhK in the NDH complex , Rps14 in ribosomes , and PetD , PetL and PetN in the cytochrome b6f complex ( Fig 4 and S6 Fig ) ., Disproportionate synthesis of PsbA is well known , and compensates for its damage and proteolysis during photosynthesis 26 ., The other proteins suggested by our data to be produced in excess may likewise be subject to more rapid turnover than their partners in the assembled complex ., A proteomic study in barley demonstrated that subunits of each photosynthetic complex generally turn over at similar rates 56 , but data for these particular proteins were not reported ., Interestingly , the inferred rates of synthesis of PsbA , PetD , and NdhK are well matched to those of their partner subunits early in development , but outpace those of their partners in mature chloroplasts ( Fig 4D , S6 Fig ) ., This feature of psbA expression coincides with the need to replace its gene product , D1 , following photo-induced damage and proteolysis 26 ., By extension , the developmental dynamics of petD and ndhK expression suggest that their gene products may turn over more rapidly than their partners as a consequence of photosynthetic activity ., In bacteria , proportional synthesis of subunits within a complex is achieved largely through the tuning of translational efficiencies among ORFs on the same mRNA 9 , 10 ., In chloroplasts , genes encoding subunits of the same complex are generally distributed among multiple transcription units 4 and RNA segments within a transcription unit often accumulate to different levels 8 ., It is interesting to consider how this shift in the gene expression landscape is reflected in the mechanisms that balance protein output among genes ., In the case of the four genes encoding the PEP RNA polymerase , relative translational outputs closely match the 2:1:1:1 protein stoichiometry , and this is programmed primarily at the level of mRNA abundance ( Fig 4B ) ., By contrast , widely varying translational efficiencies are superimposed on small variations in mRNA abundance to tune translational output to protein stoichiometry in the ATP synthase complex ( Fig 4A ) ., Genes for ribosomal proteins are distributed among ten transcription units , several of which also encode proteins involved in photosynthesis ( see Fig 3A ) ., For example , rps14 is cotranscribed with genes encoding the reaction center proteins of PSI ( psaA/psaB ) , and translational outputs within this transcription unit are balanced by large differences in translational efficiency ( Fig 4C ) ., Similarly , the psaI tr | Introduction, Results, Discussion, Materials and Methods | Chloroplast genomes in land plants contain approximately 100 genes , the majority of which reside in polycistronic transcription units derived from cyanobacterial operons ., The expression of chloroplast genes is integrated into developmental programs underlying the differentiation of photosynthetic cells from non-photosynthetic progenitors ., In C4 plants , the partitioning of photosynthesis between two cell types , bundle sheath and mesophyll , adds an additional layer of complexity ., We used ribosome profiling and RNA-seq to generate a comprehensive description of chloroplast gene expression at four stages of chloroplast differentiation , as displayed along the maize seedling leaf blade ., The rate of protein output of most genes increases early in development and declines once the photosynthetic apparatus is mature ., The developmental dynamics of protein output fall into several patterns ., Programmed changes in mRNA abundance make a strong contribution to the developmental shifts in protein output , but output is further adjusted by changes in translational efficiency ., RNAs with prioritized translation early in development are largely involved in chloroplast gene expression , whereas those with prioritized translation in photosynthetic tissues are generally involved in photosynthesis ., Differential gene expression in bundle sheath and mesophyll chloroplasts results primarily from differences in mRNA abundance , but differences in translational efficiency amplify mRNA-level effects in some instances ., In most cases , rates of protein output approximate steady-state protein stoichiometries , implying a limited role for proteolysis in eliminating unassembled or damaged proteins under non-stress conditions ., Tuned protein output results from gene-specific trade-offs between translational efficiency and mRNA abundance , both of which span a large dynamic range ., Analysis of ribosome footprints at sites of RNA editing showed that the chloroplast translation machinery does not generally discriminate between edited and unedited RNAs ., However , editing of ACG to AUG at the rpl2 start codon is essential for translation initiation , demonstrating that ACG does not serve as a start codon in maize chloroplasts . | Chloroplasts are subcellular organelles in plants and algae that carry out the core reactions of photosynthesis ., Chloroplasts originated as cyanobacterial endosymbionts ., Subsequent coevolution with their eukaryotic host resulted in a massive transfer of genes to the nuclear genome , the acquisition of new gene expression mechanisms , and the integration of chloroplast functions into host programs ., Chloroplasts in multicellular plants develop from non-photosynthetic proplastids , a process that involves a prodigious increase in the expression of chloroplast genes encoding components of the photosynthetic apparatus ., We used RNA sequencing and ribosome profiling to generate a comprehensive description of the dynamics of chloroplast gene expression during the transformation of proplastids into the distinct chloroplast types found in bundle sheath and mesophyll cells in maize ., Genes encoding proteins that make up the chloroplast gene expression machinery peak in protein output earlier in development than do those encoding proteins that function in photosynthesis ., Programmed changes in translational efficiencies superimpose on changes in mRNA abundance to shift the balance of protein output as chloroplast development proceeds ., We also mined the data to gain insight into general features of chloroplast gene expression , such as relative translational efficiencies , the impact of RNA editing on translation , and the identification of rate limiting steps in gene expression ., The findings clarify the parameters that dictate the abundance of chloroplast gene products and revealed unanticipated phenomena to be addressed in future studies . | plant cell biology, messenger rna, chloroplasts, dna transcription, plant science, molecular biology techniques, photosynthesis, cellular structures and organelles, genetic footprinting, research and analysis methods, gene expression, molecular biology, ribosomes, biochemistry, rna, genetic fingerprinting and footprinting, plant biochemistry, plant cells, cell biology, nucleic acids, protein translation, genetics, biology and life sciences, cellular types | null |
journal.pgen.1003099 | 2,012 | A Yeast GSK-3 Kinase Mck1 Promotes Cdc6 Degradation to Inhibit DNA Re-Replication | To constitute the pre-RC and initiate DNA replication , all six-components of the Origin Recognition Complex ( Orc1-6p ) bind to replication origins followed by Cdc6p , Cdt1p and the Mcm2-7p complex 1 ., Then the pre-RC has to be activated by the Dbf kinase-Cdc7p complex , resulting in the formation of a bidirectional replication fork in which the Mcm complex acts as a replicative helicase 1 ., Finally , DNA polymerase synthesizes new strands of DNA ., The cell cycle progression is driven by the Cyclin/CDK complex ., Of the nine cyclins in S . cerevisiae six are B-type cyclins ( Clb1-6 ) 2 and there is a single CDK ( Cdc28 ) ., Cdc28-Clb activity is required to initiate DNA replication 3–5 ., Eukaryotes ensure that DNA is replicated once and only once per cell cycle ., There are multiple overlapping mechanisms to prevent re-initiation of DNA replication ., Pre-RC components such as Cdc6 , Mcm2–7 , and the ORC complex are phosphorylated by Cyclin/CDK to prevent a second round of DNA replication from occurring before mitosis ., Cdc6 is phosphorylated by Cyclin/CDK complex at the N-terminal region and is targeted for ubiquitin-mediated proteolysis in S . cerevisiae 6–8 ., The MCM complex is translocated to the cytoplasm after phosphorylation by Cdk activity 9 , 10 ., Orc2 and Orc6 are also phosphorylated in a CDK-dependent manner 11 , 12 ., In addition to these mechanisms , a direct recruitment of the cyclin-CDK complex Clb5p-Cdc28p to the origin of replication is an important component of re-replication control 13 ., The Clb5p recruitment to the origin is accomplished by binding of the Clb5p hydrophobic patch substrate-targeting domain 14–16 to an Arg-X-Leu ( RXL ) target sequence in the Orc6p subunit of the ORC origin recognition complex 13 ., This Clb5 binding to Orc6 after origin licensing serves as a local switch to inhibit DNA re-replication by preventing Cdt1/Mcm2–7 loading onto the origin 17 ., The ORC6-rxl mutation strongly synergized with other mutations previously implicated in re-replication control including: N-terminal deletions in Cdc6 which stabilize the protein ( CDC6ΔNT ) 13 , mutations which force nuclear localization of the Mcm complex ( MCM7-NLS ) 11 , and mutations blocking Orc2 ( ORC2-ps ) and Orc6 phosphorylation ( ORC6-ps ) 18 ., Such multiple mutant strains strongly over-replicate DNA within a single cell cycle 13 ., ORC6-rxl GAL-CDC6ΔNT cells are viable , but show moderate DNA re-replication when incubated in galactose 19 ., The cell cycle in the ORC6-rxl GAL-CDC6ΔNT cells arrest at G2/M phase due to DNA damage checkpoint activation 19 ., Moderate cell viability in the ORC6-rxl GAL-CDC6ΔNT cells was heavily dependent on DNA damage checkpoint components such as MRE11 gene ., Cell viability was reduced and DNA re-replication was enhanced in mre11 ORC6-rxl GAL-CDC6ΔNT cells 19 ., It is known that Rad53 is phosphorylated upon DNA damage checkpoint activation ., Rad53 was hyperphosphorylated in ORC6-rxl GAL-CDC6ΔNT cells 19 , suggesting that DNA damage was induced ., We concluded that DNA re-replication most likely causes double strand breaks which in turn activates the DNA damage checkpoint response 19 ., To identify a new component that inhibits DNA re-replication in S . cerevisiae , synthetic genetic array ( SGA analysis ) 20 was performed using an ORC6-rxl strain to eliminate Clb5-Orc6 binding ., We found that mck1 deletion cells combined with the ORC6-rxl mutation showed synthetic lethality ., The MCK1 gene in S . cerevisiae encodes a serine/threonine protein kinase homologous to mammalian glycogen synthase kinase-3 ( GSK-3 ) 21 ., Mammalian GSK-3 was initially identified as an enzyme involved in the control of glycogen metabolism 22 ., GSK-3 kinase is highly conserved through evolution and plays an important role in the Wnt signaling pathway in the mammalian system ( for a review , see 23 ) ., One of the interesting features of GSK-3 kinase is its role in protein degradation ., GSK-3 phosphorylates cyclin D1 to promote its nuclear export and subsequent degradation in the mammalian system 24 ., Yeast Mck1p has diverse biological functions ., Mck1p stimulates calcineurin signaling 25–27 and binds stress-response elements to activate transcription 27 therefore cells lacking Mck1p are hot and cold sensitive 28 ., Mck1 is also implicated in mitosis and meiosis ., Yeast MCK1 has been isolated as a dosage suppressor of centromere ( CEN ) DNA mutation in CDEIII , suggesting that Mck1 has a role in centromere/kinetochore function 28 ., The mck1 mutant exhibits poor sporulation 29 , and sensitivity to benomyl , a microtubule destabilizing drug 28 ., Cdc6 levels are regulated by three distinct mechanisms: transcription 30 , ubiquitin-mediated proteolysis 7 , 8 , 31 , 32 and nuclear localization 33 ., Here we show that Mck1p has a novel function in inhibition of DNA re-replication by Cdc6p degradation through the GSK-3 consensus site at T368 ., Synthetic genetic array ( SGA analysis ) 20 was performed using ORC6-rxl , to eliminate Clb5-Orc6 binding , in order to identify a new component in the regulation of DNA re-replication in S . cerevisiae ., We found that mck1 deletion cells showed synthetic lethality in cells containing the ORC6-rxl mutation ., It is interesting that mck1 was the only deletion strain that caused synthetic lethality in the ORC6-rxl cells among 4700 deletion strains tested , and that we did not obtain other GSK-3 orthologs in this screening ., Tetrad analysis confirmed the genetic interaction between ORC6-rxl and mck1 deletion strains ( Figure 1A ) ., Haploid progenies , which contain both ORC6-rxl and Δmck1 mutations , were not able to grow on YEPD plates whereas single mutants grew fine ., We also tested if the mck1 deletion genetically interacts with the other orc mutants such as the Orc6 phosphorylation site mutant ( ORC6-ps ) and the Orc2 phosphorylation site mutant ( ORC2-ps ) ., Deletion of MCK1 reduced cell growth in the ORC6-ps cells ( Figure 1A ) ., Furthermore , the mck1 deletion caused severe growth defects in the ORC2-ps cells ( Figure 1A ) ., Thus , mck1 deletion caused synthetic lethality or semi-lethality with DNA re-replication-prone orc mutants in general ., This strongly suggests that Mck1p has a function in DNA replication control ., The mck1 deletion strain did not have genetic interactions with other pre-RC mutants such as MCM7-NLS or CDC6ΔNT ( data not shown ) ., To investigate the molecular basis of the synthetic lethality between Δmck1 and ORC6-rxl , we generated partial loss of function mutants of mck1 by PCR mutagenesis ., Among them , mck1-16 allele exhibited semi-synthetic lethality at high temperature ( 36 degrees ) when combined with ORC6-rxl mutation ( Figure 1B ) ., Consistent with this effect being due to the disruption of Clb5-Orc6 protein interaction by the ORC6-rxl mutation , the clb5 mck1-16 cells were also semi-lethal when incubated at 36 degrees ( Figure 1B ) ., To analyze the terminal phenotype of the mck1-16 ORC6-rxl strain , cells were incubated either at permissive or non-permissive temperatures and cell cycle profiles were analyzed by flow cytometry analysis ., The mck1-16 ORC6-rxl cells showed G2/M arrest after 4 hours incubation at 36 degrees ( Figure 1C , top right ) , with some cells showing a DNA content over 2C ( Figure 1C , arrow ) , suggesting re-replicated DNA ., Cell morphologies of the mck1-16 ORC6-rxl mutants were further analyzed ., The mck1-16 ORC6-rxl cells incubated at 36 degrees for 4 hours showed large budded cells with a single nuclei visualized by propidium iodide staining of DNA ( Figure 1D ) ., This phenotype is reminiscent of cells with DNA re-replication found in our previous report 19 ., Nuclear division did not occur in the mck1-16 ORC6-rxl cells ., Their cell cycle is arrested during G2 or early mitosis , most likely due to DNA damage checkpoint activated by DNA re-replication ., This is similar to our previous observation that mitotic arrest in the ORC6-rxl CDC6ΔNT cells was due to DNA damage 19 ., Previously we have shown that the ORC6-rxl mutant causes semi-synthetic lethality with a CDC6ΔNT mutant ., The ORC6-rxl CDC6ΔNT cells are arrested during mitosis with moderate DNA re-replication followed by DNA damage ., Viability of the ORC6-rxl CDC6ΔNT cells was heavily dependent on an intact DNA damage checkpoint gene such as MRE11 , a component of the MRX complex 19 ., Rad53 , a transducer kinase required for DNA damage checkpoint activation , was hyperphosphorylated in the ORC6-rxl CDC6ΔNT cells ., To directly test if DNA damage checkpoint is activated in the mck-16 ORC6-rxl cells , Rad53 phosphorylation status was analyzed by Western blotting ., Rad53 was only hyperphosphorylated in the mck-16 ORC6-rxl cells when incubated at 37 degrees ( Figure 2A ) ., We tested if the viability of the mck1-16 ORC6-rxl mutant also relies on DNA damage checkpoint ., We found that cell viability of the mck-16 ORC6-rxl cells even at the permissive temperature ( 30 degrees ) required MRE11 ( Figure 2B ) ., Next , the cell cycle profile of the mre11 mck-16 ORC6-rxl cells was examined ., DNA re-replication was greatly enhanced in the mre11 mck-16 ORC6-rxl cells at the non-permissive temperature , indicating that DNA damage checkpoint activation limits DNA re-replication in the mck-16 ORC6-rxl cells ( Figure 2C ) ., Above all , we conclude that an induction of DNA re-replication in the mck-16 ORC6-rxl cells triggered DNA damage leading to cell cycle arrest by DNA damage checkpoint activation ., Several parallel and partially overlapped molecular mechanisms ensure that cells do not re-initiate DNA replication at origins that have already fired ., We have previously shown that ORC6-rxl CDC6ΔNT cells are mitotic arrested without extensive DNA re-replication 19 ., However , multiple mutant strains such as ORC6-rxl , ps CDC6ΔNT MCM7-NLS ORC2-ps strongly over-replicate DNA within a single cell cycle 13 ., We tested if mck1 deletion also synergizes with other pre-RC mutations ., An addition of either MCM7-NLS or ORC2-ps mutation to the ORC6-rxl mck1-16 did not enhance lethality ( Figure 3A ) ., However , cells containing ORC6-rxl , ps mck1-16 MCM7-NLS and ORC2-ps mutations showed stronger lethality ( Figure 3A ) ., Flow cytometry analysis showed that DNA re-replication was enhanced in the ORC6-rxl , ps mck1-16 MCM7-NLS ORC2-ps mutant after 4 hours incubation at the non-permissive temperature ( Figure 3B , bottom right ) ., ORC6-rxl , ps MCM7-NLS ORC2-ps cells with wild type MCK1 grew normally and did not induce significant re-replication ( Figure 3A and 3B bottom left ) ., These results show that Mck1p contributes to the inhibition of DNA re-replication and suggest that the mechanism involved is likely to be distinct from the known mechanisms acting at the level of ORC and MCM proteins ., The semi-lethal phenotype of ORC6-rxl Δmck1 cells ( Figure 1D ) was reminiscent of ORC6-rxl CDC6ΔNT cells 13 ., Moreover , the deletion of MCK1 interacted genetically with ORC6-rxl ( Figure 1A ) but not CDC6ΔNT ( data not shown ) ., These observations led us to hypothesize that Mck1p could function in DNA replication control by regulating Cdc6 ., To further test this model , we examined if mck1 deletion behaved similarly to CDC6ΔNT in its interactions with mutations in the cyclin genes ., CDC6ΔNT genetically interacts with the clb5 deletion mutant , but not with other B-type cyclins 34 ., We also tested if mck1 deletion cells genetically interact with other cyclin mutants in a similar way that CDC6ΔNT does ., Table 1 summarizes the genetic interaction between mck1 and cyclin mutants ., The mck1 deletion cells were semi-lethal in the ORC6-rxl mutant cells and also showed synthetic lethality with clb5 deletion cells because ORC6-rxl is a binding mutant for Clb5p ., However , the mck1 deletion cells did not cause synthetic lethality with other B-type cyclin mutants such as clb1 , 2 , 3 , 4 or 6 ( Table 1 ) ., Therefore , mck1 deletion genetically interacts specifically with clb5 deletion ., It has been shown that Clb5p binds to Orc6p through the Clb5p hydrophobic patch substrate-targeting domain 14 ., We tested if clb5-hpm ( Clb5 hydrophobic patch mutant ) causes synthetic lethality with Δmck1 cells and found that there was a genetic interaction between clb5-hpm and Δmck1 ( Table 1 ) ., Moreover neither mck1 nor CDC6ΔNT caused lethality in clb5pCLB2 , a mutant in which Clb2 is controlled under Clb5 promoter ., Thus , we conclude that the Δmck1 cells require Clb5p-Orc6p protein binding for their survival ., We also found that deletion of CLB6 rescues Δmck1 Δclb5 semi-lethality ., We have previously shown that lethality in clb5 CDC6ΔNT cells can be rescued by the deletion of CLB6 34 and proposed the idea that the S-phase cyclin Clb6 initiates DNA replication , but fails to inhibit DNA re-replication ., Therefore , the DNA re-replication phenotype is suppressed if CLB6 is deleted by the reduction of initiation of DNA replication ., Mitotic cyclins regulate DNA replication in the clb5 clb6 ORC6-rxl cells ., We speculate that deletion of CLB6 rescues Δmck1 Δclb5 cells in the same manner ., From these results we conclude that the mck1 deletions genetically interacted with cyclin mutants in a way similar to that of stabilized CDC6ΔNT , reinforcing a model in which Mck1p acts in the same pathway as Cdc6p ., Because lack of Mck1p and stabilization of Cdc6p ( Cdc6ΔNT ) exhibited similar genetic interaction with DNA re-replication mutants , we speculated that Mck1p could control the stability of Cdc6p ., To test this possibility , the Cdc6 protein ( Cdc6-HA ) expressed under inducible GAL1 promoter in mitotically arrested cells was examined in wild type or Δmck1 backgrounds ., We found that the Cdc6 protein level was sustained at a higher level during mitosis in the mck1 deletion cells than in wild type cells even after Cdc6 expression was shut off by glucose ( Figure 4A ) ., It is important to mention that CDC6 was expressed under the GAL1 promoter , excluding possible involvement of CDC6 transcription by Mck1 in this experiment ., To test if Mck1 regulates Cdc6p post-translational levels , endogenous Cdc6 synthesis was blocked by cycloheximide ., In the mitotically arrested wild type cells , Cdc6 protein was rapidly depleted by addition of cycloheximide ( Figure S1 ) ., In the mitotic mck1 deletion cells , the cdc6 protein level was high and remained stable after cycloheximide , excluding the possibility that Mck1p regulates Cdc6p by translation ., These results strongly suggest that Mck1p controls Cdc6 protein levels by affecting degradation rates ., To further explore the possible involvement of Mck1p in Cdc6p degradation , Protein A-tagged Cdc6 protein integrated at the genome locus was examined in the wild type or mck1 deletion cells by Western blotting throughout a single cell cycle progression ., We noticed a dramatic accumulation of Cdc6 protein in the mck1 deletion cells ( Figure 4B ) ., In wild type cells , Cdc6p was expressed transiently during G1 phase , 10 minutes after alpha-factor release , and suppressed throughout S-phase ., Then Cdc6p was expressed again for a short time during mitosis , 70 minutes after alpha-factor release ( Figure 4B , upper panel ) ., This is consistent with a previous report by Drury et al 32 ., While in the mck1 deletion cells , Cdc6p was not expressed during alpha-factor arrest but was expressed 10 min after alpha-factor release and continued to accumulate during S-phase and mitosis ( Figure 4B , lower panel ) ., The increase in Cdc6 protein level is unlikely to be due to an alteration in the cell cycle progression of Δmck1 cells because the kinetics of the cell cycle progression was similar in these two strains as judged by budding index ( Figure 4B ) ., To confirm that Cdc6p is stabilized during mitosis in the mck1 deletion strain , CDC6-ProteinA or mck1 CDC6-ProteinA strains were arrested in mitosis by nocodazole and were synchronously released into the cell cycle by washing ., A small amount of Cdc6p was detectable at time zero in nocodazole arrested wild type cells ( Figure 4D , left ) ., This amount was transiently increased 10–20 minutes after release ., This is consistent with a previous report that Cdc6 protein is expressed in late mitosis and degraded after the G1/S transition 7 ., In contrast , Cdc6p was stabilized throughout mitotic progression in the mck1 deletion cells ( Figure 4D , right ) ., To further confirm if Cdc6 is stabilized in the mck1 deletion cells , we visualized Cdc6p localization in vivo ., We introduced a GFP-tag into the C-terminus of the chromosomal copy of the CDC6 gene to allow endogenous expression ., The CDC6-GFP fusion appears to be fully functional as a CDC6-GFP strain and did not show any growth defect in any of the conditions tested ( data not shown ) ., Consistent with previously published localization patterns of overexpression , Cdc6-GFP 33 , 35 protein localized and accumulated in the nucleus in late mitotic cells ( large budded cells with divided nuclei ) or in unbudded G1 cells ( Figure 4C ) ., The Cdc6-GFP signal was undetectable in the cells with small to large buds , confirming tight regulation of Cdc6 abundance by rapid degradation after S-phase onset ., In sharp contrast , Cdc6-GFP was constitutively found in the nucleus throughout the cell cycle in mck1 deletion cells ( Figure 4C ) ., This localization analysis was consistent with Western blot results that Cdc6p is stabilized in mck1 deletion cells during S-phase and mitosis , as shown in Figure 4B and 4D ., We also tested if overexpression of Mck1 promotes rapid Cdc6p degradation ., Exogenously expressed Mck1p under the GALL promoter significantly reduced Cdc6p protein levels 10 minutes after the addition of galactose ( Figure 5A , top right ) ., This result supports the idea that Mck1p promotes Cdc6p degradation ., We next examined if Mck1-mediated Cdc6 degradation is due to SCFCDC4 ubiquitin ligase ., When cdc4-1 CDC6-prA mck1 GALL-MCK1 strain was incubated at 26 degrees , Cdc6p was rapidly degraded followed by galactose addition ( Figure 5B ) ., This is consistent with results in Figure 5A ., When Cdc4 was inactivated at 36 degrees , Cdc6 became stable and was not degraded even after Mck1 overexperssion ( Figure 5B ) ., This result suggests that Mck1p phosphorylates Cdc6p to be subsequently recognized by SCFCDC4 complex for degradation ., GSK-3 kinases phosphorylate the first serine or threonine residues in the consensus site followed by a phospho-serine or phospho-threonine at the position +4 S/T-XXX-pS/T 36 ., There are two potential GSK-3 consensus phosphorylation sites in Cdc6p , TPESS ( 39–43 ) and TPTTS ( 368–372 ) ( Figure 6A ) ., To test if Mck1p binds Cdc6p at the GSK-3 consensus sites , we performed a yeast two-hybrid assay ., We examined whether Mck1p , fused with Gal4 activation domain ( GAD ) , interacts with various truncated CDC6 mutants fused to the LexA DNA binding domain ., Mck1p interacted with the C-terminal region of Cdc6p ( aa341–390 ) and not with the N-terminus ( aa 1–47 ) ( Figure 6B ) ., The mutation at T368M or S372A abolished two-hybrid interaction between Mck1p-Cdc6p indicating that Mck1p targets Cdc6p through the GSK consensus site at 368–372 ( Figure 6B ) ., The physical interaction between Mck1p and Cdc6p was also confirmed by co-immunoprecipitation ( Co-IP ) assay using the MCK1-MYC GAL-CDC6ΔNT-HA strain ., Mck1p interacted with Cdc6ΔNTp , indicating that Mck1p interacts with Cdc6p , and the protein interaction was mediated through the C-terminal region in Cdc6p ( Figure 6C ) ., The protein binding between Mck1p and Cdc6p was observed only in mitotic arrested cells blocked by nocodazole and not in asynchronous culture or G1-arrested cells ( data not shown ) ., Therefore the physical interaction between Mck1p and Cdc6p is likely primed by mitotic CDK phosphorylation of the S372 site ( see next section ) ., We also noticed that Cdc6ΔNT migrates slower in the co-IP samples than the input , consistent with the idea that only the phosphorylated form of Cdc6 , probably targeted by CDK , binds to Mck1 ( Figure 6C ) ., A GSK-3 kinase usually requires priming 36 ., In Cdc6 , the predicted priming site is located at S372 based on the amino acid sequence ., After priming , the GSK-3 kinase phosphorylates the target site at the first serine or threonine that corresponds to T368 ( see discussion ) ., Next , we tested to see if mutations at the GSK-3 consensus phosphorylation site in CDC6 cause lethality in orc mutants like the mck1 deletion does ., To prove that the C-terminus GSK-3 consensus site 368–372 in CDC6 was involved in the inhibition of DNA re-replication , the potential phosphorylation site ( T368 ) and the priming phosphorylation site ( S372 ) were altered to alanine ., The CDC6-T368A S372A in a 2 micron plasmid was transformed into wild type , ORC6-rxl , ORC6-ps or ORC6-rxl , ps mutants ., Colonies formed when either CDC6 wild type or CDC6 T368A S372A plasmids were transformed into the ORC6-wild type strain ( Figure 7B , top left ) ., In contrast , the CDC6 T368A S372A plasmid ( but not CDC6-wt ) was toxic in the ORC6-rxl cells , as transformants gave very few visible colonies ( Figure 7B , top right ) ., This effect was even more pronounced in ORC6-rxl , ps cells and , in this case , even the CDC6-wt plasmid appeared somewhat toxic ( Figure 7B , bottom right ) ., The CDC6-T368A S372A plasmid did not induce toxicity in the ORC6-ps cells ( Figure 7B , bottom left ) which confirmed the result that mck1 did not genetically interact with ORC6-ps mutation ( Figure 1 ) ., The plasmid harboring CDC6-T368A or CDC6-S372A single mutation was also toxic in the ORC6-rxl strain ( Figure S2 ) ., These results suggest that the interaction of Cdc6p with Mck1p and/or its phosphorylation by Mck1p contributes to the down-regulation of Cdc6p levels ., To confirm that Cdc6p is phosphorylated by Mck1 in vivo , we analyzed the Mck1-dependent mobility shift of Cdc6p in the cdc4-1 mutant background by western blot ., We used cdc4-1 mutant to prevent degradation of phosphorylated Cdc6 and examined the effect of Mck1 on the phosphorylation status of Cdc6p ., Cdc6p in the wild type cells migrated slower that that in the Δmck1 deletion cells indicating that Cdc6p is hyper-phosphorylated in wild type cells ., ( Figure 7C ) ., In the mck1 deletion cells , the signal of the higher molecular weight band was abrogated and the lower band was abundant suggesting that Cdc6p is less phosphorylated and more stable ( Figure 7C right ) ., To confirm that the slow migrating band of Cdc6p in the wild type cells is due to phosphorylation , protein extracts from wild type cells were treated with CIP ( calf intestine phosphatase ) ., After the CIP treatment , the slower migrating band of Cdc6p disappeared and the faster-migrating band was observed at the same level as that in Δmck1 cells ., It suggests that the band shift between wild type and Δmck1 is due to phosphorylation ( Figure 7C and 7D ) ., Finally we tested if Mck1p dependent destabilization of the Cdc6p is mediated by the T368 residue ., The mck1 GALL-MCK1 CDC6-proteinA CDC6T368A strain contains both wild type Cdc6 ( tagged with protein A ) and Cdc6T368A ( no tag ) ., First the cells were arrested in mitosis with nocodazole and then released into galactose to overexpress Mck1p ., Wild type Cdc6p was degraded rapidly after Mck1p overexpression , which is consistent with previous results in Figure 5A ( Figure 7E , upper panel ) ., In contrast , Cdc6T368A protein was resistant to degradation and was stable even after Mck1p overexpression ( Figure 7E , lower panel ) ., We also observed faster migration of Cdc6T368A protein than the wild type Cdc6p by western blot ( Figure S3 ) ., We conclude that Cdc6p is phosphorylated at T368 by Mck1p to induce its degradation ., In this study , we show that a GSK-3-like kinase , Mck1p , is involved in the inhibition of DNA re-replication through its role in Cdc6p turnover in S . cerevisiae ., There are 8 CDK consensus sites in CDC6 ., The first 47 amino acids at the N-terminus of Cdc6 are targeted by Cyclin/CDK and are critical for SCFcdc4 dependent proteolysis 7 ., Stabilization of Cdc6p in mck1 deletion cells suggests that CDK-dependent phosphorylation at the N-terminus of Cdc6 is not sufficient enough for CDC6p degradation in vivo , that Mck1-dependent phosphorylation through T368 site is also required ., The Cdc6 T368A mutant was resistant to Mck1p-dependent degradation ( Figure 7E ) ., Nocodazole was added to the media throughout this experiment , therefore Cdc6 stabilization by the T368A mutation , even after Mck1p overexpression , is not due to a change in cell cycle progression ., This is of particular interest because activation of CDK promotes both DNA replication and Cdc6p degradation at the same time ., The requirement of Mck1 for Cdc6p degradation most likely ensures that degradation of Cdc6p occurs only after origin firing has been initiated ., Three distinct Cdc6p degradation modes have been proposed by Diffleys group 32 ., Mode1 degradation during G1 phase is independent of Cdc6 CDK consensus sites and is mediated neither by SCF nor APC ., The Cdc6p degradation by Mode 2 and Mode 3 are triggered later during the cell cycle ., Mode3 is required for Cdc6 degradation during mitosis ., The Cdc6p degradation by Mck1p accounts for the mode3 mechanism based on the Cdc6p stabilization pattern during mitosis in mck1 deletion ( Figure 4A ) ., Diffleys group has reported that the Cdc6 T368M mutation leads to Cdc6p stabilization during mitosis and the mutation is resistant to mode 3 proteolysis by SCFcdc4 complex 6 ., In this study , we showed that Mck1-dependent Cdc6 phosphorylation is targeted by SCFCDC4 complex for degradation ( Figure 5B ) ., Therefore , Mck1 , most likely , phosphorylates Cdc6 and the phosphorylation at T368 is recognized by Cdc4 ., It is not clear if mode 3 requires CDK activity ., Therefore Mck1p may promote complete Cdc6 degradation during mitosis in addition to its degradation mechanism through CDK phosphorylation ., Further studies are required to test if Mck1 could also promote Cdc6 degradation via Mode 1 or Mode 2 ., There are two potential GSK-3 sites S/TXXXpS/T in Cdc6 , at 39-43 and 368-372 amino acid residues ., It has been reported that these sites share sequence similarities and are targeted for SCFCDC4 dependent proteolysis 6 ., Our yeast-two hybrid assay showed a specific interaction between Mck1p and Cdc6p through the GSK-3 consensus site located at residues 368–372 ( TPTTS ) ., This GSK-3 site in Cdc6p , amino acid 368–372 , is also shared by two potential CDK phosphorylation sites 368–371 ( TPTT ) and 372–275 ( SPVK ) ., The former partially matches with a minimal consensus CDK phosphorylation site ( S/T-P ) whereas the latter perfectly matches an optimal CDK site , with a basic residue at the +3 position ., It is important to note that Cdc6 is a very good substrate of the B-type Cyclin/CDK complex 37 ., The GSK-3 kinase and CDK could share substrate specificity 38 ., GSK-3 kinases require “priming” phosphorylation by another kinase on their substrates 36 ., The priming site is usually located C-terminally of the GSK-3 phosphorylation site , at the +4 position , which corresponds to S372 in Cdc6 ., After priming , GSK-3 recognizes its target and can phosphorylate the first serine or threonine residue , which corresponds to T368 in Cdc6 ., Thus , C-terminal Cdc6p ( aa 341–390 ) , including the GSK-3 consensus phosphorylation sequence , is sufficient for Mck1 binding and their interaction likely depends on phosphorylation of S372 by CDK ( Figure 6B ) ., We propose a model in which S372 is phosphorylated by cyclin/CDK first in order to induce phosphorylation at T368 by Mck1p kinase ., This priming model allows Cdc6 to create Cdc4 diphospho-degrons which is an efficient Cdc4 recognition site ., David Morgans group shows that Eco1 is primed by CDK and DDK in order to be targeted by Mck1 , which creates Cdc4 recognition site ( personal communication ) ., Mck1 is involved in the degradation of SCFCDC4 substrates such as Rcn1and Hsl1 25 , 26 , 39 ., Therefore , the priming model to create Cdc4 diphospho-degrons seems to be a universal mechanism to regulate protein degradation ., Mck1p protein levels are not cell cycle-regulated ( data not shown ) therefore Mck1 activation is not regulated by its own expression level ., This result supports the idea of the priming hypothesis in which Mck1 can target its substrate , Cdc6p , only after Cdc6 is phosphorylated by cyclin/CDK in a cell cycle-dependent manner ., Given the requirement of T368 for Mck1 dependent degradation of Cdc6 , Mck1 most likely phosphorylates this residue directly in vivo ., However , it is formally possible that Mck1 affects Cdc4 function other than Cdc6 ., We favor the model that Mck1 directly phosphorylates Cdc6 to promote Cdc4-dependent degradation based on our results in Figure 5B , Figure 7C and 7D ., Whether or not SCFCDC4 or other targets such as Sic1 are also phosphorylated by Mck1 is an interesting future study ., The glycogen synthase kinase-3 ( GSK-3 ) was originally identified as a kinase that inactivates glycogen synthase 40 ., In higher eukaryotes , there are two isoforms , GSK-3α and GSK-3β , that regulate various cellular processes including Wnt signaling 41 and insulin signaling 42 , 43 ., The yeast homologue of GSK3 , Mck1p , also has diverse biological functions ( see introduction ) ., This is the first evidence to show that Mck1p or any GSK-3 kinase controls DNA replication ., Whether GSK-3 kinases contribute to the regulation of DNA replication at other targets should be investigated further ., SGA analysis was performed as previously described 19 , 20 ., A query strain , MATalpha ORC6-rxl::LEU2 mfa::MFA1pr-HIS3 trp1 ade2 can1 leu2 his3 lys2 ura3 , was placed on YEPD in rectangle plates ., Then deletion mutant arrays ( MATa geneX::KanMX TRP1 ADE2 met15 leu2 ura3 his3 ) were put on top of the query strains ., The resulting diploid cells were sporulated on the plates containing 2% agar , 1% potassium acetate , 0 . 1% yeast extracts , 0 . 05% glucose , supplemented with uracil and histidine ., After incubation at 22 degrees for 5 days , the spores were pinned onto haploid selection plates ( SD-His/Leu/Arg plus canavanine ) to select for MATa mfa::MFA1pr-HIS3 ORC6-rxl::LEU2 progeny , followed by pinning onto YEPD plates containing G418 to select out the deletion array mutants ., Finally , double mutants were placed on SD-His/Leu/Arg plus canavanine plus G418 for 2 days ., The proliferation of those that contained haploid cells was scored visually ., The deletion sets used in this study were obtained from EuroScarf and are derivatives of BY4741 44 ., First , GAL-CDC6-HA or mck1 GAL-CDC6-HA strains were grown in raffinose-containing media and then galactose was added to express Cdc6-HA for 2 hours ., The cell cycle was blocked during mitosis by nocodazole at the concentration of 15 µg/ml for 2 hours ., Next , glucose was added to the media to shut off the GAL expression ( Figure 4A ) ., CDC6-PRA or mck1 CDC6-PRA strains were grown in liquid YEPD to log-phase at 30 degrees and then treated with alpha-factor at the concentration of 100 nM for 2 hours ., The cells were washed with YEPD three times to release the cell cycle from G1 ., Samples were collected every 10 minutes for 80 minutes for Figure 4B ., To block the cell cycle during mitosis , CDC6-PRA or mck1 CDC6-PRA strains were treated with nocodazole at the concentration of 15 µg/ml for 2 . 5 hours at 30 degrees ., The mitotic block was released by washing cells with YEPD twice ., Samples were collected every 10 minutes for 60 minutes for Figure 4D ., For Figure 5 and 7D , cells were treated with nocodazole for 2 hours and then switched to YEPD or YEPG containing nocodazole at 15 µg/ml ., All strains used , except for SGA analysis , are derivatives of W303 ( stra | Introduction, Results, Discussion, Materials and Methods | Cdc6p is an essential component of the pre-replicative complex ( pre-RC ) , which binds to DNA replication origins to promote initiation of DNA replication ., Only once per cell cycle does DNA replication take place ., After initiation , the pre-RC components are disassembled in order to prevent re-replication ., It has been shown that the N-terminal region of Cdc6p is targeted for degradation after phosphorylation by Cyclin Dependent Kinase ( CDK ) ., Here we show that Mck1p , a yeast homologue of GSK-3 kinase , is also required for Cdc6 degradation through a distinct mechanism ., Cdc6 is an unstable protein and is accumulated in the nucleus only during G1 and early S-phase in wild-type cells ., In mck1 deletion cells , CDC6p is stabilized and accumulates in the nucleus even in late S phase and mitosis ., Overexpression of Mck1p induces rapid Cdc6p degradation in a manner dependent on Threonine-368 , a GSK-3 phosphorylation consensus site , and SCFCDC4 ., We show evidence that Mck1p-dependent degradation of Cdc6 is required for prevention of DNA re-replication ., Loss of Mck1 activity results in synthetic lethality with other pre-RC mutants previously implicated in re-replication control , and these double mutant strains over-replicate DNA within a single cell cycle ., These results suggest that a GSK3 family protein plays an unexpected role in preventing DNA over-replication through Cdc6 degradation in Saccharomyces cerevisiae ., We propose that both CDK and Mck1 kinases are required for Cdc6 degradation to ensure a tight control of DNA replication . | DNA replication is a fundamental cellular process that takes place in all living organisms ., This cellular event has to be tightly regulated to ensure an accurate genome integrity such that DNA replication takes place only once per cell cycle ., Here we show a mechanism by which DNA re-replication is controlled by Cyclin Dependent Kinase ( CDK ) and a yeast GSK-3 kinase ( Mck1p ) in S . cerevisiae ., We found that Mck1p promoted Cdc6 protein degradation ., Mck1p targets Cdc6p through a GSK-3 consensus site ( T368 ) , and Cdc6p protein degradation was also mediated through the same T368 site ., The GSK-3 kinase has diverse cellular functions in higher eukaryotes including roles in tumorigenesis ., This finding is particularly important , since this is the first evidence to show that a GSK-3 family kinase regulates DNA replication . | biology | null |
journal.pgen.1002022 | 2,011 | Sensing of Replication Stress and Mec1 Activation Act through Two Independent Pathways Involving the 9-1-1 Complex and DNA Polymerase ε | The DNA replication machinery can experience various types of stress during S phase ., This can happen when the replisome encounters DNA lesions that hinder its progression , while traversing slow replication zones corresponding to genomic regions difficult to replicate 1 or when encountering replication fork barriers 2 ., Replication stress can also be induced by inhibiting ribonucleotide reductase ( RNR ) with hydroxyurea , which causes a global replication arrest by reducing the dNTPs pools 3 ., Under replication stress conditions , eukaryotic cells trigger a signaling cascade , known as the replication checkpoint , which , in budding yeast , culminates with the phosphorylation of Rad53 4 ., This protein kinase is essential for the activation of the molecular mechanisms required to cope with replication arrest: it promotes stabilization of stalled replication forks and allows DNA replication re-start after removal of the blocking agent 5 , 6 , 7 , 8 ., Rad53 is also responsible for inducing the transcription of RNR genes by inhibiting the transcriptional repressor Crt1 and promoting the degradation of the RNR inhibitor Sml1 9 , 10 ., Finally , Rad53 prevents the firing of late replication origins 11 and restrains spindle elongation thus preventing mitosis 12 , 13 , 14 ., The DNA damage and replication checkpoints are genetically distinct pathways; however , they are partially overlapping since they share several of the factors involved ., In fact , replication stress activates Mec1 , the same apical kinase triggered by DNA damage , which is recruited to RPA-covered ssDNA by its binding partner Ddc2 15 ., After damage , Mec1 phosphorylates the Rad9 adaptor protein , which has been loaded onto DNA via chromatin-dependent and -independent pathways: the former requiring methylation of H3-K79 and the latter depending on the 9-1-1 complex and Dpb11 16 , 17 , 18 , 19 , 20 ., Phosphorylated Rad9 , in turn , recruits Rad53 , which becomes hyperphosphorylated in a Mec1-dependent manner ., Differently , in the case of HU-induced checkpoint activation , the Rad9 adaptor protein is dispensable and its function is performed by Mrc1 , a constitutive member of the replisome complex 21 , 22 ., It is now clear that following genotoxin treatments , primary lesions are generally recognized by specific repair factors that process them to generate ssDNA regions , which elicit the DNA damage response ., On the other hand , the actual mechanism acting in the activation of the replication stress response is poorly understood ., In budding yeast , it has been suggested that replication proteins may be involved in sensing blocks of the replication fork ., Indeed , in addition to Dpb11 , the initiation factor Sld2/Drc1 and Polε itself are required for efficient checkpoint activation in response to HU treatment , although the corresponding mutants are only mildly sensitive to the drug 23 , 24 , 25 ., Sld2 is an essential CDK1 target required for initiation of DNA replication ., Its phosphorylation and subsequent interaction with Dpb11 is essential for the loading of Polε and the firing of replication origins 26 , 27 ., Polε consists of four subunits: Pol2 and Dpb2 are essential for cell viability while Dpb3 and Dpb4 appear to be non-essential ., These last two factors contains a histone-like fold motif and are also implicated in transcriptional regulation 28 , 29 ., The Polε holoenzyme is composed of two structurally distinct domains: a globular domain , made of the N-terminus of the catalytic Pol2 subunit and a tail-like domain containing the other three factors , bound to the Pol2 C-terminus 30 , 31 ., The catalytic subunit contains an N-terminal polymerase domain followed by a C-terminal region , where the checkpoint-defective mutations of POL2 map 24 ., Surprisingly , deletion of the polymerase domain does not cause cell lethality , whereas the checkpoint domain is essential for cell viability 32 ., It has been established that in response to DNA damage , the 9-1-1 clamp is loaded onto the 5′ primer-template junction adjacent to RPA-coated ssDNA 33 , 34 ., In higher eukaryotes , 9-1-1 then recruits TopBP1 which , through an interaction with ATRIP , stimulates the ATR kinase activity 35 , 36 , 37 , 38 ., Recent work in yeast demonstrated that Mec1 activation can proceed also through a 9-1-1-dependent , but Dpb11-independent pathway , mediated by an activation domain present in the Ddc1 subunit of the 9-1-1 complex 39 ., Indeed , it has been reported that S . cerevisiae 9-1-1 can directly activate the Mec1-Ddc2 kinase in vitro 40 ., The in vivo balancing between these two pathways has been recently studied , following Rad53 phosphorylation 39 , which is influenced not only by Mec1 activation , but also by the Rad9 mediator 39 ., To determine directly the relative contributions of Ddc1 and Dpb11 to Mec1 activation in different cell cycle phases , and particularly in response to replication stress , we analyzed a direct target of Mec1 kinase , histone H2A , whose phosphorylation is not dependent upon Rad9 ., In this study we found that , in G1 yeast cells , Mec1 activation induced by UV irradiation completely depends on the 9-1-1 dependent pathway , whereas Dpb11 only plays a minor role ., Conversely , in response to replication stress , Mec1 activation is achieved through two independent pathways which rely on the 9-1-1 complex and Dpb11 , respectively ., At least one of these two pathways is necessary to efficiently activate Mec1 and to allow cell growth in the presence of HU ., Finally , we provide evidence that the DNA polymerase ε complex and Sld2 are required to establish the 9-1-1 independent branch of Mec1 activation and we suggest that this could reflect strand-specificity in detecting replication stress ., We have previously shown that , in M phase , Dpb11 is required to recruit the Rad9 adaptor protein to UV-damaged DNA in a pathway that is parallel to that controlled by histone modifications 16 , 20 ., Dpb11 was also found to stimulate Mec1 kinase activity in vitro and this function appears to be modulated by its interaction with the 9-1-1 complex 41 , 42 ., To dissect the Mec1-activation role of Dpb11 in vivo and to determine the relative contribution of Dpb11 and 9-1-1 to this mechanism in different cell cycle phases , we analyzed histone H2A phosphorylation as an assay for Mec1 activity ., After UV damage H2A is phosphorylated directly on serine 129 ( γH2A ) by Mec1 kinase; indeed mec1-1 mutant cells fail to phosphorylate H2A after DNA damage and a strain deleted in TEL1 , coding for a second sensor-kinase , does not show any significant reduction in γH2A levels ( Figure S1A and S1B ) ., We used a yeast strain carrying a C-terminal deletion of Dpb11 ( Δ583―764 ) encoded by the dpb11-1 allele , which removes almost entirely the ATR Activation Domain ( AAD ) and a strain carrying the deletion of DDC1 , the gene encoding the 9-1-1 subunit involved in Mec1 activation 40 ., WT , dpb11-1 , ddc1Δ and ddc1Δdpb11-1 cells were arrested in G1 with α-factor and in M phase with nocodazole and UV irradiated ., As it is shown in Figure 1A , histone H2A is extensively phosphorylated after UV treatment in G1 and this damage-dependent modification requires the presence of a functional 9-1-1 complex , while the contribution of the AAD domain of Dpb11 is only minor ., The quantification of the signal ( shown in the lower panel of Figure 1A ) , indicates that the level of phosphorylated histone H2A ( γH2A ) in dpb11-1 is ∼50% of that found in WT cells ., In M phase cells the basal level of phosphorylated H2A-S129 is much higher ( Figure S1C ) , and this likely influences the magnitude of the increase measured after UV-irradiation ., In these conditions , Dpb11 plays a minimal role , if any , in H2A phosphorylation and also DDC1 deletion reduces γH2A only partially ( ∼50% ) ( Figure 1B ) ., However , the residual H2A phosphorylation observed in a ddc1Δ mutant strain is lost when TEL1 is deleted , ( Figure 1C ) ., On the other hand , deletion of TEL1in the dpb11-1 background does not significantly influence H2A phosphorylation ( Figure S1D ) To further elucidate the balancing between 9-1-1-dependent and Dpb11-dependent Mec1 activation in S phase , we decided to analyze this process after replication stress induced by HU ., This allowed us also to minimize the side effects due to the involvement of Dpb11 in Rad9 recruitment because , during HU treatment , Rad9 does not become hyperphosphorylated and is not expected to play any role in checkpoint activation 22 ., WT , dpb11-1 , ddc1Δ and ddc1Δdpb11-1 cells were synchronized in G1 , released into fresh medium supplemented with 200 mM HU , and checkpoint activity was monitored by measuring Rad53 phosphorylation ( Figure 2A ) ., Differently from what found in G1 and G2 cells , strains lacking either a functional 9-1-1 complex or the Dpb11 C-terminal region were fully able to phosphorylate Rad53 ., In these experimental conditions , ddc1Δ dpb11-1 double mutant cells showed a very severe defect in Rad53 phosphorylation , similar to that found in a Mec1-defective strain ., These results suggest that a dpb11-1 ddc1Δ double mutation virtually abolishes UV-induced Mec1 activation differently from what previously reported 39 , In addition , the double mutant strain showed synthetic lethality on HU plates ( Figure 2B and 43 ) ., To confirm that the dpb11-1 and ddc1Δ mutations directly affect Mec1 activity , we monitored γH2A levels in the same conditions ., As shown in Figure 2C , the ddc1Δ and dpb11-1 mutations showed a synthetic defect in the ability to phosphorylate H2A-S129 ( Figure 2D ) ., Although displaying a severe defect in Rad53 phosphorylation , ddc1Δdpb11-1 still displays a residual low level of phosphorylated Rad53 , which may be dependent upon a residual Mec1 activity ., However , Figure 2E and Figure S2A show that the residual Rad53 phosphorylation in the double mutant is instead due to Tel1 ., Indeed , an additional mutation eliminating Tel1 function completely abolishes Rad53 phosphorylation in a dpb11-1 ddc1Δ strain and strongly sensitizes cells to HU treatment , as shown in Figure S2B ., These findings further support the hypothesis that Mec1 cannot become activated in response to replication stress in the absence of both Ddc1 and Dpb11-AAD ., To verify the possibility that in dpb11-1 mutant cells an unscheduled , Ddc1-dependent , DNA damage response is triggered as a consequence of the inability to properly activate the replication stress response , similarly to what happens in an mrc1Δ strain 22 , we monitored DNA damage checkpoint activation looking at Rad9 hyperphosphorylation ., As shown in Figure 2F , differently from what found in the mrc1Δ control strain , no Rad9 hyperphosphorylation was detectable in ddc1Δ , dpb11-1 single or double mutant strains ., Consistently , rad9Δdpb11-1 double mutant cells are far less sensitive than the ddc1Δdpb11-1 strain to HU treatment ( Figure 2B and 43 ) ., Rad53 kinase activity is required to stabilize stalled replication forks 7 ., To verify whether the increased HU sensitivity of ddc1Δdpb11-1 double mutant cells was due to their inability to fully activate Rad53 and thus to stabilize the replisomes , we performed a recovery assay ., Briefly , WT , dpb11-1 , ddc1Δ , ddc1Δdpb11-1 and mec1-1sml1 mutant strains were blocked in G1 , released and exposed to HU for 90 minutes; cells were then washed and shifted into fresh medium lacking HU and allowed to recover ., As shown in the control strain mec1-1 sml1 , when Rad53 activity is impaired , cells transiently exposed to HU loose the ability to resume DNA synthesis and complete DNA replication once the drug has been removed ( 6 and Figure 3A ) ., Unexpectedly , we found that not only dpb11-1 and ddc1Δ single mutant cells , but also the double mutant strain , which has a severe Rad53 hyperphosphorylation defect , were able to recover from the HU treatment with a WT kinetics ( Figure 3A ) ., Moreover , with lower HU concentrations , ddc1Δ dpb11-1 cells were capable of completing a round of DNA replication , as demonstrated by the re-entering of the replicated chromosomes in a pulsed-field gel system ( Figure 3B ) ., Another marker of checkpoint activation by HU is the arrest of cell cycle , preventing mitosis ., When exposed to HU , checkpoint mutants fail to delay the onset of mitosis and display elongated spindles 14 ., To address the hypothesis that ddc1Δ dpb11-1 cells may die as a consequence of a premature mitosis , we measured spindle length 90 minutes after HU addition ., ddc1Δ dpb11-1 double mutant cells prevent spindle elongation in the presence of HU , a process which is clearly defective in a mec1-1 mutant strain ( Figure S3A ) , suggesting that the replication checkpoint can delay mitotic entry in the double mutant 10 ., In agreement with all these data , the HU sensitivity of ddc1Δ dpb11-1 double mutant cells can be observed only to chronic exposure to the drug , while it is virtually undetectable if cells are transiently exposed to HU ( Figure 3C ) ., ddc1Δ dpb11-1 mutant cells exhibit extremely low levels of Mec1 and Rad53 activation and , despite being sensitive to exposure to even low concentrations of HU ( Figure 2B ) , they do not show some of the most common phenotypes observed in replication checkpoint defective cells ., To better characterize the sensitivity to the drug , we monitored cell growth in the presence of 100 mM HU ., The single and double mutant ddc1Δ dpb11-1 yeast strains were synchronized in G1 , released into fresh medium supplemented with HU and cell cycle progression followed by FACS analysis ., The double mutant ddc1Δ dpb11-1 showed a small delay in progressing through S-phase in the presence of HU , compared to WT and single mutant cells ., Significantly , at late times ( 20 hours ) after the release , a large fraction of double mutant cells appeared to be arrested at different stages of S-phase , while WT and single mutant cells had regained a FACS profile with 1C and 2C peaks ( Figure 4A ) ., Consistently , PFGE analysis of genomic DNA prepared from the various strains 20 hours after release from HU showed that in ddc1Δ dpb11-1 double mutant cells most of the DNA fails to enter the gel , suggesting the presence of branched intermediates ( Figure 4B , 4C ) ., It is important to note that , differently from what found in a mec1-1 strain , the ddc1Δ dpb11-1 strain did not accumulate cells with a<1C DNA content , or low molecular weight DNA fragments ( Figure 4A–4C ) indicating a correct segregation of chromosomes ., Altogether , these findings may suggest that ddc1Δ dpb11-1 cells are unable to counteract the effect of HU by upregulating ribonucleotide reductase ( RNR ) ., Indeed , Rad53 regulates both the timely degradation of the RNR inhibitor Sml1 and the inactivation of Crt1 , which represses the transcription of RNR genes 9 , 10 ., Consistently with this interpretation , ddc1Δ dpb11-1 cells show a modest delay in Sml1 degradation and , more significantly , CRT1 deletion suppresses , although not completely , the sensitivity of the double mutant strain to HU ( Figure 4D , 4E ) ., Sld2/Drc1 and Polε participate in replication checkpoint signaling 24 , 25 ., Moreover , these factors were recently found to be part of the same pre-loading complex , together with Dpb11 and GINS 44 ., An interesting possibility is that Sld2 and Polε exert their checkpoint function by controlling Dpb11-mediated Mec1 activation ., To address this hypothesis we combined the drc1-1 allele with the DDC1 deletion ., As it is shown in Figure 5A , similarly to what reported above for Dpb11 , Sld2 also acts in a pathway that is parallel to that involving Ddc1; indeed , residual Rad53 phosphorylation present in ddc1Δ cells depends on Sld2 ., Moreover , drc1-1 cells do not show hyperphosphorylation of Rad9 in response to HU treatment , excluding the possibility of a secondary DNA damage response ( Figure S4A ) ., In agreement with these data , deletion of DDC1 displays a synergistic sensitivity to HU when combined with the drc1-1 mutation and the HU sensitivity of the double mutant strain is very similar to that observed for ddc1Δdpb11-1 cells ( Figure 5B ) ., The checkpoint function of Polε appears to reside in the C-terminal domain of Pol2 , which is bound , either directly or indirectly , by the three smaller subunits Dpb2 , Dpb3 and Dpb4 and by Dpb11 31 , 45 ., To assess if Polε participates in the Dpb11 signaling branch via its minor subunits , we deleted DPB4 in combination with the DDC1 deletion ., Figure 5C shows that Rad53 phosphorylation is severely impaired in the double mutant ddc1Δdpb4Δ , closely resembling the phenotype of a ddc1Δdpb11-1 mutant ., The same effect is measured by testing H2A phosphorylation in HU-treated samples ( Figure 5D ) ., The signals obtained for each time-point are quantified with respect to the signal detected in G1-arrested cells , in order to compensate for the higher basal level of γH2A observed in ddc1Δdpb4Δ double mutant cells in the absence of any treatment ., Moreover , no unscheduled DNA damage checkpoint activation occurs , since no Rad9 phosphorylation is detected in dpb4Δ or dpb4Δ ddc1Δ cells treated with HU ( Figure S4B ) ., Finally , the ddc1Δdpb4Δ strain shows an HU sensitivity similar to that found in ddc1Δdpb11-1 cells ( Figure 5E ) ., Apical checkpoint kinases ( Mec1/Tel1 in budding yeast , ATR/ATM in humans ) convert a structural signal coming from damaged DNA to a phosphorylation-based signaling cascade , and a large amount of work has been devoted to clarify the underlying mechanisms ., Initially , the attention was focused on the recruitment of these kinases to damaged DNA 15 , based on the assumption that binding to damaged chromatin sites would lead to their activation ., More recently , the finding that Dpb11/TopBP1 stimulates Mec1 activity suggests a more complex scenario 40 , 41 , 42 ., In vitro data obtained in Xenopus egg and mammalian cell extracts demonstrate the ability of TopBP1 to increase Mec1 kinase activity 35 , 38 ., The significance of this TopBP1 function does not appear to be specific for multicellular eukaryotes , since an interaction between Rad4/Cut5 and the checkpoint sensor kinase Rad3-Rad26 has also been found in S . pombe 46 , 47 ., More recently , in S . cerevisiae cells , Dpb11 has been demonstrated to contain an ATR activation domain ( AAD ) , which is sufficient to promote Mec1 activation in vitro 41 , 42 ., These findings apparently contradict a previous observation that Mec1 can normally phosphorylate Ddc2 in a dpb11-1 mutant , lacking part of the AAD , after UV damage in M phase 16 , while in our hands DDC1 deletion prevents Ddc2 phosphorylation ( unpublished observation ) ., Two explanations can be envisaged: in dpb11-1 mutant cells , Mec1 activity may be sufficient to phosphorylate Ddc2 , while being defective towards other substrates; alternatively , Dpb11 may play only a marginal role in response to UV irradiation in M phase ., We favored the second hypothesis because dpb11-1 mutant cells are mildly sensitive to UV irradiation and are proficient in the G2/M checkpoint; moreover , the 9-1-1 complex has also been identified as an activator of Mec1 in vitro 39 , 40 and may play a prominent role in M phase ., If this assumption is correct , Dpb11 could play a role in Mec1 activation in response to a different kind of damage or in other cell cycle phases ., Interestingly , it was demonstrated that the dpb11-1 temperature-sensitive mutant is defective in checkpoint activation after replication stress caused by HU treatment at the restrictive temperature ( 36°C ) , while it is only mildly sensitive to the drug at permissive temperature ( 23 , 25 and Figure 2B ) ., To better understand the process of Mec1 activation in vivo after DNA damage or replication stress , we analyzed the relative functions of the two putative Mec1 activators: Dpb11 and the 9-1-1 complex ., We extended our previous analysis by monitoring , in different cell cycle phases , a direct target of Mec1 kinase ( histone H2A ) as marker of Mec1 activity ., We found that , both in G1 and in M phase , the 9-1-1 complex is absolutely required for Mec1 activation in response to UV treatment , while the contribution of Dpb11 AAD is only partial ( ∼50% ) and restricted to G1 ., These in vivo findings are in agreement with the current activation model inferred from in vitro biochemical data 39 , indicating that 9-1-1 can stimulate Mec1 through both Dpb11-dependent and -independent pathways in G1 ( Figure 6 , left ) ., Differently , in M phase , the ATR activation domain of Dpb11 is dispensable for full Mec1 activation , which relies mainly on the presence of 9-1-1 ( Figure 6 , right ) ., In fact , the residual UV-induced H2A phosphorylation detectable in the ddc1Δ strain , is dependent upon the Tel1 kinase ( Figure 1 ) ., Different requirements for Mec1 activation in G1 and in M phase may reflect differences in CDK-controlled processing of DNA filament ends to generate the substrate detected by checkpoint factors 48 , 49 ., To complete studying of the pathways leading to Mec1 activation in different cell cycle stages , we analyzed the contribution of Dpb11 and Ddc1 to Mec1 activation in S phase cells challenged with replication stress ., HU decreases the cellular concentration of dNTPs available for DNA synthesis and yeast cells respond by activating the replication checkpoint ., In vivo analysis of the phosphorylation state of two Mec1 substrates , H2A and Rad53 , indicates that Dpb11 and 9-1-1 participate in Mec1 activation in response to HU treatment independently of each other in two parallel pathways ., The possibility that dpb11-1 may cause problems to the replication process triggering a DNA damage response mediated by the 9-1-1 complex , similarly to what happens in mrc1Δ cells 22 , seems unlikely ., In fact , the Rad9 DNA damage-specific adaptor does not become hyperphosphorylated in both dpb11-1 and ddc1Δ single mutants ., In agreement with such observation , rad9Δdpb11-1 cells are much less sensitive to HU than ddc1Δ dpb11-1 cells ( Figure 2 and 43 ) ., We report that the HU sensitivity of ddc1Δ dpb11-1 strain is not due to replication fork collapse or premature elongation of the mitotic spindle ( Figure 3 and Figure S2 ) , two phenotypes characteristic of mutants defective in the replication checkpoint 7 , 12 ., Accordingly , the HU sensitivity of ddc1Δdpb11-1 double mutant cells , differently from that of a mec1-1sml1 strain , is not detectable in the case of transient HU treatment ., This observation suggests that another Rad53 function activated by the replication checkpoint , and different from that responding to temporary fork arrest , is essential for sustaining growth in the constant presence of hydroxyurea ., Indeed , ddc1Δ dpb11-1 double mutant cells grown in the presence of HU show defects in completing replication and accumulate replication intermediates ., Moreover , ddc1Δ dpb11-1 cells are unable to counteract the effect of HU by upregulating ribonucleotide reductase ., Interestingly , CRT1 deletion partially suppresses HU sensitivity of the double mutant strain ( Figure 4E ) ., To obtain more insights on the pathways leading to Ddc1-dependent and Dpb11-dependent activation of replication checkpoint and to identify possible mechanisms specific for lagging or leading strand fork arrest , we analyzed mutants in the genes coding for proteins that are known to be involved in leading strand replication ., During initiation of DNA replication , Dpb11 interacts with both Sld2 and Sld3 in a phosphorylation-dependent manner , a process that is required for origin firing 26 , 27 ., Moreover , temperature sensitive drc1-1 strains , mutated in Sld2 , display the same checkpoint-deficient phenotype of dpb11-1 cells , when treated with HU at the non-permissive temperature , ( Figure 5 and 25 ) ., We tested whether Sld2 functions with Dpb11 in the same 9-1-1-independent pathway for Mec1 activation ., Combining the drc1-1 allele with the DDC1 deletion , we found that ddc1Δ drc1-1 double mutant cells display the same Rad53 phosphorylation defect and the same HU sensitivity of a ddc1Δdpb11-1 strain , indicating that Mec1 activation by Dpb11 also requires Sld2 ( Figure 5 ) ., Mutants in the Pol2 C-terminus , the enzyme replicating the leading strand 50 , are defective in the establishment of the replication checkpoint 24 , 50 and this protein region of Pol2 was suggested to be involved in its interaction with other three Polε subunits: the essential Dpb2 protein and the non-essential Dpb3 and Dpb4 subunits 31 , 45 , 51 ., Disruption of the DPB4 gene in a ddc1Δ background leads to identical phenotypes to the one observed in ddc1Δ dpb1-1 and ddc1Δ drc1-1 , strongly suggesting that the 9-1-1-independent pathway involves leading strand replication factors ., The observations that Dpb11 acts directly on Mec1 activity 41 , 42 and that , in the dpb11-1 mutant , Polε seems to be normally loaded onto replication origins 52 , strongly suggest that Dpb4 , and possibly Sld2 , function upstream of Dpb11 during checkpoint signaling ., Unfortunately , it is impossible to perform a complete formal epistatic analysis as the dpb11-1 mutation also affects replication initiation and deletion of DPB4 or mutations in SLD2 are synthetic lethal when combined with the dpb11-1 allele 28 , 53 ., In conclusion our data suggest that during exposure to hydroxyurea , two independent pathways sense replication stress and signal for Mec1 activation ., The first pathway depends on 9-1-1 , which is known to be loaded at the 5′ of primer-template junctions , when RPA covers ssDNA ahead of the primer 34 ., During unchallenged DNA replication these structures are normally formed on the lagging strand as a consequence of discontinuous DNA synthesis , and rapidly removed by refilling polymerase activity ., Inhibition of DNA polymerization by HU likely stabilizes the 5′ DNA end providing the structure required for 9-1-1 loading ., On the other hand , the higher processivity of leading strand synthesis makes it likely that the nearest 5′ end will be far away from the site of polymerase stalling , where ssDNA is generated and the Mec1-Ddc2 complex should be recruited ., The absence of such structure could prevent the 9-1-1-dependent Mec1 activation ., In this case a pathway requiring the leading strand factors Dpb4 , Dpb11 and Sld2 becomes relevant to induce Mec1 activation ( Figure 6 , center ) ., The hypothesis that Polε , Sld2 and Dpb11 work together in sensing replication stress is supported by the recent finding that an unstable complex containing Dpb11 , Sld2 , Polε and GINS is formed at the beginning of S-phase 44 ., Moreover , the demonstration that under unstressed conditions Polε acts on the leading strand while Polδ works on the lagging strand 50 , 54 supports the hypothesis that Polε and its interacting subunits may function in sensing replication stress on the leading strand , while the 9-1-1 complex may be more important to detect lagging strand fork arrest ., Additional work will be needed to confirm this model and to identify the mechanisms leading to Dpb11 recruitment at the sites of replication fork stalling , since Dpb11 appears to co-localize with Polε during initiation of DNA replication , but not during elongation 52 ., All of the strains used in this work are derivatives of W303 ( K699 MATa ade2-1 trp1-1 can1-100 leu2-3 , 12 his3-11 , 15 ura3 ) and are listed in Table 1 ., Deletion strains were generated by using the one-step PCR system 55 or by genetic crossing ., Cells were grown overnight at 25°C to a concentration of 5×106 cells/ml and arrested in G1 with 5 µg/ml α-factor for three hours ., 60 ml of cultures were spun and resuspended in the same volume of YPD supplemented with HU ( 200 mM or 100 mM , depending on the experiment ) ., 20 ml samples were taken every 30 minutes after the release ., In the case of untreated samples cells were released in fresh YPD +10 µg/ml nocodazole and every 5 minutes samples were taken for SDS-PAGE and FACS analysis ., Cells were grown in YPD medium at 25°C to a concentration of 5×106 cells/ml and arrested with nocodazole or α-factor ( 20 µg/ml ) ., 50 ml of cultures were spun , resuspended in 500 µl of sterile water , and plated on a Petri dish ( 14-cm diameter ) ., Rapidly , a 15 ml untreated sample was taken ., Plates were irradiated at 75 J/m2 and cells were resuspended in 50 ml of YPD + nocodazole or α factor ., Three 15 ml samples were taken every 10 minutes after irradiation ., Trichloroacetic acid protein extracts 56 were separated by SDS-PAGE; for the analysis of Rad9 phosphorylation , NuPAGE Tris-Acetate 3–8% gels ( Invitrogen ) were used following the manufacturers instructions ., Western blotting was performed with anti-Rad53 , anti-H2A-S129 ( Abcam #15083 ) , anti-Actin ( Sigma #A2066 ) , anti-Sml1 and anti-Rad9 antibodies , using standard techniques ., Values of phospho-H2A levels were obtained by quantifying the signal in the corresponding lanes using Quantity One software ( BioRad ) and normalizing it , first on the loading controls and then on the level of phospho-H2A in the untreated/G1-arrested sample of each strain ., 1 ml of a 5×106 cells/ml culture were fixed overnight at 4°C with fixation buffer ( 3 , 7% formaldehyde , 0 , 1 M K-phosphate pH 6 , 4 , 0 , 5 mM MgCl2 ) ., Cells were then washed three times with wash buffer ( 0 , 1 M K-phosphate pH 6 , 4 , 0 , 5 mM MgCl2 ) , one time with spheroplasting solution ( 1 , 4 M sorbitol , 0 , 1 M K-phosphate pH 6 , 4 , 0 , 5 mM MgCl2 ) and resuspended in 200 µl of the same solution ., Spheroplasts were prepared using 5 µl of 10 mg/ml Zymolyase at 37°C ., Spheroplasts were washed with the same solution and used to prepare multi-well immunofluorescence slides which were incubated overnight with α-tubulin antibody ( YOL1/34 , Seralab ) diluted 1∶100 in PBS-5%BSA ., HU plates were prepared by serial dilutions of the 2 M stock solution ., Plates containing 25 mM , 50 mM and 100 mM HU were prepared ., Overnight grown cultures were diluted to 1×106 cell/ml , then 10-fold serial dilutions were prepared and 10 µl of the suspensions were spotted on HU plates , which were incubated at 25°C ., Images were taken 2 to 7 days later ., Agarose plugs containing yeast chromosomes were prepared as described previously 57 ., These were incubated overnight at 37°C in 0 . 5 ml/plug TE containing 1 mg/ml RNAseA ., After extensive washes with Wash Buffer ( 10 mM Tris-HCl pH 7 . 5 50 mM EDTA ) , plugs were loaded on 1% agarose gel and sealed in the wells with a solution of 1% LMP agarose in TBE 0 . 5X ., Gels were run at 4°C for 24 h at 165 V , with 60 seconds pulses for 12 h and 90 second pulses for 12 h , using an Amersham Gene Navigator system . | Introduction, Results, Discussion, Materials and Methods | Following DNA damage or replication stress , budding yeast cells activate the Rad53 checkpoint kinase , promoting genome stability in these challenging conditions ., The DNA damage and replication checkpoint pathways are partially overlapping , sharing several factors , but are also differentiated at various levels ., The upstream kinase Mec1 is required to activate both signaling cascades together with the 9-1-1 PCNA-like complex and the Dpb11 ( hTopBP1 ) protein ., After DNA damage , Dpb11 is also needed to recruit the adaptor protein Rad9 ( h53BP1 ) ., Here we analyzed the mechanisms leading to Mec1 activation in vivo after DNA damage and replication stress ., We found that a ddc1Δdpb11-1 double mutant strain displays a synthetic defect in Rad53 and H2A phosphorylation and is extremely sensitive to hydroxyurea ( HU ) , indicating that Dpb11 and the 9-1-1 complex independently promote Mec1 activation ., A similar phenotype is observed when both the 9-1-1 complex and the Dpb4 non-essential subunit of DNA polymerase ε ( Polε ) are contemporarily absent , indicating that checkpoint activation in response to replication stress is achieved through two independent pathways , requiring the 9-1-1 complex and Polε . | The maintenance of genome stability is an essential process which needs a careful control ., Indeed , the checkpoints are surveillance mechanisms sensing alterations in the integrity of the genome and preventing the replication and segregation of defective DNA molecules ., The DNA integrity checkpoint is a signal transduction cascade conserved from yeast to man , and the apical factors in the pathway are protein kinases , called Mec1/Tel1 in Saccharomyces cerevisiae and ATR/ATM in mammals ., DNA integrity can be challenged by lesions caused by a variety of chemical/physical agents , or by replication stress caused by special DNA structures , or by a limited supply of deoxyribonucleotides ( dNTPs ) ., The mechanisms leading to checkpoint activation in response to DNA damage are better understood compared to the processes leading to activation as a consequence of replication stress ., We investigated the mechanisms required for Mec1 activation in response to dNTPs depletion caused by hydroxyurea treatment ., We found that Mec1 activation occurs through two independent pathways: one acting through the PCNA-like 9-1-1 complex and the second through Dpb11 and DNA polymerase ε ., The existence of these two pathways suggest a model possibly reflecting a DNA strand specificity in the detection of replication stress . | cellular stress responses, cell biology, nucleic acids, genetics, dna, biology, molecular cell biology, dna repair, genetics and genomics, molecular biology, dna synthesis | null |
journal.pcbi.1002120 | 2,011 | In vivo Conditions Induce Faithful Encoding of Stimuli by Reducing Nonlinear Synchronization in Vestibular Sensory Neurons | The vestibular system provides information about head motion relative to space that is necessary for maintaining posture , computing spatial orientation , and perceiving self-motion ., Peripheral vestibular afferents encode the detailed time course of either horizontal rotations , vertical rotations , or linear acceleration through changes in their firing rates and spike timing 1–4 ., These afferents project unto neurons within the vestibular nuclei ( VN ) 5–7 ., In vitro studies have established that VN neurons in mammals are classified into two main subpopulations ( type A and type B ) that differ in their responses to current input as well as action potential shape 8–11 ., In response to depolarizing current steps , type A neurons show a sustained tonic response while the type B neurons display spike frequency adaptation ., Type B neurons moreover display a resonance at frequencies within the behaviorally relevant range that increases the tendency of small amplitude , high-frequency synaptic inputs to trigger non-linear firing behavior in the form of synchronization to the peaks of the input 12 , 13 ., This synchronization severely limits the range of input frequencies and amplitudes for which the activity of type B neurons accurately follows the input 13–15 ., In contrast , type A neurons , despite also displaying a resonance , tend to follow the time course of current injection accurately for a much wider range of stimulus amplitudes 12 , 13 ., In contrast , the results of in vivo experiments have shown that the firing of many VN neurons accurately follows the time course of sensory stimulation over the behaviorally relevant frequency range ( 0–20 Hz ) 16 , 17 ., While this result is at odds with those of in vitro studies , it is consistent with the fact that eye movement produced by the vestibuloocular reflex ( VOR ) , which is largely driven by the activities of VN neurons , has a very short latency and is accurate over this same frequency range 18 , 19 ., How can the same neurons display nonlinear responses such as synchronization in vitro and yet accurately follow the time course of sensory input in vivo ?, The discrepancy can be dramatic ., For example , Floccular target neurons ( FTNs ) have been shown to correspond to a subpopulation of type B VN neurons 20 , 21 that display the strongest tendency for nonlinear synchronization in vitro , yet do not display such synchronization in response to sensory input in vivo 16 ., Here we test the hypothesis that the apparent discrepancy between VN response dynamics in the in vitro and in vivo conditions can be explained by an increase in trial-to-trial variability under in vivo vs . in vitro conditions ., To do so , we used a simplified biophysical model that has been previously used to describe VN neuron activity in vitro 14 ., We show that this model displays membrane potential oscillations that give rise to a resonance in the membrane potential response ., This resonance is transferred to the spiking response and causes nonlinear synchronization to sinusoidal current injections over a wide range of frequencies ( 0–20 Hz ) ., We then mimicked the high-conductance state that is typical of in vivo conditions in our model by increasing the membrane conductance ., Moreover , we mimicked their large resting discharge rates by increasing the bias current ., Interestingly , both of these changes in parameter values were not sufficient to remove this synchronization that thus severely limits the range of inputs for which our models response follows the input accurately ., However , we show that adding noise to our model in order to mimic the resting discharge variability displayed by VN neurons in vivo can be sufficient to eliminate synchronization over the full range of behaviorally relevant frequencies ., It is well known that damped or sustained membrane potential oscillations can arise from the interplay between several membrane conductances including voltage gated calcium channels 23 ., The magnitude of these oscillations is furthermore strongly dependent on the amount of depolarizing current bias 22 ., As such , we varied both the maximum calcium conductance and the bias current in our model ., We first studied the membrane potential response to step current injections as these have been previously used to demonstrate the presence of membrane potential oscillations 23 ., Our results show that the model can display damped membrane potential oscillations with different magnitudes and frequencies for a wide range of and values ( Figures 2A , B , C ) ., We characterized this dependency by systematically varying both and over a wide range of values and quantified the amplitude of these damped oscillations by computing an oscillation index ( see Methods ) ., Further , we computed the oscillation frequency from the squared magnitude of the Fourier transform of the response ( see Methods ) ., Our results show that , for a given value of the maximum calcium conductance , the oscillation index displays a maximum as a function of the bias current ( Figure 2D ) ., The oscillation frequency displayed qualitatively similar behavior to that of the oscillation index ( Figure 2E ) ., We note that the oscillation frequency was mostly within the behaviorally relevant range found in natural vestibular stimuli ( 0–20 Hz ) 24 ., This indicates that the model can display calcium induced damped membrane potential oscillations , the magnitude and frequency of which are highly dependent on the level of depolarizing bias current ., We note that qualitatively similar results were obtained when varying the persistent sodium conductance ( Figure S1 ) ., The results agree with the known effects of persistent sodium , namely to depolarize the membrane and amplify the resonant behavior 23 ., It is well known that neurons receive massive synaptic bombardment under in vivo conditions , which gives rise to a high-conductance state 25 , 26 ., Mathematically , the increased membrane conductance under such synaptic bombardment can be mimicked by increasing the leak conductance and by adding an appropriate amount of bias current 22 , 27 ., As such , we characterized the oscillation index and frequency as a function of both the leak conductance and the bias current ., Although increasing the leak conductance decreased the oscillation amplitude , it also decreased the oscillation frequency to values that were contained within the behaviorally relevant frequency range ( Figures 3A , B , C ) ., These changes were furthermore seen for a wide range of bias current values ., We observed that the oscillation index decreased as a function of the leak conductance for a given value of ( Figure 3D ) ., In contrast , the oscillation index displayed a maximum as a function of for a given value of ( Figure 3D ) ., The oscillation frequency again displayed qualitatively similar behavior to that of the oscillation index as a function of both and and remained within the behaviorally relevant range ( Figure 3E ) ., As such , we conclude that an increased leak conductance is not sufficient to eliminate our models tendency to display membrane potential oscillations ., These oscillations could potentially be detrimental to the models ability to accurately encode the timecourse of current injections as their frequency is within the behaviorally relevant range ., In order to better understand the source of these oscillations , we performed a standard perturbation analysis in our model around the resting membrane potential ( see Methods ) ., Our results show that the linearized model gave rise to oscillation indices and frequencies that were quantitatively similar to those obtained with the full model ( compare Figures 2 , 3 with Figure S2 ) ., Moreover , computing the eigenvalues of the Jacobian matrix of the linearized system revealed that they all had a negative real part ., As such , the membrane potential oscillations are unstable as our model has a stable fixed point ., This is consistent with the damped oscillations that we observed in response to steps ( Figure 2 ) ., We next investigated whether the membrane potential oscillations induced a resonance in the membrane potential response and whether this resonance causes a resonance in the spiking activity ., As such , we used a zap stimulus ( i . e . a sinusoidal waveform with a constant amplitude and a frequency that increases linearly as a function of time; Figure 4A ) as an input to our model ., Such inputs are frequently used to characterize resonant behavior 28 , 29 ., Our results show that the model does display a resonance in the membrane potential in response to zap current injection for different values of and ( Figures 4B , C , D ) ., We note that these responses show asymmetries , which is to be expected since we are using a nonlinear model ., We characterized this resonance by an oscillation index that quantifies its magnitude ( see Methods ) as well as its frequency ( i . e . the zap frequency for which the membrane potential oscillation is maximal ) ., Our results show that both the oscillation index and frequency computed from the models response to zap currents had qualitatively similar dependencies on and to those of the oscillation index and frequency computed from the models response to step currents ( compare Figures 4E , F to Figures 3D , E , respectively ) ., How does resonant behavior in the membrane potential relate to resonant behavior in the spiking activity ?, We investigated this by turning on the spiking conductances ( i . e . ) and by studying the variations in the instantaneous firing rate in response to zap current injection ., Our model displayed differential resonant behavior in its spiking activity in its response to zap current injection as a function of the leak conductance and the bias current ( Figures 5A , B , C , D ) ., We note that these responses also show asymmetries , which is to be expected since we are using a nonlinear model ., In general , parameter values that gave rise to resonance in the membrane potential also gave rise to resonance in the spiking activity ( compare Figures 4B , C , D with Figures 5B , C , D , respectively ) ., We further characterized the resonance in the spiking activity by an oscillation index that quantifies its magnitude ( see Methods ) as well as its frequency ( i . e . the zap frequency for which the ensuing variation in the instantaneous firing rate is maximal ) ., Our results show that the oscillation index and frequency computed from the spiking activity had dependencies on and that followed qualitatively similar trends to those of the oscillation index and frequency computed from the membrane potential ( compare Figures 5E , F to Figures 4E , F , respectively ) ., Note , however , that the spiking resonance frequency varied over a wider range than the membrane potential resonance ., Importantly , the resonance in the spiking regime persisted over a wide range of parameter values and its frequency overlapped with the behaviorally relevant range ., It is expected that the resonance in the spiking activity will lead to nonlinear synchronization of the response with the peaks of the input current that is expected to be detrimental to the faithful encoding of the stimulus time course through changes in firing rate ., This synchronization occurs because of the tendency of excitable systems to display n:m phase locking ( i . e . fire n spikes per m cycles of forcing ) in response to sinusoidal stimuli 30–32 ., We thus characterized the models response to sinusoidal current injections that mimicked the waveforms of sinusoidal sensory stimuli used experimentally in vivo 16 , 17 , 19 , 24 , 33–36 and systematically varied the frequency of stimulation between 0 and 25 Hz ., Our results show that the model tends to display phase locking for high ( Hz ) frequencies ( Figures 6A , B , C ) ., We therefore quantified the models accuracy at encoding the detailed time course of sinusoidal current injections through changes in firing rate by computing the variance accounted for ( VAF , see Methods ) ., Our results show that the VAF was high ( ) for a wide range of values and stimulus frequencies below 5 Hz indicating a strong tendency for faithful encoding of the current stimulus time course ( Figure 6D ) ., Increasing the baseline firing rate by increasing the bias current widened the range of stimulus frequencies for which our model displayed negligible phase locking and could faithfully encode the detailed time course of sinusoidal input from 0–5 Hz to 0–10 Hz ( Figure 6D ) ., However , we observed low VAF values ( ) for stimulus frequencies above 10 Hz for a wide range of values ., In order to test whether these low VAF values corresponded to parameter regimes for which our model displays phase locking , we computed a phase locking index ( PLI ) ( see Methods ) ., As expected , we observed that parameter regimes that gave rise to high VAF also gave rise to low PLI values and vice-versa ( compare Figures 6D and 6E ) ., This strong negative correlation between PLI and VAF for a wide range of and stimulus frequencies within the natural frequency range ( 0–20 Hz ) shows that the low VAF values correspond to a strong tendency for phase locking ., Our simulation results are largely contrary to recordings from VN neurons performed in vivo ., Indeed , many VN neurons accurately follow the time course of vestibular stimuli through changes in firing rate and do not display synchronization or phase locking for frequencies between 0 and 25 Hz 16 ., As our modeling results described above were obtained for high values of and were robust to increases in the bias current , it is unlikely that the discrepancy between our model results and experimental recordings from VN neurons in vivo is due to a change in membrane conductance or the fact that VN neurons might be in a depolarized state in vivo ., Thus , while our results show that increasing the bias current such that the firing rate increases to values seen in vivo did increase the range of frequencies for which our model could faithfully encode the time course of sinusoidal input , this alone was not sufficient to eliminate nonlinear synchronization for the full range of frequencies found in natural vestibular stimuli ( Figures 6D , 6E , 7A ) ., Thus , we hypothesized that the increased trial-to-trial variability that is characteristic of in vivo conditions 25 , 26 might explain this discrepancy ., It is expected that such variability will limit phase locking by inducing firing at all phases of the input and thus promote the faithful encoding of the stimulus waveform by changes in firing rate ( see 37 for review ) ., We thus addressed the specific question of whether the levels of resting discharge variability displayed by VN neurons in vivo are sufficient to account for the suppression of nonlinear phase locking , which is observed in vitro , thereby allowing faithful encoding of the stimulus time course through changes in firing rate ., In order to test this hypothesis , we systematically varied both the bias current as well as the noise intensity within the experimentally observed ranges of baseline firing rates ( Figure 7A ) and resting discharge variability as quantified by the coefficient of variation ( CV ) ( Figure 7B ) , respectively ., We note that previous studies have shown that VN neurons displayed values of CV in their resting discharge ranging from 0 . 05 to 0 . 7 16 , 17 and resting discharge firing rates between 6 and 170 Hz 16 , 17 , 34 ., Furthermore , we also explored the effects of such increased noise intensities on the models firing rate resonance , via repeated presentation of the zap stimulus for the same range of bias current values and noise intensities ., For higher bias currents ( ) corresponding to the baseline firing rates seen under in vivo conditions ( ) , the addition of noise is seen to reduce the oscillation index ( Figure 7C ) ., Addition of noise also decreased the oscillation frequency to values near the behaviorally relevant range ( Figure 7D ) ., As an aside , we note that , for low values of bias current ( ) , we observed a sharp increase followed by a decrease in the oscillation frequency ( Figure 7D ) ., This sharp increase at low noise intensities is consistent with previous studies showing that , for low noise , model neurons have a resonance at the spontaneous firing rate , while for higher noise intensities , the resonance frequency shifts to lower values 22 ., We do not further explore this regime since VN neurons typically have baseline firing rates under in vivo conditions that are outside those for which this regime is observed ., We first recomputed phase histograms in response to sinusoidal current injection ( Figures 8A , B , C ) for the same range of and stimulation frequencies used before but with the addition of noise with a low intensity that gave rise to low resting discharge CV values ( 0 . 04–0 . 24 ) and with bias currents giving rise to firing rates between 25–80 Hz in the absence of stimulation ., We note that these overlap with the experimentally observed ranges of values 16 ., We observed that this noise increased the range of stimulus phases that elicited spiking for higher stimulus frequencies , which reduced phase locking ( compare Figures 8B , C with Figures 6B , C , respectively ) ., However , this noise was not sufficient to completely eliminate phase locking as can be seen from the low VAF and high PLI values observed for high ( ) stimulation frequencies for a wide range of values ( Figures 8D , E respectively ) ., We next performed simulations with a higher noise intensity giving rise to higher resting discharge CV values ( 0 . 5–0 . 7 ) and bias current giving rise to firing rates from 35–85 Hz ., Our results show that the phase histograms in response to sinusoidal current injection were all sinusoidal in shape , even for parameters that gave rise to phase locking in the absence of noise ( compare Figures 9A , B , C with Figures 6A , B , C , respectively ) ., This indicates a lack of phase locking as every phase of the input can now elicit spiking ., We recomputed the VAF as a function of and stimulus frequency and found large ( ) values over the entire range explored ( Figure 9D ) ., Consequently , the model displayed negligible phase locking as quantified by the PLI ( Figure 9E ) ., Note that the range of values of VAF and PLI used in Figures 9D and 9E , respectively , were the same as those used previously ( compare Figures 9D , E with Figures 6D , E and Figures 8D , E , respectively ) ., As such , this noise intensity was sufficient to eliminate nonlinear phase locking and thereby give rise to faithful encoding of the stimulus waveform through changes in firing rate for all stimulus frequencies within the behaviorally relevant range ., In order to verify the robustness of our results , we also computed a second measure of nonlinear synchronization , the nonlinearity index ( NI , see Methods ) , that is based on the ratio of the Fourier coefficient amplitude squared at the second harmonic to that at the stimulus frequency ., This measure had qualitatively similar behavior to that of the PLI measure as a function of the bias current , stimulus frequency , and noise intensity ( compare Figure S3 to Figures 6 , 8 , 9 ) ., Finally , in order to test that these results were not an artifact of our using current input , we used conductance input rather than current input stimuli in our model ., The effect of noise on phase locking in this model ( Figure S4 ) were in qualitative agreement with those shown in Figures 6 , 8 , and 9 , illustrating the robustness of our main result to the type of input used ., We note that this outcome was expected given that increasing the membrane conductance alone was not sufficient to completely eliminate phase locking over the behaviorally relevant frequency range ., The effects of noise intensity on our models ability to accurately encode the time course of sinusoidal current injections through changes in firing rate are summarized in Figure 10 ., While the PLI rapidly decreases as a function of increasing noise intensity , the VAF rapidly increases ( Figure 10A ) ., For comparison , the resulting firing rate and CV values in the absence of stimulation are also shown for the same noise intensities ( Figure 10B ) ., Because high noise intensities were sufficient to eliminate nonlinear phase locking from our model , we used linear systems analysis to characterize the relationship between input and output in our model ., Specifically , we computed the gain ( i . e . the coefficient relating input and output ) as a function of and stimulus frequency ., Our results show that the gain increases smoothly as a function of stimulation frequency for a given value of in the presence of high noise but not so when noise is not present ( Figures 10C , D ) ., This result is important as previous studies conducted in vivo have shown that VN neurons generally display increasing gains as a function of stimulus frequency 16 , 17 ., Our results therefore suggest that the high-pass filtering characteristics seen in most VN neurons in vivo which are due , at least in part , to an intrinsic resonance ., This resonance is attenuated by the high resting discharge variability that results from the intense convergent synaptic input that the cell receives under in vivo conditions ., The goal of this study was to resolve an apparent discrepancy between the responses of VN neurons to current injection in vitro and to sensory input in vivo ., VN neurons are prone to display nonlinear responses such as synchronization to the peaks of sinusoidal current injection in vitro ., In contrast , studies performed in vivo have shown that VN neuron can respond to sensory input through changes in firing rate that accurately follow variations in sensory stimulation over a wide frequency range 16 ., We investigated the cause for this discrepancy by subjecting a mathematical model based on the Hodgkin-Huxley formalism of in vitro VN neuron activity to in vivo conditions ., Our results show that this model displays membrane potential oscillations that persisted for a wide range of parameter values ., These oscillations give rise to a resonance in the membrane potential which is transmitted to the spike train , causing nonlinear behavior such as synchronization or phase locking over the natural stimulus frequency range ( 0–20 Hz ) ., It is well known that neural variability resulting from the intense synaptic bombardment to which VN neurons are subjected to in vivo will promote faithful encoding of the stimulus waveform through changes in firing rate 37 ., As such , we tested the hypothesis that the levels of resting discharge variability seen under in vivo conditions could account for the fact that some VN neuron classes do not display synchronization in vivo ., To do so , we added noise whose intensity was calibrated in order to match the resting discharge variability experimentally observed in VN neurons under in vivo conditions ., We found that low noise intensities did not completely eliminate phase locking behavior ., In contrast , we found that high noise intensities almost completely eliminated phase locking and that our model could now faithfully encode the time course of sinusoidal current injections at frequencies contained within 0–20 Hz for a wide range of input bias currents ., These results are consistent with experimental recordings from VN neurons in vivo , suggesting that the addition of noise in the in vivo condition underlies the discrepancy between the responses of VN neurons to current injection in vitro and to sensory input in vivo ., Furthermore , they suggest that the vestibular system uses increases in variability to increase the fidelity of encoding by single neurons ., This strategy appears to be found across several sensory systems ( reviewed in 37 ) ., In the present study , we focused on the type B neurons as observed in vitro ., This is because these neurons display the greatest tendency to respond to sinusoidal current injection with synchronization as well as spike frequency adaptation ., In contrast , type A neurons show a sustained tonic response and faithfully follow the time course of sinusoidal current injections that are up to three times larger than those followed by type B neurons 8–11 , 13 ., The differences between type A and type B neurons are thought to be mediated by differences in the levels of different membrane conductances 12 , 14 ., In particular , type B neurons display larger calcium-activated conductances 13 ., Such currents mediate spike frequency adaptation ( see 38 , 39 for review ) ., Theoretical studies have shown that spike frequency adaptation leads to high-pass filtering of time varying stimuli 40–42 , which is consistent with our modeling results showing an increased gain for higher frequencies ., We note that one could use the same model as was used here in order to mimic the activity of type A VN neurons by changing membrane conductances as was done previously 14 ., We predict that a model of type A VN neuron activity would not display phase locking for the sinusoidal current injections considered here but would display phase locking for larger amplitudes ., In vivo studies have found three major functional neuronal classes in MVN that are based on the responses to voluntary eye movements and passive whole-body rotation: 1 ) Vestibular-Only ( VO ) neurons , 2 ) Position-Vestibular-Pause ( PVP ) neurons , 3 ) Floccular Target neurons ( FTN ) ., VO neurons project to the spinal cord and are thought to mediate vestibulo-spinal reflexes that control posture 43–45 , as well as cerebellum and thalamus 46 , 47 , where they are thought to play a role in spatial orientation computation ., The vestibular system also generates the vestibulo-ocular reflex ( VOR ) that functions to effectively stabilize gaze by moving the eye in the opposite direction to the on-going head motion ., The three-neuron arcs mediating the VOR are well characterized ., The primary pathway consists of projections from afferents to PVP neurons , which in turn project to extraocular motoneurons that control the eye muscles ., A secondary pathway is mediated via FTN neurons that receive direct input from the Floccular lobe of the vestibular cerebellum and also project to the extraocular motoneurons ., The correspondence between type A and B MVN neurons as observed in vitro and the different functional classes observed in vivo is not well understood in general ., The most direct link that has been made to date is based on the findings of electrophysiological and anatomical studies that suggest a subpopulation of type B neurons receive input from Floccular purkinje cells , such that they most likely correspond to the FTN neurons which have been characterized in vivo 20 , 21 ., This correspondence between type B cells and FTN cells , however , is unexpected since in vivo experiments have shown that FTN neurons do not display robust phase locking and instead respond to sinusoidal head rotations through changes in firing rate that scale with stimulus intensity for frequencies spanning the behaviorally relevant range in vivo 16 ., Thus , our results provide a potential explanation of this discrepancy originating in the intense synaptic bombardment that these neurons receive in vivo ., The correspondence between VO and PVP neurons in vivo and type A/B neurons in vitro is not known ., However , previous studies have shown that PVP neurons display nonlinear phase locking behavior in response to high frequency ( ) sinusoidal rotations 16 ., This is consistent with our modeling results showing that phase locking is not abolished for low noise intensities ( Figure 8 ) ., Our results therefore predict that:, i ) PVP neurons should have type B like responses in vitro;, ii ) PVP neurons with low resting discharge rates will display a greater tendency for phase locking and ,, iii ) : this tendency is a consequence of their low resting discharge variability ., Previous studies have reported that VO neurons do not display phase locking dynamics but have only explored frequencies between 0 and 4 Hz 48 ., Further studies are needed to explore VO neuron responses to higher stimulus frequencies and might help elucidate their correspondence with either type A or type B neurons ., In conclusion , while it is clear that the filtering properties of VN neurons as observed in vivo are shaped by intrinsic mechanisms 13 , our simulations are consistent with a growing body of literature emphasizing the role of network mechanisms 42 , 49 such as synaptic bombardment that is present under in vivo conditions affecting their responses to sensory input ., What are the sources of resting discharge variability in VN neurons ?, A unique aspect of the vestibular system , compared to other sensory systems , is that information processing is strongly multisensory and multimodal at the first stage of central processing ., This occurs because the vestibular nuclei receive inputs from a wide range of cortical , cerebellar , and other brainstem structures in addition to direct inputs from the vestibular afferents ., First , there is complete overlap in the terminal fields of regular and irregular afferents in each of the major subdivisions of the vestibular nuclei 50 , and the results of electrophysiological studies have shown that about half of the VN neuron population receive significant input from both afferent classes 5 , 6 ., Additionally , not only do neurons typically receive convergent input from otolith as well as canals afferents , but there is an impressive convergence of extra-vestibular information within the VN ( reviewed in 51 ) ., Notably , sensory inputs encoding somatosensory , proprioceptive , and visual information as well as premotor signals related to the generation of eye and head movements are sent directly to the vestibular nuclei ., In alert animals , these extra-vestibular signals strongly modify the processing of vestibular information during our everyday activities , such that this convergence plays an important role in shaping the simple sensory-motor transformations that mediate vestibulo-ocular and vestibulo-spinal reflexes as well as higher-order vestibular functions , such as self-motion perception and spatial orientation ., Thus , as a result of their cortical , cerebellar , and brainstem and afferent input afferents , VN neurons are likely to receive substantial synaptic bombardment in vivo ., For example , extracellular recordings in the cerebellar flocculus reveal irregularities in the spontaneous simple spikes firing rate of the output neurons ( i . e . Purkinje cell ) 52 ., This provides a clear source of variability to FTN neurons which might explain their lack of synchronization to sensory stimulation as predicted from our modeling results ., Previous reports have found that the high conductance state of neurons in vivo can have a significant influence on their processing of synaptic input through changes in intrinsic dynamics 27 , 53–55 ., Specifically , these changes consist of: 1 ) increased synaptic input that is dominated by excitation that acts as a net depolarizing bias; 2 ) increased membrane conductance and; 3 ) increased variability ., In general , bridging the gap between in vivo and in vitro conditions is not well understood because it is not clear which combination the three aforementioned effects is responsible for the observed changes in dynamics ., For example , both changes in the depolarization bias as well as in variability can alter burst dynamics in thalamocortical neurons 54 , 56 ., Previous studies have investigated the effects of in vivo conditions on the activity of VN neurons 14 , 57 , 58 ., In particular , it has been proposed that heterogeneities might allow for the VN neuron population to accurately encode the time course of vestibular stimuli while maintaining nonlinear synchronization at the single neuron level 58 ., This hypothesis is contra | Introduction, Results, Discussion, Methods | Previous studies have shown that neurons within the vestibular nuclei ( VN ) can faithfully encode the time course of sensory input through changes in firing rate in vivo ., However , studies performed in vitro have shown that these same VN neurons often display nonlinear synchronization ( i . e . phase locking ) in their spiking activity to the local maxima of sensory input , thereby severely limiting their capacity for faithful encoding of said input through changes in firing rate ., We investigated this apparent discrepancy by studying the effects of in vivo conditions on VN neuron activity in vitro using a simple , physiologically based , model of cellular dynamics ., We found that membrane potential oscillations were evoked both in response to step and zap current injection for a wide range of channel conductance values ., These oscillations gave rise to a resonance in the spiking activity that causes synchronization to sinusoidal current injection at frequencies below 25 Hz ., We hypothesized that the apparent discrepancy between VN response dynamics measured in in vitro conditions ( i . e . , consistent with our modeling results ) and the dynamics measured in vivo conditions could be explained by an increase in trial-to-trial variability under in vivo vs . in vitro conditions ., Accordingly , we mimicked more physiologically realistic conditions in our model by introducing a noise current to match the levels of resting discharge variability seen in vivo as quantified by the coefficient of variation ( CV ) ., While low noise intensities corresponding to CV values in the range 0 . 04–0 . 24 only eliminated synchronization for low ( <8 Hz ) frequency stimulation but not high ( >12 Hz ) frequency stimulation , higher noise intensities corresponding to CV values in the range 0 . 5–0 . 7 almost completely eliminated synchronization for all frequencies ., Our results thus predict that , under natural ( i . e . in vivo ) conditions , the vestibular system uses increased variability to promote fidelity of encoding by single neurons ., This prediction can be tested experimentally in vitro . | The vestibular system senses the motion of the head in space and is vital for gaze stability , posture control , and the computation of spatial orientation during everyday life ., The activities of single vestibular neurons recorded in the brains of awake behaving animals show that they can accurately transmit information about the time course of head motion , which is necessary for several behaviors such as the vestibulo-ocular reflex required for gaze stabilization ., In contrast , this is not the case when the same neurons are recorded in isolation and sensory stimulation is mimicked experimentally ., We investigated the cause for this discrepancy by studying how a mathematical model of vestibular neuron activity responds to mimics of sensory stimulation under different conditions ., We found that the differences in the activities of vestibular neurons recorded in awake behaving animals and in isolation can be explained by the addition of synaptic noise , which in turn , increases the variability of action potential firing that is seen in more natural conditions ., Our modeling results make a clear prediction that can be tested experimentally . | computational neuroscience, single neuron function, biology, sensory systems, neuroscience, coding mechanisms | null |
journal.pcbi.1004896 | 2,016 | Deep Neural Networks as a Computational Model for Human Shape Sensitivity | Understanding how the human visual system processes visual information involves building models that would account for human-level performance on a multitude of tasks ., For years , despite the best efforts , computational understanding of even the simplest everyday tasks such as object and scene recognition have been limited to toy datasets and poor model performances ., For instance , hierarchical architecture HMAX 1 , once known as “the standard model” of vision 2 , worked successfully on a stimulus set of paper clips and could account for some rapid categorization tasks 3 but failed to capture shape and object representations once tested more directly against representations in the visual cortex ( e . g . , 4–6 ) ., Recently , however , deep neural networks ( DNNs ) brought a tremendous excitement and hope to multiple fields of research ., For the first time , a dramatic increase in performance has been observed on object and scene categorization tasks 7 , 8 , quickly reaching performance levels rivaling humans 9 ., More specifically in the context of object recognition , stimulus representations developed by the deep nets have been shown to account for neural recordings in monkey inferior temporal cortex and functional magnetic resonance imaging data throughout the human ventral visual pathway ( e . g . , 6 , 10 , 11 ) , suggesting that some fundamental processes , shared across different hardware , have been captured by deep nets ., The stimulus sets on which DNNs have been tested in these previous studies allow the inference that there is a general correspondence between the representations developed within DNNs and important aspects of human object representations at the neural level ., However , these stimulus sets were not designed to elucidate specific aspects of human representations ., In particular , a long tradition in human psychophysics and primate physiology has pointed towards the processing of shape features as the underlying mechanism behind human object recognition ( e . g . , 12–15 ) ., Cognitive as well as computational models of object recognition have mainly focused upon the hierarchical processing of shape ( e . g . , 1 , 16 , 17 ) ., There are historical and remaining controversies about the exact nature of these shape representations , such as about the degree of viewpoint invariance and the role of structural information in the higher levels of representation ( e . g . , 18 , 19 ) ., Still , all models agree on the central importance of a hierarchical processing of shape ., For this reason we hypothesized that the general correspondence between DNNs representations and human object representations might be related to a human-like sensitivity for shape properties in the DNNs ., Here we put this hypothesis to the test through a few benchmark stimulus sets , which have highlighted particular aspects of human shape perception in the past ., We first demonstrate that convolutional neural networks ( convnets ) , the most common kind of DNN models in image processing , can recognize objects based upon shape also when all other cues are removed , as humans can ., Moreover , we show that despite being trained solely for object categorization , higher layers of convnets develop a surprising sensitivity for shape that closely follows human perceptual shape judgments ., When we dissociate shape from category membership , then abstract categorical information is available to a limited extent in these networks , suggesting that a full model of shape and category perception might require richer training regimes for convnets ., If convnets are indeed extracting perceptually relevant shape dimensions , they should be able to utilize shape for object recognition ., This ability should extend to specific stimulus formats that highlight shape and do not include many other cues , such as silhouettes ., The models have been trained for object recognition with natural images , how would they perform when all non-shape cues are removed ?, In order to systematically evaluate how convnet recognition performance depends on the amount of available shape and non-shape ( e . g . , color or texture ) information , we employed the colorized version of the Snodgrass and Vanderwart stimulus set of common everyday objects 20 , 21 ., This stimulus set consists of 260 line drawings of common objects that are easily recognizable to human observers and has been used extensively in a large number of studies ( Google Scholar citations: over 4000 to 20; over 500 to 21 ) ., In our experiments , we used a subset of this stimulus set ( see Methods ) , consisting of 61 objects ( Fig 1A ) ., Three variants of the stimulus set were used: original color images , greyscale images , and silhouettes ., First , we asked 30 human observers ( 10 per variant of the stimulus set ) to choose a name of each object , presented for 100 ms , from a list of 657 options , corresponding to the actual of these objects and their synonyms as defined by observers in 20 ., Consistent with previous studies 15 , 21 , participants were nearly perfect in naming color objects , slightly worse for grayscale objects , and considerably worse for silhouettes ( Fig 1B , gray bands ) ., Moreover , we found that participants were very consistent in their responses ( Fig 1C , gray bands ) ., We then presented three convnets with the stimuli and asked them to produce a single best guess of what might be depicted in the image ., A correct answer was counted if the label exactly matched the actual label ., We found that all deep nets exhibited a robust categorization performance on the original color stimulus set , reaching about 80–90% accuracy ( Fig 1B , with the best model ( GoogLeNet ) reaching human level of performance ., Given that the models have not been trained at all on abstract line drawings , we found it an impressive demonstration of convnet feature generalization ., As textural cues were gradually removed , convnets still performed reasonably well ., In particular , switching to grayscale decreased the performance by about 15% , whereas a further decrease by 30% occurred when inner gradients were removed altogether ( silhouette condition ) ., In other words , even when an object is defined solely by its shape , convnets maintain a robust and highly above-chance performance ., Notably , a similar pattern of performance was observed when humans were asked to categorize these objects , suggesting that models are responding similarly to humans but are overall less accurate ( irrespective of stimulus variant ) ., To investigate the consistency between human and model responses in more detail , we computed a squared Euclidean distance between the average human accuracy and a model accuracy , and normalized it to the range 0 , 1 , such that a consistency of . 5 means that a model responded correctly where a human responded correctly and made a mistake where a human made a mistake about half of the time ( Fig 1C; see Methods for reasoning behind this choice of consistency ) ., Overall , the consistency was substantial and nearly reached between-human consistency for color objects for our best model ( GoogLeNet ) ., To visualize the amount of consistency , we depicted The best model’s ( GoogLeNet ) performance on silhouettes against human performance ( Fig 1D ) ., The performances are well correlated as indicated by the slope of the logistic regression being reliably above zero ( Fig 1D; z-test on GoogLeNet: z = 2 . 549 , p = . 011; CaffeNet: z = 2 . 393 , p = . 017; VGG-19: z = 2 . 323 , p = . 020 ) ., Furthermore , we computed consistency between models and found that for each variant of the stimulus set , the models appear to respond similarly and commit similar mistakes ( the between-model consistency is about . 8 for each pairwise comparison ) , indicating that the models learn similar features ., Fig 1D also shows that the models sometimes outperformed humans , seemingly in those situations where a model could take an advantage of a limited search space ( e . g . , it is much easier to say there is an iron when you do not know about hats ) ., Overall , however , despite the moderate yet successful performance on silhouettes , it is obvious from Fig 1D that there are quite some stimuli on which the models fail but which are recognized perfectly by human observers ., Common mistakes could be divided into two groups:, ( i ) similar shape ( grasshopper instead of bee ) , and, ( ii ) completely wrong answers where the reason behind model’s response is not so obvious ( whistle instead of lion ) ., We think that the former scenario further supports the idea that models base their decisions primarily on shape and are not easily distracted by the lack of other features ., In either case , the errors might be remedied by model exposure to cartoons and drawings ., Moreover , we do not think that these discrepancies might be primarily due to the lack of recurrent processes in these models since we tried to minimize influences of possible recurrent processes during human categorization by presenting stimuli for 100 ms to human observers ., It is also possible that better naturalistic training sets in general are necessary where objects would be decoupled from background ., For instance , lions always appear in savannahs , so models might be putting too much weight on savannah’s features for detecting a lion , which would be a poor strategy in the case of this stimulus set ., Nonetheless , even in the absence of such training , convnets generalize well to such unrealistic stimuli , demonstrating that they genuinely learn some abstract shape representations ., In Experiment 2 , we wanted to understand whether convolutional neural networks develop representations that capture the shape dimensions that dominate perception , the so-called “perceived” shape dimensions , rather than the physical ( pixel-based ) form ., In most available stimulus sets these two dimensions are naturally correlated because the physical form and the perceived shape are nearly or completely identical ., In order to disentangle the relative contributions of each of these dimensions , we needed stimulus sets where a great care was taken to design perceptual dimensions that would differ from physical dimensions ., In 1987 , Biederman put forward the Recognition-by-Components ( RBC ) theory 16 that proposed that objects recognition might be based on shape properties known as non-accidental ., Under natural viewing conditions , many object’s properties are changing , depending on lighting , clutter , viewpoint and so on ., In order to recognize objects robustly , Biederman proposed that the visual system might utilize those properties that remain largely invariant under possible natural variations ., In particular , Biederman focused on those properties of object shape that remain unchanged when the three-dimensional shape of an object is projected to the two-dimensional surface on the eye’s retina , such as curved versus straight object axis , parallel versus converging edges , and so on 23 ., Importantly , RBC theory predicts that observers should notice a change in a non-accidental property more readily than an equivalent change in a metric property ., Consider , for example , geons shown in Fig 4A , top row ., Both the non-accidental and the metric variant differ by the same amount from the base geon ( as measured by some linear metric , such a pixelwise or GaborJet difference ) , yet the non-accidental one appears more distinct to us ., Over years , Biederman and others consistently found such preference to hold in a large number of studies across species 25–28 , age groups 29–31 , non-urban cultures 32 , and even in the selectivity of inferior temporal neurons in monkeys 24 , 33 ., This idea of invariants has also been shown to play an important role in scene categorization 34 and famously penetrated computer vision literature when David Lowe developed his SIFT ( Scale-Invariant Feature Transform ) descriptor that attempted to capture invariant features in an image 35 ., Thus , the sensitivity for non-accidental properties presents an important and well-tested line of research where the physical size of differences between shapes is dissociated from the effect of specific shape differences on perception ., We tested the sensitivity for non-accidental properties using a previously developed stimulus set of geon triplets where the metric variant is as distinct or , typically , even more distinct from the base than the non-accidental variant as measured in the metric ( physical ) space ., Nevertheless , humans and other species report perceiving non-accidental shapes as more dissimilar from the base than the metric ones , presenting us with a perfect test case where , similar to Exp . 2 , physical shape similarity is different from the perceived one ., We evaluated model performance on this set of 22 geons ( Fig 4A ) that have been used previously in behavioral 31 , 32 , 36 and neurophysiological studies ., A model’s response was counted as accurate if the response to a non-accidental stimulus was more dissimilar from the base than the metric one ., We found that all deep but not shallow or HMAX models ( except for HMAX’99 ) showed a higher than chance performance ( Fig 4B ) with performance typically improving gradually throughout the architecture ( Fig 4C; bootstrapped related samples significance test for deep vs . shallow , one-tailed: p < . 001; deep vs . HMAX: p = . 011 ) ., Moreover , deeper networks tended to perform slightly better than shallower ones , in certain layers even achieving perfect performance ., Overall , there was not any clear pattern in mistakes across convnets , except for a tendency towards mistakes in the main axis curvature , that is , convnets did not seem to treat straight versus curved edges as very distinct ., In contrast , humans consistently show a robust sensitivity to changes in the main axis curvature 31 , 36 ., Note that humans are also not perfect at detecting NAPs as reported by 36 ., Thus , we do not go further into these differences because the RBC theory and most previous behavioral and neural studies only address a general preference for NAP changes , and hence do not provide a systematic framework for interpreting the presence or absence of such preference for specific NAPs ., In the first three experiments , we demonstrated convnet sensitivity to shape properties ., However , these convnets have been explicitly trained to optimize not for shape but rather category , that is , to provide a correct semantic label ., Apparently , categorization is aided by developing sensitivity to shape ., But is there anything beyond sensitivity to shape then that convnets develop ?, In other words , to what extent do these networks develop semantic representations similar to human categorical representations over and above mere shape information ?, Typically , object shape and semantic properties are correlated , such that objects from the same category ( e . g . , fruits ) share some shape properties as well ( all have smooth roundish shape ) that may distinguish them from another category ( e . g . , cars that have more corners ) , making it difficult to investigate the relative contributions of these two dimensions ., To overcome these limitations , Bracci and Op de Beeck 37 recently designed a new stimulus set , comprised of 54 photographs of objects , where shape and category dimensions are orthogonal to each other as much as possible ( Fig 5A ) ., In particular , objects from six categories have been matched in such a way that any one exemplar from a particular category would have a very similar shape to an exemplar from another category ., Thus , the dissociation between shape and category is more prominent and can be meaningfully measured by asking participants to judge similarity between these objects based either on their shape or on their category ., By correlating the resulting dissimilarity matrices to human neural data , Bracci and Op de Beeck 37found that perceived shape and semantic category are represented in parallel in the visual cortex ., We employed this stimulus set to explore how categorical information is represented by convnets ., As before , participants were asked to judge similarity among stimuli based either on their shape or on their category ., Note that even for categorical judgments , participants were asked to rate categorical similarity rather than divide stimulus set into six categories , resulting in idiosyncratic categorical judgments and consistency between humans not reaching ceiling ., First , we found that convnets represented shape fairly well , correlating with perceptual human shape judgments between . 3 and . 4 , nearly reaching the human performance limit ( Fig 5C and 5D ) ., Unlike before , the effect was not specific to deep models but was also observed in HMAX and even shallow models ., This observation is expected because , unlike in previous experiments , in this stimulus set physical form and perceived shape are well correlated ., Instead , the purpose of this stimulus set was to investigate to what extent semantic human category judgments are captured by convnets , since here category is dissociated from shape ., We found that all deep but not shallow or HMAX models captured at least some semantic structure in our stimuli ( Fig 5D and 5E; bootstrapped related samples significance test for deep vs . shallow and deep vs . HMAX: p < . 001 ) , indicating that representations in convnets contain both shape and category information ., Similar to Exp . 1 , comparable correlations were observed even when the models were provided only with silhouettes of the objects ( no texture ) , indicating that such categorical decisions appear to rely mainly on the shape contour and not internal features ., The abundance of categorical information in convnet outputs is most strikingly illustrated in Fig 5B where a multidimensional scaling plot depicts overall stimulus similarity ., A nearly perfect separation between natural and manmade objects is apparent ., Note that less than a half of these objects ( 23 out of 54 ) were known to GoogLeNet , but even completely unfamiliar objects are nonetheless correctly situated ., This is quite surprising given that convnets were never trained to find associations between different categories ., In other words , there is no explicit reason why a convnet should learn to represent guitars and flutes similarly ( the category of “musical instruments” is not known to the model ) ., We speculate that these associations might be learned implicitly , since during training objects of the same superordinate category ( “musical instruments” ) might co-occur in images ., Further tests would be necessary to establish the extent of such implicit learning in convnets ., Despite its significance , the correlation with categorical judgments was much weaker than with shape , even after we restricted stimuli to the 23 objects in the ImageNet , meaning that the learned representations in convnets are largely based on shape and not category ., In other words , categorical information is not as dominant in convnets as in humans , in agreement with 6 where deep nets were shown to account for categorical representations in humans only when categorical training was introduced on top of the outputs of convnets ., ( See also Discussion where we talk about the availability of information in models . ), Our results suggest that a human-like sensitivity to shape features is a quite common property shared by different convnets , at least of the type that we tested ., However , the three convnets were also very similar , since all of them very trained on the same dataset and used the same training procedure ., Which convnet properties are important in developing such shape sensitivity ?, One critical piece of information is offer by the comparison to HMAX models ., Despite a similar architecture , in most experiments we observed that overall HMAX models failed to capture shape sensitivity to the same extent as convnets ., The most obvious difference lies in the depth of the architecture ., There are at most four layers in HMAX models but at least eight layers in the simplest of our convnets , CaffeNet ., However , HMAX’99 ( that has two layers ) did not seem to perform consistently worse than HMAX-PNAS ( that has four layers ) ., Another important difference is the lack of supervision during training ., As has been demonstrated before with object categorization 6 , unsupervised training does not seem to be sufficiently robust , at least the way it is implemented in HMAX ., Another hint that supervision might be the critical component in learning universal shape dictionaries comes from comparing our results to the outputs obtained via the Hierarchical Modular Optimization ( HMO ) that was recently reported to correspond well to primate neural responses 10 ., For Exps ., 2a and 4 , where we could obtain the outputs of the HMO layer that corresponds best to monkey neural data , we found largely similar pattern of results , despite differences in depth , training procedure , and training dataset ., The only clear similarity between the tested convnets and HMO was supervised learning ., Finally , part of convnet power might also be attributed to the fully-connected layers ., Both in CaffeNet and VGG-19 , the critical preference for perceived shape emerges at the fully-connected layers ., In GoogLeNet , the preference to perceptual dimensions is typically the strongest at the last layer that is also fully-connected , though earlier layers that are not fully-connected also exhibit a robust preference for perceived shape ., Other parameters , such as the naturalness of the training dataset or the task that convnet is optimized for , might also contribute to the representations that convnets develop ., In short , the tests and the models that we have included in the present paper provide a general answer to our hypotheses about shape representations in convnets , but there are many specific questions about the role of individual variables that remain to be answered ., In the literature , at least two theoretical approaches to shape processing have played an important role: image-based theories 19 , which capitalize on processing image features without an explicit encoding of the relation between them , and structure-based theories 18 , which emphasize the role of explicit structural relations in shape processing ., Our results do not necessarily provide support for particular theories of shape processing ., Of course , in their spirit convnets are closer to image-based theories since there is no explicit shape representation computed ., On the other hand , in Exp . 3 we also found that convnets were sensitive to non-accidental properties even without ever being trained to use these properties ., While in principle HMAX architectures can also develop sensitivity to non-accidental properties when a temporal association rule is introduced 43 , the fact that such sensitivity automatically emerges in convnets when training for object categorization provides indirect support that non-accidental properties are diagnostic in defining object categories , as proposed by the RBC theory 16 ., Of course , a mere sensitivity to non-accidental properties does not imply that convnets must actually utilize the object recognition scheme proposed by the RBC theory 16 ., For instance , according to this theory , objects are subdivided into sets of shape primitives , known as geons , and recognized based on which geons compose that particular object , referred to as a “structural description” of the object ., Finding an increased sensitivity for non-accidental properties does not necessarily imply that all these other assertions of the RBC theory are correct , and it does not by itself settle the controversy between image-based and structure-based models of object recognition ., While we demonstrate an unprecedented match between convnet representations and human shape perception , our experiments only capture a tiny fraction of the rich repertoire of human shape processing ., It is clear from Exp . 1 that despite a strong performance , convnets remain about 20% worse than human observers at object recognition on silhouettes ., Given that convnets are already very deep and were trained exhaustively , it may be a sign that in order to bridge this gap , convnets need additional layers dedicated to developing more explicit structural representations ., Another , more fundamental limitation is their feedforward architecture ., Whereas humans are thought to be able to perform many object and scene recognition tasks in a feedforward manner 44–46 , they are certainly not limited to feedforward processing and in many scenarios will benefit from recurrent processing 47 ., The role of such recurrent processes has been particularly debated in understanding perceptual organization , where the visual system is actively organizing the incoming information into larger entities 48 ., For instance , monkey neurophysiology revealed that figure-ground segmentation benefits both from feedforward and feedback processes 49 , and many models of segmentation utilize recurrent loops ( for an in-depth discussion , see 50 ) ., In contrast , despite their superior object categorization abilities , vanilla convnets show rather poor object localization results , with the top-performing model ( GoogLeNet ) in the ImageNet Large Scale Visual Recognition Challenge 2014 scoring 93 . 3% on a single object categorization task , yet localizing that object with only 73 . 6% accuracy 51 ., In other words , we showed that convnets sensitivity to shape that reflects human judgments once the object itself can be easily extracted from the image ., However , as soon as segmentation and other perceptual organization processes become more complicated , humans but not convnets can benefit from recurrent connections ., Thus , recurrent neural networks , which incorporate the feedforward complexity of the tested convnets , might provide an even better fit to human perception than purely feedforward convnets ., Finally , we have also argued in 50 that feedforward architectures such as convnets might be lacking critical mechanisms that could contribute to the initial image segmentation ., In our view , high performance at object recognition and shape processing tasks should not be taken as evidence that the “convolution-non-linearity-pooling” stack at the heart of convnets is necessarily the right or the full solution of feedforward visual processing yet ., Small modifications to this architecture , such as adding feature map correlations 52 , 53 or performing VLAD 54 or Fisher Vector 55 pooling already provides convnets with the ability to segment input images and represent textures and artistic style , all of which might be the part of feedforward computations in human visual cortex ., Taken together , we demonstrated that convolutional neural networks trained for multiclass object categorization implicitly learn representations of shape that reflect human shape perception ., Moreover , we showed that convnets also develop abstract semantic spaces independent of shape representations that provide a good , albeit weaker , match to human categorical judgments ., Overall , our results provide an important demonstration that convnets are not limited to only extracting objective information from the visual inputs ( such as object category ) but can also represent the subjective aspects of visual information in accordance to human judgments ., In other words , our work suggests that convnets might be a good candidate model for understanding various perceptual qualities of visual information ., Studies reported here were approved by the Social and Societal Ethic Committee at KU Leuven ( Exp . 1 and 2b ) and the Massachusetts Institute of Technology’s Committee on the Use of Humans as Experimental Subjects ( Exp . 1 ) ., Almost all simulations were run with Python using the psychopy-ext package 56 that provides several simple shallow models and bindings to the Caffe library 57 and to several popular computer vision models ( PHOG , PHOW , HMAX-HMIN , and HMAX-PNAS ) , written in MATLAB/C ., For online data collection , we used mturkutils , a Python interface package to Amazon Mechanical Turk ., For data collection in in Exp . 2b , we used similarity rating interface in MATLAB from 58 ., For data analysis , we used several popular free and open source Python packages , including numpy , scipy , scikits-learn , scikits-image 59 , pandas , seaborn , statsmodels , and NLTK 60 ., The code and stimulus sets for all of our simulations are available publicly at https://osf . io/jf42a , except in the cases when the stimulus set is already available online ( links to these stimulus sets are provided in the repository and in the text ) or subject to copyright restrictions ( stimulus set for Exp . 4 ) ., For a maximal reproducibility , all results reported in this manuscript can be generated with a single command: python run . py report—bootstrap ., All stimuli were scaled to 256×256 px size ., Convnets further downsampled these images to their own predefined image sizes ( typically around 224×224 px ) ., Stimuli pixel intensities were rescaled to the range between 0 and 1 , where 0 corresponds to a black pixel and 1 corresponds to a white pixel , and , for deep models , the mean of the ImageNet training set was subtracted ., No further processing was done ., We used three groups of models: shallow , HMAX , and deep ., Shallow models consist of a single layer of processing , and all features are built manually ( i . e . , there is no training ) ., In contrast , HMAX and deep networks have a hierarchical feedforward architecture and have been trained for object categorization ., However , HMAX models are not as deep ( up to four layers ) and have not been trained very optimally ( either by manual feature selection or by imprinting stimulus selectivity ) , whereas deep nets acquire their features through training by backpropagation , which operates on all the weights in the network ., We computed correlations between a particular layer of a model and behavioral data by first computing a dissimilarity matrix between stimuli and then correlating the resulting dissimilarity matrices ., We also used a one minus a Pearson correlation as a distance between stimuli as a metric ., Since these dissimilarity matrices are symmetric , we used only the upper triangle to compute correlations ., Both correlations were computed using the following formula:, ∑i=1n ( xi−x− ) ( yi−y− ) ∑i=1n ( xi−x− ) 2∑i=1n ( yi−y− ) 2 ,, where x and y correspond to either the outputs of a given model to two different stimuli ( for the dissimilarity matrix computation ) or the values in the upper triangle of these dissimilarity matrices ( for correlational analyses ) ., We also conducted the analyses using a normalized Euclidean distance as a metric for producing dissimilarity matrices , but pattern of results remained the same , indicating that the choice of a metric has little effect on our findings ., The upper and lower bounds of the ceiling performance ( shown as a gray band in figures ) were estimated using the procedure described in 70 ., The upper bound was estimated by computing the average Pearson correlation between each participant’s dissimilarity matrix and the average across all participants after z-scoring data ., The lower bound was estimated by computing the average Pearson correlation between each participant’s dissimilarity matrix and the average across the remaining participants after z-scoring data ., In Exp . 1 , the consistency between human and model ( or between two models ) was computed as one minus a normalized squared Euclidean distance between the corresponding accuracy vectors x and y:, 1−∑i=1n ( xi−yi ) 2n ., This metric is a version of the Matching distance that is used for estimating dissimilarity in binary data generalized to the non-logistic case ., We chose this metric due to the largely | Introduction, Results, Discussion, Methods | Theories of object recognition agree that shape is of primordial importance , but there is no consensus about how shape might be represented , and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed ., Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks ( DNNs ) capture important aspects of human object perception ., We hypothesized that these successes might be partially related to a human-like representation of object shape ., Here we demonstrate that sensitivity for shape features , characteristic to human and primate vision , emerges in DNNs when trained for generic object recognition from natural photographs ., We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed ., In particular , although never explicitly trained for such stimuli , DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition ., Even more strikingly , when tested with a challenging stimulus set in which shape and category membership are dissociated , the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments ., As a whole , these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes ., An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks , which is so characteristic in human development . | Shape plays an important role in object recognition ., Despite years of research , no models of vision could account for shape understanding as found in human vision of natural images ., Given recent successes of deep neural networks ( DNNs ) in object recognition , we hypothesized that DNNs might in fact learn to capture perceptually salient shape dimensions ., Using a variety of stimulus sets , we demonstrate here that the output layers of several DNNs develop representations that relate closely to human perceptual shape judgments ., Surprisingly , such sensitivity to shape develops in these models even though they were never explicitly trained for shape processing ., Moreover , we show that these models also represent categorical object similarity that follows human semantic judgments , albeit to a lesser extent ., Taken together , our results bring forward the exciting idea that DNNs capture not only objective dimensions of stimuli , such as their category , but also their subjective , or perceptual , aspects , such as shape and semantic similarity as judged by humans . | medicine and health sciences, neural networks, brain, social sciences, neuroscience, learning and memory, perception, cognitive psychology, mathematics, human performance, statistics (mathematics), cognition, memory, vision, computer and information sciences, object recognition, behavior, visual cortex, psychology, confidence intervals, anatomy, biology and life sciences, sensory perception, physical sciences, cognitive science | null |
journal.pcbi.1003655 | 2,014 | An Expanded Notch-Delta Model Exhibiting Long-Range Patterning and Incorporating MicroRNA Regulation | Differentiation of tissues during early animal development as well as tissue homeostasis during adulthood requires constant communication between cells ., One of the most common ways by which cells communicate with each other is through the Notch-Delta signaling pathway 1–4 ., Notch-Delta signaling is a fundamental cell-to-cell communication mechanism whereby a membrane-bound Delta ligand in one cell binds to a membrane-bound Notch receptor in a neighboring cell , generating a particular downstream response that depends on cellular context 1 , 5 ., Studies in several animals have shown that Notch expression is both temporally and spatially widespread 2–4 , 6 , 7 ., It is not surprising , then , that Notch-Delta signaling is involved in the development and homeostasis of many tissues , most notably those of the nervous system 7 , but also within the heart , kidney , liver , pancreas , breast , inner ear , prostate , thyroid , respiratory system , immune system , and many other cell types ( reviewed in 1 ) ., Although the specific molecular factors and interactions are remarkably complex and vary among different organisms and cell types , the core Notch signaling pathway is relatively simple and is conserved across all bilaterian animals 1 , 3 ., The core pathway consists of five main components: a Notch receptor , a CSL family transcription factor ( TF ) , the Hairy and Enhancer-of-split ( Hes ) family of TFs , the basic helix-loop-helix ( bHLH ) proneural TFs , and a Delta ligand ( Figure 1 ) ., In most animals there are multiple genes that encode each component ., For example , mammals have four Notch receptor genes and at least seven genes for Hes family members that mediate Notch-Delta signaling in different tissues 8 , 9 ., Most importantly , experimental studies have shown that neighboring cells , which communicate via Notch-Delta signaling have opposing expression patterns of these five core components 1 , 5 , 10 ., In the signal-sending or Notch-suppressed cell , only the bHLH proneural TFs and Delta are constitutively active , while Notch and Hes expression are suppressed ., This suppression is thought to be mediated in part through cis-inhibition of Notch by Delta within the same cell 2 , 11 , 12 , and through loss of signaling feedback because Delta is downregulated in the neighboring cell 13 , 14 ., Conversely in the signal-receiving or Notch-activated cell , Notch and Hes are active , while Delta and bHLH proneural gene expression , even if initially active , are eventually suppressed by a Hes family member 5 , 10 ., Notch-Delta signaling is often used in a process called lateral inhibition , where the signal-sending cell eventually differentiates into one cell type while inhibiting the signal-receiving cell from adopting the same developmental fate 15–17 ., Finally , the transcription factor CSL functions as a repressor of Hes family members in the signal-sending cell but becomes an activator of Hes genes in the signal-receiving cell 18 , 19 ., This functional switch of CSL from repressor to activator occurs when the intracellular domain ( ICD ) of Notch translocates to the nucleus where it displaces a co-repressor complexed with CSL 2 ., With this biological background in hand , several mathematical and computational models have been developed over the years to try and quantitatively explain the dynamics of Notch-Delta signaling 12 , 20–24 ., These Notch-Delta models usually fall into one of two categories: comprehensive models and minimal models ., In comprehensive models , all of the experimentally validated ( and sometimes solely computationally predicted ) molecular components are represented as separate variables , and all of the known or predicted interactions are represented as separate equations in the model 23 , 24 ., Although complex , these models have led to some key insights into the specific dynamics of particular Notch-Delta pathway genes ., For example , one model that incorporated extensive feedback between Notch , CSL , and Hes resolved the long-standing issue that Hes can act both as a bistable switch and as an oscillator by showing that the transition between these two states can occur by tuning a single parameter , the Hes1 repression constant 23 ., Another model incorporating Goodwin-modified biochemical kinetic equations for transcription , nuclear export , translation , and DNA-binding and dimerization of each factor showed the importance of the decay rate of Hes1 24 ., However , one drawback of comprehensive models is that they are usually based on experimental data from one particular cell type and , therefore , are not generalizable to other systems ., By contrast , in minimal models only the core molecular components and interactions , which capture the overall , essential Notch-Delta signaling dynamics , are represented in the differential equations ., Unlike comprehensive models , minimal models have the advantage of being applicable to many biological contexts and are also more amenable to parameter sensitivity and stability analyses , which can shed important insight into the dynamics of the system ., The first minimal Notch-Delta model was published by Monk and colleagues 20 , which at its core is a simple two-cell model with a feedback loop involving just two variables: Notch and Delta ., Because the core cascade is essentially linear , they postulated that the Notch variable could represent the quantity of activated Notch protein ( i . e . , Notch ICD ) in the cell or the quantity of downstream Hes TF 20 ., The production functions representing Notch-Delta interactions could be modeled using Hill functions , which are commonly used to model protein-protein as well as protein-DNA interactions 12 , 20 , 25 and for which we now have extensive experimental confirmation through biochemical studies 12 , 26 ., Through their model , Monk and colleagues demonstrated that such a feedback model results in a checkerboard spatial expression pattern of Notch and Delta , which mimics the Notch-Delta pattern found in several biological contexts for which lateral inhibition occurs 20 , 21 , 27 ., With lower cooperativity ( i . e . , a lower Hill coefficient ) , occasionally a spacing of two or three cells can occur 20 ., Subsequent models over the next several years were for the most part variations of the original Monk model ( e . g . , 21 , 22 ) ., Eventually , growing experimental evidence of cis-inhibition of Notch by Delta led to an updated model by Elowitz and colleagues that incorporated this interaction 12 ., Such cis-inhibition was thought to facilitate Notch-Delta lateral inhibition , and indeed the expanded model resulted in faster dynamics , sharper checkerboard patterning and greater robustness to noise 12 ., While the Monk and Elowitz models can explain the patterning in some biological systems such as ciliated cells in the early Xenopus ectoderm 21 , there are cases in both invertebrates 28–34 and vertebrates 7 , 35–37 , where Notch-Delta signaling is clearly active but the pattern is not checkerboard ., In many cases , the pattern is much more random and sparse , where the spacing between signal-sending cells can range from a single cell to dozens of cells in between 30 , 31 , 33 ., For example , studies in zebrafish and chick neuroepithelial tissues have demonstrated a gradient of expression for Notch and/or Delta 7 , 36 , 37 ., Also , the sensory organ precursor ( SOP ) cells of the Drosophila thorax that give rise to microchaetes are spaced about five cells apart when fully developed 5 , 28–30 , 38 ., A pair of studies demonstrated that SOPs in wild-type Drosophila extend dynamic projections called filopodia , and that these filopodia express graded amounts of Delta along the filopoidia and allow the SOPs to reach out and activate Notch signaling in non-neighboring cells 30 , 31 ., Another form of extended communcation in Notch signaling can occur through a process called lateral induction , in which a Delta-bound Notch receptor in the signal-receiving cell can induce the expression of other ligands , which signal Notch in downstream cells 39–41 ., Several authors analyzed more generalized models42–44 with nearest neighbor or juxtacrine inhibition and induction and found these systems could generate Turing solutions45 from a homogeneous steady-state with various wavelengths ., Thus , a model for a juxtacrine system can produce stable periodic patterns with larger spacing between peaks of Delta activity ., Hence , in addition to neighboring-cell lateral inhibition , a form of communication leading to long-range patterning can also operate in the context of Notch-Delta signaling ., Since these filopodia are wide at the base but gradually thin out towards the tip , this suggests a concentration gradient where cells touching near the base of filopodia receive stronger Notch activation compared to cells in contact with the tips ., In this report , we present a minimal Notch-Delta model , which expands upon the previous Monk and Elowitz models 12 , 20 by adding a simple nearest-neighbor Notch gradient term that makes it possible for the system to exhibit long-range effects on cell morphogenesis ., We show that incorporation of a Notch activity gradient term is able to produce a sparse pattern of Delta expression whereby Delta-expressing cells can be spaced many cells apart ., In our studies , we focus on the patterning of larval tail epidermal sensory neurons ( ESNs ) within the peripheral nervous system ( PNS ) of the ascidian Ciona intestinalis ., We quantify the number and spacing of ESNs in wild-type larvae , and show that our expanded Notch-Delta model accurately reproduces the experimentally observed ESN pattern 33 , 34 , 46 ., Ascidians are invertebrate chordates and are the closest invertebrate relatives of vertebrates 47 ., As such , they occupy an important phylogenetic position for understanding how molecular developmental pathways evolved when invertebrates and vertebrates diverged from their last common ancestor 34 , 48 ., Sensory neurons , like those in the Ciona intestinalis PNS , the mechanosensory bristles found in Drosophila , and the hair cells of the mammalian inner ear , are thought to have evolved from a common ciliated sensory-neuron precursor 34 , 49 ., Since Notch-Delta regulated tissues in flies , ascidians , zebrafish , chick and mice have all been shown to exhibit sparse spatial patterning 7 , 30 , 31 , 36 , 37 , our model suggests that Notch-Delta-mediated long-range inhibition may be broadly conserved in bilaterians ., We also demonstrate that regulation of Notch-Delta signaling by microRNAs ( miRNAs ) is conserved across bilaterians ., The miRNAs are a class of conserved small RNAs that regulate expression of target genes through transcript destabilization , deanylation and/or translational inhibition , leading to downregulation of the protein product 33 , 50 ., Previously we demonstrated that in Ciona the miRNA miR-124 downregulates Notch and all three Hes factors , and that these operate in a negative feedback loop 33 ., Here , we show that miRNA-mediated regulation of Notch signaling can be incorporated into the parameter representing the decay rate of the Notch variable , and that modulation of the Notch decay rate in the model accurately mimics the ESN pattern observed in wild type larva and in miR-124 overexpressing transgenic larvae that have altered ESN spacing patterns ., Finally , through a bioinformatics analysis we demonstrate that the majority of miRNAs expressed in sensory cell types of other animals are predicted to target Notch pathway genes in their representative systems , suggesting that miRNA interactions with the Notch signaling pathway may be functionally conserved ., In Ciona intestinalis , the tail epidermal sensory neurons ( ESNs ) differentiate from epidermal precursor cells within the dorsal and ventral midlines ., Previous work in our lab and others 33 , 34 , 46 , 51 has qualitatively shown that the midline ESN pattern is very irregular , although a quantitative investigation of the number , spacing and distribution of ESNs has not been done ., Thus , we began by quantifying ESN numbers and ESN spacings in wild-type embryos by immunohistochemically-labeling the associated cilia with an anti-acetylated tubulin antibody ., We focused on an older developmental stage ( 22 hours post-fertilization at ) , when the larvae have extended their tails and when the final midline ESN pattern has emerged 32–34 ., To identify the midlines , we generated transgenic embryos expressing either an Acete-Scute homolog ( ASH ) RFP reporter or a Delta RFP reporter ( see Materials and Methods ) 34 ., To identify the ESNs , we used fluorescent microscopy to image cilia in embryos immunohistochemically detected with an antibody against acetylated-tubulin ., ESN cell nuclei are smaller than those found in the surrounding epidermal cells , and could be visualized with DAPI staining 32 ., Figure 2 shows a representative embryo used for quantitation ., In agreement with previous qualitative observations 33 , 34 , 51 , we found that the number , distribution , and spacing of ESNs varied considerably from embryo to embryo ( embryos quantitated across three independent biological replicates ) ., Overall , we found no obvious differences between the number of midline cells , number of ESNs or the spacing between ESNs along the dorsal versus ventral midline at 22 hours post-fertilization ( see Figure S1 ) ., Therefore , we only considered statistical averages per midline without distinction between dorsal and ventral counts ., No larvae had fewer than six ESNs per midline , consistent with previous observations that six dorsal midline precursor cells express Delta early in embryogenesis prior to midline formation 32 ., We observed as many as eleven ESNs along a single midline in 22 hr larvae ., We never observed more than eight or nine ESNs in earlier embryos ( hours post-fertilization ) 34 , suggesting that ESNs continue to be specified as the larval midline develops ., We observed a variable pattern in ESN spacing with as few as one and as many as thirteen epidermal ( non-ESN ) cells separating consecutive ESNs ., We never observed two ESNs next to each other , consistent with the hypothesis that Notch-Delta-mediated lateral inhibition is active between neighboring ESN-epidermal cells 32 , 33 ., These results are summarized in Figure 3A–B ., Regarding the distribution of ESNs , we found no apparent bias of ESN position along the anterior/posterior axis ., However , we did observe that consecutive ESNs spaced at least ten cells apart were almost invariably flanked on at least one side by two or three ESNs spaced very closely ( Figure S2 ) ., With this quantitative experimental data in hand , we began drafting a Notch-Delta mathematical model that could adequately explain the patterning of midline ESNs in Ciona ., We began with a linear array of cells representing a single midline at a fixed time point ., As mentioned , we did not notice any obvious differences between the dorsal and ventral midlines at the larval stage ( see Figure S1 ) , so our model is appropriate for modeling either midline ., Future models will modify this static array into a dynamic array that includes cell division ., This 1-D model could also be easily expanded to a 2-D array for modeling planar systems such as the proneural clusters in Drosophila 5 , 12 , 20 , 30 ., Consistent with previous minimal models , each cell tracks the activity of just two biochemical species , Delta ( ) and Notch ( ) or some closely affiliated biochemical species , such as a transcription factor directly linked to these primary proteins ., Note that because our model can be applied to other biochemical and physical systems , when we present the differential equations of our model below , we will denote the Delta and Notch species more generally as and , respectively ., As discussed in the original Monk model 20 , could be taken to represent the quantity of activated Notch ( i . e . , Notch ICD ) in the cell; or it could be taken to stand for the quantity of downstream Hes TF in the cell ., In addition , since the Notch-SuH-Hes cascade is linear and exhibits bistability ( i . e . , there are only one of two stable states for each node - either all ON\\ or all OFF\\ ) , we can regard the states of Notch , SuH and Hes as equivalent , and can therefore consider any of these or all of these lumped together as the variable 52 ., Analogously , since we know that the bHLH proneural genes are expressed in a linear cascade and are upstream of Delta 34 , could represent the quantity of membrane-bound Delta in the cell or could incorporate the activity of the upstream proneural TFs 52 ., Figure 3C shows a schematic of our model for the interaction between neighboring cells ., All the cells in the linear array interact with their nearest neighbors with the exception of the end cells ., The model localizes inside the cell or expressed on the cell surface to signal only the neighboring cells ., It is repressed internally by and activates neighboring cells to stimulate production of ., The species also catalyzes the cis-inhibition of inside the same cell ., The production of depends on the activity of in the neighboring cells ., Both species have linear decay terms based on the half-lives of Notch , , and Delta , ., Finally , we include a communication term for to neighboring cells based on the gradient in activity of active Notch or a related biochemical species between the cells ., The addition of this gradient term is the primary distinction of our model from previous Notch-Delta models ., In earlier models , interactions are exclusively with neighboring cells , which restricts the patterning to primarily alternating on and off states , while our model by including a Notch activity gradient can simulate larger cell spacings , which match that found in Ciona and in other analogous Notch-Delta systems 7 , 30 , 37 ., Although the exact mechanism of long-range communication is currently unknown in Ciona , we favor a nearest-neighbor Notch gradient term versus other possibilities based on our current biological knowledge of Notch-Delta signaling in the Ciona PNS ( see Discussion ) ., All of the above interactions represent the core conserved interactions of Notch-Delta signaling and are supported by extensive experimental evidence 4 , 5 , 10 , 53 ., Let and be the activity levels of Delta and Notch in cell , respectively , then the dynamics for the model described above is given by the following system of differential equations: ( 1 ) In the system above , we let the boundaries satisfy: where and are the average activity levels of Delta and Notch over the entire array of cells ., Clearly alternate boundary conditions could be considered , although other common boundary conditions such as zero or periodic boundary conditions are not appropriate for modeling the Ciona midline ., The functions and the parameters in the model are common in biochemical control models 12 , 20 , 25 , 54 ., The essential form of each function is the same as those found for earlier minimal Notch-Delta models 12 , 20 ( Figure 3D ) ., A full explanation of each of these functions and parameters can be found in Materials and Methods , but here we briefly mention the functions and parameters that are immediately relevant for our analysis ., The first term on the RHS of the equation represents cis-inhibition by ., The parameters and are the linear decay rates of Delta and Notch or a related biochemical species , respectively ., Because our biochemical species do not distinguish between mRNA and protein levels , we may take them as representing mRNA and/or protein decay rates ., The last term in the equation is the linear gradient term representing long-range communication ., This cell-to-cell gradient term could result from bound Notch molecules self-signaling to create a gradient-like pattern of activity ., It could be the result of another signaling biochemical closely aligned with Notch , but not necessarily bound so strongly to the membrane ., From a modeling perspective this gradient form of nearest neighbor communication is the simplest mechanism of long-range patterning and makes a good first order approximation to the kinetic interactions of this signaling pathway ., For the remainder of the article , we will refer to as and as to associate the model state variables with the Delta ( ) and Notch ( ) pathways ., We wrote programs to simulate our Notch-Delta model using the Matlab solver ., We began our simulations with random low activity levels of and in all cells and first observed the qualitative behavior of our system over time ., After some time passed , a few cells developed a high level of ., The high level of in Cell suppressed in the same cell ( cis-inhibition ) and led to above average levels of in Cells and ( lateral inhibition ) ., Via the linear gradient term , subsequent neighboring cells had decreasing levels of , until some critical threshold was reached with sufficiently low that another cell could once again produce a high level of , then the pattern repeated ., The dynamical system exhibited very stable behavior for the levels of and in the immediate region near Cell ., However , we observed decreasing stability of the activity levels as levels of decrease ., When there was sufficient spacing between cells with high levels of , then we observed later development of cells with high levels of in the intervening area of cells ., These later developing cells arose from two distinct dynamical behaviors ., In one case there were sufficiently low levels of far from the ones with high levels of , resulting in the smooth development of an intervening cell with a high level of ., This case was most common early in the simulation ., In the second case , the levels of and oscillated in the regions between stable areas of high , with the amplitude of the oscillations appearing to increase with increased ESN spacing ., With enough spacing , the oscillations increased until a threshold was crossed , allowing the development of another cell with a high level of ., Because of the random initial conditions , different patterns of cells with high levels arose ., The spacings in these patterns depended strongly on the parameter values; however , after sufficient time a stable pattern emerged ., A representative example is shown in Figure 3F ., Note that spacings of more than two cells cannot be achieved with either the original Monk model 20 nor the model incorporating cis-inhibition 12 ( Figure 3E ) ., To determine if our model could explain the ESN pattern along the Ciona midline , we ran a Monte Carlo simulation with =\u200a1000 runs over =\u200a4000 time steps for each run , and compared the number , spacing , and distribution of high Delta-expressing cells with that of the ESNs from wild-type embryos ., Our simulations used the parameter values listed in Table, 1 . The parameters were chosen for the following properties ., The value for , the number of cells , was chosen to match the average number of midline cells from our experiments ., The parameters , , , , and were fairly arbitrary , although they were chosen based on our knowledge of similar biochemical control models from previous work 12 , 20 , 25 , 54 ., As off-diagonal elements , these parameters should not be as significant to the behavior of the system as the other parameters ( though the -mediated decay could be an important parameter when considering the effect of modulating cis-inhibition , as in a previous study 12 ) ., The most significant parameters for the switching behavior are the parameters and , the Hill coefficients ., These are chosen be be greater than one , but not too large to be biologically relevant ., The decay rates and along with the gradient parameter are very significant as we will see in the bifurcation analysis ., In particular , will be important when we consider the effect of microRNA-mediated regulation of Notch signaling ., For these simulations , was adjusted so that the average number of high-Delta cells over the 1000 runs closely matched the number of ESNs from wild-type experiments ., Since Delta is an epidermal sensory neuron marker 34 , throughout the text we will refer to high-Delta cells and ESNs interchangeably ., Figure 3F shows the end results of a typical run , with Movies S1 and S2 showing the dynamics of two separate runs starting with random low initial conditions for both Delta and Notch ., Both movies show the appearance of new ESNs in regions where the spacing between existing ESNs is large ., In movie S1 , the levels of Notch and Delta settle into a very stable equilibrium; while in movie S2 , the levels of Notch in the cells between the ESNs at Cells 27 and 39 show distinct stable oscillations ., Figure 3A–B shows the statistics for the number and distribution of ESNs and inter-ESN spacing from 1000 runs ., While agreement between the average number of ESNs predicted by the model and experimentally observed in larvae is expected , surprisingly the distribution of ESNs and the average ESN spacing matched very well with experimental observations ., The majority of runs in our Monte Carlo simulations produced between 6 and 11 ESNs , with a peak of 9 ESNs , matching experimental observations ., There were some instances of outliers on either side in our simulations , although if we were able to quantify an equivalent number of embryos ( ) , we might expect some experimental outliers as well ., Similarly , the ESN spacing in our simulations matched experimental observations , with the frequency histograms following an identical gamma distribution with a peak at 4 cells and dropping off after 13 cells ., There were a few rare outliers where ESN spacing exceeded 13 cells ., When we analyzed these outliers more closely , we noticed that these large spacings were flanked on at least one side by two closely ESNs ( Figure S2 ) ., These closely spaced ESNs likely stabilize the cells within the large-spacing valley ., This is in agreement with our experiments showing that cases of high inter-ESN spacing were flanked on at least one side by consecutive ESNs with tight spacing ( Figure S2 ) ., Finally , we note that our model has a disproportionate number of one-cell spacings compared with experimental observations ., This is likely due to the intense stability of the high-Delta cells and the strong effect of lateral inhibition in our model ., We chose our Hill coefficients and based on our knowledge of previous biochemical control models 12 , 20 , which produced the reasonable fits seen in Figure 3A–B ., However , we know that changing the coefficients , and , affects the lateral inhibition and induction of immediately neighboring cells and results in differing distributions of cell spacing ., Simulations with and produced significantly broader distributions ( similar means , but a much larger variance ) , while and produced a much narrower distribution ( similar mean with a smaller variance ) ., Our modeling experiments suggest that increases , especially in , would produce more two-cell spacings at the expense of one-cell spacings as suggested in the experiments ., However , since Figure 3A–B shows our model adequately represents the experiments , we chose to center our studies around the case and ., A stability analysis is used to determine equilibrium states of a system and the change in behavior of a system as the parameter values vary ., This analysis is important because it allows us to determine the possible ESN patterns that can be produced from our model , and to rigorously determine if our model can really explain the biology ., We therefore designed programs to help numerically find equilibria and allow the stability analysis of the equilibria ., The stability analysis uses the Jacobian matrix analytically derived from linearizing the system ( 1 ) ( see Materials and Methods ) ., There is a unique homogeneous equilibrium for system ( 1 ) ., Related systems 20 , 42–44 have been analyzed in terms of the stability of the homogeneous equilibrium , showing the existence of Turing solutions ., For system ( 1 ) with the parameters in Table 1 , there is a homogeneous equilibrium with and , which is unstable with multiple positive eigenvalues ., Since the experimental studies do not suggest a periodic pattern , we did not explore Turing solutions ., Our primary interest was the behavior of the many inhomogeneous equilibria ., The Monte Carlo simulations showed the variety and large number of possible stable equilibria for model ( 1 ) ., This model can easily reproduce the stable alternating pattern of the previous Monk 20 and Elowitz 12 models ., These models are very similar to ( 1 ) with =\u200a0 and =\u200a0 , respectively; however , non-zero values of and allow the richer stable patterns shown in the Monte Carlo simulations ., From the many equilibria for this system we chose to systematically explore the stability of the system with different spacings of high levels ., The numerical observations showed decreased stability of the cells some distance from the cells with high levels , so we wanted to explore the nature of any bifurcations leading to limits on the spacing of the cells ., Below we present the stability analysis for different ESN spacings , giving information about the dominant eigenvalues and commenting more about the observed eigenvalue structure ., The parameters we use in this analysis come from Table, 1 . In biological terms , the eigenvalues and eigenvectors tell us the differentiation state of each of the midline cells ., Roughly speaking , if a cell aligns with an eigenvector associated with the most negative eigenvalues , then it is stable and has fully differentiated into an ESN ., The cells that align with the largest components of the eigenvectors associated with eigenvalues with positive real part are unstable and remain bipotent ., To help minimize the effects of the boundary , we varied the number of cells in our simulations to be as close as possible to ( which is the average number of midline cells found in all of our experiments ) , while maintaining symmetry at the boundaries ., Suppose two consecutive ESNs are Cell and Cell , then define ( 1 ESN and epidermal cells ) ., We numerically find the equilibrium of ( 1 ) for each value of ., From the linearized form computed in Materials and Methods , we can readily find the eigenvalues and eigenvectors for this system ., Table 2 summarizes the results of different spacings using the parameters from Table 1 and shows the dominant eigenvalues of the system ., The linear stability analysis of ( 1 ) with the parameters from Table 1 and the spacings and numbers of cells from Table 2 gives a better understanding of this system ., The overall stability of system ( 1 ) is determined by the real part of the dominant eigenvalue , , with this system being asymptotically stable if and only if ., However , this is a high-dimensional system , and different components of the model behave differently near an equilibrium based on its structure ., The time-series local behavior of different components vary more or less depending on their location , and their fate can be understood by careful examination of the eigenvector associated with specific eigenvalues ., With MatLab we computed all eigenvalues and eigenvectors for each of the cases in Table, 2 . In every case we had the smallest eigenvalue with a multiplicity matching the number of cells with high levels of ., By examining the corresponding eigenvectors , we found the largest components centered on the highest ( lowest ) values ., ( Note that because of the scaling , the components of the eigenvectors are much smaller than the components , so we compared only relative size within or components . ), Each of the eigenvectors associated with one of the eigenvalues , , h | Introduction, Results, Discussion, Materials and Methods | Notch-Delta signaling is a fundamental cell-cell communication mechanism that governs the differentiation of many cell types ., Most existing mathematical models of Notch-Delta signaling are based on a feedback loop between Notch and Delta leading to lateral inhibition of neighboring cells ., These models result in a checkerboard spatial pattern whereby adjacent cells express opposing levels of Notch and Delta , leading to alternate cell fates ., However , a growing body of biological evidence suggests that Notch-Delta signaling produces other patterns that are not checkerboard , and therefore a new model is needed ., Here , we present an expanded Notch-Delta model that builds upon previous models , adding a local Notch activity gradient , which affects long-range patterning , and the activity of a regulatory microRNA ., This model is motivated by our experiments in the ascidian Ciona intestinalis showing that the peripheral sensory neurons , whose specification is in part regulated by the coordinate activity of Notch-Delta signaling and the microRNA miR-124 , exhibit a sparse spatial pattern whereby consecutive neurons may be spaced over a dozen cells apart ., We perform rigorous stability and bifurcation analyses , and demonstrate that our model is able to accurately explain and reproduce the neuronal pattern in Ciona ., Using Monte Carlo simulations of our model along with miR-124 transgene over-expression assays , we demonstrate that the activity of miR-124 can be incorporated into the Notch decay rate parameter of our model ., Finally , we motivate the general applicability of our model to Notch-Delta signaling in other animals by providing evidence that microRNAs regulate Notch-Delta signaling in analogous cell types in other organisms , and by discussing evidence in other organisms of sparse spatial patterns in tissues where Notch-Delta signaling is active . | The nervous system of many animals , including the marine invertebrate Ciona intestinalis in our study , develops through a cell-to-cell communication mechanism called Notch-Delta signaling ., Mathematical models for Notch-Delta signaling have been developed that can explain the development of animal nervous systems with a dense arrangement of neurons ., However , there are several cases where the spatial arrangement is much more sparse; we found that the peripheral nervous system of Ciona is one such example ., Here , we develop an expanded mathematical model that is able to account for this sparser spacing , and furthermore demonstrate that the spacing can be widened or shortened through changing a single parameter that is influenced by the concentration of a regulatory microRNA called miR-124 ., The underlying differential equations contain only two variables representing the activity levels of Notch and Delta , and are thus general enough to be applicable to a wide variety of physical and biological systems that exhibit a similar sparse patterning . | biochemistry, biochemical simulations, computer and information sciences, mathematical computing, systems science, mathematics, network analysis, genetics, regulatory networks, biology and life sciences, molecular genetics, computing methods, computational biology, nonlinear dynamics, physical sciences | null |
journal.ppat.1004500 | 2,014 | Silencing by H-NS Potentiated the Evolution of Salmonella | Horizontal gene transfer ( HGT ) has profoundly shaped the course of bacterial speciation and diversification ., The uptake of ‘pre-assembled’ genetic loci involved in antibiotic resistance , virulence , phage resistance or novel modes of metabolism can instantly confer beneficial phenotypes to the recipient cell ., HGT events have been critical in the evolution of almost all bacterial pathogens from their non-pathogenic progenitors 1–5 ., Two of the critical events when the Salmonellae diverged from their last common ancestor with E . coli were the acquisition of the Salmonella Pathogenicity Island-1 ( SPI-1 ) and the tetrathionate reductase ttr gene clusters 6–8 ., SPI-1 is a 40 kb genomic island encoding a Type 3 Secretion System ( TTSS ) required for triggering inflammation and for invasion of cells lining the intestinal mucosa 9–12 ., Together these systems enable Salmonella to outcompete other microbes in the mammalian gut where SPI-1 induces a potent oxidative inflammation that generates tetrathionate , which then serves as a terminal electron acceptor for anaerobic respiration that is available solely to Salmonella but not other gut microbes 7 ., Despite its overall importance to bacterial evolution , any individual HGT event is more likely to reduce bacterial fitness than to improve it ., Even potentially beneficial genes can disrupt regulatory networks or drain metabolic resources away from the production of energy or biomass if they are not properly regulated 13 ., Indeed , studies examining the barriers to new gene acquisition found that genes expressed at high levels are much more likely to be selected against in the new host 14 , 15 ., Virulence-associated genes , including those that encode secretion systems like the TTSS , can be particularly costly and are often lost in the absence of purifying selection ( e . g . virulence attenuation by laboratory passage ) 16–19 ., For example , triggering TTSS activation from the Shigella virulence plasmid in liquid media causes the destabilization and eventual loss of the plasmid from the population 20 ., The nucleoid associated protein H-NS was proposed to buffer the fitness costs associated with HGT by silencing genes with a %GC content significantly lower than the host genome average and are therefore likely to have been acquired from a foreign source 21–25 ., H-NS confers this benefit both by counteracting transcription at standard promoters and by preventing spurious transcription within an adenine and thymine-rich ( AT-rich ) open reading frame at sequences that can adventitiously resemble a bacterial promoter 26 ., H-NS exhibits low sequence specificity and targets DNA by recognizing specific structural features in the minor grove of AT-rich DNA 27 , 28 ., H-NS polymerizes along target AT-rich sequences by virtue of two independent dimerization domains , leading to the formation of extended nucleoprotein filaments 29–32 ., As a result of its activity , H-NS regulates the majority of horizontally acquired sequences in species such as E . coli , Yersinia , Shigella and Salmonella 1 , 33–35 ., Members of the H-NS protein family are distributed between the alpha , beta and gamma proteobacteria ., Functional analogues that bear minimal sequence or structural resemblance to H-NS have been identified in Pseudomonas sp ., ( MvaT and MvaU ) and Mycobacteria sp ., ( Lsr2 ) 36 , 37 ., While global gene expression data sets from Escherchia coli ( E . coli ) , Yersinia enterolitica ( Y . enterolitica ) , Salmonella enterica Sv ., Typhimurium ( S . Typhimurium ) , Pseudomonas aeruginosa ( P . aeruginosa ) and Mycobacteria smegmatis ( M . smegmatis ) point to a common role for the H-NS/MvaT/Lsr2 proteins as silencers of foreign AT-rich sequences , the fitness consequences of mutating the xenogeneic silencers among these species differs significantly 21–24 , 38–40 ., In P . aeruginosa , MvaT and MvaU together are essential and depletion of both of these proteins results in the activation of the Pf4 prophage , which kills the bacterial cell 41 ., In most strains of E . coli , mutations in hns mildly impede growth rates whereas failed attempts at constructing hns mutants in Y . enterolitica and Y . pseudotuberculosis strains strongly suggest hns is an essential gene in Yersinia sp ., 42 , 43 ., S . Typhimurium strain 14028s hns mutants are only viable if additional mutations are present in either the PhoP-PhoQ two component signaling system or the stationary phase sigma factor RpoS 22 ., What remains unclear is why global H-NS mediated gene silencing is so critical for the fitness of S . Typhimurium and Y . enterolitica , but is largely dispensable to other closely related species such as E . coli ., Several members of the Enterobacteriaceae including E . coli , S . Typhimurium and Shigella flexneri ( S . flexneri ) encode a second H-NS-like protein , StpA ., StpA shares 53% sequence identity with H-NS as well as several functional properties , such as the ability to self-associate and bind AT-rich DNA 44–48 ., H-NS and StpA also share a similar domain architecture exemplified by the detection of StpA/H-NS heterodimers in vivo and in vitro 49–52 ., Global transcript analysis and ChIP-on-chip data sets indicate StpA and H-NS co-localize in E . coli and S . Typhimurium , but the loss of stpA only affects the transcript levels of a subset of these loci 47 , 48 ., In fact , loss of StpA alone does not generate observable phenotypes but will further impair the fitness of strains lacking H-NS 45 , 53 , 54 ., The mild effects of stpA depletion may be attributed to low intracellular StpA concentrations 46 , 55 ., StpA is a substrate of the Lon protease and a StpA point mutation , F21C , that imparts resistance to proteolytic cleavage also restored stationary phase viability to an E . coli hns mutant strain 56 ., Other reports , however , suggest H-NS and StpA exhibit similar expression levels with the StpA protein reaching 25 000 copies per cell at mid-exponential phase and H-NS reaching 20 000 copies 57 ., Despite significant sequence homology between H-NS and StpA , the basis for their functional dissimilarities remains unknown ., In this study , we employed an experimental evolution strategy to select for mutations that compensate for the strong fitness defects of S . Typhimurium hns mutants ., Using whole genome sequencing we identified parallel adaptations in many of the hns mutant lineages including genomic deletions in the pathogenicity locus SPI-1 and non-synonomous changes in the gene encoding StpA ., The stpA mutations altered residues in the oligomerization domain and several enhanced the ability of StpA to silence hns regulated genes without having an effect on StpA expression levels ., Much of the fitness defect in the hns mutants could be attributed to overexpression of SPI-1 ., This work provides compelling evidence that H-NS potentiates bacterial speciation by improving bacterial tolerance for horizontally acquired sequences ., These findings also suggest that fitness-cost buffering by xenogeneic silencing proteins contributes to the observed tendency for genomic islands to be AT-rich ., Disruption of the hns gene in the wild type S . Typhimurium 14028s strain background severely restricts its growth rate to the point where cultivation is difficult 22 ., However , we previously demonstrated that hns mutations can be achieved in strains that harbor additional mutations in the gene encoding the alternative sigma factor RpoS ( σS or σ38 ) ., Alleles that reduce σS activity frequently arise during laboratory passage and are present in another commonly used Salmonella laboratory strain , LT2 ., The alleviating effect of rpoS mutations in the hns mutants may be due to the fact that loss of H-NS dramatically improves the stability of RpoS 58 , which may cause the inappropriate overexpression of stationary-phase genes and interfere with the expression of housekeeping genes controlled by RpoD ., To facilitate this study the hns gene from S . Typhimurium 14028s was replaced with a kanamycin resistance cassette in a background harboring a 5 amino acid in frame deletion within the coding region of rpoS that reduces RpoS activity ( referred to as rpoS* ) 22 ., Although this additional mutation improved the tolerance of 14028s for hns mutations , Δhns/rpoS* strains continue to display severe growth defects including dramatically reduced colony size ., In the course of an earlier microarray study of a S . Typhimurium Δhns/rpoS* strain we noted one isolate appeared to lose a large cluster of genes at some point during laboratory passage 22 ., To identify the nature of this deletion the isolate was further analyzed by whole genome sequencing where reads were assembled against the S . Typhimurium 14028s reference genome ( Genbank ID CP001363 . 1 ) using Geneious Pro 5 . 5 . 6 software ., This analysis revealed that the isolate incurred a 10 kb genomic deletion spanning nucleotides 1 , 334 , 560 to 1 , 344 , 664 ( Figure 1A ) ., The deleted region is highly AT-rich ( GC%\u200a=\u200a40% as compared to the genome average of %GC\u200a=\u200a52 ) and encodes several putative envelope proteins including the PhoP activated genes pagC , pagD , pliC , envE , envF and msgA 59 ., Multiple studies have shown that expression of pagC is strongly repressed by H-NS , and the spontaneous loss of these genes from the Δhns isolate suggested that hns mutants are genetically unstable and may shed horizontally acquired sequences during passage 21 , 22 , 60 ., We sought to experimentally determine if the loss of horizontally acquired sequences is a reproducible outcome of deleting hns from S . Typhimurium , as well as to identify novel compensatory mutations that may alleviate the fitness defects associated with the loss of H-NS ., Toward this end an in vitro evolution screen was performed where six independently derived freshly constructed Δhns/rpoS* mutant lineages were serially passaged alongside six lineages of the isogenic rpoS* background ( the “wild type” strain ) in Luria-Bertani broth for 30 days , or approximately 300 generations ( Figure 1B ) ., The lineages were designated WT or Δhns , “A” through to “F” ., Each day during the experiment , aliquots from the cultures were stocked and stored at −80°C to enable the retrospective analysis of genomic changes in each lineage over time ., At the end of the evolution period , the growth rates of the passaged wild type and the passaged Δhns lineages were monitored alongside their unpassaged ( day, 0 ) counterparts ( Figure 1C ) ., All six lineages lacking H-NS displayed significant increases in their growth rates compared to their respective day 0 clone , while the wild type lineages displayed modest improvements in growth ( Figure 1C ) ., Notably , by day 30 the Δhns lineages all exhibited growth rates similar to that of the wild type strains at day 30 ., To identify mutations that arose throughout the evolution period , genomic DNA from the passaged WT and Δhns lineages and their progenitor lines was analyzed by Illumina whole genome sequencing ., In total , the six Δhns lineages acquired 15 missense mutations , 2 small deletions , 2 small insertions and 5 chromosomal deletions larger than 10 kb ( Table 1 ) ., Most striking was the high degree of similarity in these mutations ., Five of six Δhns lineages incurred unique 10–50 kb deletions within the Salmonella Pathogenicity Island 1 ( SPI-1 ) and all six Δhns lineages accumulated missense mutations within the stpA gene encoding the H-NS paralogue StpA ( Figure 2 , Table 1 ) ., In agreement with our earlier observations 22 , three Δhns lineages acquired mutations in the genes encoding the PhoP/PhoQ two component system that activates many H-NS repressed genes involved in virulence , acid stress , resistance to antimicrobial peptides and intramacrophage survival 59 , 61–64 ., Specifically , lineages A and E acquired frameshift mutations in PhoP and PhoQ respectively while Δhns lineage B acquired a missense mutation ( Y320D ) in the cytoplasmic sensor kinase domain of PhoQ ., Throughout the experiment each Δhns lineage acquired a total of three to four mutations with the exception of Δhns lineage D , which acquired eight ., It is notable that Δhns lineage D incurred the largest chromosomal deletion that extended beyond SPI-1 into the locus encoding mutS and mutL , essential components of the methyl-directed mismatch repair pathway 65 ., The loss of either mutS or mutL from E . coli has been shown to result in a mutator phenotype and may explain the accumulation of other missense mutations specific to the Δhns lineage D , namely idnK ( E62G ) , mutY ( D316N ) , yecS ( P169S ) , yhfC ( M255V ) and stm1881 ( V321A ) 66 ., Analysis of the SPI-1 deletion junction regions revealed that 3 of the 5 deletions occurred without any homology in the sequences flanking the deleted segment ., The other 2 SPI-1 deletions occurred between segments homologous in only 4 nucleotides ., This suggests that RecA mediated recombination did not play a role in the loss of this island in the Δhns mutants ( Figure S1 ) ., Analysis of the wild type lineages revealed that comparatively fewer genetic changes arose during the course of the experiment ., 3 of the 6 wild type lineages acquired large chromosomal deletions that extended from 10 kb to 58 kb downstream of the uvrC locus ( Table 2 ) ., Common to all three deleted fragments were components of the uvrABC nucleotide excision pathway and constituents of the flagellar apparatus ., Under the laboratory growth conditions used in this study , expression of the uvrABC and flagellar genes likely resulted in a disadvantageous use of cellular resources ., Apart from these deletions no mutations common among the wild type lineages were observed ., To determine the timeline of the genetic changes that took place , genes of interest were PCR amplified from the frozen daily stocks of the hns mutant lineages and the PCR products were submitted for Sanger sequencing ., This assay enabled the detection of mutant alleles soon after they arose in a given lineage and the relative proportion of the wild type and mutant alleles in the population at each day could be estimated from the sequencing chromatograms by analyzing the dual fluorescence peaks at a particular nucleotide ., The relative signal strength of wild type vs . mutated nucleotides was used to approximate the emergence and dominance of each mutation in each population over time ., To determine when the large chromosomal SPI-1 deletions arose a PCR assay was employed; amplifying a region bridging the deleted segment ., This detection method did not enable us to estimate the relative proportion of SPI-1 deletion strains in the population ., We found the mutations in the PhoP/PhoQ regulatory system and the SPI-1 deletions were acquired by the hns mutant lineages in the early stages of the passaging period , prior to the missense mutations in stpA ( Figure 3 ) ., The PhoP/PhoQ and SPI-1 mutations were detected as early as day 2 of the evolution period in Δhns lineages A , B and D , suggesting these mutations confer the greatest growth advantages and/or are most easily acquired ., Of particular interest is the Δhns lineage C , which did not obtain inactivating mutations in either the PhoP/PhoQ or SPI-1 but displayed a comparable increase in fitness as Δhns lineages A , B , D , E and F in liquid growth assays ( Figure 1C ) ., Δhns lineage C acquired a stpA missense mutation ( M4T ) by day 5 that persisted at low frequency until it also acquired a second mutation in the housekeeping sigma factor RpoD ( G471D ) , at which point the Δhns/stpA/rpoD mutant rapidly outcompeted both the Δhns and Δhns/stpA mutant strains in the population by day 13 ., To address the concern that lineage C acquired SPI-1 inactivating mutations that were not detected with the Geneious Pro software we performed a reference alignment of the raw Δhns lineage C paired end reads to the S . Typhimurium 14028s reference genome using the Bowtie software package and also preformed a de novo genomic assembly of the evolved Δhns lineage C with Velvet 67 , 68 ., A list of variants from both the Bowtie and Velvet assemblies was generated with Samtools and no other mutations besides for the StpAM4T and RpoDG471D variants were identified 69 ., The fact that five out of six Δhns lineages rapidly and independently incurred deletions within the SPI-1 locus suggested that SPI-1 misregulation is a major contributor to fitness defects in S . Typhimurium Δhns mutants ., SPI-1 expression is repressed by hns and activated by a complex positive feedback loop where the production of the HilD regulatory protein induces the expression of HilA , a transcription factor that directly activates expression of the TTSS and effector proteins 70 ., To determine the degree to which SPI-1 impairs growth of the S . Typhimurium Δhns mutant , we deleted the 40 kb genomic island from a wild type strain prior to introducing the hns deletion by transduction ., The SPI-1 deletion significantly improved the growth of the Δhns strain and also provided a mild improvement in growth of the wild type strain ( Figure 4A ) ., The region of SPI-1 lost in all Δhns lineages included the promoter upstream of hilD ., Introduction of a hilD mutation into the Δhns background conferred a growth benefit similar to that of the 40 kb SPI-1 deletion ( Figure 4B ) ., These results indicate that in the absence of H-NS , SPI-1 is activated through a hilD dependent pathway and that the uncontrolled expression of SPI-1 encoded virulence determinants significantly impairs Salmonella growth ., Salmonella enterica harbors a second pathogenicity island , SPI-2 , that encodes a type-3 secretion system distinct from the one encoded on SPI-1 ., Lucchini et al . , previously reported that construction of a Salmonella ΔssrA/Δhns double mutant unable to express the genes encoded in SPI-2 significantly increased the growth rate of the Δhns strain ( grown in LB media and using strain LT2 ) 21 ., To determine if inactivation of SPI-2 encoded TTSS would offer the same fitness benefit as deletion of SPI-1 from a Δhns background , we introduced a 25 kb SPI-2 genomic deletion into Δhns and Δhns/ΔSPI-1 strains ( Figure S2 ) ., Inactivation of SPI-2 did not significantly improve growth of either the Δhns or Δhns/ΔSPI-1 14028s strain to the same extent as loss of SPI-1 ., A similar experiment was conducted in LPM ( low pH , low Mg2+ and low phosphate ) media known to activate SPI-2 to determine if fitness of the Δhns mutant would be adversely affected in a manner dependent on SPI-2 ., The Δhns mutant failed to grow in this media but this growth defect was not alleviated in the ΔssrA/Δhns double mutant indicating that other factors , not SPI-2 , impact fitness in our strain under these particular conditions ., The only gene that acquired mutations in all six passaged Δhns lineages encodes the H-NS paralogue StpA ., All of the acquired stpA mutations resulted in single amino acid substitutions or in-frame insertions that map to the predicted N-terminal and central dimerization domains of the protein ., Because disruption of stpA in a Δhns background is known to exacerbate hns mutant phenotypes , we found it unlikely that these substitutions impaired stpA function ., Intriguingly , the StpA mutations arise exclusively at sites where the unchanged amino acid is not conserved with H-NS , and the residue changes appear to render StpA more “H-NS-like” ( Figure 5A ) ., We hypothesized that the stpA mutations impart H-NS-like silencing properties to StpA and therefore partially compensated for the loss of hns at loci outside of SPI-1 in the serial passaging experiment ., To test the ability of the StpA variants to complement hns mutant phenotypes , we cloned the stpA locus from each passaged Δhns lineage and wild type stpA into a low copy vector with the native stpA promoter ., The resulting plasmids were pStpAWT , pStpAT37I cloned from Δhns lineage A , pStpAT37I/E42ins from Δhns lineages B and D which both acquired the T37I substitution and an E42 insertion , pStpAM4T from Δhns lineage C , pStpAA77D from Δhns lineage E and pStpAK38Q/F76L from Δhns lineage F . The StpA plasmids were transformed into a Δhns/ΔstpA S . Typhiumurim background in order to determine whether or not the isolated StpA variants could ameliorate bacterial fitness in the absence of hns ., Introducing either pStpAWT or StpAT37I did not significantly improve growth of the Δhns/ΔstpA mutant ( Figure 5B ) ., On the other hand expression of the StpAM4T mutant significantly improved bacterial fitness in the liquid growth assay ., Likewise , the StpAT37I/E42ins variant also offered an observable growth advantage ., The strains expressing StpAA77D and StpAK38Q/F76L initially displayed a slight growth advantage and then plateaued at a similar final optical density as the StpAWT expressing strain ., Given that expression of the StpA variants identified in the serial passaging experiment enhanced bacterial fitness to varying degrees , we next tested the ability of the modified StpA proteins to complement the impaired motility phenotype of hns mutants ., H-NS is required for both the expression and assembly of a functional flagellum 71–73 ., H-NS indirectly stimulates flagellar gene expression by repressing hdfR , a known repressor of the flhDC regulatory locus and , in addition , H-NS directly binds to the flagellar protein FliG and helps organize rotor subunit assembly 22 , 74 ., StpA has also been shown to bind FliG , but does not promote motility in the absence of H-NS unless cellular StpA levels are artificially elevated 74 ., To determine if the StpA variants stimulate motility to a greater extent than wild type StpA , we employed the same strains used in the liquid growth assays and measured their radial swarming diameters on soft agar motility plates ., After a 16 hr incubation period , wild type S . Typhimurium displayed a swarming diameter of 62 mm ( Figure 5C ) ., Similar to the hns mutant strain , the S . Typhimurium Δhns/ΔstpA strains harboring pStpAWT and pStpAT37I did not migrate beyond the original inoculation zone ., Remarkably , the StpA variants StpAM4T , StpAA77D and StpAK38Q/F76L restored motility to the Δhns/ΔstpA strain by 30% , 44% and 34% that of the wild type strain respectively ( Figure 5C ) ., StpAT37I/E42ins provided a small yet significant increase in swarming diameter to 16% the wild type diameter ., One possibility by which the StpA variants could restore motility to the Δhns mutant would be if the single amino acid substitutions increase StpA protein stability ., Intracellular StpA pools are reportedly subject to proteolysis by the Lon protease in strains lacking hns 56 ., In this study a mutation in the N-terminal dimerization domain of StpA , F21C , was shown to impart resistance to proteolysis and increase intracellular StpA concentrations ., To determine if any of the StpA mutations identified in our laboratory passage screen influenced protein levels , the amount of intracellular StpA was quantified by western blot analysis ., Δhns strains harboring epitope tagged StpA or its variants was probed with an α-FLAG antibody ., DnaK levels were analyzed on the same blot as a loading control ., Similar to the StpAF21C variant , StpAT37I and StpAT37I/E42 accumulated to higher intracellular levels than StpAWT ( Figure 6 ) ., In contrast the variants StpAM4T , StpAA77D and StpAK38Q/F76L were detected at similar levels to that of StpAWT ., This suggests that the StpA variants identified in this study fall into one of two categories , mutations that increase intracellular StpA levels similar to the previously identified StpAF21C variant , and a novel class of mutations that do not significantly alter intracellular StpA levels ., Notably , it was the latter class of variants that provided partial complementation for the loss of hns in the growth and motility assays suggesting that the amino acid substitutions M4T , A77D and K38Q/F76L alter the functional properties of StpA and not its stability ., Much like H-NS , StpA has also been implicated in silencing AT-rich regions of the genome ., Although the set of genes under control of StpA shares significant overlap with the set of genes regulated by H-NS , in the absence of H-NS , the silencing activity of StpA alone does not provide sufficient repression of H-NS regulated loci 46 , 48 , 75 ., To determine if the missense mutations acquired throughout the evolution of the Δhns lineages enhanced StpAs silencing activity , we measured the steady state transcript levels of four model H-NS and StpA regulated loci from a Δhns/ΔstpA strain harboring pStpAWT , pStpAM4T , pStpAA77D and pStpAF21C ., The StpAM4T and StpAA77D variants were chosen for transcript analysis because they provided the greatest restoration of the Δhns growth and motility defects without altering protein stability , while the StpAF21C variant was included to determine the regulatory consequences of increased intracellular StpA levels ., Also included in the analysis were a Δhns complemented strain ( Δhns+pHNS ) and a Δhns strain , which served as reference points for repressed and derepressed transcript levels ., cDNA from mid-log cultures was analyzed by Q-PCR with primers specific to proV , hilA , ssrA and yciG ., proV is a well studied H-NS regulated gene target that resides outside the Salmonella pathogenicity islands , while hilA and ssrA are transcriptional activators encoded within SPI-1 and SPI-2 respectively ., yciG is part of the rpoS regulon and was previously shown to be highly induced in a Salmonella SL1344 strain lacking stpA 48 ., Relative to the Δhns complemented strain , the transcript levels of proV , hilA , ssrA and yciG increased by 20-fold or greater in the Δhns strain ( Figure 7 ) ., The expression of yciG is highly repressed in the Δhns+pHNS strain , its transcript levels were lower than the detection limit of the Q-PCR cycler and could not be reported with confidence ., The Δhns/ΔstpA strain harboring pStpAWT displayed a greater increase in the transcripts levels of proV , ssrA and yciG compared to the Δhns strain , while hilA transcript levels were reduced by 4 . 5-fold in the presence of pStpAWT ., Substituting StpAWT with StpAM4T significantly reduced the expression levels of proV and ssrA by approximately 2-fold and 10-fold respectively ., The StpAA77D variant provided even greater repression of proV and ssrA by reducing their transcript levels by 4-fold and 20-fold relative to StpAWT ., Similar to the Δhns+pHNS strain , both StpAM4T and StpAA77D maintained yciG expression levels close to the detection limit of the sensor ., In contrast , the StpAF21C variant that accumulates to higher intracellular levels than StpAWT did not maintain significantly lower expression levels of any of the four genes tested relative to the pStpAWT strain ., This further establishes that the StpAM4T and StpAA77D variants as a novel set of mutations that enhance StpA silencing activity without affecting protein stability ., While the two single point mutations , M4T and A77D , significantly enhanced StpAs silencing activity at the proV , ssrA and yciG promoters regions these substitutions did not provide increased repression of hilA , encoding the SPI-1 transcriptional activator HilA ., hilA expression is induced by three transcriptional activators , HilC , HilD and RtsA 70 ., In the absence of H-NS it is possible that silencing complexes generated by StpAM4T and StpAA77D , although more effective than StpAWT , were unable to impede the combined HilC and HilD-mediated activation of hilA ., We repeated our in vitro evolution on an expanded number of freshly constructed hns deletion mutants to determine if loss of hns invariably led to mutations in stpA and , if so , to use this technique as a novel method of mapping functional residues in stpA ., Toward this end hns deletion mutations were introduced by transduction into the rpoS-low strain to generate 12 independent lineages ., To assess the impact SPI-1 may have on the evolution of stpA another 12 linages were generated by introducing the hns mutation into a strain already lacking SPI-1 ., Each of the 24 lineages were serially passaged in LB media over the course of 21 days and the stpA genes of each lineage were amplified by PCR and sequenced ., Sequencing of the stpA genes revealed missense mutations in 10/12 of the hns mutants in the rpoS background and 12/12 of the rpoS*/SPI-1 mutant background ( Table 3 ) ., Remarkably the two hns mutant strains that did not acquire misssense mutations in stpA did acquire silent mutations , suggesting that either that stpA is prone to mutation in the absence of hns or that the presumably silent mutations actually affect StpA levels or function by increasing mRNA stability or by altering codon usage ., As before all missense mutations mapped to the oligomerization domain between residues 2 and 80 of the stpA protein ., Furthermore some lineages acquired as many as 4 different nucleotide substitutions ., The fact that 30 independent lineages ( 24 in this experiment and 6 in the initial experiment ) acquired mutations in stpA and that none of these were nonsense mutations confirms that there is strong selective pressure to acquire mutations in stpA in the absence of H-NS ., Notably there were some differences observed in the specific mutations acquired between the two lineages ( those with or without SPI-1 ) ., In the presence of SPI-1 the StpA protein was altered at several different residues but a cluster of mutations occurred at or near codon 38 ( nucleotides 112–114 ) encoding lysine including a silent mutation at nucleotide 111 ., Strains that evolved in the absence of SPI-1 acquired a notably different set of mutations where all but one lineage acquired a mutation at nucleotide 110 resulting in the StpA ( T37I ) variant ., Additional mutations changed the asparagine at positions 2 or 7 to an aspartic acid ( N2D or N7D ) ., This suggests that the pressures that select for mutations in StpA may differ in the absence of SPI-1 ., The results of the evolution experiment provided an opportunity to map what single or double residue changes in StpA would be sufficient to engender it with H-NS-like functionality ., This functionality of each StpA variant was assessed by their ability to restore motility ( Figure 8A ) when expressed in the hns mutant background ., This assay was chosen because our data with the earlier StpA variants indicated motility restoration correlates closely with their to silence H-NS regulated loci ., These assays uncovered functional changes in single amino acids that cluster to two discrete regions of the StpA protein ( Figure 8 ) ., The functional variants StpAN2D , StpAM4T , and StpAN7D map to the short helix 1 that lies within the N-terminal dimerization domain while the variants StpAF76V , StpAF76L , StpAA77D and StpAM78K all map to helix 4 which is contained in the central dimerization domain ., Other single residue StpA variants , where changes mapped to helix 3 or the short linker segments that connect helix 3 to the other helices , failed to restore significant motility to the hns mutant ., Modeling these changes on the previously published H-NS oligomer structure show that the individual changes that confer H-NS-like function to StpA are buried within the dimerization interfaces or present on the outer , convex , surface of the H-NS filament while the residues that do not lie predominantly on the concave surface of the filament , and are largely predicted to have surface exposed side chains ( Figure 8B ) ., It is important to note the StpA residues were mostly assessed individually ( only two double-mutants were assessed ) and that some residues that appear to have no gain of function in our assays may have a more dramatic impact in combination with other changes ., Electrophoretic mobility shift assays were used to determine if changes in the StpA variants that led to increased “H-NS-like” function manifest as differences in their ability to form nucleoprotein c | Introduction, Results, Discussion, Materials and Methods | The bacterial H-NS protein silences expression from sequences with higher AT-content than the host genome and is believed to buffer the fitness consequences associated with foreign gene acquisition ., Loss of H-NS results in severe growth defects in Salmonella , but the underlying reasons were unclear ., An experimental evolution approach was employed to determine which secondary mutations could compensate for the loss of H-NS in Salmonella ., Six independently derived S . Typhimurium hns mutant strains were serially passaged for 300 generations prior to whole genome sequencing ., Growth rates of all lineages dramatically improved during the course of the experiment ., Each of the hns mutant lineages acquired missense mutations in the gene encoding the H-NS paralog StpA encoding a poorly understood H-NS paralog , while 5 of the mutant lineages acquired deletions in the genes encoding the Salmonella Pathogenicity Island-1 ( SPI-1 ) Type 3 secretion system critical to invoke inflammation ., We further demonstrate that SPI-1 misregulation is a primary contributor to the decreased fitness in Salmonella hns mutants ., Three of the lineages acquired additional loss of function mutations in the PhoPQ virulence regulatory system ., Similarly passaged wild type Salmonella lineages did not acquire these mutations ., The stpA missense mutations arose in the oligomerization domain and generated proteins that could compensate for the loss of H-NS to varying degrees ., StpA variants most able to functionally substitute for H-NS displayed altered DNA binding and oligomerization properties that resembled those of H-NS ., These findings indicate that H-NS was central to the evolution of the Salmonellae by buffering the negative fitness consequences caused by the secretion system that is the defining characteristic of the species . | H-NS is an abundant DNA-binding protein found in enteric bacteria including the important pathogens Escherichia , Salmonella , Vibrio , and Yersinia , that plays a primary role in defending the bacterial genome by silencing AT-rich foreign genes ., H-NS has been hypothesized to facilitate the evolution of bacterial species by acting as a buffer against the negative consequences that can occur when new genes are incorporated into pre-existing genetic landscapes ., Here experimental evolution and whole-genome sequencing were employed to determine the factors underlying the severe growth defects displayed by Salmonella strains lacking H-NS ., Through tracking the evolution of several independently derived mutant lineages , we find that compensatory mutations arise quickly and that they occur in loci related to virulence ., A frequent outcome was loss of the Salmonella Pathogenicity Island-1 , the defining genetic island of the genus Salmonella ., Among other things these findings demonstrate that H-NS has enabled the birth of a new and important bacterial pathogen by buffering the fitness consequences caused by overexpression of SPI-1 ., These findings are likely generalizable to pathogens such as E . coli , Yersinia , Shigella , and Vibrio cholerae , all of which maintain a pool of “expensive” AT-rich virulence genes that are repressed by H-NS . | horizontal gene transfer, medicine and health sciences, microbiology, gene transfer, bacterial diseases, enterobacteriaceae, bacteria, bacterial pathogens, foodborne diseases, infectious diseases, escherichia coli infections, medical microbiology, microbial pathogens, salmonella, genetics, biology and life sciences, evolutionary biology, evolutionary processes, organisms | null |
journal.ppat.1003381 | 2,013 | Murinization of Internalin Extends Its Receptor Repertoire, Altering Listeria monocytogenes Cell Tropism and Host Responses | Co-evolution of microbes with their hosts can select stringently specific host-microbe interactions at the cell , tissue and species levels 1 ., Species-specific host-microbe interactions , which are the rule rather than the exception , pose a challenge for the use of laboratory animal models to study human pathogens , including Listeria monocytogenes ( Lm ) , the etiological agent of listeriosis , a deadly foodborne infection ., Lm is able to actively cross the intestinal barrier , reach the systemic circulation and cross the blood-brain and placental barriers , leading to its dissemination to the central nervous system and the fetus 2 ., The mouse is a genetically amenable model that is widely used to investigate human diseases 3 , 4 ., To obtain a mouse model in which the pathogenic properties of a given pathogen are similar to what is observed in human , species specificity can be circumvented by humanizing the mouse by transgenesis 5 , 6 , 7 , 8 , knock-in 9 , knock-out 10 or xenograft techniques 11 ., One can also adapt the pathogen to the mouse by multiple passages on cell lines 12 , 13 or in vivo 14 , or specifically “murinize” a pathogen ligand so that it interacts with the mouse ortholog of a species-specific human receptor 15 , 16 ., The Lm surface protein InlA interacts with E-cadherin ( Ecad ) and mediates Lm entry into epithelial cells , which express this adherens junction protein 17 , 18 ., Cadherins constitute a family of calcium-dependent cell adhesion receptors ., Ecad is expressed mainly in epithelia , whereas N-cadherin ( Ncad ) is found primarily in neuronal cells and endothelial cells together with VE-cadherin 19 , 20 ., Ncad can also be coexpressed with Ecad in epithelial cells 21 ., Importantly , Ncad has been reported to not act as a receptor for InlA , and so far Ecad is the only known classical cadherin acting as a receptor for InlA 18 ., In contrast to Ecad from human , guinea pig , rabbit and gerbil , mouse Ecad ( mEcad ) and rat Ecad are not recognized by InlA and do not promote bacterial entry 9 , 22 ., The interaction of InlB , another Lm invasion protein , with its host receptor is also species-specific 23 ., InlB recognizes the hepatocyte growth factor receptor Met of human , mouse , rat and gerbil but not that of guinea pig and rabbit 9 , 23 , 24 ., Two mouse lines have been established to study InlA-Ecad interaction in vivo: a transgenic mouse line expressing human Ecad ( hEcad ) in enterocytes ( hEcad Tg ) 6 , and a humanized mEcad knock-in mouse line ( E16P KI ) with an E16P amino acid substitution which enables mEcad to interact with InlA without affecting Ecad homophilic interactions and allows Lm internalization 9 , 22 ., Using these two humanized mouse models , we have demonstrated that InlA mediates Lm crossing of the intestinal epithelium upon targeting of luminally-accessible Ecad around goblet cells 6 , 9 , 25 , and that InlA and InlB act interdependently to mediate the crossing of the placental barrier 9 ., Epidemiological investigations have confirmed the relevance of these experimental findings , and shown that InlA is implicated in Lm crossing of human intestinal and placental barriers 9 , 26 ., In 2007 , Wollert et al . engineered a genetically modified InlA with the purpose of increasing its binding affinity to hEcad 16 ., Two amino acid substitutions in InlA , S192N and Y369S , were shown to enhance InlA binding affinity to hEcad 16 ., Neither S192N nor Y369S substitution has been observed in the more than 500 Lm isolates InlA sequences we have checked ( our unpublished results ) ., Wollert et al . published that this increased affinity for hEcad translates into an increased bacterial entry into human epithelial cells ( Caco-2 ) 16 ., Importantly , Wollert et al . also showed that this modified InlA binds the extracellular cadherin domain 1 ( EC1 ) of mEcad in solution with a comparable affinity to that of the wild-type ( wt ) InlA for hEcad EC1 16 ., They hypothesized that this interaction would allow Lm expressing this “murinized” InlA ( InlAm ) to cross intestinal barrier and would render wt mice orally permissive to Lm infection , a phenotype which is mediated by InlA in permissive models 6 ., In support of this hypothesis , Wollert et al . found an increased intestinal , spleen and liver bacterial loads of wt mice orally inoculated with Lm expressing InlAm , yet only after 3 to 4 days post infection , which is later than in models permissive to InlA-Ecad interaction 6 , 9 , 16 ., Moreover , the ability of InlAm to mediate mEcad-dependent Lm internalization into host cells has never been tested ., In addition , InlAm unexpectedly promoted pronounced inflammation and intestinal epithelial cell damages in wt mice 16 , whereas wt InlA mediates the crossing of the intestinal barrier without inducing significant intestinal response and tissue damage in hEcad transgenic mice 6 , 27 ., This prompted us to investigate the detailed properties of InlAm in cultured cells , as well as the in vivo cell and tissue tropisms of bacteria expressing InlAm , as compared to that of its isogenic parental Lm strain that expresses wt InlA ., Here , we demonstrate that InlAm promotes bacterial entry not only into mEcad-positive but also into mEcad-negative mouse cells ., We show that InlAm-mediated entry into mEcad-negative cells is mouse Ncad ( mNcad ) -dependent ., Importantly , InlAm-mNcad interaction allows bacteria to specifically target Ncad-positive villous M cells in vivo , a cell type which is not targeted by Lm in humanized mouse models permissive to InlA-Ecad interaction ., This leads to enhanced intestinal inflammatory responses and disruption of the intestinal barrier integrity , both of which are not observed in Lm-infected humanized mice and human listeriosis ., Together , these results demonstrate that the murinization of InlA not only extends Lm host range , but also broadens its receptor repertoire , consequently changing Lm cell tropism and enhancing host immune responses to Lm ., These results challenge the relevance of using InlAm-expressing Lm to study human listeriosis and in vivo host responses to this human pathogen ., We first investigated whether the increased affinity of InlAm to hEcad translates into an enhanced invasion of hEcad-expressing cells , as proposed by Wollert et al . 16 ., To this end , we assessed InlAm-dependent entry into LoVo cell , a human epithelial cell line expressing hEcad 22 ., Lm wt strain and Lm expressing InlAm ( Lm-inlAm ) invaded LoVo cells at similar levels ( Figure 1A ) ., Because Lm can be internalized by InlA-independent pathways such as InlB-Met , we transferred either inlA or inlAm onto the chromosome of Listeria innocua ( Li ) , a naturally non-invasive and non-pathogenic Listeria species , in which heterologous expression of inlA has been shown to confer invasiveness 17 , 18 , 28 ., Li expressing either InlA ( Li-inlA ) or InlAm ( Li-inlAm ) were equally invasive in LoVo cells ( Figure 1B ) ., These results indicate that contrary to what is reported by Wollert et al . 16 , the increased affinity of InlAm to hEcad does not translate into an increased level of bacterial entry ., Both Li-inlA and Li-inlAm recruited hEcad when incubated with LoVo cells , suggesting that hEcad is involved in both InlA- and InlAm-mediated entries ( Figure 1E , upper panel ) ., Because purifed InlAm interacts with the purified EC1 domain of mEcad , Wollert et al . have proposed , although not tested , that InlAm would mediate bacterial entry into mEcad-expressing cells 16 ., We therefore tested the ability of InlAm to promote bacterial entry into the mouse epithelial cell line Nme , which expresses mEcad 29 ., InlAm promoted bacterial entry into mEcad-expressing Nme cells , although to a lower level than InlA in hEcad-expressing LoVo cells ( Figure 1C and D ) ., Li-inlAm also recruited mEcad during cell invasion , whereas as expected , Li-inlA does not ( Figure 1E , lower panel ) ., Together , these results show that, ( i ) the increased affinity of InlAm to hEcad does not enhance bacterial entry into hEcad-expressing cells , and, ( ii ) the murinization of InlA confers to Lm an enhanced ability to be internalized into mEcad-expressing cells 16 ., Monk et al . have reported that Lm-inlAm invades mouse CT26 cells more efficiently than Lm 13 ., Strikingly , CT26 cells do not express mEcad ( Figure 2A ) 30 , yet we confirmed that InlAm mediates bacterial entry into these cells ( Figure 2B ) ., Because classical cadherins exhibit a high level of conservation in their EC1 domains ( Figure S1A ) , we tested whether Li-inlAm would recruit another classical cadherin than mEcad in CT26 cells ., We labeled CT26 cells with a pan-cadherin antibody , which recognizes the cytoplasmic domain of classical cadherins 31 ., CT26 cells were strongly stained with the pan-cadherin antibody ( Figure S1B ) , indicating that they likely express classical cadherin proteins ., Furthermore , this pan-cadherin-immunoreactive protein was recruited in CT26 cells by Li-inlAm but not Li-inlA ( Figure S1B ) ., Immunoblotting and immunostaining revealed that CT26 cells express Ncad ( Figures 2C and D ) , a classical cadherin known to be expressed in endothelial cells , neurons and some transformed epithelial cells 20 ., Importantly , Li-inlAm , but not Li-inlA , recruited Ncad in CT26 cells ( Figure 2D ) ., We next tested other cell lines for Ncad expression ., We found that Nme cells ( which also express mEcad and are permissive to InlAm-mediated entry ) , human HeLa cells , and guinea pig 104C1 cells all express Ncad ( Figure 2C ) ., As in CT26 cells , InlAm promoted bacterial entry into HeLa and 104C1 cells , although these two cell lines do not express Ecad and are therefore not permissive to InlA-dependent entry ( Figure S2 ) 23 ., These results suggest that the murinization of InlA confers to this protein the ability to interact with Ncad from different species , and to enter into host cells expressing Ncad ., To investigate if mNcad serves as a receptor for InlAm-mediated entry into CT26 cells , CT26 cells were treated with mNcad-specific siRNAs or scrambled control siRNAs ., Treatment of CT26 cells with mNcad siRNAs led to a reduced expression of mNcad which correlated with a significantly decreased InlAm-dependent entry ( Figures 3A and B ) ., To directly assess the ability of mEcad and mNcad to act as receptors for InlAm , we used the BHK21 cell line , which is of hamster origin and does not express any known classical cadherin 32 , and transfected this cell line with plasmids encoding either hEcad , mEcad or mNcad ., As expected , both InlA and InlAm mediated bacterial entry into hEcad-expressing cells ( Figure 3C ) ., Moreover , InlAm mediated entry into mEcad-expressing cells , whereas as previously shown , InlA did not ( Figure 3C ) 22 ., Most importantly , we also demonstrated that InlAm mediated bacterial entry into Ncad-expressing cells , whereas , as previously shown , InlA did not ( Figure 3C ) 18 ., To investigate whether the InlAm receptor repertoire extends to other members of classical cadherins , we tested the ability of mouse P-cadherin ( mPcad ) and VE-cadherin ( mVEcad ) to serve as receptors for InlAm ( Figure S1A ) ., Neither mPcad nor mVEcad acted as a receptor for InlAm or InlA ( Figure 3C ) ., Taken together , these data confirm that InlA exhibits a species-specific and narrow repertoire for Ecad and mediates entry into hEcad- but not mEcad-expressing cells , and demonstrate that by widening InlA species spectrum from human to mouse Ecad , murinization of InlA extends its receptor repertoire to Ncad ., In order to investigate if these in vitro results translate into an in vivo phenotype , and study in particular the cell tropism of InlAm-expressing bacteria , we investigated Ncad luminal accessibility at the intestinal epithelium level , which is the portal of InlA-mediated entry of Lm ., In contrast to luminally-accessible Ecad which is mostly observed as rings surrounding goblet cells 25 , mNcad was accessible on the apical pole of villous M cells ( Figure 4 , Movie S1 ) , but not M cells of Peyers patches ( Movie S2 ) in wt mice ., The expression of luminally-accessible Ncad was also detected on the apical pole of villous M cells in E16P KI mice ( Figure S3 , Movie S3 ) ., These results suggest that InlAm may allow bacteria to target villous M cells upon mouse oral inoculation ., To specifically investigate whether InlAm-expressing bacteria target cells that express luminally-accessible Ncad , we inoculated orally wt mice with Li-inlA or Li-inlAm , and for comparison we inoculated humanized E16P KI mice orally with Li-inlA ., As expected from our recent results 25 , Li-inlA were found in goblet cells 5 hrs post oral inoculation of E16P KI mice ( Figures 5C and D ) ., In contrast , Li-inlAm targeted both goblet cells ( Figures 5A and D ) and villous M cells ( Figures 5B and D , Movie S4 ) in wt mice ., We next performed a detailed quantification of the location of bacteria in the intestinal epithelium ( i . e . goblet cells , villous M cells , other epithelial cells ) ., This demonstrated that , contrary to InlA , which targets almost exclusively goblet cells in E16P KI mice ( 82% ) , InlAm preferentially targets villous M cells ( 56% ) in wt mice , and to a lower degree goblet cells ( 34% ) ( p<0 . 001 , χ2 test analysis ) ( Figure 5D ) ., In agreement with these results obtained with Li-inlAm , Lm-inlAm also targeted both goblet cells ( Figures S4A and D , S5A , Movie S5 ) and villous M cells ( Figures S4B and D , S5B , Movie S6 ) in both wt and E16P KI mice , in contrast to Lm which exclusively targeted goblet cells , only in E16P KI mice ( Figures S4C and D , S5C , Movie S7 ) ., Together , these results demonstrate that while InlA- and InlAm-Ecad interactions both contribute to the targeting of goblet cells , InlAm-mNcad interaction allows bacteria to target villous M cells , a cell type which is not targeted when InlA interacts only with its native receptor Ecad ., To investigate the impact of InlAm-mNcad interaction on the infection process , we inoculated orally wt and E16P KI mice with Lm-inlAm or Lm ., In Lm-infected E16P KI mice in which InlA-Ecad interaction is functional , InlA promoted Lm invasion of the small intestinal tissue and bacterial dissemination to spleen and liver as early as 2 days post infection ( dpi ) ( Figure 6 ) ., In contrast , in Lm-inlAm infected wt mice , in which both InlAm-Ecad and InlAm-Ncad interactions are functional , Lm bacterial loads in the small intestinal tissue , spleen and liver were not significantly increased at 2 dpi compared to Lm-infected wt mice , but were at 4 dpi ( Figure 6 ) ., This delayed systemic dissemination was also observed when comparing Lm-inlAm to LmΔinlA in E16P KI mice ( Figure S7 ) ., These results demonstrate that , although promoting Lm crossing of the wt mouse intestinal barrier , InlAm delays bacterial systemic dissemination relative to InlA in E16P KI mice , and therefore alters the kinetics of Lm infection in vivo ., Given the changes in infection kinetics induced by InlAm , and the artifactual route of translocation taken by InlAm-expressing bacteria at the intestinal epithelium level , we investigated whether InlAm-Ncad-mediated targeting of villous M cells would have an impact on host responses ., Strikingly , oral inoculation of Lm-inlAm led to a significant neutrophil recruitment in wt ( Figures 7A and B ) , E16P KI ( Figures S8A and B ) and hEcad Tg mice ( Figures S8A and B ) , which was not observed with Lm in E16P KI ( Figures 7A and B ) and in hEcad Tg mice ( Figures S8A and B ) ., Importantly , neutrophil infiltration correlated only with InlAm-mediated invasion , and did not reflect bacterial load in the villi , which was actually the highest in Lm-infected humanized mice , in which no neutrophil infiltration was observed ( Figures 7A–C , S8A–C ) ., Moreover , a significant increase in IFN-γ and IL-1β expression was observed in the intestinal tissue of wt mice infected with Lm-inlAm , whereas no significant increase was observed in Lm-infected wt and humanized mice ( Figures 7 D and E ) ., Together , these results indicate that InlAm-Ncad-mediated intestinal invasion per se leads to exacerbated host responses compared to InlA-Ecad-mediated intestinal invasion , and are not a reflect of enhanced bacterial tissue invasion ., We next assessed intestinal barrier integrity upon infection by testing the intratissular diffusion of biotin administered intraluminally ( see Material and Methods ) 33 ., In wt and humanized mice infected by Lm for two days , biotin localized exclusively to the luminal side of the small intestine ( Figures 7F and S8D ) ., In contrast , although the intestinal villi of Lm-inlAm infected wt and humanized mice were not heavily infected , biotin accessed the lamina propria ( Figures 7F and S8D ) ., These findings indicate that InlAm-Ncad-mediated intestinal invasion leads to a disruption of intestinal barrier integrity ., Together , these results demonstrate that the murinization of InlA profoundly modifies the pathogenic properties of Lm by altering its intestinal portal of entry , host intestinal responses and intestinal barrier integrity ., InlA interaction with Ecad allows Lm translocation across the intestinal epithelium and is therefore a critical event in the development of systemic listeriosis , one of the deadliest foodborne infections in human ., Because InlA does not interact with mEcad , the discovery and characterization of this key step were made in species permissive to InlA-Ecad interaction ( guinea pig , gerbil ) and humanized mouse models ( hEcad Tg and E16P KI mouse lines ) 6 , 9 ., A genetically engineered Lm strain expressing a murinized InlA ( InlAm ) enabling interaction with mEcad in vitro has been proposed to constitute an attractive alternative model to study human listeriosis in wt mice 16 ., A practical advantage of this latter system is that it can be readily used to infect several different mouse lines ., However , a systematic study comparing the properties of Lm expressing InlAm to that of its isogenic parental strain has not been performed , neither in vitro nor in vivo ., Here we show that InlAm is able to recruit mEcad and mediate mEcad-dependent entry into cultured cells ., We also show that InlAm mediates entry into goblet cells of wt mice , which express luminally-accessible mEcad ., These results confirm that the S192N and Y369S substitutions confer to InlA a phenotype in wt mice which is observed in humanized mice permissive to InlA-Ecad interaction 25 ., Importantly , we also uncover that InlAm is able to recruit Ncad and mediate Ncad-dependent internalization ., This artifactual interaction translates in vivo into InlAm-dependent targeting of villous M cells , intestinal inflammatory responses , disruption of intestinal barrier integrity and delayed bacterial systemic dissemination in wt mice , as well as in humanized mice ., Such stricking phenotypes are not observed in humanized mice orally-inoculated with wt Lm , suggesting that they depend on InlAm-Ncad interaction and invasion of villous M cells , but not on InlAm-Ecad interaction and invasion of goblet cells ( Figure 8 ) ., It is important to note that these phenotypes are also present in E16P KI and hEcad Tg mice infected with Lm-inlAm , indicating that intestinal inflammation is a direct consequence of InlAm-mediated intestinal invasion , and proving that the absence of inflammation in Lm-infected humanized mice is not a side effect of mouse humanization , but is a genuine property of InlA-dependent intestinal invasion ., These results are in agreement with the observation by Wollert et al . that infection with Lm-inlAm leads to severe intestinal inflammation and tissue damage in wt mice 16 , and with our earlier observation that InlA has little impact on Lm intestinal responses in mice permissive to InlA-Ecad interaction 6 , 27 ., This indicates that the murinization of InlA , in addition to broadening the host range of Lm , also extends its receptor repertoire to another member of the classical cadherin family , Ncad , therefore modifying its cell tropism , host responses and the dynamics of infection ., The engineering of InlAm was based on the rational protein design of a modified InlA that would increase InlA-hEcad binding affinity 16 ., Indeed , S192N and Y369S substitutions in InlA lead to a 6 , 700-fold increase in the binding affinity of InlA to hEcad 16 ., Here we have shown that this does not translate into increased invasion of hEcad-expressing cells ., Before drawing this conclusion , we ensured that the BHK21 cell line we used does not express other cadherins than the one we intended to study ., A possible reason for the observed increased level of invasion of Lm-inlAm in Caco-2 cells observed by Wollert et al . is the coexpression of Ecad and Ncad in these cells 21 ., These results suggest that InlA-hEcad interaction , although it is of relatively low affinity ( KD\u200a=\u200a8±4 µM ) 16 , has been naturally selected to mediate an optimal level of infection ., We have shown that InlB , another major invasion protein of Lm , does not play a significant role for the crossing of the intestinal barrier 23 ., In contrast , InlB has been reported to promote Lm expressing InlAm to invade intestinal villi 34 ., Our results shed light onto these apparent contradictory results and raise the possibilty that InlAm-Ncad mediated invasion of villous M cells may involve the InlB pathway ., Shigella flexneri , the etiological agent of bacillary dysentery is associated with strong polymorphonuclear infiltration , severe local inflammation , disruption of intestinal barrier integrity , yet no systemic dissemination 35 , 36 ., In contrast , listeriosis in human and humanized mice is characterized by the paucity of intestinal symptoms , the absence of polymorphonuclear intestinal infiltration , little local inflammation , the absence of intestinal barrier disruption , but systemic dissemination 6 , 27 , 36 , 37 ., We have demonstrated that Lm-inlAm triggers pro-inflammatory response and disrupts epithelial integrity in intestinal tissue of wt and humanized mice , and exhibits a delayed systemic dissemination , compared to Lm-infected humanized mice ., These observations strongly suggest that the targeting of villous M cells by InlAm-expressing bacteria triggers pro-inflammatory host responses which contain bacterial invasion but lead to intestinal epithelium damages ., This fits with the observation that antigen delivery via villous M cells stimulates immune reponses 38 ., Like InlAm , Als3 is a Candida albicans invasin that binds both Ecad and Ncad to invade host cells 39 ., Candida albicans has been shown to favor gut inflammation and promotes food allergy accompanied by gut epithelial barrier hyperpermeability , the underlying mechanisms of which are so far unclear 40 , 41 ., Our study indicates that Candida albicans may use Als3 to target Ncad-positive villous M cells , and thereby trigger intestinal inflammation ., The specific functions of villous M cells remain poorly understood , yet villous M cells are a particularly abundant constituent of the intestinal epithelium ., Our results show that InlAm- and Als3-expressing microorganisms would be particularly instrumental to study villous M cell functions ., Repeated infection of mice in vivo or mouse cells in vitro allows the obtention of “murinized” pathogens adapted to the mouse ., Despite the great adaptability of microbes , evolutionary constraints limit pathogen variability 42 ., A mutation beneficial under certain environmental conditions may end up as disadvantageous in another , highlighting the fine-tuning of host-microbe interactions ., The structure-based rational design of InlAm was proposed as a subtle and elegant way to electively “murinize” a microbial ligand with least impact on the pathogen ., However , we provide here evidence that the rationally designed InlAm has gained the unfortunate ability to interact with another surface protein than its cognate receptor Ecad ., Even though InlAm mediates Lm crossing of the intestinal barrier , a phenotype which is strictly dependent on InlA-Ecad interaction , the way by which Lm crosses the intestinal barrier in an InlAm-dependent manner differs from what observed with wt Lm in humanized mice and humans , as does the resulting infection process ., This illustrates that murinization of human-specific pathogens , although an elegant and rational approach , may unfortunately mislead rather than ease the understanding of human infectious diseases pathophysiology ., Caution must therefore be exercised before engineering and using “murinized” pathogens to study human infectious diseases ., Bacterial strains , plasmids and primers are listed in Table S1 ., Note that the sequences of inlA , inlAm in Lm and in Li were confirmed by sequencing , as well as the integration sites of inlA and inlAm in Li and the deletion site of inlA in Lm ., Listeria and Escherichia coli strains were respectively cultivated in BHI and LB at 37°C with shaking at 180 rpm ., To deliver plasmids into Li , E . coli S17-1 ( colistin and nalidixic acid sensitive ) cells were transformed with the plasmids followed by conjugation with Li ( colistin and nalidixic acid resistant ) ., Mammalian cell lines used in this study were routinely cultured at 37°C in 5% CO2 ., Except for the culture medium for BHK21 which was supplemented with 5% fetal bovine serum , all the cell culture media were supplemented with 10% fetal bovine serum ., Human epithelium LoVo cells were cultured in F12K nutrient GlutaMax medium ., Mouse epithelium Nme cells were cultured in DMEM GlutaMax medium supplemented with 10 µg/ml insulin ., Mouse CT26 and guinea pig 104C1 cells were cultured in RPMI1640 GlutaMax medium supplemented with HEPES buffer and sodium pyruvate ., Human HeLa cells were cultured in MEM GlutaMax medium ., Hamster BHK21 cells were cultured in GMEM GlutaMax medium supplemented with tryptose phosphate buffer and HEPES buffer ., All the culture medium and related chemicals were purchased from Gibco ( Invitrogen ) ., Transient transfection of mammalian cells was performed with jetPRIME transfection kit ( Polyplus transfection ) ., The scrambled ( sc-37007 ) and mouse Ncad specific siRNAs ( sc-35999 ) were purchased from Santa Cruz ., For the transfection of siRNAs , mouse CT26 cells were seeded into the 24-well plates for 1 day and then transfected with scrambled siRNAs ( 25 nM ) or mNcad-specific siRNAs ( 25 nM ) followed by 1 day incubation and replacement of transfection medium with growth medium another 1 day of incubation before infection ., For the transfection of plasmid DNAs , BHK21 cells were transiently transfected with pcDNA3 expression vector harboring the cDNAs of each cadherin ( 1 µg DNA for each well in a 24-well plate ) followed by 2 days incubation before infection ., The strategy to express inlA or inlAm in Li is as described based on integrative plasmid pAD containing a constitutive promoter 43 ., The primers EagI_UTRhly-F and UTRhly-R were used to amplify the hly 5′ UTR of Lm EGDe ., Full length of inlA and inlAm were amplified from the genomic DNA of Lm EGDe and Lm-inlAm , respectively , with the primers UTRhly_inlA-F and SalI_inlA-R2 ., The resulting PCR products were ligated to hly 5′ UTR by splicing-by-overlap-extension ( SOE ) PCR ., The final SOE PCR products , containing the entire hly 5′ UTR sequence fused to the start codon of the inlA ( hly 5′ UTR-inlA ) or inlAm , ( hly 5′ UTR-inlAm ) , were then cloned in pCR-Blunt ( Invitrogen ) and verified by sequencing ., Plasmids containing correct sequence and pAD-cGFP were digested by EagI and SalI ., The backbone of pAD-cGFP was ligated with hly 5′ UTR-inlA and hly 5′ UTR-inlAm to form pAD-inlA and pAD-inlAm ., The mouse N-cadherin ( mNcad ) cDNA was bought from Open Biosystems ( Thermo Scientific ) and the cDNAs of mouse P-cadherin ( mPcad ) and mouse VE-cadherin ( mVEcad ) were from Riken Fantom Clones ( Dnaform ) ., To form pcDNA3-mNcad , mNcad cDNA was cloned into EcoRI-NotI site of pcDNA3 ., The plasmids pcDNA3-mPcad and pcDNA3-mVEcad were constructed by inserting mPcad and mVEcad cDNAs into NotI-KpnI site of pcDNA3 , respectively ., Cell suspensions from confluent monolayers were seeded at a concentration of 5×104 cells per well in 24-well tissue culture plates and grown for 40–48 hr in an antibiotics-free medium at 37°C ., Lm and Li strains were grown to OD600 at 0 . 8 and 0 . 6 in BHI , respectively ., Bacterial culture were then washed with PBS and diluted in cell culture medium without serum ., Bacterial suspensions were added to the cells at a multiplicity of infection ( MOI ) of approximately 50 and incubated for 1 hr ., Following wash with complete medium , 10 µg/ml of gentamicin was added to kill the extracellular bacteria for 1 hr ., The cells were then washed by complete medium and PBS , and homogenized in PBS supplemented with 0 . 4% Triton X-100 , followed by serial dilution and colony forming units ( CFUs ) counting ., For cadherin recruitment assay , the procedure was the same as the invasion assay except that the cell attachment buffer ( HEPES 20 mM , NaCl 150 mM , glucose 50 mM , MgCl2 1 mM , CaCl2 2 mM , MnCl2 1 mM , 0 . 1% BSA ) was used for infection and PBS ( Ca2+/Mg2+ ) ( Gibco ) was applied to wash the non-attached bacteria stringently followed by fixation ., Eight to 10-week old C57BL/6 female mice ( JANVIER ) and isogenic mEcad E16P KI female mice were food restricted overnight but allowed free access to water ., Lm culture was prepared as described 6 , and inoculated with a feeding needle intragastrically 44 ., Mice were then immediately allowed free access to food and water ., All the procedures were in agreement with the guidelines of the European Commission for the handling of laboratory animals , directive 86/609/EEC ( http://ec . europa . eu/environment/chemicals/lab_animals/home_en . htm ) and were approved by the Animal Care and Use Committee of the Institut Pasteur , as well as by the ethical committee of “Paris Centre et Sud” under the number 2010-0020 ., Preparation of tissue sections and whole mount tissues were as described 9 , 25 ., The following antibodies and fluorescent probes were used for immunostaining and Western blot: anti-hEcad clone HECD-1 mouse monoclonal antibody ( Invitrogen ) , anti-mEcad clone ECCD-2 rat monoclonal antibody ( Invitrogen ) , anti-β-actin clone AC-15 mouse monoclonal antibody ( Sigma ) , anti-Ncad clone 32/N-cadherin mouse monoclonal antibody ( BD ) , anti-Ncad clone GC-4 mouse monoclonal antibody ( Sigma ) , anti-pan cadherin clone CH-19 monoclonal antibody ( Sigma ) , anti-M cell clone NKM 16-2-4 rat monoclonal antibody ( Miltenyl Biotec ) , R6 anti-Li rabbit polyclonal antibody and R11 anti-Lm rabbit polyclonal antibody 45 , Rat anti-mouse Ly-6G ( BD ) , wheat germ agglutinin ( WGA ) conjugated with Alexa Fluor 647 ( Jackson ImmunoResearch ) , Alexa Fuor 488 goat anti-rabbit ( Invitrogen ) , Alexa Fluor 488 or Alexa Fluor 546 goat anti-mouse ( Invitrogen ) , Alexa Fluor 647 donkey anti-rat ( Jackson ImmunoResearch ) , Alexa Fluor 546 goat anti-rat ( Invitrogen ) , Cy3-conjugated streptavidin ( Jackson ImmunoResearch ) and Hoechst 33342 ( Invitrogen ) ., Biotin was used as a molecule to address the integrity of intestinal epithelium as described previously 33 ., Briefly , 2 mg/ml of EZ-link Sulfo-NHS-Biotin ( Pierce ) in PBS was slowly injected into the lumen of ileum loop via the open end adjacent to cecum immediatedly after removal of the entire ileum ., After 3 min , the loop was opened followed by PBS wash and 4% paraformaldehye fixation ., Four mice for each condition were sacrificed 2 days post infection ., 1 cm-long of ileal loop of each animal was applied for RNA extraction ., The RNA isolation , reverse transcription and quantitative real time PCR ( qRT-PCR ) were performed as described 46 | Introduction, Results, Discussion, Materials and Methods | Listeria monocytogenes ( Lm ) is an invasive foodborne pathogen that leads to severe central nervous system and maternal-fetal infections ., Lm ability to actively cross the intestinal barrier is one of its key pathogenic properties ., Lm crosses the intestinal epithelium upon the interaction of its surface protein internalin ( InlA ) with its host receptor E-cadherin ( Ecad ) ., InlA-Ecad interaction is species-specific , does not occur in wild-type mice , but does in transgenic mice expressing human Ecad and knock-in mice expressing humanized mouse Ecad ., To study listeriosis in wild-type mice , InlA has been “murinized” to interact with mouse Ecad ., Here , we demonstrate that , unexpectedly , murinized InlA ( InlAm ) mediates not only Ecad-dependent internalization , but also N-cadherin-dependent internalization ., Consequently , InlAm-expressing Lm targets not only goblet cells expressing luminally-accessible Ecad , as does Lm in humanized mice , but also targets villous M cells , which express luminally-accessible N-cadherin ., This aberrant Lm portal of entry results in enhanced innate immune responses and intestinal barrier damage , both of which are not observed in wild-type Lm-infected humanized mice ., Murinization of InlA therefore not only extends the host range of Lm , but also broadens its receptor repertoire , providing Lm with artifactual pathogenic properties ., These results challenge the relevance of using InlAm-expressing Lm to study human listeriosis and in vivo host responses to this human pathogen . | Co-evolution of microbes with their hosts can select stringently specific host-microbe interactions at the cell , tissue and species levels ., Listeria monocytogenes ( Lm ) is a foodborne pathogen that causes a deadly systemic infection in humans ., Lm crosses the intestinal epithelium upon the interaction of its surface protein InlA with E-cadherin ( Ecad ) ., InlA-Ecad interaction is species-specific , does not occur in wild-type mice , but does in transgenic mice expressing human Ecad and knock-in mice expressing humanized mouse Ecad ., To study listeriosis in wild-type mice , InlA has been “murinized” to interact with mouse Ecad ., Here , we demonstrate that in addition to interacting with mouse Ecad , InlAm also uses N-cadherin as a receptor , whereas InlA does not ., This artifactual InlAm-N-cadherin interaction promotes bacterial translocation across villous M cells , a cell type which is not targeted by InlA-expressing bacteria ., This leads to intestinal inflammation and intestinal barrier damage , both of which are not seen in humans and humanized mouse models permissive to InlA-Ecad interaction ., These results challenge the relevance of using InlAm-expressing Lm as a model to study human listeriosis and host responses to this pathogen ., They also illustrate that caution must be exercised before using “murinized” pathogens to study human infectious diseases . | medicine, infectious diseases, model organisms, immunology, biology, microbiology | null |
journal.pcbi.1004540 | 2,015 | Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum | Theoretical studies of decision-making have focused on the dichotomy of whether an environmental model is utilized , i . e . model-free or model-based strategies 1 , 2 ., In a typical model-free strategy , called a value-based strategy , the goodness of each action candidate is memorized and learned directly from experienced sequences of state , action , and reward in the form of an action value function 2–5 ., The hypothesis that such value-based strategies are implemented in the cortico-basal ganglia circuit1 , 6 is supported by a growing number of reports of action-value coding neuronal activities in the striatum , the input site of the basal ganglia , in rats 5 , 7 , 8 , monkeys 4 , 9–11 , and humans 12 ., By contrast , in a model-based strategy , the goodness of each action candidate is evaluated indirectly using an internal model of environmental state transitions ., Recent fMRI studies found BOLD signals correlated with estimated states and state prediction errors in the prefrontal cortex 13–15 ., While the value-based and model-based strategies have been helpful in dissecting the process of decision-making , the validity of such concepts and consequent predictions need to be assessed in light of actual animal and human behaviors ., For example , animals often utilize a simple “win-stay , lose-switch” ( WSLS ) strategy , in which the same action is repeated if it is rewarded and switched if it is not rewarded 5 , 16 ., This strategy does not conform to either the value-based or the model-based strategy ., Theoretical studies have shown that optimal behavior under uncertain state observation can be represented as a finite state machine in which an action is selected depending on the agent’s discrete internal state , and the state is updated based on sensory observation and reward feedback 17 ., The WSLS strategy is simply realized as a finite state machine with two states ., Here we consider the validity of the finite state-based strategy as another class of model-free strategy along with the value-based strategy in modeling animal choice behaviors ., We reanalyze a part of the data we published previously 18 , and we show that the finite state strategy fits the choice behavior of rats in a free-choice task more accurately than the value-based strategy and the model-based strategy ., We further reanalyze the firing of phasically active neurons ( PANs; putative medial spiny neurons ) recorded from the dorsolateral striatum ( DLS ) , dorsomedial striatum ( DMS ) , and the ventral striatum ( VS ) during the task ., We show that the individual states of the finite state strategy are encoded in DMS at the time of choice and that clusters of states are encoded in VS . Furthermore , the action values used in the value-based strategy are also encoded in DMS ., These results suggest that both the value-based strategy and the finite state strategy are implemented in the striatum ., Next we explore more detailed descriptions of choice behavior using computational models that can predict rat choices based upon past experiences ., Along with the Markov models and the value-based strategy tested in our previous study 18 , we tested the model-based strategy and the finite state strategy ., While the likelihood of a model fitted to given choice sequences is a useful criterion for comparing models , it is also important to check how the model performs when it runs autonomously ., One direct way to check this performance is to compare statistical features of the behavioral sequences produced by the model in a simulation with performance of rats in the actual task ( see Materials and Methods ) ., We simulated the Q , FQ , DFQ , and ESE models with constant parameters and the FSA models with 4 , 6 , and 8 states ., We excluded the models with variable parameters because the random walk assumption was effective for fitting a model to a given choice sequence , but not for the generation of choice sequence in a free run ., We took the number of trials required to reach the block-change criterion ( 80% or more optimal choices in the last 20 trials ) as a measure of the flexibility of adaptation ( Fig 5A–5D ) and the probability that the same action was selected after the rewarded or non-rewarded trial , P ( a ( t+1 ) = a ( t ) | r ( t ) = 1 ) and P ( a ( t+1 ) = a ( t ) | r ( t ) = 0 ) , respectively , as a measures of the robustness of the action ( Fig 5E and 5F ) ., Statistics were calculated separately for blocks with higher reward probability settings ( 90 , 50% ) and ( 50 , 90% ) and lower reward probability settings ( 50 , 10% ) and ( 10 , 50% ) ., We tested the hypothesis that data from rats could be generated from each model using the mean of the six statistics ( Fig 5C–5F ) ., Only the FSA model with 8 states was not rejected by any statistical test ( the level of the confidence interval for each statistic was set to ( 100–5/6 ) % , so that the chance of at least one false rejection is 5%; Bonferroni Method ) ., This result shows that only the FSA model with 8 states sufficiently reproduces the behavior observed in the rats , although it does not exclude the possibility that there are other models better than the FSA model with 8 states ., Previous studies have shown that striatal neurons code not only observable behavioral variables , such as action and reward 5 , 7 , 10 , 20–22 , but also hidden variables estimated from behavior using computational models , such as action values 4 , 5 , 7 , 12 , 23 ., In our previous study 18 , regression analysis revealed that action values , which were estimated from behavioral data based on the FQ-learning with variable parameters , were coded most strongly in DMS during action execution ., In this analysis , we re-analyzed the same neuronal data to examine whether a new class of hidden variables , namely , states and state clusters of the FSA with 8 states , were also coded ., However , if we use a regression model that employs only states and clusters as regressors , it would lead to Type I errors ( false positives ) ., For instance , the estimate of state 1 is strongly correlated with the left action choice in the same trial , detecting action-coding neurons as state-coding neurons ( Fig 4C ) ., To avoid this problem , we first considered a full model including all possible variables ( 30 variables ) that might be coded by striatal neurons ( Poisson regression model , see Materials and Methods ) ., Then , we extracted only the important variables to explain the output using lasso regularization 24 ( see Materials and Methods ) ., The full model we used was:, logμ ( t ) =β0+βbb ( t ) +βaa ( t ) +βrr ( t ) +βaa ( t−1 ) +βrr ( t−1 ) +βQLQL ( t ) +βQRQR ( t ) +βQCQc ( t ) +βVV ( t ) +βPLQPL:Q ( t ) +βx1x1 ( t ) +βx2x2 ( t ) +⋯+βx8x8 ( t ) +βx1x1 ( t+1 ) +βx2x2 ( t+1 ) +⋯+βx8x8 ( t+1 ) +βCLCL ( t ) +βCRCR ( t ) +βCWCWSLS ( t ) +βPL:FSACL:FSA ( t ), ( 1 ), where μ ( t ) is the expected number of spikes at trial t in a certain time bin and βi is the regression coefficient for each explanatory variable ( regressor ) ., b ( t ) is the monotonically increasing factor , namely , b ( t ) = t , which is inserted to capture the task event-independent monotonic increases or decreases in firing pattern ., The remaining regressors are classified into three types: We applied lasso to this full model , which can identify minimally important regressors among many and redundant regressors ( see Materials and Methods ) ., When lasso identified certain regressors to explain the activity of a certain neuron , we interpreted this to mean that “the neuron coded the regressors . ”, A single striatum neuron tended to code multiple variables in different time bins as shown in Fig 6 ., Lasso detected significant populations of neurons that coded observable information ( I ) and estimated information based on the FQ-learning ( II ) , similar to our previous analysis 18 ., In addition , this analysis detected neurons that coded states of the FSA model ( III ) ., Fig 6A–6C show an example of DMS neurons in which firing rate was significantly correlated with the posterior probability of states of the FSA model ., During action selection , firing rate was best explained by the regression model including not only the action , but also x5 ( t ) , in which the FSA model doubts the current belief that left hole is better and wants to choose the right hole ( see Fig 4C ) ., Fig 6D–6F show an example of DMS neurons in which firing rate was significantly correlated with the posterior probability of a transited state of the FSA model ., The firing rate during the rat’s entry to the left or right hole ( note that the reward or non-reward tone was presented at the onset of the hole poke ) was best explained by the regression model , including not only the action , reward , and x7 ( t ) , but also x7 ( t+1 ) ., Here the FSA model believes the right hole is better following an exploratory choice ( see Fig 4C ) ., Fig 6G–6I show an example of VS neurons coding the rat’s sub-strategy ( cluster ) ., There was a significant , positive correlation between neuronal firing rate during action selection and the posterior probability of the win-stay , lose-switch cluster estimated by the FSA model with 8 states ., In our previous study , we detected action-value coding neurons and state-value coding neurons by linear regression analysis , in which action values estimated by the FQ-learning were used as regressors ., In this study , we used an augmented regression model ( Poisson regression model ) , including not only variables of the FQ-learning , but also variables of the FSA models ., As a result , neurons coding variables of the FQ-learning were still detected ( Fig 7A–7C ) as in our previous analysis 18 , although the performance of the FQ-learning model was worse than that of the FSA model ., Significant proportions of neurons in which the firing rates were correlated with action values ( QL or/and QR ) were found in all regions ( Fig 7B ) ., Significant proportions of state value- ( Fig 7A ) and chosen value-coding neurons ( Fig 7C ) were found mainly in DMS and VS . A substantial proportion of striatal neurons also coded internal states of the FSA model ( Fig 7D–7F ) ., A significant proportion of cluster-coding ( CL , CR , and/or CWSLS ) neurons were found in VS ( Fig 7D ) , which might be similar to the strategy-coding neurons reported in monkey striatum 26 ., The proportion of neurons coding x ( t ) in DMS showed a peak during the action execution ( Fig 7E ) ., After entry into the left or right hole ( and the reward or no-reward tone was presented ) , populations of x ( t+1 ) in all regions were increased ( Fig 7F ) , consistent with state transition dependence on reward feedback ., Some neurons in DMS showed firing correlated with x ( t+1 ) even before presentation of the reward or no-reward tone ( Fig 7F ) , which was possible because the reward was highly predictable ( 90% or 10% ) in one of the actions in each block ., Were variables of the FQ-learning and the FSA models separately coded in different neurons ?, During action execution ( 500 ms before entry into the L/R hole ) , neurons coding only the variables of the FQ-learning model ( state value , action value , chosen value ) were 6 . 9% ( 14/204 ) in DLS , 8 . 9% ( 10/112 ) in DMS , and 2 . 9% ( 4/138 ) in VS . Neurons coding only FSA-related variables ( sub strategy , x ( t ) , x ( t+1 ) ) were 8 . 8% ( 18/204 ) in DLS , 22 . 3% ( 25/112 ) in DMS , and 14 . 5% ( 20/138 ) in VS . Neurons coding both variables were 2 . 0% ( 4/204 ) in DLS , 7 . 1% ( 8/112 ) in DMS , and 8 . 7% ( 12/138 ) in VS . While VS neurons significantly tended to code variables of both models , in DLS and DMS there were no significant tendencies ( p = 0 . 10 for DLS , p = 0 . 13 for DMS , and p < 0 . 0001 for VS , chi-squared tests ) ., Interestingly , not all states were equally coded in the striatum ( Fig 8 ) ., During action execution ( Fig 8A ) , only the proportion of state-4- and state-5-coding neurons in DMS and VS ( also state 6 and 8 in DMS ) were statistically significant , and both states preceded an exploratory action in the keep-left and keep-right clusters ( Fig 4C ) ., After execution of an action and reward feedback ( Fig 8B ) , representations of most subsequent states appeared in DLS and DMS , while representations of the same state x5 , persisted in VS . Interestingly , states 2 and 7 are major transition targets from states 4 and 5 , and these signals , especially , the signal of state 7 , were prominent in DLS ., The finite state-based strategy implemented with N = 8 states showed a significantly higher prediction accuracy ( average likelihood ) for rat choice behaviors than the best reinforcement learning model , the FQ-learning model 5 18 ., Furthermore , we compared statistical features of the time course of learning ( the number of trials to reach 80% optimality ) and the probabilities of repeating the same action after rewarded or non-rewarded outcomes of the rats and the algorithms when faced the same task ( Fig 5 ) ., We found that only the FSA model with 8 states could reproduce those features similar to the rats ., Therefore the FSA model is the best model to predict rat actions in individual trials and also to reproduce generic features of the time course of learning , although we cannot deny the possibility that there might be an even better model in both respects ., The FSA model is conceptually different from the other models ., The Q-learning ( FQ-learning ) models and the ESE models are normative models that prescribe behaviors for maximization of rewards , whereas the FSA model is a descriptive model that seeks only to describe the behavior as it appears in the data 27 ., The reformulated Baum-Welch algorithm was used not to find the parameters with which the models maximize the reward , but to find the parameters with which the models mimic the choice behavior of rats ., The FSA models do not explain why and how the rats learned the procedure ( Fig 4C ) ., If an FSA-like algorithm is implemented in the brain , how could the algorithm learn the appropriate choice and transition probabilities to efficiently obtain a reward ?, A possible scenario is that rats use the value-based strategy in the beginning of the training ., Meanwhile , the finite state strategy monitored behavior to form a procedure that mimicked the value-based strategy without explicit value evaluation ., After massive training , the procedure was formed , and the finite state strategy overrode action selection ., We speculate that the finite state strategy could be regarded as generalized habit formation ., Traditionally , habitual actions are considered automatic responses controlled by simple stimulus-response associations without any associative links to the outcome of those actions 28 ., The finite state strategy could be considered as an extended habitual action that depends not only on stimuli , but also internal states ., To test this idea , further behavioral experiment will be required ., Internal states of the FSA model were represented in the all three subregions of the striatum ( Fig 7E and 7F ) , while it has been reported that habitual actions involve DLS 28–31 ., We speculate that retention of internal states required for the FSA model involves the working memory functions of the prefrontal cortex 32 , which can explain the internal state representation in not only DLS , but also DMS and VS , where the prefrontal cortex projects 33 ., Analysis of neuronal activities suggests that all striatal areas we recorded , namely , DLS , DMS , and VS , are involved in the finite state strategy ., Interestingly , not all states were equally coded in the striatum ( Fig 8A and 8B ) ., While codings of x4 ( t ) and x5 ( t ) were found in DMS and VS , coding of x1 ( t ) , x2 ( t ) , x3 ( t ) , and x7 ( t ) was not observed in any areas ., Note that x4 ( t ) and x5 ( t ) are the states in which an action is likely to be switched after repeated unrewarded actions at x1 ( t ) , x2 ( t ) , x8 ( t ) or x7 ( t ) ., This uneven representation of states suggests that the finite state strategy is implemented in a larger brain circuit that includes the striatum ., The requirement of working memory to store the current state suggests the involvement of other brain regions , such as the prefrontal cortex and the hippocampus ., Then why are x4 ( t ) and x5 ( t ) are selectively coded in the striatum ?, It has been reported that the anterior cingulate cortex ( ACC ) plays an important role in switching behavior evoked by error feedback 34 ., The connection from the ACC to the striatum for the execution of switching 35 may be the source of strong coding of x4 ( t ) and x5 ( t ) observed in DMS and VS . Previous studies have reported that action-value signals are represented in the striatum of rodents 5 , 7 , 8 , monkeys 4 , 11 , 23 , 36 and humans 12 , suggesting that the value-based strategy is implemented in the basal ganglia ., Consistent with these reports , our previous study 18 reported that state value signals were most strongly represented in VS , and that action value signals were most strongly represented in DMS during action execution ., In the present study , we reanalyzed the same dataset as the previous study , with a more complex regression model , including not only action values , but also state values , the chosen value , and variables of the FSA model that best explained animal behaviors ., We applied lasso regularization to the augmented regression model , and similar results were reproduced; strong state-value coding in VS ( Fig 7A ) , and a peak of the proportion of action-value coding neurons in DMS during action execution ( Fig 7B ) ., In addition , we found that the signal of the chosen value , previously reported in monkeys 23 , 26 and rats 7 , was represented in VS in our dataset ( Fig 7C ) ., It has been proposed that DMS is involved in goal-directed actions 28 , 30 based on lesion studies 37 , 38 ., Formation of goal-directed action is thought to require an association between actions and outcomes , which is analogous to the action value in reinforcement learning ., Accordingly , action-value coding in DMS matches the proposal of goal-directed action in DMS ., The action value for the selected action , called the chosen value 7 , 23 , which is necessary for updating action values , was observed in VS . Furthermore , consistent with previous reports in rodents 5 , state-value representation was observed in VS ( Fig 7E ) ., These findings suggest that the value-based strategy is implemented in the striatum , although the final action choices are better characterized by the finite state-based strategy ., The likelihood of the ESE model for the model-based strategy was much lower than that of the FQ-learning model for the value-based strategy or that of the FSA model for the finite state strategy ., Thus , rats may not have estimated the reward setting in our task ., In this task , four pairs of reward probabilities were used , but in the previous report in human subjects 13 , only two pairs were used ., Therefore , it might be too difficult for rats to estimate one reward setting from four possible pairs ., The present results support the notion of a hierarchical structure in the cortico-basal ganglia loops , but suggest specific roles for different loops in implementation of the value-based and finite state-based strategies ., Representation of state values and sub-strategies ( clusters ) in VS ( Fig 7A and 7D ) suggests a role for this region in higher-level decisions , namely , selection of sub-strategies depending on the frequency of reward 39 , 40 ., Robust coding of action values and states responsible for action switching in DMS ( Fig 7D and 7G ) points to a role for this region in flexible action adaptation ., Action coding in DLS was equal to or stronger than that in DMS before movement onset 18 , suggesting a major role for this region in action preparation and initiation ., All experimental procedures were performed in accordance with guidelines approved by the Okinawa Institute of Science and Technology Experimental Animal Committee ., A part of the dataset used in our previous study 18 was reused in this study ., Behavioral and neuronal data were gathered from seven Long-Evans rats ., The number of sessions completed by each rat was from 24 to 33 ., The average ( + standard deviation ) of the trials per session was 41 . 10 ( + 27 . 58 ) trials ., Neurons stably recorded from at least two sessions were 260 in DLS , 178 in DMS , and 179 in VS ( on average , recorded from 2 . 7 sessions ) ., From this dataset , phasically active neurons ( PANs; 204 from DLS , 112 from DMS , and 138 from VS ) were extracted based on inter-spike interval statistics ., The proportion of inter-spike intervals ( ISIs ) that was > 1 s of total recoding time ( PropISIs>1s ) was calculated for each neuron 41 ., Then , neurons for which PropISIs>1s> 0 . 4 were regarded as PANs ., Intervals of the six task events ( entry into the center hole , onset of the cue tone , offset of the cue tone , exit from the center hole , entry into the left or right hole , and exit from the left or right hole ) varied by trials ., To align event timings for all trials , event-aligned spike histograms ( EASHs ) were proposed by Ito and Doya 18 ., First , the average duration for each event interval was calculated ., Then , spike timings in a certain event interval for each trial were linearly transformed into corresponding averaged event intervals ., Finally , histograms of the number of spikes for each 100 ms time window were calculated ( Fig 6A , 6D and 6G ) ., Any decision-making models for a single stimulus ( state ) and binary choice ( action ) can be defined by the conditional probability of a current action given past experiences:, PL ( t ) =P ( a ( t ) =L|e ( 1:t−1 ) ), ( 2 ), where e ( 1:t-1 ) is a simple description of e ( 1 ) , e ( 2 ) , … , e ( t-1 ) ., e ( t ) is a set of an action and a reward e ( t ) = {a ( t ) , r ( t ) } , and action a ( t ) and reward r ( t ) can be L or R and 1 or 0 , respectively ., Behavioral data are composed of a set of sequences ( sessions ) of actions and rewards ., If necessary , we use the index l as the index of sessions , for example a{l} ( t ) ., The number of trials for session l is represented by Tl , and the number of sessions is L . To fit parameters to choice data and to evaluate the models , we used the likelihood criterion , which is the probability that the observed data were produced by the model ., The likelihood can be normalized , so that it equals 0 . 5 when predictions are made with chance-level accuracy ( PL ( t ) = 0 . 5 for all t ) ., The normalized likelihood is defined by, Z=∏l=1L∏t=1Tlz{l} ( t ) 1∑l=1LTl, ( 3 ), where z{l} ( t ) is the likelihood for a single trial:, z{l} ( t ) ={PL ( t ) ifa{l} ( t ) =L1−PL ( t ) ifa{l} ( t ) =R ., ( 4 ), The ( normalized ) likelihood can be regarded as the prediction accuracy , namely , how accurately the model predicts actions using past experiences ., Generally , models that have a larger number of free parameters can fit data more accurately and thus show a higher likelihood ., However , these models may not be able to fit new data due to over-fitting ., For fair comparison of models , choice data were divided into training data ( 101 sessions ) and test data ( 101 sessions ) ., Free parameters of a model were determined to maximize the likelihood of training data ., Then , the model was evaluated by the likelihood or the normalized likelihood of the test data ( holdout validation ) ., Therefore , in this model fitting , each model was fitted to all training set trials from all seven rats with the same free parameters ., Fig 2A represents the normalized likelihood for the total of test 101 sessions ., For statistical tests of the normalized likelihood between the models ( Fig 2A ) , we compared the normalized likelihood of each session for the same parameters between the models by a paired-sample Wilcoxon test ., From the above process , we obtained the likelihood of each trial ( 4 ) in all sessions ( both training and test data ) for each model with the parameters estimated by training data ., To compare fitting performance , we averaged the sequences of the likelihoods of the last 20 trials over all blocks with higher or lower reward probabilities ( Fig 2B and 2C ) ., To test significant differences between the FSA model and the DFQ model , the Mann-Whitney U test was applied to the likelihoods for every trial ., Note that the normalized likelihood depends on the number of trials ., If an animal’s choice probability does not change over trials , namely , P ( a ( t ) = L ) = P , and model prediction PL ( t ) is also constant PL , then the expected normalized likelihood for T trials is given by, Z^ ( T ) =∑t=0T ( Tt ) Pt ( 1−P ) T−t ( PLt ( 1−PL ) T−t ) 1/T ., ( 5 ), This expected normalized likelihood rapidly decreases when the number of trials increases , and when T goes to infinite , it converges to, Z^ ( ∞ ) =PLP ( 1−PL ) ( 1−P ) ., ( 6 ), For example , let’s assume that a rat’s choice probability is P = 0 . 8 and model A predicts it perfectly by PL = 0 . 8 , the ( normalized ) likelihood is, Z^ ( 1 ) =0 . 68, , it’s less than PL , and it decreases to, Z^ ( ∞ ) =0 . 61, when T increases ., If model B predicts with PL = 0 . 7 ,, Z^ ( 1 ) =0 . 62, and, Z^ ( ∞ ) =0 . 59, , the difference in the normalized likelihood between model A and model B also decreases ( 0 . 07 → 0 . 02 ) when T changes from 1 to infinity ., This is the reason why the normalized likelihoods of models shown in Fig 2A ( T = 16856 trials ) are much less than the likelihoods shown in Fig 2B and 2C ( T = 1 trial ) ., dth-order Markov models are the simplest non-parametric models ., They predict an action at trial t , a ( t ) , from the past d-length sequence of experiences before t , e ( t-d:t-1 ) ., The prediction of the dth-order Markov model was given by the following:, PL ( t ) =NL ( e ( t−d:t−1 ) ) +1NL ( e ( t−d:t−1 ) ) +NR ( e ( t−d:t−1 ) ) +2, ( 7 ), where Ni ( e ( t − d:t − 1 ) ) is the number of i ( L or R ) chosen after every d-length sequence of the exact same sequence as e ( t-d:t-1 ) in the whole training data 5 ., The dth-order Markov model has more than 4d free parameters because there are four types of possible experiences in a single trial ( more precisely , the number of the parameters is 4d+4 ( d−1 ) +⋯+4 ., The dth-order Markov model uses the 1st-order Markov model for the prediction of the first trial in a session , and 2nd-order Markov model for the second trial ) ., The Markov models are purely descriptive models , but they provide a useful measure to objectively evaluate other models ., The DFQ-learning model 5 , 18 , which is an extension of the Q-learning model and which includes the original Q-learning model with certain parameters , is useful to test the Q-learning family ., A key component of the DFQ-learning ( and Q-learning ) model is to use action values ( QL and QR ) as predictions of the future cumulative reward that the agent would obtain after selecting left or right , respectively ., The model selects an action that has a higher action value with a higher probability:, PL ( t ) =11+exp{− ( QL ( t ) −QR ( t ) ) } ., ( 8 ), After determining the reward outcome , action values are updated by:, Qi ( t ) ={ ( 1−α1 ) Qi ( t−1 ) +α1κ1ifa ( t−1 ) =i , r ( t−1 ) =1 ( 1−α1 ) Qi ( t−1 ) −α1κ2ifa ( t−1 ) =i , r ( t−1 ) =0 ( 1−α2 ) Qi ( t−1 ) ifa ( t−1 ) ≠i , r ( t−1 ) =1 ( 1−α2 ) Qi ( t−1 ) ifa ( t−1 ) ≠i , r ( t−1 ) =0, ( 9 ), where i ∈ {L , R} , α1 is the learning rate for the selected action , α2 is the forgetting rate for the action not chosen , κ1 represents the strength of reinforcement by reward , and κ2 represents the strength of the aversion resulting from the non-reward outcome ., This set of equations can be reduced to the standard Q-learning by setting α2 = 0 ( no forgetting for actions not chosen ) and κ2 = 0 ( no aversion from a lack of reward ) ., The FQ-model is a version introducing the restriction α1 = α2 ., For the Q-learning models , we considered cases of fixed parameters and time-varying parameters ., For fixed parameter models , α1 , α2 , κ1 , and κ2 are free parameters ., For time-varying parameters , α1 , α2 , κ1 , and κ2 are not free parameters; they are assumed to vary according to the following:, αj ( t ) =αj ( t−1 ) +ςjforj∈{1 , 2}κj ( t ) =κj ( t−1 ) +ξjforj∈{1 , 2}, ( 10 ), where ζj and ξj are noise terms drawn independently from the Gaussian distribution N ( 0 , σα2 ) and N ( 0 , σκ2 ) , respectively ., σα and σκ are free parameters that control the magnitude of the change ., The predictive distribution P ( h ( t ) | e ( 1:t-1 ) ) of parameters h = QL , QR , α1 , α2 , κ1 , κ2 given past experiences e ( 1:t-1 ) was estimated using the particle filter 4 , 5 ., The action probability PL ( t ) was obtained from Eq ( 8 ) with the mean of the predictive distribution of QL ( t ) and QR ( t ) ., In this study , 5 , 000 particles were used for the estimation ., The ESE model estimates a hidden environmental state , namely , the reward setting from past experience , using the knowledge that reward probabilities should be one of the following: ( 90 , 50% ) , ( 50 , 10% ) , ( 50 , 90% ) and ( 10 , 50% ) ( five trials with zero reward probability inserted in the middle of each session were not considered . ) ., The ESE model also assumes that the reward setting is changed with a small probability ε for each trial:, P ( s ( t ) |s ( t−1 ) ) ={1−εifs ( t ) =s ( t−1 ) ε/3ifs ( t ) ≠s ( t−1 ), ( 11 ), where s ( t ) ∈ {1 , 2 , 3 , 4} is the index of reward setting at trial t corresponding to ( 90 , 50% ) , ( 50 , 10% ) , ( 50 , 90% ) and ( 10 , 50% ) , respectively ., The prediction of the reward setting at trial t for all s ( t ) is obtained using, P ( s ( t ) |e ( 1:t−1 ) ) =∑s ( t−1 ) =14P ( s ( t ) |s ( t−1 ) ) P ( s ( t−1 ) |e ( 1:t−1 ) ), ( 12 ), where P ( s ( t-1 ) | e ( 1:t-1 ) ) is the prior probability of the reward setting ., The prior probability for t = 1 was set to 1/4 for each, s . Based on this prediction , action values are given by, Qi ( t ) =κ∑s ( t ) =14P ( r ( t ) =1|s ( t ) , a ( t ) =i ) P ( s ( t ) |e ( 1:t−1 ) ), ( 13 ), where P ( r ( t ) = 1| s ( t ) , a ( t ) = i ) is the reward probability for the reward setting s ( t ) and action i ., κ is the magnitude of the reward ., An actual action , a ( t ) , is selected according to the action probability , which is calculated from Eq ( 8 ) with the action values ., After knowing the reward outcome , r ( t ) , the posterior probability of the reward setting for all s ( t ) , was updated using Bayes’ theorem:, P ( s ( t ) |e ( 1:t ) ) ∝P ( a ( t ) , r ( t ) |s ( t ) , e ( 1:t−1 ) ) P ( s ( t ) |e ( 1:t−1 ) ) ., ( 14 ), The first factor of the right side can be decomposed to, P ( a ( t ) , r ( t ) |s ( t ) , e ( 1:t−1 ) ) =P ( r ( t ) |a ( t ) , s ( t ) , e ( 1:t−1 ) ) P ( a ( t ) |s ( t ) , e ( 1:t−1 ) ), ( 15 ), where the first factor on the right side of this equation can be simply written as P ( r ( t ) | a ( t ) , s ( t ) ) because this factor comes from the reward probability setting of the task and is assumed to be independent of the past experience of rats , e ( 1:t-1 ) ., The second factor is the action probability of the agent ., Although the agent estimates the current reward setting , s ( t ) , from past experience , e ( 1:t-1 ) , the agent cannot directly observe s ( t ) ., In other words , the action probability should be the same for the same past experience , e ( 1:t-1 ) , without being affected by the true hidden state , s ( t ) ., Therefore , the second factor can be ignored because it takes the same values for all s ( t ) ., Then , Eq ( 14 ) is simplified to, P ( s ( t ) |e ( 1:t ) ) ∝P ( r ( t ) |s ( t ) , a ( t ) ) P ( s ( t ) |e ( 1:t−1 ) ) ., ( 16 ), Similar to the Q-learning models , we considered the cases of fixed and time-varying parameters ., For fixed parameter | Introduction, Results, Discussion, Materials and Methods | Previous theoretical studies of animal and human behavioral learning have focused on the dichotomy of the value-based strategy using action value functions to predict rewards and the model-based strategy using internal models to predict environmental states ., However , animals and humans often take simple procedural behaviors , such as the “win-stay , lose-switch” strategy without explicit prediction of rewards or states ., Here we consider another strategy , the finite state-based strategy , in which a subject selects an action depending on its discrete internal state and updates the state depending on the action chosen and the reward outcome ., By analyzing choice behavior of rats in a free-choice task , we found that the finite state-based strategy fitted their behavioral choices more accurately than value-based and model-based strategies did ., When fitted models were run autonomously with the same task , only the finite state-based strategy could reproduce the key feature of choice sequences ., Analyses of neural activity recorded from the dorsolateral striatum ( DLS ) , the dorsomedial striatum ( DMS ) , and the ventral striatum ( VS ) identified significant fractions of neurons in all three subareas for which activities were correlated with individual states of the finite state-based strategy ., The signal of internal states at the time of choice was found in DMS , and for clusters of states was found in VS . In addition , action values and state values of the value-based strategy were encoded in DMS and VS , respectively ., These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum . | The neural mechanism of decision-making , a cognitive process to select one action among multiple possibilities , is a fundamental issue in neuroscience ., Previous studies have revealed the roles of the cerebral cortex and the basal ganglia in decision-making , by assuming that subjects take a value-based reinforcement learning strategy , in which the expected reward for each action candidate is updated ., However , animals and humans often use simple procedural strategies , such as “win-stay , lose-switch . ”, In this study , we consider a finite state-based strategy , in which a subject acts depending on its discrete internal state and updates the state based on reward feedback ., We found that the finite state-based strategy could reproduce the choice behavior of rats in a binary choice task with higher accuracy than the value-based strategy ., Interestingly , neuronal activity in the striatum , a crucial brain region for reward-based learning , encoded information regarding both strategies ., These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum . | null | null |
journal.ppat.1005800 | 2,016 | Single-Molecule FISH Reveals Non-selective Packaging of Rift Valley Fever Virus Genome Segments | Rift Valley fever virus ( RVFV ) is a zoonotic bunyavirus of the genus Phlebovirus that causes recurrent outbreaks on the African continent , the Arabian Peninsula and several islands off the coast of Southern Africa ., The virus predominantly affects ruminants , of which sheep are the most severely affected ., Epizootics are characterized by massive abortions of pregnant ewes and high mortalities among newborns ., Infected humans generally display mild flu-like symptoms , however in a minority of cases severe complications such as retinitis , hemorrhagic fever , and delayed-onset encephalitis may develop 1 ., In humans , the overall case fatality ratio is estimated to range from 0 . 5 to 2% ., Mosquito vectors of the Aedes and Culex genera are associated with RVFV transmission in endemic areas and are also present in other regions of the world with high ruminant density ., Like all bunyaviruses , RVFV contains a tri-segmented single-stranded RNA genome of negative polarity 2 ., The large ( L ) , medium ( M ) and small ( S ) genome segments are encapsidated by the nucleocapsid ( N ) protein , which is translated from a subgenomic mRNA transcribed from the genomic-sense S RNA ., Encapsidated genome segments are referred to as ribonucleoproteins ( RNPs ) ., The antigenomic-sense S-segment additionally encodes the non-structural protein NSs ., NSs is the main virulence factor of the virus and is known to antagonize host innate immune responses 3–5 ., The M-segment encodes the two major structural glycoproteins Gn and Gc 6 which are involved in host cell entry and fusion , respectively ., The M-segment also encodes two accessory proteins , known as NSm and 78-kDa protein ., NSm was shown to have anti-apoptotic function 7 , 8 and the 78-kDa protein was shown to be incorporated predominantly into virions matured in insect cells 9 ., The L-segment encodes the RNA-dependent RNA polymerase , which is responsible for transcription of genes and replication of the viral genome 10 ., Remarkably , and in contrast to many other RNA viruses , bunyavirus mRNA synthesis is coupled to translation to prevent premature transcription termination 11 ., The termini of all bunyavirus genome segments are inverted complementary and facilitate the formation of a panhandle structure , which comprises signals for transcription , replication and encapsidation 12–19 ., Bunyavirus particles assemble in so called ‘virus factories’ , located at the Golgi network 20–23 ., In these factories viral budding is believed to be initiated by interactions of the RNPs with the cytoplasmic tail of the Gn protein 19 , 22 , 24 , 25 ., How infectious particles , containing at least one S , one M and one L RNP , assemble is not yet fully understood ., Interestingly , in 2011 Terasaki and co-workers provided some clues for a selective genome packaging process using a virus-like particle ( VLP ) system ., They suggested that copackaging of S , M and L genome segments into individual RVFV virions is mediated by direct or indirect inter-segment interactions , with a central role for the M-segment 17 ., Other findings however suggest that inter-segment interactions do not play a major role in RVFV genome packaging ., A fully viable two-segmented RVFV variant lacking the M-segment was described 26 and RVFV replicon particles that comprise only S and L genome segments can be produced very efficiently 27 , 28 ., More recent results further emphasize the flexibility of the RVFV genome ., A RVFV variant with a ‘swapped’ S segment , encoding N from the NSs locus and vice versa , is viable 29 ., Moreover , four-segmented RVFV variants were recently created , which may contain two or even three M-type segments 30 ., Here , we investigated the RVFV genome packaging process using state-of-the-art fluorescence in situ hybridization ( FISH ) ., Experiments with infected cells and mature virions revealed that copackaging of all three genome segments into individual particles is unlikely to involve the formation of a supramolecular complex ., Instead , our results reveal that RVFV genome packaging is a non-selective process ., To investigate replication and recruitment of the RVFV RNA genome segments inside an infected cell , a single-molecule multicolor RNA FISH assay was developed ., Probes were designed to be complementary to the S , M and L viral RNAs ., After confirming specificity and optimizing sensitivity ( S1 Fig ) , the assay was used to evaluate the genome segment distribution in RVFV-infected Vero cells at 2 , 4 , 6 , 8 and 10 hours post infection ( hpi ) ., At 2 and 4 hpi a single patch of up to 600 genome segments was detected in the cytoplasm of most infected cells ( Figs 1 and 2D ) ., The location of the patch varied among cells and at higher multiplicity of infection ( MOI ) cells with more than one patch of genome segments were observed as well ., Most likely , the genome segments of an infecting virion start to replicate near the site of infection immediately after fusion of the viral membrane with the endosomal membrane ., At about 4–6 hpi the total level of genome segments had increased considerably and a more random cytoplasmic distribution of genome segments was observed ., The average doubling time of a genome segment was estimated to be about 40 min ., At 6–8 hpi recruitment of genome segments to the virion assembly site at the Golgi 20–23 , 31 , 32 became evident in most of the infected cells ., The total level of cytoplasmic genome segments reached a plateau around 6 hpi , which is probably the result of ongoing replication and continuous Golgi recruitment and budding of particles containing mature RNPs ., An average cytoplasmic inter-segment ratio approaching 1:1:1 between the S , M and L segments was observed during the first 4 hrs of infection , whereas later on , due to more efficient Golgi recruitment of the M-segment , the cytoplasmic ratios slightly changed ( Fig 2G ) ., Remarkably , in about 30–40% of infected cells the segment ratios were strikingly different ., Cells with about twice as many S , M or L segments as well as cells lacking any evidence of M-segment replication ( up to 25% ) were observed frequently ( Fig 3 and S2 Fig ) ., It is important to note that cells infected with particles lacking an S and/or L segment will not reveal genome replication and are not detected by FISH ., Altogether these results suggest that during particle assembly no quality control mechanisms are present that ensure packaging of each type of genome segment ., To evaluate whether S , M and L genome segments form a supramolecular complex and comigrate to the Golgi prior virion assembly we evaluated the extent of S , M and L colocalization at 5 hpi ., The 5 hpi time point was selected because at this stage of infection the genome segment density was relatively high and the resolution of spots , corresponding to single genome segments , was still sufficient to discriminate between colocalized spots and non-colocalized spots ., Moreover , Golgi recruitment has not yet started at this time point ., As a positive colocalization control , cells were probed with two differentially labelled probe sets recognizing either the Gn or Gc gene , which are both encoded by the M genome segment ., As a negative control , cells were probed with a GAPDH mRNA probe set and a Gc probe set ., The Pearson colocalization coefficient of the probe sets recognising either the Gn or Gc-coding region was on average 0 . 65 and the colocalization coefficient of the GAPDH and Gc probe sets was below 0 . 1 ( Fig 4A and 4B ) ., These values , which are similar to what others have reported in the influenza field 33 , confirm that our FISH assay is well suited for studying genome segment colocalization ., The Pearson colocalization coefficients of the different RVFV genome segments were all below 0 . 1 ( Fig 4C ) ., This indicates that RVFV genome segments , in contrast to the genome segments of the influenza virus 33 , 34 , do not form a supramolecular complex consisting of more than one genome segment in the cytoplasm ., The important role of the RVFV glycoproteins , specifically the cytoplasmic tail of the Gn protein , in RNP incorporation into virions is well recognized 19 , 22 ., The involvement of the glycoproteins in intracellular genome segment recruitment is , however , less understood ., Here we used our previously developed RVFV replicon particles , also referred as nonspreading RVFV ( NSR ) 27 , to study the role of the glycoproteins in genome segment recruitment in more detail ., NSR particles are phenotypically similar to wild-type virus , however they cannot spread autonomously because they lack the glycoprotein-encoding M genome segment ., Vero cells were infected with NSR and the spatio-temporal distributions of the S and L genome segments were determined by FISH ( Fig 5 ) ., The results show that the total level of genome segments rapidly increased in time , similar as observed in RVFV infected cells ( Fig 2 ) ., Importantly , no evidence of Golgi recruitment was observed at any time point ( Fig 5 ) ., This suggests that in wild-type virus infected cells recruitment of genome segments is fully mediated by the glycoproteins , most likely mediated by the cytoplasmic tail of Gn , as was previously suggested by Piper and co-workers 19 ., Remarkably , starting at 8 hpi , we consistently observed aggregates of genome segments in NSR-infected cells ( Fig 5 ) ., The aggregates were randomly distributed and not associated with the Golgi ., Probably , the absence of viral budding results in accumulation and subsequent aggregation of RNPs ., In RVFV infected cells no such aggregates were found , not even at later time points ., The NSR experiments suggested a major role for the RVFV glycoproteins , probably Gn , in genome segment recruitment ., The RVFV glycoproteins Gn and Gc are normally produced from a glycoprotein precursor ( GPC ) protein that is proteolytically cleaved ., Gn and Gc subsequently form heterodimers and mature at the endoplasmic reticulum ( ER ) and Golgi ., Gn harbours a Golgi localization motif and Gc contains an ER retention signal 31 ., We previously constructed RVFV-4s variants by splitting the M segment into two M-type segments encoding either the Gn or Gc protein 30 ., We hypothesized that genome replication and recruitment is affected by changes in glycoprotein processing and genome organisation ., To test this hypothesis we evaluated the spatio-temporal distribution of genome segments in RVFV-4s infected cells by FISH ., Vero cells were infected with RVFV-4s and hybridized with probes complementary to the S , M-Gn , M-Gc and L genome segments ., The results show that the M-Gn segment is replicated more efficiently compared to the M-Gc segment ( Fig 2I ) ., Moreover , recruitment of the M-Gn segment to the Golgi was much more efficient compared to recruitment of the other segments ( Fig 6B ) ., Recruitment of M-Gn was also more efficient compared to recruitment of the wild-type M-segment in RVFV infected cells ( Figs 1 and 6B ) ., Since RVFV-4s is able to spread after infection at low MOI ( < 0 . 001 ) a significant population of particles in a virus stock is expected to contain all four genome segments ., Interestingly , the FISH experiments at 6 hpi revealed that various infected cells ( up to 40% ) did not show evidence of M-Gc replication ( Fig 6C ) ., Most likely these cells were originally infected with virions containing the S , M-Gn and L genome segments but lacking the M-Gc segment ., The number of M-Gc lacking virions correlates very well with the reduced replication of the M-Gc segment ( Fig 2I ) and , like for wild-type virus , confirms that during particle assembly no quality control mechanisms are present that ensure packaging of all different segments , including M-Gc , into a single particle ., Another interesting observation in RVFV-4s infected cells was the reduced replication of the S segment ., Most likely there is increased competition for polymerase molecules in RVFV-4s infected cells ( 4 instead of 3 segments ) resulting in reduced replication of segments with a relative low affinity for the polymerase ., Differences in polymerase affinity have already been shown at the transcription level 13 ., A final characteristic of RVFV-4s infected cells was the presence of higher densities of genome segments near the plasma membrane later on in infection ( Fig 6D ) , suggesting that in RVFV-4s infected cells , various Gn molecules move to the plasma membrane and bind genome segments during transit ., The ability of Gn to move to the plasma membrane , especially in the absence of Gc is well known 22 , 31 ., Whether RVFV-4s is able to bud at the plasma membrane awaits further study ., Altogether , the overall unbalance in genome segment replication , the enhanced Golgi recruitment of the M-Gn segment and the increased number of particles lacking one or more genome segments explain , at least partly , the observed attenuated phenotype of RVFV-4s 30 ., Although the experiments thus far show that a supramolecular complex , consisting of an S , M and L genome segment is not formed in the cytoplasm , we cannot yet rule out the possibility that a supramolecular RNP complex is formed at the virion assembly site ., During the influenza infection cycle , the formation of a supramolecular RNP complex is based on RNA-RNA interactions between the different segments and this process is believed to trigger viral budding 35 , 36 ., To obtain additional information about the putative formation of a supramolecular RNP complex during the RVFV infection cycle we tried to rescue a RVFV variant with a codon shuffled M-segment ( Fig 7 and S2 Fig ) ., Codon shuffling changes the genomic RNA sequence but does not affect the protein sequence and has limited effects on protein expression ., When RNA-RNA interactions exist between the S , M and L RNPs , a virus with a codon shuffled M segment is expected to grow less efficiently ., Interestingly , rescue of the RVFV variant with a codon shuffled M-segment , referred as RVFV-Mshuffled , was successful ., Moreover , we additionally rescued a RVFV variant with a shuffled M-segment and an optimized S segment , referred as RVFV-MshuffledSopt ( Figs 7 and S3 ) ., Both variants were able to grow with similar kinetics and to similar titers in Vero cells compared to the parental RVFV strain ( Fig 7 ) ., The efficient growth of these variants further suggests that the formation of a supramolecular RNP complex does not drive the production of infectious RVFV virions ., Altogether , the presented results suggest that RVFV genome packaging is a non-selective process ., To obtain additional evidence for this conclusion we evaluated the genome segment content of mature virions ., Virions in wild-type virus stocks ( produced on Vero cells ) were immobilized on coverslips and incubated with antibodies targeting the Gn glycoprotein and probe sets recognising the S , M and L genome segments as described in the M&M section ., After confirming specificity and the ability to determine colocalization with this assay ( Fig 8B and 8C ) the genome content of >800 virions was determined ., As expected , the results revealed a high level of heterogeneity in genome composition ., Virions were observed that did not comprise any genome segment ( about 40% ) as well as virions with only one or two segment types ( Fig 8D and 8E ) ., About 1 out of 10 virions showed evidence for the presence of all three different segments ., The relatively low abundance of virions containing all the different segments is in full agreement with the FISH data obtained with infected cells and confirms the non-selective nature of RVFV genome packaging ., Although genome packaging of viruses with segmented genomes has intrigued researchers for decades , we are only just beginning to understand the molecular processes involved ., In the field of segmented negative-strand RNA viruses , most knowledge resulted from studies with influenza virus ., In the latest influenza model , genome packaging is proposed to be a highly selective process based on the formation of a supramolecular RNP complex 33–35 , 37 , 38 ., From an evolutionary perspective , a selective genome packaging process for an 8-segmented virus is easily understood ., If not selective , the influenza virus would need to produce about 400 particles to generate 1 particle that contains each of the 8 genome segments , which is rather inefficient ., For bunyaviruses , which only have to package 3 segments , the evolutionary pressure to selectively incorporate genome segments during virion assembly is much lower ., With this study , we provide evidence that RVFV uses a non-selective genome packaging strategy ., At the beginning of this study , limited knowledge was available about the molecular mechanisms involved in RVFV genome replication , recruitment and packaging ., Moreover , as explained in the introduction section , some results pointed towards a highly selective genome packaging strategy whereas others were compatible with a non-selective packaging process ., In the current study , we investigated the molecular mechanisms involved in RVFV genome packaging by combining new tools such as replicon particles , four-segmented- and codon-shuffled viruses with state-of-the-art single molecule RNA-FISH ., The absence of colocalization of RNPs in the cytoplasm ( Fig 4 ) , the similar to wild-type growth of codon shuffled variants ( Fig 7 ) , the efficient production of replicon particles ( Fig 5B ) , the observed heterogeneity in intracellular segment replication among infected cells ( Fig 3 and S2 Fig ) and the heterogeneity in segment composition of mature virions ( Fig 8 ) demonstrate that the non-selective genome packaging model is the most plausible model to date ., The non-selective genome packaging model is in full agreement with the ability to construct a wide variety of RVFV variants without the need to conserve coding sequences and RNA structures 26 , 29 , 30 ., We here demonstrate that replication of RVFV genome segments starts locally , probably near the site of fusion of the virion with the endosome , and subsequently ( within 4–6 h ) continues to proceed throughout the cytoplasm ., After the replication phase , genome segments are recruited to the Golgi ., Recruitment is probably mediated by interactions of the nucleocapsid protein , which covers the viral RNA , with the cytoplasmic tail of Gn 19 , 22 , 24 , 25 ., After recruitment , a very heterogeneous population of virions , containing various amounts and types of genome segments , buds into the Golgi lumen ., Virions with at least one S , M and L RNP will be able to produce progeny virions upon infection ., Alternatively , co-infection with complementing particles may result in productive infection ., Interestingly , virions containing antigenomic-sense RNPs may also contribute to the RVFV infection cycle 29 , 39 ., In Fig 9 , a schematic presentation of the RVFV infection cycle , according to the newly obtained insights , is provided ., Although our results suggest that a supramolecular RNP complex is not formed , or at least does not play a critical role in the RVFV replication cycle , we cannot exclude that some degree of selectivity exists , as has been previously suggested 17 , 18 ., If some degree of selection indeed occurs , our results obtained with codon-shuffled variants suggest that this selection is mediated by the UTRs ., A major finding in the RVFV-4s infected cells was the difference in replication efficiency of the M-Gn versus M-Gc segment ., The difference in replication is not explained by large differences in segment size ( 2319 nt versus 1869 nt ) or differences in UTR sequence , since these are identical ., An explanation might be that the NSm coding region , which is present in the M-Gn segment but absent from the M-Gc segment , contains a yet unknown cis-acting replication element ., At first glance , the efficient replication of codon-shuffled variants seems to contradict this hypothesis ., However , a short stretch of nucleotides downstream of the 5’ UTR and a short stretch of nucleotides upstream of the GnGc open reading frame were maintained in these viruses ( S3 Fig ) to preserve efficient translation ., These sequences are not present in the M-Gc segment and could be involved in replication ., Future research will determine if these sequences indeed contain cis-acting replication signals ., Another very consistent finding throughout the experiments was the enhanced Golgi recruitment of the M segment compared to the S and L segments in wild-type virus infected cells and the enhanced recruitment of the M-Gn segment in RVFV-4s infected cells ., The enhanced recruitment was calculated by dividing the cytoplasmic segment ratios before ( 4 hpi ) and after Golgi localization ( 8 hpi ) ., The percentage of cytoplasmic M-segments decreased with 16% in wild-type virus infected cells and the percentage of M-Gn segments decreased with 11% compared to the other segments in RVFV-4s infected cells ., The enhanced recruitment of Gn encoding segments can be explained by the coupled transcription and translation in bunyaviruses ., Specifically , we propose the following sequence of events: transcription of the M segment is initiated in the cytoplasm , followed by translation of the Gn signal sequence by free ribosomes ., A complex of M genome segments , mRNA transcribed from this segment and ribosomes is then translocated to the ER and subsequently to the Golgi compartment to continue membrane-associated translation of M segment mRNAs ., Although the current study provides evidence for a non-selective genome packaging process during RVFV virion assembly , we do not think these results can be extrapolated to all bunyaviruses ., Whereas RVFV RNPs are expected to bind to the cytoplasmic tail of the Gn protein via the N protein , for other bunyaviruses , such as Crimean Congo hemorrhagic fever virus ( CCHFV ) , evidence was provided that the viral RNA directly interacts with the cytoplasmic tail of the Gn protein 40 ., This N-independent interaction might be segment specific and could facilitate a more selective packaging process ., The latter could also explain the lower particle to PFU ratio of CCHF compared to RVFV 41 ., In summary , this study suggests that RVFV genome packaging is a non-selective process and does not involve the formation of a supramolecular viral RNA complex ., The RVFV strain Clone 13 42 was kindly provided by Dr . Michèle Bouloy ( Institut Pasteur , France ) ., RVFV-4s , RVFV-Mshuffled and RVFV-MshuffledSopt were constructed using reverse genetics ., Sequences were based on the published Clone 13 genome ( Accession: DQ375417 . 1 , DQ380213 . 1 , DQ380182 . 1 ) ., Working stocks were obtained by low MOI ( 0 . 01 ) infections of Vero E6 cells ( ATCC CRL-1586 ) grown in Eagles Minimum Essential Medium ( EMEM ) supplemented with 5% FBS , 1% non-essential amino acids , 1% L-glutamine and 1% antibiotic/antimycotic ., RVFV replicon ( NSR ) stocks were obtained by transfection of replicon cells , which stably maintain replicating S and L genome segments with an expression plasmid expressing the RVFV glycoproteins as described previously 27 ., RVFV sequences , flanked by a minimal T7 promoter and a hepatitis delta virus ribozyme sequence , were synthesized by the GenScript Corporation ( New Jersey , USA ) and cloned into pUC57 plasmids ., RVFV-4s M-type plasmids were designed ( Clone 13 sequence based ) , as previously described , to contain half of the GPC gene , either encoding ( NSm ) Gn or Gc ( segmented at the tyrosine-675 codon of the GPC ) 30 ., The RVFV-Mshuffled segment was designed by shuffling of the NSmGnGc gene resulting in 77% homology ., The RVFV-Sopt plasmid contains a codon-optimized N gene for optimal expression in mammalian cells ., NSmGnGc shuffled and N optimized sequences are presented in S2 Fig and S3 Fig respectively ., RVFV-4s , RVFV-Mshuffled and RVFV-MshuffledSopt were rescued using a three ( or four for RVFV-4s ) plasmid system ., Briefly , BSR-T7/5 cells 43 ( previously kindly provided by Prof . Karl-Klaus Conzelmann ) were seeded in T75 flasks ( 2 , 500 , 000 cells/flask ) in GMEM containing 5% FBS and after overnight incubation medium was replaced with Opti-MEM ., Cells were transfected with a total of 20 μg pUC57 transcription plasmids per flask using TransIT transfection reagents according the manufacturers’ instructions ( Mirus , MAD ) ., Three to five days post transfection , supernatants were collected and used to infect Vero E6 cells ., All RNA-FISH assays were performed according the Stellaris FISH method originally developed by Ray , Femino and co-workers 44 , 45 ., For the RNA-FISH cell assays Vero E6 cells ( 15 , 000 cells/well ) were seeded on CultureWell 16 Chambered Coverglass ( Grace Biolabs ) ., After overnight incubation , cells were incubated with the indicated viruses for 1 h ( MOI 0 . 1–0 . 01 ) and at the indicated time points infected cells were fixed for 10 min with fixation buffer ( 75% methanol , 25% glacial acetic acid ) ., Cells were subsequently washed with PBS ( 5 min ) and pre-hybridization buffer ( 5 min ) consisting of 10% formamide and 2 mM vanadyl ribonucleoside complex ( VRC ) in 2x concentrated SSC ., Subsequently , cells were probed overnight ( 18 h ) at 37°C in hybridization buffer ( 10% formamide , 2 mM VRC , 10% w/v Dextran-Sulphate in 2 times SSC ) with the indicated probe sets ( S1 Table ) at an end concentration of 125 nM ., The probes were designed using the RNA FISH Probe Designer available online at www . biosearchtech . com and purchased from Biosearch Technologies Inc . ( Petaluma , CA ) ., After the hybridization , cells were extensively washed with pre-hybridization buffer and 2 times SSC ., Cell nuclei were visualized using DAPI and prior imaging , cells were submerged in VectaShield mounting medium ( H-1000 , Vector Laboratories ) ., For the RNA-FISH virion assays , undiluted virus stocks were incubated for 3 h in the CultureWell 16 Chambered Coverglass wells at 37°C ., The negatively charged glass binds virions relatively efficient ., After bound virions were fixed and hybridized according the procedure described for cells , with the only exception that hybridization time was reduced to 4 h , virions were visualized with the RVFV-Gn specific monoclonal antibody 4-39-cc 46 in combination with a DyLight 350 labelled Rabbit anti-Mouse ( H+L ) conjugate ( ThermoFisher Scientific ) ., Immobilized virions were finally submerged in VectaShield prior imaging ., Images of infected cells and immobilized virions were obtained with an inverted fluorescence wide-field ZEISS Axioskop 40 microscope with appropriate filters and a 1 . 3 NA 100× oil objective in combination with an Axiocam MRm CCD camera ., Raw cell images were subsequently deconvolved and analysed using Huygens deconvolution software ( SVI , Hilversum , The Netherlands ) in combination with the ImageJ program ( National Institutes of Health , USA ) ., Spots ( individual vRNAs ) were counted using the StarSearch algorithm http://rajlab . seas . upenn . edu/StarSearch/launch . html ., Images of coverslip immobilized virions were analysed by ImageJ in combination with the ComDet plugin https://github . com/ekatrukha/ComDet/wiki . | Introduction, Results, Discussion, Materials and Methods | The bunyavirus genome comprises a small ( S ) , medium ( M ) , and large ( L ) RNA segment of negative polarity ., Although genome segmentation confers evolutionary advantages by enabling genome reassortment events with related viruses , genome segmentation also complicates genome replication and packaging ., Accumulating evidence suggests that genomes of viruses with eight or more genome segments are incorporated into virions by highly selective processes ., Remarkably , little is known about the genome packaging process of the tri-segmented bunyaviruses ., Here , we evaluated , by single-molecule RNA fluorescence in situ hybridization ( FISH ) , the intracellular spatio-temporal distribution and replication kinetics of the Rift Valley fever virus ( RVFV ) genome and determined the segment composition of mature virions ., The results reveal that the RVFV genome segments start to replicate near the site of infection before spreading and replicating throughout the cytoplasm followed by translocation to the virion assembly site at the Golgi network ., Despite the average intracellular S , M and L genome segments approached a 1:1:1 ratio , major differences in genome segment ratios were observed among cells ., We also observed a significant amount of cells lacking evidence of M-segment replication ., Analysis of two-segmented replicons and four-segmented viruses subsequently confirmed the previous notion that Golgi recruitment is mediated by the Gn glycoprotein ., The absence of colocalization of the different segments in the cytoplasm and the successful rescue of a tri-segmented variant with a codon shuffled M-segment suggested that inter-segment interactions are unlikely to drive the copackaging of the different segments into a single virion ., The latter was confirmed by direct visualization of RNPs inside mature virions which showed that the majority of virions lack one or more genome segments ., Altogether , this study suggests that RVFV genome packaging is a non-selective process . | The bunyavirus family is one of the largest virus families on Earth , of which several members cause severe disease in humans , animals or plants ., Little is known about the mechanisms that facilitate the production of infectious bunyavirus virions , which should contain at least one copy of the small ( S ) , medium ( M ) and large ( L ) genome segment ., In this study , we investigated the genome packaging process of the Rift Valley fever virus ( RVFV ) by visualizing individual genome segments inside infected cells and virions ., Experiments performed with wild-type virus , two- and four-segmented variants , and a variant with a codon-shuffled M segment showed that the production of infectious virions is a non-selective process and is unlikely to involve the formation of a supramolecular viral RNA complex ., These observations have broad implications for understanding the bunyavirus replication cycle and may facilitate the development of new vaccines and the identification of novel antiviral targets . | vero cells, medicine and health sciences, rift valley fever virus, pathology and laboratory medicine, pathogens, biological cultures, microbiology, viral structure, viruses, rna viruses, bunyaviruses, microbial genomics, research and analysis methods, viral genomics, genome complexity, proteins, medical microbiology, microbial pathogens, viral replication, cell lines, virions, biochemistry, virology, viral pathogens, genetics, ribonucleoproteins, biology and life sciences, genomics, computational biology, organisms | null |
journal.pgen.1000111 | 2,008 | Identification of Neural Outgrowth Genes using Genome-Wide RNAi | Many genes required for neurodevelopment have been identified as a result of large-scale genetic screening in Drosophila ., It has been a choice model in neurogenetics since it has significant gene homology and anatomical similarity to vertebrates and the tools available for genetic manipulation are advanced ., Recently , RNA-interference ( RNAi ) has provided an important new tool for genetic analysis since it can efficiently knock down gene expression within the Drosophila nervous system to replicate genetic hypomorphic and null mutant phenotypes 1 , 2 ., In Drosophila , RNAi is mediated by the introduction of long double stranded RNAs ( dsRNAs ) ., The internalized dsRNAs are processed into 21- to 23-nucleotide segments by Dicer and are incorporated into RISC protein complexes for degradation of mRNA transcripts 3 , 4 ., In simple model organisms such as C . elegans and Drosophila , RNAi analysis has been applied to the entire genome 5 ., In addition , the use of cell-based RNAi assays enable genome-wide screens to be carried out in a high-throughput manner 6 ., However , these high-throughput screening methods have been inaccessible to address questions in neurons due to the lack of appropriate cell lines that retain neuronal morphology , gene expression profiles , and electrophysiological activity ., Thus to carry out a full genome analysis of neural development , we have adapted methods for RNAi screening by using Drosophila primary neural cultures and live-cell imaging ., A major advantage of using RNAi on primary cultures is that pleiotropic genes can be identified ., Most genes have complex expression profiles that are not restricted to a single tissue type , and that can also include mRNAs that are maternally deposited into the egg ., The primary cell culture method we present here isolates wild-type neuroblasts that are subsequently treated with RNAi , thus secondary cell defects due to disruption of tissues that form prior to neurogenesis can be avoided ., In this way , primary neural culture RNAi could offer great potential to identify interesting novel genes that would be much more difficult to find using traditional screening methods ., Here we present the results from the first genome-wide RNAi analysis of live , fluorescently labeled primary neural cells for their effects on neural outgrowth and morphology ., Through successive rounds of experimental replication , we identified 104 evolutionarily conserved genes that we implicate in neural development and function ., For the phenotypic analysis , we developed computational image analysis methods that quantify specific morphological features of the cells ., The statistical analysis can aid in the prediction of gene functions based on comparison of RNAi-induced phenotypic profiles of unknown genes to profiles that represent genes with known functions ., To explore whether in vitro RNAi phenotypes from the genome-wide screen show analogous phenotypes in vivo and across species , we chose two genes involved in the protein trafficking category for further analysis ., We found that Sec61α and Ran GTPase showed similar phenotypes in Drosophila embryos and embryonic mouse cortical neuron explants respectively ., Both genes have complex expression patterns , and are important genes in human neurological disease pathways 7 , 8 ., The work thus demonstrates the advantages of using full-genome RNAi in Drosophila primary neural cells as a tool to gain novel insights into gene functions in the nervous system ., The characteristics of primary neuronal cultures generated from gastrula stage Drosophila embryos have been well characterized and their development mirrors the phases of proliferation , differentiation , outgrowth and mature physiological function in the intact organism ., Primary neurons develop from neuroblasts that give rise to clusters of approximately 16 daughter cells 9 , 10 ., The neuronal clusters form miniature ganglia organized with neuronal cell bodies surrounding a central neuropil that contains high levels of presynaptic proteins 11–13 ., Primary neurons can be either uni- or multipolar , and they extend microtubule-rich axons out of the cell clusters using growth cones bearing filopodia and lamellipodia 13–16 ., The axons terminate on neighboring ganglia and contractile muscle fibers to form neural networks 11 , 17 ., In addition , glial cells either remain within the ganglia or extend along and wrap the axonal projections 10 , 13 , 18 ., Axons terminating on muscles develop neuromuscular junctions , which show ultrastructural specializations typical of synapses , including synaptic vesicles and electron dense material bridging regions of the synaptic cleft 10 ., The synapses of the primary neurons are electrophysiologically active and Na+-driven action potentials can be blocked by tetrodotoxin 10 , 13 , 19 , 20 ., The composition of neuronal types within primary cultures derived from gastrula stage embryos has been determined using immunostaining of molecular markers ., Primary Drosophila neurons are Elav- and HRP-positive , and the subsets of motor and sensory neurons express Fasciclin II and Futsch respectively 13 , 16 ., Other markers , such as Neuroglian , Even-skipped are expressed by primary neurons , and glia are Repo-positive 13 , 16 ., Together , the morphological , physiological , and molecular characterizations demonstrate that primary neurons and glia retain a great number of characteristics from the in vivo situation , and thus make a very appropriate system for high-throughput functional genomics applications such as RNAi ., To carry out a full-genome RNAi screen for neural outgrowth and morphology , we developed methods to generate large-scale cultures of green fluorescent protein ( GFP ) -labeled primary neurons and glia from gastrula stage embryos ., In addition , RNAi techniques were modified for higher efficacy on primary cells within a 384-well plate screening format ., It has been demonstrated that RNAi knockdowns can replicate neuronal mutant phenotypes in Drosophila embryos 21 , 22 , and that RNAi can effectively knock down specific genes in vertebrate cultured neurons 23 ., We also demonstrated that RNAi can significantly knock down gene expression in Drosophila primary neurons ., For example , cultures treated with Neuroglian dsRNA had greatly reduced immunolabeling with anti-Neuroglian as compared to controls ( Figure S1 ) ., In addition , we demonstrated that the RNAi assay can produce repeatable phenotypes within the networks of dissociated cells using positive controls that were expected to affect outgrowth in neurons ., For example , by knocking down the cytoskeletal proteins Actin and beta-Tubulin , we demonstrated that the RNAi outgrowth phenotypes were consistent , robust , and stereotyped amongst independent cell culture preparations and amongst repeated wells within the same culture preparation ( Figure S2A–C ) ., Importantly , the phenotype characteristics were distinctive depending on which gene was targeted for knockdown , with sinuous axon trajectories in Actin knockdowns versus markedly reduced axon lengths in the beta-Tubulin RNAi cultures ., Thus RNAi-induced phenotypes can result in significant visible morphological defects that are reproducible in primary neural cells ., For screening assay optimization , we performed pilot tests using a small collection of dsRNAs ., Wild-type and negative control cultures had a stereotypic morphology , characterized by cell body clusters interconnected by well-fasciculated axonal tracts ( Figure 1A ) ., In contrast , a selection of the pilot test dsRNAs caused a variety of morphological defects associated with axonal tracts ., For example , dsRNAs for hydrogen-transporting ATPase VhaAC39 and novel gene CG14883 caused excessive branching , blebbing , and defasciculation as well as disruption of cell cluster sizes ( Figure 1B–D ) ., Our observations supported previous analyses of primary neurons which showed that healthy cultured Drosophila neurons are well-fasciculated , while neurons disrupted by mutation or chemical treatments show branching abnormality , reduced axon lengths , and varicosities 13 , 14 , 16 , 17 ., For the genome-wide screen , we used a library containing ∼21 , 300 dsRNAs , representing approximately 99% of annotated genes as well as additional predicted genes 24 , 25 ., Primary cell culture preparations were applied to 384-well plates pre-aliquoted with dsRNAs and incubated for one week ( Figure S2D ) ., The morphologies of the living GFP-labeled cells in response to RNAi were imaged on a robotic microscope and also visually scored ., From visual inspection , morphological phenotypes were scored for excessive branching , defasciculation , axon blebbing , cell loss , and reduced outgrowth ., The initial full genome screen identified 336 dsRNAs that caused strong phenotypes and a further 2 , 106 dsRNAs that generated moderate to weak phenotypes ., The identified genes encompassed most functional categories , with a large proportion being novel genes with no predicted function ., In the initial screen , 136 dsRNAs that resulted in visible phenotypes represented genes that were previously shown to be required in Drosophila neurons or glia ( Table S1 ) ., These functionally diverse genes included Notch ( determination ) , MAPK/rolled ( signaling ) , Insulin-like Receptor ( growth and differentiation ) , MAP1B/Futsch ( microtubule binding ) , Rac2 ( Actin dynamics ) , Frizzled ( signal transduction ) , Synaptobrevin ( synaptic vesicle release ) , Kinesin light-chain ( axon transport ) , and V-gated K+ channel/ether a go-go ( electophysiological signaling ) ., From the full-genome screen , 125 genes were selected for confirmation of the RNAi primary cell phenotypes ., These included all the candidates that had a strongly penetrant RNAi phenotype and that have vertebrate homology using Reciprocal-Best-Blast and other criteria ., Proteasomal and ribosomal genes were excluded due to their widespread cellular functions ., The dsRNAs for the 125 candidates used in the full genome screen were resynthesized for multiple replicate analysis ., To control for potential off-target effects caused by long dsRNAs 26 , 27 , for each gene , 1–2 additional non-overlapping dsRNAs that had no homology to other genes at a statistically determined 17- to 19-basepair cut-off threshold 26 , 27 were synthesized ., The primary culture and RNAi conditions used in the full genome screen were carried out blindly on 12 replicates ., With the secondary screen , the phenotypes of 104 of the initial 125 selected hits were confirmed with 2 or more independent dsRNAs ( Table 1 ) ., To statistically analyze the morphological characteristics of the GFP-labeled cells imaged on the robotic microscope , digital image analysis tools were developed , since existing commercial image analysis software packages were far too generic for the analysis of the Drosophila neural cultures ., Image features , including the amount of branched regions , sizes of cell clusters , lengths of fascicles , and degrees of connectivity were quantified ( Figure 2A ) and normalized against controls within the replicate plates ., Using the Multivariate Students t-test with a statistical cutoff of p≤0 . 001 , 83% of the genes retested showed RNAi phenotypes significantly different than wild-type controls with two or more independent dsRNAs ( Table 1 ) ., Genes that retested as significantly different from wild-type controls with only one dsRNA are reported in Table S2 ., For the quantified image features , heat-map hierarchical clusters were generated ( Figure 2B , Figure S3 ) ., For the heat map , the quantified values of the image features were represented by a color code ( red and green , Figure 2B ) where red values show an increase in the phenotype value and green shades show decreases in the phenotype value according to the designated scale ., For example , SNAP RNAi caused disruption of cell proliferation that would normally generate healthy , large sized cell clusters ., Thus there was an overall increase in the number of small cell clusters , as represented by the red shading of the “small clusters” category ., Interestingly , by using blebbing , connectivity , and branching features for cluster analysis , we found that genes associated with vesicle and protein trafficking were localized to a similar region within the hierarchy ( Figure 2B ) ., Hierarchical cluster analysis could assist in the prediction of roles for genes with no known functional motifs , since genes of similar function are likely to have a greater chance of localizing to similar regions of the hierarchy ., For example , the novel gene CG3403 was mapped in the highlighted cluster region ( Figure 2B ) ., The rat homolog of CG3403 is Phocein , and it is reported to localize to the neuronal Golgi apparatus and dendritic spines 28 ., Based on its sequence similarity to clathrin adaptor proteins , it is hypothesized to be involved in vesicular endocytosis , however its precise function is unknown ., Importantly , growth cone dynamics are mediated by endocytosis in a similar manner to vesicle recycling at the synapse 29 ., Thus it is possible from the RNAi outgrowth phenotypes and clustering analysis that CG3403 could be involved also in protein trafficking during axon outgrowth ., General bioinformatics tools were also informative for analysis of the candidate genes ., For example , from current database information , at least 55% of the validated genes have expression within nervous system tissue during Drosophila embryogenesis ., A significant portion of the genes with embryonic neural expression also had maternally deposited mRNA for the same gene in the early embryo such as Caf1 , Lpr2 , and MAGE ., It is possible that many of these genes have not been detected in previous screens for embryonic nervous system patterning , because maternally deposited mRNAs can sometimes compensate for the loss of zygotically expressed transcripts during early development ., A selection of the validated genes was morphologically analyzed in greater detail using confocal microscopy and pan-neural markers ( Figure 3 ) ., Relative to control cultures ( Figure 1A , 3A ) , reduced fasciculation and increased branching of processes were the most widespread phenotypes observed ., While these two attributes were often observed in combination , many dsRNAs generated distinctive phenotypes ., For example , knockdowns of the translation initiation factor Int6 and ran GTPase both caused excessive branching and defasciculation , however their morphological profiles were quite different due to the relative amounts of each feature ( Figure 3B , C ) ., Further knockdowns of gene expression including: Huntingtin , Sec61α , actin binding gene diablo , novel gene CG12082 , LDL receptor Lpr2 , and Dopamine 2-like Receptor ( CG9569 ) , also showed distinctive characteristics of morphological disruption ( Figure 3D–I ) ., Given the diversity of complex phenotypes of neural morphology in cell culture , the importance of using computer algorithms to quantify specific features is underscored ., Thus image analysis algorithms such as those presented here will be useful in future suppressor/enhancer screens or chemical screens in primary neural cells ., From the RNAi phenotypes examined in greater detail , the Dopamine 2-like Receptor ( Figure 3I ) was of interest and showed an excessive branching phenotype ., Although neurotransmitter receptors are most widely known for their central role in synaptic transmission , they have also been implicated in axon outgrowth 30 and are expressed during early neurodevelopment , prior to the establishment of synapses 31–33 ., Interestingly , dopa decarboxylase deficiency mutants in Drosophila , which are unable to synthesize serotonin and dopamine , show an extensive increase in axonal branching in the larva 34 ., Ion channels were also represented amongst the validated RNAi candidates ., For example , we found that knockdown of CG16793 , a calcium channel , showed disruption of neuronal growth in culture , with increases of branch points and weakened connectivity of axons between neighboring cell body clusters ( Figure S3 ) ., During axonal outgrowth , calcium transients are important in regulating the advance of growth cones 35 and it has been demonstrated in cortical neurons that voltage-gated calcium channels mediate this activity 36 ., Increased calcium transients result in slowed growth cone advancement 36 , 37 ., Changes in the migration rates and sizes of growth cones during outgrowth are correlated with the demarcation of axonal branch points and it is now hypothesized that the local changes in calcium activity could ultimately lead to the activation local branching morphogenesis 36 ., It has also been observed that the neurotransmitter serotonin can enhance neurite outgrowth through the activation of serotonin receptors and voltage gated calcium channels 38 ., Thus our observations support the findings that altered calcium signaling can lead to changes in axon outgrowth and branch patterning ., The screen identified numerous DNA- and RNA-associated genes that have poorly understood roles in the nervous system ., It is thought that during neurodevelopment , postmitotic neurons are highly vulnerable to DNA damage 39 ., Thus genes involved in DNA repair have an influence on the genesis of the nervous system ., In our screen we identified Parp , the Drosophila homolog of PARP-1 , which has a well-characterized role in DNA repair 40 ., Our data support recent work in rat cortical neurons suggesting that PARP-1 may also have a neurotrophic role 41 ., Genes relevant to neuropathological disorders were also identified in the screen , including Presenilin , Huntingtin , and Prominin-like ., The human orthologs of these genes are implicated in Alzheimers , Huntingtons , and retinal degeneration diseases respectively ., The RNAi phenotype of Huntingtin shows increased branching and defasciculation ( Figure 3D ) , and suggests that the wild type form of Huntingtin is important for proper nervous system function ., This observation is in agreement with previous observations on Huntingtin loss 42 ., In Huntingtons disease , Huntingtin protein is thought to acquire gain of function due to expanded polyglutamine repeats ., Genetic modifier screens using in vivo Huntingtons disease models in flies have been successful in identifying evolutionarily conserved suppressors of polyglutamine expansion 43 ., Potentially , by using primary cell based RNAi , this type of screen could be carried out on the entire genome in a high-throughput manner , and thus identify potential new drug targets for the development of therapeutics ., The screen identified a significant number of genes involved in vesicle and protein trafficking including SNAP , sec23 , αCOP , γCOP , Ran GTPase , Arf102F , and both Sec61α and Sec61β ., Protein trafficking is most likely a key process in generating the highly polarized structure of neurons ., Yet this class of genes is difficult to study in a neuronal context due to their widespread expression ., Thus the use of RNAi on dissociated neurons could also yield new insights into the functions of these genes ., The Sec61α translocon gene has potential relevance to human disease , since it is implicated in polyglutamine-induced neurodegeneration 7 , 44 ., It is highly conserved , with 91% peptide identity to human Sec61α ., Sec61α dsRNA-treated cultures were scored both visually and from quantitative analysis as a strong hit that showed defasciculation and excess branching ( Figure 3E ) ., To determine whether Sec61α hypomorphic mutants have neural outgrowth defects in vivo , homozygous Sec61αk04917 7 and Sec61αl ( 2 ) SH0190 45 mutant embryos were stained with neuronal and glial markers ( Figure 4 ) ., Both alleles are P element insertions to introns of the Sec61α locus 7 , 45 ., The Sec61αk04917 allele was a stronger hypomorph than the Sec61αl ( 2 ) SH0190 allele , yet both showed similar types of nervous system disruption ., In the CNS , commissural axon tracts were poorly separated in 33/145 ( 23% ) hemisegments of Sec61αl ( 2 ) SH0190 homozygotes ( Figure 4B , E , arrows , 145 hemisegments scored ) and in 27/105 ( 27% ) of Sec61αk04917 homozygotes , while wild type embryos showed no similar commissural disruptions in 136 hemisegments scored ., In the mutants where the CNS commissures were malformed , the midline glia were also aberrantly distributed ( Figure 4A , D , arrows ) ., In the stronger Sec61αk04917 allele , the nervous system development was stunted in comparison to wild types ., After 22 hours of development , the CNS was structurally mature in all wild type embryos ( n\u200a=\u200a200 ) , showing well-fasciculated longitudinal axonal tracts and expressing strongly the synaptic marker Synaptotagmin-1 ( Figure 4H , green ) ., However after the same time duration in Sec61αk04917 homozygotes , CNS development was representative of earlier stages in all embryos scored , ( compare Figure 4H , K ) ( n\u200a=\u200a200 ) ., The PNS of both Sec61α alleles showed aberrant motor and sensory axon tracts ., The axon bundles appeared defasciculated ( Figure 4E , asterisk , Figure 4J , solid arrows ) , and hemisegments with incorrectly targeted/misbranched axonal projections were identified in 18/145 ( 12% ) of Sec61αl ( 2 ) SH0190 homozygotes , and 63/115 ( 55% ) of Sec61αk04917 homozygotes , compared to 2/138 ( 1% ) of wild types ., The profiles of the PNS glial processes were abnormal compared to wild types ( Figure 4C , F ) ., It has been previously shown that the disruption of embryonic glial development can in turn lead to errors in axon pathfinding 46 ., In the Sec61α mutants , typical neuronal patterning errors that occur as a result of disrupted glial sheaths were observed , such as the sensory system Anterior Fascicle crossing hemisegment boundaries anteriorly ( Figure 4I , L , arrows ) ., These phenotypes could arise because PNS glia contribute to the establishment of the correct positioning and bundling of the peripheral nerves at the CNS/PNS transition zone 47 ., We also observed erratic positioning of sensory neurons at the PNS/CNS transition zone in Sec61αk04917 homozygotes 89/127 ( 70% ) ( Figure G , J , compare arrowheads ) and 57/103 ( 55% ) of Sec61αl ( 2 ) SH0190 homozygotes compared to none in wild types ( n\u200a=\u200a108 ) ., Such defects are not likely to be simply due to underlying defects in musculature ( Figure S5 ) , since it has been shown that sensory neurons develop rather normally in the absence of mesoderm development 48 and additionally , muscle cells are not known to have an influence on the positioning of CNS/PNS transition zone neuronal exit and entry points ., We also observed a reduction in expression of antigens within the nervous systems of Sec61αk04917 and Sec61αl ( 2 ) SH0190 mutant embryos ., For example , all heterozygous mutant Sec61αl ( 2 ) SH0190 embryos analyzed showed reduced glial mAb 5B12 antigen labeling compared to heterozygotes stained in the same preparation , as well as wild-type embryos ., For confocal imaging of embryos in Figure 4 , the laser power was increased for the Sec61αl ( 2 ) SH0190 specimens to show mAb 5B12 staining ., Similarly we observed less Synaptotagmin-1 labeling within Sec61αk04917 mutant embryos ( Figure 4G , K , specimens imaged with equivalent laser power in green Synaptotagmin 1 channel ) ., Given the translocon function of the Sec61α protein , it is likely that many neuronal and glial proteins are not being efficiently trafficked within the hypomorphic mutants , leading to disruption of neuronal development ., The analysis of mutant embryos extend the RNAi experiments and provide further evidence that the Sec61α translocon gene is required for neural development ., Since a large number of genes identified in the RNAi screen have close homologs to vertebrate genes , we chose to validate another gene in embryonic mouse brains ., Ran GTPase was a highly conserved gene identified in the full genome screen that showed dramatic effects on neurite outgrowth when knocked down ( Figure S2E ) ., Ran GTPase is a member of the Ras superfamily that is involved in a variety of cellular process , including nucleo-cytoplasmic transport 49 and mitosis 50 ., The Drosophila Ran GTPase protein has 87% similarity to mouse and human Ran ., Ran binds to the human AR receptor protein , which shows a polyglutamine expansion in Kennedys Disease , a neurodegenerative disorder 8 , but the role of Ran in Kennedys disease , or in neurodevelopment is not known ., In Drosophila , Ran transcripts are maternally deposited into the embryo ., During later embryonic development Ran becomes zygotically expressed specifically in the CNS at stage 12 51 , which corresponds to a time of rapid neural cell division and migration ., Ran is known to be expressed in the mouse brain at early embryonic stages 52 , and is thus a good gene candidate to characterize in mouse brain development using RNAi ., We immunolabeled Ran in dissociated cortical neurons and also found high levels of expression in the nuclei of these cells ( Figure S4 ) ., Furthermore , Ran immunolabeling can be detected in the processes ( Figure S4 ) , suggesting a role for Ran in neurite outgrowth , as well as in nuclear import ., To analyze the role of Ran in mouse development , we transfected Ran RNAi constructs into the lateral ventricles of the embryonic day 14 ( E14 ) mouse brains using microinjection and electroporation techniques ., The transfected cortices were dissected and cultured as explants or dissociated cultures ., To test the efficacy of the Ran RNAi constructs ( 1 and 2 ) in reducing the levels of Ran protein , nih-3T3 cells were transfected with RNAi constructs at 70% transfection efficency ., Western blot analysis of total protein from transfected and untransfected cells showed a 64% knockdown of Ran in the presence of Ran RNAi construct number 2 ( Figure 5B ) ., Ran RNAi electroporated neurons showed processes with abnormal blebbing ( arrow in Figure 5A right panel ) compared to the normal appearing processes in the vector control ( Figure 5A left panel ) ., We observed that only 0 . 7% of control neurons presented blebs while 65 . 6% of the Ran RNAi neurons showed blebs ( Figure 5C ) ., The blebbing phenotypes in the mutant compared to wild type was statistically significant ( P<0 . 02 ) ., To ensure that the blebs present in Ran RNAi neurons were not due to the cell death we analyzed the explants with an apoptosis marker , anti-Cleaved Caspase3 ., We found that GFP-labeled neurons in the Ran RNAi explants did not colocalize with Cleaved Caspase3 ( Figure 5J ) ., Thus , the blebbing phenotype was probably due to defects in neurite outgrowth ., In addition to the blebbing phenotype , Ran-deficient neurons showed an increase in branch arborization ( Figure 5A right panel ) as compared to the normal branch morphology seen in the control ( Figure 5A left panel ) ., The number of branching points per neuron increased significantly ( P<0 . 0001 ) from 3 . 2±2 . 5 in the control to 11 . 5±3 . 6 ( Figure 5D ) ., We also analyzed the effect of Ran deficiency in vivo using explant cultures ., Analysis of 3D reconstruction of control ( Figure 5E top panel ) and Ran RNAi ( Figure 5E bottom panel ) showed that the branching and blebbing phenotype is also present in the in vivo situation ., Quantitation of the number of blebs per nuclei in explant sections showed a very significant ( P\u200a=\u200a0 . 0007 ) increase in Ran RNAi deficient explants ( Figure 5F ) ., This increased arborization phenotype observed upon knockdown of Ran protein partly resembles the effect of Rac GTPase loss-of-function in mouse and Drosophila neurons 53 ., Rac GTPases are major regulators of the actin cytoskeleton while the Ran GTPase is a major regulator of the microtubule cytoskeleton ., The interplay of both the actin and microtubule cytoskeletons is known to be important for axonal branching 54 , 55 ., To further characterize the role of Ran in neural development , we immunolabeled sections from Ran RNAi-transfected brains with various neuronal markers ., In some explants only a few cell bodies of Ran RNAi neurons were present in the intermediate zone ( IZ ) ( Figure 5G second panel ) , compared to the control-transfected cells that were observed to be closer to the IZ and in the cortical plate ( CP ) ( Figure 5G first panel ) ., In other cases , we noted an apparent increase in Ran RNAi electroporated cells close to the subventricular and ventricular zone ( SVZ/VZ ) compared to control explants in TAU1 ( axonal marker ) immunolabeled explants ( Figure 5I ) ., We analyzed the distribution of GFP-positive cells in the explants by dividing the image in Bins I through VI ( CP\u200a=\u200aBin I–III; IZ\u200a=\u200aBin III–IV; SVZ/VZ\u200a=\u200aBin IV–VI ) ( Figure 5I ) ., Three different planes were analyzed per explant ., Quantitation of GFP-cell distribution in the explants suggested that upon Ran knockdown ( Ran RNAi n\u200a=\u200a380 , 431 , 330 ) the distribution of cells might shift towards the lower areas ( Bin IV–VI ) compared to the controls ( Control n\u200a=\u200a284 , 461 , 476 ) ., Immunolabeling for the axonal marker TuJ1 colocalized with processes from Ran RNAi transfected cells ( Figure 5G right panel ) similar to the control transfected cells ( Figure 5G left panel ) ., Interestingly , analysis of MAP2 staining showed in some cases higher colocalization of MAP2 and GFP in the control neurons compared to the Ran RNAi neurons in the explant cultures ( Figure 5H left and right panels ) ., Together , these results suggest an essential and novel role for Ran GTPase primarily in regulation of neurite extension , which in turn could potentially affect neuronal polarity and migration ., RNAi in flies offers unique advantages: physiological assays in living cells are possible to carry out since Drosophila grow normally at room temperature , screening can be efficiently carried out on a genome-wide scale , full genome RNAi libraries are openly available for public use , and cell-based RNAi screening can be carried out efficiently without the use of potentially toxic transfection reagents which can significantly increase experimental noise as well as cost 24 , 25 ., To characterize genes involved in neurodevelopment , the Drosophila model is particularly attractive since it has physical and genetic similarities to vertebrates and has particularly well-developed genetic tools 56 , 57 ., Traditional chemical genetic screens in the fly have provided great insight into major evolutionarily conserved mechanisms of axon guidance by identifying genes such as slit and robo 58 , 59 ., Importantly , it is estimated that more than 60% of human disease-associated genes have a closely conserved counterpart in the fly 60 , 61 ., Recently , Drosophila has been used to model human neurological disorders , and to study the genetic pathways associated with diseases such as Parkinsons , Huntingtons , and Alzheimers diseases 62 ., For example , analyses of Drosophila Huntingtons Disease ( HD ) models have been the first to discover the role of Histone Deacetylases in suppressing HD phenotypes 63 , to show disruption of axon trafficking in HD models 42 , 64 , and to demonstrate that genetic reduction of SUMOylation reduces neurodegeneration of the HD model 65 ., There are already some neurological disease models in fly cell culture , such as for Fragile×Mental Retardation 66 and Parkinsons Disease 67 , and these could be adapted for use in high-throughput RNAi screening assays such as described here for gaining a better understanding of the disease pathways and for identification of potential therapeutic targets ., While neurobiological analysis of Drosophila have most commonly been carried out in vivo , assays in primary neural cultures have been conducted for many years and they are known to have numerous features which are | Introduction, Results, Discussion, Materials and Methods | While genetic screens have identified many genes essential for neurite outgrowth , they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula , or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote ., To address this , we developed methods to screen the Drosophila genome using RNA-interference ( RNAi ) on primary neural cells and present the results of the first full-genome RNAi screen in neurons ., We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown ., From the full genome screen , we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi , have morphological defects such as reduced axon extension , excessive branching , loss of fasciculation , and blebbing ., To assist in the phenotypic analysis of the large data sets , we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes ., The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons ., Our analysis revealed unexpected , essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules , enzymes , channels , receptors , and cytoskeletal proteins ., We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes ., We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons , respectively ., Collectively , our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes , and the screening technique can be used to identify many new genes that have important functions in the nervous system . | Development and function of the brain requires the coordinated action of thousands of genes , and currently we understand the roles of only a small fraction of them ., Recent advances in genomics , such as the sequencing of entire genomes and the discovery of RNA-interference as a means of testing the effects of gene loss , have opened up the possibility to systematically analyze the function of all known and predicted genes in an organism ., Until now , this type of functional genomics approach has not been applied to the study of very complex cells , such as the brains neurons , on a full-genome scale ., In this work , we developed techniques to test all genes , one by one in a rapid manner , for their potential role in neuronal development using neurons isolated from fruit fly embryos ., These results yielded a global perspective of what types of genes are necessary for brain development; importantly , they show that a large variety of genes can be studied in this way . | cell biology/neuronal and glial cell biology, computer science/applications, genetics and genomics/functional genomics, cell biology/cell growth and division, cell biology, cell biology/developmental molecular mechanisms, computer science/numerical analysis and theoretical computing, genetics and genomics/gene function, neuroscience/neurodevelopment, neuroscience/neuronal and glial cell biology, computational biology, neuroscience, genetics and genomics 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journal.pcbi.1002131 | 2,011 | Binding Free Energy Landscape of Domain-Peptide Interactions | Protein-protein interactions control numerous processes in the cell ., Recently , it has been realized that a significant fraction of these interactions are mediated by the binding of flexible polypeptide segments to folded domains 1–3 ., This realization is in part due to the discovery of many so-called peptide recognition domains ( PRDs ) , which function specifically by recognizing sets of short peptide sequences 4 , 5 ., PRDs often interact with their ligand peptides in a reversible , transient manner , making them particularly well suited to mediate interactions in signaling and regulatory processes , which require fast response to initiated or ceased stimuli ., A fundamental understanding of the detailed dynamics and binding free energy landscapes of these PRD-peptide interactions will therefore eventually be necessary in order to understand the finely tuned specificities and affinities which underpin many protein interaction networks ., Achieving such an understanding may also be of practical importance , as it can be a starting point towards altering signaling networks in a controlled way 6 , 7 or designing small molecules to inhibit domain-peptide binding 8 , 9 ., Modeling peptide binding in atomistic detail is a challenge ., One reason for this is the inherent flexibility of a disordered peptide chain which necessitates a statistical mechanical approach ., At the same time it is a major modeling opportunity because of the relatively small molecular interface and few amino acids involved , making the peptide binding process computationally accessible ., Several docking methods designed specifically for peptide binding have been developed 10–16 , which aim to predict the correct peptide binding pose on a protein surface ., Most of these methods require some prior knowledge of the peptide binding site , although true blind docking has also been attempted 17 , 18 ., Other in silico methods seek to provide binding predictions for whole PRD families , including SH2 19 , SH3 20 , 21 , and PDZ 22 domains ., These methods rely on structural models of domain-peptide complexes using an available experimental peptide-bound configuration as a template ., Most PRD families , however , display significant diversity in how peptides interact with domains , which fundamentally limits this approach ., In a recent effort to alleviate this problem , King et al 15 combined peptide docking and subsequent structure-based binding prediction using the Rosetta scoring function ., Molecular Dynamics simulations of domain-peptide bound states have also been carried out , emphasizing the importance of dynamics and flexibility for understanding the molecular basis of peptide binding 23–25 ., Our aim here is to go beyond docking and investigate the binding process from an equilibrium perspective ., To this end , we use a recently developed Monte Carlo-based procedure for protein-peptide binding 26 and apply it to three different PDZ domains and their target peptide sequences ., The approach combines a global conformational search of the peptide chain , as well as limited protein backbone flexibility around the native state , with an effective energy function inspired by protein folding studies 27–29 ., Rather than relying on large numbers of docking attempts , we perform fewer but long simulations such that each run exhibits multiple binding and unbinding events , thereby providing an equilibrium picture of the binding process ., In particular , this allows us to investigate and compare features of the global binding free energy landscape as determined by the interaction between the protein surface and the amino acid sequence of the peptide ., The PDZ domain is an archetypical PRD existing in large numbers in many genomes 30–32 ., It distinguishes itself from other PRDs in that it typically binds sequence motifs at the extreme C terminal end of proteins ., The architecture is mostly conserved across the domain family with a typical core structure consisting of two -helices and six -strands ., The PDZ fold includes a binding pocket between the second -helix ( ) and second -strand ( ) such that a ligand peptide can augment the -strand upon binding and pack its sidechains against the -helix ., In addition , the peptide C terminus forms hydrogen bonds with the backbone amides of a highly conserved loop on the PDZ domain ., Like many other PRD families , PDZ domains have been divided into different classes depending on which peptide sequences they preferentially bind ., The most established division of PDZ domains is into classes I , II , and III , corresponding to the sequence patterns Ser/Thr-X--COOH , -X--COOH , and Asp/Glu-X--COOH , respectively , where is any hydrophobic amino acid , X is any amino acid , and COOH is the C terminus 32 ., It can be pointed out that more fine-grained classifications are also possible 33 ., We focus here on comparing the binding behavior of class I and II domains , which represent the majority of known PDZ domains 30 , 32 ., An important aspect of any binding study is the ability to capture binding to free molecules , i . e . , to structures determined in the absence of a ligand ., This is important not the least for PDZ domains , for which only domain-peptide complexes have been solved experimentally so far 32 , compared to the almost 200 free PDZ domain structures in the Protein Data Bank ( PDB ) 34 ., We therefore start out by testing our computational procedure using two different structural forms of the domains , free and peptide-bound ., Thereafter , we describe the conformational transitions of the peptides and the binding free energy landscapes for the domains ., Finally , we perform a large number of Monte Carlo based kinetic simulations to obtain a deeper microscopic picture of the peptide binding process ., As class I and class II representatives we chose the 3rd PDZ domain of PSD-95 and the 6th PDZ domain of GRIP1 , respectively ., These are typical class I and II PDZ domains in the sense that all known binding peptides fall within their respective ideal class motifs 30 , 35 ., Free and peptide-bound X-ray structures have been determined for both domains 36 , 37 , and for PSD-95 the binding thermodynamics 38 as well as kinetics 39 , 40 have been particularly well characterized ., The ligands present in the two peptide-bound structures were derived from the C termini of the proteins CRIPT ( PSD-95 ) 36 and human Liprin- ( GRIP1 ) 37 ., We consider here the binding of these two ligands to both the bound ( b ) and free ( f ) structural forms and denote the systems by PSD95-Ib , PSD95-If , GRIP1-IIb , and GRIP1-IIf , respectively ., In addition to these class I and II domains , we include in this study the PDZ domain of PICK1 which is one of the few known PDZ domains with dual class I and II specificity ., The structure of PICK1 PDZ has been determined with class II peptides 41 , 42 ., We consider binding of ligands taken from protein kinase ( , class I ) and AMPA receptor subunit GluR2 ( GluR2 , class II ) , which are known binders to PICK1 43 , 44 , and denote the systems with PICK1-Ib and PICK1-IIb , respectively ., The PDZ domains and peptide amino acid sequences under study are summarized in Table 1 ., To simulate the domain-peptide binding process , we use the MC based approach developed in Ref ., 26 ., This simulation procedure is general in that it can in principle be applied to any protein-peptide pair as long as a protein structure is available ., Briefly , it works in the following way ., A relaxed protein domain structure is centered in a cubic box and joined by a peptide in a random conformation away from the protein surface ., The peptide is entirely free to search conformational space , restricted only by periodic boundary conditions on the box ., The protein , on the other hand , is kept close to its native structure using constraints on the -atoms , which allow limited backbone and in principle full sidechain flexibility ., We combine this simple procedure with an implicit-solvent all-atom energy function based on effective hydrogen bond , electrostatic , and hydrophobic forces 26 ., Here we improve the model by including a context-dependent desolvation effect for backbone atoms groups ( see Methods ) ., We find , in particular , that including such a context-dependence improves the challenging case of simulating peptide binding to free domain structures ., Energies E and temperatures T are given in dimensionless model units ., The thermodynamic behavior of our systems is obtained using Simulated Tempering ( ST ) 45–47 , an expanded ensemble MC method in which T is treated as a dynamical parameter ., The method is convenient both for finding global minimum-energy states and studying equilibrium behavior ., For each PSD-95 and GRIP1 structure-peptide pair , we performed 5 independent ST runs ., An example trajectory is shown in Figure S1 in Supporting Information ., In addition , fixed-T MC simulations close to the midpoint , , i . e . , where bound and unbound populations are equal , were also performed to provide additional statistics for free energy surface calculations ., 10 independent fixed-T runs were performed for each structure-peptide pair in Table 1 ., Additional details on the computational model and simulation procedure are provided in Methods ., A challenging test for our computational model , used also in guiding the development of our all-atom energy function , is the prediction of bound peptide conformations ., Figure 1 shows the model conformations found with the lowest total energy , E , across all ST and fixed-T MC runs for each system , superimposed on the corresponding experimental structures ., All 6 min-E conformations are bound at the PDZ peptide binding pocket and many of the finer atom-level details match the experimental structures ., Of special interest is to compare the two sets of results obtained for the ligand-bound and ligand-free PSD-95 and GRIP1 PDZ domain structures ., One of the most pronounced differences is due to the different sidechain orientations at P ( –2 ) between GRIP1-IIb and GRIP1-IIf docked peptides , such that the Tyr sidechain is pointing either out ( GRIP1-IIf ) or into ( GRIP1-IIb ) the peptide binding pocket ( residue positions on PDZ binding peptides are typically numbered P ( 0 ) for the C terminus residue , P ( –1 ) for the immediately preceding residue , and so on ) ., This difference in orientation is likely related to a small shift in the helix between the ligand-free and ligand-bound structures of the GRIP1 domain 37 , such that the binding pocket is slightly wider in the bound structure ., Having seen that the lowest-E states represent more or less correctly bound ligands , we turn to the equilibrium behavior of the domain-peptide interaction ., Figure 2 shows the T dependence of inter-chain hydrogen bond and hydrophobic interactions for PSD95-If/b and GRIP1-IIf/b ., Some general trends are immediately seen ., At high Ts , only limited interactions between peptides and domains occur , consistent with a process dominated by entropic effects ., As T is lowered , peptides and domains associate increasingly , making both favorable hydrogen bonds and hydrophobic interactions ., While all binding curves are smooth , the precise behavior is seen to depend on which domain structure type is used ., Particularly , we find that the free domain structures ( PSD95-If and GRIP1-IIf ) bind their ligands somewhat weaker than their respective bound structures ( PSD95-Ib and GRIP1-IIb ) ., To investigate this difference quantitatively , we fit the binding curves in Figure 2 to a simple two-state expression with 4 free parameters ., The fits are good for all binding curves and the fitted parameters are given in Tables 2 and 3 ., Of particular interest are the parameters , the midpoint temperature representing equal populations of the two states , and , the energy difference which controls the sharpness of the transition ., The midpoints obtained are and for PSD95-Ib and GRIP1-IIb , respectively ., The corresponding for PSD95-If and GRIP1-IIf are roughly 4% lower ., We also find differences in , as well as in the other 2 fit parameters , but the statistical errors for these parameters are larger ( see Table 2 and 3 ) ., One statistically significant difference is a slightly sharper binding transition for PSD95-If compared to PSD95-Ib ., This can also be seen as a relatively higher peak in the specific heat capacity curve ( ) for PSD95-If , as shown in Figure 3 ., However , all curves exhibit single peak behavior and the T-values at the peaks correspond well to the found from the fits in Figure 2 ., Hence , while we find differences in the binding behavior for bound and free domain structures , binding as an overall two-state process with a single transition appears to be a robust feature ., The variations in binding behavior between bound and free structures obtained in our simulations reflect structural differences between liganded and unliganded PDZ domain forms ., Some of these differences are likely preserved by our native state constraints ., Previous simulation results indicate that overall receptor flexibility and dynamics can play a major role in PDZ peptide binding and selectivity 7 , 25 , 48 , 49 ., Interestingly , structural differences in the binding pocket between bound and free form is significant for the GRIP1 domain 37 while quite negligible for PSD-95 36 ., Our results thus indicate that even subtle structural differences can impact binding significantly ., Regardless of these differences between bound and free form our model predicts that the GRIP1 domain binds its peptide more strongly than PSD-95 , with ( see Figure 2 ) ., Meaningful quantitative binding affinities cannot be directly obtained , however , because T is not matched to physical units ., Experimentally , the dissociation constant of the PSD-95/CRIPT interaction has been measured to at 298 Kelvin , using isothermal titration calorimetry 38 ., The binding affinity of the GRIP1 domain for the Liprin- peptide has to our knowledge not yet been determined ., The binding curves in Figure 2 report on the overall character of the binding transition but do not provide any structural details , such as where on the protein surface binding preferentially occurs or how the peptide chain dynamics is influenced by binding ., In defining a bound state , we use the root-mean-square-deviation between the atom coordinates of a model peptide conformation , , and those of the experimental ( native ) peptide structure , , i . e . , ( 1 ) where the sum goes over n peptide atoms , either all non-H or only -atoms ( indicated by superscripts ALL and , respectively ) ., An advantage of the RMSD measure is that a small value indicates that binding has occurred both at the right surface area and with a native-like internal conformation ., Any peptide with is considered correctly bound in the PDZ binding pocket ., The choice of will be discussed later ., In order to delineate the internal conformational dynamics of the peptide chain from its binding , we calculate also , where the minimization is over all rigid body translations and rotations of the peptide conformation ., Hence , is the measure typically used in the analysis of folding trajectories and its notation is chosen merely to distinguish it from the “non-optimized” RMSD measure in Equation 1 ., A small means that the peptide is native-like regardless of whether it is bound or not ., For both the PSD-95 and GRIP1 domain-peptide pairs , the probability that the peptides occupy the bound state , , increases sharply as T is lowered ( see Figure 4 ) ., It is notable that for PSD95-Ib , at the lowest T simulated , , indicating a very low probability for the peptide to bind parts of the domain surface other than the PDZ binding pocket ., values for PSD95-If , GRIP1-IIb , and GRIP1-IIf are lower but the PDZ binding pocket is the dominating binding site in these cases , too , and will likely increase further at still lower Ts ., Consistent with our results in Figure 2 , Figure 4 shows a higher peptide binding propensity for liganded ( PSD95-Ib and GRIP1-IIb ) compared to the unliganded structures ( PSD95-If and GRIP1-IIf ) ., These shifts are smaller than the differences between the two PDZ domains , as noted above ., When the peptides associate with the protein surfaces they not only bind to the peptide binding pocket , they also undergo internal conformational transitions such that they more closely resemble the native peptide structures ., This is clear from the lower panel of Figure 4 , which shows that decreases with temperature T . Hence , the peptide-binding process also leads to increasingly native-like peptide conformations ., By contrast , the peptide chains by themselves show little tendency to form any specific structure , at least over the temperatures studied , as indicated by a relative constant for isolated chains ( see Figure 4 ) ., Moreover , the chain compactness is similarly only weakly dependent on T for both peptide sequences ( see Figure S2 in Supporting Information ) ., In this sense , our peptides are intrinsically disordered and their interaction with the PDZ domains can be seen as a minimal example of coupled folding and binding ., Direct observation of such coupled folding-binding behavior in atomistic simulations has been seen previously mainly for -helical peptides 50–54 ., It must be pointed out that despite the indicated “folding , ” significant structural heterogeneity remains in the bound state ., This diversity represents the conformational entropy of the bound state and is important to take into account since it can significantly contribute to ligand binding 55–57 ., In fact , in defining the bound state , our aim was to choose large enough to comprise most of this diversity , but not too large such that incorrectly bound peptide conformations are included ., To explore this tradeoff , we show in Figure 4 curves obtained also with and for PSD95-Ib and GRIP1-IIb ., Increasing to 9 Å from 6 Å has a relatively small impact on the curves ., Most of the structural diversity is therefore included with ., At the other end , to see that is not too large , we superimposed representative sets of peptide conformations with ., This ensemble is naturally diverse but do not include conformations that can be considered misdocked ( see Figure S3 in Supporting Information ) ., Finally , we find it instructive to construct reference structures by rotating the experimental peptide structures by a half turn , such that the atoms of the first and last peptide amino acids exchange positions ., These “flipped” peptides have and for the CRIPT ( PSD-95 ) and Liprin- ( GRIP1 ) peptides , respectively ., Hence , peptide conformations of this nature would not contribute positively towards in our definition of the bound state ( and are not observed in our simulations ) ., We turn now to the binding free energy landscapes of our PDZ domains , i . e . , the free energy as a function of a set of order parameters indicating the progress of binding ., For this purpose we use , in addition to the total energy E , two standard 58 , 59 structural order parameters , and Q , defined as the distance between the centers-of-mass ( CM ) of model and experimental peptide conformations and the fraction of inter-chain native contacts , respectively ., and Q are complementary in that each provide different perspective on the peptide binding process ., The binding free energy surfaces for PSD95-Ib and GRIP1-IIb show bound and unbound states well separated with a single barrier ( the transition state , TS ) at 4–6 Å and 0 . 1–0 . 2 ( see Figure 5 ) ., The binding landscapes do not exhibit any competing deep local minima representing misdocked conformations and therefore constitute almost ideal “binding funnels” 60 ., This is reassuring in terms of the validity of the model and indicates that nonspecific binding between PDZ domain and peptide chains may be very limited ., The one-dimensional free energy profiles in , Q and E reveal a more distinct free energy barrier between the bound and unbound states for GRIP1-IIb compared to PSD95-Ib , indicating a more cooperative binding process for the class II domain ( see Figure 5 ) ., In the E parameter , a small barrier separates bound and unbound states for GRIP1-IIb while such a barrier is mostly absent for PSD95-Ib ., In the structural parameters , Q and , the barriers are overall much higher but the trend remains ., This can be seen , for example , in the free energy difference between the transition state and the native , bound state , , in the parameter ., From Figure 5 , we find that and for PSD95-Ib and GRIP1-IIb , respectively ., One could easily suspect that the relatively higher barrier for GRIP1-IIb is due to its longer peptide ., This is however not the case ., We re-made our simulations for GRIP1-IIb with a truncated , 5-amino acid version of Liprin- and found that in fact increases slightly to ., Hence , the difference between the PSD-95 and GRIP1 systems is likely mainly related to differences in the amino acid sequences ., The bound state for GRIP1-IIb is characterized by a single , deep minimum at , i . e . , with most of the native contacts formed ., The PSD-95 domain , by contrast , exhibit a significantly wider distribution of Q-values in the bound state ., In addition to a deep minimum , a second weaker minimum exists at ., Visual inspection of the minimum reveals peptide conformations in which the C terminal Val of CRIPT is tethered to the PDZ binding pocket , kept in place mainly through hydrophobic interactions involving the Val and hydrogen bonding between the peptide C terminus and the PDZ carboxylate binding loop , leaving a floppy N terminal region ., Such flexible , yet bound conformations are mostly absent for GRIP1-IIb ., Instead , its peptide typically binds through both the Cys and Tyr sidechains at P ( 0 ) and P ( –2 ) ., From the perspective of our model , we find that additional hydrophobic contacts provided by P ( –2 ) in class II domain-peptide binding give a more rigidly bound peptide ensemble , which in turn produces a higher free energy barrier for binding and a more cooperative binding process ., A question that arises in comparing features of the free energy surfaces of PSD95-Ib and GRIP1-IIb is to what extent they can be controlled by the peptide sequence ., In this regard , promiscuous PDZ domains which bind both class I and II peptides are of particular interest ., We therefore apply our method to one such domain , the PDZ domain of PICK1 , and simulate the binding of both a class I ( PICK1-Ib ) and a class II ( PICK1-IIb ) peptide , as displayed in Table 1 ., Despite that the two peptide sequences bind the same domain structure , their free energy surfaces are quite different ( see Figure 5C and D ) ., Specifically , the PICK1-Ib landscape exhibits striking similarities with PSD95-Ib , particularly with regard to a broad Q-distribution of the bound state ., PICK1-IIb , on the other hand , has a binding free energy landscape similar to GRIP1-IIb , with a single well-defined native basin of attraction ., The binding free energy barriers for PICK1-Ib and PICK1-IIb are and , respectively , such that the class II peptide again shows a relatively stronger binding cooperativity ., It is interesting to compare our results for PICK1-Ib and PICK1-IIb with those of Madsen et al . 44 ., Using an assay based on fluorescence polarization , they found that the PICK1 PDZ domain showed a higher affinity for a class II than a class I peptide ( ) ., This is in qualitative agreement with our results , as we find a higher for PICK1-IIb over PICK1-Ib ( see Figure 5 legend ) , although their class II ligand was not the same as ours ., Madsen et al . also obtained docked peptide structures using homology modeling and found to be unusually displaced from at the N terminal end , somewhat reminiscent our local free energy minimum ., However , for typical peptides in our simulations the N terminal ends have become almost entirely displaced from the -helix ., One might think that this structural diversity is exaggerated by our model because , after all , PDZ specificity is in part obtained from interactions with P ( –2 ) ., We therefore tested the PICK1 mutation Ala87Leu , which was introduced by Madsen et al . and meant to fill out the hydrophobic pocket normally occupied by the P ( –2 ) residue ., The mutation was indeed found to essentially eliminate binding to both the class I and II peptides in their assay 44 ., We find in our simulations that the Ala87Leu mutation drastically reduces from roughly 0 . 5 at in wild-type PICK1 to and 0 . 09 for the class I and II peptides , respectively ., Hence , interactions involving P ( –2 ) are still crucial for proper binding in our model despite the local minimum ., In this context , it is interesting to note that experimental PDZ domain-peptide complexes were recently obtained in which the interaction occurs mainly through the P ( 0 ) position , such that the peptides bind roughly perpendicular to the domain surface 61 ., Above we have shown that , in our model , peptide binding can be seen roughly as a two-state process in which a single free energy barrier separates the bound and unbound states ., How is this free energy barrier crossed during binding ?, To address this question and further investigate the mechanism underlying peptide binding we perform a large number of fixed-temperature simulations where the peptide chains are , as previously , initiated in random positions and conformations ., In contrast to above , the MC “kinetics” simulations are performed using only small-step updates for the peptide chain; global , unphysical pivot moves are excluded ( see Methods ) ., A fraction of rigid body translation and rotation MC moves for the peptide chain is included ., There are two processes for the peptide chain in these simulations , a search on the protein surface for the peptide-binding pocket and , subsequently , a conformational search for the correctly bound structure ., Because of the inclusion of rigid body moves , we assume a dynamics in which the search process across the protein surface is fast ., Relaxation towards equilibrium is therefore limited by a conformational reorganization of the peptide and protein chains during binding , which is the process we are primarily interested in ., We find that the relaxation behavior for both PSD95-Ib and GRIP1-IIb systems is consistent with a single-exponential curve , as can be seen in Figure 6 ., This indicates a single rate-limiting step in the peptide binding process , or , in other words , the free energy barrier is crossed without significantly populating an intermediate state ., Only a handful kinetic experiments of PDZ domain-peptide binding have been performed so far but one such study has presented results for the PSD-95 system analyzed here ., Using stopped-flow fluorescence spectroscopy , Jemth et al . 39 observed single-exponential binding traces for the PSD-95 PDZ domain and a dansylated CRIPT peptide ., Our results are therefore consistent with these observations ., However , it must be pointed out that the MC-based simulations performed here should not be seen as mimicking kinetic experiments , as chain diffusion effects are not rigorously taken into account ., A more realistic comparison is likely achieved by focusing on relative kinetic effects between peptide binding systems ., In this respect , we observe a significant difference in relaxation times between PSD95-Ib and GRIP1-IIb , such that , a prediction which may be tested experimentally ., This difference in relaxation rate between the two domains is consistent with the larger free energy barrier seen for GRIP1-IIb over PSD95-Ib ., We have developed a MC based procedure for exploring peptide binding processes and employed it to two typical PDZ class I and II domains and a dual class I–II domain ., In combining the equilibrium and small-step , fixed-temperature kinetic simulation results , a picture emerges for the binding process in which there are overall similarities but also differences in the details ., In all cases , binding is coupled to folding , and can be characterized as an overall two-state process with a free energy surface funneled towards the peptide bound state ., Binding to the PSD-95 PDZ domain involves a lower free energy barrier than the GRIP1 PDZ domain , leading to significantly faster binding kinetics , at least for the peptide sequences studied ., What is the origin of this difference ?, The shape of the near-native free energy surface for the GRIP1 PDZ domain indicates a relatively coherent ensemble of bound peptide conformations , stabilized by hydrophobic interactions with P ( 0 ) and P ( –2 ) ., As a class I domain , the PSD-95 domain lacks strong hydrophobic interactions at P ( –2 ) leading to a more conformationally diverse bound state , spanning a wider range of and Q values ., In particular , we find a weak free energy minimum corresponding to peptides bound to the PDZ binding pocket mainly through the P ( 0 ) position , with a flexible N terminal tail ., The population of such conformations are significantly smaller for the GRIP1 PDZ domain ., Our results are therefore consistent with a binding mechanism in which the rate-limiting step is the initial binding of P ( 0 ) at the PDZ peptide binding pocket ., This interpretation is also supported by recent experimental PDZ domain-peptide structures , including GRASP 61 and X11 62 , where peptides are attached in a “perpendicular” mode ., To what extent these results apply to other class I and II PDZ domains remains to be seen ., However , the fact that an analogous behavior is found for the dual class I–II PICK1 domain indicates that it may have some generality ., All simulations are performed using essentially the model described in 26 , with a small improvement described in the following ., Our original starting point was a model developed for peptide folding 27 , 28 which combines an all-atom protein representation with an effective energy function based mainly on hydrogen bonding , hydrophobicity , and electrostatic attractions ., This model was then adapted for peptide binding 26 , where , in particular , we added a context dependence to the energy function such that electrostatic attractions between partial charges buried in the protein were made effectively stronger than those solvent exposed ., This was accomplished by using a parameter , , indicating the “degree of buriedness” for any atom i ., In this work , we add a context-dependent term describing desolvation effects on backbone atom groups , ( 2 ) in which the sum goes over all backbone NH and CO groups i ., For “unsatisfied” NH and CO groups , i . e . , those not participating in any intra- or inter-chain hydrogen bond , , and for all others , ., The quantity is calculated at a point , , which for a NH group is located 2 . 0 Å from the H atom in the NH direction , and for a CO group , 2 . 0 Å from the O atom in the CO direction ., is thus found approximately in the space occupied by a potential solvent molecule hydrogen bonded to i ., indicates that this space is available to a solvent molecule while indicates it is instead occupied by other protein atoms ., Hence , “unsatisfied” NH and CO groups with ( i . e . also unlikely to participate in solvent hydrogen bonding ) are energetically penalized ., The term therefore acts as a desolvation effect for backbone atoms ., The strength chosen is ., Including this energy term yields a crucially improved performance over the previous model 26 , most notably for peptide binding to free domain structures ., Specifically , the PSD95-If domain-peptide pair exhibited almost no propensity for correct binding previously 26 while including yields reliable binding as detailed in this work ., To obtain equilibrium conformational ensembles of our domain-peptide systems we used Simulated Temperin | Introduction, Results/Discussion, Methods | Peptide recognition domains ( PRDs ) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell ., How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood ., We explore the peptide binding process of PDZ domains , a large PRD family , from an equilibrium perspective using an all-atom Monte Carlo ( MC ) approach ., Our focus is two different PDZ domains representing two major PDZ classes , I and II ., For both domains , a binding free energy surface with a strong bias toward the native bound state is found ., Moreover , both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface ., Consistent with this , various binding observables show a temperature dependence well described by a simple two-state model ., We also find important differences in the details between the two domains ., While both domains exhibit well-defined binding free energy barriers , the class I barrier is significantly weaker than the one for class II ., To probe this issue further , we apply our method to a PDZ domain with dual specificity for class I and II peptides , and find an analogous difference in their binding free energy barriers ., Lastly , we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions ., These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I . Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial , rate-limiting step . | The complex biological processes occurring in living organisms are enabled by numerous networks of interacting proteins ., It is therefore of great interest to understand the physical interplay between proteins and , in particular , how this process gives rise to highly specific network connectivities ., For a long time , the dominant molecular view of protein-protein interactions was the docking of more or less static folded structures , with specificity obtained from a complementarity in shape and charge distributions ., Lately it has been realized that many of the links in protein networks are mediated by interactions between folded domains , on the one hand , and disordered polypeptide segments , on the other ., We use an all-atom Monte Carlo based approach which attempts to capture this domain-peptide binding process in full and apply it to representative members of a common domain family ., This allows us to examine and compare detailed aspects of the binding free energy landscapes which underlie specificity and affinity ., Being able to model domain-peptide binding in a physically sound , yet computationally tractable way is essential for identifying molecular binding mechanisms and opens up possibilities for modifying interaction networks in a controlled way . | physics, biomacromolecule-ligand interactions, biophysic al simulations, biology, computational biology, biophysics simulations, biophysics | null |
journal.ppat.1004281 | 2,014 | Human APOBEC3 Induced Mutation of Human Immunodeficiency Virus Type-1 Contributes to Adaptation and Evolution in Natural Infection | The pathogenesis of HIV-1 infection correlates with the level of active viral replication and relates to a variety of factors specific to the virus , the host , and its immune system ., Mutations , insertions , deletions and recombinations that confer changes in the activity of virally encoded genes and gene products affect virus entry and post-entry events 1 ., HIV-1 shows high mutational frequency because of a combination of rapid rates of viral replication , error-prone viral reverse transcriptase ( RT ) and RNA polymerase II replicating enzymes , and recombination during concurrent infection with two or more distinct genomic RNA strands 2–4 ., In addition , nascent HIV-1 cDNA is vulnerable to mutation by host cell single-stranded cytidine deaminases that edit cytidine to uridine in the minus strand DNA copied from the viral RNA genome , giving rise to G-to-A mutation of the plus strand of viral DNA with a graded frequency of deamination from the primer binding site to the central polypurine tract and the central polypurine tract to the 3′ polypurine tract regions 5–8 ., The effect of the processes of mutation and recombination in HIV-1 is to promote genetic diversity among the viral variants and thereby allow for a faster rate of adaptation ., Seven related human apolipoprotein B mRNA-editing enzyme , catalytic polypeptide-like 3 ( APOBEC3 ) cytidine deaminases—namely , APOBEC3A , APOBEC3B , APOBEC3C , APOBEC3D , APOBEC3F , APOBEC3G , and APOBEC3H—reside in an expanded 130-kb gene cluster that likely arose through segmental duplication on chromosome 22q13 . 1 with additional modification by repeated episodes of positive selection during primate evolution 9 ., Cytidine deaminases of the APOBEC3 gene family have specificity for single-stranded DNA and inhibit infection by a diverse array of RNA and DNA viruses and retrotransposons by interfering with viral genome replication and littering the genome with deleterious mutations 10 , 11 ., Mutations mediated by APOBEC3 molecules have a strong preference for a 5′-GG-3′ and 5′-GA-3′ dinucleotide context of the edited sites ( target nucleotide underlined ) 1 , 12 , 13 ., APOBEC3D , APOBEC3F , APOBEC3G and APOBEC3H are the cellular targets for the HIV-1 accessory protein Vif 14 , 15 , which can counteract the protective role of this innate immune defense mechanism ., The HIV-1 protein Vif induces polyubiquitylation through simultaneously binding to APOBEC3 proteins and the cullin5-elongin B/C-Rbx2 ubiquitin ligase complex ., In this manner , the APOBEC3 protein serves as an adaptor that recruits the ligase complex to its substrate and induces the subsequent proteasomal degradation of APOBEC3 proteins ., This depletes the pool of APOBEC3 proteins available for incorporation into the assembling viral particle , and thereby minimizes their ability to restrict HIV-1 replication 16–19 ., Mutation of the HIV-1 genome by cytidine deamination could have a dramatic effect on viral replication ., A high rate of mutation could prevent the formation of functional proviruses and explain the G-to-A hypermutation observed in patient samples 20 , 21 ., Low levels of APOBEC3 activity that survive inhibition by the HIV-1 protein Vif may expose the virus to a broad spectrum of mutations that , rather than impeding virus replication , could provide a source of genetic variation upon which natural selection acts 22 , 23 ., Indeed , extensive sequencing of transmitted founder HIV-1 variants indicates that sequence variation bearing the hallmark of APOBEC3-mediated G-to-A mutation is commonplace and experiments employing low levels of APOBEC3G expression confirm that modest mutation frequencies , as opposed to inactivating G-to-A hypermutation ( 5′-UGG-3′ to 5′-UAG-3′; tryptophan-to-stop codon ) , can be recapitulated in cell culture experiments 21 , 24–26 ., Such sequence changes would have the potential to underlie advantageous alterations in HIV-1 phenotype , such as the appearance of mutations in HLA class I-restricted epitopes that can confer escape from immune recognition or the acquisition of drug resistance 1 , 21 , 22 , 27 ., Here , we characterized the relationship ( s ) between APOBEC3 editing of HIV-1 and the perhaps subtle contribution of mutations that could influence viral adaptation and evolution , as contrasted with destructive G-to-A hypermutation ., We applied high depth sequencing to infected cells from single-cycle APOBEC3 titration transfection experiments to confirm and extend the definitions of the DNA sequence context of the edited sites for the four cytidine deaminases of the APOBEC3 gene family that are the cellular targets for HIV-1 protein Vif and their site-specific editing frequencies 5 , 7 , 8 , 12 , 13 , 28–30 ., We then followed the evolution of sequence changes and the appearance of the G-to-A signature mutations in the consensus trinucleotide contexts for APOBEC3 protein edited sites in the Gag and Vif genes of HIV-1 in proviral DNA sampled from the peripheral blood in 10 patients with primary HIV-1 infection through time ., We found a higher frequency of substitutions within an APOBEC3 trinucleotide context of the edited sites in patients that could often resulted in sequence changes within some major histocompatibility complex ( HLA in humans ) restricted epitopes that can confer immune escape ., Thus , we provide evidence that sub-lethal levels of APOBEC3 deaminases may expose the viral genome to beneficial mutations that influence HIV-1 adaptation and evolution in natural infection ., To make predictions about the potential for APOBEC3 editing of the HIV-1 genome to influence virus diversification in natural infection , it was first necessary to carefully define the nucleotide sequence editing context preferences for the APOBEC3 proteins by titration in virus producing cells ( Table 1 ) ., Vesicular stomatitis virus G ( VSV-G ) pseudotyped HIV-1 vif-deficient ( HIV-1 pIIIB/Δvif ) stocks produced in the presence of escalating doses of each of four human APOBEC3 genes that are the cellular targets for the HIV-1 protein Vif —namely , APOBEC3D , APOBEC3F , APOBEC3G and APOBEC3H—were therefore used to challenge cultured 293T cells ., We screened for mutations in HIV-1 nascent retroviral cDNA with high throughput 454 pyrosequencing using a statistical framework to improve measurement accuracy ., Barcoded oligonucleotide primers with unique molecular identifiers were used to amplify a PCR pool that was then sequenced with sufficient depth of coverage to redundantly cover the chosen portion of the viral genome ., By stringent filtering and correcting the raw sequencing data , the platform efficiently reduces most sequencing errors generated during pyrosequencing 31 ., We separated the resulting viral sequences by sample using the index sequence ., Sequence alignments made to the HIV-1 pIIIB/Δvif reference sequence were optimized to reduce alignment errors introduced by insertions-deletions ( indels ) associated with the pyrosequencing chemistry , correct for incomplete extension miscalls , and filter out less abundant sequencing reads ., We used a statistical analytical framework to filter the data for error correction and then built the haplotypes present in the viral populations ., With this approach , we found a significantly lower average mutation frequency than the typical analysis of these data ( average of 8 . 45×10−4 ) ( Figure 1A ) , consistent with the estimations of others 2 ., The nucleotide substitution rate for each mutation type ( transition or transversion ) differed by 1 . 2 orders of magnitude ( Figure 1B ) ., The different rates of nucleotide substitution likely reflect viral RT and RNA polymerase II fidelity and the asymmetrical substitution bias for faster accumulation of G-to-A mutations , the expected result of APOBEC3 editing ., Random PCR amplification bias did not affect the reliability of the measurements 32 ., Using these results , we identified G-to-A mutations in plus strand DNA as a genetic signature to identify a posteriori APOBEC3 nucleotide contexts of the edited sites ., Each G-to-A mutation was considered independently ( Figure 1C ) ., Because there is about a two-fold increase in the frequency of adenosine relative to guanosine in the viral genome , we corrected for a bias in the 5′-GpA-3′ and 5′-GpG-3′ context raw numbers ., For each of the four human APOBEC3 proteins that are the cellular targets for the HIV-1 protein Vif , we identified positions in the Gag gene region of HIV-1 we sequenced where the site-specific G-to-A substitution frequency increased significantly with increasing APOBEC3 protein abundance ( Spearman P<0 . 05; Figure 2A ) ., The four APOBEC3 proteins had a clear bias for the plus strand 5′-GpG-3′ or 5′-GpA-3′ in the trinucleotide context of edited sites that can serve as signatures for specific APOBEC3 gene activity ( Figure 1C ) ., APOBEC3G exhibited the highest frequency of G-to-A mutations in a 5′-GGD-3′ ( where D is the IUPAC code for G , A , or T ) and 5′-GAG-3′ context of edited sites and the most hypermutated sequences ( Figure 2A ) ., APOBEC3F and APOBEC3H proteins showed high frequencies of G-to-A mutation ( range , 0 . 08 to 0 . 15 ) in both 5′-GAD-3′ and 5′-GGA-3′ contexts of edited sites ., APOBEC3D showed a high frequency of a 5′-GGD-3′ context of edited sites , but the least activity of the four APOBEC3 deaminases tested under these experimental conditions ., For the trinucleotide context of edited sites within the Gag region of HIV-1 sequenced , G-to-A mutation did not invariably happen in all the potential APOBEC3 trinucleotide context of the edited sites ., This observation suggests that other factors inherent to the sequence may affect the activity of these cytidine deaminases ., The tryptophan ( 5′-UGG-3′ ) to a stop ( 5′-UAG-3′ or 5′-UAA-3′ ) codon change , for example , occurred at these two positions at a different frequency in the four APOBEC3 deaminases ( Figure S2 ) ., Further , the 5′-GCC-3′ , 5′-GCG-3′ , and 5′-GTC-3′ trinucleotide contexts were not noticeably affected by any of the four APOBEC3 proteins ., We did not find a statistically significant enrichment for the rare cytosine-to-thymidine ( C-to-T ) mutations brought about by a conflict between APOBEC3 editing and guanosine∶uridine ( G∶U ) mismatched base pair repair in regions of the genome where the plus-strand may become briefly single-stranded during reverse transcription 8 ., In the absence of human APOBEC3 protein , the most commonly recovered mutations were random G-to-A or C-to-T transition mutations and −1 frameshift mutations from RT errors ., The mutation rate during a single-cycle of HIV-1 replication was approximately 8 . 45×10−4 mutations per nucleotide ( 95% confidence interval 7 . 5 , 9 . 6×10−4 per nucleotide substitution ) ., In the presence of increasing levels of human APOBEC3 protein , the mutation rate during a single-cycle of HIV-1 replication rose by a factor of between 2 ( for APOBEC3D ) and 20 ( for APOBEC3G ) , which at sub-lethal levels would likely increase virus diversification and allow HIV-1 to evolve at different rates ( Figure 1A ) 33 ., Once the guanosine in the trinucleotide context of edited sites was removed to correct for the substitution rate in the viral sequence , the average mutation rate corresponded to measurements for single-cycle of HIV-1 replication in the absence of functional APOBEC3 ( Figure 2B ) ., Thus , human APOBEC3 deaminase activity is evident in both a directional substitution bias and a higher substitution rate ., To explore the effects of APOBEC3 editing of HIV-1 genomes on virus diversification in patients , we produced longitudinal pyrosequencing data from the Gag and Vif genes of HIV-1 proviral DNA in peripheral blood mononuclear cells ( PBMCs ) isolated from ten patients during acute or early HIV-1 infection and at a second time point approximately 24 to 26 weeks later before the start of combination antiretroviral therapy ., Table S1 shows the clinical characteristics of the ten patients ., Transmission risk factors were not able to be determined in two patients ( S002 and S006 ) ., The range of the estimated duration of infection was between 11 and 70 days 34 ., Figure S2 shows the temporal changes in the mean levels of HIV-1 RNA in plasma ( range , 779 to 57 . 6×106 copies per ml ) and CD4+ T-cell number counts ( range , 311 to 760 cells per ml3 ) ., Table S2 shows the high-resolution HLA genotypes ., A genetic screen of the APOBEC3D , APOBEC3F , and APOBEC3G genes found no single nucleotide polymorphisms associated with loss-of-function ., The poorly expressed APOBEC3H hap I 35 , 36 was found for seven patients , four of whom were homozygotes ( S001 , S005 , S007 , and S010 ) and three of whom were heterozygotes ( S004 , S006 , S010; Table S3 ) 35–39 ., To look for changes in the genetic structure of the HIV-1 population in each patient through time , we performed high depth gene sequencing with the aforementioned performance improvements for haplotype reconstruction to achieve the sensitivity and molecular resolution necessary for distinguishing among individual viral variants 31 ., DNA isolated from patient PBMCs was subjected to HIV-1 Gag ( nucleotide positions 977–1564 corresponding to HXB2 ) and Vif ( nucleotide positions 5041–5619 ) gene sequencing using the 454 Life Sciences GS-FLX pyrosequencing system ., The median number of viral DNA template copies was 23 , 902 ( interquartile range IQR , 8 , 909 to 34 , 103 ) as measured by quantitative polymerase chain reaction ( qPCR ) ., To estimate the virus population structure of the sample from the pyrosequencing reads , we aligned the reads to a consensus sequence and collapsed the repeated reads to build the haplotypes present in the viral population ., We assembled the haplotypes in accordance with the informative de-noised reads of prescribed length such that the fewest haplotypes can account for the most reads ., We then estimated the frequency of the reconstructed haplotypes present in the population ., After error correction of the sequencing reads and reconstruction of the explaining haplotypes , the median number of the minimal inferred candidate haplotypes present in the population was 30 . 5 for the Gag ( IQR , 5 . 75 to 67 . 0 ) and 35 . 65 for the Vif ( IQR , 9 . 50 to 59 . 00 ) genes of HIV-1 ( Table S4 ) ., The number of input molecules exceeded the fold depth of sequence data attained ., This criterion is necessary to avoid bias caused by selective amplification and artifacts due to sampling and technical variability caused by pyrosequencing 40 ., Binomial power calculations suggest that a sample size of 25 , 000 sequences gives a 96% likelihood of a variant present at 0 . 02% of the virus population to occur at least twice in the sample 24 ., The depth of sequencing reads ( range , 28 , 000 to 84 , 500 ) is sufficient to detect viral variants present at or above 0 . 02% of the population ( Table S4 ) ., Nucleotide sequences corresponding to the Gag and Vif genes of HIV-1 sampled at the early and late time points during infection were subjected to maximum-likelihood methods of phylogeny estimation ., Figure 3 shows the maximum-likelihood trees of phylogenetic relationships among the aligned haplotype sequences of the Gag and Vif regions of HIV-1 from the ten patients ., The topology of the tree shows distinct patient-specific clades , each with >95% of branch support 41 , except for patient S007 ., Consistent with the short time since transmission and rapid expansion of virus from a distinct transmitted founder in the new host , the phylogenetic tree for patient S007 has short branch lengths and few internal branches ., A particular transmitted founder virus , which had been subjected to a stringent genetic bottleneck , successfully established HIV-1 infection in nine of the ten patients studied ., For seven of the ten patients , the viral sequences formed distinct patient-specific monophyletic lineages , each with high statistical support ( >99% probability ) ., Viral sequences from a multiply infected patient ( S007 ) did not coalesce at a single transmitted founder in the maximum-likelihood tree , consistent with more than one transmitted founder virus being responsible for establishing a productive infection ., In two patients of known sexual congress ( S004 and S005 ) , each of who had a distinct transmitted founder virus , there was intermixing of viral sequences at the later sample time point , which were valid and did not result from cross-contamination of amplicons ., The Highlighter plots of viral sequences from each patient showed the random distribution of nucleotide polymorphisms across them consistent with a dispersal of variants that arise from a particular transmitted founder virus ( Figure S3 ) ., Maximum diversity of sequences within the discrete viral lineages from the nine patients with a particular transmitted founder virus was low ( mean 0 . 44%; range 0 . 15 to 0 . 73% and mean 0 . 53%; range 0 . 10 to 1 . 56% for the Gag and Vif genes of HIV-1 , respectively ) ., The maximum diversity of sequences from the patient with more than one transmitted founder virus ( mean 1 . 38% and 1 . 87% for the Gag and Vif genes of HIV-1 , respectively ) exceeded that found in the viral sequences from the other patients ., Based on the evidence for trinucleotide contexts of edited sites for the APOBEC3 deaminases from titration transfection experiments , we examined the potential for APOBEC3 editing of HIV-1 DNA to contribute to adaptation and evolution in natural infection ., Analysis of specific editing frequencies at individual guanosines for each of the four APOBEC3 genes revealed a clear overlap with sequence changes observed in patients ( Figure 4 ) ., Genomic context , such as adjacent nucleotides or local structural constraints , may have moderated against the effects of APOBEC3 editing at certain nucleic acid positions ., Because the trinucleotide context of edited sites is shared among APOBEC3G , APOBEC3F and APOBEC3H , the two former cytidine deaminases could affect APOBEC3 editing for the seven patients who carried the less active form of APOBEC3H ( haplotype I; homozygotes S001 , S005 , S007 , and S010 and heterozygotes S004 , S006 , and S009; Table S3 ) ., We next assessed whether APOBEC3 editing contributes to the genetic diversification of the virus populations in these patients ., Viral sequences from the first time point differed from their respective consensus sequence by a median value of two ( 2 ) nucleotides in the Gag gene of HIV-1 and one ( 1 ) nucleotide in the Vif gene of HIV-1 ., At the second sampling time point 24 to 26 weeks later , the sequences from the Gag and Vif genes of HIV-1 differed from the particular consensus sequence by a median value of three ( 3 ) and two ( 2 ) nucleotides , respectively ., We found relatively low frequencies of per nucleotide site insertions-deletions ( indels ) ( 1 . 47×10−3 and 9 . 42×10−5 for the Gag and Vif genes of HIV-1 , respectively ) and stop codons ( 2 . 44×10−4 and 1 . 03×10−4 for the Gag and Vif genes of HIV-1 , respectively ) ., The sequence diversity within each patient was calculated as the Hamming distance between sequences after weighting by the number of collapsed sequences and correcting for the sequence length ., The frequencies of nucleotide substitutions measured across the sequenced regions gauges the proportion of nucleotide sites at which the viral sequences being compared are different ( p-distance ) ., We excluded the multiply infected patient ( S007 ) and restricted our analysis to the nine patients with infection consistent with a single transmitted founder virus in which the observed sequence diversity is expected to be due to mutations that have happened after HIV-1 transmission ., The maximum number of variable nucleotide sites within individual viral populations ranged from 0 . 6% ( with mean diversity 0 . 3% ) at the early time point to 0 . 9% ( mean\u200a=\u200a0 . 6% ) at the later time point for the Gag gene of HIV-1 and 0 . 7% ( mean\u200a=\u200a0 . 3% ) at the early time point to 2 . 4% ( mean\u200a=\u200a0 . 8% ) at the later time point for the Vif gene of HIV-1 ( Figure 5 ) ., After weighting by the number of collapsed sequences , the inter-sequence pairwise distances for the sequence sets at the early time point were significantly lower than at the later time point during infection ( Wilcoxon sum rank test , P<0 . 05 for both Gag and Vif genes of HIV-1 ) ., The overall average ratio of nonsynonymous to synonymous nucleotide substitutions ( dN/dS ) , estimated using the Nei-Gojobori algorithm as implemented in SNAP 42 , were consistent with strong purifying selection over the time points sampled ( HIV-1 Gag\u200a=\u200a0 . 37 , standard error of the mean SEM\u200a=\u200a±0 . 07; and HIV-1 Vif\u200a=\u200a0 . 23 , SEM\u200a=\u200a±0 . 04 ) ., The overall mutation frequency among the nine patients , after correction for sampling time and frequency of the collapsed haplotype sequences with a maximum-likelihood analysis assuming a strict molecular clock , was estimated to be 3 . 8×10−3 and 6 . 5×10−3 per substitution per site per year for the Gag and Vif genes of HIV-1 , respectively ., When a Bayesian approach was taken and a strict molecular clock was used , the results obtained were very similar with estimated evolutionary rates of 4 . 1×10−3 and 4 . 2×10−3 per substitution per site per year for the Gag and Vif genes of HIV-1 , respectively ., Significantly , the viral sequences were sampled over a time period in which the evolutionary rate does not mirror a compound mutation and substitution rate ., These values are consistent with the estimations of others 43–45 ., To further assess the effects of APOBEC3 editing that happen at sub-lethal levels on the genetic structure of the HIV-1 populations , we analyzed the proportion of viral sequences with nucleotide changes happening in this way as well as their contribution to the genetic diversity of the viral populations ., It follows that among the APOBEC3 trinucleotide contexts of the edited sites , which are distinguishable from the more random RT-induced G-to-A mutations , viral diversification should increase through acquisition of neutral or beneficial substitutions all the while circumventing the introduction of a deleterious stop codon or loss of an initiation codon ., The frequencies of G-to-A mutations in the HIV-1 Gag gene sequences from the first to the second time point averaged: 31% ( 1 . 9/6 . 2 potential sites ) for 5′-GGA-3′; 35% ( 3 . 1/8 . 5 ) for 5′-GGG-3′; 17% ( 0 . 5/2 . 8 ) 5′-GGT-3′; and 19% ( 1 . 2/6 . 5 ) for 5′-GAG-3′ ., In the HIV-1 Vif gene sequences , these frequencies averaged: 18% ( 1 . 6/8 . 9 ) for 5′-GGA-3′; 13% ( 1/7 . 8 ) for 5′-GGG-3′; 6% ( 0 . 3/4 . 8 ) for 5′-GGT-3′; and 15% ( 0 . 9/5 . 4 ) for 5′-GAG-3′ ., To confirm that the APOBEC3 activity in virally infected cells may influence the substitution biases that could increase the substitution rate , we compared the pairwise genetic distance ( nucleotide changes per site ) with the complete alignments after removing the guanosine position from the APOBEC3 trinucleotide contexts of the edited sites identified in the cell culture experiments ( 5′-GAD-3′ and 5′-GGA-3′ for APOBEC3F and APOBEC3H and 5′-GGD-3′ and 5′-GAG-3′ for APOBEC3G ) from the patients collapsed alignments ., Importantly , this process resulted in the pairwise distance between sequences being decreased significantly ( Wilcoxon rank sum test P-value<0 . 05; Figure 5 ) ., Transition ( purine to purine or pyrimidine to pyrimidine ) and transversion ( purine to pyrimidine or pyrimidine to purine ) median ratio values were 4 . 66 ( range 1 . 73 to 57 . 44 ) in the Gag and 5 . 34 ( range 1 . 45 to 11 . 27 ) in the Vif genes of HIV-1 ., G-to-A ( and C-to-T ) transitions accumulated 5-fold faster than A-to-G ( and T-to-C ) transitions , an inequality in the evolutionary trajectory 32 , 46 ., In sum , these results demonstrate that stochastic or transient changes in APOBEC3 deaminase activity could have relevance for the directionality of HIV-1 evolution in natural infection ., To determine whether natural selection acting on G-to-A mutations found in the APOBEC3 trinucleotide context of the edited sites could facilitate evasion of host immunity , we screened known or potential cytotoxic T lymphocyte ( CTL ) epitopes for positively selected sites ., CTL epitopes had been established experimentally by interferon-γ enzyme-linked immunospot ( ELISPOT ) or predicted on the basis of amino acids that could serve as anchors to enable HLA binding or affect proteosome cleavage sites that abolish peptide binding , lessen T cell receptor recognition , or generate antagonistic CTL responses 47–49 ., As an indicator of amount of natural selection operating on these CTL epitopes , we undertook a site-specific analysis of dN/dS in the Gag and Vif genes of HIV-1 using the Single Likelihood Ancestor Counting ( SLAC ) method implemented in HyPhy 50 ., We focused on the G-to-A changes among the positions identified by reason of their significant selection signal ( complete list and description in Table S5 ) and grouped them into positions appearing within or outside APOBEC3 trinucleotide contexts of the edited sites ., We found an overrepresentation of positively selected positions within the APOBEC3 trinucleotide context of the edited sites in the Vif gene of HIV-1 ( Fishers exact test , P\u200a=\u200a0 . 02; Table 2 ) ., Moreover , some of these positively selected positions in the Vif gene of HIV-1 appeared within a known or predicted HLA-appropriate epitope ( S004 and S009 , Table S5 ) ., HIV-1 sequences encoding variants that could result in a lower predicted peptide binding score which would potentially confer a diminished or immune escape phenotype , were found within a HLA-appropriate epitope in the Gag ( 19 of 42; per patient range , 0 to, 6 ) or Vif genes of HIV-1 ( 37 of 99; per patient range , 0 to 4 ) at the later time point during infection ( Tables 3 and S6 ) ., Most epitope escape mutations were found in those patients that carry HLA-A01:01:01 , HLA-A02:05:01 or HLA-A03:01:01 or HLA-B07:02:01 , HLA-B08:01:01 or HLA-B57:01:01 ( S001 , S002 , S003 , S009 , and S010 ) ., Of the sites under positive selection , 7 of 19 sites in the Gag and 8 of 42 sites in the Vif genes of HIV-1 were a result of G-to-A mutations in APOBEC3 editing contexts ., Clusters of G-to-A mutations in known or predicted HLA-appropriate epitopes were higher in some patients ( S001 and S009 ) than in others ( S002 , S003 , and S010 ) ., We found statistically significant evidence for G-to-A mutations in APOBEC3 trinucleotide contexts of edited sites that cause nonsynonymous substitutions in the amino acid residues located at the epitopes ( Fishers exact test P<0 . 05 ) ., These data demonstrate that APOBEC3-induced mutation embedded in the HLA-restricted epitopes can accumulate over time as a consequence of immune selection pressure ., To identify G-to-A mutations in APOBEC3 trinucleotide contexts of edited sites in known or predicted HLA-appropriate epitopes in relation to the most common haplotype in the first time point in each patient , we compared the epitopes at both early and late time points during infection ., At the second time point during infection , G-to-A mutations were preferentially found at 1/13 sites in the Gag and 6/23 sites in the Vif gene of HIV-1 ( Table S6 ) ., Though only direct experimental studies can establish which of the G-to-A mutations are associated with the evasion of host immunity , we infer that many of these are positively selected sites at low frequency variants at the first time point that transition to fixation at the second ., In this manner , APOBEC3 editing can affect the crucial interaction between the virus and the host during the earliest stages of infection , and thereby potentially influence the natural history of HIV-1 infection ., In this study we define at unprecedented depth the specific APOBEC3 trinucleotide contexts of edited sites in cell culture experiments and show that the equivalent mutations that accrue in viral DNA in cells from patients through time provide a source of genetic variation upon which natural selection acts; thus , resolving the widely debated contribution of APOBEC3 editing to the genetic changes underlying the evolution of HIV-1 populations in natural infection 13 , 22 , 33 ., Using a statistical framework that detects and corrects pyrosequencing errors , we show that APOBEC3D , APOBEC3F , APOBEC3G , and APOBEC3H , the cytidine deaminases of the human APOBEC3 gene family that are the cellular targets for HIV-1 protein Vif , have distinct , but overlapping trinucleotide contexts of the edited sites associated with antiviral defense ., Mapping these APOBEC3-mediated G-to-A mutations onto the viral sequences from ten patients with primary HIV-1 infection through time is informative of the impact that these human genes can have on virus diversification ., The over-representation of G-to-A mutations in the viral sequences compared with A-to-G or C-to-T or T-to-C mutations ( in a A-rich , C-poor genome ) suggests that accumulation of APOBEC3 mutations is well tolerated in diversifying sites and could account for the skewing of nucleotide and codon usage in the viral genome ., We note that the total number and location of the APOBEC3 trinucleotide context of the edited sites within the viral genome and the extent to which they can accumulate through time need to be accounted for in evolutionary inference at the population-level ., The tandem array of the seven human cytidine deaminases of the APOBEC3 gene family on chromosome 22 , which we distinguish by their target sequence consensus , suggest that multiple , related antiviral functions can contribute to the control of virus infection ., Differences in single-stranded DNA binding , as well as translocation along engaged templates , may explain the sequence specificity of APOBEC3 activity and processing accuracy 51 , 52 ., Nucleotides adjacent to the APOBEC3 editing context likely influence the kinetics of G-to-A mutation ., Functional biases in cytidine deaminase activity suggest that people may differ in the predominant expression of APOBEC3 and that these functionality-related genes may play a role in the spectrum of innate resistance that protects against invading viruses and contributes to phenotype ., This conclusion , which could apply to other types of viruses or retroviral elements , suggests that human APOBEC3 proteins have clear impact at the boundary between the virus and its host ., It has been posited that limiting-levels of APOBEC3 activity could result in lethal mutations rather than rapid adaptation through acquisition of neutral or potentially beneficial mutations 33 ., Further , that a single incorporated APOBEC3 unit is likely to cause extensive and inactivating levels of HIV-1 hypermutation ., These conjectures are based on in silico analyses of optimized reference sequences that would be estimated to account for the mutation levels of 39 near-full length patient-derived hypermutated viral sequences selected from the HIV Sequence Database ., As the analyses began with highly mutagenized HIV-1 genomes with 5′-GpG-3′ or 5′-GpA-3′ signatures of APOBEC3 editing from which the non-hypermutated reference sequences were derived , the estimated effect is distorted by a clear selection bias ., Studies that have original patient-derived non-hypermutated reference sequences clearly corroborate the relevance of small increases in mutation frequency affected by APOBEC3 for genetic changes underlying virus evolution 53 ., Natural selection during virus infection can create advantageous mutations or eliminate deleterious ones ., The rate of fixation of advantageous mutations , which is faster than the rate of fixation of neutral mutations , increases with the strength of selection ., We found statistically significant evidence for positive selection acting on the Vif region of HIV-1 in the APOBEC3 trinucleotide context of the edited sites ., Even though blockade by the HIV-1 protein Vif effectivel | Introduction, Results, Discussion, Methods | Human APOBEC3 proteins are cytidine deaminases that contribute broadly to innate immunity through the control of exogenous retrovirus replication and endogenous retroelement retrotransposition ., As an intrinsic antiretroviral defense mechanism , APOBEC3 proteins induce extensive guanosine-to-adenosine ( G-to-A ) mutagenesis and inhibit synthesis of nascent human immunodeficiency virus-type 1 ( HIV-1 ) cDNA ., Human APOBEC3 proteins have additionally been proposed to induce infrequent , potentially non-lethal G-to-A mutations that make subtle contributions to sequence diversification of the viral genome and adaptation though acquisition of beneficial mutations ., Using single-cycle HIV-1 infections in culture and highly parallel DNA sequencing , we defined trinucleotide contexts of the edited sites for APOBEC3D , APOBEC3F , APOBEC3G , and APOBEC3H ., We then compared these APOBEC3 editing contexts with the patterns of G-to-A mutations in HIV-1 DNA in cells obtained sequentially from ten patients with primary HIV-1 infection ., Viral substitutions were highest in the preferred trinucleotide contexts of the edited sites for the APOBEC3 deaminases ., Consistent with the effects of immune selection , amino acid changes accumulated at the APOBEC3 editing contexts located within human leukocyte antigen ( HLA ) -appropriate epitopes that are known or predicted to enable peptide binding ., Thus , APOBEC3 activity may induce mutations that influence the genetic diversity and adaptation of the HIV-1 population in natural infection . | Cytidine deaminases of the human APOBEC3 gene family act as an intrinsic defense mechanism against infection with HIV-1 and other viruses ., The APOBEC3 proteins introduce mutations into the viral genome by inducing enzymatic modification of nucleotide sequences and inhibiting synthesis of cDNA strands from the viral RNA ., Viral Vif counters this impediment to the fidelity of HIV-1 replication by targeting the APOBEC3 proteins for degradation ., Low-level APOBEC3 activity that outlasts blockade by viral Vif may foster infrequent mutations that provide a source of genetic variation upon which natural selection acts ., Here , we defined the APOBEC3 nucleotide contexts of the edited sites by titration of the wild type and non-editing APOBEC3 mutant in cultured cells ., We then followed the patterns of G-to-A mutations we identified in viral DNA in cells obtained from ten patients with acute infection ., Our deep sequencing analyses demonstrate an association between sub-lethal APOBEC3 editing and HIV-1 diversification ., Mutations at APOBEC3 editing contexts that occurred at particular positions within specific known or predicted epitopes could disrupt peptide binding critical for immune control ., Our findings reveal a role for human APOBEC3 in HIV-1 sequence diversification that may influence fitness and evolution of beneficial variants and phenotypes in the population . | immunodeficiency viruses, organismal evolution, innate immune system, medicine and health sciences, infectious diseases, immunity, medical microbiology, microbial evolution, viral pathogens, virology, microbial pathogens, biology and life sciences, immunology, microbiology, evolutionary biology, viral evolution, immune system | null |
journal.pgen.1006238 | 2,016 | Pfh1 Is an Accessory Replicative Helicase that Interacts with the Replisome to Facilitate Fork Progression and Preserve Genome Integrity | Faithful and efficient replication of the genome is essential in every cell cycle , yet there are many naturally occurring obstacles that impede fork progression ., These sites include stable protein complexes , DNA secondary structures , and ongoing transcription , each of which can challenge replication fork progression ., Failure to circumvent these obstacles can cause DNA double strand breaks ( DSBs ) that impair genome integrity and increase the risk of cancer and other disorders that are associated with genome instability ., As many of the proteins involved in DNA replication are highly conserved , model organisms such as S . pombe provide genetically tractable systems to identify and characterize genes with important roles in genome preservation whose human orthologs might have similar functions ., DNA replication is accomplished by the multi-subunit replisome , a complex that is assembled at and moves bi-directionally away from replication origins ., Replicative helicases , such as the Escherichia coli DnaB and the eukaryotic hexameric MCM complex , are required to unwind the double helix to allow DNA polymerases access to the replication template ., In addition , accessory helicases , such as the E . coli rep , dinG and UvrD proteins help the polymerase maneuver past protein complexes , RNA transcripts , and other naturally occurring impediments 1–5 ., In Bacillus subtilis the essential accessory DNA helicase PcrA promotes fork movement through transcribed genes 6 , 7 ., E . coli rep physically interacts with the replicative DnaB helicase to bypass protein complexes on DNA 8 ., The best-studied eukaryotic accessory DNA helicases are the two budding yeast enzymes , ScRrm3 and ScPif1 , which are both members of the Pif1 family 9 ., Although these two helicases have largely non-overlapping functions , they both promote progression past naturally occurring hard-to-replicate sites ., ScRrm3 acts at over 1000 sites , including RNA polymerase III transcribed genes , the replication fork barrier ( RFB ) within ribosomal DNA ( rDNA ) , inactive replication origins , silencers , telomeres , centromeres , and converged replication forks 10–14 ., The diverse ScRrm3-sensitive sites have the common feature of being assembled into stable protein complexes ., Removal of these proteins in trans or mutation of their binding sites in cis relieves the requirement for ScRrm3 at the affected site ., In its absence , forks tend to stall and break at ScRrm3-sensitive sites ., Rather than being recruited to its sites of action , ScRrm3 moves with the replisome through both ScRrm3-sensitive and insensitive sites 15 and interacts with leading strand DNA polymerase ε and PCNA 15 , 16 , suggesting that it is a replisome component ., ScPif1 also promotes fork progression , but so far this activity has been observed only at putative G-quadruplex ( G4 ) structures 17–21 ., G4 DNA is a stable , four-stranded secondary structure held together by non-canonical G-G base pairs reviewed in 22 ., In cells lacking ScPif1 , DNA replication slows and DNA damage is detected at many G4 motifs ., Current evidence suggests that ScPif1 is recruited to G4 motifs after their replication 18 , and the abundance of ScPif1 , unlike that of ScRrm3 , is cell cycle regulated , peaking in late S phase 15 , 23 ., Thus , unlike ScRrm3 , ScPif1 probably does not move with the leading strand DNA polymerase ., ScRrm3 is a backup helicase for ScPif1 at G4 motifs 17 ., As ScRrm3 and ScPif1 are both associated with stalled replication forks 24 , ScPif1 might be a backup for ScRrm3 at some of its targets , such as RNA polymerase III transcribed genes ., ScPif1 actions are not limited to its being an accessory DNA helicase , as it also inhibits telomerase by displacing it from DNA ends 25 , 26 , promotes formation of long flap Okazaki fragments 27 , 28 , and is needed for the stable maintenance of mitochondrial DNA 29 and break-induced replication 30–33 ., Unlike budding yeast , most eukaryotes , including fission yeast and humans , encode a single Pif1 family helicase ., While neither ScRrm3 nor ScPif1 is essential , the fission yeast Pfh1 DNA helicase is required for maintenance of both the mitochondrial and nuclear genomes 34 ., Pfh1 also affects nuclear DNA repair: it localizes to DNA damage foci upon exogenous DNA damage , and its absence results in spontaneous DNA damage foci 34 ., In earlier work , we and others used two-dimensional gels to show that Pfh1 , like ScRrm3 promotes fork progression through specific stable protein complexes , including RNA polymerase II and III transcribed genes , silencers , converged replication forks 35 , 36 , and telomeres 37 ., In addition , microscopic studies show that Pfh1 suppresses formation of ultrafine anaphase bridges that arise at incompletely replicated regions , such as Lac repressor bound LacO arrays 38 , supporting the idea that it is needed to complete DNA replication ., Like ScPif1 , Pfh1 binds to and suppresses DNA damage at G4 motifs 39 ., Also , ScPif1 and Pfh1 both unwind G4 structures in vitro 17 , 40–43 ., In this paper , we address two fundamental questions about Pfh1 function: where does Pfh1 act along the genome and how is it recruited to its sites of action ?, Earlier studies focused on Pfh1’s role in replication of a few examples of hard-to-replicate sites 35 , 37 , 39 ., Here we used chromatin immunoprecipitation combined with genome-wide deep sequencing ( ChIP-seq ) to study the full range of Pfh1-sensitive sites ., This analysis revealed hundreds of sites of Pfh1 binding where replication slows and DNA damage occurs , especially in the absence of Pfh1 ., These Pfh1-sensitive sites included all previously identified hard-to-replicate sites as well as novel sites , such as nucleosome depleted regions ( NDR ) ., Second , we assayed binding and fork progression to determine if Pfh1 is nearby the replisome during S phase or , if it is recruited to its sites of action ., These analyses revealed that Pfh1 and the leading strand DNA polymerase bind both Pfh1-sensitive and Pfh1-insensitive sites to a similar extent ., Likewise , mass spectrometry ( MS ) found that Pfh1 interacts with many key replisome components ., Together these data argue that Pfh1 is not just recruited to its sites of action , but that it is in proximity to the replisome during DNA synthesis and functions as an accessory DNA helicase at all known classes of hard-to-replicate sites ., These results inform our understanding of Pif1 helicases in higher eukaryotes , such as humans , which like S . pombe , encode only one Pif1 family helicase ., Given that the helicase domains of Pfh1 and hPIF1 are related ( 36% sequence identity ) 25 , hPIF1 may have similar functions in preserving genome integrity ., We analyzed 621 previously identified Pfh1 binding sites across the S . pombe genome from ChIP-seq on cycling WT cells ( S1 Table ) 39 ., Given the low coverage of rDNA repeats and telomeres in the S . pombe annotated genome , the rest of this paper considers only non-telomeric , non-rDNA Pfh1 binding sites ., We determined if the peaks of Pfh1 binding correlated with any of sixteen annotated genomic features ( Table 1; Methods ) ., Because Pfh1 bound preferentially to GC-rich sites , we determined the significance of its association with features after accounting for GC content using random GC-matched controls ( Methods ) ., Pfh1 peaks were significantly associated with many known hard-to-replicate sites , such as tRNA genes , 5S ribosomal RNA ( rRNA ) , and highly active RNA polymerase II transcribed genes ( Table 1 ) ., For example , Pfh1 binding occurred ≤ 300 base pairs ( bp ) , the shearing size of the ChIP DNA , from 80 out of 171 ( 47% ) tRNA genes , 18 of 33 ( 55% ) 5S rRNA genes , and 302 of the top 500 ( 60% ) most highly transcribed RNA polymerase II genes ( as defined in Rhind , Chen ( 44 ) ) ., In addition , Pfh1 binding sites were significantly associated with meiotic double strand break ( DSB ) hotspots , nucleosome depleted regions ( NDRs ) , 3’ untranslated regions ( UTRs ) , and mating type loci ( Table 1 ) ., In all following association analyses , associations with p-values less than the Bonferroni multiple testing adjusted threshold of 0 . 003 will be interpreted as significant ., A recent study reported that highly transcribed S . cerevisiae genes are over-represented in ChIP experiments carried out with diverse nuclear proteins , suggesting that their presence might be a technical artifact caused by their high transcription rate 45 ., The specific cause of the “hyper-ChIPability” of these regions has not been resolved ., It has been proposed that , because highly transcribed genes are more accessible during the pull-down , they may be more likely to interact with beads or antibodies during the IP , and therefore be subject to nonspecific precipitation by the antibody ., To ensure that the association with highly transcribed RNA polymerase II genes was not due to this artifact , we used ChIP combined with quantitative PCR ( qPCR ) to examine Pfh1 association to four highly transcribed genes , hsp90+ , tdh1+ , adh1+ , and hta1+ , which are among the top 500 most highly transcribed genes and were all Pfh1-associated sites in the genome-wide analysis ., Transcription of hsp90+ , tdh1+ , adh1+ occurs throughout the cell cycle , while hta1+ transcription peaks in S phase 46 ., In fission yeast , the G2 phase comprises about 75% of the cell cycle , so the majority of cells in an asynchronous culture are in G2 phase , and most genes are transcribed in this interval 47 ., Thus , if the association of Pfh1 with highly transcribed genes was non-specific , it should occur to a similar extent in asynchronous and G2-arrested cells for hsp90+ , tdh1+ , adh1+ , but not hta1+ ., We performed ChIP-qPCR experiments in both asynchronous and G2 arrested cells in a temperature sensitive cdc25-22 strain that expressed Pfh1-13Myc ., Pfh1 was significantly associated to all four genes in asynchronous cells grown at 25°C , compared to an untagged control strain ( S1 Fig ) , which validated the peaks found in the ChIP-seq data ., To arrest the cells in G2 phase , cells growing logarithmically at 25°C were shifted to 37°C for 4h ., Consistent with the expectation in the absence of bias , high Pfh1 binding to all four of the highly transcribed genes varied across the cell cycle; it was approximately four times higher in asynchronous compared to G2 arrested cells in the ChIP-qPCR assay ( p ≤ 0 . 016; Fig 1 ) ., In contrast , Pfh1 binding to the much less frequently transcribed ade6+ gene was not significantly different in asynchronous versus G2 arrested cells ( p = 0 . 14 , Fig 1 ) ., Thus , the ChIP-qPCR experiment confirmed the high Pfh1 binding to these highly transcribed genes seen by ChIP-seq and established that this binding is not an artifact of the ChIP ., To identify genomic sites that require Pfh1 for their timely replication , we analyzed genome-wide occupancy of Cdc20 , the catalytic subunit of the leading strand DNA polymerase ε 48 in WT and Pfh1-depleted cells ., As previously reported 39 , there are 485 peaks of high Cdc20 occupancy in WT cells and 517 in Pfh1-depleted cells ( S1 Table ) ., Although all genomic sites are Cdc20-associated when they are being replicated , high DNA polymerase occupancy correlates with replication fork slowing in both S . pombe and S . cerevisiae 39 , 49 ., Most of the high Cdc20 occupancy sites in WT cells were also found in Pfh1-depleted cells ( 390/485 , 80% ) and vice versa ( 389/517 , 75% ) ( Fig 2A ) ., In an earlier study , we used these data to demonstrate that many G4 motifs bind Pfh1 and that fork slowing and DNA breakage is more frequent at G4 motifs than at other G-rich regions in Pfh1-depleted cells ( S2 Fig ) 39 ., Here we extend this analysis from G4 motifs to the rest of the genome ., This analysis showed that in addition to G4 motifs , tRNA genes , 5S rRNA genes , NDRs , replication origins , RNA polymerase II promoters , RNA polymerase II transcribed genes , and meiotic DSB hotspots were significantly enriched among high Cdc20 occupancy sites in both WT and Pfh1-depleted cells ( Table 2 and S2 Table; p < 0 . 001 ) ., The sets of high Cdc20 occupancy were identical in the two conditions except that dubious genes were enriched in Pfh1-depleted but not in WT cells ., Despite this similarity , the evidence for elevated Cdc20 occupancy was significantly greater in Pfh1-depleted cells ( p ≈ 0 , Wilcoxon signed-rank test , p-values < 10−50 are reported as ≈0; Fig 2B ) ., This pattern held for all genomic features tested ( Fig 2C–2F; S3 Table ) ., For example , 69% of Cdc20 peaks near NDRs ( p = 2 . 4x10-5; Fig 2C ) and 62% of peaks near highly transcribed RNA polymerase II genes ( p ≈ 0; Fig 2D ) were significantly stronger in Pfh1-depleted cells ., This effect was particularly striking at tRNA ( p ≈ 0; Fig 2E ) and 5S rRNA ( p = 1 . 4x10-6; Fig 2F ) genes , where over 97% of the peaks showed evidence of significantly elevated Cdc20 occupancy when cells were Pfh1-depleted compared to WT cells ., These findings argue that these genomic features , all of which had significant Pfh1 occupancy in WT cells ( Table 1 ) , were particularly dependent on Pfh1 for timely replication ., To compare the Pfh1-dependent effects in more detail , we analyzed the genomic features associated with the 95 peaks of high Cdc20 binding unique to WT cells versus those associated with the 128 peaks of high Cdc20 binding that were found only in Pfh1-depleted cells ., No features were enriched among the unique WT peaks ., In sharp contrast , 5S rRNA and tRNA genes , meiotic DSB hotspots , NDRs , and dubious genes were all significantly enriched amongst the Cdc20 peaks unique to Pfh1-depleted cells ( S2 Table ) ., Taken together , our results show that several classes of genomic features , especially RNA polymerase III transcribed genes , depend on Pfh1 for normal fork progression ., In cases where sites were hard to replicate even in WT cells , fork pausing at these sites was significantly more pronounced in Pfh1-depleted cells ., In virtually all eukaryotes , including S . pombe , phosphorylation of H2A ( γ-H2A ) marks sites of DNA damage , typically DSBs 50 ., To determine if the site-specific increases in replication pausing detected in Pfh1-depleted cells were associated with DNA damage , we analyzed peaks from previous ChIP-seq experiments using anti-γ-H2A antibodies in WT or Pfh1-depleted cells ( S1 Table ) 39 ., We quantified the genomic distribution and Pfh1-dependence of the γ-H2A peaks with the same methods used for Cdc20 ., As demonstrated in previous work , WT cells had 179 γ-H2A peaks and Pfh1-depleted cells had 582 peaks ( Fig 3A ) 39 ., Only two genomic features , the mating type loci and origins of replication , were significantly enriched near high occupancy γ-H2A sites in WT cells ( p < 0 . 001 for both; S5 Table ) ., However , in Pfh1-depleted cells , 5S rRNA and tRNA genes , meiotic DSB hotspots , NDRs , the mating type loci , and origins of replication all overlapped significantly with γ-H2A peaks ( p < 0 . 001; Table 2 and S5 Table ) ., We also determined the degree of overlap between Cdc20 and γ-H2A peaks in Pfh1-depleted cells ., Of the 582 γ-H2A peaks in Pfh1-depleted cells , 71% ( 411 ) also had high Cdc20 occupancy; this number is significantly more than expected by chance ( p < 0 . 001 ) ., γ-H2A sites that were high occupancy for both γ-H2A and Cdc20 were enriched for multiple genomic features including 5S rRNA and tRNA genes , origins of replication , meiotic DSB hotspots , 3’ and 5’ UTRs , and promoters ( p < 0 . 001 for all; S6 Table ) ., These features include most of those with significant Pfh1 association in WT cells ., In contrast , γ-H2A peaks without corresponding Cdc20 peaks were enriched only in 3’ and 5’ UTRs and promoters ( p < 0 . 001 for both ) ., The significant overlap between peaks of Cdc20 and γ-H2A binding supports the connection between Pfh1-dependent fork slowing ( as marked by Cdc20 peaks ) and DNA damage ( as marked by nearby γ-H2A ) at several classes of hard-to-replicate sites ., These patterns are illustrated for two specific sites , a tRNA gene ( Fig 4A ) and a 5S rRNA gene ( Fig 4B ) ., The strengths of the Pfh1 , Cdc20 , and γ-H2A binding are shown relative to input for the different strains in a 10 kilobase ( kb ) window around each gene ., At both sites , a Pfh1 peak overlapped the gene ( grey vertical lines mark centers of genes ) ., A Cdc20 peak showed a similar overlap with the gene in both WT ( dashed blue line ) and Pfh1-depleted cells ( solid blue line ) , but the peak was stronger in Pfh1-depleted cells ., Broad peaks of γ-H2A flanked both genes in Pfh1-depleted cells , consistent with the 40 kb domains of γ-H2A that flank DSB sites 50 ., Plots for all tRNA and 5S rRNA genes are shown in S3 and S4 Figs ., When the strengths of all the γ-H2A peaks between WT and Pfh1-depleted cells were compared , 88% of all peaks were higher in Pfh1-depleted cells ( p ≈ 0 , Wilcoxon signed-rank test; Fig 3B ) ., This pattern held for the subsets of γ-H2A peaks associated with nearly all classes of genomic features ( Fig 3C–3F; S3 Table ) ., However , as seen for Cdc20 , the magnitude of the difference was strongest for the RNA polymerase III transcribed genes: 190 of 192 γ-H2A peaks near tRNA genes ( p ≈ 0 , Fig 3E ) and all 67 γ-H2A 5S rRNA peaks ( p ≈ 0 , Fig 3F ) were greater in Pfh1-depleted cells ., Our data indicate that Pfh1 promotes replication and suppresses DNA damage at many discrete sites in the genome ., We considered two models to explain this pattern of Pfh1 action ., First , Pfh1 could act by binding nearby the replisome and mitigating replication obstacles as the replisome moves past Pfh1-sensitive sites ., Alternatively , Pfh1 could be recruited only to sites that are hard to replicate or to stalled replication forks ., In that case , Pfh1 would have low or no binding to sites that are Pfh1-insensitive ., To distinguish between the two possibilities , we used ChIP-qPCR to monitor association of Pfh1 and Cdc20 in synchronized cells ., For these experiments , we used a cdc25-22 strain that expressed Pfh1-13Myc inserted at the leu1+ locus under the control of the pfh1+ promoter ( the endogenous pfh1+ locus was unaltered ) ., This strain also expressed Cdc20-3HA from its endogenous location ., Cells were arrested in G2 phase by incubation at the non-permissive temperature ( 37°C ) and then released at permissive temperature ( 25°C ) ., The position in the cell cycle and the quality of the synchrony were determined by FACS analysis ( Fig 5A ) ., To determine association and movement of the replication fork , samples were taken throughout one synchronous cell cycle ., At each time point , we examined association of Pfh1-13Myc and Cdc20-3HA to three origins of replication and their adjacent regions ., We examined binding to the efficient ars3002 and a region18 kb away from ars3002 ( ars3002_18kb ) , ars2004 and a region 30 kb from ars2004 ( ars2004_30kb ) , and ars3005 and a region 26 kb from ars3005 ( ars3005_26kb ) ( Fig 5B ) ., If Pfh1 were nearby or associated with the replisome , Pfh1 and Cdc20 would have similar temporal patterns of binding to the three origins and their adjacent sites ., If Pfh1 were recruited only to hard-to-replicate sites , then Cdc20 and Pfh1 would have different binding patterns ., In fact , under the second model , Pfh1 should not bind to any of these sites , as none of them were Pfh1-dependent in the whole genome analysis ., Consistent with the first model , Cdc20 and Pfh1 displayed similar association patterns at all three origins and their adjacent regions ( Fig 5C–5E ) ., We first examined the binding to ars3002 and its adjacent region ars3002_18kb ( Fig 5C ) ., Although Cdc20 bound in early S phase to ars3002 , earlier than Pfh1 , the peak binding for Cdc20 and Pfh1 was reached at 95 min ( Fig 5C ) ., Both proteins had their start of binding to ars3002_18kb at 80 min after release from G2 phase and their peak binding at 95 min ( Fig 5C ) ., Thus , Pfh1 and Cdc20 bound to the Pfh1-insensitive site located 18 kb downstream of ars3002 similarly ., However , while clear movement of Cdc20 was detected in this region , movement from this origin to the downstream regions was not visible for Pfh1 ., Next , we examined the binding of Pfh1 and Cdc20 to the four other Pfh1-insensitive sites ars2004 , ars2004_30kb , ars3005 , and ars3005_26kb ., Pfh1 bound to all these four regions , similarly to Cdc20 ( Fig 5D and 5E ) ., However , movement of neither Cdc20 nor Pfh1 was detected at any of these origins to their adjacent sites ., Because the dynamics of Pfh1 and the replisome were not clear from the above experiments , we further investigated the binding pattern of Pfh1 and Cdc20 at five other regions , including both Pfh1-sensitive and Pfh1-insensitve sites ( Fig 5F ) ., We tested two regions that were not origins of replication on chromosome II: one Pfh1-sensitive ( Chr II_nonars_1236154 ) , and a Pfh1-insensitive site 36 kb away ( Chr II_nonars_1272741 ) ., Finally , we examined two Pfh1-sensitive tRNAs ( tRNAGLU . 05 and tRNAASN . 05 ) and one Pfh1-insensitive site 6 kb away from tRNAASN . 05 ( tRNAASN . 05_6kb ) ., At all five regions , both Pfh1 and Cdc20 had peak binding at 95 min after release from the G2 arrest , which by FACS analysis is mid-S phase ( Fig 5 ) ., To determine if Pfh1 was recruited only to Pfh1-sensitive sites , we calculated the ratio of Pfh1 to Cdc20 binding at Pfh1-sensitive and -insensitive sites ( IP/input of Pfh1 divided by IP/input for Cdc20 ) at the peak of replication for all sites ( Fig 5F ) ., If Pfh1 were recruited solely to Pfh1-sensitive sites , the ratio of Pfh1/Cdc20 would be higher at Pfh1-sensitive sites compared to Pfh1-insensitive sites ., If Pfh1 were in proximity to the replisome , the ratios should be similar at the two classes of sites ., Indeed , the Pfh1/Cdc20 ratio was on average 1 . 1 and 0 . 9 for Pfh1-sensitive and Pfh1-insensitive sites , respectively ( Fig 5F ) ., Thus , Pfh1 binds similarly to both Pfh1-sensitive and -insensitive sites ., These data suggest that Pfh1 is near the replisome during S phase , rather than being recruited to its sites of action at Pfh1-sensitive sites ., However , we cannot rule out the possibility that additional Pfh1 molecules are recruited to some or even all Pfh1-sensitive sites upon replisome pausing ., If Pfh1 maintains proximity to the replisome , as suggested by its pattern of binding to chromosomal DNA ( Fig 5 ) , then Pfh1 should be associated in vivo with known replisome subunits ., To address this possibility , we used immunoaffinity purification mass spectrometry ( IP-MS ) to identify the Pfh1 protein interactions in S phase cells ( Fig 6A ) ., In these experiments , Pfh1 was expressed under its endogenous promoter as a GFP fusion ( Pfh1-GFP ) , allowing immunoaffinity isolation of Pfh1 and its associated proteins through the GFP tag 51 ., Cells expressing a SV40 nuclear localization signal-GFP fusion ( NLS-GFP ) were used as a negative control for non-specific association of proteins to GFP ., Two biological replicates of both Pfh1-GFP and NLS-GFP were isolated in parallel from S phase cells ( Fig 6A ) using anti-GFP antibodies ( Fig 6B ) ., Following mass spectrometry analysis of Pfh1-GFP and NLS-GFP immunoisolates , the interaction specificity of individual co-isolating proteins was assessed using the SAINT ( significance analysis of interactome ) algorithm 52 ., SAINT determines confidence scores ( ranging from 0 to, 1 ) for protein-protein interactions based on the spectrum count distributions obtained from bait ( Pfh1-GFP ) isolations relative to the negative control ( NLS-GFP ) ., High confidence Pfh1 interactions were defined as proteins having a SAINT score ≥ 0 . 80 , a threshold used previously to identify functional protein interactions 53 , 54 ., By this metric , there were 50 high confidence Pfh1 protein associations that comprise the Pfh1 S phase interactome ( Table 3 and S7 Table ) ., Although five of the Pfh1 interacting proteins are uncharacterized , there is functional data for 45 of the 50 proteins ., Table 3 lists these proteins ., We also assessed the relative abundance of individual Pfh1 interacting proteins within the interaction network by calculating the normalized spectrum abundance factor ( NSAF ) for each protein relative to its proteome abundance value ( PAX ) 55 ., NSAF values provide a measure of protein abundance by accounting for factors such as protein length and sample complexity that can influence the number of spectra acquired for a given protein within a sample ., Normalizing NSAF values to PAX values , as described in 56 , provides insight into proteins and functional protein classes that are enriched in the Pfh1 isolation relative to their abundances in the cellular proteome ., These data are presented in Fig 6C , which also categorizes interacting proteins by function ., The replisome is the multi-protein complex that is present at the replication fork as it moves through the chromosome ., Multiple replisome components interacted with Pfh1 with high specificity ( SAINT score ≥ 0 . 80; Fig 6C and Table 3 ) ., These proteins were: ( 1 ) five of the six subunits of the replicative helicase , the MCM complex ( Mcm2 and Mcm4-7 ) ; ( 2 ) catalytic subunits of two of the three replicative polymerases ( DNA Pol1 from DNA polymerase α; DNA Pol2/Cdc20 , from DNA polymerase ε ) ; ( 3 ) Pol12 , the β subunit of DNA Pol1; ( 4 ) proliferating cell nuclear antigen ( PCNA , Pcn1 ) , a processivity factor that encircles and slides along the DNA; ( 5 ) the three subunits of the single-strand binding replication factor A ( RPA , Ssb1 , 2 and 3 ) ; ( 6 ) the Dna2 helicase-nuclease that is required for Okazaki fragment maturation; and ( 7 ) the two subunits of the FACT complex ( facilitates chromatin transcription ) , Pob3 and Spt1 , which facilitates nucleosome remodeling during both transcription and DNA replication ., The association of FACT subunits with Pfh1 suggests that FACT and Pfh1 might act synergistically to promote replication through hard-to-replicate sites ., Four mismatch repair ( MMR ) proteins , Msh1 , 2 , 3 and 6 , were also Pfh1-associated ., The Msh2/6 and Msh2/3 heterodimers interact directly with DNA for the recognition of base pair mismatches ., Because MMR and DNA replication are strongly coupled in budding yeast , MMR proteins are proposed to track with the replisome and hence can also be considered replisome components 57 , 58 ., Additional replisome components were present in the Pfh1-GFP isolations but did not meet our SAINT score criterion ., These proteins were: ( 1 ) the sixth Mcm subunit ( Mcm3; SAINT score , 0 . 68 ) ; ( 2 ) the catalytic subunit of the lagging strand DNA polymerase; Pol3 ( SAINT score , 0 . 15 ) ; ( 3 ) Dpb2 , the second largest subunit of Pol ε ( SAINT score 0 . 31 ) ; ( 4 ) Pri1 and Pri2 , the primase subunits that function together with DNA polymerase α to synthesize the primers on the leading and lagging strand ( SAINT scores of 0 . 65 and 0 . 33 , respectively ) ; and ( 5 ) Mcl1 , the S . cerevisiae Ctf4 homologue that interacts with DNA polymerase α ( SAINT score 0 . 70 ) ., While SAINT scores point to high confidence interactions , being based on detected protein spectral counts , they are influenced by sample complexity and the dynamic range of the co-isolated proteins , and thereby weighted towards large and abundant proteins , and stable interactions ., Pfh1-associated replisome components with lower SAINT scores may be smaller proteins , have lower cellular abundances , and/or form transient interactions 59 ., We performed two additional experiments to confirm the association of Pfh1 with the replisome ., First , we isolated Pfh1 from asynchronous cells both in the presence and absence of DNase ( S7 Table and S5 and S6 Figs ) ., Because this experiment was performed with an asynchronous population , only a subset of the proteins that interacted with Pfh1 in S phase was detected ( S6 Fig ) , even without DNase treatment ., Of the 19 replisome/replication proteins that passed our stringent SAINT score threshold , ten were found in the DNase untreated sample , and nine of these retained their Pfh1 association after DNase treatment: Ssb1 and 2 , Msh2 , Mcm4 , 5 , and 6 , Cdc20 ( Pol2 ) , Pol12 , and Spt16 ., Because these interactions were not DNA-dependent , they are likely due to protein-protein interactions ., Second , we isolated Pfh1 and its associated proteins from G2 arrested cells ., We detected eight Pfh1-associated replication/replisome proteins in G2 , and all eight were detected with fewer spectral counts in G2 extracts than in S phase extracts ., The remaining eleven were not detected at all as Pfh1-interacting proteins in G2 phase ( Table 3; S7 Table ) ., Thus , as expected for a replisome component , Pfh1 association with known replisome subunits was either lost or diminished in G2 phase ., Together , these results show that Pfh1 associated in vivo with numerous replisome proteins , and that replisome and replication-related proteins represent a substantial subset of specific Pfh1 interactions ( 19 out of 50 proteins ( 38% ) with SAINT score of ≥0 . 8 ) ( Table 3; in bold; Fig 6C ) ., Almost all of these associations were S phase-limited or S phase-enriched as well as DNA independent ., Pfh1 is a multi-functional protein: in addition to its role in nuclear DNA replication , it promotes DNA repair and is essential for maintenance of mitochondrial ( mt ) DNA 34 ., Consistent with Pfh1 having mt function , 8 of the 50 high confidence Pfh1 interaction proteins have mt annotations ( Fig 6C; mt proteins are underlined in Table 3 ) ., This subset includes several proteins implicated in mtDNA replication , such as ( 1 ) Rim1 , the mt single-strand DNA binding protein ( MS analyses reveal that ScPif1 is also ScRim1-associated; 60 ) , ( 2 ) Rpo1 , the mtRNA polymerase that is thought to prime mtDNA replication , and ( 3 ) Mgm101 , which is required for maintenance of mtDNA by an unknown mechanism ., Consistent with the reported DNA repair functions of Pfh1 34 , we observed multiple repair proteins among the high confidence Pfh1 interactions ( Table 3 , italics ) , including Rad22 , the S . pombe homolog of budding yeast Rad52 , which is required for homologous recombination 61 , Rad3 , the ATR-like checkpoint kinase 62 , and both subunits of the non-homologous end joining Ku complex , pKu70 and 80 ., In addition , Rqh1 ( homolog of human BLM ) DNA helicase and its two interacting partners , the topoisomerase Top3 and Rmi1 , were Pfh1-associated ., This highly conserved heterotrimeric complex has multiple functions , but is best known for suppressing DNA damage at hard-to-replicate sites , such as converged forks 63 and/or collapsed replication forks—functions relevant to those of Pfh1 ., Finally , six subunits of the 26 subunit RNA polymerase III complex were Pfh1-associated with high confidence ( Rpc1 , 2 , 3 , 4 , 25 and 37 ) , while four others were Pfh1-associated but had SAINT scores <0 . 8 ( Rpc6 , 0 . 71; Rpc9 and 10 , 0 . 30; Rpc19 , 0 . 76 ) 64 ., This finding is probably related to RNA polymerase III transcribed genes being among the most potent replication impediments in Pfh1-depleted cells ( Figs 2–4; see Discussion ) ., We used genome-wide assays to determine sites where replication and genome integrity are Pfh1-dependent ., The most striking aspect of these data is the strong dependence of RNA polymerase III transcribed genes on Pfh1 ., 2D gel analyses showed previously that replication of five of five tested tRNA genes is Pfh1-dependent , and this dependence is seen regardless of whether replication is co-directional or opposite to the direction of transcription through the gene 35 ., Here we show that close to 50% ( 80/171 ) of the tRNA genes bound Pfh1 ( Table 1 ) ., Moreover , nearly all of the tRNA genes that bound Pfh1 were sites of fork pausing and DNA damage in both WT and Pfh1-d | Introduction, Results, Discussion, Materials and Methods | Replicative DNA helicases expose the two strands of the double helix to the replication apparatus , but accessory helicases are often needed to help forks move past naturally occurring hard-to-replicate sites , such as tightly bound proteins , RNA/DNA hybrids , and DNA secondary structures ., Although the Schizosaccharomyces pombe 5’-to-3’ DNA helicase Pfh1 is known to promote fork progression , its genomic targets , dynamics , and mechanisms of action are largely unknown ., Here we address these questions by integrating genome-wide identification of Pfh1 binding sites , comprehensive analysis of the effects of Pfh1 depletion on replication and DNA damage , and proteomic analysis of Pfh1 interaction partners by immunoaffinity purification mass spectrometry ., Of the 621 high confidence Pfh1-binding sites in wild type cells , about 40% were sites of fork slowing ( as marked by high DNA polymerase occupancy ) and/or DNA damage ( as marked by high levels of phosphorylated H2A ) ., The replication and integrity of tRNA and 5S rRNA genes , highly transcribed RNA polymerase II genes , and nucleosome depleted regions were particularly Pfh1-dependent ., The association of Pfh1 with genomic integrity at highly transcribed genes was S phase dependent , and thus unlikely to be an artifact of high transcription rates ., Although Pfh1 affected replication and suppressed DNA damage at discrete sites throughout the genome , Pfh1 and the replicative DNA polymerase bound to similar extents to both Pfh1-dependent and independent sites , suggesting that Pfh1 is proximal to the replication machinery during S phase ., Consistent with this interpretation , Pfh1 co-purified with many key replisome components , including the hexameric MCM helicase , replicative DNA polymerases , RPA , and the processivity clamp PCNA in an S phase dependent manner ., Thus , we conclude that Pfh1 is an accessory DNA helicase that interacts with the replisome and promotes replication and suppresses DNA damage at hard-to-replicate sites ., These data provide insight into mechanisms by which this evolutionarily conserved helicase helps preserve genome integrity . | Progression of the DNA replication machinery is challenged in every S phase by active transcription , tightly bound protein complexes , and formation of stable DNA secondary structures ., Using genome-wide analyses , we show that the evolutionarily conserved fission yeast Pfh1 DNA helicase promotes fork progression and suppresses DNA damage at natural sites of fork pausing , which occur at “hard-to-replicate” sites ., Our data suggest that Pfh1 interacts with the replication apparatus ., First , mass spectrometry revealed that Pfh1 interacts with many components of the replication machinery ., Second , Pfh1 and the leading strand DNA polymerase occupy many common regions genome-wide , not only hard-to-replicate sites , but also sites whose replication is not Pfh1-dependent ., The human genome encodes a Pfh1 homolog , hPIF1 , and contains all of the same hard-to-replicate features that make fission yeast DNA replication dependent upon Pfh1 ., Thus , human cells likely also require replicative accessory DNA helicases to facilitate replication at hard-to-replicate sites , and hPIF1 is a good candidate for this role . | transfer rna, enzymes, cell cycle and cell division, cell processes, dna-binding proteins, enzymology, dna damage, fungi, model organisms, polymerases, dna replication, dna, synthesis phase, schizosaccharomyces, research and analysis methods, proteins, rna polymerase, schizosaccharomyces pombe, yeast, biochemistry, rna, helicases, cell biology, nucleic acids, genetics, biology and life sciences, yeast and fungal models, non-coding rna, organisms | null |
journal.pgen.1007834 | 2,019 | Genetics of cocaine and methamphetamine consumption and preference in Drosophila melanogaster | Illicit use of cocaine and methamphetamine constitutes a significant public health problem that incurs great socioeconomic costs in the United States and worldwide 1–3 ., Cocaine and the amphetamine class of drugs are potent central nervous system stimulants that act by raising synaptic concentrations of biogenic amines ., Cocaine inhibits neurotransmitter reuptake at dopaminergic , serotonergic and noradrenergic synapses 4 , 5 ., Amphetamine increases neurotransmission by promoting the release of dopamine from presynaptic vesicles through its actions on the vesicular monoamine transporter and subsequent reverse flux of dopamine via the dopamine transporter and through the plasma membrane into the synaptic cleft 6 , 7 ., Amphetamine and methamphetamine are used clinically to treat attention deficit hyperactivity disorder and narcolepsy ., Long term use of these compounds , however , can lead to addiction , and ultimately death 8 ., The addictive properties of these drugs are mediated through the dopaminergic mesolimbic reward pathway , which projects from the ventral tegmental area via the nucleus accumbens to prefrontal cortex 9 ., Although most studies on psychostimulants focus on addiction , addiction represents only one facet of the diverse organismal effects that result from psychostimulant drug abuse ., These drugs exert a wide range of physiological and behavioral effects , including suppression of appetite , which can result in malnutrition , and severe cardiovascular , respiratory and renal disorders ., Use of cocaine and amphetamine can also cause mental disorders , including paranoia , anxiety , and psychosis 10 , 11 ., Susceptibility to the effects of cocaine and methamphetamine is likely to vary among individuals and be determined both by environmental and genetic factors ., However , there is limited information regarding the genetic basis of susceptibility to the effects of these drugs in human populations 12 ., Twin and adoption studies have focused primarily on alcohol abuse and illicit drugs , such as cannabis , with heritability estimates ranging from ~30–70% 13 , 14 ., Most studies on psychostimulant addiction to date have centered on candidate genes associated with neurotransmission in the mesolimbic projection 12 , and many of these are inconclusive or contradictory ., For example , some studies reported that alleles of the dopamine D2 receptor were associated with substance abuse 15–18 , whereas others did not replicate this finding 19–24 ., Similar contradictory results have been obtained for association analyses between polymorphisms in the dopamine transporter gene and cocaine-related phenotypes 24–28 ., These contradictory findings may be due in part to failure to account for multiple testing or population structure 29 ., However , genetic studies of substance abuse and addiction in human populations are challenging due to diverse social conditions and physical environments , confounding factors with comorbid conditions such as alcoholism or psychiatric disorders , and difficulty to recruit large numbers of study subjects due to criminalization ., Drosophila melanogaster is an excellent model for identifying genes that affect drug consumption behaviors since both the genetic background and environment , including exposure to drugs , can be controlled precisely ., These results have translational potential since 75% of disease-causing genes in humans have a fly ortholog 30 ., High resolution X-ray crystallography has shown that the D . melanogaster dopamine transporter has a central conformationally pliable binding site that can accommodate cocaine , methamphetamine and their closely related analogues 31 ., Similar to its effects in humans , methamphetamine suppresses sleep , causes arousal and suppresses food intake in flies 32–34 ., In addition , cocaine and amphetamine exert quantifiable locomotor effects in flies 35–41 ., Thus , despite profound differences between the neuroanatomical organization of the fly and vertebrate brains , it is likely that behavioral and physiological effects of methamphetamine and cocaine are mediated , at least in part , by analogous mechanisms ., Here , we used the inbred , sequenced lines of the D . melanogaster Genetic Reference Panel ( DGRP 42 , 43 ) to investigate the genetic basis for variation in psychostimulant drug consumption ., We used a four-capillary Capillary Feeding ( CAFE ) assay 44–46 to quantify voluntary consumption , preference and change of consumption and preference over time for cocaine and methamphetamine ., Since cocaine and methamphetamine both target dopaminergic synaptic transmission , but through different mechanisms , we asked to what extent genetic networks that underlie variation in consumption of cocaine and methamphetamine incorporate the same or different genes ., We also sought to determine the extent of sexual dimorphism for naïve and experience-dependent voluntary drug intake ., In addition , we asked how much variation in voluntary drug consumption exists among different DGRP lines and what fraction of that variation is accounted for by genetic variation ., We showed that there is naturally occurring genetic variation for all drug consumption traits with strong sex- , drug- and exposure time-specific components ., We performed genome wide association ( GWA ) analyses to identify candidate genes associated with the drug consumption behaviors that could be mapped to a genetic interaction network ., We tested the effects of RNAi mediated suppression of gene expression 47 on all consumption behaviors for 34 candidate genes and found that all affected at least one behavior in at least one drug and sex ., Finally , we used RNAi to suppress gene expression in neurons , glia , the mushroom bodies and dopaminergic neurons in a subset of genes and showed that innate preference and the development of preference for psychostimulant drugs involves dopaminergic neurons and the mushroom bodies , neural elements associated with experience-dependent modulation of behavior ., We used a four-capillary CAFE assay 44–46 to enable flies to choose to consume either sucrose or sucrose supplemented with 0 . 2 mg/ml cocaine ( or 0 . 5 mg/ml methamphetamine ) , analogous to the two-bottle choice assay used in rodent studies 48 ( Fig 1 ) ., We quantified consumption for three consecutive days for males and females from each of 46 DGRP lines that were unrelated , free of chromosomal inversions , and free of infection with the endosymbiont Wolbachia pipientis 43; S1 Table ., These data enabled us to assess whether there is naturally occurring genetic variation in this population for naïve consumption of each solution and preference , and change of consumption and preference upon repeated exposures ( i . e . , experience-dependent modification of behavior ) ., We performed four-way mixed model analyses of variance ( ANOVA ) to partition variation in consumption between DGRP lines , males and females , drug vs . sucrose , and the three exposures ., All main effects were significant for both drugs ( Table 1 ) , indicating genetic variation for consumption , difference between amount of sucrose and drug consumed , sexual dimorphism , and experience-dependent modulation of behavior ., We are most interested in the two- and three-way interaction terms involving Line , as they indicate genetic variation in sexual dimorphism ( L×X ) , change of consumption between exposures ( L×E ) , preference for sucrose or drug solution ( L×S ) , and change of preference for sucrose or drug between exposures ( L×E×S ) ., With the exception of L×S , these interaction terms were significant for both the cocaine and methamphetamine analyses ( Table 1 ) ., We next performed reduced ANOVA models to quantify broad sense heritabilities ( H2 ) for consumption and change in consumption traits ( S2 Table ) ., We found significant genetic variation in consumption of both drugs and sucrose alone within each sex and exposure , with H2 ranging between 0 . 20 and 0 . 38 for cocaine consumption and between 0 . 22 and 0 . 30 for methamphetamine consumption ( Fig 2 , S2 Table ) ., Further , there was significant genetic variation for the change in consumption of sucrose alone or drug in both sexes between the third and first exposures , with H2 ranging between 0 . 14 and 0 . 18 for cocaine and between 0 . 17 and 0 . 22 for methamphetamine ( Fig 2 , S2 Table ) ., Thus , there is genetic variation for both consumption and experience-dependent consumption of both drugs and sucrose alone in the DGRP ., Finally , we defined preference in two ways: as the difference between amount of drug and sucrose alone consumed ( Preference A ) , and as this difference scaled by the total amount of both solutions consumed ( Preference B ) ., Variation in Preference A is the statistical interpretation of the line by solution interaction; Preference B is the metric commonly used in rodent studies 48 ., Preference values of 0 indicate equal consumption of sucrose alone and sucrose containing drug; values > 0 represent preference for the drug and values < 0 indicate drug avoidance ., Both preference metrics were significantly genetically variable for each sex and exposure for cocaine , with H2 ranging from 0 . 06–0 . 16; while for methamphetamine , both preference metrics were significantly genetically variable in females for all exposures ( H2 from 0 . 05–0 . 18 ) and for males in the second and third exposures ( H2 from 0 . 08–0 . 11 ) ( S2 Table ) ., For cocaine , the difference in Preference A between exposures 3 and 1 was significant only in females ( H2 = 0 . 11 ) while the difference in Preference B was significant for females ( H2 = 0 . 13 ) and males ( H2 = 0 . 05 ) ., For methamphetamine , the difference in Preference A was significant in males ( H2 = 0 . 04 ) and the difference in Preference B was significant in females ( H2 = 0 . 04 ) ( S2 Table ) ., Thus , there is genetic variation for both innate drug preference and experience-dependent drug preference in the DGRP ., The heritabilities of consumption traits are low , as is typical for behavioral traits , indicating that environmental factors , including previous experience , predominantly contribute to the observed phenotypic variation ., The advantage of performing multiple replicate measurements of each DGRP line is that the broad sense heritabilities of line means ( S3 Table ) used in the GWA analyses ( see below ) are much greater than heritabilities based on individual vials ( S2 Table ) ., We computed the genetic and phenotypic correlations between males and females for the consumption behaviors , between exposures for consumption and preference , and between solutions ( S4 Table ) ., Cross-sex genetic correlations for consumption tended to decrease with the number of exposures for both cocaine and methamphetamine , suggesting that the experience-dependent modification of consumption is sex-specific ., Consumption of drugs and sucrose is highly correlated across the three exposures ( albeit significantly different from unity ) , while the correlations of drug preference across exposures are low to moderate for both cocaine and methamphetamine in both sexes ., Although the consumption of drugs and sucrose for cocaine and methamphetamine are genetically and phenotypically correlated in both sexes , preference for the two drugs is not significantly correlated ., Finally , Preference A and Preference B within each exposure are nearly perfectly correlated , as expected since the difference in consumption is in both metrics ., In summary , we found that there is extensive genetic variation in consumption and preference as well as change in consumption and preference with repeated exposures for both cocaine and methamphetamine across different genetic backgrounds , and that genetic variation for these traits has significant sex- and drug-specific components ., Our quantitative genetic analyses of consumption in the DGRP indicate that there is genetic variation for all traits assessed , and that the traits have a complex correlation structure indicating partially common and partially distinct genetic bases ., Therefore , we performed GWA analyses for 12 traits ( drug and sucrose consumption exposure 1 , drug and sucrose consumption exposure 3 , change in drug and sucrose consumption , Preference A exposure 1 , Preference A exposure 3 , Preference B exposure 1 , Preference B exposure 3 , change in Preference A , and change in Preference B ) for cocaine and methamphetamine , separately for males and females ., We performed association tests for 1 , 891 , 456 DNA sequence variants present in the 46 DGRP lines with minor allele frequencies greater than 0 . 05 43 ., At a lenient significance threshold of P < 5 x 10−5 , we identified 1 , 441 polymorphisms in or near ( within 1 kb of the start and end of the gene body ) 725 genes for all consumption behaviors related to cocaine , and 1 , 413 polymorphisms in or near 774 genes for methamphetamine exposure ( S5 Table ) ., The majority of these variants had sex-specific effects ., A total of 40 variants and 141 genes overlapped between cocaine and methamphetamine ., The variants in or near genes implicate candidate genes affecting consumption behaviors , while the intergenic variants could potentially contain regulatory motifs for transcription factor-binding sites or chromatin structure regulating these traits ., Only two variants are formally significant following a Bonferroni correction for multiple tests ( 0 . 05/1 , 891 , 456 = P\u2009<\u20092 . 64\u2009×\u200910−8 ) ., 2L_10179155_SNP is located within an intronic region in CG44153 and affects experience-dependent development of methamphetamine preference in both sexes ., Its human homolog ADGRB3 encodes a G-protein coupled receptor , which contributes to the formation and maintenance of excitatory synapses 49 and has been implicated in GWA studies on human addiction 50 ., 3R_27215016_SNP is a synonymous SNP in the coding sequence of CG1607 and affects naïve consumption of sucrose ., CG1607 encodes an amino acid transmembrane transporter ., One of its human orthologs , SLC7A5 , is an amino acid transporter , mutations in which are associated with autism spectrum disorder and defects in motor coordination 51 ., While not formally significant , we identified genes previously associated with cocaine-related behaviors ( Bx Lmo , loco , Tao ) and ethanol-related behaviors ( Bx , DopR , Egfr , hppy , Tao , Tbh ) 52 in D . melanogaster ., In addition , the genes implicated by the GWA analyses are enriched for multiple gene ontology ( GO ) categories and pathways 53 , 54 at a false discovery rate < 0 . 05 ( S5 Table ) ., GO terms involved in nervous system development and function were among the most highly enriched , consistent with the known neurobiological mechanisms of action of these drugs ., Finally , we note that ~ 70% of the candidate genes from the GWA analyses have human orthologs , and many of these genes have previously been associated with cocaine or methamphetamine abuse in humans or with behaviors associated with intake and response to various psychoactive substances ( alcohol , cannabis , nicotine , opioids ) in humans as well as zebrafish , mouse and rat models ( S6 Table ) ., This suggests that cocaine and methamphetamine exert their effects in flies and humans through evolutionarily conserved neural mechanisms ., These results suggest a highly polygenic architecture for variation in consumption and drug preference , and that the genetic underpinnings for variation in consumption or preference are both shared and distinct for cocaine and methamphetamine , consistent with the quantitative genetic analyses ., We next asked whether the genes we identified in the GWA analyses belonged to a known genetic interaction network ., Since the consumption behaviors are highly inter-correlated , we queried whether all 1 , 358 candidate genes from the GWA analyses for both cocaine and methamphetamine combined could be clustered into significant sub-networks based on curated genetic interactions in Drosophila ., If we do not allow any missing genes , we find a significant ( P = 9 . 99 × 10−4 ) network of 81 candidate genes ( Fig 3 , S7 Table ) , most of which ( 88 . 9% ) are predicted to have human orthologs 55 ., We performed enrichment analyses 53 , 54 to gain insight in the biological context for genes in the network using a false discovery rate < 0 . 05 ., Surprisingly , many canonical signaling pathways are highly enriched , including the Wingless ( Wnt ) , Cadherin , Cholecystokinin Receptor ( CCKR ) , Transforming Growth factor beta ( TGF ) , and Fibroblast Growth Factor ( FGF ) signaling pathways ., Concomitantly , we find high enrichment of molecular function GO terms associated with regulation of transcription and DNA and protein binding , and biological function GO terms associated with development ( including the development of the nervous system; S7 Table ) ., These results suggest that naturally occurring genetic variation in nervous system development is associated with variation in propensity to consume psychostimulant drugs ., Furthermore , our results indicate that natural variants in key genes regulating all aspects of fly development and function can be associated with variation in drug consumption behaviors ., We used RNA interference ( RNAi ) to functionally test whether reduced expression of candidate genes implicated by the GWA analyses affect consumption phenotypes ., We selected 34 candidate genes for RNAi mediated suppression of gene expression from the set of GWA analyses ( S5 Table ) ., A total of nine of the candidate genes were in the network; the others were chosen based on gene expression in the nervous system and their known role in nervous system function , as well as belonging to enriched pathways and gene ontology categories ., We measured consumption of cocaine and sucrose ( S8 Table ) and methamphetamine and sucrose ( S9 Table ) for three consecutive days , separately for males and females , for each of the RNAi and control genotypes , exactly as described for the DGRP lines ., We performed three-way fixed effect ANOVAs for each UAS-RNAi and control genotype , separately for males and females ( S10 and S11 Tables ) ., The main effects in these models are genotype ( L , RNAi and control ) , solution ( S , sucrose and drug ) and exposure ( E , first and third ) ., A significant L effect denotes a difference in overall consumption between the RNAi and control genotypes; a significant S effect indicates a difference in preference between sucrose alone and sucrose with drug; and a significant E effect indicates a difference in consumption between exposures 1 and 3 ., Significant L×S and L×E interaction terms denote , respectively , a difference in preference between the RNAi and control genotypes , and a difference in consumption between exposures 1 and 3 between the two genotypes ., A significant L×S×E interaction indicates a change in preference with repeated exposure between the RNAi and control genotypes ., We are most interested in the main effect of genotype and interactions with genotype; i . e . , consumption , preference , change of consumption and change of preference ., First , we used a weak ubiquitous GAL4 driver crossed to all 34 UAS-RNAi genotypes and their respective controls ., All candidate genes had a significant ( P < 0 . 05 ) effect on at least one of the consumption traits in at least one drug or sex combination ., A total of 22 ( 25 ) genes affected consumption of cocaine ( methamphetamine ) , 21 ( 23 ) affected a change of consumption with exposure to cocaine ( methamphetamine ) , 16 ( 10 ) affected cocaine ( methamphetamine ) preference , and 11 ( 11 ) affected a change in cocaine ( methamphetamine ) preference with exposure in males and/or females ( S10 and S11 Tables , S1–S3 Figs ) ., There were pronounced sex- and drug-specific effects for all drug-related traits ., The majority of RNAi genotypes showed reduced consumption of cocaine and/or methamphetamine compared to their controls , dependent on exposure and sex ., If consumption is positively associated with gene expression , this suggests that the products of these genes contribute to drug consumption ., On the other hand , several RNAi constructs caused increased drug consumption , suggesting that naturally occurring variants that decrease expression of these genes could predispose to drug preference ., Finally , several RNAi-targeted genes exhibit a relative increase or decrease in drug consumption compared to the control at the third exposure , indicating experience-dependent change in preference ., To extend and refine our RNAi analysis , we next selected 10 genes ( Dop1R1 , Ect4 , ed , mld , msi , Oct-TyrR , olf413 , Snoo , Vha100-1 , wmd ) from among those that showed phenotypic effects when targeted by RNAi under the ubiquitous driver and which have known effects on the nervous system ., We assessed functional effects of these genes on consumption traits when their corresponding RNAi constructs were expressed under the control of the neuronal-specific elav driver or glial-specific repo driver ., All of these genes had a significant ( P < 0 . 05 ) effect on at least one of the consumption traits in at least one drug or sex combination under the elav driver , and all but Snoo had significant effects on at least one of the consumption traits in at least one drug or sex combination under the repo driver ., With neuronal-specific suppression of gene expression , 9 ( 10 ) genes affected consumption of cocaine ( methamphetamine ) , 6 ( 7 ) affected a change in consumption with exposure to cocaine ( methamphetamine ) , 2 ( 7 ) affected cocaine ( methamphetamine ) preference , and 3 ( 6 ) affected a change in cocaine ( methamphetamine ) preference with exposure in males and/or females ( S10 and S11 Tables , Fig 4 , S4 Fig ) ., With glia-specific suppression of gene expression , 4 ( 7 ) genes affected consumption of cocaine ( methamphetamine ) , 7 ( 6 ) affected a change in consumption with exposure to cocaine ( methamphetamine ) , 3 ( 0 ) affected cocaine ( methamphetamine ) preference , and 2 ( 3 ) affected a change in cocaine ( methamphetamine ) preference with exposure in males and/or females ( S10 and S11 Tables , Figs 4 and 5 , S4 Fig ) ., These effects were largely sex- , drug- and driver-specific ., We infer from these results that variation in gene expression in both neurons and glia contributes to phenotypic variation in drug intake behaviors ., In humans , the mesolimbic dopaminergic projection plays a role in drug addiction ., In Drosophila , the mushroom bodies could play an analogous role , as they are integrative centers in the fly brain associated with experience-dependent learning 56 , 57 , dependent on dopaminergic input ., To test whether the mushroom bodies and dopaminergic projection neurons could serve as neural substrates that contribute to variation in drug consumption or preference , we focused on four genes ( Dop1R1 , ed , msi , Snoo , ) that showed robust phenotypic effects when targeted with a corresponding elav-driven RNAi ., Knockdown of all four genes with a mushroom body specific driver resulted in significant effects on consumption of cocaine and/or methamphetamine for at least one drug and sex combination ( S10 and S11 Tables , Figs 4 and 5 , S5 Fig ) ., Expression of RNAi in mushroom bodies affected change in consumption of cocaine and methamphetamine for Dop1R1; cocaine preference and change of methamphetamine preference for ed; change in consumption of cocaine for msi; and cocaine and methamphetamine preference , cocaine preference , change of cocaine preference and change of consumption of methamphetamine for Snoo ., Expression of RNAi in dopaminergic neurons affected change of consumption of cocaine and change in methamphetamine preference for Dop1R1; consumption for cocaine and methamphetamine , change of consumption of methamphetamine and cocaine preference for ed; consumption of cocaine and methamphetamine , change of consumption of cocaine , and cocaine preference for msi; and all four traits for Snoo ( S10 and S11 Tables , Figs 4 and 5 , S5 Fig ) ., These effects are largely sex- , drug- and driver-specific ., These results suggest that , despite differences in the genetic underpinnings of susceptibility to cocaine and methamphetamine , phenotypic manifestation of genetic variation in consumption and development of preference for both drugs is channeled in part through a neural network that comprises dopaminergic projections to the mushroom bodies ., Although studies using mice 58 , 59 , rats 60 , 61 , primates 62 and humans 63 provide important information about the cellular , developmental , physiological , and behavioral effects of psychostimulants , these systems are less suited to dissecting the relationship between naturally occurring genetic variation and phenotypic variation in individual susceptibility to drug consumption and/or preference ., Here , we show that D . melanogaster harbors substantial naturally occurring variation for all consumption-related behaviors , including experience-dependent change in consumption , innate drug preference and experience-dependent change in preference , under conditions where we can obtain replicated measurements of consumption for each genotype in a choice assay performed over three successive days under controlled environmental conditions ., We show that genetic variation for consumption and preference metrics is both shared between males and females and the different exposures , but is also sex- , exposure- and drug-specific ., Sex differences in drug self-administration and addiction have also been shown in humans and mammalian animal models 64–72 ., The Diagnostic and Statistical Manual of Mental Disorders , Fifth Edition ( DSM-V ) defines 11 criteria for substance use disorder in humans , all related to continuing to use of the substance despite adverse social and physiological effects and the development of tolerance with repeated exposure ., The DSM-V also recognizes that there is individual variability of unknown etiology for the propensity both to experiment with psychostimulants and to develop symptoms of substance abuse following initial exposure ., Previous studies of effects of cocaine 35 , 37–39 , 73–76 and methamphetamine 77 in Drosophila examined mutations and pharmacological interventions using locomotor-based assays , clearly demonstrating an adverse effect of these substances ., However , previous Drosophila studies have not assessed naturally occurring variation in drug self-administration and change in this behavior on repeated exposure , which may better model the genetic basis of individual susceptibility–or resistance–to substance abuse and the development of tolerance ( increased drug preference over time ) ., To begin to understand the nature of the genetic basis for variation in drug consumption and preference , we performed GWA analyses for all consumption traits , separately for cocaine and methamphetamine , using 1 , 891 , 456 DNA sequence variants present in the 46 DGRP lines with minor allele frequencies greater than 0 . 05 43 ., We identified 1 , 358 unique candidate genes using a lenient significance threshold of P < 5 x 10−5 ., We hypothesized that these candidate genes would be enriched for true positive associations despite the low power of the GWA analyses and that choosing genes for functional evaluation from this list would be more productive than choosing genes at random ., Observations supporting this hypothesis are that mutations in several candidate genes have previously been shown to affect cocaine or ethanol-related phenotypes in Drosophila 52 , that the candidate genes are highly enriched for GO terms involved in the development and function of the nervous system , and that 81 candidate genes can be assembled into a known genetic interaction network ( Fig 3 ) , which is highly unlikely ( P = 9 . 9 x 10−3 ) to occur by chance ., The candidate genes in the significant genetic interaction network are enriched for several canonical signaling pathways as well as all aspects of development , including nervous system development ., These observations suggest that subtle genetic variation in nervous system development is associated with variation in propensity for consumption of psychostimulant drugs ., Nearly 90% of the genes in the network have human orthologs and are candidates for future translational studies ., We selected nine candidate genes in the significant genetic network and 25 additional candidate genes to assess whether RNAi reduction using a weak ubiquitous GAL4 driver affected consumption traits , using the same experimental design as for the DGRP lines ., All of these genes affected at least one consumption trait/sex/drug ., However , there is considerable variation in the effects of different drivers on consumption , preference and change in preference for cocaine and methamphetamine , which likely reflects variation in the effects of RNA interference on different neural elements of a complex integrated neural circuitry ., Indeed , several candidate genes , functionally implicated by RNAi , are associated with neural development and represent several early developmental signaling pathways ., Snoo has been identified as a negative regulator of the decapentaplegic signaling pathway 78 , 79 and has been implicated in dendritic patterning 80 ., Echinoid , the gene product of ed , is an immunoglobulin domain containing membrane protein of adherens junctions that interacts with multiple developmental signaling pathways , including Egfr , Notch and Hippo signaling 81–83 ., Musashi , encoded by msi , is a neural RNA binding protein that interacts with Notch signaling to determine cell fate 84 ., RNAi targeting of expression of these genes under MB-GAL4 or TH-GAL4 drivers show different effects on consumption , change in consumption , preference and change in preference for the two drugs ( S5 Fig ) ., Among the functionally validated candidate genes , Oct-TyrR and Dop1R1 are of special interest ., Oct-TyrR encodes an octopamine-tyramine receptor expressed in mushroom bodies 85 , and Dop1R1 , which encodes a dopamine receptor enriched in the mushroom bodies , has previously been implicated in aversive and appetitive conditioning 86 , innate courtship behavior 87 and sleep-wake arousal 88 ., Loss-of-function mutations of Dop1R1 increase sleep and these effects are reversed by administration of cocaine 88 ., Octopamine and tyramine act on astrocytes via the Oct-Tyr1 receptor and this activation of astrocytes can in turn modulate dopaminergic neurons 89 ., Thus , we can hypothesize that combinations of octopaminergic and dopaminergic signaling in the mushroom bodies can modulate drug consumption and/or experience-dependent changes in consumption or preference following repeated exposure to cocaine or methamphetamine ., Finally , genes which were functionally validated with RNAi represent evolutionarily conserved processes ., Future studies can assess whether their human counterparts play a role in variation in susceptibility to psychostimulant drug use in human populations ., The DGRP , UAS-RNAi and GAL4 driver lines used are listed in S12 Table ., The DGRP lines are maintained in the Mackay laboratory ., RNAi lines 47 were obtained from the Vienna Drosophila Resource Center and the GAL4 driver lines from the Bloomington , Indiana Drosophila stock center ., All lines were maintained on standard cornmeal/yeast/molasses medium at 25°C on a 12 hour light/dark cycle with constant humidity of 50% ., We used a four-capillary Capillary Feeder ( CAFE ) assay 44–46 to measure drug consumption ., Briefly , five 3–5 day old flies per genotype/sex were anesthetized using CO2 and placed on cornmeal/yeast/molasses/agar medium one day prior to the assay ., Flies were transferred without anesthesia 45 minutes prior to the assay to vials containing 4-5ml of 1 . 5% agar ( Sigma Aldrich ) ., Two capillaries ( VWR International: 12 . 7 cm long , 5 μl total volume ) containing 4% sucrose ( Sigma Aldrich ) + 1% yeast ( Fishe | Introduction, Results, Discussion, Materials and methods | Illicit use of psychostimulants , such as cocaine and methamphetamine , constitutes a significant public health problem ., Whereas neural mechanisms that mediate the effects of these drugs are well-characterized , genetic factors that account for individual variation in susceptibility to substance abuse and addiction remain largely unknown ., Drosophila melanogaster can serve as a translational model for studies on substance abuse , since flies have a dopamine transporter that can bind cocaine and methamphetamine , and exposure to these compounds elicits effects similar to those observed in people , suggesting conserved evolutionary mechanisms underlying drug responses ., Here , we used the D . melanogaster Genetic Reference Panel to investigate the genetic basis for variation in psychostimulant drug consumption , to determine whether similar or distinct genetic networks underlie variation in consumption of cocaine and methamphetamine , and to assess the extent of sexual dimorphism and effect of genetic context on variation in voluntary drug consumption ., Quantification of natural genetic variation in voluntary consumption , preference , and change in consumption and preference over time for cocaine and methamphetamine uncovered significant genetic variation for all traits , including sex- , exposure- and drug-specific genetic variation ., Genome wide association analyses identified both shared and drug-specific candidate genes , which could be integrated in genetic interaction networks ., We assessed the effects of ubiquitous RNA interference ( RNAi ) on consumption behaviors for 34 candidate genes: all affected at least one behavior ., Finally , we utilized RNAi knockdown in the nervous system to implicate dopaminergic neurons and the mushroom bodies as part of the neural circuitry underlying experience-dependent development of drug preference . | Illicit use of cocaine and methamphetamine is a major public health problem ., Whereas the neurological effects of these drugs are well characterized , it remains challenging to determine genetic risk factors for substance abuse in human populations ., The fruit fly , Drosophila melanogaster , presents an excellent model for identifying evolutionarily conserved genes that affect drug consumption , since genetic background and exposure can be controlled precisely ., We took advantage of natural variation in a panel of inbred wild derived fly lines with complete genome sequences to assess the extent of genetic variation among these lines for voluntary consumption of cocaine and methamphetamine and to explore whether some genetic backgrounds might show experience-dependent development of drug preference ., The drug consumption traits were highly variable among the lines with strong sex- , drug- and exposure time-specific components ., We identified candidate genes and gene networks associated with variation in consumption of cocaine and methamphetamine and development of drug preference ., Using tissue-specific suppression of gene expression , we were able to functionally implicate candidate genes that affected at least one consumption trait in at least one drug and sex ., In humans , the mesolimbic dopaminergic projection plays a role in drug addiction ., We asked whether in Drosophila the mushroom bodies could play an analogous role , as they are integrative brain centers associated with experience-dependent learning ., Indeed , our results suggest that variation in consumption and development of preference for both cocaine and methamphetamine is mediated , at least in part , through a neural network that comprises dopaminergic projections to the mushroom bodies . | genome-wide association studies, invertebrates, alkaloids, medicine and health sciences, genetic networks, rna interference, chemical compounds, disaccharides, social sciences, carbohydrates, organic compounds, animals, animal models, behavioral pharmacology, drosophila melanogaster, model organisms, network analysis, experimental organism systems, genome analysis, pharmacology, cocaine, epigenetics, drosophila, research and analysis methods, computer and information sciences, genomics, genetic interference, animal studies, behavior, gene expression, chemistry, insects, arthropoda, biochemistry, rna, psychology, eukaryota, organic chemistry, nucleic acids, gene identification and analysis, genetics, biology and life sciences, physical sciences, computational biology, recreational drug use, sucrose, organisms, human genetics | null |
journal.pcbi.1006333 | 2,018 | Use of an individual-based model of pneumococcal carriage for planning a randomized trial of a whole-cell vaccine | For encapsulated bacteria such as Streptococcus pneumoniae 1 , Haemophilus influenzae 2 , and Neisseria meningitidis 3 , asymptomatic carriage in the human upper respiratory tract is a precursor to mucosal or invasive disease ., The population of bacteria in the upper respiratory tract , which may be sampled in the oropharynx or nasopharynx , is also the primary or sole source of transmission of these bacteria ., Because carriage is far more common and typically longer in duration than disease with these bacteria , it is often a more convenient endpoint for clinical trials of vaccines against them ., If a vaccine can prevent or terminate carriage , then it is likely to reduce both the risk of disease and the opportunities for transmission , leading to herd immunity effects ., Many of the current generation of vaccines against these organisms , made from their capsular polysaccharides chemically conjugated to a protein carrier ( conjugate vaccines ) , have been evaluated in randomized controlled trials ( RCTs ) where carriage was the primary endpoint 4–10 , and the case for carriage as an endpoint in vaccine licensure has been put forth by an international consortium 11 ., Carriage endpoints have also been used for RCTs of other vaccines against encapsulated bacteria , such as the protein-based vaccine designed to protect against group B meningococci 12 ., While the use of carriage as an endpoint in an RCT is often convenient and offers the possibility of smaller sample sizes than disease endpoints , it presents added complexities ., Carriage is almost always measured as prevalence ( whether the target organism is present at a particular time ) rather than as incidence ( the rate at which individuals acquire the organism ) , the more traditional endpoint in vaccine trials ., For vaccines such as conjugate vaccines that are thought to act directly on vaccinated persons by reducing the incidence of acquiring colonization , the proportional reduction in prevalence due to a vaccine will in general be smaller than the proportional reduction in incidence it causes 13 , because prevalence increases less than linearly with incidence ., Under certain assumptions , the estimated impact on prevalence can be converted into an estimate of the impact on incidence 13 , though this becomes more complex when there are multiple serotypes targeted by the vaccine 14 ., At a practical level , decisions must be made about when to sample the carriage population to estimate efficacy , with the goal of observing the largest effect possible ( to reduce sample size ) and also of being able to estimate a meaningful efficacy parameter 15 ., Moreover , immunity to carriage of S . pneumoniae ( also called pneumococci , the species on which this paper and the remainder of this introduction will focus ) likely involves at least two different parts of the immune system: antibodies that act in a serotype-specific fashion to reduce the risk of acquisition 16 and T-helper cells that act in a serotype-independent manner to reduce the duration of a carriage episode 17 ., Both of these forms of immunity are imperfect: even after multiple exposures to pneumococci , a human can acquire colonization and will not clear it immediately 16 , 18 , 19 ., Vaccines typically augment or hasten the acquisition of immunity , but vaccine-induced immunity against carriage is also only partially effective 13 ., In a vaccine trial conducted in infants or toddlers , participants in both the vaccine group and the control group will be repeatedly challenged by exposure to pneumococci ., Through the experience of acquiring and clearing colonization , these individuals will develop immune responses that reduce their rate of acquisition on exposure and increase the rate at which they clear the colonization episode 16 , 20 ., Further complexity arises from the fact that individuals may be colonized simultaneously with multiple strains of pneumococci 21–23 , some of which may be undetected at sampling time and not all of which may be affected by the vaccine ., Given these complexities , design of an RCT for a new vaccine involves challenging questions of choosing the best population and inclusion criteria to improve the chances of seeing a real effect of the vaccine , choosing at what time after vaccination to measure carriage , and estimating power and sample size requirements ., Mathematical simulations 15 , 24–26 have been used to assist in the design of intervention trials for infectious diseases ., These approaches have been needed , and useful , because standard assumptions about the magnitude of effect size and predictable event rates in controls are often not met in the setting of a transmissible pathogen , particularly when accounting for complexities like those mentioned above ., An inactivated whole cell pneumococcal ( wSP ) vaccine has recently been manufactured under Good Manufacturing Practices 27 and has been employed in dose-finding , immunogenicity , and safety studies in Kenyan adults and toddlers ( clinicaltrials . gov NCT02097472 ) 28 ., Although not powered for efficacy evaluation , this study was extended to evaluate nasopharyngeal carriage in toddlers participating in the trial ., Based on murine data , it is believed that the primary impact of such a vaccine is to hasten the development of T-cell-mediated immunity to colonization , thereby reducing the duration of carriage episodes 17 , 29 ., To aid in evaluating the results of this study and in planning future , larger studies , we undertook simulation modeling of such a trial in different age groups and settings to answer several questions:, Our simulation study was based on a published individual-based model of pneumococcal transmission that incorporates many of the complexities described above 30 ., To this model , we added the ability to simulate vaccine trials , and implemented an algorithm to fit parameters to carriage prevalence data ., The wSP vaccine was modeled as accelerating the exposure-dependent development of non-serotype-specific immunity against carriage duration , i . e . vaccination was immunologically equivalent to having cleared more colonizations ., Three possible vaccine efficacies were considered: 3 , 5 , or 10 “colonization equivalents” ( “c . e . ” ) , which correspond , respectively , to an additional 26% , 39% , or 63% reduction in carriage duration ., We assumed a minimum carriage duration of 20 days , and so reductions in duration affect the duration of carriage beyond the first 20 days ., Trial participants in the model were vaccinated once , either as infants , at 60 days of age , or as toddlers , at 360 days , and the vaccine was assumed to be effective immediately upon receipt ., Simulated trials took place in two settings that differed in their transmission intensity: the higher transmission setting had an under-five carriage prevalence of 66%; the lower transmission setting , 55% ., For the higher transmission setting , we ran 50 simulations of the vaccine trial using different random seeds and recorded the carriage prevalence every month ( defined as 30 days ) , starting from birth to 24 months after vaccination ( Fig 1 ) ., For both infants and toddlers , all vaccine efficacies led to reductions in prevalence throughout the follow-up period ., Higher efficacies resulted in greater reductions in carriage ., However , that marginal benefit attenuated with time as both controls and vaccinees acquired more natural immunity from carriage episodes ., Similar patterns were observed in the toddler trials , but with smaller reductions in prevalence ( Fig 2A–2C ) ., For the infants , the prevalence in the control and vaccine arms followed non-monotonic trajectories over the course of the follow-up period ., In the infants , the median prevalence in the control arms started at 74% at 2 months of age , peaked at 91% at 8 months of age , and then declined ( Figs 1A and 2A–2C ) ., The timing of the peak is consistent with previously reported data from Kilifi , Kenya 31 ., In the vaccinated infants , the median prevalence peaked at the same time , at 8 months of age for the 3 c . e . vaccine efficacy , or slightly earlier , at 5 months of age for the 5 c . e . and 10 c . e . wSP vaccine efficacies ( Fig 2A–2C , blue ) ., For the toddlers , who are vaccinated later in life at 12 months of age , the age-specific prevalence in both the control and vaccine arms steadily declined across the 24-month follow-up period ( Fig 2A–2C , purple ) ., From the joint trajectory of the control and vaccine arm prevalence over the follow-up period , we determined how the sample size required for a two-sample test of equal proportion varied with sampling time ., We assumed a 5% type I error probability , 80% power , and balanced arms , and use the term “sample size” to refer to the combined size of both arms ., In infants , for all vaccine efficacies , the median sample size decreased dramatically—almost ten-fold or more—in the period 3 to 9 months post-vaccination , plateaued , and then started increasing around 18 months post-vaccination ., In toddlers , the median sample size over time was also U-shaped , reaching a minimum at 9 months post-vaccination before increasing ( Fig 2D–2F , purple ) ., At virtually all sampling times and for all vaccine efficacies , the median sample size was larger in the toddler trials than in the infant trials ( Fig 2D–2F ) ., To examine the impact of transmission intensity in the population on carriage prevalence in the trial , we also ran 50 simulations of the vaccine trial in the lower transmission setting ., As in the higher transmission setting , all vaccine efficacies resulted in reductions in carriage prevalence at all sampling times ., The prevalence peak previously observed in infants was delayed , due to the slower acquisition of non-serotype-specific immunity in a lower transmission setting ( Fig 1 ) ., Thus , the prevalence trajectories in controls and vaccinees followed non-monotonic trajectories in both infants and toddlers ( Fig 3A–3C ) ., In the infant arms , the kink in the prevalence trajectory between 9 and 12 months post-vaccination was due to the change in age-specific contact patterns as the participants aged into the next age group ( Fig 3A–3C , S1 Table ) ., As in the higher transmission setting , the total sample size decreased substantially in the period 3 to 9 months post-vaccination , and reached similar minimums ., In the infant arms , the total sample size remained close to the minimum until the end of the 24-month follow-up period ., In the toddler arms , the median sample size increased slightly near the end of the follow-up period ., However , this rebound was considerably smaller than in the higher transmission setting , and the median sample size at 24 months post-vaccination was roughly five- to six-fold smaller ., The sample sizes for the infant and toddler arms were more similar than in the higher transmission setting , particularly for later sampling times ( Fig 3D–3F ) ., Using a computational , individual-based transmission model of pneumococcal carriage , we estimated that a vaccine that enhances the immune response by an amount corresponding to 3 , 5 , or 10 carriage episodes could reduce age-specific carriage prevalence up to 7% , 10% , and 17% , respectively , compared to control in a setting similar to that of the wSP vaccine trial in Kenya , but that the magnitude of the reduction would depend strongly on the age at which participants were sampled ., We found , however , that larger reductions could be observed if the same trial were performed in infants , in a lower-transmission setting , or both ., Altogether , this analysis indicated that an infant trial conducted in a lower-transmission setting for a vaccine simulating 3 , 5 , or 10 exposures could be adequately powered with fewer than 800 , 330 , or 110 participants respectively , if the sampling window were chosen to be 15 to 24 months post-vaccination ., Suboptimal choices of setting , age group , and sampling time could multiply the required sample size by a factor of ten or more ., The individual-based computational model 30 on which our work is based was originally used to explain serotype diversity and explore serotype replacement following the introduction of conjugate vaccines ., With modifications , this model is also well suited to address our modeling questions , because it incorporates many processes , epidemiological and immunological , that complicate the relationship between the efficacy of a vaccine believed to reduce carriage duration but not risk of acquisition , and its effect on carriage prevalence ., Our extensions—an algorithm to fit the model to specific epidemiological settings and the ability to randomize trial participants to different vaccine interventions—allow this model to be used for vaccine trial planning ., Our simulated vaccine trials show that sampling time and participant age greatly influence the number of participants needed to detect a protective effect of a vaccine whose effect is accelerating the development of immunity against carriage duration , as the wSP vaccine and perhaps other protein-based vaccines targeting carriage are expected to do ., Across different combinations of vaccine efficacies and participant ages , the required sample size reached a minimum approximately 9 months post-vaccination before rebounding in later months ., This favorable sampling time is consistent with simulation results by Scott et al . , who explored similar questions , but more generally and for vaccines whose primary effect is on acquisition rather than duration , and using a non-serotype-specific compartmental transmission model 15 ., This timing is also consistent with what Auranen et al . , who explored pneumococcal trial design questions with a Markov transition model , suggest: waiting at least twice the average carriage duration after immune response before sampling 32 ., In our simulations , the U-shaped trajectory of sample size over the follow-up period indicates a window of favorable sampling times , when the sample size is relatively small as compared to earlier or later ., We found that sample sizes are lower , and the favorable window longer , when trial participants were younger , and when the transmission level was lower ., In these scenarios , natural immunity is weaker initially or develops more slowly , and thus immune enhancement by the vaccine is more apparent ., This intuition is what our simulation study attempts to quantitate , in terms of sample size , for different trial conditions ., Certain model assumptions may affect our conclusions ., Our formulation of vaccine efficacy requires estimating the acquisition rate of exposure-dependent immunity ., Direct estimates of vaccine efficacy against carriage , when they become available , can be used instead ., We assume that the vaccine shortens only future carriage episodes , but not ones already present at the time of vaccination ., Since the intrinsic duration of the fittest serotype is five months , this assumption would delay the vaccine’s effect on carriage prevalence , and thus , our reported favorable sampling times ., This delay would affect infants more than toddlers , as they are more immunologically naïve and experience longer carriage durations ., Auranen et al . , in their study , report that sampling time is determined by the rate of clearance rather than rate of acquisition , which reinforces the importance of determining whether a vaccine accelerates the clearance of pre-existing carriage episodes 32 ., Another important assumption is that exposure , rather than age alone , is responsible for the progressive shortening of carriage episodes as an individual gets older ., If immune maturation due to calendar age , rather than or in addition to increased exposure , actually reduces carriage duration , then that would bolster the case for younger trial participants ., Regardless of age at vaccination , the favorable sampling windows will likely be shortened as well ., Our simulation framework can be easily updated should future evidence suggest revisiting these assumptions ., In its current form , our current simulation framework is already adaptable enough to examine a variety of scenarios ., The ability to tailor simulations to specific settings is particularly useful—vaccine trials take place in countries with different age and serotype distributions , and Phase I/II and Phase III trials of the same vaccine may be conducted in the different locations ., While we present results for a vaccine against carriage duration , we can also model vaccine protection against acquisition , and specify whether a vaccine effect is serotype-specific ., The analysis presented here can be easily repeated , without changes to the source code , for trials involving polysaccharide conjugate vaccines , which protect against acquisition 4 and whose protection is serotype-specific 10 , and novel vaccines with both polysaccharide and protein antigens 33 , which may elicit a combination of serotype-specific and cross-reactive responses against carriage ., The general population can also be vaccinated ., Hence , our framework can be used to simulate trials—such as those comparing dosing schedules—that take place in countries with existing vaccination programs ., In addition to planning future trials , our simulation framework can be used to examine completed trials ., For completed trials with carriage endpoints that have not found a statistically significant vaccine effect , such as a recent phase II trial of a protein and polysaccharide-based vaccine in Gambian infants 33 , simulation studies such as this can help assess whether inadequate power is a compelling explanation ., The analysis presented in this paper does not consider the effect of vaccination on carriage density or other factors ( apart from duration ) that would affect the infectiousness of a person who is vaccinated yet still becomes colonized ., More generally , we do not consider the impact of vaccination on transmission at all in our simulations: simulated trial participants are computationally isolated from other hosts to approximate an individually randomized trial in which the participants are a negligible fraction of the population ., However , our current framework can also simulate roll-outs of vaccination programs in the simulated population , where there is transmission between individuals , thus allowing the indirect effect of vaccination to be included ., Vaccines with direct effects against transmissibility , possibly via reducing bacterial density in the nasopharynx , can be incorporated into our framework as well , with minimal modifications to the source code ., We considered two settings that differ in their transmission intensity ., The higher transmission setting was chosen to approximate Kenya , the site of a recent dose-finding and safety study 28 ., The age distribution of simulated hosts was matched to that of Kenya’s population in 2015 38 , the second year of the study , which ran from April 2014 to December 2015 ., The age-specific mixing matrix was estimated from a social contact study in Kilifi , Kenya from 2011–2012 39 and can be found in S1 Table ., The age structure in the model is described in more detail in S1 Text ., We fixed the non-serotype-specific immunity acquisition rate so the simulated age-specific carriage durations are consistent with the age-specific rates of clearance in Kenyan toddlers estimated by Abdullahi et al . 40 ( S3C Fig ) ., The serotype fitness parameters were fit to serotype-specific carriage prevalences from a cross-sectional study in Kilifi from 2006 to 2008 31 , before the introduction of the conjugate vaccine PCV10 ., We chose to fit using only pre-PCV10 data ., Trying to reproduce changes in serotype distribution due to PCV10 would have introduced additional complications , while being unlikely to yield further insight into our modeling questions given that the wSP vaccine is expected to act in a serotype-agnostic manner 41 ., A mathematical description of the fitting algorithm can be found in S2 Text and the fitted serotype fitness parameters are listed in S2 Table ., For the lower transmission setting , we used a smaller overall contact rate , so the simulated carriage prevalence at 12 months of age resembles preliminary estimates from a study in Indonesia 42 , the proposed site for a follow-up wSP vaccine efficacy trial ( S3B Fig ) ., To facilitate comparisons between settings , we kept the same age distribution , age-specific mixing pattern , and fitness parameters used in the higher transmission setting ., A summary of the model parameters and their values can be found in Table 1 ., To isolate the effect of transmission intensity in our main analyses , we had used the same age-specific mixing pattern–based on Kenya contact survey data 39–in both the higher and lower transmission settings ., Real-world vaccine trials , however , will take place in the context of different mixing patterns , or may be planned in the absence of reliable social contact data ., To examine the robustness of our findings to the pattern of age-specific mixing , we repeated our analyses assuming random mixing between individuals , i . e . , equal contact rate for all pairs of individuals ., We re-fit the model to the observed Kenya carriage survey data 31 , and ran a set of 50 simulations ., With a random mixing pattern , there was a slightly higher carriage prevalence in trial participants during the first two years of follow-up ., However , the total sample sizes , in both magnitude and trend across sampling time , remained similar to those from the main analyses ( S4 Fig , Fig 2 ) ., We also confirmed that the inflection in the prevalence trajectories at 12 months of age ( Figs 1 and 2 blue ) were due to changes in age-specific mixing when infants age into the next age group ( from 0–1 years to 1–6 years ) ; this inflection was not seen in simulations with a random mixing pattern ( S4 Fig blue ) ., Other potential sources of bias were the population and trial arm sizes ., In the main analyses , we chose values that were small enough to allow simulations to finish reasonably quickly , and reduced the effect of simulation variability by running multiple simulations and considering sample median ., To assess whether the sample median may be biased , we performed univariate sensitivity analyses of the population and trial arm size ., Specifically , within the higher transmission setting , we varied population size between 10K , 25K , and 50K individuals ( not including trial participants ) , with the trial arm size fixed at 5K ., We also varied the trial arm size between 2 . 5K , 5K , or 10K participants , with the population size fixed at 25K ., Note that the middle values , a population size of 25K and a trial arm size of 5K , were the ones used in the main analyses ., Twenty-five simulations were run for each set of parameter values ., Varying the population and varying the trial arm size did not appreciably alter the sample median of the simulated carriage prevalences ( S5 Fig ) ., Larger population sizes led to smaller variability between simulations , which is expected given the stochastic nature of transmission in the model ( S5A and S5B Fig ) ., Larger trial arm sizes did not reduce variability , suggesting that the epidemiological dynamics in the general population are driving the variability in the trial arm prevalences , at least for the trial arm sizes examined ( S5C and S5D Fig ) ., C++11 code for fitting and simulating the individual-based model can be found in the Github repository linked here: https://github . com/ocsicnarf/vaccine-trial-planning . | Introduction, Results, Discussion, Methods | For encapsulated bacteria such as Streptococcus pneumoniae , asymptomatic carriage is more common and longer in duration than disease , and hence is often a more convenient endpoint for clinical trials of vaccines against these bacteria ., However , using a carriage endpoint entails specific challenges ., Carriage is almost always measured as prevalence , whereas the vaccine may act by reducing incidence or duration ., Thus , to determine sample size requirements , its impact on prevalence must first be estimated ., The relationship between incidence and prevalence ( or duration and prevalence ) is convex , saturating at 100% prevalence ., For this reason , the proportional effect of a vaccine on prevalence is typically less than its proportional effect on incidence or duration ., This relationship is further complicated in the presence of multiple pathogen strains ., In addition , host immunity to carriage accumulates rapidly with frequent exposures in early years of life , creating potentially complex interactions with the vaccine’s effect ., We conducted a simulation study to predict the impact of an inactivated whole cell pneumococcal vaccine—believed to reduce carriage duration—on carriage prevalence in different age groups and trial settings ., We used an individual-based model of pneumococcal carriage that incorporates relevant immunological processes , both vaccine-induced and naturally acquired ., Our simulations showed that for a wide range of vaccine efficacies , sampling time and age at vaccination are important determinants of sample size ., There is a window of favorable sampling times during which the required sample size is relatively low , and this window is prolonged with a younger age at vaccination , and in a trial setting with lower transmission intensity ., These results illustrate the ability of simulation studies to inform the planning of vaccine trials with carriage endpoints , and the methods we present here can be applied to trials evaluating other pneumococcal vaccine candidates or comparing alternative dosing schedules for the existing conjugate vaccines . | Streptococcus pneumoniae , a bacterium carried in the nasopharynx of many healthy people , is also a leading cause of bacterial pneumonia , sepsis , and ear infections in children aged five years and younger ., Vaccines targeting select strains of S . pneumoniae have been effective , and the development of new vaccines , particularly those that target all strains , can further lower disease burden ., For clinical trials of these vaccines , the number of study participants needed depends on the expected effect of the vaccine on a conveniently measured outcome: asymptomatic carriage ., The most economical way to test a vaccine for its effect on carriage is by measuring prevalence at a specific time , and comparing vaccinated to unvaccinated participants ., The relationship between incidence ( or duration ) and prevalence is complex , and changes with time as children develop natural immunity ., We explored this relationship using a mathematical model ., Given a vaccine efficacy , our computer simulations predict that fewer study participants are needed if they are vaccinated at a younger age , taken from a population with intermediate levels of transmission , and sampled for carriage at a certain time window: 9 to 18 months after vaccination ., Our study illustrates how simulation studies can help plan more efficient vaccine trials . | children, medicine and health sciences, immunology, simulation and modeling, vaccines, preventive medicine, age groups, infants, infectious disease control, vaccination and immunization, families, research and analysis methods, public and occupational health, infectious diseases, people and places, toddlers, immunity, population groupings, biology and life sciences, conjugate vaccines, vaccine development | null |
journal.pgen.1000118 | 2,008 | Novel Association of ABO Histo-Blood Group Antigen with Soluble ICAM-1: Results of a Genome-Wide Association Study of 6,578 Women | ICAM-1 is a member of the immunoglobulin superfamily of adhesion receptors and consists of 5 immunoglobulin-like extracellular domains , a transmembrane domain and a short cytoplasmic domain ., ICAM-1 , present on endothelial cells , serves as a receptor for the leukocyte integrins LFA-1 ( lymphocyte function-associated antigen-1 ) and Mac-1 ( CD11b/CD18 ) , facilitating leukocyte adhesion and migration across the endothelium 1 ., A soluble form of ICAM-1 ( sICAM-1 ) is found in plasma and consists of the extra-cellular domains of ICAM-1 ., Although the process leading to the formation of sICAM-1 is not entirely clear , sICAM-1 is thought to be shed from the cell membrane via proteolytic cleavage of ICAM-1 ., Because sICAM-1 binds to LFA-1 , it is capable of inhibiting lymphocyte attachment to endothelial cells 2 ., Furthermore , sICAM-1 has been shown to bind human rhinoviruses , the etiologic agent of 40–50% of common colds , and to inhibit rhinovirus infection in vitro 3 ., Likewise , a circulating fragment of sICAM-1 binds to erythrocytes infected with Plasmodium falciparum , the etiologic agent of malaria 4 ( MIM 611162 ) ., Finally , plasma concentration of sICAM-1 has been shown to provide unique predictive value for the risk of myocardial infarction ( MIM 608446 ) , ischemic stroke ( MIM 601367 ) , peripheral arterial disease ( MIM 606787 ) and noninsulin-dependent diabetes mellitus ( MIM 125853 ) in epidemiological studies 5–7 ., Despite relatively high heritability estimates ( from 0 . 34 to 0 . 59 ) 8 , 9 for sICAM-1 , few genetic variants are known to influence its concentrations ., Two recent linkage studies have shown evidence for genetic association at the ICAM1 ( GeneID 3383 ) locus ( 19p13 . 3-p13 . 2 ) 8 , 9 and two candidate SNPs within the extracellular domains of ICAM-1 itself , G241R ( rs1799969 ) and K469E ( rs5498 ) , have been correlated with circulating sICAM-1 levels 10 , 11 ., By contrast , a recent genome wide association study ( GWAS ) from the Framingham investigators involving 1006 participants and 70 , 987 SNPs revealed no association reaching a genome-wide level of significance , including the ICAM1 locus itself , although this study had no genetic marker within 60 kb of the gene 12 ., To more comprehensively explore this issue , we performed a larger GWAS , evaluating 336 , 108 SNPs in 6 , 578 apparently healthy women ., All participants in this study were part of the Womens Genome Health Study ( WGHS ) 13 ., Briefly , participants in the WGHS include American women from the Womens Health Study ( WHS ) with no prior history of cardiovascular disease , diabetes , cancer , or other major chronic illness who also provided a baseline blood sample at the time of study enrollment ., The WHS is a recently completed 2×2 randomized clinical trial of low-dose aspirin and vitamin E in the primary prevention of cardiovascular disease and cancer ., For all WGHS participants , EDTA anticoagulated plasma samples were collected at baseline and stored in vapor phase liquid nitrogen ( −170°C ) ., Circulating plasma sICAM-1 concentrations were determined using a commercial ELISA assay ( R&D Systems , Minneapolis , Minn . ) ; the assay used is known not to recognize the K56M ( rs5491 ) variant of ICAM-1 14 and the 22 carriers of this mutation were therefore excluded from further analysis ., This study has been approved by the institutional review board of the Brigham and Womens Hospital ., Additional clinical characteristics of these subsets are provided in Table S1 ., Genotyping was performed in two stages , a first sample being used to discover new associated loci and the second sample being used to validate them by replication ., These two samples were genotyped independently of one another in two batches ., The first ( WGHS-1 ) and second ( WGHS-2 ) batches included 4 , 925 and 2 , 056 self-reported Caucasian WGHS participants , respectively ., No related individuals were detected when tested with an identity by state analysis 15 ., Samples were genotyped with the Infinium II technology from Illumina ., Either the HumanHap300 Duo-Plus chip or the combination of the HumanHap300 Duo and I-Select chips was used ., In either case , the custom content was identical and consisted of candidate SNPs chosen without regard to allele frequency to increase coverage of genetic variation with impact on biological function including metabolism , inflammation or cardiovascular diseases ., Genotyping at 318 , 237 HumanHap300 Duo SNPs and 45 , 571 custom content SNPs was attempted , for a total of 363 , 808 SNPs ., Genetic context for all annotations are derived from human genome build 36 . 1 and dbSNP build 126 ., SNPs with call rates <90% were excluded from further analysis ., Likewise , all samples with percentage of missing genotypes higher than 2% were removed ., Among retained samples , SNPs were further evaluated for deviation from Hardy-Weinberg equilibrium using an exact method 16 and were excluded when the P-value was lower than 10−6 ., Samples were further validated by comparison of genotypes at 44 SNPs that had been previously ascertained using alternative technologies ., SNPs with minor allele frequency >1% in Caucasians were used for analysis ., After quality control , 307 , 748 HumanHap300 Duo SNPs and 28 , 360 custom content SNPs were left , for a total of 336 , 108 SNPs ., From the initial 4925 WGHS-1 and 2056 WGHS-2 individuals genotyped , 4582 WGHS-1 individuals and 2014 WGHS-2 individuals were kept for further analysis ., Because population stratification can result in inflated type I error , a principal component analysis using 1443 ancestry informative SNPs was performed using PLINK 17 in order to confirm self-reported ancestry ., Briefly , these SNPs were chosen based on Fst >0 . 4 in HapMap populations ( YRB , CEU , CHB+JPT ) and inter-SNP distance at least 500 kb in order to minimize linkage disequilibrium ., Different ethnic groups were clearly distinguished with the two first components ., Out of 4582 WGHS-1 and 2014 WGHS-2 self-identified Caucasians , 12 and 6 were removed from analysis because they did not cluster with other Caucasians , leaving 4570 ( WGHS-1 ) and 2008 ( WGHS-2 ) participants for analysis , respectively ., Two more analyses were undertaken to rule out the possibility that residual stratification within Caucasians was responsible for the associations observed ., First , association analysis was done with correction by genomic control ., This method estimates the average effect of population substructure in the sample ( based on median T values ) and accordingly corrects the test statistics 18 ., Second , a principal component analysis 19 was performed in Caucasians ( only ) using 124 , 931 SNPs chosen to have pair-wise linkage disequilibrium lower than r2\u200a=\u200a0 . 4 ., The first three components were then used as covariates in the association analysis ., As adjustment by these covariates did not change the conclusions , we present analysis among the WGHS-1 and WGHS-2 Caucasian participants without further correction for sub-Caucasian ancestry unless stated otherwise ., To identify common genetic variants influencing sICAM-1 levels , we first attempted to discover which loci significantly contributed to sICAM-1 concentrations in WGHS-1 ., Plasma concentrations of sICAM-1 were adjusted for age , smoking , menopause and body mass index using a linear regression model in R to reduce the impact of clinical covariates on sICAM-1 variance ., The adjusted sICAM-1 values were then tested for association with SNP genotypes by linear regression in PLINK 17 , assuming an additive contribution of each minor allele ., A conservative P-value cut-off of 5×10−8 was used to correct for the roughly 1 , 000 , 000 independent statistical tests thought to correspond to all the common genetic variation of the human genome 20 ., Replication of genome-wide significant associations was performed on adjusted sICAM-1 values from the replication sample ( WGHS-2 ) , using a Bonferroni correction to account for multiple hypothesis testing ., To further define the extent of genetic associations , a forward selection linear multiple regression model was used at the previously identified loci ., Briefly , all genotyped SNPs within 100 kb of the most significantly associated SNP at each replicated locus and passing quality control requirements were tested for possible incorporation into a multiple regression model ., In stepwise fashion , a SNP was added to the model if its multiple regression P-value was less than 10−4 ( to account for all the SNPs being considered ) and if it had the smallest P-value among all the SNPs not yet included in the model ., This analysis was done on WGHS-1 individuals using adjusted sICAM-1 values ., We then proceeded to validate our multiple regression model in WGHS-2 samples ., Using only the SNPs previously selected in WGHS-1 , we added them in a multiple regression model in the same order as they were chosen in WGHS-1 ., We considered the model validated if each time a SNP was included in the model , its regression P-value was lower than 0 . 01 ( to account for multiple testing ) and the direction of effect consistent ., Plasma from A blood group individuals was mixed 1∶1 or 1∶2 with a monoclonal anti-A antibody ( Ortho-Clinical Diagnostics , Rochester NY ) , and allowed to incubate 10 minutes or 60 minutes at room temperature , or 60 minutes or 12 hours at 4°C before assaying sICAM-1 levels by the standard technique ., To exclude the possibility that the antibody itself interfered with the assay , the same procedure was repeated with plasma from O blood group individuals ., Finally , plasma from O group individuals , which is expected to contain both anti-A and anti-B polyclonal antibodies , was mixed with plasma from A group individuals in 1∶1 ratio , again with incubation as above and measurement of sICAM-1 levels ., As shown in Table 1 , 19 SNPs passed our stringent genome-wide significance threshold when tested in WGHS-1 individuals , clustering within two loci in the vicinity of the ICAM1 ( 19p13 . 2 ) and ABO ( GeneID 28 ) ( 9q34 . 2 ) genes ( Figure 1 ) ., The replication threshold in WGHS-2 was conservatively set at a 2-sided P-value of 0 . 002 , applying a Bonferroni correction to account for 19 tests ., Using this cutoff , we were able to replicate 17 of the 19 associated SNPs , including SNPs at both the ICAM1 and ABO loci ., Only rs2116941 ( 19p13 . 2 ) and rs7256672 ( 19p13 . 2 ) did not replicate using this standard ., Nevertheless , each of these SNPs had a P-value lower than 10−9 when tested on the combined sample ( i . e . WGHS-1 and WGHS-2 pooled together ) ., Among the replicated SNPs , only rs7258015 ( 19p13 . 2 ) deviated from Hardy-Weinberg equilibrium ( p\u200a=\u200a0 . 00007 ) , but visual inspection of the raw genotyping signal for this SNP did not reveal any obvious artifact ., Major and minor alleles are shown in Table S2 ., We then applied our model selection algorithm in WGHS-1 individuals ( see Methods ) using 54 SNPs at 19p13 . 2 ( ICAM1 locus ) and 68 SNPs at 9q34 . 2 ( ABO locus ) ., As can be seen in Table 2 , 3 out of 54 SNPs at 19p13 . 2 were selected by our algorithm and 1 out 68 SNPs at 9q34 . 2 was selected ., All four SNPs selected in WGHS-1 were validated in WGHS-2 ., Pairwise linkage disequilibrium between these SNPs was low ., For instance , r2 was lower than 0 . 35 between ICAM1 SNPs while it was lower than 0 . 002 between the ABO SNP rs507666 and the ICAM1 SNPs ., Among these SNPs , there was no strong evidence for non-additive effects of the minor allele as judged by lack of significance for a likelihood ratio test comparing the additive regression model to an alternative genotype model with an additional degree of freedom ., Interestingly , one of the four selected SNPs ( rs281437 ) was non-significant in univariate analysis , illustrating that its inclusion in the model and significant association are conditional on the genotypes at rs5498 and rs281437 ., No gene-gene interaction was observed between ICAM1 and ABO SNPs ., The 3 SNPs at 19q13 . 2 ( ICAM1 ) collectively explained 6 . 9% of the total variance in sICAM-1 concentrations ( pooling WGHS-1 and WGHS-2 together ) , whereas the ABO SNP rs507666 explained 1 . 5% ., In comparison , clinical covariates accounted for 18 . 8% of the variance ( Table 3 ) , and together the candidate loci and the clinical variables accounted for 27 . 3% of total variance ., It should be noted that the estimated effect sizes of the ICAM1 and ABO loci are minimums since the genotyped variants might not be the actual functional variants ., The 3 SNPs at the 19p13 . 2 ( ICAM1 ) locus selected by our algorithm were also used in haplotype analysis using WHAP 21 , as implemented in PLINK 17 ( Table 4 ) ., The estimate of the proportion of variance attributable to haplotypes , as well as their regression coefficients , is consistent with the linear model of these same SNPs , reinforcing the adequacy of a strictly additive model to explain the association ., The ABO histo-blood group antigen is the most important blood group system in transfusion medicine ., Using data from Seattle SNPs ( http://pga . mbt . washington . edu ) as well as from the Blood Group Antigen Mutation Database ( www . ncbi . nlm . nih . gov ) , it can be demonstrated that rs507666 is a perfect surrogate for type A1 histo-blood group antigen ., Moreover , using rs687289 as a marker for the O allele , rs8176746 for the B allele and rs8176704 for the A2 allele , complete blood group antigen phenotype can be re-constructed by haplotype analysis ( no serotype data is available in WGHS ) ., Imputed haplotypes perfectly fitted the pattern expected from the literature and their association with sICAM-1 is shown in Tables 5 and 6 ., The A1 allele is associated with the lowest sICAM-1 concentrations while the A2 allele is associated with low concentrations , intermediate between the A1 and O allele ., In comparison , the B allele is associated with slightly higher concentrations than the O allele ., Because ABO histo-blood group antigens are known to vary in frequency among Caucasian sub-populations , we sought to investigate the potential effect of population stratification on the observed association even though adjustment of sICAM-1 values for the top ten components of our principal component analysis did not change our conclusions ( see Methods ) ., Visual inspection of the clustering pattern from the top two components confirmed a match with previously published work of sub-Caucasian stratification 22 ( data not shown ) ., Since these two components were reproducibly shown to correspond to a Northwest-Southeast European gradient 22 and the A1 allele follows such a gradient 23 , we hypothesized that they would be tightly linked to A1 allele frequencies ., Indeed , the second component showed evidence of association with A1 allelic frequencies ( p\u200a=\u200a2 . 5×10−6 ) , while the first component was only weakly associated ( p\u200a=\u200a0 . 08 ) ., Nevertheless , neither the first nor second component was very tightly linked to sICAM-1 values ( p\u200a=\u200a0 . 69 and 0 . 0006 respectively with corresponding R2 of 3 . 8×10−5 and 0 . 0019 ) , implying that stratification has no major effect on the sICAM-1 association ., Furthermore , the weak association with the second component could be partially explained by the correlation with A1 alleles , with corrected P-value of 0 . 004 and R2 of 0 . 0013 ., Adjustment of sICAM-1 values for the first and second components did not substantially change the association between the A1 allele and sICAM-1 ( unadjusted p\u200a=\u200a5 . 1×10−29 and adjusted p\u200a=\u200a5 . 5×10−28 ) , demonstrating that stratification on a Northwest-Southeast European axis is not responsible for the association ., We conclude that the data does not support the hypothesis that Northwest-Southeast sub-Caucasian stratification is responsible for the association of ABO variants with sICAM-1 concentrations since the A1 allele varies in frequency according to a Northwest-Southeast European axis while the slight variation in sICAM-1 among this same axis is at least partially dependent on the A1 allele ., Indeed , there is no evidence in the literature that mean sICAM-1 concentrations vary at all among Caucasian sub-populations , and this lack of evidence is supported by an overall R2 of 0 . 005 ( P-value of 0 . 0007 ) for the association between sICAM-1 concentrations and the top 10 principal components ., The Secretor phenotype ( as defined by rs601338 on chromosome 19q13 . 33 ) and the Lewis antigen phenotype ( as defined by rs812936 on chromosome 19p13 . 3 ) are additional important members of the histo-blood group antigen system ., These were therefore tested for association with sICAM-1 levels as well as for interaction with rs507666 ., No significant effect was observed ., Although the sICAM-1 molecule itself is not known to bear the ABO histo-blood group antigen , this possibility could not be ruled out , especially given its extensive glycosylation 24 , 25 ., We therefore sought to exclude the remote chance that the association between A histo-blood group antigen and lower sICAM-1 values was the consequence of a lower affinity of the antibodies used in the sICAM-1 assay for sICAM-1 carrying the A antigen ., In other words , if sICAM-1 does carry ABO histo-blood group antigen , then the allelic composition at the ABO locus could dictate the glycosylation status of the sICAM-1 molecule and possibly interfere with the immunoassay used ., While there is no evidence that the two plasma proteins known to contain ABO histo-blood group antigen ( von Willebrand factor and alpha 2-macroglobulin ) 26 suffer from such analytical interference , immunoassays are potentially susceptible to differential glycosylation of their target protein 27 ., We thus hypothesized that blocking the A antigen sites with either polyclonal or monoclonal antibodies would result in spuriously low sICAM-1 values if sICAM-1 does indeed carry ABO histo-blood group antigen and if the A antigen is located in the vicinity of one of the two antibody binding sites used by the immunoassay ., No differential effects of the mixing procedures ( see Methods ) were observed suggesting that the A blood group antigen was not interfering with measurement of sICAM-1 levels ., We therefore conclude that the genetic association of the ABO variant is not due to analytic interference ., However , we can not exclude that sICAM-1 bears the ABO histo-blood group antigen ., Finally , we sought to assess the presence of other associations that did not pass our stringent genome-wide P-value cut-off ., We therefore repeated the whole-genome association analysis on the combined sample ( i . e . WGHS-1 and WGHS-2 pooled together ) ., While no new locus was associated at a genome-wide level , rs9889486 had the lowest p-value ( outside of 9q34 . 2 and19p13 . 2; p\u200a=\u200a3 . 2×10−6 ) with a false discovery rate 28 of 0 . 03 ., This SNP is intronic to CCDC46 ( GeneID 201134 ) ( 17q24 . 1 ) , a gene whose function is not well characterized ., Among other low p-value SNPs , we note rs1049728 ( p\u200a=\u200a1 . 3×10−5 ) with a false discovery rate of 0 . 08 and the 51st most strongly associated SNP overall ., This SNP is located in the 3′ untranslated region of RELA ( GeneID 5970 ) ( 11q13 . 1 ) , which is part of the NFKB signaling complex , arguably the most important known regulator of ICAM1 expression 29 ., The non-synonymous coding ICAM1 SNPs rs1799969 ( G241R ) and rs5498 ( K469E ) were previously described as being associated with sICAM-1 levels10 , 11 whereas the association involving rs281437 is unreported ., The later SNP is in the 3′ untranslated region of ICAM-1 ., Of interest , the minor allele of rs1799969 ( arginine ) is correlated with lower sICAM-1 and has been associated with lower risk of type I diabetes30 , while the minor allele of rs5498 ( glutamic acid ) is correlated with higher sICAM-1 levels and has been associated with lower risk of asthma 11 ( MIM 600807 ) , inflammatory bowel disease 31 ( MIM 266600 ) and type I diabetes 32 ( MIM 222100 ) ., Furthermore , it has been demonstrated in vitro that this SNP affects ICAM-1 mRNA splicing pattern and apoptosis in human peripheral blood mononuclear cells 33 ., It is also noteworthy that sICAM-1 has been shown to inhibit insulitis and onset of autoimmune diabetes in a mouse model of type I diabetes 34 whereas ICAM1 itself was proven to be crucial to the priming of T cells against beta cells 35 ., The most striking result of this report is the association between sICAM-1 levels and rs507666 , a SNP intronic to the ABO gene ., The ABO gene encodes glycosyltransferase enzymes which transfer specific sugar residues to a precursor substance , the H antigen ., There are three major alleles at the ABO locus: A , B and O . Variation at the ABO locus is remarkable in that these alleles encode enzymes with different specificities as well as activities ., The A allele encodes the enzyme alpha1→3 N-acetylgalactosamyl-transferase which forms the A antigen from the H antigen ., The A allele ( as well as the B and O alleles ) is itself heterogeneous and comprises several subgroups , of which A1 and A2 are the most important ., As compared to A1 , the A2 allele has 30–50 fold less A transferase activity 36 ., The B allele encodes the enzyme alpha1→3 galactosyltransferase which forms the B antigen from the H antigen ., The O allele does not produce an active enzyme 37 ., Consistent with the A antigen being associated with lower sICAM-1 concentrations and with the A1 allele having 30–50 fold more A transferase activity than the A2 allele , the A1 allele is associated with the lowest sICAM-1 concentrations while the A2 allele is associated with low concentrations as well , but still higher than the A1 allele ( Table 5 ) ., Although we excluded the possibility of an analytical interference to explain the association , the exact mechanism linking histo-blood group antigen to sICAM-1 concentrations remains elusive ., Among the different hypotheses , it remains possible that sICAM-1 bears the A antigen , a modification that might increase its clearance by increasing its affinity for its receptor ( s ) and/or decrease its secretion , perhaps by decreasing its affinity for the protease ( s ) producing sICAM-1 from membrane-bound ICAM1 ., Alternatively , lower sICAM-1 concentrations might be the result of the presence of the A antigen on its receptor ( s ) and/or protease ( s ) ., ABO histo-blood group phenotype has been linked to a plethora of diseases , including infectious diseases , cancers and vascular diseases 38 ., Particularly interesting is the association of non-O histo-blood groups — and group A in particular 39 , 40 — with a higher risk of myocardial infarction , peripheral vascular disease , strokes and venous thromboembolism 41 ( MIM 188050 ) ., While this phenomenon is partially explained by higher concentrations of the coagulation factors vonWillebrand and VIII ( presumably because of decreased clearance ) 42 , the exact mechanism is not entirely understood ., Underlining the complex nature of the biological processes involved , the A1 group ( rs507666 ) is associated with lower levels of sICAM-1 , a ( positive ) predictor of vascular diseases in epidemiological studies 5 , 6 , 43–46 ., Among potential explanations as to this apparent disparity , it is possible that decreased sICAM-1 leads to increased adhesion of leukocytes on endothelial surface and therefore increased vascular inflammation , an important component of atherosclerosis 47 ., Moreover , because group A individuals have been shown to have higher blood cholesterol 48 and coagulability 42 , the decrease in sICAM-1 seen in these individuals could be offset by the increased susceptibility to vascular diseases conferred by these risk factors , even if sICAM-1 mechanistically causes these diseases ., Alternatively , sICAM-1 might merely be a marker of increased inflammation and coagulation 49 , both risk factors for vascular diseases ., Also of special interest , group A antigen carriers have been recognized as having a higher risk of suffering from severe malaria when infected by Plasmodium falciparum 50 ., Plamodium infected erythrocytes express a receptor ( PfEMP-1 ) that binds specifically to cell-surface group A and B antigen as well as ICAM-1 51 , a major step in the sequestration of infected erythrocytes leading to the clinical complications of severe and cerebral malaria ., The lower concentrations of sICAM-1 found in A1 group carriers could therefore be hypothesized to contribute to this higher risk either directly , if sICAM-1 can inhibit the sequestration process , or indirectly , if sICAM-1 levels reflect differences in the processing of the ICAM1 receptor itself ., Several limitations warrant discussion ., First , this study was conducted in Caucasian women ., It is therefore difficult to generalize our results to other ethnicities or to men ., Second , effect estimates derived from this study might be higher than in other populations as these are initial findings and because of the winners curse 52 ., Third , although we were able to rule out a technical artifact as the cause of our results , no mechanistic link is identified to explain the association between ABO histo-blood groups and sICAM-1 ., In particular , one pending question is whether or not ICAM-1 bears any ABO antigen at all ., In this report , we demonstrate that sICAM-1 concentrations are associated with genetic variation at the ABO and ICAM1 loci in women ., To our knowledge , this represents the first published genetic evidence that ABO may have a regulatory role on an inflammatory mediator , a finding with potential implication on a diverse array of immune-mediated disorders ., Especially interesting is the fact that both ABO and ICAM1 have been previously related to vascular disease and malaria , two major causes of mortality and morbidity worldwide ., The current study indicates a genetic link between histo-blood group antigen and inflammatory adhesion processes , providing the basis for physiological studies of this interaction . | Introduction, Methods, Results/Discussion | While circulating levels of soluble Intercellular Adhesion Molecule 1 ( sICAM-1 ) have been associated with diverse conditions including myocardial infarction , stroke , malaria , and diabetes , comprehensive analysis of the common genetic determinants of sICAM-1 is not available ., In a genome-wide association study conducted among 6 , 578 participants in the Womens Genome Health Study , we find that three SNPs at the ICAM1 ( 19p13 . 2 ) locus ( rs1799969 , rs5498 and rs281437 ) are non-redundantly associated with plasma sICAM-1 concentrations at a genome-wide significance level ( P<5×10−8 ) , thus extending prior results from linkage and candidate gene studies ., We also find that a single SNP ( rs507666 , P\u200a=\u200a5 . 1×10−29 ) at the ABO ( 9q34 . 2 ) locus is highly correlated with sICAM-1 concentrations ., The novel association at the ABO locus provides evidence for a previously unknown regulatory role of histo-blood group antigens in inflammatory adhesion processes . | Soluble Intercellular Adhesion Molecule 1 ( sICAM-1 ) is an inflammatory marker that has been associated with several common diseases such as diabetes , heart disease , stroke , and malaria ., While it is known that blood concentrations of sICAM-1 are at least partially genetically determined , our current knowledge of which genes mediate this effect is limited ., Taking advantage of new technologies allowing us to interrogate genetic variation on a whole genome basis , we found that a variation in the ABO gene is an important determinant of sICAM-1 blood concentrations ., Since the ABO gene is responsible for the ABO blood groups , this discovery sheds light on a new role for blood groups and offers novel mechanisms to explain the association between sICAM-1 blood concentrations and various common diseases . | genetics and genomics/genetics of the immune system, genetics and genomics/complex traits, cardiovascular disorders/cardiovascular diseases in women, hematology/blood transfusion, infectious diseases/tropical and travel-associated diseases, cardiovascular disorders/myocardial infarction | null |
journal.pcbi.1001051 | 2,011 | Theoretical Analysis of the Stress Induced B-Z Transition in Superhelical DNA | DNA often occurs in an underwound , negatively superhelical topological state in vivo ., In bacteria , gyrase enzymes act to generate negative supercoils , while topoisomerases dissipate them ., The dynamic balance between these two processes determines a basal level of superhelicity that can change according to the environmental or nutritional state of the organism 1 ., In addition , RNA polymerase translocation leaves a wake of negative supercoils and generates a bow wave of positive supercoils 2–4 ., Together these effects induce substantial amounts of superhelicity in the topological domains into which bacterial genomes are subdivided ., A variety of regulatory processes in prokaryotes , including the initiation of transcription from specific genes , are known to vary with the level of superhelicity experienced by the DNA involved 5 ., It has long been thought that unconstrained superhelicity was not a factor in eukaryotic genomic regulation ., Eukaryotes do not commonly have negatively supercoiling gyrases while they do have relaxing topoisomerases ., Also nucleosomal winding both stabilizes supercoils and could inhibit the transmission of unconstrained superhelicity ., However , it is now known that substantial amounts of transcriptionally induced negative superhelicity occur upstream ( i . e . 5′ ) of RNA polymerases in the human genome 6 , 7 ., A superhelix density of is achieved there by a single transcriptional initiation event , while divergently oriented transcription can produce superhelix densities of in the region between the polymerase complexes ., This superhelicity extends over at least kilobase distances , hence must be transmitted either through or around nucleosomes ., Kinetically , this transcription driven superhelicity is generated faster than topoisomerases act to relieve it , so it abides long enough to be able to affect subsequent regulatory processes ., The levels of negative superhelicity achieved in both prokaryotes and eukaryotes are sufficient to drive in vivo structural transitions to alternative DNA conformations 7 , 8 ., The most studied DNA transition is superhelically induced duplex destabilization ( SIDD ) , which facilitates or creates local sites of strand separation ., SIDD has been implicated in a wide variety of regulatory processes , including the initiation of transcription from specific promoters in both prokaryotes and eukaryotes 9–17 ., Here we focus on the transition from B-form to Z-form , a left-handed double helix ., When the discovery of Z-DNA was announced this transition was predicted to occur at physiologically attained levels of negative superhelicity 18–20 ., Z-DNA has been experimentally detected at inserted Z-susceptible sites in bacterial genomic DNA both in vitro and in vivo 21–26 ., The study of alternate DNA structures in eukaryotes is more challenging , in part because DNA superhelicity in these organisms seems not to be stable , but rather is a transient state driven by transcriptional activity ., However , there is substantial indirect evidence that Z-DNA also can occur in vivo in eukaryotes ., Z-DNA has been implicated in a variety of regulatory events relating to replication , transcription , recombination , and other biological processes 27 ., For example , it has been shown that the negative torsional stress induced by polymerase translocation during transcription can stabilize Z-DNA near transcription start sites 28 ., The amount of Z-DNA found in these experiments was directly related to transcriptional activity , and thus to the level of transcription-driven superhelicity ., Another set of experiments studied the formation of Z-DNA in the 5′ flank of the human c-myc gene 29 , 30 ., Three Z-susceptible regions were identified near the promoters of this gene ., These experimental results suggest that the regions involved transform to Z-form during c-myc transcription , but revert to B-form when transcription is inhibited ., These experiments indicate that transcriptionally driven superhelical stresses can drive B-Z transitions in mammalian cells ., Many attempts have been made to identify proteins that bind selectively to Z-DNA ., A powerful method developed by Herbert 31 led to the isolation of double-stranded RNA adenosine deaminase ( ADAR1 ) 32 , a Z-DNA binding enzyme , as well as other Z-binding proteins ., It has been shown that E3L , a Z-DNA binding protein found in poxviruses , inhibits the host cells ability to perform transcription or mount an anti-viral response when it is bound to Z-DNA near transcription start sites 33 ., On this basis it was suggested that an inhibitor of E3L binding might protect against poxviral infection ., Although there are some indications that Z-binding proteins may be involved in gene regulation , this remains an active area of research 27 ., The Z-form helix has dinucleotide repeat units , one of which must be in the syn- and the other in the anti-conformation , with helicity of −12 base pairs per turn 34 ., ( The minus sign indicates the left-handedness of the helix . ), The free energy required for the B-Z transition under low salt conditions has been determined for each of the ten dinucleotides 21 , 35–39 ., The Z-form is energetically most accessible for certain alternating purine-pyrimidine sequences , the most favored being , with guanine in the and cytosine in the conformations ., Z-formation has also been observed in sequences , although transitions there are almost twice as costly as at GC runs ., The remaining alternating purine/pyrimidine sequence , , has a very high transition energy and is not normally found in Z-form ., Perturbations which break the purine/pyrimidine alternation , although energetically costly , have also been observed in Z-DNA , as will be discussed below ., The substantial nucleation energy for initiating a run of Z-DNA , which may be regarded as the cost of generating two junctions between B-form and Z-form , also has been determined 21 , 40 ., Soon after the discovery of Z-DNA several simple theoretical analyses of superhelical B-Z transitions were developed ., These all assumed the simplest conditions of a single , uniformly Z-susceptible site embedded in an entirely Z-resistant background ., The first such analysis simply predicted that physiological levels of negative superhelicity could drive B-Z transitions 18 ., This approach was subsequently used to investigate the basic properties of these transitions , and to assess how the B-Z transition might compete with others in simple paradigm cases 19 , 36 , 41–43 ., Finally , these simple theoretical approaches were applied to determine the energy parameters of the transition from experiments in which a single uniform insert ( commonly ) placed within a superhelical plasmid was observed to undergo transition 21 , 36 , 40 ., In this paper we present the first method to analyze the superhelical B-Z transition in its full complexity ., This method , which we call SIBZ , can calculate the B-Z transition behavior of multi-kilobase length genomic DNA sequences under superhelical stress ., It specifically includes the competition for transition among all sites within the sequence ., SIBZ analyzes the states available to the entire sequence , where each base can be found in either the B-conformation or as a part of a Z-form dinucleotide pair ., It then uses statistical mechanics to determine the equilibrium distribution among these states ., Specifically , it calculates the probability of B-Z transition for each base pair in the sequence under the given conditions ., In this way it identifies the Z-susceptible regions within the sequence , and assesses how they compete at any given level of superhelicity ., SIBZ was developed by modifying the SIDD algorithm to treat the B-Z transition , as described in the following section ., Several other theoretical strategies have been developed or proposed for analyzing superhelical DNA transitions , which also might have been modified for this purpose ., Although a formally exact method has been suggested based on recursion relations , it was found to be too computationally inefficient to warrant development 43 , 44 ., So an approximate algorithm was presented in the same paper that could make base pair-specific calculations ., This method has not been made available for public use or evaluation ., An alternative exact algorithmic strategy also has been developed and presented 45 ., Although this approach could compute transition profiles ( i . e . transition probabilities for each base pair ) , it too was found to be too computationally cumbersome to be practical ., So a more efficient approximate method based on its approach was also presented ., To create SIBZ we chose to modify the SIDD approach because it has been extensively developed , optimized and implemented in this group , and it features an attractive combination of high accuracy and computational efficiency ., There have been three previous theoretical methods implemented that analyze DNA sequences to identify potential Z-DNA forming regions 35 , 46–48 ., The first method , developed by the Jovin group , seeks to identify Z-susceptible sites based solely on their sequence characteristics 46 ., The energetics of transition were not considered in this approach ., Another method , called Z-Catcher , performs a mechanical calculation , but does not consider the thermodynamic equilibrium of the system 47 ., Z-Hunt 35 , 48 uses statistical mechanics , but only calculates the propensity of each fixed region within the sequence to form a Z-helix in isolation ., Since the superhelical stresses that drive B-Z transitions couple together the transition behaviors of all base pairs that experience them , these approaches do not give information about how these competitive transitions behave in situ ., There are possible states available to a sequence of base pairs that is subject to a monomeric two-state transition ( that is , one in which any individual base pair can either be in the B-form or in the alternate state ) ., This number does not depend on whether the sequence is linear or circular ., This is the situation for the strand separation transition , in which the repeat units of both states are monomeric ., However , it does not hold for the B-Z transition because the repeat unit of Z-DNA is two base pairs ( dimeric ) , while that of the B-form is a single base pair ( monomeric ) ., We will first derive an expression for the number of Z-form states available to a linear molecule of base pairs , and then use this result to determine the number of states of a circular molecule having the same length ., Let denote the number of states available to a linear molecule comprised of base pairs experiencing the B-Z transition ., In any given state each base pair in the sequence is either a monomer ( i . e . in B-form ) or part of a dimeric pair with one of its neighbors ( i . e . in Z-form ) ., There are two possible arrangements for the first base pair in the sequence ., It can be a monomer ( i . e . in B-form ) , in which case there are ways that the rest of the sequence can be arranged ., Otherwise , it can be in a dimeric unit with the second base pair ( i . e . in Z-form ) ., In this case the disposition of the first two base pairs is determined , so there are ways to arrange the rest of the sequence ., Because these two alternatives are mutually exclusive and exhaust the possibilities , it follows that ( 1 ) Using the fact that and , one can calculate for any length from Eq ., ( 1 ) ., This recursion relation together with these initial conditions show that , the st Fibonacci number ., An excellent approximation to this number for all is given by ( 2 ) This shows that the number of possible state in the linear B-Z transition grows exponentially with sequence length , but with base equal to the golden ratio , rather than base 2 as holds for transitions in which both states have monomer units ., All the states found above for a linear molecule also are available to a circular molecule of the same length ., However , in this case it is also possible that base pairs 1 and can form a dimeric unit , provided neither is already dimerized with its other neighbor ., The number of states of the linear molecule in which neither base pair 1 nor base pair are dimerized is ., So this is the number of states of the circular molecule in which base pair 1 dimerizes with base pair ., It follows that the number of states available to the circular molecule is ( 3 ) In principle all states available to the molecule compete for occupancy ., Once the free energy associated to each state has been evaluated , the partition function may be calculated as ( 4 ) where the sum is over all states , and , where is the Boltzmann constant and is the temperature ., At thermodynamic equilibrium the available states are weighted according to the Boltzmann distribution ., That is , in the equilibrium distribution each state occurs with relative frequency ( 5 ) This means that the occupancy of states decreases exponentially as their free energies increase , so only the relatively low energy states are significantly occupied ., At equilibrium the ensemble average value of a parameter , that has value in state , is given by ( 6 ) The equilibrium probability of transition for base pair is found by averaging the parameter according to the above equation , where in any state where base pair is transformed , and in all other states ., The transition profile is the graph of vs ., It shows the probability of transition for each base pair in the sequence under the assumed conditions ., As will be shown below , this profile can change significantly as the imposed superhelix density changes ., We consider a DNA molecule containing base pairs of defined sequence , on which a superhelical density is imposed ., Here , where bp/turn is the helical twist rate for the B-form ., A state of this molecule assigns to each base pair one of two conformations , either B-form or Z- form ., This is done in a manner consistent with the dinucleotide repeat unit of Z-DNA , as described below ., The residual superhelicity in that state is the linking difference remaining to stress the molecule after the change of twist consequent on transition ., This includes the untwisting of the transformed base pairs from the right-handed B-form to the left-handed Z-form , together with a small amount of untwisting of the two stands at each B-Z junction 21 ., Thus , in a state where bases pairs are in the Z-form the residual superhelical linking difference is given by ( 7 ) Here bp/turn is the twist rate for the B-helix , bp/turn is the twist rate of the Z-helix , and is the number of runs of Z-form DNA ., ( A run of transformed base pairs is defined as a maximal segment in which all base pairs are in the non-B structure . ), The twist at a B-to-Z junction has been measured to be turns 21 ., In our current applications the superhelicity is regarded as remaining constant ., Next we determine the free energy of each state of this molecule ., This is comprised of the energy cost of the transition , plus the energy of accommodating the residual superhelicity ., The quadratic free energy associated to residual superhelicity 52 is given by ( 8 ) Here 53 , 54 , is the gas constant , and is the absolute temperature ., The energy cost of the transition includes two factors ., First , a nucleation energy for each run of Z-DNA is required to form the junctions between the B-form and the Z-form ., This has been measured to be approximately 5 . 0 kcal/mol/junction , so the nucleation energy for each Z-DNA run is kcal/mol 21 , 36 , 40 ., Second , the energy to transform the specified base pairs into the Z-DNA conformation is also needed ., The published values of the B-Z transition free energies for all 10 dinucleotide pairs are given in the second and third columns of Table 1 35 ., The bases in each dinucleotide pair must alternate , one in anti and the other in syn conformation ., As the values in the table show , the transition energy of a particular dinucleotide depends strongly on whether it is AS ( 5′ anti 3′ syn ) or SA ( 5′ syn 3′ anti ) ., A Z-Z junction occurs when adjacent dinucleotides have different anti-syn alternations , either ( AS ) ( SA ) or ( SA ) ( AS ) ., This violation is energetically costly , as shown in the last column of the Table ., Some of the energy values shown in Table 1 are calculated estimates , while others were measured experimentally 21 , 35 , 37–39 ., The energies shown can be evenly divided between the two base pairs involved , whether in a dinucleotide or in a Z-Z junction , to determine the transition free energy associated with each base pair ., It is energetically most favorable for purines and pyrimidines in the Z-form to be in the syn and the anti states , respectively ., There is a substantial energy cost for a base pair to have the opposite conformation ., Although in principle every dimeric sequence can be driven into Z-form , four ( CG , GC , CA\u200a=\u200aTG , and AC\u200a=\u200aGT ) have substantially lower transition energies than do the others ., The equilibrium distribution will be dominated either by the untransformed state or by states in which transition occurs at sequences composed of the energetically most favored dinucleotides ., The total free energy of a state that has specified base pairs comprising dinucleotide repeat units in runs of Z-form is given by ( 9 ) The precise manner in which the base pair transition energies are determined from Table 1 is described below ., We evaluate the equilibrium B-Z transition in a negatively supercoiled DNA molecule by appropriately modifying the previously developed SIDD method , which has been described elsewhere 45 , 49 , 51 , 55 ., Briefly , one first finds the lowest energy state of the system , whose free energy is denoted by ., Then one sets an energy threshold , and finds all states whose free energy does not exceed ., This is done by an exhaustive enumeration procedure 45 , 49 ., From these states one calculates an approximate partition function and the equilibrium ensemble average values of all quantities of interest ., Since the population of a state at equilibrium decreases exponentially as its free energy increases , the states that are neglected because their energies exceed the threshold are occupied with very low frequencies ., If , for example , the threshold is set at kcal/mol , as is used below , the neglected states are occupied no more than times the frequency of the lowest energy state at K . A careful density of states calculation was performed to assess the aggregate influence of the neglected high energy states , and to approximately modify the calculated ensemble averages to account for them 49 , 51 ., Although this correction could not be performed on the transition probabilities of individual base pairs , this approach showed that all other ensemble averages were accurate to between four and five significant figures ., Subsequently , we developed an exact , but very slow algorithm that performs these calculations 50 ., By comparing its results with those of SIDD at different energy thresholds it was determined that , when thresholds in the range 10 to 12 kcal/mol were used , the accuracy of all calculated parameters , including the transition probabilities of individual base pairs , exceeds four significant digits 45 ., Since there is a substantial nucleation energy kcal/mol associated with opening each run 21 , 36 , 40 , states with numerous Z-runs will be correspondingly less populated ., This allows us to impose a cutoff on the number of runs that may occur ., In the SIDD analysis of strand separation it was found that three run states were the most that were encountered at any reasonable energy threshold and superhelix density ., However , sequences that are energetically most susceptible to the B-Z transition tend to be shorter than the A+T-rich regions that favor denaturation ., As shown by the energies in Table 1 , there are many very costly types of “imperfections” which may hinder the extension of a Z-DNA run ., So in the B-Z transition it can be energetically less expensive to initiate a new run at another favorable site than to extend an existing run into an energetically unfavorable region ., Extensive sample calculations performed in tuning the SIBZ algorithm have shown that limiting consideration to states with a maximum of four runs is sufficient for high accuracy at the superhelical densities and sequence lengths of interest in this paper ., One important step in the SIBZ algorithm is the assignment of transition energies to the dinucleotide repeat units in each Z-run ., This is complicated because the transition energy associated to each unit can have any of three significantly different values depending on the anti and syn characters of the base pairs in that unit and in its neighbors ., Briefly , within each unit one base pair must be in syn and the other in anti ., Also there is a significant energy penalty assessed in cases where there are Z-Z junctions with one or both neighbors ., In the SIBZ algorithm we use the following procedure to assign the energetically most favorable anti or syn conformations to all base pairs in a potentially Z-forming run ., First , since the unit cell of Z-DNA is a dinucleotide , we allow only an even number of base pairs in any Z-run ., We assign all purines in the run to be syn and all pyrimidines to be anti , as the dinucleotides with the lowest transition energies have this character ., ( See Table 1 . ), Since in most cases forming a Z-Z junction is more costly than flipping a base pair into its non-favorable conformation , we next change the conformation of a base pair if it has the same anti or syn character as both of its nearest neighbors ., This procedure eliminates any repetition of the same conformation ( anti or syn ) longer than two base pairs ., Then we search for quartets of the form AASS or SSAA ., In these we flip the central two bases so that an alternating syn-anti character is obtained ., Finally , when two bases within a dinucleotide unit have the same conformation , which is not permitted in Z-DNA , we flip the base pair which yields the minimum Z-Z junction energy with its neighbor ., There is an ambiguity in this procedure for internal runs of even length at least four , such as ASSSSA , due to the order in which they are flipped ., However , the states involved are always high energy because they will have at least one Z-Z junction as well as at least two unfavorable dinucleotides ., To determine the B-Z transition energies of a region , we scan its sequence by dinucleotide units , adding energies of syn-anti or anti-syn pairs according to Table 1 ., Whenever the alternating anti-syn character is disturbed we add the appropriate Z-Z junction energy ., Also , we set the minimum length of a Z-run to be eight base pairs , as shorter regions have not been seen experimentally to form Z-DNA 21 ., In this way we assign a B-Z transition energy to each segment in the DNA sequence of even length between 8 and 250 base pairs , which is a reasonable maximum cutoff for a Z-run length ., The algorithmic strategies for finding the lowest energy state , identifying all states that satisfy the threshold condition , and analyzing them to determine equilibrium values of parameters of interest are the same as those developed previously in the SIDD algorithm 45 , 49 ., We treat circular sequences as described there ., A linear sequence is circularized by joining its end to its start with 50 T bases , and the resulting sequence is treated using the same method ., In this case we do not report the information for the augmenting segment ., These issues and methods have been described previously 45 , 49 , 51 ., To assess its performance characteristics , we used SIBZ to analyze the pBR322 plasmid ( bp ) using an energy threshold of kcal/mol ., At superhelical density this analysis took 0 . 12 minutes to run on a MacBook Pro with dual Intel processors ., On average there were 23 . 9 Z-form base pairs and 2 . 3 runs of transition ., A total of 15 , 829 , 349 states were found to satisfy the energy cutoff condition ., At superhelical density this analysis took 2 . 25 minutes to run on the same machine ., In this case there were averages of 44 . 2 Z-form base pairs and 3 . 9 runs of transition , and 1 , 047 , 067 , 293 states satisfied the energy cutoff condition ., We find that the execution time is almost constant at superhelix densities , and increases quadratically thereafter ., The algorithm scales approximately linearly with sequence length ., These performance characteristics suggest that the SIBZ analysis of the complete human genome at would take approximately 12 hours on a 100 CPU cluster of slightly faster ( viz . Opteron ) processors , if the sequence was partitioned into 5 kb segments that were analyzed individually ., A similar analysis at would take approximately ten days ., We note that there is substantial variability of execution speed depending on the attributes of the sequence being analyzed ., SIBZ executes quickly on sequences that have one dominant Z-susceptible site ., However , the analysis under identical conditions of a sequence in which there is substantial competition among numerous sites can take up to ten times longer ., Z-Hunt was the first algorithm to predict Z-forming regions in DNA sequences based on energy considerations 35 ., Although the original version only accepted sequences shorter than 1 Mbp , recently Z-Hunt was implemented to identify potential Z-forming regions in longer sequences , and specifically in the human genome 48 ., In both versions of Z-Hunt a series of fixed length segments within a sequence are separately tested for their Z-forming potential ., This is done by inserting the segment in a standard background , which is a circular plasmid in which the inserted segment is the only site that can undergo a structural transition ., Z-Hunt then calculates the propensity of the segment to form Z-DNA under these standardized conditions ., A Z-score is assigned to each segment by comparing its ability to adopt Z-form with those of a collection of randomly generated sequences ., Unlike in SIBZ where we assign a superhelical density , Z-Hunt bases its Z-score on the superhelix density at which onset of transition occurs in this standard background ., So there is no direct relationship between a segments Z-score and its probability of transition at a specific superhelix density ., Z-Hunt also provides no information about the competition among multiple Z-susceptible regions within the sequence ., Z-Catcher uses a different approach to identify sites with Z-forming potential 47 ., This algorithm includes a superhelix density as one of its inputs 47 ., It treats the B-Z transition as a simple binary , “on-off” process at a single site ., A critical threshold superhelix density is calculated for each individual segment of the sequence being analyzed , at which the energy required by the B-Z transition of that site exactly balances the stress energy released from this transition when it occurs alone in a standard background ., If the input superhelix density is more negative than this critical , the region is said to be Z-forming ., Its output is a list of predicted Z-forming sites , with no weight or probability assigned to them ., Z-Catcher analyzes individual sites as though complete transition at that site is the only possibility ., No consideration is given to how each site competes with all other sites having Z-forming potential within the rest of the sequence ., This algorithm is purely mechanical; it does not analyze the equilibrium behavior of the sequence ., SIBZ is the only method developed to date that analyzes the fully competitive B-Z transition behavior of DNA sequences in situ at thermodynamic equilibrium under any level of negative superhelicity ., It is the only approach that calculates the equilibrium probabilities of transition for each base pair under the given conditions ., This provides a more realistic and rigorous analysis , and enables more direct comparisons to be made between its predictions and experimental results than are possible with either Z-Hunt or Z-Catcher ., The B-Z transition behavior of susceptible regions within a DNA sequence can vary in complicated and highly interdependent ways ., This complexity arises because superhelical stresses globally couple together the transition behaviors of all base pairs that experience them ., When one region undergoes transition , the change of twist involved fractionally relaxes the level of stress experienced by all other base pairs in the domain ., This can be seen from Eq ., ( 7 ) , where a change in the number of transformed base pairs causes a corresponding change in the residual superhelicity experienced by the entire domain ., In consequence , the transition behavior of each base pair is affected by the transformation of any other base pair ., This global coupling is the primary reason why superhelical transitions cannot be understood by studying individual sites in isolation , but must be considered in their actual context ., This competition between different Z-forming regions within a sequence can lead to a rich repertoire of complex , interactive behaviors ., We illustrate this with sample calculations on a designed sequence containing two regions susceptible to Z-DNA formation ., This sequence consists of 5000 T base pairs , into which we insert two Z-susceptible regions at distant locations ., The thymidine background is chosen to insure that only our inserted segments are susceptible to Z-formation ., The first insert is a segment while the second is a segment that contains six Z-Z junctions ., The segment is less costly to transform , because the Z-Z junctions in the segment are energetically expensive ., However because it is shorter , transition at the segment also relieves less superhelicity ., We used SIBZ to calculate the probability of transition of each of these regions over a range of negative superhelix densities ., Plots of these probabilities as a function of are shown in Fig ., 1 . Just beyond the onset of transition where is still small , the superhelical free energy of the untransformed state also is relatively small ., Under these circumstance the energy relief afforded by transition is less than it is at more extreme superhelicities ., So in this regime the magnitude of the transition energy is the dominant factor in determining which regions transform ., This is why the shorter but energetically less costly Insert 1 is the first to transform , as shown in the figure ., As increases the superhelical free energy becomes quadratically larger ., Now transitions at longer sequences become more desirable because they relieve more superhelical stress energy ., Under these circumstances the difference in transition free energy due to the ZZ-junctions becomes less important than the benefit afforded by transforming a substantially longer segment ., For this reason a coupled transition-reversion event occurs around , in which transition of Insert 2 is coupled to the reversion of Insert 1 back to B-form ., In the range it is energetically too costly for both segments to transform to Z-DNA simultaneously , so such states occur infrequently at equilibrium ., Transition of the long Insert 2 has caused substantial relaxation , which decreases the residual superhelicity felt by Insert 1 below the value that would drive it to transform ., So at these stress levels the probability of transformation of Insert 1 drops to near zero ., As increases beyond the point where Insert 2 has a high probability of being entirely in Z-form , the additional stress accumulates as negative residual superhelicity ., When this reaches a sufficient level Insert 1 again transforms to Z-DNA ., Beyond both inserts have high probabilities of simultan | Introduction, Methods, Results, Discussion | We present a method to calculate the propensities of regions within a DNA molecule to transition from B-form to Z-form under negative superhelical stresses ., We use statistical mechanics to analyze the competition that occurs among all susceptible Z-forming regions at thermodynamic equilibrium in a superhelically stressed DNA of specified sequence ., This method , which we call SIBZ , is similar to the SIDD algorithm that was previously developed to analyze superhelical duplex destabilization ., A state of the system is determined by assigning to each base pair either the B- or the Z-conformation , accounting for the dinucleotide repeat unit of Z-DNA ., The free energy of a state is comprised of the nucleation energy , the sequence-dependent B-Z transition energy , and the energy associated with the residual superhelicity remaining after the change of twist due to transition ., Using this information , SIBZ calculates the equilibrium B-Z transition probability of each base pair in the sequence ., This can be done at any physiologically reasonable level of negative superhelicity ., We use SIBZ to analyze a variety of representative genomic DNA sequences ., We show that the dominant Z-DNA forming regions in a sequence can compete in highly complex ways as the superhelicity level changes ., Despite having no tunable parameters , the predictions of SIBZ agree precisely with experimental results , both for the onset of transition in plasmids containing introduced Z-forming sequences and for the locations of Z-forming regions in genomic sequences ., We calculate the transition profiles of 5 kb regions taken from each of 12 , 841 mouse genes and centered on the transcription start site ( TSS ) ., We find a substantial increase in the frequency of Z-forming regions immediately upstream from the TSS ., The approach developed here has the potential to illuminate the occurrence of Z-form regions in vivo , and the possible roles this transition may play in biological processes . | We present the SIBZ algorithm that calculates the equilibrium properties of the transition from right-handed B-form to left-handed Z-form in a DNA sequence that is subjected to imposed stresses ., SIBZ calculates the probability of transition of each base pair in a user-defined sequence ., By examining illustrative examples , we show that the transition behaviors of all Z-susceptible regions in a sequence are coupled together by the imposed stresses ., We show that the results produced by SIBZ agree closely with experimental observations of both the onset of transitions and the locations of Z-form sites in molecules of specified sequence ., By analyzing 12 , 841 mouse genes , we show that sites susceptible to the B-Z transition cluster upstream from gene start sites ., As this is where stresses generated by transcription accumulate , these sites may actually experience this transition when the genes involved are being expressed ., This suggests that these transitions may serve regulatory functions . | physics, biophysics/theory and simulation, computational biology, mathematics/statistics, biophysics | null |
journal.ppat.1002549 | 2,012 | Transcriptional Activation of the Adenoviral Genome Is Mediated by Capsid Protein VI | DNA viruses require the transport of their genome into the nucleus to initiate replication ., Cells perceive the introduction of foreign nucleic acids or unscheduled replication as danger signals and activate a DNA damage response that leads to cell cycle arrest and/or apoptosis ., To ensure proper replication , DNA viruses express ‘early’ viral genes to degrade or displace key regulators of cellular antiviral machinery ., In return , cells repress incoming viral genomes through a network of transcriptional repressors and activators that normally control cellular homeostasis reviewed in 1 , 2 ., The nuclear domains thought to be responsible for repressing viral genomes are ND10 or promyelocytic nuclear bodies PML-NBs; reviewed in 3 , 4 named after the scaffolding PML protein ., PML-NBs are interferon inducible , dot-like nuclear structures associated with proteins with transcriptional repressive functions ., These include HP-1 , Sp100 , ATRX and Daxx summarized in 4 , 5 ., Daxx ( death domain associated protein ) was first described as a modulator of Fas-induced apoptotic signaling 6 ., When chromatin-bound , Daxx inhibits basal gene expression from various promoters by binding to transcription factors ( e . g . p53/p73 , NF-kappaB , E2F1 , Pax3 , Smad4 or ETS1 ) , ATRX , histone deacetylases and core histones to form a repressive chromatin environment 7–13 ., In contrast , Daxx localization to PML-NBs reduces its repressive capacity and facilitates apoptosis through p53 family members 5 , 7 , 14 ., PML-NBs are found in close proximity to replication centers of DNA viruses ( e . g . adenoviruses ( Ads ) , herpes simplex virus ( HSV-1 ) , human cytomegalovirus ( HCMV ) and human papillomavirus HPV; 15 , 16–18 ., Gene expression from these viruses is repressed via the PML-NBs , suggesting a role in antiviral defense 19–22 ., To counteract genome repression , viral genome activation involves PML-NB disruption or degradation of Daxx , Sp100 and/or PML via different mechanisms ., HCMV gene expression is initiated by proteasomal degradation of Daxx via tegument protein pp71 of the incoming particle 23 ., Early HSV-1 gene expression requires PML degradation , mediated by the virus encoded ubiquitin ligase ICP0 ., Furthermore , in order to activate viral gene expression , transcriptional repression by Daxx and ATRX needs to be relieved 3 , 24 , 25 ., HPV early gene expression is supported by reorganization of PML-NBs through the minor capsid protein L2 26 ., At the beginning of infection , Ads express the immediate early protein E1A from the E1A promoter ., E1A binds and displaces the transcriptional repressor Rb from E2F transcription factors ., This results in the auto-stimulation of E1A expression and the activation of the downstream viral expression units E1B , E2 , E3 and E4 as well as promoting cellular gene expression ., The early E1B-55K protein forms a SCF-like E3-ubiquitin ligase complex with the viral E4orf6 and several cellular factors ., This complex degrades factors ( for example , factors of the DNA damage response ) to ensure progression of the replication cycle summarized in 1 , 2 , 27 ., E1B-55K protein complex also targets Daxx for proteasomal degradation counteracting its repressive effect 21 ., In contrast to HSV-1 , PML is not degraded by Ads but relocalized into track-like structures through the E4orf3 protein 28 , 29 ., Despite the well-characterized mechanism of E1A dependent transactivation of early Ad genes , it is unclear how the E1A transcription is efficiently initiated before other viral genes are expressed ., The genome enters the cell as a transcriptionally inactive nucleoprotein complex , which is highly condensed by the histone-like viral protein VII inside the capsid shell ., Partial disassembly of the endocytosed capsid releases the endosomolytic internal capsid protein VI , permitting endosomal membrane penetration 30 , 31 and transport towards the nucleus ., After import through the nuclear pore complex , Ad genomes associate with PML-NBs and replication centers are established 30 , 31 , reviewed in 32 , 33–35 ., Endosomal escape and subsequent transport are facilitated by Nedd4 ubiquitin ligases , which are recruited through a conserved PPxY motif in protein VI ., Ads with mutated PPxY motif do not bind Nedd4 ligases and have reduced infectivity , showing the importance of this interaction for the onset of gene expression from the viral genome 36 ., Here we report that Ad capsid proteins and cytoplasmic entry steps are linked to initiation of the adenoviral E1A expression by counteracting Daxx mediated transcriptional repression ., Using the Ad system , we further show that capsid proteins from several other DNA viruses share and complement this function ., This suggests a conserved mechanism among DNA viruses and provides insights into the very early virus-host interactions required to establish an optimal cellular environment for productive infection ., The capsid protein VI participates in two crucial steps in the nuclear delivery of the Ad genome ., Firstly , protein VI is required for lysis of endosomal membranes ., Secondly , it is needed for efficient post-endosomolytic transport , mediated by the cellular ubiquitin ligase Nedd4 that binds to a conserved PPxY motif in protein VI ., Mutating the PPxY motif interferes with capsid transport toward the nucleus and efficient viral gene expression 30 , 36 ., To investigate the role of protein VI during post-endosomolytic steps required for the onset of viral replication , we constructed replication competent Ads containing the E1 region with either wildtype ( wt ) protein VI ( HH-Ad5-VI-wt , depicted in the Figure S1 ) or mutant “M1” protein VI in which the PPSY motif was mutated to PGAA that abolished Nedd4 interaction HH-Ad5-M1; Fig . S1; 36 ., Following infection of U2OS cells , we observed that M1 virus replication was attenuated compared to wt ( Figure 1A and S1B ) ., This is in agreement with our previous observations showing reduced infectivity of an E1-deleted M1 Ad vector compared to the corresponding E1-deleted wt Ad vector 36 ., To distinguish between capsid transport and possible more downstream effects , we infected cells with different amounts of replication competent wt and M1 viruses ., Then , we determined the genome copy numbers in nuclear and cytoplasmic fractions by qPCR and the efficiency of the initiation of virus replication by quantification of E2A stained replication centers ( detailed in Figure S2 ) ., Compared to wt , fewer M1 virus genomes accumulated in the nucleus associated fraction , independent of the amount of input virus ., In contrast , initiation of virus replication for M1 genomes was reduced for low , but not at high physical particle per cell ratios ( Figure S2 ) suggesting defects downstream of virus nuclear transport ., Therefore , the expression of the early viral proteins E1A , E1B-55K and E2A in wt and M1 infected cells was analyzed by western blot , starting 8 h post infection ( p . i . ) and throughout the whole replication cycle ( Figure 1B , left panel ) ., We observed that expression of E1A in M1 virus infected cells was reduced compared to wt ( Figure 1B , right panel ) and accordingly , all other gene products were expressed with a delayed kinetic ., This observation can be explained by the initial lower levels of E1A expression , because E1A is required for full activity of Ad downstream promoters 37 ., Thus , we next investigated if the reduced E1A protein expression in M1-infected cells was due to reduced transcriptional activation of the E1A promoter following infection ., We isolated and quantified newly synthesized E1A mRNA from cells infected with wt and M1 virus starting as early as 1–2 h p . i . ( Figure 1C ) ., The results confirmed that , at 1–4 h p . i . , M1-infected cells showed reduced levels of newly synthesized E1A mRNA compared to wt-infected cells ., Interestingly this reduction was gradually compensated throughout the first hours of infection ( Figure 1C , compare 1–2 h , 3–4 h and 5–6 h ) suggesting that low levels of initially made E1A were sufficient to compensate for the M1-defect in E1A transcription ., The high particle per cell ratio requirement for transcriptional activation and the reduced levels of E1A mRNA and E1A protein expression for the M1 virus indicated that the PPxY motif in protein VI not only affects transport towards the nucleus , but also early viral gene expression , presumably through separate mechanisms ., We previously showed that protein VI contains nucleo-cytoplasmic transport signals 38 ., To test if protein VI could play a direct role in the initial activation of the viral genome , we first analyzed whether protein VI from incoming Ad capsids is imported into the nucleus ., Using nucleo-cytoplasmic fractionation , we observed rapid protein VI accumulation in the nuclear fraction after infection ( Figure 2A ) ., Fractionation does not discriminate between nuclear ( inside ) or nucleus-associated ( outside ) accumulation of protein VI ( e . g . capsid-associated at the microtubule organizing center ) ., Thus , we investigated the subcellular localization of protein VI derived from entering viral particles by confocal microscopy in synchronous infected cells ., Within one hour , we observed protein VI specific signals in dot-like structures inside the nucleus for wt- and the M1-virus ., Using antibodies ( Ab ) against PML , we showed some protein VI associated with PML-NBs ( Figure 2B ) ., We confirmed the association of some protein VI with PML-NBs in a virus free system by transfecting protein VI-mRFP alone or together with EGFP-PML expressing plasmids into U2OS cells ., Transfected proteins were detected via the mRFP and EGFP signal or with specific Ab for endogenous PML ( “endogenous” highlighted throughout the text and in figures by the suffix “e” , e . g . ePML ) ., The results show that protein VI was able to independently associate with PML-NBs ( Figure 2C ) ., Using a serie of protein VI mutants , we mapped the region of protein VI required for PML-NB association ( Figure S3 ) ., This analysis revealed that the N-terminal amphipathic helix was required for efficient PML-NB targeting , because a mutant ( VI-delta54 ) deleted of the amphipathic helix showed a diffuse nuclear distribution ( Figure S3 ) ., We repeatedly observed the clustering of PML in transfected cells , suggesting PML-NB structure modulation resulting from protein VI expression ., In summary , these data showed that some protein VI from incoming Ad particles is targeted into the nucleus , where some of it consistently localizes adjacent to PML-NBs , suggesting an involvement in additional intranuclear steps ., It was recently reported by some of the co-authors of this work that the transient PML-NBs resident factor Daxx suppressed Ad replication and was degraded late in the infection cycle 21 ., The observation that some protein VI was associated with PML-NBs prompted us to investigate whether PML itself , or PML-NB-associated factors such as Daxx , interact with protein VI ., These interactions could provide an explanation for the reduced transcription of the E1A promoter observed for the M1 virus ., Cells were infected with HH-Ad5-VI-wt or -VI-M1 and harvested after 24 h ., Lysates were subjected to immunoprecipitation ( IP ) using PML or Daxx specific Ab and analyzed by western blot ( Figure 3A ) ., The data showed that protein VI could be precipitated from both wt and M1 infected cells using either PML or Daxx specific Ab ., In contrast to virus infected cells , we did not detect co-precipitated protein VI following cotransfection and IP with different PML isoforms , suggesting an indirect association of PML and protein VI , presumably bridged by other viral or infection induced factors ( Figure 3B ) ., In contrast , co-IP of protein VI with Daxx also occurred after isolated transfection of protein VI-wt as well as protein VI-M1 suggesting that the interaction is independent of other viral factors ( Figure 3C ) ., We next asked whether Daxx interaction with protein VI could explain the reduced replication of HH-Ad5-VI-M1 ., For these assays , we used the hepatoma derived cell line HepaRG , because of its close resemblance to primary cells 39 , and HepaRG cells depleted of Daxx ( HAD , Daxx was depleted with shRNA expressing lentiviral vectors 20 ) ., We infected Daxx-depleted HAD and HepaRG parental cells with HH-Ad5-VI-wt and HH-Ad5-VI-M1 and determined virus yields and gene expression at 12 , 24 and 72 h p . i . ( Figure 3 ) ., The M1 virus was more strongly attenuated in HepaRG cells than in U2OS cells ( compare to Figure 1 ) , while Daxx depletion strongly enhanced virus production for both viruses and nearly restored the M1 virus yields to wt levels ( Figure 3D ) ., This improvement of Ad permissivity was confirmed by an increase of expression of all analyzed viral genes , including gene products from the E1A and E1B promoters ( Figure 3E ) ., The data showed that Daxx depletion was sufficient to increase Ad gene expression for both viruses , emphasizing the role of Daxx in viral genome repression ., In addition , wt but not M1 mutant protein VI could counteract Daxx mediated inhibition indicating that the PPxY motif of protein VI plays a significant role in initiating viral gene expression ., Next , we asked whether the Ad immediate early E1A and early E1B promoters are targeted by Daxx mediated repression and if this is the case whether it can be reversed by protein VI ., To this end , we constructed luciferase expression vectors controlled by the Ad E1A and E1B promoters and measured luciferase expression in protein VI-wt or protein VI-M1 transfected H1299 cells ( Figure 4A ) ., Unlike VI-M1 , VI-wt was able to stimulate expression from the E1A promoter ∼2 . 5-fold and ∼1 . 5-fold from the E1B promoter ( Figure 4A ) ., To show direct association of protein VI with E1 promoters , we performed chromatin immunoprecipitation assays ( ChIP ) at 48 h p . i from M1- or wt virus infected cells , using protein VI specific serum and Ad promoter-specific primers ( Figure 4B ) ., The results show that the VI-wt protein was much more strongly associated with the E1A and E1B promoter in infected cells than the VI-M1 protein , which is also reflected in their relative activation ability ( Figure 4B , compare with 4A ) ., To analyze whether protein VI associated activation of Ad early promoters is involved in Daxx de-repression , we cotransfected the E1B promoter driven luciferase expression vector in absence or presence of Daxx with protein VI-wt or VI-M1 expression vectors ., Protein VI-wt , but not VI-M1 , alleviated Daxx repression implying a role for the PPxY motif ( Figure 4C ) ., Although there was less binding to protein VI compared to the E1A promoter , we observed a strong effect on the activation of luciferase expression in that experiment ., We also tested if protein VI ( wt or M1 ) stimulates other Ad promoters using luciferase expression vectors for all viral promoters ., The data showed that protein VI-wt was able to stimulate most of the Ad promoters in absence of other viral factors to various degrees ( Figure S4 ) ., The strongest induction was observed for the immediate early E1A and E2A early promoter , which is in agreement with the weak E2A expression observed in HepaRG cells in M1-virus infected cells and the restoration of E2A expression following Daxx depletion ( see Figure 3E ) ., In contrast , E3 and E4 promoter activation was weak with no clear difference between wt and M1 ., In the context of an ongoing virus infection , the transcriptional activation of both promoter groups ( E1/E2 vs . E3/E4 ) was shown to be regulated by E1A but via different mechanisms 40 , 41 ., Thus , our data showed that protein VI might also play a minor role in the transcriptional activation of the E1/E2 promoter group ., Altogether , the promoter analysis suggests that protein VI plays a so far not recognized role in the Ad gene expression program ., We next asked how the PPxY motif of protein VI contributes to Daxx de-repression ., In previous work , we showed that this motif mediates protein VI interaction with cytoplasmic Nedd4 ubiquitin ligases 36 ., Overexpression of protein VI and/or Nedd4 did not result in a change of steady-state Daxx levels ( data not shown ) suggesting that de-repression was not achieved through Daxx degradation as e . g . as shown for HCMV ., However , when we tested if protein VI targets Nedd4 ligases to PML-NBs our analysis showed that protein VI-wt , but not VI-M1 targets Nedd4 ligases towards PML-NBs ., This targeting required the PPxY motif and the amphipathic helix , but was independent of catalytical Nedd4 activity suggesting that Nedd4 ligases could be involved in other steps of counteracting Daxx repression by protein VI ( Figure S5 ) ., As a next step , we therefore analyzed whether the subcellular distribution of Daxx was altered in response to protein VI and Nedd4 expression ., In non-transfected cells , endogenous Daxx ( eDaxx ) is nuclear in steady state with some Daxx localizing to dot-like intranuclear structures resembling PML-NBs ( Figure 5a ) ., When we transfected expression vectors for protein VI-wt or VI-M1 into U2OS cells , nuclear localization of eDaxx was lost and eDaxx colocalized with transfected protein VI in the cytoplasm ( Figure 5b and e ) ., In contrast , following transfection of expression vectors for protein VI-wt and Nedd4 ligases , eDaxx remained nuclear and instead protein VI-wt colocalized with Nedd4 ligases in the cytoplasm ( Figure 5c ) ., When we transfected expression vectors for Nedd4 ligases and protein VI-M1 , protein VI retained the capacity of translocating eDaxx to the cytoplasm ( Figure 5f ) ., These data suggested that binding of Nedd4 to the PPxY motif of protein VI efficiently competed with protein VI-dependent cytoplasmic translocation and/or cytoplasmic retention of Daxx ., This effect did not require Nedd4 ubiquitin ligase activity ( Figure 5d ) ., Thus , our results suggested that the PPxY motif present in wt protein VI could influence the dynamic nucleo-cytoplasmic distribution of Daxx ., To continue our analysis in a more physiological setting , we analyzed the subcellular localization of Daxx during Ad entry ( Figure 6 ) ., In uninfected control cells , Daxx localized to the nucleoplasm and into PML-NBs ., Within the first hour of infection , Daxx remained largely nuclear in wt- as well as M1-virus infected cells ., Occasional cytoplasmic Daxx was never virus particle-associated ., In contrast to non-infected cells , we observed a trend towards intranuclear displacement of Daxx from PML-NBs and PML clustering following infection ( Figure 6A , red arrows ) , which could be clearly distinguished from Daxx spots in uninfected cells ., This suggests that incoming viruses displace Daxx from PML-NBs by a mechanism independent of the PPxY motif of protein VI and prior to initial viral gene expression ., Because we noticed occasionally large PML-NBs in infected cells , we next quantified the number of PML-NBs in wt- and M1-infected cells compared to non-infected cells ., The results showed that on average , infected cells had less PML-NBs than non-infected cells , supporting our observation that PML-NBs were clustering ( Figure 6B ) and that the effects where PPxY motif independent ., To show that the Daxx displacement from PML-NBs in the very early infection phase was caused by protein VI , we analyzed Daxx dissociation from PML-NB also in VI-wt and VI-M1 transfected cells ( Figure S6 ) ., Compared to non-transfected cells , expression of protein VI-wt or VI-M1 led to translocation and cytoplasmic colocalization of Daxx ( as seen in Figure 5 ) ., In addition , in several cells , Daxx was partially or completely displaced from PML-NBs and PML formed large nuclear clusters similar to those observed in infected cells ( Figure S6 , red arrows ) ., We also transfected cells with expression vectors for HCMV pp71 tegument protein , known to interact with Daxx 42 ., Unlike for protein VI , in pp71 transfected cells , Daxx remained nuclear and localized to some degree with PML into pp71 induced , ring-like structures also partially displacing Daxx from PML-NBs ( Figure S6 ) ., To directly follow Daxx displacement from PML-NBs and from the nucleus , we used microinjection of recombinant protein VI ( Figure 7 and Videos S1 , S2 , S3 ) ., We transfected U2OS cells with Daxx-mCherry and PML-GFP expression constructs , and injected the cytoplasm with either control buffer , recombinant VI-wt or with recombinant VI-M1 ( Figure 7B ) and followed the distribution of Daxx-mCherry using live-cell imaging ( Figure 7A ) ., Daxx-mCherry was exclusively localized to the nucleoplasm and PML-NBs , while PML-GFP showed an intranuclear dot-like distribution with some cytoplasmic aggregates at higher levels of expression ., Cytoplasmic injection of protein VI-wt or VI-M1 led to displacement of Daxx from PML-NBs and cytoplasmic accumulation of Daxx within minutes of injection ( Figure 7A , first and second row compared to buffer controls in the last row ) ., We quantified the cytoplasmic accumulation of Daxx by measuring nuclear Daxx fluorescence loss following microinjection ., This quantification revealed that Daxx nuclear export occurred more rapidly post injection of protein VI-wt than VI-M1 , suggesting that the PPxY motif accelerated the process of Daxx displacement ( Figure 7C ) ., Notably , Daxx displacement was paralleled by a strong increase in intranuclear mobility of PML-GFP and by fusion events between individual bodies ( Videos S1 and S2 ) , thus providing evidence that the large clustered PML-NBs , observed in fixed cells , result from the mobilization of Daxx out of the bodies ., We also microinjected recombinant protein VI ( VI-delta54 ) , lacking the amphipathic helix required for PML-NB targeting of protein VI , to see whether PML-NBs association is required for Daxx displacement ., In contrast to protein VI-wt and VI-M1 , injection of VI-delta54 only transiently displaced Daxx from PML-NBs and did not result in Daxx cytoplasmic translocation ( Figure 7A third row and Video S3 ) ., The Daxx residence time in PML-NBs is ∼2 seconds 43 ., Therefore our observation could be explained by competitive binding of VI-delta54 to Daxx , which could transiently prevent Daxx from association with PML-NBs ., In summary , these data strongly suggested that protein VI from incoming adenoviral capsids can displace Daxx from PML-NBs , which in turn affects the PML-NB architecture leading to the accumulation of PML in large intranuclear clusters ., Our analysis further indicate that association of protein VI with PML-NBs through the amphipathic helix is not strictly required for Daxx displacement from PML-NBs and that the PML-NB rearrangements take place prior to or are concomitant with the initiation of adenoviral transcription ., Our data showed that protein VI activates the Ad E1 promoters by reversing Daxx repression , presumably until newly synthesized E1A can secure the Ad gene expression program ., In this case , virion proteins derived from other DNA viruses known to abrogate Daxx repression should be able to substitute this function ., To test this possibility , we tested whether the expression from the E1A promoter can be activated by the HCMV pp71 tegument protein or by the HPV L2 minor capsid protein , which both target Daxx 26 , 44 ., Similar to protein VI-wt , pp71 and L2 were able to stimulate the Ad E1A promoter ( Figure 8A ) ., Furthermore , we observed that like protein VI-wt , pp71 and L2 could also drive efficient E1A and E1B expression from a subviral construct , preserving the virus context encoding the E1A and E1B transcription units ( Figure 8B , lane 3 , 6 and 7 ) ., These results show that non-adenoviral virion proteins are also capable of inducing immediate early adenoviral gene expression in the absence of any further Ad protein ., This induction of gene expression was through mediating transcriptional activation , as shown by elevated E1A and E1B mRNA levels ( Figure 8C ) ., Similarly , this result confirmed that elevated E1A mRNA and protein expression levels driven by protein VI require the PPxY motif , thus directly linking entry and early viral gene expression ( Figure 8B , lanes 1–4 ) ., To extend the analysis for other regions of protein VI , we used the expression construct encoding protein VI-delta54 , lacking the amphipathic helix , which is required to target protein VI to PML-NBs ( Figure S3d ) ., The results showed that like protein VI-M1 , the construct expressing VI-delta54 only marginally stimulated the E1A promoter ( compare wt- , M1 and delta54 in Figure 8A and C ) ., In contrast , the expression of protein VI-delta54 resulted in somewhat elevated protein expression levels compared to VI-M1 suggesting that it might promote E1A expression on a post-transcriptional level ., This could result from the diffuse localization of VI-delta54 in the nucleoplasm of transfected cells ( compare with Figure S3 ) ., In summary , this analysis showed that efficient transcriptional activation of the E1A promoter requires the amphipathic helix in addition to the PPxY motif ., If the HCMV tegument protein pp71 , that is known to remove Daxx repression from the immediate early HCMV promoter 45 , activates the Ad E1A promoter , it was conceivable to speculate that protein VI would also be able to stimulate the immediate early HCMV promoter ., To test this hypothesis , we constructed viral vectors encoding wt- or M1-mutated protein VI where the E1 region was replaced by a HCMV promoter controlled GFP ( wt ) or mCherry ( M1 ) expression unit ., We transduced U2OS cells with M1-vectors and increasing amounts of wt virus and quantified gene expression using fluorescent activated cell sorting ., The results showed partial restoration of the ( HCMV promoter controlled ) marker gene expression from VI-M1 vector transduced cells only in cells that were co-transduced with the M1-vector and the wt-vector ( Figure S7 ) ., This analysis suggested that protein VI stimulated the HCMV promoter in trans , like pp71 could stimulate the Ad E1A promoter in trans ( Figure S7 ) ., Taken together the effects that protein VI has on the E1A promoter are comparable , and moreover compatible and interchangeable , with the HCMV or papillomavirus virion derived immediate early enhancing activities ., Because protein VI , pp71 and L2 can stimulate Ad E1A expression independently , we next asked if they could compensate for the lack of functional PPxY motif in the replication competent HH-Ad5-VI-M1 virus ., We transfected cells with expression vectors for protein VI-wt , VI-M1 and VI-delta54 ( Figure 9A ) and HCMV tegument protein pp71 and HPV small capsid protein L2 ( Figure 9B ) followed by infection with HH-Ad5-VI-wt or HH-Ad5-VI-M1 virus ., The analysis showed that protein VI-wt was able to fully compensate for the M1 mutation in the virus and restored progeny virus production to wt levels , while protein VI-M1 was not able to rescue virus production and VI-delta54 resulted only in partial rescue ( Figure 9A ) ., Amazingly , HCMV pp71 and HPV L2 were also fully capable of complementing the M1 mutant virus and restored progeny virus production to wt levels ( Figure 9B ) ., Lastly , we wanted to know if the adenoviral protein VI capsid protein was also able to stimulate an immediate early promoter in the context of a non-related virus infection ., We transfected U2OS cells with protein VI-wt and VI-M1 or a control vector and infected the transfected cells with a murine cytomegalovirus ( MCMV ) expressing luciferase under the control of the HCMV immediate early promoter ( MCMV-Luc ) ., Luciferase expression was measured 2 h after a synchronized infection to quantify the activation of the immediate early promoter ., The results showed that only protein VI-wt was able to stimulate immediate early promoter in the context of MCMV infection ( Figure 9C ) ., Taken together these results showed that protein VI promotes immediate early gene expression from the adenoviral E1A promoter , but it was also able to act on the immediate early gene expression of a non-related virus ., In summary , our analysis provides an intriguing mechanistic basis for cross genome activation of at least three unrelated DNA viruses ., Our data suggest that initiation of viral gene expression can be achieved in cases where the respective virion proteins of one virus are capable of removing Daxx dependent transcriptional repression from the genome of the other virus ., Here , we show that the capsid protein VI is necessary for efficient initiation of Ad gene expression by activating the E1A promoter and promoting initial expression of the E1A transactivator , a function that had not been previously identified ., E1A is a crucial global transcriptional activator promoting early adenoviral gene expression 37 ., We show that E1A transcription and E1A protein expression at the onset of viral gene expression are reduced when cells are infected with an Ad mutant in which the PPxY motif in the capsid protein VI is inactivated ., E1A mRNA production in this mutant increases with time and reaches wildtype levels , suggesting that newly expressed E1A compensates for the mutation in protein VI and drives adenoviral gene expression as soon as critical concentrations have been reached 37 ., In addition , protein VI also stimulates other E1A dependent Ad promoters in the absence of any viral protein suggesting that it may act as a capsid derived E1A surrogate prior to the onset of E1A expression ., Thus , protein VI is an important regulator of viral gene expression and links virus entry to the onset of gene expression ., This is at least in part mediated by counteracting transcriptional repression imposed by the cellular Daxx protein and can be substituted by functionally homologous capsid proteins from unrelated DNA viruses ., In the nucleus , Daxx associates with chromatin and PML-NBs ., PML-NB association with Daxx is thought to alleviate gene repression and activate apoptosis , while chromatin bound Daxx is thought to act in a transcriptionally repressive manner 7 , 46 , 47 ., A dynamic equilibrium of Daxx between PML-NBs and chromatin association may thus govern the response status of the host cell upon infection ., Moreover , an antiviral interferon response increases expression of PML and sensitizes cells for apoptosis ., Artificial knock down of PML increases replication of Ad and other viruses , an observation that supports antiviral functions of PML reviewed in 4 , 21 ., However , PML knock down also decreases Daxx steady state levels by an unknown mechanism , showing that antiviral activity might be mediated by Daxx rather than PML 21 ., This would be in line with our observation that Daxx knock down has much stronger pro-replicative effects on Ads ., Here we demonstrate that Daxx directly represses Ad E1 promoters ., So far , it has been shown that Daxx inactivates the major immediate early promoter of HCMV 45 , is recruited to HSV genomes via SUMO dependent pathways 48 and is likely to associate with incoming avian sarcoma virus ( ASV ) and human immunodeficiency virus ( HIV ) genomes 49 , 50 ., Therefore , Daxx could act as a cytoplasmic and/or nuclear DNA sensor and may be part of a cellular innate defence mechanism against DNA virus infection ( or other pathogens ) by simply assembling repressive complexes on incoming DNA 51 ., This is supported by two recent studies showing that Daxx selectively represses procaryotic DNA expression 52 and that frequent epigenetic silencing of integrated retroviral genomes could be reversed by Daxx depletion , showing epigenetic control of pathogen DNA by Daxx associated mechanisms 53 ., Daxx mutants that fail to associate with the HSV genome also fail to induce repression on the HSV genome , underlining the important role of Daxx as part of the cellular innate antiviral defence mechanism 48 ., If Daxx serves in ant | Introduction, Results, Discussion, Materials and Methods | Gene expression of DNA viruses requires nuclear import of the viral genome ., Human Adenoviruses ( Ads ) , like most DNA viruses , encode factors within early transcription units promoting their own gene expression and counteracting cellular antiviral defense mechanisms ., The cellular transcriptional repressor Daxx prevents viral gene expression through the assembly of repressive chromatin remodeling complexes targeting incoming viral genomes ., However , it has remained unclear how initial transcriptional activation of the adenoviral genome is achieved ., Here we show that Daxx mediated repression of the immediate early Ad E1A promoter is efficiently counteracted by the capsid protein VI ., This requires a conserved PPxY motif in protein VI ., Capsid proteins from other DNA viruses were also shown to activate the Ad E1A promoter independent of Ad gene expression and support virus replication ., Our results show how Ad entry is connected to transcriptional activation of their genome in the nucleus ., Our data further suggest a common principle for genome activation of DNA viruses by counteracting Daxx related repressive mechanisms through virion proteins . | To initiate infection , DNA viruses deliver their genome to the nucleus and express viral genes required for genome replication ., Efficient transport is achieved by packing the viral genome as a condensed , transcriptionally inactive nucleo-protein complex ., However , for most DNA viruses , including Adenoviruses ( Ads ) , it remains unclear how the viral genome is decondensed and how transcription is initiated inside the nucleus ., Cells control unwanted gene expression by chromatin modification mediated through transcriptionally repressive complexes ., A key factor in repressive complex assemblies is the transcriptional repressor Daxx ., The Ad structural capsid protein VI is required for endosomal escape and nuclear transport ., Here we show that protein VI also activates the Ad E1A promoter to initiate Ad gene expression ., This is achieved through the removal of Daxx repression from the E1A promoter , which requires a conserved ubiquitin ligase interacting motif ( PPxY-motif ) in protein VI ., We further show that capsid proteins from other unrelated DNA viruses also activate the Ad E1A promoter and support Ad replication by counteracting Daxx repression , functionally replacing protein VI ., Our data suggest that reversal of Daxx repression by virion proteins is a widespread mechanism among DNA viruses that is not restricted to a single virus family . | viral transmission and infection, virology, biology, microbiology, viral replication | null |
journal.pntd.0003127 | 2,014 | Viral Aetiology of Central Nervous System Infections in Adults Admitted to a Tertiary Referral Hospital in Southern Vietnam over 12 Years | Central nervous system ( CNS ) infections are important diseases worldwide ., The spectrum of infections is broad , encompassing bacterial meningitis , aseptic meningitis and encephalitis ., The estimated incidence of encephalitis worldwide is between 3 . 5 and 7 . 4 cases per 100 , 000 person years 1 ., While the clinical course of viral meningitis and encephalitis may overlap , viral meningitis is usually self-limiting 2 , whereas the mortality from viral encephalitis ranges from 4 . 6% to 29% 3–8 and nearly 50% of survivors have persistent neurological sequelae after 6 months follow up 7 ., Viruses are regarded as the most common causes of encephalitis and aseptic meningitis , and the specific viral aetiology of the diseases is diverse and dependent on geographical , temporal , host-immunity and age factors 3 , 7–10 ., The aetiology is in part driven by the introduction of immunization programs and/or the ( re ) emergence of ( new ) pathogens ., In Southeast Asia , in 1998 Nipah virus emerged in Malaysia and Singapore 11 , 12 and spread to Bangladesh where it causes annual outbreaks of fatal encephalitis 13 ., Over the last 16 years , enterovirus 71 has emerged and caused large outbreaks of hand foot and mouth disease , sometimes associated with fatal encephalitis in young children ., Likewise , Japanese encephalitis virus ( JEV ) is a leading cause of encephalitis in children and occasionally in young adults in many countries in Asia including Vietnam 9 , 14–16 ., In Vietnam , JEV vaccine was first introduced in 1997 , and had been administered to all children 1–5 years of age in 437 ( 65% ) of 676 districts by 2007 14 ., By 2008 , 91% of the target population in Vietnam had received JEV vaccination 17 ., Studies in Western countries have revealed that herpes simplex virus ( HSV ) varicella-zoster virus ( VZV ) and enteroviruses ( EVs ) are the leading causes of encephalitis/aseptic meningitis in adults 4 , 7 , 18 , whereas in two recent prospective descriptive studies in Vietnam JEV , dengue virus ( DENV ) , HSV , EVs and VZV were frequently detected in adults CNS infections of presumed viral origin 9 , 19 ., Of note , in these two studies virus diagnostic tests were limited to these 5 types , possibly underestimating the aetiological diversity of the infections ., Better understanding of the causes of the diseases is of public health importance , in order to better inform immunization policy , and may influence clinical management ., Herein we report the results of a 12 year study investigating the clinical and laboratory features of 291 HIV uninfected adults with presumed viral CNS infections admitted to a tertiary referral hospital in southern Vietnam ., The study was conducted at an infectious disease ward of the Hospital for Tropical Diseases ( HTD ) in Ho Chi Minh City , a primary , secondary , and tertiary referral hospital for infectious diseases in both children and adults for all southern provinces of Vietnam ., The hospital has 550 beds , 35 , 000 admissions annually , and serves a population of 42 million people ., Any adult with severe CNS infections in southern Vietnam is referred to HTD ., The patients who present to this hospital are therefore representative of the whole of southern Vietnam ., Patient enrolment started in December 1996 and is on-going ., The present study reports findings of the 291 consecutive patients enrolled between December 1996 and May 2008 ., All adult patients ( age ≥15 years ) presenting with clinically suspected CNS infections of presumed viral origin , based on the clinical judgment of admitting physicians , negative HIV serology , and with no evidence of purulent bacterial , eosinophilic , cryptococcal and tuberculous meningitis by CSF cell count , culture and/or microscopy , were eligible to enter the study ., Detailed demographic and clinical data , including routine blood and CSF haematology and biochemistry were collected on case record forms at enrollment and during hospitalization ., Clinical outcomes were assessed at discharge using the Glasgow Outcome Scale and was defined as death , full recovery ( no abnormality ) , and severe ( greatly affecting function , i . e . dependence ) , moderate ( deficit affecting function but not dependence ) , or minor ( abnormality detectable but not affecting function ) neurological sequelae , based on neurological examination , degree of independent functioning and controllability of seizures 20 ., For microbiological investigations , acute CSF and serum were collected at enrolment ., As part of routine care , CSF specimens of the enrolled patients were cultured and/or examined by microscopy for detection of bacterial/C ., neoformans/M ., tuberculosis infection with the use of standard methods if clinically indicated ., Eosinophilic meningitis was diagnosed by CSF examination , and defined by the presence of more than 10 eosinophils per ml of CSF or >10% of total CSF white cells ., All patients were tested for antibodies to HIV ., Between 1997 and 2004 , oral acyclovir 4 g per day was given if herpes simplex encephalitis ( HSE ) was suspected because intravenous ( IV ) acyclovir was not available in our hospital at that time ., From 2005 , patients with suspected HSE were either given oral valacyclovir 3 g per day or oral acyclovir 4 g per day ., IV acyclovir 1 . 5 g per day was only given to patients who could afford to pay for their medications pending HSV PCR results ., Irrespective of the aetiology , supportive therapy for patients with encephalitis of presumed viral infection was an important cornerstone of management ., Seizures were controlled with IV benzodiazepines , phenytoin or phenobarbital , and where necessary sedation and mechanical ventilation ., Medical management of raised intracranial pressure included elevating the head of bed , IV mannitol , and intubation with mechanical hyperventilation ., Careful attention was paid to the maintenance of respiration , cardiac rhythm , fluid and electrolyte balance , prevention of deep vein thrombosis , aspiration pneumonia , and secondary bacterial infections ., Patients were categorised as having confirmed , probable or possible diagnoses , and no aetiological agent found ( see Table 1 ) ., A confirmed diagnosis was established if viral nucleic acid or viral specific IgM was detected in CSF for JEV , DENV , HSV , EVs , mumps and VZV ., In some instances , the detection of the virus in CSF or in other body fluids alone is insufficient and requires further supporting evidence for interpretation of the results ., Details are presented in Table 1 ., Chi-square test , Fishers exact test , independent samples t test and the Wilcoxon rank-sum test were used to compare data between groups of patients when appropriate by using either SPSS for Windows version 14 ( SPSS Inc , Chicago IL , USA ) or statistical software R version 2 . 9 . 0 ( http://www . r-project . org ) ., For the period between 1996 and October 2007 , the samples analysed were anonymized , residual CSF and blood specimens that were collected as part of routine care ., The use of these clinical specimens for the purpose of improving knowledge about the causative agents of CNS infections in Vietnam was approved by the Scientific and Ethical Committee of the Hospital for Tropical Diseases , Vietnam ., From November 2007 onward , the prospective study of patients was approved by the Scientific and Ethical Committee of the Hospital for Tropical Diseases , Vietnam and the Oxford University Tropical Research Ethics Committee , UK , OxTREC number: 004-07 ., Physicians entering patients into the study were responsible for obtaining their written informed consent from the patient ., If the patient was unconscious , the written informed consent was obtained from a relative or a family member ., Between December 1996 and May 2008 , 1601 patients with CNS infections were admitted to the infectious ward of the HTD , of whom 291 fulfilling the entry criteria of CNS infections of presumed viral origin were enrolled ., 190 ( 65% ) were male , and 235 ( 89% ) were referred from other hospitals ., The median duration of hospital stay was 10 days interquartile range ( IQR ) : 7–18 ., A total of 71 ( 25% ) patients received acyclovir or valacyclovir: oral acyclovir ( n\u200a=\u200a39 ) , IV acyclovir ( n\u200a=\u200a2 ) and oral valacyclovir ( n\u200a=\u200a30 ) ( Table 2 ) ., A low Glasgow coma score ( ≤9 ) was recorded in 82 ( 29% ) of the patients on admission ., A fatal outcome was recorded in 28 ( 10% ) patients; and 78 ( 27% ) patients suffered neurological sequelae at discharge , which was severe in 49 ( 17% ) , moderate in 17 ( 6% ) and mild in 12 ( 4% ) ., There were no differences in outcome detected between patients who had confirmed/probable causes identified versus those with possible/no aetiological agent identified ( Table 2 ) ., The patients characteristics , clinical outcome and distribution of the enrolled patients over the study period are presented in Table 2 ., Virus and bacterial specific PCR investigations were performed on CSF specimens of all 291 enrolled patients , while serologic tests for IgM antibodies against DENV and JEV were done in 278 patients due to insufficient volumes of CSF from the remaining patients ( Table 3 ) ., Rabies PCR was performed on saliva of 1 patient with a clinical syndrome suggestive of rabies ., Serologic testing for IgM antibodies to rubella virus was performed on sera of six patients with a non-confluent maculopapular rash , suggestive of rubella on admission ., Further testing for the presence of antibodies to EBV ( VCA IgM , VCA IgG and EBNA IgG ) and CMV ( IgM and IgG ) were done on available acute blood samples from 22/24 and 4/5 patients that had viral DNA detected in CSF by EBV and CMV PCRs , respectively ( Table 3 ) ., We found confirmed/probable viral infection in 93 ( 32% ) patients ( see Table 4 ) ., JEV was the most common virus detected ( n\u200a=\u200a36 , 12% ) followed by DENV ( n\u200a=\u200a19 , 6 . 5% - CSF serology , n\u200a=\u200a17 and CSF PCR , n\u200a=\u200a2 ) , HSV ( n\u200a=\u200a19 , 6 . 5% ) and EVs ( n\u200a=\u200a8 , 2 . 7% ) ( Table 4 ) ., Characteristics and clinical outcome of patients with or without a confirmed/probable viral aetiology , and patients with JEV , DENV , EVs and HSV infections , are presented in Tables 2 , 3 and Table 5 ( with additional data for DENV patients ) ., Overall , the characteristics of patients with or without a confirmed/probable viral aetiology were similar , although patients with a confirmed/probable viral aetiology were significantly younger , had higher frequency of history of fever , vomiting and headache on admission , and higher CSF white blood cell counts , but they required a shorter hospital stay ( p<0 . 05 for all ) ., The incidence of death ( 47% ) and neurological sequelae ( 26% ) by discharge in the 19 patients with HSV encephalitis ( HSE ) was significantly higher than that of patients with other infections ( Table 3 ) ., Nine ( 47% ) patients with HSE received acyclovir/valacyclovir; in one of whom the discharge outcome was not fully assessed because of being transferred to another hospital ., There was a trend towards better outcome amongst those who received drug earlier ., The median days of illness before acyclovir/valacycolovir administration was 3 days ( range 3–5 ) in survivors compared to 7 days ( range 5–13 ) ( P\u200a=\u200a0 . 05 ) in those who died ., Patients infected with JEV were mostly young adults and were significantly younger than the other patients: median age 18 years ( IQR 16–22 ) compared to 28 years ( IQR 17–38; P\u200a=\u200a0 . 02 ) for those with DENV and 32 years , ( IQR 21–41; P<0 . 001 ) for those with HSE ., A possible viral cause was detected in 28 ( 10% ) patients ., EBV DNA was detected by PCR in CSF of 24 patients ( Table 4 ) ., Among these , evidence of past EBV infection was seen in 22/22 ( 100% ) tested patients ( VCA IgG and EBNA IgG positive ) but none had evidence of acute infection ( negative VCA IgM ) ( Table 4 ) ., Similarly , CMV DNA was detected in the CSF of 5 patients of whom 4 had acute sera available for serologic tests ., All had detectable CMV specific IgG , but three had undetectable IgM ( Table 4 ) ., Of 291 patients fulfilling the inclusion criteria of CNS viral infections , evidence of bacterial infection by PCR analysis of CSF was found in 8 ( 2 . 7% ) , including 5 with S . pneumoniae , 2 with S . suis serotype 2 , and one with PCR positive for both S . suis and N . meningitidis ., Three of the 8 bacterial PCR positive patients were treated with antibiotics prior to admission ., Clinical outcomes and CSF laboratory data of these patients are detailed in Supplementary Table S1 ., Twenty-three patients ( 8% ) had tests that were positive for more than one agent ( Table 4 ) ., In the majority ( 16/23 , 70% ) of these , the CSF was PCR positive for EBV or CMV plus another agent ., In those with positive CSF EBV PCR , 6 were positive for JEV , 2 with DENV , and 1 each with HSV , VZV and S . pneumoniae , respectively ., In those with positive CSF CMV PCR , 2 were also positive for JEV ( n\u200a=\u200a2 ) , and 1 each with mumps and EVs , respectively ., Among EBV patients there was no statistical difference in viral load ( as suggested by the obtained Ct values ) between patients with or without other positive results ( median Ct value 37 , range 34–40 vs . 35 , range 32–41 , respectively , P\u200a=\u200a0 . 19 ) ., Twenty-eight ( 10% ) patients died during hospitalization ., 60% died within the first two weeks of illness , and 93% died within the first two weeks of hospital admission ., Two hundred and ninety one patients were enrolled over a 12-year period ( Figure 1A ) ., Encephalitis cases distributed throughout the year with a slight peak in October and November ., Similarly , there is no clear seasonal trend for specific viruses except for JEV cases , which peaked in June ( Figure 1B ) , when the southern part of Vietnam enters the rainy season ., We describe the clinical features and infectious aetiology of 291 adults with suspected CNS infections of presumed viral origin admitted to a tertiary referral hospital in southern Vietnam over 12 years ., The incidence of death in this study was high at 10% ( 28/291 ) ., And this was particularly marked in patients with confirmed HSE ( 9/19 , 47% ) ., The mortality from viral encephalitis reported in other studies ranged from 4 . 6% to 20% 3–7 ., Studies assessing outcome from HSE have reported mortality ranging from 5% in France 4 , to 11% in the UK 7 and 18% in the USA 3 ., High-dose IV acyclovir is recommended for the treatment of HSE 24 , but is often unavailable or unaffordable in resource-poor countries , such as Vietnam ., The limited availability of acyclovir/valacyclovir and the lack of its empirical prescription may explain the high mortality from HSE in our study ., According to a recent study conducted in our hospital , oral valacyclovir , which is considerably cheaper , may be an acceptable early treatment for suspected HSE in resource limited settings 25 ., Neurological sequelae were also frequent in our patients , affecting 27% ( 78/291 ) ., More subtle impairments of cognitive function may have been missed in our assessment; a recent study performed in the UK found 45% of patients to have persistent neurological sequelae 6 months after diagnosis 7 ., The lack of specialist rehabilitation services in less well-resourced countries makes the problems of neurological sequelae particularly serious for patients and their families ., The diagnostic yield of 32% of the present study is in accordance with previous findings ( 16–52% ) 3–7 , 18 , and further illustrates the big challenge to establish a confirmed infectious aetiology in patients with acute CNS infections ., Together with our previous reports , the data confirm that JEV , DENV , HSV and EVs are the leading causes of CNS viral infections in Vietnam 8 , 9 , 19 , and highlights much-needed efforts for national vaccination campaigns against vaccine preventable diseases due to viruses as JEV , rubella and rabies in Vietnam ., As of 2008 , which is at the end of this report , it was recorded that JEV vaccine was administered to 91% of the target population in Vietnam 17 ., Because , data on vaccination status of the patients was not available , it remains unknown whether the JEV patients in the present study had received ( sufficient doses of ) JE vaccine ., Of note , in our previous study in children with viral encephalitis in southern Vietnam in 2004 , of 191 enrolled children , only 19 . 5% had received at least one dose of JE vaccine ., 8 ., Follow-up study is therefore needed to assess the effect of this immunization campaign on the overall incidence of JEV in Vietnam ., Currently , Vietnam is amongst the countries that have yet to include rubella vaccination in their routine immunization programmes 26 , and has experienced notable outbreaks between 2005–2007 with ≥3000 cases/year , and ∼800 cases in 2008 27 ., We may have underestimated the number of cases of encephalitis due to rubella in our study , since only patients with rashes where the diagnosis was suspected were tested ., Likewise , rabies is responsible 100 cases per year annually in Vietnam ., Rabies control can be achieved through vaccination of humans and animals , dogs in particular 28–30 ., Rabies control in Vietnam is challenging because of limited public awareness ( particularly among pet owners ) , large numbers of stray dogs , and the lack of a national vaccination program 30–32 ., Neurological manifestations of DENV infection have been recorded in about 0 . 5–20% and 4–47% of patients admitted with classical dengue and encephalitis-like illness , respectively in endemic areas 33 ., Similarly , the 19 DENV patients were all admitted with clinical sing/symptom of acute CNS infection without a typical picture of classical dengue infection ., Although currently there are no standardised case definitions or diagnostic criteria available for this clinical entity 33 , 34 , according to criteria recently proposed by Carod-Artal et al . 33 , 15/19 ( 79% ) dengue patients in our study can be classified as having dengue encephalitis ., While the biological significance of the detection of EBV/CMV DNA in CSF remains unknown , the high frequency of EBV/CMV DNA detection in CSF together with other potential CNS pathogens observed in this study confirms the findings of previous reports 35–38 ., Past infection was also documented in 22/22 and 3/4 EBV and CMV patients , respectively ., The detection of EBV/CMV DNA in CSF of these patients may be a result of the inflammatory processes and white blood cell recruitment leading to CSF entry of EBV/CMV infected cells ., However , it cannot be ruled out that under certain circumstances ( e . g . co-infection with another CNS pathogen ) the virus may reactivate and cause or aggravate CNS infection 39–41 ., Evidence of bacterial infection was detected in 2% of patients with negative CSF Gram stains and culture and with clinical and laboratory data compatible with CNS viral infections , suggesting that PCR should always be considered to exclude CNS infections in patients with treatable bacterial meningitis , particularly in countries as Vietnam where antibiotics are frequently prescribed in community and hospital settings prior to presentation at a facility where a definitive microbiological diagnosis could be made 42 ., Our study has some limitations ., First , this is a hospital-based descriptive study and patient admission to the research ward is biased by the availability of beds at the time the patients are admitted to the hospital ., This in part , explains the fluctuation in patient numbers enrolled over the study period ( Figure 1A ) ., Therefore the data may not closely represent for the wider community ., Second , we did not look for all potential infectious causes of CNS infections in Vietnam ( including measles virus 43 ) , or for non-infectious ( immune or endocrine ) causes ., Third , diagnostics by IgM testing of acute samples might be suboptimal ( e . g . in case of JEV and DENV ) both in terms of sensitivity and specificity ., Fourth , samples from other body compartments such as rectal and throat swabs were not collected for aetiological investigation in this study ( e . g . in case of enterovirus infection ) , which could have increased the total diagnostic yield 8 ., Fifth , undiagnosed cases could be caused by novel pathogens which would have gone undetected ., However , this study represents the broadest investigation yet of the possible viral causes of the CNS infections in adults in Vietnam , with a diagnostic yield of 32% ., The results suggest that the majority of morbidity/mortality amongst patients with a confirmed/probable viral CNS infection is preventable by adequate vaccination and/or treatment , and are therefore of public health significance . | Introduction, Materials and Methods, Results, Discussion | Central nervous system ( CNS ) infections are important diseases in both children and adults worldwide ., The spectrum of infections is broad , encompassing bacterial/aseptic meningitis and encephalitis ., Viruses are regarded as the most common causes of encephalitis and aseptic meningitis ., Better understanding of the viral causes of the diseases is of public health importance , in order to better inform immunization policy , and may influence clinical management ., Study was conducted at the Hospital for Tropical Diseases in Ho Chi Minh City , a primary , secondary , and tertiary referral hospital for all southern provinces of Vietnam ., Between December 1996 and May 2008 , patients with CNS infections of presumed viral origin were enrolled ., Laboratory diagnostics consisted of molecular and serological tests targeted at 14 meningitis/encephalitis-associated viruses ., Of 291 enrolled patients , fatal outcome and neurological sequelae were recorded in 10% ( 28/291 ) and 27% ( 78/291 ) , respectively ., Mortality was especially high ( 9/19 , 47% ) amongst those with confirmed herpes simplex encephalitis which is attributed to the limited availability of intravenous acyclovir/valacyclovir ., Japanese encephalitis virus , dengue virus , herpes simplex virus , and enteroviruses were the most common viruses detected , responsible for 36 ( 12% ) , 19 ( 6 . 5% ) , 19 ( 6 . 5% ) and 8 ( 2 . 7% ) respectively , followed by rubella virus ( 6 , 2% ) , varicella zoster virus ( 5 , 1 . 7% ) , mumps virus ( 2 , 0 . 7% ) , cytomegalovirus ( 1 , 0 . 3% ) , and rabies virus ( 1 , 0 . 3% ) ., Viral infections of the CNS in adults in Vietnam are associated with high morbidity and mortality ., Despite extensive laboratory testing , 68% of the patients remain undiagnosed ., Together with our previous reports , the data confirm that Japanese encephalitis virus , dengue virus , herpes simplex virus , and enteroviruses are the leading identified causes of CNS viral infections in Vietnam , suggest that the majority of morbidity/mortality amongst patients with a confirmed/probable diagnosis is preventable by adequate vaccination/treatment , and are therefore of public health significance . | Central nervous system ( CNS ) infections are important diseases worldwide ., The spectrum of infections is broad , encompassing bacterial/aseptic meningitis and encephalitis ., Viruses are regarded as the most common causes of encephalitis and aseptic meningitis ., Better understanding of the causes of the diseases is of public health importance , in order to better inform immunization policy , and influence clinical management ., We describe the clinical features and infectious causes of 291 adults with clinically suspected CNS infections of presumed viral origin ., We show that CNS viral infections in Vietnam are associated with high morbidity and mortality ., Mortality was especially high ( 47% ) amongst those with herpes simplex encephalitis which is attributed to the limited availability specific antiviral drugs in our setting ., Japanese encephalitis virus , dengue viruses , herpes simplex virus and enteroviruses were the most common viruses detected , followed by rubella virus , varicella zoster virus , mumps virus , cytomegalovirus , and rabies virus ., Our study represents the broadest yet investigation of the possible viral causes of the CNS infections in adults in Vietnam , with a diagnostic yield of 32% ., The results show that the majority of morbidity/mortality amongst patients with a confirmed/probable diagnosis could be prevented by adequate vaccination or treatment , and are therefore of public health significance . | infectious diseases, medicine and health sciences, biology and life sciences, immunology, microbiology, molecular biology | null |
journal.pgen.1001305 | 2,011 | Two Frizzled Planar Cell Polarity Signals in the Drosophila Wing Are Differentially Organized by the Fat/Dachsous Pathway | Planar Cell Polarity ( PCP ) describes the orientation of a cell within the plane of an epithelium ., A primary model for studying the genetic control of PCP has been the organization of an array of cell hairs that point toward the distal tip of the Drosophila wing 1 ., Two signaling pathways are known to control Drosophila wing PCP , the Frizzled ( Fz ) PCP pathway and the Fat/Dachsous ( Ft/Ds ) pathway 2 , although the functional relationship between these two pathways remains subject to debate 3 ., One model , the Tree-Amonlirdviman model , proposes a tiered structure in which long-range gradients of Ft/Ds signaling provide global polarity information that controls the direction of a local Fz PCP signal 4 ., In the case of the wing , the proximal expression of Ds and distal expression of Four-jointed ( Fj ) are proposed to generate opposing activity gradients along the proximal-distal ( P-D ) wing axis that control the direction of the Fz PCP signal 5 , 6 ., In contrast , studies in the Drosophila abdomen have led to an alternative ‘Two Pathway’ model in which the Ft/Ds and Fz PCP pathways function independently to organize PCP 7 ., The resolution of these distinct models is important since both Fz PCP and Ft/Ds pathways have also been shown to be critical for PCP in vertebrate development 8 , 9 and are implicated in human disease 9–11 ., In an earlier paper we showed that , in addition to organizing wing hair polarity , the Fz PCP pathway is required for the integrity and orientation of cuticle ridges that traverse the adult wing membrane 12 ., However , although wing hairs have a common orientation across the wing , ridges are aligned with the anteroposterior ( A-P ) axis in the anterior wing and with the P-D axis in the posterior wing ., Consequently , hair and ridge orientation are approximately orthogonal in the anterior wing , but are closely matched in the posterior wing ., This presents the problem of how Fz PCP signaling can lead to these two distinct outcomes in anterior and posterior wing cells ., Data from our work , and from other labs , has led us to propose a Bidirectional-Biphasic ( Bid-Bip ) model in which two distinct Fz PCP signaling events occur along different axes of the wing ( Figure 1 and 12 ) ., In the model , there is an Early Fz PCP signal aligned with the A-P axis that is approximately symmetric in the anterior and posterior wing ., This is followed by a Late Fz PCP signal aligned with the P-D axis ., For the model , the direction of Fz PCP signaling is defined as the hair polarity that would be specified by the signal ., The concept of two Fz PCP signaling events during wing development is not novel; the existence of a distinct Early Fz PCP signal around 18 hours after pupal formation ( a . p . f . ) has been well established by work in the Strutt lab 13 , 14 ., However , the notion that the Early Fz PCP signal is oriented along the A-P axis appears to be a novel feature of our Bid-Bip model 12 ., Previous evidence for Fz PCP signaling along the A-P axis of the wing has come from the Adler lab ., Adler showed that , in addition to the distal cell non-autonomy associated with fz mutant wing clones 15 , fz clones in the anterior wing show cell non-autonomy posterior to the clone , whilst posterior fz clones show cell non-autonomy anterior to the clone 16 ., Moreover , as well as the proximal cell non-autonomy associated with mutant clones of the Fz PCP pathway gene Van Gogh/strabismus ( Vang/stbm ) 17 , anterior Vang/stbm clones show anterior cell non-autonomy and posterior Vang/stbm clones show posterior cell non-autonomy 16 ., Adlers data argue for Fz PCP signaling along the A-P axis of the wing that has an opposite orientation in the anterior and posterior wing ., This matches the description of our proposed Early Fz PCP signal ( Figure 1 ) ., Key to our Bid-Bip model is the notion that different features of the wing are organized by the two Fz PCP signaling events ., The model proposes that posterior ridges are organized by the Early Fz PCP signal , while anterior ridges and wing hairs are organized by the Late Fz PCP signal ( Figure 1 ) ., This is supported by our finding that early over-expression ( e . g . 10 hours a . p . f . ) of the Sple isoform of the PCP protein Prickle reorients hairs and ridges in both the anterior and posterior wing , whereas late Sple over-expression ( e . g . 19 hours a . p . f . ) reorients hairs and anterior ridges , but not posterior ridges 12 ., This observation suggests that posterior ridges are specified earlier than anterior ridges ., Consequently , the Bid-Bip model proposes that ridges and hairs are organized by the same Fz PCP signaling event in the anterior wing , but by different ( and differently oriented ) Fz PCP signaling events in the posterior wing ., Thus , the model accounts for the differing relationships between ridge and hair orientation observed in the anterior and posterior wing ., The model also implies that orthogonal hair and ridge orientation is the normal outcome of a single Fz PCP signaling event in the wing ., One further feature of our Bid-Bip model is the proposal that the Early and Late Fz PCP signals differ in the use of the Prickle protein isoforms Pk and Sple within the Fz PCP pathway ., Pk and Sple share a C-terminus containing a PET domain and three LIM domains , but the 13 N-terminal amino acids in Pk are replaced by 349 N-terminal amino acids in Sple 18 ., In the model , the Early Fz PCP signal employs the Sple isoform and the Late Fz PCP signal employs the Pk isoform ., ( For this reason , we will refer to the Early Fz PCP signal as Fz ( Sple ) and the Late Fz PCP signal as Fz ( Pk ) in this paper ., ) This agrees with previous work from Strutt that showed the Pk isoform is only required for Late Fz PCP signaling 13 ., Consequently , a prediction of the Bid-Bip model is that loss of Pk isoform activity ( i . e . a pkpk mutant ) blocks Late Fz PCP signaling and so only the Early Fz PCP signal occurs ., Consistent with this prediction , there is a regular , approximately orthogonal , relationship between hair and ridge orientation across the entire pkpk mutant wing suggesting that only a single Fz PCP signaling event ( i . e . Fz ( Sple ) ) has occurred 12 ., Moreover , Adler has shown that in a pkpk mutant wing , the cell non-autonomy of fz− clones is primarily posterior to anterior clones and anterior to posterior clones 16 , suggesting that Fz PCP signaling is principally along the A-P axis and in opposite directions in the anterior and posterior wing ., This fits our models proposal that only the Early Fz PCP signal is active in a pkpk mutant wing ( Figure 1 ) ., Our Bid-Bip Fz PCP signaling model differs significantly from previous models of PCP in the Drosophila wing ., Therefore , it provides an alternative template for an evaluation of the role of the Ft/Ds pathway in wing PCP ., The work presented in this paper addresses the relationship of the Ft/Ds and Fz PCP pathways in the context of our model and concludes that a primary role of the Ft/Ds pathway in wing PCP is to control the direction of the Early Fz ( Sple ) signal ., Membrane ridge orientation differs between the anterior and posterior of the wild-type Drosophila wing 12 ., The boundary between these two regions lies in the vicinity of the L3 vein , but is not possible to pinpoint on wild-type wings , as ridge orientation is difficult to determine adjacent to wing veins ., Homozygous rhove-1 , vn1 wings lack wing veins L2-5 and display altered wing shape 19 ., Using our Cuticle Refraction Microscopy ( CRM ) technique 12 in conjunction with conventional light microscopy , we find that rhove-1 , vn1 wings retain wild-type hair polarity and ridge orientation ( compare Figure 2A with Figure 3A ) ., In the absence of veins on the rhove-1 , vn1 wing , it becomes clear that the boundary between anterior A-P and posterior P-D ridge orientation can be mapped to a narrow region , about 2–3 cells wide , that forms an approximately straight line along the P-D axis of the wing ( yellow shaded region in Figure 2A and 2B ) ., Our ability to finely map this region implies an abrupt change in PCP on the wing and for this reason we refer to it as a ‘PCP Discontinuity’ ( PCP-D ) ., The absence of veins and unusual wing morphology of the rhove-1 , vn1 wing make the location of the PCP-D difficult to pinpoint ., To overcome this problem , we over-expressed Argos uniformly during dorsal wing development ( MS1096-gal4; UAS-argos ) ., The Argos protein is a negative regulator of EGF signaling and Argos over-expression in the dorsal wing antagonizes longitudinal vein development resulting in variable loss of dorsal longitudinal veins including L3 ( Figure 2C and 20 ) ., These wings reveal that the discontinuity in ridge orientation ( i . e . the PCP-D ) maps to the normal location of the L3 vein ( Figure 2D ) ., According to our Bid-Bip Fz PCP signaling model ( Figure 1 ) , loss of Pk isoform activity ( i . e . a pkpk mutant ) inactivates the Late Fz ( Pk ) signal , but not the Early Fz ( Sple ) signal 12 ., Therefore , since only Early Fz ( Sple ) signaling is active on a pkpk mutant wing , the pkpk mutant hair polarity pattern should reflect the direction of the Fz ( Sple ) signal ., At first glance , the intricate swirling hair patterns observed on a pkpk wing appear an improbable signaling output 12 , 18 , 21 , 22 ., However , since hair whorls , and other abrupt changes in hair polarity , on a pkpk wing are normally adjacent to wing veins , we hypothesized that an alternate hair pattern might appear in the absence of vein differentiation ., To test this , we generated pkpk30; rhove-1 , vn1 homozygous flies , which lack wing veins L2-5 and have no Pk isoform activity ., We found that these wings lack most of the abrupt changes in hair polarity normally found on a pkpk mutant wing ( see , for example , Figure S1 ) ., However , the approximately orthogonal relationship between hair polarity and ridge orientation , seen in both the anterior and posterior wing of a pkpk mutant wing 12 , is maintained ( Figure 2E and 2F ) ., On a pkpk30; rhove-1 , vn1 wing , anterior hairs consistently have a posterior component to their polarity and posterior hairs have an anterior component to their polarity ., The boundary between anterior and posterior pointing hairs can be mapped to an approximately straight line , around 2–3 cells wide , along the P-D axis ( yellow shaded region in Figure 2E and 2F ) ., This position is also associated with a discontinuity in ridge orientation , which changes abruptly in this region ( Figure 2F ) ., To localize this PCP discontinuity , we over-expressed Argos in a pkpk mutant wing ( MS1096-gal4; pkpk30/pkpk30; UAS-argos ) , to induce partial loss of dorsal longitudinal veins ( Figure 2G ) ., On these wings , it is clear that the discontinuity in hair and ridge orientation maps to the site of the L3 vein ( Figure 2H ) ., In summary , we have identified a PCP discontinuity ( PCP-D ) in the Drosophila wing that maps to the site of the L3 wing vein ( although physical differentiation of the L3 vein is not required for the formation of the PCP-D ) ., In wild-type wings , the PCP-D represents a discontinuity in ridge orientation , but not hair polarity ., However , in wings lacking Pk isoform activity , the PCP-D represents a discontinuity in both ridge orientation and hair polarity ., According to our Bid-Bip Fz PCP signaling model ( Figure 1 ) , only Early Fz ( Sple ) signaling is active in a pkpk mutant wing , therefore we conclude that there is a discontinuity in Fz ( Sple ) signaling at the site of the L3 wing vein ., We also note that , although hair polarity in a wild-type wing is not disrupted by the removal of wing veins , pkpk mutant hair polarity is significantly modified by wing vein removal ( see Figure S1 ) ., This suggests that the output of the Early Fz ( Sple ) signal is significantly influenced by wing vein differentiation , whereas the Late Fz ( Pk ) signal is not ., This observation is not consistent with a previous report which concluded that altered wing vein formation does not affect the pkpk mutant wing hair phenotype 21 ., However , we note that the genes we have used to alter vein formation ( rho , ve , argos ) are all components of the EGF signaling pathway , whereas the genes used in the early work ( knirpsri , cubitus interruptus and plexus ) are not EGF components ., This raises the possibility that it is altered EGF signaling that modifies the Early Fz ( PCP ) signal rather than the physical differentiation of wing veins ., The hypomorphic fat1 ( ft1 ) mutant allele is homozygous viable and affects wing shape , but not hair polarity ( Figure 3D ) ., We mapped ridge orientation on a ft1 homozygous mutant wing using our CRM technique 12 , and found that ridges in distal regions of the posterior wing show an A-P orientation , in contrast to the normal P-D orientation ( compare Figure 3F with Figure 3C ) ., In contrast , anterior ridges on the ft1 wing retain the normal A-P orientation ( compare Figure 3E with Figure 3B ) ., The abnormal wing morphology of viable dachsous ( ds ) mutants makes analysis of wing ridges by our CRM technique challenging , although we were able to confirm that the D region ( between veins L4 and L5 ) of a dsUA071/ds05142 heterozygous wing , retains wild-type hair polarity , but has primarily A-P ridges ( data not shown ) ., However , we were largely able to overcome this problem by expressing gene-specific RNAi uniformly in the developing dorsal wing ., Uniform expression of ds RNAi ( VDRC transformant 36219GD 23 ) in the dorsal wing ( MS1096-gal4; UAS-ds ( IR ) ) alters wing shape and disrupts crossveins ( Figure 3G ) , but produces only localized hair polarity changes in the proximal wing ( red shaded oval in Figure 3G ) ., CRM analysis shows that uniform ds RNAi expression alters posterior ridges to a more A-P orientation ( Figure 3I ) , but does not affect anterior ridges ( Figure 3H ) ., Uniform expression of ft RNAi ( VDRC transformant 9396GD 23 ) in the dorsal wing ( MS1096-gal4; UAS-ft ( IR ) ) results in a very similar wing phenotype to ds RNAi expression ( data not shown ) ., The control of PCP by the Ft/Ds pathway also requires the four-jointed gene 24 , and we have mapped ridge orientation on wings homozygous for the amorphic fjD1 allele ., Homozygous fjD1 wings have altered shape , but hair polarity is disrupted in only a small proximal region ( red shaded oval in Figure 3J ) , the same region affected by uniform ft or ds knockdown ., We found that posterior ridges on fjD1 homozygous wings also have a more A-P orientation than wild-type ( Figure 3L ) , but anterior ridges are unchanged ( Figure 3K ) ., The phenotypes generated using the VDRC ft and ds RNAi lines are unlikely to result from off-target RNAi activity as they phenocopy the established mutant phenotype of these genes ., In addition , we were able to reproduce these phenotypes using independent ft ( JF03245 ) and ds ( JF02842 ) RNAi lines from the TRiP project ( Transgenic RNAi Project , Harvard Medical School ) in combination with the same Gal4 driver ( data not shown ) ., Curiously , uniform fj RNAi expression using either the VDRC ( transformant 6774GD 23 ) or TRiP ( JF02843 ) stocks failed to give the characteristic fj mutant wing morphology and so these stocks were excluded from this study ., Our findings show that reduced activity of the Ft/Ds pathway genes ft , ds and fj alter ridge orientation in the distal posterior wing to a more A-P orientation , without affecting hair polarity in the same region or anterior ridge orientation ., Since our Bid-Bip model proposes that posterior ridges are organized by the Early Fz ( Sple ) signal whereas anterior ridges and wing hairs are organized by the Late Fz ( Pk ) signal ( see Figure 1 ) , these results suggest that reduced activity of Ft/Ds pathway genes can alter the Early Fz ( Sple ) without affecting the Late Fz ( Pk ) signal ., The fact that posterior ridges still form when Ft/Ds pathway activity is reduced suggests that the role of the Ft/Ds pathway is not to specify posterior ridges , but to direct ridge orientation , presumably by controlling the direction of the Early Fz ( Sple ) signal ., Our analysis of wing ridge phenotypes led us to conclude that reduced Ft/Ds pathway activity can affect the direction of the Early Fz ( Sple ) signal without altering the Late Fz ( Pk ) signal ., Since the Late Fz ( Pk ) signal is inactivated in a pkpk mutant wing , the pkpk hair polarity phenotype should reflect the direction of the Early Fz ( Sple ) signal 12 ., Consequently , if reducing Ft/Ds pathway activity affects the orientation of the Early Fz ( Sple ) signal , we predict that it should significantly modify the pkpk mutant wing hair phenotype ., This turns out to be the case ., For example , although a ft1 homozygous wing has wild-type hair polarity , the pkpk30 hair polarity phenotype is substantially modified in a ft1 , pkpk30 double mutant wing ( compare Figure 4A with 4D ) ., Specifically , in comparison to a pkpk30 homozygote , ft1 , pkpk30 hair polarity is more distal in both the anterior wing ( compare Figure 4B with 4E ) and in distal regions of the posterior wing ( compare Figure 4C with 4F ) ., We see a similar modification of the pkpk hair phenotype when driving uniform ft RNAi expression ( VDRC transformant 9396GD ) in a pkpk mutant wing ( MS1096-gal4; pk30 , UAS-ft ( IR ) /pk30 ) , but with more extensive regions of distal hair polarity in the posterior wing and an anterior component to anterior hair polarity ( data not shown ) ., Driving uniform expression of ds RNAi ( VDRC transformant 36219GD ) in the dorsal wing of a pkpk mutant also modifies the pkpk hair phenotype to a more distal polarity in the anterior and distal posterior wing ( Figure 4G , 4H and 4I ) ., We also generated flies homozygous for both a pkpk allele and for an amorphic allele of lowfat ( lft ) , a recently identified modulator of Ft/Ds signaling 25 ., lftTG2 homozygous wings display altered wing morphology and aberrant posterior ridges , but wild-type hair polarity ( 25 and data not shown ) ., In lftTG2 , pkpk homozygous wings , the pkpk hair phenotype is modified to a more distal polarity in the anterior and distal posterior wing ( Figure 4J , 4K and 4L ) , in a similar manner to when ft or ds activity is reduced ., Hair polarity on fjD1 , pkpk30 homozygous wings is also more distal than the pkpk30 phenotype ., However , this effect is less than observed for reduced ft , ds or lft activity and appears region specific ., For example , hair polarity in the A region ( anterior to the L2 vein ) of a fjD1 , pkpk30 wing is entirely distal , but in the B region ( between the L2 and L3 vein ) retains a significant posterior component and so is closer to the pkpk30 phenotype ( data not shown ) ., These findings show that reduced activity of the Ft/Ds pathway genes ft , ds , fj and lft modify the pkpk30 hair polarity phenotype to a more distal polarity in the anterior wing and distal regions of the posterior wing ., This is despite the fact that hair polarity in these regions is not affected by the reduced activity of the same Ft/Ds pathway genes in a wild-type background ( see Figure 3 ) ., In the context of our Bid-Bip model ( Figure 1 ) , this supports our proposal that reduced levels of Ft/Ds pathway activity can alter the direction of the Early Fz ( Sple ) signal without affecting the Late Fz ( Pk ) signal ., Moreover , our results suggest that the role of Lft in wing PCP is entirely restricted to regulating the Early Fz ( Sple ) signal ., In the posterior wing , reduced Ft/Ds pathway activity modifies the pkpk30 hair phenotype to a more distal polarity in the same regions in which reduced Ft/Ds pathway activity alters ridge orientation to a more A-P orientation ( see Figure 3 ) ., Since we propose that a single Fz PCP signal specifies orthogonal hair and ridges , we would expect that a change in the Fz ( Sple ) signal direction that results in distal hair polarity should be associated with A-P ridges ., To complement the studies described above , we looked at the effect of over-expressing Ft/Ds pathway genes on the Early Fz ( Sple ) and Late Fz ( Pk ) signals ., Uniform over-expression of ft ( MS1096-gal4; UAS-ft ) results in similar wing morphology to loss of ft activity ( Figure 5B ) and alters posterior ridges ., ft over-expression alters hair polarity in the same proximal region of the wing affected by reduced Ft/Ds pathway gene activity ( see Figure 3 ) , but also generates variable hair polarity changes in more distal regions of the wing ( red ovals in Figure 5B ) ., Uniform over-expression of ds or fj results in a similar wing shape , posterior ridge and hair polarity phenotype to reduced activity of the same genes ( Figure 5C and 5D ) ., When ft , ds or fj are uniformly over-expressed in a pkpk mutant wing , the pkpk wing hair phenotype is modified to a more distal polarity in the anterior wing and in distal regions of the posterior wing ( Figure 5F , 5G and 5H ) ., These modifications of the pkpk hair phenotype are similar to those generated by reduced activity of the same Ft/Ds pathway genes ( see Figure 4 ) ., These results show that uniform over-expression of ft , ds or fj modify the pkpk hair polarity phenotype in regions of the wing not affected by over-expression of these genes alone ., In the context of our Bid-Bip model ( Figure 1 ) , this suggests that ft , ds and fj over-expression can alter the Early Fz ( Sple ) signal without affecting the Late Fz ( Pk ) signal ., The results also imply that both over-expression , and reduced activity , of Ft/Ds pathway genes modify the direction of the Fz ( Sple ) signal to a more distal orientation ., Conventionally , gradients of Ft/Ds activity , arising from localized expression of one or more Ft/Ds pathway genes , have been proposed to organize epithelial PCP 26 ., In the wing , proximal Ds expression and distal Fj expression have been proposed to generate Ft/Ds activity gradients that organize hair polarity 6 , 14 , 27 ., This proposal is supported by studies that show Ds expression is primarily in the proximal wing at 24–26 hours a . p . f . , shortly before the Late Fz PCP signal 6 , 27 ., However , at 17 hours a . p . f . , immediately before the Early Fz PCP signal 12 , 14 , Ds protein is present in a P-D stripe along the centre of the wing blade ( see Figure 6H in 27 ) ., We stained ds-lacZ wings at 18 hours a . p . f . and detected a corresponding stripe of beta-galactosidase activity that extends along the majority of the wing blade ( Figure 6A ) ., Beta-galactosidase activity reduces gradually both anterior and posterior to this stripe , suggesting symmetric gradients of ds expression along the A-P axis ., To localize this ds expression , we stained for beta-galactosidase activity in an 18 hours a . p . f . ds-lacZ wing that also expressed Green Fluorescent Protein ( GFP ) under the control of the engrailed ( en ) promoter ( en-gal4 , UAS-gfp ) ., The en promoter drives GFP expression throughout the posterior wing with a sharp anterior boundary 4–5 cells posterior to the L3 vein ( Figure 6B ) ., In ds-lacZ/en-gal4 , UAS-gfp wings , the peak of beta-galactosidase activity ( red arrowheads in Figure 6C and 6D ) is located anterior to the anterior boundary of GFP expression ( Figure 6D ) implying that the peak of ds expression maps close to the site of the L3 vein ., There is no ds expression within the wing pouch of 3rd instar imaginal wing discs 28–30 , and little ds expression within the pupal wing blade at 24–26 a . p . f . 6 , 27 ., Therefore , we conclude that ds is expressed transiently at the site of the L3 vein around 18 hours a . p . f . , the time Strutt has defined for the Early Fz PCP signal 13 , 14 ., Since we propose that the Early Fz ( Sple ) signal converges at the site of the L3 vein and that ds is required for the normal orientation of the Fz ( Sple ) signal , this makes localized ds expression a strong candidate for an organizer of the Fz ( Sple ) signal ., fj expression has previously been proposed to form an opposing gradient to ds in the wing , eye and abdomen 6 , 27 , 31–33 ., However , although there is beta-galactosidase activity at the anterior and posterior wing margin of a fj-lacZ wing at 18 hours a . p . f . , there is also expression at the distal margin and in distal intervein regions ( 5 and data not shown ) ., This pattern of fj expression does not suggest that there are simple opposing gradients of ds and fj expression in the anterior and posterior wing during the period of Early Fz PCP signaling ., If gradients of Ft/Ds pathway gene activity control the direction of the Early Fz ( Sple ) signal , we would expect that altering local levels of Ft/Ds pathway gene expression in the pupal wing should reorient the Fz ( Sple ) signal ., We initially generated marked clones of ft , ds and fj knockdown or over-expression in a pkpk mutant wing to identify hair polarity changes that result from inducing novel gradients/boundaries of Ft/Ds signaling ., However , interpreting the effects of clones of variable shape , size and position on the pkpk mutant hair phenotype proved unfeasible ., To overcome this problem , we used the well-characterized sal-Gal4 driver to drive localized over-expression or knockdown of ft , ds and fj in both wild-type and pkpk mutant wings ., The sal-Gal4 driver expresses Gal4 protein in the spalt expression pattern 34 ( i . e . between the L2 vein and midway between the L4 and L5 veins ( Figure 7A ) ) , and has been used successfully to generate gradients of Ft/Ds pathway gene expression along the A-P wing axis 27 ., Using the sal-Gal4 driver to knockdown ds or ft , or to over-express ds , ft or fj resulted in changes in wing morphology , but did not affect hair polarity outside the main sal-Gal4 expression domain ( see Figure 7D , 7F , 7H , 7J and 7L ) ., However , when the same experiments were done in a pkpk mutant wing , specific changes of hair polarity were observed outside of the main sal-Gal4 expression domain ., For example , in the A region of the wing ( anterior to the L2 vein ) hair polarity on a pkpk mutant wing is posterior ( see Figure 4 and 12 , 18 , 21 ) ., However , hair polarity in the A region of a pkpk mutant wing becomes anterior when sal-Gal4 is used to drive ds knockdown or ft or fj over-expression ( Figure 7E , 7K and 7M ) ., In contrast , pkpk mutant wings in which sal-Gal4 drives ds over-expression or ft knockdown retain posterior hair polarity in the A region ., In each case , hair polarity within the main sal-Gal4 expression domain resembles the modified pkpk phenotype seen when the same Ft/Ds pathway genes were knockdown or over-expressed uniformly in the wing ( see Figure 4 and Figure 5 ) , with the exception of fj over-expression which maintained the normal pkpk mutant phenotype within the sal-Gal4 expression domain ., This last observation is curious , but may be due to the relative levels of expression driven by the MS1096-Gal4 and sal-Gal4 drivers ., We note that Ft/Ds pathway gene misexpression can affect hair polarity on a pkpk mutant wing ten or more cell diameters anterior to the main sal-Gal4 expression domain , suggesting a substantial degree of cell non-autonomy ., We have found that driving RNAi knockdown of the cell-autonomous tricornered ( trc ) ( VDRC transformant 107923KK 23 ) or forked ( f ) ( VDRC transformant 33200GD 23 ) genes using the sal-Gal4 driver generates occasional cells carrying a mutant hair phenotype anterior to the L2 vein ( data not shown ) ., This raises the possibility there may be a gradient of sal-Gal4 expression extending several cell diameters anterior to the L2 vein that could generate corresponding gradients of Ft/Ds pathway gene activity ., However , it is also possible that the boundary of Ft/Ds pathway gene expression generated using the sal-Gal4 driver may cause propagation of PCP changes outside of the expression domain , as has been observed in the Drosophila abdomen ( 7 and see discussion ) and in the control of cell proliferation by the Ft/Ds pathway 35 ., In the context of our Bid-Bip model , these results suggest that generating gradients/boundaries of Ft/Ds pathway gene expression along the A-P wing axis can alter the direction of the Early Fz ( Sple ) signal , without affecting the Late Fz ( Pk ) signal ., Specifically , we find that the Fz ( Sple ) signal is reoriented to point away from a region of reduced ds expression , but not from a region of ds over-expression ., This is consistent with the observation that the Early Fz ( Sple ) signal normally points towards high levels of ds expression at the site of the L3 vein ., The Early Fz ( Sple ) signal also points away from over-expressed ft or fj , which suggests that there are activity gradients of Ft and Fj that oppose the Ds expression gradient during the period of Early Fz ( Sple ) signaling ., To test if gradients/boundaries of Ft/Ds pathway gene expression can alter PCP in the absence of both the Pk and Sple protein isoforms , we used sal-Gal4 to drive ft or fj over-expression , and ft knockdown , in a pkpk-sple-14 homozygous mutant wing ., These localized changes in ft or fj expression altered the morphology of the pkpk-sple-14 wing ( compare Figure S2B with S2D , S2F and S2H ) , however , there were no significant changes in hair polarity at the boundaries of the sal-gal4 expression domain ., For example , in the A region of a pkpk-sple-14 homozygous wing hair polarity is slightly more anterior than wild-type ( 18 , 22 and see Figure S2C ) , but is not altered when sal-gal4 is used to drive ft or fj over-expression , or ft knockdown ( see Figure S2E , S2G and S2I ) ., These results show that gradients/boundaries of Ft/Ds pathway gene expression , which can reorient the Fz ( Sple ) signal , do not alter PCP in the absence of Pk and Sple isoform activity ., The Ft/Ds pathway controls wing morphogenesis by determining the orientation of cell divisions and clonal growth 36 and it has been proposed that altered wing hair polarity associated with loss of Ft/Ds pathway activity might also be a consequence of abnormal cell division 37 ., Our data show that altered Ft/Ds pathway activity can change wing morphology without affecting hair polarity across most of the wing ( see Figure 3 ) ., In the context of our Bid-Bip model , this suggests that the role of the Ft/Ds pathway in wing morphogenesis is largely separable from its role in organizing the Late Fz ( Pk ) signal ., However , we find that changes in Ft/Ds activity that alter wing shape consistently modify the pkpk mutant hair phenotype ., This suggests that we have been unable to separate the role of Ft/Ds in wing morphogenesis from its role in organizing the Early Fz ( Sple ) signal ., To attempt to unlink these activities , we controlled the timing of ds RNAi expression during the development of a pkpk mutant wing ., Constitutive expression of ds RNAi in the developing pkpk wing ( using the MS1096-Gal4 driver ) alters wing morphology and changes pkpk wing hair polarity to a more distal orientation ( see Figure 4G , 4H and 4I ) ., We controlled the timing of ds RNAi expression in MS1096-Gal4; UAS-ds ( IR ) wings by constitutive expression of Gal80ts , a temperature-sensitive Gal4 inhibitor , that binds and inactivates Gal4 at 18°C , but not at 30°C 38 ., Consequently , animals of the genotype MS1096-Gal4/+; pk30 , ds ( IR ) /pk30 , tubP-GAL80ts can be cultured at 18°C ( when Gal80ts is active and inhibits Gal4 ) and then shifted to 30°C at specific times a . p . f . to induce ds RNAi expression in the wing ., When flies of this genotype were cultivated continuously at 18°C , they showed a typical pkpk mutant wing phenotype ( Figure 8A and 8B ) , indicating that Gal80ts effectively inhibited Gal4 at this temperature ., In contrast , when flies of this genotype were cultivated continuously at 30°C , they displayed wing morphology typical of reduced ds activity ( Figure 8C ) , combined with more distal hair polarity than | Introduction, Results, Discussion, Materials and Methods | The regular array of distally pointing hairs on the mature Drosophila wing is evidence for the fine control of Planar Cell Polarity ( PCP ) during wing development ., Normal wing PCP requires both the Frizzled ( Fz ) PCP pathway and the Fat/Dachsous ( Ft/Ds ) pathway , although the functional relationship between these pathways remains under debate ., There is strong evidence that the Fz PCP pathway signals twice during wing development , and we have previously presented a Bidirectional-Biphasic Fz PCP signaling model which proposes that the Early and Late Fz PCP signals are in different directions and employ different isoforms of the Prickle protein ., The goal of this study was to investigate the role of the Ft/Ds pathway in the context of our Fz PCP signaling model ., Our results allow us to draw the following conclusions: ( 1 ) The Early Fz PCP signals are in opposing directions in the anterior and posterior wing and converge precisely at the site of the L3 wing vein ., ( 2 ) Increased or decreased expression of Ft/Ds pathway genes can alter the direction of the Early Fz PCP signal without affecting the Late Fz PCP signal ., ( 3 ) Lowfat , a Ft/Ds pathway regulator , is required for the normal orientation of the Early Fz PCP signal but not the Late Fz PCP signal ., ( 4 ) At the time of the Early Fz PCP signal there are symmetric gradients of dachsous ( ds ) expression centered on the L3 wing vein , suggesting Ds activity gradients may orient the Fz signal ., ( 5 ) Localized knockdown or over-expression of Ft/Ds pathway genes shows that boundaries/gradients of Ft/Ds pathway gene expression can redirect the Early Fz PCP signal specifically ., ( 6 ) Altering the timing of ds knockdown during wing development can separate the role of the Ft/Ds pathway in wing morphogenesis from its role in Early Fz PCP signaling . | Planar Cell Polarity ( PCP ) describes the orientation of a cell within the plane of a cell layer ., The precise control of PCP has been shown to be vital for normal development in both vertebrates and invertebrates , and failures of PCP have been implicated in human disease ., Studies in the fruit fly Drosophila have identified two genetic pathways , the Frizzled and Fat/Dachsous pathways , that are required to organize PCP , although the functional relationship between the two pathways remains unresolved ., We have previously proposed a model of Frizzled pathway activity in the Drosophila wing that invokes two consecutive Frizzled signaling events oriented in different directions ., The Early and Late Fz PCP signals use different isoforms of the Prickle protein ., The goal of this study was to define the activity of the Fat/Dachsous pathway in the context of our Frizzled signaling model ., Our results suggest that the Fat/Dachsous pathway has a different functional relationship with each of the Frizzled signaling events ., Specifically , we find that by altering Fat/Dachsous pathway activity , we can reorient the Early Frizzled signal without affecting the Late Frizzled signal ., This suggests that the functional relationship between the Fat/Dachsous pathway and the Frizzled pathway can vary , even between consecutive Frizzled signaling events within the same set of cells . | developmental biology/developmental molecular mechanisms, cell biology/cell signaling | null |
journal.pgen.1008103 | 2,019 | Ribosomal RNA gene repeats associate with the nuclear pore complex for maintenance after DNA damage | DNA damage can lead to deletion , translocation and amplification of DNA in the genome , which may result in cell death , cancer and cellular senescence 1 ., The most hazardous forms of genomic damage is the DNA double-strand break ( DSB ) that can occur randomly in the chromosome during replication , mainly in the S phase of the cell cycle , when the replication fork is arrested by DNA damage , torsional stress , modified nucleotides , or colliding transcription complexes ., Stalled replication forks are thought to be targets of endonucleases that induce a DSB 2 ., Downstream events of a DSB , such as DNA damage checkpoint control and DSB repair , have been analyzed 3 ., Nonetheless , the mechanism of DSB repair in repetitive sequences without rearrangement is not well understood ., Insights into the cellular mechanisms that prevent these rearrangements while allowing the broken genome to be repaired will contribute to the development of novel cancer treatments and broaden our understanding of the aging process ., Here , we focus on the ribosomal RNA gene repeat ( rDNA ) to investigate the mechanism by which genome rearrangement is prevented after a DSB at a site with a stalled replication fork ., In eukaryotic cells the rDNA forms a huge , conserved , tandem repeating structure ( > 100 copies ) on the chromosome ., Transcription at this locus generates ribosomal RNA ( rRNA ) that , together with the ribosomal proteins , is assembled into ribosomes ., A large number of ribosomes are needed to sustain cell-growth ., Indeed , rRNA comprises approximately 80% of the total RNA in a cell 4 and , in the case of budding yeast Saccharomyces cerevisiae , ~ 150 rDNA copies are present on chromosome XII ., Each repeating unit contains 35S and 5S rRNA genes , which are transcribed by RNA polymerases I and III , respectively ( Fig 1A ) ., The transcript of the 35S rRNA gene is subsequently processed into mature 5 . 8S , 18S and 25S rRNA ., The stability of rDNA is affected by recombination among the repeats , which can be easily detected by pulsed field gel electrophoresis 5 ., For the upkeep of repeat number , cells can use a gene amplification mechanism that helps to maintain copy number by recombination 6 ., In this system , replication is arrested at the replication fork barrier ( RFB ) site , located near the 3’ termination site of the 35S ribosomal RNA gene ( Fig 1A and S1 Fig ) ., A complex formed by the binding of Fob1 to the RFB site inhibits replication against the direction of rDNA transcription 7 ., A DSB is subsequently induced at the RFB site ( ~6% of arrested forks at the RFB site result in a DSB ) and repaired by recombination with the sister-chromatid 5 , 8 , 9 ., When the broken end recombines unequally with a homologous site on the sister chromatid and replication restarts , some copies are replicated twice resulting in an increased copy number ( S1B-1 Fig ) ., Thus , cells can use the rearrangement for copy number maintenance ., This mechanism is regulated by the interplay between Sir2 , a histone deacetylase , and transcription from the nearby bidirectional promoter E-pro ( S1 Fig ) ., In a cell with a wild-type rDNA copy number ( ~150 ) , E-pro transcription is repressed by Sir2 , but this repression does not occur in cells with a low rDNA copy number 10 ., Non-coding transcription from E-pro , which prevents sister-chromatid cohesion , stimulates unequal sister-chromatid recombination 8 ., When the copy number reaches the wild-type level , amplification stops ., Alternatively , a DSB in the rDNA of a strain with a normal copy number can be repaired by a mechanism that does not involve homologous recombination , which reduces the risk of rearrangement ( and thus copy number instability ) ., In this mechanism , as we have shown recently , a replisome component Ctf4 protects arrested forks from breakage and end resection ., Although this pathway needs to be elucidated in more detail , it appears that DSB repair at arrested forks is regulated differently from replication-independent DSBs 9 ., By using the unstable nature of rDNA as a measure , we screened a yeast library of ~4 , 800 deletion mutants of non-essential genes and identified ~700 ribosomal RNA gene unstable mutants ( RiUMs ) 11 , 12 ( http://lafula-com . info/kobayashiken/geldata/index . php ) ., Among the RiUMs there was a deletion in TEL1 , which is an orthologue of the human ataxia-telangiectasia mutated ( ATM ) gene that responds to DNA damage and functions in telomere maintenance , damage checkpoint control and DSB repair 13 ., Ataxia-telangiectasia or Louis–Bar syndrome is a rare , neurodegenerative , autosomal recessive disease that causes severe disability ., In budding yeast , Tel1 regulates telomere length through phosphorylation of proteins involved in DSB repair and promotes elongation of telomere repeats 14 ., Although Tel1 functions redundantly with the ATR orthologue Mec1 as S phase checkpoint kinases ( reviewed in 15 ) , the function of these proteins in rDNA maintenance has not been determined ., Certain types of DNA repair appear to arise through recruitment of damage to specific subnuclear sites ( reviewed in 16 ) ., TEL1 is involved in the relocation of DNA to the nuclear pores after inducing DSBs by means of endonuclease HO during the G1 and S/G2-phases of the cell cycle 17 ., This irreparably damaged DNA also binds to the essential Sad1/UNC-84 ( SUN ) domain protein Mps3 in the inner nuclear membrane , but only when DSBs are induced during the S/G2-phase 18–20 ., The rDNA instability in tel1Δ observed in our screen prompted us to investigate whether naturally occurring DSBs formed after replication arrest cause rDNA to translocate to the nuclear envelope ., Using chromatin immunoprecipitation ( ChIP ) assays , we detected binding of rDNA to the nuclear pores , which required Tel1 and Mec1 , indicating this localization is DNA-damage dependent ., In addition , Tof1 , a component of the replisome , which is necessary for fork arrest at the RFB , together with condensin recruiting factors were also found to be required for localization of rDNA to the nuclear pores ., Defective association to nuclear pores reduced rDNA stability , suggesting that this association helps to maintain repeat stability ., Recently , we screened a yeast deletion library for factors involved in the maintenance of rDNA stability and identified ~700 ribosomal RNA unstable mutants ( RiUM ) 11 , 12 ., Among these , there were genes related to DNA repair for which the molecular mechanism with respect to rDNA was not known ., In this category , we focused on a protein kinase Tel1 that regulates telomere length through phosphorylation of proteins mediating DSB repair and that enhance elongation of telomere repeats 14 ., We first introduced the tel1 deletion to our laboratory strain to confirm the generality of the phenotype ., We performed PFGE assays three times and one of the trials was followed by Southern blotting with an rDNA probe ( Fig 1B , S2A and S2B Fig ) ., Although the effect was relatively modest as that of the library strain , quantitative analysis revealed that the bands of rDNA-containing chromosome XII were broader in the tel1Δ compared to wild-type ( Fig 1C . See S2B Table and Materials and methods for about the quantification ) ., Such variable copy numbers are a hallmark of unstable rDNA 5 ., In this assay , the bands of chromosome XII in fob1Δ were not shaper compared to wild-type ., The similar observation was made in a previous study illustrating the inherent difficulty of the detection of a more stable band than that of the wild-type strain 12 ., To test whether rDNA instability in the tel1Δ is related to replication fork barrier activity that induces a DSB , we made a double mutant , tel1Δ fob1Δ ., In the double mutant , the bands of chromosome XII became as sharp as that of the fob1Δ ( Fig 1B and 1C ) , indicating that rDNA instability in the tel1Δ is caused downstream of Fob1 ., Thus , Tel1 functions after replication fork arrest mediated by Fob1 and before involvement in rDNA maintenance ., We reasoned tel1Δ might have an effect on replication fork blocking activity and therefore DSB frequency at the RFB site ., Thus , we examined this possibility by two dimensional gel electrophoresis ( 2D gel assay ) in which the amount of replication fork arrest can be determined from the signal intensity of the “RFB-spot” corresponding to the number of Y-shaped replication intermediates accumulating at the RFB site 21 , 22 ., In the tel1Δ , the “Y-arc , Double-Y and RFB-spot” signals , corresponding to replication intermediates , was slightly weaker than that in the wild-type cells , probably because of the reduced number of S-phase cells in the mutant ( Fig 2A ) ., To compare these strains , RFB-spot intensity was normalized to the replication intermediates ., No significant difference in stalling of the replication forks was observed ( RFB-spot , Fig 2A and 2B and S2B Table ) ., The 2D gel-assay also gave insight into the frequency with which a DSB is formed after replication-fork arrest by means of the “DSB-spot” i . e . a signal that corresponds to broken fragments at the RFB site ., The signal of the spot ( ~2 . 3 kb ) disappeared in the fob1Δ because there was no arrest of the replication fork 8 , 23 ., Relative to the RFB spot , the intensity of the DSB spot was not affected in the tel1Δ ( Fig 2A and 2C and S2B Table ) ., Thus , the increased levels of replication fork blocking activity and resulting increased DSBs are unlikely to be the cause of rDNA instability in the tel1Δ ., Although the frequency of DSB was not increased in tel1Δ compared to wild-type , the mutant exhibited Fob1-dependent rDNA instability ( Figs 2 and 1 , respectively ) ., A previous study demonstrated that Tel1 is required for translocation of HO-induced persistent DSBs to the nuclear pore and pore-binding is implicated in alternative recombination-mediated repair pathways 17 ., Therefore , we hypothesized that replication-dependent DNA damage in rDNA might be associated with nuclear pores in a Tel1-dependent manner ., To test this hypothesis , we performed chromatin immunoprecipitation ( ChIP ) assays with mAB414 , which is an anti-nucleoporin antibody 20 ., Five PCR primer sets in an rDNA unit were designed to detect precipitated rDNA , while two primer sets in SMC2 and CUP1 were used to detect control loci ( Fig 3A ) ., The precipitated rDNA was assessed by quantitative real-time PCR ( qPCR ) and relative enrichment was normalized against CUP1 ., Our results show that rDNA is enriched at the nucleoporins , which constitute nuclear pores , by 4 . 4- to 8 . 1-fold relative to the CUP1 locus ., By contrast , the control SMC2 locus did not display any enrichment ( Fig 3A ) ., Intriguingly , enrichment immediately adjacent to the RFB was relatively weak by comparison to the surrounding regions ( Fig 3A and S2A Table ) ., Similar results were observed for the HO induced-DSB 17 ., Although the underlying mechanism remains unclear , it may involve phosphorylation of histone H2A , recruitment of DNA repair proteins and/or DSB end resection around the DSB ., To evaluate the differences between wild-type and mutant strains , we calculated the relative enrichment of mutant strains to wild-type in each ChIP assay and compared the means of three independent assays ( Fig 3B and S2A Table ) ., The rDNA association with nuclear pores was significantly reduced both in tel1Δ and mec1Δ sml1Δ , suggesting that association of rDNA with the nuclear pores is dependent on DNA damage checkpoint kinases Tel1 and Mec1 ., Tof1 is a component of the replisome and , like Fob1 , is required for the arrest of the replication fork at the RFB and the formation of a DSB 24 , 25 ., To test whether the nuclear-pore association depends on the replication block in the rDNA , we performed the ChIP assay with the fob1Δ and tof1Δ , both of which do not exhibit the replication fork block at the RFB 8 , 24 , 26 ., In the absence of Tof1 , rDNA association with the nuclear pores was significantly reduced ( Fig 3B and S2A Table ) ., In contrast , the reduction was smaller for the fob1Δ and was not statistically significant ., The reason for the observed differences between these two mutants is unclear ., One possible explanation is that Fob1 is responsible for RFB only , while Tof1 might be related to replication fork arrest at any sites in rDNA as it travels with the replication fork ., Indeed , there was no difference in binding to the nuclear pore at RFB between the fob1Δ and tof1Δ mutants ( P-value = 0 . 303477 . S2A Table ) ., For tof1Δ , however , nuclear pore-binding was significantly decreased at non-RFB loci in rDNA ( P-value < 0 . 05 ) , except at the 3 end of 35S rDNA ( P-value = 0 . 050003 ) ., This observation suggests , unlike Fob1 , the role of Tof1 in nuclear pore binding is not limited to RFB sites ( see Discussion section ) ., Because the replication fork block induces DNA damage only in S-phase , the association was expected to occur in this phase of the cell cycle ., To confirm that , we synchronized cells in G1 phase and tested the association ., Contrary to our expectation , the nuclear-pore association was detected even in G1 phase ( S3 Fig ) ., This raises the possibility that the association may be maintained throughout mitosis ( see Discussion ) ., In budding yeast , persistent DNA damage is recruited to the nuclear periphery and is associated with nuclear pores through the Nup84 subcomplex 17 , which contains Nup133 , Nup120 , Nup145C , Nup85 , Nup84 , Seh1 , and Sec13 27–29 ., The nuclear pore association of rDNA compromised both the nup84Δ and nup120Δ and the effect was more pronounced in the deletion of NUP120 , suggesting that rDNA association with nuclear pores requires intact Nup84 complex ( Fig 4 and S2A Table ) ., The rDNA gives rise to the nucleolus , which is a membrane-less organelle that appears to assemble through phase separation ., Importantly , recombination foci are excluded from the nucleolus indicating that rDNA repair occurs in a specific environment distinct from the nucleolus 30 ., Although Mec1/Tel1 have been implicated in nuclear pore association of DSB , there may be rDNA-specific factors that are involved in the nuclear pore association ., We speculated that putative candidates would interact both with rDNA and with the nuclear pores or the surrounding nuclear membrane proteins ., This holds for condensin recruiters Tof2 , Csm1 and Lrs4 , which have been identified as synthetic lethal mutants with a condensin conditional mutant ( smc2-157 ) and that interact with Fob1 and recruit condensin to the rDNA 31 ., Csm1 and Lrs4 are also known as cohibin that associates with CLIP ( chromosome linkage inner nuclear membrane proteins , Src1 and Nur1 ) and localizes the rDNA to the CLIP to maintain rDNA stability , even though it has not been shown whether the binding is damage-dependent 32 , 33 ., To test the contribution of these proteins to the association of rDNA with nuclear pores , we performed a ChIP-qPCR assay with deletion mutants for the factors ., The rDNA association with the nuclear pores in all these mutants was reduced compared to wild-type , indicating that condensin recruiters are required for rDNA relocation to the nuclear pores ( Fig 5 and S2A Table ) ., Sir2 also acts as a bridge between rDNA and the nuclear pores as is the case for CLIP ( Fig 5 and 32 ) ., For sir2Δ , the association of rDNA with the nuclear pores was also reduced ( Fig 5 and S2A Table ) ., To determine the subnuclear localization of spontaneously damaged rDNA , we used a strain in which each copy of the rDNA repeat has a lacO array that associates with LacI-GFP 34 ., We scored DSBs on the rDNA by monitoring the foci of Rad52-CFP , a factor essential for homologous recombination that accumulates at DSBs ( S4A and S4B Fig ) ., The Rad52 focus was barely detected under normal physiological conditions ( 4 cells scored from 875 asynchronous cells; 1 . 26% ) and colocalization of Rad52-CFP and LacI-GFP / rDNA-lacO was even less frequent ( 0 . 46% ) ., Note that Rad52 foci are formed only when the DSBs are excluded from the nucleolus 30 and we estimate that less than 21% of DSBs are marked by discrete Rad52 foci in the rDNA ( see legend to Fig 6C ) ., This may result in a loss of data for a large fraction of DSBs if we use Rad52 as a marker of DSB in the rDNA ., Instead , we used I-SceI endonuclease to induce DSB in the rDNA 30 ., In this assay , I-SceI cleaves the recognition sequence inserted in the rDNA and the location of DSB is detected by TetI fused with mRFP ( monomeric red fluorescent protein ) that associates with the adjacently located tetO array 30 ( Fig 6A ) ., The I-SceI induced DSB is known to shift away from the nucleolus to complete homologous recombinational repair 30 ., Using this system , we scanned the position of the TetI-mRFP focus and classified them into three zones compared with mRFP-fused nuclear pore proteins 35 ( Fig 6B ) ., Before induction of I-SceI , the TetI-mRFP locus was preferentially positioned in the nuclear center ., Strikingly , the locus was relocated to the nuclear periphery both in the G1 and S phases within 2 hours of DSB induction ( Fig 6C and 6D ) ., No enrichment was observed in the strain lacking the I-SceI endonuclease , confirming the association is damage-specific ( Fig 6E ) ., These results indicate that DSB in the rDNA is localized in the nuclear periphery ., To test whether rDNA association with the nuclear pores has a biological role in maintaining rDNA stability , we analyzed the migration of chromosome XII in mutants that fail to relocate rDNA to the nuclear pores ( sir2Δ , tel1Δ , nup84Δ , nup120Δ , tof2Δ , csm1Δ , and lrs4Δ ) by pulsed field gel electrophoresis ( PFGE , Fig 7A and 7B ) ., The fob1Δ and sir2Δ were used as the negative and the positive control , respectively ., All mutants except for nup84Δ exhibited an unstable chromosome XII compared to the wild-type ( Fig 7A and 7B ) ., Nup84 and Nup120 belong to the same heptameric Nup84 complex of nuclear pore complex 28 , 29 , 36 ., However , the nuclear pore association and the stability of rDNA were differentially affected in these mutants ( Figs 4 and 7 ) ., These findings are consistent with the fact that DNA damage sensitivity in the nup120Δ is stronger than that in the nup84Δ 37 ., Taken together , these data suggest that Nup120 plays a more prominent role than Nup84 in DNA repair through an unknown mechanism ., Mps3 acts as an alternative anchoring site of HO-induced DSBs on the nuclear membrane 18 , 19 , 38 ., A mutant form of the essential Mps3 ( mps3Δ65–145 ) , truncated at the N-terminal acidic domain , did not affect rDNA stability according to PFGE analysis ( Fig 7A and 7B , 39 ) ., Furthermore , nup120Δ mps3Δ65–145 double mutations did not show any additive effect in terms of rDNA-stability compared to the corresponding single mutations , suggesting that Mps3 does not make a significant contribution to rDNA stability ., Given that rDNA instability in tel1Δ was dependent on Fob1 ( Fig 1B and 1C ) , the replication-dependent DNA damage in rDNA appears to bind to the nuclear pores for its maintenance ., rDNA is one of the most unstable regions in the genome due to its repetitive nature ., Recombination among the repeats would result in deletions ( loss of copies ) leading to copy number instability ., Nonetheless , cells appear to have evolved mechanisms to avoid such instability , which would be deleterious ., Association of rDNA to the nuclear pores seems to be one such mechanism ., By this change in location , the broken rDNA unit is isolated from intact copies and the risk of hazardous recombination thereby reduced ., Moreover , alternative repair pathways at the nuclear pore might be facilitated 17 , 40 ., In Fig 8 , we summarize how the damaged rDNA is repaired ., Recently , we found that the ends of a DSB formed after stalling of a replication fork at the RFB are not resected in a strain with a normal rDNA copy number , and that the DSB is repaired through a pathway that does not involve homologous recombination 9 ., In this pathway , the DSB can be repaired without alteration of rDNA copy number ., Therefore , we proposed that this homologous recombination-independent repair is the default mechanism used for rDNA repair ( 1st stage , Fig 8 ) ., In contrast , when the rDNA copy number is reduced in a strain , resection of the DSB is induced , which triggers unequal sister-chromatid recombination that may amplify the number of rDNA copies 9 ., For this reaction , the DSB together with the surrounding region needs to be moved from the nucleolus to the nucleoplasm where the homologous recombination enzymes , including Rad52 , form distinct foci ( 2nd stage ) 30 ., Previously , we found that E-pro transcription is activated and cohesin dissociates from the rDNA in the absence of Sir2 ., As a result , unequal sister-chromatid recombination was increased and the copy number changed with a high frequency 10 ( S1 Fig ) ., The E-pro regulated recombination may occur at this stage just outside of the nucleolus ., Finally , if the DSB cannot properly be repaired at the 2nd stage , the DSB with the surrounding region relocates to the nuclear envelope where it is trapped by the nuclear pores ( 3rd stage ) ., In the presence of a repair template , no binding of the DSB to the nuclear periphery was observed in a previous HO-induced DSB assay 17 , 19 ., Although there are abundant repair templates in the case of damaged rDNA , the locus is relocated to the nuclear pores presumably because it is isolated from the majority of templates at the 2nd and 3rd stages ., The 3rd stage may work as a back-up system for the 1st and the 2nd stages and could prevent aberrant genomic changes such as the generation of a large deletion ., The isolated broken ends around the nuclear pores may be repaired by homologous recombination with chromosomal rDNA or an ERC ., Otherwise , repair of the broken ends may occur via the single strand annealing ( SSA ) pathway that connects repetitive sequences using the homologous sequence without introducing mutations 41 ., In this study , proteins involving replication fork bock , DNA damage checkpoint and condensing loading were implicated in the rDNA-nuclear pore binding ., Unraveling the hierarchy of these factors is an exciting challenge for future studies ., In the tof1Δ , defects in the association to the nuclear pores were more obvious than in the fob1Δ ( Fig 3B ) ., The reason for the difference in dissociation between these mutants is unclear ., One possible explanation is that Fob1 is specifically responsible for the RFB , while Tof1 might be associated with replication fork arrest at any site in rDNA given that it travels with the replication fork ., In the fob1 mutant with a low rDNA copy number , collision between 35S transcription and replication machineries causes inhibition of the replication fork and induces rDNA instability 42 ., This damage to the DNA may occur to some extent in a normal copy strain and trigger the relocation ., By contrast , in the tof1 mutant , such RFB independent damage might also be reduced , resulting in a lower level of nuclear pore binding ., The binding of rDNA to nuclear pores was detected even in the G1 phase ( S3 Fig ) ., Because no replication-dependent DSB is induced in G1 phase , the data does not easily fit the DSB dependent-binding model ( Fig 8 ) ., Nonetheless , there are several possible explanations for the cell cycle independent association of rDNA to nuclear pores ., The first interpretation is that the binding is caused by extra-chromosomal rDNA circles ( ERCs ) that are produced by unequal sister chromatid recombination ., However , the ChIP data in sir2Δ does not support this hypothesis ( Fig 5 ) ., Because sir2Δ leads to instability of rDNA and produces vast amounts of ERCs , the strains should show an accumulation of rDNA-nuclear pore binding if ERCs bind to the nuclear pores ., However , no such accumulation was observed ., An alternative interpretation is that a DSB in rDNA that is not repaired in S/G2 phases might be carried into the next cell cycle ., It is known that damage in the rDNA does not induce checkpoint control 43 ., Once a DSB in rDNA is carried over to the next cell cycle , it can be recruited to or maintained at the nuclear periphery in G1 phase as seen in endonuclease-induced damage ( Fig 6 ) ., A third interpretation of cell-cycle independent interaction of rDNA to nuclear pores is that the rDNA binds to the nuclear pore and is maintained at the site even after repair is completed ., The replication-dependent rDNA damage occurs in S-phase and rDNA is relocated to the nuclear periphery ., The DSB in rDNA is repaired in S/G2 phases and the locus might be kept at the nuclear periphery until the next G1 phase ., In either case , we hypothesize that a small portion of damaged rDNA remains in the mother cell with the nuclear envelope , which may be carried into the next cell cycle ., Indeed , we detected stacked rDNA in the wells during pulse-field gel electrophoresis specifically of mother-cells in G1 phase ( three or four budded age ) ., This observation suggests an accumulation of unstable rDNA in the G1 phase of mother cells 44 ., We propose that this accumulation of broken ends could be a cause for senescence of the mother cell ., Several recent papers highlight the importance of perinuclear anchoring for continuing damage repair ., It has been shown that replication damage associated with expanded triplet repeats and eroded telomeres shift transiently to the nuclear pores 45 , 46 ., Su et al . showed that an artificially inserted CAG repeat is localized to the nuclear pores in a replication-dependent manner and this localization was important for CAG repeat stability 45 ., As the repeat may form a secondary structure and arrest replication , the CAG repeats and rDNA are expected to share a common mechanism that localizes them to the nuclear periphery , at least partially ., Churikov et al . showed that shortened telomeres in a telomerase-deficient yeast strain are relocated to the nuclear pores and this localization was required for type II survivors in which the short terminal TG-tract is elongated by recombination ( ALT in mammals ) 46 ., Although the relationship between the shortened telomere recombination and rDNA stability is not known , localization at the nuclear pore seems to be important for many aspects of genome maintenance ., In this study , we identified a mechanism that protects damaged repetitive rDNA sequences from undergoing rearrangement ( copy number variation ) by association with the nuclear pores ., In this way rDNA stability is maintained probably via the SSA pathway , which cannot be applied to DSBs in non-repetitive sequences ., Likewise , in Drosophila cells , a DSB in heterochromatin that mostly comprises repetitive sequences relocates to the nuclear pores for repair in a SUMOylation-dependent manner 47 ., SUMOylation also mediates relocation of the DSB in the rDNA to outside of the nucleolus and the eroded telomere to the nuclear periphery in Saccharomyces cerevisiae 30 , 46 ., It has been reported that damaged rDNA is relocated to specific loci around the nucleolus of mammalian cells and most of the factors required for this relocation , which were identified in yeast , are well conserved 48 ., Because mammalian genomes contain large stretches of repetitive sequences , such as retrotransposons and Alu-repeats , a similar mechanism may operate to maintain genome integrity in higher eukaryotes ., Future studies will shed light on the involvement of human homologues in the repair of damaged repetitive DNA ., Yeast strains used in this study were derived from NOY408-1b ( a W303 derivative ) ., Strains were grown at 30°C in YPD ( YPDA for Figs 1 , 3 , 4 , 5 , 7 and S3 Fig ) medium ., YPD ( yeast extract-peptone-dextrose ) and YPDA ( YPD with 0 . 4% adenine ) are rich media used for normal culture ., Synthetic complete ( SC ) medium lacking the appropriate amino acids 49 was used for gene marker selection ., Yeast strains used in this study are listed in S1 Table ., If necessary , G418 ( Sigma ) or clonNAT ( WERNER ) was added to the medium at the following concentration , 500 μg/ml ( G418 ) or 100 μg/ml ( clonNAT ) ., Yeast genetic transformation was performed by using Frozen-EZ Yeast Transformation II Kit ( Zymo Research Corporation ) according to the manufacturer’s instructions ., To test rDNA stability by pulsed field gel electrophoresis , we used cells that had divided ~45 times after transformation ., For the DSB localization assay , yeast cells were grown at 30 °C for 2 days on selective synthetic medium containing 2% glucose ( SD ) ., The cells were inoculated in synthetic medium containing 2% raffinose ( SR ) and grown overnight ., The culture was diluted to SR next morning and grown for about 4 hours ., When the exponentially growing cell population reached around 2 . 5 × 106 cells ml−1 , we added 20% galactose ( final 2% ) to the medium to induce I-SceI ., The living cells were directly subjected to microscopy on an SR agarose pad ., We used SD/SR-lacking tryptophan and uracil for YCH-252 or lacking tryptophan , uracil and histidine for YCH-244 in these experiments ., Samples for pulsed-field gel electrophoresis ( PFGE ) were prepared as described previously 50 ., Electrophoresis was performed in a 1% ( 0 . 8% for S2B Fig ) agarose gel with 0 . 5×Tris-borate-EDTA ( TBE ) buffer , using CHEF-MAPPER ( Bio-Rad ) ., The conditions were a 300–900 sec pulse time and 100 V for 68 hours at 14 °C ., For S2B Fig , after electrophoresis , the rDNA was detected by Southern blot analysis with an rDNA specific probe ., To quantify instability of rDNA in PFGE ( Figs 1C and 7B ) , the signal intensities of Chr ., XII and Chr ., IV were measured by Image J ( Fiji ) using the image of an EtBr stained gel ., The signal intensities of Chr ., XII were divided by that of Chr ., IV , which was expected to be constant between mutants ., Broader unstable bands reduce signal intensities in the area ., Moreover , chromosomes with an unusual structure cannot enter the gel and thereby reduce signal intensity ., Normalization of the Chr ., XII band intensity in the mutants to that of Chr ., IV , yielded values reflecting their rDNA stability ., In the tof2 , csm1 and lrs4 mutants , several minor bands were observed ., This suggests some of the cells contained multiple copies of chromosome XII because of chromosome missegregation caused by condensation defects in these mutants 31 ., In such cases , the major band was measured ., 2D gel electrophoresis was performed as previously described 51 ., DNA from early log phase cells ( ~3x106 cells/ml in YPD medium ) were digested in agarose plugs ( 5x107 cells/plug ) using BglII for 4 h at 37 °C ., The reaction was carried out in 200 μl reaction buffer with 150 units of BglII ., After electrophoresis , the rDNA was detected by Southern analysis with an rDNA specific probe ., RFB and DSB signals were quantified by ImageQuant ( GE ) ., The signal intensity of the RFB spot was divided by the signal intensity of total replication intermediates signal for normalization ., The signal intensity of the DSB spot was normalized to the RFB signal to show the relationship between the DSB and the arrested fork it was derived from ., ChIP was carried out as previously described 52 with minor modifications described below ., Yeast cells cultured in 45 ml medium were cross-linked with 1% formaldehyde at 30 °C for 20 min ., Cell pellets were resuspended in 600 μl of lysis buffer ( 50 mM HEPES-KOH at pH 7 . 5 , 500 mM NaCl , 1 mM EDTA at pH 8 . 0 , 1% Triton X-100 , 0 . 1% sodium deoxycholate and protease inhibitors ) and disrupted with zirconia beads using a Multi-bead shocker ( Yasui Kikai ) ., The recovered chromatin fraction was subjected to sonication using a Bioruptor ( Cosmo | Introduction, Results, Discussion, Materials and methods | The ribosomal RNA genes ( rDNA ) comprise a highly repetitive gene cluster ., The copy number of genes at this locus can readily change and is therefore one of the most unstable regions of the genome ., DNA damage in rDNA occurs after binding of the replication fork blocking protein Fob1 in S phase , which triggers unequal sister chromatid recombination ., However , the precise mechanisms by which such DNA double-strand breaks ( DSBs ) are repaired is not well understood ., Here , we demonstrate that the conserved protein kinase Tel1 maintains rDNA stability after replication fork arrest ., We show that rDNA associates with nuclear pores , which is dependent on DNA damage checkpoint kinases Mec1/Tel1 and replisome component Tof1 ., These findings suggest that rDNA-nuclear pore association is due to a replication fork block and subsequent DSB ., Indeed , quantitative microscopy revealed that rDNA is relocated to the nuclear periphery upon induction of a DSB ., Finally , rDNA stability was reduced in strains where this association with the nuclear envelope was prevented , which suggests its importance for avoiding improper recombination repair that could induce repeat instability . | Ribosomal RNA genes ( rDNA ) comprise an unstable region of the genome due to their highly repetitive structure and elevated levels of transcription ., Collision between transcription and replication machineries of rDNA , which may lead to DNA damage in the form of a double-stranded break , is avoided by the replication fork barrier ., When such a break is repaired by homologous recombination with a repeat on the sister chromatid , the abundance of homologous sequences may lead to a change in copy number ., In most organisms , however , only small variations in copy number are observed , indicating that the rDNA is stably maintained ., Our results suggest that some parts of rDNA become localized to the nuclear pore complex in a DNA double-strand break-dependent manner ., This localization requires the protein kinase Tel1 , which is involved in the DNA damage response pathway , and factors that recruit condensin , which facilitates condensation and segregation of rDNA during mitosis ., We found that the rDNA becomes unstable when association with the nuclear envelope was prevented ., Thus , the localization represents a unique strategy for maintaining repeat integrity after DNA damage . | cell cycle and cell division, cell processes, dna damage, fungi, model organisms, dna replication, experimental organism systems, dna recombination, dna, cell nucleus, cellular structures and organelles, nuclear pores, homologous recombination, research and analysis methods, saccharomyces, animal studies, ribosomes, yeast, biochemistry, rna, eukaryota, ribosomal rna, cell biology, nucleic acids, genetics, biology and life sciences, dna repair, saccharomyces cerevisiae, yeast and fungal models, non-coding rna, organisms | null |
journal.pcbi.1006459 | 2,018 | An information theoretic treatment of sequence-to-expression modeling | Cellular processes are determined by the response of regulatory sequences in DNA to signals from specific proteins called transcription factors ( TFs ) , leading to up- or down-regulation of gene expression 1 ., A major class of regulatory sequences is that of cis-regulatory modules ( CRMs , also called enhancers ) : these are regions of DNA , about 500–2000 base pairs long , harboring TF binding sites that control the transcriptional levels of nearby genes ., Variation of the DNA sequence in CRMs can affect gene expression , and has been linked to developmental defects and disease 2 ., Even minor variations , such as single nucleotide polymorphisms ( SNPs ) , in CRMs can have significant functional impact , such as problems in fetal development 3 ., Our ability to predict the impact of non-coding sequence variations on gene expression is very limited , in part due to the complexity of CRMs , and in part because such impact depends not only on the sequence itself but also the abundance and activities of relevant TFs in the cellular conditions of interest ., Statistical methods based on correlations among diverse data types such as TF-ChIP , histone modifications , gene expression , etc . can reveal salient properties of CRMs such as their tissue-specific activities 4 and their major regulators TFs 5 , 6 , and can in some cases predict the effect of removing a TF’s influence on a CRM or gene 7–9 ., Statistical and machine learning methods have recently been developed that can to some extent predict the effects of single nucleotide mutations on TF binding levels , DNA accessibility 10 , 11 , and even gene expression 12 , but these are typically not amenable to mechanistic interpretations , and are in a relatively early stage of exploration ., On the other hand , biophysical models based on equilibrium thermodynamics that explicitly incorporate key interactions among TFs , DNA and the transcriptional machinery have proven powerful for mechanistic understanding of the gene regulation process 13 ., Thermodynamics-based modeling of gene expression reveals the precise mapping between CRM sequence and the associated gene expression in a variety of cellular contexts , the so called ‘readout’ of the CRM ., These models provide a means to formalize our assumptions about a CRM’s cis-regulatory logic , especially how its functional elements combine to regulate a transcriptional output 14–19 ., They can generate predictions that can be empirically tested 20 , e . g . , by targeted misexpression or mutagenesis experiments ., Indeed , they have been used to predict effects of site mutations 21 , and also promise to provide precise , mechanistically grounded predictions of the effect of minor sequence changes in CRMs 20 ., Furthermore , these models can reveal ambiguities in our mechanistic knowledge about a system given existing data; pinpointing these ambiguities helps with choosing the future experiments that would best improve knowledge of the system ., The success of thermodynamic models has been demonstrated in the context of systems with high-resolution gene expression measurements , such as early-stage Drosophila embryonic development 15–17 , 22 ., Mechanisms influencing the precise function of a regulatory system include the number , accessibility , affinities and relative arrangement of TF binding sites within a CRM , as well as cellular concentrations of the TF molecules , and protein-protein interactions; all of these mechanisms affect the rate of transcription of the gene 23 ., Thermodynamic models of CRM function encode these mechanistic factors in their parameters , which correspond to biochemical properties of the molecules controlling the gene expression ., These parameters are typically computationally optimized to be assigned values that can best explain the gene expression patterns attributable to a set of CRMs 16 , 22 ., When used to investigate the regulatory function of a single CRM , the thermodynamic modeling approach faces a significant challenge: non-uniqueness of the optimal models ., For instance , a CRM mediating control by five or more TFs will include 10 or more free parameters in the GEMSTAT thermodynamic model 22 and we have shown previously that parameter training will converge to one of many local optima 21 ., Each optimal model explains the data equally well , but uses parameters that correspond to significantly varying , often mutually incompatible mechanistic hypotheses 24 , 25 ., For example , consider a gene regulated by a CRM that is under the control of two activators ., Assume there are two models that explain the wild-type expression pattern ., One model predicts the correct expression by using ( assigning function to ) only one activator , while the other model uses both activators ., In the absence of additional biological experiments that confirm the role of each activator , both models are equally plausible ., Problems arise when we try to predict , using the model , the effect of knocking down an activator or mutating its binding site ( s ) ., Depending on which model we use , the predicted effect of the perturbation experiment is different: a model that does not use an activator will not predict a change due to removal of that activator’s influence ., However , there is no reason to prefer one model’s prediction over the other , and the biology remains ambiguous until a new experiment is performed ., We believe it is important to respect this ambiguity of knowledge when modeling gene expression data and making predictions about future experiments ., In agreement with the above proposal , Samee et al . 21 laid out a new paradigm of gene expression modeling where one searches within the model’s parameter space for as many optima as possible , resulting in an “ensemble” of optimal models ., ( Henceforth , different assignment of values to the model’s tunable parameters will be considered as different models . ), Each model in the ensemble is a hypothesis about the cis-regulatory mechanisms encoded in the CRM , and is also capable of making specific predictions about perturbation experiments ., A simple approach to working with an ensemble of models is to make predictions by uniformly aggregating predictions of its member models ., It has been shown that this “wisdom of crowds” approach can be effective: aggregated votes of many models can predict the effect of site mutations more accurately than any individual model 21 ., We noticed , however , that a typical ensemble of sequence-to-expression models , e . g . , that created by Samee et al . 21 in modeling embryonic expression of the Drosophila gene ind , is not uniformly distributed in the parameter space ., Rather , they are clustered in the parameter space ( Fig 1D ) , with models within a cluster predicting similar effects for a particular perturbation , but each cluster’s consensus predictions being qualitatively different from those of other clusters ., Different clusters can have different ‘spans’ , i . e . , the extent to which models in that cluster differ from each other quantitatively ( in parameter values ) while producing equally good fits to available data and essentially the same predictions for future experiments ., For instance , the cluster at the bottom of Fig 1D has greater span than the cluster on the top-left , which is relatively tight ., The span of a cluster pertains to parameter sensitivity 24 , 26 in that region of parameter space ., Furthermore , different clusters may have different representation ( number of models ) in the ensemble , and the number of represented models may not correlate with the span of the cluster ., This is because we do not make strong assumptions about how the ensemble of models was obtained , beyond that it is a collection of models that fit the available data and may be located in different regions of parameter space ., With these observations about ensembles of models , we sought the most appropriate way to use ensembles for making predictions and for designing future experiments ., We describe here one such procedure that we developed and implemented , which allows us to make predictions with ensembles of models , and also offers a principled approach to experiment design in gene regulation studies ., Briefly speaking , our modeling approach involves ( 1 ) creating a large ensemble of models that fit the available data accurately , following the sampling and optimization strategy of Samee et al 21 and ( 2 ) defining a probability distribution over the parameter space such that the ensemble of models represents regions of high probability and where each cluster of models ( roughly speaking , a distinct mechanistic hypothesis ) has approximately the same total probability as other clusters ., This distribution provides a principled way for us to make aggregated predictions about any particular perturbation experiment , and to describe the uncertainty in such predictions ., Additionally , we show how to measure the entropy of this probability distribution , thereby quantifying the uncertainty in parameter space 27 that remains after fitting the models to available data ., Noting that the ensemble of models consistent with available data changes ( typically shrinks ) upon performing an additional experiment , we suggest that the difference of entropies of the probability distributions before and after an experiment ( i . e . , information gain ) may be used to score the ‘value’ of the experiment ., We can use this value as a score to compare different experiments , the experiment with greater score being deemed the more informative experiment ., The ability to assign information theoretically-grounded ‘values’ to experimental results is significant , since it paves the way for principled experiment design 28 , 29, We consider the class of mathematical models that predict the gene expression level driven by a cis-regulatory module ( CRM ) from the latter’s sequence , given prior knowledge of relevant transcription factors ( TFs ) , their in vitro DNA-binding affinities ( motifs ) , and their concentration levels in the cellular context of interest ., Several such models have been investigated in the literature 15–17 , 20 , and we work with the GEMSTAT model 22 , which we developed previously and which we are most familiar with ., The GEMSTAT model has two free ( tunable ) parameters for each relevant TF , one corresponding to its binding strength for the consensus site and one corresponding to its potency as an activator or repressor ., The model also has optional free parameters for any TF-TF cooperative interactions that the modeler may choose to include ., Assigning values to these free parameters specifies a model completely , allowing it to predict gene expression in any cellular context where TF concentrations are known ., Typically , optimization strategies are used to identify the parameter setting ( s ) that accurately predict gene expression driven by a CRM in multiple cellular contexts 30 ., In light of the observations made in Introduction , we sought to first construct an ensemble of models that are widely spread in parameter space , and thus represent different mechanistic explanations of data ., A model is included if its goodness-of-fit score–sum of squared errors or ‘SSE’ between known and predicted expression levels in multiple cellular conditions–is below a threshold ., We noted that the number of TFs in common modeling scenarios is less than 10 15–17 , 20 , 22 , and the number of free parameters in the range of 10–20 ., This led us to consider uniform sampling of the parameter space as the first step of ensemble construction ., We followed the approach of Samee et al . 21 ( Fig 1A–1D ) , performing extensive uniform sampling from the space ( millions of samples ) , followed by filtering of promising models ( SSE score below a modest threshold ) , local optimization seeded by these promising models , and a final round of filtering on the optimized models ( SSE score below a strict threshold ) ., ( See Methods for details . ), This procedure allows us to construct a large ensemble of models representing many or all optimal regions of the parameter space ., We provide more details of ensemble size and composition later , in the context of specific gene models ., An ensemble of models can be used to make predictions by aggregating ( averaging ) the predictions made by each member model ., However , this approach ignores the fact that the ensemble construction ( outlined above or by a similar method ) likely results in some regions of parameter space being over-represented in the ensemble ., Models belonging to the same region , i . e . , proximal to each other in the parameter space , are presumed to represent qualitatively similar mechanisms of CRM function ., Thus , the ensemble’s aggregate predictions may be biased towards one or a few mechanistic hypotheses ., We therefore sought a more nuanced way to aggregate model predictions , by defining a probability distribution over parameter space that captures how the fit models are spread across different regions of the space but discounts for unequal representations of ( number of models in ) different regions ., Such a probability distribution can then be used to make predictions about new experiments and also to score the uncertainty of mechanistic explanations offered by the ensemble ., We also note that constructing this distribution has close ties to the kernel density estimation problem 31 but is different because the ensemble is not a collection of IID samples drawn from the desired population ., The simplest distribution to consider is a discrete uniform distribution over the models in the ensemble; e . g . , Fig 1E shows such a distribution over an ensemble of four models in a toy 1-dimensional parameter space ., In the continuous parameter space , highly proximal models are likely to have similar goodness-of-fit , therefore we smoothen the discrete distribution by centering a Gaussian distribution at each model in the ensemble and constructing a uniform mixture ( Fig 1F ) ., This mixture of Gaussian distributions provides a continuous distribution , but if one region of the space is over-sampled in the ensemble , the distribution puts undue weight in that region; e . g . , the three closely-related models on the left in Fig 1F together carry about three times the probability mass as that around the isolated model on the right ., In light of this observation , we first cluster models , each cluster roughly corresponding to a distinct mechanistic hypothesis , and define the overall probability distribution to be a mixture of distributions representing each cluster ., Since we lack any additional knowledge to prefer one cluster over another , we assume uniform mixture weights for the clusters ., The probability distribution representing each cluster , in turn , is a mixture of Gaussian distributions whose means are the models in that cluster ., Thus , Fig 1G shows a mixture of two distributions ( red and blue ) representing the two clusters , with the red distribution in turn being a uniform mixture of Gaussians centered on the three models in that cluster ., Fig 1H shows a similar construction , now for a 2D parameter space , beginning with the given filtered ensemble ( θ1 , θ2 , … θM ) , identifying three clusters and constructing the mixture probability distribution ., ( For more details , especially the construction of covariance matrices for these distributions , see Methods . ), The probability distribution over models , constructed as above , can be used in the following ways: Sequence-to-expression models enable us to propose mechanisms for gene expression regulation , that may then be confirmed by performing perturbation experiments such as TF knockout or site mutagenesis , followed by expression assays that inform us about how the gene expression changes in the perturbation condition ., Some experiments result in greater gene expression changes than others , and it is natural to want to characterize ‘what was learned’ from each experiment , as well as quantify how informative that experiment was ., Here , we demonstrate such an exercise in systematic experimentation in the gene regulation context , using the ensemble modeling framework described above ., Our first set of demonstrations are in the context of the regulatory mechanisms of an early development gene in D . melanogaster—the intermediate neuroblasts defective ( ind ) gene ., We chose this gene because it is known to be regulated by a well-defined enhancer , and its major regulatory inputs are well characterized ., The gene was characterized by Weiss et al . 32 and Stathopoulos and Levine 33 , among others , and was the subject of systematic modeling by Samee et al . 21 ., It is expressed in a lateral stripe along the dorso-ventral axis of the early embryo ( S1A Fig , black curve ) , with activation from the TFs Dorsal ( DL ) and Zelda ( ZLD ) , and repression by the TFs Snail ( SNA ) , Ventral nervous system defective ( VND ) and Capicua ( CIC ) ( S1A Fig ) ., In addition to the wild-type expression pattern of this gene , its expression has been experimentally recorded under several perturbation conditions ( S1 Table ) , surveyed by Samee et ., al 21 and further discussed below ., Despite the knowledge of a fairly complete set of regulatory inputs , several ambiguities remain about the cis-regulatory logic of the ind enhancer ., This is evident when we construct an ensemble of models that predict the known expression pattern of ind from its enhancer sequence along with TF concentration profiles along the D/V axis ., S1B Fig shows that the ensemble’s mean prediction ( magenta curve ) for these wild-type conditions fits the wild-type expression profile accurately , and with little variation among different models ( pink curves are models in the ensemble ) but S1C Fig reveals that most of the 13 parameters of the model exhibit substantial variability , a point also illustrated by the marginal distributions of ten of the parameters ( S1D–S1F Fig ) ., The high degree of uncertainty is not surprising , given that data from only one experiment–the wild type condition–for a single enhancer was used to train the ensemble ., It also means that results of various perturbation experiments may prove informative about this gene’s regulatory mechanisms , an avenue that we pursue next ., First , we worked with a ‘synthetic true model’ MST that allows us to predict results of various perturbation ‘experiments’ in silico ., This synthetic true model MST was carefully chosen from among the ensemble of models consistent with wild-type data , described above ., ( See S3 and S4 Figs for details . ), We used MST to individually predict the effects of, ( a ) each TF’s knockout and, ( b ) removing the strongest site of each TF in the enhancer , and treated these predicted gene expression patterns ( Fig 2A , green curves ) as the ‘true’ results of those hypothetical or ‘in silico perturbation experiments’ ., We used each of the 10 in silico experiments to construct a ‘filtered ensemble’ ( average predictions shown in Fig 2A , magenta curves ) , computed its entropy score , and thus assigned an information theoretic ‘value’ to the experiment ( Fig 2B ) ., We noted that the magnitude of change in the expression profile resulting from a perturbation experiment does not necessarily reflect the value of the experiment ., For instance , it is possible to obtain new information from a perturbation experiment where the expression pattern remains unchanged from wild-type , a case in point being the SNA knockout experiment ( Fig 2A ) , with assigned value 1 . 66 –apparently many models consistent with wild-type data cannot explain this experiment and are removed in the filtered ensemble from its results ., Conversely , an experiment with a more substantial expression profile change may not add anything to our knowledge of the regulatory mechanism ., For instance , the DL knockout experiment shows peak ind expression diminishing by ~60% ( Fig 2A ) but is assigned a value of 0 . 32 ( Fig 2B ) , among the lowest of the 10 experiments; this is because most models capable of explaining the wild-type ind pattern apparently use DL as activator , so knocking out DL does not provide much new information ., The same is not true of the experiment where the strongest DL site is removed , an experiment with minor impact on expression ( Fig 2A ) but a relatively high assigned value of 1 . 34 ., This points out that even if the involvement of a TF is beyond doubt , there may be uncertainty regarding the strength of its regulatory input and the mediatory role of each of its binding sites ., We noted ( Fig 2B ) the same trend—that the value of strongest site mutagenesis is greater than that of TF knockout–for the other activator ( ZLD ) ., On the other hand , for perturbations involving repressors ( SNA , VND , CIC ) the value of the site mutagenesis experiment is less than that of TF knockout in all three cases ., Also , for comparison , we show in Fig 2C the relative values of the 10 ‘experiments’ under a more simplistic scheme that evaluates each experiment by the reduction in entropy assuming a discrete uniform distribution on all models in an ensemble ., We note that the two schemes largely agree with each other in this evaluation , though this may not be true in general , depending on how an ensemble of models is generated ., Finally , we note that the observations above were made with a specific choice of the ‘synthetic true model’ MST , that furnished ‘experimental’ results , but the reported trends , e . g . , large information gain from a perturbation experiment with little effect on expression , or little gain from an experiment with large effect , were unchanged when we repeated the entire exercise with a different choice of MST ( S5 and S3B Figs ) ., In this section , we will examine results of real perturbation experiments pertaining to the ind gene reported in the literature and evaluate each experiment in the way described above ., In addition to the wild type gene expression pattern of the ind gene ( S1A Fig ) , we have information from six different biological perturbation experiments ( S1 Table ) ., It is known that ind expression is abolished in DL mutants 34 and becomes weaker in ZLD mutants 35 ., Its peak expression reduces to ~50% of its wild-type level upon mutation of the four strongest ZLD binding sites 21 ., ( We call this experiment ‘ZLD site mut . ’ . ), Removal of the strongest DL site ( ‘DL 1 site mut . ’ ) has no observable effects on the expression 36 and removing three overlapping DL sites ( ‘DL 3 site mut . ’ ) greatly diminishes peak expression 21 ., Knockout of SNA ( experiment ‘SNA KO’ ) leaves ind expression unaltered 17 , while knocking out VND ( ‘VND KO’ ) causes the domain of expression to expand ventrally 37 , and CIC site mutagenesis ( ‘CIC site mut . ’ ) expands ind expression dorsally 38 ., We evaluated each of the six perturbation experiments ( two TF knockouts and four site mutagenesis experiments ) using the approach introduced in the previous section–begin with the ensemble of models that explain wild-type gene expression , construct a filtered ensemble that additionally explains the perturbation results ( see Methods and S2 Fig ) , and calculate the difference in entropy ( ‘information gain’ ) ., The values assigned to these experiments are shown in Fig 3A , and we note that the SNA and VND knockout experiments were the most informative in this group ., Evaluating a new experiment , in our scheme , involves ruling out from the original ensemble a subset of models inconsistent with that new experiment ., Recall that models in the ensemble were clustered , with the informal understanding that each cluster represents a distinct mechanistic hypothesis ., Thus , if an entire cluster is ruled out by a particular experiment , one may interpret it as ruling out a particular mechanistic hypothesis ., Table 1 shows the sizes of clusters in the original ( wild-type ) ensemble of models and the effect of filtering with each perturbation experiment ., We note that an experiment ( ‘DL 3 site mut . ’ in Table 1 ) may remove just one cluster , while retaining other clusters of models as feasible ., There may also be experiments ( ‘SNA KO’ and ‘VND KO’ in Table 1 ) that rule out the majority of mechanistic hypotheses , retaining only 2–3 of the original clusters ., The other scenario–where all clusters are retained but rendered substantially sparser–is also seen , indicating that the information gained by those experiments was more along the lines of quantitative refinement rather than qualitative pruning of the space of possible mechanisms ., Fig 3B shows the above information theoretic evaluation of each experiment , compared to a simpler scoring scheme where entropy of an ensemble is simply the logarithm of the size of that ensemble , i . e . , where we assume a uniform discrete distribution on models ., As expected , the two scores are highly correlated ., Note that experiments were assigned values above under the assumption that they were the sole ( or first ) perturbation experiment performed ., In reality , of course , a line of enquiry proceeds via a series of such experiments , begging the question whether a perturbation experiment can be informative on its own but not so much if it follows another perturbation experiment ., We explored this question further , by examining every possible pair of experiments ( performed sequentially ) , and noted that there are indeed such examples ., However , in the interest of continuity we do not discuss this analysis here , referring the interested reader to S3 Table ., We next moved beyond asking ‘how much’ information was gained from an experiment to the more subjective question of ‘what’ information was gained ., To answer this , it seems natural to compare the original ( wild-type ) ensemble of models to the filtered ensemble that is additionally consistent with the new experiment’s results ., The challenge then becomes: how do we compare these two ensembles in a language that appeals to the biologist’s intuition ?, One pragmatic approach that we devised , and illustrate here , is to identify a second experiment for which the two ensembles make markedly different predictions , and use this difference to illustrate the distinction between ensembles ., For instance , consider the ‘CIC site mut . ’ experiment , which we saw above to be of modest information theoretic value ( Fig 3A ) ., We also noted in Table 1 that this experiment induces a filtered ensemble with two of the eight original clusters completely ruled out and two additional clusters drastically reduced in size ( from ~900 and ~700 models to 2 and 1 models respectively ) , suggesting that certain plausible mechanistic hypotheses were indeed ruled out by it ., To interpret this further , we considered the predictions of this filtered ensemble on the ‘DL 1 site mut . ’ experiment ( Fig 3E ) and found these to be in fair agreement with the true results from the literature 36 ( Fig 3C ) ., We then noted that the wild-type ensemble , not filtered by the ‘CIC site mut . ’ experiment , is far more uncertain in its predictions about the ‘DL 1 site mut . ’ experiment ( Fig 3D ) ., Thus , the ‘CIC site mut . ’ experiment informs us , correctly , that mutagenizing the strongest DL site in the enhancer should not result in a significant reduction in peak ind levels , a point that was ambiguous in the original ensemble ., A similar approach can be adopted to interpret the information provided by other perturbation experiments ., In our second example , we interpreted the ‘DL 1 site mut . ’ experiment by examining the predictions of its filtered ensemble on the ‘CIC site mut . ’ experiment , which according to the literature 38 shows an extension of the dorsal boundary of ind expression ( Fig 3F ) This derepression effect is much more accurately predicted by the filtered ensemble ( Fig 3H ) , while the original ensemble’s average prediction is less definitive in predicting this effect ( Fig 3G ) ., In other words , the ‘DL 1 site mut . ’ experiment informs us that CIC is an important repressor of the ind gene , setting up its precise dorsal boundary ., For our third example , we note that the filtered ensemble of the ‘VND KO’ experiment accurately predicts that a genetic knockout of SNA will not affect the ventral boundary of ind expression ( Fig 3I and 3K ) , while the original ensemble erroneously predicts ventral de-repression ( Fig 3J ) ., In other words , the ‘VND KO’ correctly informs us that SNA does not position the ventral boundary of ind expression ., Thus , these three examples show how the information gained by an experiment can be interpreted by examining unique aspects of predictions of that experiment’s filtered ensemble on a second experiment ., Similar to ind , single minded ( sim ) is dorso-ventral patterning gene in D . melanogaster that has been the subject of many biological experiments that describe the regulators of the gene , delineate its enhancer 39–41 , and characterize the combinatorial action of multiple TFs and cell signaling in the formation of the precise expression pattern driven by the sim enhancer 42 ., The sim gene is initially expressed at the cellular blastoderm stage in a narrow row of width equal to two cells along the dorso-ventral axis at the mesectoderm ( the boundary between mesoderm and neural ectoderm ) 40 , 41 ( Fig 4A ) ., Sim acts as a master regulator during the development of central nervous system ( CNS ) 43 and the confinement of its expression to the narrow line of cells is essential for the formation of the ventral midline and CNS during gastrulation 39 , 44 ., This precise pattern of expression can be explained by a complex regulatory mechanism that involves Notch signaling 45–47 ., On the ventral side , DL and Twist ( TWI ) activate but SNA represses the expression in the mesoderm 39 , 44 ., Expression on the dorsal side is inhibited directly by Suppressor of hairless ( Su ( H ) ) , which is the only known repressor of sim in the neuroectoderm 45 , but is believed to have an activating influence on sim in the mesoderm region 45 , 48 , 49 ., The sharp dorsal boundary of sim is formed because Notch signaling converts the ubiquitously expressed Su ( H ) from a repressor in dorsal regions to an activator in ventral regions exactly at the mesectoderm 37 , 45 , 48 , 50 ., With these pieces of mechanistic information in hand , we employed GEMSTAT to model the expression driven by the sim enhancer ., Then , we used the procedure introduced above to examine different perturbation experiments related to this enhancer reported in the literature , and quantify and interpret the ‘value’ of these experiments , after the fact ., To our knowledge , this work is the first attempt to computationally model the expression of the sim enhancer , using the combinatorial action of TFs and signaling 45 , 47 , 51 ., We built an ensemble of models that predicts the wild-type expression profile of sim accurately ( Fig 4B and Methods ) from its wild-type enhancer ( ‘2 . 8sim’ ) ., We then considered several experiments reported in the literature pertaining to this gene , with the goal of computing the information gain from each experiment and interpreting the information they provide ., Each of the nine experiments considered is a reporter assay with a variant of the wild-type sim enhancer , and we used its observed readout to construct objective criteria ( S2 Table ) for filtering models and creating a ‘filtered ensemble’ for that experiment ( S6 Fig ) ., This allowed us to quantify the information gain score of each experiment , using the procedure described in previous sections ( Fig 4C and 4D ) ., This revealed that the experiment ‘2 . 8simΔSD16’ , representing a deletion of two segments ( harboring a SNA site and an E-box element respectively ) from the wild-type enhancer 2 . 8sim , is the most informative ( value 2 . 25 ) , while seven of the other eight experiments are substantially less informative ( about 0 . | Introduction, Results, Material and methods, Discussion | Studying a gene’s regulatory mechanisms is a tedious process that involves identification of candidate regulators by transcription factor ( TF ) knockout or over-expression experiments , delineation of enhancers by reporter assays , and demonstration of direct TF influence by site mutagenesis , among other approaches ., Such experiments are often chosen based on the biologist’s intuition , from several testable hypotheses ., We pursue the goal of making this process systematic by using ideas from information theory to reason about experiments in gene regulation , in the hope of ultimately enabling rigorous experiment design strategies ., For this , we make use of a state-of-the-art mathematical model of gene expression , which provides a way to formalize our current knowledge of cis- as well as trans- regulatory mechanisms of a gene ., Ambiguities in such knowledge can be expressed as uncertainties in the model , which we capture formally by building an ensemble of plausible models that fit the existing data and defining a probability distribution over the ensemble ., We then characterize the impact of a new experiment on our understanding of the gene’s regulation based on how the ensemble of plausible models and its probability distribution changes when challenged with results from that experiment ., This allows us to assess the ‘value’ of the experiment retroactively as the reduction in entropy of the distribution ( information gain ) resulting from the experiment’s results ., We fully formalize this novel approach to reasoning about gene regulation experiments and use it to evaluate a variety of perturbation experiments on two developmental genes of D . melanogaster ., We also provide objective and ‘biologist-friendly’ descriptions of the information gained from each such experiment ., The rigorously defined information theoretic approaches presented here can be used in the future to formulate systematic strategies for experiment design pertaining to studies of gene regulatory mechanisms . | In-depth studies of gene regulatory mechanisms employ a variety of experimental approaches such as identifying a gene’s enhancer ( s ) and testing its variants through reporter assays , followed by transcription factor mis-expression or knockouts , site mutagenesis , etc ., The biologist is often faced with the challenging problem of selecting the ideal next experiment to perform so that its results provide novel mechanistic insights , and has to rely on their intuition about what is currently known on the topic and which experiments may add to that knowledge ., We seek to make this intuition-based process more systematic , by borrowing ideas from the mature statistical field of experiment design ., Towards this goal , we use the language of mathematical models to formally describe what is known about a gene’s regulatory mechanisms , and how an experiment’s results enhance that knowledge ., We use information theoretic ideas to assign a ‘value’ to an experiment as well as explain objectively what is learned from that experiment ., We demonstrate use of this novel approach on two extensively studied developmental genes in fruitfly ., We expect our work to lead to systematic strategies for selecting the most informative experiments in a study of gene regulation . | information entropy, ecology and environmental sciences, gene regulation, experimental design, research design, regulator genes, probability distribution, mathematics, gene types, research and analysis methods, computer and information sciences, gene expression, soil science, soil perturbation, probability theory, mutagenesis, genetics, information theory, biology and life sciences, physical sciences | null |
journal.pcbi.1004493 | 2,015 | Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance | In this study we present a model that abstracts the evolutionary dynamics of an asexually reproducing population with unspecified , but variable , population size in which individuals are subject to point mutation at reproduction ., We model the genotype of each individual as a length N sequence of 0s or 1s indicating the absence or presence of N known fitness conferring mutations of interest ., In particular , our results are derived from a model of E . coli with genotypes of length N = 4 , indicating the presence of four possible amino acid substitutions ( specifically M69L , E104K , G238S and N276D ) giving 24 = 16 possible genotypes in total ., The fitness values associated with individuals in this model are , in general , abstracted away from biological reality but in the context of this work are empirically determined by average growth rates of specific strains under different drugs ( see 33 for details ) ., Our model builds on the well–studied Strong Selection Weak Mutation ( SSWM ) model derived by Gillespie 34 , 35 which assumes that the disease population is isogenic and that evolution proceeds as the population genotype is periodically replaced by a fitter mutant ., This model is valid under a broad range of circumstances provided that the mutation rate is not too high or the population size too small ( a precise description of the necessary relationship between population size and mutation rate is provided in the Materials and Methods ) ., The benefit of this abstraction is that , provided they fall within acceptable limits , we are able to ignore the population size and mutation rate in predicting evolutionary trajectories ., This allows us to efficiently determine evolutionary trajectories and to consider trajectories either at the patient scale or at the whole clinic scale as in Goulart et al 21 , although without explicit knowledge of the population parameters we are unable to predict the time taken to traverse these evolutionary trajectories ., Under the assumption of SSWM the evolutionary trajectory of a population can be viewed as a weighted random walk through genotype space which is determined by the fitness landscape imposed by a drug ., Our work differs from previous models that utilize the SSWM model in that we encode this random walk formally as a Markov chain ., This enables us to determine the probability of evolutionary trajectories to the fitness optima of landscapes through matrix multiplication ., Specifically , we encode uncertainty about the current population genotype as a probability distribution vector μ with length equal to the number of genotypes ., We can then determine the probability of reaching different fitness optima of a landscape by multiplying μ by a limit matrix that is determined by successive multiplication of the Markov chain transition matrix associated with that landscape ., This encoding assumes that drugs are prescribed for sufficiently long for evolution to proceed to equilibrium in the fitness landscape ., Here , we present the construction of the Markov chain from fitness landscapes and use this construction to derive mathematical results regarding drug ordering and cycling from the algebraic properties of its transition matrix ., In particular , we demonstrate that the order in which drugs are prescribed can have significant effects on the final population configuration—a phenomenon we call non–commutativity of selective pressures ., Using previously measured landscapes for 15 β-lactam antibiotics we illustrate how the emergence of high resistance , which we take throughout the following to mean evolution to the highest fitness peak of the landscape , can be both hindered and promoted by different orderings of selective pressures ., Finally , we exhaustively explore all possible ordered sequences of two , three and four antibiotics , finding that the majority , approximately 70% , of arbitrary drug sequences promote the emergence of resistance ., These findings suggest new treatment strategies which use rational orderings of drugs to shepherd evolution through genotype space to a configuration that is sensitive to treatment , as in the work of Imamovic et al 32 , but also from which resistance cannot emerge ., Using the Markov chain model presented in the Materials and Methods , we can formally prove that for a large class of fitness landscape pairs there is non-commutativity in the evolutionary process as described by the SSWM assumptions ., Suppose that there are two drugs , X and Y , with corresponding fitness landscapes x and y , and that we wish to determine what , if any , difference there is between applying X followed by Y to a population as opposed to applying Y followed by X . We can construct the Markov chain transition matrices Px and Py corresponding , respectively , to x and y according to Eq 3 and take the limits P x * and P y * of these matrices under successive multiplication ., For a given initial population distribution vector μ , we can find the distribution over genotypes after evolution proceeds to equilibrium in the fitness landscapes by matrix multiplication ., For example , the distribution after drug X is prescribed is given by μ ′ = μ P x * ., Hence , our model predicts that the ordering makes no difference to the final population distribution on an initial population with genotype i if , and only if ,, μ i P x * P y * = μ i P y * P x * , ( 1 ), where μi is the population vector whose ith component is one and all of whose other components are zero ., Supposing we do not know the initial population genotype , we can only guarantee that the order of application is irrelevant when the outcome is the same regardless of the starting genotype ., We thus require that μ i P x * P y * = μ i P y * P x * for each genotype i ., Since these unit vectors form a basis of ℝN this occurs precisely when P x * P y * = P y * P x * ., It follows that drug application will commute precisely when the corresponding limit matrices commute ., In practice we may be able to narrow down which genotypes are likely to constitute the population through bacterial genotyping or by observing that certain strains are not viable in the wild due to the high fitness cost of certain mutations ., Mira et al 33 empirically determined the fitness landscapes for E . coli in the presence of N = 4 resistance–conferring mutations under 15 commonly used antibiotics using average growth rates as a proxy for fitness ., We tested commutativity between each pair of these 15 antibiotics and found no commuting pairs ., We then tested 106 pairs of random fitness landscapes with varying ruggedness generated according to Kauffman’s NK model for generating “tunably rugged” fitness landscapes 36 , 37 using a random neighborhood Boolean function for determining the fitness contributions of each locus ., We fixed N = 5 and generated each landscape by first drawing K uniformly from {0 , … , N − 1} and then using Kauffman’s model to build a landscape ., We found that 0 . 132% of the landscape pairs generated had limit matrices which commuted , suggesting that commutativity is rare and the order in which drugs are prescribed will be significant in almost all instances ., We now turn our attention to finding antibiotic cycling strategies as considered by Goulart et al 21 ., Unless x is a flat landscape ( taking equal values for all genotypes ) there must exist at least one genotype j whose fitness is a minimum and which has a fitter neighbor ., Such a genotype satisfies ℙ ( i → j ) = 0 for all genotypes i ., Hence if x is not flat , the limit matrix P x * has at least one column of all zeros and is singular , so there cannot exist a second landscape y for which P x * P y * = I . Hence for any second landscape y there must exist a unit row vector μi for which μ i P x * P y * ≠ μ i ., This means that natural selection in our model is irreversible , in the sense that for a given ( non-flat ) landscape we cannot find a second which is guaranteed to reverse its effects unless we first measure the population genotype—a measurement which is non–trivial and not currently common clinical practice ., This result precludes the existence of a general cycling strategy that returns the disease population to its original genotype regardless of that starting genotype ., If we do in fact know the starting genotype , as we might if the disease is contracted in the wild where resistance–conferring mutations often carry a cost 38 which makes the wild–type genotype most likely , then cycling strategies can be found efficiently by our model ., If the initial genotype is known to be i , the initial population distribution will be μi and a sequence of drugs X1 , …Xk with fitness landscapes x1 , …xk will constitute a cycling strategy precisely when μiPx1*…Pxk*=μi ., This criterion will be satisfied when μi is a left 1–eigenvector of Px1*…Pxk* ., As such , we can find cycling strategies directly using algebra and avoid the graph–search technique used by Goulart et al 21 ., Prescriptions of sequences of drugs occur frequently in the clinic , and often without any guidelines as to which orderings are preferable ., Common examples of this include treatment of H . pylori 39 , Hepatitis B 40 and the ubiquitous change from broad to narrow spectrum antibiotics 41 ., The ordering of the sequence is therefore often determined arbitrarily , by the individual clinician’s personal , or historical experience or from laboratory data ., However , our model predicts the order in which the drugs are given is likely to have an effect on how the disease evolves and further , once a drug has been given it is not guaranteed that we will be able to reverse the effects ., Ideally , we would like to be able to identify drug orderings that lower the probability of a highly resistant disease population emerging during treatment ., To consider optimal drug orderings in the context of our model we first need to know the fitness landscapes ( or proxies of the fitness landscapes ) of a number of antibiotics used to treat a given bacterial infection ., Experimentally determining these landscapes requires one to consider all possible 2N combinations of genotypes in a set of N loci , a task which is prohibitively difficult for all but small values of N . De Visser and Krug found that there have been less than 20 systematic empirical studies of fitness landscapes 26 and that landscapes have been calculated for a number of model organisms including E . coli 21 , 29 , 42 , 43 , Saccharomyces cerevisiae 44 , Plasmodium falciparum 45 and type 1 Human Immunodeficiency Virus 46 ., Recent work by Hinkley et al 47 , which utilizes regression methods to approximate large fitness landscapes from samples of the space , could help ameliorate the complexity of experimentally determining fitness landscapes ., Mira et al 33 investigated the fitness landscapes of E . coli under 15 different β-lactam antibiotics using the average growth rates of isogenic populations of each genotype under the drug as a proxy for fitness for a total of N = 4 resistance conferring mutations ., Fig 1 shows the evolutionary graphs of the fitness landscapes for three of these antibiotics , Ampicillin ( Amp ) , Ampicillin+Sulbactam ( Sam ) and Cefprozil ( Cpr ) ., We will first use these three fitness landscapes to demonstrate the steering hypothesis explicitly ., In the case of a single peaked landscape , such as that for Sam , we cannot reduce the likelihood of resistance as all evolutionary trajectories lead to the global fitness optimum ., It is only when a drug has a multi–peaked landscape that we may be able to avoid resistance through careful choice of preceding drug ., Of the 15 landscapes determined empirically by Mira et al 33 only the landscape for Sam is single peaked ., In their review of empirical fitness landscapes , de Visser et al 26 find that biological landscapes show a variable but substantial level of ruggedness , suggesting that multi–peaked landscapes could be quite common ., In the following we take the parameter r in Eq ( 3 ) , which determines how the probability of a mutation fixing is biased by the fitness increase it confers , to be zero ., Note that changing the value of r will not change the accessibility of an evolutionary trajectory , hence by taking a different value of r ≥ 0 we will only change the result quantitatively ( the probabilities may change ) but not qualitatively ., We begin by supposing that we do not know the initial population genotype ., This assumption gives us worst case scenario results , and allows pre–existence of any resistant genotype ., We model this situation by taking as our prior population distribution μ = 1/2N , … , 1/2N specifying that each genotype is equally likely to constitute the starting population ., If we apply the drug Amp to this population distribution we find that in the expected distribution μ * = μ P A m p * ( shown in the first diamond in the top row of Fig 2 ) each of the fitness peaks can be found ., In particular , the most highly resistant genotype 1111 can arise in the population with probability 0 . 62 ., Suppose instead we apply Sam first ., In this case as the landscape is single peaked the population will converge to the global optimum genotype 1111 ., This genotype is also the global optimum of the Amp landscape and hence if we apply Amp after Sam we will encounter high resistance ., We have steered the population with one drug to a configuration which increases the probability of resistance to a second ., Next suppose that we instead apply Cpr after Sam; in this case the population is guaranteed to evolve to a local optimum 0110 of the Cpr landscape ., 0110 is the least fit local optimum of the Amp landscape ., Thus if we apply Amp to the population primed by Sam → Cpr then evolution to the global optimum 1111 is not possible ., This example demonstrates the steering hypothesis , that evolution can be shepherded through careful orderings of multiple drugs to increase or decrease the likelihood of resistance emerging ., To test our steering hypothesis more rigorously , we performed an in silico test of steering using combinations of one , two or three preceding drugs for each of the 15 drugs for which we know the landscapes ., Table 1 shows , for each of the 15 antibiotics , the steering combinations predicted to minimize the probability of evolution to the most resistant genotype in the landscape of that antibiotic when applied in order before it ., Again we took μ = 1/2N , … , 1/2N to model the worst case scenario for pre–existing resistance ., We found that for 3 of the 15 drugs there exists another which steers the initial population μ to a configuration from which evolution to the global fitness optimum of the drug landscape is prevented entirely ., This number rose to 6 when pairs of drugs applied sequentially are used to steer the population and to 7 when triples were applied in sequence ., We then performed a second in silico experiment to find combinations of steering antibiotics maximizing the probability that evolution proceeds to the least fit local optima in the landscape of a final antibiotic ( Table 2 ) ., We found that , excluding the single peaked landscape for Ampicillin with Sulbactam , there exist 0 drugs for which a single other drug is able to steer the population to a configuration from which evolution to only the least fit optimum is possible ., If pairs of drugs are used to steer there are 3 such drugs ( including the example presented in the above demonstration ) and if triples of steering drugs are considered there remains only 3 ., These findings suggest that through careful choice of steering drugs we may be able to prevent the emergence of resistance ., During these experiments we found that 14 of the 15 antibiotics in our experiment ( Cefpodoxime ( CPD ) being excluded ) appeared in an optimal steering combination of some length ., Whilst careful selection of drugs for steering can prevent the emergence of resistance , arbitrary drug orderings can also promote it ., We performed an exhaustive in silico search of all singles , pairs , and triples of steering drugs applied sequentially to prime the initial population μ for a final application of each of the 15 antibiotics ( Table 3 ) ., We found that steering with a single drug increased the likelihood of the most resistant genotype emerging in 57 . 3% of cases and decreased the likelihood in 29 . 8% of cases ., Steering with pairs of drugs increased the likelihood in 64 . 1% of cases and decreased it in 28 . 4% of cases and steering with triples increased the likelihood in 65 . 6% of cases and decreased it in 27 . 5% ., We tested the robustness of these results to changes in the value of r in Eq ( 3 ) and found that each of these values are changed by less than 2% for r = 1 ., For r → ∞ we found that 56 . 0% , 68 . 1% and 71 . 2% of singles , doubles and triples ( respectively ) of steering drugs increased the likelihood of the most highly resistant genotype being found whereas only 22 . 2% , 20 . 0% and 19 . 2% of singles , pairs and triples ( respectively ) decreased it ., For each of the antibiotics , except Cefaclor , Cefprozil and Ampicillin+Sulbactam ( which is single peaked making steering irrelevant ) , we found that a random steering combination of length one , two or three is more likely to increase the chances of resistance than to reduce it ., Indeed , for Piperacillin+Tazobactam and Ceftizoxime we found that a random steering combination will increase the probability of the most highly resistance genotype occurring in more than 80% of cases , suggesting that sequential multidrug treatments which use these very common antibiotics should proceed with caution ., These findings suggest that the present system of determining sequential drug orderings without quantitative optimization based guidelines could in fact be promoting drug resistance and that to avoid resistance we must carefully consider the order in which drugs are applied ., We have reduced the evolutionary dynamics of an asexually reproducing population to a biased random walk on a fitness landscape ., Through this reduction we explored the evolutionary trajectories of a population by considering the algebraic properties of the Markov chain transition matrix associated with the random walk ., We have demonstrated that evolution on fitness landscapes is non-commutative , in the sense that the same drugs applied in different orders can result in different final population configurations , through parallels with the non-commutativity of matrix multiplication ., Further , we demonstrated that it is possible to find sequences of drugs that can be applied to both avoid and promote the emergence of resistance ., In particular , we performed an exhaustive analysis of the evolutionary trajectories of E . coli under short drug sequences ( two to four drugs ) chosen from 15 β–lactam antibiotics using empirically determined fitness landscapes and found that the majority , approximately 70% , of sequential treatments with 2–4 drugs increase the likelihood of resistance emerging ., In light of the slow pace of novel antibiotic discovery and the rapid emergence of resistance to the presently most utilized antibiotics , these findings suggest a new treatment strategy—one in which we use a sequence of drugs ( or even treatment breaks which themselves impose a selective pressure 38 ) to steer , in an evolutionary sense , the disease population to avoid resistance from developing ., Further , the drugs used to prime the disease population for treatment by an effective antibiotic do not themselves need to be the most effective drugs available ., This means that there could be a large pool of potential steering drugs in the form of antibiotics which have gone unused for many years due to their inefficacy as a single agent ., However , in the same way that the drug ordering can be used to steer away from resistance we have shown it can also be used to make resistance more likely ., Our results show that we may be inadvertently selecting for highly resistant disease populations through arbitrary drug ordering in the same way that highly resistant disease can emerge through irresponsible drug dosing ., The most striking example is that of Piperacillin with Tazobactam , a drug often prescribed in a hospital setting after others fail , which has an increased likelihood of resistance when prescribed after a pair or triple of others drugs in over 90% of cases ., If we are to avoid resistance to our most effective drugs we must carefully consider how they are used together with other drugs , both in combination and in sequence , and take appropriate steps to reduce the risk ., A major difficulty in using sequential drug treatments to steer disease populations is that in order to predict the outcomes we must know the fitness landscapes of the drugs involved ., De Visser and Krug 26 state that there exist fewer than 20 systematic studies of fitness landscapes and that these studies consider between 3 and 9 possible mutations ., For steering to be fully effective we must account for all likely fitness conferring mutations and their effects on fitness under many drugs ., Thus , many of the studies reviewed by de Visser and Krug are insufficient for determining clinically actionable steering strategies for certain diseases ., Fortunately , for a number of highly resistant infectious diseases 48 , 49 and cancers 50 , 51 , only a small number of mutations appear to contribute to resistance ., Further , recent work by Hinkley et al 47 in HIV has introduced a method to approximate large fitness landscapes from relatively fewer data points using a regression method ., It follows that determining the landscapes is not an entirely intractable problem ., A further complication in determining steering strategies is that fitness landscapes can be dependent on the disease microenvironment and have the potential to change from patient to patient or throughout the course of treatment ., The consequences of such effects on fitness landscapes have not yet been experimentally determined ., Two major assumptions within our modeling are that drugs are administered for sufficiently long that evolution can converge to a local fitness optimum and that this convergence is guaranteed to occur ., This assumption poses two potential problems in converting our model predictions to predictions of real–world bacterial evolution ., The first is that if selection is strong and mutations are rare then there is a possibility of the population being driven to extinction before an adaptive mutation occurs ., We have chosen to ignore this possibility within our modeling as in the context of treating bacterial infections this would constitute a success ., The second is that the time to convergence could be prohibitively long for steering to constitute a realistic treatment strategy ., We believe that the assumption of reasonable convergence times could be valid as adaptive walks in rugged landscapes are often short 52 ., However , it has been shown that for certain landscapes there can exist adaptive walks of length exponential in the number of loci 53 , but as we get to choose those drugs with which to steer we can avoid landscapes for which the convergence time is prohibitively long ., Further , our model is not necessarily restricted to the dynamics within a single patient ., Goulart et al 21 used fitness landscapes to explore whole hospital scale antibiotic treatment strategies and our model , as an encoding of evolution on fitness landscapes , is capable of making predictions at this scale as well ., As such , even if evolutionary convergence is experimentally determined to be prohibitively slow for steering to be effective as a treatment for bacterial infection within a single patient , our results will still hold in scenarios which admit longer timescales ., Specifically , evolutionary steering could provide an effective means to avoid the emergence of drug resistance at the hospital scale or in long–term diseases such as HIV or Tuberculosis ., The Strong Selection Weak Mutation model we have used here is a highly simplified , yet well studied model of evolution ., The model ignores much of the complexity of the evolutionary process , specifically simplifying the genotype–phenotype map , ignoring the disease microenvironment and making the assumption of a monomorphic disease population in which deleterious and neutral mutations cannot fix ., Under certain regimes of population size and mutation rates these simplifying assumptions break down 54 ., For example , if the population is sufficiently large then stochastic tunneling 55—where double mutations can occur allowing the crossing of fitness valleys—can arise causing a breakdown of the Strong Selection assumption ., Similarly , if the mutation rate is sufficiently high then the population ceases to be monomorphic and forms a quasispecies 56 , 57 ., Conversely , if the population is sufficiently small then it becomes possible for deleterious mutations to fix 58–60 ., Finally , we have ignored the possibility of neutral spaces in the fitness landscape which have been shown to have significant impact on whether non-optimal genotypes can fix in the population as well as the time taken for evolution to find a locally optimal genotype 61 , 62 ., The only neutral mutation present in the empirical landscapes we use in this work is in the single–peaked landscape for Ampicillin+Sulbactam ( Fig 1 ) ., As the landscape is single peaked , omitting this mutation does not prevent any evolutionary trajectories ., Thus , the results of our exhaustive search of sequential treatments are unaffected by the assumption that neutral mutations cannot fix ., We believe that each of the possible breakdowns of the SSWM model will have important consequences for the possibility and efficacy of steering , especially as larger landscapes are considered ., However , a proper treatment of their implications is beyond the scope of this paper ., In our future work we aim to undertake a comprehensive study of the implications of population size , mutation rate , neutral drift and evolutionary convergence times on our steering hypothesis ., To further develop the theory of evolutionary steering as a clinically viable strategy for preventing or treating highly resistant disease we must begin to collect data regarding empirical landscapes ., The data cannot be collected at a large scale by the existing method of engineering all genotypes of interest and testing their fitness ., Such experiments are intractable for all but the smallest landscapes ., Instead we must begin to measure and collect the genotypes and fitness of pathogens that appear in the clinic ., Hinkley et al 47 attempted to reconstitute the empirical landscapes for HIV-1 under different drugs which were later analyzed by Kouyos et al 63 ., This analysis was only possible due to the availability of a data set of over 70 , 000 clinical samples of HIV-1 with recorded values of fitness under a number of antiretroviral drugs ., Such data sets for bacterial pathogens are not yet available but should become easier to obtain as the cost of genome sequencing continues to fall ., Once the data are available we will be able test the validity of many of the assumptions of our model ., Such a data set will also have many uses beyond the work presented here , for example in tracking the spread and evolutionary history of highly resistant disease through phylodynamics 64 ., The model presented here is a simplification of the evolutionary process; however , given that non-commutativity is present in this highly simplified model , it is unlikely that commutativity will emerge as more complexity is introduced ., It follows that the cautionary message regarding sequential drug application which results from our simplified model merits serious consideration ., Whether or not measuring fitness landscapes provides sufficient information to correctly identify , or to serve as a heuristic in identifying , optimal drug orderings in vivo is a question that cannot be answered through mathematical modeling alone ., It is only by verifying the predictions of steering strategies given by our model through biological experiment that we can determine whether they are viable ., Supposing our model predictions are indeed viable , then knowledge of some approximation to the fitness landscapes of the presently most used antibiotics could , in combination with our model , provide at least a good heuristic for how to proceed with multi-drug treatments , future antibiotic stewardship programs and clinical trial design ., We begin with the concept of a fitness landscape introduced by Wright 59 and used by Weinreich et al 27 and Tan et al 29 to study evolutionary trajectories in asexually reproducing populations ., We represent the genotype of an organism by a bit string of length N and model mutation as the process of flipping a single bit within this string ., This model of mutation only accounts for point mutations and ignores the possibility of other biologically relevant mutations such as gene insertions , gene deletions and large structural changes to the genotype ., This gives a set of 2N possible genotypes in which individuals of a given genotype , say g , can give rise to mutated offspring which take genotypes given by one of the N mutational neighbors of g—precisely those genotypes g′ for which the Hamming distance 65 , Ham ( g , g′ ) , from g is 1 ., As such , our genotype space can be represented by an undirected N-dimensional ( hyper– ) cube graph with vertices in {0 , 1}N representing genotypes and edges connecting mutational neighbors ( Fig 3a ) ., We define a selective pressure on our graph that drives evolution , for example through an environmental change or drug application , as a fitness function, f : { 0 , 1 } N → R ≥ 0 ., ( 2 ), This fitness function represents a genotype-phenotype map in the simplest sense—assigning to each genotype a single real-valued fitness ., Gillespie 34 , 35 showed that if the mutation rate u and population size M of a population satisfy Mu logM ≪ 1 , and if we assume that each mutation is either beneficial or deleterious , then each beneficial mutation in the population will either reach fixation or become extinct before a new mutation occurs ., Further , selection will be sufficiently strong that any deleterious mutation will become extinct with high probability and hence we may assume that this always occurs ., In the case that Mu2 ≈ 1 stochastic tunneling 55 , 66 , 67 through double mutations can occur and we cannot ignore deleterious mutations ., Assuming Mu logM ≪ 1 , then after each mutation the population will stabilize to consist entirely of individuals with the same genotype and this genotype will be eventually replaced by a fitter neighboring genotype whenever one exists ., This observation gives rise to the Strong Selection Weak Mutation ( SSWM ) model , which models a population as isogenic and occupying a single vertex on a directed graph on the set of 2N possible genotypes , {0 , 1}N , in which there exists an edge from vertex a to a neighboring vertex b if , and only if , f ( b ) > f, ( a ) ( see Fig 3b and 3c ) ., This population undergoes a stochastic walk on the graph in which the population genotype is replaced by a fitter adjacent genotype with some probability ., In fact , this model still holds in the case that Mu ≫ 1 ≫ Mu2 26 ., Several ‘move rules’ have been proposed which can be used to select an adjacent fitter neighbor during this stochastic walk 52 and which of these move rules is most accurate depends on the population size 26 ., Common move rules include selecting the fittest neighbor 36 , 68 , selecting amongst fitter neighbors at random 69–71 or selecting fitter neighbors with probability proportional to the fitness increase conferred 34 , 35 , 72 ., We encapsulate each of these variants of the SSWM model within our model ., The SSWM model of evolution reduces the evolutionary process to a random walk on a directed graph and hence can be modeled by a Markov chai | Introduction, Results, Discussion, Materials and Methods | The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health ., Here , we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape ., This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies ., Using this formalism , we analyze 15 empirical fitness landscapes of E . coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application ., Specifically , we demonstrate that the majority , approximately 70% , of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic ., Further , we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance ., This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy . | Increasing antibiotic resistance , coupled with the slowing rate of discovery of novel antibiotic agents , is a public health threat which could soon reach crisis point ., Indeed , the last decade has seen the emergence of deadly , highly resistant forms of pathogens , such as Escherichia coli , Acenitobacter baumanii , Klebsiella pneumoniae , Enterococcus and Staphylococcus aureus as well as non–bacterial pathogens including malaria and viruses such as HIV ., Here , we develop a mathematical model of an evolving bacterial population , which allows us to predict the probability of resistant strains emerging ., Using this model we show how sequences of drugs can be prescribed in order to prevent resistance where each drug alone may fail ., These model predictions suggest a novel treatment strategy: using sequences of antibiotics to ‘steer’ the evolution of a pathogen to a configuration from which resistance to a final antibiotic cannot emerge ., Further , we test the likelihood of resistance emerging when arbitrary sequences of antibiotics are prescribed , finding that approximately 70% of arbitrary sequences of 2–4 drugs promote resistance to the final drug ., This result serves as a cautionary warning that we may be inadvertently promoting resistance through careless ( or random ) prescription of drugs . | null | null |
journal.pgen.1003757 | 2,013 | Functional Dissection of Regulatory Models Using Gene Expression Data of Deletion Mutants | The complex functions in eukaryotic cells are implemented through a highly organized regulatory network composed of concerted activities of many genes and gene products ., Gene expression can be directly regulated by transcription factors ( TFs ) 1 , the states of chromatin structures 2 , 3 and small RNAs , and interactions among them 4–6 ., In other words , the mRNA expression level of a gene is the output synthesized from the information of several input signals ., Gene knockout is a classic approach to studying gene functions and the collection of yeast knockout strains has enabled systematic genome-wide functional analysis 7 ., Transcriptional profiles of mutant strains have been used as molecular phenotypes for functional analysis and genetic epistasis analysis 8 , 9 ., In addition , the expression profiles of single , double and triple deletion mutants of chromatin machinery components , protein kinases and phosphatases were used to analyze the functional overlaps among these proteins 3 , 10 ., Dion et al . constructed 15 mutants of lysines 5 , 8 , 12 , and 16 to arginine in the histone H4 tail and characterized the resulting genome-wide gene expression changes 11 ., Transcriptional regulatory networks in different cellular contexts have been constructed through the DNA microarray analysis of transcription factor deletion mutants and over expression strains in S . cerevisiae 1 , 12 by directly linking the genetically perturbed transcription factors ( TFs ) with the genes that change expression in response to the perturbations ., As none of the regulators works alone , probably more important than constructing such regulator-target networks is to understand how the regulators cooperate to form regulatory pathways to specifically regulate a transcriptional program or biological processes 3 ., Here we use the transcriptional profiles of deletion mutants as the molecular phenotypes of the mutants to determine how the regulators interact genetically or cooperate functionally with each other to modulate gene expression ., We propose a Bayesian network ( BN ) approach to reverse engineer regulator networks from these gene expression profiles ., The approach excels previous methods such as context-dependent regulation and correlation coefficient analysis 12–14 in that it can easily integrate different datasets and infer causalities in the regulatory program ., Nodes in the network are the genes deleted in the mutants and the algorithm greedily searches over all possible Bayesian network structures for the one that best summarizes the relationships among the global differential expression change profiles upon deleting these genes ., Thus by exploring the relationships among the global differential gene expression profiles for the deletion mutant genes , we can obtain valuable causal or non-causal relationships among these regulatory deletion-mutant genes through the inferred BN structure ., Then , we used the above approach to analyze the global differential gene expression profiles of 544 single or double deletion mutants of transcription factors , chromatin machinery components , protein kinases and phosphatases in S . cerevisiae ., The BN inferred identified with high precision and recall causal regulatory and non-causal interaction relationships among these regulators in different cellular contexts ., The deletion mutants of transcriptional regulators used in this study are nonessential genes in yeast under rich medium growth conditions , yeast extract peptone dextrose medium ( YPD ) or synthetic complete medium ( SC ) ., We compiled expression profiles of sequence-specific DNA binding transcription factors ( STFs ) deletion strains grown in SC and YPD mediums 12 , 15 ., We also collected the expression profiles for deletion mutants of protein kinases , phosphatases 10 and chromatin machinery components 3 ( See Methods for more detailed data descriptions ) ., To confirm that regulators belonging to the same protein complex or regulatory pathway tend to share common targets 15 , we used Jaccard similarity index ( JI ) to examine the similarities between targets profiles of the perturbed regulators ( see Methods , Figure S1A , Table S1 ) and we observed that a STF is more likely to connect with another STF than with a general transcription regulator ( GTFs e . g . chromatin modifiers and remodelers ) whether the regulators are derived from the same data set or different data sets ., Indeed , the percentage of known physical interactions or genetic interactions ( downloaded form SGD ) present among predicted gene pairs increases as the threshold of pair-wise Jaccard similarity index ( JI ) used in prediction increases ( Figure S1B ) , suggesting that the similarities of gene expression profile changes after genetic perturbation of transcriptional regulators can be used to infer relationships among these regulators ., However , JI is only a crude measure that is subject to different cutoffs and cannot infer directionality or causality of regulatory relationships ., In contrast , Bayesian network is a solid statistical inference method that can infer directions or causality of regulatory relationships and is more appropriate for this task ., A Bayesian network 16 is a directed probabilistic graphical model which represents conditional independency relationships between variables ., The BN learning approach has been extensively used in previous works to analyze gene expression and other high throughput data sets 14 , 17 , 18 ., Suppose that the expression of a deletion mutant gene ( denoted by G ) is fully determined by its three intermediate regulator genes ( denoted by A , B , C ) , if the expression of genes A , B , C can be controlled precisely , we can find a specific expression configuration of A , B , C ( e . g . , A is up-regulated and B , C are down-regulated ) so that the expression of G is as small as possible just like being deleted ., As such , we can anticipate that the global differential gene expression profile of deleting G versus the wild type strain can be well predicted from the global differential expression profiles of deleting B , deleting C and over-expressing A , respectively ., Although the datasets contain only genetic deletion strains , no over-expression strains , the global differential expression profile of the profile of over-expressing A is often opposite to that of deleting A , we can thus well predict the differential gene expression pattern of deleting G from the three differential gene expression profiles of deleting genes A , B and C , respectively ., In general , if one gene is combinatorially regulated by a set of other genes , usually we can approximate its deletion-mutant differential expression ‘phenotype’ fairly well by the deletion-mutant differential expression ‘phenotypes’ of its regulator genes ., However , in deletion mutant experiments , it is typical that most genes have small expression changes in deletion mutant strains compared to their WT ., For instance , 80% yeast genes have similar expressions to the WT strain in protein kinase or phosphatases deletions under the same growth condition 19 ., Thus , the differential expression profiles of these regulators are sparse ., The majority of ‘neutral’ gene expression changes ( represented by ‘0s ) in the differential expression profiles will artificially induce a high similarity between the deletion mutant genes ( regulators ) in classic BN learning methods ., To this end , we developed a new Bayesian network structure-learning algorithm called Deletion Mutant BN ( DM_BN ) ( Figure 1 ) , which is specifically designed for reverse engineering regulatory networks of deletion mutant genes from differential gene expression profiles in the corresponding deletion mutant strains ., Note that , the input of this algorithm is a matrix of discrete values: 1 , −1 , 0 , which denote the differential gene expression of the mutant strain versus the WT ., Each column of the matrix records the differential gene expression profile for one deletion mutant gene ., As described above , the training data for Bayesian network is skewed towards 0 , it is not viable to exploit classical Bayesian network learning approaches based on discrete data 20 ., Indeed , in our extensive comparison of the proposed DM_BN algorithm with state-of-the-art BN learning algorithms with three other scoring metrics 20–23 , a well-known software package for BN learning 24 and two widely used non-Bayesian approaches to building regulatory networks 25 , 26 on the yeast deletion mutant datasets , the significantly improved network inference quality fully confirmed the advantage of the DM_BN algorithm ( See below ) ., The main technical contribution of the DM_BN algorithm is to employ the kernel based approach to Bayesian network inference 27 and the introduction of a novel kernel for discrete data that is specifically designed for characterizing the deletion mutant data sets ., Specifically , suppose and are two discrete variables which could take values 1 , −1 , 0 , the trivial kernel for discrete data in 27 is defined as: , i . e . , when ; and when ., This is not viable for dealing with the deletion mutant data sets since the dominant value in such data is 0 , the trivial kernel for discrete data will induce a large similarity output ( 1 . 0 ) for all most all gene pairs which are neither up-regulated nor down-regulated ., To prevent this biased effects , we modified the trivial kernel to the DM kernel below:The implication from the new DM kernel is clear: the differential expression changes of two genes in a deletion mutant experiment are considered similar ( with kernel output 1 . 0 ) if they are either up-regulated or down-regulated simultaneously ., The similarity between genes that are not responsive to the deletion mutant experiment is abandoned ( with kernel output 0 . 0 ) ., In this way , only the information of the co-regulation activity is fed into the Bayesian network-learning algorithm ( Methods ) ., Another contribution of the DM_BN algorithm is the incorporation of the a priori knowledge from deletion mutant experiments into Bayesian network learning ., For this purpose , we employ a network template to constrain the space of graph search in Bayesian network learning and to provide additional causal information in the learning and interpretation of Bayesian network structure ., The basic idea of constructing the adjacency matrix of the network template ( template matrix for short ) is as follows: First , we start with an empty template matrix of zeros ., Then , we define the list of target genes of a deletion mutant gene to be the genes whose mRNA levels either up- or down-regulated compared to the WT strain ., If both deletion mutant genes A and B ( with indices , respectively ) are not in the target gene list of each other , the two genes do not seems to have a direct regulatory relationship , but they could cooperate to regulate other genes ., So , if the target gene list of A and B overlap ( i . e . , at least one gene appear in both of the two target gene lists ) , the elements of the template matrix are set to 1 , which means that either one of the two edges might appear in the final BN ., Finally , if gene B appears in the target gene list of A , but A is not in the target gene list of B , we set , which means that could appear in the final BN while the reversed edge is forbidden ., In rare occasions , when both A , B appear in the target gene list of each other , we set , since we do not know which direction of the interaction represents the dominant regulatory effect while the other represents the secondary feedback effect ( Figure 1 ) ., To identify potential causal interactions from Bayesian network structure , we have to determine whether the directionality of each edge in the network is reversible or not 28 ., In this step , the template matrix again provides a priori causal information to guide the algorithm to disambiguate more edge directionalities ., More details of the algorithm are presented in Methods ., To quantitatively compare the performance of the DM_BN learning algorithm with other approaches to infer regulatory networks , we curate a database of ground-truths protein-protein interactions , regulatory interactions , genetic/epistatic interactions and protein complexes from the KEGG and SGD databases ., Here , methods being compared include alternative Bayesian network learning algorithms ( the WinMine Toolkit 24 , the BDeu scoring approach 22 with optimized prior 23 and the BIC scoring approach 20 , 21 and non-Bayesian network approaches ( the ARACNE 25 software , the Disruption Network 26 and the Jaccard similarity index ( JI ) approach ) ., Details of these algorithms and the strategies used in the testing are described in Methods and Supplemental Note 1 ( Text S1 ) ., Basically , two key performance indicators are important for comparing the above algorithms:, 1 ) Precision-recall curve , which quantifies the ability of an algorithm to correctly predict bona fide interactions between these regulators;, 2 ) The precision of orientation , which measures the ability of an algorithm to predict correct directionality for each causal interaction ., We first calculated the precision and recalls of all the predicted yeast regulator networks ( Methods ) ., In this computation , directionality is not considered in matching a predicted edge and a known interaction in the database , which is partly because we only have very limited knowledge about the causality of these ground-truth interactions ., By plotting the corresponding precision-recall points ( or point , if an algorithm predicts only one network ) for each algorithm , we found that DM_BN algorithm outperforms all the alternative network construction approaches in both precision and recall ( Figure 2 , Table S2 ) ., In other words , regardless of causality , DM_BN algorithm has the highest precision of de novo network predictions over the whole range of recall rates ., A close examination of the BN inferred by the DM_BN algorithm suggested it indeed recapitulated many interactions in protein complexes or pathways ., Specifically , the BN structure visualized in Figure 3 with precision 0 . 4704 and recall rate 0 . 0323 ( Figure, 2 ) includes both causal ( represented by directed edges ) and non-causal ( by undirected edges ) relationships among these regulators , which are known to take place in diverse biological processes to combinatorially regulate the expression of target genes ., Moreover , we also computed the functional enrichment of these regulators based on their target genes ( Methods ) ., The result suggests that regulators that tightly interconnected in the BN more significantly share common functions than other regulator pairs ( Figure 3 , Table S3 ) ., The network learned by DM_BN algorithm further predicts how these regulators interact with each other in different cellular processes ( Figure 3 ) ., For example , the predicted network module among subunits of the chromatin remodeling machinery complex ( Figure 3 , shown by purple nodes ) has a high precision of 0 . 85217 ( Table S4 ) ; the network module consisting of protein kinases Vps15 , Ark1 , Prk1 , Cdk8 , Cka2 and protein phosphatase Ptc1 , Ptc2 , Pph3 , Ptp3 is involved in three interrelated cell processes: cell wall organization or biogenesis , amino acid metabolism and carbohydrate metabolism , which is consistent with biological knowledge; and the predicted network suggests that Rpd3 complex , Sir complex and Ste11 mediated MAPK kinase cascades pathway cooperate with each other in mating process ( Figure 3 , Table S3 ) ., We also observed that a STF is more likely to connect with another STF than with a GTF , which is similarly observed in the densely connected network inferred by the Jaccard index ( JI ) similarity measure ( Figure S1A ) ., Our results are also consistent with the E-MAP results , which are quantitative genetic interactions between phosphorylation related genes in S . cerevisiae 19 ., For instance , it is known that histone variant H2A . Z ( encoded by the Htz1 gene ) exchange with histone H2A in nucleosomes through the SWR1 complex 29 , 30 and that Htz1 displays positive genetic interactions with SWR1 ( +3 . 5 ) , Vps71 ( +3 . 9 ) and Vps72 ( +3 . 5 ) 19 ., These interactions are all predicted by the network inferred by DM_BN ( Figure 3 ) ., Indeed , the target sets of Htz1 , SWR1 , Vps71 and Vps72 deletion mutants have high similarity , with Jaccard indices ( Methods ) ( Figure 3 ( blue circle ) , Table S5 ) ., Moreover , the functional enrichment of the predicted SWR1 complex target gene sets for vesicle organization ( Table S3 ) is consistent with the fact that SWR1 complex is required for vacuolar protein sorting 31 ., More examples of the inferred pathway relationships are listed in Table S6 , S7 ., Inferring correct directionalities for causal interactions or epistasis is an important aspect for regulatory network predictions ., However , most non-Bayesian network algorithms are unable to do so ., For example , the ARACNE 25 software and the Jaccard similarity index ( JI ) approach could only predict undirected interactions ., Although the Disruption Network 26 could predict the direct causal relationships between deletion mutant genes and differentially expressed genes , such knowledge is derived from the deletion mutant experiments without performing causality inference ., It is of special interest to see if an algorithm can make de novo predictions about causality among deletion mutant genes from the similarity of their genome-wide differential expression profiles ., In principle , Bayesian network learning algorithms hold this promise and thus we compare the performance of the four BN learning algorithms ( DM_BN , Winmine toolkit 24 , the BDeu 22 and BIC scoring approaches 20 , 21 ) in predicting causal relationships ., Since the ground-truth causal relationships derived from existing databases for the 378 regulator genes is very limited , and also because we do not know the exact cellular contexts in which those causal relationships hold true , to quantify the performance in predicting causal relationships , we calculated the recall and the precision of all these network inference approaches except BDeu using the four MAPK ( mitogen-activated protein kinase ) cascades where clear causal relationships are well described among these kinases ., The exclusion of BDeu here is simply because it does not have a tunable parameter to generate a relative sparse network that is comparable to the size of networks generated by the other three approaches ., However , using a different evaluation approach , BDeus causality prediction apparently does not perform as well as DM_BN and BIC ( see below ) ., Yeast contains at least four MAPK ( mitogen-activated protein kinase ) cascades that convert extracellular stimuli into intracellular signals during a variety of cellular processes , such as mating , cell wall remodeling and high osmolarity adaption 32 ., We found that when all tools predicted roughly the same number of edges , the DM_BN algorithm with prior information can predict more interactions with correct orientations than other tools among kinases involved in the same signal transduction pathway ( Table S8 , S9 , S10 , S11 ) ., To test whether the correct inference of edge direction is solely the result of applying a template , we examined the directionality of edges in BNs inferred by DM_BN without any template ., At various parameters , 73 . 3–89 . 2% of the edges have the same direction as the regulator-DEG relationships identified in the deletion mutants experiments ., These proportions are significantly higher than that expected by chance ( random coin tossing p\u200a=\u200a0 . 5 , Binominal test p\u200a=\u200a0 . 0625∼5 . 42e-07 , Table 1 ) ., This indicates that the correct inference of edge directions by DM_BN is largely not attributed to using the template ., However , the BN inferred by DM_BN with the template , did correct a small number edges incorrectly predicted when not using the template ( 1/34∼4/15 edges , Table 1 ) ., This is because the network template not only corrects edge orientation errors inconsistent , but also improves the global causal structure in the BN through the cascading interactions between edges ., Therefore , a template is included in the actual implementation of the DM_BN algorithm as the default setting ., Using a similar approach , we also compared with other BN inference algorithms , the performance of DM_BN in de novo predicting causal relationships without using the a priori information encoded by the template ., The DM_BN algorithm and the BIC scoring approach 20 , 21 generally predict non-compelled directed edges remarkably more precise than the BDeu scoring method 22 or the WinMine toolkit 24 ( Supplemental Note 2 and 3 in Text S1 , Figure S2 ) ., In particular , the causal relationships inferred by the DM_BN algorithm ( with the network template ) correctly recapitulated the linear cascade structure for regulators in the HOG signaling pathway involved in the osmotic stress response ( Figure 4A ) ., For instance , Ste11 MAPK kinase ( MAPKKK ) phosphorylates Pbs2 MAPK kinase ( MAPKK ) ., Then , the activated Pbs2 phosphorylates Hog1 in the MAPK kinase cascade pathway for osmostress adaptation 33 ., In the mating process , DM_BN not only accurately grouped the SIR complex and the Ste11 mediated MAPK cascade pathways , but also correctly predicted the connectivity among components of the complex or the pathway ( Figure 4B ) ., The results correctly recovered the role of Ste7 and Ste11 protein kinases in two different MAPK Fus3 and Kss1 cascade pathways that controls mating , respectively 33 , 34 ., The inferred causal relationships or non-causal interactions between these gene expression regulators not only confirmed known relationships , such as physical interactions and genetic epistasis relationships among these regulators , but also predicted many novel relationships that could be important in gene regulation ., For example , the DM_BN algorithm not only correctly predicted the connection between components in the SIR complex or in the MAPK pathway , but also predicted the dense connection between the SIR complex and Ste11-mediated MAPK cascades ( Figure 4B , Table S3 and Table S12 ) ., Clustering of the expression profiles of the genes in these network modules shows that the genes up-regulated in the deletion mutants of Sir2 , Sir3 and Sir4 ( Figure 5A , right panel ) are all within 10 kb to their nearest telomere ., Meanwhile , the predicted functions of the genes down-regulated by all deletion mutants in Figure 5A are enriched for mating process ( see also Table S3 ) ., All these findings are consistent with the knowledge that SIR complex plays roles in silencing at HML , HMR loci which carry unexpressed copies of mating-type genes and telomeres 35 and that SIR complex is comprised of two structural proteins Sir3 and Sir4 , deletion of which will cause reduced mating rate at different levels 36 ., The mRNA expression levels of Fus3 and Fus1 are very low in the deletion mutants of Sir2 , Sir3 and Sir4 in all the three data sets ( Figure 5A , left panel ) ., However , the mRNA levels of other genes in the model are not changed compared to WT ( except the expression levels of the deletion mutant genes themselves ) ( Figure 5A , left panel ) ., From the causal , non-causal relationships predicted by DM_BN ( Figure 4B ) and the expression profiles of deletion mutants experiments ( Figure 5A ) , we can infer a novel model implying that the Ste11 mediated MAPK cascades pathway may have overlapping functions with the SIR complex ( Figure 5B ) ., Thus , SIR complex could indirectly influence the mRNA expression of kinase Fus3 , which is involved in the MAPK cascades pathway in mating process ., Although a STF is more likely to have a similar gene expression pattern with another STF than with a GTF generally ( Figure 3A ) , the network suggests that STFs Cst6 , Sfp1 , Bas1 , Mac1 , Gsm1 , Ixr1 , haa1 , Ume6 and Cad1 connect densely to GTFs ( some subunits of SIR complex , SWI/SNF complex and SAGA complex ) ( Figure 5C ) ., Clusters ( Figure S3 ) of the expression profiles of these regulators revealed high similarities between the target profiles of the STFs , Sfp1 and Cst6 , and the GTFs SWI/SNF complex and SAGA complex ., Another example is the high similarity between the targets profiles of STFs: Ixr1 , Cad1 , Bas1 and Stp4; and GTFs: SIR complex , SAGA complex ., Although no physical interactions or binding relationships between them have been reported in the literature , SAGA subunit Spt3 has been reported to have negative genetic interactions with Stp4 and Ixr1 10 ., These novel predictions by the DM_BN algorithm may serve as blueprints for further experimental explorations ., Uncovering complex regulatory networks is an important and challenging task 1 , 12 , 19 , 37 ., Here , we introduced a new Bayesian network inference algorithm “DM_BN” , specifically designed to infer regulatory networks from gene expression profiles generated by gene perturbations , such as gene deletions ., DM_BN can work with both small and large datasets and infer causal and non-causal relationships among the perturbed genes ., To address the sparsity of gene expression changes in the perturbation experiments , we developed a kernel-based BN learning algorithm DM_BN , which is appropriate for modeling such gene expression data sets ., Comparing with known biological interactions , both the recall and the precision of the network inferred by the proposed DM_BN algorithm are significantly higher than that inferred by WinMine and by the Jaccard Index ( JI ) similarity measure ( Figure 2 ) ., The DM_BN network model not only successfully recapitulated known interactions among the yeast transcriptional regulators , but also predicted many novel interactions among these regulators and regulatory protein complexes , offering new insights into the yeast transcriptional regulatory network ., Our results show that the improved performance of the DM_BN algorithm can be mainly ascribed to the new kernel ., Since an edge between two regulator genes is allowed in the network template if they share at least one target gene , the template matrix actually allowed all the possible interactions between these genes , hence has a very little predictive value by itself ., However , the DM_BN algorithm still benefits from using the network template in two aspects ., First , by eliminating all impossible edges , the template effectively reduced the search space to speed up the DM_BN algorithm ., Second , by encoding the a priori regulator-target causal knowledge in deletion mutant experiments , the network template not only corrects edge orientations that are inconsistent with such information ( Table 1 ) , but also improves the global causal structure predicted by DM_BN through edge-edge interactions , as we demonstrated in the inference of MAPK pathways ( Table S11 ) ., Although the DM_BN approach has achieved big success in inferring yeast regulatory network from perturbation-based gene expression data sets , there are still a few limitations to its applications ., For example , the mRNA expression levels of target genes are not fully representative of the activities and interactions of the regulators in modulating gene expression ., This is because post-transcriptional changes and the regulators context-specific transient activity were not measured in the experiments ., Due to the intrinsic limitation of mRNA expression data , our method failed to identify certain relationships among the regulators under certain conditions , especially when the activity of the regulators is not screened in the microarray experiments ., Nevertheless , these problems are not the fault of the proposed BN inference algorithm but rather inherent limitations of current experimental systems , which are expected to overcome by introducing other types of high-throughput datasets ., In this sense , the application of the DM_BN algorithm is not limited to microarray expression profiles of genetic perturbations , it can actually be extended to work on many kinds of high-throughput data , such as epigenomic , transciptomic , proteomic data sets , and even quantitative phenotype data ., All the gene expression profiles are downloaded from the Gene Expression Omnibus ( GEO ) database , including 269 transcription factors knockout strains grown in yeast extract peptone dextrose medium ( YPD ) 12 , 150 deletion mutants of protein kinases and phosphatases 10 , 165 mutants of chromatin machinery components 3 and 52 sequence-specific DNA binding transcription factors ( STFs ) deletion strains grown in synthetic complete medium ( SC ) 15 ., Altogether the four data sets above contain gene expression profiles of 544 yeast deletion mutants ., The series accession numbers of these data sets are GSE4654 12 , GSE25644 10 , GSE25909 3 and GSE2324 15 ., The detailed DNA microarray normalization and statistical analysis procedures see described in Supplemental Methods ( Text S1 ) ., After processing , the gene expression changes are represented by discrete values: 1 ( significant up-regulation ) , −1 ( significant down-regulation ) and 0 ( no significant expression change ) ., We employ the kernel-based Bayesian network learning algorithm 27 with three modifications ., First , we use the ‘DM kernel’ instead of the trivial kernel to handle the yeast deletion mutant datasets ., Second , we use a template matrix to constrain the space of all possible Bayesian network structures ., Details of the ‘DM kernel’ and the construction of the template matrix are described in the Results and will not be repeated here ., Finally , we modified the BIC scoring function by increasing the weight of the complexity term for penalizing the Kernel Generalized Variance 38 measure ., This is necessary for removing biological noise and increasing the precision and sparsity of the finally obtained network structure ., Formally , the Bayesian network scoring function is modified as follows ( cf . eqn . 4 in ref 27 for details ) :Here , is the BIC score for node and its parents , and the overall score for a full Bayesian network is: ., and are the Kernel Generalized Variance 38 for node sets and ., is the multiplicative weight that we impose on the second term of the scoring function ., With the DM kernel inside the KGV measure , the template matrix as a structural constraint and the modified scoring function , we can search for the Bayesian network structure that optimally fit the yeast deletion mutant datasets ., Specifically , in each step , we consider, 1 ) adding an edge that is consistent with the template;, 2 ) deleting an edge from the current BN structure;, 3 ) reversing the direction of an edge that will not violate the causal constraints embodied the template ., In accordance with previous studies , we use the greedy ascent TABU search method 39 to find the ideal Bayesian network structure ., Here , ‘TABU’ denotes a Meta searching strategy that prohibits the algorithm from ‘undoing’ a recent operation ., It helps the search procedure from being getting stuck in the local optima regions 39 ., Finally , we adopt an efficient dynamic graph acyclicity checking method 40 in the Bayesian network structure search , since the most computational intensive task in this study involves inferring a Bayesian network of up to ∼400 nodes , using the conventional static graph acyclicity checking method would be fairly slow ., Interpreting the causalities in the Bayesian network structure is not a straight forward task ., This is because there are equivalence classes of Bayesian network structures ., All BNs in the equivalence class are semantically equivalent ., They share the same set of skeletons ( edge connections regardless of arrows ) , but differ in the directionalities of some edges 41 ., As such , there are two types of edges in a Bayesian network: compelled edges , whose | Introduction, Results, Discussion, Methods | Genome-wide gene expression profiles accumulate at an alarming rate , how to integrate these expression profiles generated by different laboratories to reverse engineer the cellular regulatory network has been a major challenge ., To automatically infer gene regulatory pathways from genome-wide mRNA expression profiles before and after genetic perturbations , we introduced a new Bayesian network algorithm: Deletion Mutant Bayesian Network ( DM_BN ) ., We applied DM_BN to the expression profiles of 544 yeast single or double deletion mutants of transcription factors , chromatin remodeling machinery components , protein kinases and phosphatases in S . cerevisiae ., The network inferred by this method identified causal regulatory and non-causal concurrent interactions among these regulators ( genetically perturbed genes ) that are strongly supported by the experimental evidence , and generated many new testable hypotheses ., Compared to networks reconstructed by routine similarity measures or by alternative Bayesian network algorithms , the network inferred by DM_BN excels in both precision and recall ., To facilitate its application in other systems , we packaged the algorithm into a user-friendly analysis tool that can be downloaded at http://www . picb . ac . cn/hanlab/DM_BN . html . | The complex functions of a living cell are carried out through hierarchically organized regulatory pathways composed of complex interactions between regulators themselves and between regulators and their targets ., Here we developed a Bayesian network inference algorithm , Deletion Mutant Bayesian Network ( DM_BN ) to reverse engineer the yeast regulatory network based on the hypothesis that components of the same protein complexes or the same regulatory pathways share common target genes ., We used this approach to analyze expression profiles of 544 single or double deletion mutants of transcription factors , chromatin remodeling machinery components , protein kinases and phosphatases in S . cerevisiae ., The Bayesian network inferred by this method identified causal regulatory relationships and non-causal concurrent interactions among these regulators in different cellular processes , strongly supported by the experimental evidence and generated many testable hypotheses ., Compared to networks reconstructed by routine similarity measures or by alternative Bayesian network algorithms , the network inferred by DM_BN excels in both precision and recall ., To facilitate its application in other systems , we packaged the algorithm into a user-friendly analysis tool that can be downloaded at http://www . picb . ac . cn/hanlab/DM_BN . html . | null | null |
journal.pgen.1000912 | 2,010 | A cis-Regulatory Signature for Chordate Anterior Neuroectodermal Genes | The concept of “selector genes” was introduced 30 years ago by Garcia-Bellido to define genes that interpret a transient regulatory state and specify the identity of a given developmental field 1 ., The question of how embryos execute distinct and unique differentiation programs using these selector genes can be tackled by focusing on how gene expression is encoded in cis-regulatory elements and their field-specific trans-acting factors ( TF ) ., This concept was more recently extended to terminal selector genes that coordinate the expression of differentiation genes to determine a given cell type 2 ., In vertebrates , examples include the Crx TF that interacts with another TF to control the expression of target genes in rod photoreceptors 3–5 ., In vertebrates as well as in flies , Crx and its Drosophila homolog Otd act through a small cis-regulatory motif overrepresented in the elements flanking the target genes 6–10 ., In addition to this evolutionary conserved network , many others in Caenorhabditis elegans and Drosophila melanogaster have shown that cell specific enhancers contain a common “tag” corresponding to a specific cis-regulatory motif , and that this motif is linked to one or a few terminal selector genes 11 , 12 ., In contrast , during early development , very few studies have reported how a set of region-specific cis-regulatory elements responds to field-specific selector genes ., In insects , one of the best characterized sets of functionally related cis-regulatory elements responds to the gradient of nuclearized dorsal TF in the early Drosophila embryo 13 , 14 ., However , the regulatory mechanism of dorsal-ventral patterning is not enough conserved in chordates to allow comparative studies of the regulatory network ., A more general character of bilaterians is the tripartite organization of the nervous system along the antero-posterior axis 15 ., In the posterior part ( hindbrain and nerve cord ) , Hox genes are expressed in a colinear order ., In the domain anterior to the Hox genes , several striking similarities in the relative expression patterns of other transcription factors have been noted in bilaterians 16–18 ., The OTX-like homeobox transcription factors ( otd in insects ) are expressed in the anteriormost part of animals as diverse as cnidarians , insects , annelids , urochordates and vertebrates 19–21 ., In chordates , OTX has a sustained expression in the anterior neuroectoderm and in derivatives of anterior ectoderm such as placodes , stomodeum 20 , 22 ., In mice , null-mutants of this gene lack various head structures 23 ., These results suggest that OTX-like proteins belong to a conserved developmental control system operating in the anterior parts of the brain , different from the one encoded by the Hox complexes 24 ., Many homeodomain proteins bind to the core DNA sequence ATTA , but several subfamilies have longer binding specificities around this core 25 , 26 ., OTX homeodomain proteins contain a lysine at position 50 which confers them additional specificity to guanines 5′ of the ATTA motif , resulting in a core recognition sequence of GATTA/TAATC 27 ., The DNA binding domains of homeobox gene families are highly similar over large evolutionary distances and cross-species experiments have demonstrated that the OTX proteins can be exchanged between flies , mice and human without major developmental defects 28 , 29 , and more recently between ascidians and mice 24 , 30 ., For studies of anterior nervous system development , the ascidian Ciona intestinalis offers the advantage of a simple chordate body plan with the canonical tripartite brain along the antero-posterior axis 31 ., In addition , the genome is small , with short intergenic regions which can be aligned with another ascidian species , thus simplifying the identification of cis-regulatory elements 32 ., Moreover , complete expression patterns have been determined for thousands of genes and are readily available in public databases 33–35 ., Therefore , Ciona intestinalis constitutes an ideal model system for combining whole genome bioinformatics and experimental cis-regulatory analyses ., Here , we first focus on one single anterior ectodermal enhancer in Ciona intestinalis ., Its detailed analysis points to an internal tandem-like structure and underscores the key role of the selector gene Otx ., We then examine if other duplicated putative binding sites for OTX preferentially flank anteriorly expressed genes in the genome ., We have previously described an enhancer sequence ( called “D1” , 323bp ) that controls expression of the Ciona intestinalis Pitx gene in a sub-region overlapping the neural and the non neural ectoderm called the anterior neural boundary ( ANB ) 36 ., For the sake of simplicity , and although ANB has a dual origin , we label it as a derivative of the neuroectoderm and call the region composed of anterior epidermis , ventro-anterior sensory vesicle and ANB , the “anterior neuroectoderm” ., For this study , we used a minimal 206 bp fragment of D1 that is sufficient to drive reporter gene expression in the ANB and divided it into five parts ( D1a-e , Figure 1A and Figure 2A ) for further analysis ., Deletion of the first 16pb ( D1a , Figure 2A ) resulted in the D1bcde fragment ( Figure 1A ) and led to ectopic reporter gene expression in the anterior epidermis ( ae ) and ventro-anterior sensory vesicle ( vasv ) in addition to the expected expression in the ANB ., All these elements indicate that D1 responds to neuroectodermal trans-activating factors that are not restricted to the ANB and that D1a contains motifs bound by a repressor factor that restricts D1 expression to the sole ANB ., We tested whether D1bcde controls the onset of Ci-pitx expression in the ANB ., Endogenous Ci-Pitx-gene expression was not detected in ANB cells before the initial tailbud stage 37 , 38 , suggesting that it starts at this stage ., To test whether D1bcde recapitulates the temporal pattern of Ci-Pitx expression , we assayed reporter gene expression by either X-gal staining or lacZ in situ hybridization on the same batch of electroporated embryos fixed at successive stages ., The rationale is to take advantage of the delay in β-galactosidase protein synthesis ( e . g . 39 ) , which should produce a marked difference between X-gal and in situ staining shortly after the onset of reporter gene expression ., We could detect neither lacZ RNAs nor β-galactosidase activity before the initial tailbud stage ., At this stage , however , lacZ transcripts could be detected in 55 . 4% ( n\u200a=\u200a46 of N\u200a=\u200a71 ) of the embryos while only 7% ( n\u200a=\u200a5 of N\u200a=\u200a83 ) showed positive ANB cells after X-gal staining ( Table S1 ) ., Hence , D1bcde-driven transcription starts at the same time as the endogenous pitx gene , which indicates that the D1bcde enhancer element triggers the initiation of Ci-pitx expression in ANB cells ., Conservation between Ciona intestinalis and savignyi genomic sequences is not uniformly distributed throughout conserved non coding elements ( CNEs ) but rather concentrated in short blocks of identical nucleotides , which point to candidate transcription factor binding sites ( TF-BS; Figure 1A , Figure 2A ) ., We identified four classes of putative TF-BS based on nucleotide composition and by querying binding site databases 40 , 41 ., One of them matches the OTX/K-50 paired homeodomain consensus sequence ( sites O1 and O2 , Figure 1A and Figure 2A ) ., Other sites , called T ( T/A-rich ) , G ( G/C-rich ) and M , bear resemblance to Forkhead , Smad and Meis family factors , respectively ( Figure 1A ) ., Some of them ( P , T1 , T2 were not completely conserved in the genome alignment ., But each class of these candidate binding sites was represented at least twice in the minimal D1bcde element ., The function of these candidate TF-BS was tested by introducing point mutations in the corresponding blocks of conserved sequences , followed by reporter gene expression assays ., With the exception of mutations disrupting the “M” sites , each one of the individual modifications of O , T and G sequences reduced reporter gene expression in the anterior neuroectoderm derivatives ( Figure 1B ) ., Taken together , these observations indicate that D1 enhancer activity requires at least two copies of each one of three distinct classes of conserved putative TF-BS ( Figure 1 ) ., The aforementioned observation that the essential putative binding sites occur several times in the enhancer led us to investigate whether the structure of D1 bears functional significance to its enhancer activity ., Notably , the 54-bp D1 ( ab ) element ( Figure 2A ) contains the three previously mentioned conserved motifs O , T and G in addition to a putative Pax binding site ( P ) , but D1 ( ab ) is not sufficient to enhance reporter gene transcription ( Figure 2C ) ., Since each of the critical sites is represented at least twice in the full length enhancer , we asked whether D1 enhancer activity relies on this tandem-like repetition of essential binding sites ., We created artificial enhancers containing multiple copies of D1 ( ab ) and found that as little as two copies of D1 ( ab ) were sufficient to drive strong lacZ expression in the anterior neuroectoderm ( 88% of 167 tailbud embryos ( Figure 2D and 2E ) ) ., To test whether enhancer activity of the D1 ( ab ) dimer relies specifically on the duplication of O , T and G sites , we introduced point mutations in the second D1 ( ab ) copy ., Each of these mutations strongly reduced enhancer activity ( Figure 2D ) ., These observations are reminiscent of the requirement for multiple copies of bicoid binding sites for target gene activation during Drosophila head development 42 and the general tendency of binding sites to occur in clusters 43 ., Our results demonstrate that duplications of critical binding sites are essential for D1 enhancer activity and do not constitute mere redundancy ., We next asked whether the distance between the duplicated 54bp elements influenced the activity of the artificial D1 ( ab ) dimer ., To this aim , we designed sequences that are not predicted to bind any characterized transcription factors from the Uniprobe database ( see Materials and Methods ) and inserted 25 , 50 , 75 and 150bp spacers between the D1 ( ab ) duplicates ., Overall , enhancer activity of these constructs is reduced compared to the original D1 ( ab ) dimer and almost completely abolished with the 75bp and 150bp spacers ( Figure 2F ) ., Similar structural constraints were reported in the Drosophila knirps enhancer , which was shown to require a specific arrangement of duplicated bicoid binding sites for activation 44 , 45 ., Similarly , even-skipped enhancers contain a conserved structure of paired binding sites 46 and duplicated and relatively distant ( 30–200bp ) TFBS are necessary for a correct activity of the SV40 enhancer 47 and the lac operon 48 ., Taken together , our observations demonstrate that D1 enhancer activity relies on the clustering of duplicate short conserved sequences ., Among D1 ( ab ) essential putative binding sites , the GATTA/TAATC “O” sequences correspond to the consensus for K50-Paired homeodomain proteins ., In ascidians , this family includes Goosecoid , Pitx and Otx ., Only Otx , is expressed in the right time and place to account for D1 enhancer activation in the anterior neuroectoderm in Ciona 20 and there is only one Otx gene in the Ciona intestinalis genome ., A functional study using morpholino antisense oligonucleotides in Halocynthia roretzi - another ascidian species - showed that the Hr-Otx knockdown strongly perturbs anterior neuroectoderm development , mostly because it is required for early specification events in the gastrula 49 ., To avoid this early effect , we used targeted expression of dominant-negative and hyper-active versions of the Ci-OTX protein to interfere with its endogenous activity specifically after gastrulation ., We thus engineered protein chimeras between the Ci-OTX homeodomain and the Drosophila engrailed repressor peptide or the VP16 trans-activation domain to create dominant-negative ( OTX:EnR ) or hyper-active ( OTX:VP16 ) forms , respectively ., We then used the Ci-Six3 cis-regulatory DNA to drive expression of these fusion proteins in a region that encompasses the ANB ( Figure S1 ) ., These constructs were co-electroporated with the Ci-Distal-Pitx reporter plasmid , which contains the D1 enhancer with the two essential O1 and O2 K50-Paired binding sites 36 , and the number of anterior neuroectodermal cells expressing the reporter gene was scored at the mid-tailbud stage ( Figure 3 ) ., In control embryos expressing a Ci-Six3:Venus construct , an average of 2 . 78 anterior neuroectodermal cells per embryo activated the Ci-Pitx reporter construct , which can be accounted for by the mosaic incorporation of the transgene in the four ANB cells ( Figure 3A and 3C ) ., In contrast , targeted expression of Ci-OTX fusion proteins significantly altered Ci-Pitx reporter gene expression in the anterior neuroectoderm: the engrailed fusion inhibited ANB expression , while OTX:VP16 produced ectopic activation in surrounding neuroectodermal cells ( Figure 3B–3D ) ., Notably , OTX:VP16 also boosts expression of the ab dimer construct , and is not sufficient to induce overexpression when coelectroporated with one dimer construct bearing one O mutation ( data not shown ) ., This indicates that OTX:VP16 indeed binds to the GATTA binding sites ., These observations strongly suggest that Ci-OTX trans-activating inputs are required for D1 enhancer activity in the anterior neuroectoderm ., In addition , widespread expression of Ci-Otx in the anterior neuroectoderm contributes to the broad D1 trans-activation potential that encompasses the ANB , anterior epidermis and anterior sensory vesicle and is probably defined in D1 by the conserved GATTA/TAATC duplicated sequences ., We cannot exclude the possibility that endogenous Ci-Pitx maintains its own expression through the same GATTA/TAATC BS , which binds PITX as well as OTX proteins ., However , Otx is the best candidate for the onset of D1 activity , which begins exactly at the same time as the onset of the endogenous Ci-Pitx expression ., Of the three different duplicated BS that we identified for the ANB expression domain of Pitx and that we suppose to be specific to this restricted area of the anterior neuroectoderm , we concentrated our effort only on the binding sites for OTX as these are the only ones assignable to a well-characterized transcription factor ., The observation that the transcriptional response to the broadly expressed head field-selector gene Otx is mediated by duplicated GATTA motifs led us to investigate whether this regulatory architecture was overrepresented in candidate Otx target genes in early tailbud embryos ., At this stage , Ci-Otx expression extends over a broad domain referred to as in the anterior neuroectoderm , which derives from the a-line blastomeres and encompasses the ANB as well as other specific neurectodermal territories such as the anterior sensory vesicle , palps , a-line epidermis and rostral trunk epidermal neurons ( RTEN ) ., Therefore , we reasoned that candidate Otx target genes could , in principle , be expressed in all or part of the anterior neurectoderm ., Hence , we asked whether duplicated GATTA motifs –the candidate signature for Otx binding- were enriched in the conserved noncoding sequences flanking genes with conspicuous expression in the anterior neurectoderm ., To this end , we obtained whole mount in situ hybridization data for 1518 genes showing tissue-specific expression from the model organism database ANISEED ( December 2007 , http://crfb . univ-mrs . fr/aniseed , see also Protocol S1 ) ., From these , we selected genes that are expressed in the central nervous system ( CNS ) and the ANB and classified them into different territories according to their expression along the antero-posterior axis: following previous reports 49–51 , the ascidian visceral ganglion and the nerve cord were considered as “posterior” CNS whereas the whole sensory vesicle , including the ANB , constitute the “anterior” nervous system ., This lead to a detailed annotation of nervous system expression patterns for 258 genes ( Table S2 ) ., From this list we retained only those 100 genes that are specifically expressed in the anterior and not the posterior parts of the CNS ., Finally , we obtained annotations for additional genes expressed in tissues like muscle , epidermis or notochord , from the database ANISEED ., This latter set of genes was used as negative controls , which allowed for background definition for further statistical analyses ., In total , our set includes annotations for 904 genes ., We then aimed at studying the distribution of duplicated short DNA motifs around these 904 genes to find those that show a bias towards genes expressed in the anterior or posterior nervous system , muscle , epidermis or notochord ., We concentrated on conserved non-coding elements ( CNEs ) , as these have been shown to be enriched in developmental enhancers 52 , 53 ., To obtain these elements for the genome of Ciona intestinalis , we created a whole-genome alignment with Ciona savignyi 54 and removed aligned positions in transcribed regions from it ., This results in 168306 CNEs with an average length of 143 bp ., Then , we searched for duplicate matches to all 512 possible pentamers within 125 bp of all CNEs in the Ciona intestinalis genome and subsequently calculated the number of tissue-specific neighboring genes associated to each duplicated conserved pentamer and tissue ., The rationale for using consensus and not matrix-based searches was that all subclasses of homeodomain proteins have well characterized binding sites that resemble pentamer motifs without degenerate positions 25 , 26 ., For the window size parameter , we observed from our case study that the sites had to occur in duplicates with a maximum distance of about 125bp , which was the total length of the fragment between both OTX-sites in the 75bp spacer construct ., The score we chose was inspired by 55; it does not require a sequence background model ., This “motif-tissue-score” is the negative logarithm of the binomial probability to obtain a certain number of annotated genes from a given tissue by chance and therefore reflects the association of individual pentamer motifs with specific tissues ., Our first observation was that a duplicated OTX ( GATTA ) motif within 125 basepairs appears among the motifs with the highest score in the anterior CNS region ( Table S3 ) ., For instance , genes containing duplicated GATTA motifs within 125bp in their flanking conserved genomic DNA are more likely to be expressed in the anterior nervous system than in any of the other tissues used in this analysis , including the posterior CNS ( 26% versus 12% or less , Table 1 ) ., We then set out to assess the robustness of this analysis to variations of all three parameters: copy-number , window size and gene annotation ., We varied the number of motif-duplicates from one to four and still obtained the highest motif-tissue scores in the anterior region with two copies ., Increasing the window size from 25bp to 300bp did not change the scores to a large extent and the relative order between the anterior nervous system and other tissues always remained the same ( Figure S3 ) ., The influence of errors in the manual annotation process was investigated by a simulation: we randomized 10% of all gene annotations and repeated this procedure 100 times ., The 95% confidence intervals from these are small compared to the total differences between the tissues ( Figure 4 ) ., These results indicate that a biased distribution of GATTA motifs in CNEs supports the model of anterior ectodermal expression based on D1 enhancer analysis ., We conclude that the presence of duplicated and conserved OTX binding sites in a cis-regulatory element is a signature for anterior neuroectoderm enhancer activity ., We then sought to test whether conserved sequences containing duplicated GATTA motifs act as enhancers in the anterior neuroectoderm ., Out of all 53 CNEs with at least two conserved GATTAs in a 125 bp window that flank genes expressed in the anterior nervous system , we selected 30 CNEs ., We succeeded in cloning 23 of them into a lacZ expression vector ., After electroporation , we observed that ten of them are active enhancers in various domains of the anterior neuroectoderm derivatives , where Otx is expressed at the tailbud stage ( Figure 5 , Figure S2 , and Table S4 ) ., The remaining non-coding regions were inactive or drove non-specific expression in the mesenchyme , as is often observed in electroporated ascidian embryos 56 , 57 ., This ratio of positive elements is high compared to a previously published enhancer screen of random DNA fragments ( 5 active enhancers out of 138 tested fragments ) 57 and similar to a prediction based on binding site occurrences in Drosophila muscle founder cells ( 6 out of 12 tested elements ) 58 ., We were unable to identify additional motifs that would be predictive of enhancer activity in the anterior neurectoderm ( Figure S4 ) ., However , additional motifs are required in natural enhancers , as we showed that pentamers of GATTA alone were unable to drive reporter gene activity in the Otx expression domain ( data not shown ) ., The diversity of expression patterns obtained with the ten active enhancers rather suggests that different transcription factors , each specific for a subdomain of the anterior neuroectoderm , might be implied in the activity of these elements ., Thus , while there might be additional motifs necessary for anterior neuroectoderm expression , this study shows the importance of the duplicated GATTA regulatory architecture as a predictive tag for the identification of anterior enhancers in chordates ., Could a signature based on GATTA-sites also be predictive in vertebrates ?, 52 reported that GATTA is over-represented in forebrain enhancers and used it as one of six motifs to predict forebrain enhancers in the mouse genome ., We also found other overrepresented motifs in anteriorly expressed genes ( see Table S3 ) ., Therefore , as determined experimentally with the D1 element , additional complexity must supplement the duplicated GATTA sites to achieve a cell-specific expression ., Similar approaches performed in Drosophila and Caenorhabditis have identified several binding sites , which correspond to factors that specify a particular fate or behaviour in a combinatorial fashion , such as the myogenic factors 58 , 59 ., However , our study identifies for the first time a cis-regulatory signature that determines the transcriptional response to a “master” homeobox gene in a simple chordate and establishes a model for genome-wide predictions of tissue-specific enhancers ., Adult Ciona intestinalis were purchased at the Station de Biologie Marine de Roscoff ( France ) and maintained in artificial sea water at 15°C under constant illumination ., Eggs and sperm were collected from dissected gonads and used in cross fertilizations ., Electroporations , using 70 µg of DNA , and LacZ stainings were performed as previously described 36 ., Embryo staging at 13°C were done according to 60 , 61 ., Images were taken on a Leica DMR microscope ., For the mutational analysis of the enhancer D1bcde ( Figure 1 ) , we omitted the first 16 bp ( AAACGCGACGACCTCC ) of D1abcde that were not conserved between Ciona intestinalis and savignyi ., Each of the mutations was designed to perturb DNA-binding of the candidate trans-acting factors following various reports in the literature ., Mutations were performed using the Stratagene QuickChange Kit ., Seven new constructs called m0 , m1 , m2 , m3 , m4 , m5/6 , m7/8/9 were generated ., After each electroporation , we observed LacZ expression in the tissues of the anterior neural boundary , anterior epidermis , ventro-anterior sensory vesicle and mesenchyme ., We obtained a semi-quantitative estimation of the promoter activity by calculating the percentage of positive embryos ., Plasmids with artificial enhancers were designed by cloning inserts into the pCES2::lacZ vector that contains the basal Ci-Fkh/FoxA promoter 57 ., Insert D1 ( ab ) was generated by cloning two long complementary primers with XhoI/XbaI cohesive ends into pCES2 ., Inserts ( abde ) , ( abd ) , ( ab ) ( ab-Pdel ) , ( ab ) ( ab-Omut ) , ( ab ) ( ab-Tmut ) , ( ab ) ( ab-Gmut ) were generated by cloning a second insert consisting of another couple of long complementary primers into the XbaI/BamHI site of D1 ( ab ) ., The insert of D1 ( ab ) ×5 was designed in silico , synthetized by Genecust Europe ( Luxembourg ) and cloned into pCES2::LacZ between XhoI and BamHI ., To obtain D1 ( ab ) ( ab ) , we cut out the first two parts of D1 ( ab ) ×5 with SalI/XhoI and ligated them into pCES2 ., The spacer sequence between both ( ab ) parts of D1 ( ab ) -xx- ( ab ) constructs was created in silico by avoiding all octamers bound by homeodomain factors from a large-scale DNA-protein binding assay 25 ., We recursively added random nucleotides to an unbound sequence and backtracked if the new sequence contained an octamer with PBM enrichment score >0 . 3 from the UniProbe database 62 ., These constructs , D1 ( ab ) -xx- ( ab ) are also derived from D1 ( ab ) , but the insert was synthesized by GeneScript Corporation ( Piscatway , NJ , USA ) ., We amplified spacers of the appropriate length by PCR from the longer fragment and cloned them between the two duplicated ( ab ) fragment by restriction/ligation ., A pSix3:Venus plasmid was digested by BamHI/EcoRI to eliminate the Venus/YFP reporter ., Plasmids containing non-coding elements were created with the Gateway Technology System ( Invitrogen Carlsbad , CA , USA ) ., We cloned an AttR3/AttR4 Gateway Cassette from 63 into the XhoI/XbaI-site of pCES2 and called the resulting construct AttR3R4-pCES2 ., Predicted fragments were first amplified by primers including part of the flanking AttB3/AttB4-sequences and then extended by a subsequent PCR to the full length sequences of AttB3/AttB4 ., These fragments were recombined with BP clonase into the P3/P4-donor Vector 63 and the resulting entry vectors recombined with LR clonase into AttR3R4-pCES2 producing expression vectors ., Computational methods are described in Protocol S1 ., Programs that were used for whole-genome analyses are accessible at http://genome . ciona . cnrs-gif . fr/scripts/ . | Introduction, Results/Discussion, Materials and Methods | One of the striking findings of comparative developmental genetics was that expression patterns of core transcription factors are extraordinarily conserved in bilaterians ., However , it remains unclear whether cis-regulatory elements of their target genes also exhibit common signatures associated with conserved embryonic fields ., To address this question , we focused on genes that are active in the anterior neuroectoderm and non-neural ectoderm of the ascidian Ciona intestinalis ., Following the dissection of a prototypic anterior placodal enhancer , we searched all genomic conserved non-coding elements for duplicated motifs around genes showing anterior neuroectodermal expression ., Strikingly , we identified an over-represented pentamer motif corresponding to the binding site of the homeodomain protein OTX , which plays a pivotal role in the anterior development of all bilaterian species ., Using an in vivo reporter gene assay , we observed that 10 of 23 candidate cis-regulatory elements containing duplicated OTX motifs are active in the anterior neuroectoderm , thus showing that this cis-regulatory signature is predictive of neuroectodermal enhancers ., These results show that a common cis-regulatory signature corresponding to K50-Paired homeodomain transcription factors is found in non-coding sequences flanking anterior neuroectodermal genes in chordate embryos ., Thus , field-specific selector genes impose architectural constraints in the form of combinations of short tags on their target enhancers ., This could account for the strong evolutionary conservation of the regulatory elements controlling field-specific selector genes responsible for body plan formation . | Regional identity in embryos is defined by a few specific transcription factors that activate a large number of target genes through binding to common tags in regulatory sequences ., In chordates it is unclear if such tags can be identified in the cis-regulatory regions of regionally expressed genes ., To address this question we focused on the anterior nervous system where Otx codes for a transcription factor that triggers expression of many other head-specific genes ., We analyzed an element that is active in the region bordering the anterior nervous system in the marine invertebrate Ciona intestinalis ., We found that the crucial binding sites have to be duplicated and close enough ., One of the pairs is bound by OTX ., We showed that anterior nervous system genes are often flanked by duplicated OTX binding sites ., We confirmed by transgenic assays that about half of these genomic sequences are active and drive expression anteriorly ., This study unravels a simple regulatory logic in the anterior enhancers ., It indicates that although there are major changes in the organization of the binding sites at short evolutionary range , conserved expression patterns are partly generated by a duplicated organization of conserved binding sites for region-specific transcription factors . | genetics and genomics/comparative genomics, computational biology/sequence motif analysis, computational biology/transcriptional regulation, developmental biology/developmental evolution, developmental biology/pattern formation, developmental biology/neurodevelopment, computational biology/genomics, evolutionary biology/developmental evolution | null |
journal.pntd.0004418 | 2,016 | Albendazole and Corticosteroids for the Treatment of Solitary Cysticercus Granuloma: A Network Meta-analysis | Neurocysticercosis ( NCC ) , a parasitic disease of the nervous system caused by Taenia solium ( pork tapeworm ) , is a leading cause of acquired epilepsy worldwide 1 , 2 ., The disease is widely prevalent around the world , and has pleomorphic clinical and radiologic manifestations 1 ., Solitary cysticercus granuloma ( SCG ) , presenting as a single small enhancing lesion , is found in ~20% of NCC cases in endemic areas , and is the commonest type of NCC in the Indian subcontinent as well as in travelers of industrialized countries returning from endemic zones 3 , 4 ., SCG has traditionally been considered the degenerating form of long-established vesicular cyst that cannot maintain immune evasion and thus is under the host’s immune attack ., A recent hypothesis proposes that SCG represents fresh infection that is rapidly detected and destroyed by the host’s immune system ., 5 Treatment might be different for patients with live and degenerative/dead parasite ., While there is sufficient information in support of the use of the combination of anthelmintics and corticosteroids in patients with viable cystic parenchymal NCC 6–10 , the treatment of SCG has not been optimally defined 11 ., Besides , the recent American Academy of Neurology ( AAN ) evidence-based guideline on NCC didn’t address management issues of different types of lesion independently 12 ., Currently , the overall evidence from randomized clinical trials ( RCTs ) on drug therapy for SCG consists of comparisons between the combination of anthelmintics and corticosteroids therapy , anthelmintics therapy alone , corticosteroids therapy alone and conservative treatment ( limited to treatment of symptoms ) , such as antiepileptic drugs alone without anthelmintics or corticosteroids ., Several pairwise meta-analyses have evaluated the independent efficacies of anthelmintics and of corticosteroids 9 , 13 , 14 ., However , multiple different regimens have never been compared with each other simultaneously ., The network of evidence can be better examined in a mixed treatment comparison framework with Bayesian method 15 , 16 ., This approach fully respects randomization , accounts for the correlation of multiple observations within the same trial , and allows estimation of relative efficacies of different drugs and their combination ., Here , we systematically reviewed and analyzed RCTs on drug therapy for SCG and conducted a Bayesian network meta-analysis to determine the effect of different therapies on seizure control and on radiological resolution of the disease ., We searched the electronic databases of PubMed , EMBASE and the Cochrane Library ( from inception until June 1 , 2015 ) without restrictions on language or publication date ., The logic combinations of the following terms were searched in the Title/Abstract: cysticercosis , neurocysticercosis , solitary cysticercus granuloma , single small enhancing computed tomographic lesion , cysticidal , anticysticercal , anthelmintic , albendazole , praziquantel , corticosteroid , steroid , prednisolone , methylprednisolone , and dexamethasone ., The computer retrieval was supplemented by manual search of reference lists of identified studies and ( systematic ) reviews on neurocysticercosis ., The identified citations were initially screened at the title and abstract level , and then retrieved as full-text copies if they reported potentially relevant studies ., To be included in the analysis , studies had to be randomized clinical trials ( RCTs ) that included patients with new onset seizures and diagnosed with SCG based on clinical and imaging studies according to the accepted criteria 18 ., All studies compared the efficacy of anthelmintics ( albendazole and/or praziquantel ) or corticosteroids , or both , head to head or with placebo or no drugs ., Studies were excluded if they compared different dosages or durations of the same medication , if only patients with cystic or multiple enhancing lesions were included , and if none of the quantitative outcomes of interest ( see below ) were reported ., Studies using concomitant drugs , such as antiepileptic drugs ( AEDs ) were not excluded if balanced among the trial arms ., When more than one report describing the same study were published , the one with the most recent or complete data was used for meta-analysis ., Two researchers independently reviewed the studies with disagreements in eligibility , methodological quality or data extraction resolved through discussion and consensus ., Data were collected for each eligible RCT on study characteristics , patient characteristics , and outcome results ., The goal of this study was to compare the efficacies of different drug therapies in the following aspects: seizure recurrence , defined as the occurrence of one or more convulsions after the beginning of treatment , and lesion resolution , defined as complete disappearance of the granuloma with no residual scar , calcification or perilesional edema on imaging studies , by MRI or CT scan ., If a study reported outcomes at multiple time points , only data from the most recent follow-up were extracted for analysis ., The methodological quality of the included RCTs was appraised using the Cochrane Collaboration’s tool for assessing risk of bias 19 , which consists of seven items: sequence generation; allocation concealment; blinding of participants and personnel; blinding of outcome assessors; incomplete outcome data; selective outcome reporting; and other bias ., Blinding and incomplete outcome data were assessed separately for the two primary outcomes ., The overall risk of bias of a trial was expressed as low , moderate , or high ., Therapeutic interventions were included in quantitative analyses if they had been studied in at least two trials ., We conducted Bayesian network meta-analysis using the binomial likelihood model for multi-arm trials 20 , 21 , given the outcomes were dichotomous and included multi-arm trials ., Our model adopted random effects because it is the most appropriate and conservative analysis to account for variance among trials ., The Markov Chains Monte Carlo method was used for analysis ., Three Markov chains ran simultaneously with different initial values ., 150 , 000 simulations were generated for each of the three sets of initial values , with the first 50 , 000 discarded to avoid the influence of initial unstable values ., The convergence was assessed with trace plots and the Brooks-Gelman-Rubin statistic ., The odd ratios ( OR ) were estimated from the median of the posterior distribution and the accompanying 95% credible intervals ( CrI ) , which can be interpreted in the same manner as the conventional 95% confidence interval ( CI ) ., For comparison , the estimates from just the head-to-head evidence for each pair of comparison were also worked out with the Mantel-Haenszel method of the conventional pairwise meta-analysis ., Furthermore , for each outcome , we estimated the probability that each treatment regimen was the most , the second , the third , and the least efficacious , based on their ranks in each iteration of Markov chain ., These probability values were used for generating cumulative probability plots and calculating the Surface Under the Cumulative RAnking curve ( SUCRA ) , with 1 representing the best treatment and 0 the worst 22 ., We examined the validity of the network models by evaluating three of their important characteristics ., The goodness of model fit was measured by the posterior mean of the residual deviance , which should be close to the data points when the model can provide adequate fit ., Heterogeneity was defined as the variability of the results across trials ., It was estimated from the posterior median between-study variance τ2 , with τ2 < 0 . 04 indicating a low level of heterogeneity and τ2 > 0 . 40 a high level 23 ., Consistency , defined as agreement between direct and indirect sources of evidence , was first assessed visually by comparing the results of network meta-analysis and pairwise meta-analysis , and then tested statistically by calculating the ratio of two odds ratios ( RoR ) from direct and indirect evidence in each closed loop in the network of interventions ., RoR values close to 1 mean that the two sources are in agreement 24 ., The considerable variation in follow-up duration among the included RCTs and the fact that probability of both primary outcomes are related with time 25 did not allow calculation of the absolute rate difference and number needed to treat for each intervention by using the baseline rates across the conservative treatment arms ., Considering that evidence may be different from RCTs with different follow-up duration , we performed meta-regression analysis with follow-up duration ( ≤ 6 months versus ≥ 9 months for seizure recurrence , 3 months versus 6 months for lesion resolution ) as an interaction 26 ., We calculated the subgroup interaction term β and checked whether its 95% credible interval included the possibility of no interaction ., We performed further sensitivity analysis by sequentially removing one study at a time from the overall dataset ., A post hoc analysis was performed to compare different treatments on the risk of residual calcification during the evolution of SCG lesions ., Assessment of publication bias using the funnel plots was precluded by the small number of studies included in the meta-analysis ., Conventional pairwise meta-analysis was performed with Review Manager 5 . 3 . 3 ( Cochrane Collaboration , Nordic Cochrane Centre , Denmark ) ., Network meta-analysis including meta-regression analysis was performed with winBUGS 1 . 4 . 3 ( MRC Biostatistics Unit , Cambridge , UK ) ., Test for consistency was conducted with Stata 12 . 0 ( StataCorp LP , College Station , TX ) ., Fig 1 is a flow chart of the study and summarizes the process of trial selection ., Twenty articles reporting 16 RCTs met the inclusion criteria 27–42 ., The included RCTs covered six different treatment regimens for SCG: albendazole ( evaluated in 5 trials ) , praziquantel ( 1 trial ) , corticosteroids ( 9 trials ) , albendazole plus corticosteroids ( 6 trials ) , albendazole and praziquantel plus corticosteroids ( 1 trial ) , and conservative treatment ( 11 trials ) ., The two praziquantel-containing regimens were evaluated only in one trial , so that they and the corresponding trials were not suitable for the network meta-analysis ., The main features of the RCTs included in the quantitative analysis are summarized in Table 1 ., Fourteen trials involving 1 , 277 randomized patients were included ., All the included RCTs were conducted in India where cysticercosis is highly endemic ., The proportion of women ranged from 29 . 7% to 47 . 2% , and the mean age of patients at baseline ranged from 7 . 4 to 24 years ., Each participant in each RCT , but two , was diagnosed with a solitary enhancing lesion ., In one RCT 31 , only 70 . 8% of the patients had a single enhancing lesion while the others carried two or more lesions ., However , it was possible to extract the data of patients with single lesions , thus allowing the inclusion in the analysis of data of only these patients ., In another trial 32 , some patients ( 18% ) had two rather than one enhancing lesions and the outcome data could not be separated ., We decided to include this trial given the small proportion of patients with two lesions ., All trials had two arms , except one in which the three active treatments were compared directly with each other 40 ., The dosage of albendazole and corticosteroids were similar across the trials , but the duration of treatment varied from 3 to 28 days ., Antiepileptic drugs were used in all trials ., The follow-up period ranged from 6 to 18 months for seizure recurrence and from 2 to 6 months for lesion resolution ., There was high risk of selection bias in most studies because they used random number tables to generate random number sequences with no or unclear method of allocation concealment ., The performance bias was high in more than half of the studies due to lack of blinding of participants ., Blinding of seizure assessment was unclear or non-existent in those studies too , but for the assessment of lesion resolution , blinding was generally well maintained ., S1 Fig shows the assessment process of the risk of bias of the studies included in this meta-analysis ., Thirteen RCTs were used for the quantitative analysis of seizure recurrence ., The network diagram for this outcome is presented in S2 ( A ) Fig . Network meta-analysis showed that albendazole plus corticosteroids was the only treatment protocol that significantly decreased the recurrence of seizure during the follow-up period compared with conservative treatment ( OR 0 . 32 , 95% CrI 0 . 10–0 . 93 , Figs 2 and 3 ) ., The results were similar in the only direct comparison RCT that evaluated albendazole plus corticosteroid versus conservative treatment ( OR 0 . 31 , 95% CI 0 . 11–0 . 89 ) 32 ., The risk reduction for corticosteroids alone was marginal outside the level of significance ( 0 . 46 , 0 . 19–1 . 01 ) , and the efficacy of albendazole alone did not even approach statistical significance ( 0 . 66 , 0 . 22–2 . 17 ) ., While there were no significant differences among the three active treatments , the ranking probabilities and cumulative probability plots indicated that the combination of albendazole and corticosteroids was superior to either treatment alone ( Fig 3 ) ., The combination therapy had the greatest probability of being the best treatment ( Pbest 73 . 3% ) , and the SUCRA values were 0 . 884 , 0 . 637 , and 0 . 388 for albendazole plus corticosteroid , corticosteroid , and albendazole , respectively ., A test of subgroup interaction between RCTs with follow-up period of ≥9 months and those with ≤ 6 months was not statistically significant ( subgroup interaction term β 0 . 05 , -1 . 73–1 . 77 ) , adding support to the conclusion that the combination of the two groups of RCTs was not inappropriate ., The fit of model was good with the posterior mean of the residual deviance of 26 . 77 , compared with 27 data points ., However , the estimated between-study variance was 0 . 54 ( 0 . 03–2 . 52 ) , which is potentially considerable with its uncertainty caused by the relatively small number of studies ., Visual inspection of the results from pairwise and network meta analyses showed obvious inconsistency between direct and indirect estimates for the contrast albendazole versus corticosteroids , and this was confirmed by a large RoR value ( 3 . 15 ) of the corresponding loop in the network ( S3 ( A ) Fig ) ., Since only one RCT 40 supplied direct evidence for the comparison , we investigated the inconsistency by removing this trial in a sensitivity analysis ., The result is presented in S1 Table ., The combination of albendazole plus corticosteroid ( 0 . 31 , 0 . 11–0 . 76 ) and corticosteroid alone ( 0 . 37 , 0 . 17–0 . 68 ) both significantly reduced the risk of seizure recurrence , with combination therapy being the better one in probability analysis ( Pbest 67 . 1% versus 31 . 8% , SUCRA 0 . 882 versus 0 . 767 ) ., The probability analysis suggested that the efficacy of albendazole monotherapy was even worse than conservative treatment although there was no significant difference between the two ( OR conservative treatment versus albendazole 0 . 73 , 0 . 23–2 . 23 ) ., Note that the pooled estimates of network meta-analysis generally overlapped with the results of conventional pairwise meta-analysis ( when available ) and that the estimated between-study variance decreased from 0 . 54 to 0 . 16 ., The outcome of lesion resolution was analyzed in all 14 RCTs ., The network diagram is presented in S2 ( B ) Fig . In the network meta-analysis , compared with conservative treatment , the efficacy of albendazole plus corticosteroids combination therapy in inducing resolution of SCG was the highest ( 3 . 05 , 1 . 24–7 . 95 ) , followed by albendazole alone ( 2 . 63 , 1 . 61–6 . 34 ) , and corticosteroids alone ( 2 . 32 , 1 . 20–4 . 75 , Figs 2 and 3 ) ., The same order was also identified in conventional pairwise meta-analysis but the confidence intervals were wider and only the efficacy of corticosteroid therapy reached statistical significance ., The differences between the three treatments were not conclusive ., Nevertheless , the combination of albendazole plus corticosteroid ( Pbest 53 . 9% , SUCRA 0 . 789 ) was more likely the best treatment for this outcome in probability analysis , compared with the monotherapy of albendazole ( 33 . 9% , 0 . 659 ) and corticosteroid ( 12 . 1% , 0 . 541 ) ( Fig 3 ) ., The posterior mean residual deviance was close to the number of data points ( 29 . 71 compared with 29 ) and thus the model fit was adequate ., Heterogeneity was high ( between-study variance 0 . 41 ) but acceptable ., RoR values all close to 1 demonstrated no significant inconsistency between direct and indirect evidence for any of the pairwise treatment comparisons ( S3 ( B ) Fig ) ., The effect of interaction between the trials with 3-month follow-up and those with 6-month was insignificant ( subgroup interaction term β 0 . 40 , -0 . 84 to 1 . 75 ) , although the point estimate was positive , suggesting that the efficacy of the combination therapy in promoting lesion resolution could be more obvious in short follow-up ., This is reasonable since many SCGs resolve spontaneously with time 25 ., Sensitivity analyses by sequentially removing one study at a time yielded largely the same results ., During review of the literature , 8 studies were identified that included data describing the frequency of residual calcification on follow-up imaging 27–29 , 31 , 34 , 35 , 41 , 42 ., Since calcific residue is one of the major predictors for future seizure recurrence 25 , we did an additional post hoc analysis to evaluate the effect of different therapies on reducing the risk of residual calcification of SCG ., Only pairwise meta-analysis was conducted because a closed loop for network meta-analysis was not formed ., All pooled ORs are close to 1 with wide 95% CIs ( S4 Fig ) , indicating that none of regimens showed a better effect on reducing the risk of residual calcification compared with others ., SCG is the commonest form of NCC seen in India and high-income countries , and is also found in about 20% of NCC cases elsewhere 3–5 ., Although the granuloma shows spontaneous resolution with time , complete resolution can take anywhere from a few weeks to several years 43 ., Since 1993 when albendazole was first shown to hasten the resolution of long persistent SCG 44 , several clinical trials have been conducted to evaluate the effects of albendazole and other treatment options ., Based on a Bayesian network of 14 RCTs that included 1277 patients , the results of the present meta-analysis showed that the combination therapy of albendazole and corticosteroids for SCG reduced the risk of seizure recurrence by two thirds and tripled the odds of lesion resolution during a short follow-up period of around one year , compared with conservative treatment ., Although the differences in the beneficial effects of the combination therapy of albendazole plus corticosteroids compared with either treatment alone did not reach statistical significance , the combined therapy consistently showed higher probabilities of being at superior ranking positions for both outcomes ., The albendazole and corticosteroids monotherapies showed similar and significant efficacy in promoting lesion resolution , but their benefits failed to translate into better seizure outcome during the follow-up ., The superiority of the combination therapy was robust and changed little in trials with different follow-up durations and in sensitivity analyses ., Previous meta-analyses 9 , 10 , 13 reported that anthelmintic therapy with albendazole improved the seizure-free rate and hastened the resolution of the granuloma ., However , these analyses combined clinical trials with different comparison groups ( anthelmintics versus conservative treatment , anthelmintics versus corticosteroids , combination of anthelmintics and corticosteroids versus conservative treatment , and combination of anthelmintics and corticosteroids versus corticosteroids ) making it impossible to determine the efficacy of anthelmintics itself and of the combination therapy ., In fact , only three studies directly compared albendazole alone with placebo or no drugs , and the pooled estimates showed borderline significant improvement in lesion resolution and no difference in seizure outcome ., Our network meta-analysis confirmed that albendazole alone did not improve the seizure-free rate although more lesions showed radiological resolution ., The efficacy of corticosteroids alone in the treatment of SCG was evaluated in two pairwise meta-analyses with inconsistent results 13 , 14 ., Both studies used the same set of trials , yet Otte et al . 13 in their meta-analysis incorrectly extracted the data from the trial by Kishore et al . 34 ., They misused the data of the placebo arm for the prednisolone arm and that of prednisolone for the placebo , thus yielding pooled effects that fell just short of statistical significance ., In our study , we also used the pairwise meta-analysis , which confirmed the benefits of corticosteroids monotherapy for both outcomes ., These benefits remained significant in network meta-analysis , although the corticosteroids monotherapy tended to be inferior to the combination therapy and albendazole monotherapy in promoting lesion resolution and inferior to the combination therapy in preventing seizure recurrence ., Based on RCTs and pairwise meta-analysis , an expert consensus on diagnostic and therapeutic schemes for SCG recommended a short course ( 1–2 weeks ) of albendazole with or without corticosteroids be prescribed soon after the first seizure 11 ., Our study suggests that albendazole alone may not be effective on seizure control , and that the combination therapy of albendazole and corticosteroids should be initiated to bring the most benefit for patients with SCG ., The observed effects of albendazole and corticosteroids are supported by their mechanisms of actions and the histopathology of the granulomatous lesion ., The cysticercus granuloma consists of a dying parasite surrounded by fibrosis , angiogenesis and infiltration of inflammatory cells 45 ., The parasite or its parts are still present , offering a target for the anthelmintics to act on ., The attack on parasite accelerates its destruction and leads to a faster and more efficient lesion resolution , but at the same time hastens the release of parasitic antigens and exacerbates local inflammation 46 ., The study by Robinson et al . demonstrated that substance P produced within cysticercosis granulomas is capable of inducing seizure activity 47 ., The anti-inflammatory and immunosuppressive properties of corticosteroids seem to reduce the generation of the seizure-inducing mediators , limit the inflammatory damage to neural tissue and control perilesional edema ., Corticosteroids also interact with albendazole by reducing the elimination rate of albendazole sulfoxide , the active component of albendazole , thus increasing its plasma concentrations 48 ., The clinical synergism between albendazole and corticosteroids results in better seizure control as well as early resolution of the granulomatous lesion ., However , because the analyzed clinical trials do not provide information on the timing of seizure recurrence in relation to drug administration , it is not clear whether the favorable seizure outcome achieved by the combination therapy is the result of a reduced likelihood of seizure activity during and shortly after the administration of albendazole and corticosteroids or due to a more sustained effect ., Our study has several limitations ., First , the majority of RCTs included in the analysis were at high risk of bias mainly because of inadequate allocation concealment and blinding ., Only four studies were considered to have low-to-moderate risk of bias for the two outcomes , respectively , so that sensitivity analyses with only high quality studies were not possible ., Second , all included RCTs were conducted in India ., It is not certain whether the conclusions of this study apply to other populations ., Third , under each class of treatment , there were variations in dosage and duration of the drugs used ., This might have introduced some heterogeneity into the network meta-analysis ., Treating them as different regimens , however , would not be feasible due to the insufficient number of studies to form a well connected network ., The optimal match of dosages and durations of albendazole and corticosteroids needs further research ., Fourth , the duration of the follow-up period varied among the included RCTs , making it another source of heterogeneity ., Previous meta-analyses have tried some resolutions to the problem , such as performing separate meta-analyses at different time points of follow-up 13 or extracting data in the form of number of events per person-years observed 49 ., In fact , estimating person-year of follow-up in these trials is very imprecise , and to our knowledge , there are currently no suitable methods that allow inclusion of all time points in a network meta-analysis ., Here we explored the effects of differences in the duration of the follow-up period by meta-regression ., The follow-up period was found not to significantly influence the results ., Nevertheless , the average duration of the follow-up period of the included RCTs was generally short ., Currently we cannot make firm conclusions on the effects of therapies more than one year after treatment ., Future studies should focus on the efficacy of treatment in long-term seizure recurrence and granuloma resolution ., Finally , limited data were available for two praziquantel-containing regimens to include in the analysis ., In one trial 38 , 26 patients were assigned to receive single-day praziquantel therapy or no therapy ., Complete resolution was found in 78% ( 11 out of 14 ) and 50% ( 6 out of 12 ) of patients , respectively ., Another trial compared the combination of albendazole , praziquantel and prednisolone with the combination therapy of albendazole and prednisolone 33 ., After 6-month follow-up , complete lesion resolution was observed in 72% ( 38 out of 53 ) of patients of the praziquantel-treated group , versus 52% ( 26 out of 50 ) of the control group ., The differences were not statistically significant in both studies ., Although a previous meta-analysis showed that praziquantel might be less effective than albendazole in the treatment of NCC 49 , the two anthelmintics have different mechanisms of action and have synergistic effects when used in combination 50 ., More data are required before praziquantel can be added to the combination of albendazole and corticosteroids therapy for the treatment of SCG ., Despite the above limitations , based on the comprehensive review and robust statistical method , our network meta-analysis provides a complete picture for the efficacy of different management options for patients with SCG ., The combination of albendazole and corticosteroids performs better than other therapies in reducing seizure recurrence and promote lesion resolution during a follow-up period of around one year ., Until more direct active comparisons are available , it should be recommended for the treatment of SCG . | Introduction, Methods, Results, Discussion | Solitary cysticercus granuloma ( SCG ) is the commonest form of neurocysticercosis in the Indian subcontinent and in travelers ., Several different treatment options exist for SCG ., We conducted a Bayesian network meta-analysis of randomized clinical trials ( RCTs ) to identify the best treatment option to prevent seizure recurrence and promote lesion resolution for patients with SCG ., PubMed , EMBASE and the Cochrane Library databases ( up to June 1 , 2015 ) were searched for RCTs that compared any anthelmintics or corticosteroids , alone or in combination , with placebo or head to head and reported on seizure recurrence and lesion resolution in patients with SCG ., A total of 14 RCTs ( 1277 patients ) were included in the quantitative analysis focusing on four different treatment options ., A Bayesian network model computing odds ratios ( OR ) with 95% credible intervals ( CrI ) and probability of being best ( Pbest ) was used to compare all interventions simultaneously ., Albendazole and corticosteroids combination therapy was the only regimen that significantly decreased the risk of seizure recurrence compared with conservative treatment ( OR 0 . 32 , 95% CrI 0 . 10–0 . 93 , Pbest 73 . 3% ) ., Albendazole and corticosteroids alone or in combination were all efficacious in hastening granuloma resolution , but the combined therapy remained the best option based on probability analysis ( OR 3 . 05 , 95% CrI 1 . 24–7 . 95 , Pbest 53 . 9% ) ., The superiority of the combination therapy changed little in RCTs with different follow-up durations and in sensitivity analyses ., The limitations of this study include high risk of bias and short follow-up duration in most studies ., Dual therapy of albendazole and corticosteroids was the most efficacious regimen that could prevent seizure recurrence and promote lesion resolution in a follow-up period of around one year ., It should be recommended for the management of SCG until more high-quality evidence is available . | Neurocysticercosis is an infection of the central nervous system by the larva of Taenia solium ( pork tapeworm ) ., It is a leading cause of epilepsy in the world ., The disease takes many different forms , each with different optimal treatment ., In this study , we focused on the treatment of solitary cysticercus granuloma ( SCG ) , previous evidence on which is inconclusive ., Since many different regimens have been compared in clinical trials of SCG , we conducted a network meta-analysis ., This method is powerful as it can analyze quantitatively all of the data from all comparisons together ., The result can tell us how different treatments perform against each other and how treatments should be ranked ., The outcomes of our meta-analysis suggest that the combination of albendazole and corticosteroids is the most efficacious regimen to control seizures in affected patients and to promote the total disappearance of the lesion , compared with albendazole alone , corticosteroids alone , and conservative treatment . | medicine and health sciences, immune cells, diagnostic radiology, pathology and laboratory medicine, granulomas, immunology, corticosteroid therapy, database searching, neuroscience, physiological processes, mathematics, signs and symptoms, statistics (mathematics), pharmaceutics, neuroimaging, research and analysis methods, imaging techniques, animal cells, mathematical and statistical techniques, lesions, steroid therapy, tomography, computed axial tomography, radiology and imaging, diagnostic medicine, cell biology, meta-analysis, physiology, database and informatics methods, biology and life sciences, cellular types, physical sciences, drug therapy, calcification, statistical methods | null |
journal.pgen.1003758 | 2,013 | High-Throughput Genetic and Gene Expression Analysis of the RNAPII-CTD Reveals Unexpected Connections to SRB10/CDK8 | The largest subunit of RNA polymerase II , Rpb1 , has a unique C-terminal domain ( CTD ) composed of the repeated sequence Tyr-Ser-Pro-Thr-Ser-Pro-Ser ( Y1 S2 P3 T4 S5 P6 S7 ) 1 , 2 ., Although the CTD is highly conserved across species , the number of repeats varies in a manner resembling genomic complexity , with 25/26 repeats in Saccharomyces cerevisiae and 52 in humans 3 ., Deletion of the entire CTD is lethal in budding yeast , while strains carrying 9–13 repeats are viable but display conditional phenotypes 4 , 5 ., While not required to support basal transcription in vitro , the CTD is critical for the response to activator signals in vivo 6 , 7 ., For example , CTD truncation mutants exhibit reduced activation of INO1 and GAL10 upon switching to inducing conditions 7 ., The CTD is a scaffold for the recruitment of RNA processing and chromatin remodeling factors , a function linked to its differential phosphorylation at specific residues of the heptapeptide repeat 3 ., Transcription begins with the recruitment of RNAPII with an unphosphorylated CTD to promoters , where it interacts with components of the transcription pre-initiation complex ( PIC ) 8 , 9 ., Following , it is phosphorylated at S5 and S7 by the general transcription factor TFIIH , facilitating recruitment of capping enzymes and release of RNAPII from promoter-bound elements 10–13 ., Elongation is characterized by phosphorylation of S2 by Ctk1 and Y1 and T4 by yet unidentified kinases 14 , 15 ., S2 and Y1 phosphorylation play a role in the temporal recruitment of elongation and termination factors 15 ., Subsequently , termination entails removal of all phosphorylation marks by Fcp1 and Ssu72 to regenerate an initiation competent RNAPII molecule 16–18 ., While early work aimed at understanding CTD function uncovered a set of SRB ( Suppressor of RNA Polymerase B ) genes , a comprehensive genetic network governing CTD function has yet to be fully elucidated 19 ., Of the identified SRB genes many encode members of a large multisubunit complex known as Mediator 20 ., Mediator was first identified in vitro as a cellular fraction that stimulates RNAPII transcription , and is now known to not only physically interact with the CTD , but also to be important for the response to up-stream regulatory signals 21 ., Although primarily associated at RNAPII gene promoters , Mediator also resides at open reading frames ( ORFs ) 22 , 23 ., Furthermore , Mediator is organized into four functionally distinct submodules: head , middle , tail and Cdk8 module 24 ., The head module interacts with the CTD while the tail and middle modules interact with gene-specific and general transcription factors 25 , 26 ., The Cdk8 kinase module likely associates transiently with the core Mediator complex and has roles in both transcriptional activation and repression 27 , 28 ., This dual activity is in part mediated by Cdk8s ability to phosphorylate multiple regulatory components of the transcription machinery ., These include several transcription factors as well as factors more generally required for transcription such as the CTD itself 27 , 29–31 ., While the mechanistic role of some of these phosphorylation events is unclear , CTD phosphorylation by Cdk8 prior to promoter association inhibits RNAPII recruitment and transcription initiation in vitro 29 ., In contrast , CTD phosphorylation by Cdk8 and Kin28 following promoter association promotes RNAPII release from the PIC and thus stimulates transcription activation 30 ., The work here highlighted the functional circuitry between the RNAPII-CTD and Mediator in the regulation of cellular homeostasis , gene expression , and the transcription factor Rpn4 ., Our data uncovered a length-dependent requirement of the CTD for genetic interactions and mRNA levels of genes expressed under normal growth conditions ., Truncating the CTD primarily resulted in increased expression and RNAPII association at a subset of genes , in part mediated by changes to transcription initiation ., These genes had preferential association of Cdk8 at their promoters and were regulated by the transcription factor Rpn4 ., The expression and RNAPII binding defects of the majority of this subset of genes were suppressed by deleting SRB10/CDK8 , suggesting that in CTD truncation mutants , Cdk8 functioned to enhance transcription and RNAPII association at a subset of genes ., Conversely , our data also revealed that deletion of CDK8 suppressed the activation defects of CTD truncation mutants at the INO1 locus thus indicating that Cdk8 also functioned to repress transcription and RNAPII association in CTD truncation mutants ., To broadly determine the requirement of CTD length for cellular function , we used Epistasis Mini Array Profiling ( E-MAP ) to generate genetic interaction profiles of CTD truncation mutants containing 11 , 12 , 13 or 20 heptapeptide repeats ( rpb1-CTD11 , rpb1-CTD12 , rpb1-CTD13 and rpb1-CTD20 respectively ) against a library of 1532 different mutants involved principally in aspects of chromatin biology and RNA processing 32 ( Table S1 ) ., CTD truncations were created at the RPB1 locus by addition of a TAG stop codon followed by a NAT resistance marker ., As a control for the genetic integration strategy we also generated RPB1-CTDWT , which contained a NAT resistance marker following the endogenous stop codon ., While the minimal CTD length for viability is 8 repeats , we focused on strains starting at 11 repeats as mutants bearing shorter CTDs were significantly unstable in our hands , consistent with previous findings 33 ., Overall our data revealed a greater number of significant genetic interactions as the CTD was progressively shortened , an effect consistent with increasingly disrupted function ( Figure 1A ) ., Furthermore , while hierarchical clustering based on Spearmans rho correlation delineated two major clusters , the first including rpb1-CTD11 , rpb1-CTD12 and rpb1-CTD13 and the second consisting of rpb1-CTD20 and RPB1-CTDWT ( Figure 1B ) , individual genetic interactions revealed more nuanced CTD length-dependent genetic interaction patterns ( Figure S1 ) ., For example , aggravating interactions were observed with strains lacking ASF1 , RTT109 and DST1 when the CTD was truncated to 13 repeats or shorter , while truncation to 11 repeats was required for aggravating interactions with SET2 , RTR1 and SUB1 ., Collectively , this data revealed significant and specific functional alterations to the CTD as a result of shortening its length and suggested that individual pathways required different CTD lengths for normal function ., Finally , given that we identified significant genetic interactions with genes involved in a variety of processes , we compared the E-MAP profile of our shortest CTD truncation with all previously generated profiles to determine which pathways were principally affected by truncating the CTD ., This analysis revealed that four of the ten most correlated profiles belonged to loss of function alleles of genes encoding subunits of TFIIH and Mediator ( RAD3 , MED8 , MED31 and MED20 ) suggesting that shortening the CTD results in genetic interaction patterns most similar to mutants affecting transcription initiation ( Figure 1C ) ., Although the CTD plays a major role in the response to activator signals in vivo , its general involvement in transcription is less well defined ., To investigate this important aspect , we generated gene expression profiles of CTD truncation mutants in normal growth conditions ( Table S2 ) ( Complete dataset can be found in array-express , code E-MTAB-1431 ) ., Similar to the E-MAP data , the expression data revealed a length-dependent requirement for CTD function , with the severity and number of transcriptional changes increasing as the CTD was progressively shortened ( comparison of E-MAP vs . expression profiles Pearsons rho 0 . 57 ) ( Figure 2A and 2B ) ., This gradient effect was clearly visible in the group of genes whose transcript levels decreased upon truncation of the CTD ( Figure 2A groups A , B and C constitute genes requiring greater than 13 , 12 , and 11 repeats for normal transcription respectively ) , and thus provided strong evidence of a gene-specific CTD length requirement for normal transcription ., Surprisingly , given the central role of the CTD in RNAPII function , our microarray data identified only 127 genes with significant increases in mRNA levels and 80 genes with significant decreases ( p value <0 . 01 and fold change >1 . 7 compared to wild type ) , in strains carrying the shortest CTD allele , rpb1-CTD11 ., Functional characterization of the set of genes with increased and decreased mRNA levels suggested that the transcriptional alterations were not affecting a random group of genes ., Instead , using previously published transcription frequency data , we found that the genes with decreased mRNA levels tended to be highly transcribed with short mRNA half-lives , while the genes with increased mRNA levels were mostly lowly transcribed with long mRNA half-lives ( Figure 2C and 2D ) 34 ., In addition , these genes belonged to different functional gene ontology ( GO ) categories ., The genes with increased mRNA levels were enriched for proteasome and proteasome-associated catabolism processes while the genes with decreased levels were enriched for iron homeostasis , purine metabolism and pheromone response ( Table S3 ) ., Finally , these genes were differentially regulated by transcription factors ( Figure 2E ) ., The genes whose expression levels decreased were principally bound by Ste12 , while those with increased expression were bound by Ume6 , Met31 , Gcn4 and most significantly by Rpn4 which bound 46% of these genes ( p value 1 . 46E-41 ) ., The measured gene expression changes in CTD truncation mutants could result from either effects on the synthesis or stability of the mRNA ., To differentiate between these two possibilities , we measured RNAPII occupancy genome-wide and determined if the changes in gene expression correlated with alterations in RNAPII occupancy ( Complete dataset can be found in array-express , code E-MTAB-1341 ) ., Specifically , we measured RNAPII in rpb1-CTD11 and wild type cells by chromatin immunoprecipitation followed by hybridization on a whole genome tiled microarray ( ChIP-on-chip ) using an antibody specific to the RNAPII subunit Rpb3 ., Despite the use of different platforms , antibodies and normalization methods , the obtained genome-wide Rpb3 occupancy profiles obtained in wild type cells were highly correlated with those previously published by several groups ( Figure S2 ) 35–39 ., Furthermore , the occupancy maps revealed highly correlated profiles between rpb1-CTD11 and wild type cells ( Spearmans rho 0 . 85 ) , agreeing with the limited transcriptional differences detected by the expression analysis ., Nonetheless , our Rpb3 occupancy plots showed clear RNAPII occupancy differences along genes that were identified as either having increased or decreased mRNA levels in the rpb1-CTD11 mutant ( Figure 3A and B ) ., Accordingly , plotting the average Rpb3 occupancy scores of the differentially regulated genes in rpb1-CTD11 versus wild type cells revealed that the genes with increased mRNA levels had a significant increase in Rpb3 binding levels along their coding regions while the genes with decreased mRNA levels had a significant decrease ( one-tailed t-test p value 2 . 98e-22 and 3 . 36e-7 , respectively ) , thus suggesting a direct effect of truncating the CTD on RNAPII levels and mRNA synthesis at specific loci ( Figure 3C ) ., To better understand the effect of truncating the CTD on transcription , we generated genome-wide association profiles of representative transcription associated factors ., These factors included the initiation factor , TFIIB which is encoded by the SUA7 gene , the capping enzyme Cet1 , the elongation factor Elf1 , and the Set2-dependent elongation associated chromatin mark histone H3 lysine 36 trimethylation ( H3K36me3 ) ( Complete dataset can be found in array-express , code E-MTAB-1379 ) ., We note that with the exception of CET1 ( which was not present on our E-MAP array ) , the genes encoding these factors had negative genetic interactions with our shortest CTD truncation allele ., Our genome-wide occupancy profiles under wild type conditions were highly correlated to those previously reported ( Figure 4 and Figure S3 ) 35 , 40 ., Overall , genome-wide occupancy was independent of CTD length for TFIIB , Elf1 and H3K36me3 , despite the latter having decreased bulk levels in CTD truncation mutants ( Figure S3 ) 41 ., In contrast , Cet1 chromatin association decreased primarily in genes with lower transcriptional frequencies , perhaps reflective of its decreased binding to RNAPII with a shortened CTD ( Figure S3B ) 42 ., Focusing on only the genes whose expression levels were altered in the CTD truncation mutants , we observed several interesting patterns ., First , the levels of H3K36me3 correlated well with the transcription changes as its occupancy was decreased in genes whose expression decreased and increased in genes whose expression increased in the rpb1-CTD11 mutant ( paired t-test p value 8 . 68e-6 and 9 . 34e-23 respectively ) ( Figure 4A ) ., Second , the levels of Cet1 were greatly reduced at the promoters of genes whose expression increased in rpb1-CTD11 while only slightly reduced at those whose expression decreased ( Figure 4B ) ( paired t-test p value 7 . 82e-25 and 2 . 72e-7 respectively ) ., Lastly , both TFIIB and Elf1 had statistically significant CTD-length dependent occupancy changes , although the overall magnitude of change was minor compared to that of H3K36me3 and Cet1 ( Figure 4C and D ) ., The genetic similarity of CTD truncation mutants with mutants encoding initiation factors along with the ChIP-on-chip profiles of RNAPII and transcription associated factors suggested that possible changes to transcription initiation in the CTD truncation mutants might mediate some of the effects on gene expression ., Using a LacZ reporter gene strategy we tested if the promoter elements of a set of exemplary genes sufficed to recapitulate the observed changes in expression ., These assays revealed significant increases in β-galactosidase activity when the promoter regions of a subset of genes with increased mRNA levels were tested in the rpb1-CTD11 mutant compared to wild type ., These data confirmed that alterations to promoter-directed initiation events were in part responsible for the increased expression observed for these genes at their native loci ( Figure 5 ) ., In contrast , the promoters of the genes with decreased mRNA levels in rpb1-CTD11 mutants showed no significant differences in β-galactosidase as compared to wild type cells ., We next expanded our characterization of the CTD to explore the well-established connection to Cdk8 in more detail ., First , we showed that in addition to suppressing the cold sensitive phenotype of CTD truncation mutants , loss of CDK8 could also suppress other known CTD growth defects ( Figure S4 ) 19 ., Second , despite Cdk8 being able to phosphorylate the CTD , its loss had only very minor effects on the bulk CTD phosphorylation defects seen in CTD truncation mutants 43 , 44 ( Figure S4 ) ., Third , we found that loss of CDK8 had striking effects on the mRNA levels of genes whose expression was dependent on the CTD ., Specifically , comparison of mRNA expression profiles for rpb1-CTD11 cdk8Δ and rpb1-CTD12 cdk8Δ double mutants to the single mutants revealed wide-spread and robust restoration of most of the genes with increased mRNA levels in rpb1-CTD11 , while only a few of the genes with decreased mRNA levels appeared to be suppressed ( Figure 6A ) ., The restoration of mRNA levels in the genes with increased expression in the rpb1-CTD11 mutant was mediated by regulation of RNAPII levels , as Rpb3 occupancy changed from an elevated state in the rpb1-CTD11 mutant to close to wild type levels in the rpb1-CTD11 cdk8Δ mutant ( Figure 6B ) ., Accordingly , the average Rpb3 binding scores at these genes in the rpb1-CTD11 cdk8Δ mutant were significantly lower than the scores of the rpb1-CTD11 mutant and were not statistically different from the scores of wild type cells ( one-tailed t-test p value 7 . 17e-18 and 0 . 159 respectively ) ( Figure 6C ) ., Consistent with fewer genes being suppressed in the set of genes with decreased mRNA levels in the rpb1-CTD11 mutant , a restoring effect on RNAPII levels was not observed at this set of genes ( Figure 6C ) ., A previously characterized phenotype of CTD truncation mutants is reduced activation of INO1 and GAL10 upon switching to inducing conditions ., Therefore , we investigated if loss of CDK8 could also suppress these expression defects of CTD truncation mutants 7 ., Focusing on INO1 , a gene important for the synthesis of inositol and survival in response to inositol starvation , we measured INO1 mRNA levels in wild type , rpb1-CTD11 , cdk8Δ and rpb1-CTD11 cdk8Δ mutants before and after induction ., In agreement with previous work , rpb1-CTD11 mutants had an impaired ability to activate INO1 expression upon induction ( Figure 7A ) 7 , 45 ., Upon deletion of CDK8 , INO1 mRNA levels were robustly and reproducibly restored ., This effect was corroborated with the suppression of the growth defect of CTD truncation mutants in media lacking inositol upon removal of CDK8 ( Figure 7B ) ., Consistent with this being a direct effect on mRNA synthesis , Rpb3 levels throughout the INO1 gene in rpb1-CTD11 mutants were significantly lower as compared to wild type ., Furthermore , upon deletion of CDK8 , the levels of RNAPII associated with the INO1 gene were restored ( Figure 7C ) ., While not statistically significant , we nevertheless observed a tendency for increased Rpb3 occupancy at the 3′ end of the gene in cdk8Δ and rpb1-CTD11 cdk8Δ mutants ., To understand the mechanism underlying the restoration of the transcription and RNAPII recruitment changes in the rpb1-CTD11 mutant upon loss of CDK8 , we first tried to understand the role of Cdk8 in regulating these genes ., To determine if Cdk8 played a direct regulatory role at these genes , we generated a genome-wide map of Cdk8 occupancy under wild type conditions ( Complete dataset can be found in array-express , code E-MTAB-1379 ) ., The average gene occupancy of Cdk8 showed clear enrichment at promoters , although we did identify Cdk8 binding to a small number of ORFs ( Figure S5 ) 22 , 23 , 46 ., Focusing on CTD-length dependent genes , we observed Cdk8 occupancy at the promoters of genes with increased mRNA levels in the rpb1-CTD11 mutant ( Figure 8A ) , while very little Cdk8 was observed at the set of genes with decreased levels ( data not shown ) ., Importantly , Cdk8 occupancy was not significantly altered in strains with a truncated CTD ( Figure 8A ) ., In both situations , the preferential association of Cdk8 with the genes having increased expression was significant even when compared to all genes in the genome ( one-tailed , unpaired t-test p-value 0 . 0001079 for wild-type and 0 . 005898 for rpb1-CTD11 , respectively ) , thus supporting a direct regulatory role for Cdk8 at these loci ( Figure 8B ) ., However , despite its significant association and robust effect on normalizing the expression levels of this set of genes , our gene expression analysis clearly showed that Cdk8 was not the sole regulator of these genes as these were generally normal in cdk8Δ mutants ( Figure 6A ) 47 ., Using strict criteria , our profiles of rpb1-CTD11 and rpb1-CTD11 cdk8Δ mutants revealed robust restoration of mRNA levels at 45% of the genes with increased expression levels in the rpb1-CTD11 mutant and 24% of the genes with decreased levels when CDK8 was deleted ( Figure 6A ) ., Among the genes with increased expression , those suppressed were involved in proteasome assembly and proteasome catabolic processes ( Table S4 ) ., Consistently , these genes were primarily regulated by Rpn4 ( Bonferroni corrected p value of hypergeometric test 1 . 06E-26 ) ., Of the genes with decreased expression , the suppressed set were mainly involved in iron transport , assimilation and homeostasis , however , no significantly associated transcription factors were identified ., Given that our data thus far suggested that the restoring effect was at the level of initiation and mediated by Cdk8 , we concentrated our efforts in determining if Rpn4 , the only transcription factor found to be significantly involved in regulating the expression of the suppressed set of genes , contributed to the suppression ., First , we determined if RPN4 was genetically required for the suppression of CTD truncation phenotypes by loss of CDK8 by generating rpb1-CTD11 , cdk8Δ and rpn4Δ single , double and triple mutants and testing their growth on different conditions ., To test for specificity we also investigated whether the suppression was affected by GCN4 , which encodes for a transcription factor involved in the regulation of the genes whose expression increased in the rpb1-CTD11 mutant but not on those suppressed by deletion of CDK8 ., Deletion of RPN4 in the rpb1-CTD11 cdk8Δ background abolished the suppression , indicating that RPN4 was genetically required ( Figure 8B; compare rpb1-CTD11 cdk8Δ to rpb1-CTD11 cdk8Δ rpn4Δ ) ., In contrast , deletion of GCN4 in the rpb1-CTD11 cdk8Δ background had no effect on the suppression , suggesting that the genetic interactions with RPN4 were specific ( Figure S8 ) ., Considering that Rpn4 is a phospho-protein , we also tested the involvement of two previously identified phosphorylation sites that are important for its ubiquitin-dependent degradation 48 ., Introduction of the RPN4 S214/220A mutant restored the suppression in a rpb1-CTD11 cdk8Δ rpn4Δ strain in most of the conditions tested , thus demonstrating a general lack of involvement of these phosphorylation sites in the suppression ( Figure S8 right panel: compare rpb1-CTD11 cdk8Δ and rpb1-CTD11 cdk8Δ rpn4Δ ) 48 ., Despite our inability to link Rpn4 phosphorylation to the suppression mechanism , the genetic analysis showed that the growth of rpb1-CTD11 rpn4Δ double mutants was more compromised than that of rpb1-CTD11 mutants alone , indicating a clear dependence on Rpn4 function for maintaining rpb1-CTD11 cell fitness ( Figure 8B compare rpb1-CTD11 and rpb1-CTD11 rpn4Δ mutants ) ., This phenotypic pattern contrasted the apparent increase in Rpn4 function in a rpb1-CTD11 mutant as suggested by our gene expression analysis , and indicated that mutating CDK8 normalized , rather than abolished Rpn4 activity in rpb1-CTD11 mutants ., To test this hypothesis , we measured the levels of Rpn4 fused to a hemagglutinin ( HA ) tag in rpb1-CTD11 and cdk8Δ single and double mutants ., Consistent with an increase in Rpn4 function , Rpn4 protein levels were increased in rpb1-CTD11 mutants compared to wild type cells ( Figure 8D ) ., Surprisingly , Rpn4 protein levels were reduced upon deletion of CDK8 in the rpb1-CTD11 mutant , consistent with the observed restoration in gene expression of Rpn4 target genes ., In addition , the initial gene expression analysis as well as detailed RT-qPCR analysis of the RPN4 locus did not detect significant alterations in RPN4 mRNA levels in rpb1-CTD11 and CDK8 single and double mutants , suggesting that the effect of the CTD and Cdk8 on Rpn4 was most likely at the protein level ( data not shown ) ., In support of this and consistent with the slightly elevated level of Rpn4 in the cdk8Δ strain ( Figure 8D ) , loss of CDK8 increased the half-life of Rpn4 ( Figure 8E ) ., This suggested that Cdk8 was a regulator of Rpn4 stability in vivo ., Our genetic interaction , mRNA profiling , and RNAPII binding studies illuminated key linkages between CTD function , gene expression , mediator function , and the transcription factor Rpn4 ., We found distinct CTD- length dependent genetic interactions and gene expression alterations during steady state growth ., The majority of the expression changes in the CTD mutants were in genes whose mRNA levels increased and these were accompanied by increased RNAPII binding across their coding regions ., CTD truncation mutants were primarily defective in transcription initiation as suggested by our E-MAP profile of the rpb1-CTD11 mutant and further supported by reporter assays ., Removal of the mediator subunit , Cdk8 , in cells with shortened CTD restored the original mRNA levels and RNAPII occupancy profiles at a subset of genes whose expression was increased in the CTD truncation mutant , highlighting an activating role for Cdk8 in gene expression regulation ., In contrast , loss of CDK8 also restored the reduced activation of the INO1 gene exemplifying the more established repressive role for Cdk8 ., Finally and highly consistent with the expression results , shortening the CTD resulted in increased cellular amounts of the transcription factor Rpn4 , which was normalized upon concomitant removal of CDK8 ., Underscoring its role , we found that RPN4 was genetically required for the suppression of CTD truncation phenotypes by loss of CDK8 ., The mRNA analysis identified genes whose expression levels during normal growth were dependent on CTD length , thus expanding the existing knowledge of CTD function in vivo , which has been derived from a primary focus on genes activated in response to specific conditions including INO1 and GAL10 7 ., Despite the CTD being essential for viability in vivo , we detected a seemingly low number of genes with altered expression levels in rpb1-CTD11 mutants ., We reconcile this with the fact that our shortest allele was four repeats above the minimum required for viability in S . cerevisiae , suggesting that we were predominantly assaying those genes most sensitive to changes in CTD length rather than the essential function of the CTD ., Nonetheless , using stringent criteria our data identified a set of over 200 genes whose transcription was CTD length-dependent ., As expected from the well-documented role of the CTD in transcription activation , about 40% of CTD-dependent genes had decreased expression ., Surprisingly , we found that about 60% of CTD-dependent genes had increased expression ., Functional analysis of the genes with increased or decreased expression upon CTD truncation revealed key differences in mRNA stability , transcriptional frequency , GO categories and associated transcription factors , suggesting differential effects on groups of genes with distinct properties ., In addition , for both groups there was a high correlation between mRNA levels and RNAPII occupancy suggesting a direct effect on RNAPII function rather than changes in posttranscriptional RNA processing ., Furthermore , truncating the CTD also caused changes in the association of Cet1 and H3K36me3 at genes whose expression was altered in the rpb1-CTD11 mutant ., Finally , our data linked the alterations observed at the genes with increased mRNA levels to changes in transcription initiation using promoter-fusion experiments ., How this latter finding can be reconciled with the minor changes in TFIIB association at the promoters of these genes remains to be determined ., The increased mRNA levels and concurrent increase in occupancy of RNAPII in rpb1-CTD11 mutants presents an interesting conundrum ., Seemingly , these results pointed to a previously unreported inhibitory function of the CTD , as shortening it relieved the inhibition and resulted in higher RNAPII occupancy ., However , we favor a model in which these relationships are reflective of a cellular stress response elicited by impairing CTD function ., Consistent with this hypothesis , CTD truncation mutants displayed heightened sensitivity to a variety of stressors , as shown by others and us 4 , 19 , 49 ., Furthermore , CTD truncation mutants had increased levels of Rpn4 protein and the genes that had increased mRNA levels tended to be regulated by Rpn4 , consistent with their important contributions to the cellular stress response 50–52 ., In addition , we investigated the molecular underpinnings of the well-established connection between Cdk8 and the RNAPII CTD ., To this end , we found that deletion of CDK8 normalized the expression of genes with increased mRNA levels in the CTD truncation alleles ., This observation is consistent with the less-understood role for CDK8 as an activator of transcription , likely acting by enhancing recruitment of RNAPII with a shortened CTD to its target genes ., Given that Cdk8 was found to be preferentially associated with the promoters of these genes regardless of CTD length , it is likely that this represents a direct mechanism ., Importantly , our data clearly showed that Cdk8 was not the sole regulator of this subset of genes as a single deletion of CDK8 does not alter their expression ., Thus , in wild type cells Cdk8 associated at these genes promoters but it only enhanced transcription when CTD function was disrupted ., This observations are in agreement with Cdk8s well-established role in the response to environmental signals 31 , 53 , 54 ., Furthermore , we show that Cdk8s role in activating CTD-dependent genes with increased mRNA levels was in part mediated by increasing the protein levels of the transcription factor Rpn4 , which we found to be genetically required for the suppression ., Accordingly , the levels of Rpn4 protein correlated with the mRNA levels of Rpn4 targets genes in rpb1-CTD11 and cdk8Δ single and double mutants ., This is consistent with the known role of Cdk8 in regulating protein levels of transcription regulatory proteins and the established function of Rpn4 in activating gene expression as a result of stress 55 ., Reminiscent of recent work by several groups showing that loss of Cdk8 stabilizes Gcn4 protein levels , our data on Rpn4 protein stability provided further support of a close linkage between Cdk8 and Rpn4 , although the mechanistic details remain to be determined 56–58 ., In addition , we note that not all suppressed genes are known targets of Rpn4 , suggesting that it is likely not the only factor linking the RNAPII CTD and Cdk8 function ., The fact that removal of Cdk8 also suppressed defects in activated transcription suggested an entirely different relationship between the RNAPII-CTD and Cdk8 from the one described above , this time involving a negative role for Cdk8 ., This is exemplified by the INO1 locus , where rpb1-CTD11 mutants have decreased mRNA expression and RNAPII association when grown in inducing conditions , a defect that was restored upon deletion of CDK8 ., While reminiscent of the model postulating that Cdk8-catalyzed phosphorylation of the CTD prevents promoter binding of RNAPII and thus results in transcriptional repression , we do not think this is the mechanism of suppression described here 29 ., First , deletion of CDK8 had no alleviating effects on the bulk phosphorylation status of either full-length or truncated CTD ., Second , deletion of CDK8 alone under non-inducing conditions did not result in de-repression of INO1 , in contrast to well-characterized Cdk8 target genes 47 ., Lastly , despite our genome-wide Cdk8 occupancy data showing a reproducible , albeit slight , enrichment of Cdk8 at the INO1 promoter , it does not meet our enrichment criteria , making it unclear if Cdk8 directly associates and functions at this locus ( data not shown ) ., In conclusion , our data revealed a tight link between Cdk8 and the RNAPII-CTD in transcription regulation , where Cdk8 can both enhance and repress transcription , the former in part mediated by regulating the levels of the transcription factor , Rpn4 ., Strains and plasmids are listed in Supplementary materials ., Partial , complete gene deletions or integration of a 3XFLAG tag was achieved via the one-step gene replacement method 59 ., CTD truncations were created at the RPB1 locus by addition of a TAG stop codon followed by a NAT resistance marker and confirmed by sequencing ., As a control for E-MAP and gene expression analysis we used RPB1-CTDWT ., This strain co | Introduction, Results, Discussion, Materials and Methods | The C-terminal domain ( CTD ) of RNA polymerase II ( RNAPII ) is composed of heptapeptide repeats , which play a key regulatory role in gene expression ., Using genetic interaction , chromatin immunoprecipitation followed by microarrays ( ChIP-on-chip ) and mRNA expression analysis , we found that truncating the CTD resulted in distinct changes to cellular function ., Truncating the CTD altered RNAPII occupancy , leading to not only decreases , but also increases in mRNA levels ., The latter were largely mediated by promoter elements and in part were linked to the transcription factor Rpn4 ., The mediator subunit Cdk8 was enriched at promoters of these genes , and its removal not only restored normal mRNA and RNAPII occupancy levels , but also reduced the abnormally high cellular amounts of Rpn4 ., This suggested a positive role of Cdk8 in relationship to RNAPII , which contrasted with the observed negative role at the activated INO1 gene ., Here , loss of CDK8 suppressed the reduced mRNA expression and RNAPII occupancy levels of CTD truncation mutants . | RNA Polymerase II ( RNAPII ) is the enzyme responsible for the transcription of all protein-coding genes ., It has a unique extended domain called the C-terminal domain ( CTD ) ., This domain is highly conserved across species and is composed of repeats of a seven amino acid sequence ., The CTD functions as a recruiting platform for regulatory and RNA processing factors , making the CTD a master orchestrator of transcription ., Previous work revealed a critical role for CTD length in the transcription of induced genes ., However , how CTD length is generally required for transcription is currently unclear , as is the mechanism underlying the observed suppression of CTD truncation phenotypes by loss of the SRB10/CDK8 gene ., Here , using gene expression microarrays , we determined the set of genes most sensitive to alternations in CTD function and uncovered unexpected links between RNAPII-CTD and Cdk8 . | null | null |
journal.ppat.1005186 | 2,015 | Comparative Life Cycle Transcriptomics Revises Leishmania mexicana Genome Annotation and Links a Chromosome Duplication with Parasitism of Vertebrates | Trypanosomatids , vector-borne protists of the order Kinetoplastida , infect humans , animals and plants and pose a heavy global burden on health and economic development 1 , 2 ., The human pathogenic Trypanosoma brucei , T . cruzi and Leishmania spp ., affect mostly people in developing countries and together account for 4 . 4M disability-adjusted life years 3 ., Infections with Leishmania spp ., present as a spectrum of diseases ranging from cutaneous lesions to fatal visceral infections 4 , estimated to cause 20 , 000 to 40 , 000 deaths per year 5 ., Uniquely among trypanosomatids , Leishmania spp ., are adapted to survival and replication in the phagolysosome of professional phagocytes , a niche in which only few pathogens of any lineage thrive ., The shared biology and unique pathogenicity mechanisms of Leishmania spp ., and trypanosomes have been the focus of intense research 1 and the genetic basis for the species-specific differences in disease manifestations remain key questions in post-genome analyses of these parasites ., Sequencing of over 20 trypanosomatid spp ., genomes to date has revealed an extreme degree of synteny and high proportion of shared genes ., Of the ~8 , 000 annotated genes in Leishmania spp ., genomes , ~6 , 000 are shared with other trypanosomatids and 95% of genes are conserved between L . major , L . infantum , L . braziliensis and L . mexicana ., Only ~200 to 400 genes were found to be absent from one or more of these genomes and surprisingly few genes are unique to any one Leishmania species 6 , 7 ., Instead , heterogeneity has arisen through large-scale variation in gene and chromosome copy number 7 with widespread aneuploidy in natural Leishmania populations 8 , 9 ., One event that is shared by all examined Leishmania spp ., is a duplication of chromosome 31 ( in L . mexicana , a fusion event joined chromosomes 8 and 29 and as a result the homologue of chromosome 31 is called “chromosome 30” ) ., It is unknown what role this duplication event may have played in the evolution of the parasite ., To pinpoint genetic adaptations that allowed Leishmania spp ., to parasitise mammalian macrophages requires better knowledge about gene expression patterns specific to the intracellular amastigote forms ., Amastigotes are formed when metacyclic promastigote forms are egested by an infected sandfly during a bloodmeal and phagocytosed by a professional phagocyte ., During the subsequent differentiation , the morphology of the parasite’s cell body changes from an elongated to an ovoid shape and the cells lose their motility; only the tip of their short flagellum remains external to the flagellar pocket and the flagellar axoneme is devoid of the molecular motors and accessory structures required for beating the flagellum 10 ., The properties of the cell surface change: the promastigote lipophosphoglycan ( LPG ) coat is lost and amastin surface proteins are upregulated 11 ., A change in metabolism shifts the cells from using glucose and proline as their carbon source to beta-oxidation of fatty acids and increased use of amino acids 12 , 13 ., Known virulence factors expressed in amastigote forms include superoxide dismutases , which protect against reactive oxygen species produced by the host cell 14 , the major surface protease gp63 ( also known as leishmanolysin ) 15 , cysteine peptidases 16 , the iron transporter LIT1 17 and A2 proteins , which were linked to the establishment of visceral infections 18 ., To study amastigote biology , parasites have been isolated from infected animals or in vitro infected macrophages or alternatively , generated from promastigotes in cell-free medium through a decrease of pH and increase in temperature ( “axenic amastigotes” ) ., The latter show the characteristic amastigote morphology and exhibit many of the molecular and biochemical characteristics of lesion-derived amastigotes 19–21 but their virulence was shown to be attenuated compared to lesion-derived amastigotes 22 ., Thus , whilst they offer the opportunity to study amastigote-specific molecular processes in a system that yields much higher numbers of cells than purification of amastigotes from macrophages and free of contaminating host cell material , it remains controversial how representative their biology is of “true” amastigotes ., Microarray-based studies comparing gene expression profiles of promastigotes and amastigotes ( generated axenically , or isolated from tissue culture macrophages or lesions ) found that most transcripts were constitutively expressed but each study identified a few genes that showed strong stage-regulated mRNA expression , including genes affecting morphology , translation and amastigote-specific virulence factors 23–26 , with notable differences between L . infantum axenic and intracellular amastigotes 27 ., Combined proteomic and transcriptomic studies comparing axenic amastigotes to promastigotes found overall modest correlations between mRNA and protein levels 28 ., A time-course analysis of differentiation revealed that early during differentiation changes in RNA levels were pronounced while at later time points downregulation of translation dominated 29 ., RNA-sequencing ( RNA-seq ) technology now allows discovery of new information about the transcriptomes of Leishmania 30–32: it yields measurements of relative transcript abundance over a larger dynamic range ( capturing most of the genes in the genome ) and identifies the precise nucleotide sequence of transcripts including transcript boundaries ., This is particularly important in Leishmania spp ., , where regulation of gene expression occurs post-transcriptionally and sequences in 5’ and 3’ UTRs have been shown to mediate differential transcript abundances and translation between life cycle stages 33–37 ., Moreover , RNA-seq analyses readily uncover novel transcripts and have facilitated refinement of genome annotations of a variety of species ranging from bacteria to metazoa 38–41 including T . brucei procyclic forms 42 and L . major promastigote forms 30 ., Finally , RNA from intracellular pathogens can be sequenced together with host cell RNA ( “dual RNA-seq” 43 , 44 ) , eliminating the need for cell purification procedures that might affect gene expression patterns prior to RNA extraction ., Here we used RNA-sequencing of L . mexicana to profile the transcriptomes of promastigotes and early amastigotes , 24 hours after exposure to differentiation conditions , when morphological transformation is complete and well-characterised molecular markers of amastigotes are upregulated ., The amastigotes were derived from the same population of promastigotes either by infection of bone marrow derived murine macrophages or differentiation in axenic culture , allowing a comparison of gene expression patterns of intracellular and axenic amastigotes with a known history and at a defined stage in development ., We utilised the RNA-seq data to define precisely the genomic positions of spliced leader acceptor sites and poly-A addition sites and used this information to refine the current set of gene model predictions for L . mexicana ., Here we found evidence for extensions and truncations of annotated coding sequences and 936 novel transcripts ., Using this novel RNA-seq guided annotation of 9 , 169 predicted coding sequences , we quantified transcript abundances and tested for differential expression between life cycle stages ., We found that 41% of all genes showed statistically significant changes in relative mRNA abundance between promastigotes and intracellular amastigotes and 13% between axenic and intracellular amastigotes ., Whilst this showed that axenic differentiation did not fully replicate the intracellular development of amastigotes , less than 1% of all transcripts varied more than two-fold between the two amastigote forms , pointing to a fundamentally similar pattern of gene expression ., Over one third of amastigote enriched transcripts encode novel and hypothetical proteins , many conserved only within Leishmania spp ., Furthermore , genes upregulated in amastigotes are significantly enriched on chromosome 30 , suggesting that amastigote-specific functions may be a driving force in maintaining supernumerary copies of this chromosome ., To map the 5’ ends of transcripts , defined by the position of spliced-leader acceptor sites ( SLAS ) , reads generated from the random primed library that contained the spliced leader sequence ( SL ) were mapped to the L . mexicana genome ( see materials and methods and 46 ) ., In total , 6 , 942 , 183 SL-containing reads were mapped to 21 , 249 positions in the genome ( S1 Table ) ., Ninety-six percent of SLAS mapped to an AG dinucleotide consistent with the known conservation of this dinucleotide at the vast majority of mapped kinetoplastid trans-splice sites 30 , 42 ., To map the sites at which poly-A tails were added to transcript 3’ ends ( PAS ) a second T15VN-primed library was generated from two of the RNA samples for each cell type ( see materials and methods ) ., 3 , 939 , 551 reads containing at least 5 consecutive A nucleotides at the 3’ end were mapped to 96 , 522 positions in the genome ( materials and methods , 46 ) ( S2 Table ) ., Assignment of SLAS and PAS to genes was initially performed using version 6 . 0 of the L . mexicana genome ., We found that in the majority of cases ( 6 , 796 annotated protein coding genes ) there was good correspondence between an annotated coding sequence ( CDS ) , RNA-seq read coverage and positions of SLAS and PAS ., We did however find many loci bounded by SLAS at the 5’ end PAS at the 3’ end suggestive of processed transcripts from genes that had not been annotated ., Many of these putative novel transcripts contained open reading frames that could represent unannotated CDS ., This prompted us to define gene models for predicted protein coding genes guided by the RNA-sequencing data ( for details of gene predictions see materials and methods ) ., In total our combined analysis of the transcriptome of PRO , AMA and AXA and the existing annotation of the L . mexicana genome predicted a total of 9 , 169 putative protein coding genes , of which 936 have not been previously described ., A SLAS could be assigned to 8 , 882 genes and a PAS to 8 , 769 genes; for 8 , 540 genes both a SLAS and PAS were assigned and only 58 genes had neither ., The position of the SLAS indicated that 1 , 253 genes had an upstream ATG start codon in-frame with the annotated CDS ( ‘extended CDS’ ( S3 Table ) ) and for 184 genes the SLAS was mapped to a position downstream of the annotated ATG ( ‘truncated CDS’ ( S4 Table ) ) ., The majority of genes had between 1 and 3 SLAS , with a mean of 2 . 4 ( Fig 2A ) ., For 8 , 045 transcripts ( 90 . 6% ) the SLAS with the most counts was the same in PRO and AMA ., The mapped 3’ ends of transcripts showed greater heterogeneity than the 5’ ends , with a mean of 10 . 9 PAS per gene ( Fig 2B ) ., The median lengths of the untranslated regions ( UTRs ) , based on the gene models defined above , was 242 nt for 5’ UTRs ( Fig 2C ) and 584 nt for 3’ UTRs ( Fig 2D ) ., On average , UTRs and intergenic regions are longer in Leishmania mexicana than in T . brucei 42 , 47 ., There was no correlation between 5’ UTR and 3’ UTR length on the same gene , or between the length of a UTR and the abundance of that mRNA within the cell ( S3 Fig ) ., We next analysed transcript abundances in each of the three L . mexicana cell forms by calculating the number of fragments per kilobase of transcript per million mapped reads ( FPKM ) for each sample ( S12 Table and S4A Fig ) ., The correlation between biological replicates was between 0 . 90 and 0 . 99 ( R2 ( Pearson ) , log10 FPKM values ) ( S13 Table ) with low coefficient of variation ( S4B Fig ) ., These results demonstrate high levels of agreement and low amounts of variability across the range of expression levels observed in each of the biological replicates ., The three AMA samples showed lower FPKM values than the AXA and PRO samples because the AMA reads were derived from a mixed library of leishmanial and murine RNA ., We examined the genes comprising the top FPKM percentile in each cell form ( 91 genes; ( S14 Table ) ) to discover the most abundant transcripts in each condition and to assess the extent of overlap ., Sixty-eight genes ( 75% ) were shared between the top FPKM percentile in PRO , AXA and AMA ( Fig 6 and S14 Table ) , including 45 encoding ribosomal proteins ( 42 in reference annotation , 4 encoded by novel transcripts LmxM . 18_241026 , LmxM . 24_804446 , LmxM . 24_805244 , LmxM . 24_806159 ) , 10 histones ( 9 in reference annotation , 1 novel LmxM . 21_369741 ) , 2 heat shock proteins and 2 novel proteins of unknown function ( no Pfam domains LmxM . 20_617046 , LmxM . 32_1186260 ) ., Thirty-three genes ( 25% ) were only in the top FPKM percentile in one of the cell forms ( 13 in PRO , 6 in AXA and 16 in AMA ) ., The latter included genes encoding 3 cysteine peptidases ( known amastigote virulence factors ) , amastin , 2 hypothetical and 2 novel proteins ( LmxM . 19_375604 , LmxM . 33_1093342 ) ., Closer inspection of all thirteen novel transcripts in the top FPKM percentiles showed that one ( LmxM . 16_570431 ) corresponded to the 3’ UTR of PFR2 , indicating that collapsed gene arrays in the genome assembly could cause false positives in the annotation of novel transcripts ., Conversely , MS evidence proved the existence of a protein product from three of the remaining twelve , including LmxM . 19_375604 , which subsequent analysis ( next section ) showed to be significantly upregulated in AMA compared to PRO ., To identify the genes that were differentially expressed we performed pairwise differential expression testing between all three cell forms ., The results showed that there was a significant ( Benjamini-Hochberg corrected p-value ≤ 0 . 05 ) difference between PRO and AMA in the abundance of 3 , 832 transcripts ( 388 of these represent novel genes and 1 , 290 showed at least a 2-fold change ) ( Fig 7A and S15 Table ) , 2 , 176 transcripts differed in abundance between PRO and AXA ( 232 novel genes; 361 with ≥ 2-fold change ) ( Fig 7B and S16 Table ) and 1 , 234 transcripts differed in abundance between AMA and AXA ( 119 novel genes; 67 genes with ≥ 2-fold change ) ( Fig 7C and S17 Table ) ., We validated the differential expression data by analysis of 13 genes where published Northern blot data was available comparing L . mexicana RNA abundance in PRO with AMA or AXA ( S18 Table ) : five genes linked to the PFR2 locus 50 , three glucose transporter genes , LmGT1 , LmGT2 and LmGT3 51 and five other genes ., For 9 of the 13 genes the differential expression analysis fully agreed with the published data ., For three genes , where different expression levels had been detected by Northern blot , our analysis found no significant difference ( LmxM . 16 . 0390 , LmxM . 16 . 1410 and LmxM . 16 . 1410 ) ., For LmGT3 , reported to be expressed at similar levels in PRO and AXA 51 , the RNA-seq data showed a small but significant increase in AXA ., Interestingly , our analysis found an even more pronounced increase in LmGT3 transcript levels in AMA compared to PRO ., This is consistent with results of genetic studies that indicated LmGT3 may have an essential role in the parastiophorous vacuole 52 ., Examination of the 13 control genes also found strong agreement ( R2 = 0 . 88 ) between the transcript sizes measured in Northern blots and the transcript lengths established by RNA-seq ( S18 Table ) ., We tested the differentially expressed genes for enrichment of GO terms , metabolic pathways , Pfam domains , transmembrane domains and signal peptides ., We found enrichment in PRO for the GO terms concerning tRNA charging , glycolysis , sterol biosynthesis , central carbon metabolism , respiration ( anaerobic ) and TCA cycle ., In addition we found enrichment in PRO for GO terms plausibly linked to the function of the motile flagellum ( microtubule motor activity , dynein complex , microtubule-based flagellum and microtubule based movement ) and calcium signalling ( calmodulin binding and calcium ion binding ) ( S19 Table ) ., No GO terms were enriched in the gene set expressed higher in AXA or AMA compared to PRO; this reflects the lack of functional information that is known about these genes ., In the gene set expressed higher in AXA than AMA , GO terms associated with proteolytic activity , DNA binding and nucleosomes were enriched ( S19 Table ) , the latter possibly reflecting the higher rate of cell proliferation in AXA ., Analysis for Pfam domain enrichment showed that in PRO , the enriched Pfam domains , like the GO terms , point to functions of the motile flagellum ( S20 Table ) ., Both amastigote forms were characterised by higher expression of amastin genes compared to PRO , consistent with the known stage-specificity of a subset of amastin genes 11 , 53 ., Predicted transmembrane domain-containing proteins were significantly enriched overall in both AMA ( p = 4 . 29x10-10 ) and AXA ( p = 1 . 13x10-16 ) compared to PRO ., Taken together the enrichment analysis indicates that transcripts with higher expression in PRO include those linked to the function of the motile flagellum , while the gene set with higher expression in early amastigote forms points to a change in surface proteome during differentiation ., Analysis of the presence and absence of the differentially regulated genes in other sequenced trypanosomatid genomes revealed that genes upregulated in the mammalian host were more often found only in L . mexicana ( and other Leishmania spp . ) than genes that were upregulated in the insect vector ( S21 Table ) ., For example , the 1 , 979 genes that are upregulated in PRO compared to AMA can be clustered into 1 , 837 orthogroups , of these 1 , 754 ( 95% ) are found in at least one other Leishmania sp ., , 1 , 413 ( 77% ) are found in at least one other Phytomonas sp ., and 1 , 590 ( 87% ) are found in at least one other Trypanosoma sp ., genome ., In contrast , the 1 , 853 genes that are upregulated in AMA compared to PRO are clustered into 1 , 718 orthogroups of which 1 , 448 ( 84% ) are found in at least one other Leishmania sp ., , 1 , 019 ( 59% ) are found in at least one other Phytomonas sp ., and 1 , 183 ( 69% ) are found in at least one other Trypanosoma sp ., genome ., Genes that are differentially expressed between PRO and AMA were found across all chromosomes ( Fig 8 ) but deviations from the expected numbers ( p≤0 . 05 , χ2-test ) were found for ten chromosomes ( S22 Table ) ., Genes upregulated in PRO were over-represented on chromosomes 5 , 14 and 21 and underrepresented on chromosomes 08_29 and 30 ., Genes upregulated in AMA were overrepresented on chromosomes 29 , 30 and 33 and underrepresented on chromosomes 4 , 14 and 15 ., The deviation from the expected numbers of transcripts upregulated in AMA was most striking for chromosome 30 ( p = 4 . 03x10-09 , χ2-test ) and this was not explained simply by the presence of an amastin gene array on chromosome 30: removal of all amastin genes ( defined as genes with a Pfam domain PF07344 ) from the analysis still showed a significant enrichment of AMA-upregulated genes on chromosome 30 ( p = 1 . 46x10-06 , χ2-test ) ( S22 Table ) ., A ≥2-fold transcript enrichment in AMA compared to PRO was found for 79 ( 19% ) genes on chromosome 30 , of which 20 are novel transcripts , 15 are amastins and the remaining genes encode several amino acid- and other transporters and hypothetical proteins ., L . mexicana chromosome 30 is the homologue of chromosome 31 in other Leishmania spp ., , which has been shown to be supernumerary ( typically tetraploid ) in all examined Leishmania spp ., and isolates 7 , 8 ., Our finding that amastigote-upregulated genes are over-represented on this chromosome , together with the independent duplication event of homologous sequences in T . brucei 54 strongly links the duplication of this chromosome to the adaptation to vertebrate hosts ., This study analysed the transcriptomes of promastigote and early amastigote forms of L . mexicana to refine gene models and compare transcript abundances in a parasite strain and under culture conditions widely used in studies of Leishmania biology ., The single nucleotide resolution of the RNA-sequencing data allowed for the first time mapping of L . mexicana SLAS and PAS on a genome-wide scale and thereby definition of processed transcript boundaries ., This allowed a fresh interrogation of gene models and led to the prediction of 9 , 169 potentially protein coding genes , of which 936 have not been previously described ., The majority of these novel transcripts contain open reading frames that are shorter than the CDS of genes in the current genome annotation ., Short open reading frames ( sORFs , <100 codons ) , including those upstream of recognised CDS ( uORFs ) , and peptides encoded by sORFs have attracted a lot of interest since evidence has accumulated in many species from bacteria to humans ( reviewed in 55 , 56 , and recently T . brucei 49 ) , that some have important functions ., Examples range from regulation of protein expression 57 , 58 to signal transduction within and between cells 59 , 60 and development 61 ., Differentiation of functional sORFs from spurious ORFs that occur in a genome by chance is difficult 62 and we cannot rule out the possibility that some novel transcripts represent trans-spliced and poly-adenylated non-coding transcripts 63 or possibly intermediate products of pre-mRNA processing ., Whilst we do not expect that all of these novel ORFs encode proteins , integration of transcriptomic evidence with comparative sequence analysis , protein feature predictions and experimental evidence increases confidence in the prediction of bona fide short CDS ., In our study , direct MS evidence for 47 of the predicted novel proteins and detection of Pfam-A and-B domains in another 53 sequences provided strong evidence that at least 100 of the novel L . mexicana transcripts are protein-coding ., This is likely to be an underestimation of the true number of novel proteins because the small size of the peptides biases against their detection by conventional protein sample preparation and MS 64 , 65 ., Reciprocal best tblastx analysis of trypanosomatid genomes uncovered a high degree of conservation of the derived amino acid sequences of the novel transcripts ., Our results converge with other recent studies 42 , 49 on a small set of novel transcripts that are widely conserved across trypanosomatids and revealed a larger set of several hundred novel transcript sequences specific to Leishmania spp ., Small proteins are now recognised to play diverse and important roles , acting predominantly as regulators of diverse cellular processes 66 ., 126 novel transcripts are ≥2-fold more abundant in AMA compared to PRO and future studies of their function should consider the possibility that amastigote-derived peptides may have targets in the host cell ., At the level of annotation , we identified two potential causes for mis-annotations of novel transcripts: first , overlap with non-coding RNA loci decreases the confidence three novel genes adjacent to annotated tRNA loci and 18 novel genes that are syntenic with snoRNA loci in L . major ., Few non-coding RNA genes are currently annotated in the L . mexicana genome and detailed mapping of those was outside the scope of this study but future refinements of the L . mexicana genome annotation will clarify the status of some of these novel transcripts ., Second , 41 novel transcripts mapped to regions of the genome which contain assembly gaps or are known to be incorrectly assembled , including the PFR2 locus , and should be viewed with caution ., Together these make up a small proportion of the 936 novel transcripts and overall we conclude that a substantial proportion of novel transcripts represent a previously undiscovered fraction of the L . mexicana transcriptome , that may have important , potentially Leishmania-specific functions ., The amastigote form of Leishmania spp ., remains a relatively poorly understood cell and its intracellular lifestyle complicates laboratory studies of its biology ., Isolation of amastigotes before RNA extraction could alter gene expression profiles properties , as demonstrated for example for DNA polymerase β transcripts 67 ., The dual-RNA sequencing approach allowed us to establish the first global gene expression profile of undisturbed intracellular amastigotes at an early time point after differentiation ., Differential expression testing showed a statistically significant difference in abundance ( p ≤ 0 . 05 ) between PRO and AMA for 41% of transcripts , with 14% of transcripts showing a ≥ 2-fold difference ., It remains a disputed question to what extent axenically differentiated amastigotes can serve as useful models for amastigote biology ., There was a significant difference in abundance in 13% of transcripts between AMA and AXA , but very few showed a ≥ 2-fold difference ( only 0 . 7% of all genes ) ., Thus on a global scale , the transcriptomes of recently differentiated AXA and AMA were much more similar to each other than either was to the promastigote form ., Importantly , consultation of published studies of well-characterised stage-regulated transcripts showed good agreement of our RNA-seq results with published Northern blot or qPCR data of intracellular or axenic amastigotes , validating our data ., It will be interesting to compare the gene expression patterns of the early amastigotes analysed here with those of amastigotes from infected animal tissues at different time points in an infection ., While we observed roughly equal numbers of differentially regulated genes between PRO and AMA the presence/absence of those genes in other kinetoplastid genomes was markedly different ., Specifically , more of the genes that were upregulated in the insect adapted life cycle stage were detectable in other trypanosomatid genomes than those that were upregulated in the mammalian adapted life cycle stage ., This raises the question as to the evolutionary history of these genes , i . e . were they invented in the ancestor or Leishmania mexicana after its divergence from other lineages ( such as Phytomonas and Trypanosoma ) or were they lost from other lineages following divergence from the last common ancestor ., Additional genome resources across the breadth of the kinetoplastid tree will help resolve this question ., The gene models reported here enable genome-wide searches for sequence elements contributing to stage-regulation of gene expression ., Whilst post-transcriptional control of gene expression in kinetoplastids operates at multiple levels 68 and RNA abundance shows limited correlation with protein levels globally 28 , 29 , our RNA-seq analysis , consistent with many earlier transcriptomic studies , identified genes where stage-regulation of the transcripts correlates strongly with the expression pattern of the protein ., These include genes encoding flagellar proteins and surface proteins , both of which have provided paradigms for control of mRNA levels by cis-acting elements such as the regulatory elements in the UTRs of PFR2 and the major surface proteins of amastigotes , the amastins 33 , 69 , 70 ., Further investigation into functionally related cohorts of transcripts might prove fruitful not only for the discovery of additional features and sequence elements regulating their transcript abundances but also master regulatory factors acting on these elements , controlling surface proteome composition or flagellum formation during differentiation ., Whilst alternative uses of SLAS could provide mechanisms for stage-regulation of gene expression , we found that globally , 90% of genes shared the same major SLAS between PRO and AMA , indicating that despite the heterogeneity of sites , a dominant site is used for most genes in both stages ., Few of the mapped sites were exclusive to one cell type but differential use of a dominant site in nearly 10% of genes warrants further investigation since alternative trans-splicing may affect the expression or localisation of the protein product , as suggested by recent analyses of differential splicing between bloodstream and procyclic forms of T . brucei 71 , 72 ., The small numbers of minor SLAS precluded a rigorous in silico analysis of their differential usage , a limitation akin to that reported in another comparison between T . brucei life cycle stages 47 ., The organisation of functionally related genes into polycistronic transcription units ( PCU ) could add another level of control over stage-specificity ., Siegel et al . 47 analysed this for T . brucei and found no evidence for co-regulation of genes within a given PCU 47 ., However some evidence suggests that the order of genes in their PCU is important for their expression during the cell-division cycle 73 ., Transcriptional start sites in L . mexicana have not yet been mapped but once their locations become known our RNA-seq data of promastigotes and amastigotes can be mapped onto PCUs to test this idea in Leishmania ., Leishmania spp ., are remarkable for their plasticity in chromosome copy number ., Amplification of a given chromosome will increase the gene copy number of all genes on that chromosome and , assuming gene dosage affects the level of gene expression , one might expect to find functionally linked genes in Leishmania spp ., clustered by chromosomes ., Our data shows that the distribution of genes up- or downregulated in PRO or AMA diverges significantly from the genome average on ten chromosomes ., Chromosome 30 showed the most striking enrichment of AMA-upregulated transcripts , distributed over the entire chromosome ., Interestingly , the syntenic block that constitutes L . mexicana chromosome 30 was duplicated in the T . brucei clade to form parts of chromosomes 4 and 8 , providing opportunities for evolutionary innovations through divergence of paralogous sequences ., The 47% of duplicated genes that were retained as paralogous loci showed an enrichment of genes containing TMDs or SPs , suggestive of a function at the host-parasite interface 54 ., Independent duplication of this region in Leishmania spp ., may have assisted adaptation to vertebrate parasitism in both these sister lineages ., Our data supports this because of the functionally annotated transcripts that were at least two-fold upregulated in AMA , several are plausibly advantageous to survival in the mammalian host cell , including amino acid transporters ( LmxM . 30 . 0330 , LmxM . 30 . 0571 , LmxM . 30 . 0870 , LmxM . 30 . 1820 ) , tryparedoxin ( LmxM . 30 . 1960 ) for detoxification of reactive oxygen species , and one member of the ABC transporter superfamily ( LmxM . 30 . 1290 ) ) ., Aquaglyceroporin 1 ( AQP1 ) ( LmxM . 30 . 0020 ) transports solutes and protects amastigotes from hypoosmotic shock 74 ., Advantages of high AQP1 expression in amastigotes are however counterbalanced by the facilitated influx of antimonials , linking higher expression of the gene to greater drug-sensitivity 74 , 75 ., The majority of the upregulated transcripts ( including 20 novel transcripts ) from chromosome 30 have no known function and focused experiments are now required to discover their importance in amastigote biology ., Attributing functions to these hypothetical proteins is a key challenge for the future and may well identify as yet completely uncharacterised aspects of amastigote biology and virulence and shed more light on the evolution of parasitism and adaptations to specific niches in the host ., About half of the genes in the L . mexicana genome encode hypothetical proteins and our study shows that proteins of unknown function ( including putative novel small proteins ) dominate among the genes that are most upregulated in amastigotes , not only on chromosome 30 but genome-wide ., The ability to perform large-scale unbiased loss of function screens would facilitate the identification of essential ge | Introduction, Results, Discussion, Materials and Methods | Leishmania spp ., are protozoan parasites that have two principal life cycle stages: the motile promastigote forms that live in the alimentary tract of the sandfly and the amastigote forms , which are adapted to survive and replicate in the harsh conditions of the phagolysosome of mammalian macrophages ., Here , we used Illumina sequencing of poly-A selected RNA to characterise and compare the transcriptomes of L . mexicana promastigotes , axenic amastigotes and intracellular amastigotes ., These data allowed the production of the first transcriptome evidence-based annotation of gene models for this species , including genome-wide mapping of trans-splice sites and poly-A addition sites ., The revised genome annotation encompassed 9 , 169 protein-coding genes including 936 novel genes as well as modifications to previously existing gene models ., Comparative analysis of gene expression across promastigote and amastigote forms revealed that 3 , 832 genes are differentially expressed between promastigotes and intracellular amastigotes ., A large proportion of genes that were downregulated during differentiation to amastigotes were associated with the function of the motile flagellum ., In contrast , those genes that were upregulated included cell surface proteins , transporters , peptidases and many uncharacterized genes , including 293 of the 936 novel genes ., Genome-wide distribution analysis of the differentially expressed genes revealed that the tetraploid chromosome 30 is highly enriched for genes that were upregulated in amastigotes , providing the first evidence of a link between this whole chromosome duplication event and adaptation to the vertebrate host in this group ., Peptide evidence for 42 proteins encoded by novel transcripts supports the idea of an as yet uncharacterised set of small proteins in Leishmania spp ., with possible implications for host-pathogen interactions . | Leishmania are single-celled parasites that are transmitted between animal hosts by the bite of sand flies ., Once inside their animal hosts they abandon their extracellular habit and invade cells of the immune system , called macrophages ., This oscillation between hosts requires the parasite to be able to adapt to dramatically different environments ., To help unravel the multitude of biochemical , ultrastructural and lifestyle differences that distinguish these specialised life cycle stages we characterised and contrasted the transcriptomes of insect and mammalian adapted forms ., Using bioinformatic approaches we revised the genome annotation and discovered nearly 1 , 000 new genes that had not been described before ., We found that over 3 , 000 genes change in their expression to facilitate the change in host environment including those involved in specifying cell shape , extracellular appearance and biochemistry ., Furthermore we reveal that an ancient chromosome duplication shared by all Leishmania species may have contributed to the adaptation of these globally important parasites to parasitism of vertebrates . | null | null |
journal.pntd.0007251 | 2,019 | A little goes a long way: Weak vaccine transmission facilitates oral vaccination campaigns against zoonotic pathogens | 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 | Introduction, Materials and methods, Results, Discussion | 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 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 . | 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 | null |
journal.pgen.1000732 | 2,009 | Exceptional Diversity, Non-Random Distribution, and Rapid Evolution of Retroelements in the B73 Maize Genome | Transposable elements ( TEs ) were first discovered in maize ( Zea mays ) 1 , but have subsequently been found in almost every organism investigated , from archaea and eubacteria to animals , plants , fungi and protists 2 ., TEs are dynamic , abundant and diverse components of higher eukaryotic genomes , where they play key roles in the evolution of genes and genomes ., The class I TEs transpose through reverse transcription of a transcribed RNA intermediate , while most class II TEs transpose through a cut-and-paste mechanism that mobilizes the DNA directly ., However , there are some class II TEs , for instance IS91 of bacteria and Helitrons in eukaryotes , that are believed to transpose through a rolling-circle DNA replication process that does not involve element excision 3 , 4 ., In most plant species , a particular type of class I element , the long terminal repeat ( LTR ) retrotransposons , has been observed to be the major TE , accounting for >80% of the nuclear DNA in many angiosperms 5 ., The other two types of class I elements , LINEs and SINEs , have also been observed in all carefully annotated flowering plant genomes , but their copy numbers and overall contributions to genome composition have not usually been large ., However , in lily ( Lilium speciosum ) and grapevine ( Vitis vinifera ) , LINEs appear to be more numerous and/or active than in most plant species investigated 6 , 7 ., A wealth of recent studies has indicated that the class I elements , especially LTR retrotransposons , are primary contributors to the dynamics of genome structure , function and evolution in higher plants ., Even within species , the LTR retrotransposon arrangement and copy number can vary dramatically in different haplotypes 8–11 ., Some LTR retrotransposons acquire and amplify gene fragments 12 , 13 , and sometimes fuse their coding potential with those of other genes 14 , to create “exon shuffled” candidate genes that have the potential to evolve novel genetic functions 15 ., Retroelements of all types may also serve as sites for the ectopic recombination events that can cause chromosomal rearrangements: duplications , deletions , inversions and translocations ., Retroelement insertions can donate their transcriptional regulatory functions to any adjacent gene , and the prevalence of this process over evolutionary time is indicated by the many fragments of retroelements and other TEs that are found in current plant gene promoters 16 ., In angiosperms , polyploidy and retroelement amplification are the major factors responsible for the greater than 1000-fold variation in genome size 5 ., In some lineages , amplification of only one or a few LTR retrotransposon families has been observed to more than double genome size in just a few million years 17 , 18 ., In other organisms , like maize , many different LTR retrotransposon families have amplified in recent times to create a large and complex genome 19 ., Despite the abundance , ubiquity and genetic contributions of TEs in plants , no previous investigation has made comprehensive efforts to fully discover or characterize all of the TEs in any angiosperm genome ., Even the best annotated plant genomes , those of Arabidopsis thaliana and rice ( Oryza sativa ) , were initially examined only at a cursory level to find highly repetitive elements and those with homology to previously known TEs ., Hence , subsequent studies on these genomes continue to yield new families of TEs of various types ., The first exception to this rule has been the draft sequence analysis of the ∼2300 Mb maize genome , where a consortium of TE researchers has used several independent approaches in an attempt to discover and describe as many TEs as possible 20 ., Even before its nearly full genome analysis , maize was the source of the best-studied TE populations in plants , including the LTR retrotransposons , where detailed analysis of small segments of the genome uncovered a great diversity of elements in different families that are mostly arranged as nested insertions 21 ., The maize LTR retrotransposons were classified into 47 families 22 , and comparisons between families indicated differences in their times of transposition 23 , their preferential associations with different chromosomal regions 23–25 , and their levels of expression 26 ., In order to fully describe the contributions of TEs to genome structure and function , one needs to first find and describe all of the TEs in a genome ., Given that that average flowering plant genome is ∼6500 Mb 27 , they are expected to be composed of complex intermixtures and highly variable structures of TEs , so identification and analysis of the complete TE set will be a daunting task ., Hence , we know very little about TE abundances and arrangements in anything but unusually tiny plant genomes , like those of Arabidopsis , rice and sorghum ., Here , a comprehensive identification and characterization of retroelements is reported for the maize genome from inbred line B73 20 ., Hundreds of new retroelement families were discovered , and dramatic preferences in their distributions , associations and activities were uncovered ., These first comprehensive studies open a window onto the true complexity of genome structure and evolution in a moderate-sized angiosperm genome ., In order to find all elements , LTR retrotransposons were sought by a combination of approaches that relied on both structure and homology , as described in Materials and Methods ., The structure of an integrated LTR retrotransposon can be simply described as a terminal 5′ repeat that starts/ends in TC/GA ) , followed by a primer binding site that is used for the initiation of reverse transcription ( i . e . , replication ) , followed by polycistronic ( and sometimes frame-shifted ) genes that encode for several proteins necessary for element replication and integration , followed by a polypurine tract that is involved in the switch to second strand DNA synthesis , followed by the 3′ LTR ., Searching for these canonical structures employed LTR_STRUC 28 , combined with custom Perl scripts ., All intact LTR retrotransposons were identified in a set of 16 , 960 sequenced maize BACs ( bacterial artificial chromosomes ) 20 ., In addition , LTR retrotransposons homologous to known TEs in the maize LTR retrotransposon exemplar database ( http://maizetedb . org/ ) were found by running the RepeatMasker program ( vers 3 . 19 ) 29 on the assembled B73 genome using default parameters ., The element discovery process yielded 406 unambiguously distinct families of LTR retrotransposons that contained at least one intact member ( Table 1 ) , with intact being defined as the presence of two LTRs flanked by target site duplications ( TSDs ) ., Families were defined by established sequence relatedness criteria 30 , and most families were named using the sequence-based criteria developed by San Miguel and coworkers 31 ., Of these families , the great majority ( 363 ) were found by this structure-based screen and had not been previously described ., A few ( 90 ) additional full-length LTR retrotransposons were identified that lacked sufficient structural or internal sequence information to allow one to determine their family status , and these are currently given the generic family name “unknown” ( see Materials and Methods ) ., LINEs were detected by their TSDs flanking a block of sequence of appropriate length ( 5–10 kb for L1-like superfamily member searches and 3–5 kb for RTE-like superfamily member searches ) , terminated on one end with a simple sequence repeat , usually poly A . Further , these candidates were required to encode at least one LINE-specific protein motif ., SINEs are non-autonomous retroelements that use the enzymatic machinery of autonomous LINEs to retropose ( for a review see 32 ) ., SINE discovery was mainly based on the detection of the characteristic internal RNA polymerase III promoter , as described in Materials and Methods ., Prior to this search , only the ZmAU SINE family had been identified in maize 33 ., Using a structure-based approach , an additional three SINE families were discovered , and are now named ZmSINE1 , ZmSINE2 and ZmSINE3 ( Figure 1A ) ., All four maize SINE consensus sequences possess an internal RNA polymerase III promoter composed of conserved A and B boxes , suggesting an ancestral relationship to tRNAs ., As for the pSINE family in rice and the TS SINE family in tobacco 34 , 35 , ZmAU , ZmSINE1 and ZmSINE2 members ends with a poly ( T ) stretch of 4 to more than 20 bases , a feature found only in these five plant SINE families 32 ., In contrast , ZmSINE3 members end with a poly ( A ) stretch , a feature found for Brassicaceae SINEs 36 as well as for all other eukaryotic tRNA-related SINEs 32 ., Despite this structural difference , ZmSINE2 and ZmSINE3 likely have the same LINE partner as they show strong 3′-end sequence homologies with the maize LINE1-1 consensus sequence ( Figure 1B ) ., This implies that , in the target-primed reverse transcription process leading to SINE integration by the LINE machinery , the same LINE reverse transcriptase can prime reverse transcription on a poly ( A ) as well as a poly ( U ) -ending RNA template ., Because TEs in maize and other organisms tend to insert into each other , it was possible that other TE sequences inside a retroelement might be misidentified as an intrinsic part of the retroelement ., Hence , all of the retroelements identified in maize were carefully compared to the comprehensive databases for other ( i . e . , class I ) TEs in maize 20 to produce a filtered set of retroelement sequences ., The filtered LTR retrotransposon sequences for all 406 families were used with a RepeatMasker approach 29 to find all of the significant homologies in the B73 draft sequence 20 ., At the default settings employed , similarity as small as a contiguous perfect match of 24 bp was identified as a valid homology ., With this approach , over 1 . 1 million LTR retrotransposon fragments were identified in the B73 maize genome , contributing ∼1 . 5 Gb , or about 75% of the ∼2 . 05 Gb of the genome that has been sequenced ( Table 1; 20 ) ., As expected , the most abundant families were those that had been previously known , like Huck , with the four most numerous families each contributing 7–12% of the nuclear DNA ., The 20 most numerous LTR retrotransposon families generate ∼70% of the sequenced B73 genome ( Table 2 ) , while the remaining 386 families mostly consist of low-copy-number families with a high diversity but lesser genomic abundance ( Figure 2 and Table S1 ) ., Many cases were observed of gene fragments inside LTR retrotransposons ( Table S2 ) ., A total of 425 intact LTR retrotransposons were observed to contain gene fragments , from a minimum of 189 independent gene fragment captures ., No case was identified , under the conditions employed , where a single LTR retrotransposon contained inserted fragments from more than one standard nuclear gene ., Other classes of TEs in maize are even more active in gene fragment acquisition , including 1194 gene fragment captures by Helitrons and 462 by other DNA transposons , including Pack-MULEs 20 ., It is not known whether these gene fragments play any role in maize genetic function , for instance in the creation of a new gene or in epigenetic regulation of their donor loci ., Thirty different families ( with family members defined as those with >80% sequence identity 30 ) of LINEs were detected in the maize genome , with 13 of these not having been previously found and/or identified as separate families ( Table 1 ) ., Approximately 35 , 000 LINEs ( many as fragments of intact elements ) were found in the B73 sequence , but this number is certain to be an overestimate caused by the many gaps and incorrect assemblies that are expected in the current maize genome draft sequence 20 ., These LINEs contribute 20 Mb of DNA to the draft genome sequence , or about 1% of the total ( Table 1 ) ., Overall , SINEs represent around 0 . 5 Mb and 0 . 02% of the sequenced portion of the B73 maize genome 20 ., The copy numbers are 49 , 134 and 23 for the ZmAU , ZmSINE1 and ZmSINE3 families , respectively ., ZmSINE2 is the major SINE family , with 1382 members ., Based on phylogenetic criteria ( Figure S1 ) , the ZmSINE2 family can be further divided into three distinct subfamilies ., A phylogenetic approach was used to study the amplification dynamics of SINEs in maize ., The ZmSINE1 , ZmSINE2 and ZmSINE3 families contain very young members ( Figure S1 ) , close to the family consensus , suggesting very recent transposition activity ., Tree topologies for these families are also typical of the “gene founder” model wherein a very small number of “master” elements are active while the vast majority of derived copies have no significant amplification potential 37 ., The ZmAu family is mainly composed of more diverged members , suggesting little or no activity in the recent past ., In order to look at the behaviors ( e . g . , insertion specificities or amplification level ) of the TEs across a genome , it is essential to determine their relatedness and then use this information to generate families of close relatives ., Once families are generated , then family-specific behaviors can be investigated ., Transposable elements of all classes tend to vary in relatedness across a spectrum , such that two TEs recently derived by transposition from the same parent element may be 100% identical in sequence , while others with a more ancient relationship can show any degree of further divergence ., However , the very rapid removal of DNA from higher plant genomes 38 , 39 , especially from maize 40 , by the progressive accumulation of small deletions indicates that TEs that last shared a common ancestor more than a few million years ago ( mya ) are usually largely or fully deleted from the genome ., Hence , TE families can be defined by an arbitrary but consistent criterion of nucleotide sequence divergence , and a value of 80% identity has been selected by a consortium of researchers in this field 30 ., In the maize genome , the classification of LTR retrotransposons into families was a major challenge because of the exceptional complexity that was observed ., Nonetheless , similar to the case in the much simpler rice genome 41 , all-by-all BLAST analysis of LTRs was sufficient to unambiguously define families by the 80% identity rule ., Not all families could be classified in their appropriate superfamily ( i . e . , copia or gypsy ) , usually because of an absence of the genes needed for the definitive gene order criterion or for phylogenetic analysis , and these were dubbed RLX ., The individual family identifications were clear , however , and each family was given a unique name ., Some of these family designations conflict with previous names 42 , but these earlier names were not applied with any specific rule , and thus were certain to be both misleading and temporary ., For instance , the LTR retrotransposon collection called CRM 20 was actually found to represent four related , but clearly separate , LTR retrotransposon families that we have now named CRM1 , CRM2 , CRM3/CentA , and CRM4 ., Our consistent analysis using agreed-upon criteria 30 caused other such splittings of previously lumped families , and also lumped some different named families into single families that fit the 80% identity criterion ( e . g . , Cinful and Zeon are actually a single family that has now been named Cinful-zeon ) ., The new names , and the names that had previously been applied by unspecified and/or inconsistent homology criteria , are now shown in Table S1 ., The assembled physical and genetic map of maize inbred B73 20 allows placement of any class of sequence along that portion of the genome that was sequenced ., Overall , LTR retrotransposons are found to be most abundant in pericentromeric heterochromatin and least abundant in the more gene-rich arms on all chromosomes ( Figure 3 ) ., However , different LTR retrotransposons are found to be differentially clustered in such analyses , with the general observation that the gypsy superfamily of LTR retrotransposons is concentrated in the pericentromeric heterochromatin while the copia superfamily shows a preferential accumulation in the more euchromatic regions of the chromosome arms 20 ., Despite this general pattern , individual families show deviations from the rule ., For instance , the gypsy family Huck was found to exhibit a more ‘copia-like’ distribution on chromosome 1 ( Figure S2 ) ., Another gypsy family , Grande , shows a relatively even distribution across 10 Mb bins of this same chromosome ., Hence , there are families that accumulate in a pattern that contrasts with the general behavior of their superfamilies in maize ., A more dramatic correlation between LTR retrotransposon family property and insertion/accumulation pattern was observed by comparing the copy numbers of intact elements in a LTR retrotransposon family with the nature of the sequences within 500 bp ( on each side ) of the insertion site ., Low-copy-number families were found to be most often inserted into the regions in or near genes ( or gene fragments ) , while high-copy-number families were observed to primarily accumulate inside other LTR retrotransposons ( Figure 4 ) ., LINEs of both RIT and RIL ( L1-like ) families were found to be fairly evenly distributed across all chromosomes , with a higher abundance in distal regions of the chromosomes ( Figure S3 ) ., Although maize LINEs have been observed to show a preferential association with genic regions , especially introns 43 , their common occurrence in pericentromeric DNA suggests that many insertions are not in or near genes ., Of the 1991 SINEs discovered , 1174 were found in the introns or UTRs ( untranslated regions ) of genes and 21 in putative coding exons ( data not shown ) ., Only 796 were found in the intergenic space that makes up more than 85% of the sequenced B73 genome 20 ., Hence , like SINEs in other species , these small TEs show a very strong preference for association with genes in the maize nuclear genome ., In this regard , the general distribution of SINEs across the maize chromosomes ( Figure S4 ) was found to exhibit a pattern quite similar to the gene distribution 20 ., As previously observed in other organisms by numerous scientists studying many different genomes , maize TEs were found to make up a greater quantity of the total DNA in the gene-poor pericentromeric regions than in other parts of the genome ( Figure 3 ) ., However , as mentioned above and observed previously ( reviewed in 44 ) , LINEs , SINEs and some LTR retrotransposon families accumulate preferentially in areas that are near genes ., Figure 5 shows the relationship between LTR retrotransposon abundance and LTR retrotransposon family richness across chromosome 1 of maize inbred B73 , and this general pattern was found to be the same across all other chromosomes ( data not shown; Table S3 ) ., Hence , on all maize chromosomes , those regions that have the most total LTR retrotransposons also have the fewest kinds of LTR retrotransposons ., This observation echoes the relationship between the number of species and the abundance of individual species in both terrestrial and aquatic environments , but has no precedent that we are aware of in TE studies ., The insertion dates of intact LTR retrotransposons was observed to vary according to the distance from the centromere ., Younger elements are enriched in the euchromatic regions whereas older elements are most abundant in the pericentromeric regions ( Figure 6 ) ., An analysis of variance showed that the average insertion date per 1 Mb bin varied according to distance from the centromere ( F\u200a=\u200a2 . 08; P<0 . 0001 ) , and this relationship held across most of the chromosomes ( Table 3 ) ., The average date of LTR retrotransposon insertion for a given family was also observed to correlate with the current perceived copy numbers of the LTR retrotransposon families ., As a general pattern , the lower-copy-number elements were more ancient insertions ( averaging about 1 . 2 mya ) compared to the highest-copy-number elements ( averaging about 0 . 7 mya ) ( Figure 7 ) ., Because most of the higher-copy-number LTR retrotransposons are of the gypsy superfamily ( Table 2 ) , and show an overall pericentromeric accumulation bias 20 , one expected the opposite result because of the slower rate of LTR retrotransposon removal in gene-poor ( and thus recombination-poor ) regions like the pericentromeres 45 ., The landmark sequencing of the very complex and fairly large maize genome was accomplished at a small fraction of the cost of previous clone-by-clone sequencing projects because of the expertise of the researchers involved , a low redundancy of initial shotgun sequencing , and because of a decision to not finish any regions of the genome that appeared to lack gene candidates 20 ., Hence , a very comprehensive TE discovery and masking process was necessary to facilitate finishing that was efficiently targeted on genes ., One disadvantage of this approach , however , is that most sequenced regions are composed of many tiny contiguous sequences ( contigs ) ., Our analysis of the current B73 assemblies ( data not shown ) indicates a median contig size of ∼7 kb with ∼60% of the assembly occurring in contigs larger then 30 kb ., Thus , a structure-based search approach that requires intact elements , like the one employed in this project , will miss any families where the only intact members are fractured by sequence gaps or inaccurate scaffolding of contigs ., This is expected to be most problematic for large TEs ( like LTR retrotransposons ) and for those that only have a few intact members ., Hence , our prediction that ∼75% of the B73 maize genome is composed of LTR retrotransposons is a minimum estimate ., Also because of the many tiny sequence gaps in the assembly , there will be many occasions when an intact retroelement was identified by RepeatMasking as several fragments of an element ., Hence , calculation of the ratio of intact to fragmented LTR retrotransposons is not valid with this dataset ., In contrast , this same analysis with the random sampling of fully sequenced and annotated clones known as the GeneTrek approach does allow accurate quantification of the relative abundance of different TE structures ., In such a GeneTrek analysis , the ratio of intact to truncated LTR retrotransposons in maize was found to be ∼2∶1 40 , 46 , quite different from the ratio of ∼1∶27 that was calculated ( Baucom and Bennetzen , data not shown ) as an artifact of this same analysis on the currently fractured B73 assembly 20 ., There are also many large sequence gaps , and numerous sequenced BACs with no home in the assembly , for the B73 draft sequence 20 ., It is likely that about 90% of the maize nuclear genome is present in the current assembly ( ∼2005 Mb out of ∼2300 Mb ) ., From all previous full genome sequences in multicellular eukaryotes that have centromeres , the standard observation has been that the majority of the unsequenced regions are in the gene-poor areas around the centromeres and in other heterochromatic blocks ., Because these gene-poor chromosome segments also tend to be LTR retrotransposon-rich , these results provide a further reason to believe that the B73 maize genome contains more than 75% LTR retrotransposons , with an upper limit of ∼85% ., Importantly , however , the overall quantitation of retroelement contributions to the B73 genome is not dramatically biased by the gaps and other intrinsic errors in the current assembly ., As shown in Figure S5 , most LTR retrotransposons exhibit the same relative abundance when used to mask the current B73 draft assembly as they do when used to mask a shotgun dataset from the same B73 line ( R2\u200a=\u200a0 . 99 , p<0 . 0001 ) ., The few exceptions to this observation ( e . g . , Ipiki ) are likely to be LTR retrotransposons that are preferentially abundant in that ∼10% ( e . g . , near centromeres ? ) of the maize genome that is not present in the assembly 20 ., Previous maize studies had uncovered primarily the high-copy-number retroelements 21 , 23 , with some exceptions of low-copy-number TE discovery associated with particular mutations 47 , 48 or carefully sequenced and annotated small segments of the maize genome 46 ., All of the LTR retrotransposons found in these earlier studies were also found in this analysis , at the approximate predicted frequencies ., The major difference , however , was the large dataset available in the current study , and thus the discovery of hundreds of additional LTR retrotransposon families ., Only by this comprehensive analysis on the majority of the maize genome was it possible to determine the exceptional complexity of retroelements in maize , and their different properties of dispersal and divergence ., Rice , with an ∼400 Mb nuclear genome , has 172 identified LTR retrotransposon families that contribute ∼97 Mb , distributed across 48% with only a single intact element , 20% with 2 intact elements and 32% with 3 or more intact elements 41 ., Maize , in contrast , has 406 identified LTR retrotransposon families , just over twice as many , but they contribute ∼1700 Mb of DNA to the maize nuclear genome ., These maize elements are distributed across 42% singleton intact elements , 21% with 2 intact elements and 37% of families with 3 or more intact elements ., Hence , the >17X greater amount of LTR retrotransposons in maize compared to rice is not primarily caused by a greater number of element families in maize but instead by a much higher copy number of a very small number of superabundant families ., Two of the many misconceptions about TE properties in higher eukaryotes are that they are highly repetitive and are randomly scattered about the genome ., In fact , many TE families are present in very low copy numbers ., The median family copy number of intact LTR retrotransposon with TSDs in B73 maize was measured to be 2 ( mean ∼77 ) , with a total of 256 families that contained only one or two intact LTR retrotransposons that were detected ., Most LTR retrotransposon families are distributed quite unevenly across the genome , probably an outcome of both differences in insertion preferences and different rates of LTR retrotransposon removal in different chromosomal locations 44–46 , 49 ., The previous observation that LTR retrotransposons show a dramatic bias in whether they insert into LTRs or the internal regions of other LTR retrotransposons 21 was not observed , however , and it now seems likely that the previous conclusion was an artifact of a small sample size ., Studies in rice and other organisms suggest that LTR retrotransposons are more rapidly removed ( sometimes by unequal homologous recombination to generate solo LTRs ) in regions with high recombination rates , like areas around genes and in the cores of centromeres 45 , 46 ., One example of this analysis was that the ratio of solo LTRs to intact elements was found to be much higher in gene-rich and recombination-rich euchromatic regions than in gene-poor and recombination-poor pericentromeric regions 44 ., Although natural selection should also more rapidly remove individuals from a population that contain retroelements or other TEs detrimentally inserted into coding and gene regulatory regions , this process alone cannot explain the differential retroelement accumulation properties that we observe ., For instance , why would LINEs , SINEs and low-copy-number LTR retrotransposons not be depleted in genic regions , while high-copy-number LTR retrotransposons are ?, A simpler explanation is that different retroelements are directed to preferentially insert in different parts of the genome by the biases of their integrases for association with specific chromatin proteins , as observed with Ty elements in yeast 50 ., We have no idea how many types of DNA::protein configurations are actually present in plants , of course , but it is very clear that chromatin consists of more than just hetero- and eu- varieties 51 , so sufficient variability should be present to allow a great wealth of different TE insertion specificities , as has been recently reported in Arabidopsis 52 ., Particularly fascinating are the high-copy-number LTR retrotransposons like Ji and Opie that preferentially avoid insertion into genes , but primarily insert into heterochromatin near genes , while other high-copy-number elements like Gyma avoid inserting into genes or heterochromatin near genes , preferring instead an accumulation into large gene-free heterochromatic blocks 46 ., Unlike low-copy-number LTR retrotransposons , which are associated with de novo mutations in many plant species , neither class of high-copy-number LTR retrotransposons is associated with a mutation caused by insertion into a gene ., Perhaps TE insertion profiles will be a uniquely useful route to uncover and map a broad spectrum of novel chromatin structures ., Genomic complexity is not just a matter of the number of different sequences , but also of the variability in their arrangement and stability ., The factors that determine differences in these arrangements , such as differential insertion specificities and differences in retention , are only beginning to be understood ., It is already clear , though , that TE insertion and retention biases are the major forces that determine local genome structure in maize and other complex plant genomes ., The mechanisms responsible for these biases , and their outcome vis-à-vis gene/genome function and evolution , are only now beginning to be understood ., Viewed from the standpoint of the TE , much of the diversity in TE populations and their arrangement takes on a new and informative light ., A previous model proposed that low-copy-number TEs must insert near or into genes so that they have a reasonable chance of expression and activity in subsequent generations , while highly repetitive TEs need to avoid insertions that disrupt genes in most cases because 1000 or 10 , 000 such insertions would lead to a dead host 44 ., Hence , abundant TEs rely on their abundance per se to guarantee transmission and the opportunity for activity in future generations ., The data for LTR retrotransposon abundance versus copy number shown here agrees with this model , as does the fact that ( to date ) none of the high-copy-number LTR retrotransposons have been shown to cause a de novo mutation , while low-copy-number LTR retrotransposons ( e . g . , Bs1 , Tnt1 , Tos17 ) that make up a relatively small part of their genomes have caused many new mutations 47–49 , 53 ., The analysis of the maize genome suggests that the copy number for this transition is fairly low , 10–100 intact copies per genome ( Figure 4 ) , for this change in lifestyle ., LTR retrotransposon families with copy numbers less than ten were usually found to preferentially accumulate in genic regions , while most LTR retrotransposon families with copy numbers higher than 100 were found to be enriched in gene-poor regions like pericentromeric heterochromatin ., The insertion preferences of LTR retrotransposons can contribute to their potential for more than just transcriptional activity ., Elements that land in recombination-rich regions have a greater chance of inter-element unequal events that can create novel LTR retrotransposons with possible new properties 38 ., Insertion into an LTR provides the opportunity to acquire the gene regulatory properties of the target LTR retrotransposon ., Moreover , insertion of an LTR retrotransposon into an LTR retrotransposon would usually eliminate the target element as a potential competitor for future amplification ., The observed relationship between LTR retrotransposon family richness and LTR retrotransposon abundance across the maize chromosomes is the most compelling indicator , in this study , of the validity of the conceptualization of TEs as | Introduction, Results, Discussion, Materials and Methods | Recent comprehensive sequence analysis of the maize genome now permits detailed discovery and description of all transposable elements ( TEs ) in this complex nuclear environment ., Reiteratively optimized structural and homology criteria were used in the computer-assisted search for retroelements , TEs that transpose by reverse transcription of an RNA intermediate , with the final results verified by manual inspection ., Retroelements were found to occupy the majority ( >75% ) of the nuclear genome in maize inbred B73 ., Unprecedented genetic diversity was discovered in the long terminal repeat ( LTR ) retrotransposon class of retroelements , with >400 families ( >350 newly discovered ) contributing >31 , 000 intact elements ., The two other classes of retroelements , SINEs ( four families ) and LINEs ( at least 30 families ) , were observed to contribute 1 , 991 and ∼35 , 000 copies , respectively , or a combined ∼1% of the B73 nuclear genome ., With regard to fully intact elements , median copy numbers for all retroelement families in maize was 2 because >250 LTR retrotransposon families contained only one or two intact members that could be detected in the B73 draft sequence ., The majority , perhaps all , of the investigated retroelement families exhibited non-random dispersal across the maize genome , with LINEs , SINEs , and many low-copy-number LTR retrotransposons exhibiting a bias for accumulation in gene-rich regions ., In contrast , most ( but not all ) medium- and high-copy-number LTR retrotransposons were found to preferentially accumulate in gene-poor regions like pericentromeric heterochromatin , while a few high-copy-number families exhibited the opposite bias ., Regions of the genome with the highest LTR retrotransposon density contained the lowest LTR retrotransposon diversity ., These results indicate that the maize genome provides a great number of different niches for the survival and procreation of a great variety of retroelements that have evolved to differentially occupy and exploit this genomic diversity . | Although TEs are a major component of all studied plant genomes , and are the most significant contributors to genome structure and evolution in almost all eukaryotes that have been investigated , their properties and reasons for existence are not well understood in any eukaryotic genome ., In order to begin a comprehensive study of TE contributions to the structure , function , and evolution of both genes and genomes , we first identified all of the TEs in maize and then investigated whether there were non-random patterns in their dispersal ., We used homology and TE structure criteria in an effort to discover all of the retroelements in the recently sequenced genome from maize inbred B73 ., We found that the retroelements are incredibly diverse in maize , with many hundreds of families that show different insertion and/or retention specificities across the maize chromosomes ., Most of these element families are present in low copy numbers and had been missed by previous searches that relied on a high-copy-number criterion ., Different element families exhibited very different biases for accumulation across the chromosomes , indicating that they can detect and utilize many different chromatin environments . | genetics and genomics/genome projects, genetics and genomics, genetics and genomics/plant genomes and evolution, genetics and genomics/genomics | null |
journal.pgen.1007616 | 2,019 | Artificial selection on storage protein 1 possibly contributes to increase of hatchability during silkworm domestication | The silkworm , Bombyx mori , is the only fully domesticated insect species , originating from its wild ancestor , B . mandarina , approximately 5000 years ago ., During the domestication process , the domestic silkworm evolved rapidly under human-preferred selection ., Deciphering the way in which artificial selection acts on the silkworm genome to produce human-favored domestication traits will provide clues from a unique insect model for understanding Darwins theory of artificial selection 1 ., Recently , through genome-wide screening of selection signatures in a large batch of domestic and wild silkworms , we identified candidate domestication genes that enriched nitrogen and amino acid metabolism pathways , specifically in glutamate and aspartate metabolism ., Knockout of two involved genes resulted in abnormal metamorphosis and decreased cocoon yield 2 ., These findings suggest that , like domestic plants and animals , domestic silkworms also tend to have efficient utilization of nitrogen resources to adapt to human-preferences 2–4 ., In addition to the glutamate and aspartate metabolism , which is an ammonia re-assimilated system 5 , we further wonder whether other kinds of nitrogen supplies are also affected by artificial selection ., If this is the case , how have they contributed to silkworm phenotypic changes during domestication ?, Insect storage proteins ( SPs ) are another important resource of amino acids and nitrogen ., Specifically , SPs are repositories of stored amino acids that belong to a special conserved arthropod hemocyanin superfamily 6 ., Most insects have at least two main types of storage proteins , i . e . , arylphorin and methionine-rich storage proteins; some species have other atypical SPs 7 ., SPs have been cloned or predicted in many insect species , including Lepidoptera moths and butterflies 8–11 ., Insect SPs are believed to serve as a source of amino acids and nitrogen for pupae and adults during metamorphosis and reproduction 12 , however there is little solid functional evidence of their biological significance 10 , 13 ., In plants , storage proteins are mainly reserved in seeds , where , along with other nutrients such as oil and starch , they supply energy for seed germination and growth 14 , 15 ., Particularly in crops , seed SPs act to provide energy for humans and animals and thus are of great interest and a target for breeding and improvement 14–16 ., In the domestic silkworm , previous studies preliminarily characterized the gene and protein expression patterns of four SPs 8 , 17–19 ., SP1 is female-biased expressed when entering the last instar , and only accumulates in the female pupa 8 , 20 , 21 ., It has been suggested that SP1 contributes to adult female characters and is related to the synthesis of vitellogenin ( Vg ) , the precursor of yolk protein 17 ., SP2 couples with SP3 to form a heterohexamer and has inhibitory effects on cell apoptosis 18 , 19 ., SSP2 was a heat resistant protein and suggested has a cell-protective function 19 ., The determination as to whether or not SPs are also important in silkworm domestication , as is the case in domesticated plants , awaits a thorough exploration of their biological and evolutionary significance ., Development of genomics and genome-editing techniques provide tools for efficiently deciphering the evolutionary and functional significance of particular genes 22 , 23 ., In this study , we conducted a genome-wide identification of the silkworm SPs ., Taking advantage of the genomic data resource of a batch of representative domestic and wild silkworms 2 , we performed selection signature screening of the silkworm SPs followed by functional verification via the CRISPR/Cas9 knockout system and comprehensive comparative ova transcriptomes of wild-type and mutant silkworms as well as domestic and wild silkworms ., Our findings suggest that artificial selection on SP1 contributes to increased egg hatchability during silkworm domestication , possibly by promotion of vitellogenin , influence of hormone synthesis and egg development and eggshell formation ., These results provide a novel case with functional evidence for the determination of a regulatory framework on a silkworm domestication gene , revealing that artificial selection acting on the nitrogen and amino acid supply is also required for improved silkworm reproduction ., In total , we identified 8 SPs in the silkworm genome by means of a blast search ., Among which SP2 were not annotated in the gene list ., Of these , SP1 exhibited the highest methionine content ( 10 . 98% ) ( Table 1 ) ., Phylogenetic analysis showed that SP1 was located in one distinct clade , whereas other SPs were in another , indicating an obvious divergence between SP1 and the remaining SPs ( Fig 1A ) ., SP1 is located on chromosome 23 while the other SPs are clustered on chromosome 3 , suggesting possible tandem duplication events during evolution ., Interestingly , by screening artificial selection signatures on the genomic region bearing SP1 and the other SPs respectively , we detected a strong selection signature in the SP1 region of the domestic silkworm ( see Material and methods ) , since there was notably reduced nucleotide diversity in the domestic silkworm group ( Fig 1B and 1C ) ., Furthermore , we detected strong differentiation in allelic frequency upstream of SP1 ( Fig 1D ) ., Correspondingly , SP1 was differentially expressed in the ova of domestic and wild silkworms , with higher expression in the domestic one ( Fig 1E , S1 Fig ) ., We also detected 11 SNPs that caused amino acid changes in the coding sequence of the gene ( S2 Fig ) ; the biological significance of these SNPs requires further evaluation ., These results suggest that artificial selection acting on SP1 during silkworm domestication may affect the function of this gene in domestic silkworms ., At the very least , we can infer that selection may favor higher expression in the domestic silkworm ., To explore the possible phenotypic influence of artificial selection of SP1 acting on the domestic silkworm , we first investigated the biological role of this gene in the silkworm using CRISPR/Cas9 knockout system ., For the single guide RNA ( sgRNA ) design , we selected highly specific targets in the first exon , close to the translation starting site; namely , S1 and S2 ( Fig 2A ) ., We chose another site S3 close to the end of the first exon , more than 60 bp downstream from S1 and S2 ( Fig 2A and Table 2 ) to obtain a potentially large fragment deletion by injecting the pool of three gRNAs ., After mutation screening of the injected eggs ( G0 generation ) , the gRNAs targeting the above three sites successfully guided DNA editing and generated a variety of mutation types , including 4–9 bp deletions or small insertions followed by a large deletion ( Fig 2B ) ., Through screening of the exuviae of the fifth instar larvae in the G0 cocoons , we successfully identified 26 mosaic mutant G0 moths ., We then generated pairwise crosses of those G0 mutants with similar mutant genotypes from the G1 populations ., After mutation screening of the G1 eggs , we selected two populations with large deletions for further feeding and mutation screening ( see Material and methods ) ., Finally , in the G2 generation we obtained two types of homozygous mutants , i . e . , MU1 and MU2 ( Fig 2C ) ., In MU1 , there was an 8 bp insertion followed by a 63 bp deletion in the SP1 coding sequences ., In MU2 , there was a 4 bp insertion followed by a 65 bp deletion ., The mutations occurred at +29 and +26 bp of the first SP1 exon in MU1 and MU2 , respectively ( Fig 2C ) , resulting in reading frame shift mutations and severe premature termination close to the translation starting site , with stop signals at +10 aa and +37 aa of the SP1 protein ( Fig 2D ) ., We selected and maintained the MU1 population for assay on phenotypes related to reproduction and metamorphosis , such as the number of eggs , hatching rate , pupa weight , and cocoon weight ., Compared with the wild-type , which exhibited hatching rates of approximately 90% , the hatching rates of the SP1 mutants were dramatically lower , with a mean value of about 40% ( Fig 2E ) , although neither the number of eggs produced nor the whole pupa weight or cocoon shell weight were noticeably affected ( Fig 2E ) ., Given that the data were obtained from large replicates ( 83 replicates for the hatchability assay and 240 replicates for the pupa and cocoon weights ) , the results are robust ., Loss-of-function mutation resulted in significantly decreased expression of SP1 and Vg in the ova , based on the RNA-seq data ( Fig 2F ) ., These results suggest that in the silkworm SP1 may positively affect the expression of ova Vg and contribute to silkworm egg development ., Given that knockout of SP1 resulted in a reduced hatching rate ( Fig 2E ) and that it is female-specific expressed in the pupa and adult stages , we suspect that it plays an important role in ova development , thus contributing to an efficient hatching process ., During domestication , artificial selection preferred higher expression of SP1 , thus may improve the silkworm hatching rate ., As expected , we found that the hatching rate of the domestic silkworm was significantly higher than that of the wild one ( Fig 3A ) ., No obvious differences in egg production was detected between wild and domestic silkworms ( Fig 3A ) ., The lower hatching rate of the wild silkworm has also been reported in other studies 24 , 25 ., We further tested expression of Vg in the ova and discovered that consistently , it was significantly higher expressed in the domestic silkworm than in the wild silkworm ( Fig 3B , S1 Fig ) ., Promotion of SP1 expression in the domestic silkworm thus results in the corresponding up-regulation of Vg , which further contributes to increased hatchability during silkworm domestication ., In order to further explore the regulation network and possible molecular mechanisms of female-specific SP1 on egg hatchability , we generated comprehensive ova comparative transcriptome analyses between the wild-type and the mutant , as well as the domestic and wild silkworm ( Bombyx mandarina ) , with 4 . 87~9 . 15 Gb RNA-seq data for each sample ( S1 Table ) ., We chose ova instead of fertilized eggs as target because silkworm SP1 is female-biased expressed when entering the last instar and only accumulated in the female 8 , 20 , 21 ., Comparative transcriptomics in this target tissue would directly focus mechanism of SP1 on female reproductivity and avoid potential influence from the male ., In total , there were 561 genes identified as differentially expressed genes ( DEGs ) in the SP1 knockout mutants ( MU1 ) compared to the wild-type silkworm , with significantly more down-regulated genes ( 341 ) than up-regulated ( 220 ) ( p = 0 . 0003 , Chi-squared test with Yates continuity correction ) ( Fig 4A and S2 Table ) ., As expected , we found many more DEGs ( 2882 ) between the wild and domestic silkworms , since wild silkworms are much more genetically and phenotypically different from the domestic one , compared with the silkworm mutant from the wild-type ., It is interesting that in the 2882 DEGs , there were also significantly more lower expressed genes ( 1761 ) than higher expressed ( 1121 ) ( p = 2 . 2e-16 , Chi-squared test with Yates continuity correction ) ( Fig 3A and S3 Table ) in the wild silkworm ., These results suggest that transcriptome repression in ova might be an output of SP1 depletion in the SP1 mutant ( Fig 2F and Fig 4A ) and a low expressional level of SP1 in the wild silkworm ( Fig 1E; Fig 4A and S1 Fig ) ., We identified 302 common genes in the two sets of DEGs ., KEGG enrichment analysis indicated that these common DEGs were significantly enriched in pathways related to cell proliferation , such as ECM-receptor interaction and folate biosynthesis , as well as the hormone synthesis pathway , which is important in adult ovary development and female production 26 , 27 ( Fig 4B ) ., Gene ontology ( GO ) enrichment analysis indicated that the common genes were enriched in reproduction related biological processes , such as chorion-containing eggshell formation ( Fig 4B ) ., These genes were also enriched in the molecular functions of the structural constituents of chorion ( Fig 4B ) ., In fact , they are all annotated as chorionic proteins , including 3 chorion class CB protein M5H4-like genes ( BGIBMGA009720 , BGIBMGA009719 , BGIBMGA009715 ) as well as a chorion class B protein PC10 gene ( BGIBMGA009721 ) ., All these chorion like genes showed significantly higher expression in the domestic silkworm , compared with both the mutant and the wild silkworm ( Fig 4B , S2 Table , S3 Table ) ., This pattern was also supported by Real-Time PCR validation in the domestic and wild silkworm ( S1 Fig ) ., Genes in ECM-receptor interaction pathway include collagens and integrins ( S3 Fig ) and those in folate biosynthesis include folylpolyglutamate synthase , which involves in 7 , 8-Dihydrofolate ( DHF ) and 5 , 6 , 7 , 8-Tetrahydrofolate ( THF ) , substrates for subsequent one carbon pool mediated by folate ( S4 Fig ) ., The enriched hormone synthesis pathway includes genes functioning in both juvenile and molting hormones ( S5 Fig ) ., Extend to all the enriched genes , it is notable that most of these enriched genes were relatively highly expressed in the domestic silkworm , compared with SP1 mutant and the wild silkworm ( Fig 4B ) ., We further generated enrichment analyses on DEGs on the two sets of DEGs independently and observed consistent pattern ( Tables 3 and 4 ) ., Functional enrichment analysis of DGEs between wild-type silkworm and SP1 mutant silkworm revealed a significantly enriched the KEGG pathway “ECM-receptor interaction” as well as other three pathways with marginal significances , insect hormone biosynthesis , Glycine , serine and threonine metabolism and Folate biosynthesis ( Table 3 ) ., The enriched GO terms included eggshell formation process and structural constituent of chorion ., Most of the genes in these two GO terms were down-regulated in the mutant ( Table 3 ) ., Consistently , These GO items were also in the top rank with the lowest p values when analyzing the DEGs between wild and domestic silkworm ( Table 4 ) ., Nearly all of the genes involved in these KEGG and GO terms showed significant lower expression level in the wild silkworm , i . e . , up-regulated in the domestic silkworm ( Table 4 ) ., These results further supported that in the wild silkworm , low expression level of SP1 may be associated with suppressed expression of genes in the eggshell formation process ., DEGs between domestic and wild silkworms were significantly enriched in function of structural constituent of ribosome ., The related genes are mostly ribosome proteins ( S6 Fig ) ., We also noted that the related biological processes , such as amide and peptide biosynthesis , was also in the top rank with the lowest p value ( Table 4 ) ., The related genes were also up-regulated in the domestic silkworm ., During domestication , there might be other factors that contribute to improved hatchability , such as , improved amide and peptide biosynthesis and activated ribosome activities in the ovaries ., Nitrogen resources are very important for silkworm domestication ., The domestic silkworm tends to efficiently utilize nitrogen resources to yield protein outputs to adapt to human-preference , such as the economically important product , the cocoon ., In this study , we discovered that artificial selection could directly act on a nitrogen resource gene , i . e , storage protein 1 ( SP1 ) , to improve silkworm hatchability ., SPs are also target loci of breeding in crops 28 ., However , with edible crops , human can directly benefit from the nutrients of these improved SPs 16 , whereas in the silkworm , the SPs benefit is in the form of increased silkworm reproductive capacity ., Among all the SPs identified , SP1 is quite divergent and somewhat unique from the others , both in terms of genomic location and phylogenetic position ., A similar pattern was also observed in other Lepidoptera species , such as the tobacco hornworm , Manduca sexta 29 , suggesting that SP1 may have evolved independently , while the other types of SPs might have experienced duplication during Lepidoptera evolution ., Methionine-rich SP1 seems to be of special interest , since methionine is reported to be an important amino acid in the trade-off between growth and reproduction 30 ., In Drosophila , dietary methionine restriction extends lifespan 30 , while in grasshoppers , a reduced reproduction-induced increase in expression methionine-rich protein occurred during life extension 31 ., Similarly , in the beet armyworm , silencing of SP1 by RNA interference ( RNAi ) decreases larval survival , which indicates the role of the methionine-rich SP in growth and metamorphosis13 ., We therefore added a new evidence that different to grasshopper 31 and the beet armyworm13 , but similar to Drosophila30 , silkworm methionine-rich SP1 functions in the reproduction process but does not obviously affect growth ., Given that in cocoon-producing silk moths , other nitrogen utilization system such as the glutamate /glutamine cycle , have been reported to be vital in metamorphosis silk-cocoon production 2 , 5 , 32 , we suspect that the strategy of nitrogen resource allocation via storage proteins may have diverged or modified during Lepidoptera insect evolution ., In the silkworm , the function of SP1 is limited to influencing the egg hatching rate ., Artificial selection acted only on SP1 rather than other SPs , suggesting the importance of SP1 for human-preferred domestication traits , i . e . , increased hatchability ., Ova comparative transcriptome analyses further illustrated a framework of regulatory network of SP1 on hatchability ., Firstly , there are many genes near the bottom of the regulatory network , including vitellogenin ( Vg ) , chorion proteins , structural component proteins in the extracellular matrix ( ECM ) -interaction pathway such as collagen and integrins , and synthetase in folate biosynthesis are all generally repressed in both the SP1 mutant and the wild silkworm ., Thus , artificial selection acts on SP1 for increased hatchability , possibly associated with the influence of those genes , pathway or biological processes , and finally contributes to an improved performance of ovary ., Vg is the main nutrient for silkworm egg formation and embryonic development33 ., It appears and accumulates at the stage when SP1 rapidly declines and disappears in the fat body , shortly before the emergence of the adult silkworm 17 , 34 ., SP1 may supply amino acids for the synthesis of Vg , as previously reported in Plutella xylostella 35 ., We therefore suspect that deficiency of SPs might directly trigger an as yet unknown regulatory pathway for the expression or synthesis of Vg ., Chorion proteins are the major component of the silkworm eggshell and perform the essential function of protecting the embryo from external agents during development , while simultaneously allowing gas exchange for respiration ., Eggshell ( chorion ) is constructed by the ovarian follicle cells ., The follicle cell epithelium surrounds the developing oocyte and , in the absence of cell division , synthesizes a multilayer ECM 36 ., Eggshell ECM was usually linked by integrins , a family of transmembrane receptor proteins to the cytoskeleton of the oocyte ., Via a series of signal transductions , ECM-integrins function in oocyte movement , differentiation , and proliferation 36 ., Integrins were reported to function in formation of actin arrays in the egg cortex 37 and they were also involved in tracheole morphogenesis which affects respiration 38 ., Repression of these genes are directly associated with deficient development and function of the ovary ., Loss of function of SP in the mutant or low expression level in the wild silkworm of SP might influence development and function of the ovary , further reducing the expression of Vg and chorion genes ., Secondly , folate is known to be important for human fetal development 39 ., In insects , folate also plays an important roles in egg development , possibly promoting the biosynthesis of nucleic acids in the ovaries , and evoking mitoses in cells of the collicular epithelium 40–42 ., Last and interestingly , we found that the hormone synthesis pathway was also repressed in response to SP1 deficiency ., Recent advances in hormone signaling indicate that in the adult insect , juvenile ( JH ) and molting hormones may cooperate to promote Vg expression and oocyte development 27 , 43 , 44 ., Therefore , hormone signaling pathway might function in the regulatory network of SP in these downstream genes , although there are still black boxes in the regulation connections of these genes , which require further in-depth experimental exploration ., Notably , increased hatchability during domestication may not be solely attributed to the increased expression of SP1 and the associated downstream genes , given that artificial selection acts on hundreds of gene loci in the silkworm genome 2 , 45 and that the ova comparative transcriptome between wild and domestic silkworms identified many more genes than that between SP1 mutant and wild-type silkworm ., We observed significantly enriched pathway and structural constituent of ribosome , the protein translation machinery and the biological processes involved in nitrogen metabolism and , are generally up-regulated in the domestic silkworm compared with the wild one ( Table 4 ) ., These results again supported the importance of nitrogen and amino acids in silkworm domestication , not only for silkworm protein output 2 , but also for productivity ., Similar to other domesticates , hatchability of silkworm eggs directly determines the quantity of offspring , and thus it is an important productivity trait for human to favorably select during domestication ., Based on the above results and the discussion , we propose that artificial selection , favors higher expression of SP1 in the domestic silkworm , which would subsequently up-regulate the genes or pathways vital for egg development and eggshell formation ., On the other hand , artificial selection consistently favors activated ribosome activities and improved nitrogen metabolism in the ova , as it might act in the silk gland for increased silk-cocoon yield 2 ., In result , the domestic silkworm demonstrates improved egg hatchability compared with it wild ancestor ., A multivoltine silkworm strain , Nistari , was used in all experiments ., Larvae were reared on fresh mulberry leaves under standard conditions at 25°C ., The wild silkworms were collected in Zhejiang Province , China and maintained as laboratory population in our lab ., The genomic single nuclear polymorphic data ( SNP ) file ( the VCF ) for the domestic and wild silkworm obtained from DEYAD platform ( https://doi . org/10 . 5061/dryad . fn82qp6 ) 2 ., Reference genome and the annotation file used for RNA-seq data mapping were obtained from the Ensemble database ( http://metazoa . ensembl . org/Bombyx_mori/Info/Index ) ., The reference sequences of B . mori storage proteins ( SP1 , SP2 , SSP2 and SP3 ) were retrieved from the NCBI GenBank ., These sequences were used as query , searching for homologs in the B . mori genome by tblastn with e-value <10−7 ., Other insect homologs of the silkworm SPs were searched in GenBank ( https://blast . ncbi . nlm . nih . gov/ ) by BLASTP with an e-value <10−7 ., We selected sequences from several representative Lepidoptera species and Drosophila melanogaster as candidate proteins for further analyses ., The sequences of the SP1 homologs were aligned using MEGA 6 . 0 software 46 ., A gene tree was constructed using MrBayes-3 . 1 . 2 with GTR + gamma substitution model 47 ., The gene-ration number was set as 1000000 and the first 25% was set as burn-in ., Other parameters were set as default ., Based on the available whole genomic single nuclear polymorphic data ( SNP ) of domesticated and wild silkworm populations 2 , ( https://doi . org/10 . 5061/dryad . fn82qp6 ) , we screened the selection signatures of the silkworm SPs , according to Xiang et al’s pipeline 2 ., Specifically , data from 19 samples of the early domesticated group ( i . e . , trimoulting local strains CHN_L_M3 ) of the domestic silkworm Bombyx mori and 18 samples of wild silkworm B . mandarina were used ., The SNP data of the two chromosomes that SP1 ( Chromosome 23 ) and the cluster of the other SPs ( Chromosome 3 ) were located were used to screen for the domestication signature ., Chr ., 23 is 20 , 083 , 478 bp in length and 2 , 046 , 397 SNPs were identified ., Chr ., 3 is 14 , 662 , 804 bp in length and 1 , 448 , 852 SNPs were identified , based on the published data ., Allelic frequency and SNP annotation were calculated using in-house Perl scripts ., For the detection of selection signature during silkworm domestication , we set a very stringent threshold to screen out regions significantly deviated from the overall distribution ., We only used windows within the top 1% of selective signatures ( the corresponding p value of a Z test < 0 . 001 ) and applied Fst ( fixation index ) between the two groups to represent the selective signatures , taking the highest 1% value as the cutoff ., The selection in the domestic silkworm group ( i . e . , the early domesticated group ) was further confirmed by limiting π at a relatively low level ( the lowest 5% ) ., The 20 bp sgRNA targets immediately upstream of PAM were designed by the online platform CRISPRdirect ( http://criSpr . dbcls . jp/ ) 48 ., The sgRNA DNA template was synthesized by PCR , with Q5 High-Fidelity DNA Polymerase ( NEB , USA ) ., The PCR conditions were 98°C for 2 min , 35 cycles of 94°C for 10 s , 60°C for 30 s , and 72°C for 30 min , followed by a final extension period of 72°C for 7 min ., The sgRNA were synthesized based on the DNA template in vitro using a MAXIscript T7 kit ( Ambion , Austin , TX , USA ) according to the manufacturer’s instructions ., The Cas9 construct was a kind gift provided by the Shanghai Institute of Plant Physiology and Ecology ( Shanghai , China ) ., The Cas9 vector was pre-linearized with the NotI-HF restriction enzyme ( NEB , USA ) ., The Cas9 mRNA was synthesized in vitro with a mMESSAGE mMACHINE T7 kit ( Ambion , Austin , TX , USA ) according to the manufacturer’s instructions ., All related primers are shown in Table 2 ., Fertilized eggs were collected within 1 h after oviposition and microinjection was within 4, h . The Cas9-coding mRNA ( 500 ng/μL ) and total gRNAs ( 500 ng/μL ) were mixed and injected into the preblastoderm Nistari embryos ( about 8 nl/egg ) using a micro-injector ( FemtoJet , Germany ) , according to standard protocols ( Tamura , 2007 ) ., The injected eggs were then incubated at 25°C for 9–10 d until hatching ., To calculate the effect of Cas9/sgRNA-mediated gene mutation in the injected generation ( G0 ) , we collected ~10% of the eggs ( 64 out of 600 ) 5 d after injection to extract genomic DNA for PCR , with primers Sp1-F and Sp1-R ( Table 2 ) ., The amplified fragments were cloned into a pMD19-T simple vector ( Takara , Japan ) and sequenced to determine mutation type ., When the injected G0 silkworms pupated , we collected silkworm exuviae from fifth instar larvae in each cocoon ., Genomic DNA was extracted using a TIANamp Blood DNA Kit ( Tiangen Biotech , Beijing ) according to the manufacturer’s instructions ., Individual mutation screening was generated with PCR at 94°C for 2 min , 35 cycles of 94°C for 30 s , 57°C for 30 s , and 72°C for 45 s , followed by a final extension period of 72°C for 5 min ., The PCR products were cloned into the pMD19-T simple vector ( Takara , Japan ) and sequenced ., Mosaic mutant moths were obtained from the above mutation screening of exuviae DNA from fifth instar larvae ., Moths with the same mutation site were pairwise crossed with each other to acquire G1 offspring ., About 7 d after the G1 eggs were laid , we collected ~30 eggs from each offspring population from one parental pair and pooled them to extract genomic DNA for mutation screening by PCR ., The amplified fragments were cloned into a pMD19-T simple vector ( Takara , Japan ) and sequenced to determine the exact mutation type ., Two G1 offspring populations with large deletions in BmSp1 were selected for further breeding ., At the pupa stage , 20 randomly selected individuals within each population were subjected to mutation screening of exuviae DNA ., Homozygous mutant moths with the same identified mutant genotype were crossed to acquire G2 offspring ., Mutation effects on proteins were evaluated using MEGA 6/0 software46 through codon alignment of the wild-type and the mutant ., On the fourth day of pupation ( P4 ) , we weighed and recorded the whole cocoon weight , pupa weight , and cocoon shell weight ., In total , data from 240 SP1-MU1 mutants and 110 wild-type silkworms were recorded respectively ., Offspring of the homozygous mutants and wild-type silkworms were incubated at 25°C for 9–10 d until hatching ., The number of eggs produced and hatched from each female moth were recorded respectively ., The egg hatching rates were then determined ., Eighty-three replicates were set for the SP1 mutant and 17 for the wild-type populations , respectively ., These assays were also generated for 20 wild silkworms ., Student’s t-test was used to analyze the significance of the differences ., For comparisons of datasets with unbalanced size , Student’s t-test with FDR ( false discovery rate ) correction was used ., Specifically , for the cocoon- and pupal- related traits , we divided the samples of SP1 mutant to two groups , consisting of 120 samples for each , and generated t-test with the wild type respectively ., The average p value followed by FDR correction was used to verify the significance ., As for the analysis of the number of eggs and hatching rates , we divided the samples of SP1 mutant to 4 groups , consisting of 20 or 21 samples for each , and generated t-test with the comparable data from the wild-type silkworms , respectively ., Average p value followed by FDR correction was used to verify the significance ., Ova from newly emerged virgin moth of the domestic wild type silkworm , SP1 mutant and the wild silkworm were dissected and collected for RNA extraction with three replicates set for each ., Total RNA were isolated using TRIzol ( Invitrogen ) ., For each sample , RNA were sent to Novogene Bioinformatics Institute ( Beijing , China ) for cDNA library construction and RNA-seq by Illumina Hiseq 2500 ( Illumina , San Diego , CA , USA ) with 125 bp paired-end reads according to the manufacturer’s instructions ., Raw data were filtered with the following criteria: ( 1 ) reads with ≥ 10% unidentified nucleotides ( N ) ; ( 2 ) reads with > 10 nt aligned to the adapter , allowing ≤ 10% mismatches; and ( 3 ) reads with > 50% bases having phred quality < 5 ., The clean data were mapped to the Bombyx mori reference genome using Tophat with 2 nt fault tolerance and analyzed using Cufflinks 49 ., The relative expression value of each gene was calculated using the widely used approach ,, i . e ., , fragments per kilobase of exon per million pair-end reads mapped ( FPKM ) 49 , using Cuffdiff In order to identify differentially expressed genes ( DEGs ) , Cuffdiff was further used to perform pairwise comparisons between wild-typed and SP1 mutant samples , as well as the wild and domestic silkworm , respectively , with corrected P-value of 0 . 05 <5 and Log2-fold change>1 ., KEGG and GO enrichment analyses of DEGs were performed with an online platform ( http://www . omicshare . com/tools/ ) , using all the expressed genes ( FPKM >1 ) in the ova of virgin moth of Bombyx mori as background ., We used real-time PCR to evaluate the results of RNA-seq data ., The Ova o | Introduction, Results, Discussion, Materials and methods | Like other domesticates , the efficient utilization of nitrogen resources is also important for the only fully domesticated insect , the silkworm ., Deciphering the way in which artificial selection acts on the silkworm genome to improve the utilization of nitrogen resources and to advance human-favored domestication traits , will provide clues from a unique insect model for understanding the general rules of Darwins evolutionary theory on domestication ., Storage proteins ( SPs ) , which belong to a hemocyanin superfamily , basically serve as a source of amino acids and nitrogen during metamorphosis and reproduction in insects ., In this study , through blast searching on the silkworm genome and further screening of the artificial selection signature on silkworm SPs , we discovered a candidate domestication gene , i . e . , the methionine-rich storage protein 1 ( SP1 ) , which is clearly divergent from other storage proteins and exhibits increased expression in the ova of domestic silkworms ., Knockout of SP1 via the CRISPR/Cas9 technique resulted in a dramatic decrease in egg hatchability , without obvious impact on egg production , which was similar to the effect in the wild silkworm compared with the domestic type ., Larval development and metamorphosis were not affected by SP1 knockout ., Comprehensive ova comparative transcriptomes indicated significant higher expression of genes encoding vitellogenin , chorions , and structural components in the extracellular matrix ( ECM ) -interaction pathway , enzymes in folate biosynthesis , and notably hormone synthesis in the domestic silkworm , compared to both the SP1 mutant and the wild silkworm ., Moreover , compared with the wild silkworms , the domestic one also showed generally up-regulated expression of genes enriched in the structural constituent of ribosome and amide , as well as peptide biosynthesis ., This study exemplified a novel case in which artificial selection could act directly on nitrogen resource proteins , further affecting egg nutrients and eggshell formation possibly through a hormone signaling mediated regulatory network and the activation of ribosomes , resulting in improved biosynthesis and increased hatchability during domestication ., These findings shed new light on both the understanding of artificial selection and silkworm breeding from the perspective of nitrogen and amino acid resources . | Like other domesticates , nitrogen resources are also important for the only fully domesticated insect , the silkworm ., Deciphering the way in which artificial selection acts on the silkworm genome to improve the utilization of nitrogen resources , thereby advancing human-favored domestication traits , will provide clues from a unique insect model for understanding the general rules of Darwins theory on artificial selection ., However , the mechanisms of domestication in the silkworm remain largely unknown ., In this study , we focused on one important nitrogen resource , the storage protein ( SP ) ., We discovered that the methionine-rich storage protein 1 ( SP1 ) , which is divergent from other SPs , is the only target of artificial selection ., Based on functional evidence , together with key findings from the comprehensive comparative transcriptome , we propose that artificial selection favored higher expression of SP1 in the domestic silkworm , which would influence the genes or pathways vital for egg development and eggshell formation ., Artificial selection also consistently favored activated ribosome activities and improved amide and peptide biosynthesis in the ova , like what they may act in the silk gland to increase silk-cocoon yield ., We highlighted a novel case in which artificial selection could directly act on a nitrogen resource protein associated with a human-desired domestication trait . | invertebrates, animal types, moths and butterflies, artificial selection, domestic animals, animals, invertebrate genomics, silkworms, zoology, nutrient and storage proteins, proteins, insects, animal genomics, arthropoda, biochemistry, eukaryota, genetics, biology and life sciences, biosynthesis, genomics, evolutionary biology, evolutionary processes, organisms | null |
journal.pgen.1003586 | 2,013 | Interplay between Structure-Specific Endonucleases for Crossover Control during Caenorhabditis elegans Meiosis | Structure-specific endonucleases are required for several kinds of DNA repair processes such as nucleotide excision repair ( NER ) , DNA interstrand crosslink repair ( ICL ) and double-strand break repair ( DSBR ) ., Homologous recombination is an error free repair pathway because the broken DNA ends are repaired from templates consisting of either homologous sequence at the sister chromatids or the homologous chromosomes ., During meiotic recombination , at least one DNA double-strand break has to be repaired as a crossover ( obligate crossover ) by homologous recombination between non-sister chromatids of a homologous pair of chromosomes ., Crossover formation is essential for generating genetic diversity and promoting accurate chromosome segregation ., The double ( or single ) Holliday junction is believed to be the intermediate required to make a crossover product 1 ., The opposite sense resolution of the double Holliday junction results in crossover products , while the same sense resolution results in non-crossover products 2 ., Moreover , the convergent branch migration and decatenation of such intermediates , referred to as double Holliday junction dissolution , also results in non-crossover products ., Unprocessed double Holliday junctions are toxic for cycling cells ., Usually , branch migration during Holliday junction dissolution depends on the Bloom syndrome helicase ( BLM ) and the decatenation process is catalyzed by topoisomerase III 3 ., RMI1 and RMI2 are the essential cofactors of the dissolvasome , BTR ( BLM-TOP3-RMI1-RMI2 ) complex 4 , 5 ., If double Holliday junctions are not processed by the BTR complex then Holliday junction resolvases play an essential role in avoiding breaks observed at anaphase ., This outcome allowed for a synthetic lethal screen with sgs1 , which encodes the BLM helicase homolog in yeast , and identified Mus81/Slx3-Mms4/Slx2 and Slx1–Slx4 6 ., Importantly , this screening strategy did not identify Rad1-Rad10 , orthologs of the human XPF-ERCC1 , and Yen1 , the ortholog of human GEN1 , because sgs1Δyen1Δ and sgs1Δrad1Δ are viable 7 ., In vitro , MUS81-EME1 , SLX1–SLX4 , and GEN1 have Holliday junction resolvase activity 8–14 ., While the 3′flap nuclease activity of Rad1 in yeast constitutes its main function during homologous recombination 15 , the XPF homolog MEI-9 is required for the majority of the crossovers formed in Drosophila 16 ., MUS81 , SLX1 and XPF require EME1 , SLX4 and ERCC1 , respectively , for nuclease activity ., SLX4 also acts as a scaffolding protein for several DNA repair proteins including MUS81 and XPF 10–12 ., It is reported that the D . melanogaster and C . elegans orthologs of SLX4 ( MUS312 and HIM-18 , respectively ) are required for crossover formation during meiosis 17 , 18 ., In C . elegans meiosis , it is proposed that between 5 and 12 DSBs are evenly distributed along each pair of chromosomes 19–21 and one of the DSB sites is designated as a future crossover site by COSA-1 , MSH-5 and ZHP-3 22 , 23 ., The number of crossovers is tightly regulated as only a single crossover occurs between each homologous chromosome pair ., Crossover distribution is also regulated in many organisms ., For example , crossover formation is suppressed at centromeres and telomeres in budding yeast 24 ., It is also known that the single interhomolog crossover is frequently located at the terminal quarters of the autosomes and the terminal thirds of the X chromosome in C . elegans 25 , 26 ., Interestingly , crossover formation is suppressed at the center of the chromosomes compared to the arm regions ., Recently three studies identified mutants that showed decreased levels of crossover suppression at the center of the autosomes 21 , 27 , 28 ., The molecular mechanisms responsible for this suppression remain to be elucidated , and one of the factors required for this crossover suppression at the center region is SLX-1 21 ., Despite the importance of Holliday junction resolution , severe meiotic defects have not been reported among single mutants for most of the structure-specific endonucleases in yeast , flies , mice or worms , with notable exceptions including mus81 and eme1 mutants in fission yeast , and mei-9 in flies 9 , 16 ., Moreover , it is still not known whether these structure-specific endonucleases exhibit a Holliday junction resolution activity in vivo ., To investigate whether these four structure-specific endonucleases coordinately function to form crossovers during meiotic prophase , likely as Holliday junction resolvases , we took advantage of the ease of genetic analysis and the power of high-resolution imaging in the well-defined spatial-temporal distribution of germline nuclei in C . elegans ., We made single , double , triple and quadruple mutants of the structure-specific nucleases and analyzed phenotypes indicative of errors in chromosome segregation ( decreased brood size , increased embryonic lethality , larval arrest and incidence of male offspring ) , as well as crossover designation , frequency and distribution , and bivalent morphology ., Our studies demonstrate that:, 1 ) HIM-18 interacts with MUS-81 , SLX-1 and XPF-1;, 2 ) XPF-1 acts redundantly with MUS-81 and SLX-1 for crossover formation; and, 3 ) SLX-1 exhibits a region-specific crossover suppression activity ., We propose that the structure-specific endonucleases coordinately function for both positive and negative control of a crossover ., Moreover , this is the first report implicating the redundant actions of XPF-1 with both MUS-81 and SLX-1 in crossover formation ., To understand the interaction networks between structure-specific endonucleases , we performed a matrix-based yeast two-hybrid assay by using full-length constructs generated from cDNA for him-18 , slx-1 , xpf-1 , ercc-1 , mus-81 , eme-1 ( F56A6 . 4 ) , gen-1 and rad-54 ( Figure 1 ) ., Although the ortholog of human EME1 ( F56A6 . 4 ) was not previously known in C . elegans , we found it through a BLAST search ., We detected seven protein-protein interactions , HIM-18-SLX-1 , HIM-18-XPF-1 , HIM-18-MUS-81 , XPF-1-ERCC-1 , MUS-81-EME-1 , HIM-18-HIM-18 and ERCC-1-ERCC-1 ., We confirmed conserved interactions between each nuclease and its non-catalytic subunit , HIM-18-SLX-1 , XPF-1-ERCC-1 and MUS-81-EME-1 ., Similar to mammals , HIM-18 interacts with three structure-specific endonucleases , namely SLX-1 , XPF-1 and MUS-81 ., We detected two novel self-interactions: HIM-18-HIM-18 and ERCC-1-ERCC-1 ., These interactions support the suggestion that SLX4 and ERCC1 could make a very large ( 2M Dalton ) protein complex in human HEK293 cells 10 ., Although it is known that Mus81 interacts with Rad54 in S . cerevisiae 29 , we did not detect this interaction in C . elegans ., Interestingly , GEN-1 did not interact with any structure-specific endonucleases or their regulatory subunits ., However , we cannot rule out the possibility that post-translational modification-dependent interactions might have been missed in a yeast two-hybrid assay ., These data suggest that the structure-specific endonucleases identified thus far can be categorized into two classes , one consisting of HIM-18-associated nucleases ( SLX-1 , XPF-1 and MUS-81 ) and the second consisting of GEN-1 ., To investigate whether the structure-specific nucleases play a role in the germline , we measured the brood size in mus-81 , slx-1 , xpf-1 and gen-1 mutants ( Figure 2A and Table S1 ) ., A decreased brood size is suggestive of increased sterility ., The brood size was reduced to 60 . 5% and 68% of wild type levels in mus-81 and slx-1 single mutants , respectively , while there was no significant reduction in brood size for either xpf-1 ( 89% ) or gen-1 ( 111 . 9% ) single mutants compared to wild type ., The increased sterility observed for mus-81 and slx-1 mutants suggests that MUS-81 and SLX-1 are required for normal germline function ., It is known that mutants of Holliday junction resolvases show synthetic lethality with mutants of the Bloom syndrome helicase gene in yeast and mammals 6 , 30–32 ., To investigate whether mus-81 , slx-1 , xpf-1 and gen-1 genetically interact with him-6 , we made double mutants of each nuclease with him-6 and examined the brood size , levels of embryonic lethality , larval arrest and the incidence of males detected among their progeny ( Figure 2 and Table S1 ) ., Increases in either embryonic lethality or larval arrest are suggestive of mitotic defects ., A high incidence of males ( Him phenotype ) is indicative of increased X chromosome nondisjunction and correlates with meiotic defects , whereas a combination of increased embryonic lethality accompanied by a high incidence of males is suggestive of increased aneuploidy resulting from meiotic missegregation of both autosomes and the X chromosome , respectively 33 ., mus-81 and slx-1 show synthetic mitotic defects with him-6 while xpf-1 and gen-1 do not ., However , due to the lack of viable adult progeny , which impedes assessing the frequency of males , we cannot rule out the possibility of synthetic meiotic defects as well ., These results suggest that MUS-81 and SLX-1 , but not XPF-1 and GEN-1 , are essential in processing recombination intermediates in the absence of HIM-6 ., To examine whether the structure-specific nucleases play either distinct or overlapping roles in the mitotic and/or meiotic programs , we measured embryonic lethality , larval arrest and the incidence of males observed among the progeny of single , double , triple and quadruple mutants of mus-81 , slx-1 , xpf-1 and gen-1 ( Figure 2 and Table S1 ) ., While only an average of 7% and 7 . 6% embryonic lethality was observed respectively in mus-81 and xpf-1 single mutants , 70% embryonic lethality was observed in mus-81;xpf-1 double mutants ( Figure 2B and Table S1 ) ., Synergistic effects were also observed regarding the phenotype of larval arrest in mus-81; xpf-1 double mutants , where 57 . 1% larval arrest was observed among the surviving progeny , compared to 3 . 9% and 1 . 3% in the mus-81 and xpf-1 single mutants , respectively ( Figure 2C and Table S1 ) ., mus-81;xpf-1 double mutants also exhibited a higher incidence of males ( 8% males ) among their progeny , indicative of X chromosome nondisjunction , compared to mus-81 and xpf-1 single mutants with 0 . 5% and 1 . 5% , respectively ( xpf-1 vs . xpf-1;mus-81 , P<0 . 0025 ) ( Figure 2D and Table S1 ) ., These results suggest that compensating activities of MUS-81 and XPF-1 are required for embryonic viability , larval development and proper X chromosome segregation ., A similar outcome is observed when examining the same phenotypes described above in slx-1;xpf-1 double mutants compared to each single mutant ( P<0 . 0001 ) ( Figure 2B–2D and Table S1 ) ., These results suggest that SLX-1 and XPF-1 exhibit synergistic roles when it comes to embryonic , larval and meiotic development ., Unlike slx-1 single mutants , gen-1 single mutants exhibit neither increased sterility nor increased larval arrest compared to wild type ( Figure 2A and Table S1 ) ., However , the brood size of slx-1;gen-1 double mutants decreased to 46% of slx-1 and 28% of gen-1 single mutants ( P<0 . 0001 ) ( Figure 2A and Table S1 ) ., The frequency of larval arrest observed among the progeny of slx-1;gen-1 double mutants increased 5 . 3-fold compared to slx-1 and 26-fold compared to gen-1 single mutants ( P<0 . 0001 ) ( Figure 2C and Table S1 ) ., However , there is no genetic interaction between slx-1 and gen-1 with regard to embryonic lethality or X chromosome nondisjunction ( Figure 2B–2D and Table S1 ) ., Therefore , these results suggest that SLX-1 can fully compensate for absence of GEN-1 during gametogenesis and larval development , whereas GEN-1 can only partially compensate for loss of SLX-1 ., It is known that a single crossover occurs at the terminal quarters along autosomes and at the terminal thirds along the X chromosome in C . elegans meiosis 25 , 26 ., Recently , the boundaries between different chromosome domains ( tips , arms and center regions ) have been reported using high-density single-nucleotide polymorphism ( SNP ) genotyping on a large panel of recombinant inbred advanced intercross lines ( RIAILs ) in C . elegans 26 ( Figure 3A ) ., Recombination was not observed at either the left or the right tips ( ∼0 . 5 Mb from telomeric ends ) of each chromosome 26 ., To examine whether the four structure-specific nucleases show either distinct or overlapping roles in meiotic crossover formation we compared crossover frequencies and distribution as in 34 along both chromosomes V and X between wild type and all single , double , triple and quadruple mutants ( Figure 3 ) ., Specifically , we monitored four SNP sites located near the ends of chromosomes V and X ( positions a and, d ) , and at the boundaries between the arms and the center ( positions b and, c ) of these two chromosomes ( Figure 3A ) ., The SNP sites selected were closely juxtaposed to the boundaries defined in the Rockman and Kruglyak study 26 ., This analysis allowed us to compare the crossover frequency and distribution on the left arm , center and right arm of these chromosomes ., We assayed 48 cM and 49 . 6 cM intervals corresponding to 96 . 7% and 96 . 9% of the whole lengths of chromosomes V and X , respectively ( Figure 3A to 3D ) ., None of the single mutants tested exhibited a statistically significant change in the crossover frequency in this interval ( interval a–d ) for either chromosome compared to wild type ( Figure 3D ) ., However , we observed a significant reduction of crossover frequency in all mus-81; xpf-1 and slx-1; xpf-1 backgrounds ( Figure 3D ) ., Specifically , we observed the following frequencies compared to wild type on chromosomes V and X , respectively: mus-81;xpf-1 ( 65% and 41% of wild type ) , mus-81;xpf-1;gen-1 ( 67% and 55% ) , mus-81slx-1;xpf-1;gen-1 ( 67% and 31% ) , slx-1;xpf-1 ( 81% and 68% ) , and slx-1;xpf-1;gen-1 ( 76% and 69% ) ( Figure 3D ) ., Notably , while the following mutants exhibited a significant decrease ( P<0 . 05 ) in crossover frequency when compared to wild type for the a–d interval on chromosome V: mus-81; xpf-1 ( P\u200a=\u200a0 . 0041 ) , slx-1;xpf-1 ( P\u200a=\u200a0 . 0133 ) , mus-81 slx-1; xpf-1 ( P\u200a=\u200a0 . 0870 , borderline significance likely due to the low N-value ) , and mus-81;xpf-1;gen-1 ( P\u200a=\u200a0 . 0142 ) ( Table S2 ) , we no longer observed this significance after Sidak correction for multiple comparisons ( Figure 3D and Table S3 ) ., However , this may be an artifact of the stringency of the Sidak correction as suggested by our observations of increased embryonic lethality and frequency of males in these mutants accompanied by chromosomal abnormalities in late meiotic prophase I ( Figure 2 and see below ) ., These data therefore suggest that MUS-81 , XPF-1 and SLX-1 all contribute to crossover formation , with XPF-1 functioning redundantly with MUS-81 and SLX-1 , respectively ., This is further supported by the high embryonic lethality observed in all the mus-81; xpf-1 and slx-1; xpf-1 , but not mus-81slx-1 , backgrounds ( Figure 2B and Table S1 ) ., Interestingly , no further significant reduction was observed when a gen-1 mutation was introduced into the mus-81;xpf-1 or slx-1; xpf-1 backgrounds ., Therefore , C . elegans GEN-1 does not seem to be involved in crossover formation , although the accompanying study by ONeil et al . 35 observed that microinjection of human GEN1 rescues the accumulation of joint molecules in mus-81;xpf-1 double mutants ., Finally , crossover frequency on the X chromosome of mus-81;xpf-1 double mutants is 60% of the level observed in slx-1;xpf-1 double mutants ( P\u200a=\u200a0 . 0101 ) ., Furthermore , introduction of an slx-1 mutation did not further affect the crossover frequency observed in mus-81;xpf-1 double mutants ., Thus , a mus-81 mutation causes a more severe effect than an slx-1 mutation in the xpf-1 background ., Taken together , these data suggest that both MUS-81 and SLX-1 have either redundant or compensatory roles with XPF-1 in regulating crossovers on both autosomes and the X chromosome ., In general , only a single crossover occurs between each pair of homologous chromosomes during prophase I of C . elegans meiosis 36 ., Therefore , crossover interference , by which the formation of an interhomolog crossover in a given chromosome region discourages formation of additional crossovers nearby , is strictly enforced in C . elegans 37 ., Thus , the occurrence of multiple crossover events between homologous chromosomes indicates misregulation of crossover interference in this system ., While we did not observe multiple crossovers in wild type for either chromosomes V or X , these were observed in multi-nuclease mutants ( Figure 3D ) ., For example , 4 . 1% and 7 . 1% of total crossover events , calculated as in 34 , were double crossovers on chromosome V in slx-1;xpf-1;gen-1 triple and mus-81slx-1;xpf-1;gen-1 quadruple mutants , respectively ., These results are consistent with the observations by Agostinho et al . 38 and suggest that structure-specific nucleases may be involved in the regulation of crossover interference ., As we previously reported , crossover distribution in slx-1 mutants shifts to the center of chromosome V ( 35 . 7% of total crossovers ) where crossover formation is tightly suppressed in wild type ( 20 . 7% of total crossovers ) ( P\u200a=\u200a0 . 0312; Fishers Exact test ) ( Table S2 ) ., Notably , this statistical significance is no longer observed following Sidak correction for multiple comparisons ( P\u200a=\u200a0 . 3784 ) ( Figure 3D and Table S3 ) ., However , there are additional phenotypes that can be explained , at least in part , by a deregulation in crossover distribution , such as the increased embryonic and larval lethality , decreased brood size , and increased chromosome abnormalities ( see below ) observed in slx-1 single mutants ., In addition , a similar shift was observed in Agostinho et al . 38 ., These results suggest that SLX-1 exhibits either anti-crossover activity or pro-non-crossover activity at the center region of the autosomes ., The increased crossover levels previously detected on the center of the X chromosome in slx-1 mutants compared to wild type 21 were not recapitulated here ., This is due to the interval previously used in this analysis , which included regions of both the center and the right arm of this chromosome ., In our current study , the more strictly defined SNP sites at the boundaries of the arms and the center , allow us to observe a more precise crossover distribution in each chromosome domain and to correlate our findings to the global analysis presented for wild type in Rockman and Kruglyak 26 ., Therefore , we conclude that SLX-1 does not inhibit interhomolog crossover formation at the center region of the X chromosome ., ZHP-3 , the C . elegans homolog of S . cerevisiae Zip3 , is a crossover-promoting factor 23 , 39 ., ZHP-3 initially localizes along the length of chromosomes , but becomes restricted to six distinct foci per nucleus in late pachytene , marking the six crossover precursor sites ( one per homolog pair ) 23 , 39 ., Since SNP analysis revealed that crossover frequency was reduced in mus-81;xpf-1 and slx-1;xpf-1 double mutants , we assessed the number of ZHP-3 foci per nucleus in the nuclease-defective mutants to determine if a similar reduction in ZHP-3 foci could be observed ( Table 1 ) ., All single mutants , as well as all double , triple , and quadruple nuclease mutant combinations did not result in changes in the number of ZHP-3 foci ( ∼6 per nucleus ) ., This is consistent with the observed number of ZHP-3 foci in the single nuclease-defective mutants and the double nuclease-defective mutants by Agostinho et al . and ONeil et al . 35 , 38 ., Thus , the reduction in crossover frequency observed for the mus-81;xpf-1 and slx-1;xpf-1 backgrounds by SNP analysis could not be detected by cytological crossover analysis based on scoring the number of ZHP-3 foci per nucleus ., This outcome suggests that crossover designation is not drastically affected in the nuclease-defective mutants , but that subsequent resolution of those events into crossovers is impaired ., Increased sterility , embryonic lethality , and X chromosome nondisjunction in the absence of the MUS-81 , XPF-1 , SLX-1 , and GEN-1 endonucleases indicate defects in meiotic chromosome segregation ., To observe the meiotic chromosomal defects , we examined chromosome morphology in −1 oocytes at diakinesis and +1 oocytes with chromosomes at anywhere from prometaphase I to anaphase I in the endonuclease mutants ( Figure 4A–H ) ., Following formation of the single off-centered crossover between homologous chromosomes , the bivalents remodel around the crossover site adopting a cruciform-structure comprised of a short and long arms ( Figure 4C ) 40 ., To monitor bivalent morphology , and precisely examine homolog attachment , we stained oocytes with DAPI and antibodies recognizing the meiosis-specific α-kleisin REC-8 and the aurora B kinase AIR-2 41 , 42 ., REC-8 localizes along both the long and short arms during diakinesis , and is removed at the short arm at anaphase I , thereby allowing homologs to segregate to opposite poles of the spindle 41 ., AIR-2 localizes as two rings only along the short arm of the bivalents until the metaphase to anaphase I transition and it has been proposed to phosphorylate REC-8 along the short arm , thus promoting its turnover 18 , 43 ., The bivalents in the endonuclease-defective mutants exhibited a range of chromosome defects suggestive of impaired DSBR and/or lack of mature interhomolog crossover formation , which included chromatin bridges , premature homolog separation , DNA fragments , and a frayed appearance ( Figure 4 ) 18 , 44–48 ., The xpf-1 and slx-1 single mutants exhibited elevated numbers of oocytes carrying chromosomal aberrations ( 38% ( 17/45 ) and 43% ( 10/23 ) ; P<0 . 0001 , respectively; Figure 4A ) , whereas these were not drastically increased in the mus-81 and gen-1 single mutants ( 0% ( 0/21 ) , P\u200a=\u200a1 , and 4% ( 1/26 ) , P\u200a=\u200a0 . 23 , respectively ) ., However , all double , triple , and quadruple nuclease mutant combinations showed an increase in the frequency of oocytes with chromosomal abnormalities compared to wild type ( Figure 4A ) ., Thus , the absence of either SLX-1 or XPF-1 individually increases the frequency of oocytes with aberrations and any combination of mutations in slx-1 , xpf-1 , mus-81 , or gen-1 also results in defective bivalent morphology ., Analysis of chromosomes in the oocytes in the nuclease-defective mutants revealed chromatin bridges within bivalents ( intrabivalent ) and between bivalents ( interbivalent; Figure 4 ) ., With the exception of the mus-81 single mutant ( 0%; 0/21 ) and the gen-1 single mutant ( 0%; 0/26 ) , all single , double , triple , and quadruple nuclease mutants had higher than wild type levels ( 0%; 0/88 ) of chromatin bridges ( intrabivalent and/or interbivalent ) , observed in anywhere from 6% to 93% of oocytes ., Among the other single nuclease mutants ( xpf-1 ( 16%; 7/45 ) and slx-1 ( 26%; 6/23 ) ) , the slx-1 mutant had the highest frequency of oocytes with chromatin bridges within bivalents and/or between bivalents ., The double mutant combinations with the highest frequency of oocytes with chromatin bridges included: the slx-1;xpf-1 ( 85%; 17/20 ) and the mus-81; xpf-1 mutants ( 45%; 15/33 ) ., Interbivalent chromatin bridges were observed in the slx-1 single mutant ( 9%; 2/23 ) , slx-1;xpf-1 double mutant ( 15%; 3/20 ) , and the xpf-1;gen-1 double mutant ( 21%; 5/24 ) ., We hypothesize that the chromatin bridges between bivalents arise from multichromatid strand invasions that can be removed by nucleases or are typically prevented by helicases ., High levels of oocytes with chromatin bridges have also been observed for these double mutants in the accompanying studies by Agostinho et al . and ONeil et al 35 , 38 ., The xpf-1 mutation tended to increase the frequency of oocytes where homologs have dissociated prematurely , as indicated by bivalents that have been separated at the short arm and confirmed by the presence of uncoupled AIR-2 rings ( 6%–30%; Figure 4 ) ., Separation at the short arm likely indicates that fragile chromatin connections between homologs have been broken ., With the exception of the mus-81; xpf-1 double mutant and mus-81 slx-1; xpf-1 triple mutant , all strains lacking XPF-1 had a statistically significant increased level of dissociated bivalents compared to wild type ( Figure 4A ) ., The mus-81; xpf-1 and mus-81 slx-1; xpf-1 mutants had a borderline increase in prematurely dissociated homologs ( P\u200a=\u200a0 . 079 and P\u200a=\u200a0 . 063 , respectively ) ., The slx-1 single mutant ( 9%; 2/23 ) , most double mutant combinations , all triple mutants , and the quadruple mutant had DNA fragments in 9% to 29% of oocytes ( Figure 4 ) ., The mus-81 slx-1 and xpf-1; gen-1 double mutants were the only mutant combinations that did not exhibit increased DNA fragments compared to wild type ( P\u200a=\u200a0 . 35 and P\u200a=\u200a0 . 22 , respectively ) ., Thus , absence of XPF-1 , SLX-1 , or combinations of any two of the four nucleases generally increased the occurrence of chromosomal abnormalities , which is consistent with the genome instability revealed by their concomitant increase in sterility , embryonic lethality , and male progeny ., These plate phenotypes likely resulted from failure to properly process recombination intermediates without those endonucleases as apparent by the aberrant bivalent morphology ., The nuclease-defective mutants were prone to contain oocytes with chromatin bridges , which supports the role of these nucleases in joint molecule resolution ., Based on our crossover analysis , MUS-81 , SLX-1 and XPF-1 are factors contributing to obligate crossover formation ( Figure 5A ) ., However , because mus-81slx-1; xpf-1; gen-1 quadruple mutants still show 67% and 31% of crossovers on chromosomes V and X , respectively , this suggests that additional nucleases involved in meiotic crossover formation may exist in C . elegans ., Recently , it has been shown that the biochemically characterized resolvases , Yen1 , Mus81-Mms4 , Slx1–Slx4 , the Bloom syndrome helicase homolog Sgs1 and a mismatch repair complex , Exo1-Mlh1-Mlh3 , are required for joint molecule resolution in yeast 49 ., Although exo-1 and mlh-1 single mutants are viable in C . elegans ( T . Saito et al . , unpublished results and 35 , 50 , 51 ) , we cannot eliminate the possibility that EXO-1 and MLH-1 may act in a redundant manner during crossover formation in this organism ., There is no MLH-3 ortholog in C . elegans; however , we have found that its potential nuclease motif , DQHAX2EX4E , is conserved in PMS-2 52 ., Furthermore , FAN-1 , which interacts with MLH-1 and is required for interstrand crosslink repair , may also act as a Holliday junction resolvase because the VRR_NUC domain , which is conserved in the FAN-1 homolog of the archaeon Sulfolobus solfataricus , can cleave Holliday junctions 53 ., Another aspect to consider , regarding Holliday junction resolution , is that it is reminiscent of the decatenation activity exhibited by the type I topoisomerase ., This is supported by reports that the vaccinia virus topoisomerase and the human topoisomerase I can resolve synthetic Holliday junctions 54 , 55 ., Thus , it will be interesting to investigate whether the C . elegans topoisomerase family of proteins can cleave artificial Holliday junctions in vitro and whether they are required for crossover formation in vivo ., In addition , the SLX4/HIM-18 complex remains a possible candidate because recently another nuclease , the human SNM1B/Apollo , was found to interact with SLX4 56 ., The SNM1B homolog in C . elegans is MRT-1 , which is required for DNA crosslink repair and telomerase activity 57 ., Finally , LEM-3/Ankle1 , a protein that contains an Ankyrin repeat , LEM domain ( for lamina-associated polypeptide , emerin , MAN1 domain ) and a GIY-YIG type nuclease domain that is also found in SLX-1 , has been recently identified ., LEM domain-containing proteins connect the nuclear membrane and chromatin ., Chromatin immunoprecipitation analysis ( ChIP-on-chip and ChIP-seq ) revealed that histone H3K9me and LEM-2 are enriched at the arm regions of the chromosomes , where crossovers are frequently observed 58–60 ., Further studies will therefore aim to identify the additional crossover-specific Holliday junction resolvases operating during meiosis ., It has been reported that XND-1 , HIM-5 and SLX-1 are required for the suppression of crossovers at the center of the autosomes 21 , 27 , 28 ., However , the molecular mechanism for this chromosome region-specific crossover suppression is unknown ., A possible mechanism for crossover suppression may involve same sense resolution of double Holliday junctions at the center of chromosomes by SLX-1 ( Figure 5B and 5C ) ., In addition , it is known that there are epigenetic differences between the arms and the center region of chromosomes in C . elegans ( Figure 5C ) 60 ., Specifically , histone H3K9 me1/2/3 is enriched at the arm regions and H3K4me3 is enriched at the center in embryonic and larval stages ., Whether this kind of epigenetic mark is maintained during meiotic recombination in the mature germline ( adult worms ) remains to be determined ., Given the presence of a PHD-finger in SLX-1 , it remains to be tested whether SLX-1 may act as a region specific crossover suppressor in part by recognizing these or other epigenetic marks defining chromosome domains or boundaries ., How is crossover formation regulated in C . elegans meiosis ?, It has been previously estimated that the number of DSBs is around 5–12/homologous chromosome pair during meiotic prophase in C . elegans 19–21 ., Therefore , it remains unclear how only one of these DSBs is destined for repair as an interhomolog crossover while all other DSBs are repaired as noncrossovers including interhomolog noncrossovers , intersister crossovers and intersister noncrossovers ., If there is no bias in how either a single or double Holliday junction is resolved , the expectation is that the crossover/noncrossover ratio should be 1∶1 ., However , only opposite-sense resolution of a double Holliday junction allows for crossover formation ., Since crossovers are essential during meiosis , biasing factors that reinforce opposite-sense resolution must exist to ensure crossover formation ., COSA-1 , MSH-5 and ZHP-3 are factors that promote crossover formation but it is not known if their biochemical activities directly promote opposite-sense resolution ., Based on our observations , Holliday junction resolvases do not play a role in designating a single DSB as the site destined for repair as an interhomolog crossover ( Table 1 ) ., One possible hypothesis to explain the single crossover at one of the arm regions is that there are both interhomolog and noncrossover biases operating at the arm regions ( Figure 5C ) ., Once one of the DSBs at the arm region is marked by pro-crossover factors , such as ZHP-3 , MSH-4 , MSH-5 and COSA-1 , the designated DSB site may undergo resolution by the redundant activities of HIM-18-binding nucleases , SLX-1 , MUS-81 and XPF-1 ., A double nicked Holliday junction cleaved by Mus81-Eme1 in yeast 61 has been suggested to only result in a crossover ., In C . elegans , SLX-1 , XPF-1 or MUS-81 may act on a recombination intermediate consisting of a D-loop and a half junction , which resembles two nicked Holliday junctions , to form a crossover ( Figure 5C ) ., Further studies will reveal the biochemical activities of these proteins in more detail ., Given the role of RTEL-1 in catalyzing D-loop disruption in vitro 48 , we propose that all undesignated DSBs at the arm regions are converted into noncrossover products via an RTEL-1-dependent SDSA pathway ., In buddin | Introduction, Results, Discussion, Materials and Methods | The number and distribution of crossover events are tightly regulated at prophase of meiosis I ., The resolution of Holliday junctions by structure-specific endonucleases , including MUS-81 , SLX-1 , XPF-1 and GEN-1 , is one of the main mechanisms proposed for crossover formation ., However , how these nucleases coordinately resolve Holliday junctions is still unclear ., Here we identify both the functional overlap and differences between these four nucleases regarding their roles in crossover formation and control in the Caenorhabditis elegans germline ., We show that MUS-81 , XPF-1 and SLX-1 , but not GEN-1 , can bind to HIM-18/SLX4 , a key scaffold for nucleases ., Analysis of synthetic mitotic defects revealed that MUS-81 and SLX-1 , but not XPF-1 and GEN-1 , have overlapping roles with the Bloom syndrome helicase ortholog , HIM-6 , supporting their in vivo roles in processing recombination intermediates ., Taking advantage of the ease of genetic analysis and high-resolution imaging afforded by C . elegans , we examined crossover designation , frequency , distribution and chromosomal morphology in single , double , triple and quadruple mutants of the structure-specific endonucleases ., This revealed that XPF-1 functions redundantly with MUS-81 and SLX-1 in executing crossover formation during meiotic double-strand break repair ., Analysis of crossover distribution revealed that SLX-1 is required for crossover suppression at the center region of the autosomes ., Finally , analysis of chromosome morphology in oocytes at late meiosis I stages uncovered that SLX-1 and XPF-1 promote meiotic chromosomal stability by preventing formation of chromosomal abnormalities ., We propose a model in which coordinate action between structure-specific nucleases at different chromosome domains , namely MUS-81 , SLX-1 and XPF-1 at the arms and SLX-1 at the center region , exerts positive and negative regulatory roles , respectively , for crossover control during C . elegans meiosis . | Abnormalities in the number and/or structure of the chromosomes can result in cancer , birth defects , miscarriages and infertility ., In particular , the exchange of genetic material ( crossover formation ) between maternal and paternal chromosomes during the cell division program of meiosis is essential to produce normal sperm and eggs ., Homologous recombination is the pathway utilized to make crossovers , and resolution of recombination intermediates known as Holliday junctions is the final step of homologous recombination ., Four structure-specific endonucleases , MUS-81 , SLX-1 , XPF-1 and GEN-1 , have been recently proposed to act as Holliday junction resolvases ., However , how these nucleases work in vivo was unknown ., Using Caenorhabditis elegans as a model system , we analyzed all possible mutant combinations for these structure-specific endonucleases ., We found that XPF-1 has a redundant role with both MUS-81 and SLX-1 in promoting crossover formation ., Interestingly , SLX-1 is required for proper suppression of crossovers at the center region of the autosomes ., Therefore , these studies shed new light on our understanding of the mechanisms regulating both the frequency as well as the distribution of crossover recombination events during meiosis . | biology | null |
journal.ppat.1002788 | 2,012 | Control of Virulence by Small RNAs in Streptococcus pneumoniae | Gene regulation and intercellular communication are fundamental aspects of bacterial adaptation to dynamic environments ., As such , bacteria have evolved numerous strategies to facilitate tight control of genetic networks in response to diverse extracellular stimuli ., Roles have been described for DNA , RNA and protein in gene regulation ., Only recently have we begun to appreciate the global roles of sRNAs , particularly in regards to bacterial pathogenesis , as the traditional genetic screens for virulence factors have typically not focused on these small , rarely annotated sRNAs ., In recent years there has been a constantly expanding repertoire of sRNAs being identified in a number of bacterial pathogens using both tiling arrays as well as high-throughput sequencing of RNA ( RNA-seq ) ., Bioinformatic approaches have also predicted numerous sRNAs in many bacterial pathogens indicating a high prevalence of sRNAs encoded by diverse bacterial species 1 , 2 ., The increasingly important role of sRNAs in controlling gene expression in bacteria suggests a subset of these molecules may have roles in bacterial virulence 3 , 4 ., One of the more compelling cases for the role of sRNAs in bacterial pathogenesis arose from studies of Hfq , a chaperone providing stability to sRNA , which substantially advanced our knowledge of the diversity and functional roles of sRNAs in bacteria 5 ., Homologs of Hfq are found in diverse species of Gram-negative and Gram-positive bacterial pathogens 6 ., Deleting Hfq , which has pleiotropic effects on the stability of several sRNAs , predictably results in numerous phenotypes , mainly consisting of resistance to various environmental stresses , suggesting potential roles in host pathogenesis 6 , 7 , 8 ., There are also numerous examples of sRNAs that function independently of Hfq , even in bacterial species that encode the chaperone ., While deletion of Hfq in Listeria has a discernable effect on virulence , its absence does not affect the level of expression of sRNAs 7 , 9 ., Additionally , deletion of Hfq in S . aureus was found to have no detectable effect on the microbial stress response nor the function of sRNAs 10 ., Despite the apparent absence of Hfq , pathogenic streptococci nonetheless encode and express an abundance of sRNAs 11 , 12 , 13 ., In S . pyogenes , the regulatory RNAs RivX and FasX have been implicated in virulence gene regulation and interactions with host cells , respectively 14 , 15 , 16 , 17 ., Additionally , a specific sRNA , tracrRNA , serves a central function in the CRISPR system that mediates the silencing of foreign nucleic acid sequences 18 ., Regulatory RNAs targeting virulence gene expression in streptococci function both at the transcriptional and translational levels 19 ., The interactions of sRNAs are complex , with examples of the same sRNA functioning to both activate and repress target genes by a number of mechanisms 20 ., Despite the increase in our knowledge of sRNAs , their contribution to virulence has been much less well established though examples have been demonstrated 3 , 21 , 22 ., In S . pyogenes , deletion of the 4 . 5S RNA component of the signal recognition particle pathway results in significant attenuation of tissue disease 23 ., S . aureus encodes numerous sRNAs , of which the best characterized example is RNAIII , which coordinates the expression of virulence genes 24 , 25 , 26 , 27 , 28 ., Examination of the transcriptome of L . monocytogenes indicated the presence of several sRNAs implicated in pathogenesis that were not found in closely related non-pathogenic species 9 , 29 ., Recent reports have also shown sRNAs being involved in pathogenesis in Salmonella and Yersinia 30 , 31 ., Despite these examples , the contribution of the vast majority of sRNAs to bacterial pathogenesis , particularly in Streptococcus pneumoniae , remains uncharacterized ., S . pneumoniae is a leading cause of childhood mortality worldwide and is a major health concern despite widespread vaccination ., The pneumococcus is remarkably adept at colonizing and infecting diverse niches in the human body , readily establishing itself as a commensal in the nasopharynx in over 40% of healthy individuals as well as being a major causative agent of pneumonia , otitis media , sepsis , and meningitis 32 , 33 ., A number of well characterized virulence genes have tissue-restricted virulence phenotypes , underscoring the diverse pneumococcal arsenal for targeting dissimilar host tissues 34 , 35 ., One major facet of gene regulation is the set of 13 two-component systems ( TCSs ) encoded in the pneumococcal genome that control a multitude of gene networks and are implicated in pathogenesis 36 ., Included in these networks are sRNAs , some of which are controlled by the CiaR response regulator in the pneumococcus 37 ., This phenomenon is not restricted to pneumococci , as other streptococcal species harboring CiaR also are predicted to encode numerous sRNAs , indicating that downstream sRNAs may be an important facet of regulation by this TCS 38 ., Of the sRNAs identified thus far in the pneumococcus , none have been found to play a definitive role in the regulation of virulence genes or networks ., A substantial number of sRNAs have been predicted in the sequenced pneumococcal reference strains D39 and TIGR4 using bioinformatics , tiling arrays , and sequencing 11 , 12 , 39 ., However , none have been assigned a role in host pathogenesis ., To address this possibility , we undertook a sequencing based approach to identify sRNAs in pneumococcus coupled with both targeted and random gene deletions to ascertain the impact of sRNAs on pneumococcal disease ., We present data identifying sRNAs in the pneumococcus by RNA sequencing ( RNA-seq ) ., Furthermore , using both transposon mutagenesis ( Tn-seq ) and targeted deletions , we describe data indicating that many sRNAs play vital roles in progression of infection with unique sRNAs being required for specific tissue tropism ., These data provide the first comprehensive analysis of the contribution of sRNAs to pneumococcal pathogenesis and greatly expand the repertoire of sRNAs that play definitive roles in bacterial virulence ., To initially identify sRNAs , we isolated , enriched , and fully sequenced small ( <200 nt ) transcripts of the TIGR4 strain of pneumococcus ., To broaden sRNA capture , we also analyzed mutants in genes encoding the response regulator of three two-component systems ( TCS ) : GRR ( TCS03 ) , CbpR ( TCS06 ) , and VncR ( TCS10 ) - all of which influence the expression of many transcripts in pneumococcus 40 , 41 ., TCSs monitor environmental cues to precisely control networks of gene expression; elimination of TCS control could potentially broaden total transcript abundance and thereby capture sRNAs that would otherwise be overlooked ., In addition , TCSs have been shown to control the expression of sRNAs both in Gram-negative and Gram-positive bacteria , both as positive and negative regulators 37 , 42 ., The TCS mutants and TIGR4 were sequenced individually and the data were pooled to generate the composite of sRNAs ., For each strain analyzed , coverage exceeded 99 . 9% with a read depth ranging from 100–400 providing high confidence in sequence quality ., The data were next processed to eliminate all sequences within known ORFs to focus on intergenic regions or those running antisense to known ORFs as well as further constraints as detailed in the methods ., The position of the identified sRNAs both from our analysis and previous reports were mapped to the TIGR4 genome ., The sRNAs were found to be more abundant on the positive strand , though numerous sequences were identified on the negative strand ( Figure 1 ) ., We identified 89 putative sRNAs ( Table 1 ) ., Of these , 56 were novel and the rest have been recently identified by various studies ( Table 1 , column 11 ) ., By BLAST analysis , 85 sRNAs were highly conserved ( >90% ) amongst pneumococci , 11 were conserved amongst streptococci , and 17 were conserved amongst other Gram-positive bacteria , typically other lactic acid bacteria ., Figure 2 outlines the order of analyses applied to the identified sRNAs ., Of the 89 sRNAs identified by sequencing , 41 were confirmed for expression and size via Northern blot analysis ( Figure S1 in Text S1 ) , an additional 4 were confirmed by qRT-PCR analysis ( Table 1 ) , and 10 sRNAs were confirmed by previous studies ., Seventeen of the novel sRNAs contained a highly conserved BOX element , making specific detection by Northern blotting or qRT-PCR difficult as the BOX element encompassed a majority of the predicted sRNA sequence in many instances ., RNA-seq of the TCS knockouts allowed for the identification of additional sRNAs that were not expressed in the parental TIGR4 ., An example is shown in Figure S2 in Text S1; the F13 sRNA had high expression in the TCS knockout while being undetectable in the parental TIGR4 ., In total , there were 24 sRNA candidates that failed to meet the cutoff criteria in all three TIGR4 RNA-seq assemblies but were present in at least one of the TCS knockouts ., These data indicate the pneumococcus expresses numerous , highly conserved sRNAs ., We next sought to determine if any of the sRNAs detected by RNA-seq shared any conserved motifs that could facilitate the identification of additional sRNA candidates ., Five sequence motifs were conserved across several sets of sRNAs ( Figure S3 in Text S1 ) ., Each of these motifs was found at additional locations in intergenic regions in the TIGR4 genome , raising the possibility that these motifs could be used to identify additional sRNAs ( Table S3 in Text S1 ) ., Part of Motif 1 shares homology with a boxA BOX element ., The areas around 17 of these motifs had increased signal based on the Illumina reads compared to the nearby flanking region , indicating the possibility of sRNAs being encoded in these domains ., Northern Blots using probes against flanking regions immediately outside the conserved motif for these 17 putative sRNAs identified detectable bands between 250–350 bps for each of these new putative sRNAs ( Figure S1 in Text S1 ) , indicating that the conserved motifs can be used to predict additional sRNAs ., All identified sequences were also analyzed by using Rfam to identify potential RNA families ., The R6 and F17 were predicted to be members of the T-box family; F26 and R15 were predicted to be members of Pyr; F27 and F32 were predicted to be members of the TPP and tmRNA families , respectively ., Members of these families were found upstream of the class of genes typically regulated by cis-acting riboswitches , namely tRNA synthases and amino acid biosynthesis genes in the case of the T-box , and genes involved in pyrimidine biosynthesis for the Pyr families , indicating these regulatory RNAs may function in a similar manner ., The remaining identified sequences had no significant homology to described RNA families ., As indicated in Figure 2 , the sRNAs were next analyzed for a role in virulence ., Fifteen sRNAs were chosen for further study on the basis of favorable predicted free energy for folding into secondary structures and high levels of expression by Northern blot ., These included ΔF6 , ΔF7 , ΔF20 , ΔF22 , ΔF24 , ΔF25 , ΔF32/tmRNA , ΔF41 , ΔF42 , ΔF43 , ΔF44 , ΔF48 , ΔF55 , ΔR6 , and ΔR12 ., These sRNAs were deleted with most having no polar effects on flanking genes ( Figure S4 in Text S1; note SP0625 is a pseudogene and partially overlapping with ΔF22 ) ., One mutant , ΔF48 resulted in approximately 20-fold upregulation of the upstream gene sp1872 ., The mutants were assessed for their ability to establish invasive disease in a murine model of infection in which intranasal challenge progresses to pneumonia , sepsis , and meningitis ., All mutants caused equivalent levels of bacteremia 24 hours post challenge ( data not shown ) but further progression of sepsis was attenuated in 8 of the sRNA knockouts tested ( p<0 . 05 , Mantel-Cox log rank test ) : ΔF20 , ΔF32/tmRNA , ΔF41 , ΔF44 , ΔF48 , ΔF22 , ΔF7 , and ΔF25 ( Figure 3 ) ., These data represent the first report of sRNAs playing a definitive role in pneumococcal pathogenesis whereby deletion of the sRNA results in a significant attenuation of invasive disease ., In order to obtain organ-specific information on the relative contribution of the identified sRNAs to pneumococcal pathogenesis , we next utilized Tn-Seq , an approach that measures the relative fitness of bacterial mutants in different environments ( Figure 2 , right arm of flowchart ) ., We also included the sequences for the sRNAs identified in TIGR4 by previous studies to obtain the most comprehensive analysis of the contribution of sRNAs to pathogenesis ., We analyzed three sites of the host that are vital for the progression of pneumococcal disease- the nasopharynx , lungs , and bloodstream ., A comprehensive , large pool of pneumococcal mutants generated by random transposon insertions was administered to these respective host sites and bacteria were harvested subsequent to disease progression ., By sequencing the respective mutants in the input and output pools , the relative fitness level of the sRNA mutants was quantified ( Table 2 , unfiltered data Table S4 in Text S1 ) ., A fitness level below 1 means the mutant had decreased fitness whereas a fitness level of 0 indicates that the mutant was attenuated to a degree that no mutants were recovered from the output pools ., A number of sRNAs were found to have reduced fitness during colonization of the nasopharynx including F14 , F20 , F38 , F41 , F63 , and F66 ., A further 12 sRNAs identified by other groups were also found to have significantly reduced fitness during nasopharyngeal colonization ., During lung infection , sRNAs F7 and F32/tmRNA were among the 5 genes identified in our study to be significantly impaired during infection ., When the comprehensive list of sRNAs was included , a total of 28 sRNA mutants were predicted to have defects during lung infection ., In the sepsis model of infection , a total of 18 sRNA mutants were found to have highly significant reductions in fitness in the bloodstream , including the F25 and F41 that were amongst the knockouts originally tested ., These data were in agreement with and further supportive of our data from the targeted genetic knockouts ( 5 of the 8 attenuated knockouts predicted from RNA-seq were also identified by Tn-seq ) ., In order to confirm the Tn-seq analysis , individual sRNA knockouts were tested in a competitive index model of infection in which the sRNA mutant was inoculated together with the TIGR4 wild type into the nasopharynx , lung , or blood and differential bacterial density was determined at 24 hours post infection ., The capacity of a subset of sRNA mutants predicted by Tn-seq to colonize the nasopharynx , infect the lungs , and replicate in the bloodstream were analyzed in respect to TIGR4 ( Figure 4A–C ) ., The ΔF24 strain which was avirulent in sepsis showed a slight decrease in colonization of the nasophaynx ( Figure 4B ) ., In addition , ΔR12 , which was not significantly attenuated in our initial model of infection , showed dramatic differences in both nasopharyngeal colonization and in the intraperitoneal bacteremia model ( Figure 4 ) ., In addition , two new sRNA mutants were generated from the Tn-Seq predictions , ΔF5 and ΔF62 , both of which displayed defects in their respective host niches of the bloodstream and lung ., RNA-seq coupled with Tn-seq and validated with targeted knockout mutants proved to be a robust method for determining the contribution of sRNAs to pathogenesis ., A total of 28 sRNAs in the lung , 26 in the nasopharynx , and 18 in the blood were predicted to have significantly altered fitness in these respective host niches ., While a majority of the Tn-seq sRNA mutants attenuated the bacteria , it should be noted that a small number of mutations actually resulted in a fitness benefit in certain host sites ( Table 2 ) ., In addition , most of the attenuated sRNAs were predicted to be defective in only one host organ , underscoring the contribution of these sRNAs to these distinct environments ., These data indicate that sRNAs contribute to pneumococcal pathogenesis both for systemic infections as well as for tissue specific tropisms ., To identify the step in host-bacterial interactions affected by the attenuated sRNA knockouts , the ability of the mutants to adhere to and invade endothelial and nasopharyngeal cell lines was determined ., The sRNA mutant F20 had a significant defect in adhesion and invasion of Detroit nasopharyngeal cells ( Figure S5 in Text S1 ) , a finding in agreement with the decreased nasopharyngeal fitness ( Table 2 ) ., A striking defect in adherence to activated endothelial cells was observed in six of the sRNA mutants , while invasion of endothelial cells was only impaired in F20 and F32/tmRNA ., These data indicate that many of the attenuated sRNAs have specific defects in interactions with host cells , an underlying cause for attenuation of disease ., We then hypothesized that sRNAs could target either gene networks or individual genes ., To investigate global gene expression , we compared the transcriptome of TIGR4 to that of each of the attenuated sRNA mutants via microarray analysis ., Several pathways were significantly different upon deletion of the sRNAs ( Table S4 in Text S1 ) ., The ΔF25 , ΔF41 , and ΔF44 mutants upregulated a putative ABC transporter encoded by SP1688–1690 that is predicted to be involved in carbohydrate transport ., The SP1721–1725 genes , predicted to play roles in sucrose metabolism , were also highly differentially regulated in several of the sRNA mutants ., The ΔF32 mutant substantially downregulated several metabolic networks encompassing the lactose transport system and multiple PTS systems ., This highlights the potentially pleiotropic effects that the deletion of the sRNAs could have on pneumococcal biology and pathogenesis in the host ., Many sRNAs function at the post-transcriptional level 43 , suggesting that there may be important changes in bacterial physiology that potentially could have been missed by global transcriptional analysis ., We next sought to determine the effect of the deletion of sRNAs on the global proteome of the pneumococcus ., Replicate two-dimensional gels were analyzed for each attenuated sRNA mutant and compared to the parental TIGR4 ., Every individual protein spot on the gels was then quantified from these duplicate gels to obtain a comprehensive analysis of changes in protein abundance resulting from the deletion of the respective sRNA ., The quantitation of the respective spots for each bacterial strain , along with both the predicted pI and molecular weight of the protein , are listed in Table S6 in Text S1 ., The image of a TIGR4 gel with the individual spot identifications is provided in Figure S6 in Text S1 ., A number of proteins spots found in increased or decreased abundance are summarized in Figure 5 ., Deletions in F20 and F32/tmRNA resulted in dramatic alterations in abundance , of 88 and 100 proteins respectively ., Of note is that both the ΔF20 and ΔF32 mutants were the only attenuated sRNA mutants to have significant defects in the invasion of endothelial cells , indicating that a subset of these misregulated proteins are important for cell-cell interactions ., Analysis by mass spectrometry ( Figure 5 ) indicated that the ΔF20 mutant had decreased abundance of two proteins involved in purine biosynthesis , PurM and PurC , potentially explaining the defect in virulence ., The overexpression of the NrdI flavoprotein , essential for the conversion of nucleotides to deoxynucleotides , suggests defects in DNA synthesis and repair 44 ., These data indicate that the deletion of sRNAs can have multiple effects on bacterial pathogenesis by influencing numerous putative targets ., Advances in sequencing technologies have driven an explosion in our knowledge of the non-coding genetic repertoire of bacterial species ., This study illustrates the first example of a global approach to both sRNA identification and pathogenesis profiling , an amalgamation of RNA-seq and Tn-seq ., The RNA-seq tactic identified 89 putative pneumococcal sRNAs , capturing both sRNAs previously detected by sequencing and tiling arrays and many additional previously unknown sRNAs 11 , 12 , 37 , 39 ., Use of RNA-seq has certain advantages for the identification of sRNAs ., The mean level of sequence coverage was over 100-fold on both the forward and reverse strands , with each sRNA corresponding to a minimum of 10x coverage allowing for high confidence in the data ., It should be noted that low abundance sRNAs identified in other studies from a single read will likely be missed by our analysis 39 ., Unlike tiling arrays , RNA-seq identifies the origin of transcription ., This permits the precise mapping of sRNAs that contain highly repetitive regions , such as the over 100 BOX elements found in intergenic regions of the pneumococcal genome ., BOX elements are short AT-rich repeats that are highly transcribed and were also detected in sRNA searches using tiling arrays , though precise locations could not be mapped 11 ., Eighteen BOX element containing sRNAs were mapped , a finding particularly important as the Tn-seq analysis implicated a subset of four BOX-element sRNAs in pathogenesis ., Although BOX elements have traditionally been thought to be parasitic sequences mobilized by transposases 45 , recent evidence supporting their placement in sRNAs indicates that they can form RNA structures with riboswitches 46 ., In addition , BOX elements can stimulate expression of downstream genes by increasing the half-lives of the mRNA 47 ., Another important aspect of this study was the identification of five novel shared sRNA sequence motifs that were conserved at multiple locations in the pneumococcal genome ., Upon closer examination of the sequence read depth in the areas surrounding these motifs , we identified 17 with increased signal compared to the surrounding region ., All 17 of these predicted sRNAs were subsequently validated by expression analysis underscoring the robustness of the predictions ., While members of the T-box , Pyr , TPP , and tmRNA sRNA families described in other bacteria were also found in pneumococcus , a majority of the predicted pneumococcal sRNAs could not be assigned to a functional family ., These data indicate that the pneumococcus is a rich source of new motifs that can expand sRNA prediction algorithms in Gram-positive bacteria ., Although numerous sRNAs have been identified in the pneumococcus , there have been no sRNAs implicated in pathogenesis and more broadly , there have been no attempts to apply transposon-mediated mutagenesis to determine the role of sRNAs in bacterial virulence in specific host tissues ., This study represents the first use of transposon-mediated mutagenesis to address the global role of sRNAs in discrete host tissues during disease ., Using a comprehensive list of sRNAs identified in this study together with those found by others , we identified a number of sRNAs that played distinct roles in pathogenesis in the nasophaynx , the lung , or the bloodstream ., The lungs provided the most comprehensive analysis of the contribution of sRNAs to virulence , since bottleneck constraints in the nasophaynx and the blood imposed by a limitation of bacterial binding sites and clearance by the spleen , respectively , may have impaired detection in these sites ., A number of sRNAs had no inserts in the Tn-seq deletion library ( n . i . in Table S4 in Text S1 ) and it is tempting to speculate that there is a selective pressure against the loss of these sRNAs; however this observation could be random due to their small size ., All three body sites had a distinct list of sRNA candidates that were involved in pathogenesis ., The Tn-seq analysis proved to be robust , as mutants predicted to be attenuated in their respective host niches were confirmed in in vivo competition experiments pitting each sRNA mutant individually against wild type ( Figure 4 ) ., Thus the multi-organ Tn-Seq approach captured this diversity as exemplified by R12 that did not have a significant virulence defect in overall survival in our initial studies but was attenuated both during colonization of the nasophaynx and in the blood following intraperitoneal infection ., The Tn-seq analysis also provides insight into the organ-specific defects of the sRNAs found to have reduced virulence in Figure 3 ., Both the ΔF41 and ΔF25 strain had greatly reduced fitness in the blood , in agreement with their inability to progress to sepsis ., The ΔF7 and ΔF32/tmRNA strains were both defective in the lung infection , indicating that this might be the most crucial site for clearance of these mutants ., This comprehensive analysis of the contribution of all the identified sRNAs to pneumococcal pathogenesis in discrete host sites can provide a framework for future investigations elucidating the precise functions of these sRNAs ., These data add to the growing understanding of the contribution of sRNA in the virulence of bacterial pathogens 3 ., The sRNA mutants displaying defects in virulence exhibited a number of characteristics that could potentially explain an inability to cause disease ., Several of the attenuated sRNA mutants had defects in adhesion and invasion of nasopharyngeal or endothelial cells , capabilities important to the progression of invasive disease ., ΔF20 and ΔF32/tmRNA showed decreased adhesion/invasion of nasopharyngeal or endothelial cells , respectively , in concert with Tn-seq and competitive index data indicating lack of fitness in the nasopharynx and lung ., F32 encodes a tmRNA and these have been implicated in the pathogenesis of other bacteria 48 , 49 ., The central role of tmRNA in the rescue of ribosomes on stalled mRNA as well as targeting defective mRNA for degradation , is consistent with the strong defect in pathogenesis observed in the ΔF32 strain 50 ., In the case of the ΔF20 mutant , proteomic analysis indicated proteins responsible for purine metabolism were strongly down regulated whereas DNA synthesis and repair pathways were greatly increased ., Thus deletion of F20 had pleiotropic effects on DNA metabolism that could explain attenuation of the mutant ., Taken together , these data provide compelling evidence that sRNAs play important roles in virulence , that their affects can arise at several levels of control of virulence gene/protein expression , and that these roles can be restricted to specific host tissues ., Our study expanded the search for sRNAs and their role in gene regulation to three mutants in TCSs ., Control over gene networks by TCSs is typically mediated by a direct interaction of the response regulator with a target sequence shared by many genes dispersed over a genome ., However , TCSs have also been found to control the expression of sRNAs in pneumococcus and other bacteria 37 , 51 ., For example , control of porin expression in E . coli involves multiple sRNAs that exert posttranscriptional control over the targets of TCSs 42 ., The prospect of sRNA functioning as an intermediary , finely tuning the control of and expanding the regulatory scope by a TCS , would allow for another layer of control for more precise regulation ., Our observation that the abundance of sRNAs was altered when each of the three TCSs were disrupted is consistent with TCSs acting through sRNAs to broadly control gene expression ., This is further supported by the observed alterations of the global transcriptome as well as the abundance of multiple protein targets upon deletion of an individual sRNA ( Tables S5 and S6 in Text S1 , Figure 5 ) ., These data suggest that the impact of sRNAs on multiple aspects of pneumococcal biology and pathogenesis could potentially be exerted by an additional layer of posttranscriptional control over the gene networks controlled by TCSs ., The widespread utilization of RNA-mediated regulation of diverse processes has a number of potential advantages for bacteria 52 ., Protein regulators incur greater metabolic costs to the cell , being encoded by larger segments of the genome and requiring translation ., In contrast , sRNAs do not require translation and occupy a very limited amount of the genome ., The additional layer of regulation conferred by sRNAs may also allow for more precise control of gene expression , as evidenced by the fact that sRNAs can have multiple targets as well as the fact that multiple sRNAs can regulate a single target under different conditions 4 , 53 ., Additionally , sRNAs can have dramatically different half-lives in the cell , ranging from under 2 minutes to greater than 30 minutes 54 ., Such differences in stability could potentially mediate the duration of control mediated by sRNAs ., The challenging task that remains following the identification and characterization of sRNAs in pathogenesis is assigning discrete functional roles to these molecules ., We have shown the feasibility of applying Tn-seq to identify changes in bacterial fitness in response to deletion of the corresponding sRNA in various host tissues ., The feasibility of this approach to investigate the gene networks and functional roles of sRNAs suggest the combination of RNA-seq and Tn-seq will be a unique and powerful tool for future investigations of the precise functional roles of these sRNAs in the pneumococcus ., The S . pneumoniae strains used are listed in Table S1 in Text S1 ., All experiments were conducted in the sequenced TIGR4 strain 55 ., Cultures were grown overnight on tryptic soy agar plates supplemented with 3% sheep blood and were transferred to a defined semisynthetic casein liquid medium supplemented with 0 . 5% yeast extract ( i . e . , C+Y ) 56 ., To initially identify sRNAs in Streptococcus pneumoniae , we designed a method to isolate , enrich , and fully sequence small ( <200 nt ) transcripts of the TIGR4 strain of pneumococcus ., Cultures were grown in triplicate in C+Y ( 200 mL ) until an OD620 of 0 . 5 was reached , corresponding to mid log phase growth ., Bacteria were diluted ( 1∶2 ) in RNAProtect stabilization buffer ( Qiagen ) and centrifuged; the resulting bacterial pellets were then frozen at −80°C ., The pellets were thawed and resuspended in Lysis Buffer Mirvana miRNA Isolation Kit ( Applied Biosystems ) ., To each sample , 200 µL of 0 . 1 mm glass beads ( Sigma ) were added before they were lysed using a mini-beadbeater ., Samples were incubated for 10 minutes at 70°C and subsequently processed through a Qiashredder column ( Qiagen ) ., sRNA was purified using organic extraction and sRNA enrichment procedures as described in the Mirvana protocol ., Purified sRNA was DNAse-treated by using Turbo DNAse ( Applied Biosystems ) according to the manufacturers instructions ., Purified sRNA was prepared for sequencing by using the Small RNA Sample Prep kit ( Illumina ) ., Details about the cluster generation , sequencing , and Northern Blot confirmation are provided in the Supplementary Materials section ., Detection of biologically meaningful sRNA regions was based on the assumption that sequence reads are enriched in such regions ., The sequence reads were first mapped to the T4 genome using the program GMAP recursively by quality based trimming ., Then the coverage information for both strands was calculated based on high quality matches ., When a read mapped to multiple positions on the genome , the highest quality match was selected ., For each intergenic region and anti-sense coding region of size greater than 150 bases , a simple method was used to identify a potential read enriched region ( peak ) ., Due to the degradation of the sample mRNA , these reads were mapped all over the genome and it was necessary to remove those background signals ., Signal noise was not uniformly distributed along the genome , so a baseline detection algorithm ( linear inter | Introduction, Results, Discussion, Materials and Methods | Small noncoding RNAs ( sRNAs ) play important roles in gene regulation in both prokaryotes and eukaryotes ., Thus far , no sRNA has been assigned a definitive role in virulence in the major human pathogen Streptococcus pneumoniae ., Based on the potential coding capacity of intergenic regions , we hypothesized that the pneumococcus produces many sRNAs and that they would play an important role in pathogenesis ., We describe the application of whole-genome transcriptional sequencing to systematically identify the sRNAs of Streptococcus pneumoniae ., Using this approach , we have identified 89 putative sRNAs , 56 of which are newly identified ., Furthermore , using targeted genetic approaches and Tn-seq transposon screening , we demonstrate that many of the identified sRNAs have important global and niche-specific roles in virulence ., These data constitute the most comprehensive analysis of pneumococcal sRNAs and provide the first evidence of the extensive roles of sRNAs in pneumococcal pathogenesis . | Pneumonia is a leading cause of childhood mortality worldwide , resulting in more deaths in young children than any other infectious disease ., One of the leading causes of pneumonia is the human pathogen , Streptococcus pneumoniae , the causative agent of over six million infections each year in the United States ., Understanding how bacterial pathogens rapidly respond to dynamic host environments is a central aspect of microbial pathogenesis ., Accumulating evidence has implicated sRNAs as vital regulators in a number of important cellular processes though few have been implicated in virulence ., In our investigations we have applied next-generation sequencing to define the sRNA repertoire of S . pneumoniae ., In addition , we utilized both targeted genetic knockouts and transposon mutagenesis to show that a significant portion of these sRNAs play important roles at various stages of pneumococcal pathogenesis ., These data represent the first example of sRNAs being involved in pneumococcal pathogenesis and greatly expand the number of sRNAs that play important roles in bacterial pathogenesis . | biology, microbiology | null |
journal.pcbi.1000930 | 2,010 | Evolution and Optimality of Similar Neural Mechanisms for Perception and Action during Search | Neurophysiology studies of the macaque monkey 1–3 support the existence of two functionally distinct neural pathways in the brain mediating the processing of visual information ., The behavior of patients with brain damage has led to the proposal that perception is mediated by the ventral stream projecting from the primary visual cortex to the inferior temporal cortex , and that action is mediated by the dorsal stream projecting from the primary visual cortex to the posterior parietal cortex 4–6 ( Figure 1a ) ., Although there has been debate about whether this separation into ventral/dorsal streams implies that the brain contains two distinct neural representations of the visual world 7–12 , there has been no formal theoretical analysis about the functional consequences of the two different neural architectures on an animals survival ., Visual search requires animals to move their eyes to point the high-resolution region of the eye , the fovea , to potentially interesting regions of the scene to sub-serve perceptual decisions such as localizing food or a predator ., What is the impact of having similar versus different neural mechanisms guiding eye movements and mediating perceptual decisions on visual search performance for an organism with a foveated visual system ?, We consider two leading computational models of multiple-fixation human visual search , the Bayesian ideal searcher ( IS ) 13–15 and the ideal saccadic targeting model ( maximum a posteriori probability , MAP 16 , 17 ) for a search task of a target in one of eight locations equidistant from initial fixation ( Figure 1b ) ., The ideal searcher uses knowledge of how the detectability of a target varies with retinal eccentricity ( visibility map ) and statistics of the scenes to move the fovea to spatial locations which maximize the accuracy of the perceptual decision at the end of search 13 ( Figure 1b ) ., The saccadic targeting model ( MAP ) makes eye movements to the most probable target location 6 , 17 which is optimal if the goal was to saccade to the target rather than collect information to optimize a subsequent perceptual decision 1 ( Figure 1b ) ., Depending on the spatial layout of the possible target locations and the visibility map , the IS and MAP strategies lead to similar ( Figure 1c ) or diverging eye-fixations ( Figure 1d–e ) ., For example for a steeply varying visibility map ( Figure 1c ) both models make eye movements to the possible target locations while for a broader visibility map ( Figure 1d–e ) the ideal searcher tends to make eye movements in between the possible target locations attempting to obtain simultaneous close-to-fovea processing for more than one location ., Covert attention allows both models to select possible target locations and ignore locations that are unlikely to contain the target when deciding on saccade endpoints and making perceptual search decisions 18 , 19 ., Perceptual target localization decisions for both models are based on visual information collected in parallel over the whole retina , temporally integrated across saccades , and based on the location with highest sensory evidence for the presence of the target ., Critically , we implemented the models to have two processing pathways , one determining where to move the fovea and the other stream processing visual information to reach a final perceptual decision about the target location ., Rather than having a single linear mechanism or perceptual template ( Figure 1b ) , each pathway in the model had its own neural mechanism which is compared to the incoming visual data at each possible target location ., Likelihood ratios 20 of the observed responses for each of the mechanisms under the hypothesis that the target is present or absent at that location are used to make decisions about where to move the eyes and perceptual decisions ( see Materials and Methods ) ., We used a genetic algorithm as a method to find near-optimal solutions for perception and action mechanisms but also to simulate the effects of the evolutionary process of natural selection on the neural mechanisms driving saccadic eye movements and perceptual decisions during search ., The computational complexity of the ideal Bayesian searcher makes it difficult to virtually evolve the model ( see note 1 in Text S2 ) and thus we used a recently proposed approximation to the ideal searcher that is computationally faster ( Entropy Limit Minimization , ELM 15 , 21 ) ., The ELM model chooses the fixation location that minimizes the uncertainty of posterior probabilities over the potential target locations ., The decision rule can be simplified to choose the fixation location with the maximum sum of likelihood ratios across potential target locations , each weighted by its squared detectability given the fixation location 15 ., The ELM model can be shown to approximate the fixation patterns of the ideal searcher 15 and capture the main characteristics of the fixation patterns of the IS for our task and visibility maps ( Figure 1c–e; ELM ) ( see note 2 in Text S2 ) ., The process of virtual evolution started with the creation of one thousand simulated individuals with separate linear mechanisms for perception ( ventral ) and eye movement programming ( dorsal; Figure 2a ) ., Each pathways template for each individual was created from independent random combinations of the receptive fields of twenty four V1 simple cells ., Each simulated individual was allowed two eye movements ( see note 3 in Text S2 ) before making a final perceptual search decision about the location of the target ., Performance finding the target in one of eight locations for five thousand test-images ( one thousand for natural images ) was evaluated and the probability of survival of an individual was proportional to its performance accuracy ., A new generation was then created from the surviving individuals through the process of reproduction , mutation and cross-over ( Figure 2a ) ., The process was repeated for up to 500 generations ., We first evolved the ideal searcher approximation ( ELM model ) for different shape luminance targets ( isotropic Gaussian , vertical elongated Gaussian and cross pattern consisting of a positive and negative polarity elongated Gaussian ) embedded in 1/f noise and a steep visibility map ( Figure 1c ) ., Irrespective of the target shape , virtual evolution led to converging perception ( ventral ) and saccade ( dorsal ) mechanisms that are similar to the target ( Figure 2b; see Video S1 , Video S2 , and Video S3 for virtual evolution ) ., To further investigate the generality of the result we evolved the ELM model to search a circular Gaussian target added to backgrounds with different statistical properties: white noise , 1/f noise and importantly , a calibrated set of natural image backgrounds 22 ., Figure 3 ( 2nd row ) presents the distribution of perceptual decision accuracies across individuals in a generation and shows that perceptual performances of simulated individuals in the population improve with generations and then converge to an asymptote ., We characterized the similarity between the perception and saccade mechanisms by computing the correlations between the 2 dimensional linear mechanisms for each individual in each generation ., Figure 3 ( 3rd row ) shows that the distribution of correlations across individuals in the population evolves to unity irrespective of the background type ., To visualize in detail the shape of the evolved templates , we analyzed the radial profile of the templates of the highest performing simulated individuals in the last generation ( Figure 3; 4th row ) ., For all three backgrounds the saccade and perception templates converge to similar shapes ( perception and saccade 2-D template correlations for the best performing templates in the last generation: 0 . 990±0 . 006 , 0 . 986±0 . 013 , 0 . 982±0 . 013 ) ., In addition , the linear mechanisms for the 1/f noise and natural scenes are narrower than those for the white noise and show an inhibitory surround ( Figure 3 ) ., These previous results were based on a visibility map that steeply declines with eccentricity and rely on the assumption that humans are near-ideal searchers ., We , thus , evolved the mechanisms for the case of a broader visibility map that is similar to that measured for human observers in 1/f noise 15 ( Figure 4a ) and showed that the convergence of neural mechanisms generalizes to different visibility maps ( Figure 4a ) and also to a model in which eye movement planning is assumed to follow a saccadic targeting strategy ( MAP ) rather than approximating an ideal strategy ( Figure 4a ) ., Furthermore , Figure 4b shows that there is nothing particular about the symmetry of the eight location configuration search task since similar convergent evolution is observed for an asymmetric four location task ( Figure 1e ) ., We also evaluated whether our results would change if the model included the increasing size of V1 receptive fields and lower frequency tuning with retinal eccentricity ( see note 4 in Text S2 ) ., Figure 5a ( right graph ) shows the center frequency and bandwidth ( standard deviation ) of the oriented Gabor receptive fields as a function of retinal eccentricity ., The computational time demands of this simulation restricted us to evaluate this model for a fixed set of receptive field weights across eccentricities ( see note 5 in Text S2 ) and limited set of scenarios: 1/f noise , steep visibility map and two targets: a low frequency Gaussian ( Figure 5b; left ) and a Difference of Gaussians ( DoG ) with a center frequency of 8 c/deg ( Figure 5b; right ) ., Due to the fixed set of weights across eccentricity , in this model the spatial profile of the linear combination of receptive fields scales up with eccentricity ., Thus , for each retinal eccentricity category there was a pair of evolved template profiles ., Figure 5c shows that convergent evolution still results when receptive field size increases with eccentricity and irrespective of the spatial frequency of the target ., Figure 5d presents the similar radial profiles of the of evolved perception and saccade mechanisms for the fovea and a sample peripheral retinal location ( perception and saccade 2-D template correlations for the best performing templates in the last generation averaged across retinal eccentricities were: Gaussian target: 0 . 963±0 . 008; DoG target: 0 . 961±0 . 004 ) ., Do all scenarios lead to converging evolution of the perception ( ventral ) and action ( dorsal ) pathways ?, No , if we take a case in which the sought target is equally detectable across the retina ( flat visibility map ) , the results show the correlations between the perceptual and saccade templates do not converge to unity ( Figure 6a ) ., A second example is a case in which the organism makes a decision after eight eye movements rather than two eye movements ( Figure 6b ) ., Because the organism gets to fixate on all eight target locations , there is little added benefit of an efficient saccadic system and the co-evolution is much slower ( Figure 6b ) ., A third scenario of partial convergence results if we adopt a strong model of two visual processing streams which spatially pre-filter the visual input based on the properties of the cells in the parvocellular and magnocellular lateral geniculate nucleus ( LGN ) ( 23; see Figure 6d ) and assume no further interaction across pathways ., The differential spatial frequency filtering of the two pathways can introduce constraints in the frequency content of the evolved mechanisms preventing a full convergence of the templates ( Figure 6e; perception and saccade 2-D template correlations for the best performing templates in the last generation for: 1/f noise: 0 . 603±0 . 082 ) ., A similar simulation with the same target but white noise instead of 1/f noise also resulted in partial convergence ( perception/saccade 2-D template correlation of 0 . 856±0 . 046 ) ., We used an approximation to an Ideal Bayesian Searcher ( Entropy Limit Minimization model; ELM ) to virtually evolve separate linear mechanisms for eye movements and perceptual decisions during visual search for a variety of targets embedded in various synthetic and natural image backgrounds ., Evolved templates contain similarities to the target but for the 1/f and natural images they are narrower than the target and contain a subtle inhibitory surrounding not present in the signals but often present in monkey neuronal receptive fields and human behavioral receptive fields 9 , 19 ( see blue outline in Figure 2b ) ., A previous study has shown that such inhibitory surrounds serve to suppress high amplitude noise in the low frequencies and optimize the detection of spatially compact signals in natural images 24 ., The current result extends previous results 24 to show the optimality of inhibitory surrounds during visual search in natural images for an organism with a foveated visual system and saccadic eye movements ., Central to this paper , the mechanisms for perception and saccades evolved to similar representations ., This result is robust across different types of backgrounds , signals , visibility maps , and spatial distributions of possible target locations ., Due to computational constraints we did not investigate the more general case of allowing the target to appear at any location within the image but there is no particular reason to suggest that our result would differ for this latter general case ., In addition , similar convergence between mechanisms was found for what arguably are the most common contender algorithms to model how humans plan eye movements during search: an approximation to the ideal searcher , ELM and a saccadic targeting model; MAP model; 13 ., For simplicity our original models did not include receptive fields that increased with retinal eccentricity but an implementation of such a model led to similar convergent evolution for a low and a higher spatial frequency target ., The scenarios for which we did not find full convergent evolution of the linear mechanisms were for cases for which the target was either equi-detectable across the retina or the organism had enough time to fixate all of the possible target locations ., Note , however , that for both cases , performance of the evolved individuals does improve with increasing generations ( Figure 6a–b ) through the evolution of the perceptual template to a target-like structure ., Yet , there is no performance advantage for evolving a neural mechanism for saccades that encodes target information because , for these cases different eye movement patterns have little or no impact on perceptual performance ., A third scenario which resulted in partial convergence was a two stream model with pathway-specific pre-filtering of the visual input ., A strong assumption that there are no interconnections between the two pathways would result in processing constraints based on the early stages of visual processing of both pathways ., Inclusion of pre-filtering properties of the parvocellular and magnocellular LGN cells restricted the full convergence of the evolved mechanisms ., These finding suggest that if we adopt a strict separation of pathways and take into account properties of LGN cells we should not always expect similar mechanisms driving perception and saccadic decisions during search ., The specific circumstances for which we will not find convergent evolution and the degree of similarity between evolved templates will depend on the spatial frequency of the target and background statistical properties ( see results for 1/f noise vs . white noise ) ., Yet , is the strict separation of pathways and constraints to the filtering properties of parvocellular ( perception ) and magnocellular ( action ) LGN cells tenable for the case of eye movements and perceptual decisions during search ?, A recent psychophysical study 9 used the same Gaussian target as in the simulations and reverse correlation to show that estimated underlying templates mediating human saccadic actions and perceptual search decisions are similar ., Thus , these psychophysical findings would suggest that the strong assumption of no interconnections across pathways and constraints by the early LGN processing might not hold at least for the case of perception and eye movements during visual search ., Together , our present results suggest a theory of why evolution would favor similar neural mechanisms for perception and action during search 9 and provide an explanation for the recent study finding similar estimated underlying templates mediating human saccadic decisions and perceptual decisions ., Our findings and theory do not necessarily imply either that one pathway mediates both perception and action nor are they incompatible with the existence of separate magnocellular and parvocellular pathways ., Instead , our theory would be consistent with the idea that pathways for perception and oculomotor largely overlap , leading to significant sharing of visual information across pathways 8 , 12 , 25 , 26 ., For the case of saccadic eye movements , visual cortical pathways through the frontal eye fields 27 and the lateral intra-parietal cortex 28 play critical roles , as well as brainstem and cortical pathways through the superior colliculus 29 ., In addition , studies have related areas in the ventral stream ( V4 ) to target selection of saccades 30 , 31 ., In addition , the results do not prohibit small differences in visual processing for perception and saccadic action but provide functional constraints on how much discrepancy can exist between neural mechanisms without jeopardizing the survival of the organism ., In the larger context , the similar neural mechanisms for perception and saccade actions should be understood as another effective strategy implemented in the brain , in addition to guidance by target properties 13 , 14 , 32 , 33 , optimal saccade planning 15 , contextual cues 34 , 35 and miniature eye movements 36 to ensure successful visual search ., Finally , the approach of the present study demonstrates how the rising field of natural systems analysis 37 , 38 can be used in conjunction with virtual evolution and physiological components of the visual system to evaluate whether properties of the human brain might reflect evolved strategies to optimize perceptual decisions and actions that are critical to survival ., We assumed a viewing distance of 50cm for the models ., Search targets for simulations were:, a ) A Gaussian target with 0 . 5539 square root contrast energy ( SCE ) and a standard deviation of 0 . 1376 degrees ( Figure 1c; 2b left column; 3 ) ;, b ) An elongated Gaussian with 0 . 9594 SCE , standard deviations of 0 . 4128 deg ., in the vertical direction and 0 . 1376 degrees in the horizontal direction ( Figure 2b center column , Figure 4 ) ;, c ) The difference of a vertically oriented and a horizontally oriented elongated Gaussians with 0 . 8581 SCE ( Figure 2b , right column ) ., The white noise root mean square contrast ( rms ) was 0 . 0781 ., The same rms was used for white noise filtered with the 1/f function ( 1/f noise ) ., Possible target locations were equidistant 7 degrees from the center fixation cross ., Independent external and internal noise samples were refreshed with each saccade for the white and 1/f noise ., For the natural images the external backgrounds were fixed but the internal noise refreshed across saccades ., Here , we briefly describe the models implementations ( see Text S1 for detailed mathematical development and details ) ., The initial stage of all three models investigated ( ideal searcher , IS; entropy limit minimization , ELM; and saccadic targeting , MAP ) is the dot product of a perceptual and saccade template ( w ) with the image data ( g ) at all possible target locations , where r is the resulting scalar response and w and g are expressed as 1-D vectors ., The templates for the perceptual decisions and saccade planning were independent and random linear combinations of 24 Gabor functions that spanned the targets: spatial frequencies , 0 . 5 , 1 , 2 , 4 cycles/degree for 6 different orientations , 30 degrees apart , and with octave bandwidths ., A subset of simulations ( Figure 6 ) also modeled pre-processing of the image by separate LGN cells corresponding to the magnocellular ( dorsal ) and parvocellular ( ventral ) cells ., The filtering was done using DoG functions with different center frequencies ( see Text S1 for mathematical details ) prior to the processing by the Gabor functions ., Use of a larger number of Gabor functions did not significantly change the evolved templates for the targets considered but required prohibitively longer computational times due to the dimensionality explosion ., For the template derived for the case of the isotropic Gaussian target we used an additional constraint of equal weighting for all orientations of the Gabor functions for a given spatial frequency ., Most of the simulations used the fixed 24 Gabor functions irrespective of retinal eccentricity ., A subset of simulations ( see Figure 5 ) used sets of 24 Gabor functions that increased linearly in size and also decreased in the central frequency tuning with retinal eccentricity ( see details in effects of retinal eccentricity section ) ., Template responses were integrated across saccades ., Calculation of likelihood ratios use Gaussian probability density functions which depend on the image parameters for the white and 1/f Gaussian noisy images ., For the natural images , the likelihood calculation required estimating the probability density function from a training set of 3000 images and fitting the probability density functions with Laplacian distributions convolved with a Gaussian distribution representing the internal noise ( see Text S1 ) ., Two methods were used to model the detrimental effect of retinal eccentricity on the detectability of the target ., The first method which is similar to Najemnik and Geisler 13 was implemented by adding internal noise to the scalar template response: , where the additive internal noise scalar value is sampled from a Gaussian distribution which standard deviation ( ) is dependent on the distance ( i . e . retinal eccentricity ) between the tth fixation and the template response location i out of m possible target locations ., Also the internal noise was proportional to the templates response standard deviation resulting from the external image variability ., The visibility maps referred to as steep and broad ( see also Figure S1 ) were obtained with internal noise standard deviations given by: ( 1a ) ( 1b ) where σo is the standard deviation of the template response due to external noise , e is the eccentricity in degrees , and the subscripts k refer to the fixation location , and i to the possible target location ., For all models , independent samples of internal noise were used for each saccade and pathways ., The second method to model the effects of retinal eccentricity included internal noise ( see above ) and also varied the sets of 24 Gabor functions with retinal eccentricity ., The size of Gabor functions increased with the retinal eccentricity ( e ) so that the standard deviation of the spatial Gaussian envelope is given by: ( 2 ) where is the bandwidth and is the center frequency of Gabor function in the fovea ., Thus , the standard deviation in the frequency domain of each Gabor function ( Figure 5a; right graph ) decreases as: ( 3 ) The center frequency tuning of the Gabor functions ( s ) linearly decreased with retinal eccentricity: ., The saccadic targeting or maximum a posteriori probability model ( MAP ) chooses the location of the next fixation with the maximum product of likelihoods ratios ( ) across previous and present fixation ( t\u200a=\u200a1 , … , T ) : ( 4 ) For the case of white noise and 1/f Gaussian noise the expression can be simplified to the sum of log-likelihood ratios: ( 5 ) where Δμ is the difference in mean response of the template to the signal plus background and background only and all other symbols are defined above ., The ideal searcher selects as the next fixation the location that will maximize the probability of finding the target after the eye movement is made: ( 6 ) where is the proportion correct ( PC ) given that the target location is i , and the next fixation is ., The term is the prior that the ith location contains the target given the sensory evidence collected up to the present fixation: and m is the number of possible target locations ., For white noise and 1/f noise Gaussian noise , becomes: ( 7 ) where is the probability density function of the Gaussian function in Equation ( 9a ) , the cumulative density function of the Gaussian function in Equation ( 9b ) , and , are the log-likelihood ratios which are known scalar values based on acquired visual information , ( 8a ) ( 8b ) while and are random variables describing log-likelihoods after the next fixation and described by normal probability density functions: ( 9a ) ( 9b ) where ( ) is the detectability at target location i ( j ) , given fixation at location ., The present formulation is identical to that of Najemink and Gielser 13 but uses likelihood ratios rather than product of posteriors ., The entropy limit minimization model chooses as the next fixation the locations that minimize the expected entropy and can be approximated by maximizing the expected information gain ., This can be shown to be approximated by calculating for each potential fixation location , , a sum of the posterior probability for each location weighted by the squared detectability given the fixation location 15: ( 10 ) where is the Shannon entropy of , and is the information gain ., For all models , the final perceptual decision about the target location was obtained by combining the likelihood ratios for each possible target locations across all fixations and choosing the location with the highest product of likelihood ratios: ( 11 ) where the likelihoods of the responses given the background only and the target are given and which are the probability density functions ( pdf ) assumed to be Gaussian ( white noise and 1/f noise ) or empirically estimated from samples ( see next section ) for the natural images ., The distribution of template responses for the natural image dataset 22 were estimated from 24 , 000 image patches extracted from the eight possible target locations for 3000 natural images ., We fit the distribution of these responses for each template of each simulated individual with a Laplacian distribution: ( 12 ) where is the mean parameter and is a scale parameter ., To take into account the effect of additive Gaussian internal noise on the probability density function of the template responses we convolved the Laplacian distribution with the Gaussian distributions: ( 13 ) where and are Gaussian and Laplace probability density functions respectively ( see Figure S2 ) ., We used the Genetic Algorithm Optimization Toolbox ( GAOT ) 39 ., Arithmetic crossover parameter was set to operate 50 times per generation , and uniform mutation to operate 50 times per generation ., The selection process used a real-valued roulette wheel selection 38 ., A generation consisted of 1 , 000 individual parameter settings ., All individuals were randomly initialized , and allowed to evolve over 500 generations ( see Text S1 for additional details ) ., Reported results for each scenario/model were averages across ten simulated evolution runs . | Introduction, Results, Discussion, Materials and Methods | A prevailing theory proposes that the brains two visual pathways , the ventral and dorsal , lead to differing visual processing and world representations for conscious perception than those for action ., Others have claimed that perception and action share much of their visual processing ., But which of these two neural architectures is favored by evolution ?, Successful visual search is life-critical and here we investigate the evolution and optimality of neural mechanisms mediating perception and eye movement actions for visual search in natural images ., We implement an approximation to the ideal Bayesian searcher with two separate processing streams , one controlling the eye movements and the other stream determining the perceptual search decisions ., We virtually evolved the neural mechanisms of the searchers two separate pathways built from linear combinations of primary visual cortex receptive fields ( V1 ) by making the simulated individuals probability of survival depend on the perceptual accuracy finding targets in cluttered backgrounds ., We find that for a variety of targets , backgrounds , and dependence of target detectability on retinal eccentricity , the mechanisms of the searchers two processing streams converge to similar representations showing that mismatches in the mechanisms for perception and eye movements lead to suboptimal search ., Three exceptions which resulted in partial or no convergence were a case of an organism for which the targets are equally detectable across the retina , an organism with sufficient time to foveate all possible target locations , and a strict two-pathway model with no interconnections and differential pre-filtering based on parvocellular and magnocellular lateral geniculate cell properties ., Thus , similar neural mechanisms for perception and eye movement actions during search are optimal and should be expected from the effects of natural selection on an organism with limited time to search for food that is not equi-detectable across its retina and interconnected perception and action neural pathways . | The brain has two processing pathways of visual information , the ventral and dorsal streams ., A prevailing theory proposes that this division leads to different world representations for conscious perception than those for actions such as grasping or eye movements ., Perceptual tasks such as searching for our car keys in a living room requires the brain to coordinate eye movement actions to point the high resolution center of the eye , the fovea , to regions of interest in the scene to extract information used for a subsequent decision , such as identifying or localizing the keys ., Does having different neural representations of the world for eye movement actions and perception have any costs for performance during visual search ?, We use computer vision algorithms that simulate components of the human visual system with the two separate processing streams and search for simple targets added to thousands of natural images ., We simulate the process of evolution to show that the neural mechanisms of the perception and action processing streams co-evolve similar representations of the target suggesting that discrepancies in the neural representations of the world for perception and eye movements lead to lower visual search performance and are not favored by evolution . | neuroscience/psychology, neuroscience/cognitive neuroscience, neuroscience/natural and synthetic vision, computational biology/computational neuroscience | null |
journal.ppat.1002188 | 2,011 | Histo-Blood Group Antigens Act as Attachment Factors of Rabbit Hemorrhagic Disease Virus Infection in a Virus Strain-Dependent Manner | Rabbit hemorrhagic disease virus ( RHDV ) , a single stranded positive-sense RNA virus belonging to the Lagovirus genus of the Caliciviridae family , is the cause of rabbit hemorrhagic disease ( RHD ) , a disease affecting wild and domestic rabbits of the Oryctolagus cuniculus species ., RHD was first described in Angora rabbits in China in 1984 ., By 1987 RHD was detected in Czechoslovakia and Italy , and rapidly expanded to most European countries 1 ., RHDV usually kills rabbits within 48 to 72 hours of infection ., The disease is characterized by acute necrotizing hepatitis and haemorrhages , sometimes preceded by tracheitis and generally associated with disseminated intravascular coagulation in many organs , particularly the lungs , heart and kidneys ., There are three different clinical courses of RHD , the peracute form is distinguished by sudden death with no previous clinical signs ., The acute form of RHD involves depression , anorexia , apathy , rapid respiration , anemia and some animals show signs of abdominal distress ., Animals perish after one to three days ., The sub acute form involves slight clinical symptoms and the animals recover within 2–3 days 2 , 3 ., Mortality rates are as high as 50–90% although rates are lower in young animals less than 6–8 weeks-old , and no mortality occurs in animals less than 4 weeks-old ., Kittens can become infected and shed virus but do not show clinical signs of the disease ., The most common routes of infection are the oral and upper respiratory routes , mainly through direct contact between animals or through contact with water or contaminated food ., The virus is present in the blood , organs , secretions and skin or fur of infected animals ., It is excreted in large amounts through urine and feces and can also be spread by insects 4 ., In addition RHDV is resistant in the environment particularly in dry conditions and to date there is no evidence that RHDV can infect other species 5 ., RHDV has become endemic in the original distribution range of the rabbit , Spain , Portugal and France , where it has caused severe long term decline of rabbit population size 6 , 7 ., The drastic decline also threatens species dependent on rabbits such as the Iberian lynx ( Lynx pardinus ) and the Spanish imperial eagle ( Aquila adalberti ) , which are specialist predators , and to a lesser extent the Bonelli eagle ( Hieraaetus fasciatus ) ., The decline of wild rabbit populations has also had an impact on lizard populations , which use rabbit warrens during hot summer periods 8 ., In addition , RHDV can cause devastating losses for rabbit producers , although efficient vaccines are commercially available allowing protection of farmed rabbits 9 ., Anti-RHDV antibodies have been detected in wild rabbit serum sampled prior to the reported emergence of RHDV 10 ., This has led to speculation that non-pathogenic RHDV strains may have been circulating in rabbit populations prior to the first detected RHDV outbreak ., Such strains were indeed discovered later in countries such as Italy , France and Australia 11 , 12 , 13 , 14 ., Infection by these non-pathogenic strains has been reported to confer complete protection 11 , partial protection 15 or no protection 12 , 16 against RHDV in rabbits through cross-recognizing antibodies ., The genetic diversity between RHDV isolates is quite low even between isolates that are not geographically correlated ., Indeed , the nucleotide and amino acid differences between strains range between 1–10% and 1–6% , respectively , which is far lower than the differences observed for other caliciviruses 12 , 16 ., Nevertheless , it has been suggested that French RHDV isolates can be assigned into six genetic groups , G1 to G6 following spatio-temporal distribution 17 ., In France , G1 has almost completely disappeared and is found exclusively in the south-west near the Spanish border ., G2 , in which had been included the strain isolated in the first reported outbreak in China in 1984 , has not been isolated recently ., The genetic group G4 emerged from G3 , while G5 and G6 appeared as new independent groups 17 , the latter corresponding to the first antigenic variant identified , RHDVa 18 ., More recently , the pathogenic forms of RHDV were shown to cluster into four major groups 19 , 20 with the genetic groups G3 , G4 and G5 as an artificial subdivision of Group 4 identified by Kerr and co-workers , which clusters Western Europe and Bahrain strains collected from 1989 onward ., RHDV has previously been shown to bind the oligosaccharide H type 2 and A type 2 ( Fig 1A ) , histo-blood group antigens ( HBGAs ) expressed on the duodenal surface and trachea of rabbits , two possible doors of entry for the virus 21 ., HBGAs are polymorphic carbohydrate structures representing terminal exposed portions of larger glycans O- or N-linked to proteins or to glycolipids ., In many vertebrate species they are mainly expressed on epithelial cells and only a few primate species , including humans , express them on vascular endothelial cells and erythrocytes ., They are synthesized by stepwise addition of monosaccharide units from several precursors by specific glycosyltransferases ( Fig . 1 ) ., The recent occurrence of the highly pathogenic RHDV with a documented HBGA binding ability can be expected to provide a useful model to study the impact of the virus on the hosts HBGA diversity and reciprocally of the host diversity on the virus HBGA-binding properties ., We have here determined host diversity regarding HBGA expression on the duodenum surface , a likely point of entry for the virus , and strain-specific binding of RHDV by examining the binding to synthetic sugars , haemagglutination and binding to the duodenal mucosa of both wild and domestic rabbits ., The role of each glycan for binding was determined through specific enzymatic removal of the monosaccharide comprising the A , B or H epitopes ., Host variation was determined through semi-quantitative A , B and H phenotyping of rabbit duodenums and structural characterization of glycans by mass spectrometry ., The role of HBGA binding in RHDV infection was further tested by challenging AB negative and AB positive rabbits with a strain largely dependent on AB binding , revealing an important role of HBGA binding at lower dose infections ., In addition , preliminary evidence for selection of weak-binding ABH phenotypes were detected in wild rabbit populations following RHDV outbreaks ., Understanding the selection of weak-binding RHDV phenotypes is important as resistance to infection generates problems for controlling the large rabbit population in Australia and provides possibilities of selecting RHDV resistant animals in areas where the rabbit populations are threatened ., The strains used in this study were chosen to represent each of the six chronologically established genetic groups ( G1–G6 ) previously identified by Le Gall-Reculé and co-workers 17 ., To analyze the phylogenetic position of these strains , a neighbor-joining tree was constructed using complete nucleotide sequences of the capsid gene ., Similar to the finding of Kerr et al . 20 , the resulting phylogenetic diagram shows a division of the analyzed strains into four rather than six clades with G3 , G4 and G5 forming a major group while G1 , G2 and G6 are clearly distinct highly supported groups ( Fig 2 ) ., G1 corresponds to strains that circulated in Western Europe during the first RHDV outbreaks 17 and now exclusively circulate in the Iberian Peninsula and sporadically in the South of France 19 , 22 ., G6 corresponds to the antigenic variant strains ( RHDVa ) first described by Capucci et al 18 ., To address the question of carbohydrate binding of RHDV , six different strains designated G1–G6 were used ., RHDV liver extracts with high virus titres , at least 1×1010 viral RNA copies , were used to screen a panel of 38 polyacrylamide ( PAA ) -conjugated oligosaccharides and 19 human serum albumin ( HSA ) - conjugated oligosaccharides ( see Table S1 ) ., The carbohydrates with any capacity to bind RHDV were then used to determine binding over a range of RHDV dilutions ( Fig 3 ) ., The dilution corresponding to equivalent amounts of viral RNA , determined through real time RT-PCR , are shown in the figure with a vertical line ., The antigenic variant G6 was detected with a mouse monoclonal antibody 2G3 previously determined to bind G6 strains as well as all other pathogenic RHDV strains ( kindly provided by Lorenzo Capucci ) ., G1–G5 were on the other hand detected with a high-titered anti-RHDV rabbit serum and the amount of G6 may therefore not be completely comparable to that of the other strains ., All strains showed strongest binding to B type 2 ., G1 was the only strain showing strong binding to Ley and the binding to H type 2 , A type 2 and B trisaccharide varied between the strains tested ., Therefore H type 2 is not the only HBGA which may be of relevance for RHDV binding and individual strains show distinct specificities for synthetic oligosaccharides ., Human red blood cells ( RBC ) carry A , B and H type 2 on their surface , all of which may be ligands of the G1–G6 strains tested on synthetic sugars ., Therefore the ABH expression in this non-synthetic system was used to test for RHDV strain binding ., Moreover , RHDV strains have previously been described to be either haemagglutinating or non-haemagglutinating 23 ., We tested all 6 strains of RHDV on human A , B and O blood ( Table 1 ) ., All RHDV strains were able to agglutinate B blood consistent with B type 2 recognition ., The G2 and G3 strains also showed strong binding to H type 2 on O RBC ., G1 and G5 showed weak binding to O RBC , while G4 and G6 did not agglutinate O blood ., G1 was able to agglutinate A blood to the same extent as B , whilst all other strains showed weak agglutination of A , indicating B type 2 as a ligand for G1–G6 , H type 2 as a ligand for G2 and G3 and A as a ligand for G1 ., To study HBGAs expression and distribution in the rabbit duodenum and trachea , the proposed sites of viral entry , as well as in the liver , a major site of replication , monoclonal antibodies against A and B as well as Ulex europaeus lectin ( UEA-I ) were used to detect A , B and H type 2 ( Ley ) on wild rabbit tissue sections , respectively ( Fig 4 ) ., HBGA expression was always restricted to epithelial cells ., The nine French wild rabbits tested were found to be either A and B positive ( A+B+ ) at the duodenum surface or A and B negative ( A−B− ) ., Staining with UEA-I was much stronger and homogenous on sections from A−B− rabbits ( data not shown ) , suggesting partial masking of H type 2 by the A and B epitopes ., In addition , expression of the B antigen appeared to differ from those of the A and H antigens ., A , B and H were expressed on the crypts of Lieberkühn ( surface layer of the mucosa ) and not on the Brünners glands ( deep layer ) ., Yet , staining by the anti-B appeared more patchy and irregular than staining by the anti-A and it was always weaker ., In addition , in A+B+ animals , A antigen , but not B antigen was detected on the surface of the trachea and on the biliary ducts of portal spaces in the liver ., Neither A , B or H antigen could be detected on the liver parenchyma ., In order to get insights into the diversity of HBGA expression in rabbits , a more quantitative assay was needed ., Therefore , fresh rabbit duodenums were collected from 84 rabbits of both wild and laboratory origin for more detailed studies of ABH expression and RHDV binding ., A semi-quantitative ELISA system for rabbit duodenum scrapings determined that all animals do express either H type 2 , A or B . No animal was found to be of a clear non-secretor phenotype as described for humans , and all rabbits expressed detectable levels of either H type 2 or A and/or B , albeit with great individual variations and where A expression generally was stronger than B . Ranking animals by increasing H type 2 detection clearly showed an inverse relationship with A expression and to a lesser degree with B expression , indicating that A and B epitopes mask the H motif ( Fig 5 and Table S2 ) ., Based on the detection of the A or B antigens , animals were phenotyped A+B+ , A−B− or A+B− with the respective frequencies 0 . 52 , 0 . 38 and 0 . 1 respectively ., The carbohydrates of the rabbit duodenum were further characterized through mass spectrometry ( MS ) ., Both N-linked and O-linked glycans were analyzed and blood group antigens have been mainly found on O-glycans , while N-glycans are largely terminated with galactose or sialic acid with virtually no fucose on their antennae ( Fig S1 ) ., Many of the O-glycans peaks identified through MALDI-TOF analysis ( Table S4 ) have been shown to be a mixture of different structures after MS/MS analysis ( Fig S2 ) ., In O-glycans , we observed a predominance of core 2 structures , followed by core 3 ( Fig 1C ) ., The smallest fucosylated glycan was observed at m/z 708 , corresponding to a core 1 fucosylated trisaccharide bearing a blood group H epitope , while the largest fucosylated glycan had a composition of 8 residues arranged on a biantennary core 2 structure ( m/z 1822 ) with one antenna carrying an A blood group epitope ( Fig 6 ) ., Most of the samples analyzed showed high abundance peaks at m/z 708 , and/or at mass 954 , a trisaccharide with composition HexNAc2 , Fuc , Hex , corresponding to different structures in different samples: after MS/MS sequencing , a mixture of core 1 , 2 and 3 structures bearing blood group H , A and Lewis epitopes have been observed ( Fig 6 and Fig S2 ) ., A peak at m/z 1199 , with composition HexNAc3 , Fuc , Hex , has also been observed in most of the samples analyzed , showing after MS/MS sequencing a core 3 structure carrying an H or Lewis X epitope in some of the rabbits , while in other rabbits it was recognized as a core 2 , 3 and 4 with terminal A blood group on one antenna ( Fig 6 and Fig S2 ) ., In 3 of the 10 samples analyzed , a peak at m/z 1373 was found , with composition HexNAc3 , Fuc2 , Hex , and after further analysis it was found to be a core 3 structure carrying a blood group A epitope ( Fig S3 ) ., Detailed compositions of O-glycans of the 10 samples analyzed are reported in Table S3 ., In summary , we observed a high variety of O-glycan structures carrying blood group antigens H , A and B ( Table S3 ) ., Blood group antigen B has been detected in samples 1 , 4 , 5 , 6 , 8 , and 9 for a total of six B positive rabbits out of ten analyzed , in accordance with data from antibody binding analysis , while blood group antigen H has been detected in nine out of ten samples analyzed and only sample 9 did not show any glycan bearing H blood group epitopes ., Mass spectrometry analysis could not distinguish between type 1 or type 2 based antigens but we observed poor reactivity on rabbit duodenal extracts of an anti-H type 1 specific antibody compared to the UEA-I reactivity , recognizing H type 2 ., On human saliva from O secretors , both reagents reacted equally well , suggesting that in rabbit duodenum , there is little , if any , type 1 based histo-blood group antigens ( data not shown ) ., In all of the 10 rabbits , both wild and domestic rabbits , detection of the B type 3 structure by mass spectrometry matched the B phenotype obtained with an anti-B antibody showing broad specificity toward all types of B antigens ( Table 2 ) ., In contrast , detection of A histo-blood group structures by mass spectrometry and using a broadly reactive anti-A did not match ., Thus , although three animals were unequivocally phenotyped as A- , the A type 3 structure was found in all 10 rabbit duodenum samples analyzed by mass spectrometry ., A type 2 was only found in the rabbits that through antibody detection were determined to be A+ , though two of the A+ phenotyped rabbits did not express A type 2 ., In order to determine if the discrepancy between the MS and the phenotyping results for the A type was not due to the specific anti-A that was used in the first place , the A- rabbits were then phenotyped again with several other broad binding , anti-A specific antibodies with confirmed recognition of the PAA-conjugated A trisaccharide ., Yet , despite attempts to amplify the signal , the A- phenotyped rabbits remained A- , indicating that in some animals , although present , A type 3 epitopes are not detected on duodenum extracts by ELISA or immunohistochemistry , and the phenotypes of the 10 rabbit duodenum samples as determined by ELISA are noted in Table 2 ., Despite this discrepancy , it remains that both the A+ versus A− and B+ versus B− phenotypic dichotomies are as clear-cut as the A or B versus O distinction between humans ., To determine the sites of RHDV attachment to the duodenum , A+B+ rabbit duodenum sections were used and all six strains were found to bind to the duodenum surface but not the Brünners gland , in accordance to A , B and H type 2 expression as discussed above ., Binding was also detected on the surface epithelium of the trachea that expresses detectable amounts of H type 2 epitopes despite expression of A antigen , but not on the biliary ducts in the liver where B and H type 2 epitopes are not detectable in A+B+ animals ( Fig 4 ) ., The six strains of RHDV were then analyzed for binding to duodenum extracts using the same semi-quantitative system as for ABH phenotyping ., Virus binding and ABH phenotypes were normalized , to account for variation of duodenum scraping , against Concanavalin A binding , a lectin which binds mannose of N-glycans ., To analyze relationships between A , B or H expression and virus binding , animals were separated into three equal groups of weak , medium and strong binders and correlated to A , B and H presence or absence ., G2 binding was significantly correlated to H type 2 expression ., G3 binding did not correlate with the presence of either A , B or H antigen ., G4 , G5 , G6 and G1 binding significantly correlated to A and B expression and inversely correlated to H type 2 expression , with the exception of G6 binding which did not correlate to B expression ( Table 3 , Table S5 ) ., It should be noted that most animals that express A also express B ( A+B+ or A−B− phenotypes ) ., Therefore in this association study , A and B antigens are linked ., Nevertheless , a small subgroup of rabbits expresses A independently of B ( A+B− phenotype ) ( Table S2 ) ., This subgroup was significantly associated with low G2 binding ( p\u200a=\u200a0 . 006 ) and inversely with strong G4 , G5 , G6 and G1 binding ( p<0 . 05 ) ., In order to visualize differences in binding of these strains to individual animals , rabbits were ranked according to G4 binding in increasing order ( Fig 7 ) ., Keeping the same order of relative binding for the five other strains clearly showed important individual differences with animals strongly recognized by some strains but poorly by others ., Each strain showing a unique binding pattern , despite similar binding characteristics of G4 , G5 and G6 as described above ., To confirm that binding of each strain to rabbit duodenum extracts required the presence of A , B or H epitopes and to get a more complete picture of the strains specificities , A , B or H antigens of duodenal extracts were removed with specific glycosidases prior to virus binding assay ., Extracts from three rabbits of three different phenotypes ( A−B− , A+B+ and A+B− ) were treated with either a galactosidase or an N-acetyl galactosaminidase , removing the galactose ( Gal ) of B or the N-acetylgalactosamine ( GalNAc ) of A respectively , to determine the role of A and B binding in the duodenum , followed by an α1-2 fucosidase to remove the fucose of H type 2 ( Fig 8 ) ., Efficacy of each enzyme was determined by testing the binding of either an anti-A , an anti-B or UEA-I before and after treatment ., The results shown in Fig 8D indicate that the fucosidase removed H type 2 almost completely , whereas treatments with either the N-acetyl galactosaminidase or the galactosidase only resulted in a partial removal of the A and B antigens , respectively ., Removing the fucose of H type 2 confirmed that binding of all RHDV strains to A−B− rabbits was dependent on H ( Fig 8A ) ., In an A+B− rabbit , cleavage of the GalNAc sugar of A followed by removal of the fucose of H type 2 confirmed the significance of RHDV binding and the importance of A antigen expression ( Fig 8B ) ., Indeed , for G2 which was the only strain unable to recognize the A synthetic oligosaccharide and to show no relationship between RHDV binding and A expression , cleavage of the GalNAc residue of A allowed binding since it resulted in appearance of the underlying H type 2 , which became accessible for binding ., Inversely , the G1 strain preferred binding to A over H type 2 , as seen in the decreased binding after removal of the A epitope , consistent with the results of agglutination ., The other strains were not affected by the removal of A but showed a clear decrease of binding following removal of both A and H , indicating similar binding to A as to H type 2 on the duodenum extracts ., Cleavage of the Gal and GalNAc followed by removal of H type 2 of an A+B+ rabbit indicated a major importance of B in the presence of A , as removal of the Gal of B followed by removal of the fucose of the underlying H type 2 abolished binding for G1 , G2 , G3 and G5 ( Fig 8C ) ., G4 and G6 binding also preferably bound B over A as removal of B decreased binding , whereas removal of A did not affect binding , or even slightly increased RHDV binding ., However , neither G4 nor G6 binding was further decreased after removal of H as A was still expressed in the duodenal scrapings , allowing for binding of G4 and G6 ., Thus , the results of RHDV strain binding so far indicates binding of G2 to H type 2 and B , while the other strains are able to bind A , B and H type 2 with variable strength ., Human norovirus binding to HBGAs has been determined to correlate with symptomatic infection in human volunteer studies and in outbreak studies ., To determine the importance of carbohydrate binding regarding RHDV infection , domestic rabbits were challenged with the G4 strain ( GenBank accession number AJ535094 ) ., This genetic group was chosen because it proved to be strongly dependent on binding to A and B antigens and because a breed of rabbits with a previously determined high A−B− frequency ( 63% ) was available at the animal facility ., Pre-challenge serum was collected from 6 of the animals , and no RHDV antibodies were detected ., In addition , the rabbits from this animal facility proved negative during routine screening for the non-pathogenic calicivirus strain ., Furthermore , any protection from cross-reacting antibodies can be excluded as non-pathogenic calicivirus strains circulating in France have been shown to provide no protection against RHD 12 ., The 12 week old rabbits were then infected orally with 105 , 107 or 109 genome copies of a G4 liver extract ., At necropsy of dead rabbits , typical RHD lesions were observed ., Surviving animals were sacrificed 11 days after infection , however one rabbit within the highest infectious dose group was sacrificed 7 days post infection due to ethical considerations as it seemed severely ill ., Post-mortem examination confirmed the presence of RHD lesions ., Duodenum and liver samples were collected from all of the rabbits post-mortem ., 7/10 , 6/11 and 3/10 rabbits died from RHDV with an average survival time of 3 . 5 days , 3 . 5 days and 5 days in the groups infected with 109 , 107 and 105 genome equivalents , respectively ( Fig S5 ) ., Post-mortem ABH duodenum phenotyping of the rabbits determined 3 , 3 and 4 A+B+ rabbits in the 105 , 107 and 109 infectious dose groups , respectively , the remaining animals being A−B− ( Table 4 ) ., Analysis of the ABH duodenum phenotype and G4 binding to the duodenum of all infected animals resulted in a B expression well correlated with virus binding of the rabbit duodenum of the infected animals ( r2\u200a=\u200a0 . 78 ) ( Fig 9A ) ., Nevertheless , it should be noted that all animals were recognized by the G4 strain , albeit with great individual variation ., Real time RT-PCR of RNA isolated from the duodenum and liver of the rabbits revealed viral replication in the liver of all animals ( Fig 9B ) ., The RNA levels in both liver and duodenum of rabbits succumbed to infection were significantly higher than viral RNA levels of rabbits sacrificed at 11 days post infection ( Mann-Whitney , p<0 . 0001 ) ., It should be noted that this difference might be partly due to the difference in sampling time between dead and surviving rabbits ( 3–5 days vs 7–11 days ) ., Within the rabbits infected with the lowest dose of 105 genome copies all A+B+ rabbits ( n\u200a=\u200a3 ) died , while all A−B− rabbits ( n\u200a=\u200a7 ) survived the infection ( p\u200a=\u200a0 . 008 , Fischers exact test ) ( Fig 9C , D , Table 4 ) ., This was however not the case for the two higher-dose challenges ( Fig S4 , Table 4 ) , indicating that A and B binding facilitates infection , though the lack of A and B antigens can be overcome by a high viral dose ., In order to study possible selection of ABH phenotypes after RHDV outbreaks , rabbit duodenums were collected from two populations located 15 km apart near Perpignan , southern France , where detailed information was available regarding RHDV ., Rabbits were sampled by hunters from the two different populations , Claira and Canohès ., The Claira population had never been affected by an RHDV outbreak ., The Canohès population was heavily reduced during September 2006 by RHDV , where G5 was the primary RHDV- circulating in the area , though Iberian G1 strains were also detected during this period , and the population size strongly decreased ( Stéphane Marchandeau , personal communication ) ., Twenty two rabbit duodenums were collected in 2009 from the Claira population and only 5 from Canohès due to the low density of rabbits in the population ., All of the 5 rabbits sampled from the Canohès population were B- , and all of them , either of the A+B− and A−B− subtypes , were significantly lower binders of G5 , and similar binding results were seen with G1 ( Table 5 ) ., In contrast , the Claira population where RHDV had never been detected had a high frequency of A+B+ ( 82% ) and therefore only few B− animals ( 18% ) ., Since the G5 strain binds preferentially to the B antigen , these results suggest that the B− phenotype could have been selected at Canohès following the devastating 2006 outbreak ., In Australia , rabbits have been repeatedly infected with the G2 Czech strain of RHDV to control the rabbit population ., Infecting a rabbit population with the same strain gives an interesting perspective to study selection from a single RHDV strain ., Rabbits were sampled at three different locations , Hattah , Bendigo and Bacchus Marsh ., Experimental challenges with RHDV have shown the Hattah and Bacchus Marsh populations to have developed partial resistance to infection ( Brian Cooke , personal communication ) and the non-pathogenic , partially protecting virus RCV-A1 has been detected in the Bendigo and Bacchus Marsh populations , but not in the Hattah population 15 ., Rabbit duodenum extracts were analyzed for ABH phenotype and G2 strain binding ., Similar to the above described French G2 strain , the Czech G2 strain bound Australian A+B− rabbits poorly ( Table 6 ) ., The G2 Czech strain also showed binding to synthetic B and H , but not A , similar to the French G2 strain used above ., In addition , both strains showed poor binding to the A+B− individuals regardless of the animals origin ( data not shown ) ., Hattah was the population of the significantly highest frequency of A+B− rabbits and inversely with the lowest frequency of A−B− animals , which are most frequently strongly recognized by the G2 strains , suggesting selection for a subgroup of rabbits with potential of protection against infection with a G2 strain ., A recent study of the phylodynamics of RHDV indicated that France has been the most important source population for RHDV 20 ., Although this may be due to sampling bias , a chronological relationship matching their phylogenetic positions has been established in France for the G2 to G5 strains 17 ., The G1 strain used in the present study is a recent strain of Iberian origin and G6 strains showed no apparent chronological link with other strains ., In France , G1 and G2 , which includes the strain isolated in the first reported outbreak in China in 1984 , have not been isolated since 1990 , though G1 currently circulates almost exclusively on the Iberian Peninsula ., In addition , since 2000 a few Iberian G1 strains have been identified in the South of France , along the Spanish border 19 ., This may be the result of virus spread across the Pyrenean Mountains via insects or the wind 22 ., The Iberian strains , suspected to originate from a single introduction of G1 , have evolved separately from the other RHDV strains and cluster into 6 Iberian clades ( IB1 to IB6 ) 22 ., G3 has been isolated in France between 1990–1997 and G4 has been isolated from 1993–1999 ., G5 , originally first detected in 1994 and G6 first detected in 1999 are both currently circulating in France ., G6 corresponds to the first antigenic variant identified , RHDVa 18 ., Although the neighbor-joining tree constructed using nucleotide sequences allowed the allocation of the strains tested into each of the six previously identified genetic groups , it is comparable to the topology presented by Kerr et al . 20 ., That the G3 , G4 and G5 genetic groups did not appear as clearly independent as previously reported 17 might be due to the use of complete nucleotide sequences of the capsid in the more recent studies 20 rather than just partial sequences used in the earlier study 17 ., Alternatively , this can also be a result of the inclusion of strains that cover most of the worldwide genetic diversity ., Nevertheless , the topology of the tree is highly supported by the bootstrap values which are all above 90% for the major nodes ., Neighbour joining trees were constructed using amino acids sequences of the entire capsid or of the P2 subdomain with the aim to infer a correlation between the HBGA binding profiles and the evolution of RHDV ., No such correlation was observed ( data not shown ) ., Regardless , the trees constructed using either nucleotide or amino acid sequences showed that that the six selected virus strains G1 to G6 represent a good cross section of the antigenic diversity amongst the known pathogenic forms of RHDV ., The gastrointestinal tract is protected by a thick layer of O-glycans constituting the glycocalix of epithelial cells or presented as soluble mucins ., It is therefore not uncommon for pathogens of the gastrointestinal tract to interact with such carbohydrates to be able to access the underlying epithelial cell membrane ., A major route of transmission of RHDV is the fecal-oral route and so far viral RNA of the closely related non-pathogenic viruses have been exclusively recovered from the small intestine 11 , 12 , 13 , suggesting that these viruses are primarily enteric viruses but that the pathogenic strains do not remain confined in the gut 24 , 25 ., Since an RHDV strain was previously shown to bind to a carbohydrate structure expressed in the gastrointestinal and upper respiratory tracts of rabbits 21 , in the present study we first aimed at determining if this characteristic was shared by other pathogenic strains belonging to different clusters of the RHDV phylogeny and this was analyzed by several methods ., Analysis of the binding to a set of HBGA related synthetic neoglycoconjugates revealed distinct binding patterns between strains , although strong binding to the B type 2 motif was common to all strains ., The strong H type 2 binding previously observed for a G2 strain was confirmed but the magnitude of H type 2 binding was quite variable among strain | Introduction, Results, Discussion, Material and Methods | Rabbit Hemorrhagic disease virus ( RHDV ) , a calicivirus of the Lagovirus genus , and responsible for rabbit hemorrhagic disease ( RHD ) , kills rabbits between 48 to 72 hours post infection with mortality rates as high as 50–90% ., Caliciviruses , including noroviruses and RHDV , have been shown to bind histo-blood group antigens ( HBGA ) and human non-secretor individuals lacking ABH antigens in epithelia have been found to be resistant to norovirus infection ., RHDV virus-like particles have previously been shown to bind the H type 2 and A antigens ., In this study we present a comprehensive assessment of the strain-specific binding patterns of different RHDV isolates to HBGAs ., We characterized the HBGA expression in the duodenum of wild and domestic rabbits by mass spectrometry and relative quantification of A , B and H type 2 expression ., A detailed binding analysis of a range of RHDV strains , to synthetic sugars and human red blood cells , as well as to rabbit duodenum , a likely gastrointestinal site for viral entrance was performed ., Enzymatic cleavage of HBGA epitopes confirmed binding specificity ., Binding was observed to blood group B , A and H type 2 epitopes in a strain-dependent manner with slight differences in specificity for A , B or H epitopes allowing RHDV strains to preferentially recognize different subgroups of animals ., Strains related to the earliest described RHDV outbreak were not able to bind A , whereas all other genotypes have acquired A binding ., In an experimental infection study , rabbits lacking the correct HBGA ligands were resistant to lethal RHDV infection at low challenge doses ., Similarly , survivors of outbreaks in wild populations showed increased frequency of weak binding phenotypes , indicating selection for host resistance depending on the strain circulating in the population ., HBGAs thus act as attachment factors facilitating infection , while their polymorphism of expression could contribute to generate genetic resistance to RHDV at the population level . | Rabbit hemorrhagic disease virus ( RHDV ) , detected as late as 1984 , has spread to large parts of the world , threatening rabbit populations and other species dependent on rabbits in many European countries ., Mortality has been shown to be as high as 90% and rabbits are killed 48 to 72 hours after infection ., Related viruses called noroviruses , infect humans in a manner dependent on the expression of histo-blood group antigens ( HBGAs ) , which are not only expressed on red blood cells , but also on epithelial cells , in saliva and on mucins of the intestinal tract ., RHDV also binds to HBGA and in this report we characterize binding of strains of all genetic groups of RHDV to different HBGAs ., We also demonstrate HBGAs to function as attachment factors in a challenge experiment ., As polymorphisms of genes involved in HBGA synthesis divide the rabbit population into different subgroups , we find selection of low-binding subgroups of wild rabbits in populations recovering from devastating outbreaks of RHDV ., This is the first demonstration of differential HBGA specificities of RHDV strains , description of function in infection and demonstration of host selection due to RHDV infection based on HBGA phenotype . | veterinary diseases, emerging infectious diseases, veterinary microbiology, virology, microbial pathogens, biology, evolutionary biology, microbiology, host-pathogen interaction, evolutionary genetics, animal management, veterinary science | null |
journal.pgen.1002430 | 2,011 | Azospirillum Genomes Reveal Transition of Bacteria from Aquatic to Terrestrial Environments | Fossil records indicate that life appeared in marine environments ∼3 . 5–3 . 8 billion years ago ( Gyr ) 1 and transitioned to terrestrial ecosystems ∼2 . 6 Gyr 2 ., The lack of fossil records for bacteria makes it difficult to assess the timing of their transition to terrestrial environments; however sequence analysis suggests that a large clade of prokaryotic phyla ( termed “terrabacteria” ) might have evolved on land as early as 3 Gyr , with some lineages later reinvading marine habitats 3 ., For example , cyanobacteria belong to the terrabacterial clade , but one of its well-studied representatives , Prochlorococcus , is the dominant primary producer in the oceans 4 ., Bacteria of the genus Azospirillum are found primarily in terrestrial habitats , where they colonize roots of important cereals and other grasses and promote plant growth by several mechanisms including nitrogen fixation and phytohormone secretion 5 , 6 ., Azospirillum belong to proteobacteria , one of the largest groups of “hydrobacteria” , a clade of prokaryotes that originated in marine environments 3 ., Nearly all known representatives of its family Rhodospirillaceae are found in aquatic habitats ( Figure 1 and Table S1 ) suggesting that Azospirillum represents a lineage which might have transitioned to terrestrial environments much later than the Precambrian split of “hydrobacteria” and “terrabacteria” ., To obtain insight into how bacteria transitioned from marine to terrestrial environments , we sequenced two well studied species , A . brasilense and A . lipoferum , and a third genome of an undefined Azospirillum species became available while we were carrying out this work 7 ., Horizontal gene transfer has been long recognized as a major evolutionary force in prokaryotes 12 ., Its role in the emergence of new pathogens and adaptation to environmental changes is well documented 32 ., While other recent studies indicate that HGT levels in natural environments may reach as much as 20% of a bacterial genome 33 , our data suggest that HGT has affected nearly 50% of the Azospirillum genomes , in close association with dramatic changes in lifestyle necessary for transition from aquatic to terrestrial environments and association with plants ., Emergence of these globally distributed plant-associated bacteria , which appear to coincide with radiation of land plants and root development , likely has dramatically changed the soil ecosystem ., The genome of Azospirillum lipoferum 4B was sequenced by the whole random shotgun method with a mixture of ∼12X coverage of Sanger reads , obtained from three different libraries , and ∼18X coverage of 454 reads ., Two plasmid libraries of 3 kb ( A ) and 10 kb ( B ) , obtained by mechanical shearing with a Hydroshear device ( GeneMachines , San Carlos , California , USA ) , were constructed at Genoscope ( Evry , France ) into pcDNA2 . 1 ( Invitrogen ) and into the pCNS home vector ( pSU18 modified , Bartolome et al . 34 ) , respectively ., Large inserts ( 40 kb ) ( C ) were introduced into the PmlI site of pCC1FOS ., Sequencing with vector-based primers was carried out using the ABI 3730 Applera Sequencer ., A total of 95904 ( A ) , 35520 ( B ) and 15360 ( C ) reads were analysed and assembled with 504591 reads obtained with Genome Sequencer FLX ( Roche Applied Science ) ., The Arachne “HybridAssemble” version ( Broad institute , MA ) combining 454 contigs with Sanger reads was used for assembly ., To validate the assembly , the Mekano interface ( Genoscope ) , based on visualization of clone links inside and between contigs , was used to check the clones coverage and misassemblies ., In addition , the consensus was confirmed using Consed functionalities ( www . phrap . org ) , notably the consensus quality and the high quality discrepancies ., The finishing step was achieved by PCR , primer walks and transposon bomb libraries and a total of 5460 sequences ( 58 , 602 and 4800 respectively ) were needed for gap closure and quality assessment ., The genome of strain Azospirillum brasilense Sp245 was sequenced by the whole random shotgun method with a mixture of ∼10X coverage of Sanger reads obtained from three different libraries and ∼25X coverage of 454 reads ., A plasmid library of 3 kb , obtained by mechanical shearing with a Hydroshear device ( GeneMachines , San Carlos , California , USA ) , were constructed at Plant Genome Mapping Laboratory ( University of Georgia , USA ) into pcDNA2 . 1 vector ( Invitrogen ) ., Large inserts ( 40 kb ) were introduced into the PmlI site of pCC1FOS ., Sequencing with vector-based primers was carried out using the ABI 3730 Applera Sequencer ., The Arachne “HybridAssemble” version combining 454 contigs with Sanger reads was used for assembly ., Contig scaffolds were created using Sequencher ( Gene Codes ) and validated using clone link inside and between contigs ., AMIGene software 35 was used to predict coding sequences ( CDSs ) that were submitted to automatic functional annotation 36 ., The resulting 6233 A . lipoferum 4B CDSs and 7848 A . brasilense Sp245 CDSs were assigned a unique identifier prefixed with “AZOLI” or “AZOBR” according to their respective genomes ., Putative orthologs and synteny groups were computed between the sequenced genomes and 650 other complete genomes downloaded from the RefSeq database ( NCBI ) using the procedure described in Vallenet et al . 36 ., Manual validation of the automatic annotation was performed using the MaGe ( Magnifying Genomes ) interface ., IS finder ( www-is . biotoul . fr ) was used to annotate insertion sequences 37 ., The A . lipoferum 4B nucleotide sequence and annotation data have been deposited to EMBL databank under accession numbers: FQ311868 ( chromosome ) , FQ311869 ( p1 ) , FQ311870 ( p2 ) , FQ311871 ( p3 ) , FQ311872 ( p4 ) , FQ311873 ( p5 ) , FQ311874 ( p6 ) ., The A . brasilense Sp245 nucleotide sequence and annotation data have been deposited at EMBL databank under accession numbers: HE577327 ( chromosome ) , HE577328 ( p1 ) , HE577329 ( p2 ) , HE577330 ( p3 ) , HE577331 ( p4 ) , HE577332 ( p5 ) , HE577333 ( p6 ) ., In addition , all the data ( i . e . , syntactic and functional annotations , and results of comparative analysis ) were stored in a relational database , called AzospirilluScope 36 , which is publicly available at http://www . genoscope . cns . fr/agc/mage/microscope/about/collabprojects . php ?, P_id=39 ., BLAST searches were performed using NCBI toolkit version 2 . 2 . 24+ 38 ., Multiple sequence alignments were built using the L-INS-i algorithm of MAFFT 39 with default parameters ., Phylogenetic tree construction was performed using PhyML 40 with default parameters unless otherwise specified ., 16S rRNA sequences were retrieved from the Ribosomal Database Project 41 ., A concatenated ribosomal protein tree was constructed from sequenced members of alpha-proteobacteria with a 98% 16S rRNA sequence identity cutoff to limit overrepresentation ., The following ribosomal proteins were used: L3 , L5 , L11 , L13 , L14 , S3 , S7 , S9 , S11 , and S17 ., The proteins were identified using corresponding Pfam models and HMMER 42 searches against the genomes of sequenced alpha-proteobacteria selected above ., The sequences were aligned and concatenated ., GBlocks 43 with default parameters was used to reduce the number of low information columns ., The tree was constructed using PhyML with the following options: empirical amino acid frequencies , 4 substitution categories , estimated gamma distribution parameter , and NNI tree topology search ., Protein sequences queries from all 3 Azospirillum genomes were used in BLAST searches against the non-redundant microbial genome set constructed by Wuichet and Zhulin 26 supplemented with sequenced members of Rhodospirillales absent in the original set ( Acetobacter pasteurianus IFO 3283-01 , alpha proteobacterium BAL199 , Magnetospirillum gryphiswaldense MSR-1 , and Magnetospirillum magnetotacticum MS-1 ) ., E-value cutoff of 10∧−4 was used ., Only the first occurrence of each species was used in ancestry assignment ., Proteins were assigned as being ancestral or horizontally transferred , with varying degrees of confidence , based on the presence of members of Rhodospirillales and Rhodospirillaceae in the top eight BLAST hits ., Ancestral assignment was based on the top 8 hits , based on the number of Rhodospirillaceae genomes in the database: 2 Azospirillum , 3 Magnetospirillum , 2 Rhodospirillum , and Nisaea sp ., BAL199 , excluding the organism on which ancestry assignment is being performed ., High confidence ancestral proteins have at least 6 of the top 8 species belonging to Rhodospirillales or all but 1 , if the BLAST result had less than 8 species ., This rule allows for 1–2 independent events of HGT from Rhodospirillales to other distantly related species ., Medium confidence ancestral proteins have at least 4 Rhodospirillaceae in the top 8 ., Low confidence ancestral proteins have at least 1 Rhodospirillaceae in the top 8 , excluding hits to other Azospirillum genomes ., High confidence horizontally transferred proteins have 0 hits to Rhodospirillales in the top 10 , excluding hits to other Azospirillum genomes ., Medium confidence horizontally transferred proteins have 0 hits to Rhodospirillales in the top 5 , excluding hits to other Azospirillum genomes ., Low confidence horizontally transferred proteins have 0 hits to Rhodospirillaceae in the top 8 , excluding hits to other Azospirillum genomes ., Unassigned proteins either have no BLAST hits outside Azospirillum , or simultaneously classify as medium confidence horizontally transferred and medium or low confidence ancestral ., Bidirectional BLAST was used to identify orthologs of the putative glycoside hydrolase ( GH ) genes ., Phyml package was used to confirm evolutionary relationships and visualize the results ., Domain architectures were obtained through Pfam 53 search for each protein ., Then information from CAZy 54 and recent analysis 55 was used to assign putative activities of the predicted GHs ., Chemotaxis proteins were identified in genomic datasets as previously described 56 ., Using CheA sequences from a recent chemotaxis system classification analysis 26 , alignments of the P3–P5 regions of CheA were built for each class and for the entire set of CheA sequences ., Each alignment was made non-redundant so that no pair of sequences shared more than 80% sequence identity ., Hidden Markov Models ( HMMs ) were built from each non-redundant alignment and used to create library via the HMMER3 software package ( version HMMER 3 . 0b3 ) 42 and default parameters ., The rhizosphere CheA sequences from a recent study 25 were run against the CheA HMM library ., Unclassified sequences ( Unc ) are those with top hits to the full CheA set HMM rather than a class-specific HMM ., The remaining sequences were assigned to the class of the top scoring HMM ., Azospirillum strains and control strains ( Dickeya dadantii 3937 as a positive control , A . tumefaciens NT1 as a negative control ) were cultured for 16 h in liquid AB minimal medium 57 containing 0 . 2% malate and 1 mg/L biotin ., An aliquot of 107 cells ( for Dickeya dadantii 3937 ) or 2 . 107 cells ( for all other strains ) was deposited on top of AB plates containing 0 . 1% carboxymethylcellulose instead of malate ., Plates were incubated for 5 days before being stained as previously described 58 ., A 211-bp cpaB ( AZOBR_p460079 ) internal fragment was amplified by PCR with primers F6678 ( GCGTGGACCTGATCCTGAC ) and F6679 ( GTGACCGTCTCGCTCTGAC ) and subcloned into pGEM-T easy ( Promega ) ., White colonies were screened by PCR with primers F6678 and F6679 for correct insertion in pGEM-T easy , resulting in pR3 . 37 ., The insert of plasmid pR3 . 37 was digested with NotI and cloned into the NotI site of pKNOCK-Km 59 , resulting in pR3 . 39 after transfer into chemically-competent cells of E . coli S17 . 1 λpir ., pR3 . 39 was introduced into A . brasilense Sp245 by biparental mating ., Transconjugants resulting from a single recombination event of pR3 . 39 were selected on AB medium containing 0 . 2% malate , ampicillin ( 100 mg/mL ) and kanamycin ( 40 mg/mL ) ., The correct insertion of pKNOCK into cpaB was confirmed by PCR with primers ( F6678 and F5595 TGTCCAGATAGCCCAGTAGC , located on pKNOCK ) and sequencing of the PCR amplicon ., Sp245 and Sp245cpaB were labelled with pMP2444 60 allowing the constitutive expression of EGFP ., The strains were grown in NFB* ( Nitrogen free broth containing 0 . 025% of LB ) with appropriate antibiotics in glass tubes containing a cover-slide , under a mild lateral agitation for 6 days ., After the incubation , the liquid and the cover-slide were removed from the tubes and the biofilm formed at the air/liquid interface was colored by 0 . 1% crystal violet ., After two washings with distilled water , crystal violet was solubilized by ethanol and quantified by spectrophotometry at 570 nm ., The experiment was performed twice in triplicate ., In parallel , the colonization of the glass cover-slide was monitored by confocal laser scanning microscopy ( 510 Meta microscope; Carl Zeiss S . A . S . ) equipped with an argon-krypton laser , detectors , and filter sets for green fluorescence ( i . e . , 488 nm for excitation and 510 to 531 nm for detection ) ., Series of horizontal ( x-y ) optical sections with a thickness of 1 µm were taken throughout the full length of the Sp245 and Sp245cpaB biofilms ., Three dimensional reconstructions of biofilms were performed using LSM software release 3 . 5 ( Carl Zeiss S . A . S . ) . | Introduction, Results/Discussion, Materials and Methods | Fossil records indicate that life appeared in marine environments ∼3 . 5 billion years ago ( Gyr ) and transitioned to terrestrial ecosystems nearly 2 . 5 Gyr ., Sequence analysis suggests that “hydrobacteria” and “terrabacteria” might have diverged as early as 3 Gyr ., Bacteria of the genus Azospirillum are associated with roots of terrestrial plants; however , virtually all their close relatives are aquatic ., We obtained genome sequences of two Azospirillum species and analyzed their gene origins ., While most Azospirillum house-keeping genes have orthologs in its close aquatic relatives , this lineage has obtained nearly half of its genome from terrestrial organisms ., The majority of genes encoding functions critical for association with plants are among horizontally transferred genes ., Our results show that transition of some aquatic bacteria to terrestrial habitats occurred much later than the suggested initial divergence of hydro- and terrabacterial clades ., The birth of the genus Azospirillum approximately coincided with the emergence of vascular plants on land . | Genome sequencing and analysis of plant-associated beneficial soil bacteria Azospirillum spp ., reveals that these organisms transitioned from aquatic to terrestrial environments significantly later than the suggested major Precambrian divergence of aquatic and terrestrial bacteria ., Separation of Azospirillum from their close aquatic relatives coincided with the emergence of vascular plants on land ., Nearly half of the Azospirillum genome has been acquired horizontally , from distantly related terrestrial bacteria ., The majority of horizontally acquired genes encode functions that are critical for adaptation to the rhizosphere and interaction with host plants . | genome sequencing, genome complexity, genome evolution, microbial evolution, biology, genomics, comparative genomics, microbiology, genetics and genomics | null |
journal.ppat.1000187 | 2,008 | Transmission of Vibrio cholerae Is Antagonized by Lytic Phage and Entry into the Aquatic Environment | Diarrheal disease is the second most common cause of death among children under 5 years of age globally – it is the leading cause of morbidity 1 , 2 ., The Gram-negative bacterium Vibrio cholerae is a facultative pathogen having both human and environmental stages , and is the etiologic agent of the secretory diarrheal disease cholera 3 ., Today , the burden of cholera is estimated to reach several million cases a year in both Asia and Africa , with fewer cases in Latin America 4 ., Aquatic reservoirs harbor V . cholerae during extended periods between outbreaks 5 , but there is little known about how fast V . cholerae moves from one patient to the next during an outbreak ., Transmission between patients may be quite rapid ., For example , two devastating outbreaks strike Dhaka , Bangladesh annually ., The high burden of disease 6 , collapsed water infrastructure , poverty , and crowding make Dhaka an ideal setting for the fast transmission of a facultative pathogen such as V . cholerae ., At the host population level , first degree relatives in households are more likely to be infected with V . cholerae 7 ., At the pathogen level , the di-annual cholera outbreaks may be clonal 8 , 9 , 10 , and there are rapid shifts in drug resistance patterns 11 , 12 ., Despite these epidemiological observations that support a model for rapid transmission during an outbreak , little is known about the selective forces that drive facultative pathogens – like V . cholerae – out of the environment and into the next host ., Using the infant-mouse model of cholera , we recently demonstrated that genes induced late in the infection provide a fitness advantage for the transition to aquatic environments 13 ., In this study , V . cholerae from cholera patients or in vitro culture were transferred to an aquatic environment ., We tested three factors as potential selective forces for driving V . cholerae out of the aquatic environment and into the next host ., These factors are shared among several facultative pathogens and are as follows: the viable but non-culturable state , hyperinfectivity , and lytic phage ., Escherichia coli , Shigella sonnei , Listeria monocytogenes , Campylobacter jejuni , and V . cholerae are examples of facultative pathogens that lose the ability to culture on standard media upon transfer to aquatic environments 14 , 15 ., This phenotype was traditionally called the viable but non-culturable state ( VBNC ) because the cells maintain the capacity for metabolic activities such as protein synthesis , respiration , and have intact membranes despite their inability to culture 16 ., However , we prefer to use the active but non-culturable ( ABNC ) term for reasons explained by Kell et al 17 ., The critical debate over terminology is if it is possible for bacteria with a known in vitro growth condition to be viable and ( but ) nonculturable ., Since the answer to this question seems unresolved , the ABNC term is a more conservative definition ., In the case of V . cholerae , animals become infected when inoculated with high doses of ABNC bacteria ( >106 or >1000-fold above the typical ID50 in animal models ) suggesting that ABNC bacteria can be rescued for vegetative growth in vivo 14 ., The experimental designs in these studies were unfortunately not overly relevant to conditions in the field; the ABNC state was induced by prolonged incubation at 4°C ., ABNC V . cholerae have been observed in rural and urban water samples in Bangladesh between and during outbreaks 5 ., ABNC V . cholerae are found as single cells or associated in aggregates with phytoplankton and zooplankton 5 , 18 , 19 , 20 ., For these reasons , ABNC V . cholerae are proposed to be the environmental reservoir that maintains V . cholerae between outbreaks and seeds new outbreaks ., However , the role this reservoir plays during an outbreak is unclear because cholera outbreaks accelerate faster than the stochastic contribution of V . cholerae from an environmental reservoir 21 ., The second factor we measured was hyperinfectivity ., This phenotype was discovered when V . cholerae from patients were found to be more infectious in the infant-mouse model than in vitro grown V . cholerae 22 , 23 ., This phenotype has also been documented in Citrobacter rodentium 24 , and can be modeled with mouse passaged bacteria 25 ., The role hyperinfectivity plays in transmission is largely unknown , but V . cholerae from patients remain hyperinfectious for at least 5 h in pond water 23 ., Models suggest that outbreaks start when an index case consumes V . cholerae from an environmental reservoir , but the acceleration of the outbreak is driven by hyperinfectious V . cholerae ., Unlike the stochastic contribution of environmental V . cholerae , mathematical models that incorporate hyperinfectivity produce the steep rise in case numbers that are consistent with the actual rise in cases observed in Dhaka , Bangladesh during an outbreak 21 ., The third factor we examined was lytic phage; we note here that this report concerns only lytic vibriophage and not cholera toxin phage or other lysogenic phage ., Lytic vibriophage in the environment have been studied from almost the time that V . cholerae was first discovered 26 , but recent phage epidemiology papers provide new insights into the role phage play in outbreaks ., The percentage of patients passing lytic phage rises as a cholera outbreak progresses; at the same time , phage titers in the environment increase 27 , 28 ., Towards the end of an outbreak , the vast majority of cholera patients ( >90% ) void lytic vibriophage in addition to V . cholerae ., Over a 5-year study of patients at the International Centre for Diarrhoeal Disease , Bangladesh ( ICDDR , B ) , at least half of cholera patients harbored lytic vibriophage 29 ., The ubiquity of lytic phage at the end of an outbreak suggests phage may play an important role in stopping an outbreak ., This hypothesis is also supported by mathematical models 30 , as well as epidemiological data that indicate household contacts of an index case that does not harbor lytic phage are at an increased risk of infection with V . cholerae 29 ., In summary , a cholera outbreak is currently modeled as follows: An outbreak begins with the consumption of ABNC V . cholerae from the environment , is accelerated by hyperinfectious bacteria shed from patients , and is terminated by a rise in lytic phage ., This model however does not provide a reason ( selective pressure ) for V . cholerae to leave the aquatic environment and go to the next host ., Contrary to the current model regarding the importance of ABNC V . cholerae for transmission , we show that the loss of culturability is a negative selective pressure for transmission , and non-culturable cells are not the major contributors to infection ., Instead we show here that culturable V . cholerae recently shed by patients are the major contributors to infection , and upon prolonged incubation in pond water , lytic phage and ABNC cells rise in the aquatic environment to cooperatively block transmission ., In addition , transcriptional analysis suggests that bacteria quickly adjust to the stresses of the aquatic environment , and lytic phage have an undetectable influence on this adaptation ., Despite this adaptation , rice-water stool V . cholerae rapidly become ABNC ., In the absence of high-titer phage , our results support the model that recently shed hyperinfectious V . cholerae drive cholera outbreaks ., The strains used in this study are provided in Table S1 ., Strains were grown on Luria-Bertani ( LB ) agar or in LB broth with aeration at 37°C with streptomycin ( SM ) 100 µg/ml unless otherwise specified ., SM sensitive strains were cultured on LB or a Vibrio spp ., selective medium , TTGA 31; the plating efficiency on TTGA and LB was equivalent ( data not shown ) ., The in vitro derived V . cholerae were prepared by growth for 4 h at 37°C with gentle rocking in M9 minimal medium ( pH 9 . 0 ) supplemented with trace metals , vitamins ( Gibco MEM Vitamins , Invitrogen ) , and 0 . 5% glycerol 32; this medium is referred to as ‘M9 pH 9’ ., Water was collected from a pond in central Dhaka each day of experimentation using a mechanical pump and intake hose system that collected water approximately 0 . 5 m below the water surface to avoid fluctuations in osmolarity due to rain water stratified at the top layer of the pond ., This pond has historically cultured positive for V . cholerae; however in this study , V . cholerae and phage lytic for V . cholerae were below the limit of detection by standard methods 29 on the days of experimentation ., Eighty liters of unfiltered pond water were transferred to a barrel lined with a pond-water washed autoclave bag , an aquarium bubbler was placed in the barrel to oxygenate the water as well as to avoid stratification , the barrels were positioned in an open shed shielded from direct sunlight but freely exposed to the outside air: water temp ., 26–28°C , dissolved oxygen ≈6% , conductivity 260–300 µS/cm , total dissolved solutes 137 . 8 mg/l , salinity\u200a=\u200a0 . 1 ppt , and pH 6 . 6–6 . 9 ., One liter of the water was centrifuged at 2 , 744 g at room temperature ( RT ) for 5 min , and filter sterilized through a 0 . 2 µm filter ( FS pond water ) ., This FS pond water was used to resuspend in vitro and in vivo derived V . cholerae , as well as for chemical analysis ., After each experiment , bleach was added to each barrel to 0 . 5% and held for 24 hrs to sterilize the contents ., Cultures on LB agar were taken to confirm complete sterilization before the water and bag were disposed ., Inorganic chemical analysis on stool supernatant and pond water samples was performed by Dr . R . Auxier at the Center for Applied Isotope Studies ( U . of Georgia , Athens , GA ) ., Dr . A . Parastoo at the Complex Carbohydrate Research Center ( U . of Georgia , Athens , GA ) determined the major sugars in the FS pond water samples using mass spectrometry ., Stool samples were collected from adult patients ( >15 yrs of age ) with acute watery diarrhea and no prior treatment with antibiotics ., The samples were examined by darkfield microcopy to confirm the presence of V . cholerae 29 , and were included in the study if >95% of the cells were highly motile and vibrioid in shape ., All samples were screened and found to be negative for ETEC , the ratio of V . cholerae to non-V ., cholerae bacteria was determined , and the presence of lytic phage was assayed as previously described 29 ., Stool samples meeting the inclusion criteria were clarified of mucus and debris by centrifugation at 988 g for 3 minutes at RT , and then V . cholerae were pelleted by 15 minutes of centrifugation at 26 , 892, g . Bacterial pellets were resuspended in an equal volume of FS pond water at a final concentration of approximately 1×108 CFU/ml; alternatively , pellets were resuspended in RNAlater ( Ambion , INC ) , flash frozen , and stored at −80°C for subsequent microarray analysis ., Fifty ml aliquots of the resuspension were transferred to dialysis tubes with a 12 KDa cutoff ( Fisher Scientific INC ) , and the tubes were immediately transferred to the pond microcosm described above ., The tubes were kept just below the surface of the water , and bacteria and phage did not traverse the dialysis tubing ( data not shown ) ., The time from stool collection in the hospital to incubation in pond water was under 1, h . The collection of the rice-water stool from human subjects was reviewed and approved by both the Research Review Committee and Ethical Review Committee at the International Centre for Diarrhoeal Disease Research , Bangladesh , and by the Human Research Committee at the Massachusetts General Hospital ., V . cholerae were isolated by single colony purification from stool on either LB SM or TTGA media 31 ., The in vitro derived V . cholerae were prepared as described above in M9 pH 9 at a final concentration of 1×108 cfu/ml ( approximately equivalent to the density in stool ) ., After incubation at 37°C with gentle rocking for 4 h , the cells were then pelleted by centrifugation at 26 , 892 g for 15 min at RT , resuspended in an equal volume of FS pond water , transferred to dialysis tubes , and placed in the pond microcosm in a manner similar to the stool derived V . cholerae described above ., Additionally , a portion of the pellets were stored in RNAlater as above for subsequent microarray analysis ., At 5 and 24 h , the contents of the dialysis tubes ( patient and in vitro derived ) were transferred to sterile centrifuge tubes ., For microarray analysis , the contents were centrifuged at 26 , 892 g for 15 min at RT , and the pellets were stored in RNAlater as above ., At the 0 , 5 , and 24 h time points of collection for patient or in vitro derived cells for microarray analysis , paired samples were simultaneously taken for animal experiments described below ., The competitive index of V . cholerae pre-incubated in M9 pH 7 , M9 pH 9 , or rice-water stool supernatant was determined using 5 to 6-day-old Swiss Webster mice as described previously 22 ., In brief , the O1 El Tor Inaba strain N16961 ( LacZ− ) of V . cholerae was grown overnight on LB agar with SM , and colonies were resuspended in LB broth ., The cells were washed and incubated in M9 pH 7 , M9 pH 9 , or phage negative stool supernatant ., During the incubation of the in vitro samples , stool samples from cholera patients were screened for V . cholerae and processed as described above ., After the 1 h incubation of the in vitro grown strains , infant mice were inoculated intragastrically with 105 CFU of a 1∶1 mixture of the paired LacZ+ stool V . cholerae and in vitro grown LacZ− wild-type N16961 strain ., At 24 h post inoculation , the small intestine was harvested and the homogenized contents were serially diluted and plated on LB SM , X-gal 40 µg/ml agar plates ., After overnight incubation at 37°C , blue and white colonies were counted to determine the competitive index ., To study the infectivity of V . cholerae transferred to the pond microcosm , the ID50 was determined for both stool and in vitro derived V . cholerae after 0 , 5 , and 24 h of incubation in pond water ., At 0 h , stool derived V . cholerae were prepared as described above and serially diluted in LB ., The in vitro derived V . cholerae were prepared as described above with the 4 h preincubation at 37°C in M9 pH 9 , and subsequent serial dilution in LB ., Groups of 5–6 day-old Swiss Webster mice were then inoculated intragastrically with doses that ranged from approximately 1 to 105 CFU per mouse ., Mice were euthanized at 24 h post inoculation , and the small intestinal homogenates were plated as described above ., Values of ≥1 , 000 CFU/mouse ( limit of detection\u200a=\u200a100 CFU ) were recorded as positive for infection ., A dose-response curve was made by plotting the fraction of infected mice against the log10 of the input V . cholerae cell count – either by CFU or direct counts ., The ID50 was estimated from this curve by a standard nonlinear regression using the Hill Equation – the Hill slope was fixed at 1 . 0 when there were <3 data points between the values of 0 . 1 and 0 . 9 on the Y axis ., The 95% confidence intervals for the ID50 ( CI ) and coefficient of determination ( R2 ) are provided ., The ID50 for the stool and in vitro derived V . cholerae incubated in the pond microcosm was determined at 5 h and 24 h in the same manner ., The in vivo dynamic between V . cholerae and lytic phage was investigated by the co-infection of both the bacteria and lytic phage in ID50 experiments as described above ., Lytic phage isolates were obtained in a pair-wise manner from the same patients that the V . cholerae isolates were obtained ., Phage were isolated from stool supernatant by a standard plaque assay on a bacterial lawn made of the V . cholerae isolate from the same stool sample 33 ., Phage were picked from 3 serial clear lytic plaques ., V . cholerae were prepared for the animal studies by overnight growth and a 4 h incubation at 37°C in M9 pH 9 as described above ., The V . cholerae isolated from a given patient and the paired lytic phage were combined for 8 min prior to infection at a phage::bacterium multiplicity of infection ( MOI ) that reflected what was observed in the rice-water stool and pond microcosm in this project: 0 . 001 to 5 PFU/CFU ., The inocula were then serially diluted and groups of at least five infant mice were inoculated with doses that ranged from approximately 1 to 105 CFU per mouse ., The ID50 was calculated for each experiment as described above ., At a given dose of bacteria and phage , the burden of infection was determined by calculating the median CFU/ml for each group of at least five mice ., This was repeated for a total of 3 strains at all doses of bacteria and paired phage ., The three medians were plotted individually , and the average of the three medians was also plotted ., A Students t-test was performed between the average for the no-phage control and each phage dose ., We investigated if colonization of infant mice by V . cholerae in the presence of lytic phage was because the bacteria had become resistant to the phage ., One way that bacteria can become resistant to phage is by altering the phage receptor which is most commonly LPS for vibriophage 34 , 35 ., A basic test for putative LPS mutants is agglutination in LB 36; this test lacks absolute specificity as other phenotypes can also cause agglutination such as expression of the toxin co-regulated pilus ( TCP ) , but TCP is not expressed in LB 37 ., We validated the agglutination assay with LPS extraction and gel electrophoresis of several putative LPS mutants identified by agglutination ( below ) ., We chose strain EN159 for this study because it is SM resistant ., From each animal coinfected with EN159 ( all doses ) and the paired EN159 phage ( all doses ) , eight isolates were colony purified ( 3× ) and frozen for further evaluation of phage sensitivity ., These isolates were grown in LB SM broth overnight and agglutination of the cells was assessed if the media clarified after 20 min of static incubation ., The fraction of the 8 isolates from a given mouse that agglutinated was recorded as a fraction of isolates from a given mouse that were putative LPS mutants 36 ., All isolates from mice infected with the highest dose of V . cholerae ( 1 . 5×105 CFU/mouse ) and all phage MOIs ( 0 , 0 . 005 , 0 . 1 , and 2 . 0 ) were further tested for phage resistance by the standard plaque assay ., For validation of the agglutination assay , LPS was extracted from a total of five isolates from 5 different mice infected with the highest bacterial dose ( 1 . 5×105 CFU/mouse ) and at highest MOI ( 2 . 0 ) ., As a control , LPS was extracted from a total of five isolates from 5 different mice infected with the highest dose of bacteria ( 1 . 5×105 CFU/mouse ) and no phage ., The input strain also served as an additional control ., Cell surface polysaccharides from the eleven strains were isolated and analyzed as described recently 38 , 39 ., Briefly , Proteinase K-digested whole cell extracts were isolated according to Hitchcock and Brown 40 and analyzed by electrophoresis on 16 . 5% SDS-polyacrylamide gels ., The complete synthesized LPS and the lipid A-core oligosaccharide precursor were visualized by silver staining 41 ., RNA was prepared from the samples collected at 0 , 5 , and 24 h of dialysis in pond water ( described above ) ., The frozen suspensions of bacteria in RNAlater ( Qiagen ) were thawed on ice , spun at 15 , 000 g for 20 min at 4°C , the supernatant was discarded , RNA was extracted from the pellet using the Qiagen ( Valencia , CA ) RNeasy Mini Kit , and DNA was removed using the Qiagen on-column RNase-Free DNase set ., For qRT-PCR validation , complete DNA removal was achieved using the Ambion ( Applied Biosystems/Ambion , Austin , TX ) DNA-free DNase Treatment kit ., Each RNA sample was spiked with an in vitro transcribed Arabidopsis RNA which served as a reference for color balancing during scanning; the control RNA was provided by the Pathogen Functional Genomics Resource Center ( PFGRC ) at the J . Craig Venter Institute ( formerly TIGR ) ., Labeling of cDNA was performed as described previously 42 with the exception that the reverse transcription reaction used Superscript III ( Invitrogen , Carlsbad , CA ) at a reaction temperature of 52°C for 1 h and 8 µg RNA ., The cDNA from each reaction was split and labeled with either Cy3 or Cy5 ( dye swapped ) ., Unless indicated otherwise , at least 4 technical microarray replicates ( 2 dye swaps ) were performed per biological replicate ., There were two biological replicates for each condition: patient derived with phage , patient derived with no phage , and in vitro derived ., Microarrays were provided by PFGRC and consisted of glass slides with genes spotted in quadruplicate with 70 bp oligonucleotides for each of 3810 V . cholerae ORFs ., Hybridizations were performed as described previously 42 ., Microarrays were scanned with a Perkin-Elmer Scanner , and the raw data were analyzed using the Perkin-Elmer Scan Array Express , Imigene , and Spotfire software packages ., Cy3 and Cy5 data from each slide were split into the relevant biological groupings as single channel data ., All items with a raw intensity of less than 50 were assigned a minimum intensity value of 49 . 9 43 ., The complete data set was log2 transformed and normalized against all other scans by the 75th percentile ., The values for a given gene across all scans were then normalized by the z-score for that specific gene ., The normalized data was then compared by ANOVA according to the relevant biologic grouping 44 , 45 , 46 ., For ANOVA analysis between 6 groups , a Bonferroni correction was applied to account for bias due to multiple tests by dividing the desired level of significance ( α\u200a=\u200a0 . 01 ) by the total number of comparisons performed ( 22 , 860\u200a=\u200a3 , 810 genes with 6 comparisons ) 44 , 45 , 46 ., Therefore , the corrected false-positive rate was α\u200a=\u200a4 . 4×10−7 which was rounded to α\u200a=\u200a1 . 0×10−7; P values that fell below 1 . 0×10−7 were considered statistically significant ., Cluster analysis was performed by Spotfire with the following metrics: clustered by Unweighted Pair-Group Method with Arithmetic mean ( UPGMA ) , correlated by Pearson Product Momentum Correlation , and ordered by Input Rank ., As an independent measure of similarity between biological groupings , Principal Component Analysis ( PCA ) was performed on all samples using Spotfire ., After the ANOVA , all replicates were ungrouped , and the cluster analysis and PCA were performed in an unsupervised fashion with respect to the technical replicates and biological groupings ., Fold-changes between two biological groupings were calculated using distinction calculations performed by Spotfire , and fold-changes with P values<6 . 6×10−6 ( Bonferroni corrected ) were considered significant ., Microarray data are available in the supplemental material ( Tables S3 , S4 , S5 , S6 , S7 , S8 , S9 and S10 ) ., There was sufficient sample to obtain cDNA template from three phage positive patients ( EN159 , EN182 , EN191 ) , and three phage negative patients ( EN124 , EN150 , EN174 ) ., RNA was isolated as described above , and qRT-PCR was performed as previously described 13 ., In brief , cDNA was synthesized from 1 µg of RNA using the SuperScript II First Strand Synthesis System for qPCR ( Invitrogen Inc . ) ., The qRT-PCR experiments were performed with iQ SYBR Green supermix ( Biorad ) ., Each reaction contained 200 nM primers , approximately 10 ng of the template , and the ROX reference dye ., All primer pairs ( Table S1 ) amplified the target with efficiencies of 92% or greater ( data not shown ) ., The mean cycle threshold for the test transcript was normalized to the reference transcript sanA 47 and argS ., The reference argS was chosen because no expression changes were detected in this microarray project as well as all publicly available V . cholerae microarray databases ., Values >1 indicate that the transcript is in higher concentration than the reference ., This project focuses on rice-water stool samples collected from three patients ( EN159 , EN182 , and EN191 ) who harbored lytic vibriophage for V . cholerae , and the respective phage and V . cholerae isolates from these three patients ., In addition , rice-water stool was collected from three patients who did not harbor lytic vibriophage ( EN124 , EN150 , and EN174 ) , and V . cholerae was isolated from each of these patients ., Therefore , the biological replicates for each arm of the study were three unless stated otherwise; sufficient numbers of infant mice for ID50 testing were available only for the three patient samples that harbored phage ., At the time of collection , all patients were severely dehydrated as defined by the World Health Organization 48 ., As V . cholerae passes from the patient into pond there is dramatic shift in osmolarity and in the concentrations of inorganic nutrients and carbon sources ., Some of these factors are depicted in Fig . 1 ., NaCl and KCl are major contributors to osmolarity and both have a decline from 2 , 600 to 22 ppm ( 120-fold ) and 820 to 6 ppm ( 140-fold ) between the rice-water stool and pond supernatant , respectively ., The conductivity difference between the rice-water stool ( as well as LB broth ) and pond water is approximately a 50-fold decline ., Phosphate and fixed nitrogen are typically limiting inorganic nutrients in fresh water ponds ., Phosphate and fixed nitrogen ( NH4+ ) decline from 160 to 0 . 1 ppm ( 1 , 600-fold ) and 52 to 0 . 5 ppm ( 104-fold ) , respectively ., V . cholerae was placed in filtered pond water and then dialyzed in 12 KDa tubing with live pond water ., Therefore , carbon sources such as large polymers like chitinous exoskeletons would not be present in the dialysis bags ., Carbon sources detected were rhamnose ( 29 Mol . %; 16 nM ) , fucose ( 20% Mol . %; 11 nM ) , glucose ( 2 . 7 Mol . %; 1 nM ) , and unidentified sugars ( 48 . 9 Mol . % ) ., This chemistry collectively framed many of the physiological events that occurred as V . cholerae adapted to the aquatic system ., This adaptation and pond microcosm system is not necessarily specific to Bangladesh as the chemical composition shown herein is comparable to pond water used in transition studies with pond water obtained in Boston , MA 13 ., The culturability of V . cholerae transferred to the pond microcosm was monitored by culture and direct microscopy counts ., We define the non-culturable cells as ‘active but non-culturable’ ( ABNC ) because there were clear transcriptional changes between 5 and 24 h detected by both microarray and qRT-PCR analysis ( below ) ., Thus , our measure of ‘active’ was global transcriptional change ., Culturability was rapidly lost upon transfer to the pond microcosm at 5 and 24 h with declines of 63% ( SD+/−16% ) and 98% ( SD+/−1 . 0% ) , respectively ( Fig . 2A ) ., The V . cholerae isolates from the respective patients were grown in vitro ( M9 pH 9 ) and transferred to the pond microcosm; the declines in culturability in the pond microcosm were similar for the in vitro derived samples compared to the patient derived samples ( Fig . 2A ) ., Despite the drop in culturable cells , the total cell numbers remained constant by direct counts ( Fig . 2B ) for all sample types; the cell number was also constant for phage negative patient samples and the paired in vitro grown strains ( Fig . 2B ) ., The culture counts are not available for the phage negative patient samples because two isolates were unexpectedly SM sensitive ., The plating efficiency of starting cultures neared 100% ., For example , the average concentration of V . cholerae from patients ( EN159 , EN182 , EN191 ) at 0 h by culture counts and direct counts was 1 . 0×108 CFU/ml ( +/−1 . 1×108 CFU/ml ) and 1 . 65×108 CFU/ml ( +/−0 . 35×108 CFU/ml ) , respectively ., The PFU titer was monitored at 0 , 5 and 24 h in the pond microcosm ( Fig . 2C ) ., At 0 h , the average ratio of phage to V . cholerae for all three patient stools was 2 . 2×10−6 ( SD+/−3 . 5×10−6 ) ., At 5 h , this ratio increased by 4 orders of magnitude to 1 . 0×10−2 ( SD+/−1 . 2×10−2 ) by culture counts , or 3 orders of magnitude to 1 . 5×10−3 ( SD+/−1 . 3×10−3 ) by direct counts ., At 24 h , this ratio increased an additional 2 orders of magnitude to 4 . 0×10−1 ( SD+/−3 . 9×10−1 ) by culture counts , but remained steady at 3 . 8×10−3 ( +/−3 . 2×10−3 ) by direct counts ., From 5 to 24 h , this ratio changed because the culturable counts decreased 14-fold ., These findings are supported by micrographs that illustrate altered morphology of V . cholerae only in the patient derived samples from phage positive patients ( Fig . 3A ) ., Lytic and lysogenic vibriophage have been previously characterized from patients 27 , 33 , 35 , 49 , 50 , 51 , 52; our phage isolates are consistent in terms of the tropism of those lytic phage previously published 27 because our phage had specificity for the Inaba or Ogawa serotype of the O1 El Tor V . cholerae biotype , and the phage were unable to form plaques on O139 V . cholerae ( data not shown ) ., These data indicate the phage receptor may be O1 LPS as has been demonstrated previously 34 , 35 ., Support for this hypothesis is the generation of LPS mutants in the presence of lytic phage ( presented below ) ., The ID50 for V . cholerae freshly shed from the patients ( 113 CFU; 95% confidence interval CI\u200a=\u200a65–196 CFU ) was lower compared to the in vitro grown reference ( 596 CFU; 95% CI\u200a=\u200a193–1834 CFU; Fig . 4A ) ., Hyperinfectivity was also observed after 5 h of dialysis between the patient ( 51 CFU; 95% CI\u200a=\u200a13–202 CFU ) and in vitro culture ( 680 CFU; 95% CI\u200a=\u200a276–1673 CFU; Fig . 4B ) ., These findings are consistent with competition experiments previously published that suggest V . cholerae maintains hyperinfectivity for at least 5 h after exit from the patient 23 ., We tested if hyperinfectivity could be induced by the medium alone ( stool-supernatant ) , and we found that hyperinfectivity could not be induced in vitro by incubation in stool supernatant ( pH 9 ) or minimal media ( M9 pH 9 ) ( Fig . S1 ) ., Unique to the present study was that the single strain infection experiments revealed that the fraction of mice infected with high doses of patient derived V . cholerae was reduced at 5 h and 24 h compared to the in vitro reference ( Fig . 4B–E ) ., Indeed , the ID50 was not able to be calculated for the patient derived samples at 24 h because less than 50% of the animals were infected ( Fig . 4D–E ) ., The 24 h time point corresponds with the point when the titer of PFU was highest and the titer of culturable cells was lowest ( Fig . 2 ) ; note again that the no phage control for this experiment are in vitro derived cells ., We hypothesized , and show below , that the incomplete colonization observed is due to the presence of lytic phage in the inocula ., We wanted to investigate the relevance of the ABNC state to the transmission of V . cholerae ., To do this we tracked the ID50 over time by both culturable counts and direct counts ., We focus here on the ID50 data from the in vitro derived V . cholerae because the phage positive patient derived samples failed to fully colonize at 5 and 24 h ., In the context of the pond system , the total cell counts remained constant but the proportion of culturable cells decreased over time ., We tested three competing hypotheses:, ( i ) If culturable cells are equally infectious as non-culturable cells , then the ID50 by total cell counts will be constant as the percent of culturable cells decreases ., ( ii ) If culturable cells are more infectious than non-culturable cells , then the ID50 by total cell counts will increase as the percent of culturable cel | Introduction, Materials and Methods, Results, Discussion | Cholera outbreaks are proposed to propagate in explosive cycles powered by hyperinfectious Vibrio cholerae and quenched by lytic vibriophage ., However , studies to elucidate how these factors affect transmission are lacking because the field experiments are almost intractable ., One reason for this is that V . cholerae loses the ability to culture upon transfer to pond water ., This phenotype is called the active but non-culturable state ( ABNC; an alternative term is viable but non-culturable ) because these cells maintain the capacity for metabolic activity ., ABNC bacteria may serve as the environmental reservoir for outbreaks but rigorous animal studies to test this hypothesis have not been conducted ., In this project , we wanted to determine the relevance of ABNC cells to transmission as well as the impact lytic phage have on V . cholerae as the bacteria enter the ABNC state ., Rice-water stool that naturally harbored lytic phage or in vitro derived V . cholerae were incubated in a pond microcosm , and the culturability , infectious dose , and transcriptome were assayed over 24 h ., The data show that the major contributors to infection are culturable V . cholerae and not ABNC cells ., Phage did not affect colonization immediately after shedding from the patients because the phage titer was too low ., However , V . cholerae failed to colonize the small intestine after 24 h of incubation in pond water—the point when the phage and ABNC cell titers were highest ., The transcriptional analysis traced the transformation into the non-infectious ABNC state and supports models for the adaptation to nutrient poor aquatic environments ., Phage had an undetectable impact on this adaptation ., Taken together , the rise of ABNC cells and lytic phage blocked transmission ., Thus , there is a fitness advantage if V . cholerae can make a rapid transfer to the next host before these negative selective pressures compound in the aquatic environment . | The biological factors that control the transmission of water-borne pathogens like Vibrio cholerae during outbreaks are ill defined ., In this study , a molecular analysis of the active but non-culturable ( ABNC ) state of V . cholerae provides insights into the physiology of environmental adaptation ., The ABNC state , lytic phage , and hyperinfectivity were concurrently followed as V . cholerae passaged from cholera patients to an aquatic reservoir ., The relevance to transmission of each factor was weighed against the others ., As the bacteria transitioned from the patient to pond water , there was a rapid decay into the ABNC state and a rise of lytic phage that compounded to block transmission in a mouse model ., These two factors give reason for V . cholerae to make a quick transit through the environment and onto the next human host ., Thus , in over-crowded locations with failed water infrastructure , the opportunity for fast transmission coupled with the increased infectivity and culturability of recently shed V . cholerae creates a charged setting for explosive cholera outbreaks . | microbiology/environmental microbiology, infectious diseases, public health and epidemiology, infectious diseases/neglected tropical diseases, molecular biology, infectious diseases/epidemiology and control of infectious diseases, molecular biology/bioinformatics, microbiology, infectious diseases/bacterial infections, microbiology/applied microbiology, microbiology/microbial physiology and metabolism, infectious diseases/tropical and travel-associated diseases, microbiology/cellular microbiology and pathogenesis, microbiology/medical microbiology, infectious diseases/gastrointestinal infections | null |
journal.pgen.1003192 | 2,013 | Single-Stranded Annealing Induced by Re-Initiation of Replication Origins Provides a Novel and Efficient Mechanism for Generating Copy Number Expansion via Non-Allelic Homologous Recombination | Duplication or amplification of chromosomal segments is important for evolution , phenotypic variation , human genetic disorders , and cancer 1–5 ., Many of these duplications or amplifications are arranged in direct tandem repeat and have homologous sequence elements at their boundary , suggesting they were formed through recombination between non-allelic homologous sequences ., Evidence for such non-allelic homologous recombination ( NAHR ) events is found in the genomes of nearly all species , including humans , where almost half of the human genome is comprised of low or high copy number repeat sequences 6–8 ., The mechanisms responsible for these duplications or amplifications have been difficult to discern because these events are usually too rare to characterize their molecular intermediates ., Nonetheless , studies primarily in microorganisms have led to a number of models for how these duplications/amplifications might arise ., The most established model assumes that these NAHR events occur through the same fundamental mechanism as allelic homologous recombination 9–11 ., In this model NAHR is initiated by a simple DNA double-strand break ( DSB ) in a repeat sequence , which normally provokes a homology search for the intact allelic counterpart as a repair template ., An imperfect search arising from misalignment of sister chromatids or homologs , however , would lead to establishment of a double Holliday junction structure between non-allelic homologous sequences that can resolve into an unequal crossover ., Evidence of the reciprocal copy number expansions and contractions expected to arise from such unequal crossing over is limited , having only been observed in the context of large tandem arrays of rDNA 12 , 13 or CUP1 repeats 14 , at subtelomeric repeats 15 , and in some human genetic disorders 16 ., A recent variation on this model suggests that NAHR-mediated tandem duplications/amplifications may be generated by break-induced replication ( BIR ) 17 ., In this model , a broken chromosomal end initiates strand invasion and replication fork assembly at a non-allelic homologous sequence ., The fork then duplicates the chromosomal segment between the homologous sequences before proceeding to the end of the chromosome ., Again , direct support for this model is minimal ., Recently , we demonstrated that re-replication of a chromosomal segment due to dysregulation of replication controls can efficiently induce NAHR-medicated tandem duplication/amplification of that segment in the budding yeast Saccharomyces cerevisiae 18 ., Importantly , introduction of simple DSBs failed to induce duplication/amplification with similar efficiency ., Our findings raise the possibility that an alternative mechanism initiated by the loss of replication control might be responsible for some NAHR-mediated tandem duplications/amplifications ., We have thus been eager to elucidate the mechanism of re-replication induced gene amplification ( RRIGA ) ., The initiation of eukaryotic DNA replication is controlled by a battery of overlapping mechanisms that prevent re-initiation of DNA replication from the hundreds to thousands of replication origins in eukaryotic genomes ., Replication initiation normally occurs at these origins in a two-stage process 19 , 20 ., In G1 phase , origins are licensed for initiation by loading the Mcm2-7 core replicative helicase onto them , a process that requires the origin recognition complex ( ORC ) , Cdc6 , Cdt1 , and Mcm2-7 ., During S-phase , licensed origins are triggered to initiate DNA replication ., To ensure that none of these origins re-initiate , multiple mechanisms inhibit ORC , Cdc6 , Cdt1 , and Mcm2-7 to minimize the chance that origins will be relicensed after they have initiated 19–21 ., Consistent with the non-redundant nature of these controls , experimentally inactivating increasing numbers of these mechanisms leads to progressively increasing amounts of re-initiation and re-replication in budding yeast 22 , 23 ., These replication controls are critical for cell viability and genome stability ., When sufficient controls are disrupted to cause overt re-replication ( i . e . an increase in genomic DNA content detectable by flow cytometry ) , extensive DNA damage and a major DNA damage response is observed 24–31 ., While the source of damage is not well understood , the amount of damage apparently overwhelms the DNA damage response and leads to massive cell death ., In budding yeast , we developed the ability to induce and detect much lower levels of re-replication compatible with cell viability 23 ., Retention of viability allowed us to examine the effect of re-replication on genome stability 18 ., We found that limited , transient re-replication of a chromosomal segment induced tandem duplication and occasionally higher order amplification of that segment at a rate approximating 10−2 per cell division , about five orders of magnitude higher than spontaneous duplication rates 17 ., The tandem duplications were bounded by Ty retrotransposon elements , a class of repetitive elements scattered throughout the yeast genome 32 ., Here we uncover the mechanism of this re-replication induced gene amplification ., These studies show that re-replication induces DNA damage because re-replication forks are highly susceptible to breakage ., Our data support a model for RRIGA in which the two forks of a re-replication bubble both proceed beyond repetitive sequence elements flanking the re-initiating origin and then break ., Should these breaks occur in trans with respect to the chromosome axis , normal 5′ to 3′ strand resection at each break will expose complementary strands of the non-allelic repetitive elements , providing a ready substrate for recombination by a single strand annealing ( SSA ) mechanism ., Such repair of these broken re-replication bubbles will result in a tandem duplication arranged in direct repeat ., In this model both the susceptibility of re-replication forks to breakage and the special structural context provided by the re-replication bubble contribute to the extraordinary efficiency of RRIGA ., Importantly , the critical event triggering the formation of these tandem direct duplications is re-initiation of DNA replication within the duplicated segment ., The remarkably efficient channeling of these re-initiation events into tandem direct duplications raises the possibility that even rare spontaneous re-initiation events may be a potent source of copy number variation in evolution and disease ., RRIGA generates gene duplications and amplifications arrayed in head-to-tail orientation at the original chromosomal locus with boundaries corresponding to Ty retrotransposable elements 18 ., We previously reported that the inter-amplicon junctions generated by RRIGA had hybrid sequences consistent with a non-allelic homologous recombination event between Ty retrotransposable elements that flank the re-initiating origin ( Figure 1A, ( i ) ) ., The two Ty elements most frequently involved in our RRIGA experiments share a 1 . 3 kb region of 99% sequence identity where the recombination events occurred ( Figure S1A ) ., What we did not know was whether the homology between Ty elements is sufficient to promote RRIGA or whether other Ty-associated elements or biology are also important ., Most Ty elements , including those involved in our RRIGA studies , are surrounded by tRNA genes and long terminal repeats ( LTRs ) in inverted orientation ., These associated elements are known to cause replication forks to pause and possibly to break 33 , 34 , disruptions that could stimulate recombination ., Hence , if such associated elements are important for RRIGA , it might constrain RRIGA to specific repetitive elements in budding yeast ., On the other hand , if homology is sufficient for sequences to serve as RRIGA boundary elements , RRIGA could offer a potential mechanism for a broad range of NAHR-mediated copy number variations ., To address this question we used our previously described RRIGA assay , which exploits colony sectoring 35 to screen for amplification events 18 ., In this assay , an origin particularly prone to re-initiate ( ARS317 ) when Cdc6 , Orc6 , and the MCM complex are deregulated is integrated at 567 kb on Chromosome IV , along with a color based copy number reporter gene ( ade3-2p ) ., Cells with a single copy of ade3-2p are pink , while those with two or more copies are red ., After transiently inducing re-initiation at ARS317 during a nocodazole arrest ( G2/M ) , cells are plated for single colonies and possible amplification events are identified from pink colonies with red sectors that comprise 1/2–1/8 of the colony ., We then verify and characterize amplifications in the red sectors by array Comparative Genomic Hybridization ( aCGH ) ., The vast majority of amplifications identified using this assay span the region from 515–650 kb on Chromosome IV , with YDRCTy2-1 and YDRCTy1-1 at the left and right boundaries , respectively ., These are the closest Ty elements flanking the re-initiating ARS317 origin at 567 kb , and both are surrounded by tRNA genes and LTRs ( Figure S1A ) ., To determine whether homology is sufficient to support RRIGA , we constructed strains in which: ( 1 ) YDRCTy2-1 was replaced by a 3′ portion of the URA3 gene; ( 2 ) YDRCTy1-1 was replaced by a 5′ portion of the URA3 gene; or ( 3 ) both Ty elements were replaced by their respective URA3 gene fragments ( Figure S1 ) ., Two versions of these strains were generated ., In version 1 some of the adjacent LTRs were replaced along with each Ty element , but tRNA genes and inverted LTR repeats were preserved ( Figure S1B ) ., In version 2 , all of the adjacent tRNA genes and LTRs were replaced along with each Ty element ( Figure S1C ) ., Importantly , the URA3 fragments share a 390 bp overlapping region of 100% sequence identity ., Thus , sequence homology was present at positions 515 kb and 650 kb on Chromosome IV in the strains in which both endogenous Ty elements were intact , as well as the strains in which both Ty elements were replaced by URA3 fragments ., In contrast , no significant homology was present at these loci in strains in which only one Ty element was replaced by a URA3 fragment , and we refer to these as non-homologous boundary strains ., The non-homologous boundary strains showed a 5- to 10-fold decrease in sector frequency ( Figure 1B, ( i ) , Figure S2A , Table S1 ) ., Subsequent aCGH analysis of a dozen residual sectors induced in version 2 of each of these non-homologous boundary strains failed to detect any amplifications with endpoints at 515 kb and 650 kb on Chromosome IV ( Figure S3 , Table S2 ) ., Thus , when RRIGA frequencies for the 515–650 kb segment were estimated by multiplying sector frequencies by the percent of sectors that amplified this segment , there was at least a 50- to 100-fold reduction in frequency ( Figure 1B, ( ii ) ) ., We note that many red sectors derived from the strain without the right hand Ty element at 650 kb ( YDRCTy1-1 ) did have an extra copy of the ade3-2p reporter , but achieved this either by using Ty elements further to the right as the right hand RRIGA boundary element , or through translocation or aneuploidy ., In contrast , most red sectors derived from the strain without the left hand Ty element at 515 kb ( YDRCTy2-1 ) did not have an extra copy of the ade3-2p reporter , presumably because there are no other Ty elements on Chromosome IV to serve as left-hand RRIGA boundaries ( these red sectors presumably arose from other genomic changes that altered the rate of red pigment accumulation in ade3-2p cells ) ., These findings confirmed that homology at the boundaries of amplicons is necessary for efficient RRIGA in budding yeast ., More importantly , when sequence homology was restored by replacing the remaining Ty element with the appropriate URA3 fragment , RRIGA frequencies were also restored ., In strains with Ty elements at both 515 kb and 650 kb replaced by either version 1 or version 2 of the overlapping URA3 fragments , sectoring occurred at a frequency comparable to the strain with endogenous Ty elements intact ( Figure 1B, ( i ) , Figure S2A , Table S1 ) ., Furthermore , most ( 14/16 ) of the red-sectors that were examined by aCGH bore an amplification of the 515 kb to 650 kb region of Chromosome IV ( Figure S3 , Table S2 ) ., Importantly , RRIGA frequency in the context of overlapping URA3 fragments was unaffected by the presence or absence of the tRNA genes or LTRs ( compare Figure 1B and Figure S2A , Figure S2B ) ., Thus , sequence homology at the boundaries of amplicons is sufficient to support RRIGA ., Given the prevalence of homologous repetitive elements in eukaryotic genomes 36 , this finding implies that these genomes are a potentially rich source of substrates for re-replication induced gene amplification ., The fact that RRIGA in budding yeast results in NAHR between homologous sequences flanking a re-initiating origin allowed us to develop a more rapid and sensitive selection-based assay for quantifying RRIGA ., We designed the URA3 fragments replacing the Ty elements at 515 kb and 650 kb such that NAHR between the fragments during RRIGA reconstitutes a full length , functional URA3 gene at the inter-amplicon junction ( Figure 1A, ( ii ) ) ., Thus , in addition to scoring RRIGA between these two endpoints by colony sectoring , we could select for these events on media lacking uracil ( Figure 2A , Table S1 , Table S3 ) ., We note that the selection assay consistently gave a higher frequency than the sectoring assay , most likely because our visual criterion restricted the sectoring assay to capturing amplification events that occurred within 2–3 generations of cell plating ( see Text S1 ) ., We characterized the genetic alterations in the URA3 prototrophs recovered from our selection assay to ensure that they structurally resembled the RRIGA amplifications previously recovered from the sectoring assay ., aCGH demonstrated that all prototrophs did indeed bear an amplification that spans the region from 515–650 kb on Chromosome IV ( Figure 2B , Table S4 ) ., PCR across potential amplicon junctions confirmed that the original amplicon boundaries were intact and that the regenerated URA3 gene was created from a new head-to-tail amplicon junction ( Figure 2C ) ., Such a junction could arise from tandem intrachromosomal amplicons in head-to-tail orientation , as previously observed for RRIGA , but could also arise from circularization of an extrachromosomal amplicon via NAHR between the two URA3 fragments ., These two possibilities can be distinguished by the spontaneous loss rate of the regenerated URA3 gene , because the latter will be lost at a much higher rate than the former ., This loss rate can be estimated by the frequency of cells lacking URA3 ( and thus resistant to the drug 5-fluoroorotic acid ( 5-FOA ) ) that accumulate in a population when selection for the gene is removed ., As shown in Figure 2D , all the URA3 prototrophs obtained from our selection assay accumulated 5-FOA resistance at a frequency expected for an intrachromosomal amplification ., Thus , the amplifications detected using the URA3 selection assay were structurally identical to those observed using the sectoring screen ., Three major forms of homologous recombination have been characterized in budding yeast and shown to have distinct genetic dependencies ( Figure 3A ) : gene conversion ( GC ) , break induced replication ( BIR ) , and single-strand annealing ( SSA ) 37 ., BIR can be further subdivided into a form that requires the RecA homolog Rad51 and one that is independent of Rad51 38 ., We could thus narrow down the form of homologous recombination responsible for RRIGA by using the URA3 selection assay to quantify the dependence of RRIGA on various recombination genes ., Because the frequency of RRIGA is dependent on the amount of induced re-replication , we normalized the measured frequency against the height of the induced re-replication peak ( Figure S4A ) The genetic dependencies for RRIGA most closely resembled those for SSA ( Figure 3B , Table S3 ) ., First , RRIGA was independent of Rad51 , which is required for strand invasion in GC and Rad51-dependent BIR but is not required for SSA 39–44 ., Second RRIGA was dependent on Rad1 and Msh3 ., The former functions as part of the Rad1-Rad10 structure specific endonuclease , which removes non-homologous 3′ tails during SSA ., The latter functions as part of the Msh2-Msh3 complex to stabilize the SSA structure that is recognized by Rad1-Rad10 45–47 ., Finally , RRIGA was independent of Pol32 , a non-essential subunit of DNA Polymerase δ that is important for BIR 48 ., Similar results for RAD51 and RAD1 were observed using the colony sectoring assay , although a partial dependence on Rad51 suggests that a subset of these RRIGA events may require this protein ( Figure S4B , S4C , Table S1 ) ., Taken together , these results indicate that most of the NAHR observed in RRIGA is mediated by SSA ., Such a central role for SSA both restricts the possible mechanisms for RRIGA and expands the genetic alterations associated with SSA ., SSA is almost always associated with deletion of chromosomal segments that lie between flanking homologous sequences 37 ., A break between those sequences followed by 5′ end resection past both sequences allows them to anneal and initiate repair through NAHR , but at the cost of deleting the intervening segment ., In the context of a re-replication bubble , however , SSA could generate a tandem duplication if both forks of the bubble travel beyond flanking homologous sequences and break in trans relative to the chromosome axis ., The dual fork breaks would cleave the re-replicated sister chromatid in two , leaving a copy of its re-replicated portion at each broken end ., Subsequent 5′ end resection back toward the re-replicated homologous sequences closest to each end would then expose complementary strands of these non-allelic sequences for annealing and SSA repair , resulting in a head-to-tail tandem duplication in loco ( Figure 3C ) ., Such a model provides the most straightforward explanation for how SSA can be responsible for RRIGA ., Moreover , in this model , the special context provided by the re-replication bubble to exploit SSA for tandem duplications suggests one reason why re-replication is such a potent inducer of gene amplification ., A key requirement in our SSA model for RRIGA is that each re-replication fork must break origin-distal to the homologous sequence element that will undergo NAHR ( Figure 3C ) ., Although re-replication is known to induce double-strand breaks ( DSBs ) , or at least a robust DNA damage response , in most cases the source of those breaks is unknown and actual breakage of re-replication forks has not been directly implicated 24–31 ., We therefore asked whether there is a correspondence between the position of DSBs and re-replication forks and whether DSB do in fact arise distal to both flanking repetitive elements ., To map the location of DSBs that arise during re-replication from ARS317 , we sized chromosomal fragments generated by these breaks using pulsed field gel electrophoresis ( PFGE ) ( Figure 4 , Figure S5 ) ., By preparing genomic DNA from cells embedded within agar plugs , this technique minimizes breakage from in vitro manipulations ., Cells were harvested for PFGE after inducing re-replication for 0 , 3 , or 6 hr; as a control we harvested cells from a congenic non-re-replicating strain at the same time points ., Prior to PFGE , chromosomal DNA was digested with the I-SceI endonuclease , which cuts a single unique I-SceI recognition site engineered very close to ARS317 ., This digest divides Chromosome IV into two fragments containing sequences to the left and right of ARS317 , respectively ., For those molecules that re-initiated from ARS317 , the digestion will convert the resulting bubble intermediates into left and right Y-shaped chromosome fragments with ARS317 near the arm tips , telomere at the stem base , and re-replication fork at the branch point ., Hence , a DSB in the re-replicated segment will cleave off an arm of the Y , generating a truncated chromosomal fragment whose length defines the position of the break relative to ARS317 ( Figure 4A ) ., After size separation by PFGE , these fragments were detected by southern analysis using probes just to the left or right of the I-SceI cut site ( Figure 4B , Figure S5 ) ., Using this approach , we found that re-replication dependent DSBs did indeed arise with significant frequency on both sides of ARS317 ( Figure 4B , Figure S5 ) ., Full-length right and left fragments from I-SceI-digested Chromosome IV were detected as discrete bands ., Truncated fragments arising from DSBs migrated as a smear representing a range of sizes below the full-length fragments ., These truncated fragments were specific to the re-replicating strain and became more abundant with longer induction of re-replication ., Quantifying the amount of each fragment length relative to the starting amount of G2/M chromosomes before re-replication ( 0 hr ) allowed us to estimate the percent of these chromosomes that acquired a DSB at each chromosomal position as a consequence of re-replication ( see Text S1 ) ., Figure 4C shows a plot of this DSB percentage as a function of distance from ARS317 ., The distribution formed a broad peak centered about the origin similar to the distribution of re-replication forks around ARS317 ( Figure S4A: WT ) ., The similarity of these distributions is consistent with the notion that the DSBs arise from breakage of re-replication forks ., Importantly , many of the DSBs we mapped arose origin-distal to the two flanking repetitive Ty elements that are closest to ARS317 and that participate most frequently in RRIGA ., We suspect our analysis undercounts DSB formation because some re-replication forks may not break until after the re-replication induction period , and some forks that break early in this period may already have been repaired ., Nonetheless , the data provide a ballpark estimate of the percent of G2 chromosomes that acquire a double strand break beyond the most proximal Ty element as a consequence of re-replication ., After 3 hr of re-replication this estimate is roughly 10–15% for either side of ARS317 ( see Text S1 ) ., After 6 hr of re-replication the estimate is roughly 30–45% ., Thus , these breaks are not rare , and there is a reasonable probability that a re-replication bubble will break at both forks at the positions needed to stimulate the use of homologous sequences in our model for SSA-mediated RRIGA ., Our model predicts that the further away a homologous sequence is from the origin , the lower the frequency of RRIGA involving that sequence , as fewer re-replication forks will be able to reach that sequence and break beyond it ., The fact that RRIGA amplicons preferentially arise from NAHR between the two closest Ty elements flanking the re-initiating origin as endpoints is consistent with this prediction ., However , to test this prediction directly we used the URA3 selection assay to quantify the frequency of RRIGA in a series of strains where the flanking URA3 fragments were placed at increasing distance from ARS317 ( Figure 5A ) ., The overall trend supports the prediction ., As the flanking URA3 fragments are moved further away from ARS317 , RRIGA frequencies drop ( Figure 5B , Table S3 ) ., RRIGA isolates from each starting strain were examined by aCGH to confirm that their amplicons did indeed extend from one URA3 fragment to the other ( Figure S6A , Table S4 ) ., Importantly , despite the large range of amplicon sizes ( 11 kb to 585 kb ) there was no detectable difference in the growth rates of these isolates ( Figure S6B ) ., Hence , the decrease in RRIGA frequencies cannot be explained by a decrease in fitness of those RRIGA isolates with larger amplicons ., Instead these results support the requirement for forks to replicate and break beyond flanking homologous sequences ., In the SSA model of RRIGA , after a re-replication fork breaks origin-distal to a homologous sequence element , 5′ end resection from the break must proceed back to the homologous sequence to make it available for SSA ., With resection rates in S . cerevisiae estimated at 4 kb per hour 49 , breaks that arise tens of kilobases past the homologous sequence will require many hours of resection before they can facilitate RRIGA , increasing the likelihood that the break will be repaired by an alternative mechanism or fail to occur before chromosomes finally segregate ., Hence , one might expect some constraint on how far a fork break can occur beyond a homologous sequence and still stimulate the use of that sequence for RRIGA ., Such a constraint would influence the optimum position of a re-initiating origin relative to homologous boundaries of a potential amplicon ., One would predict that RRIGA should be more efficient when the origin is within the amplicon than when it is outside ( see Figure 6A ) ., In the latter case , any distance traveled by the re-replication fork that initially moves away from the amplicon will have to be completely retraced during resection followed by further resection from the origin to the closest boundary ., To test this prediction , we generated a series of re-replicating strains in which the right amplicon boundary was held fixed while the left amplicon boundary lay either to the left of ARS317 ( positioning the origin within the amplicon ) or at two sites to the right of ARS317 ( positioning the origin outside of the amplicon ) ( Figure 6B ) ., In this series , the rightward re-replication fork has to travel the longest distance to reach the right homologous sequence boundary , and this distance is unchanged ., In contrast , the leftward re-replication fork has little or no distance to travel to get past the left homologous sequence boundaries , but wherever it might break the resection distance back to those boundaries increases ., In accordance with the prediction , the RRIGA frequency tracks inversely with the anticipated resection distance ., The frequency is highest for the strain with the origin contained within the amplicon and becomes progressively lower as the left amplicon boundary is positioned further to the right of the origin ( Figure 6C , Table S3 ) ., Because the size of the amplicons varied in this series of strains , we also compared strains with relatively constant amplicon size in which the re-initiating origin was effectively repositioned outside of the amplicon ( Figure S7A ) ., In one set of strains , amplicon boundaries approximately 140 kb apart were moved to the right of ARS317 , causing a precipitous drop in RRIGA ( Figure S7B , Table S3 ) ., Although part of this drop can be attributed to the increased distance re-replication forks have to travel to the rightmost boundary ( see Figure 5B , strains YJL9118/9119 versus strains YJL9121/9122 ) , the remainder is likely due to the repositioning of the origin outside of the amplicon ( compare Figure S7B strains YJL9145/9146 versus Figure 5B strains YJL9121/9122 ) ., Similarly , in strains where the amplicon size is maintained at ∼100 kb there is a dramatic decrease in RRIGA frequency when the origin is positioned outside of the amplicon ( see Figure S7B , strains YJL9115/9116 versus YJL9147/9148 ) ., Thus , as expected from the SSA model for RRIGA , a re-initiating origin is most efficient at inducing amplification if the origin lies between the homologous sequences that define the amplicon boundaries ., We have previously shown that re-replication in budding yeast is remarkably efficient at inducing NAHR events that result in tandem gene amplifications oriented in direct repeat 18 ., A transient , localized , limited pulse of re-replication from a single origin induced segmental amplifications on the order of 10−2 per cell per generation ., This efficient amplification appeared to be specific to re-replication , as disruption of S-phase replication with mutant replication proteins or hydroxyurea did not induce equivalent amplification frequencies ., In this paper , we propose a model for re-replication induced gene amplification ( RRIGA ) that helps explain why this amplification is so efficient and that provides a new mechanism for NAHR-mediated copy number variation ., Such efficiency makes it conceivable that rare or sporadic re-replication events might contribute to DNA copy number changes observed during oncogenesis or evolution ., In our model for RRIGA ( Figure 3C ) , bidirectional re-replication forks proceeding outward from a re-initiating origin can stimulate an efficient NAHR event between flanking homologous sequence elements by replicating beyond them and generating DSBs ., Normal processing of these breaks will involve 5′ to 3′ single-strand resection back toward the homologous sequences ., In those cases where the two forks break in trans , this resection can expose complementary sequences in non-allelic homologous sequences , resulting in annealing and repair of the break by an SSA mechanism ., The result is a head-to-tail tandem duplication at the endogenous chromosomal locus ., Such tandem duplications can provide a stepping stone for higher order amplifications 50 ., Expansion of the duplication might occur readily without further re-replication , as the initially duplicated segments provide a much larger NAHR substrate ., On the other hand , if re-replication were to recur in subsequent generations , it could stimulate a series of stepwise expansions that would lead to multi-copy amplification ., An important premise for our RRIGA model is the ability of re-replication to induce frequent chromosomal breaks ., There are many reports associating the deregulation of replication initiation proteins with the generation of chromosomal breaks or the induction of a DNA damage response 24–31 ., However , in most cases , this deregulation has been imposed constitutively throughout the cell cycle , making it hard to distinguish whether these breaks are due to re-replication per se or arise from possible disruption of S-phase replication ., Because we induced re-replication after completion of an intact S-phase , the chromosomal breaks we observed and mapped can be specifically attributed to re-replication ., Importantly , the correspondence between the distribution of breaks and the distribution of re-replication forks along the chromosome suggests that these forks are the source of these DSBs ., Formally , it is possible that the DSBs we mapped were actually the free ends of newly synthesized DNA fragments extruded by head-to-tail fork collisions during multiple rounds of re-replication , as has been proposed to explain induction of a DNA damage response during re-replication in Xenopus extracts 51 ., However , this scenario is unlikely in our gene amplification studies , where we induced on average only half a round of re-replication near ARS317 ( i . e . copy number increase from 2C to 3C ) ., Hence , it appears that the re-replication forks themselves are breaking , leading to chromosome fragmentation ., The distribution of re-replication-induced breaks did not reveal any striking hotspots , indicating that these breaks do not depend on special DNA elements or structures that are suspected of potentiating DSB formation by promoting fork stalling and/or collapse 52–54 ., The independence of these breaks from the inverted LTR repeats and multiple tRNA genes that often surround Ty elements is consistent with our ability to replace entire clusters of these elements with simple homologous sequences and still observe high frequency RRIGA ., Our results therefore raise the possibility that re-replication forks are particularly susceptible to breakage ., Supporting this notion is our previous observation that the induction of re-replication can lead to a rapid and massive Rad9-dependent DNA damage response , a response that is not seen during unperturbed S-phase 24 ., In fact , even when S-phase was subjecte | Introduction, Results, Discussion, Materials and Methods | Copy number expansions such as amplifications and duplications contribute to human phenotypic variation , promote molecular diversification during evolution , and drive the initiation and/or progression of various cancers ., The mechanisms underlying these copy number changes are still incompletely understood , however ., We recently demonstrated that transient , limited re-replication from a single origin in Saccharomyces cerevisiae efficiently induces segmental amplification of the re-replicated region ., Structural analyses of such re-replication induced gene amplifications ( RRIGA ) suggested that RRIGA could provide a new mechanism for generating copy number variation by non-allelic homologous recombination ( NAHR ) ., Here we elucidate this new mechanism and provide insight into why it is so efficient ., We establish that sequence homology is both necessary and sufficient for repetitive elements to participate in RRIGA and show that their recombination occurs by a single-strand annealing ( SSA ) mechanism ., We also find that re-replication forks are prone to breakage , accounting for the widespread DNA damage associated with deregulation of replication proteins ., These breaks appear to stimulate NAHR between re-replicated repeat sequences flanking a re-initiating replication origin ., Our results support a RRIGA model where the expansion of a re-replication bubble beyond flanking homologous sequences followed by breakage at both forks in trans provides an ideal structural context for SSA–mediated NAHR to form a head-to-tail duplication ., Given the remarkable efficiency of RRIGA , we suggest it may be an unappreciated contributor to copy number expansions in both disease and evolution . | Duplications and amplifications of chromosomal segments are frequently observed in eukaryotic genomes , including both normal and cancerous human genomes ., These copy number variations contribute to the phenotypic variation upon which natural selection acts ., For example , the amplification of genes whose excessive copy number facilitates uncontrolled cell division is often selected for during tumor development ., Copy number variations can often arise when repetitive sequence elements , which are dispersed throughout eukaryotic genomes , undergo a rearrangement called non-allelic homologous recombination ., Exactly how these rearrangements occur is poorly understood ., Here , using budding yeast to model this class of copy number variation , we uncover a new and highly efficient mechanism by which these variations can be generated ., The precipitating event is the aberrant re-initiation of DNA replication at a replication origin ., Normally the hundreds to thousands of origins scattered throughout a eukaryotic genome are tightly controlled such that each is permitted to initiate only once per cell cycle ., However , disruptions in these controls can allow origins to re-initiate , and we show how the resulting DNA re-replication structure can be readily converted into a tandem duplication via non-allelic homologous recombination ., Hence , the re-initiation of DNA replication is a potential source of copy number variation both in disease and during evolution . | cancer genetics, genetic mutation, genome evolution, microbiology, mutational hypotheses, mutation, model organisms, molecular cell biology, mutation types, dna replication, dna recombination, molecular genetics, dna, dna structure, genetics and genomics, structural genomics, dna amplification, biology, evolutionary genetics, molecular biology, nucleic acids, genetics, gene duplication, yeast and fungal models, saccharomyces cerevisiae, genomics, evolutionary biology, genomic evolution, genetics of disease, evolutionary processes, dna repair | null |
journal.ppat.1007509 | 2,019 | Identification of a short, highly conserved, motif required for picornavirus capsid precursor processing at distal sites | Picornaviruses comprise a large family of non-enveloped RNA viruses that includes important human and animal pathogens ., Examples include poliovirus ( PV ) ( genus: Enterovirus ) , hepatitis A virus ( Hepatovirus ) , encephalomyocarditis virus ( Cardiovirus ) and foot-and-mouth disease virus ( FMDV ) ( Aphthovirus ) ., In picornavirus particles , the RNA genome ( ca . 7 , 100–8 , 900 nt ) is surrounded by a protein shell ( capsid ) consisting of the four structural proteins VP1 , VP2 , VP3 and VP4 1 , with the exception of parechoviruses and kobuviruses in which the VP0 ( the precursor of VP2 and VP4 ) remains uncleaved ( reviewed by 2 ) ., The capsid is composed of 60 copies of each of these structural proteins; VP1 , VP2 and VP3 are exposed on the surface of the particle while VP4 is entirely internal 3–5 ., Translation of the positive-sense RNA genome is dependent on the internal ribosomal entry site ( IRES ) within the 5′ untranslated region ( UTR ) that directs cap-independent translation initiation 6 ., During and after translation of the single open reading frame , processing of the newly synthesized polyprotein occurs ( reviewed in 2 ) ., Usually three or four primary products are formed , namely the Leader ( in many picornaviruses ) , the capsid precursor P1 or P1-2A ( depending on the genus ) and the precursors of the non-structural proteins , namely P2 and P3 ., Many of these viruses , e . g . members of the Cardiovirus , Hepatovirus and Aphthovirus genera , have a Leader protein at the N-terminus of the polyprotein , i . e . upstream of the capsid precursor ., In the Aphthoviruses , the Leader protein is a protease ( Lpro ) , which cleaves itself from the N-terminus of the P1-2A precursor , see Fig 1 ., Cleavage of the junction between the structural and non-structural proteins , at either the VP1/2A or the 2A/2B junction , is usually mediated by the 2A protein , but the function of the 2A protein varies between the genera 7 , see Fig 1A ., In the cardio- and aphthoviruses cleavage at the 2A/2B junction ( at the C-terminus of 2A ) is protease independent and happens during translation by a process termed “ribosomal skipping” 8 or “StopGo” 9 ., In this case , the 2A protein remains attached to the precursor of the structural proteins ( as P1-2A ) until it is removed by the 3C protease ( 3Cpro ) , see Fig 1B ., In the enteroviruses , the cleavage at the VP1/2A junction ( i . e . at the N-terminus of 2A ) , to release P1 , is mediated by the 2A protein that is a chymotrypsin-like protease 10 , 11 ., The P2-P3 junction and the other protein junctions within these precursors are cleaved by 3Cpro to produce the mature non-structural proteins ., However , the P1 capsid precursor of enteroviruses requires the 3CD protease ( 3CDpro ) for its processing 12 , 13 whereas for the cardio- and aphthoviruses the 3Cpro is sufficient to cleave the P1-2A precursor into three structural proteins ( VP0 , VP3 and VP1 ) plus 2A 1 , 14 , see Fig 1B ., During capsid assembly , VP0 is cleaved ( in most picornaviruses ) to generate VP2 and VP4 by a process that is currently not understood ., There are seven different serotypes of FMDV: O , A , C , SAT 1 , SAT 2 , SAT 3 and Asia 1 ., There is a high level of sequence variation between the surface exposed structural proteins of these different serotypes ., The internal VP4 protein is the most conserved of the capsid proteins with 81% of the residues being invariant 15 ., In contrast , only 26% of the VP1 protein residues are invariant and furthermore it ranges in size ( 209–213 aa ) between serotypes 16 ., VP1 is the most surface exposed capsid protein 3 and has been one of the most studied FMDV proteins due to its antigenic importance and role in virus attachment 17 ., One of the antigenic sites in VP1 is located on the G-H loop ( including residues 141–160 ) , which contains an arginine-glycine-aspartate ( RGD ) motif that is involved in the attachment of the virus to cellular integrin receptors 18 , 19 ., Surprisingly , previous work has demonstrated that a cell-culture adapted FMDV , lacking part of this G-H loop ( aa 142–154 ) , is still able to replicate and grow normally in cell culture through the use of heparan sulfate proteoglycans ( HSPG ) as receptor 20 ., Viruses have only a very limited coding capacity within their genomes and thus they rely on cellular factors and pathways to complete their life cycle ., Several studies have suggested that cellular chaperones , including various different heat shock proteins ( Hsps ) , are required to facilitate virus entry , genome replication , protein expression and protein assembly for a variety of viruses , including picornaviruses ., Viral proteins , like cellular proteins , are dependent on such chaperones for their correct folding and assembly 21–24 ., Studies on the role of Hsp90 , using specific inhibitors , have shown that these agents reduce the replication of diverse viruses in vitro ., The Hsp90 appears to be involved in the regulation of viral polymerase function in the case of herpesvirus 25 and hepatitis B virus 21 , whereas this chaperone seems to be required for capsid processing and assembly in different picornaviruses 23 , 26 ., Hsp90 and Hsp70 have been reported to interact with the PV capsid precursor , P1 23 , 27 ., The interaction between PV P1 and Hsp90 ( possibly together with Hsp70 ) , and likely in conjunction with its co-chaperone p23 , is believed to protect the P1 from degradation by proteasomes ( which remove misfolded proteins ) and is also involved in the folding of P1 allowing it to be correctly processed by the 3CDpro 23 ., Recently , we have shown that impeding the processing of one of the cleavage sites within the FMDV P1-2A , at either the VP0-VP3 or the VP3-VP1 junctions , did not block processing of the other cleavage sites , indicating that processing of these junctions is mutually independent 28 ., However , in an earlier study , it was shown that truncation of VP1 ( removing the C-terminal 42 amino acids of VP1 ) completely blocked processing of the residual capsid precursor at both the VP0-VP3 and the VP3-VP1 junctions by 3Cpro in a cell-free system 29 ., Similarly , truncating the PV P1 precursor , by removing 50 aa from the C-terminus of VP1 ( 302 residues in length ) , blocked cleavage of the 2 junctions within the P1 precursor in vitro 30 ., The basis for these effects has not been explained ., However , taken together , these results suggest that the C-terminus of VP1 is important in relation to the processing of the entire capsid precursor of picornaviruses ., In this study , we have now identified a short region within the C-terminus of VP1 that is critical for the processing of the FMDV capsid precursor ., This region contains a stretch of five amino acids that are very highly conserved amongst all FMDVs ., Furthermore , this region is also strongly conserved between most other picornaviruses , including PV , suggesting a shared role for this motif for capsid processing and assembly within the picornavirus family ., Previous studies have shown that truncation of the FMDV P1-2A , by removal of the 2A peptide and the C-terminal 42 residues of VP1 , completely abrogated processing by 3Cpro in vitro 29 even though the cleavage sites between VP0 and VP3 and between VP3 and VP1 were unmodified ., To confirm these observations , within cells , stop codons were introduced at different positions within the P1-2A coding sequence ., Transient expression assays were used to express the FMDV A22 Iraq P1-2A capsid precursor and its derivatives , within BHK cells , both in the absence and presence of the FMDV 3Cpro ., The plasmids encoding both the P1-2A ( wt ) and the P1 alone ( truncated to the first amino acid of the 2A peptide ) served as positive controls ., Both of these controls yielded the expected products corresponding to the P1-2A precursor and the P1 precursor ( approximately 85 kDa ) , respectively in the absence of 3Cpro ( Fig 2 , lanes 1 and 3 ) ., When these plasmids were co-transfected with a plasmid that expresses the 3Cpro , both of these products were efficiently processed as indicated by the production of VP0 ( approximately 37 kDa ) ( Fig 2 , lanes 2 and 4 ) ., Thus , the absence of the 2A peptide did not affect processing of the capsid precursor by 3Cpro ( as observed previously 14 , 26 ) ., Plasmids encoding mutant precursors , truncated to residue 205 in VP1 and 199 in VP1 ( VP1 being 211 aa in length in FMDV A22 Iraq ( wt ) ) , generated products of approximately 85 kDa in the absence of 3Cpro ( Fig 2 , lanes 5 and 7 ) , and these were efficiently processed in the presence of 3Cpro ( Fig 2 , lanes 6 and 8 ) ., The four additional mutants , P1 ( VP1 Y185Stop ) , P1 ( VP1 L158Stop ) , P1 ( VP1 A107Stop ) and P1 ( VP1 L53Stop ) all yielded products corresponding to their expected size in the absence of 3Cpro ( Fig 2 , lanes 9 , 11 , 13 and 15 ) , however it is noteworthy that these truncated products accumulated to a lower level in the cell lysates ., Strikingly , no processing of these truncated precursors was detected for any of these four mutants in the presence of 3Cpro ( Fig 2 , lanes 10 , 12 , 14 and 16 ) although each of these products contained the unmodified VP0/VP3 and VP3/VP1 junctions ., As expected , no products were detected in the negative control ( no DNA ) ( Fig 2 , lane 17 ) ., In order to map the determinants of capsid processing more precisely , plasmids were constructed to express mutant forms of the P1-2A precursor with fairly small internal deletions within the C-terminal portion of VP1 ., To serve as positive controls , both the P1-2A ( wt ) and a mutant form with a deletion within VP1 , designated P1-2A ( VP1 Δ142–154 ) , were included ., The latter deletion is tolerated by the infectious virus 20 and thus it was expected that 3Cpro should be able to fully process all of the junctions in this deletion mutant ., As expected , expression of both the P1-2A ( wt ) and the P1-2A ( VP1 Δ142–154 ) led to the synthesis of products corresponding to the P1-2A precursor ( approximately 85 kDa ) ( Fig 3 , lanes 1 and 13 ) ., Furthermore , both the P1-2A ( wt ) and the P1-2A ( VP1 Δ142–154 ) products were efficiently processed in the presence of 3Cpro ( Fig 3 , lanes 2 and 14 ) ., Notice that the VP1 product derived from the P1-2A ( VP1 Δ142–154 ) mutant migrated faster than the VP1 produced from the P1-2A ( wt ) ( ( approximately 28 kDa ) due to the internal deletion ( note that these antibodies do not recognize VP3 31 , but presumably this was also made ) ., Five different short deletions were introduced into the region of VP1 spanning residues 185–199 ( the region found to be critical by the truncation analysis ) , namely P1-2A ( VP1 Δ185–199 ) , P1-2A ( VP1 Δ185–189 ) , P1-2A ( VP1 Δ188–192 ) , P1-2A ( VP1 Δ191–195 ) and P1-2A ( VP1 Δ194–199 ) ., Each of these constructs generated products that were very similar in size as the wt P1-2A in the absence of 3Cpro ( Fig 3 , lanes 3 , 5 , 7 , 9 and 11 ) ., However , in the presence of 3Cpro the mutant having the largest deletion , P1-2A ( VP1 Δ185–199 ) could not be processed ( Fig 3 , lane 4 ) ., The same product , corresponding to the P1-2A precursor , was observed both in the absence and presence of 3Cpro ., Similarly , the mutants P1-2A ( VP1 Δ185–189 ) and P1-2A ( VP1 Δ188–192 ) were also not processed in the presence of 3Cpro ( Fig 3 , lanes 6 and 8 ) ., It is again noteworthy that the mutant P1-2A products that could not be processed accumulated to a lower level in the cell lysates than the P1-2A precursors that could be processed ( c . f . lanes 3 , 5 , 7 and 1 , 9 , 11 , 13 ) ., In contrast , co-expression of 3Cpro with the P1-2A ( VP1 Δ191–195 ) and P1-2A ( VP1 Δ194–199 ) led to production of VP0 indicating that processing of these mutant precursors had occurred ( Fig 3 , lanes 10 and 12 ) ., However , it is noteworthy that no product corresponding to VP1 was detected , when P1-2A ( VP1 Δ191–195 ) was co-expressed with 3Cpro ( Fig 4A , lane 4 ) ., Furthermore , unexpectedly , when the P1-2A ( VP1 Δ194–199 ) was co-expressed with 3Cpro a major product corresponding to the intermediate VP3-VP1 ( approximately 49 kDa ) was detected ( Fig 4A , lane 6 ) ., Only a weak signal corresponding to the mature VP1 was detected indicating severe inhibition of processing at the VP3/VP1 junction in this mutant ( Fig 4A , lane 6 ) , n . b . this cleavage site is located over 190 residues away in the linear sequence ., No products were detected in the negative control lane ., ( Fig 4A , lane 9 ) ., Due to inefficient detection of VP1 from some of the mutant precursors , an extra modification that blocks processing of the VP1/2A junction ( 2A L2P ) 32 was introduced into the plasmids that express P1-2A ( VP1 Δ191–195 ) , P1-2A ( VP1 Δ194–199 ) and the positive controls; P1-2A ( wt ) and P1-2A ( VP1 Δ142–154 ) ., The additional modification ( 2A L2P ) ensured that the 2A peptide remained fused to the VP1 ( as VP1-2A ) ., Each of these constructs generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( Fig 4B , lanes 1 , 3 , 5 and 7 ) ., The 2A L2P substitution increased the sensitivity of VP1 detection when using the anti-FMDV A-Iraq antibody ., This showed that the P1-2A ( wt + 2A L2P ) and the P1-2A ( VP1 Δ142–154 + 2A L2P , positive control ) precursors were fully processed to yield VP0 and VP1-2A in the presence of 3Cpro as expected ( Fig 4B , lanes 2 and 8 ) ., It also verified that cleavage at the VP3-VP1 junction in the P1-2A ( VP1 Δ194–199 +2A L2P ) occurred at a slower rate compared to wt , since the VP3-VP1-2A intermediate was far more abundant for the P1-2A ( VP1 Δ194–199 + 2A L2P ) than for the P1-2A ( wt +2A L2P ) in the presence of 3Cpro ( compare lanes 6 and 2 in Fig 4B ) ., It should be noted that some mature VP1-2A could be detected from the P1-2A ( VP1 Δ194–199 + 2A L2P ) and thus cleavage of the VP3/VP1 junction was not completely blocked ( Fig 4B , lane 6 ) ., Furthermore , the P1-2A ( VP1 Δ191–195 + 2A L2P ) could be processed to generate VP0 and VP1-2A ( Fig 4B , lane 4 ) ., However , the VP3-VP1 intermediate produced from the P1-2A ( VP1 Δ191–195 +2A L2P ) mutant was also more abundant than the intermediate seen with the P1-2A ( wt + 2A L2P ) indicating that this mutant also had a slower processing at the VP3/VP1 junction ( Fig 4B , lane 4 ) ., The cleavage of the unmodified VP1/2A junction in the P1-2A precursors with different internal deletions , was investigated using an anti-2A antibody ., As expected , both the P1-2A ( wt ) and the positive control P1-2A ( VP1 Δ142–154 ) generated products of approximately 85 kDa corresponding to the P1-2A precursor in the absence of 3Cpro ( see supplementary material S1 Fig , lanes 1 and 13 ) ., In the presence of 3Cpro , no products were detected by the anti-2A antibodies from either the P1-2A ( wt ) or the positive control P1-2A ( VP1 Δ142–154 ) indicating that the VP1/2A junction had been processed ( S1 Fig , lanes 2 and 14 ) ; note the 2A peptide itself is only 18 residues long and is not detected by immunoblotting ., The two mutants , P1-2A ( VP1 Δ191–195 ) and P1-2A ( VP1 Δ194–199 ) that showed slower processing of the VP3-VP1 junction also generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( S1 Fig , lanes 9 and 11 ) ., However , in the presence of 3Cpro , no products were detected by the anti-2A antibodies ( S1 Fig , lanes 10 and 12 ) , indicating that these two deletions in VP1 did not affect processing of the VP1/2A junction ., Surprisingly , the non-processable precursors , i . e . P1-2A ( VP1 Δ185–199 ) , P1-2A ( VP1 Δ185–189 ) and P1-2A ( VP1 Δ188–192 ) , could not be detected using the anti-2A antibody , either in the absence or presence of 3Cpro , and thus we cannot conclude whether cleavage of this junction was affected by the deletions ( S1 Fig , lanes 3–8 ) ., No products were detected in the negative control ( No DNA , S1 Fig , lane 15 ) ., Alanine-scanning mutagenesis was employed to identify individual residues within the C-terminal region of VP1 ( between residues 185 and 199 of VP1 ) that are required for 3Cpro processing of the P1-2A precursor ., The wt and mutant precursors were expressed alone and also in the presence of the FMDV 3Cpro as above ., As expected , the P1-2A ( wt ) and all 15 of the single amino acid substitution mutants each generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( see Figs 5 , 6 and S2 ( supplementary material ) , odd numbered lanes ) ., The wt and some 13 different mutant P1-2A precursors , excluding the mutants P1-2A ( VP1 Y185A ) and P1-2A ( VP1 R188A ) , were processed by 3Cpro to yield VP0 and VP1 ( Figs 5 , 6 and S2 ( supplementary material ) even numbered lanes ) ., In contrast , the P1-2A ( VP1 Y185A ) and P1-2A ( VP1 R188A ) mutants were highly resistant to cleavage by the 3Cpro ( Fig 5 , lanes 4 and 10 ) ., Furthermore , it was again apparent that the accumulation of these mutant P1-2A products in the cell lysates was lower than for the wt precursor and for the other mutants that could be processed ( Fig 5 , lanes 3 and 9 ) ., Thus , the single amino acid substitutions VP1 Y185A and VP1 R188A were individually able to severely inhibit processing at both the VP0/VP3 and the VP3/VP1 junctions within the P1-2A precursor and had a deleterious effect on the level of the unprocessed product generated within cells ., Surprisingly , none of the single alanine substitutions in the VP1 194–199 region had any effect on the processing of the junctions within the P1-2A precursor ( S2 Fig , lanes 4 , 6 , 8 , 10 , 12 and 14 ) ., None of these produced the severe block on cleavage of the VP3-VP1 junction that was detected with the P1-2A ( VP1 Δ194–199 ) mutant ( Fig 4 , lane 6 ) ., However , interestingly , the P1-2A ( VP1 V193A ) was processed more slowly at the VP3-VP1 junction compared to the P1-2A ( wt ) and the other alanine mutants ( Fig 6 , lane 12 ) ., The cleavage of the VP1/2A junction of the P1-2A precursors with different alanine substitutions , was also investigated using the anti-2A antibody ., As expected , the P1-2A ( wt ) generated a product of approximately 85 kDa corresponding to the P1-2A precursor in the absence of 3Cpro ( see supplementary material S3 Fig , lane 1 ) ., However , in the presence of 3Cpro , no product ( containing 2A ) was observed from the P1-2A ( wt ) showing that VP1/2A junction had been processed ( S3 Fig , lane 2 ) ., The two mutants , P1-2A ( VP1 C186A ) and P1-2A ( VP1 P187A ) that were correctly processed at the VP0/VP3 and the VP3/VP1 junction also generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( S3 Fig , lanes 5 and 7 ) ., However , as with the wt protein , in the presence of 3Cpro no products including 2A could be detected , indicating that these two substitutions individually did not prevent processing at the VP1/2A junction ., Neither of these mutant precursors , with single amino acid substitutions , which were highly resistant to cleavage at the VP0/VP3 and the VP3/VP1 junctions , i . e . P1-2A ( VP1 Y185A ) and P1-2A ( VP1 R188A ) , could be detected by the anti-2A antibody , either in the absence or presence of 3Cpro ., Thus , we cannot conclude whether this junction was affected by these substitutions ( S3 Fig , lanes 3 , 4 , 9 and 10 ) ., These results are consistent with the inability to detect the mutant capsid precursors P1-2A ( VP1 Δ185–199 ) , P1-2A ( VP1 Δ185–189 ) and P1-2A ( VP1 Δ188–192 ) , with the anti-2A antibody , as shown in S1 Fig ( see above ) ., To confirm the importance of the YCPRP motif in the context of the virus itself , specific mutations have been introduced into the full-length FMDV cDNA , that encode single amino acid substitutions ( to Ala ) within the YCPRP motif ., In addition , a deletion of the sequence encoding residues VP1 185–190 from the full-length FMDV cDNA was also made ., RNA transcripts were prepared in vitro from each of the mutant plasmids and introduced into BHK cells ., The initial harvests , prepared after 24h , were passaged onto fresh BHK cells and the appearance of cytopathic effect ( CPE ) observed ., Clear CPE was observed with the wt transcript and from the mutants encoding the VP1 C186A and P189A substitutions ., In contrast , no CPE was apparent for the mutants encoding the VP1 Y185A , P187A and R188A substitutions or with the mutant lacking residues VP1 185–190 ( see Table 1 ) ., Sequencing of the P1-2A coding region from the rescued viruses ( FMDV VP1 C186A and FMDV VP1 P189A revealed that the introduced mutations were retained and that no secondary mutations had occurred ., These results verified the critical importance of residues Y185 and R188 in VP1 for P1-2A processing ( Fig 5 ) and for virus infectivity ., It is noteworthy that the P187A mutant was also non-infectious ( Table 1 ) although the capsid precursor processing could be observed in the transient expression assay ( see Fig 5 , lane 8 ) ., The FMDV 3Cpro is able to cleave a variety of different junction sequences in the virus polyprotein 33 ., We have shown previously that blocking cleavage of one junction in the FMDV P1-2A did not affect processing of the other junctions 28 ., In the current studies , it has been shown that modifications that modify or delete a short motif in the C-terminus of VP1 , can prevent processing of the FMDV capsid precursor P1-2A at each of the usual cleavage sites , which are far separated , in the linear sequence , from the site of the modifications ., The VP0/VP3 cleavage site is more than 400 amino acids away from the modified motif in the linear sequence while the VP3/VP1 junction is almost 200 amino acids away ., It seems very likely that this reflects a major change in protein conformation for these mutant proteins ., Viral proteins , like cellular proteins , are dependent on cellular chaperones for correct folding , assembly and function 24 ., The viral capsid precursor must fold to a conformation that is soluble and recognizable by the viral protease to be processed ., After the cleavage of the precursor , the mature capsid proteins assemble around the viral genome to form the protein shell , which contains 60 copies of each of the subunits ., These structures must be stable both within , but also outside , the host cells to permit virus spread ., Moreover , the virus particle must also be able to disassemble upon entry into cells to deliver the viral genome to initiate a new infection ., Thus , the core structure of the capsid proteins ( as distinct from the antigenic loops ) is probably tightly constrained ., Within the picornavirus family , the general structure of the capsid proteins are very similar 2 ., Several chaperones are known to facilitate folding of picornavirus capsid proteins 23 , 26 ., The mature picornavirus capsid proteins are generated by cleavage of the P1 , P1-2A or L-P1-2A precursors ., Both Hsp90 and p23 , a co-chaperone of Hsp90 , have been reported to be required for processing of the PV P1 precursor into the mature structural proteins 23 ., Similarly , inhibitors of Hsp90 have been shown to impede processing of the wt FMDV capsid precursor in cell-free assays 26 ., However , interestingly , hepatitis A virus ( HAV ) is not sensitive to the inhibition of Hsp90 function 34 ., This indicates that HAVs might employ other strategies for correct folding of the capsid precursor ., However , it is noteworthy that HAV also has several unique characteristics that distinguish it from most other members of the picornavirus family , e . g . slow growth rate , lack of capsid protein myristoylation and use of only a single viral protease ( 3Cpro ) for polyprotein processing 35–38 ., An earlier study showed that Hsp90 mediates PV P1 folding in cells ., Inhibition of this chaperone lead to misfolding of P1 , which resulted in the targeting of the PV P1 for degradation by the cellular quality-control system ( proteasome pathway ) , and thus the level of the PV P1 was strongly reduced 23 ., These observations are consistent with the results presented here on the FMDV P1-2A ., All of the FMDV P1-2A precursors that cannot be processed by 3Cpro accumulated to a lower level than the P1-2A ( wt ) ., This was apparent for the truncated precursors ( VP1 Y185Stop , VP1 L158Stop , VP1 A107Stop , VP1 L53Stop ) , precursors with small internal deletions ( VP1 Δ185–199 , VP1 Δ185–189 , VP1 Δ182–192 ) and two precursors with single amino acid substitutions ( VP1 Y185A and VP1 R188A ) ., Thus , it may be that the mutant precursors , which cannot be processed , are misfolded and therefore targeted for degradation , hence the reduced level of these products within cells ., Interestingly , Geller et al . , 23 showed that inhibition of the Hsp90 chaperone in a cell-free system ( rabbit reticulocyte lysate ) , where the proteasomal degradation system is inhibited by free hemin , did not reduce the yield of P1 23 ., However , even in the absence of proteasomal degradation , the Hsp90 was still required for P1 to fold into a processing-competent conformation , since the PV P1 precursor , in the absence of Hsp90 , adopted a misfolded conformation that could not be recognized by the 3CDpro and thus could not be processed into the mature capsid proteins 23 ., The clear resistance to processing of certain mutant FMDV P1-2A proteins ( in which the YCPRP motif is modified or deleted ) and their reduced accumulation within cells is entirely consistent with these results ( see Fig 2B , Fig 3 , Fig 4 and Fig 5 ) ., As indicated above , a critical region that is required for the correct processing of the FMDV capsid precursor has now been identified ., This motif ( YCPRP ) is very highly conserved among FMDVs ., Indeed , the YCPR sequence was found to be completely conserved in over 100 FMDV strains , with representatives from all 7 serotypes 15 ) ; only variation to YCPRA has been observed ( Fig 7 ) ., However , previously , no function for this conserved sequence had been identified ., The YCPRP motif is also highly conserved among other picornaviruses as well , e . g . it exists as FCPRP in cardioviruses and WCPRP in enteroviruses , see Fig, 8 . Indeed , both Y to F and Y to W are very conservative amino acid substitutions , since all three amino acids have similar properties with non-polar , aromatic side chains ., This high conservation likely reflects its importance for correct folding of the capsid precursor ., The high resistance to cleavage of the junctions between the structural proteins following substitution of residues VP1 Y185 and VP1 R188 individually indicates that correct cleavage may be dependent on the interaction with several amino acids in this region and thus the whole motif seems to be of high importance for correct folding and subsequent processing of the capsid precursor ., Furthermore , these results are consistent with the observations that the substitutions VP1 Y185A and VP1 R188A , that each prevent P1-2A processing by 3Cpro in cells ( Fig 5 ) , also block FMDV infectivity ( Table 1 ) ., It is interesting to note that the VP1 P187A mutant was also non-infectious ( Table 1 ) even though processing of the P1-2A could still be observed ( Fig 5 , lane 8 ) ., The high conservation of this motif clearly reflects its sensitivity to modification ., An earlier study has shown that removing 50 residues from the C-terminus of the PV VP1 prevented cleavage of the two junctions , VP0/VP3 and VP3/VP1 , within the capsid precursor in vitro 30 ., Significantly , these 50 amino acids include the highly conserved motif ( WCPRP ) identified here , and thus indicates the importance of this motif , not only for FMDV , but also more widely within the picornavirus family ., Similarly , as indicated above , removal of 42 residues ( including the YCPRP ) from the C-terminus of the FMDV VP1 protein completely prevented cleavage of the capsid precursor by the 3Cpro in a cell-free system 29 ., Recently , we have shown that blocking cleavage of one of the junctions within the FMDV P1-2A precursor did not block the cleavage of the other junction within the capsid precursor 28 ., Thus , the severe inhibition of cleavage of both junctions likely reflects a changed overall structure of the capsid precursor , thereby preventing cleavage of both junctions ., It is interesting to note that in HAV , the equivalent region of VP1 has the sequence YFPRA , perhaps the two substitutions together account for the lack of sensitivity of HAV assembly to Hsp90 inhibitors 34 ., It can be proposed that the conserved motif serves as a binding site for an important chaperone , e . g . Hsp90 ( or its partners ) , that is necessary for correct protein folding ., A proposed model for this interaction is shown in Fig, 9 . A co-chaperone of Hsp90 , called p23 , also seems to be involved in the correct folding of the PV P1 ., It has been reported that treatment with geldanamycin ( GA ) did not affect the PV P1-Hsp90 interaction , but abolished the P1-p23 interaction and thereby affected P1 maturation 23 , thus indicating different possibilities for chaperone interaction at this specific site ., Picornaviruses are able to adapt very rapidly since they have an RNA dependent RNA polymerase with a high error rate and no error correction mechanism ., However , Geller et al . , 23 showed that PV was unable to adapt to an Hsp90-independent P1 folding pathway during several passages in cells in culture or in PV-infected mice when the function of Hsp90 was inhibited by the presence of GA ., Thus it seems that for the virus to adapt to a folding pathway without the involvement of Hsp90 requires extensive change 23 ., It is interesting that the deletion VP1 Δ194–199 strongly inhibited cleavage at the VP3-VP1 junction , without affecting the cleavage of the VP0-VP3 junction ( Fig 4 , lane 6 ) ., Surprisingly , the alanine scanning substitutions through this specific region did not identify any individual residue that affected cleavage of any of the junctions ( S2 Fig , lanes 4 , 6 , 8 , 10 , 12 and 14 ) ., However , interestingly the P1-2A ( VP1 V193A ) mutant , modified at a residue adjacent to the deletion , also displayed a slower processing rate of this VP0/VP3 junction compared to the wt and the other alanine mutants ( Fig 6 , lane 12 ) ., However , this VP1 V193A mutant does not seem to affect the processing of the VP3-VP1 junction to the same extent as the VP1 Δ194–199 mutant ., In addition , the VP1 Δ191–195 mutant also showed a lower processing of this VP3/VP1 junction ( Fig 4B , lane 4 ) as judged by the elevated level of the VP3-VP1 product ., These results indicate that the cleavage of the VP3-VP1 junction , may be dependent on the interaction with several amino acids and that residues within the VP1 aa 193–199 region are important for optimal processing of the VP3-VP1 junction , more than 190 aa away from the site ., We have noted previously that the K210E change in VP1 that severely limited processing at the VP1/2A junction also enhanced the yield of VP3-VP1-2A 14 ., These studies also identified a genetic link between the processing of the VP1/2A junction and the substitution E83K in VP1 ., Furthermore , Escarmis et al . , 39 showed that the substitution M54I within the VP1 of serotype C FMDV resulted in less efficient processing at the VP3/VP1 junction ., Thus , there are multiple , complex , interactions , some of which operate “at a distance” , that govern picornavirus capsid protein processing and assembly ., The plasmid pO1K/A22 contains a T7 promoter upstream of a full-length FMDV cDNA with the A22 Iraq capsid coding sequence within an FMDV O1K backbone as previously described 28 , 40 , 41 ., To investigate the effect of different modifications within the P1-2A , the FMDV cDNA was digested with ApaI and then religated to remove most of the sequence encoding the non-structural proteins ( including the 3Cpro ) downstream of the 2A-peptide , as described previously 28 ., These constructs contained a modified form ( W52A substitution ) of the Lpro to overcome the negative effect of the L prot | Introduction, Results, Discussion, Methods | Many picornaviruses cause important diseases in humans and other animals including poliovirus , rhinoviruses ( causing the common cold ) and foot-and-mouth disease virus ( FMDV ) ., These small , non-enveloped viruses comprise a positive-stranded RNA genome ( ca . 7–9 kb ) enclosed within a protein shell composed of 60 copies of three or four different capsid proteins ., For the aphthoviruses ( e . g . FMDV ) and cardioviruses , the capsid precursor , P1-2A , is cleaved by the 3C protease ( 3Cpro ) to generate VP0 , VP3 and VP1 plus 2A ., For enteroviruses , e . g . poliovirus , the capsid precursor is P1 alone , which is cleaved by the 3CD protease to generate just VP0 , VP3 and VP1 ., The sequences required for correct processing of the FMDV capsid protein precursor in mammalian cells were analyzed ., Truncation of the P1-2A precursor from its C-terminus showed that loss of the 2A peptide ( 18 residues long ) and 27 residues from the C-terminus of VP1 ( 211 residues long ) resulted in a precursor that cannot be processed by 3Cpro although it still contained two unmodified internal cleavage sites ( VP0/VP3 and VP3/VP1 junctions ) ., Furthermore , introduction of small deletions within P1-2A identified residues 185–190 within VP1 as being required for 3Cpro-mediated processing and for optimal accumulation of the precursor ., Within this C-terminal region of VP1 , five of these residues ( YCPRP ) , are very highly conserved in all FMDVs and are also conserved amongst other picornaviruses ., Mutant FMDV P1-2A precursors with single amino acid substitutions within this motif were highly resistant to cleavage at internal junctions ., Such substitutions also abrogated virus infectivity ., These results can explain earlier observations that loss of the C-terminus ( including the conserved motif ) from the poliovirus capsid precursor conferred resistance to processing ., Thus , this motif seems essential for maintaining the correct structure of picornavirus capsid precursors prior to processing and subsequent capsid assembly; it may represent a site that interacts with cellular chaperones . | The picornavirus family includes clinically important human and animal pathogens , for example: poliovirus , rhinovirus ( causing the common cold ) and foot-and-mouth disease virus ( FMDV ) that infects cloven-hoofed animals ., Picornaviruses contain a positive-sense RNA genome surrounded by a protein shell , also called a capsid ., The capsid proteins are made from a precursor and correct processing and assembly of these capsid proteins is necessary in the virus life cycle to create new infectious virus particles ., In this study , we have identified a short motif ( just 5 amino acids long ) within the capsid precursor , which is highly conserved among picornaviruses ., Deletion of this motif inhibited processing of the junctions between the mature structural proteins within this precursor , with one junction being more than 400 amino acids away from this region ., This motif also seems to be required for the optimal accumulation of the capsid precursor in cells ., We hypothesize that the motif may be involved in binding to a cellular protein , such as a chaperone , to stabilize the capsid precursor and promote its correct folding to allow it to be processed by the viral protease prior to capsid assembly . | blood serum, animal diseases, medicine and health sciences, body fluids, foot and mouth disease, microbiology, precursor cells, vertebrates, animals, mammals, viruses, animal models, rna viruses, experimental organism systems, sequence motif analysis, zoology, research and analysis methods, sequence analysis, bioinformatics, animal cells, proteins, animal studies, structural proteins, viral packaging, viral replication, picornaviruses, guinea pigs, biochemistry, rodents, eukaryota, blood, cell biology, anatomy, virology, database and informatics methods, physiology, biology and life sciences, cellular types, immune serum, amniotes, organisms | null |
journal.pntd.0000282 | 2,008 | Targets of the Entamoeba histolytica Transcription Factor URE3-BP | The early branching eukaryote Entamoeba histolytica is a human parasite that is the etiologic agent of amebic dysentery and liver abscess ., Only one of every five infections leads to disease 1 , and the parasite and host factors that control the outcome of infection are not well understood ., Alteration in transcription of certain crucial genes may contribute to the expression of a virulence phenotype ., Distinct gene expression profiles which may be associated with pathogenicity have been identified by comparing the transcriptome of laboratory-cultured HM-1:IMSS E . histolytica to trophozoites growing in vivo , as well as to that of less virulent strains and recent clinical isolates 2 , 3 , 4 , 5 , 6 ., Here we have attempted to study the molecular mechanisms involved in the transcriptional regulation of virulence in E . histolytica by investigating further the role of the upstream regulatory element 3-binding protein ( URE3-BP ) transcription factor ., URE3-BP is a calcium regulated transcription factor , that is known to bind to the URE3 motif and thereby modulate transcription of both the Gal/GalNAc-inhibitable lectin hgl5 and ferredoxin 1 ( fdx ) genes ., Mutation of the URE3 motif within the hgl5 and fdx1 promoter led to a four-fold rise and a two-fold drop in gene expression respectively , indicating that URE3 may function as a repressor or activator depending on context 7 , 8 ., Previously a yeast one hybrid screen was used to identify an E . histolytica cDNA encoding a protein ( URE3-BP ) that recognized the URE3 DNA motif 9 ., The URE3-BP protein was present in the E . histolytica nucleus and cytoplasm with an apparent molecular mass of 22 . 6 KDa ., Two EF-hand motifs were identified in the amino acid sequence of URE3-BP ., Binding of URE3-BP to the URE3 motif was inhibited in vitro by addition of calcium ., Mutation of the second EF hand motif in URE3-BP resulted in the loss of calcium inhibition of DNA binding , as monitored by an electrophoretic mobility shift assay ., Chromatin immunoprecipitation experiments confirmed the calcium-dependent interaction of URE3-BP with both the hgl5 and fdx1 promoter DNA 10 ., Because the Gal/GalNAc inhibitable lectin is an important virulence factor of E . histolytica it may be coordinately regulated at the transcription level with other virulence genes ., In this light , it was intriguing that the mRNA of ( URE3-BP ) was down regulated two-fold in vivo 2 ., The discovery of direct downstream targets of URE3-BP therefore may identify other genes important in E . histolytica pathogenesis and help delineate molecular and cellular mechanisms involved in the expression of virulence ., Position-specific variability in the sequence of transcription factor binding sites renders recognition of valid targets by computational methods alone extremely challenging 11 , 12 ., Most work has been performed in the yeast model organism or the well-studied human transcriptome ., The parameters affecting transcription regulation in early branching eukaryotes are only beginning to be deciphered 4 , 13 , 14 , 15 , 16 , 17 ., The sequencing of the E . histolytica genome identified homologues of most of the RNA polymerase II subunits 18 , 19 , however the structure of E . histolytica core promoter varies from the conventional norm by containing a third regulatory sequence GAAC in addition to the TATA box and INR ., This may have an unpredictable impact on the machinery necessary for regulation of transcription 7 , 20 ., A bioinformatics approach was used by Hackney et al to correlate potential E . histolytica DNA motifs with high and low gene expression 21 ., In our study we have focused on using not only computational but also experimental approaches to discover the gene regulatory network of the URE3-BP transcription factor ., To identify the consensus binding site sequence , a position weight matrix ( PWM ) of transcription factor binding to the URE3 motif was developed ., To test the validity of the matrix , selected mutants within the URE3 motif of the hgl5 promoter were assessed for promoter activity in an episomal reporter construct ., Finally , to identify additional genes regulated by URE3-BP , genome-wide expression profiling of transcripts from strains over-expressing a calcium insensitive URE3-BP mutant was performed ., E . histolytica strain HM1:IMSS trophozoites were grown at 37°C in TYI-S-33 medium containing penicillin ( 100 U/ml ) and streptomycin ( 100 μg/ml ) ( GIBCO/BRL ) 22 ., Amebae in logarithmic phase growth ( ∼6×104 trophozoites/ml ) were used for nuclear extract preparation ., Crude nuclear extracts were prepared by the method previously described 8 , 23 with the following modifications: the protease inhibitors 2 mM ( 2S , 3S ) -trans-epoxysuccinyl-L-leucylamido-3-methylbutane and 2 mM 4- ( 2-aminoethyl ) benzenesulfonylfluoride , HCl were added to both cell and nuclear lysis buffers , and dithiothreitol was omitted from the nuclear lysis buffer ., Stable transfection of E . histolytica trophozoites was achieved by use of the previously described lipofection technique 24 , 25 ., Briefly , amebae were washed and suspended ( 2 . 2×105 amebae per ml ) in Medium 199 ( Invitrogen , CA ) supplemented with 5 . 7 mM cysteine , 1 mM ascorbic acid , 25 mM HEPES pH 6 . 8 ( M199s ) 3 μg of DNA and 15 μl of Superfect ( Qiagen ) was added ., Treated amebae were left for 3 hours at 37°C , then growth media was added , and incubation at 37°C was continued overnight ., The expression of all the recombinant proteins was confirmed by western blotting ., Nuclear and cytoplasmic extracts were prepared using standard techniques 9 ., Transfected amebae were selected with either G418 ( 6 μg/ml ) or hygromycin ( 15 μg/ml ) ., Transient transfection was achieved using the electroporation protocol described by Purdy et al . Briefly trophozoites were washed and suspended in 120 mM KCI , 0 . 15 mM CaCl2 , 10 mM K2HPO4/KH2PO4 , pH 7 . 5 , 25 mM HEPES , 2 mM EGTA , 5 mM MgC12 , 50 μg/ml of plasmid and 3 . 1 μg/ml of DEAE-dextran , and electroporated at 500 μF and 500 V/cm ( Gene Pulser , Bio-Rad ) 7 ., URE3-BP , has been shown to bind specifically to the TATTCTATT ( URE3 ) DNA motif in Gilchrist et al 2001 9 ., In these conditions antibodies raised against URE3-BP blocked the formation of the URE3 DNA-protein complex by native nuclear extracts and competition with a 60 fold excess of the nonspecific oligonucleotide ( Olig-1 ) did not interfere with the formation of the specific complex ., EMSA assays were performed with a Klenow-radiolabeled double stranded DNA oligonucleotide that spans the URE3 motif within the hgl5 promoter TGTTCCAAAAAGATATATTCTATTGAAAATAAAAGAAG ( hgl5-URE3 ) ., The protein-DNA interaction occurred in band shift buffer ( 10 mM Tris-HCl pH 7 . 9 , 50 mM NaCl , 1 mM EDTA , 0 . 05% nonfat milk powder , 3% glycerol , 0 . 05 mg of bromophenol blue ) to which 0 . 2 μg of poly ( dIdC ) , 10 fmol of DNA probe , and 2 μg of nuclear extract were added ., The reaction mixture was allowed to incubate at room temperature ( 20°C ) for 1 h prior to electrophoresis on a nondenaturing polyacrylamide gel for 2 to 3 h ., The gel was then fixed and dried , and the signal from the protein-DNA complex was quantitated after exposure of the gel to a phosphorimage screen as described previously 8 ., A ten fold or six fold excess of either cold hgl5-URE3 ( wt ) or oligonucleotides wherein a base pair alteration within the URE3 motif had been made were added to the assay and the amount of competition was quantitated using a PhosphorImager ., A double stranded oligonucleotide ( Olig1 ) with the sequence AGAAAGCGTAATAGCTCA was used as an irrelevant control ., Experiments were performed in triplicate , gels scanned ( Molecular Dynamics , Model 425 ) and relative density of the EMSA assessed by use of the ImageQuant program ( IQMac v1 ) ., The stable construct ( pHTP . luc ) contained the luciferase structural gene under the control of the E . histolytica hgl5 gene 26 ., The promoter was mutated at the URE3 motif as described in results ., Inducible vectors were based on the tetracycline inducible gene expression system of Ramakrishnan et al . 27 ., An N-terminal myc tag was introduced by the amplification using the oligonucleotide TGCGGATCCAAATGGAACAAAAATTAATTTCAGAAGAAGATTTA-ATGCAACCACCTGTAGCTAATTTCC , and a control generated using an oligonucleotide that incorporated two stop codons directly after the myc tag ( CTTGTATTTAACAATAGCTAACATC ) ., Both amplicons were subcloned into the pCR2 . 1 TOPO expression vector ( Invitrogen ) and sequenced to confirm the presence of the desired mutations ., The DNAs were then subcloned into the tetracycline-inducible gene expression system ., One ml of Trizol ( Invitrogen ) was added to 2×106 amebae collected by centrifugation at 900 rpm for 5 min and an initial RNA preparation performed according to the manufacturers directions ., RNA greater than 200 nucleotides in length was separated from total RNA by the RNeasy protocol ( Qiagen ) ., RNA was isolated from at least two independent cultures on the same day for microarray analysis ., Reverse transcription real time PCR ( qRT-PCR ) was used to independently measure mRNA abundance in independently transformed amebae ., The cDNA was subjected to 40 amplification cycles with HotStarTaq ( Qiagen ) ., Primers were designed to amplify 100–300 base pairs using genomic sequences from the E . histolytica Genome Sequencing Project ( http://www . tigr . org/tdb/e2k1/eha1/ , http://pathema . tigr . org/tigr-scripts/Entamoeba/PathemaHomePage . cgi ) and the Primer3 program ( Table S1 ) 28 ., The fluorescent dye SYBR Green I ( Molecular Probes ) was used to detect amplified cDNA ., Continuous SYBR Green I monitoring during amplification using the MJR Opticon II machine was done according to the manufacturers recommendations ., All real time amplification reactions were performed in triplicate and the resulting fluorescent values averaged ., In all experiments utilizing qRT-PCR the cycle threshold values ( CT , the cycle number at which fluorescence exceeds the threshold value ) were linked to the quantity of initial DNA after calibration of the effectiveness of the amplifying primer pair 29 ., The relatively invariant lgl1 transcript was used to compensate for the variation in the amount of amebic mRNA isolated ., Quality control of RNA samples was performed by use of the Agilent Bioanalyser Nano Assay ., The standard protocol for hybridization of eukaryotic mRNA to Affymetrix arrays was followed ( http://www . affymetrix . com/support/technical/manual/expression_manual . affx ) ., Two micrograms of total RNA was used for cDNA and subsequent biotinylated cRNA synthesis ., This labeled RNA probe was hybridized to the Affymetrix custom array designed using information generated from the E . histolytica genome sequencing project release date 12/08/04 as previously described 2 , 18 ., The affymetrix probes were mapped to the new Genome Assembly and recognized 6385 of the reannotated open reading frames ( 78% of E . histolytica Open Reading Frames ( ORF ) 8197 http://pathema . tigr . org/ ) ., The ORF probe sets were preferentially selected from the 600 bases proximal to the 3′ end of the E . histolytica sequences ., The arrays were scanned with an Affymetrix Gene Chip scanner 7G and report files were generated to determine the percentage of present calls of each array ., The detection calls ( present , marginal , absent ) for each probe set were obtained using the GCOS system ( http://www . affymetrix . com/products/software/specific/gcos . affx ) ., Only genes with at least one “present” call were used in assessment of the data ., Raw data from the arrays were normalized at probe level by the gcRMA algorithm and then log2 transformed 30 ., Genome analysis and datasets− The dataset used in this analysis was that of the reannotated E . histolytica genome of Caler et al . ( manuscript in preparation ) publicly available at http://pathema . tigr . org ( Genebank accession number ( AAFB00000000 ) ) ., The reannotated genome was searched for the URE3 motif with a custom motif search script ( Table 1 ) ., Microarray data analysis was performed using the Array Data Analysis and Management System ( VBI ) ( http://pathport . vbi . vt . edu/main/microarray-tool . php ) ., The system uses publicly available tools such as Bioconductor 31 for analysis of the data ., Briefly , statistical significance was determined for the microarray data using the Linear Models for Microarray Data ( LIMMA ) program as described in the results section 32 , 33 ., The statistical significance p values were corrected using the Benjamini and Hochberg false-discovery-rate test ( FDR≤0 . 05 ) 34 ., Our comparisons were both between the two strains , and between different time points giving us potentially three control conditions ., The most comprehensive comparison was between the test and control strains at 9 h post-induction ., Statistical significance was determined for the qRT-PCR results using the students T test and the non-parametric Kruskal-Wallis Test was used to determine significance in the reporter gene assays ., URE3 associated promoters were compared to the frequency of motif appearance in all E . histolytica promoters using the chi-squared test ( InStat 2 . 03 program ( GraphPad Software ) ) ., Transwell migration assays were performed using 5 mm transwell inserts ( 8 μm pore size Costar ) suspended by the outer rim within individual wells of 24-well plates ., Briefly , ameba trophozoites were incubated in serum free growth media containing 2 μg/ml CellTracker Green CMFDA ( Molecular Probes ) for 1h 35 ., Trophozoites were then washed and suspended at a concentration of 2×105/ml in serum free media and 500 μl loaded into the upper chamber ., The plates were then placed in anaerobic bags ( GasPak 100 Anerobic system; BD Biosciences ) and incubated at 37°C for 3 h ., Inserts and media were removed and fluorescence measured using a SpectraMax M2 fluorescent plate reader ., Fluorescence versus concentration for each sample was determined by using a standard curve ., Ameba numbers confirmed in selected experiments by microscopic counting and by use of the Techlab E . histolytica II antigen test used according to the manufacturers directions ., Electrophoretic mobility shift analysis ( EMSA ) was used with base substituted oligonucleotides to define the consensus URE3 motif ., The impact of adding an excess of a non-radioactive oligonucleotide with a base pair alteration within the URE3 motif T1A2T3T4C5T6A7T8T9 ., was measured ., A representative gel showing competition with the motif modified at positions 1 ( AATTCTATT , GATTCTATT , CATTCTATT ) or 4 ( TATACTATT , TATGCTATT , TATCCTATT ) is shown in Figure 1A ., The efficacy of a substituted base in competition assays was compared to the wild type motif ( 100% ) and an irrelevant control ( 0% ) , as shown in Figure 1B ., The percent contribution of each base to the total competition occurring at each position ( from each of the four bases ) was then calculated and is shown graphically in Figure 1C ., The consensus URE3 motif incorporated base substitutions that maintained at least 15% competition of the gel shifts ., The prototypic URE3 motif T1A2T3T4C5T6A7T8T9 as a result was modified to a consensus motif of T1atg2T3tc4cg5T6at 7tgc8tg9 ., We tested whether URE3 mutations that prevented competition in EMSAs ( Figure 1 ) , also blocked URE3 function in a transfected promoter ., Key bases within the hgl5 promoter URE3 motif were mutated: T4A , T4C T4G and C5A ., These mutant promoter sequences were placed upstream of the luciferase reporter gene ., Luciferase values from at least three independent experiments with two different DNA preparations were performed ( Figure 2 ) ., De-repression of the promoter in all base changes assayed indicated that these bases were critical for the binding of URE3-BP ( which acts as a repressor in the hgl5 promoter context ) ., This included the promoter with the mutation T4C ., In the EMSA assay the T4C oligonucleotide affinity for URE3-BP was approximately 50% of the wild type oligonucleotide ., We interpreted this as a consequence of the lower sensitivity of the episomal reporter assays , likely due to over-expression of episomal constructs ., To further evaluate the physiological relevance of the URE3 matrix , a calcium-insensitive mutant of URE3-BP ( EF ( 2 ) mutURE3-BP ) ( Figure 3A ) , and therefore constitutively active , was inducibly expressed and the changes in gene expression measured by use of an Affymetrix custom array ( E_his-1a520285 ) ., The array included probes to 6 , 385 E . histolytica ORFs ., Total RNA ( 12 μg ) was isolated before induction ( –Tet ) and after 9 h of induction ( +Tet ) from cells carrying the myc-tagged recombinant URE3-BP mutant or the control construct ( containing a stop codon immediately after the N terminal myc tag ) ., The expression of the mRNA encoding the recombinant calcium-insensitive dominant positive mutant URE3-BP was induced 10–15 fold at nine hours post induction as indicated by myc specific qRT-PCR ( Figure 3B ) ., A western blot of E . histolytica nuclear and cytoplasmic proteins , probed with a myc-specific antibody , confirmed the cytosolic and nuclear distribution of both wild type and recombinant protein ( Figure 3C ) ., A calcium insensitive EMSA with hgl5-URE3 occurred only in nuclear extracts prepared from EF ( 2 ) mutURE3-BP transformed trophozoites ( Figure 3D ) ., In low calcium conditions EF ( 2 ) mutURE3-BP and STOP-EF ( 2 ) mutURE3-BP had equivalent URE3 binding capacity ( data not shown ) ., The complete microarray data ( deposited in NCBIs Gene Expression Omnibus 36 and accessible through GEO Series accession number GSE12188 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgiaccGSE12188 ) was normalized using gcRMA and statistical significance determined by LIMMA statistical analysis ( Table S2 ) ., A total of fifty mRNAs were increased or decreased ≥2-fold at 9h post-induction compared to the induced control strain in which an N-terminal stop codon was present in the EF ( 2 ) URE3-BP sequence ( Figure 4 ) ., The filtered transcripts had a normalized signal intensity of >50 in at least one microarray experiment , a change of greater than 2 fold , and were statistically significant by LIMMA ., A total of fifty mRNAs were increased ( 8 ) or decreased ( 42 ) ≥2-fold at 9h post-induction compared to the induced “control stop” strain , which had a stop codon inserted after the sequence encoding the myc tag ., To identify the novel URE3-BP regulated genes , the promoters of transcripts significantly modulated by two-fold or greater were scored for the presence or absence of the URE3 matrix ., The DNA Pattern Find program ( http://bioinformatics . org/sms/ ) was used to locate the URE3 matrix in putative promoters of URE3-BP responsive genes ( Figure 4 and Table 2 ) ., In cases where the probe set represented a ‘family’ of highly similar transcripts the probe set was scored positive if any of the promoters contained a URE3 motif ., The three family probe sets are indicated in Table 2 ., The URE3 matrix was found in 23% of all predicted promoter regions , however the matrix appeared at a statistically greater frequency ( 54% ) in the URE3-BP modulated transcripts predicted by LIMMA ( chi-square test p>0 . 0001 ) ., Alternative analysis using the motif frequency or requiring the presence of two or more motifs for the positive designation also confirmed the correlation between the URE3 motif and transcript modulation ( Table 1 ) ., The presence of the URE3 motif in the 3′ UTR regions was not above background values ., The breakdown of the motifs found in the promoters of putative URE3-BP targets is shown in Table 2 and a graphical representation of the observed URE3 motifs is shown in Figure 4C ., The sequence consensus of the URE3 motifs found 5′ of the modulated transcripts displayed only A or T residues at motif positions 2 and 7 ., While positions 2 and 7 were found to be the least conserved positions in the URE matrix consensus the predominant substitution of A/T may be a reflection of the AT bias of the Entamoeba genome ., The other predominant change was a G substitution at position five which was half as effective as the wild type motif in EMSA assays ( C5G ) ., InterPro was used to scan the open reading frames of the significantly modulated genes to obtain additional information on protein function , TMpred to predict transmembrane regions , big-PI Predictor to identify Glycosylphosphatidylinisotol ( GPI ) anchored proteins ( GPI-anchor ) and SignalP to identify signal peptides 37 , 38 , 39 ., Sequences 150 bp 5′ and 3′ of the annotated ATG start codons were also checked and any additional in-frame peptides also examined for the presence of a signal peptide ., On the basis of this information , the majority of the transcripts ( 47 of 50 ) could be subdivided into four categories: membrane proteins , metabolism , cytoskeleton , and transcription & translation ., The URE3 associated transcripts are shown in Table 2 ., A gene was assigned to the membrane encoding group on the basis of the annotated GO term , the presence of a signal peptide , a GPI-anchor signal , or transmembrane domain ., The majority of the membrane gene promoters contained a URE3 matrix ( 73% p<0 . 0001 ) ., The encoded membrane proteins were quite distinct at the protein level ., However , a subgroup of these proteins had highly similar promoter , and amino- and carboxyl-terminal sequences ( sites of signal peptide and transmembrane domains ) ( Figure 5 ) ., With one exception ( EHI_163360 ) , the predicted sizes , pI , and length of the proteins were also quite similar ( molecular mass between 29 to 47 kDa , and pI 4 . 3 to 5 . 5 ) ., In addition , all these proteins contained a hydrophobic domain at the carboxyl terminus , and an anterior potential GPI anchor cleavage/addition site 40 ., Most of the promoters of the small group of genes encoding metabolic enzymes also contained a URE3 matrix ( 86% p<0 . 0001 ) ., The enzymes encoded by these genes were linked to phospholipid metabolism ., The opposing regulation of two enzymes that catalyze the addition of Coenzyme A to fatty acids ( EHI_079300 and EHI_185240 ) might reflect different substrate specificities of these enzymes 41 ., Both could potentially use the fatty acids , which are produced as a consequence of the breakdown of phospholipids by phospholipid:diacylglycerol acyltransferase ( PDAT ) ( Figure 6 ) 42 ., No URE3 matrix was found upstream of the fourth transcript , fatty acid elongase ( EHI_092190 ) , which could also be potentially involved in this potential scavenger cell pathway ., To determine whether URE3-BP regulated the promigratory effects of trophozoites , transwell migration assays were performed as described in materials and methods ., A two fold increase in migrating trophozoites was observed when comparing ameba induced to express EF ( 2 ) mutURE3-BP to uninduced controls ( p\u200a=\u200a0 . 04 ) or to the induced control stop strain transfected with the construct STOP- EF ( 2 ) mutURE3-BP ( p\u200a=\u200a0 . 02 ) ( Figure 7 ) ., No difference was observed in migration when uninduced or induced STOP- EF ( 2 ) mutURE3-BP were compared ( data not shown ) ., In this work the DNA consensus motif recognized by the URE3-BP transcription factor was experimentally defined , and then used to identify a subset of E . histolytica transcripts modulated by inducible expression of URE3-BP ., URE3-BP had previously been shown to regulate the expression of two virulence factors in the parasite ., The current studies provide a more global picture of its role in control of gene expression ., The key experimental approach was the inducible expression of a dominant positive URE3-BP mutant and the subsequent identification of uniquely altered transcripts ., The majority ( 42/50 ) of transcripts were repressed ., Over half ( 54% ) of the modulated genes had a URE3 matrix in the promoter region while the other half was comprised of genes presumably downstream of control by URE3 ., The URE3 matrix was present in the 5′ sequences of URE3-BP modulated genes involved in fatty acid metabolism and in potential membrane or secreted proteins ., The latter suggests that phenotypic changes due to the expression of the dominant positive URE3-BP mRNA could occur most noticeably at the cell surface of E . histolytica trophozoites ., URE3-BP regulated genes , which encoded proteins with an N terminal signal peptide , included the potential virulence factor EhCP-A7 , a cysteine protease , an asparagine-rich antigenic surface protein ariel 43 , 44 , a novel lectin-like protein , and a subgroup of genes encoding potential surface proteins which appear to have highly conserved promoters and signal peptides ., Most unusually the conservation in this group of potential surface proteins was greater at the DNA rather than the protein level ., This may represent a gene duplication followed by functional divergence , or possibly a gene recombination event ., A technical limitation of the gene expression analysis was the inability to measure transcript levels of the hgl5 and fdx1 genes that contain URE3 in their promoters , which cannot be distinguished from highly related gene family members that lack URE3-containing promoters ., The hgl5 gene belongs to a family of five highly similar genes ( up to 99% ) , and ferredoxin is encoded by two identical ORFs , fdx1 , and fdx2 ( confirmed by Gilchrist et al unpublished data ) ., The presence of the URE3 matrix was not much higher than background in the promoters encoding genes involved in either transcription/translation ( 25% p\u200a=\u200a0 . 035 ) or cytoskeletal function ( 25% p\u200a=\u200a0 . 73 ) ., However while we could not demonstrate changes in the level of the ferredoxin transcript , a URE3 associated Fe-hydrogenase EHI_005060 ( EC 1 . 12 . 7 . 2 ) 45 , which may be expected to reduce ferredoxin was statistically significantly up-regulated ( over two-fold ) ., Four of the other six metabolic enzymes identified by inducible expression of URE3-BP could be linked in a phospholipid degradation/ fatty acid assimilation pathway ( Figure 6 ) ., A potentially rate limiting step in a fatty acid biosynthesis pathway appeared to be closely modulated by opposing regulated acyl-Coenzyme A synthetases ( acyl-CoA synthetases ) ., The modulated pathway may be involved in the hydrolysis of phospholipids to form fatty acids and important in modification of the cell membrane lipid content 46 ., The inclusion of short chain fatty acids in the E . histolytica growth media has no impact on either the URE3-BP transcript or on the genes involved in this pathway , suggesting the lack of feedback inhibition of URE3-BP from the products of this pathway 17 ., A limitation of this study was that the microarray analysis measured the steady state mRNA levels and we therefore may have missed changes in newly transcribed RNA , especially for abundant transcripts ., Changes occurring in mRNA stability and/or transcript processing may obscure changes occurring at the level of transcription 47 , 48 ., A second limitation is that the high ‘background’ incidence of the URE3 motif ( 23% ) in the promoters of all E . histolytica may indicate that there are other factors not yet identified involved in promoter specific recognition by URE3-BP ., Because appreciable levels of wild type URE3-BP were still present , this might have contributed to the failure to observe changes in the roughly 2000 genes with putative URE3-BP binding sites for which no change was seen following induction of EF ( 2 ) mutURE3-BP ., Because of these issues it is a reasonable conclusion that the 50 changed transcripts are an underestimate of the genes regulated by URE3-BP ., The URE3 matrix was absent in 23 of the regulated promoters ., Amebae were harvested at nine hours after the addition of tetracycline and shortly after appreciable induction of recombinant URE3-BP protein ( Figure 3B ) ., Therefore it is possible that at this time point URE3-BP regulated transcripts may have in turn induced the expression of a set of secondary-response genes 49 ., The URE3 associated EHI_004480 ORF encoding a protein with a basic leucine zipper domain , and the EHI_000780 transcript that encodes a potential chromodomain protein , could act as regulators of a secondary response ., Among the modulated non-URE3 associated transcripts are members of the virulence associated EhSTIRP family 3 , 50 and cytoskeletal genes suggesting a potential involvement in attachment or motility 19 ., The promigratory impact of URE3-BP overexpression shown in Figure 7 supported this correlation however identifying truly co-regulated genes is very difficult with this limited data set 51 , 52 ., In conclusion , we have identified a group of genes , which appear to be regulated by URE3-BP ., These genes and their products may represent a network of interconnected responses to environmental signals ., The biological consequences of these changes may impact the ability of the organism to colonize the host , and/or control its invasive behavior . | Introduction, Methods, Results, Discussion | The Entamoeba histolytica transcription factor Upstream Regulatory Element 3-Binding Protein ( URE3-BP ) is a calcium-responsive regulator of two E . histolytica virulence genes , hgl5 and fdx1 ., URE3-BP was previously identified by a yeast one-hybrid screen of E . histolytica proteins capable of binding to the sequence TATTCTATT ( Upstream Regulatory Element 3 ( URE3 ) ) in the promoter regions of hgl5 and fdx1 ., In this work , precise definition of the consensus URE3 element was performed by electrophoretic mobility shift assays ( EMSA ) using base-substituted oligonucleotides , and the consensus motif validated using episomal reporter constructs ., Transcriptome profiling of a strain induced to produce a dominant-positive URE3-BP was then used to identify additional genes regulated by URE3-BP ., Fifty modulated transcripts were identified , and of these the EMSA defined motif TatgTtccgTattgctg was found in over half of the promoters ( 54% p<0 . 0001 ) ., Fifteen of the URE3-BP regulated genes were potential membrane proteins , suggesting that one function of URE3-BP is to remodel the surface of E . histolytica in response to a calcium signal ., Induction of URE3-BP leads to an increase in tranwell migration , suggesting a possible role in the regulation of cellular motility . | Most infections with Entamoeba histolytica are asymptomatic ., However , in a minority of cases , they develop into invasive and even life-threatening amebiasis ., We suspect , based on prior studies of invasive amebae , that changes in amebic gene expression enable the transition from asymptomatic to invasive infection ., Our long-term goal is to identify the genetic program required to cause amebic colitis ., Here , we studied a transcription factor named URE3-BP that controls the expression of two virulence genes , the Galactose and Galactose N- acetyl- galactosamine inhibitable lectin ( Gal/GalNAc lectin ) and ferredoxin ., We suspected that this factor might coordinate invasiveness by co-regulating additional virulence factors ., The consensus DNA motif that is recognized by URE3-BP was identified by reporter gene assays and by electromobility shift assays ., We then inducibly expressed a constitutively active form of the transcription factor , and measured the changes in total amebic gene expression mediated by overexpression of this dominant-positive version of URE3-BP ., This analysis allowed for a further definition of the functional URE3 motif ., Inducible expression of URE3-BP led to changes in the transcript levels of several novel amebic membrane proteins ., In conclusion , this genome-wide analysis of a transcription factor and its cis-acting regulatory sequence in Entamoeba histolytica has identified new transcripts regulated by URE3-BP that may play a role in trophozoite motility within a coordinated virulence-specific gene regulatory network . | molecular biology/transcription initiation and activation, infectious diseases/neglected tropical diseases, infectious diseases/protozoal infections, biochemistry/transcription and translation, infectious diseases/tropical and travel-associated diseases, infectious diseases/gastrointestinal infections | null |
journal.pgen.1001178 | 2,010 | Allele-Specific Down-Regulation of RPTOR Expression Induced by Retinoids Contributes to Climate Adaptations | A major goal of human genetics is to identify functional genomic regions , especially those containing variants that influence common disease susceptibility or disease-related phenotypes ., However , due to the complexity of the genome , it is not easy to distinguish functional from non-functional regions , especially for regulatory elements , which can lie far from the target gene ., Because adaptive variation must necessarily have functional , in addition to fitness effects , signals of positive natural selection have been proposed as an informative approach to the functional annotation of the genome ., Many genome-wide selection scans have been performed to date based on different approaches ( as reviewed by references 1–5 ) ., These studies have generated a large number of signals , most of which await validation through functional or phenotypic analyses ., One approach to the detection of local adaptations searches for correlations between allele frequencies and environmental variables , e . g . latitude or temperature; this approach assumes that the intensity of selection varies across environments and that the variables correlated with allele frequencies are good proxies for the true selective pressure ( e . g . temperature is a proxy for cold or heat stress ) ., This approach may be particularly informative for human populations who originated in Sub-Saharan Africa and migrated out of Africa 60–100k years ago to occupy most of the earth landmass 6 , 7 ., During this dispersal , human populations have been exposed to extremely diverse environments , which differ in terms of climate , including temperature , day length , UV radiation , pathogen diversity and other factors ., These aspects of human environments have important effects on physiological and developmental processes and , therefore , exerted strong selective pressures on the human genome 8 ., Consistent with the action of spatially-varying selective pressures , human skin pigmentation 9 , body mass 10 , basal metabolic rates ( BMR ) 11 , and cranial form 12 vary across human populations and are associated with climate variables ., It was recently shown that polymorphisms in candidate genes for metabolic disorders 13 , salt homeostasis 14 , 15 , response to stress 16 , 17 , and circadian signaling 18 , are strongly correlated with climate variables , thus providing a possible genetic mechanism for the observed distribution of human phenotypes across populations ., One of these studies identified the RPTOR gene as a target of spatially-varying selective pressures because many variants within the gene exhibited particularly strong correlations between allele frequency and latitude 13 ., The RPTOR gene codes for a protein involved in the target of rapamycin ( MTOR ) pathway , which in turn is important in cell growth , proliferation , apoptosis 19 , and immune response 20 ., Two multiprotein complexes , MTORC1 and MTORC2 , constitute the core of this pathway 19; MTORC1 is the target of and sensitive to rapamycin , an immunosuppressant and anti-cancer agent , while the other complex is not 19 ., Under the regulation of nutrient , energy , and stress , MTORC1 can transfer the proliferation signal to the downstream proteins mainly by phosphorylating two substrates , ribosomal protein S6 kinase , 70kDa , polypeptide 1 ( RPS6KB1 ) and eukaryotic translation initiation factor 4E binding protein 1 ( EIF4EBP1 ) 19 ., The regulatory associated protein of MTOR ( RPTOR ) is a crucial component of the MTORC1 21 , which works both as a scaffold and a regulatory protein 21 , 22 ., In particular , RPTOR can bind to TOR signaling ( TOS ) domain of EIF4EBP1 and RPS6KB1 23– and to the HEAT repeat domain of MTOR 21 , thus making the phosphorylation reaction possible ., Therefore , in absence of RPTOR , the kinase activity of MTOR is mainly reduced or inhibited 21 ., Given the function of this pathway and its regulation in response to environmental stimuli , it is plausible that the RPTOR variants correlated with latitude , or one in strong linkage disequilibrium ( LD ) with these SNPs , conferred adaptations to selective pressures that vary across environments ., However , the mechanism through which this variation affects the function of the RPTOR gene remains unknown ., In the present study , we used population genetics analyses and in vitro functional assays to localize the most likely target of selection and to propose a mechanism underlying its effect on RPTOR gene function ., More specifically , we used the results of a genome-wide selection scan to identify the variant with the strongest evidence as a target of climate adaptations ( Hancock and Di Rienzo , personal communication ) ., This SNP lies within a predicted POU class 2 homeobox 1 ( POU2F1 ) binding site and near a retinoid acid receptor ( RAR ) binding site identified by Chromatin immunoprecipitation ( ChIP ) -chip 26 ., Given that POU2F1 and RAR are known to cooperate in the regulation of gene expression , we hypothesized that this SNP is located within an enhancer that regulates RPTOR expression in response to retinoid acid ( RA ) ., Consistent with this prediction , we observed a significant increase of RPTOR expression in both MCF-7 and HepG2 cell lines after treatment with RA ., We further showed that both POU2F1 and RAR bind to the region spanning the SNP of interest in both cell lines ., Finally , we determined that the two alleles at this SNP influence RA-mediated transcriptional response by means of reporter gene assays using enhancer constructs containing , respectively , the ancestral and the derived allele ., Based on these results , we propose that the SNP that is strongly correlated with climate variables affects fitness by influencing RPTOR gene expression ., To refine the location of the polymorphism targeted by climate-related selective pressures , we mined the results of a recent genome-wide scan for signals of allele frequency correlation with climate variables ( Hancock and Di Rienzo , personal communication ) ., Allele frequencies for a total of 156 SNPs were obtained in the genomic region spanning the RPTOR gene and 100 kb upstream and downstream of the gene ( See Table S1 for detailed information ) ., The evidence for the action of selective pressures related to climate was assessed by means of a Bayesian method that yields a Bayes factor ( BF ) , which is a measure of the support for a model in which a SNP allele frequency distribution is linearly dependent on a climate variable in addition to population structure , relative to a model in which the allele frequency distribution is dependent on population structure alone 27 ., A transformed rank statistic ( sometime referred to as an ‘empirical p-value’ ) was also calculated to determine whether the BF value of a SNP of interest is unusual relative to those of other SNPs matched by allele frequency; as with formal p-values , a low rank indicates strong evidence for a correlation ( i . e . a large BF ) 27 ., We examined the following climate variables: mean , minimum and maximum temperature , precipitation rate , relative humidity and solar radiation; for all climate variables we considered the value in the winter and summer , respectively ., Among the 156 SNPs tested , 56 had a rank lower that 5% for at least one climate variable ., One SNP , rs11868112 , had particularly large BFs with winter temperatures ( rank statistic\u200a=\u200a0 . 0082 , 0 . 0064 , and 0 . 0039 for minimum , mean and maximum winter temperature , respectively . See Figure 1 and Figure 2 , Table S2 and Table S3for detail . ) ., Therefore , this SNP , or one in strong LD with it , is a candidate target of selective pressures related to climate ., To determine whether an unknown coding SNP could account for the above correlations with climate variables , we re-sequenced the RPTOR cDNA from a small , but diverse group of HapMap lymphoblastoid cell lines ., As shown in Figure S1 , 38 SNPs were identified; 5 and 17 of them were located in 5′ and 3′ untranslated regions , respectively ., No non-synonymous SNP was identified ., Moreover , all of them showed a relatively low LD with rs11868112 ( r2<0 . 62 , 0 . 34 , and 0 . 69 in YRI , CEU , and ASN populations , respectively ) ., These results indicate that the selection signal is not due to a non-synonymous SNP and suggest that the selection target is not likely to be within the coding region ., Because the true target of selection could be a regulatory SNP , we re-sequenced also 22 . 6kb of conserved non-coding elements near the RPTOR gene , the RPTOR promoter , and a 2 . 6 kb region spanning rs11868112 ., As shown in Figure S2 , 11 , 16 , and 135 SNPs were found in the intergenic , promoter , and intron regions , respectively; polymorphism levels were within the range of genome-wide variation ( see Table S4 ) 28 , 29 ., Moreover , none of the additional 161 SNPs discovered in this survey showed strong LD with rs11868112 ( r2<0 . 23 , 0 . 48 , and 0 . 54 in YRI , CEU , and ASN populations , respectively , result not shown ) ., Therefore , our targeted re-sequencing survey did not identify SNPs with likely functional effects and that could drive the signal seen at rs11868112 ., We used the re-sequencing data to perform neutrality tests based on the allele frequency spectrum , but no significant departure was detected ( Table S4 ) ., This may be due to the fact that these tests are known to have inadequate power under a range of selection scenarios , including models in which selection acted on an allele occurring at appreciable frequencies prior to the onset of selection 30–32 ., SNP rs11868112 lies 26 . 2 kb 5′ to the RPTOR gene , which is ubiquitously expressed and is a strong biological candidate for adaptations to different local environments , and 41 . 3 kb 3′ to the NPTX1 gene , which is transcribed in the opposite orientation relative to RPTOR ., NPTX1 codes for neuronal pentraxin 1 that is expressed only in the central neurons of the nervous system where it plays a role in synaptic plasticity 33; given its function , the NPTX1 gene is a less likely target of adaptations to different climates ., We hypothesized that the SNP rs11868112 is located within a long-distance regulatory element and that this SNP influences the activity of this regulatory element ., This hypothesis was bolstered by the fact that this SNP lies less than 1 kb away from a retinoic acid receptor α ( RARA ) binding site detected by ChIP-chip in the breast cancer cell line MCF-7 26 ., We also found that rs11868112 resides within a canonical POU2F1 binding site , as predicted by the Match program in the TRANSFAC database ( http://www . gene-regulation . com ) ., Since POU2F1 is known to cooperate with RARA to regulate gene expression 34 , we hypothesize that RPTOR gene expression is regulated by retinoids via activation of the RARA and that rs11868112 modulates the activation of RPTOR expression by modifying POU2F1 binding affinity to the DNA ., To investigate the effects of retinoids on RPTOR expression , we treated HepG2 and MCF-7 cell lines with the selective RARA agonist AM580 , which has greater specificity for RARA compared to all-trans RA 35 , for different time periods ( 2–48 hrs ) and measured RPTOR mRNA levels by quantitative real time PCR ., The MCF-7 cell line was included because the RARA binding site was originally identified in these cells while the human hepatocellular carcinoma cell line HepG2 was included because the liver plays a prominent role in biological processes relevant to energy metabolism ( e . g . carbohydrate and lipid metabolism ) ., The genotype of rs11868112 is TT and CT for MCF-7 and HepG2 , respectively ., As shown in Figure 3A , RPTOR expression in HepG2 varied substantially across time points for vehicle ( DMSO ) control treatment ., A 33 . 7% higher expression level was observed for 12 hrs treatment with AM580 versus DMSO ( P\u200a=\u200a0 . 01 ) ., For all other time points , no significant difference ( P>0 . 05 ) was observed for AM580 and DMSO treatment ., In MCF-7 cells , where we observed considerably less variation in RPTOR expression for DMSO treatment across time points , we found a significant increase of RPTOR mRNA levels upon AM580 treatment at 12 , 24 and 48 hrs ( 38 . 1% , 50 . 8% , and 62 . 5% higher expression , and P<0 . 001 , P\u200a=\u200a0 . 01 , and P\u200a=\u200a0 . 02 respectively , Figure 3B ) ., A relatively late ( 12 hrs or more ) transcriptional response to retinoids has been observed for many other target genes of RARA 34 , 36 , 37; however , it remains unclear whether the RPTOR gene is a direct or indirect target ., These findings suggest that RPTOR expression may be regulated by RARA binding to the genomic region adjacent to SNP rs11868112 ., To investigate the role of SNP rs11868112 in the regulation of RPTOR expression , we performed ChIP assays followed by quantitative PCR to determine whether RARA and/or POU2F1 bind the DNA near the SNP ., First , we treated HepG2 and MCF-7 cells with AM580 and DSMO and performed a ChIP with antibodies against RARA followed by real time PCR quantification of the region spanning the RARA binding site detected by ChIP-chip 26 ., We found a significant enrichment ( P<0 . 02 ) of the putative RARA binding region for the chromatin immunoprecipitated DNA with the RARA antibody ( Figure 4A and 4B ) , which confirms RARA binding to the region near rs11868112 ., This enrichment was observed in both DMSO and AM580 treated cells ( Figure 4A and 4B ) , which is consistent with the model for the genomic actions of retinoic acid receptors 38 ., To investigate whether POU2F1 binds to the region encompassing rs11868112 ( in a canonical POU2F1 motif ) and to study the retinoic acid dependency of POU2F1 recruitment , we performed ChIP with antibodies against POU2F1 in the same cell lines ., Upon AM580 treatment , we observed a significant enrichment ( P<0 . 01 ) of the putative POU2F1 binding region in the chromatin immunoprecipitated with the POU2F1 antibody in both HepG2 and MCF-7 cells ( Figure 5A and 5B ) , which indicates POU2F1 binding to the genomic region encompassing rs11868112 ., For vehicle treatment we observed no significant POU2F1 binding in HepG2 cells ( P>0 . 2 , Figure 5A ) , but a significant enrichment in MCF-7 cells ( Figure 5B ) , which suggests that POU2F1 binding to this specific genomic locus may not require liganded RARA dependent on the specific cell lineage ., Overall , our findings indicate that RARA and POU2F1 bind to the region adjacent to and encompassing rs11868112 , respectively , suggesting that this region acts as a cis-regulatory module with POU2F1 and RARA-binding elements ., To test whether POU2F1 and RARA binding to this module elicits cis-regulatory effects dependent on the allele status of rs11868112 , we performed luciferase reporter gene assays with the cloned regions of the ancestral and the derived allele ., In HepG2 cells , the reporter gene construct containing the ancestral allele ( C ) exhibited a 19 . 2% higher luciferase activity than the construct for the derived allele ( T ) allele ( P\u200a=\u200a0 . 011 ) 12 hrs after AM580 treatment ( Figure 6A ) ., Before and after this time point , no significant difference was observed between the reporter gene constructs for the C and T alleles ( P>0 . 09 ) ., This observation is consistent with the maximal RA-dependent induction of RPTOR expression at 12 hrs after AM580 treatment ., Similar results were obtained with the MCF-7 cell line , where the reporter construct for the ancestral allele showed a 24 . 1% higher luciferase activity than those for the derived allele at 12 hrs after AM580 treatment ( P\u200a=\u200a0 . 0053 , Figure 6B ) ., These findings suggest that the region 26 . 2 kb upstream of RPTOR acts as an RA-dependent enhancer in human cells and that the activity of this enhancer depends on the allele status within the POU2F1 binding site at rs11868112 ., In this study , we combined population genetics , bioinformatics , and experimental approaches to identify a cis-regulatory element harboring a SNP ( rs11868112 ) associated with a strong signal of selection identified in a genome-wide study ., The allele frequencies at this SNP are strongly correlated with latitude and winter temperature variables ., A re-sequencing survey did not identify additional SNPs that are in strong LD with rs11868112 and that are likely to have functional effects ., Because SNP rs11868112 lies within a predicted POU2F1 binding site located close to a RARA binding site identified by ChIP-chip , we hypothesized that this SNP influences the transcriptional response to RA ., Consistent with this hypothesis , we showed that POU2F1 and RARA do bind to the genomic region spanning and near SNP rs11868112 , respectively ., Furthermore , reporter gene assays suggest that this region functions as a RA-dependent enhancer and that the allele status at rs11868112 affects enhancer activity ., Although we cannot conclusively identify the target gene of this enhancer , RPTOR appears to be a strong candidate because it is induced by the selective RARA agonist AM580 in two different cell lines ., The fact that the time of differential RPTOR expression in response to RA treatment coincides with the time of allele-specific down-regulation in reporter gene assays further supports this proposal ., Overall , these results provide an example of how a selection signal can identify a functional SNP and suggest a role for the regulation of RPTOR expression in human adaptations to different environments ., Despite the clear signal of selection given by the correlation between allele frequency and climate variables , standard neutrality tests did not detect a significant departure from expectations ., This included tests of the frequency spectrum as well as haplotype homozygosity ( as assessed by the extended haplotype homozygosity 39 or integrated haplotype score statistics 40 ) ., However , these tests are powerful when selection acts on a new rather than an existing mutation 30 , 31 and when selection acts on a dominant or codominant allele 41 ., In the case of SNP rs11868112 , the beneficial allele in cold climates segregates at appreciable frequencies in sub-Saharan African populations ( 8%–43% ) , thus suggesting that this variant predates the dispersal of human populations out of Africa and that this allele was neutral before becoming advantageous when humans moved to colder climates ., Modeling studies have shown that under these circumstances standard neutrality tests have inadequate power to detect a signal of selection ., For example , simulations of a model of directional selection on standing variation determined that , if a neutral allele occurred at frequency greater than 5% prior to becoming advantageous , virtually no signature is expected on the frequency spectrum , on patterns of linkage disequilibrium and on polymorphism levels 31 ., Given the relatively high frequency of the derived allele at rs11868112 in sub-Saharan African populations , it is therefore not surprising that we detected a signature of natural selection only by using the climate correlation approach ., The molecular function of RPTOR is consistent with a role in local adaptations ., This is because the MTORC1 complex , which contains RPTOR as a critical component , integrates environmental signals to regulate cell growth , metabolism and survival ( 42 and references therein ) ., However , given the diversity of biological processes regulated by MTORC1 and by retinoids , it is difficult to pinpoint the function of RPTOR that is the most likely target of selection ., One possibility is that SNP rs11868112 influences the regulation of energy metabolism and mitochondrial function; under this model , the derived allele would have conferred a selective advantage by increasing thermogenesis during the dispersal to progressively colder climates ., This scenario is supported by the fact that the MTOR pathway controls mitochondrial function , especially ATP synthetic capacity 43 , directly 44 and indirectly 45 ., Tissue-specific knockouts ( KOs ) of raptor have provided further support for the critical role of mTORC1 signaling on whole body metabolism ., Adipose-specific raptor KO mice are resistant to diet-induced obesity , due to an increased mitochondrial uncoupling in white adipose tissue 46 , 47 ., Because mitochondrial uncoupling is an important mechanism for generating body heat , it is possible that the selective advantage conferred by SNP rs11868112 is due to its effect on thermogenesis and cold tolerance ., Adaptations to cold climates are evident in the geographic distributions of many traits ., For example , significant correlations exist between body mass and temperature 10 , 48 , consistent with the long standing hypotheses that variation in body size and proportions are adaptations to maintain temperature homeostasis 49 , 50 ., Furthermore , there is evidence that human metabolism has been shaped by adaptations to cold stress from studies of arctic populations , which exhibit elevated basal metabolic rates compared to non-indigenous populations 11 ., Although these geographic patterns of human phenotypes are well established , the genetic factors contributing to these adaptations are only partially understood ., Genetic variants that can increase mitochondrial uncoupling efficiency might be advantageous in cold climates and thus may have been selected during human migrations northward ., The derived T allele rs11868112 , which is associated with lower RA-dependent transcription levels , may result in increased mitochondrial uncoupling in adipose tissue and , hence , higher thermogenesis ., Consistent with the hypothesis that this allele confers resistance to cold stress , it increases in frequency with decreasing winter temperatures in worldwide population samples , and it is relatively rare in the equatorial populations of sub-Saharan Africa and South East Asia ( see Figure 2 ) ., Although a role for RA in the transcriptional induction of RPTOR was not previously reported , retinoids have been long known to regulate thermogenesis and energy expenditure through their effects on the expression of the major uncoupling protein gene , UCP1 , in brown adipose tissue ( BAT ) 51 ., This protein plays a key role in nonshivering thermogenesis , which is the main mechanism for heat generation in human infants ., As with the RPTOR gene , a distal enhancer upstream to the proximal UCP1 promoter was found to contain a RAR response element , which mediates its transcriptional induction 36 , 52 ., Therefore , our finding that the transcription of the RPTOR gene is induced by RA treatment in liver and breast epithelial cells is consistent with the known function of RAR in regulating the expression of another gene with a clear role in thermogenesis ., More recently , experiments in mice identified a role for retinoids in adipose tissue remodeling and , more specifically , in the acquisition of BAT-like properties in white adipose tissue 53 ., These findings further support the notion that the biological functions of RAR include the regulation of energy balance and thermogenesis ., Moreover , our observation that RPTOR is induced by retinoids provides a possible mechanistic link connecting the action of retinoids in adipose tissue remodeling and the finding that the adipose-specific raptor KO exhibits increased mitochondrial uncoupling ( i . e . a typical BAT property ) in white adipose tissue ., Another possible explanation for the signal of selection observed at SNP rs11868112 may be related to the role of the MTOR pathway in the regulation of the immune response ., Indeed , the MTOR pathway plays multiple roles in immunity , especially in the activation and proliferation of T cells 20 , and has been implicated in the etiology of autoimmune disorders , such as systemic lupus erythematosus ( SLE ) 54 ., Moreover , the specific inhibitors for this pathway , rapamycin and its derivatives , can decrease proliferation of T lymphocytes and are used as immunosuppressant to avoid allograft rejection 20 or to treat autoimmune patients 55 , 56 ., Since pathogen diversity decreases with latitude mainly as a result of climatic factors 57 , the optimal level of immune response is also expected to vary according to latitude and climate ., Therefore , we hypothesize that the increase in the frequency of the derived T allele with decreasing temperatures is due to selective pressures acting on the MTORC1 function in the regulation of the immune response ., Under this scenario , the decrease in RPTOR expression associated with the T allele at high latitudes could reflect a shift to maintain the appropriate balance between pathogen pressures and immune response , with an exaggerated immune response possibly resulting in increased risk for autoimmune diseases ., It might be argued that the modest ( ∼20% ) decrease of RPTOR expression associated with the T allele is not sufficient to generate significant phenotypic and fitness differences between arctic and tropical populations ., One possibility is that RPTOR and the MTOR pathway are located at the top of the signaling cascade 19 , therefore , a subtle change in its expression can have major consequences ., Alternatively , as observed for most susceptibility SNPs identified through GWAS of common diseases , rs11868112 may be just one of many SNPs with small effects on the phenotypes that are adaptive in different climates ., Genotype data in the RPTOR gene for HGDP individuals was obtained from published Illumina HumanMap 650Y data ( http://hagsc . org/hgdp/files . html ) ., The genotype data for the same SNPs from four HapMap Phase III populations ( Luhya , Maasai , Tuscans , and Gujarati ) ( http://www . hapmap . org ) and five additional populations ( Vasekela ! Kung from South Africa , lowland Amhara from Ethiopia , Naukan Yupik and Maritime Chukchee from Siberia , and Australian Aborigines ) were also incorporated ( Hancock and Di Rienzo , personal communication ) ., In total , 61 human populations were included in the current study ., This study uses the Bayesian geographic analyses method of Coop et al . ( 2010 ) 27 , which is a model-based method that tests whether a linear relationship between allele frequency and a variable provides a significantly better fit to the data than the null model alone ( where the null model is given by a matrix of the covariance of allele frequencies between populations ) ., The environmental variables included latitude and seven climate variables in the summer and winter seasons ., Twenty-four unrelated Hapmap samples ( 8 YRI , 8 CEU and 8 ASN ) were chosen for re-sequencing the coding regions and 48 unrelated HapMap samples ( 16 YRI , 16 CEU , and 16 ASN ) for re-sequenced in the non-coding regions ., cDNA was synthesized from RNA extracted from the lymphoblastoid cell lines of the HapMap samples using the Super Transcript III First-Strand Synthesis System for RT-PCR ( Invitrogen , Carlsbad , CA ) and utilized as template ., Conserved non-coding regions were identified by using the ECR genome browser ( http://ecrbrowser . dcode . org/ ) and by choosing regions conserved between human and at least two additional species ( see Figure S3 ) ., A 2 . 6 kb segment spanning SNP rs11868112 as well as the RPTOR promoter were also included in the resequencing survey ., PCR was performed by using the primers in Table S5 ., After exonuclease I and Shrimp Alkaline Phosphatase ( United States Biochemicals , Cleveland , OH ) treatment , sequencing was performed by using internal primers in Table S5 and BigDye Terminator v3 . 1 ( Applied Biosystems , Foster City , CA ) ., In total , 34 . 4 kb ( 6 . 0 kb for coding and 28 . 4 kb for non-coding ) were amplified and re-sequenced ., Polymorphisms were scored by PolyPhred 58 and confirmed visually ., Visual genotype and LD between SNPs were determined by using the Genome Variation Server ( http://gvs . gs . washington . edu/GVS/ ) ., Population genetics indices , including segregating sites ( S ) , nucleotide diversity ( π ) 59 , Wattersons estimator of the population mutation rate parameter ( θw ) 60 , Tajimas D 61 , were calculated by Slider ( http://genapps . uchicago . edu/labweb/index . html ) ., The expected distribution of nucleotide diversity and Tajimas D was generated by coalescent simulations using the software ms 62 with appropriate demographic models 63 ., All re-sequencing data will be made publicly available in PharmGKB ( http://www . pharmgkb . org ) ., The human hepatocellular carcinoma cell line HepG2 was cultured in minimum essential medium ( MEM , ATCC , Manassas , VA ) supplemented with 10% fetal bovine serum ( FBS , Invitrogen , Carlsbad , CA ) ., The human breast cancer cell line MCF-7 was maintained in Dulbeccos Modified Eagles Medium ( DMEM; Invitrogen ) with 10% FBS and 0 . 1% insulin ( Sigma , St . Louis , MO ) ., Before any AM580 or DMSO treatment , cells were grown for 48 hrs in medium with 10% charcoal-stripped FBS ( Invitrogen ) ., Cells were treated with 100 nM AM580 ( Sigma ) or DMSO ( Sigma ) for 2hrs , 4hrs , 8hrs , 12hrs , 24hrs , and 48hrs , and then harvested ., RNA was extracted using the RNeasy Mini Kit ( Qiagen , Valencia , CA ) and cDNAs were synthesized with the High Capacity Reverse Transcription Kit ( Applied Biosystems ) ., RPTOR mRNA levels were determined by real time PCR using the power SYBR green ( Applied Biosystems ) with primers 5′-CGGGGAGGTCTGGGTCTTCAA-3′ and 5′-CTCCTGCTCCCGCTGTAGTGC-3′ 64 ., β-actin was used as a calibrator gene in real time PCR with the primers 5′-ACGTGGACATCCGCAAAGAC-3′ and 5′-CAAGAAAGGGTGTAACGCAACTA-3′ 65 ., For each of three independent biological replicates , three technical replicates were performed for each time point on a StepOne Plus Realtime PCR System ( Applied Biosystems ) ., ChIP was carried out using the ChIP Assay Kit ( Upstate , Indianapolis , IN ) according to the manufacturers protocol ., Briefly , 107 cells grown for 48 hours in medium with charcoal-stripped FBS and then treated with 100nM AM580 or DMSO for 1 hr , were incubated for 10 minutes with 1% formaldehyde at room temperature ., The fixed cells were treated with 1 . 25 M glycine for 5 minutes , washed twice with ice-cold phosphate buffered saline ( Invitrogen ) containing protease inhibitor cocktail ( PIC , Sigma ) and phenylmethylsulfonyl fluoride ( PMSF , Fisher , Pittsburgh , PA ) , scraped , lysed and sonicated to obtain 200–800 bp fragments with the Sonicator 4000 ( MISONIX , Farmingdale , NY ) ., The solubilized chromatin was diluted 10-fold with dilution buffer , and pre-cleared with protein A beads ., After centrifuging and transferring the supernatant , 1% sample was stored as input and the remaining chromatin was incubated with rabbit polyclonal anti-POU2F1 ( sc-232X ) or anti-RARA ( sc-551X; Santa Cruz Biotechnology , Santa Cruz , CA ) and normal rabbit IgG ( Santa Cruz Biotechnology ) and immunoprecipitated with protein A beads ., After washing with low salt , high salt , LiCl and TE buffer twice , the immunoprecipitated chromatin was eluted and de-crosslinked ., Upon proteinase K treatment ( Qiagen ) DNA was recovered by QIAquick PCR purification kit ( Qiagen ) ., The obtained DNA was quantified by real time PCR with iQ SYBR green ( Bio-Rad , Hercules , CA ) and primer pairs 5′- AGGTCTGCAACACAGCACAT -3′ and 5′- CTGGGAGCTATGCCTGGTC -3′ , and 5′-CTAAGTGCTGGGTCGTAAGTTGT-3′ and 5′-GAATGCAGGCTATAAATCAGGAG-3′ to quantify the enrichment for POU2F1 and RARA binding site , respectively ., For each ChIP assay , three technical replicates were performed for three biological replicates on a StepOne Plus Realtime PCR System ( Applied Biosystems ) ., A 3 . 7 kb segment containing the derived T allele of rs11868112 and the putative RAR binding site ( see Figure S4 ) was amplified by nested PCR ., In the first round of PCR the primers 5′-TTGCGAAAGTAAATGCTAT-3′ and 5′-CAGAGGGGCCTTGAGATGACCA-3′ were used ., In the second round of PCR the primers 5′-CAGTC-GCTAGC-TTCCCTCACTCTGTCCCCCAATG-3′ and 5′-CAGTC-CTCGAG-TTCCTGAC | Introduction, Results, Discussion, Materials and Methods | The mechanistic target of rapamycin ( MTOR ) pathway regulates cell growth , energy homeostasis , apoptosis , and immune response ., The regulatory associated protein of MTOR encoded by the RPTOR gene is a key component of this pathway ., A previous survey of candidate genes found that RPTOR contains multiple SNPs with strong correlations between allele frequencies and climate variables , consistent with the action of selective pressures that vary across environments ., Using data from a recent genome scan for selection signals , we honed in on a SNP ( rs11868112 ) 26 kb upstream to the transcription start site of RPTOR that exhibits the strongest association with temperature variables ., Transcription factor motif scanning and mining of recently mapped transcription factor binding sites identified a binding site for POU class 2 homeobox 1 ( POU2F1 ) spanning the SNP and an adjacent retinoid acid receptor ( RAR ) binding site ., Using expression quantification , chromatin immunoprecipitation ( ChIP ) , and reporter gene assays , we demonstrate that POU2F1 and RARA do bind upstream of the RPTOR gene to regulate its expression in response to retinoids; this regulation is affected by the allele status at rs11868112 with the derived allele resulting in lower expression levels ., We propose a model in which the derived allele influences thermogenesis or immune response by altering MTOR pathway activity and thereby increasing fitness in colder climates ., Our results show that signatures of genetic adaptations can identify variants with functional effects , consistent with the idea that selection signals may be used for SNP annotation . | Climate has exerted strong selective pressures in human populations during their dispersal , and signatures of these adaptations are still detectable in the geographic distribution of polymorphisms ., RPTOR is a key component of the mechanistic target of rapamycin pathway , which regulates cell growth , metabolism , and immune response; and its deregulation is associated with human diseases , including cancer and diabetes ., Previous studies showed that variation in RPTOR carry strong signatures of adaptations to different climates ., Here , we used evolutionary genetics approaches coupled with transcription factor motif data mining to refine the location of the selection target ., We then used functional assays to show that the selected polymorphism resides in a sequence element that regulates gene expression levels in response to retinoids ., The derived allele at this SNP , which results in lower expression levels , increases in frequency with decreasing temperatures , consistent with the notion that it confers a selective advantage in colder climates possibly through its effects on energy metabolism or immune response ., These results suggest a novel regulatory role for retinoids in MTOR signaling ., Moreover , they support the proposal that evolutionary approaches can be informative for SNP functional annotation . | evolutionary biology, genetics and genomics | null |
journal.pgen.1003349 | 2,013 | Human Spermatogenic Failure Purges Deleterious Mutation Load from the Autosomes and Both Sex Chromosomes, including the Gene DMRT1 | Male infertility is a multifaceted disorder affecting nearly 5% of men of reproductive age ., In spite of its prevalence and a considerable research effort over the past several decades , the underlying cause of male infertility is uncharacterized in up to half of all cases 1 ., Some degree of spermatogenic impairment is present for most male infertility patients , and , in its most severe form , manifests as azoospermia , the lack of detectable spermatozoa in semen , or oligozoospermia , defined by the World Health Organization as less than 15 million sperm/mL of semen ., Spermatogenesis is a complex multistep process that requires germ cells to, ( a ) maintain a stable progenitor population through frequent mitotic divisions ,, ( b ) reduce ploidy of the spermatogonial progenitors from diploid to haploid through meiotic divisions , and, ( c ) assume highly specialized sperm morphology and function through spermiogenesis ., These steps involve the expression of thousands of genes and carefully orchestrated interactions between germ cells and somatic cells within the seminiferous tubules 2 ., It is likely that a large proportion of idiopathic cases of spermatogenic failure are of uncharacterized genetic origin , but measuring the heritability of infertility phenotypes has been challenging ., Known genetic causes of non-obstructive azoospermia ( NOA ) include deletions in the azoospermia factor ( AZF ) regions of the Y chromosome 3 , Klinefelters syndrome 4 , and other cytogenetically visible chromosome aneuploidies and translocations 5 ., Beyond these well-established causes , which are observed in 25–30% of cases , the genetic architecture of spermatogenic impairment is currently unknown ., One might expect a priori that rare or de novo , large effect mutations will be the central players in genetic infertility , and indeed other primary infertility phenotypes like disorders of gonadal development , isolated gonadotropin-releasing hormone deficiency , and globozoospermia , a disorder of sperm morphology and function , appear to be caused by essentially Mendelian mutations operating in a monogenic or oligogenic fashion 6 , 7 , 8 ., Similarly , recurrent mutations of the AZF region on the Y chromosome are either completely penetrant ( AZFa , AZFb/c ) or highly penetrant ( AZFc ) risk factors for azoospermia ., Our working model at the start of this study was that additional “AZF-like” loci existed in the genome , either on the Y chromosome or elsewhere , and that , much like recent progress in the analysis of developmental disorders of childhood , a large number of causal point mutations and submicroscopic deletions could be revealed in idiopathic cases by the appropriate use of genomic technology ., In this paper , we employ oligonucleotide SNP arrays as discovery technology to conduct a whole-genome screen for two rare genetic features in men with spermatogenic failure ., First , we extract and analyze the probe intensity data to find rare copy number variants ( CNVs ) ., A growing number of CNVs have been associated with a host of complex disease states 9 including neurological disorders 10 , 11 , 12 , 13 , several autoimmune diseases 14 , 15 , type 2 diabetes 16 , cardiovascular disease 17 , and cancer 18 , 19 , 20 , 21 ., Now , a role for CNVs in male infertility is beginning to emerge 22 , 23 , 24 , 25 ., As a second approach to identify rare genetic variants , we use a population genetics modeling framework to identify large homozygous-by-descent ( HBD ) chromosome segments that may harbor recessive disease alleles ., When applied to consanguineous families , so-called “HBD-mapping” has been an unequivocal success in identifying the location of causal variants for simple recessive monogenic diseases 26 ., HBD analysis can also be used to screen for the location of rare variants in common disease case-control studies of unrelated individuals , using either a single-locus association testing framework or by testing for an autozygosity burden , frequently referred to as “inbreeding depression”: an enrichment of size or predicted functional impact of HBD regions aggregated across the genome ., This approach has produced results for a growing list of common diseases , including schizophrenia 27 , Alzheimers disease 28 , breast and prostate cancer 29 ., In this study , we screened three cohorts of men with idiopathic spermatogenic failure in an attempt to identify rare , potentially causal mutations , and to better understand the genetic architecture of the disease ( Table 1 ) ., We found a genomewide enrichment of large , rare CNVs in men with spermatogenic failure compared to normozoospermic or unphenotyped men ( controls ) ., We also identify a number of cases with unusual patterns of homozygosity , possibly the result of recent consanguineous matings ., Our results show that spermatogenic output is a phenotype of the entire genome , not just the Y chromosome , place spermatogenic failure firmly among the list of diseases that feature a genomewide burden of rare deleterious mutations and provide a powerful organizing principle for understanding male infertility ., When restricting our analysis to CNVs with a call frequency of less than 5% , a subset likely to be enriched for pathogenic events , we observed pronounced differences among groups ( Table S1 ) ., Azoospermic and oligozoospermic men have nearly twice the amount of deleted sequence genomewide when compared to controls ( p\u200a=\u200a1 . 7×10−4 , Wilcoxon rank sum test ) , and a nonsignificant 12% increase in the number of deletions per genome ., When examining the even more restricted set of rare CNVs larger than 100 kb ( Dataset S1 ) , these associations are more pronounced: the rate of deletions in cases was twice that of controls ( 1 . 12 vs . 0 . 55 , p\u200a=\u200a9 . 7×10−4 ) and the amount of deleted sequence 2 . 6 times greater in cases ( p\u200a=\u200a8 . 8×10−4 ) ., In order to replicate these initial findings , we assayed two additional cohorts – one group of 61 Caucasian men with severe spermatogenic impairment and 100 ethnicity-matched , unphenotyped controls , both collected at Washington University in St . Louis ( WUSTL ) , and a larger case cohort of 179 Caucasian men with idiopathic azoospermia , primarily from medical practices in Porto , Portugal , matched to an unphenotyped control set of 974 Caucasian men collected by the UK National Blood Service ( NBS , 30 ) ., Although using different array platforms ( Text S1 ) , we observed replication of our initial association ( Table S2 and Table S3 ) ; in the WUSTL cohort a 20% increase in the rate ( p<0 . 05 ) and in the Porto cohort a 31% increase in rate ( p<5×10−3 ) ., We excluded several artifactual explanations for this burden effect , including specific batch phenomena or population structure ( Text S1 , Figures S1 , S2 , S3 , S4 , S5 ) ., To better characterize these genomewide signals , we set out to search for clustering of pathogenic mutations on specific chromosomes ., We focused first on the Y chromosome as it is the location of most known mutations modulating human spermatogenesis ( Figure 1 , Figure S6 ) ., Y-linked microdeletions of the AZFa , AZFb , and AZFc regions are well-established causes of spermatogenic impairment , and thus we excluded from this study cases with AZF microdeletions visible by STS PCR ., In the array data , we found no significant difference in the frequency of rare Y deletions between case and controls groups; however rare duplications were more abundant in Porto cases compared to the NBS controls ( a 3-fold enrichment in Porto cohort , p\u200a=\u200a1 . 9×10−3 ) ., We could classify the majority ( >90% ) of our samples to major Y haplogroups using SNP genotypes ( Text S1 ) , and , as expected , most of these samples fall into the two most common European haplogroups: I ( 22% ) and R ( 70% ) ., The observed duplication burden was not an artifact of differences in major Y haplogroup frequency between cases and controls , as association was essentially unchanged when only considering samples with haplogroup R1 ( p\u200a=\u200a3 . 3×10−3 ) ., Due to low probe coverage , only one Y-linked duplication was called in the Utah cohorts ( in a control individual ) and two in the WUSTL cohort ( both in cases ) , so this burden of Y duplications was not replicated ., Next we turned to the X chromosome , which is highly enriched for genes transcribed in spermatogonia 31 ., In the Utah cohorts there were 71 gains and losses with a frequency of less than 5% on the X chromosome , cumulatively producing three times as much aneuploid sequence in azoospermic and oligozoospermic men compared with normozoospermic men ( 89 kb/person azoo , 45 kb/person oligo , 27 kb/person normozoospermic men , all cases versus controls p<0 . 03 ) ., This burden was strongly replicated in the Porto samples , which displayed a 1 . 6 fold enrichment of rare CNV on the X ( p\u200a=\u200a5×10−4 ) and the WUSTL samples ( 31% of cases with a rare X-linked CNV versus 16% of controls , p\u200a=\u200a0 . 02 by permutation ) ., The genome-wide signal of CNV burden was not driven solely by sex chromosome events: considering only autosomal mutations in Utah samples there was an enrichment of aneuploid sequence in large deletions in azoospermic men ( 268 kb/person ) and oligozoospermic men ( 308 kb/person ) compared to control men ( 189 kb/person , p\u200a=\u200a9 . 8×10−3 ) , and an enrichment in the rate of deletions in all cases when considering just events >100 kb ( 1 . 9 fold enrichment , p\u200a=\u200a6×10−3 ) ., In the Porto cohort , there was modest evidence for a higher rate of rare deletions of all sizes in azoospermic men ( 1 . 27 fold enrichment , not significant ) as well as an increase in total amount of deleted sequence ( 345 kb/case vs . 258 kb/control , p<0 . 003 ) ., In order to cleanly summarize our findings across all cohorts , we fit logistic regression models for each cohort , regressing case status onto CNV count for different classes of CNV ., We also fit a linear mixed-effects logistic regression model to the total dataset for each CNV class , treating cohort as a random factor ( Figure 1 ) ., In each regression model we controlled for population structure by including eigenvectors from a genomewide principal components analysis ( Methods ) ., On the basis of the combined analysis , we estimate that each rare autosomal deletion multiplicatively changes the odds of spermatogenic impairment by 10% ( OR 1 . 10 1 . 04–1 . 16 , p<2×10−3 ) , each rare X-linked CNV ( gain or loss ) by 29% , ( OR 1 . 29 1 . 11–1 . 50 , p<1×10−3 ) and each rare Y-linked duplication by 88% ( OR 1 . 88 1 . 13–3 . 13 , p<0 . 03 ) ., Deletions of the AZF regions of the Y chromosome are often mediated by non-allelic homologous recombination ( NAHR ) between segmental duplications and are the most common known cause of spermatogenic failure ., Because of their prognostic power and high rate of recurrence in the population , screening for AZF deletions is a standard part of the clinical workup for azoospermia ., It would be of high clinical value if additional azoospermia susceptibility loci with significant recurrence rates could be identified ., We screened all cohorts for large ( >100 kb ) rearrangements flanked by homologous segmental duplications capable of generating recurrent events by NAHR 32 ., There was no significant enrichment of gains or losses in cases across these hotspot regions when considered as an aggregate ., Due to small sample sizes we found no single-locus associations , at these hotspot loci , or elsewhere , that met the strict criteria of genomewide significance in both the discovery and replication cohorts ., Many of our single-cohort associations from one platform lack adequate probe coverage on other platforms for robust replication ( Text S1 ) ., However , several loci were significant on joint analysis of all cohorts ., The best candidate for a novel locus generating NAHR-mediated infertility risk mutations is a 100 kb segment on chromosome Xp11 . 23 flanked by two nearly identical ( >99 . 5% homology ) 16 kb segmental duplications containing the sperm acrosome gene SPACA5 ( Figure 2a , Figure S7 ) ., We identified 9 deletions of this locus spread across all patient cohorts ( 3 in PT , 1 in UT , 5 in WUSTL ) compared to 8 in the pooled 1124 controls ( 2 . 8% frequency versus 0 . 7% , odds ratio\u200a=\u200a3 . 96 , p\u200a=\u200a0 . 005 , Fisher exact test ) ., We genotyped the deletion by +/− PCR in an additional cohort of 403 men with idiopathic NOA from Weill Cornell , and observed an additional 3 deletions ( Figure S8 , Text S1 ) ., In a prior case-control study of intellectual disability , investigators using qPCR estimated the allele frequency of this deletion to be 0 . 47% ( 10/2121 ) in a large Caucasian male control cohort 33 ., Combining these data , we estimate the allele frequency of the deletion to be 1 . 6% in Caucasian cases , compared to 0 . 55% in Caucasian controls ( OR 3 . 0 , 95% CI 1 . 31–6 . 62 , p\u200a=\u200a0 . 007 ) ., The deleted region contains the X-linked cancer-testis ( CT-X ) antigen gene SSX6; the CT-X antigen family is a highly duplicated gene family on the X chromosome comprising 10% of all X-linked genes and is expressed specifically in testis ., After controlling for differences in coverage across the array platforms used in this study , we find a significant enrichment of rare deletions of CT-X genes in all cases ( p\u200a=\u200a0 . 02 ) ; this finding did not extend to duplications or CT antigen genes on the autosomes ( Table 2 ) ., When analyzing all cohorts jointly , our strongest association ( genomewide corrected p-value <0 . 002 ) is to both gains and losses involving a 200 kb tandem repeat on Yq11 . 22 , DYZ19 ( Figure S6 , Figure S9 ) , a human-specific array of 125 bp repeats first discovered as a novel band of heterochromatin in the Y chromosome sequencing project 34 ., Tandem repeat arrays are often highly unstable sequence elements that can mutate by both replication-based and recombination-based ( e . g . NAHR ) mechanisms ., In our data there were 9 gains and 11 losses at DYZ19 in 323 cases ( combined frequency 6 . 1% ) , compared to 3 gains and 12 losses in 1136 controls ( combined frequency 1 . 3% ) ., While this finding may ultimately require painstaking technical work to conclusively validate , we have several reasons to believe the association is real ., First , we have previously shown that it is possible to identify real copy number changes at VNTR loci using short oligonucleotide arrays 35; second , copy number changes at this locus were identified by multiple platforms in the current study; third , the association is nominally significant in both the Utah and Porto cohorts; fourth the locus is within the AZFb/c region ., The direction of copy number changes does appear to track with haplogroup – while 12/13 duplications occur on the R1 background , 14/15 deletions for which haplogroup could be determined occur on I or J background ., Haplogroup assignments for the carriers of these CNVs were confirmed by standard short tandem repeat analysis ( Text S1 ) ., The strong association between haplogroup and direction of copy number change is noteworthy; it may indicate that DYZ19 CNVs are merely correlated with other functional changes on these chromosomes , or perhaps the structure of these chromosomes predisposes them to recurrent gains ( R1 ) or losses ( I/J ) ., The gene DMRT1 is widely believed to be the sex-determination factor in avians , analogous to SRY in therians , and may play the same or similar role in all species that are based upon the ZW sex chromosome system 36 ., DMRT1 encodes a transcription factor that can activate or repress target genes in Sertoli cells and premeiotic germ cells through sequence-specific binding 37 ., In humans , DMRT1 is located on 9p24 . 3 in a small cluster with the related genes DMRT2 and DMRT3 ., Large terminal deletions of 9p are a known cause of syndromic XY sex-reversal , and although the role of the DMRT genes in the 9p deletion syndrome phenotype has not yet been defined , mouse experiments have shown that homozygous deletion of DMRT1 causes severe testicular hypoplasia 38 , 39 , 40 ., We found two , perhaps identical , 132 kb deletions spanning DMRT1 in the Utah cohort in men with azoospermia , and a 1 . 8 Mb terminal duplication of 9p , spanning these genes , was seen in a single normozoospermic control from Utah ( Figure 2b ) ., All three of these rearrangements were validated by TaqMan assay ( Figure S10 , Text S1 ) ., Both men were recruited into the study in Salt Lake City , UT between 2002 and 2004 ., They self-reported their ancestry as Caucasian , and in both cases this assumption was clearly verified by principal components analysis of their genetic data ( Figure S2 ) ., There was no evidence that the two deletion carriers were closely related upon comparison of their whole-genome SNP genotypes ., Testis biopsies were performed on both men; these indicated apparent Sertoli cell only syndrome in the first and spermatocytic arrest in the second ., Both men exhibited apparently normal male habitus and virilization with no phenotypic similarities to 9p deletion syndrome ., We obtained Affymetrix 6 . 0 array data from a previously published genomewide association study of idiopathic NOA in Han Chinese 41 comprised of 979 cases and 1734 controls ( Text S1 ) ., After processing these samples with our CNV calling pipeline , we observed an additional 3 deletions of DMRT1 exonic sequence in cases ( 0 . 3% ) and none in controls ( Figure 2B , Figure S11 ) ., From these combined array data we estimate a frequency of DMRT1 exonic deletion of 0 . 38% ( 5/1306 ) in cases and 0% ( 0/2858 ) in controls ( OR\u200a=\u200aInfinity , 2 . 0-Inf , p\u200a=\u200a0 . 003 ) ., We obtained the two largest control SNP array datasets in the Database of Genomic Variants ( DGV ) , representing CNV calls from 4519 samples typed with platforms of equal or higher probe density to the ones used here 42 , 43 ., None of these samples contained CNV of any sort affecting DMRT1 ., Finally , we screened an additional set of 233 idiopathic NOA cases from Weill Cornell , and 135 controls with the TaqMan validation assay and identified an additional 3 deletions ( 2 in cases , 1 in controls , Text S1 , Figure S12 ) ., As this qPCR assay interrogates intronic sequence , the functional consequences of these 3 deletions are unclear ., Our array data have revealed some of the smallest coding deletions of DMRT1 reported to date in humans , and should help to clarify the critical regions of 9p involved in testicular development and function ., Notably , using a bespoke reanalysis of the intensity data , we did not see evidence for CNVs involving the gene PRDM9 , a recently characterized zinc finger methyltransferase that appears to control the location of recombination hotspots in a diversity of mammalian species ., Heterozygosity of PRDM9 zinc finger copy number has been shown to cause sterility in male hybrids of Mus m ., domesticus and Mus m ., musculus due to meiotic arrest 44 ., The identification of functional or physical annotations enriched in case-associated CNVs can be a powerful step in constructing models to classify pathogenic variants ., We searched for significant case-specific aggregation of CNVs in several classes of functional sequence , including 195 genes previously shown to result in spermatogenic defects when mutated in the mouse 45 , all protein and non-protein coding genes , and 525 testis genes that are differentially expressed during human spermatogenesis ( Text S1 ) ., Deletion of X- or Y-linked exonic sequence conferred the strongest risk ( OR\u200a=\u200a1 . 87 1 . 30–2 . 68 , p<1×10−3 ) ., Very similar risk was associated with deletion of exonic sequence from testis genes differentially expressed during spermatogenesis , despite the fact that only 15% of these genes are located on the sex chromosomes ( OR\u200a=\u200a1 . 85 1 . 01–3 . 39 , p<0 . 05 ) ., Deletion of any exonic sequence was also associated with disease ( OR\u200a=\u200a1 . 25 1 . 07–1 . 46 , p<5×10−3 ) ., Deletion of miRNAs was not associated , nor was deletion of the 195 mouse spermatogenic genes 45 , which were very rarely deleted in either cases or controls ., We hypothesized that at least some of the functional impact of CNV burden on fertility was a result of disruption of haploinsufficient ( HI ) genes , as has been demonstrated for neuropsychiatric and developmental disease 46 ., For each singleton deletion in our collections we used a recently described modeling framework to calculate the probability that the deletion is pathogenic due to dominant disruption of a haploinsufficient gene 47 ., Much to our surprise , HI scores from deletions in infertility cases were much smaller than those from cases of autism and developmental disorders and in fact indistinguishable from controls ( mean HI score −1 . 16 in controls , −1 . 02 in all spermatogenic impairment cases , p\u200a=\u200a0 . 49 by Wilcoxon rank sum test; Figure 3 ) ., Likewise there was no enrichment of large rearrangements within 45 known genomic disorder regions in cases 46 ., In contrast to previously described diseases that feature CNV burden , spermatogenic impairment may be more likely to result from large effect recessive mutations , or perhaps the additive effect of deleterious mutations across many loci ., We sought to uncover support for recessive mutation load in our cases by assessing the impact of inbreeding , or elevated rates of homozygosity , on disease risk by applying a population genetic approach to the SNP genotype data from our samples 48 ., The major genetic side effect of consanguineous mating is a genome-wide increase in the probability that both paternal and maternal alleles are homozygous-by-descent ., This probability is often summarized as the inbreeding coefficient , F , and can be estimated from analysis of pedigree structure or by direct observation of genomewide SNP genotypes ., Due to differences in demographic history and culture , the extent of background homozygosity in the genome is expected to vary when comparing diverse populations throughout the globe ., The haplotype modeling algorithms implemented in the software package BEAGLE estimate the background patterns of linkage disequilibrium and homozygosity across a set of samples , allowing population-specific information to be used to assess the evidence that any given section of a genome is likely to be homozygous-by-descent ( HBD ) ., During the course of our study we concluded that standard PCA-based approaches to stratification are insufficient to correct for population structure during the analysis of inbreeding , even when using population genetic methods like BEAGLE ( Text S1 , Figure S13 ) ., The problem comes not from spurious identification of HBD , but from spurious association of HBD with disease status when case and controls are sampled from groups with different levels of background relatedness ., For instance , in a recent survey of 17 Caucasian cohorts , estimates of the average inbreeding coefficient , F , varied from 0 . 09% to 0 . 61% , with UK-based cohorts showing the lowest F and the one Portuguese cohort showing the highest 27 ., While PCA-based methods traditionally detect and correct for differences in allele frequencies among groups , we believe that they do not detect differences in inbreeding that can be readily incorporated into a case-control testing framework ., In the following section , we use data from 622 healthy adults from Spain , who we believe form a more appropriate control group for the Porto case cohort ( Methods , Text S1 , Figure S13 ) ., Analyzing each cohort separately , BEAGLE identified 5343 chromosome segments likely to represent HBD regions ( HBDRs ) across all samples ., We excluded low-level admixture as a spurious source of HBD ( Figure S3 ) ., Only three of these segments were identified as apparent artifacts induced by large heterozygous deletions ( 287 kb , 817 kb , and 877 kb in size ) and were removed before subsequent analyses ., As expected , the distribution of HBD across all samples was L-shaped , with the majority of HBDRs shorter than 1 Mb and a few intermediate and very large events observed ( Figure 4b ) ., The largest HBDR identified spanned all of chromosome 2 in an azoospermic individual , indicative of uniparental isodisomy of the entire chromosome ., Clinical reports of UPD2 are extremely rare – there are 7 previous reports of UPD2 that have been ascertained through association with an autosomal recessive disorder 49 ., In each of these cases a recessive disorder that lead to clinical presentation was identified ., There is currently no proof of imprinted genes on chromosome 2 from either mouse or human data ., We performed whole exome sequencing on this individual , and using a simple scoring scheme based on functional annotation and population genetic data , identified a homozygous missense mutation of the INHBB gene as the most unusual damaging homozygous lesion in the genome of this individual ( Figure 5 , Text S1 ) ., The biology of the INHBB gene product strongly implicates this mutation as a causal factor but without additional functional or epidemiological evidence such a conclusion is speculative ( Figure 6 ) ., Setting aside this case of UPD2 , we found only modest evidence for an enrichment of homozygosity in men with spermatogenic impairment ( Figure 4a , Table 3 ) ., Our hypothesis was that , if a large percentage of cases of azoospermia were attributable to large-effect autosomal recessive Mendelian mutations , we would see a corresponding increase in the proportion of cases with large values of F . The average inbreeding coefficient was numerically higher in each case cohort compared to its matched control cohort ( Table 3 ) ., We used a logistic regression mixed model framework to test for association between autozygosity and disease , while controlling for population structure , fitting models that treated autozygosity as both a categorical variable ( e . g . inbreeding coefficient >6 . 25% , yes or no ) and a continuous variable ( F , Methods ) ., While the estimated effect of inbreeding on disease risk was positive in every model that we tested , the corresponding odds ratios did not differ significantly from 1 in any version ( Table 3 ) ., There were fewer than 10 HBD regions shared by 2 or more cases , supporting the model that spermatogenic efficiency has a polygenic basis ., We also tested for case-specific aggregation of HBD segments using the same association framework as that used for CNVs ., We did not identify any significant patterns ., Based on published analyses of small-effect recessive risk mutations in other complex diseases , we believe our current sample size would be underpowered to detect association between very old inbreeding ( e . g . due to shared ancestors 15 generations ago ) ., It is possible that large cohorts , consisting of over 10 , 000 cases , may be needed to accurately estimate the relationship between low-level variation in inbreeding ( F values smaller than 0 . 1 ) and azoospermia risk , as well as map specific risk alleles 27 , 50 ., We report here the largest whole genome study to date investigating the role of rare variants in infertility , examining data from 323 cases of male infertility and 1 , 136 controls ., These data demonstrate that rare CNVs are a major risk factor for spermatogenic impairment , and while confirming the central role of the Y chromosome in modulating spermatogenic output , our risk estimates for autosomal and X-linked CNVs indicate that this phenotype is influenced by rare variation across the entire genome ., The controls from two of the cohorts were unphenotyped , and given the estimated prevalence of azoospermia ( 1% ) , we may have underestimated the risk associated with these large rearrangements ., We observed 5 deletions of DMRT1 coding sequence in cases and none in over 7 , 000 controls ., These deletions ranged in size from 54 kb to over 2 Mb ( Table 4 ) ., DMRT1 is situated in a region of chromosome 9p that has been identified as a source of syndromic and non-syndromic forms of XY gonadal dysgenesis ( GD ) ., The deletions of this region that are associated with syndromic forms of GD are usually 4–10 Mb in size , while isolated GD has been reported for deletions smaller than 1 Mb 40 , 51 , 52 ., Despite frequent involvement of DMRT1 in these putative causal mutations , there is variability in both the phenotypic outcome affiliated with each deletion and the extent of DMRT1 coding sequence contained therein ., At least two cases of GD have been linked to deletions near but not overlapping DMRT1 – one 700 kb mutation 30 kb distal to DMRT1 in a case of complete XY GD that was inherited from an apparently normal mother , and a second 260 kb de novo deletion about 250 kb distal to DMRT1 39 , 40 ., Both of these deletions overlapped the genes KANK1 and DOCK8 ., On the other hand , two smaller deletions , one a 25 kb deletion of DMRT1 exons 1 and 2 , and one a 35 kb deletion of exons 3 and 4 , have been observed in patients with complete GD and bilateral ovotesticular disorder of sexual development , respectively 51 , 52 ., Based on the clinical records of patients in our current study , there is no chance that our DMRT1 deletion carriers could represent misdiagnosis of a condition as severe as complete XY GD , which presents with the appearance of female genitalia ., Indeed , two of our DMRT1 deletion carriers were subject to testicular biopsies ., Our observations here suggest that hemizygous deletion of DMRT1 is a lesion that shows variable expressivity that may depend on the sequence of the undeleted DMRT1 allele , variation in other sequences on chromosome 9p , and the state of other factors in the pathways regulating testicular development and function ., Strictly speaking , statements that hemizygous deletions of DMRT1 are “sufficient” to cause GD or spermatogenic failure need to be qualified at this point until we gain a better understanding of the effects of genetic background ., For instance , in most studies of DMRT1 deletion , the undeleted DMRT1 allele is rarely sequenced ., Is the mode of action dominant or recessive ?, Deletions of the Y chromosome have long been appreciated as a cause of azoospermia , and we have now shown here that Y-linked duplications are also significant risk factors for spermatogenic failure ., The precise definition of the duplication sensitive sequences awaits further investigation ., Historically , Y duplications have been much less studied than Y deletions , as +/− STS PCR is the standard assay for assessing Y chromosome copy number variation in both the clinical and research setting ., Quantitative PCR methods for measuring Y chromosome gene dosage have been described in the literature , and applied almost exclusively to studying the phenotypic effects of duplication of genes in the AZFc region 53 ., Results of these investigations are conflicting , with studies of Europeans reporting no association between AZFc partial duplication and spermatogenic impairment 54 , while reproducible associations have been reported in east Asian cohorts 55 , 56 ., Notably , we identified some duplications on the Y chromosome greater than 2 . 5 Mb in size , all spanning the AZFc locus ( Figure S6 ) , in 8/179 cases ( those typed on Affymetrix 6 . 0 ) , compared to 13/972 controls ( OR 3 . 45 1 . 21–9 . 12 , p<0 . 01 ) ., Rearrangements of this size on the autosomes confer staggering risk for other forms of disease; for example , by one recent estimate CNVs larger than 3 Mb have an OR of 47 . 7 for intellectual disability and/or developmental delay 46 ., Our results suggest that Y chromosome structure may be more dosage sensitive than previously appreciated , and we speculate that some genes and non-coding sequences of the Y chromosome may be under stabilizing selection for copy number 57 ., Three recent studies have used array-based approa | Introduction, Results, Discussion, Methods | Gonadal failure , along with early pregnancy loss and perinatal death , may be an important filter that limits the propagation of harmful mutations in the human population ., We hypothesized that men with spermatogenic impairment , a disease with unknown genetic architecture and a common cause of male infertility , are enriched for rare deleterious mutations compared to men with normal spermatogenesis ., After assaying genomewide SNPs and CNVs in 323 Caucasian men with idiopathic spermatogenic impairment and more than 1 , 100 controls , we estimate that each rare autosomal deletion detected in our study multiplicatively changes a mans risk of disease by 10% ( OR 1 . 10 1 . 04–1 . 16 , p<2×10−3 ) , rare X-linked CNVs by 29% , ( OR 1 . 29 1 . 11–1 . 50 , p<1×10−3 ) , and rare Y-linked duplications by 88% ( OR 1 . 88 1 . 13–3 . 13 , p<0 . 03 ) ., By contrasting the properties of our case-specific CNVs with those of CNV callsets from cases of autism , schizophrenia , bipolar disorder , and intellectual disability , we propose that the CNV burden in spermatogenic impairment is distinct from the burden of large , dominant mutations described for neurodevelopmental disorders ., We identified two patients with deletions of DMRT1 , a gene on chromosome 9p24 . 3 orthologous to the putative sex determination locus of the avian ZW chromosome system ., In an independent sample of Han Chinese men , we identified 3 more DMRT1 deletions in 979 cases of idiopathic azoospermia and none in 1 , 734 controls , and found none in an additional 4 , 519 controls from public databases ., The combined results indicate that DMRT1 loss-of-function mutations are a risk factor and potential genetic cause of human spermatogenic failure ( frequency of 0 . 38% in 1306 cases and 0% in 7 , 754 controls , p\u200a=\u200a6 . 2×10−5 ) ., Our study identifies other recurrent CNVs as potential causes of idiopathic azoospermia and generates hypotheses for directing future studies on the genetic basis of male infertility and IVF outcomes . | Infertility is a disease that prevents the transmission of DNA from one generation to the next , and consequently it has been difficult to study the genetics of infertility using classical human genetics methods ., Now , new technologies for screening entire genomes for rare and patient-specific mutations are revolutionizing our understanding of reproductively lethal diseases ., Here , we apply techniques for variation discovery to study a condition called azoospermia , the failure to produce sperm ., Large deletions of the Y chromosome are the primary known genetic risk factor for azoospermia , and genetic testing for these deletions is part of the standard treatment for this condition ., We have screened over 300 men with azoospermia for rare deletions and duplications , and find an enrichment of these mutations throughout the genome compared to unaffected men ., Our results indicate that sperm production is affected by mutations beyond the Y chromosome and will motivate whole-genome analyses of larger numbers of men with impaired spermatogenesis ., Our finding of an enrichment of rare deleterious mutations in men with poor sperm production also raises the possibility that the slightly increased rate of birth defects reported in children conceived by in vitro fertilization may have a genetic basis . | genome-wide association studies, aneuploidy, x-linked, chromosomal disorders, genetics, biology, human genetics, genetics of disease, chromosomal deletions and duplications, genetics and genomics, y-linked | null |
journal.pntd.0005230 | 2,017 | Defining Seropositivity Thresholds for Use in Trachoma Elimination Studies | Trachoma is caused by ocular infection with the bacterium Chlamydia trachomatis ( Ct ) 1 ., It is the leading infectious cause of blindness worldwide 2 ., The World Health Organization ( WHO ) estimates that over 200 million people in 42 countries are at risk from trachoma blindness 3 , that 1 . 4 million people experience moderate to severe visual impairment because of the disease and that of these , around 450 , 000 have been irreversibly blinded 4 ., The most commonly used system for estimating the prevalence of trachoma uses the WHO simplified grading system 5 of clinical signs of trachoma ., These include trachomatous inflammation—follicular ( TF ) , trachomatous inflammation—intense ( TI ) and trachomatous trichiasis ( TT ) , which is the rubbing of the eyelashes against the globe of the eye ., WHO guidelines recommend the SAFE strategy to combat trachoma: Surgery to treat trichiasis , annual mass-drug administration ( MDA ) of Antibiotics to treat Ct infection and Facial cleanliness and Environmental improvement to reduce transmission ., Implementation of the SAFE strategy and cessation of MDA depends on the prevalence of TF in children aged 1–9 years ., Concerns have been raised about the appropriateness of having treatment guidelines based on clinical signs such as TF and TI ., In some low endemicity 6 , 7 and post-MDA settings 8 , 9 , both TF and TI correlate poorly with the prevalence of Ct infection and both clinical signs are sometimes associated with bacteria other than Ct 10 , 11 ., Tests for infection have been suggested as possible tools for trachoma control programmes ., Numerous nucleic acid-amplification tests ( NAATs ) have been developed , including the adapted use of commercial kits originally designed for diagnosing genitourinary Ct infections 12–16 ., NAATs have been shown to be cost-effective in some settings 17 but concerns have been raised that the per-sample cost of NAATs can be too much for national eye health programmes in countries where trachoma remains a problem 18 ., The cost of specialist devices and platforms for deploying NAATs can also be prohibitive ., Serology has been suggested as a possible alternative to clinical signs and infection testing , as it indicates the cumulative exposure to Ct 19 , 20 , with the potential to assess the impact of intervention efforts 21 ., By monitoring the exposure to Ct of the youngest age groups , born after implementation of MDA , serology may prove useful for confirming that transmission has been interrupted 22 ., Serology has recently been used in several studies 19 , 20 , 22–24 , three of which have taken place in districts that have completed three or more rounds of MDA 22–24 ., These studies have used the multiplex bead array platform ( Bio-rad , Hercules , California ) to detect antibodies against Pgp3 and CT694 , antigens thought to be highly immunogenic 25 ., Because this platform is costly , technically complex and unlikely to be found in most laboratories in resource-limited regions , alternative , simpler methods of antibody detection have been proposed 22 , 26 ., To make serological testing more widely accessible , the Pgp3/CT694 assay used in previous studies 19 , 20 , 22–24 has been adapted for use in a simple Pgp3-specific enzyme-linked immunosorbent assay ( ELISA ) ., Pgp3 is a Ct-specific 84kDa heterotrimeric protein 27 and is recognised by specific IgG 28 ., It is thought to be the most immunodominant of the proteins encoded by the Ct plasmid that is unique to Ct 29 ., ELISAs are routinely used to detect specific IgG in dried blood spots 30–34 ., ELISA data , measured as optical density ( OD ) is quantitative and continuous ., It is desirable to be able to assign a classification ( seronegative , seropositive ) to each sample , but this can be challenging because the distributions of OD values in the negative and positive populations may overlap to a greater or lesser extent 34 ., The aim of this study was to determine the most appropriate method for setting the threshold for positivity as well as to determine the usefulness of an anti-Pgp3-specific ELISA for identifying communities in which the transmission of ocular Ct has been interrupted ., We tested dried blood spots collected as part of trachoma surveys in three countries: Laos , Uganda and The Gambia ., We evaluated the age-specific seroprevalence using four methods and compared the resulting estimates of prevalence of seropositivity based on six possible thresholds ., We discuss the merits of the different methods in the context of programmes seeking to monitor the elimination of trachoma as a public health problem ., This study was conducted in accordance with the Declaration of Helsinki ., This study received approval from the Ethics Committee of the London School of Hygiene & Tropical Medicine ( LSHTM; references 6319 , 6514 , 8355 , 8918 ) , UK; the Ministry of Health of the Lao People’s Democratic Republic ( No:48 NIOPH/NECHR ) , Ugandan Ministry of Health ( VCD-IRC/053 ) and The Gambia government/Medical Research Council ( MRC ) Joint Ethics Committee ( SCC1408v2 ) ., In all countries , a local health official explained the study to each head of household , answered any questions and explained the written consent form before requesting their agreement and signature ., Written ( thumbprint or signature ) consent was obtained from each participant or the parent or guardian of each child under 18 who participated; assent was sought from children aged 12–17 ., Trachoma graders were trained according to the Global Trachoma Mapping Project ( GTMP ) protocols and were required to score a minimum kappa of 0 . 7 for the diagnosis of TF in an inter-grader agreement test with 50 eyes of 50 children 35 , 36 ., The samples in Laos were collected in November 2014 as part of a follow-up study to the GTMP work completed there ., Three districts in three regions were selected based on baseline trachoma survey findings that indicated potential ‘hot spots’ 37 ., From these three regions , all children aged 1–9 in selected villages were invited to participate ., Trachoma elimination programmes have never been undertaken in Laos ., In Uganda , samples were collected as part of a trachoma impact survey in May 2014 , following three years ( 2010–2012 ) of implementation of the A , F and E components of the SAFE strategy in two regions ( Pader and Agogo ) ., Prior to MDA , trachoma was considered highly endemic in these regions , although no data is publicly available ., This study was a population based prevalence survey , which used a two stage sampling strategy; villages were selected with probability proportional to size , and households were randomly selected within each selected village based on a household list produced by the village chief and local health officials ., All children aged 1–9 years in the selected households were invited to participate ., In The Gambia , a population based prevalence survey using a two stage sampling strategy was undertaken in February-March 2014; villages were selected with probability proportional to size , and households were randomly selected within each selected village based on a household list produced by the village chief and local health officials ., One region , Lower River Region ( LRR ) had undergone three rounds of annual ( 2007–2009 ) MDA for trachoma , while the other , Upper River Region ( URR ) , has never had trachoma elimination activities because trachoma has not been of a sufficiently high prevalence to justify implementation ., All members of randomly selected households were invited to participate , regardless of age ., After informed consent was obtained , a trachoma grader examined both eyes for signs of trachoma using a binocular loupe ( 2 . 5× ) and a torch ., The grader changed gloves between each participant to minimise the risk of carry-over contamination ., Antibiotics were provided to individuals with evidence of active trachoma and/or the affected household , according to each country’s national policy ., Each participant had a finger-prick blood sample collected onto filter paper ( Trop-Bio Pty , Townsville , Australia ) , using a sterile single-use lancet ( BD Microtrainer , Dublin , Ireland ) ., Each filter paper had six extensions , calibrated to absorb 10 μL of blood ., Samples were air-dried for approximately five hours and then stored in individual Whirl-Pak plastic bags ( Nasco , Modesto , California ) with desiccant sachets ( Whatman , Little Chalfont , UK ) before being stored at -20°C ., All samples were shipped to LSHTM for testing ., Dried blood spots ( DBS ) were tested for antibodies against Pgp3 ., One whole filter paper extension per sample was eluted in 250 μL PBS + 0 . 3% v/v Tween-20 ( PBSTw ) ( Sigma-Aldrich , Dorset , UK ) + 5% w/v non-fat milk powder ( PBSTw-milk ) ( AppliChem , Maryland Heights , USA ) overnight at 4°C ., Immulon 2HB 96-well plates ( VWR International , Lutterworth , UK ) were coated with recombinant Pgp3 protein 19 overnight at 4°C ( 25ng per well in 0 . 1M sodium carbonate buffer , pH 9 . 6 ) ., Plates were washed with PBSTw to remove unbound protein , blocked with 100 μL PBSTw for 1 hour at 4°C and washed two times ., Control sera with known ratios of Pgp3 antibodies ( 1000 units , 500 units , 200 units , 50 units and negative control serum ) and a blank consisting of PBSTw-milk were run on every plate ., All samples and controls were tested in triplicate at a 1:50 dilution in PBSTw-milk ., After 2 hours incubation on an orbital shaker at room temperature , wells were washed 5 times and 50 μL of an HRP-labelled mouse anti-human IgG ( Fc ) -HRP ( Southern Biotech , Birmingham , USA ) diluted 1:32 , 000 was added ., Plates were incubated for 1 hour on an orbital plate shaker at room temperature then washed 5 times to remove unbound antibody ., Fifty microliters of TMB ( KPL , Gaithersburg , USA ) was added and the mixture was incubated in the dark for 9 minutes at room temperature ., The reaction was stopped with 50 μL 1N H2SO4 and optical density was read at 450 nm ( OD450 ) on a Spectramax M3 plate reader ( Molecular Devices , Wokingham UK ) ., Readings were corrected for background by subtracting the average absorbance of three blank wells containing no serum , using Softmax Pro5 software ( Molecular Devices , Wokingham UK ) ., Blanked OD450 values for samples and controls were normalised by dividing the mean of the three wells against the mean of 200 unit control included on each plate ., This was done for each plate ., Data analysis for ELISA was performed separately and masked to the results of demographic and clinical information ., Statistical analysis was carried out using R 38 ., We used four different methods for establishing a threshold for seropositivity: visual inspection of the inflection point ( VIP ) , a finite mixture model ( FMM ) 39 , the expectation-maximisation algorithm ( EM ) 40 and an receiver operating characteristic ( ROC ) curve based on previously-assayed dried blood spots from children in Tanzania 19 ., There are as yet no accepted guidelines as to what level of sensitivity or specificity is required of a serological test; thus we referred to a previously published template 18 and established three possible thresholds from the ROC curve: one maximising specificity , one with a sensitivity greater than 80% 18 and one optimising the balance between sensitivity and specificity , by maximising Youden’s J-index 41 ., We asked 12 arbitrarily selected non-laboratory staff and students at LSHTM to visually examine a simple plot of the sorted OD450 data curves and determine the inflection point for each sample set ., For this exercise , we defined the inflection point as the point on the data curve where the curve changes from predominantly horizontal to predominantly vertical ., The 12 values were then averaged to determine the threshold and standard deviations ( SDs ) were calculated ., A finite mixture model 42 was used to classify the samples as seropositive or seronegative based on normalised OD450 values ., The data were fitted using maximum likelihood methods , estimating the distribution parameters for each classification group ( seropositive or seronegative ) as well as the proportion of samples in each category to fit the overall distribution of results 34 , 43 , 44 ., The threshold for seropositivity was then defined as the mean of the Gaussian distribution of the seronegative population plus three SDs of the seronegative population 44 , 45 ., FMM was performed on each set of samples , based on country of origin ., The expectation-maximisation algorithm is similar to FMM in that it classifies samples based on population parameters ., It relies on the Bayesian information criterion to select an appropriate model ., EM is an iterative optimization method to estimate some unknown parameter 40 , in this case the threshold between seropositive and seronegative , given the number of clusters and the normalised OD450 values ., EM estimates where to set the threshold while maximising the likelihood of each sample parameter 40 ., Using the ‘mclust’ package in R , parameters were set to specify a univariate model with equal variance between 2 clusters 46 ., Serum samples from 122 children from the United States and blood spots from 11 Ct-specific PCR-positive children from Tanzania were used to make the original ROC curve 19 ., A second set of 124 Tanzanian dried blood spots were assayed using the multiplex bead array and dichotomised based on the original threshold ., These samples were then re-tested with the ELISA and the data from this assay were used to generate the ROC curve used in this manuscript ., The R package ‘Epi’ 47 was used to generate three different thresholds: the first of which maximises Youden’s J-index to balance sensitivity and specificity 41 , the second and third were set for high sensitivity ( minimum 80% ) and high specificity ( minimum 98% ) , respectively ., The prevalence of signs of trachoma and the exact binomial confidence intervals were calculated using the R ‘Stats’ package 38 ., Due to the low prevalence of clinical signs , Fisher’s exact test was used to test for association 48 ., Seroprevalence in each population was calculated using each of six thresholds ., We also examined the relationship between the clinical data and serological data ., Due to the low prevalence of clinical signs , data for clinical signs were pooled across all three studies ., We recruited 978 Laotian children aged 1–9 years from the provinces of Attapu ( n = 406 ) , Houaphan ( n = 307 ) and Phôngsali ( n = 239 ) ., Twenty-six participants had incomplete clinical records and were excluded from further study ., The proportions of the sample populations who were male were 52 . 9% , 60 . 3% and 54 . 0% in Attapu , Houaphan and Phôngsali , respectively ., The median age was five years in all three provinces ., Fifteen cases of TF were diagnosed ( 1 . 6% , exact binomial CI = 0 . 9%-2 . 6% ) , 11 of which were bilateral cases ( Table 1 ) ., No cases of TI were observed ., There was a higher prevalence of TF in Attapu ( 2 . 7% , 11/406 ) than in either Houaphan ( 1 . 0% , 3/307 ) or Phôngsali ( 0 . 4% , 1/239 ) , ( p = 0 . 02 ) using Fisher’s exact test 49 with the Simes-Bonferroni correction for multiple tests 50 ., 2738 children aged 1–9 years were recruited in the Ugandan districts of Agogo ( n = 1388 , 49 . 7% male ) and Pader ( n = 1377 , 50 . 4% male ) ., 38 participants were missing complete clinical data and were excluded from further study ., The median age was five years in both districts ., 93 cases of TF were diagnosed ( 3 . 4% , exact binomial CI = 2 . 8%-4 . 2% ) , 44 of which were bilateral ., Eight cases of TI were diagnosed ( 0 . 3% , exact binomial CI = 0 . 1%-0 . 6% ) ( Table 1 ) ., No other clinical signs were assessed ., The prevalence of TF was 3 . 2% in Agogo and 3 . 7% in Pader ., There was no significant difference between the estimated prevalence of TF in the two districts ( TF: Χ2 = 0 . 429 , p = 0 . 5125; TI: Χ2 = 3 . 1566 , p = 0 . 07562 ) ., In the Gambia we recruited participants of all ages from the Lower River Region ( LRR , n = 1028 , 41 . 9% male ) and Upper River Region ( URR , n = 840 , 42 . 5% male ) ., Ten participants were excluded from the study because they either declined to provide a blood sample ( n = 1 ) or had incomplete clinical data ( n = 9 ) ., The median age in LRR was 13 years ( range: 1–88 ) and 11 years in URR ( range: 1–90 ) ., Overall , 30 cases of TF were diagnosed ( 1 . 6% , exact binomial CI = 1 . 1%-2 . 3% ) , 19 of which were bilateral ( Table 1 ) ., There were 25 cases of TF in children aged 1–9 years ., Four cases of TI were observed ( 0 . 2% , exact binomial CI = 0 . 06%-0 . 6% ) , two of which were in children aged 1–9 years ., Examiners found 78 cases of TS ( 4 . 2% , exact binomial CI = 3 . 3%-5 . 2% ) , eight cases of TT ( 0 . 4% , exact binomial CI = 0 . 2%-0 . 8% ) and one case of CO ( 0 . 05% , exact binomial CI = 0 . 001%-0 . 3% ) ., There was a significant difference in TS prevalence between the URR and LRR ( Χ2 = 7 . 2435 , p = 0 . 007116 ) ; the difference in TF prevalence was non-significant ( Χ2 = 0 . 1343 , p = 0 . 714 ) ., The prevalence of TI , TT and CO in this population was too low for meaningful statistical analysis ., Observed frequencies of clinical signs of trachoma in the various samples are summarised in Table, 1 . A more detailed description , including prevalence by age and gender , is presented in Supplementary S1 , S2 and S3 Tables ., The five serum controls were tested in triplicate and the mean values for each plate were tracked across each sample set ., The coefficient of variation was less than 10% in each of the three replicates of each control specimen ., Inter-plate variation of controls was less than 15% across all plates in each sample set as shown in Table, 2 . A plate was permitted to have no more than one control with >15% variation from the sample set mean for that control; if a plate had two or more controls with values more than 15% greater or lesser than the sample set mean , the plate was re-run ., Less than 5% of plates were re-run due to this ., Table 2 shows the mean values and the accepted 15% range for the five controls ., The sample set for each country was tested separately ., Each plate showed a large but narrowly distributed proportion of low-OD specimens , with a smaller proportion of higher-OD specimens ., Fig 1 shows typical results from an ELISA plate ., In all three sample sets , density data peak around 0 . 25 OD450; this can be seen in centre panels B in Figs 2 , 3 and 4 ., The leftmost panels of Figs 2A , 3A and 4A were shown to 12 people , each of whom was asked to determine each graph’s point of inflection ., The mean of the inflection points was calculated for each sample set and the SD and range were calculated ., For Laos , the mean threshold was calculated to be 0 . 619 OD450 ( SD = 8 . 2% , range 0 . 485–0 . 750 ) ; for Uganda the threshold was calculated to be 0 . 641 OD450 ( SD = 14 . 4% , range 0 . 410–0 . 795 ) and for The Gambia the threshold was calculated to be 0 . 579 OD450 ( SD = 7 . 3% , range 0 . 402–0 . 673 ) ., The sorted normalised OD450 values are shown in Figs 2A , 3A and 4A ( leftmost panels ) , alongside marginal density distribution plots of the same values ( centre panels ) and boxplots ( rightmost panels ) showing the range of the 12 threshold values that were selected by the volunteers ., A finite mixture model was tested on all three sample sets , setting the threshold at the mean of the seronegative population plus three SDs 44 , 45 ., The thresholds were set at 0 . 6963 OD450 , 0 . 5537 OD450 and 0 . 6725 OD450 for Laos , Uganda and The Gambia , respectively ., The FMMs are shown in Figs 2B , 3B and 4B ., An EM model was fitted to all three sample sets , specifying parameters for a univariate model with equal variance between 2 clusters 45 ., The thresholds were set at 0 . 65 OD450 , 0 . 45 OD450 and 0 . 57 OD450 for Laos , Uganda and The Gambia , respectively ., The EM-derived threshold selections are shown in Figs 2B , 3B and 4B ., Using the ROC curve to set a threshold optimising Youden’s J-index to balance specificity and sensitivity resulted in a threshold at 0 . 870 OD450 ( specificity 93 . 9% , sensitivity 91 . 4% ) ., Setting the threshold to ensure a minimum sensitivity of 80% resulted in a threshold at 0 . 965 OD450 ( specificity 94 . 8% , sensitivity 89 . 4% ) ., Setting the threshold for a minimum specificity of 98% resulted in a threshold at 1 . 951 OD450 ( specificity 98 . 28% , sensitivity 43 . 94% ) ., Fig 5 shows the ROC curve with the three thresholds identified ., Panels 2C , 3C and 4C show all six thresholds in relation to the normalised OD450 data in each of the three populations ., The internally calibrated methods ( i . e . , VIP , FMM and EM ) were reasonably conformant and appeared to favour threshold placements that were substantially lower than those set by the ROC , which is calibrated with Tanzanian specimens , even when a higher sensitivity ( i . e . , lower threshold value ) test was specified in the ROC analysis ., As a consequence of this , the seroprevalence estimates that were determined by VIP , EM and FMM were similar to one another , while the seroprevalence estimates set by any of the ROC curve thresholds were much lower in all three populations ( Table 3 ) ., Seroprevalence for each sample set , using the six different thresholds were calculated , along with 95% confidence intervals ., As the threshold increases in value , fewer specimens are classified as being seropositive , decreasing the seroprevalence ., The seroprevalence for each sample set at each threshold is presented in Table 3 ., Seroprevalence for each country by sex , region and age is provided in Supplementary S4 , S5 and S6 Tables ., Table 4 presents the proportion of seropositive samples by clinical grade , as estimated by each threshold specification ., Due to the relatively low prevalence of all clinical signs , prevalence values for have been pooled ., Several previous studies have used anti-Pgp3-specific ELISAs to test for genital chlamydial infection 21 , 51–54 but only one 55 has used the method for the detection of antibodies against ocular chlamydial infection ., In this study , we used an ELISA test to detect IgG antibodies specific to the Ct protein Pgp3 in studies with large sample sizes from three countries ., To date , this is the largest study to measure antibodies to Ct in trachoma-endemic populations and the first to look at populations from more than one country , including East Africa , West Africa and Southeast Asia ., We have shown that within and between runs there is a low coefficient of variation in the assay and that the bimodal data distribution of normalised OD450 values in those samples reflects that which would be expected in populations where a minority of individuals are seropositive and where there is a broad range of antibody titres in the seropositive sub-population ., This is best observed in the data from the Gambia ( Fig 4 ) , where we included adults in the sample and where the more substantial seropositive sub-population can be accounted for by both sexually transmitted Ct infection and the formerly high level of endemicity of trachoma in the Gambia ., Clinical specimens without any Ct-specific IgG still have some degree of baseline reactivity in ELISA tests because of non-specific binding of irrelevant antibodies ., There is also substantial between-specimen variation in seropositives , which reflects natural variation in the antibody titre ., The potential for there being substantial overlap between the seronegative specimens with high baselines and the seropositives with low anti-Pgp3 antibody titres means that it can be difficult to differentiate between the two groups ., There is very little published information on the prevalence of trachoma in Laos and Uganda 56 , but on the evidence of our analysis , clinical signs of disease are rare and the levels of seropositivity appear to be comparable to those in The Gambia , where elimination has been declared ., We have no data on the prevalence of Ct infection in the communities in Laos and Uganda , nor is there any longitudinal data to monitor changes in antibody levels following documented infection ., Numerous studies have looked at the prevalence of ocular Ct infection in The Gambia and shown it to be negligible 7 , 57 , 58 ., All the populations we studied have received MDA and we did not screen a population with higher prevalence levels ., Further research in meso- and hyper-endemic populations will be needed in order to assess the utility of this method in other settings ., We have shown how the method that is selected for the statistical interpretation of ELISA data ( with particular regard to the method of threshold specification ) can greatly change the population prevalence estimates that are derived ., Methods that indicate the use of a higher threshold value are likely to be more specific and have a higher positive predictive value ( PPV ) , but they do incur a penalty in the form of reduced sensitivity ., In the context of post-MDA trachoma control , a test with high PPV is more desirable as over-diagnosis might lead to the inappropriate continuation of MDA interventions ., Meanwhile a lower sensitivity test , applied in a low prevalence setting such as the post-MDA population of the Gambia , is likely to have a high negative predictive value ( NPV ) and the clinical impact of the false negative rate is likely to be modest as long as the sensitivity does not fall too far ., In our hands , the ROC analysis supported the use of higher thresholds than did the other methods ., Unfortunately the reference material was not sampled from any natural population and so the estimated sensitivity and specificity of the test based on ROC were unlikely to reflect the true performance in the populations that were sampled in this study 59 ., We explored three internally calibrated thresholding methods ( i . e . using only data generated during the study ) , all of which specified thresholds at approximately the same OD450 value ., This was true across sample sets from all three countries ., It is perhaps unsurprising that similar estimates emerged from FMM and EM , as there are methodological similarities in the two approaches ., At face value the VIP method might seem arbitrary and crude , but the human brain can outperform computers in some aspects of pattern recognition and by obtaining a threshold estimate that closely matches that of EM and FMM , our data indicate that the results of a conditionally independent method ( VIP ) correlate closely with the computational approaches and are able to successfully determine where the most obvious bimodal split in the data occurs ., What gives FMM and EM the edge over VIP is that they are more replicable and that the different requirements for higher or lower specificity and sensitivity in different clinical settings can be controlled by changing the number of SDs that the algorithm uses to determine the cut point ., For instance , an increasingly specific test could be implemented by setting the threshold at four , five or six SDs of the negative population , rather than three SDs we used here ., None of the populations that we surveyed would be expected ( based on clinical signs ) to have a high level of Ct seropositivity and it may be that the data in Tables 3 and 4 ( and Supplementary Data S4 , S5 and S6 Tables ) reflect a high false positive rate , low positive predictive value ., By adjusting the parameters of the algorithms we might achieve a prevalence estimate that is more accurate , but without any gold standard we can never truly assess how accurate our estimates are ., In the Gambian data , using respectively 4 or 5 SDs would have led to cut points at respectively OD = 0 . 81 and OD = 0 . 95 , values much closer to the cut-points recommended by the ROC analysis ., For programmatic purposes , the absolute value and accuracy of the prevalence estimate is actually somewhat less important than the precision of that estimate and the longitudinal change in repeat measures from the same population across the lifetime of the intervention and monitoring programme ., This is because the absolute estimate is clearly highly variable given quite arbitrary choices made during data analysis , whilst percentage changes in population seroprevalence across time ( regardless of the actual number values ) can be indicative of the effectiveness of MDA ., As long as the method is fixed and replicable , then both longitudinal and between–population comparisons are appropriate and will have a fixed level of error , even though the absolute accuracy will remain unknown ., The real value of using an internally controlled method such as FMM or EM is that it is possible to use an algorithmic approach that is simple to apply to any data set and which requires no additional testing of external specimens or controls ., In this study , we generated a ROC curve based on specimens that had previously been calibrated against the original reference standards described by Goodhew et al 19 ., There is no gold standard for serological testing of chlamydia , and mis-classification in the reference standards is likely to have introduced error in the reference panel ., Goodhew described how one PCR-positive DBS tested negative for antibodies against Pgp3 , while three samples that were in the negative reference group tested positive for antibodies against Pgp3 19 ., As these original reference standards were no longer available , we have had to rely on a second set of standards that were tested against the original standards ., Problems relating to the ROC reference specimens could be solved by the establishment of a fully maintained and quality controlled international standard , but this is unlikely to happen as it is would be very difficult to identify a reliable source of large volumes of seropositive plasma ., FMM has been used in numerous serological studies 34 , 39 , 43 , 45 , 60–66 and we propose that it , or the closely related EM , should be considered as the method of choice when performing data analysis for trachoma serology data ., In trachoma control programmes , the SD parameter should be adjusted to favour high specificity and a larger number of SDs than used here would seem appropriate ., One attractive option would be to use data from a post-elimination country ( i . e . the Gambia ) to subtract out the background positivity and by doing so calibrate or normalise the test for use in populations where elimination has not yet been reached and prevalence is unknown ., Variability and error are inherent to any diagnostic test and with every change in reference standard and assay technique , variability and error increase over and above any variation that may be inherent in a test due to inter- or intra- centre and user variation ., Thus , we believe that an alternate approach to assay design , reference selection and threshold specification should be considered ., For all the sample sets included in this study , the density data peak around 0 . 25 OD450 ( Figs 2A , 3A and 4A ) , suggesting that a comparison of seroprevalence levels between populations is possible ., Compared to ROC curves , internally-referenced thresholds inherently account for differing background levels in each population ., If not accounted for using the ROC curve , this may result in an under- or over-estimation of seroprevalence ., This will facilitate the programmatic usage of seroprevalence levels set by the finite mixture model or expectation-maximisation algorithm if serology is to be adopted as an alternative monitoring method ., The ELISA assay presented in this paper is easy-to-use , affordable in terms of both reagents and equipment required , and can potentially be deployed in low- and middle-income countries ., The unit cost per sample was less than £4 . 00; this includes all materials required fo | Introduction, Methods and Materials, Results, Discussion, Conclusion | Efforts are underway to eliminate trachoma as a public health problem by 2020 ., Programmatic guidelines are based on clinical signs that correlate poorly with Chlamydia trachomatis ( Ct ) infection in post-treatment and low-endemicity settings ., Age-specific seroprevalence of anti Ct Pgp3 antibodies has been proposed as an alternative indicator of the need for intervention ., To standardise the use of these tools , it is necessary to develop an analytical approach that performs reproducibly both within and between studies ., Dried blood spots were collected in 2014 from children aged 1–9 years in Laos ( n = 952 ) and Uganda ( n = 2700 ) and from people aged 1–90 years in The Gambia ( n = 1868 ) ., Anti-Pgp3 antibodies were detected by ELISA ., A number of visual and statistical analytical approaches for defining serological status were compared ., Seroprevalence was estimated at 11 . 3% ( Laos ) , 13 . 4% ( Uganda ) and 29 . 3% ( The Gambia ) by visual inspection of the inflection point ., The expectation-maximisation algorithm estimated seroprevalence at 10 . 4% ( Laos ) , 24 . 3% ( Uganda ) and 29 . 3% ( The Gambia ) ., Finite mixture model estimates were 15 . 6% ( Laos ) , 17 . 1% ( Uganda ) and 26 . 2% ( The Gambia ) ., Receiver operating characteristic ( ROC ) curve analysis using a threshold calibrated against external reference specimens estimated the seroprevalence at 6 . 7% ( Laos ) , 6 . 8% ( Uganda ) and 20 . 9% ( The Gambia ) when the threshold was set to optimise Youden’s J index ., The ROC curve analysis was found to estimate seroprevalence at lower levels than estimates based on thresholds established using internal reference data ., Thresholds defined using internal reference threshold methods did not vary substantially between population samples ., Internally calibrated approaches to threshold specification are reproducible and consistent and thus have advantages over methods that require external calibrators ., We propose that future serological analyses in trachoma use a finite mixture model or expectation-maximisation algorithm as a means of setting the threshold for ELISA data ., This will facilitate standardisation and harmonisation between studies and eliminate the need to establish and maintain a global calibration standard . | Trachoma is caused by the bacterium Chlamydia trachomatis ( Ct ) ., Individuals who have previously been infected with Ct carry specific antibodies in their blood ., Recent studies have suggested that these antibodies may be a good way to estimate the intensity of transmission of this bacterium in a population ., Among people who do have antibodies ( seropositives ) there is variation in the amount that is detectable in their blood ., Some people have such low levels that differentiating them from those who don’t have antibodies ( seronegatives ) is challenging ., We used a new test for Ct antibodies on blood specimens from three countries ., Our test worked extremely well , giving reproducible results when we tested the same samples multiple times ., We compared four different methods for setting the position of the threshold line between seronegatives and seropositives ., The estimated transmission intensity in each country varied depending on the threshold method used , but two methods that used statistical modelling algorithms to define the two groups performed consistently across all three countries’ samples ., We recommend that future studies should consider adopting the statistical modelling approaches , as they are objective tests that require no reference material and allow for standardisation between studies . | medicine and health sciences, enzyme-linked immunoassays, body fluids, pathology and laboratory medicine, immune physiology, immunology, tropical diseases, geographical locations, uganda, bacterial diseases, eye diseases, neglected tropical diseases, antibodies, immunologic techniques, africa, research and analysis methods, laos, immune system proteins, infectious diseases, serology, proteins, immunoassays, hematology, people and places, biochemistry, blood, anatomy, asia, gambia, physiology, ophthalmology, biology and life sciences, trachoma | null |
journal.pgen.1004128 | 2,014 | Extreme Population Differences in the Human Zinc Transporter ZIP4 (SLC39A4) Are Explained by Positive Selection in Sub-Saharan Africa | Zinc homeostasis is critically important for human health ., Similarly to iron , zinc has manifold functions in the body , such as in the immune system 1 , aging 2 , DNA repair 3 , signaling 4 and in diseases such as diabetes 5 and cancer 6 ., On the molecular level , zinc acts as a co-factor in hundreds of metallo-enzymes as well as in hundreds of DNA-binding proteins ( e . g . zinc finger proteins ) ., Zinc homeostasis is tightly regulated by 10 zinc efflux transporters and 14 zinc influx transporters ( encoded by the SLC30A and SLC39A gene families , respectively ) ., ZIP4 ( SLC39A4 ) is the most important intestinal zinc uptake transporter and is expressed at the apical membrane of enterocytes 7 , 8 ., Loss-of-function mutations in ZIP4 cause acrodermatitis enteropathica 9 , 10 MIM 201100 , a congenital disease characterized by extreme zinc deficiency if left untreated without supplemental zinc 11 , 12 ., Fittingly , it was recently reported that the loss of expression of this gene in a ZIP4 intestine-specific knockout mouse caused systemic zinc deficiency , leading to disruption of the intestine stem cell niche and loss of intestine integrity 13 ., The single nucleotide polymorphism ( SNP ) c . 1114C>G ( rs1871534 ) in the ZIP4 gene ( SLC39A4; NM_130849 . 2 ) results in the substitution of leucine for valine at amino acid 372 ( Leu372Val ) in the human ZIP4 transporter ., This non-synonymous SNP is one of the most markedly differentiated genetic variants in the genome in terms of allele frequency differences between populations 14–16 , according to data from HapMap 17 , the Human Genome Diversity Panel ( HGDP ) 18 and the 1000 Genomes Project 16 ., Extreme population differentiation is a signature of local positive selection 15 , 19–21 , but genomic scans for targets of natural selection based on other criteria , such as extended long haplotypes 22–24 or selective signatures in the allele frequency spectrum 25 , have failed to identify ZIP4 as a candidate gene for positive selection ., To date , whether this variant has evolved under positive selection or neutrality , and its potential functional significance , has not been examined ., In the work reported here , we had three main objectives:, ( i ) to investigate evolutionary explanations for the extreme population differentiation of the ZIP4 Leu372Val polymorphism by use of coalescent simulations;, ( ii ) to test for functional differences in cellular zinc transport between the alleles of the Leu372Val polymorphism using a heterologous expression system; and, ( iii ) to discuss potential selective forces behind this possibly adaptive event and their implications for zinc homeostasis in modern humans ., We have extensively characterized the extreme geographical differentiation of the Leu372Val substitution and provide evidence that it has been subject to a nearly complete but mild selective sweep in Sub-Saharan Africa ., Our simulations show how the extreme pattern of population differentiation , yet absence of other classical signatures of positive selection , can be explained by directional selection accompanied by the effects of a recombination hotspot near the polymorphic adaptive site ., Additionally , our data demonstrate in vitro functional differences between the two human polymorphic alleles at codon 372 of the human ZIP4 transporter in surface protein expression , basal intracellular levels of zinc and zinc uptake ., We hypothesize that the reduction in intracellular zinc levels mediated by the Val372 allele may have been advantageous in Sub-Saharan Africa , possibly by restricting access of a geographically restricted pathogen to this micronutrient , and that other possible secondary consequences for disease risk and health may result from the differential activity of the ZIP4 alleles ., Five common non-synonymous SNPs are known in the human ZIP4 gene ( Table 1 ) : Glu10Ala ( rs2280839 ) , Ala58Thr ( rs2280838 ) , Ala114Thr ( rs17855765 ) , Thr357Ala ( rs2272662 ) and Leu372Val ( rs1871534 ) ., However , only the latter two SNPs show elevated levels of population differentiation in the 1000 Genomes Phase1 sequencing data when comparing the Yoruba from Ibadan , Nigeria ( YRI ) with either the Han Chinese from Beijing , China ( CHB ) or Utah residents of Northern and Western European origin ( CEU ) ., As shown in Figure 1A and 1B , their FST values fall above the 99 . 999 th percentile of the genome-wide FST distributions between CEU-YRI ( with FST values for rs2272662 and rs1871534 of 0 . 48 and 0 . 98 , respectively ) and between CHB-YRI ( with FST values of 0 . 51 and 0 . 98 , respectively ) ., We therefore verify that the Leu372Val substitution encoded by SNP rs1871534 is the non-synonymous polymorphism exhibiting the most extreme allele frequency differences in the human ZIP4 gene ., Next , we genotyped the 51 populations from the Human Genome Diversity Panel ( HGDP ) and compiled additional allele frequencies for this position in worldwide populations from the Alfred database 26 , 27 ., Additionally , we obtained new data from a Pygmy population from Gabon and North African populations of Western Sahara , Morocco , and Libya ., These new data confirm that the Leu372 variant is the most common allele outside of Africa , and provide a more detailed picture of the geographical allele frequency distributions of this non-synonymous polymorphism ( Figure 1C and Table S1 ) ., Overall , the Val372 variant showed the highest frequencies in Sub-Saharan Africa , with populations such as the Ibo or the Yoruban people exhibiting the most extreme derived allele frequencies worldwide ( 0 . 99 and 0 . 96 , respectively ) ., Interestingly , two presumably early-branching groups in Sub-Saharan Africa , the Pygmy and the San people , showed opposing trends in the derived allele frequency ( 0 . 94 and 0 . 0 , respectively ) ., Even though the small sample size from the San ( only six individuals ) means that a population frequency of up to 0 . 221 cannot be excluded ( with p\u200a=\u200a0 . 05 based on assuming Hardy-Weinberg equilibrium and a binomial approach ) , such divergent tendencies in these two Sub-Saharan populations are maintained ., Given the elevated levels of population differentiation of the SNP rs2272662 , we also genotyped the HGDP panel for the Thr357Ala polymorphism ., However , compared with the Leu372Val substitution , the derived allele at this non-synonymous SNP displayed intermediate frequencies worldwide ( Figure S1 and Table S1 ) and less extreme allele frequency differences between populations ., Given the allele frequency differences observed in the Leu372Val polymorphism between the two early human branches in Africa and the uncertainty associated with the low coverage of the Neanderthal genome draft sequence 28 , we resequenced the corresponding orthologous positions for rs1871534 and rs2272662 in an additional Neanderthal sample , labeled SD1253 and excavated at El Sidrón site in Spain 29 ., The two positions were amplified in a multiplexed reaction , along with a diagnostic Neanderthal mitochondrial DNA ( mtDNA ) fragment , to monitor contamination in the PCR reaction ., For the L16230-H16262 diagnostic mtDNA fragment , 64 clones were generated ( Figure S2 ) , all of which show the Neanderthal-specific 16234T-16244A-16256A-16258G haplotype 28 ., This again supports the very low level of contamination in this particular sample ., For the orthologous positions of the human rs1871534 and rs2272662 SNPs , 19 and 14 sequences were successfully obtained , respectively ., With the exception of one clone in the second position , all sequences showed the previously inferred ancestral alleles , in agreement with the reads present for the Vindija individuals 33 . 16 ( one read for each position ) , 33 . 25 ( two for rs1871534 and none for rs2272662 ) and 33 . 26 ( two and one , respectively ) ( Figure 2 ) ., The successful resequencing of this Neanderthal individual , together with published reads from additional Neanderthals 28 and from the Denisovan individual 30 , strongly suggests that the Leu372 variant ( encoded by the C allele in rs1871534 ) is the ancestral human form , which is also in agreement with the chimpanzee state ( Figure 2 ) ., Together with the extreme population differentiation pattern , these results suggest that a selective sweep may have taken place in Sub-Saharan Africa , where the derived variant is nearly fixed ., Next we examined the complete genomic region around ZIP4 ( Figure 3 ) in the 1000 Genomes sequencing data ., Whereas we found a cluster of three strongly elevated FST scores between CEU and YRI in the neighboring SNPs rs1871535 ( intronic ) , rs1871534 and rs2272662 ( further suggesting directional selection in a specific geographical region ) , in both populations there was a clear absence of extreme values in neutrality statistics such as Tajimas D or Fay and Wus H ( Figure S3 ) ., Notably , no other polymorphism in the flanking region of the human ZIP4 displays the high levels of population differentiation of the Leu372Val substitution ., Interestingly , in both African and non-African populations there is a recombination hotspot in the ZIP4 gene , which could have reduced any signature of selection on the surrounding linked variation , thereby explaining the apparent lack of significant departures from neutrality ., To further investigate this possibility , we carried out coalescence simulations under a variety of recombination and selection scenarios using a well-established demography 31 ., As shown in Figure 3D , the observed values for FST and most of the different neutrality statistics cannot be explained by neutral evolution or positive selection with a constant recombination rate ., Instead , this atypical pattern of extreme population differentiation , yet seemingly neutral Tajimas D and other neutrality statistics , showed a higher recovery in simulations with directional selection on the derived allele in Sub-Saharan African populations in the context of the observed recombination landscape , including the hotspot ( Figure 3D and 3E , Figure S4 ) ., In a more formal evaluation of the results , we quantified the empirical probability for each scenario and neutrality test as well as for different combinations of tests by using composite scores encompassing at least three complementary signatures of positive selection:, ( i ) site frequency spectrum ,, ( ii ) population differentiation , and, ( iii ) haplotype structure ., The scenario of “weak selection ( s\u200a=\u200a0 . 005 ) + hotspot” is the most likely among the different ones tested ( Table S2 ) ., Moreover , all the empirical likelihoods calculated for the different composite scores indicate that the proposed scenario of “weak selection ( s\u200a=\u200a0 . 005 ) + hotspot” is more likely than the neutral scenario ( Table 2 ) ., Therefore , our simulation results indicate that the atypical patterns of selection in the ZIP4 gene can indeed be explained by positive selection having acted upon the Val372 allele in Sub-Saharan African populations and that recombination has erased further accompanying signatures of the selective sweep ., Selection coefficients lower than the ones tested ( 3 . 0% , 1 . 0% , 0 . 5% ) further dilute the signal of selection in the site frequency spectrum based neutrality tests ( results not shown ) , but require such long duration times of the sweep that would substantially predate the population split between African and Eurasian populations ., We observed that the Leu372Val polymorphism affects a highly conserved amino acid ( Figure 4 ) and that the same codon position has been altered in acrodermatitis patients carrying missense mutations Leu372Arg 32 and Leu372Pro 8 ., Moreover , both PolyPhen 33 and SIFT 34 algorithms predict functional effects for the Leu372Val substitution ( see Table 1 ) ., These observations led us to test the Leu372Val polymorphism for a possible functional change in the ZIP4 transporter , using transiently transfected HeLa cells ., To be able to control for possible haplotypic effects between the two most highly differentiated non-synonymous SNPs in the ZIP4 transporter , we also considered variation at the Thr357Ala polymorphism in the functional analyses ., Furthermore , we introduced the pathological mutations Leu372Arg and Leu372Pro in the Ala357 background of the human ZIP4 gene and analyzed them as well ., The pathological impact of the Leu372Pro mutation on ZIP4 protein biology and function has already been evaluated in the mouse ZIP4 protein 10 , but not the Leu372Arg mutation ., Besides providing confirmation of their impact in the context of the human gene , the use of these pathological mutations provided us with an extreme phenotype to which to compare the phenotype associated with the ZIP4 non-synonymous polymorphisms ., In all cases , functional analyses were carried out to determine effects on expression , subcellular localization , and zinc transport ., As shown in Figure 5 , human ZIP4 proteins carrying the Leu372Pro and Leu372Arg mutations showed an absence of surface protein expression ( P<0 . 001 , one way ANOVA versus the Ala357-Leu372 isoform ) , consistent with the known causal role of these variants in the zinc deficiency disorder , acrodermatitis enteropathica ., Interestingly , the derived Val372 variant also showed significantly decreased surface expression , but to a much lesser extent , and independently of the Thr357Ala substitution ( P<0 . 05 in both Ala357 and Thr357 backgrounds; one way ANOVA versus the Ala357-Leu372 isoform ) ., Overall , the Leu372Val substitution had a highly significant effect on surface expression ( ANOVA , p\u200a=\u200a0 . 00021 ) , while there was no effect ascribable to the Thr357Ala replacement ( p\u200a=\u200a0 . 579 ) ., Western blot analysis of all isoforms revealed a remarkable decrease in detection of the Ala357-Pro372 isoform ( Figure S5A ) ., However , the reduced expression of this isoform was not due to a defect in the construct sequence but to a higher protein degradation rate , as shown in Figure S5B ., Further analysis showed that the Ala357-Leu372 and Ala357-Val372 isoforms do not differ in protein degradation rate ., Therefore , the differences in the surface expression experiment must be due to a different trafficking pattern of these variants ., In this sense , co-localization of ZIP4 with calnexin ( a protein present in the lumen of the endoplasmic reticulum ) indeed showed that those proteins presenting lower surface expression were partially retained in the endoplasmic reticulum ( Figure S6 ) ., Zinc transport analysis of the different ZIP4 isoforms was performed in two ways ., First , we quantified basal zinc content with FluoZin-3 in HeLa cells overexpressing the various ZIP4 variants during a 24-hour period ( Figure 6A ) , and second , we recorded intracellular zinc uptake upon perfusion with an external solution containing 200 µM Zn2+ ( Figure 6B ) ., Our results show that basal zinc content in cells overexpressing pathological variants Pro372 and Arg372 did not differ from surrounding non-transfected HeLa cells ., On the contrary , all common ZIP4 variants ( Ala357 , Thr357 , Leu372 and Val372 ) promoted increased intracellular zinc levels ., However , and in agreement with their reduced surface expression , Val372 variants ( in both Ala357 and Thr357 backgrounds ) presented lower basal zinc content compared to Leu372 ( P<0 . 01 and P<0 . 05 , respectively; one way ANOVA versus the Ala357-Leu372 isoform; Figure 6A ) ., As shown in Figure 6B , cells overexpressing the pathological Leu372Arg and Leu372Pro mutations did not uptake zinc , consistent with their inability to traffic to the plasma membrane ., Zinc uptake mediated by the Val372 variants was also consistent with their reduced membrane expression; i . e . the Val372 variants in both Ala357 and Thr357 backgrounds presented significantly lower maximum transport ( Tmax ) compared to the Leu372 variant ( P<0 . 01 in each case; Figure 6B ) ., However , the time to reach half-maximal transport ( t1/2 ) showed no significant difference , indicating that transport kinetics were not markedly different among the four common variants ( Figure 6 ) ., Overall , these results support the idea that the Val372 variant does not disturb the kinetics of the ZIP4 transporter but leads to lower zinc uptake transport due to reduced surface expression ., Our study was triggered by the observation of extreme population differentiation between Sub-Saharan African and non-African populations involving the Leu372Val polymorphism in the ZIP4 gene , unaccompanied by any other signals of a classic hard sweep , such as long extended haplotype homozygosity , in either population ( Figures S3 , S7 and S8 ) ., By interrogating and compiling allele frequencies in more than 100 worldwide human populations , we further characterized the extreme population differentiation of the Leu372Val polymorphism and confirmed that this result was not an artifact of allele switching 15 ., Given the worldwide distribution of the human derived and ancestral alleles ( confirmed by sequencing a Neanderthal and phylogenetic conservation ) , we conclude that this sweep must have taken place within Africa , probably in Sub-Saharan Africa , and not outside the African continent ., Notably , the extreme population differentiation of the Leu372Val polymorphism represents the top fourth region within the global genome-wide FST distribution between CEU-YRI obtained from the 1000 Genomes Project data ., The only CEU-YRI FST values that are more extreme all involve well-known examples of local geographical adaptation in humans: the SLC24A5 and SLC45A2 genes ( with an FST of 0 . 9826 and 0 . 9765 , respectively ) , which have been associated with light skin pigmentation in Europeans; and the DUFFY gene ( with an FST of 0 . 9765 ) , which provides resistance to the malaria pathogen Plasmodium vivax ., Moreover , with the notable exception of DUFFY FY*O allele 35 , 36 , most of the extreme FST values obtained when comparing Africans with non-Africans are usually attributed to local adaptation outside of Africa ., Our detection of such a rare signature of natural selection in the African continent is therefore quite remarkable ., Interestingly , it is congruent with a recent study that has found only limited evidence for classical sweeps in African populations , which is likely due to a combination of limitations of the currently used methodology and specific characteristics of African population history 37 ., Notably , we observed a nearly complete but mild selective sweep for the Val372 variant in Africa , which involves three SNPs with extremely high population differentiation , whereas most other commonly used tests for selection show values not even close to genome-wide significance ., Our coalescent simulations indicate that this unusual pattern might be explained by local positive selection in combination with an observed recombination hotspot of moderate strength ., At approximately 7 cM/Mb , the recombination rate is only around 7-fold higher than the genomic background , but the hotspot is extended over 3–4 kb ., Therefore , a similar number of recombination events may accumulate over time corresponding to a more typically sized hotspot of 1 kb and a recombination rate of around 25 cM/Mb ., To our knowledge , this is the first example of a nearly complete selective sweep that is obscured by the effect of a recombination hotspot ., It is compatible with earlier theoretical observations that instances of weaker selection in the presence of recombination may not always have an influence on polymorphism statistics 38 and with the observed effect of recombination on the partial sweep around the malaria-related β-globin gene 39 ., Because of the unclear effects of the recombination hotspot , it was not possible to estimate the age of the sweep using linkage disequilibrium decay related methods ( e . g . 40 ) ., It is likely that a mild selection pressure would have needed a long time to reach the extreme population differentiation values observed , indicating this may be an ancient event ., The fact that the high frequency of the Val372 allele is restricted to Sub-Saharan African populations suggests that the selection process started after the Out of Africa expansion of modern humans ( i . e . sixty thousand years ago ) ., Alternatively , it is also possible that the bottleneck in the Out of Africa expansion did not sample the Val372 allele , which in turn could explain its absence in most non-African populations ., This implies that the Out of Africa event is not a hard upper limit for the age of the selection process ., Other more complex evolutionary scenarios cannot be entirely ruled out , and could warrant a more detailed investigation ., For example:, ( i ) selection acting on standing genetic variation , in the sense that the Val372 variant was already segregating when it came under the influence of local selection;, ( ii ) additional directional selection against the Val372 allele in non-African populations;, ( iii ) selection favoring the Leu372 variant on multiple , geographically independent origins mostly in non-African populations , in addition to positive selection on the Val372 variant in Africa; and, ( iv ) ‘gene surfing’ of any of the two variants on the wave of a population range expansion 41 ., However , we consider it is unnecessary to invoke such complex scenarios in preference to the simpler one we propose based on coalescent simulations ., Moreover , back-and-forth migrations between Sub-Saharan African , Northern African and Middle Eastern populations after the first Out-of-Africa wave of migration 42 could easily explain the observed low-intermediate allele frequencies in Middle Eastern populations without invoking additional selection events ., In the absence of additional linked functional variants in the region , we infer that directional selection has acted on the ZIP4 gene ., This conclusion is supported by:, ( i ) the disease phenotype of acrodermatitis enteropathica , which involves extreme and potentially lethal zinc deficiency and is caused by , among others , diverse mutations at amino acid position 372 in ZIP4 43;, ( ii ) the absence of cellular zinc transport in Leu372Arg and Leu372Pro acrodermatitis mutants;, ( iii ) the finding that the Val372 variant leads to reduced zinc transport at the cellular level; and finally, ( iv ) the conservation of this amino acid position across diverse species ( Figure 4 ) ., Furthermore , we infer that the Leu372Val substitution was the functional site targeted by selection due to its location in the predicted center of selection ( highest FST ) , and since it is the only putative functional polymorphism in the ZIP4 gene ., Of the other two polymorphic variants with somewhat high allele frequency differences between populations , the Thr357Ala substitution ( rs2272662 ) does not show any functional effect and the intronic rs1871535 cannot be associated with any known regulatory function ( according to information on DNAse I hypersensitivity clusters , CpG Islands and transcription factor binding sites available from the ENCODE data ( http://genome . ucsc . edu/ENCODE 44 ) ., Therefore , both rs1871535 and rs2272662 are likely to be neutral ., Other non-synonymous polymorphisms with intermediate allele frequencies in the ZIP4 gene ( Glu10Ala , Ala58Thr , and Ala114Thr ) have very low FST scores and are therefore not considered candidate variants for selection ., Our functional results in transfected HeLa cells indicate that the Val372 form of the ZIP4 receptor has lower relative cell surface expression , despite no expected differences in mRNA expression and protein synthesis ., Interestingly , we found that this decreased expression translated into reduced zinc transport of the derived Val372 variant at the cellular level ., That is , we observed differences in the maximal transport ( Tmax ) with no significant differences in the transport kinetics ( T1/2 ) between Leu372 and Val372 ., The functional results observed in transfected HeLa cells are likely to be transferable to other epithelial cells , in accordance with independent experiments showing an effect of acrodermatitis variants at position 372 on surface expression ( in CHO cells ) and on zinc transport ( in HEK293 cells ) when using mouse cDNA 10 ., However , the critical function of ZIP4 in knockout studies has been shown to primarily affect intestinal zinc uptake 13 ., In contrast to the Leu372Pro and Leu372Arg acrodermatitis mutations , which served as controls and showed an almost complete absence of zinc transport , both the Leu372 and Val372 variants must be capable of carrying out zinc transport in the normal range of concentrations , given their high frequency in the healthy population ., The consequences of this difference in zinc transport at the organ and organismal level are currently unclear , although there is a strong indication that this variant may indeed be phenotypically relevant ., For example , a similar non-synonymous mutation in the porcine homologue of ZIP4 leads to non-pathogenic reduced tissue concentrations of zinc in piglets 45 ., Could the concept of “nutritional immunity” 46 , 47 involving zinc explain a putative selective force in Sub-Saharan Africa ?, According to this hypothesis , the human host restricts access to certain micronutrients , so that pathogens become less virulent ., This is a well-known mechanism of immune defense mediated by iron metabolism 48 , and there are indications that zinc metabolism could have a similar function 47 , 49 ., For example , hypoferremia and hypozincemia are both part of the acute phase response to infection and both seem to be influenced by a different zinc transporter from the same family , ZIP14 50 ., We speculate that the selective force behind the extreme FST pattern of the Leu372Val substitution may be related to pathogens or infectious diseases ., It is known that decreased zinc uptake mediated by ZIP4 leads to decreased zinc concentrations in the major organs , as shown in a mouse knockout model 13 ., While the phenotypic effect of the Val372 allele in humans is currently unknown , we conjecture that the in vitro difference may indeed translate into physiological differences , possibly leading to a slightly decreased uptake of dietary zinc ., Fittingly , there is suggestive evidence that African genetic ancestry may involve lower serum levels of zinc 51 , as African-American children have a fourfold risk of zinc deficiency compared to Hispanic children ., This result would suggest that African ancestry may be associated with lower serum zinc levels , although these results may be biased due to differences in lifestyle , socio-economic status etc . , and this observation would need to be confirmed by controlled studies ., Alternatively , lower zinc concentrations mediated by the Leu372Val substitution in the enterocyte cells could facilitate early diarrheal episodes during a digestive infection in order to reduce the pathogen load on the luminal surface 52 , 53 ., Similarly , the lower level of expression of the ZIP4 isoform carrying the Val372 variant could also be advantageous if any parasite uses the ZIP4 receptor to enter enterocytes ., Furthermore , the selective force may be related to pre-historic differences in dietary zinc due to lifestyle or to local levels of zinc concentrations in soil and the food chain ., No large-scale ethnic comparisons related to serum or tissue zinc concentrations are available ., To our knowledge , rs1871534 has not been tested in case-control studies in African populations related to one of the numerous existing infectious diseases like malaria , trypanosmias or Lhassa fever ., It is therefore possible that important evidence for a possible selective force has been missed ., In future research , the inclusion of additional cell lines , and genotype-phenotype association studies in diverse ethnic populations may help to clarify further phenotypic consequences of this non-synonymous polymorphism ., Genotype-phenotype association studies should involve African-American or East African populations in which the Val372 allele is segregating at intermediate frequencies ., Candidate phenotypes and traits to interrogate could be serum zinc concentrations , zinc content in hair and nails , serum zinc concentrations after controlled zinc supplementation , and a range of disease traits , especially diseases with an elevated risk in different populations , for example , diverse types of cancer in African Americans ., As this SNP was not included in the commonly used Affymetrix and Illumina SNP arrays with up to one million variants ( although it is included in several of the latest arrays ) , potential clinically relevant associations may have been missed ., Interestingly , common polymorphisms in other zinc transporters show genome-wide associations with disease traits , such as a non-synonymous variant in the zinc efflux transporter ZnT8 ( SLC30A8 ) and diabetes incidence 54 , as well as a regulatory variant in the zinc influx transporter ZIP6 ( SLC39A6 ) and survival in esophagal cancer 55 ., The identification of a high-frequency derived allele polymorphism in the ZIP4 zinc transporter gene ( SLC39A4 ) , combined with a more complete picture of worldwide allele frequencies and in-depth coalescent simulations , is consistent with a long lasting selective event in Sub-Saharan Africa driven by a moderate selection coefficient ., This event did not leave the typical footprint of a selective sweep with long haplotypes or detectable neutral deviations in the allele frequency spectrum of the surrounding region , most likely because of the presence of a moderate recombination hotspot ., Through functional experiments we have verified the Leu372Val substitution as the likely causal site ., Given that two functionally different alleles of this key component of cellular zinc uptake are distributed so divergently across worldwide populations , our results may point to functional differences in zinc homeostasis among modern human populations with possible broader relevance for health and disease ., The G and C alleles at rs1871534 ( Leu372Val ) have been swapped in various public sources such as HapMap ( http://www . hapmap . org ) or dbSNP ( http://www . ncbi . nlm . nih . gov/SNP ) that report conflicting allele frequencies in populations with a similar geographical origin ., This situation led us to repeat the genotyping of this SNP in the Human Genome Diversity Panel ( HGDP-CEPH ) 18 ., We also genotyped rs2272662 ( which causes the Thr357Ala substitution ) because , within the ZIP4 gene , it shows the second highest allele frequency differences between CEU and YRI HapMap populations and allele frequencies were not available at the worldwide level ., The rs1871534 and rs2272662 loci were genotyped in the H971 subset 56 of the HGDP-CEPH 18 , representing 51 worldwide populations , and in an additional population from Africa: Pygmies from Gabon ( N\u200a=\u200a39 ) 57 ., We also genotyped rs1871534 in North African populations from Western Sahara ( Saharawi , N\u200a=\u200a50 ) , Morocco ( Casablanca , N\u200a=\u200a30; Rabat , N\u200a=\u200a30; Nador , N\u200a=\u200a30 ) and Libya ( Libyans , N\u200a=\u200a50 ) ., Genotyping was performed using Taqman assays C__11446716_10 and C__26034235_10 on an Applied Biosystems Light Cycler ( 7900HR ) , according to standard protocols ., Additional genotypes for rs1871534 were obtained from the Alfred database ( http://alfred . med . yale . edu ) 26 , 27 ., Informed consent was obtained for all human samples analysed and genotyping analyses were performed anonymously ., The project obtained the ethics approval from the Institutional Review Board of the local institution ( Comitè Ètic dInvestigació Clínica - Institut Municipal dAssistència Sanitària ( CEIC-IMAS ) in Barcelona , Spain ., The El Sidrón Neanderthal sample SD1253 has been used in many paleogenomic studies due to its high endogenous DNA content | Introduction, Results, Discussion, Materials and Methods | Extreme differences in allele frequency between West Africans and Eurasians were observed for a leucine-to-valine substitution ( Leu372Val ) in the human intestinal zinc uptake transporter , ZIP4 , yet no further evidence was found for a selective sweep around the ZIP4 gene ( SLC39A4 ) ., By interrogating allele frequencies in more than 100 diverse human populations and resequencing Neanderthal DNA , we confirmed the ancestral state of this locus and found a strong geographical gradient for the derived allele ( Val372 ) , with near fixation in West Africa ., In extensive coalescent simulations , we show that the extreme differences in allele frequency , yet absence of a classical sweep signature , can be explained by the effect of a local recombination hotspot , together with directional selection favoring the Val372 allele in Sub-Saharan Africans ., The possible functional effect of the Leu372Val substitution , together with two pathological mutations at the same codon ( Leu372Pro and Leu372Arg ) that cause acrodermatitis enteropathica ( a disease phenotype characterized by extreme zinc deficiency ) , was investigated by transient overexpression of human ZIP4 protein in HeLa cells ., Both acrodermatitis mutations cause absence of the ZIP4 transporter cell surface expression and nearly absent zinc uptake , while the Val372 variant displayed significantly reduced surface protein expression , reduced basal levels of intracellular zinc , and reduced zinc uptake in comparison with the Leu372 variant ., We speculate that reduced zinc uptake by the ZIP4-derived Val372 isoform may act by starving certain pathogens of zinc , and hence may have been advantageous in Sub-Saharan Africa ., Moreover , these functional results may indicate differences in zinc homeostasis among modern human populations with possible relevance for disease risk . | Zinc is an essential trace element with many biological functions in the body , whose concentrations are tightly regulated by different membrane transporters ., Here we report an unusual case of positive natural selection for an amino acid replacement in the human intestinal zinc uptake transporter ZIP4 ., This substitution is recognized as one of the most strongly differentiated genome-wide polymorphisms among human populations ., However , since the extreme population differentiation of this non-synonymous site was not accompanied by additional signatures of natural selection , it was unclear whether it was the result of genetic adaptation ., Using computer simulations we demonstrate that such an unusual pattern can be explained by the effect of local recombination , together with positive selection in Sub-Saharan Africa ., Moreover , we provide evidence to suggest functional differences between the two ZIP4 isoforms in terms of the transporter cell surface expression and zinc uptake ., This result is the first genetic indication that zinc regulation may differ among modern human populations , a finding that may have implications for health research ., Further , we speculate that reduced zinc uptake mediated by the derived variant may have been advantageous in Sub-Saharan Africa , possibly by reducing access of a geographically restricted pathogen to this micronutrient . | mutation, haplotypes, genomics, adaptation, genetic mutation, genetic polymorphism, natural selection, genetics, population genetics, evolutionary selection, biology, comparative genomics, evolutionary biology, evolutionary immunology, population biology, evolutionary processes, evolutionary genetics, gene function | null |
journal.pcbi.1006171 | 2,018 | Thalamocortical and intracortical laminar connectivity determines sleep spindle properties | Sleep marks a profound change of brain state as manifested by the spontaneous emergence of characteristic oscillatory activities ., In humans , sleep spindles consist of waxing-and-waning bursts of field potentials oscillating at 11–15 Hz lasting for 0 . 5–3 s and recurring every 5–15 s ., Experimental and computational studies have identified that both the thalamus and the cortex are involved in the generation and propagation of spindles ., Spindles are known to occur in isolated thalamus after decortication in vivo and in thalamic slice recordings in vitro 1 , 2 , demonstrating that the thalamus is sufficient for spindle generation ., In in-vivo conditions , the cortex has been shown to be actively involved in the initiation and termination of spindles 3 as well as the long-range synchronization of spindles 4 5 ., Multiple lines of evidence indicate that spindle oscillations are linked to memory consolidation during sleep ., Spindle density is known to increase following training in hippocampal-dependent 6 as well as procedural memory 7 memory tasks ., Spindle density also correlates with better memory retention following sleep in verbal tasks 8 , 9 ., More recently , it was shown that pharmacologically increasing spindle density leads to better post-sleep performance in hippocampal-dependent learning tasks 10 ., Furthermore , spindle activity metrics , including amplitude and duration , were predictive of learning performance 11–13 , suggesting that spindle event occurrence , amplitude , and duration influence memory consolidation ., In human recordings , spindle occurrence and synchronization vary based on the recording modality ., Spindles recorded with magnetoencephalography ( MEG ) are more frequent and less synchronized , as compared to those recorded with electroencephalography ( EEG ) 14 ., It has been proposed that the contrast between MEG and EEG spindles reflects the differential involvement of the core and matrix thalamocortical systems , respectively 15 ., Core projections are focal to layer IV , whereas matrix projections are widespread in upper layers 16 ., This hypothesis is supported by human laminar microelectrode data which demonstrated two spindle generators , one associated with middle cortical layers and the other superficial 17 ., Taken together , these studies suggest that there could be two systems of spindle generation within the cortex and that these correspond to the core and matrix anatomical networks ., However , the network and cellular mechanisms whereby the core and matrix systems interact to generate both independent and co-occurring spindles across cortical layers are not understood ., In this study , we developed a computational model of thalamus and cortex that replicates known features of spindle occurrence in MEG and EEG recordings ., While our previous efforts have been focused on the neural mechanisms involved in the generation of isolated spindles5 , in this study we identified the critical mechanisms underlying the spontaneous generation of spindles across different cortical layers and their interactions ., Histograms of EEG and MEG gradiometer inter-spindle intervals are shown in Fig 1C ., For neither channel type are ISIs distributed normally as determined by Lilliefors tests ( D2571 = 0 . 1062 , p = 1 . 0e-3 , D4802 = 0 . 1022 , p = 1 . 0e-3 ) , suggesting that traditional descriptive statistics are of limited utility ., However , the ISI at peak of the respective distributions is longer for EEG than it is the MEG ., In addition , a two-sample Kolmogorov-Smirnov test confirms that EEG and MEG ISIs are not drawn from the same distribution ( D2571 , 4802 = 0 . 079 , p = 1 . 5e-9 ) ., While the data where not found to be drawn from any parametric distribution with 95% confidence , an exponential fit ( MEG ) and lognormal fit ( EEG ) are shown in red overlay for illustrative purposes ., These data are consistent with previous empirical recordings 18 and suggest that sleep spindles have different properties across superficial vs . deep cortical layers ., To investigate the mechanisms behind distinct spindle properties across cortical locations as observed in EEG and MEG signals , we constructed a model of thalamus and cortex that incorporated the two characteristic thalamocortical systems: core and matrix ., These systems contained distinct thalamic populations that projected to the superficial ( matrix ) and middle ( core ) cortical layers ., Four cell types were used to model distinct cell populations: thalamocortical relay ( TC ) and reticular ( RE ) neurons in the thalamus , and excitatory pyramidal ( PY ) and inhibitory ( IN ) neurons in each of three layers of the cortical network ., A schematic representation of the synaptic connections and cortical geometry of the network model is shown in Fig 2 ., In the matrix system , both thalamocortical ( from matrix TCs to the apical dendrites of layer 5 pyramidal neurons ( PYs ) located in the layer 1 ) and corticothalamic synapses ( from layer 5 PYs back to the thalamus ) formed diffuse connections ., The core system had a focal connection pattern in both thalamocortical ( from core TCs to PYs in the layer III/IV ) and corticothalamic ( from layer VI PYs to the thalamus ) projections ., Because spindles recorded in EEG signal reflect the activity of superficial layers while MEG records spindles originating from deeper layers ( Fig 1 and 19 ) , we compared the activity of the model’s matrix system , which has projections to the superficial layers , to empirical EEG recordings and compared the activity in model layer 3/4 to empirical MEG recordings ., In agreement with our previous studies 3 , 5 , 20 , 21 , simulated stage 2 sleep consisted of multiple spindle events involving thalamic and cortical neuronal populations ( Fig 3 ) ., During one such typical spindle event ( highlighted by the box in Fig 3A and 3B ) , cortical and thalamic neurons in both the core and matrix system had elevated and synchronized firing ( Fig 3A bottom ) , consistent with previous in-vivo experimental recordings 22 ., In the model , spindles within each system were initiated from spontaneous activity within cortical layers and then spread to thalamic neurons , similar to our previous study5 ., The spontaneous activity due to miniature EPSPs in glutamergic cortical synapses led to fluctuations in membrane voltage and sparse firing ., At random times , the miniature EPSPs summed such that a small number of locally connected PY neurons spiked within a short window ( <100ms ) , which then induced spiking in thalamic cells through corticothalamic connections ., This initiated spindle oscillations in the thalamic population mediated by TC-RE interactions as described before 20 , 23 , 24 ., Thalamic spindles in turn propagated to the neocortex leading to joint thalamocortical spindle events whose features were shaped by the properties of thalamocortical and corticothalamic connections ., In this study , we examined how the process of spindle generation occurs in a thalamocortical network with mutually interacting core and matrix systems , wherein the thalamic network of each system is capable of generating spindles independently ., Based on the anatomical data 16 , the main difference between the modeled core and matrix systems was the radii or fanout of connections in thalamocortical and corticothalamic projections ( in the baseline model , the fanout was 10 times wider for the matrix compared to the core system ) ., Furthermore , the strength of each synaptic connection was scaled by the number of input connections to each neuron 25 , 26 , leading to weaker individual thalamocortical projections in the matrix as compared to the core ., These differences in the strength and fanout of thalamocortical connectivity resulted in distinctive core and matrix spindle properties ( see Fig 3A , right vs left ) ., First , both cortical and thalamic spindles were more spatially focal in the core system as only a small subset of neurons was involved in a typical spindle event at any given time ., In contrast , within the matrix system spindles were global ( involving the entire cell population ) and highly synchronous across all cell types ., These results are consistent with our previous studies 5 and suggest that the connectivity properties of thalamocortical projections determine the degree of synchronization in the cortical network ., Second , spindle density was higher in the core system compared to the matrix system ., At every spatial location in the cortical network of the core system , the characteristic time between spindles was shorter compared to that between spindles in the matrix system ( Fig 3A left vs right ) ., In order to quantify the spatial and temporal properties of spindles , we computed an estimated LFP as an average of the dendritic synaptic currents for every group of contiguous 100 cortical neurons ., LFPs of the core system were estimated from the currents generated in the dendrites of layer 3/4 neurons while the LFP of the matrix system was computed from the dendritic currents of layer 5 neurons , located in the superficial cortical layers ( Fig 2 ) ., After applying a bandpass filter ( 6–15 Hz ) , the spatial properties of estimated core and matrix LFP ( Fig 3C ) closely matched the MEG and EEG recordings , respectively ( Fig 1 ) ., In subsequent analyses , we used this estimated LFP to further examine the properties of the spindle oscillations in the core and matrix systems ., We identified spindles in the estimated LFP using an automated spindle detection algorithm similar to that used in experimental studies ( details are provided in the method section ) ., The spindle density , defined as the number of spindles occurring per minute of simulation time , was greater in the core compared to the matrix ( Fig 4A ) as confirmed by an independent-sample t-test ( t ( 18 ) = 7 . 06 , p<0 . 001 for across estimated LFP channels and t ( 2060 ) = 19 . 2 , p<0 . 001 across all spindles ) ., The results of this analysis agree with the experimental observation that MEG spindles occur more frequently than EEG spindles ., While the average spindle density was significantly different between the core and matrix , in both systems the distribution of inter-spindle intervals peaks below 4 seconds and has a long tail ( Fig 4B ) ., A two sample KS test comparing the distributions of inter-spindle intervals confirmed that the intervals were derived from different distributions ( D1128 , 932 = 0 . 427 , p<0 . 001 ) ., The peak ISI of the core was shorter than that of the matrix system , suggesting that the core network experiences shorter and more frequent quiescence periods than the matrix population ., Furthermore , maximum-likelihood fits of the probability distributions ( red line in Fig 4B ) confirmed that the intervals of spindle occurrence cannot be described by a normal distribution ., The long tails of the distributions suggest that a Poisson like process , as oppose to a periodic process , is responsible for spindle generation ., This observation is consistent with previous experimental results 18 , 27 and suggests that our computational model replicates essential statistical properties of spindles observed in in vivo experiments ., Several other features of simulated core and matrix spindles were similar to those found in experimental recordings ., The average spindle duration was significantly higher in the core compared to the matrix system ( Fig 4C ) as confirmed by independent-sample t-test ( t ( 2060 ) = 16 . 3 , p<0 . 001 ) ., To quantify the difference in the spatial synchrony of spindles between the core and matrix systems , we computed the spatial correlation 28 between LFP groups at different distances ( measured by the location of a neuron group in the network ) ., The correlation strength decreased with distance for both systems ( Fig 4D ) ., However , the spindles in the core system were less spatially correlated overall when compared to spindles in the matrix system ., Simultaneous EEG and MEG measurements have found that about 50% of MEG spindles co-occur with EEG spindles , while about 85% of EEG spindles co-occur with MEG spindles 29 ., Further , a spindle detected in the EEG signal is found to co-occur with about 66% more MEG channels than a spindle detected in MEG ., Our model generates spindling patterns consistent with these features ., The co-occurrence probability revealed that during periods of spindles in the matrix system , there was about 80% probability that core was also generating spindles ( Fig 4E ) ., In contrast , there was only a 40% probability of observing a matrix spindle during a core system spindle ., An independent-sample t-test confirmed this difference between the systems across estimated LFP channels ( t ( 14 ) = 31 . 4 , p<0 . 001 ) ., Furthermore , we observed that the number of LFP channels that were simultaneously activated during a spindle event in the core system was higher when a spindle co-occurred in the matrix versus times when the spindles only occurred in the core ( Fig 4F , t ( 14 ) = 67 . 2 , p<0 . 001 ) ., This suggests that the co-occurrences of spindles in both systems are rare events but lead to the wide spread activation in both the core and matrix when they take place ., Finally , we examined the delay between spindles in the core and matrix systems ( Fig 4G ) ., We observed that on average ( red line in Fig 4G ) , the spindle originated from the core system then spread to the matrix system with a mean delay of about 300 ms ( delay was measured as the difference in onset times between co-occurring spindles within a window of 2 , 500 ms; negative delay values indicate spindles in which the core preceded matrix ) ., The peak at -750 ms corresponds to spindles originating from the core system , while the peak at +750 ms suggests that at some network sites , spindles originated in the matrix system and then spread to the core system ., While there were almost no events in which the matrix preceded the core by more than 1 sec ( right of Fig 4G ) , many events occurred in which the core preceded the matrix by more than 1 sec ( left of Fig 4G ) ., In sum , these results suggest that spindles were frequently initiated locally in the core system , then propagate to and spread throughout the matrix system ., This can trigger spindles at the other locations of the core , so eventually , even regions in the core system that were not previously involved become recruited ., These findings explain the experimental result that spindles are observed in more MEG channels when they also co-occur in the EEG 29 ., We leveraged our model to examine factors that may influence spindle occurrence across cortical layers ., The main difference between the core and matrix systems in the model was the breadth or fanout of the thalamic projections to the cortical network ., Neuroanatomical studies suggest that the core system has focused projections while matrix system projects widely 16 ., Here , we assessed the impacts of this characteristic by systematically varying the connection footprint of the thalamic matrix to superficial cortical regions , while holding the fanout of the thalamic core to layer 3/4 projections constant ., We also modulated the corticothalamic projections in proportion to the thalamocortical projections ., Using the estimated LFP from the cortical layers corresponding to core and matrix system , respectively , we quantified various spindle properties as the fanout was modulated ., Spindle density ( the number of spindles per minute ) in both layers was sensitive to the matrix system’s fanout ., ANOVA confirmed significant effects of fanout and layer location , as well as an interaction between layer and fanout ( fanout: F ( 6 , 112 ) = 66 . 4; p<0 . 01 , Layer: F ( 1 , 112 ) = 65 . 18; p<0 . 01 and interaction F ( 6 , 112 ) = 22 . 8; p<0 . 01 ) ., When the matrix and core thalamus had similar fanouts ( ratio 1 and 2 . 5 in Fig 5B ) , we observed a slightly higher density of spindles in the matrix than in the core system ., This observation is consistent with the properties of these circuits ( see Fig 2 ) , wherein the matrix system contains direct reciprocal projections connecting cortical and thalamic subpopulations and the core system routes indirect projections from cortical ( layer III/IV ) neurons through layer VI to the thalamic nucleus ., When the thalamocortical fanout of the matrix system was increased to above ~5 times the size of the core system , the density of spindles in the matrix system was reduced to around 4 spindles per minute ., Interestingly , the density of spindles in the core system was also reduced when the thalamocortical fanout of the matrix system was further increased to above ~10 times of that in the core system ( ratio above 10 in Fig 5B ) ., This suggests that spindle density in both systems is determined not only by the radius of thalamocortical vs . corticothalamic projections , but also by interactions between the systems among the cortical layers ., We further expound on the role of these cortical connections in the next section ., We also examined the effect of thalamocortical fanout on the distribution of inter-spindle intervals ( Fig 5C ) ., Although the mean value was largely independent of the projection radius , a long tailed distribution was observed for all values of fanout in the core ., Contrastingly , in the matrix system the mean and peak of the inter-spindle interval shifted to the right ( longer intervals ) with increased fanout ., With large fanouts , the majority of matrix system spindles had very long periods of silence ( 10-15s ) between them ., This suggests that thalamocortical fanout determines the peak of the inter-spindle interval distribution , but does not alter the stochastic nature of spindle occurrence ., The degree of thalamocortical fanout also influenced the co-occurrence of spindles in the core and matrix systems ( Fig 5D ) ., Increasing the fanout of the matrix system reduced spindle co-occurrence between two systems ., This reduction resulted mainly from lower spindle density in both layers ., However , the co-occurrence of core spindles during matrix spindles was higher for all values of fanout when matrix thalamocortical projections were at least 5 times broader than core projections ., This suggests that the difference in spindle co-occurrence between EEG and MEG as observed in experiments 14 depends mainly on the difference in the radius of thalamocortical projections between the core and matrix systems , while overall level of co-occurrence is determined by the interaction between cortical layers ., We examined how spatial correlations during periods of spindles vary depending on the fanout of thalamocortical projections ., The spatial correlation quantifies the degree of synchronization in the estimated LFP signals of network locations as a function of the distance between them ., As expected , increasing the distance reduced the spatial correlation ( Fig 4D ) ., We next measured the mean value of the spatial correlation for each fanout condition ., The mean correlation increased as a function of the fanout in the matrix system ( Fig 5A ) ., However , the spatial correlation within the core , and between the core and matrix systems , did not change with increases in the fanout , suggesting that the spatial synchronization of core spindles is largely influenced by thalamocortical fanout but not by interactions between the core and matrix systems as was observed for spindle density ., Does intra-cortical excitatory connectivity between layer 3/4 of the core system and layer 5 of the matrix system affect spindle occurrence ?, To answer this question , we first varied the strength of excitatory connections ( AMPA and NMDA ) from the core to matrix pyramidal neurons ( Fig 6A and 6B ) ., Here the reference point ( or 100% ) corresponds to the strength used in previous simulations , i . e . half the strength of a within-layer connection ., The spindle density varied with the strength of the interlaminar connections ( Fig 6A ) ., For low connectivity strengths ( below 100% ) , the spindle density of the matrix system was reduced significantly , while at high strengths ( above 140% ) the matrix system spindle density exceeded that of the control ( 100% ) ., There were significant effects of connection strength and layer on the spindle density , as well as an interaction between the two factors ( connection strength: F ( 5 , 96 ) = 24 . 7; p<0 . 01 , layer: F ( 5 , 96 ) = 386 . 6; p<0 . 01 and interaction F ( 5 , 96 ) = 36 . 9; p<0 . 01 ) that suggests a layer-specific effect of modulating excitatory interlaminar connection strength ., Similar to the spindle density , spindle co-occurrence between the core and matrix systems also increased as a function of interlaminar connection strength , reaching 80% for the both core and matrix at 150% connectivity ., In contrast , changing the strength of excitatory connections from layer 5 to layer 3/4 had little effect on the spindle density , ( Fig 6C ) ., Taken together , these results suggest that the strength of the cortical core-to-matrix excitatory connections is one of the critical factors in determining spindle density and co-occurrence among spindles across both cortical lamina and the core/matrix systems ., Using computational modeling and data from EEG/MEG recordings in humans we found that the properties of sleep spindles vary across cortical layers and are influenced by thalamocortical , corticothalamic and cortico-laminar connections ., This study was motivated by empirical findings demonstrating that spindles measured in EEG have different synchronization properties from those measured in MEG 14 , 29 ., EEG spindles occur less frequently and more synchronously in comparison to MEG spindles ., Our new study confirms the speculation that anatomical differences between the matrix thalamocortical system , which has broader projections that target the cortex superficially , and the core system , which consists of focal projections which target the middle layers , can account for the differences between EEG and MEG signals ., Furthermore , we discovered that the strength of corticocortical feedforward excitatory connections from the core to matrix neurons determines the spindle density in the matrix system , which predicts a specific neural mechanism for the interactions observed between MEG and EEG spindles ., There were several novel findings in this study ., First , we developed a novel computational model of sleep spindling in which spindles manifested as a rare but global synchronous occurrence in the matrix pathway and a frequent but local occurrence in the core pathway ., In other words , many spontaneous spindles occurred locally in the core system but only occasionally did this lead to globally organized spindles appearing in the matrix system ., As a result , only a fraction of spindles co-occurred between the pathways ( about 80% in matrix and 40% in core pathway ) ., This is consistent with data reported for EEG vs MEG in vivo ( Fig 1 ) ., In contrast , in our previous models 3 , 5 , spindles were induced by external stimulation and always occurred simultaneously in the core and matrix systems , but with different degrees of internal synchrony ., In addition , these studies did not examine how the core and matrix pathways interact during spontaneously occurring spindles ., Second , in this study we found that the distribution of the inter-spindle intervals between spontaneously occurring spindles in both the core and matrix pathways had long tails similar to a log-normal distribution ., This result is consistent with analyses of MEG and EEG data reported in this study and in our prior study 18 ., In our previous models 3 , 5 , spindles were induced by external stimulation and the statistics of spontaneously occurring spindles could not be explored ., Third , we demonstrated that the strength of thalamocortical and corticothalamic connections determined the density and occurrence of spontaneously generated spindles ., The spindle density was higher in the core pathway as compared to the matrix pathway with high co-occurrence of core spindles with matrix spindles ., These findings were corroborated with experimental evidence from EEG/MEG recordings ., Finally , we reported that laminar connections between the core and matrix could be a significant factor in determining spindle density , suggesting a possible mechanism of learning ., When the strength of these connections was increased in the model , there was a significant increase in spindle occurrence , similar to the experimentally observed increase in spindle density following recent learning 10 ., The origin of sleep spindle activity has been linked to thalamic oscillators based on a broad range of experimental studies 2 , 30 , 31 ., The excitatory and inhibitory connections between thalamic relay and reticular neurons are critical in generating spindles 20 , 23 , 32 , 33 ., However , in intact brain , the properties of sleep spindles are also shaped by cortical networks ., Indeed , the onset of a spindle oscillation and its termination are both dependent on cortical input to the thalamus 3 , 34 , 35 ., In model studies , spindle oscillations in the thalamus are initiated when sufficiently strong activity in the cortex activates the thalamic network , and spindle termination is partially mediated by the desynchronization of corticothalamic input towards the end of spindles 3 , 32 ., However , in simultaneous cortical and thalamic studies in humans , thalamic spindles were found to be tightly coupled to a preceding downstate , which in turn was triggered by converging cortical downstates 36 ., Further modeling is required to reconcile these experimental results ., In addition , thalamocortical interactions are known to be integral to the synchronization of spindles 5 , 33 ., In our new study , the core thalamocortical system revealed relatively high spindle density produced by focal and strong thalamocortical and corticothalamic projections ., Such a pattern of connectivity between core thalamus and middle cortical layers allowed input from a small region of the cortex to initiate and maintain focal spindles in the core system ., In contrast , the matrix system had relatively weak and broad thalamocortical connections requiring synchronized activity in broader cortical regions in order to initiate spindles in the thalamus ., We previously reported 5 that ( 1 ) within a single spindle event the synchrony of the neuronal firing is higher in the matrix than in the core system; ( 2 ) spindle are initiated in the core and with some delay in the matrix system ., The overal density of core and matrix spindle events was , however , the same in these earlier models ., In the new study we extended these previous results by explaining differences in the global spatio-temporal structure of spindle activity between the core and matrix systems ., Our new model predicts that the focal nature of the core thalamocortical connectivity can explain the more frequent occurrence of spindles in the core system as observed in vivo ., The strength of core-to-matrix intracortical connections determined the probability of core spindles to “propagate” to the matrix system ., In our new model core spindles remained localized and have never involved the entire network , again in agreement with in vivo data ., We observed that the distribution of inter-spindle intervals reflects a non-periodic stochastic process such as a Poisson process , which is consistent with previous data 18 , 27 ., The state of the thalamocortical network , determined by the level of the intrinsic and synaptic conductances , contributed to the stochastic nature of spindle occurrence ., Building off our previous work 21 , we chose the intrinsic and synaptic properties in the model that match those in stage 2 sleep , a brain state when concentrations of acetylcholine and monoamines are reduced 37–39 ., As a consequence , the K-leak currents and excitatory intracortical connections were set higher than in an awake-like state due to the reduction of acetylcholine and norepinephrine 40 ., The high K-leak currents resulted in sparse spontaneous cortical firing during periods between spindles with occasional surges of local synchrony sustained by recurrent excitation within the cortex that could trigger spindle oscillations in the thalamus ., Note that this mechanism may be different from spindle initiation during slow oscillation , when spindle activity appears to be initiated during Down state in thalamus 35 ., Furthermore , the release of miniature EPSPs and IPSPs in the cortex was implemented as a Poission process that contributed to the stochastic nature of the baseline activity ., All these factors led to a variable inter-spindle interval with long periods of silence when activity in the cortex was not sufficient to induce spindles ., While it is known that an excitable medium with noise has a Poisson event distribution in reduced systems 41 , here we show that a detailed biophysical model of spindle generation may lead to a Poission process due to specific intrinsic and network properties ., Layer IV excitatory neurons have a smaller dendritic structure compared to Layer V excitatory neurons 42 ., Direct recordings and detailed dendritic reconstructions have shown large post-synaptic potentials in layer IV due to core thalamic input 42 , 43 ., We examined the role of thalamocortical and corticothalamic connections in a thalamocortical network with only one cortical layer ( S1 Fig ) ., We found that increasing the synaptic strength of thalamocortical and corticothalamic connections both increased the density and duration of spindles , however it did not influence their synchronization ( S1A Fig ) ., In contrast , changing fanout led to an increase in spindle density , duration , and synchronization ., Furthermore , we examined the impact of thalamocortical and corticothalamic connections individually without applying a synaptic normalization rule ( see Methods ) ., We observed that the thalamocortical connections had a higher impact on spindle properties than corticothalamic connections ( S1B Fig ) ., In our full model with multiple layers , which included a weight normalization rule and wider fanout of the matrix pathway ( based on experimental findings16 ) , the synaptic strength of each thalamocortical synapse in the core pathway was higher than that in the matrix pathway ., The exact value of the synaptic strength was chosen from the reduced model to match experimentally observed spindle durations , as observed in EEG/MEG and laminar recordings 17 ., The simultaneous EEG and MEG recordings reported here and in our previous publications 14 , 29 revealed that, ( a ) MEG spindles occur earlier compared to the EEG spindles and, ( b ) EEG spindles are seen in a higher number of the MEG sensors compared to the spindles occurring only in the MEG recordings ., This resembles our current findings , in which the number of regions that were spindling in the core system during a matrix spindle was higher than when there was no spindle in the matrix system ., Further , the distribution of spindle onset delays between the systems indicates that during matrix spindles some neurons of the core system fired early , and presumably contributed to the initiation of the matrix spindle , while others fired late and were recruited ., Taken together , all the evidence suggests a characteristic and complex spatiotemporal evolution of spindle activity during co-occurring spindles , where spindles in the core spread to the matrix and in turn activate wider regions in the core leading to synchronized activation across cortical layers that is reflected by strong activity in both EEG and MEG ., Thus , the model predicts that co-occurring spindles could lead to the recruitment of the large cortical areas , which indeed has been reported in previous studies 28 , 44 ., At the same time , local spindles occurring in the model within deep cortical l | Introduction, Results, Discussion, Materials and methods | Sleep spindles are brief oscillatory events during non-rapid eye movement ( NREM ) sleep ., Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance , suggesting spindle involvement in memory consolidation ., Here , using computational models , we identified network mechanisms that may explain differences in spindle properties across cortical structures ., First , we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems ., The matrix system , projecting superficially , has wider thalamocortical fanout compared to the core system , which projects to middle layers , and requires the recruitment of a larger population of neurons to initiate a spindle ., This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers , as observed in the EEG signal ., In contrast , spindles in the core system occurred more frequently but less synchronously , as observed in the MEG recordings ., Furthermore , consistent with human recordings , in the model , spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles ., We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system , leading to widespread spindle activity ., Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning . | The density of sleep spindles has been shown to correlate with memory consolidation ., Sleep spindles occur more often in human MEG than EEG recordings ., We developed a thalamocortical network model that is capable of spontaneous generation of spindles across cortical layers and that captures the essential statistical features of spindles observed empirically ., Our study predicts that differences in thalamocortical connectivity , known from anatomical studies , are sufficient to explain the differences in the spindle properties between EEG and MEG which are observed in human recordings ., Furthermore , our model predicts that intracortical connectivity between cortical layers , a property influenced by sleep preceding learning , increases spindle density ., Results from our study highlight the role of intracortical and thalamocortical projections on the occurrence and properties of spindles . | learning, medicine and health sciences, sleep, brain electrophysiology, brain, electrophysiology, social sciences, neuroscience, learning and memory, physiological processes, clinical medicine, cognitive psychology, brain mapping, network analysis, bioassays and physiological analysis, neuronal dendrites, neuroimaging, electroencephalography, research and analysis methods, computer and information sciences, imaging techniques, clinical neurophysiology, animal cells, electrophysiological techniques, thalamus, cellular neuroscience, psychology, cell biology, anatomy, physiology, neurons, biology and life sciences, cellular types, magnetoencephalography, cognitive science, neurophysiology | null |
journal.pcbi.1000670 | 2,010 | A Kinetic Model of Dopamine- and Calcium-Dependent Striatal Synaptic Plasticity | The present study reviews electrophysiological studies on corticostriatal synapse plasticity of medium spiny neurons and molecular biological studies focused on intracellular signaling cascades involved in this plasticity ., All signaling pathway reactions shown in Fig . 2 are represented by binding and enzymatic reactions ., Binding reaction of molecule A and molecule B to form molecule AB ( 1 ) where and are rate constants for forward and backward reactions , is simulated by the ordinary differential equation: ( 2 ) The rate constants and were related to the dissociation constant and the time constant , i . e . , and ., An enzymatic reaction of substrate S with enzyme E to produce product P was simulated by a collection of two elementary processes:, 1 ) enzyme E bound to substrate S to form the enzyme-substrate complex ES; and, 2 ) the complex ES dissociated into enzyme E and product P . The chemical equation can be written as ( 3 ) The Michaelis-Menten formulation was avoided due to problems with the steady-state assumption 21 , 43 ., However , many papers and databases have provided only and the Michaelis constant rather than and ., In such cases , it was assumed that was four times larger than ( i . e . and ) , based on the default setting in GENESIS/Kinetikit simulator ., ( Tables S1 , S2 , S3 ) ., Postsynaptic spines receive two presynaptic inputs: glutamatergic terminals from the cerebral cortex and dopaminergic terminals from the substantia nigra pars compacta ., Plasticity of corticostriatal synaptic input results from phosphorylation of AMPA-type glutamatergic receptors , which promotes insertion into the postsynaptic membrane 20 , 44 , 45 ., Below , the pathways linking glutamatergic and dopaminergic input to phosphorylation of AMPA receptors are delineated ., The above-described signaling cascade , which links glutamatergic and dopaminergic inputs to AMPA receptor regulation , includes multiple excitatory and inhibitory pathways and feedback loops ., This makes logical or intuitive inference of network behaviors virtually impossible; the outcomes depend on the strength and delay associated with each arrow in the diagram . However , logical or intuitive inference of network behaviors becomes virtually impossible , because the outcomes depend on strength and delay associated with each arrow in the diagram ., This necessitates numerical simulation of a quantitative model of a signaling cascade to understand and prediction the dynamic behavior ., Therefore , the present study designed a kinetic model of the cascade with the concentrations of intracellular calcium and extracellular dopamine as the inputs and AMPA receptor concentration in the postsynaptic membrane as the output ., However the cascade , which links glutamate stimulation to calcium response was not included in this model but will be addressed in a future study ., Similar to most large-scale cascade models , many reactions were adopted from previously published model 21 , 22 , 55 , 66 , 68 , 69 or deposited the DOQCS database 42 ., When available , models of striatal spiny neurons were utilized ( e . g . , DARPP-32 , D1R , and AC5 ) ., Otherwise , Otherwise , hippocampal neuron models were adopted ( e . g . , CaM , CaMKII , PP2B , I-1 , and AMPA receptor ) by assuming that molecular processes are common between different brain areas ., If no previous model was available ( e . g . , PP2A , PP1 , CK1 , and Cdk5 ) , a reaction model was designed based on previous literature ., Because many of the reactions remain poorly understood , a number of assumptions and simplifications were necessary to design the cascade models ., For instance , although DARPP32 contains at least four phosphorylation sites that affect its enzymatic properties , phosphorylation of Ser102 by CK2 , which facilitates phosphorylation of Thr34 by PKA , was not modeled 70 ., This was because the upstream regulation mechanisms for CK2 are now well known ., Therefore , an 8-state model was designed for DARPP-32 , with three phosphorylation sites: Thr34 , Thr75 , and Ser137 ., CK1 activation is required for Cdk5 activation 64 ., Although the cascade linking these two molecules has not yet been identified , a direct pathway from CK1 to Cdk5 has been hyphothesized 23 ., Although reports have described PP1 phosphorylation by Cdk5 71 , a simple model was adopted from the DOQCS database , where only inhibition and disinhibition by I-1 and Thr34 were taken into account 72 ., AMPA receptor trafficking in the postsynaptic membrane was modeled using the state transition diagram shown in Fig . 3 ., AMPA receptors contain two phosphorylation sites - Ser845 phosphorylated by PKA and Ser831 phosphorylated by CaMKII ., Therefore , a serial phosphorylation model was proposed for hippocampal neurons 73 where Ser831 was phosphorylated after Ser 845 phosphorylation ., Initially , the model was tested to determine whether it reproduced known features of calcium- and dopamine-dependent plasticity in medium spiny neurons ., Subsequently , the dynamic characteristics of the model were analyzed to predict effects of experimental manipulation ., The entire model consisted of 72 reactions , with 132 reaction parameters ., Among these , 83 parameters were retrieved from literature and model database ., The remaining 49 parameters were hand-tuned to qualitatively reproduce the following properties: Forms and parameters of all reactions are listed in Tables S1 , S2 , S3 ., Because many of the parameters affected multiple features of the model behavior , it was difficult to specify which parameter was responsible for the replication of each property ., Numerical simulations were implemented by GENESIS/kinetikit ( http://www . genesis-sim . org/GENESIS/ ) ., It was assumed that the postsynaptic spine was a homogeneous volume of ( cubed ) , so that each molecular species concentration represented the state variables ., The two inputs to the cascade model comprised the concentrations of intracellular calcium , which were evoked by cortical glutamatergic input , and extracellular dopamine , which were evoked by nigral dopaminergic input ., The time courses of the concentrations were approximated by the alpha function ( 4 ) which takes a maximum value of 1 when ., The intracellular calcium concentration induced by a train of cortical spikes , which begin at time with inter-spike interval ( ISI ) , was simulated by ( 5 ) where and were the basal level and stimulus amplitude of calcium concentration , respectively ( Fig . 4A ) ., The maximum function , rather than temporal summation , of calcium transients was used to replicate calcium response data from D1R-expressing striatal neurons 76 ., The time constant of the alpha function was 77 , 78 ., spikes at ISI ( 100 Hz ) were simulated and repeated six times with 10-sec intervals ( Fig . 4B ) ., The concentrations used in the simulation were as follows: and ., The extracellular dopamine concentration , which was induced by a single presynaptic spike at time , was simulated by: ( 6 ) where and were the basal level and stimulus amplitude of dopamine concentration , respectively ( Fig . 4C ) ., The time constant of the alpha function was 78 , 79 ., Dopamine input simulation was repeated six times with 10-sec intervals ( Fig . 4D ) ., The concentrations used in the simulation were as follows: and ., The activities of intracellular molecules were simulated in response to four input conditions:, i ) weak calcium input alone ( and ) ;, ii ) strong calcium input alone ( and ) ;, iii ) dopamine input alone ( and ) ., iv ) weak calcium input coincident with dopamine input ( and ) ; The detailed input forms are explained by Eqs ., ( 4 ) – ( 6 ) in Materials and Methods , and the transient time courses are shown in Fig ., 4 . Results are shown in Fig ., 5 . Direct downstream of calcium , CaM ( Fig . 5A ) , PP2B ( Fig . 5B ) , and PP2A ( Fig . 5C ) were moderately activated by weak calcium input ( cyan ) , but more highly activated by strong calcium input ( blue ) ., In contrast , CaMKII ( Fig . 5D ) , which self-phosphorylates , did not respond to weak calcium input ( cyan ) , but responded drastically to strong calcium input ( blue ) ., The differential activation profiles of PP2A , which dephosphorylates AMPA receptors , and CaMKII , which phosphorylates AMPA receptors , can be a source of bi-directional plasticity due to calcium input ., CK1 ( Fig . 5E ) was activated by PP2B , but the response to strong calcium input was saturated due to a self-inhibitory mechanism ., CK1 subsequently activated Cdk5 ( Fig . 5F ) and the Ser137 phosphorylation site of DARPP-32 ( Fig . 5G ) ., Phosphorylation of Thr75 in DARPP-32 ( Fig . 5H ) increased with weak calcium input ( cyan ) via Cdk5 activation ( Fig . 5 ) , but decreased with strong calcium input ( blue ) via PP2A activation ( Fig . 5C ) ., This bi-directional calcium effect on Thr75 was consistent with experiments showing phosphorylation of Thr75 with a glutamate receptor agonist 17 , 18 ., Downstream of the D1Rs , AC5 ( Fig . 5I ) increased with dopamine input , but decreased with strong calcium input due to calcium inhibition ., cAMP concentration ( Fig . 5J ) increased or decreased depending on AC5 activation level , and subsequently slowly decayed ., Phosphorylated PKA ( Fig . 5K ) decreased with weak calcium input ( cyan ) and increased with strong calcium input ( blue ) , mirroring the bi-directional changes of Thr75 ( Fig . 5H ) ., PKA increased at a slower rate with dopamine input ( red ) , subsequent to increased cAMP ., Simultaneous stimulation of weak calcium and dopamine resulted in a bi-phasic response , including an initial dip followed by a sustained elevation ., PDE activation ( Fig . 5L ) was similar to the activation profile of PKA ., Dopamine input ( red ) resulted in increased Thr34 phosphorylation of DARPP-32 ( Fig . 5M ) via PKA activation ., Calcium input ( cyan and blue ) reduced Thr34 phosphorylation due to stronger inhibition by PP2B ., The decreased Thr34 phosphorylation due to calcium input was consistent with experimental results utilizing AMPA and NMDA 17 ., Coincident calcium input ( magenta ) reduced the response of Thr34 to dopamine input ( red ) ., These results were consistent with experimental responses to different levels of dopamine and NMDA inputs 74 ., Dopamine input alone increased phosphorylation of Inhibitor-1 ( I-1 ) ( Fig . 5N ) via PKA activation ., However , I-1 phosphorylation decreased due to either weak or strong calcium input , or simultaneous calcium and dopamine inputs , via PP2B inhibition ., Phosphorylation of PP1 ( Fig . 5O ) was opposite to that of I-1 by dopamine input ( red ) , but similarly phosphorylated by both strong ( blue ) and weak ( cyan ) calcium inputs , even under simultaneous dopamine input ( magenta ) ., Finally , via phosphorylation by CaMKII ( Fig . 5D ) and PKA ( Fig . 5K ) , and dephosphorylation by PP2A ( Fig . 5C ) and PP1 ( Fig . 5O ) , AMPA receptor phosphorylation at Ser845 decreased due to weak calcium input , but increased due to strong calcium input and simultaneous calcium and dopamine inputs ( Fig . 5P ) ., Fig . 6A shows the time course of synaptic efficacy ( AMPA receptor concentration in the post-synaptic membrane ) induced by different levels of dopamine input coincident with a weak calcium input ., While the absence of dopamine input caused depression of the synapse ( solid ) , increased dopamine levels resulted in potentiation ., Fig . 6B shows the time course of synaptic efficacy in three different levels of calcium input without dopamine input ., While weak calcium input causes depression , increased calcium input resulted in potentiation ., Synaptic efficacy was evaluated 10 min after conditioning as an index of long-term synaptic plasticity ., Synaptic efficacy increased with increasing dopamine input coincident with calcium input ( Fig . 6 ) ., In conditions of dopamine depletion , where both and were set at , the calcium input did not alter synaptic efficacy ., These results were in accordance with dopamine-dependent synaptic plasticity 27 , as characterized in Fig . 1A ., Fig . 6D shows synaptic plasticity dependence on calcium input levels in the absence of dopamine input ., Weaker calcium input resulted in LTD , but stronger calcium input caused LTP ., These results were consistent with previous experimental observations 11 , 28 , 29 , as schematized in Fig . 1B ., To further clarify the interactions between calcium and dopamine inputs and the roles of molecules in the signaling cascade , 2D maps of synaptic plasticity were plotted with different levels of calcium and dopamine inputs using standard and modified models ., Fig . 7A shows synaptic plasticity after 10 minutes stimulation in the standard model ., LTD was induced by weak calcium input in the absence of dopamine ( blue area ) , and LTP was induced by either strong calcium or strong dopamine input ( red area ) ., When CaMKII activation was fixed at a steady-state level ( Fig . 7B ) , increased calcium input did not induce LTP ., Rather , LTD occurred only at low levels of dopamine input ., When PKA was fixed at the steady-state level ( Fig . 7C ) , dopamine-dependent plasticity disappeared ., Fixing PP1 produced LTP , regardless of the strength of calcium and dopamine inputs ( Fig . 7D ) ., The potentiation induced by strong dopamine alone disappeared , because the disinhibition due to decreased PP1 ( corresponding to the red line in Fig . 5O ) was removed ., Several studies have modeled signal transduction in medium spiny neurons 21–23 ., The novelty of the present model is the incorporation of AMPA receptor phosphorylation and membrane trafficking to directly assess the effects of cascade dynamics on striatal synaptic plasticity ., This allowed for the reproduction of both LTD and LTP in calcium- and dopamine-dependent plasticity and to predict interactions between calcium and dopamine inputs , as shown in Fig . 7 , and effects of various manipulations on striatal synaptic plasticity ., Embedding of the present model in a complete neuronal model , or even a neural network model , enables the assessment of the role of calcium- and dopamine-dependent plasticity in cellular and network functions ., The model can also serve as the basis for building simplified signaling cascade models for large-scale simulation and theoretical analysis ., The present signaling cascade model involving DARPP-32 differs from previous models in several points ., The factors incorporated by this model but not by existing models 21–23 were inhibition of PDE by PKA , Ser137 effect on Thr34 , and inhibition of PP1 by I-1 ., The CK1-Cdk5 pathway , which has been previously described 23 , was critical for reproducing bidirectional phosphorylation of Thr75 , which was dependent on calcium input intensity ., In addition , the present study performed a rigorous analysis of bistability of positive feedback loop formed by PKA , PP2A , and DARPP-32 on Thr75 , which was a source of a threshold-like response of PKA activity to both dopamine and calcium inputs ., The model prediction of Thr34 and Thr75 responses to dopamine and calcium input were consistent with the Fernandez model 21 if the calcium input levels from the Fernandez model were regarded as the strong calcium input for the present model ., However , simultaneous calcium and dopamine inputs resulted in Thr34 dephosphorylation in the present model , but phosphorylation in the Lindskog model 22 ., This discrepancy could be due to inactivation by the calcium-PP2B-Thr34 pathway was stronger than activation by the PKA-Thr34 pathway in present model ., DARPP-32 phosphorylation on Thr75 has been shown to because of glutamate , AMPA , or NMDA exposure , but returns to normal level within 10 min 17 , 18 ., In addition , an mGluR agonist has been shown to potentiate Cdk5 activation and phosphorylation of DARPP-32 on Thr75 and Ser137 , and returns to baseline levels after peaking at 2 min 64 ., Assuming that an mGluR agonist induced weak calcium levels , and glutamate , AMPA , or NMDA produced strong calcium input , those experimental results were consistent with the present results , as shown in Fig . 5H ., In present model , phosphorylation of DARPP-32 on Thr75 , as a result of weak calcium input , takes place through the CK1-Cdk5 pathway ., Although CK1 activation is required for Cdk5 activation through signaling from mGluR 64 , it is not known whether the pathway from CK1 to Cdk5 is direct ., Similar to a previous model , the present study assumed direct activation of Cdk5 by CK1 for simplicity 23 ., Alternative mechanisms for inhibition of PP2A dephosphorylation on Thr75 exist - either through the calcium-AC5-cAMP-PKA pathway or the calcium-CaM-PDE-cAMP-PKA pathways ., More quantitative data on the strengths of these pathways and additional in silico experiments are necessary to definitely determine the role of the CK1-Cdk5 pathway in calcium-dependent LTD ., AMPA receptor trafficking in the present model was derived from Hayers model 82 ., The primary modification comprised sequential phosphorylation of Ser845 by PKA followed by Ser831 phosphorylation by CaMKII , as proposed by Lee et . al . 83 ., However , the LTP mechanism in the present striatal model differed from the hippocampal LTP by Lee et . al . 83 ., Previous results demonstrated that the phosphorylation of Ser845 did not increase during LTP 83 , and the present model showed that the phosphorylation of Ser845 increased during dopamine-dependent LTP , but did not increase during calcium-dependent LTP ., In addition , PKA was required for striatal LTP 75 To address this feature in the present striatal model , most of the AMPA receptors were dephosphorylated at the baseline ., This prediction was consistent with the lower phosphorylation level of Ser845 by reduced PKA levels due to inhibition by DARPP-32 in the striatum 69 ., It should be noted , however , that the observation of sequential AMPA receptor phosphorylation by Lee et al . 83 in the hippocampus did not exclude a parallel phosphorylation model ., It could be interpreted as a result of high PKA and low PPI concentration at the baseline in the hippocampus ., It is a subject of future study whether a parallel phosphorylation model can also reproduce the striatal synaptic plasticity ., D1-type neurons express GluR1 and GluR2/3 in the spines 84 , 85 ., A previous hippocampal study 86 showed that GluR1 subunit trafficking was a result of stimulation , but that GluR2 subunit trafficking was constitutive ., In addition , chronic treatment with the antidepressant maprotiline increases GluR1 , but not GluR2 87 ., Moreover , GluR2-lacking AMPA receptors exhibit larger single-channel currents than GluR2-expressing AMPA receptors 88 ., For these reasons , trafficking of GluR1 , but not GluR2 , was modeled in the present study to ascertain whether synaptic plasticity responded to stimulus ., Some theoretical studies 89 , 90 have predicted that NMDA receptor-mediated calcium influx results in bidirectional synaptic change ., However , these studies modeled only AMPA receptor phosphorylation , but not trafficking , and also did not consider striatal synaptic plasticity ., Although the present model considered the number of AMPA receptors in the postsynaptic membrane as a measure of synaptic efficacy , previous studies have suggested that the conductance of AMPA receptor varies according to the phosphorylation state ., For example , Ser831 phosphorylation increases conductance 91 and Ser845 phosphorylation increases open probability 92 , 93 ., If these effects are taken into consideration , the amplitude of LTP could be larger , as observed in experiments 8–11 ., Threshold dynamics due to the bistability of the positive feedback loop of PKA , PP2A , and Thr75 on DARPP-32 played an important role in reverting the LTD to LTP in dopamine-dependent plasticity ., However , when embedded into the entire system , the loop did not exhibit complete bistability , as demonstrated by gradual conversion of synaptic conductance to baseline levels ( Fig . S2 ) ., The possible mechanisms for longer-lasting synaptic plasticity are described below ., First , bistability of some proteins in the cascade has been reported , such as the bistability of CaMKII phosphorylation 82 ., However , CaMKII activity did not last for an extended period of time in the present model ., This was consistent with a previous study 94 , which reported that CaMKII activity returns to baseline within 2–5 min ., Hayer et . al . observed bistability of AMPA receptor phosphorylation and Catellani et . al . 73 mathematically determined bistability in the sequential AMPA receptor phosphorylation model ., These bistable mechanisms were not incorporated in the present model , but may contribute to synaptic changes over longer periods of time ., Second , the present model did not consider increased levels of AMPA receptors and other proteins as a result of gene transcription ., A possible link from the current model to longer-term synaptic plasticity is cAMP-response element binding protein ( CREB ) , which controls gene transcription for longer-term synaptic plasticity in the striatum 95 ., CaMKII , PKA , and PP1 directly activate CREB , but also indirectly via extracellular signal-regulated kinase ( ERK ) , which activates CREB 35 , 96 ., In addition , calcium activates mitogen-activated protein kinase kinase ( MEK ) , which activates ERK 97 ., PP1 activates striatal enriched phosphatase ( STEP ) 98 , which inhibits ERK , and PKA inactivates STEP ., As a result , CREB is inhibited by PP1 and activated by CaMKII and PKA ., Therefore , activation of CaMKII and PKA , as well as inhibition of PP1 , which results in AMPA receptor phosphorylation , can also trigger gene transcription through CREB activation ., Approximately half of the model parameters were based on previous reports and databases 21 , 22 , 42 , 55 , 66 , 68 , 69 , and the remaining half were designed to reproduce experimental findings 15–18 , 39 , 46 , 64 , 74 ., Model behavior robustness was determined by altering the kinetic parameters of the PKA-PP2A-Thr75 loop up to ten-fold ( Fig . 13 ) ., Persistence of nonlinear threshold behavior , despite shifts in thresholds , was also verified ., Although the present model parameters reflected some uncertainty , the model served as a useful starting point for exploring the mechanisms influencing corticostriatal synaptic plasticity by testing alternative parameter values or incorporating additional pathways ., The present model did not include a number of known pathways such as the effect of DARPP-32 Ser102 on phosphorylation of Thr75 99 ., Membrane potential of striatal medium spiny neurons shifts between up- and down-states , depending on cortical inputs 100 ., During the up-state , LTP is induced by cortical stimuli , even without dopamine input 31–34 ., LTP is also induced by cortical stimulation in a magnesium-free solution 11 , 28 , 29 ., Both cases reflect calcium-dependent plasticity because of the large calcium influx through NMDA receptors ., Two types of medium spiny neurons exist: D1 receptor-expressing neurons that project to the direct pathway , and D2 receptor-expressing neurons that project to the indirect pathway 101 , 102 ., In D1 neurons , dopamine increases cAMP via G-proteins and AC5 , similar to the present model ., However , in D2 neurons dopamine inhibits AC5 and decreases cAMP so the effect of dopamine input is opposite to that in D1 neurons ., Schultz et . al . recorded the activities of dopamine neurons in the substantia nigra in monkeys and found that dopamine neurons encode error signals of reward prediction 103 ., The reinforcement learning model of the basal ganglia posits that striatal neurons learn to compute expected reward based on the reward prediction error signal carried by dopamine neuron firing 103 ., Dopamine-dependent synaptic plasticity plays a major role in learning ., The medium spiny neurons are depolarized by glutamatergic inputs from the cortex that represent a sensory or a contextual state ., When the acquired reward is more than expected , phasic dopamine neuron firing would induce LTP of the activated cortico-striatal synapses ., On the other hand , if the reward is less than expected , a pause in dopamine neuron firing would cause LTD of those synapses ., The glutamatergic input would not only cause depolarization and firing , but also induce changes in molecular states , such as the phosphorylation level of DARPP-32 and/or shift the threshold of the positive feedback loop , which would serve as the short-term memory of preceding states ., To support this scenario , the temporal order of calcium and dopamine input is a critical factor ., Assuming that calcium flux by glutamatergic input is a fast process , the synaptic efficacy should be potentiated when calcium input ( associated with a sensory or contextual state ) precedes dopamine input ( associated with a reward prediction error signal ) ., Our model is consistent with this point ( Fig . S1 ) ., On the other hand , our model also predicts that the effect of the temporal order on synaptic plasticity is not strong enough ., This suggests additional interactions between dopamine and calcium signaling ., For example , dopamine facilitates L-type calcium channels , which affect the calcium influx through the interaction of glutamate receptor activation and and back-propagating action potentials ., To more precisely simulate calcium dynamics , we have to construct a whole neuron model 104 and combine it with the signaling cascade model ., There are several interaction pathways between calcium and dopamine signaling ., In the upstream of PKA , calcium directly inhibits AC5 and indirectly cAMP through CaM and PDE ., While calcium inhibition of AC5 depended on the timing between calcium and dopamine , PDE inhibition of cAMP did not depend on this timing very much ., The stronger interaction of dopamine and calcium on PKA was through DARPP-32 ., Weak calcium input inhibited PKA through the phosphorylation of Thr75 by Cdk5 , but strong calcium input activated PKA through the dephosphorylation of Thr75 by PP2A ., While dopamine input reduced the increase of Thr75 by a weak calcium input , it did not affect the decrease of Thr75 by a strong calcium input ., Furthermore , the subsystem around the PKA-PP2A-DARPP-32 positive feedback loop showed bistability while PKA activity showed a threshold like response to cAMP activation by dopamine input ., However , this loop became mono-stable with both activation of Cdk5 by a weak calcium input , leading to a low level of PKA , and by activation of PP2A by a strong calcium input , leading to a high level of PKA ., Addictive drugs ( e . g . cocaine and amphetamine ) increase the basal level of dopamine by inhibiting the reuptake of dopamine and facilitating the release of presynaptic dopamine 5 ., They ultimately decrease DARPP-32 phosphorylation on Thr75 and increase it on Thr34 105 ., In our model , increased basal dopamine levels caused LTD with the calcium and dopamine inputs which caused LTP under control conditions ( Fig . 14 ) ., This result is consistent with the theory that the value of everything except for drugs decreases because of the impairment of appropriate learning in drug addiction 106 . | Introduction, Materials and Methods, Results, Discussion | Corticostriatal synapse plasticity of medium spiny neurons is regulated by glutamate input from the cortex and dopamine input from the substantia nigra ., While cortical stimulation alone results in long-term depression ( LTD ) , the combination with dopamine switches LTD to long-term potentiation ( LTP ) , which is known as dopamine-dependent plasticity ., LTP is also induced by cortical stimulation in magnesium-free solution , which leads to massive calcium influx through NMDA-type receptors and is regarded as calcium-dependent plasticity ., Signaling cascades in the corticostriatal spines are currently under investigation ., However , because of the existence of multiple excitatory and inhibitory pathways with loops , the mechanisms regulating the two types of plasticity remain poorly understood ., A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity ., The model incorporated all major signaling molecules , including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa ( DARPP32 ) , as well as AMPA receptor trafficking in the post-synaptic membrane ., Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity ., Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A ( PKA ) , protein phosphatase 2A ( PP2A ) , and the phosphorylation site at threonine 75 of DARPP-32 ( Thr75 ) served as the major switch for inducing LTD and LTP ., Calcium input modulated this loop through the PP2B ( phosphatase 2B ) -CK1 ( casein kinase 1 ) -Cdk5 ( cyclin-dependent kinase 5 ) -Thr75 pathway and PP2A , whereas calcium and dopamine input activated the loop via PKA activation by cyclic AMP ( cAMP ) ., The positive feedback loop displayed robust bi-stable responses following changes in the reaction parameters ., Increased basal dopamine levels disrupted this dopamine-dependent plasticity ., The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction . | Recent brain imaging and neurophysiological studies suggest that the striatum , the start of the basal ganglia circuit , plays a major role in value-based decision making and behavioral disorders such as drug addiction ., The plasticity of synaptic input from the cerebral cortex to output neurons of the striatum , which are medium spiny neurons , depends on interactions between glutamate input from the cortex and dopaminergic input from the midbrain ., It also links sensory and cognitive states in the cortex with reward-oriented action outputs ., The mechanisms involved in molecular cascades that transmit glutamate and dopamine inputs to changes in postsynaptic glutamate receptors are very complex and it is difficult to intuitively understand the mechanism ., Therefore , a biochemical network model was constructed , and computer simulations were performed ., The model reproduced dopamine-dependent and calcium-dependent forms of long-term depression ( LTD ) and potentiation ( LTP ) of corticostriatal synapses ., Further in silico experiments revealed that a positive feedback loop formed by proteins , the protein specifically expressed in the striatum , served as the major switch for inducing LTD and LTP ., This model could allow us to understand dynamic constraints in reward-dependent learning , as well as causes and therapies of dopamine-related disorders such as drug addiction . | computational biology/computational neuroscience, computational biology/signaling networks | null |
journal.pgen.1004662 | 2,014 | Signature Gene Expression Reveals Novel Clues to the Molecular Mechanisms of Dimorphic Transition in Penicillium marneffei | Systemic dimorphic fungi are a group of phylogenetically diverse fungal pathogens which are often geographically restricted but pose an increasing threat to the general population , particularly for immunosuppressed hosts ., When transferring between their inhabited environments and human body , morphologic shifts seem necessary for dimorphic fungi to adapt to new circumstance 1 ., The phase transitions are regulated by temperature in systemic dimorphic fungi , which take the saprotrophic mycelial form at the lower ambient temperature and the pathogenic yeast form at the higher host body temperature 2 ., The mycelium-to-yeast ( M-Y ) transition is believed to be critical for the pathogenicity of systemic dimorphic fungi because the yeast form is the in vivo cellular form that is capable of evading the host immune system 3 , while the yeast-to-mycelium ( Y-M ) transition is crucial to maintain an environmental reservoir , since these fungi are not directly transmitted between mammalian hosts 4 ., Thus , the mechanisms of dimorphism attract great interest within the scientific community 5 ., Penicillium marneffei is a strictly thermally dimorphic fungus 6 , recently renamed Talaromyces marneffei 7 ., At 25°C , P . marneffei grows vegetatively as mycelia and shows typical multinuclear mold morphology ., At 37°C , the fungus undergoes the phase transition with concomitant coupling of nuclear and cellular division to form uninucleate , single-celled yeasts ., To date , over forty genes have been functionally characterized in P . marneffei ( see review 8 ) , yet genetic mechanisms underlying dimorphism remain elusive ., Dimorphic development in P . marneffei is a complex process controlled by a suite of genetic elements ., The recent advent of high-throughput approaches has brought new promise for the utilization of genomic and systematic applications to complement the conventional single-gene approaches to identify factors and processes that contribute to dimorphism 9–13 ., In the present study , we employed next-generation sequencing technologies to revisit the genome sequences of P . marneffei , and systematically identified signature expression changes associated with the dimorphic switch ., Specifically , we conducted a hybrid assembly of the P . marneffei genome with data derived from three different sequencing technologies ., We also utilized RNA-seq to characterize P . marneffei transcriptomes at various stages of its life cycle ., Using the over-expression experiment , we investigated the function of an important transcription factor and showed that the activation of this transcription factor can induce mycelial growth of P . marneffei at 37°C ., We provided evidence for the potential roles of secreted proteins , non-coding RNAs , and secondary structural transition of mRNA transcripts in regulating thermal dimorphism in P . marneffei ., We previously sequenced the genome of P . marneffei PM1 strain using Sanger sequencing and obtained 190 . 3 Mb of shortgun sequences 14 ., In the present study , we re-sequenced the genome using Illumina and PacBio sequencing technologies ., We obtained 4 . 12 Gb of Illumina reads and 91 . 70 Mb of PacBio reads ., The length of PacBio reads ranged from 50 to 15 , 433 bp with an average of 1 , 885 bp ., As a result , we have sequenced the P . marneffei genome using all three generations of sequencing technologies ., To take full advantage of reads generated by these different technologies , we adapted a hybrid assembly strategy ., The first step of the hybrid assembly involves the error correction for PacBio long reads using massive high-throughput short Illumina reads 15 ., This step is essential because the high error rate of PacBio reads would otherwise interfere with the overall assembly ., The error correction algorithm is implemented in PacBioToCA of Celera Assembler 16 ., The length of seed is a key parameter that influences the results of mapping and error correction ., To determine the influence of the seed length on the performance of error correction , we compared the error-corrected PacBio reads against the reads from the Sanger assembly ., We found that the accuracy of error correction was not sensitive to the length of seed , while the seed length of 12 produced the largest yield of error-corrected PacBio reads ( Figure S1 ) ., Thus , the seed length of 12 was used for the error correction ., The second step of hybrid assembly is to determine the optimal seed length for full assembly and use it to assemble all three types of reads simultaneously ., To do so , we performed the full assembly multiple times by setting the length of seed from 16 to 75 ., We evaluated the assembly results by the N50 scaffold size ( Figure S2 ) ., We chose the optimal seed length 62 for the full assembly ., The final full assembly was performed using Celera Assembler 16 ., To illustrate the performance of hybrid assembly , we also assembled the genome using only Sanger reads and Illumina reads , without PacBio reads ., Phrap was used to assemble Sanger reads , while ABySS 17 and SOAPdenovo 18 were used to assemble Illumina reads ., Indeed , hybrid assembly produced results better than those obtained by the other non-hybrid means of assembly ( Table 1 ) ., In addition , we adapted a procedure described in 19 to use the paired-end RNA-seq reads ( described below ) to further improve the assembly ., The newly assembled genome consists of 28 . 35 Mb of sequences , distributed on 216 scaffolds ., The N50 reaches 678 . 24 kb , which is 3 . 5 times longer than the draft assembly we previously reported 14 ., The longest scaffold is 1 . 28 Mb ., To our knowledge , this is the first time that all three generations of sequencing technologies were used in de novo genome assembly for a fungal genome ., Using ab initio gene prediction , subsequently improved by using expression data , we annotated 9 , 480 protein-coding genes and 571 non-coding RNA genes ( i . e . , genomic loci that can be transcribed into mRNA molecules but with minimal protein-coding potential ) ( Table S1 ) ., For these protein-coding genes , we annotated 6 , 066 by searching the Swiss-Prot database using BLASTP ( Table S2 ) , 5 , 890 with 1 , 687 gene ontology ( GO ) terms ( Table S3 ) , and 7 , 358 with 5 , 340 IPR names ( Table S4 ) ., We used RNA-seq to determine the global gene expression of P . marneffei grown on PDA media under four experimental treatments: ( 1 ) stable growth at 37°C as yeasts ( stable yeast , Y ) , ( 2 ) yeasts grown at 37°C transferred to 25°C for 6 hours ( yeast-to-mycelium , Y-to-M ) , ( 3 ) mycelia grown at 25°C transferred to 37°C for 6 hours ( mycelium-to-yeast , M-to-Y ) , and ( 4 ) stable growth at 25°C as mycelia ( stable mycelium , M ) ., For each treatment , two biological replicates were performed ., Highly consistent measures between two replicates were obtained for all treatments ( Figure S3 ) ., Among 10 , 051 genes , 92 . 5% were expressed ( FPKM>1 . 0 in at least one condition ) ., We used a four-digit code to denote the expression pattern for each gene ., The code is a combination of four “1” or “0” , indicating relatively high or low expression of a gene , respectively , under the four treatments ., For example , the expression level of GQ26_0010080 under the second treatment was significantly higher than those of the other three treatment ( average FPKM: 182 . 3 , 518 . 8 , 176 . 8 , and 181 . 9 ) ; the genes expression pattern is “0100” ., Note that the expression levels of a gene were compared between treatments of the same gene , not against expression levels of other genes ., Genes with the same expression pattern code do not necessarily have the similar overall expression level ., This four-digit code system allowed us to create 16 expression patterns and classified all genes into one of pattern groups ., The 16 patterns included “0000” for genes that were not expressed ( FPKM<1 . 0 ) under all four conditions and “1111” for genes expressed under four conditions almost equally ., The rest of 14 patterns ( such as “0100” and “0011” ) were collectively named signature patterns ., A total of 2 , 718 P . marneffei genes were classified into one of signature pattern groups ( Table S1 ) ., Each of the 14 signature patterns is presumably implicated at a certain stage in the life cycle of P . marneffei ( Figure 1 ) ., For genes in each pattern group , we tabulated the GO terms associated with gene functions and used REVIGO 20 to summarize information by merging semantically similar GO terms into non-redundant , high-level phrases ( Figure 2 , Table S5 ) ., We examined the distribution of genes with different expression patterns along each scaffold ., We found that a number of genes with the same expression patterns form gene clusters ( Figure 3 ) ., The similarity in expression patterns suggest that the genes sharing the same clusters and thus genetically linked may also play similar or related roles in regulating the life cycle of P . marneffei ., Of 2 , 718 genes with 14 signature patterns , 283 ( 10 . 4% ) are located in 73 clusters ( Table 2 ) ., These clusters are composed of 3 to 13 genes , scattered all over scaffolds ., The size of the clusters ( i . e . , the number of genes in a cluster ) is independent of the type of expression pattern ., For example , 23 . 5% “0100” genes are located within the same clusters ., In contrast , 11 . 4% “0011” and 8 . 2% “1000” are located in clusters , but the clusters representing each of these patterns have comparable total numbers of genes ( 498 , 517 , and 499 for “0100” , “0011” and “1000” , respectively ) ., Transcriptional activation or suppression of downstream target genes in response to different stimuli is often accomplished by transcription factors 21 , 22 ., In P . marneffei , genes with three transcription factor domains are the most abundant: MADS-box ( IPR002100 ) , CBF/NF-Y/archaeal histone ( IPR003958 ) , and Fork head ( IPR001766 ) ., In particular , the MADS-box transcription factor gene family is clearly expanded in the P . marneffei lineage ( Figure 4 ) , while the numbers of the other two types of transcription factors are comparable to other fungal species ., MADS-box transcription factors are known to regulate cell-type-specific transcription in Saccharomyces cerevisiae 23 and Schizosaccharomyces pombe 24 , 25 ., Interestingly , three ( out of eight ) P . marneffei MADS-box transcription factors are separately located in three “0100” clusters ( highly expressed in Y-to-M transition ) ., We determined the expression level for genes in one of the clusters using quantitative RT-PCR ( qRT-PCR ) ., In this particular cluster , the MADS-box transcription factor ( GQ26_0030130 ) is located in the middle of a group of 12 genes with the expression pattern “0100” ., Our qRT-PCR results confirmed that the expression level of all genes in this cluster is significantly up-regulated during Y-M transition ( Figure 5 ) ., We named the gene GQ26_0030130 madsA ., In wild-type P . marneffei , the expression of madsA is up-regulated during Y-M transition , which suggests the role of this gene in stimulating mycelial development ., To characterize its function , we overexpressed madsA in P . marneffei ( madsAOE ) ( Materials and Methods ) ., At 25°C , the madsAOE mutant grew as mycelia , showing no morphological differences compared to the wild-type strain ., Strikingly , at 37°C , mycelial cells were induced in the madsA-overexpressed strain ( Figure 4 ) , the wild type cells grew strictly as yeasts at this high temperature ., This further supports our hypothesis that MadsA controls the phase transition from yeast to mycelium in P . marneffei ., Secreted proteins facilitate the attachment of P . marneffei conidia to the bronchoalveolar epithelium of the host 26 ., In the newly assembled P . marneffei genome , we predicted 434 proteins that are likely to be secreted extracellularly ( Materials and Methods ) ., The majority of them ( 339 or 78 . 1% ) were among those in the 14 signature expression patterns ., These predicted secreted proteins appear disproportionally enriched in most of the signature patterns ( Table 3 ) ., This finding suggests that secreted proteins may play diverse roles at different stages of the P . marneffei life cycles ., Furthermore , clusters of genes encoding secreted proteins have been identified in non-human pathogenic fungi 27 , 28 ., For example , 12 clusters containing 79 secreted proteins and ranging from 3 to 26 genes were identified in Ustilagos maydis 27 , and 121 gene clusters containing 453 secreted proteins and ranging from 3 to 11 genes per cluster in Monacrosporium haptotylum 28 ., However , in the P . marneffei genome , we only found 5 clusters of secreted proteins , each with just 3 genes , suggesting that the clustering organization of secreted proteins per se is not important for P . marneffei pathogenicity ., In a previous study , we found that the expression of most fungal heat-responsive genes in P . marneffei are not up-regulated at 37°C 13 ., This led us to believe that P . marneffei may take a distinct strategy of genetic regulation at the elevated temperature beyond known heat-shock proteins 13 ., RNA structure is crucial for gene regulation and function 29 ., For example , RNA structures near the start codon of the URE2 transcript reduced its translation rate in S . cerevisiae 30 ., Parallel analysis of RNA structures with temperature elevation ( PARTE ) of S . cerevisiae revealed that thermodynamically unstable structures are enriched in ribosome binding sites in the 5′-UTRs of mRNAs 31 , which suggested that RNA thermometers can function as an evolutionarily conserved heat shock mechanism in eukaryotes 32 ., Here we hypothesized that the structural transition of mRNAs at different temperatures is one of the mechanisms underlying thermal dimorphism of P . marneffei ., To this end , we employed a computational approach based on RNAfold v2 . 1 . 7 33 to determine the secondary structure of P . marneffei mRNAs at 25 and 37°C ., Through the structural comparison , we identified the mRNAs whose predicted structures are substantially different at the two temperatures ., The structural differentiation was assessed by focusing on the region of −9 to +6 base positions around the translation initiation codon ., Nucleotides in this region have been shown to be important for the regulation of translation initiation 34 ., We expected that this region in mRNAs of temperature-sensitive genes would be more “structurally open” ( i . e . , contains more unpaired bases ) at 37 than at 25°C , facilitating the translation of the mRNAs into proteins ., Accordingly , the expression of these genes might also be up-regulated at 37°C ., We identified 59 mRNAs structurally more open at 37 than 25°C ( Table S6 ) , which was indicated by at least eight more unpaired bases in the translation initiation region at 37°C ., Fourteen of these mRNAs are transcribed from genes with one of signature expression patterns ( Table S7 ) ., Three are transcribed from genes with the expression pattern of “1010” , which indicates that their transcription is highly sensitive to 37°C ( Figure 6 ) ., We predicted 571 potential non-coding RNA ( ncRNA ) genes whose transcripts have no or minimal protein-coding potential , indicated by the lack of significant hits when comparing the transcripts against sequences of the Genbank database using the BLASTX algorithm ., The expression patterns of these ncRNAs are more likely to be “0010” , “1010” , “1110” , and “1100” ( Table 3 ) ., Notably , 8 . 4% ( 49 of 571 ) of the ncRNAs have an expression pattern of “0010” ., This figure is significantly higher than the background frequency of 1 . 9% ( i . e . , 186 of 10 , 051 total genes have the pattern of “0010” ) ., Because ncRNAs are often partially complementary to other molecules and take effect through binding to their targets , we searched all the potential binding sites of the 571 ncRNAs in P . marneffei transcripts using the BLASTN-short algorithm ., We found a total of 569 genes containing at least one potential ncRNA binding site ( Table S8 ) ., The expression patterns of these target genes tended to be those related to M-Y transition , including 37°C-sensitive ( “1010” ) , M-Y transition specific ( “0010” ) , and mycelium and M-Y transition ( “0011” ) ( P\u200a=\u200a5 . 6×10−8 , 3 . 5×10−5 , and 0 . 013 , respectively; χ2 test ) ., Additionally , we found 89 potential binding sites located in the structurally flexible regions as indicated by the differential secondary structure prediction at 25 and 37°C ., Whole-genome sequencing represents a powerful and critical tool for functional genomic studies ., Various sequencing technologies have been developed for genome sequencing , but none of them is prefect—all of these technologies have their own advantages and drawbacks ., Illumina , for example , delivers high-throughput , inexpensive , and accurate sequence information 35 ., However , template amplification is required before sequencing with Illumina , which could cause amplification artifacts 36 and biased coverage 37 related to the chemical-physical properties of the genome ., More importantly , Illumina produces short reads , which decreases the continuities of contigs 38 ., By contrast , the PacBio sequencer does not require template amplification and thus reduces the composition bias , and is able to produce reads over 10 kb long 39 ., These features allow PacBio to bypass the short-read issues of Illumina , and confer the potential advantage to resolve complex repeat regions of the genome ., However , the error rate of PacBio reads is as high as 15% 40 ., To solve the problem , we used the error correction algorithm to correct as many as possible of the errors in PacBio reads by mapping Illumina reads to them ., The Sanger sequencing , representing an earlier generation technology , is still the “gold standard” for validation; however , partially due to the financial considerations , researchers are inclined to use as few Sanger reads as possible ., Here , we have set to combine three different generations of sequencing technologies , namely Sanger , Illumina , and PacBio , to produce a better reference P . marneffei genome ., The hybrid assembly allowed the advantages of different technologies to complement to each other to improve the quality of assembly ., There were more reads that could be mapped to the hybrid assembly of PM1 than the previous one assembled with only Sanger reads 14 ., Our results showcased the possibility of improving the overall quality of genome assembly through re-sequencing with diverse platforms ., Our strategy is a significant improvement in the balance of cost and genome sequence quality ., It is noteworthy that genome sequences of another P . marneffei strain ATCC18224 are available in Genbank ( Accession ABAR00000000 ) , which are different from those of P . marneffei strain PM1 14 ., Our assembly showed that PM1 may have eight chromosomes because telomere tandem repeat sequences were identified at the ends of 16 scaffolds ., In contrast , seven chromosomes are present in the ATCC18224 assembly ., A karyotype study also suggested seven chromosomes , but , at the same time , the same study estimated that the genome size was between 25 . 7 to 26 . 7 Mb 41 , which was smaller than the estimates for ATCC18224 and PM1 ( ∼29 . 0 Mb for both ) ., It is possible that a chromosome in size of ca ., 2 Mb was overlooked by the karyotype study ., Nevertheless , more studies are needed to confirm the number of chromosomes of P . marneffei and reconcile assemblies from different strains in the future ., Expression levels at four conditions were considered for each gene as part of the expression pattern analysis ., This allowed the multiple-way comparisons of gene expression in all conditions and could , in theory , produce a large number of patterns ( i . e . , all possible combinations of the results of gene expression level comparison between conditions ) ., To simplify the patterns , we employed the “0”–“1” schema to define signature patterns that only capture the essential differences between conditions ., As a result , in the experimental data we collected , only 15 patterns ( including “1111” representing equally expressed in all conditions ) needed to be considered ., This significant reduction of the theoretical number of possible expression-level combinations to the number of observed expression patterns in reality suggested a functional constraint in the biological system of P . marneffei gene expression ., We mapped all expression patterns with associated genes onto the different biological stages in the life cycle of P . marneffei ( Figure 1 ) ., This represents , so far , the most comprehensive expression pattern-based classification of P . marneffei genes ., One immediate insight we gain based on the classification was the discovery of expression pattern clusters ., Genes in the same cluster have the same expression patterns , probably due to coordinated regulation of these genes that have similar or related functions ., The madsA gene is in the cluster of high expression during the morphological transition from the yeast to mycelium form ., Indeed , over-expression of this MADS-box gene resulted in the morphological change to mycelium form at 37°C that restricts the wild type P . marneffei in the yeast form ., This MADS-box transcription factor is located in the cluster of a series of genes with the same expression patterns ., The induced morphological change validated the function of this gene in regulating morphological development ., This result also demonstrated the usefulness of the signature expression patterns and the clusters of genes with the same patterns ., This concept was further illustrated by a series of discoveries involving secreted proteins , RNA structural transition , and ncRNAs ., First , 78 . 1% of predicted secreted proteins have expression patterns that are found among the 14 signature patterns ., The portion is significantly higher than that of randomly selected genes that have signature expression patterns ( P<10−16 , Χ2 test ) ., Second , 23 . 7% ( 14 out of 59 ) of mRNAs with a highly differential structure at 25 and 37°C have a signature expression pattern ., This suggests a widely-spread impact of the mRNA structural transition at different stages of P . marneffei development ., Finally , our results describing the expression patterns of ncRNAs and their target genes suggest that ncRNAs may play a role in M-Y transition ., In summary , we have made several steps toward a better understanding of P . marneffei thermal dimorphism ., Hybrid assembly combined the advantages of different sequencing technologies to improve the quality of genome assembly ., Signature expression patterns allowed the prioritization of genes that potentially play important roles in growth regulation and dimorphic development ., This strategy was applied to identify the potential master transcription factor , madsA , whose function in regulating yeast-to-mycelium transition was then experimentally validated ., We anticipate that our overall strategy and approaches can also be used for studying other systemic fungal pathogens ., Sanger sequencing of P . marneffei strain PM1 was conducted in our previous study 14 ., Briefly , genomic DNA was prepared from the arthroconidia of PM1 grown at 37°C as described 42 ., Two genomic DNA libraries were made in pUC18 carrying insert sizes from 2 . 0–3 . 0 kb and 7 . 5–8 . 0 kb , respectively ., DNA inserts were prepared by physical shearing using the sonication method 43 ., A total of 190 . 3 Mb of sequence data , which is equivalent to ∼6 . 6× coverage of the genome , were generated by random shotgun Sanger sequencing ., The draft genome sequences were assembled using Phrap ( http://www . phrap . org/ ) ., In the present study , the P . marneffei strain PM1 genome was re-sequenced using Illumina and PacBio sequencing technologies ., The strain was cultured in Sabourauds Dextrose Agar ( SDA ) at 25°C for 7 days ., Sporulating colonies were covered by 1 ml of sterile phosphate buffed saline ( PBS ) containing 0 . 05% ( v/v ) TWEEN-20 ., The resulting mixture of conidia and hyphal fragments was withdrawn and filtered by Miracloth ., The density of conidia in the filtrate was adjusted to 1×108 conidia/ml ., 100 µl of adjusted conidial suspension was inoculated into 50 ml of Sabourauds Dextrose Broth ( SDB ) ., After culturing at 37°C for 7 days , 5 ml of culture was transferred into another 45 ml of SDB and cultured at 37°C for 16 hours ., Cells were collected by centrifugation at 3000×g for 5 min ., Genomic DNA was extracted using E . Z . N . A . Fungal DNA Kit ( Omega Bio-Tek Inc . ) , following the manufacturers instructions with a slight modification: RNA was digested by RNase I instead of RNase A . High-throughput Illumina sequencing was performed by the Beijing Genomics Institute ( BGI ) Americas ., Extracted genomic DNA was fragmented randomly and then DNA fragments around 500 bp in size were retained by electrophoresis ., Adapters were ligated to DNA fragments ., After cluster preparation , 90-bp pair-end sequencing was conducted ., This resulted in raw data of 22 , 861 , 112 pairs of 90 bp pair-end reads ., The reads were trimmed using fastx-trimmer version 0 . 0 . 13 , based on the base-calling quality reported by FastQC v0 . 10 . 1 ., PacBio sequencing was conducted by the Biomedical Genomics Microarray Facility at University of California , San Diego ., The template library , with 10-kb insert was prepared according to the manufacturers specifications ., The sequencing was carried out on a PacBio RS platform following the standard protocol with a C2 sequencing kit at the 1×120-min acquisition mode ., The run was carried out with diffusion-based loading and analyzed using the standard primary data analysis ., Finally , the SMRT cell of PacBio system yielded 91 . 70 Mb of sequences from a total of 48 , 645 continuous long reads ., We employed Phrap v1 . 090518 or the assembly of Sanger reads alone using various values of -minmatch ranging from 14 ( default value ) to 30 ., The default value provides the best assembly with a total length of 28 . 92 Mb and a scaffold N50 of 24 . 08 kb after removing contigs shorter than 500 bp ., Two software tools were used independently for assembling Illumina reads ., For ABySS v1 . 3 . 7 17 , different values for parameter k-mer size ( -k ) were used , ranging from 13 to 79 ., A run with a minimum-required contig size of 100 bp and a k-mer length of 28 nt resulted in an assembly with a total length of 28 . 50 Mbp and a scaffold N50 of 211 . 24 kbp after removing scaffolds less than 500 bp ., For SOAPdenovo v2 . 01 18 , different values for parameter k-mer size ( -K ) were used with odd numbers of 13 to 79 ., A run with a k-mer length of 57 nt resulted in an assembly with a total length of 27 . 95 Mbp and a scaffold N50 of 170 . 68 kbp after removing scaffolds less than 500 bp ., Error correction of PacBio RS reads were implemented by PacBioToCA of Celera Assembler version 7 . 0 16 using Illumina reads 15 with seed length ( -MerSize ) ranging from 10 to 20 ., Corrected reads were aligned to the contigs assembled with Sanger reads by BLASTN to estimate the accuracy of error correction ., Because seed length affected amount of corrected reads but not the identity , the error correction of PacBio reads was performed with a seed length of 12 to produce the largest amount of corrected read outputs ., Hybrid de novo assembly was performed using Celera Assembler with Sanger , Illumina , and corrected PacBio reads ., The different seed lengths ranging from 19 to 79 were tested ., A run with a seed length of 62 resulted in the best assembly as indicated by the N50 scaffold size ., RNA-seq reads were used to improved scaffolds using customized scripts based on the principle described in a previous study 19 ., Protein-coding genes were predicted by using FGENESH ( SoftBerry , Mount Kisco , NY ) 44 with genome-specific parameters of Penicillium ., Aided by a total of 107 . 4 million paired-end RNA-seq reads , 739 additional genes were annotated that had been missed by the ab initio prediction ., These genes were then searched against the non-redundant protein database of Genbank using BLASTX to measure the coding potential ., Together , we annotated a total of 9 , 480 protein-coding genes and 571 non-coding RNA genes ., To further annotate predicted genes , we searched motifs and domains using InterProScan v5 . 3-46 . 0 45 against publicly available databases , including Pfam , PRINTS , PROSTIE , ProDom , SMART , and PANTHER , and then retrieved the GO annotation from the results of InterProScan for all genes ., At the beginning of the experiment , conidia of strain PM1 were inoculated onto SDA plates and cultured at 25 and 37°C for a week ., The germinated cells were transferred onto new SDA plates every week for 2 weeks to establish stable colonies of either the mycelial or yeast growth form ., One week before the extraction of total RNA , the homogenous cells were cultured on new SDA plates to obtain fresh cells ., For temperature switch experiments , yeast or mycelial growth plates were transferred to 25 or 37°C for 6 hours , respectively ., The total RNAs were extracted from each condition for two independent biological replicates using the E . Z . N . A . fungal RNA kit ( Omega Bio-Tek ) , following the manufacturers instructions with DNase I digestion to eliminate genomic DNA ., We adjusted the total RNA concentration according to the DNA content before the standard poly ( A ) + RNA-seq was performed ., RNA was quantified using Qubit 2 . 0 fluorometer ( Life Technologies , Grand Island , NY ) ., Finally , we obtained ∼13 million 90-bp paired-end reads for each sample ., The reads were trimmed using fastx-trimmer based on the quality reported by FastQC ., RNA-seq short reads were mapped to the annotated genomes using Tophat v2 . 0 . 11 46 ., For each sample , ∼2 . 0 Gb of reads was mapped , representing ∼100× coverage of P . marneffei transcriptome ., Using SAMMate 47 , the gene expression level was measured in FPKM ( fragments per kilobase of exon per million fragments mapped ) 48 ., For each gene , expression levels associated with each of the four experimental treatments were compared to each other and the relative levels are marked by 1 indicating highly expressed and 0 indicating lowly expressed ., Genes that were not expressed at all for the four conditions have a pattern of “0000” , while the house-keeping genes consistently expressed at similar levels under all four conditions have a pattern of “1111” ., Besides the two patterns for un-expressed and house-keeping genes , 14 other signature patterns ( e . g . , “1100” and “0100” ) were denoted ., The P . marneffei strain was pre-cultured under the same conditions for preparing the RNA-seq samples ., The total RNAs were also extracted using the same procedure described above ., The concentrations of total RNA were adjusted to 100 µg/ml ., Real-time RT-PCR assays were performed using iTaq Universal SYBR Green One-Step Kit ( Bio-Rad Laboratories ) with primers shown in Table S9 ., Template total RNA was reverse transcribed and amplified in a Bio-Rad CFX96 Real-Time PCR Detection System ( Bio-Rad Laboratories ) in 20-µl reaction mixtures containing 10 µl of iTaq universal SYBR Green reaction mix ( 2× ) , 0 . 25 µl of iScript reverse transcriptase , 2 µl of 100 nM of forward and reverse primers mix , 1 µl of total RNA template , and 6 . 75 µl of nuclease-free water , at 50°C for 10 min , 95°C for 1 min , followed by 30 cycles of 95°C for 10 s and 58°C for 30 seconds ., Melting curves were measured from 65°C to 95°C with 0 . 5°C of increment ., Results from actin ( actA ) were used for normalization ., There is no significant difference in the gene expression levels of actA in different cell types ( conidia , mycelia , an | Introduction, Results, Discussion, Materials and Methods | Systemic dimorphic fungi cause more than one million new infections each year , ranking them among the significant public health challenges currently encountered ., Penicillium marneffei is a systemic dimorphic fungus endemic to Southeast Asia ., The temperature-dependent dimorphic phase transition between mycelium and yeast is considered crucial for the pathogenicity and transmission of P . marneffei , but the underlying mechanisms are still poorly understood ., Here , we re-sequenced P . marneffei strain PM1 using multiple sequencing platforms and assembled the genome using hybrid genome assembly ., We determined gene expression levels using RNA sequencing at the mycelial and yeast phases of P . marneffei , as well as during phase transition ., We classified 2 , 718 genes with variable expression across conditions into 14 distinct groups , each marked by a signature expression pattern implicated at a certain stage in the dimorphic life cycle ., Genes with the same expression patterns tend to be clustered together on the genome , suggesting orchestrated regulations of the transcriptional activities of neighboring genes ., Using qRT-PCR , we validated expression levels of all genes in one of clusters highly expressed during the yeast-to-mycelium transition ., These included madsA , a gene encoding MADS-box transcription factor whose gene family is exclusively expanded in P . marneffei ., Over-expression of madsA drove P . marneffei to undergo mycelial growth at 37°C , a condition that restricts the wild-type in the yeast phase ., Furthermore , analyses of signature expression patterns suggested diverse roles of secreted proteins at different developmental stages and the potential importance of non-coding RNAs in mycelium-to-yeast transition ., We also showed that RNA structural transition in response to temperature changes may be related to the control of thermal dimorphism ., Together , our findings have revealed multiple molecular mechanisms that may underlie the dimorphic transition in P . marneffei , providing a powerful foundation for identifying molecular targets for mechanism-based interventions . | Penicillium marneffei is a significant dimorphic fungal pathogen capable of causing lethal systemic infections ., It grows in a yeast-like form at mammalian body temperature and a mold-like form at ambient temperature ., The thermal dimorphism of P . marneffei is closely related to its virulence ., In the present study , we re-sequenced the genome of P . marneffei using Illumina and PacBio sequencing technologies , and simultaneously assembled these newly sequenced reads in different lengths with previously obtained Sanger sequences ., This hybrid assembly greatly improved the quality of the genome sequences ., Next , we used RNA-seq to measure the global gene expression of P . marneffei at different phases and during dimorphic phase transitions ., We found that 27% of genes showed signature expression patterns , suggesting that these genes function at different stages in the life cycle of P . marneffei ., Moreover , genes with same expression patterns tend to be clustered together as neighbors to each other in the genome , suggesting an orchestrated transcriptional regulation for multiple neighboring genes ., Over-expression of the MADS-box transcription factor , madsA , located in one of these clusters , confirms the function of this gene in driving the yeast-to-mycelia phase transition irrespective of the temperature cues ., Our data also implies diverse roles of secreted proteins and non-coding RNAs in dimorphic transition in P . marneffei . | next-generation sequencing, genome expression analysis, fungal genomics, mycology, genomics, fungal genomes, penicillium marneffei, medical microbiology, genome analysis, fungal genetics, transcriptome analysis, genome annotation, biology and life sciences, genetics, microbiology, computational biology, microbial pathogens, fungal pathogens | null |
journal.ppat.1005855 | 2,016 | Impact of Microbiota on Resistance to Ocular Pseudomonas aeruginosa-Induced Keratitis | The importance of microbiota in regulating lymphocytic development and inflammatory responses in the gut has been demonstrated in studies using germ-free ( GF ) or antibiotic-treated ( ABX ) mice 1–5 ., Loss of intestinal microbiota diversity alters the host resistance to gut pathogens such as Salmonella typhimurium , Listeria monocytogenes , and Clostridium difficile to name a few 6–8 ., Consistently , reconstitution of commensal bacterial communities facilitates the clearance of enteric opportunistic pathogens 9 ., This suggests that transferring defined commensal bacterial populations into the host to re-establish microbiota offers an antibiotic–independent approach to combat infections ., These approaches may not be exclusive for intestinal pathogens ., A recent study demonstrated that antibiotic-treated mice showed increased sensitivity to viral infections ., Housed under conventional conditions influenza virus–infected mice displayed lower viral titers and virus–associated mortality when compared to antibiotic–treated mice 10 ., In lieu with these data , murine gut microbiota , particularly the Segmented Filamentous bacteria , promoted pulmonary type 17 immunity and resistance to S . aureus pneumonia 11 ., Despite the growing understanding of the impact of the host–microbe alliance on immunity in the gastrointestinal tract , the extent to which individual microenvironments such as that of the eye are controlled by resident or distant microbiota remains unclear ., Unlike in the skin or gut , the ocular commensals are limited in abundance and richness ., The most frequently identified species from the conjunctival surfaces in healthy humans are the Coagulase Negative Staphylococci sp ., ( CNS spp . ) , which include Staphylococcus epidermidis 12 , 13 ., The less frequently present microbial species are Propionibacterium sp ., , Corynebacterium sp ., , Staphylococcus aureus , Streptococcus sp ., , Micrococcus sp ., , Bacillus sp ., , and Lactobacillus sp ., 14 ., While the Gram-positive species above are sparingly detected in ocular environment , the Gram-negative species are even less frequently detected and include Pseudomonas aeruginosa , Enterobacter sp ., , Escherichia coli , Proteus sp ., , and Acinetobacter sp ., 12 , 15–19 ., Numerically , the conjunctival surfaces harbor 10–100 CFU/swab in 20–80% of the swabs , a figure remarkably different from the number of commensals present in the oral mucosa where 100% of the swabs yield 107−108 CFU/ml of cultivatable bacterial species 20 , 21 ., Utilizing 16S rRNA gene amplicon sequencing to analyze samples from contact lens wearers versus non–lens wearers , Shin et al . observed a shift of the conjunctival microbiota in the lens wearers towards relatively higher abundance of Methylobacterium , Lactobacillus , Acinetobacter , and Pseudomonas , and lower relative abundance of Corynebacterium , Staphylococcus , Streptococcus , and Haemophilus , suggesting that contact lens wearing alters the composition of ocular microbiota towards skin-like microbiota 22 ., In agreement , the extended wear of contact lenses is associated with increased numbers of pathogenic organisms in conjunctival tissues 23–26 ., Cumulatively , these studies raised important questions ., Namely , how does ocular microbiota affect local immune responses to infectious pathogens; does wearing of contact lenses increase the frequency of keratitis in patients due to contamination of the contact lenses with species derived from the skin of the hand; and does ocular microbiota exert immune functions that are required for the maintenance of ocular health ?, In the present study , we compared the impact of gut and ocular commensals on the susceptibility to P . aeruginosa-induced keratitis ., We found that the GF Swiss Webster ( SW ) mice displayed a significantly higher susceptibility to P . aeruginosa–induced infection compared to conventional specific pathogen free ( SPF ) mice ., This is indicated by higher bacterial burden and increased pathology scores in the corneas ., Reducing the numbers of gut commensals significantly increased susceptibility to bacterial challenge , as did reduction of ocular commensals , albeit to a lesser effect ., Mechanistically , the presence of microbiota elevated IL-1ß production in the P . aeruginosa-infected corneas ., Monocolonizing GF mice with CNS sp ., restored resistance to infection , implicating that CNS sp ., are sufficient to induce protection against keratitis ., To evaluate how microbiota alters baseline ocular immune barrier , eye washes were collected from GF and SPF SW mice and their protein signature was characterized using quantitative LC-MS/MS ( S1 Table ) ., The total protein levels of the ocular washes obtained from GF and SPF SW mice were comparable ( S2 Fig ) ., Peptides derived from 63 proteins were differentially present in SPF SW mice compared to GF SW mice at baseline ( S2 Table ) ., Important innate immune effectors including complement component C3 , factor B , and complement component C9 were significantly reduced in GF-derived tear films ( Fig 1A , S2 Table ) ., Iron-scavenging protein like serotransferrin was also significantly reduced , suggesting decreased baseline anti-microbial activity ( Fig 1B and S2 Table ) ., Expectedly , peptides derived from Ig kappa constant region and IgA heavy chain- constant region were significantly reduced in GF mice ( Fig 1C ) ., Interestingly , neutrophil-derived peptides such as lipocalin 1 ( Lcn2 ) , neutrophil cytosolic factor ( Ncf2 ) , neutrophil cytosolic factor ( Ncf1 ) , chitinase-3-like protein 1 ( Fig 1D , for complete list see S2 Table ) were less abundant in GF-derived tear films compared to SPF-derived washes , illustrating decreased neutrophil trafficking to the ocular surfaces at steady state in the absence of microbiota ., In contrast , only 8 identified proteins were upregulated in GF-derived ocular washes when compared to SPF ( S3 Table ) , neither of which had previously identified antimicrobial activity ., Cumulatively , these data demonstrated compromised innate and adaptive effectors at the ocular surface in the absence of microbiota and suggested increased susceptibility to infection ., To determine the effect of the presence of microbiota on P . aeruginosa–induced keratitis , GF and SPF SW mice were infected with P . aeruginosa strain 6294 ., At 24 h after infection corneas of infected GF SW mice exhibited a significantly higher bacterial burden ( p = 0 . 01 , Student’s t-test ) than conventionally maintained control animals ( Fig 2A ) ., This correlated with increased histopathology scores in the GF mice ( Fig 2B , p = 0 . 0004 , Mann-Whitney test ) ., Comparison of the inflammatory profiles of infected GF and SPF SW mice is shown in Fig 3 ., A panel of cytokines , TNF-α , KC , IL-6 , IL-12p40 , IFN-γ , and IL-10 were quantified at the protein level in infected corneas harvested from the individual animals ., Corneal homogenates of infected GF mice contained significantly higher levels of IL-6 ( p = 0 . 0001 ) , IL-12 p40 ( p<0 . 0001 ) , and KC ( p = 0 . 0001 ) compared to the infected SPF mice corneas ., There were no significant differences in the levels of TNF-α , IFN-γ , or the anti-inflammatory cytokine IL-10 , although there was a tendency for increased presence in the GF mice ( Fig 3 ) ., These data showed elevated proinflammatory responses to infection in the absence of microbiota ., Further , quantitative proteomic analysis of the tear film from the infected GF and SPF mice showed that P . aeruginosa infection induced differential upregulation of 293 proteins in the GF group compared to the non-infected GF baseline , whereas only 106 proteins were upregulated in the SPF group when compared to the non-infected baseline ., When comparing the protein signature of infected GF mice to that of infected SPF mice , 24 proteins were differentially present including proteins with antimicrobial activity ( e . g . , lactotransferrin , myeloid bactenecin , neutrophil gelatinase-associated lipocalin ) , and proteins with enzymatic activity ( e . g . , neutrophil collagenase and myeloperoxidase ) ( S4 Table ) ., String-based analysis of the upregulated proteins showed enrichment for phagocytosis , phagosome , and leukocyte transmigration KEGG pathways ( Fig 4 ) , further supporting the observation of elevated susceptibility of GF mice to infection ., Cumulatively , these data demonstrate that GF SW mice are capable of mounting a protective anti-P ., aeruginosa immune response by stimulating increased recruitment of neutrophils to the site of infection despite the initially low innate immune response ., To evaluate the impact of ocular microbiota on regulating the resistance to keratitis , separate cohorts of SPF SW mice were pretreated topically with gentamycin to reduce the numbers of commensals prior to infection ., The recoverable cultivatable conjunctival bacterial presence included from SPF SW mice included mannitol-fermenting and non-fermenting Staphylococcus spp ., and minor proportion of other species such as Streptococcus sp ., ( Fig 5 ) ., The presence of these bacterial commensals was completely ablated after a 4-day topical treatment with gentamycin ( Fig 5 ) ., Upon infection , the corneas of gentamycin-treated mice displayed moderate , but significantly higher numbers of P . aeruginosa ( p = 0 . 0001 , Student’s t-test ) compared to age- and gender-matched control mice ., Correspondingly , the pathology scores were significantly increased in the topical treatment group when compared to the controls ( p = 0 . 0001 , Mann-Whitney test ) , illustrating that the ocular commensals contributed to the resistance to infection ., Next , we evaluated the requirement for non-ocular microbiota in regulating the susceptibility to bacterial keratitis ., SPF SW mice were treated orally with an antibiotic cocktail before the infections experiments , which resulted in a significant decrease in numbers of gut microbiota ( Fig 6A ) while preserving the ocular commensals in the conjunctiva ( Fig 6B ) ., ABX mice showed increased susceptibility to ocular P . aeruginosa challenge , which was exemplified by elevated bacterial burden in the corneas ( p = 0 . 001 , Student’s t-test ) and increased corneal pathology ( p = 0 . 0005 , Mann-Whitney test ) ( Fig 6C ) ., Combined treatment of SPF mice with gentamycin and oral antibiotics did not result in increased susceptibility to infection when compared to infected mice treated with oral antibiotics alone , demonstrating a non-cumulative effect ( Fig 6D ) ., Consistent with these observations , baseline bactericidal properties of neutrophils derived either from GF and ABX mice were significantly reduced compared to SPF mice ( Fig 7 ) ., GF-derived BM PMNs exhibited 50% reduced killing of P . aeruginosa PAO1 ( Fig 7A ) and PA14 ( S3 Fig ) when compared to PMNs isolated from the SPF SW animals ., Reactive oxygen species ( ROS ) released by GF PMNs in response to P . aeruginosa 6294 were significantly less than those released by the SPF PMNs ( S4 Fig ) ., To further characterize the phenotype of GF-derived neutrophils , RNA sequencing experiments were carried out using bone marrow derived purified mature Ly6G+ , CD11b+ PMNs ( Fig 7C ) ., Similar total number of reads was obtained from the individual biological duplicates ., Upon mapping of these reads to the reference genome database , approximately 270 differentially expressed transcripts were identified ( S5 Table ) ., Ingenuity pathway–based prediction for upstream regulators suggested the involvement of the LPS ( p = 5 . 95E-19 ) , IFN type 1 ( p = 3 . 36E-18 ) , IL-1ß ( 8 . 93E-15 ) , ACKR ( 1 . 16E-14 ) , TNF-α ( 2 . 57E-14 ) , MyD88 ( p = 2 . 7E-13 ) , TICAM ( p = 9 . 6E-13 ) , and IFN-γ ( p = 6 . 24E-12 ) pathways ., These data demonstrated significant phenotypic alterations between the GF- and SPF-derived neutrophils and support the conclusion that microbiota promotes neutrophil bactericidal activities against P . aeruginosa ., Since deficiency in the IL-1ß signaling pathways sensitizes to P . aeruginosa–induced infections 27 , ocular IL-1ß levels were quantified at baseline and during P . aeruginosa-induced infection ., The levels of the IL-1ß mRNA transcripts in the GF-derived conjunctival tissues were two-fold less than in the SPF-derived tissues ( p = 0 . 002 , Student’s t-test ) ., This was in contrast to the levels of IL-1α RNA transcripts , which were comparable in both groups of mice ( Fig 8A ) ., Consistent with differential microbiota-driven priming , GF mice mounted a significantly weaker IL-1ß response when compared to the SPF mice ( Fig 8B , p = 0 . 0001 , Student’s t-test ) during infection with P . aeruginosa 6294 ., In contrast to the levels of IL-1ß , other proinflammatory cytokines , which are typically upregulated during bacterial keratitis , like IL-6 , KC , and IL-12p40 levels were significantly increased in infected corneal tissues from GF mice ( Fig 3 ) ., Importantly , local gentamycin treatment resulted in two-fold lower IL-1ß levels in P . aeruginosa PAO1-infected tissues ( Fig 8C , p = 0 . 02 , Student’s t-test ) , illustrating that local microbiota promoted the magnitude of IL-1ß released during infection at the ocular mucosa ., Lastly , neutralizing anti-IL-1ß antibody in the naturally more resistant SPF SW mice prior to infection led to an increase in corneal P . aeruginosa burden ( Fig 8D , p = 0 . 002 , Student’s t-test ) confirming the requirement for IL-1ß in regulating susceptibility to infection ., To evaluate which microbial communities or individual commensal species promote ocular health , GF mice were reconstituted with either mouse- or human-derived gut microbiota ., The reconstituted mice showed less susceptibility to P . aeruginosa 6294 , reduced pathology , and decreased bacterial presence in the cornea ( Fig 9A ) ., While the gut microbiota in these reconstitution experiments was of different origin: mouse- or human–derived , all reconstituted animals retained CNS sp ., at the ocular surface ., To evaluate the impact of the CNS sp ., on regulating susceptibility to keratitis , monocolonization experiments were carried out ., A single topical application of CNS sp ., was sufficient to colonize the ocular surface ., CNS sp ., were recovered two weeks after the initial commensal exposure from the cornea as well as from feces of monocolonized mice , indicating gut colonization in these animals ( Fig 9B ) ., The monocolonized mice showed restored PMN bactericidal properties against P . aeruginosa ( Fig 9C ) , increased IL-1ß conjunctival transcripts ( Fig 9D ) and similar resistance to P . aeruginosa-induced infection as the SW mice ( Fig 9E ) ., Cumulatively , these data underline the importance of CNS sp ., in regulating neutrophil activation and resistance to P . aeruginosa-induced keratitis ., Unlike any other body site , the ocular mucosal surfaces harbor very few cultivatable bacterial species 17 , 21 ., A lower percentage of the conjunctival swabs give rise to cultivatable bacteria that are in stark contrast to the number of recovered bacteria from the skin or oral mucosa where 100% of the swabs result in microbial growth 28–30 ., The cultivatable commensal species from the eye are limited in repertoire and include Coagulase Negative Staphylococcus sp ., , Propionibacterium sp ., , Corynebacterium sp ., , S . aureus , Streptococcus spp ., , Micrococcus sp ., , Bacillus sp ., , and Lactobacillus sp ., 12 , 15–19 ., These observations prompted the inquiry into whether small numbers of bacterial species have measurable and significant impact on ocular immunity 21 ., We examined this question by comparing the ocular immune responses to P . aeruginosa , a frequent opportunistic ocular pathogen , in GF mice , in mice treated with topically applied antibiotics to reduce the ocular microbiota , in mice treated with oral antibiotics to reduce gut microbiota , and in GF mice monocolonized with CNS sp ., First , we examined whether depletion or absence of microbiota leads to increased susceptibility to P . aeruginosa–induced keratitis in naturally resistant SW mice and , thereby , to altering the degree and quality of the commensal-driven immune priming ., We found that GF mice were significantly more susceptible to ocular challenge with P . aeruginosa compared to SPF-maintained SW mice ., This was exemplified by increased bacterial presence in the cornea , elevated inflammatory cytokine responses , and higher ocular pathology scores at the peak of infection ( Fig 2 ) ., These data reveal the importance of microbiota in regulating corneal resistance to P . aeruginosa ., Local treatment with gentamycin , and thereby reduction of local microbiota , elevated the susceptibility to infection in SPF SW mice demonstrating that local microbiota plays a measurable and significant , albeit moderate , role in maintaining ocular immunity ( Fig 5 ) ., There are significant implications that stem from these findings ., In the USA , one in 2 , 500 daily contact lens wearers develops P . aeruginosa keratitis ., Therefore , there is a long-lasting interest in the understanding of how contact lenses predispose to this infection ., Recently , Shin et al . reported that contact lens wear significantly alters the conjunctival commensal community 22 ., Interestingly , the alterations were associated with reduced abundance of Corynebacterium sp ., , Staphylococcus sp ., , Streptococcus sp ., , and Haemophilus sp ., in the contact lens wearers 22 ., However , the biological implications of this difference are uncertain ., Based on our results , we propose that contact lenses are not simply a vector for pathogenic organisms but that their use lowers immune effector responses elicited by commensal microbiota , and that this sensitizes to infection ., We hope that our data will lead to exploring novel designs and regimens of contact lens wear to achieve minimal impact on the commensal communities , thereby decreasing complications by infections ., Previous studies have demonstrated that deficiency in IL-1ß signaling rendered the C57BL/6 mice more susceptible to P . aeruginosa–induced eye infection 31 ., In these experiments , IL-1ß was released by corneal macrophages in response to P . aeruginosa–stimulated TLR4 and TLR5 activation and by neutrophils 27 , 31 ., Consequently , IL-1ß –dependent signaling promoted neutrophil recruitment during keratitis and P . aeruginosa–induced pneumonias 31–35 ., Our data not only confirm the critical role of IL-1ß signaling in mediating protection against P . aeruginosa–induced keratitis by converting the resistant SW mice to susceptible ( Fig 8D ) but also show that microbiota regulates the magnitude of IL-1ß released during infection ., Consistently , GF mice displayed fewer IL-1ß transcripts ( Fig 8A ) and , conversely , monocolonizing GF mice with CNS sp ., increased IL-1ß transcript levels ( Fig 8D ) ., Currently , the type of the cells that produce IL-1ß transcripts is under investigation and includes epithelial cells , conjunctival antigen presenting cells , and goblet cells as they are exposed to commensal-derived products ., The ability of commensals to prompt IL-1ß –driven responses has been previously recognized at mucosal sites exposed to much more prominent commensal presence such as the gut 36 , 37 ., Vancomycin-sensitive Gram-positive commensals promoted the expansion of IL-1R1+ γδ T cells , which elicited protection against peritoneal E . coli infection via improved neutrophil recruitment ., In the skin , S . epidermidis primed the CD11b+ CD11c+ dendritic cell–derived IL-1ß production , thereby promoting the maintenance of CD8+ and CD4+ T cells 37 ., Exposure of S . epidermidis–loaded dendritic cells to CD8+T cells triggered potent IL-17A and IFN-γ production , which had protective consequences against Candida albicans challenge ., Whether microbiota-driven γδT cells or adaptive CD8+ , CD4+ T cells are critical in regulating PMN recruitment during infection in the ocular mucosa remains to be clarified ., While depletion of the local microbiota resulted in a moderate increase in the susceptibility to infection , the reduction of gut microbiota had a stronger impact ., Of note , when oral antibiotics were combined with topical gentamycin treatment to ablate the conjunctival commensal presence , no significant differences were observed in the recovered corneal P . aeruginosa burden between the different treatment groups , suggesting that the pathways were not synergistic ( Fig 6D ) ., The observation that the ocular microbiota was less efficacious in regulating resistance to infection when compared to gut microbiota , depends on gut microbiota–driven neutrophil maturation ., Depletion of gut microbiota significantly reduced neutrophil maturation and bactericidal activities against P . aeruginosa ( Fig 7 ) ., These data are consistent with recent publications reporting that GF mice had reduced proportions and differentiation potential of the granulocytic progenitors in the bone marrow which consequently rendered them susceptible to Listeria monocytogenes , Streptococcus pneumoniae , Staphylococcus aureus , and Escherichia coli infections 38–41 ., The observation that the reconstitution with mouse or human gut derived microbiota or monocolonizing the animals with CNS sp ., restored the bactericidal properties of PMNs against P . aeruginosa ( Fig 9 ) is also in accord with the recently published results by Balmer et al . where reconstitution experiments with E . faecalis , E . coli K-12 , and S . xylosus were sufficient to restore neutrophil maturation 38 ., Aiming at identifying microbiota-derived molecules and , ultimately , the underlined pathways , serum transfer experiments from SPF mice to GF mice pointed to bacteria-derived , heat stable , soluble compounds that triggered TLR signaling 38 ., “Triple” colonization of MyD88-/-/TICAM-/- mice with E . faecalis , E . coli K-12 , and S . xylosus failed to stimulate granulopoiesis highlighting the importance of commensal-induced MyD88 signaling ., Intriguingly , the transcriptomic signature of GF-derived neutrophils suggest that these neutrophils can respond to LPS , IL-1ß , and type I and type II interferon challenges , thereby providing evidence that neutrophils display significant degree of transcriptional plasticity when generated in the bone marrow ., Consistently , challenges of GF mice with either E . coli-derived LPS or peptidoglycan enhanced the neutrophil priming 40 , 41 ., Cumulatively , these data suggest that tonic gut microbiota–derived signals not only stimulate neutrophil survival as documented in 39 , but also increase the bactericidal activity of neutrophils ., Neutrophil trafficking to the ocular surfaces at steady state has been reported and is considered to promote resistance to infections , however the stimuli that trigger neutrophil recruitment are not known 42–44 ., The LC-MS3 data clearly show increased neutrophil presence at the ocular surfaces of conventionally housed SPF mice , which was less pronounced in GF mice ( Fig 1 ) ., Consistently , there was a delayed initial recruitment of PMNs to the eye in the GF mice during P . aeruginosa–induced infection , which as disease progressed resulted in a more significant pathology at the later time points ( Fig 2 ) ., These data are congruent with previous results showing delayed neutrophil recruitment to the cornea in the Staphylococcus aureus infected GF mice 45 , and further elucidate the importance of microbiota-driven neutrophil recruitment to the ocular surfaces ., In conclusion , we demonstrate a clear role for microbiota in regulating the immune response at the ocular surface ., Mechanistically , gut microbiota primes the development and activation of neutrophils in the bone marrow , thereby regulating the pool of mature neutrophils and their activation state ., Locally , microbiota provides signals that regulate the magnitude of neutrophil recruitment to the ocular tissues during infection in an IL-1ß –dependent manner ., Identifying the commensal species and the biochemical composition of microbiota-induced signals that regulate neutrophil recruitment and bactericidal activities will allow development of new antibiotic-adjunctive therapeutic approaches ., All animal experiments were performed following National Institutes of Health guidelines for housing and care of laboratory animals and performed in accordance with institutional regulations after protocol review and approval by the Harvard Medical School Animal Care and Use Committee and were consistent with the Association for Research in Vision and Ophthalmology guidelines for studies in animals ( protocol 404R98 ) ., Mice were housed and bred in the Channing Laboratory Animal Care Facilities ., The SW GF mice were purchased from the Gnotobiotic Core Facility , Harvard Medical School ., Age and gender matched SW mice were purchased from Taconic Farms ., 8–10 week old , gender-matched mice were used throughout the experiments ., Invasive P . aeruginosa strains 6294 and PAO1 were used throughout these experiments ., The bacterial strains were grown overnight at 37°C on Tryptic Soy Broth agar plates supplemented with 5% sheep blood ., The bacterial suspensions were prepared in saline solution and used for subsequent infection experiments ., Infections were carried out as described previously 46 ., Briefly , mice were anesthetized with intraperitoneal ketamine and xylazine injections ., Three 0 . 5 cm scratches were made on the cornea with 25G needle tip and an inoculum of 1 x 107 cfu of P . aeruginosa delivered in 5 μl onto the eye ., Mice remained sedated for approximately 30 min ., For evaluation of corneal pathology , daily scores were recorded by an observer unaware of the experimental status of the animals based on the following scoring system using a graded scale of 0 to 4 as follows: 0 , eye macroscopically identical to the uninfected contra-lateral control eye; 1 , faint opacity partially covering the pupil; 2 , dense opacity covering the pupil; 3 , dense opacity covering the entire anterior segment; and 4 , perforation of the cornea , phthisis bulbi ( shrinkage of the globe after inflammatory disease ) , or both ., To determine corneal bacterial counts at 24h after infection , mice were sacrificed , the eyes were enucleated , and the corneas were dissected from the ocular surface ., To quantify P . aeruginosa levels , corneas were suspended in PBS , 0 . 05% Triton X100 , serially diluted and plated on P . aeruginosa selective McConkey agar plates ., Mouse and human gut reconstituted GF mice were generated as previously described 47 ., To generate monocolonized animals , 1000 CFU CNS sp ., were placed onto the ocular surface of GF mice ., Mice were rested for 2 weeks to allow for colonization prior to infection experiments ., Ocular swabs were collected and plated on blood-agar plates and mannitol agar plates to determine the levels of ocular colonization ., Ten μl PBS were instilled onto the ocular surface of each eye , pipetted up and down for five times and then pooled from both eyes ., Protein levels from pooled eye washes of 4 to 6 individual mice were quantified using a standard Bradford reagent ( BioRad ) , 20 μg of total protein were subjected to trypsin digestion and subsequent LC-MS/MS quantification ( The Thermo Fisher Center for Multiplexed Proteomics ) 48 ., Sample processing steps included SDS-PAGE purification of proteins , in-gel protein digestion using trypsin and peptide labeling with TMT 10-plex reagents ., Multiplexed quantitative mass spectrometry data were collected on an Orbitrap Fusion mass spectrometer operating in a MS3 mode using synchronous precursor selection for MS2 fragment ion selection 49 ., MS2 peptide sequence data were searched against a Uniprot mouse database with both the forward and reverse sequences using the SEQUEST algorithm ., Further data processing steps included controlling peptide and protein level false discovery rates , assembling protein groups , and protein quantification from peptides ., The p-values were calculated using Benjamini-Hochberg FDR correction 50–54 ., Eyes were enucleated from euthanized mice , fixed in 4% ( v/v ) paraformaldehyde , and subsequently embedded in paraffin ., Four μm sections were cut and stained with hematoxylin-eosin to visualize tissue morphology following previously used techniques 55 ., The levels of ocular inflammation in the corneal sections was quantified on a scale of 1 to 4 , with “1”being reflective of no neutrophil influx in the cornea or anterior chamber and healthy appearance; “2” denoting mild inflammation , preserved corneal epithelial layer , presence of neutrophils in the conjunctival tissues; “3” being reflective of moderate inflammation , loss of epithelial layer , influx of neutrophils in the corneal epithelium , less than 50 cells/ field of vision at 40X magnification , neutrophils lining the anterior chamber; and “4” denoting severe inflammation , lost corneal epithelial layer , massive influx of neutrophils in the cornea ( more than 50 cells/field of vision at 40X magnification ) ; numerous neutrophils present and scattered thought the anterior chamber ., Histological scoring was carried out by Dr . Roderick Bronson , ( HMS , Histopathology core ) blindly using sections which did not display genotypic and phenotypic information ., Cytokine levels ( IL-1ß , KC , IL-6 ) of corneal lysates were determined by commercially available ELISA assays ( R&D Systems ) ., In addition , IL-12p70 , IL-10 , IFN-γ were measured using a Meso Scale Discovery ( MSD ) multiplex 7-spot electrochemiluminescence ( ECL ) assay and outputs measured by an ultra-low noise charge-coupled device ( CCD ) Imager 2400 ( Meso Scale Discovery , Gaithersburg , MD , USA ) ., The MSD ECL platform has been previously validated against cytokine standards recommended by WHO and U . K . National Institute for Biological Standards and Control ( NIBSC ) and by comparison to traditional ELISA 56 ., Four week old SW SPF mice ( Taconic ) were treated with antibiotic cocktail in the drinking water containing carbenicillin ( 1g/L ) , neomycin ( 1g/L ) , metronidazole ( 1g/L ) , vancomycin ( Henry Shein ) ( 0 . 5g/L ) , and levaquin ( 0 . 15g/L ) for 4 weeks 4 , 41 , 57 ., One packet of a sucralose-based artificial sweetener ( Splenda , Heartland Food Products Group ) was used to make the antibiotics containing water palatable ., Fecal pellets were collected before and every week during the antibiotic treatment to enumerate the intestinal cultivable aerobic and anaerobic bacteria ., Antibiotic containing water was renewed every three days and animal cages were replaced every three days ., Fecal samples were serially diluted and plated on blood , McConkey and mannitol agar plates in duplicates ., Plates were incubated at 37°C both , aerobically and anaerobically ., To evaluate the impact of local microbiota on immunity to infection , separate cohorts of 8-week old , gender matched , SPF SW mice ( Taconic ) were treated with Gentak eye ointment ( Patterson Veterinary ) twice daily for four days ., The control group was treated with sterile saline ., Mice were rested for 4 days , and infections with P . aeruginosa were carried out as described above ., Conjunctival swabs were collected before and after treatment to enumerate conjunctival bacterial presence ., Murine bone marrow was flushed from both hind limbs with PBS supplemented with 2% fetal bovine serum and 1 mM EDTA ., The cells were washed , erythrocytes in the cell pellet were lyzed using the Mouse Erythrolysis Kit ( R&D Systems ) according to the manufacturer’s instruction , and neutrophils were isolated using the EasySep Mouse Neutrophil Enrichment Kit ( Vancouver , Canada ) ., Neutrophils were incubated with P . aeruginosa strain PA01 at an MOI of 100:1 for 90 min at 37°C on a rotator ., Aliquots taken at time 0 and 90 min were serially diluted and plated on McConkey agar to determine numbers of live P . aeruginosa ., Percentage of killing ability of neutrophils was cal | Introduction, Results, Discussion, Materials and Methods | The existence of the ocular microbiota has been reported but functional analyses to evaluate its significance in regulating ocular immunity are currently lacking ., We compared the relative contribution of eye and gut commensals in regulating the ocular susceptibility to Pseudomonas aeruginosa–induced keratitis ., We find that in health , the presence of microbiota strengthened the ocular innate immune barrier by significantly increasing the concentrations of immune effectors in the tear film , including secretory IgA and complement proteins ., Consistent with this view , Swiss Webster ( SW ) mice that are typically resistant to P . aeruginosa–induced keratitis become susceptible due to the lack of microbiota ., This was exemplified by increased corneal bacterial burden and elevated pathology of the germ free ( GF ) mice when compared to the conventionally maintained SW mice ., The protective immunity was found to be dependent on both eye and gut microbiota with the eye microbiota having a moderate , but significant impact on the resistance to infection ., These events were IL-1ß–dependent as corneal IL-1ß levels were decreased in the infected GF and antibiotic-treated mice when compared to the SPF controls , and neutralization of IL-1ß increased the ocular bacterial burden in the SPF mice ., Monocolonizing GF mice with Coagulase Negative Staphylococcus sp ., isolated from the conjunctival swabs was sufficient to restore resistance to infection ., Cumulatively , these data underline a previously unappreciated role for microbiota in regulating susceptibility to ocular keratitis ., We predict that these results will have significant implications for contact lens wearers , where alterations in the ocular commensal communities may render the ocular surface vulnerable to infections . | Contact lens wear is associated with frequent Pseudomonas aeruginosa–induced keratitis , however the reasons for this association remain unclear ., Recent genomics–based approaches revealed that contact lens wearers harbor altered ocular commensal communities when compared to non-lens wearers raising important questions , namely , does wearing of contact lenses increase the frequency of keratitis in patients due to contamination of the contact lenses with species derived from the skin or does ocular microbiota exert immune functions that are required for the maintenance of ocular health ?, We demonstrate a clear role for ocular microbiota in regulating protection against Pseudomonas aeruginosa–induced infections ., At the ocular surface , commensal bacteria provide signals that regulate the magnitude of neutrophil recruitment during infection ., These events may be driven by a frequent gram-positive commensal–Coagulase Negative Staphylococcus ( CNS ) sp ., In addition to the impact of ocular microbiota , there is an important contribution of gut microbiota that stimulate neutrophil development in the bone marrow , thereby regulating the pool of mature neutrophils and their activation state ., Cumulatively , these data show for the first time a role for microbiota in regulating the susceptibility to P . aeruginosa–keratitis . | blood cells, antimicrobials, medicine and health sciences, immune cells, microbiome, pathology and laboratory medicine, keratitis, pathogens, drugs, immunology, ocular anatomy, microbiology, pseudomonas aeruginosa, antibiotics, eye diseases, pharmacology, eyes, bacteria, neutrophils, bacterial pathogens, microbial genomics, pseudomonas, white blood cells, animal cells, medical microbiology, microbial pathogens, head, eye infections, cell biology, anatomy, cornea, ophthalmology, genetics, microbial control, biology and life sciences, cellular types, ocular system, genomics, organisms | 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journal.pcbi.1000616 | 2,009 | Meta-analysis of Inter-species Liver Co-expression Networks Elucidates Traits Associated with Common Human Diseases | The advent of expression profiling and other high throughput technologies has enabled us to systematically study complex human diseases by simultaneously measuring tens of thousands of molecular species in any given cell-based system 1 ., It is now routine to organize such large-scale gene expression data into co-expression networks to shed light on the functional relationships among genes , and between genes and disease traits 2 , 3 , 4 , 5 ., Analysis of co-expression networks can be used to study any tissue or organ ( such as liver , which plays a key role in the metabolism of glucose , lipids and toxic compounds ) , as long as the samples from such organs are collected in a population setting ., Given that mouse and rat populations are commonly used to study human diseases in this manner , it is important to understand the mechanisms that are conserved between human and the rodent species , especially as we seek better predictions of the efficacy of drug targets identified from mouse or rat in human populations ., In addition , identifying mechanisms that differ between humans and rodents can help to improve the design and interpretation of toxicity studies that involve rodent models ., Meta-analysis is the statistical synthesis of data by aggregating results from a set of comparable studies 6 ., It can be used to systematically examine similarities and differences between molecular profiling studies carried out in populations from different species 7 ., In a gene co-expression network , relationship between gene pairs is usually measured by correlation coefficients of different forms , such as Pearson correlation , Spearman correlation , or Mutual Information ., Therefore , the problem of combining or comparing co-expression relationships across multiple datasets can be framed in the context of a meta-analysis of correlation coefficients , for which various methods have already been introduced ., One method is Fishers Inverse test , which computes a combined statistic ( S ) from the p-values of the correlation coefficients obtained from ( k ) individual datasets as , ., Under fairly general conditions this statistic follows a distribution with degrees of freedom under the joint null hypothesis of no correlation , making it possible to compute p-values of the combined statistic ., Another widely used meta-analysis method involves computing a weighted average of a common metric ( i . e . effect size ) derived from correlation coefficients in the individual datasets ., Such statistic can then be used to test for homogeneity over the individual measures and for statistical significance ., Datasets in this type of meta-analysis are typically weighted by the accuracy of the effect size they provide , which is a function of the individual sample sizes ., Once the mean effect size is calculated , its statistical significance can be assessed by estimating the pooled variance of the mean effect size ., In defining the effect size , Hedges and Olkin 8 and Rosenthal and Rubin 9 both advocated converting the correlation coefficient into a standard normal metric using Fishers Z-transformation and then calculating a weighted average of these transformed scores ., Depending on whether the effect sizes are assumed to be equal or not in the multiple datasets , fixed effect as well as random effect models can be employed ., In the fixed effect models , the effect size in the population is a fixed but unknown constant and therefore is assumed to be the same for all datasets included in the meta-analysis ., For random effect models , effect sizes may vary from dataset to dataset , and are assumed to be a random sample of all population effect sizes ., Hunter and Schmidt 10 introduced a single random-effects method based on untransformed correlation coefficients ., One important feature of this type of method is that heterogeneity of the effect sizes can be estimated , which provides a way to assess the difference in correlation coefficients across multiple datasets ., Schulze 11 provided a thorough review of these meta-analysis methods and their applications ., For a meta-analysis of co-expression networks from diverse datasets , such as those constructed from different species , one central issue is that it is often unreasonable to assume that every gene pair has a unique , true effect size across evolutionarily diverse species ., Although random effect models provide a more realistic way to accommodate cross species variation , it still assumes a parametric distribution on the population effect sizes ., To circumvent this problem , a non-parametric meta-analysis method was introduced for the identification of conserved co-expression modules from human , fly , worm and yeast 7 ., In this method , Pearson correlation coefficients of expression profiles between every gene pair were computed in each organism and then rank-transformed according to their correlations with all other genes ., A probabilistic test based on order statistics was then applied to evaluate the probability of observing a particular configuration of ranks across the different organisms by chance ., The advantage of this method is two-fold:, 1 ) because the method is based on non-parametric statistics , it makes no assumption on the underlying distribution of correlation coefficients across multiple datasets; and, 2 ) the effect size ( i . e . the rank ratio statistic for every gene pair ) is defined in a gene-centric fashion such that for any given gene , correlations with all other genes are considered ., However , the method also has several limitations including, 1 ) the loss of power in general given the non-parametric formulization 12 , 13 , and, 2 ) the meta-analysis results cannot be represented in the same format as the individual datasets given there is no concept of a mean effect size ., The details of individual methods are presented in the Methods section ., Their pros and cons are summarized in Supplementary Table S1 ., In this paper , we develop a method for the meta-analysis of diverse datasets generated across multiple species ., Our method is semi-parametric in nature , requiring fewer assumptions on the distribution of the effect size than a purely parametric approach while retaining better statistical power than a fully non-parametric method ., It also, 1 ) defines an effect size that is gene centric ,, 2 ) allows for the computation of a mean effect size , and, 3 ) leads to a heterogeneity statistic to test for differences in correlation structures among distinct datasets ., Unlike most network alignment algorithms 14 , 15 , 16 , 17 , 18 ( with the exception of 19 ) or connectivity-based approaches 20 , our method does not rely on the networks inferred a-priori from individual datasets , but instead focuses on the development of rigorous statistics to test directly the relationship between every gene pair ., The simulation results showed that our method is robust against noises ., When applied to a human , mouse and rat cross species meta-analysis of liver co-expression networks , we demonstrate that our method out-performs existing methods in identifying functionally coherent gene pairs that are conserved among the three species ., Our method also leads to the identification of modules of co-expressed genes that represent core functions of the liver that have been conserved throughout evolution ., Both highly replicated and less confident genome-wide association study ( GWAS ) candidate genes for blood lipid levels are found to be enriched in the conserved modules , providing a systematic way to elucidate the mechanisms affecting blood lipid levels ., Application of our test for homogeneity leads to the identification of a single sub-network driven by ApoE that distinguishes two nearly identical experimental cross populations whose genetic backgrounds only vary with respect to the gene ApoE ., We further demonstrate that genes involved in human- or rodent- specific liver interactions tend to be under positive selection throughout evolution ., Finally , we identified a human-specific sub-network regulated by RXRG , which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse ., Taken together , our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only conserved information but also differences that are dataset-specific ., The intuition behind our meta-analysis approach in the cross-species setting is that , instead of directly comparing the correlation coefficients of a gene pair as an absolute measure of co-expression , which depends on many features such as sample size , expression dynamics , measurement noise , and confounding factors that are usually not well-controlled among the individual datasets , we measure the co-expression relationship as a relative distance with respect to each genes total relationship to all other genes in each dataset ., When the correlation coefficients between a given gene and all other genes were rank-transformed into a uniform distribution , the inter-relationships among the correlations were destroyed ., Unlike the previous method 7 we assume the distribution of correlation coefficients of one gene to all other genes follows a normal distribution under the condition that the numbers of samples and genes are large ( see Materials and Methods section for details ) ., In fact , for roughly 70–90% of the expression traits in our datasets , the distributions of their correlation coefficients to all other expression traits are well supported as being normal by the Kolmogorov-Smirnov test ( Figure S1 ) ., Based on this assumption , we define for gene pair ( i , j ) in dataset , the effect size of its co-expression according to Glasss d score definition 21 as:where is the correlation coefficient between the expression profiles of ( i , j ) in dataset , and and are the mean and standard deviation of the null distribution , respectively , of the correlation coefficients between gene and all other genes ., Essentially , by this definition we transform the correlation measure into a relative distance to the gene-centric mean in terms of standard deviation units ., This transformation not only normalizes all effect sizes , but also takes into account the context of each gene in individual datasets ., It is of further note that our effect size definition is directional , i . e . is usually different from due to differences in the neighborhoods of gene and ., For simplicity , we drop the superscript so that represents the effect size for any gene pair in dataset ., Using a meta-analysis procedure for d score that developed by Hedges and Olkin 8 , we can compute the mean effect size as:and the standard deviation of the mean effect size as:The statistical significance of the mean effect size can then be assessed by forming the Z-score statistic: In addition , heterogeneity of the effect sizes across the datasets can be estimated by the statisticwhich follows a distribution with degree of freedom under the null hypothesis of homogeneous effect sizes ., Given the mean effect size and heterogeneity statistic , a flowchart of our method is summarized in Figure 1 ., Briefly , the first step begins by computing correlation coefficients for all gene pairs in every dataset ., Correlation can be measured by the Pearson or Spearman correlation , depending on the properties of the datasets being analyzed ., The method then proceeds by iterating through all gene-pairs one at a time , computing the heterogeneity statistic for every gene-pair ., If homogeneity is not rejected at a pre-specified significance level , the mean effect size for the gene-pair is computed and tested for deviation from zero ., A statistically significant mean effect size is then considered as a conserved co-expression relationship among the datasets being compared ., On the other hand , if the homogeneity of the effect sizes is rejected , the gene-pair is considered as a candidate for change in co-expression relationships , termed differential interactions hereafter , between the datasets ., In this case , the direction of change can be determined by examining the actual effect sizes in single datasets ., To compare the performance of our semi-parametric method with the existing parametric and non-parametric methods , we ran several simulations ., In each simulation , 3 independent data sets were generated assuming the underlie structure is modular as shown in Figure S2 ( see Materials and Methods section for details ) ., There were 150 samples and 2000 genes in each data set ., The signal strength is measured by the correlation between the latent regulators and their downstream genes ., The signal strengths were different for the 3 simulated data sets , shown in Figure 2A ., When there was no systematic noise , the parametric methods ( FEM Fisher-Z and combine p-value ) performed better than non-parametric method , shown in Figure 2B ., It is consistent with other studies results that there are power losses in general for non-parametric methods 12 , 13 ., The performance of our semi-parametric method was between the parametric methods and the non-parametric method ., It is consistent with the nature that our semi-parametric is a hybrid of parametric and non-parametric methods ., It is worth to note that the random effect model ( REM Fisher-Z ) performed worst among methods tested even though the effect sizes were different as shown in Figure 2A ., When the systematic noises were moderate ( measured by the correlation between genes and systematic noises ) as shown in Figure 2C , the performances of our semi-parametric method and the parametric methods were similar , shown in Figure 2D ., When the systematic noises were stronger ( shown in Figure 2E ) , the performances of parametric methods decreased significantly , and our semi-parametric and non-parametric methods were robust against systematic noises ( shown in Figure 2F ) ., Under all conditions , our semi-parametric method performed better than the non-parametric method ., We applied our method to identify conserved co-expression interactions among 6 , 455 orthologous genes in human , mouse and rat ( see Materials and Methods for details about the data , data preparation and orthologous gene identification . The 6 , 455 genes are listed in Table S2 . The 2-D hierarchical clustering views of individual data sets are shown in Figure S3 , and ordered sample and gene annotations are listed in Table S3 , S4 , S5 , S6 , S7 , S8 ) ., We used the absolute Spearman correlation coefficient between the expression profiles of a gene pair as the measure of co-expression interaction ., By doing this we considered only the magnitude of gene-gene correlation , but not its direction , since the same gene-gene relationship may manifest as either a positively or negatively correlated expression profile due to feedback control 4 ., Specifically , our method inferred 20 , 230 conserved co-expression interactions , covering 4 , 885 genes , at a p-value cutoff of , corresponding to a Bonferroni corrected false positive rate of 5% ( i . e . ) for both effect size and the heterogeneity ., The false discovery rate ( FDR ) of this result is estimated to be based on a permutation test procedure where sample labels were randomly shuffled for each gene independently in every dataset ( see Materials and Methods for details ) ., These conserved interactions represent approximately 2 . 4–15 . 2% of the co-expression interactions obtained using single species data , given there were 828 , 031 , 334 , 721 and 132 , 884 interactions in human , mouse and rat , respectively , at the same statistical significance p-value threshold ., We benchmarked the performance of our method against existing meta-analysis methods in the literature , as well as against the interactions previously reported for single species co-expression networks 22 ., The number of predictions ( i . e . conserved interactions ) inferred by our method lies in between the numbers predicted by existing parametric and non-parametric meta-analysis methods at a common FDR threshold , shown in Table S9 , consistent with the semi-parametric nature of our approach ., When only considering the same number of top confident predicted pairs , the qualities of the semi-parametric method were better than other methods in terms of coherences with both Gene Ontology ( GO ) biological processes and curated KEGG pathways ( shown in Table S10 ) ., To test the full range of predictions , we generated precision vs . coverage curves for each method by varying the statistical significance thresholds and computing, 1 ) the percent of inferred gene pairs that share a common GO biological process annotation , and, 2 ) the percent of inferred gene pairs that share a common curated KEGG pathway ( Figure 3 ) ., Two conclusions stand out from these results ., First , all meta-analysis methods outperform the inference based only on single species datasets , likely due to the increased precision achieved by incorporating evolutionary information and the added power achieved by integrating multiple datasets ., Second , our method clearly outperformed all existing meta-analysis methods across the full spectrum of coverage , but most significantly at the stringent p-values ., This demonstrates the added value of combining the advantages of existing methods ., We next performed spectral clustering of the orthologous genes based on their interconnectivity in the conserved co-expression network and identified co-expressed gene modules , shown in Figure S4 ( see Materials and Methods for the spectral clustering method ) ., Table 1 summarizes the top 13 modules comprised of greater than 20 genes and their enrichment for GO biological process terms ., Almost all of the modules are observed to be coherent with respect to some biological processes and many of the indicated processes represent core biological processes in the liver , including immune response ( p<2 . 70×10−43 ) , carboxylic acid metabolic process ( p<6 . 6×10−16 ) , and sterol biosynthetic process ( p<1 . 9×10−27 ) ., It is of particular note that these modules differ from modules identified in single species datasets in that the genes in modules of the conserved co-expression network are functionally related based on evolutionary conservation , rather than on correlated gene expression alone ., Recent human genome-wide association studies have identified many candidate genes affecting blood lipid concentrations ., However , the mechanisms by which many of these candidate genes contribute to blood lipid concentration remains unclear 23 ., In addition , there are potentially many SNPs with weaker associations to lipid concentration that are difficult or impossible to detect or replicate given the lack of power in current GWAS 24 ., Therefore , an open question is whether there are many more genes harboring common variation that affect the polygenetic nature of lipid concentration regulation ., Because liver is a key tissue for lipid metabolism , we can use the liver networks to interpret the GWAS results and generate hypothesis regarding the mechanisms of the candidate genes ., Toward this end , we selected 30 recently identified lipid-associating loci 25 and assessed the ability of our conserved modules to annotate the 45 candidate causal genes nominated from these 30 loci ., Of the 45 candidate genes , 26 have orthologs in human , mouse and rat and were therefore included in our study ., Nineteen of these genes reside in human , mouse and rat conserved modules ( Table 2 ) , where the putative mechanisms with respect to lipid regulation can be annotated based on the module functions ., The results suggest that cellular processes such as sterol biosynthetic process and cell-cell communication are involved in regulating blood lipid concentration ., Of particular note is SORT1 , a gene that resides at the locus most significantly associated with LDL cholesterol 25 ., Based on the conserved modules , SORT1 belongs to module 1 , a module enriched for genes involved in cell-cell signaling ( p-value<6 . 51×10−23 ) ., Other candidate genes at lipid associated loci , such as GALNT2 and NCAN , also reside in module 1 , suggesting that cell-cell signaling is important for blood lipid regulation ., PCSK9 is clearly annotated as being involved in the sterol biosynthetic process along with FADS1 , FADS2 , HMGCR and MVK ., In contrast , only 14 of 26 candidate genes can be annotated based on modules derived from the human co-expression networks alone ( Table 2 ) ., The annotations of these genes based on the conserved modules are closer to their known functions than ones based on the human modules ( shown in Table S11 ) ., For example , MAFB is annotated as “transcription regulation” based on the conserved modules , but as “carboxylic acid metabolic” based on the human-only modules , whereas its annotation in GO is “positive regulation of transcription from RNA polymerase II promoter” ., These examples illustrate how the conserved human , mouse and rat modules can enhance the interpretation of GWAS and the annotation of candidate genes identified from these studies ., Blood lipid concentration regulation is a complex process , involving many different cellular pathways ., We have recently demonstrated that common variation of complex traits is caused by networks of genes as opposed to single genes 4 ., To assess whether GWAS results associate with entire networks of genes , we overlapped blood lipid concentration results from the Framingham heart study 26 and the Broad Institute lipid study 27 with the human , mouse and rat conserved liver network ., In this analysis , we consider a gene as associated with the blood lipid trait if any SNP associated with the trait in these studies lies within 50Kb of the gene ., Then , at a p-value threshold of 0 . 001 , 22 . 2% of the genes with human , mouse and rat orthologs are associated with blood lipid concentration in either study ., At the same p-value cutoff , 19 . 7% of all human genes in our dataset were associated with blood lipid concentration , suggesting that the lipid concentration regulation mechanism is conserved globally ( ∼1 . 13 fold enrichment , Fishers Exact Test ( FET ) p-value\u200a=\u200a5 . 38×10−11 , permutation adjusted p-value<0 . 001 , Figure S5A ) ., The distribution of genes associated with blood lipid concentration among the modules is shown in Figure 4A ., Seven of the 13 modules were observed to have a higher concentration of genes associated with blood lipids than the background ., Modules 1 , 7 and 11 were significantly enriched for genes associated with blood lipid levels ( 1 . 14 , 1 . 41 and 1 . 55 fold enrichment with FET p-values of 1 . 7×10−3 , 6 . 6×10−3 , and 7 . 4×10−3 , respectively ) ., These results suggest that cell-cell signaling , cell-adhesion and sterol biosynthesis pathways are associated with variation in blood lipid concentration regulation in the human population ., In contrast , a similar test was applied to modules identified from human expression profile data alone ., The module with the highest overlap with genes associated with blood lipid traits was not enriched for a coherent biological process and the module enriched for carboxylic acid metabolism were not significantly enriched for genes associated with blood lipid traits ( Figure 4B ) ., We have further showed that these results are not sensitive to the window size around the lipid-associating loci for selecting lipid-associating genes ., The trends of the global conservation of lipid-associating genes and results in Figure 4 hold true also for window size of 10K , 20K , 30K and 40K ( Table S12 and Figure S6 ) ., Genetic loci associating with blood lipid traits from both Framingham and Broad studies may harbor many genes in each of these regions ., Dissecting the true causal genes from those irrelevant ones remains a significant challenge ., We have previously shown that cis eSNPs – SNPs that are associated with the mRNA levels of genes residing in the same genomic regions – are enriched for functionally relevant genes associating with the trait of interest 28 ., In addition to the cis eSNPs , functionally coherent gene modules , representing the cellular processes associated with the trait of interest , can also help pinpoint the true causal genes ., By filtering the Framingham and Broad candidate lipid-associating genes with genes that either, 1 ) harbor a cis eSNP in its vicinity , or, 2 ) belongs to any of the three conserved co-expression modules enriched in lipid-associating genes , the overlap between the two studies becomes more significant than the un-filtered sets , demonstrating the utilities of cis eSNP and conserved co-expression modules in teasing out irrelevant candidate genes ( shown in Table 3; in this case , the cis eSNP genes we previously identified from a liver expression study were used 28 ) ., There were 395 genes ( Table S13 ) that are associated with a cis eSNP in the human liver , and are also in the three conserved co-expression modules we identified as associated with the blood lipid trait ., These genes represent the most likely causal genes controlling the blood lipid concentration by integrating GWAS candidate loci , human cis eSNP genes and conserved co-expression modules between human and rodent species ., Among these genes , four of them , SORT1 , FADS1 , FADS2 and GALNT2 , are recently reported as candidate genes at highly replicated genetic loci contributing to polygenic dyslipidemia 25 ., This result is statistically significant given there are only 26 such candidate genes in our initial set of 6455 orthologous genes between human and rodents ( a 2 . 51-fold enrichment , FET p-value<0 . 0189 , permutation adjusted p-value<0 . 015 , Figure S5B ) ., These results demonstrate that the combination of multiple types of information can provide an objective way to infer causal genes under the loci of interest ., Many factors contribute to the identification of differential interactions between human , mouse and rat , such as evolution differences , genetic background differences , and perturbation differences in the data sets ( such as genetic diversity in human liver data vs . diverse compound treatments in rat liver data ) , to name just a few ., As a proof of concept , we applied our meta-analysis approach to identify differential interactions between the liver co-expression networks from two previously reported F2 intercrosses ., The first F2 intercross was constructed between C57BL/6J ApoE null ( B6 . ApoE−/− ) mice and C3H/HeJ ApoE null ( C3H . ApoE−/− ) mice ( referred as BXH/apoe−/− ) 29 ., The second F2 intercross was constructed between C57BL/6J ( B6 ) wild type mice and C3H/HeJ ( C3H ) wild type mice ( referred as BXH/wt ) 30 ., These two crosses are essentially identical from the standpoint of genetic background , diet , and rearing , except that in one of the crosses the ApoE gene is knocked out ., Given this single gene difference between the crosses , we hypothesized that differentially connected genes would be enriched for genes associated with ApoE related pathways ., Our method identified 500 differentially connected genes involving 1 , 023 differential interactions between the BXH/wt and BXH/apoe−/− crosses ., GO enrichment analysis for this set of genes revealed that the only over represented biological process were those involving ApoE 31 , albeit these processes are highly overlapping , including the cholesterol metabolic process ( 4 . 5% vs . 0 . 7% background , p<5 . 6×10−6 ) , the sterol metabolic process ( 4 . 5% vs . 0 . 9% background , p<1 . 2×10−4 ) and the lipid metabolic process ( 15 . 2% vs . 7 . 2% background , p<3 . 3×10−4 ) ., Interestingly , no core biological processes in liver that do not involve ApoE ( e . g . , immune response ) were enriched , which serves as a negative control for our results ., To test whether these differential interactions were mainly driven by expression dynamic changes as the result of the ApoE gene knockout , we selected a set of 500 genes with the largest difference in expression variation between the two crosses ., GO enrichment analysis revealed no coherent biological functions represented in this set , indicating that the observed network changes could not be explained simply by dynamic differences in gene expression ., We further examined the mouse protein-protein and protein-DNA interaction networks curated from interaction databases and literature , including Ingenuity , GeneGO and HPRD , around the ApoE gene ., Of the 22 genes in the immediate neighborhood of ApoE , including ApoE itself , 4 ( 18 . 2% ) were inferred as differentially connected between the wild type and ApoE−/− crosses , and this proportion was highly significant ( ∼8 . 1 fold enrichment , FET p-value<1 . 1×10−4 , permutation adjusted p-value<0 . 001 ) ( Figure 5 and Figure S5C ) ., Taken together , these results demonstrate the ability of our meta-analysis procedure to dissect differentially regulated pathways around specific molecular perturbations ., Although our method is purely expression profile based , it can also recapitulate known physical interactions in the region of the source perturbation , which further supports the validity of our approach ., Differential interactions among diverse organisms can result from true evolutionary differences or from incomplete perturbations in the datasets we examined , leading to reduced expression dynamics in one or both of the interacting genes ., Here we assumed that the gene expression system in each species we examined was extensively perturbed , either directly or indirectly ( via second or higher order effects ) ., The human samples were collected from more than 400 unrelated individuals , making up an out-bred population comprised of 400 diverse genetic backgrounds ., The F2 mice obtained from the BXH crosses represent an in-bred population in which differences in the genetic background of the parental strains are randomly shuffled in each of the individual mice ., The rat expression profiles were generated by treating rats with a compendium of drug compounds with various mechanisms of action ., Therefore , although liver gene expression in each species is measured under different sets of perturbations , the extensiveness of these diverse perturbations was likely to render that most pathways were perturbed given there are a finite number of pathways ., We carried out the cross-species meta-analysis in a pair-wise fashion to produce human vs . mouse and human vs . rat comparisons ., For the human vs . mouse comparison our method identified 8 , 706 conserved interactions involving 3 , 205 genes , in addition to 613 differential interactions involving 547 genes ., For the human vs . rat comparison , we identified 10 , 809 conserved interactions among 3 , 310 genes , as well as 447 differential interactions among 420 genes ., All results were obtained using a p-value cutoff of ., We further characterized each orthologous gene considered in the comparisons by classifying each genes involvement in, 1 ) only conserved interactions ,, 2 ) at least one differential interaction ., Since it has been shown that genes differentially connected in the co-expression and physical interaction networks tend to evolve at different rate 32 , 33 , we also attempted to characterize the evolutionary rate for each group by measuring the ratio between the rate of non-synonymous to synonymous substitution ( Ka/Ks ) 34 in the protein coding regions of the respective genes ., Interestingly , for both comparisons we found that genes involved in a larger number of differential connections tend to have a higher Ka/Ks ratio ( Figure S7 ) ., These results suggest that stronger positive selection ( or relative weaker negative selection ) may lead to new advantages for a given gene by increasing or decreasing the number of its co-expression partners ., To further illustrate this point , we expanded our analysis to include genes that are non-orthologous between human and | Introduction, Results, Discussion, Materials and Methods | Co-expression networks are routinely used to study human diseases like obesity and diabetes ., Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species , as well as those that are species-specific characterizing evolutionary plasticity ., We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species ., The simulation results showed that the semi-parametric method is robust against noise ., When applied to human , mouse , and rat liver co-expression networks , our method out-performed existing methods in identifying gene pairs with coherent biological functions ., We identified a network conserved across species that highlighted cell-cell signaling , cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels ., We further developed a heterogeneity statistic to test for network differences among multiple datasets , and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution ., Finally , we identified a human-specific sub-network regulated by RXRG , which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse ., Taken together , our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific . | Two important aspects of drug development are drug target identification and biomarker discovery for early disease detection , disease progression , drug efficacy and drug toxicity , etc ., Recently , many single nucleotide polymorphisms ( SNPs ) associated with human diseases are discovered through large genome-wide association studies ( GWAS ) ., However , it is still largely unclear how these candidate SNPs may cause human diseases ., The ultimate aim of this paper is to put these GWAS candidate SNPs and their associated genes into a network context to understand their mechanism of action in human diseases ., In addition to large-scale human data sets that are often heterogeneous in terms of genetic and environmental factors , many high quality data sets in rodents exist and are frequently used to model human diseases ., To leverage such information , we developed a method for combining and contrasting gene networks between human and rodents , specifically to elucidate how GWAS candidate SNPs may contribute to human diseases ., By identifying mechanisms that are conserved or divergent between human and rodents , we can also predict which disease causal genes can be studied using rodent models and which ones may not . | genetics and genomics/bioinformatics, computational biology/systems biology, genetics and genomics/functional genomics | null |
journal.pcbi.1002419 | 2,012 | Effect of Network Architecture on Synchronization and Entrainment Properties of the Circadian Oscillations in the Suprachiasmatic Nucleus | In mammals , the suprachiasmatic nucleus ( SCN ) of the hypothalamus constitutes the central circadian pacemaker 1 , 2 ., The SCN comprises about 20000 densely packed neurons organized into bilateral pairs of nuclei on each side of the third ventricle , above the optic chiasm 2 ( Fig . 1 ) ., The cells receive light signals from the retina via the optic nerve ., The SCN controls circadian rhythms in other parts of the brain including the cortex and the pineal gland , as well as in peripheral tissues such as the liver , kidney , and heart ., This hierarchical organization of the circadian system ensures the proper timing of physiological processes and behavior 1 , 3 ., In natural conditions , the organism is subject to the alternation of days and nights ., In response and anticipation to this cycling environment , the circadian pacemaker adjusts the phase of clock-controlled processes with respect to the light-dark cycle ., Each SCN neuron expresses clock genes ., Interconnected transcriptional and translational feedback loops form the core circadian network allowing each cell to produce circadian oscillations 4 , 5 ., Such oscillations still subsist in cultured cells ., However , in dispersed culture , the oscillator population is highly heterogeneous: many cells present damped oscillations 6 and the period of the oscillations varies from cell to cell 7 ., To produce a reliable global rhythm , the SCN cells must oscillate in synchrony ., Synchronization is achieved via intercellular coupling mechanisms 8 , 9 ., The SCN can thus be regarded as a network of coupled oscillators ., Cells of the SCN can be roughly divided in two groups of neurons that differ by their light sensitivity , the neurostransmitters they produce , and consequently by their coupling properties 2 ( Fig . 1 ) ., Besides GABA which is expressed by all SCN neurons 10 , several region-specific neurotransmitters have been identified ., In the ventro-lateral region ( VL ) , the neurons mainly express vasoactive intestinal peptides ( VIP ) , whereas the neurons of the dorso-medial region ( DM ) express a different neural hormone , the arginine-vasopressin ( AVP ) ., When the two regions are dissociated , the VL cells remain synchronized while the DM cells run out of phase 11 ., Such results suggest that the two SCN regions differ by their intercellular coupling properties ., Additionally , only the VL region is light-sensitive and just a distinct subset of VL neurons is directly influenced by the photic input 12 , 13 ., Little is known about the connectivity and topological properties of the SCN cellular network ., However the characterization of anatomical and functional connectivity in other regions of the brain ( e . g . cortex ) revealed small-world properties 14 , 15 , 16 ., Small-world topology combines local and long-range connections , thereby decreasing the average path length between cells 17 ., Such organization was shown to lead to more efficient synchronization at a lower energy cost ( because fewer connections are needed ) 18 , 19 , 20 , 21 , 22 ., It is thus reasonable to assume that the SCN also exploits such network properties to efficiently synchronize neurons ., In this paper we developed a multi-oscillator model for the SCN and investigated the implication of the network topology on synchronization and entrainment properties ., The model studied here extends the work previously published by Bernard et al . 23 in three main directions: we introduced heterogeneity among the different SCN cells , we systematically compared generic network topologies , we proposed a model accounting for the distinction between two distinct subareas in the SCN , and investigated the possible role of this separation in the response of the SCN to light signals ., The core cellular oscillator is a molecular model of intermediate complexity , which is based on interlocked feedback loops 24 ., In the present work , we introduced cellular heterogeneity through variability in parameter values to mimic experimental observations and various topologies for the coupling of the oscillators: random , scale-free , and local networks ., Long connections are present in the random and scale-free topologies forming a small-world network 25 , 26 ., Scale-free networks are characterized by a skewed distribution of the connections where a few cells ( hubs ) are connected to a large number of cells while the rest have few outgoing edges ., On the contrary in a local topology , cells are only connected to their close neighbors ., We compared the dynamical properties of the different networks: resynchronization time after a temporary arrest of the oscillations or after a transient decoupling , the synchronization and entrainment performances , as well as the response of the system to jet lags ., Finally , we proposed a coupled dual network as a model of the VL-DM organization of the SCN ., Several models have been proposed for the cellular mammalian circadian clock ., Earlier models are mostly phenomenological and rely either on abstract equations 27 , 28 , or on simple biomolecular mechanisms 29 ., More recently , detailed molecular models have been proposed 24 , 30 , 31 , 32 ., For our purpose we have chosen the model of intermediary complexity proposed by Becker-Weimann et al . 24 ., This models does not explicitly incorporate all clock components ( for example no distinction is done between Per1–3 and Cry1–2 , the Per/Cry complex is denoted by ) , but accounts for the core architecture of the circadian clock , involving interlocked positive and negative feedback loops ( Fig . 2 ) ., To take into account coupling and light entrainment , the Becker-Weimann model was extended to include a neurotransmitter and a signaling cascade 23 ., The coupling between the molecular oscillators is accomplished by a neurotransmitter , released upon Per/Cry complex activity in the upstream cell ., The neurotransmitter triggers , in the target cell , a signaling cascade involving PKA and CREB that have been experimentally shown to activate Per/Cry transcription 33 , 34 ., The resulting two-step cascade can be seen as a generic signaling pathway ., In addition to a modulation by CREB , the production of Per/Cry mRNA ( ) is also increased by light in the light-sensitive cells ., Overall , the model we used comprises ten state variables that represent different molecular species or complexes ( Fig . 2 ) ., Reaction rates are modeled using mass-action kinetics , except for the regulated mRNA production rate where Hill-type functions are used ( see Eqs ., ( 1 ) in Models ) ., To mimic the experimentally observed population heterogeneity , we introduce variability in all model parameters except Hill coefficients ., For each cell , the parameters ( see Eqs ., ( 1 ) and Tab ., S1 ) are uniformly distributed in the logarithmic space around the original parameter values where the variability is controlled by the heterogeneity parameter : with , being a uniform distribution in the range ., All individual parameters are randomly chosen without any intra- or intercellular correlations ., Examples of individual oscillators are shown in figure 3A ., We tested different values of between 0 . 025 and 0 . 3 ( Fig . 3B–C ) and observed that small values of generate a population where about half of cells have limit-cycle oscillations ( Fig . 3B ) , but the pseudo-periods ( defined as the average duration between two peaks , see Models ) have little variability ( Fig . 3C ) ., On the other hand , large values of lead to high heterogeneity where some cells are overdamped and the pseudo-periods are broadly distributed ., In the intermediate regime ( ) , the results are not very sensitive to , therefore for the different simulations , we chose a value of ., For this value , about 35–40% of the cells oscillate in isolation as observed experimentally 6 ( Fig . 3D ) ., The distribution of the pseudo-period of oscillation is centered on a value of 21 . 2 hours with a standard deviation of 0 . 7 hour ( Fig . 3E ) which is in the range of experimental results 35 ., To form the SCN network , we supposed that the cells are connected with directed ( unidirectional ) edges through the dendrites ., The upstream cell produces a neurotransmitter acting on a signaling cascade in the downstream cell that increases expression , the coding mRNA ( Fig . 2 ) ., The effect of the incoming signals from the different cells sums up until saturation ( see Eq ., ( 3 ) in Models ) ., The coupling parameter , that represents the strength of the effect of on PKA activation in the downstream cell , was set to a value of 0 . 5 for most simulations ( the effect of the value of will be discussed later ) ., Note that , although is identical for all cells , intercellular heterogeneity causes variability in the connection strengths due to differences in the dynamics of the species involved in the cell-cell communication ( , PKA and CREB ) ., In this analysis , we mainly focused on the effect of the network topology on the synchronization properties and ignore the effect of individual parameters ., We selected three generic types of networks: random connections between cells ( ) , scale-free distribution of the outgoing edges ( ) , or local connections only ( ) ( see Models and Fig . 4 ) ., Each type of network contains 200 cells and we tested different values of , the average number of edges per cell , ranging from 3 to 15 ., For simulations with light , in agreement with the experimental observations 13 , we assumed that only 20% of cells , on average , are light-sensitive and the distribution of light-sensitive cells can be either random ( , or respectively , second column in Fig . 4 ) , biased to favor the cells with the highest outgoing degree ( or ) or spatially localized in the case of the local topology ( , third column in Fig . 4 ) ., In the six topologies , the average degree and the fraction of light-sensitive cells are identical to allow a fair comparison ( see Tab . S2 ) ., We first performed the following in silico experiments 9 , 11 , 36: interruption of the protein production due to an administration of cycloheximide ( CHX ) or interruption of the cell-cell communication through exposure to tetrodotoxin ( TTX , see Models for implementation ) , both in a network without light entrainment ., Our results are consistent with the experiments: cells stop oscillating upon exposure but quickly resynchronize after CHX ( Fig . 5A ) or TTX wash-out ( Fig . 5B ) ., The phase of the individual oscillators ( measured at the stationary state , about 30 cycles after the perturbation ) is conserved after both CHX ( Pearsons , in Fig . 5C ) 11 and TTX ( Pearsons , in Fig . 5D ) perturbations 36 ., Results in figure 5 were made using a scale-free network , but the other topologies tested in this work ( random and local ) display similar results ., As previously reported 37 , the period of the individual oscillators is negatively correlated with the difference between the phase of the same oscillator and the phase of the network ( Pearsons , , Fig . S1 ) ., To compare the different networks , we focused on the concentration of the PER/CRY complex ( variable ) averaged over all cells ( , see Eq ., ( 7 ) in Models ) and evaluated its amplitude and period of oscillation ( see Fig . 6A and Models ) ., As the networks are randomly generated , all results in figures 6 and 7 represent the mean of the value measured over 30 different networks ., We also defined two different order parameters as described in the Models section , equations ( 8 ) and ( 9 ) : the state order parameter that measures how synchronized are the individual oscillators over the length of the simulation , and the phase order parameter , that measures how the individual oscillators are in phase at a given time ( note that this measure is independent of the magnitude of the amplitude ) ., It is worth noting that the coupling function implies that a cell always acts on the dynamics of its downstream cells even if they are synchronized ., This differs from a diffusive interaction ( e . g . Kuramoto oscillators 28 , 38 in which the coupling depends on the phase difference ) for which synchronized oscillators have no influence on each other ., This property , along with cellular heterogeneity , prevent the application of theoretical results found in the literature 39 , 40 , and require a numerical analysis ., In the following , we distinguish two different conditions of simulation: in constant dark ( DD , no entrainment , Fig . 6A ) or in a light-dark cycle ( LD , period of 24 hours with 12 hours of entrainment , Fig . 6B ) ., The results of the mean amplitude and the oscillatory period for different network architectures ( average of 30 randomly chosen networks for each condition ) are shown in figure 6C–F ., Considering the random networks , the amplitude of oscillations strongly depends on the average degree with the maximum value seen for an intermediate connectivity: 5 edges per cell for the case without entrainment ( Fig . 6C ) and 7 with entrainment ( Fig . 6D ) ., This dependence on the number of edges is also reflected in the order parameters and whose values are maximal for an intermediate connectivity of ( Fig . S2 ) ., The period in DD conditions is around 25 hours for low connectivity which is closer to experimental evidence 35 than the period of 22 hours found in highly connected networks ., In LD conditions , all random networks have a 24-hour period , reflecting proper entrainment by light ., For the scale-free networks , the amplitude the system exhibits in darkness is the largest of all topologies , also for very low connectivity ( Fig . 6C ) ., It drops when increasing the number of edges and converges to the results of the other networks ., The period of networks without entrainment is around 26 hours for and decreases down to 22 hours for higher connectivity as for the random networks ., In light-entrained conditions , a significant difference can be noticed between the case where the light-sensitive cells are randomly distributed ( ) , and the case where the cells with high outgoing degree are light-sensitive ( ) ., Although both network types show large amplitude , the networks do not systematically have the same period as the entrainment signal for because a significant fractions of the cells are not located downstream of a light-sensitive cell ., On the other hand , networks have a period of 24 hours for all tested values which means that networks are more suitable to represent the SCN ., For the local topology , we observed that , without entrainment ( left column in Fig . 6 ) , local networks have a low amplitude due to a lack of synchronization throughout the network ( see also Fig . S2A–B ) ., Clearly , since the connections are only local ( Fig . 4 ) , the network does not have the small-world property 25 ., On the other hand , with light entrainment , local networks with a random distribution ( ) of the light-sensitive cells have ample oscillations and a 24-hour period ., In this specific case , due to the random distribution of light-sensitive cells , most of the cells are directly downstream of a cell entrained by light even for small ( Fig . S3 ) ., In the case where the light-sensitive cells are closely localized ( ) , the entrainment efficiency is weak and the oscillation amplitude of is low ., These results suggest that , in constant dark , the scale-free , and to a lesser extent , the random architectures with an intermediate connectivity ( 5–7 edges per cell on average ) seem to represent the experimental data best ., In contrast , local architectures as defined in our work impede an efficient synchronization of the cells and therefore show small oscillations ., In LD conditions , the distribution of the light-sensitive cells plays a significant role and the networks that have a smaller average distance to a light-sensitive cell ( Fig . S3 ) , i . e . the , , or networks , show a larger oscillatory amplitude ( Pearsons , over all networks types and average degrees ) ., The relationship between the average number of degrees and the amplitude in both DD and LD conditions ( Fig . 6C–D ) suggests that a strong connectivity is detrimental for system performance ., This raises the question of how the value of the coupling constant affects the network oscillations ., While maintaining , a stronger coupling constant ( larger ) decreases the amplitude and the period of oscillations in DD conditions ( Fig . S4A , C ) ., In light/dark conditions , the relation between and oscillation amplitude follows a bell-shaped curve , the maximum of which depends on the network type ., For networks , a weak coupling ( ) is optimal , whereas an intermediate coupling ( ) favors networks and a strong coupling ( ) is preferred for random networks ., Note that , for most of the values the performance ranking of the network types remains the same ( scale-free networks showing largest amplitude ) ., In addition , although can be fine-tuned to increase the performance of a given network type , the results we obtained with are qualitatively similar to results with other values which is why will be used for further analyses ., We then considered the case of a perturbation in the entrainment pattern of light/dark alternation ., Since one of the goals of the circadian clock is to ensure the adaptation to the day-night cycle , an efficient clock should resynchronize rapidly after a jet lag ., We chose the case of an 8-hour shift resulting in a long night of 20 hours , followed by the regular 12 h∶12 h LD cycle ., As a measure of resynchronization , we considered the number of cycles until the system recovers , i . e . has a phase difference between the peak of and the beginning of the night similar to the one prior to the jet lag 41 ., We also determined the maximal decrease of the phase order parameter after the jet lag as a measure of how the individual cells desynchronize as a consequence of the jet lag ., As shown in figure 7A–C , the effect of a long night depends on the network type ., In the case of an topology , the synchronization of the system is hardly perturbed ( blue line in Fig . 7A ) and the phase difference between the peak of and the beginning of the night recovers its value prior to the jet lag in about 3 cycles ., On the contrary , the network needs about 6 cycles to regain the proper phase with a strong decrease of synchronization ( Fig . 7B ) ., For the network , although the system experiences desynchronization , the phase difference is recovered in about 4 cycles ( Fig . 7C ) ., A systematic analysis of the different network types shows that random networks ( and ) and scale-free networks with biased distribution of the light-sensitive cells ( ) undergo very little desynchronization ( Fig . 7D–E ) ., Note that the results for the networks are less relevant because these networks display very low amplitude ., In order to generalize the measured advantage of the , and network types for resynchronization after a jet lag , we tested 3 other types of 8-hour shifts: a short night , a long and a short days ., The results ( summarized in Fig . S5 ) show that these three types of networks are also the best performers when experiencing other types of jet lags , but also that the long day or night ( delay shifts ) have less impact than the short day or night ( advance shifts ) ., We further investigated this difference between delays and advances for and networks with ., For different shifts ranging from 4 to 10 hours , long shifts induce longer resynchronization time ( Fig . 7F–G ) , but additionally , the network resynchronizes significantly faster after a delay than an advance of the same shift duration ( Wilcoxons with n\u200a=\u200a30 for all shifts and both networks , expect for with a 4-hour shift ) ., Remarkably , this corresponds to experimental evidence on mice 42 and physiological observations showing that recovery from a jet lag due to westbound flights ( long day or night ) is easier than recovery from eastbound ones 43 ., The next question we addressed concerns the separation of the SCN in two different regions , namely ventro-lateral ( VL ) and dorso-medial ( DM ) ., Experimental observations have shown that the VL is entrained by light but oscillates with large amplitude even in dark conditions 11 , 12 ., These properties closely correspond to networks with , or architectures ., On the other hand , the current consensus for the DM , is an entrainment through the VL and not directly by light 11 ., Additionally , when detached from the VL , the cells of the DM hardly oscillate and are not synchronized ., When looking for these features in the network types studied above , a local network with random distribution of the entrained cells seems to best represent the DM ., In terms of geometry , the VL forms a core surrounded by the DM which would lead to the hypothesis that connections between the VL and the DM regions occurs locally on the border between the two regions ., A biased distribution of the light-sensitive cells in the VL is also plausible as the SCN is located above the optical chiasm ( Fig . 1 ) and thus the cells located in the lower part of the VL could be more sensitive to the light clues ., Such configuration would allow a compact organization of the SCN without long neuronal connections ( Fig . 8A–B and S7 ) ., To test this hypothesis , we performed simulations of a SCN composed of two regions with the following properties ., The VL is modeled by an network composed of 200 cells and has an average value of edges ., Random networks are also able to produce ample oscillations in the VL ( Fig . S6A , C ) , however the local networks are not plausible due to their low amplitude in DD conditions ., For the DM , we chose a local network of 200 cells with surrounding the VL region as other topologies would require long connections across the VL ., Cells of the VL and the DM are heterogeneous with parameters distributed as previously ( ) ., Entrainment of the DM by the VL is made by local connections with an average outgoing degree of ( Fig . S7 ) ., Note that this architecture implies that no DM cells can be upstream of a VL cell ., The simulations of this system show good synchronization and entrainment of the DM part in both dark and light/dark conditions ( Fig . S8 ) ., However we saw a delay of the DM phase in comparison to the VL ( Fig . S8B , D ) , which contradicts the experimental results 11 ., To counter this problem , we used faster oscillating cells for the DM ( see Eq ., ( 6 ) in Models ) as suggested by experimental data 44 ., With this adjustment , the DM is not properly entrained by the VL because the free-running period of the whole DM is too short ., This can be improved by decreasing to in the DM only ( Fig ., 8 ) which results in oscillations with larger amplitude ( Fig . S4B ) in LD conditions , as well as an increase of the free-running period ( Fig . S4C ) ., Additionally , reducing the coupling has also been suggested as a way of facilitating the entrainment 45 ., With this configuration , the center of the DM is in phase with the VL and some exterior cells are in phase advance ( Fig . 8A , B ) ., When isolated from the VL , the DM cells are not synchronized ( Fig . S9 ) which is in agreement with experimental observations 11 ., Note that for a core formed of a random network , the DM is delayed in LD conditions despite these adjustments ( Fig . S6B , D ) ., This suggests that a scale-free architecture is the most plausible topology for the VL region of the SCN ., A possible advantage of a division of the SCN in two regions can be to filter disturbances of the entraining LD cycle ., To test this hypothesis , we perturbed the light inputs in two different ways and measured the effect on in the VL and the DM regions of the SCN ., The first perturbation is an interruption of 4 hours of the light cue during the day ( a pulse of light during the night has only a marginal effect and was therefore not studied further ) ., In this case , ( Fig . 9A ) , the amplitude of the average concentration over the VL cells rises before dropping by about 20% ., The initial value is recovered after about 10 cycles ( Fig . 9C ) ., The phase is also affected , first delayed by about 1 . 5 hours and then advanced by the same value ( Fig . 9E ) ., However , the amplitude of the DM part is hardly affected by the perturbation , although the maximal phase shift is similar ., To quantify the effect of the perturbation , we defined as the average normalized difference between the peaks and the stationary peaks over 300 hours after the perturbation ( see Eq ., ( 10 ) in Models ) ., Averaged over 30 different networks , the effect of the 4 h light interruption on the VL is , which is 33% more ( Wilcoxons ) than on the DM: ., The second perturbation studied is , as previously , a jet lag of 8 hours occurring during the night ( resulting in a long night of 20 hours ) ., The VL cell reacts strongly by increasing the peak value of oscillations by about 50% ( Fig . 9B , D ) ., As already measured ( Fig . 7E ) , the phase of the VL adjusts precisely to the new entrainment pattern in about 4 cycles ( Fig . 9F ) ., The phase of the DM follows the VL within one cycle reaching the correct phase in 5 cycles ., Here also , we observed a strong difference between the VL and the DM parts of the SCN: whereas ( Wilcoxons ) ., These results suggest that the separation of the SCN in two parts with different topologies allows the DM region to have a lower sensitivity to short entrainment perturbations while at the same time better adapts to long term changes than a network formed of a unique topology such as the VL ., In this work , we addressed the question of the organization of the neuronal cells in the SCN by assessing the synchronization properties of different types of networks ., In these networks , each cell is a circadian oscillator but the population shows heterogeneity in its oscillatory behavior as observed experimentally 7 , 6 ., We found that , in general , the network is able to cope with cellular heterogeneity and the system oscillates with large amplitude and a period slightly longer than the individual period which is consistent with in vitro measures 46 ., Our results show that the architecture of the network , independently of the number of cells in the network ( Fig . S10 ) , plays a significant role in the synchronization properties ., In general , we observed that a strong connectivity , either due to a high number of connections or a strong value of the coupling constant , is detrimental for the amplitude of oscillations ., The distribution of the edges also plays a critical role: Vasalou et al . 25 already observed that small-world networks are better synchronized than networks with local connections ., Our results not only confirm that random networks better synchronize than our local networks , but also show that scale-free networks exhibit larger oscillations and better synchrony with fewer connections in DD conditions ., In LD conditions , a strong correlation exists between the average distance to a light-sensitive cell and the performance of the network ( Fig . S3 ) ., In our work , two types of networks result in a short average distance and therefore ample oscillations in LD conditions: ( 1 ) networks with a uniform degree distribution ( local or random ) and uniformly distributed light-sensitive cells , or ( 2 ) scale-free networks where the cells with high outgoing degree are light-sensitive ., These results were obtained with a variability as the distribution of individual cells properties matched experimental data ., We now briefly comment on the effect of the value for the different types of networks ., To simplify the analysis , we varied only for networks with an average degree of as all types of networks show good performance for this value ., In both DD and LD conditions , although the synchronization increases , oscillation amplitude remains similar for values of between 0 and 0 . 1 ( Fig . S11A–B ) , reflecting that the networks can efficiently cope with some cell-to-cell variability and that a tight tuning of individual oscillators is not necessary ., This property holds for all types of networks ., Cell heterogeneity also induces phase fluctuation 47 and we found a rather weak correlation ( Pearson correlation coefficient ) between individual phase differences and the period of the cellular oscillators ( Fig . S1 ) which is closer to experimental observations 47 than the high correlation reported for simpler models where heterogeneity was only introduced at the level of the period 37 ., One of the properties of the circadian clock is adaptation to changes in the entrainment pattern for example after a jet lag or a long period of dark ( hibernation ) ., Although circadian rhythms and chronotherapy play an important role in medicine , the specific case of jet lag has only been marginally discussed in the modeling literature 41 ., Our contribution to this question shows that the network topologies are strongly related to the resetting of the SCN with an advantage for small-world networks ( such as random or scale-free networks with biased distribution of light-sensitive cells , ) with an intermediate connectivity of 5–7 edges per cell ., When comparing our results to experiments 42 , 43 , we observed that the networks are closer to the experimental results where resynchronization is fast ( 2–3 cycles ) for delay , and slower ( 4–5 cycles ) for advance in the entrainment , confirming the observations that the circadian rhythm is more affected by eastbound than westbound-induced jet lags ., It is also interesting to notice that a heterogeneous cell population seems to enhance resynchronization after a jet lag for the , and network types ( Fig . S11C–D ) ., Remarkably , experimental observations already suggested that the SCN regional heterogeneity and the multiple phase relationships among SCN cells could contribute to the photoperiodic adaptation 48 ., Alternatively , a different entrainment pattern with a shorter light exposure ( diurnal duration of 8 hours with a period of 24 hours ) , results in ampler oscillations than a system with a 16-hour light exposure especially for networks ( Fig . S12 ) , which is once again consistent with experimental observations 49 ., Finally , the last and probably most ambitious part of this work consisted of coupling two networks with different properties to mimic the two regions of the SCN , namely the ventro-lateral and the dorso-medial parts ., From our previous results , we selected a network combination that matched experimental facts: namely a core ( VL ) that is entrained by light and oscillates on its own , and a shell ( DM ) that can have sustained oscillations only while entrained by the VL ., A scale-free network with biased distribution of the light-sensitive cells for the VL combined with a local network for the DM results in the desired properties with minimal connections ., To more accurately match experimental data , we had to decrease the period of the cells in the DM as well as their coupling strength ., With these adjustments , we obtained waves of expression through the SCN ( Fig . S13 ) as observed in cultured SCN slices 47 , 50 ., Other combinations of parameters can possibly reproduce the properties of the VL and DM parts but our exploration of the different types of networks was not exhaustive , due to high number of possible combinations ., We nevertheless tried different types of connectivity for the DM as well as different distribution of the edges ( allowing longer connections ) and eventually obtained a valid model of the SCN that can be used for further analysis ., In this work , we found that combining two networks with different connectivity properties ( both in the topology , the strength of connections and the oscillation speed of the individual cells ) showed better results than a homogeneous network ., These results may provide insigh | Introduction, Results, Discussion, Models | In mammals , the suprachiasmatic nucleus ( SCN ) of the hypothalamus constitutes the central circadian pacemaker ., The SCN receives light signals from the retina and controls peripheral circadian clocks ( located in the cortex , the pineal gland , the liver , the kidney , the heart , etc . ) ., This hierarchical organization of the circadian system ensures the proper timing of physiological processes ., In each SCN neuron , interconnected transcriptional and translational feedback loops enable the circadian expression of the clock genes ., Although all the neurons have the same genotype , the oscillations of individual cells are highly heterogeneous in dispersed cell culture: many cells present damped oscillations and the period of the oscillations varies from cell to cell ., In addition , the neurotransmitters that ensure the intercellular coupling , and thereby the synchronization of the cellular rhythms , differ between the two main regions of the SCN ., In this work , a mathematical model that accounts for this heterogeneous organization of the SCN is presented and used to study the implication of the SCN network topology on synchronization and entrainment properties ., The results show that oscillations with larger amplitude can be obtained with scale-free networks , in contrast to random and local connections ., Networks with the small-world property such as the scale-free networks used in this work can adapt faster to a delay or advance in the light/dark cycle ( jet lag ) ., Interestingly a certain level of cellular heterogeneity is not detrimental to synchronization performances , but on the contrary helps resynchronization after jet lag ., When coupling two networks with different topologies that mimic the two regions of the SCN , efficient filtering of pulse-like perturbations in the entrainment pattern is observed ., These results suggest that the complex and heterogeneous architecture of the SCN decreases the sensitivity of the network to short entrainment perturbations while , at the same time , improving its adaptation abilities to long term changes . | In order to adapt to their cycling environment , virtually all living organisms have developed an internal timer , the circadian clock ., In mammals , the circadian pacemaker is composed of about 20 , 000 neurons , called the suprachiasmatic nucleus ( SCN ) located in the hypothalamus ., The SCN receives light signals from the retina and controls peripheral circadian clocks to ensure the proper timing of physiological processes ., In each SCN neuron , a genetic regulatory network enables the circadian expression of the clock genes , but individual dynamics are highly heterogeneous in dispersed cell culture: many cells present damped oscillations and the period of the oscillations varies from cell to cell ., In addition , the neurotransmitters that ensure the intercellular coupling , and thereby the synchronization of the cellular rhythms , differ between the two main regions of the SCN ., We present here a mathematical model that accounts for this heterogeneous organization of the SCN and study the implication of the network topology on synchronization and entrainment properties ., Our results show that cellular heterogeneity may help the resynchronization after jet lag and suggest that the complex architecture of the SCN decreases the sensitivity of the network to short entrainment perturbations while , at the same time , improving its adaptation abilities to long term changes . | systems biology, biology, computational biology, genetics and genomics | null |
journal.pcbi.1005953 | 2,018 | SozRank: A new approach for localizing the epileptic seizure onset zone | Epilepsy is one of the most common neurological disorders affecting about 70 million people worldwide ., It is characterized by recurrent episodes of abnormal neural activity in the central nervous system 1 ., This activity leads to transient occurrence of signs and/or symptoms , also known as epileptic seizures ., The clinical symptoms of epileptic seizures range from auras , to spasmodic muscular contractions , up to loss of consciousness 2 , 3 ., Epileptic seizures can be roughly divided into two groups , based on the location in the brain from which the abnormal neural activity originates and how it propagates ., In partial , or focal , seizures the abnormal neural activity originates from a limited area in the brain , commonly referred to as the seizure onset zone ( SOZ ) ., On the other hand , primary generalized seizures begin with a widespread electrical discharge that involves most of the brain ., In this work we consider focal epilepsy and present an algorithm for SOZ localization , that is , determining the area in the brain where the abnormal neural activity leading to a focal seizure originates ( a guiding hypothesis throughout our work is that in focal seizures there is a singular focal point , from which this activity originates ) ., The common and simplest approach to treat epilepsy is using antiepileptic drugs ., Yet , in about 25–33% of the patients this approach is not effective 4 , and a patient is diagnosed with refractory epilepsy ., A possible treatment approach for refractory epilepsy is a resective surgery procedure to remove the areas in the brain that are necessary and sufficient to generate the abnormal neural activity that leads to epileptic seizures ., Currently , it is not known how these areas , also referred to as the epileptogenic zone ( EZ ) , can be mapped ., Therefore , in clinical practice , the SOZ is used as an approximation for the EZ 5 , and in the resective surgery the estimated SOZ is removed ( assuming this region is not responsible for indispensable brain functions ) ., Recent longitudinal trials indicate that long-term seizure freedom can be achieved in up to two thirds of the patients who undergo surgery 6 ., The main tool for SOZ identification ( localization ) , in cases where the SOZ is not evident in a non-invasive electrocorticography ( EEG ) or in an MRI , is invasive EEG ( also known as electrocorticography ( ECoG ) ) ., In ECoG grids or strips of electrodes are placed on the cortex 2 , allowing a direct measurement and recording of the brain’s electrical activity ( local field potentials ) ., These recordings , together with video monitoring , are used by expert neurologists to approximate the electrodes associated with the area within which the SOZ lies ., In this paper we describe an algorithm that localizes the SOZ based on the ECoG recordings ., Such an automated solution will provide a valuable tool for neurologists to assist in SOZ localization and perhaps increase localization accuracy over current methods ., The algorithm proposed in this paper builds upon a fundamental property of focal seizures reported in 7: the abnormal neural activity associated with focal seizures starts in the SOZ and spreads to other areas in the brain ., Therefore , at the beginning of such activity , signals recorded at electrodes located in vicinity of the SOZ should have a relatively large causal influence on the rest of the recorded signals ., This calls for an algorithm that estimates and incorporates the causal influence between the different recorded signals into its SOZ localization ., Since the electrodes in an ECoG grid are relatively close together 8 , the signals recorded in the different electrodes are statistically dependent ., In such a case , to fully quantify the statistical causal influence between two electrodes , one must evaluate this influence when conditioning on the rest of the electrodes 9 , 10 ., Unfortunately , even for moderate-size grids with 16 electrodes , this task is too computationally demanding and requires a huge amount of data per each seizure ., Therefore , in this paper we take a different path and approximate the underlying causal influence structure ., Instead of ( statistically ) conditioning on the rest of the recordings , the proposed algorithm applies a practical approximation by considering the electrodes as nodes in a directed graph , where the edges’ weights are estimations of the pair-wise causal influences ., In this work we focus on the following question: how should the SOZ be inferred from the estimated graph ?, The procedure for estimating the graph is discussed in S1 Text ., The method of representing causal influences among a set of random variables using directed graphs is not new ., In 11 this approach was used while adding the constraint that the graph should be acyclic ., In this case it is assumed that the joint density follows a causal Markov condition 11 ., However , with ECoG recordings the Markovian structure of the underlying density is not known , and it must be estimated from the recordings ., A possible approach for estimating this structure is via minimizing the KL divergence 12 , Sec . 8 . 5 between the true density and an approximated density induced by a spanning tree ., As shown in 13 , the best tree ( in terms of minimizing the KL divergence ) can be found using the maximum-weight spanning tree algorithm ( Edmonds’ algorithm ) 14 ., Moreover , the underlying hypothesis for localization based on this approach is that the root of the spanning tree should correspond to the origin of the causal activity ., This localization approach was taken in 15 , which our work improves upon in multiple dimensions ., In particular , 15 assumes that the underlying density follows a specific structure in order to apply Edmonds’ algorithm ., However , it is not clear if this structural assumption accurately describes the observed signals ., In addition , the algorithm of 15 localizes the SOZ using only the outgoing weights , whereas other works 16 , 17 use both incoming and outgoing node weights ., Hence , our work contrasts with 15 by using the PageRank algorithm to account for the structure of the estimated graph rather than assuming a specific structure , and basing our localization on both the incoming and outgoing weights to each node ., Another approach for inferring the SOZ from the estimated graph was proposed in 16 , 17: based on the findings of 7 , the nodes in the SOZ should have properties of “sources of causal influence” with large outgoing flow and small incoming flow ( the total incoming flow subtracted from the total outgoing flow is referred to as the net-flow ) ., The two main drawbacks of this approach is that it ignores the structure of the estimated graph , and ranks the nodes ( electrodes ) based only on their one-step neighbors ., Our study shows that for some of the patients this approach works well , while for others the results can be improved by a more sophisticated inference approach ., To account for the structure of the graph ( and for multi-step neighbors ) , we propose to use a variant of Google’s famous PageRank algorithm 18 , 19 ., The PageRank algorithm , initially designed for ranking web pages , is based on the following thesis 20: A web page is important if it is pointed to by other important pages ., Motivated by this thesis , the PageRank algorithm views the web as a directed graph with pages as nodes and hyperlinks as edges , and ranks the web pages based on the steady-state probability of a random surfer visiting each page ., Using terminology taken from another web ranking algorithm , the hyperlink-induced topic search ( HITS ) algorithm 21 , the PageRank algorithm can also be viewed as assigning authority scores to the nodes ., A high authority score is given to a page that is linked by many other pages with high authority scores ., Thus , we use PageRank to calculate an in-flow ( authority ) score for each node in the graph ., To calculate an out-flow score we use the Reverse PageRank algorithm 22 ., As PageRank ranks based on the dominant right eigenvector of the directed graph , it accounts for its structure ., We emphasize that in our algorithm the PageRank does not model the propagation of the abnormal neural activity ., Instead , it is used to evaluate the importance of a node in terms of its causal influence on the rest of the network ., It should further be noted that the PageRank algorithm was already used in the context of neuroscience problems ., For example , 23 studied the network architecture of functional connectivity within the human brain connectum , and used four centrality measures , of which PageRank was one , to provide insights on this connectivity ., Numerous works have studied the problem of localizing the SOZ using ECoG recordings ., We refer the reader to 2 , 16 and references therein for background on this topic ., Many of the algorithms proposed in previous studies are based on some form of a ( causal ) connectivity graph and use the following three main steps:, i ) Pre-processing the ECoG recorded signals;, ii ) Estimating the connectivity graph from the processed ECoG signals; and, iii ) Inferring the SOZ from the estimated connectivity graph ., While the algorithm proposed in the current paper follows a similar approach , it uses an improved method to estimate the connectivity graph and applies a novel method to infer the SOZ from it ., Specifically , the proposed algorithm analyzes two types of 10 seconds blocks: at the beginning of a seizure ( an ictal block ) as well as blocks randomly sampled when the patient is resting ( rest blocks ) ., By using information from both types of blocks , the proposed algorithm accounts for the structure of the estimated network when no seizure is evolving ., The value of 10 seconds was chosen to provide a good tradeoff between the number of samples in a block and the stationarity of the observed signals over a block ., When the block is much longer than 10 seconds the data may not be stationary , while when the block is much shorter than 10 seconds the number of samples available for estimating the pair-wise causal influences is too small ( the estimation may not be accurate enough ) ., In contrast to the proposed algorithm , previous studies used significantly longer blocks 16 , 17 ., To quantify the pair-wise causal influences between the recordings , the proposed algorithm uses a combination of a parametric causality measure , Granger causality 24 , and a non-parametric measure , directed information 25 ., Our results show that this combined procedure improves upon using each of the above approaches ( parametric or non-parametric ) separately ., Finally , the proposed algorithm uses a novel approach to infer the SOZ from the estimated graph ., In particular , by using a variation of the PageRank algorithm , a score is assigned to each node ., The algorithm then selects the SOZ nodes as the nodes that have high scores compared to other nodes , as well as compared to scores calculated based on the rest blocks ., We emphasize that previous studies 16 , 17 did not account for the rest blocks as part of the localization procedure ., Our analysis , on the other hand , suggests that rest block should be taken into account when localizing the SOZ in order to avoid biased results ., The proposed algorithm was tested on 19 data-sets , taken from patients undergoing surgical treatment for medically refractory epilepsy ., These data-sets are listed on the online iEEG portal 26 ( http://www . ieeg . org ) ., The patient-specific information is detailed in Table 1 ., The ECoG signals were sampled at rates between 500 Hz and 5 KHz: data-set I001_P034_D01 was sampled at 5 KHz ( Mayo Clinic , Rochester , MN ) ., Data-sets Study_004-2—Study_037 were sampled at 500 Hz ( Mayo Clinic , Rochester , MN ) , and data-sets HUP64_phaseII—HUP87_phaseII ( Hospital of the University of Pennsylvania , Philadelphia , PA ) were sampled at 512 Hz ., Each of the data-sets contains ECoG recordings , as well as annotations indicating which time intervals in the recordings correspond to seizures ., The video recordings are used to generate these annotations ., The data-sets also include reports describing the spatial locations , on the cortex , of the electrodes , and comments by expert neurologists as to where the seizures originate from ., We refer to an electrode that is highlighted in these comments as an electrode of interest ( EOI ) ., Some of these data-sets contain recordings from several strips and grids ., In these cases , the proposed algorithm analyzed the largest grid of electrodes ( in all considered cases the largest grid was located over the suspected SOZ ) ., The name of the analyzed grid and its size are specified in Table 1 ., Table 1 also specifies the surgical outcome ( class ) for each patient ., Note that three patients were not resected , and there is no follow-up for three other patients ., We summarize the localization results using the following two metrics: Next , we provide a detailed description of our localization results ., The localization results for the patients detailed in Table 1 are presented in Figs 1–5 ., As a ground truth we use the EOIs indicated by the neurologists and detailed in the data-sets reports ., In Figs 1–5 , the EOI electrodes ( nodes ) are marked by a bold annulus , whereas the nodes detected by our proposed algorithm are marked by solid brown circles ., The reports in the iEEG portal contain a unique numbering for each electrode in each of the grids ., This numbering is also included in the grids presented in Figs 1–5 ., For instance , in Fig 1-, ( a ) , node 1F is marked by 1 which corresponds to the numbering used in the report ., This , together with the fact that node 2F is marked by 7 , implies that node 3A corresponds to node 18 in the report ., The proposed algorithm applies a variant of PageRank on the estimated causal influence graph to calculate a rank ( score ) for each of the nodes ., Then , natural candidates for the SOZ are the nodes with the top p0 percentile of scores ., In order to verify that the calculated ranks are not due to chance and indeed capture an evolving abnormal neural activity that leads to a seizure , the proposed algorithm also calculates similar scores for an ensemble of recordings taken while the patient is resting ., From this ensemble the algorithm creates an empirical distribution of the scores for each electrode , and requires electrodes in the SOZ to have a score in the top p1 percentile of the calculated empirical distribution ., A detailed description of the inference procedure is provided in the Methods section ., The results in Figs 1–5 were obtained using p0 = 10 and p1 = 5 ., The values of p0 and p1 control the tradeoff between the false positives ( identifying electrodes not in the SOZ ) and the false negatives ( SOZ electrodes not identified ) ., Note that the number of indicated EOIs can be relatively large , for instance , in Fig 1-, ( b ) , 10 nodes out of 36 are indicated as EOIs ., This number also differs between data-sets ., The value of p0 was selected to provide a good balance between the success rate and the FPR , namely , inferring at most 10% of nodes in the grid as SOZ candidates ., The value of p1 controls the significance level ( enables the algorithm avoiding the possible bias caused by an inherent property of the patients’ brain ) ., We discuss the implications of this parameter in the Discussion section ( see the The structure of the estimated causal influence graph subsection and the A comparison with different inference approaches subsection ) ., Closely examining the localization maps in Figs 1–5 , it can be observed that our algorithm successfully localized the SOZ ( using the terminology defined above ) in 17 out of the 19 data-sets ., The two exceptions are data-set study_023 in Fig 3-, ( a ) and data-set HUP64_phaseII in Fig 3- ( d ) ., Regarding data-set study_023 ( Fig 3-, ( a ) ) , it can be observed that the localization concentrates in the lower left corner of the grid ., While the reports for this data-set clearly indicate that the SOZ is nodes 2H–3H ( electrodes 58–59 ) , they also state the following: “The EEG showed fast activity at LTG #59 at 01:20:59 , which then evolves into spike activity in LTG #58 and 59 . At 01:21:10 , there was spread of spike and wave activity to LTG #2 , 3 , 10 , 11 , 18 , and 19” ., Thus , our algorithm accurately inferred the area to which the activity spread ., By analyzing a time interval that significantly precedes the seizure start point marked in the reports ( see the Methods section for a discussion regarding the analyzed time intervals ) , the inference can be significantly improved ., Regarding data-set HUP64_phaseII ( Fig 3- ( d ) ) , it can be observed that four of the inferred nodes are concentrated around the EOI while the other four are spread over the grid ., The reason for marking this inference as non-successful is the fact that exactly 50% of the electrodes overlap with the EOI , or with the nodes strictly adjacent to the EOI ., Thus , this localization can be viewed as a partial success ., As mentioned above ( see also the Methods section ) , the ( statistical ) significance of the calculated scores is evaluated in order to preclude scores which were obtained by chance or which are not a result of the evolving seizure activity ., In other words , the objective of the post-processing is to verify that the high scores are due to the evolving activity of an epileptic seizure and not an inherent property of the patients’ brain ., To test this hypothesis , the algorithm generates an empirical distribution of the scores calculated over random blocks recorded while the patient is resting , see the Methods section for a detailed description of this procedure ., Our study shows that , for a specific patient , the estimated causal influence graph has patterns that are common between a rest state and the beginning of a seizure , namely , the beginning of the ictal state ., This implies that one must account for rest blocks in order to avoid having the localization results biased by the inherent structure of the causal influence graph ., Figs 6 and 7 demonstrate that the causal influence graph estimated in rest blocks and in a block at the beginning of a seizure indeed have a common structure ., Each of the sub-figures in Figs 6 and 7 is a heat map of an estimated graph ( the entries are the estimations of the pair-wise causal influences ) ., The procedure for creating ( estimating ) this graph is briefly discussed in the Methods section , while a detailed description is provided in S1 Text ., The left column corresponds to the ictal blocks ( beginning of a seizure ) , while the middle and right columns correspond to random blocks used as part of the post-processing procedure ., Each row corresponds to a different data-set: HUP65_phaseII , HUP70_phaseII , HUP78_phaseII , and HUP87_phaseII ., In each sub-figure , a yellow in the ( i , j ) location implies high estimated causal influence from node i to node j in the respective graph ., The main dark blue diagonal in each of the heat maps corresponds to the causal influence between an electrode to itself that is set to zero ., It can be observed that , per data-set , i . e . , in the same row in Fig 6 or in Fig 7 , the heat maps follow a similar structure ., On the other hand , this structure is different from one data-set to another ( between different rows ) ., In the first row of Fig 6 , corresponding to data-set HUP65_phaseII , one can observe hot super and sub diagonals ., In the second row of Fig 6 , corresponding to HUP70_phaseII , one can observe a small hot region in the bottom right of the map ., In the first row of Fig 7 , corresponding to data-set HUP78_phaseII , one can also observe a hot region in the bottom right of the map , yet , this region is significantly smaller than the one in the second row of Fig 6 ., Finally , in the second row of Fig 7 , that corresponds to HUP87_phaseII , one can observe small hot squares at the upper-left part of the map ., These findings indicate that an inference procedure that ignores the structure during rest times , e . g . , 16 , 17 , may not be aware of the structure that is present when there is no neural activity leading to a seizure ., This may result in a biased inference ., One may conjecture that the structural resemblance demonstrated in Figs 6 and 7 is due to epileptic activity in a rest state , commonly referred to as interictal discharges 27 , 28 ., Yet , we note here that the starting point of the evaluated rest block is randomly selected ( see the Methods section for details ) ., Moreover , the patterns depicted in Figs 6 and 7 appear in all analyzed rest blocks ., Thus , as interictal discharges are relatively sparse , we conjecture that this structure is not due to the interictal discharges ., At the same time , we note that interictal discharges can be used to assist in localizing the SOZ 29 ., Designing a robust method to incorporate the interictal discharges in our algorithm is part of our future research plans ., A natural question is how good are the results reported in the Results section compared to the performance of other inference algorithms ., To answer this question we tested two alternative inference approaches as well as two methods for estimating the pair-wise causal influences ., Before discussing the alternative inference approaches we first provide some background on the problem of estimating the causal influence graph ., The proposed algorithm uses ECoG signals from two types of intervals ( blocks ) : 10 seconds at the beginning of a seizure ( an ictal block ) and 10 seconds randomly selected from a period in which the patient is resting ., Intuitively , in ictal blocks the seizure activity has not spread out across the brain yet , and therefore these blocks should give clear insights as to the SOZ location ., The length of the analyzed blocks is chosen to be 10 seconds ., This follows as these blocks are used to estimate the weights in the causal-influence graph , and this places two contradicting constraints on their length ., On the one hand , the analyzed ECoG signals should be approximately stationary ., According to 35 , ECoG signals are approximately stationary only for a few seconds ., On the other hand , the considered blocks should be long enough to facilitate non-parametric accurate estimation of the causal influence ., Our study shows that blocks of 10 seconds provide a good tradeoff between the above two constraints ( see the detailed discussion in the Description of the setup subsection ) ., The heat maps in Figs 6 and 7 indicate that the causal influences in rest blocks ( the middle and right column ) are lower compared to those in the ictal blocks ( depicted on the left column ) , namely , the graphs are more blue and less yellow ., Extending this observation , our study shows that the seizure evolution process can be examined in terms of the causal influence graph , as depicted in Fig 8 for data-set HUP65 phaseII ., Similarly to Figs 6 and 7 , each of the sub-figures in Fig 8 is a heat map of an estimated graph ( the entries are the estimations of the pair-wise causal influences ) , where in each sub-figure the graph was estimated from a different time window ., The procedure for estimating these graphs is briefly discussed in the Methods section , while a detailed description is provided in S1 Text ., It can be observed that in Fig 8-, ( a ) ( which corresponds to a rest state ) , the causal influence is relatively low ( yet the pattern of hot super and sub diagonals is apparent ) ., The causal influence is higher in Fig 8-, ( b ) that shows the graph estimated from the recordings of pre-ictal state ( 10 seconds before the seizure starting point ) ., The causal influence increases in Fig 8-, ( c ) –8-, ( e ) , corresponding to the first 10 seconds ( ictal block ) , 10 to 20 seconds after the seizure starts , and 20 to 30 seconds after the seizure starts , respectively ., Finally , in Fig 8-, ( f ) , that corresponds to 30 to 40 seconds after the seizure starts , there is a decrease in the causal influence compared to Fig 8-, ( e ) ., A possible explanation for this decrease is that after 30 seconds from the seizure starting point it already spread throughout the grid ., Indeed , the reports corresponding to data-set HUP65_phaseII indicate that after about 30 seconds from the seizure starting point the activity was apparent in the whole grid: “rhythmic sharps of variable amplitudes are recorded throughout the grid diffusely ( generalized seizure electrographically ) ” ., High frequency oscillations were recently suggested as good bio-markers for the epileptogenic zone 36–38 ., Yet , as stated in 36 , to record high frequency oscillations , the ECoG recordings must be sampled at a minimum rate of 2 KHz ., The oscillatory events can then be visualized by applying a high-pass filter and increasing the time and amplitude scales ., As 18 of the 19 data-sets were sampled at approximately 500 Hz , analysis of high frequency oscillations cannot be applied ., Moreover , as discussed in S1 Text , to efficiently estimate the pair-wise causal influence graph we down-sample the recorded signals , see the discussion about the impact of the sampling rate on the signals memory order and the resulting number of samples required for accurate estimation ., While the analyzed signals can represent any limited frequency band ( not necessarily the low frequencies ) , the results presented in this work were obtained by analyzing the activity in frequencies below 100 Hz ., We note that filtering out the high frequencies was also applied in 39 ., On top of the sampling frequency limitations described in the previous section , it must be noted that the ECoG recordings in general , and the estimated causal-influence graph in particular , do not provide a complete representation of the epileptic network ., Since it is not possible to record the electrical activity from the whole brain ( the grids’ size is limited ) , the true SOZ may not be covered by the recording grid ., In this work , we assume that the preceding analysis was executed , e . g . , using EEG or MRI imaging , and that the grid was located based on a good ( yet rough ) estimation of the SOZ location ., Another source of inaccuracy is the fact that the recorded signals might be influenced by ( or correlated with ) a strong signal originating from a location out of the grid ., This may call for analysis of causal influence graphs in the presence of latent variables , see , for example , 40–42 and references therein ., However , these works either assume a linear model , or derive estimation methods which require a very large number of samples ., As discussed in S1 Text , the number of available samples for estimating the causal influences is inherently small , and thus these techniques cannot be used ., Finally , as discussed above , since the electrodes in an ECoG grid are closely located , the recorded signals might be statistically dependent ., In this case , to fully quantify the statistical causal influence between two electrodes , one must evaluate this influence when statistically conditioning on the rest of the electrodes ., However , even for small grids , this task is too computationally demanding and requires a huge number of samples ., Despite the incomplete representation of the causal-influence network via the pair-wise causal influence graph , the inference results presented above suggest that the used approximation is accurate enough for the purpose of localizing the SOZ ., A major concern regarding any automatic localization algorithm is the computational aspects 16 , 17 ., In particular , for large grids , the computational complexity of estimating the causal influence graph is high since N ( N − 1 ) values must be estimated ., This leads to the question: can the proposed algorithm be executed in real-time to yield results within minutes from the time that the recording session ends ?, We assert that it can , given that the main computational load of our algorithm is the estimation of the causal-influence graphs of the random rest blocks , see the Methods section ., This follows as the number of seizures per patient is relatively small ( see Table 1 where data-set Study_033 is the largest with 17 seizures ) , while in order to create the empirical distributions we estimate the causal influence graph for 200 rest blocks ., Note that there is no need to wait until the end of the recording session to execute this estimation task ., In fact , estimation of the causal influence graphs for the rest blocks can be executed in parallel to the recording procedure , thus , significantly reducing the computational load at the end of the procedure ., We further note that estimating the graph can be performed using a dedicated hardware ( Graphics Processing Unit ) and in parallel over several processors 43 , reducing the required time even further ., Finally , we emphasize that from the perspective of graph theory , the estimated graphs are very small ( compared to graphs with thousands or even millions of nodes ) ., Therefore , the computational complexity of the inference procedure based on the PageRank algorithm is negligible ., The impact of an automatic localization algorithm could be significant , in particular in view of the improved inference performance reported in Tables 2 and 3 ., First , it can provide an objective point of view regarding the SOZ location ., Second , the proposed algorithm can save analysis time for the neurologists by providing a pointer to a set of electrodes suspected to be located over the SOZ ., Third , while the proposed algorithm focuses on inferring the SOZ , the techniques developed in this work can be used to learn other mechanisms and dynamics of the brain ., For instance , understanding modifications in the neural network due to learning 44 , or extending the above discussion on seizure evolution ., Finally , we note that the proposed PageRank-based analysis of the graph can be applied after any procedure for developing a weighted directed graph indicative of causal influences ., While due to computational complexity constraints , and the limited number of available samples , the proposed algorithm uses the pair-wise DI , in principle , any procedure that estimates a weighted directed graph ( for instance , estimating the causally conditioned DI ) can be used before applying the PageRank algorithm ., In terms of future research , we currently have three main directions: First , the proposed algorithm uses the rest periods to create the empirical distributions used in the post-processing stage ., An interesting question is how to use these blocks to learn about the epileptic activity of the patient , thus improving the inference accuracy ., We believe that by identifying rest blocks with interictal discharges , it will be possible to further take advantage of the recorded rest blocks ., Second , the similarity between the structure of the graphs estimated in rest and pre-ictal blocks motivates analyzing these structures also during the seizure itself ., Such an analysis can shed light on the transition from rest to seizure and on the propagation of the seizure activity over the network ., Third , an important aspect in estimating the pair-wise causal influences ( or any statistical functional that involves memory ) is estimating the length of the auto-time-dependence of the ECoG recordings ( for Gaussian signals this reduces to the actual length of the auto-correlation function ) ., The parameter can also be interpreted as the Markov order of the sequence ., Using tools from the theory of machine learning , namely , a data-driven estimator of the Markov order , in 45 we are studying the empirical distribution of the estimated Markov order over different states ( rest and ictal ) and different patients ., The patients included in this study ( l | Introduction, Results, Discussion, Methods | Epilepsy is one of the most common neurological disorders affecting about 1% of the world population ., For patients with focal seizures that cannot be treated with antiepileptic drugs , the common treatment is a surgical procedure for removal of the seizure onset zone ( SOZ ) ., In this work we introduce an algorithm for automatic localization of the seizure onset zone ( SOZ ) in epileptic patients based on electrocorticography ( ECoG ) recordings ., The proposed algorithm builds upon the hypothesis that the abnormal excessive ( or synchronous ) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain ., Thus , when this abnormal activity starts , signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals ., The SOZ localization is executed in two steps ., First , the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges’ weights quantify the pair-wise causal influence between the recorded signals ., Then , the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase ., Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists ( success rate of 17 out of 19 ) . | Epilepsy is a common neurological disorder characterized by abnormal electrical disturbances in the brain that result in transient occurrence of signs and/or symptoms , also known as seizures ., In focal epilepsy , this electrical activity originates from a limited area in the brain , commonly referred to as the seizure onset zone ( SOZ ) ., For patients with focal epilepsy that cannot be treated with medications , the common treatment is a resective surgery to remove the SOZ ., This work presents an algorithm for SOZ localization based on electrocorticography recordings ., Such an automatic solution has the potential to increase the localization accuracy , to provide a validation of the neurologist’s SOZ region , and to ultimately reduce or eliminate the analysis time of the neurologist ., Inference results for 19 patients show a close match between the SOZ inferred by the proposed algorithm and the SOZ estimated by expert neurologists . | machine learning algorithms, medicine and health sciences, applied mathematics, neuroscience, surgical and invasive medical procedures, health care, simulation and modeling, algorithms, mathematics, artificial intelligence, membrane electrophysiology, brain mapping, electrocorticography, bioassays and physiological analysis, directed graphs, neuroimaging, research and analysis methods, epilepsy, computer and information sciences, imaging techniques, treatment guidelines, electrophysiological techniques, graph theory, electrode recording, neurology, biology and life sciences, physical sciences, health care policy, machine learning | null |
journal.pcbi.1006594 | 2,018 | Mechanistic insight into spontaneous transition from cellular alternans to arrhythmia—A simulation study | Cardiac alternans is comprised of beat-to-beat alterations in cardiac electrical and mechanical activities 1 ., At the cellular level , cardiac electrical alternans ( CEAs ) manifests as alterations either in the duration of the action potential ( APD alternans ) or/and in the cytosolic calcium transient amplitude ( CaT alternans ) 2 ., Clinically , cardiac alternans especially that associated with the APD alternans can be detected as electrocardiographic T-wave alternans ( TWA ) , which has been recognised as a biomarker for predicting the onset of cardiac arrhythmias and sudden cardiac death ( SCD ) 3–5 ., As TWA is associated with increased risk of cardiac arrhythmogenesis in many heart diseases , such as heart failure 6 , ischemia 7 and long QT syndromes 8 , 9 , it is crucially important to understand possible underlying mechanism ( s ) of arrhythmogenesis in association with cardiac alternans ., Previous experimental and simulation studies have unravelled possible mechanisms underlying the onset of cardiac alternans 10 , 11 ., One of the most well-known hypotheses for the genesis of APD alternans is the APD restitution theory , first established by 12 , which theoretically attributes the generation and sustainability of cardiac alternans to the slope of APD restitution curve ., When the maximal slope of the APD restitution curve is greater than 1 , then sustained APD alternans can be produced at fast pacing rates ., This theory has been supported by some experimental and simulation studies ( e . g . 13–15 ) ., However , due to the effect of cardiac excitation memory , some other studies 16–18 have found that the APD restitution theory is not sufficient to produce stable alternans and more complicated dynamic processes are involved ., Another theory is about the primary role of CaT alternans , which may be generated by a stiff relationship between the Ca2+ content in the SR and calcium release from the Ryanodine receptors ( RyRs ) 18 , 19 ., By mechano-electrical coupling 20 , such CaT alternans may be reflected as APD alternans , then reflected as TWA ., This theory is supported by some experimental studies in observing CaT alternans with flat APD restitution curve or under voltage clamp conditions 19 , 21 , 22 ., APD alternans promoted by CaT alternans may account for cardiac alternans at slow pacing rates 23 ., At the tissue level , cellular APD alternans can be manifested as spatially concordant and/or discordant excitation wave alternans 24 ., With concordant alternans , the whole tissue exhibits uniform alternation of long or short APD of one excitation waves ., However , with discordant alternans , non-uniform distribution of long and short APD within the tissue at the same excitation wave can be seen ., Several mechanisms have been proposed to be responsible for the formation of discordant alternans 25 , 26 ., One is the intrinsic heterogeneous electrical properties of the tissue , by which cells in different regions have different repolarisation properties ( such as epicardium to endocardium heterogeneity , or apex to base heterogeneity ) 24 ., Such heterogeneous electrical properties of tissue may produce out-of-phase excitation waves manifested as discordant alternans ., In conditions with a decreased inter-cellular electrical coupling ( e . g . due to fibrosis ) , individual cells’ intrinsic heterogeneous properties can be preserved due to reduced electronic interactions from neighbouring cells , enabling tissue to generate discordant alternans 27 , 28 ., However , other studies found intrinsic heterogeneity of tissue is an unnecessary condition for the genesis of discordance 29–31 ., Watanabe et al . 29 demonstrated that discordant alternans can be developed without spatial inhomogeneity in electrophysiology due to the nature of conduction velocity ( CV ) restitution , which has also been observed in other studies 30 , 31 ., In the case when discordant alternans is associated with unstable dynamics of intracellular calcium cycling , no contribution of CV restitution is required 32 , 33 ., Discordant alternans is believed to be strongly arrhythmogenic as it may increase spatial dispersion of refractoriness , thus facilitating uni-directional conduction block and leading to formation of re-entry 24 , 30 ., However , due to the complexity of cardiac excitation and propagation , the mechanism by which APD alternans at the cellular level spontaneously evolves into re-entrant excitation waves at the tissue level remains incompletely elucidated ., TWA has been known to be associated with the repolarisation phase of the action potential , during which L-type calcium and various potassium channel currents play important roles 5 , 34–36 ., However , it is also associated with the depolarisation phase 37–39 , during which the sodium channel current ( INa ) plays an important role in determining the maximal upstroke velocity of action potentials ., It has been found that malfunction of INa is associated with various cardiac conduction diseases 40 , 41 , modulates the effective refractory period between cardiac action potentials 42 , which may also promote the genesis of alternans at the cellular level and discordant alternans at the tissue level ., So far , the role of INa in perpetuating the genesis of discordant alternans leading to formation of re-entry has not been completely elucidated ., In this study , we used a simulation approach to investigate the mechanism ( s ) by which cardiac alternans in a single cell transitions spontaneously into re-entrant arrhythmia in both homogeneous and heterogeneous cardiac tissues ., The role of INa in generating and transitioning the APD alternans to discordant alternans that perpetuates re-entrant excitation waves was also investigated ., The rabbit ventricular epicardial cell model developed by Aslanidi et al . 43 was used in this study ., This model was chosen as it is able to generate stable large and small AP alternations at pacing cycle length < 188 ms , and is suitable for long time period simulations ., At the cellular level the typical Hodgkin-Huxley model of a cardiac cell was implemented , by which the cell membrane is modelled as a capacitor aligned in parallel with ion channel currents , Na+-Ca2+ exchangers and Na+-K+ pumps that are responsible for generating cardiac action potentials ., In brief , the model equation is represented as:, ∂V∂t=−Iion+IstimCm, ( 1 ), where V is the membrane potential , t the time , Iion the sum of all transmembrane ionic currents 43 , Istim the externally applied stimulus current and Cm the cell capacitance per unit surface area ., In order to investigate the role of INa in the generation of alternans and its transition into re-entrant excitation waves , a scaling factor ( SgNa ) was used to modulate the macroscopic conductance of the sodium channel by the following equation:, INa=SgNagNam3hj ( Vm−ENa ), ( 2 ), where gNa is the maximal channel conductance , m the activation gate , h and j the fast and slow inactivation gate respectively 44 , Vm the membrane potential , and ENa the channel’s reversal potential 43 ., In order to allow the cell model to generate AP alternans at a wide pacing cycle length ( PCL ) range , the time constant of the inactivation gate h of the INa channel was increased by 30ms at membrane potentials negative to -70mV , mimicking a prolonged recovery time for the INa channel as seen in some familial arrhythmia syndromes associated with sodium channel dysfunction caused by mutations in the SCN5A gene ( for details see a review in 45 ) ( see Online Supplement Material S1 Text for details ) ., In order to characterise fully the effects of varying the recovery time of INa on alternans genesis and conduction , simulations with graded increases in the time constant of the inactivation of h , ranging from 0 to 40 ms , were also conducted ., Multicellular tissue model for simulating the AP propagation was based on the well-established mono-domain equation 46:, ∂V∂t=∇⋅ ( D∇V ) −IionCm, ( 3 ), where D is the diffusion coefficient matrix determining the APs conduction velocity in tissue ., For one-dimensional ( 1D ) simulations , it can be presented as:, ∂V∂t=D ( ∂2V∂x2 ) −IionCm, ( 4 ), where the diffusion coefficient D is a scalar value ., For 1D simulations , a ventricular strand of a total length of 120 mm was discretised by a spatial resolution of 0 . 15 mm to form 800 interconnected nodes , each of which was modelled by the Aslanidi et al . cell model ., In the model , the diffusion coefficient D was set to 0 . 15 mm2/ms , giving a conduction velocity ( CV ) of planar excitation waves of 57cm/s through the strand , which matches experimental data from the rabbit ventricles 47 ., For two-dimensional ( 2D ) simulations , an idealised geometry of cardiac tissue sheet with dimensions of 120×120 mm2 were used , which was discretised by a spatial resolution of 0 . 15 mm to form an 800×800 nodes discrete lattice ., In isotropic tissue models , the diffusion coefficient D was set to be the same value as that used in the 1D simulation ., In anisotropic tissue models , D for the direction in parallel to the fibre direction remains the same as that in the 1D , and for the direction perpendicular to the fibre was set be a quarter of that along the fibre , which gave a 2:1 ratio of the CV for along and perpendicular to the fibre according to the experimental data 48 ., In order to consider the non-uniform anisotropic property of cardiac tissue , an idealised elliptical fibre orientation was implemented following a similar approach as used in 49 in the anisotropic 2D tissue by the following equation:, θ ( x0 , y0 ) =arctan ( −x04y0 ), ( 5 ), where ( x0 , y0 ) represents the coordinates of a point in the 2D tissue sheet with the origin being at the left-top corner , and θ denotes the fibre direction of the point ., At the single cell level , a steady-state protocol was used to evoke action potentials , from which cardiac alternans were analysed and the APD rate dependent curves were determined ., In this protocol , a sequence of 20 supra-threshold stimuli with a fixed PCL was applied to the cell model to evoke APs until a steady-state was reached ., Then the last two APs were recorded for analysis , for each of which the time interval between the upstroke of the AP and 90% repolarisation was measured as APD ., The measured APD was plotted against variant PCLs to get the APD rate dependent curves ., In addition , an S1-S2 protocol 50 was utilised to obtain the APD restitution curve ., With the S1-S2 protocol , 20 S1 stimuli with a PCL of 800 ms and one extra S2 stimulus with decreasing diastolic intervals ( DIs ) were applied to the model ., Then the APDs evoked by the S2 at various DIs were obtained for the APD restitution curves ., Furthermore , the S1-S2 protocol was also used for computing the effective refractory period ( ERP ) rate dependent curves ., For each simulation , the model was given a sequence of 20 S1 stimuli with a fixed PCL and one extra S2 stimuli with decreasing time intervals between the last two stimuli ., Then the smallest time interval that could evoke an AP whose overshoot is over 0 mV was recorded and used to calculate ERPs ., By varying the conditioning PCLs , the ERP rate dependence relationship was obtained by plotting the computed ERPs against PCLs ., Similarly , the same two protocols described above were used in 1D tissue models to obtain the CV rate dependence and CV restitution relationships respectively ., In the 1D tissue model , excitation waves were evoked by a sequence of supra-threshold stimuli applied at one end of the strand ., Then the CV was measured as the ratio between the distance and the excitation time interval between the 25th and 75th node to avoid effects from the boundary 51 and heterogeneous wave propagations at fast pacing condition ., In the 2D tissue model , excitation waves were evoked by applying supra-threshold stimuli at the left-bottom corner of the tissue ., To test possible model-dependence of simulation results and their human relevance , a well-established model for the human atrial action potentials developed by Courtemanche et al . ( denoted as CRN model , 52 ) was also implemented in cellular , 1D and 2D tissue models ., Details of the model implementation and simulation protocols are documented in the Online Supplement S1 Text ., At single cell level , Eq ( 1 ) and all gating variables were solved by Forward Euler ( FE ) method with a time step of t = 0 . 005 ms . At tissue level , Crank-Nicolson ( CN ) scheme 53 was implemented to solve Eq ( 3 ) for 1D and 2D simulations with a space step of x = 0 . 15 mm and a time step of t = 0 . 005 ms . At tissue boundaries , Neumann boundary conditions with zero-flux was implemented ., All simulations were carried out on a system with 2 Intel Xeon E5 2680v2 10 core processors ( 40 logical cores ) and 128 GB RAM memory , and OpenMP 54 was implemented for parallelising ., In response to a series of rapid stimuli using the steady-state protocol , the Aslanidi et al . model was able to generate AP alternans ., Fig 1 shows a representative period of the time course of the simulated AP alternans with PCL at 160 ms ( Fig 1A ( i-ii ) ) , during which distinctive APs showed alternating large and small amplitudes and durations ., The underlying INa and ICaL also showed significant large and small variations ( Fig 1B and 1C ) , which were in phase with the AP variations during AP alternans ., Generation of the AP alternans was rate-dependent as shown in the computed APD restitution curve ( Fig 1D ) ., By decreasing PCL from 800 ms to 140 ms , a bifurcation point at PCL of 188 ms was observed , marking the PCL threshold for the genesis of AP alternans ., Similarly , the computed ERP restitution curve also showed the bifurcation point at PCL of 188 ms , by which the ERP alternans was generated ( Fig 1G ) ., Using the CRN model of the human atrial cell , obvious alternans in the action potential was also observed , which was associated with alternating INa and ICaL as shown in the Online Supplement ( S3 . 1 Fig ) ., The role of INa in modulating the profiles of large and small APs in their amplitudes and durations , as well as the PCL threshold for generating AP alternans was investigated ., In Fig 1 , simulation results with a reduced INa ( SgNa = 0 . 6 ) and increased INa ( SgNa = 1 . 5 ) on alternans were shown and compared with those in the control condition ( SgNa = 1 . 0 ) ., It was shown that reducing INa by 40% produced a marked effect on APD shortening at large PCLs; however , it only had a noticeable effect for the large AP of the alternans at small PCLs ( Fig 1A–1C ) ., It shifted the bifurcation point to the right , indicating a provocative role of INa reduction on the genesis of AP alternans , by which AP alternans was able to be generated by large PCLs ( i . e . , slow heart rates ) ., INa reduction also reduced the APD difference ( ΔAPD , Fig 1E ) between the alternating large and small APs , so was the difference between the maximal upstroke velocities ( ΔdV/dtmax , Fig 1F ) ., For the whole range of the PCL in the bifurcation area , the averaged differences of the APD and the maximal upstroke velocity between the alternating APs were also reduced by INa reduction ., However , INa reduction increased the ERP at both large and small PCLs ( Fig 1G ) , and reduced the maximal slope of the APD restitution curve obtained by the S1-S2 protocol ( Fig 1H and 1I ) ., It is interesting to note that INa reduction resulted in a shortened APD , but an increased ERP , implying a predisposing role of INa on arrhythmogenesis , as determined below ., On the other hand , an increased INa by 50% did not produce a noticeable effect on modulating the AP profiles for either the large or the small AP , although it decreased slightly the averaged difference of APD ( ΔAPD ) , and increased that of the maximal upstroke velocity ( ΔdV/dtmax ) between them ( both measured locally at the bifurcation point and averagely over the whole bifurcation area ) ( Fig 1E and 1F ) ., However , it increased the maximal slope of APD restitution curves computed from the S1-S2 protocol ( Fig 1H and 1I ) ., These results illustrated a higher degree of the difference between the large and small APs with an increased INa as compared to the control condition , which might also be associated with abnormal AP propagation in the tissue level ., Alternating APs at the cellular level were reflected as alternans of AP conduction velocity as observed at the 1D tissue level ., Fig 2 shows the computed conduction velocity ( CV ) restitution curves by the steady-state ( Fig 2A ) and the S1-S2 protocol ( Fig 2B ) ., As shown in Fig 2A, ( i ) , large and small CV alternations was generated at the PCL bifurcation point which was correlated with the one generating AP alternans , demonstrating that large and small APs were associated with fast and slow wave propagation in tissue respectively ., It was also shown that a reduced INa resulted in a decreased difference of the CV between fast and slow conduction ( Fig 2A, ( ii ) ) , accompanying decreased maximal slope of the CV restitution ( Fig 2B, ( ii ) ) ., On the other hand , an increased INa produced an increased difference of CV between fast and slow conduction , as well as an increased maximal slope of the CV restitution curve ., AP alternans at the cellular level might be mapped into spatio-temporally heterogeneous conduction , resulting in functional heterogeneity leading to impaired excitation wave conduction at 1D tissue level ., Fig 3 shows results of simulated excitation wave conduction paced at PCL = 140 ms in a 1D homogenous strand model in control condition ( SgNa = 1 . 0 ) ., In the figure , the evoked action potential propagation along the strand was colour mapped and plotted in the space-time domain , in which space goes vertically from the bottom to the top , and time goes horizontally from the left to the right ( Fig 3A ) ., It was shown at the local region of the stimulation site , the stimuli evoked a series of APs , with a large one being followed by a small one , showing apparent electrical AP alternans ., Correspondingly , a fast wave followed by a slow one was observed initially at the vicinity of the stimulation site ( Fig 2A, ( i ) ) , which was mapped into distant tissue regions later on ., To illustrate electrical and conduction alternans , time courses of the computed APD and CV near the stimulus region ( a red line marked by a in Fig 3A ) were plotted simultaneously in Fig 3B ( i-ii ) ., Distinctive oscillating AP and CV were observed from the 6th beat , with a larger APD being correlated with a greater CV , and vice versa ., With a small AP , a slow CV at the vicinity of the stimulation site was observed , resulting in a conduction delay , allowing other part of the tissue more time to recover from a previous excitation ., When the small AP excitation wave reaches the more recovered part of tissue , it became large and conducted relatively quickly until the excitation wave reaches the refractory tail of the previous excitation ., In such a way , a profound functional spatial heterogeneity was generated in the intrinsically homogeneous tissue ., With time , the spatial heterogeneity evolves into a large scale spatial-temporal heterogeneity , by which standing waves were observed ., In this case , along the strand , regions of larger AP were alternated by those of smaller AP , each of which was associated with a faster or a slower conduction velocity accordingly ., Evolution of the functional spatial-temporal heterogeneity along the strand was characterised by the spatial distribution of the APD and CV from beat to beat as illustrated in Fig 3C ( i-ii ) ., At beat 1 , both APD and CV showed almost a homogeneous spatial-distribution ( a slight inhomogeneous distribution of APD and CV along the strand was due to the boundary effect of the tissue model 51 ) ., Following a premature beat that failed to evoke propagation , the spatial heterogeneity was built up and became noticeable at beat 4 , a smaller APD and CV was observed at the region near the stimulation site as compared to those at distance ., With time , a significant spatial heterogeneity with respect to both APD and CV through the whole tissue strand was produced and became markedly at beat 14 and beat 15 , leading to the formation of standing waves ., With the formation of standing waves , electrical alternans in the tissue showed both concordant and discordant features , depending on the spatial scale of observation ., Fig 3D shows the time course of AP traces recorded from position a to position e ( marked in Fig 3A ) ., APs registered from different sites of the strand showed either concordant or discordant alternans ., Within a small observation scale ( within ( a , b , c ) or ( d , e ) ) , recorded APs ( from a to c: marked by red arrows; from d to e: marked by blue arrows ) showed clearly concordant alternans ., Within a large observation scale ( between ( a , b , c ) and ( d , e ) ) , APs showed significantly discordant alternans ., Note that there is a singularity point , by which in-phase and out-of-phase APs were separated ., As such , APs recorded from c and d , though the distance between them was small , clearly showed discordant alternans ., Correlation between alternating APD and alternating CV was also observed in the CRN model of the homogeneous human atrial strand , which generated the functional heterogeneity leading to formation of standing waves as shown in S3 . 2 Fig in the Online Supplement ., The role of INa in the conduction of AP alternans was further investigated in the 1D tissue model ., Results are shown in Fig 4 for the space-time plot of AP conduction with a reduced ( Fig 4A ) , control ( Fig 4B ) and an increased INa ( Fig 4C ) ., At PCL = 140ms , a reduced INa facilitated the transition from a standing wave to a conduction block ( Fig 4A ) at some of the singularity points ., Similarly , an increased INa also led to a conduction failure of the standing waves ( Fig 4C ) , though with a greater distance from the stimulation site as compared to the case of INa reduction ., These results suggested that INa plays an important role in the conduction of AP alternans: either reducing or increasing it may result in the transition from discordant alternans to conduction failure ., The genesis of discordant alternans was also PCL-dependent , as shown in Fig 4B and 4D ., By increasing PCL from 140 ms to 150 ms , the observed discordant alternans became concordant alternans in the tissue ., A complete map in the 2D PCL-SgNa parameter space was constructed to demonstrate the combined effects of altered PCL and SgNa on the genesis of concordant , discordant alternans and the generation of conduction failure ., Results are shown in Fig 4E ., In the figure , white blocks represented the area within which only concordant alternans appeared , grey blocks for the area of discordant alternans with conducting standing waves , and black blocks for the discordant alternans with conduction failure ., It was shown that when SgNa was smaller than 0 . 6 , a conduction block always occurred either at the vicinity of the stimulus site or in the middle of the strand for a wide range of PCLs ., This suggested a decreased INa might facilitate the genesis of discordant alternans and conduction failure at low heart rate , resulting in an increased risk of arrhythmogenesis ., On the other hand , an increased INa also led to discordant alternans and conduction failure at fast pacing rates ( i . e . , small PCLs ) , but not at slow pacing rates ., This is paradoxical in light of the prediction of single cell simulations which showed an increased maximum slope of APD and CV restitution curves with an increased INa ( Fig 1I and Fig 2B, ( ii ) ) , by which one would expect a more pronounced genesis of alternans ., Such a discrepancy between cell and tissue modelling may be attributed to different mechanisms by which a reduced or increased INa facilitates alternans conduction ., In the case with a reduced INa , the reduction in the upstroke velocity of the small AP resulted in a remarkable slow CV during propagations ., In the case when AP was small enough to reach the threshold for propagation , conduction failure occurred ., For an increased INa , the difference between large and small APs became more pronounced , leading to a large gradient of APD dispersion in the tissue , which finally caused conduction block ., To test this hypothesis , the diastolic interval ( DI ) between the failing wave and the previous one at the conduction block sites was measured ( marked in Fig 4A–4C as a , b , c ) and presented in Fig 4F ., It was shown that the computed DI with a reduced INa was significantly greater than that in control and increased INa condition , suggesting the conduction failure for an increased INa was due to a reduced DI , by which the excitation wave collided with the tail of the previous excitation , leading to self-termination ., However , with a reduced INa , a large DI suggested that the conduction failure was attributable to a small AP that failed to provoke excitation ., Effects of varied recovery time of INa on alternans genesis and conduction was investigated , and results are shown in S4 Fig in the Online Supplement for the 1D tissue model with a reduced ( SgNa = 0 . 6 ) , normal ( SgNa = 1 . 0 ) , and increased ( SgNa = 1 . 5 ) INa conditions at different PCLs ., For each condition , the distance between the stimulation site and the first APD node , i . e . , the point where distinctive discordant alternans first appeared was computed ( see the black lines marked in S4A ( i-ii ) Fig ) Fig to measure a minimal tissue size required for developing and sustaining discordant alternans ., It was shown that a shortened APD node distance was associated with a longer INa recovery time , suggesting a facilitative role of impaired INa recovery in generating discordant alternans ., Excitation waves in cardiac tissue resemble curved waves more than planar waves as seen in the 1D tissue ., Therefore , further simulations were conducted to investigate the conduction of curved excitation waves associated with alternans in a homogeneous 2D tissue model ., Effects of a decreased and increased INa on the conduction of the excitation waves were also investigated ., Results are shown in Fig 5 . Fig 5A shows snapshots of excitation waves in the 2D tissue with a reduced INa ( SgNa = 0 . 6 ) ., These excitation waves were evoked by a series of stimuli applied at the left bottom corner with PCL = 140ms ( Fig 5A, ( i ) ) ., With such a PCL , AP alternans and discordant alternans were observed at the cellular and 1D tissue levels respectively ., However , in the 2D tissue model , due to the combined effects of wave curvature and electronic interactions between cells , 1:1 alternans seen at the 1D tissue disappeared ., Instead , intermittent excitations switching between fast and slow rates were observed as indicated by the time series of action potentials ( Fig 5A, ( ii ) ) recorded from registration sites of cell A , cell B and cell C ( sites marked in Fig 5A, ( i ) ) ., Interestingly , some beats of the stimuli failed to evoke excitation wave propagation in the tissue ., For example , for the 12th and 13th stimulus beats marked as black and red star respectively in the stimuli time series as shown in Fig 5A, ( ii ) , one generated excitation propagation ( 12th beat ) , and the other failed ., In this way , 1:1 response of the tissue to stimuli failed ., Conduction failure gave tissue enough time to recover from the previous excitation , leading to a normal conduction across the 2D tissue with no alternations ., However , the excitation rate was halved ., For the control condition ( SgNa = 1 . 0 ) , by which standing waves were observed in the 1D strand model , functional heterogeneity was generated in the 2D tissue model ( Fig 5B, ( i ) ) ., In this case , the small AP of the alternans conducted slowly at some local regions ( marked by black star of the wavefront in Fig 5B, ( i ) ) , allowing the other parts of the tissue more time to recover from the previous excitation ., When the excitation wave reached the regions with more recovered excitability , it conducted relatively faster until it hit the refractory tail of the previous excitation , where the conduction became slowed down again or even stopped ., This led to the formation of excitation-refractory islands , generating spatial functional heterogeneity in the tissue , which became more pronounced with time ., For example , at the 12th beat of stimulus with wave front marked by the black star in Fig 5B, ( i ) , the wave front caught up the rear of the wave of the previous excitation , resulting in a small AP ( therefore small CV ) at recording site of cell B ( Fig 5B, ( ii ) ) , which evolved into a repolarisation island ( a green strip after the wave front marked by the black star in Fig 5B, ( i ) ) ., In this case , discordant alternans was observed as shown in the time series of recorded APs ( Fig 5B, ( ii ) ; see black stars and red stars ) ., With an increased INa , AP propagation in the 2D tissue model was similar to those observed in 1D model ., In addition to discordant alternans , conduction might fail when the wave front hit the refractory tail of the previous AP excitation as shown in Fig 5C, ( i ) ., For example , at the 12th beat of stimuli , the excitation wavefront ( marked by the black star ) collided with its processor , leading to terminated propagation ( Fig 5C, ( i ) ) ., This formed a conduction block zone to the wave front of the next excitation ( i . e . , evoked by the 13th stimulus; marked by the red star ) , leading to conduction failure ., APs recorded from registration sites clearly showed missing beats at the distant site ( cell C ) from the stimulus region ., In such a way , tissue failed to respond 1:1 to the stimuli ., In the following simulations , we investigated the transition from conduction alternans to re-entrant arrhythmias in homogeneous , inhomogeneous and anisotropic 2D tissue models in order to understand possible roles of tissue inhomogeneity and anisotropy in perpetuating the formation of re-entrant excitation ., Due to conduction failure in 2D tissue with reduced INa , simulations results with control ( SgNa = 1 . 0 ) and increased INa ( SgNa = 1 . 5 ) at PCL = 140 ms were shown in the following sections ., In the 2D homogeneous tissue model with increased INa ( SgNa = 1 . 5 ) , alternating excitation wave conduction might transit spontaneously to paroxysmal re-entrant excitation waves when conduction failure associated with the small AP occurred near the tissue boundary as shown in Fig 6 . In simulations , condition and parameters of the model were the same as used in Fig 5 , but with a slight increase in the conduction velocity ( by about 10% ) , allowing conduction slowing down to occur near the tissue boundary ., With normal INa ( SgNa = 1 . 0 ) , though conduction slowed down when the excitation wavefront ran into the refractory tail of the previous excitation ( snapshot at 1885 ms ) , it continued to propagate without breakup due to sufficient gap between the two consecutive waves ., However , with SgNa = 1 . 5 ( Fig 6B, ( i ) ) , after a transition period of 1880 ms during which functional heterogeneity developed in the tissue , the wavefront of the excitation wave ( i . e . , the one in the middle of the tissue ) collided with the refractory tail of the previous excitation , leaving a very narrow refractory gap between the two due to more pronounced AP alternans as compared to the case of SgNa = 1 . 0 ., In this case , the conduction of wavefront slowed down , or stopped especially in the middle parts with a greater curvature ., However , at the tissue boundaries , as the Neumann non-flux boundary condition was implemented , effective intercellular coupling of cells was slightly less loaded as compared to other parts of tissue , leading to a slightly increased excitability of t | Introduction, Methods, Results, Discussion, Conclusions | Cardiac electrical alternans ( CEA ) , manifested as T-wave alternans in ECG , is a clinical biomarker for predicting cardiac arrhythmias and sudden death ., However , the mechanism underlying the spontaneous transition from CEA to arrhythmias remains incompletely elucidated ., In this study , multiscale rabbit ventricular models were used to study the transition and a potential role of INa in perpetuating such a transition ., It was shown CEA evolved into either concordant or discordant action potential ( AP ) conduction alternans in a homogeneous one-dimensional tissue model , depending on tissue AP duration and conduction velocity ( CV ) restitution properties ., Discordant alternans was able to cause conduction failure in the model , which was promoted by impaired sodium channel with either a reduced or increased channel current ., In a two-dimensional homogeneous tissue model , a combined effect of rate- and curvature-dependent CV broke-up alternating wavefronts at localised points , facilitating a spontaneous transition from CEA to re-entry ., Tissue inhomogeneity or anisotropy further promoted break-up of re-entry , leading to multiple wavelets ., Similar observations have also been seen in human atrial cellular and tissue models ., In conclusion , our results identify a mechanism by which CEA spontaneously evolves into re-entry without a requirement for premature ventricular complexes or pre-existing tissue heterogeneities , and demonstrated the important pro-arrhythmic role of impaired sodium channel activity ., These findings are model-independent and have potential human relevance . | T-wave alternans ( TWA ) , manifested as beat to beat alterations between large and small T-wave amplitudes on the electrocardiogram ( ECG ) is one of the prevalent clinical observations that are closely associated with cardiac arrhythmias and sudden death ., TWA is believed to be underlined by cardiac alternans at the cellular level , but the extract mechanism for the transition from cellular alternans to that at the tissue level , and how this further spontaneously evolves into cardiac arrhythmias remains incompletely elucidated ., In this study , multiscale rabbit ventricular computational models were used to address this issue by investigating the underlying mechanism ( s ) for the arrhythmogenesis of cardiac alternans , as well as a possible role of sodium channel on perpetuating cardiac arrhythmias ., Our results demonstrated a spontaneous development of re-entry from cellular alternans , arising from a combined action of APD and CV restitution properties with the curvature-dependence of CV ., Tissue inhomogeneity and anisotropy further promoted break-up of excitation waves , leading to multiple re-entrant excitation waves ., It was also found impaired sodium channel with either increased or decreased channel current facilitated the arrhythmogenesis ., This study provides new insights into underlying the mechanism , by which cellular cardiac alternans spontaneously evolves into cardiac arrhythmias ., Similar results were observed in human atrial tissue models , suggesting our major findings are model-independent and of potential clinical relevance . | medicine and health sciences, action potentials, membrane potential, condensed matter physics, brain electrophysiology, vertebrates, electrophysiology, anisotropy, neuroscience, animals, mammals, rabbits, simulation and modeling, animal models, ion channels, clinical medicine, materials science, brain mapping, experimental organism systems, bioassays and physiological analysis, event-related potentials, waves, electroencephalography, neuroimaging, research and analysis methods, cardiology, sodium channels, arrhythmia, imaging techniques, animal studies, proteins, clinical neurophysiology, electrophysiological techniques, biophysics, physics, biochemistry, leporids, eukaryota, physiology, biology and life sciences, wavefronts, physical sciences, material properties, amniotes, neurophysiology, organisms | null |
journal.pcbi.1005524 | 2,017 | A mathematical model coupling polarity signaling to cell adhesion explains diverse cell migration patterns | Rho GTPases are central regulators that control cell polarization and migration 15 , 16 , embedded in complex signaling networks of interacting components 17 ., Two members of this family of proteins , Rac1 and RhoA , have been identified as key players , forming a central hub that orchestrates the polarity and motility response of cells to their environment 18 , 19 ., Rac1 ( henceforth “Rac” ) works in synergy with PI3K to promote lamellipodial protrusion in a cell 16 , whereas RhoA ( henceforth “Rho” ) activates Rho Kinase ( ROCK ) , which activates myosin contraction 20 ., Mutual antagonism between Rac and Rho has been observed in many cell types 19 , 21 , 22 , and accounts for the ability of cells to undergo overall spreading , contraction , or polarization ( with Rac and Rho segregated to front versus rear of a cell ) ., The extracellular matrix ( ECM ) is a jungle of fibrous and adhesive material that provides a scaffold in which cells migrate , mediating adhesion and traction forces ., ECM also interacts with cell-surface integrin receptors , to trigger intracellular signaling cascades ., Important branches of these pathways are transduced into activating or inhibiting signals to Rho GTPases ., On one hand , ECM imparts signals to regulate cell shape and cell motility ., On the other hand , the deformation of a cell affects its contact area with ECM , and hence the signals it receives ., The concerted effect of this chemical symphony leads to complex cell behavior that can be difficult to untangle using intuition or verbal arguments alone ., This motivates our study , in which mathematical modeling of GTPases and ECM signaling , combined with experimental observations is used to gain a better understanding of cell behavior , in the context of experimental data on melanoma cells ., There remains the question of how to understand the interplay between genes ( cell type ) , environment ( ECM ) and signaling ( Rac , Rho , and effectors ) ., We and others 19 , 21–27 have previously argued that some aspects of cell behavior ( e . g . , spreading , contraction , and polarization or amoeboid versus mesenchymal phenotype ) can be understood from the standpoint of Rac-Rho mutual antagonism , with fine-tuning by other signaling layers 28 ., Here we extend this idea to couple Rac-Rho to ECM signaling , in deciphering the behavior of melanoma cells in vitro ., There are several overarching questions that this study aims to address ., In experiments of Park et al . 11 melanoma cells were cultured on micro-fabricated surfaces comprised of post density arrays coated with fibronectin ( FN ) , representing an artificial extracellular matrix ., The anisotropic rows of posts provide inhomogeneous topographic cues along which cells orient ., In 11 , cell behavior was classified using the well-established fact that PI3K activity is locally amplified at the lamellipodial protrusions of migrating cells 36 ., PI3K “hot spots” were seen to follow three distinct patterns about the cell perimeters: random ( RD ) , oscillatory ( OS ) , and persistent ( PS ) ., These classifications were then associated with three distinct cell phenotypes: persistently polarized ( along the post-density axis ) , oscillatory with two lamellipodia at opposite cell ends oscillating out of phase ( protrusion in one lamellipod coincides with retraction of the other , again oriented along the post-density axis ) , and random dynamics , whereby cells continually extend and retract protrusions in random directions ., The fraction of cells in each category was found to depend on experimental conditions ., Here , we focus on investigating how experimental manipulations influence the fraction of cells in different phenotypes ., For simplicity , we focus on the polarized and oscillatory phenotypes which can be most clearly characterized mathematically ., The following experimental observations are used to test and compare our distinct models of cell signaling dynamics ., For a graphical summary of cell phenotypes and experimental observations , see Fig 1 ., We discuss three model variants , each composed of ( A ) a subsystem endowed with bistability , and ( B ) a subsystem responsible for negative feedback ., In short , Model 1 assumes ECM competition for ( A ) and feedbacks mediated by GTPases for ( B ) ., In contrast , in Model 2 we assume GTPase dynamics for ( A ) and ECM mediated feedbacks for ( B ) ., Model 3 resembles Model 2 , but further assumes limited total pool of each GTPase ( conservation ) , which turns out to be a critical feature ., See Tables 1 and 2 for details ., We analyze each model variant as follows: first , we determine ( bi/mono ) stable regimes of subsystem ( A ) in isolation , using standard bifurcation methods ., Next , we parameterize subsystem ( B ) so that its slow negative feedback generates oscillations when ( A ) and ( B ) are coupled in the model as a whole ., For this to work , ( B ) has to force ( A ) to transition from one monostable steady state to the other ( across the bistable regime ) as shown in the relaxation loop of Fig 2d ., This requirement informs the magnitude of feedback components ., Although these considerations do not fully constrain parameter choices , we found it relatively easy to then parameterize the models ( particularly Models 1b and 3 ) ., This implies model robustness , and suggests that broad regions of parameter space lead to behavior that is consistent with experimental observations ., Parameters associated with rates of activation and/or feedback strengths are summarized in the S1 Text ., The parameters γi represent the strengths of feedbacks 1 or 2 in Fig 2, ( b ) and 2, ( c ) ., γR controls the positive feedback ( 2 ) of Rac ( via lamellipod spreading ) on ECM signaling , and γρ represents the magnitude of negative feedback ( 1 ) from Rho to ECM signaling ( due to lamellipod contraction ) ., γE controls the strength of ECM activation of Rho in both feedbacks ( 1 ) and ( 2 ) ., When these feedbacks depend on cell state variables , we typically use Hill functions with magnitude γi , or , occasionally , linear expressions with slopes γ ¯ i ., ( These choices are distinguished by usage of overbar to avoid confusing distinct units of the γ’s in such cases . ), Experimental manipulations in 11 ( described in Section “Experimental observations constraining the models” ) can be linked to the following parameter variations ., In view of this correspondence between model parameters and experimental manipulations , our subsequent analysis and bifurcation plots will highlight the role of feedback parameters γR , ρ , E in the predictions of each model ., Rather than exhaustively mapping all parameters , our goal is to use 1 and 2-parameter bifurcation plots with respect to these parameters to check for ( dis ) agreement between model predictions and experimental observations ( O1–O3 ) ., This allows us to ( in ) validate several hypotheses and identify the eventual model ( the Hybrid , Model 3 ) and set of hypotheses that best account for observations ., We first investigated the possibility that lamellipod competition is responsible for bistability and that GTPases interactions create negative feedback that drives the oscillations observed in some cells ., To explore this idea , we represented the interplay between lamellipodia ( e . g . , competition for growth due to membrane tension or volume constraints ) , using an elementary Lotka-Volterra ( LV ) competition model ., For simplicity , we assume that AE , LE depend linearly on Rac and Rho concentration , and set BE = 0 ., ( This simplifies subsequent analysis without significantly affecting qualitative conclusions . ), With these assumptions , the ECM Eq ( 3c ) reduce to the well-known LV species-competition model ., First consider Eq ( 3c ) as a function of parameters ( AE , LE ) , in isolation from GTPase input ., As in the classical LV system 45 , competition gives rise to coexistence , bistability , or competitive exclusion , the latter two associated with a polarized cell ., These regimes are indicated on the parameter plane of Fig 3a with the ratios of contractile ( LE ) and protrusive ( AE ) strengths in each lamellipod as parameters ., ( In the full model , these quantities depend on Rac and Rho activities; the ratios LE ( ρk ) /AE ( Rk ) for lamellipod k = 1 , 2 lead to aggregate parameters that simplify this figure ., ) We can interpret the four parameter regimes in Fig 3a as follows: I ) a bistable regime: depending on initial conditions , either lamellipod “wins” the competition ., II ) Lamellipod 1 always wins ., III ) Lamellipod 2 always wins ., IV ) Lamellipods 1 and 2 coexist at finite sizes ., Regimes I-III represent strongly polarized cells , whereas IV corresponds to an unpolarized ( or weakly polarized ) cell ., We next asked whether , and under what conditions , GTPase-mediated feedback could generate relaxation oscillations ., Such dynamics could occur provided that slow negative feedback drives the ECM subsystem from an E1-dominated state to an E2-dominated state and back ., In Fig 3a , this correspond to motion along a path similar to one labeled, ( d ) in Panel ( a ) , with the ECM subsystem circulating between Regimes II and III ., This can be accomplished by GTPase feedback , since both Rho and Rac modulate LE ( contractile strength ) and AE ( protrusion strength ) ., We show this idea more explicitly in Fig 3, ( c ) –3, ( e ) by plotting E1 vs LE1 while keeping LE1 + LE2 constant ., ( Insets similarly show E2 vs LE1 . ), Each of Panels ( c-e ) corresponds to a 1-parameter bifurcation plot along the corresponding path labeled ( c-e ) in Panel ( a ) ., We find the following possible transitions: In Fig 3c , we find two distinct polarity states: either E1 or E2 dominate while the other is zero regardless of the value of LE1; a transition between such states does not occur ., In Fig 3d , there is a range of values of LE1 with coexisting stable low and high E1 values ( bistable regime ) flanked by regimes where either the lower or higher state loses stability ( monostable regimes ) ., As indicated by the superimposed loop , a cycle of protrusion ( green ) and contraction ( blue ) could then generate a relaxation oscillation as the system traverses its bistable regime ., In Fig 3e , a third possibility is that the system transits between E1-dominated , coexisting , and E2-dominated states ., In brief , for oscillatory behavior , GTPase feedback should drive the ECM-subsystem between regimes I , II , and III without entering regime IV ., Informed by this analysis , we next link the bistable ECM submodel to a Rac-Rho system ., To ensure that the primary source of bistability is ECM dynamics , a monostable version of the Rac-Rho sub-system is adopted by setting n = 1 in the GTPase activation terms AR , Aρ in Eqs ( 3a ) and ( 3b ) ., We consider three possible model variants ( 1a-1c ) for the full ECM / GTPase model ., In view of the conclusions thus far , we now explore the possibility that bistability stems from mutual antagonism between Rac and Rho , rather than lamellipod competition ., To do so , we chose Hill coefficients n = 3 in the rates of GTPase activation , AR , Aρ ., We then assume that ECM signaling both couples the lamellipods and provides the requisite slow negative feedback ., Here we consider the case that GTPases are abundant , so that the levels of inactive Rac and Rho ( RI , ρI ) are constant ., We first characterize the GTPase dynamics with bR , ρ as parameters ., Subsequently , we include ECM signaling dynamics and determine how the feedback drives the dynamics in the ( bR , bρ ) parameter plane ., Isolated from the ECM influence , each lamellipod is independent so we only consider the properties of GTPase signaling in one ., This mutually antagonistic GTPase submodel is the well-known “toggle switch” 50 that has a bistable regime , as shown in the ( bR , bρ ) plane of Fig 4a ., ECM signaling affects the Rac / Rho system only as an input to bρ ., A linear dependence of bρ on Ek failed to produce an oscillatory parameter regime , so we used a nonlinear Hill type dependence with basal and saturating components ., Furthermore , for GTPase influence on ECM signaling we use Hill functions for the influence of Rho ( in LE ) and Rac ( in BE ) on protrusion and contraction ., We set AE = 0 in this model for simplicity ., ( Nonzero AE can lead to compounded ECM bistability that we here do not consider . ), Given the structure of the bρ − bR parameter plane and the fact that ECM signaling variables only influence bρ , we can view oscillations as periodic cycles of contraction and protrusion forming a trajectory along one of horizontal dashed lines in Fig 4a ., This idea guides our parametrization of the model ., We select a value of bR that admits a bistable range of bρ in Fig 4a ., Next we choose maximal and minimal values of the function bρ ( EK ) that extend beyond the borders of the bistable range ., This choice means that the system transitions from the high Rac / low Rho state to the low Rac / high Rho state over each of the cycles of its oscillation ., With this parametrization , we find oscillatory dynamics , as shown in Fig 4b ., We now consider the two-lamellipod system with the above GTPase module in each lamellipod; we challenge the full model with experimental observations ., Since each lamellipod has a unique copy of the Rac-Rho module , ECM signaling provides the only coupling between the two lamellipods ., First , we observed that inhibition of ROCK ( reduction of γρ in Fig 4b ) suppress oscillations ., However the resulting stationary state is non-polar , in contrast to experimentally observed increase in the fraction of polarized cells ( O1 ) ., We adjusted the coupling strength ( lc ) to ensure that this disagreement was not merely due to insufficient coupling between the two lamellipods ., While an oscillatory regime persists , the discrepancy with ( O1 ) is not resolved: the system oscillates , but inhibiting ROCK gives rise to a non-polarized stationary state , contrary to experimental observations ., Yet another problematic feature of the model is its undue sensitivity to the strength of Rac activation ( bR ) ., This is evident from a comparison of the dashed lines in Fig 4a ., A small change in bR ( vertical shift ) dramatically increases the range of bistability ( horizontal span ) , and hence the range of values of bρ to be traversed in driving oscillations ., This degree of sensitivity seems inconsistent with biological behavior ., It is possible that an alternate formulation of the model ( different kinetic terms or different parametrization ) might fix the discrepancies noted above , so we avoid ruling out this scenario altogether ., In our hands , this model variant failed ., However a simple augmentation , described below , addresses all deficiencies , and leads to the final result ., In our third and final step , we add a small but significant feature to the bistable GTPase model to arrive at a working variant that accounts for all observations ., Keeping all equations of Model 2 , we merely drop the assumption of unlimited Rac and Rho ., We now require that the total amount of each GTPase be conserved in the cell ., This new feature has two consequences ., First , lamellipods now compete not only for growth , but also for limited pools of Rac and Rho ., This , along with rapid diffusion of inactive GTPases across the cell 30 , 31 , 51 provides an additional global coupling of the two lamellipods ., This seemingly minor revision produces novel behavior ., We proceed as before , first analyzing the GTPase signaling system on its own ., With conservation , the bR − bρ plane has changed from its previous version ( Fig 4a for Model 2 ) to Fig 5a ., For appropriate values of bR , there is a significant bistable regime in bρ ., Indeed , we find three regimes of behavior as the contractile strength in lamellipod k , bρ ( Ek ) , varies: a bistable regime where polarity in either direction is possible , a regime where lamellipod j “wins” ( Ej > Ek , left of the bistable regime ) , and a regime where lamellipod k “wins” ( right of the bistable regime ) ., Only polarity in a single direction is possible on either side of the bistable regime ., As in Model 2 , we view oscillations in the full model as cycles of lamellipodial protrusion and contraction that modify bρ ( Ek ) over time , and result in transitions between the three polarity states ., To parameterize the model , we repeat the process previously described ( choose a value of bR consistent with bistability , then choose the dependence of bρ on ECM signaling so as to traverse that entire bistable regime . ) We couple the GTPase system with ECM equations as before ., We then check for agreement with observations ( O1–O3 ) ., As shown in Fig 5, ( e ) and 5, ( f ) , the model produces both polarized and oscillatory solutions ., To check consistency with experiments , we mapped the dynamics of this model with respect to both ROCK mediated contraction and PI3K mediated protrusion ( Fig 5c ) ., Inhibiting ROCK ( Fig 5b , decreasing γρ ) results in a transition from oscillations to polarized states , consistent with ( O1 ) ., PI3K upregulation promotes oscillations ( increasing γR , Fig 5c ) , characteristic of the more invasive cell line 1205Lu ( consistent with O2 ) ., Finally , increased fibronectin density ( increased γE , Fig 5d ) also promotes oscillations , consistent with ( O3 ) ., We conclude that this Hybrid Model can account for polarity and oscillations , and that it is consistent with the three primary experimental observations ( O1–3 ) ., Finally , Model 3 can recapitulate such observations with more reasonable timescales for GTPase and ECM dynamics than were required for Model variant 1b ., It is apparent that Model 3 contains two forms of lamellipodial coupling: direct ( mechanical ) competition and competition for the limited pools of inactive Rac and Rho ., While the former is certain to be an important coupling in some contexts or conditions 52 , we find that it is dispensable in this model ( e . g , see lc = 0 in Fig 5c ) ., We comment about the effect of such coupling in the Discussion ., In the context of this final model , we also tested the effect of ECM activation of Rac ( in addition to the already assumed effect on Rho activation ) ., As shown in Fig 5d ( dashed curves ) , the essential bifurcation structure is preserved when this modification is incorporated ( details in the S1 Text , and implications in the Discussion ) ., To summarize , Model 1b was capable of accounting for all observations , but required conservation of GTPase to do so ., This model was however rejected due to unreasonable time scales needed to give rise to oscillations ., Model 2 could account for oscillations with appropriate timescales , but it appears to be highly sensitive to parameters and , in our hands , inconsistent with experimental observations ., Model 3 , which combines the central features of Models 1b and 2 , has the right mix of timescales , and agrees with experimental observations ., In that final Hybrid Model , ECM based coupling ( lc ) due to mechanical tension or competition for other resources is not essential , but its inclusion makes oscillations more prevalent ( Fig 5b and 5e ) ., Furthermore , in this Hybrid Model , we identify two possible negative feedback motifs , shown in Fig 2b ., These appear to work cooperatively in promoting oscillations ., As we have argued , feedbacks are tuned so that ECM signaling spans a range large enough that bρ ( Ek ) traverses the entire bistable regime ( Fig 5a ) ., This is a requirement for the relaxation oscillations schematically depicted in Fig 2c ., Within an appropriate set of model parameters , either feedback could , in principle , accomplish this ., Hence , if Feedback 1 is sufficiently strong , Feedback 2 is superfluous and vice versa ., Alternatively , if neither suffices on its own , the combination of both could be sufficient to give rise to oscillations ., Heterogeneity among these parameters could thus be responsible for the fact that in ROCK inhibition experiments ( where Feedback 1 is essentially removed ) , most but not all cells transition to the persistent polarity phenotype ., The Hybrid Model ( Model 3 ) is consistent with observations O1–O3 ., We can now challenge it with several further experimental tests ., In particular , we make two predictions ., Migrating cells can exhibit a variety of behaviors ., These behaviors can be modulated by the cell’s internal state , its interactions with the environment , or mutations such as those leading to cancer progression ., In most cases , the details of mechanisms underlying a specific behavior , or leading to transitions from one phenotype to another are unknown or poorly understood ., Moreover , even in cases where one or more defective proteins or genes are known , the complexity of signaling networks make it difficult to untangle the consequences ., Hence , using indirect observations of cell migration phenotypes to elucidate the properties of underlying signaling modules and feedbacks are , as argued here , a useful exercise ., Using a sequence of models and experimental observations ( O1–O3 ) we tested several plausible hypotheses for melanoma cell migration phenotypes observed in 11 ., By so doing , we found that GTPase dynamics are fundamental to providing, 1 ) bistability associated with polarity and, 2 ) coupling between competing lamellipods to select a single “front” and “rear” ., ( This coupling is responsible for the antiphase lamellipodial oscillations ) ., Further , slow feedback between GTPase and ECM signaling resulting from contraction and protrusion generate oscillations similar those observed experimentally ., The single successful model , Hybrid Model ( Model 3 ) , is essentially a relaxation oscillator ., Mutual inhibition between the limited pools of Rac and Rho , sets up a primary competition between lamellipods that produces a bistable system with polarized states pointing in opposite directions ., Interactions between GTPase dynamics and ECM signaling provide the negative feedback required to flip this system between the two polarity states , generating oscillations for appropriate parameters ., Results of Model 3 are consistent with observations ( O1–O3 ) , and lead to predictions ( P1–P2 ) , that are also confirmed by experimental observations 11 ., In 11 , it is further shown that the fraction of cells exhibiting each of these behaviors can be quantitatively linked to heterogeneity in the ranges of parameters representing the cell populations in the model’s parameter space ., In our models , we assumed that the dominant effect of ECM signaling input is to activate Rho , rather than Rac ., In reality , both GTPases are likely activated to some extent in a cell and environment-dependent manner 41 , 42 ., We can incorporate ECM activation of Rac by amending the term AR so that its magnitude is dependent on ECM signaling ( Ek ) ., Doing so results in a shift in the borders of regimes we have indicated in Fig 5d ( dashed versus solid borders , see S1 Text for more details ) ., So long as Rho activation is the dominant effect , this hardly changes the qualitative results ., As the strength of feedback onto Rac strengthens , however , the size of the oscillatory regime is reduced ., Thus if feedback onto Rac dominates , loss of oscillations would be predicted ., This is to be expected based on the structure of these interactions ., Where ECM → Rho mediates a negative feedback , ECM → Rac mediates a positive feedback , which would be expected to suppress oscillatory behavior ., Thus while the ECM likely mediates multiple signaling feedbacks , this modeling suggest feedback onto Rho is most consistent with observations ., We have argued that conservation laws ( fixed total amount of Rac and fixed total amount of Rho ) in the cell plays an important role in the competition between lamellipods ., Such conservation laws are also found to be important in a number of other settings ., Fully spatial ( PDE ) modeling of GTPase function has shown that conservation significantly alters signaling dynamics 27 , 31 , 54 ., In 55 , it was shown that stochastically initiated hot spots of PI3K appeared to be globally coupled , potentially through a shared and conserved cytoplasmic pool of a signaling regulator ., Conservation of MIN proteins , which set up a standing wave oscillation during bacterial cell division , has been shown to give rise to a new type of Turing instability 56 ., Finally , interactions between conserved GTPase and negative regulation from F-actin in a PDE model was shown to give rise to a new type of conservative excitable dynamics 46 , 47 , which have been linked to the propagation of actin waves 57 ., These results provide interesting insights into the biology of invasive cancer cells ( in melanoma in particular ) , and shed light onto the mechanisms underlying the extracellular matrix-induced polarization and migration of normal cells ., First , they illustrate that diverse polarity and migration patterns can be captured within the same modeling framework , laying the foundation for a better understanding of seemingly unrelated and diverse behaviors previously reported ., Second , our results present a mathematical and computational platform that distills the critical aspects and molecular regulators in a complex signaling cascade; this platform could be used to identify promising single molecule and molecular network targets for possible clinical intervention . | Introduction, Results, Discussion | Protrusion and retraction of lamellipodia are common features of eukaryotic cell motility ., As a cell migrates through its extracellular matrix ( ECM ) , lamellipod growth increases cell-ECM contact area and enhances engagement of integrin receptors , locally amplifying ECM input to internal signaling cascades ., In contrast , contraction of lamellipodia results in reduced integrin engagement that dampens the level of ECM-induced signaling ., These changes in cell shape are both influenced by , and feed back onto ECM signaling ., Motivated by experimental observations on melanoma cells lines ( 1205Lu and SBcl2 ) migrating on fibronectin ( FN ) coated topographic substrates ( anisotropic post-density arrays ) , we probe this interplay between intracellular and ECM signaling ., Experimentally , cells exhibited one of three lamellipodial dynamics: persistently polarized , random , or oscillatory , with competing lamellipodia oscillating out of phase ( Park et al . , 2017 ) ., Pharmacological treatments , changes in FN density , and substrate topography all affected the fraction of cells exhibiting these behaviours ., We use these observations as constraints to test a sequence of hypotheses for how intracellular ( GTPase ) and ECM signaling jointly regulate lamellipodial dynamics ., The models encoding these hypotheses are predicated on mutually antagonistic Rac-Rho signaling , Rac-mediated protrusion ( via activation of Arp2/3 actin nucleation ) and Rho-mediated contraction ( via ROCK phosphorylation of myosin light chain ) , which are coupled to ECM signaling that is modulated by protrusion/contraction ., By testing each model against experimental observations , we identify how the signaling layers interact to generate the diverse range of cell behaviors , and how various molecular perturbations and changes in ECM signaling modulate the fraction of cells exhibiting each ., We identify several factors that play distinct but critical roles in generating the observed dynamic: ( 1 ) competition between lamellipodia for shared pools of Rac and Rho , ( 2 ) activation of RhoA by ECM signaling , and ( 3 ) feedback from lamellipodial growth or contraction to cell-ECM contact area and therefore to the ECM signaling level . | Cells crawling through tissues migrate inside a complex fibrous environment called the extracellular matrix ( ECM ) , which provides signals regulating motility ., Here we investigate one such well-known pathway , involving mutually antagonistic signalling molecules ( small GTPases Rac and Rho ) that control the protrusion and contraction of the cell edges ( lamellipodia ) ., Invasive melanoma cells were observed migrating on surfaces with topography ( array of posts ) , coated with adhesive molecules ( fibronectin , FN ) by Park et al . , 2017 ., Several distinct qualitative behaviors they observed included persistent polarity , oscillation between the cell front and back , and random dynamics ., To gain insight into the link between intracellular and ECM signaling , we compared experimental observations to a sequence of mathematical models encoding distinct hypotheses ., The successful model required several critical factors ., ( 1 ) Competition of lamellipodia for limited pools of GTPases ., ( 2 ) Protrusion / contraction of lamellipodia influence ECM signaling ., ( 3 ) ECM-mediated activation of Rho ., A model combining these elements explains all three cellular behaviors and correctly predicts the results of experimental perturbations ., This study yields new insight into how the dynamic interactions between intracellular signaling and the cell’s environment influence cell behavior . | cell physiology, cell motility, engineering and technology, enzymes, signal processing, biological cultures, enzymology, cell polarity, developmental biology, gtpase signaling, cell cultures, melanoma cells, cellular structures and organelles, research and analysis methods, extracellular matrix signaling, proteins, extracellular matrix, guanosine triphosphatase, biochemistry, signal transduction, hydrolases, cell biology, cell migration, biology and life sciences, cultured tumor cells, cell signaling | null |
journal.pcbi.1005728 | 2,017 | Population FBA predicts metabolic phenotypes in yeast | A cell’s phenotype—its set of distinguishing observable traits—can be as much an emergent property of the cell’s environment and gene expression state as it is a result of the cell’s genotype ., While some observables , like an organism’s response to Gram staining , can be immutable and tied to specific genes , others can be more fluid , varying from cell-to-cell with the random fluctuations in each cell’s molecular makeup 1–4 ., A cell might by chance over- or under-express the enzymes involved in a given biosynthetic pathway , in which case the over- or underproduction of that pathway’s end product might signify a naturally occurring phenotype ., Understanding this type of phenotypic variability requires models capable of connecting comprehensive gene expression profiles with cellular function ., Constraint-based methods like flux balance analysis ( FBA ) have proven to be among the more successful approaches to modeling complex enzyme-mediated biochemistry at the cell scale ( for recent reviews and a primer , see 5–9 ) ., In its simplest form , FBA seeks the flux distribution through a biochemical network that maximize the production of some specific objective , like biomass , while requiring that the concentrations of all other metabolites remain fixed ( i . e . the flux into and out of each metabolite is balanced ) ., Parsimonious FBA ( pFBA ) improves on the predicted flux distribution 10 by minimizing the total flux through all reactions while maintaining optimal objective function ., Minimizing total flux reduces the number of feasible flux distributions and represents efficient enzyme usage by the cell ., By imposing constraints on the flux allowable through certain reactions ( such as substrate uptake reactions , or reactions catalyzed by mutated , knocked-out , or low-copy number enzymes ) , different environments , genetic perturbations , or gene expression states can be modeled ., The use of FBA and related techniques has grown to include a large user-base that actively contributes to the development of both methods and models , and metabolic reconstructions now exist for a variety of model organisms ranging from bacteria and yeast up through humans 11–15 ., A particularly vibrant area of research in the field has been the use of large -omics data sets to constrain models in ways that reflect the influence of the cell’s regulatory machinery ., RNA microarray and RNA-Seq data can be used to impose reaction constraints according to the expression levels of the genes that encode their associated enzymes 16–20 ., More recently the development of coupled metabolism and expression ( ME ) models has allowed for the direct prediction of the enzyme expression state that optimizes growth , yielding results that agree with experimental data sets 21 ., While these methods yield insight into the average behavior of a population , they say little about cell-to-cell variability among sub-populations ., Heterogeneity in gene expression has been the subject of intense experimental and theoretical research over the past several years 22–32 , but relatively few studies have attempted to understand its effects on cellular function 33 , 34 ., Gene expression is known to correlate with growth rate 35; Labhsetwar et al . 34 developed the Population FBA methodology ( Fig, 1 ) in order to show that by sampling experimentally determined enzyme copy number distributions in a correlated fashion and using them as constraints on a genome-scale model of Escherichia coli metabolism , independently simulated cells exhibit a broad distribution of growth rates and several behavioral phenotypes ( e . g . some cells secrete acetate while others do not , or some cells make heavy use of the Entner-Doudoroff pathway while others predominantly use the Embden-Meyerhof-Parnas pathway ) ., This study was made possible by the results of Taniguchi et . al . ’s groundbreaking fluorescence microscopy investigation of protein expression in single cells with single molecule sensitivity 36 ., With recent developments in microscopy and microfluidics , a number of research teams have begun to report direct observations of single-cell growth rates 37–39 ., Intriguingly , growth rate distributions reported in yeast 38 bear a striking resemblance to that predicted in the Labhsetwar et . al . , article; in particular both show a broad “shoulder” of slow-growing cells and a distinctive peak of fast-growers ( see Fig, 2 ) 34 , 38 ., Here we extend the Population FBA approach developed in 34 in order to predict and characterize emergent metabolic phenotypes within yeast populations ., Employing the yeast 7 . 6 metabolic reconstruction—the latest and most complete and predictive genome-scale metabolic reconstruction of Saccharomyces cerevisiae to date 14 , 41—along with comprehensive proteomics 42 and microarray 43 data sets , we construct highly realistic populations of independent in silico S . cerevisiae cells in two different growth media ( the first , denoted SD represents the same synthetic defined medium used in 42 , while the second , denoted 13C , represents the minimal medium used in a recent experimental fluxomics study 40 , See Table A1 in S1 Text ) ., By ensuring these populations realize experimentally observed growth-rates , levels of gene expression , and correlations among co-regulated genes , we are able to create detailed models of the intracellular metabolic fluxes of every individual cell ( ∼ 100 , 000 ) ., We show that the Population FBA methodology using scaled protein counts and the yeast 7 . 6 metabolic reconstruction give quantitative and qualitative agreement with experimentally observed intracellular fluxes ( as determined by a 13C study 40 ) ., The use of transcriptomics data in order to impose correlations among co-regulated genes marginally improves the fidelity of our predicted intracellular fluxes ., We then characterize the dominant metabolic phenotypes within our modeled populations ., Specifically we find a shift in the balance between fermentation and respiration among fast-growing cells , we find cells in amino acid-rich media that make use of a complex set of reactions involving the glycine cleavage system , we find cells in minimal media that leverage the pentose phosphate pathway in order to conserve NADH , and we find slow-growing cells whose uptake of certain amino acids from the media exhibits a distinctive bimodality ., And finally , we characterize the degeneracy of the possible sets of enzyme-related constraints that can give rise to the experimentally observed growth rate distributions ., The consensus yeast metabolic model version 7 . 6 14 was chosen to describe the metabolic pathways in our simulations ., This model ( available from yeast . sourceforge . net ) represents the most comprehensive yeast metabolic reconstruction to date 41 ., All FBA calculations were performed using the COBRA toolbox version 2 . 0 44 or COBRApy 45 ., Gurobi 6 was used to perform all linear programming optimizations ., Flux variability analysis ( FVA ) was performed using COBRA function fluxVariability ( ) to determine robustness ( minimum and maximum ) in flux values given a percentage optimality ., FVA was also used to identify proteins with significant copy numbers but zero predicted flux in their associated reactions ., For every cell in our modeled populations we sampled fluorescence values out of 535 experimentally determined distributions and converted them to enzyme copy number using Eq 1 ( see Methods Section Conversion of fluorescence to protein copy numbers and scaling ) ., Each sampled enzyme copy number was paired with a turnover rate corresponding to that enzyme’s function ( kcat ) , and the product of these and a conversion factor yielded the upper bounds for the fluxes through the reactions catalyzed by each enzyme in each cell ( See Methods Section Constraint relaxation for realistic growth ) ., The conversion factor used was 3 . 0 × 10−7 s cell−1 mmol gDwt−1 hr−1 , given by the number of seconds in an hour ( 3 , 600 ) divided by the average dry mass of a haploid yeast cell ( 2 . 0 × 10−11 g 46 ) and the number of particles in a mmol ( 6 . 02 × 1020 ) ., In cases where multiple enzymes catalyze a given reaction , Gene-Protein-Reaction ( GPR , part of the metabolic model ) rules were used to determine the effective upper bound for the reaction from the upper bounds calculated for the individual enzymes ., In cases involving “AND” relationships ( i . e . an enzyme is made up of two subunits and both need to be present ) , the minimum of the individual upper bounds was used , whereas in cases involving “OR” relationships ( i . e . different proteins can catalyze the same reaction ) , the sum of the individual upper bounds was used ., If a count was missing for one of the enzymes involved in an “OR” relationship , the upper bound was left at the default value of 1 , 000 mmol gDwt−1 hr−1 ., After setting all protein-associated constraints , parsimonious FBA 10 was performed in order to predict the internal fluxes of each modeled cell ., Upper bounds for the uptake substrates were applied depending on the growth medium being modeled ., The SD medium included glucose , 19 amino acids , uracil , citrate , vitamins , and minerals; the upper bounds for the amino acids , uracil , citrate and the vitamins were estimated based on experimental data 47 ( when no data was available the maximum experimental uptake was set , See Table A1 in S1 Text ) , those for oxygen and the minerals were unconstrained , and glucose upper bound was scaled to match experiment 40 ., The strain , BY4741 , used in the growth rate distribution 38 and proteomics 42 studies—both grown in SD medium—contained several gene deletions , including his3Δ1 , leu2Δ0 , met15Δ0 , and ura3Δ0 ., To account for this , the genes YCL018W , YLR303W , YEL021W were inactivated , leading to zero flux being allowed through five reactions: 3-isopropylmalate dehydrogenase ( r_0061 ) , cysteine synthase ( r_0312 ) , O-acetylhomoserine ( thiol ) -lyase ( r_0812 , and r_0813 ) and orotidine-5-phosphate decarboxylase ( r_0821 ) ., The histidine biosynthesis knockout is recovered when GFP is tagged to any protein , so the gene YOR202W was kept active ., The 13C medium included only glucose , some vitamins , and minerals ( See Table A1 in S1 Text ) ., As in the SD medium , vitamin uptake upper bounds were set based on experimental data 47 , the oxygen and minerals were unconstrained and glucose upper bound was scaled to match experiment 40 ., Glucose uptake upper bound of 20 mmol gDwt−1 hr−1 is also supported by Diderich et al . 48 ., The strain used in the 13C medium , FY4 Mat a , is a wild-type strain , so no modifications were done to original yeast 7 . 6 model to simulate this ., Protein abundances were obtained from single cell fluorescence measurements on yeast strain BY4741 grown on glucose SD medium 42 ., The authors reported fluorescence distributions that were calculated from average pixel intensities over entire cells; we therefore considered all protein abundances to be size-normalized ., For each GFP-labeled protein , Dénervaud et al . 42 deconvoluted the single cell fluorescence signal from the autofluorescence signal , and fitted the results to gamma distributions , providing shape and scale parameters for 4 , 159 proteins measured at 40 time points , taken 20 minutes apart ( totaling 166 , 360 fluorescence distributions ) ., Since the study aimed at observing changes in the proteome in response to stress , only 18 of the 40 time steps could be used for each protein ( the ones before the induction of stress factors ) ., Of the 74 , 862 remaining distributions , several displayed significant abnormalities , most likely resultant from the automated deconvolution procedure used to separate weak GFP fluorescence signals from the cell’s autofluorescence ., The abnormality consisted of fluorescence distributions that were extremely narrow and usually had low mean fluorescence , hereafter referred to as “spikes” ., Examples can be seen in Fig 3 ., A multi-step procedure was developed to automate the processing of the almost 75 thousand fluorescence distributions , and when appropriate , censoring of spikey distributions ., Only proteins that had data for all 18 time points were subjected to this process , which led to the removal of 59 proteins ., First , a conservative lower bound of 0 . 1 was placed on standard deviations to remove the most obvious spikes , leading to the removal of all 18 time points for 7 proteins and a total of 2948 total fluorescence distributions being discarded across all proteins ., Then , for each protein , the remaining distributions were used to determine a central reliable region for means and standard deviations , which were defined as the range from 1 . 5 times their IQR ( inter quartile range ) below the 25% quantile to 1 . 5 times the IQR above the 75% quantile ., If a fluorescence distribution had either its mean or standard deviation outside this range , the distribution was discarded , leading to the removal of another 4175 distributions ., After this step , only proteins that had 3 or more fluorescence distributions out of the original 18 were kept , which lead to the removal of another 3 proteins ., Finally , the third step calculated the coefficient of variation ( CV , defied as standard deviation over the mean ) of the means and the CV of the standard deviation of the remaining distributions for all proteins ., Only proteins whose distributions had both means and standard deviations with CVs lower than an upper bound of 0 . 5 were kept , removing 201 additional proteins ., Proteins with mean fluorescence less than 7 . 98 A . U . were also removed because Dénervaud et al . 42 considered them unreliable ., We found that these proteins had significantly less noise than proteins with means higher than 7 . 98 A . U . The final set of reliable fluorescence distributions represented a total of 3 , 647 proteins , which covered diverse cellular processes and compartments ., The full dataset acquired after this process is reported in the S2 File , including parameters for fluorescence distributions in individual timesteps , and full plots for all fluorescence distributions used in our simulations ., The fluorescence distributions which were found to be reliable were then converted to absolute protein copy distributions ., We used single cell quantification of 10 proteins ( Table 1 ) from mass spectrometry ( MS ) 49 in order to relate fluorescence values to single cell copy numbers ( Fig 3 ) ., The quantitative protein abundance from the MS study were determined using the same yeast strain as used in Dénervaud et al . , but were grown on complex media ., In order to estimate protein counts for synthetic defined ( SD ) media , we used expression ratios observed in a single cell proteomics study 50 , where protein abundances were measured in both complex and synthetic defined media ., Finally , a linear fit between log values for protein counts and fluorescence was used to obtain the Eq ( 1 ) for converting fluorescence into protein counts:, p = 2 ., 87 * f 1 ., 5577 ( 1 ), where p represents the single cell protein copy number , and f represents the fluorescence value ., During sampling , we ensured a lower bound of 2 . 87 for all enzymes ( if a copy number was sampled lower , it was replaced with 2 . 87 ) , This was because we expect fluorescence values less than 1 to be unreliable ., Protein counts were scaled in case of simulation for 13C medium because the protein distributions measured by Dénervaud et al . are in SD medium ., Ratios to scale the protein counts were found from microarrays comparing gene expression of cells grown in SD medium to cells grown in SD medium without amino acids for 6 hours after being transferred from SD medium 51 ., Top 10 proteins downregulated and top 10 proteins upregulated in minimal medium are shown in Table A2 in S1 Text ., Microarray datasets from Kemmeren et . al . 43 ( available from the GEO database , Accession No . GSE42528 ) , were used to calculate correlation coefficients among the 532 out of 535 metabolic proteins we sampled ., Rest of the three proteins were not measured in these microarray experiments ., This microarray data is well-suited for our study because it was produced using an almost identical strain of yeast , BY4742 which has same deletions but different mating type , and under similar growth conditions as that used in the proteomics study we rely on 42 ., Absolute fluorescence values for the sample channel of the two-channel microarrays were used; because almost all of the genes evaluated had two probes on the microarray chip ( Accession no . GPL11232 ) , the mean value of the two probes was computed ., Fluorescence values were then quantile normalized across the entire set of microarray data 52 ., Correlation coefficients were calculated from these normalized fluorescence values ., These correlation coefficients were then used to create correlated samples of protein counts using the usual Cholesky decomposition methodology 34 ., The correlations observed show clear biological relevance ., For example , the Crabtree effect , which is well known in S . cerevisiae , can be seen in the positive correlations among the Glucose transporter HXT1 and genes in the fermentative pathway as well as the negative correlations between HXT1 and genes involved in the TCA cycle and oxidative phosphorylation ( see Fig A11 in S1 Text ) ., Moreover , the correlations we see recover many experimentally known regulatory links in yeast ( see Fig A12 and Section Reliability of mRNA Microarray Correlation Data in S1 Text ) ., Without internal constraints , the metabolic model iJO1366 for E . coli and the yeast version 7 . 6 model both return higher growth rates for a given glucose uptake rate than is experimentally observed ., However , as previously reported in the population studies on E . coli 34 , imposing all of the possible constraints arising from the measured protein distributions and turnover rates does not allow the population to grow ., The problem lies with either some of the protein counts or some of the turnover rates ., In converting the fluorescence data to protein distributions , we already removed spurious data and low counts , so we were confident in the remaining distributions ., Moreover , because a third of the kcat values obtained from BRENDA have changed in the span of a year , we chose to keep the protein counts and raise the appropriate turnover rates in order to allow for growth ., To deterministically find problematic turnover rates , we iteratively simulated populations of 400 cells and identified the reaction whose flux most often reached its imposed upper bound ., kcat for the enzyme associated with that reaction was doubled ., If that reaction was catalyzed by multiple proteins , we doubled the kcat value for the protein with highest protein mean count in case of isozymes ( ‘OR’ relationship ) and all the subunits in case of an protein enzyme complex ( ‘AND’ relationship ) ., We continued this procedure iteratively until the mean growth rate of the sampled population reached 0 . 35 hr−1 , the bulk growth rate measured in both the proteomics 42 and single cell ( microcolony ) experiments 38 ., First round of doublings helped us to focus on proteins which needed excessive doublings and hence manual search for those kcat values was performed and any higher kcat reported in literature was accepted ., Manually found kcat values can be found in Table A3 in S1 Text ., Before going through the doubling procedure , we also raised kcat values of all subunits in a protein complex to the highest kcat among the subunits ., Each yeast cell in our modeled populations had a unique protein copy number for 535 genes , and a unique flux distribution throughout the metabolic network of over 3 , 400 reactions ., Different fluxes in this metabolic network are linearly dependent on each other and constitute metabolic pathways ., To find pathways that were differentially used by different segments of our modeled populations , we used principal component analysis ( PCA ) as implemented in MATLAB’s pca ( ) function to elucidate orthogonal directions ( in the 3 , 400-dimensional flux-space ) in which the cells in our populations varied most ., We chose 1 , 000 cells at random from the population for this analysis ., Since the members of this population grew at different growth rates , we normalized all fluxes by the cell’s growth rate , allowing us to identify growth-independent differences in pathway usage ., This methodology is similar to that used previously 34 , but we didn’t need to rotate the components coming out of PCA as they aligned with canonical metabolic pathways ., A new procedure for filtering overly-constraining turnover rates based on the Micro Genetic Algorithm ( GA ) formalism was developed 53 ., This method utilizes an entire growth distribution as a target for optimizing the selection of experimental constraints ., Micro Genetic Algorithm was chosen instead of a “regular” Genetic Algorithm solely for computational cost concerns ., In a “regular” GA algorithm in dozens to hundreds of genomes would have to be simulated at each generation , and several hundred generations could need to be evaluated to reach the same results ., The computational cost would be extremely higher as compare to our GA implementation ., In our attempt to reduce the size of search space we have restricted GA variables to binary values representing weather to use a particular kcat or 38 , 000 s−1 rather than more flexible values kcat can take in the doubling procedure ., Briefly , a population of 10 “genomes” was simulated , each one composed of a list of “genes” that indicated if a protein’s kcat would be kept at its BRENDA value , or if it would be raised to 38 , 000 s−1 ., The genomes were allowed to evolve by exchanging information , and each new generation was created by a random selection of solutions biased by their fitness , while always taking the best solution to the next generation ( see SI Section Extended Methodology: Genetic Algorithm for Constraint Selection for details ) ., The fitness of each genome was determined by simulating a cell population based in its kcat selection , and then calculating the goodness-of-fit between the resulting growth rate distribution and the observed distribution 38 ., The basic Population FBA methodology has been described previously 34 ., Briefly , enzyme copy numbers are sampled from experimentally-determined distributions 42 from a single cell proteomics study; each sampled set of enzyme copy numbers represents a unique cell in its own gene expression state ., Assuming Michaelis-Menten kinetics , each copy number—paired with an appropriate enzyme turnover rate ( kcat ) —represents the maximum reaction flux that the cell can maintain through the reaction ( s ) mediated by that enzyme ., Many genes are known to exhibit some correlation in their expression levels ., For bacteria , 34 , this effect was handled fairly simply; proteins in the “extrinsic noise limit” , noise floor observed in proteins with high means , were assumed to exhibit a correlation coefficient of 0 . 66 suggested by the single cell proteomics study 36 ., Due to the availability of large transcriptomics datasets , we are now able to take a more refined approach in which we systematically impose the types of correlations that should naturally arise among the copy numbers of co-regulated proteins ., This was accomplished by extracting correlation coefficients for ∼ 4 , 000 S . cerevisiae gene products from an expansive collection of microarray gene expression datasets 43 and using them to draw correlated sets of protein copy numbers Constraints of this type were then imposed throughout a genome scale flux balance model of metabolism , and parsimonious flux balance analysis ( pFBA ) 10 was used to predict each cell’s metabolic behavior ., The copy number distributions that were used were adapted from a recent article by Dénervaud et al . 42 for yeast grown in SD medium ., The authors used a GFP fusion library spanning 4 , 159 S . cerevisiae proteins and a unique parallel microchemostat microfluidic device to measure single cell fluorescence intensity distributions—representative of protein expression distributions—for approximately 2/3 of the yeast proteome ., Intensities sampled from these distributions were transformed to copy numbers using a calibration curve ( see Fig 3A ) ., Several of the measured fluorescence distributions were abnormally “spikey , ” likely as a result of poor deconvolution of the GFP signal and the cell’s own autofluorescence ( see Fig 3B and 3C ) ., We removed these spikey distributions by determining which distributions had abnormal means and standard deviations using a simple outlier-detection protocol ( see Methods Section Conversion of fluorescence to protein copy numbers and scaling ) ., Among the remaining 3 , 885 distributions , those with mean fluorescence lower than 7 . 98 A . U . ( as measured by Dénervaud et al . 42 ) had significantly lower noise than the proteins with similar means hence they were also removed ., The 3 , 647 distributions that remained after this censoring procedure showed noise characteristics that agreed qualitatively with previously published results in E . coli ( see Fig A1 in S1 Text ) ., Only 535 of these remaining distributions were associated with enzymes involved in the yeast 7 . 6 metabolic reconstruction 14 ( see S1 File ) , and thus only these were used in our study ., We would like to note that GFP is extremely stable protein which might affect stability of tagged protein and hence bias the protein counts towards higher number than their numbers in untagged cells ., Metabolic reconstruction of yeast accounts for 13 compartments which represent various organelles and their membranes ., All metabolites are assigned to one of these compartments and reactions are either localized in a compartment if all the reactants and products are present in the compartment or facilitate transport of metabolites across compartments ., When we associate an enzyme with a reaction using the gene-protein-reaction associations of the reconstructions , we assume all the copies of the enzyme are available to the reaction it is associated with ., So even though the copies of the enzyme might be spread out over multiple compartments in real cells , in lack of that information we make all the copies available to all the reaction the enzyme is associated with ., Two sets of simulations were performed , corresponding to the two different environmental conditions ., The first was intended to replicate the cell growth media used in a study ( 40 ) of 13C-labeled glucose utilization by several strains of yeast ., This was done in order to accurately compare our predicted intracellular metabolic fluxes with those determined experimentally ( see Results Section Population FBA yields intracellular fluxes that agree with 13C fluxomics data ) ., The synthetic defined ( SD ) medium replicates the conditions used in single cell proteomics 42 and growth rate distribution studies 38 ., This SD media included approximately the same concentrations of salts , double the glucose , and several metabolites not present in the 13C media ., These included 19 amino acids ( including the histidine , leucine , and methionine necessary for the growth of the his3Δ1 , leu2Δ0 , and met15Δ0 experimental strain ) , as well as citrate , and uracil ( necessary for the ura3Δ0 also present in the experimental strain ) ., Our modeled cells contained the same knockouts as the cells used in the experiments ., Modeling of 13C media involved modifying relevant uptake rates in the metabolic model and scaling protein copy numbers measured in SD media to 13C media ., Details of both modeled media and rescaling can be found in Section Extended Methods: Metabolic Model and Experimental Data and Table A1 and Table A2 in S1 Text ., We assume that the relative composition of biomass is the same in all members of the population , although some experiments indicate the composition may change as a function of the growth rate 54 , 55 ., FBA models in general are underdetermined ., By adding constraints in the form of reaction upper- and lower-bounds , modelers are able to whittle down the solution space ( the right null space of the stoichiometry matrix ) to the flux distributions that most accurately describe real cells ( 7 ) ., Metabolic reconstructions already include topological and thermodynamic constraints in terms of stoichiometric matrix and reaction reversibilities ., Additional constraints are also routinely added to reflect the genetics of the strain ( for example by fixing the flux through a reaction mediated by a “knocked-out” gene to zero ) as well as the growth medium used ( for example , by limiting the uptake of substrates absent from the media to zero ) ., In Population FBA we add additional constraints based on protein copy numbers and their kinetic capacity ., After censoring proteomics data to remove unreliable distributions , we believe we have good quality of protein copy number distributions ., As for the kinetic capacity , we rely primarily on the BRENDA database 56 , 57 ., The BRENDA database often contains several sets of kinetic parameters for a given reaction ., These can include values for enzymes from different organisms , strains , and most often in vitro conditions ., A recent study concluded that kcat measured in vitro generally agree with max kcat in vivo estimated using omics data 58 ., Whenever possible the largest kcat value available for a wild-type S . cerevisiae strain was taken , otherwise the largest value reported for any mutant or other species was used ., If no kcat was available for an enzyme-mediated reaction , a value of 38 , 000s−1 ( corresponding to the largest kcat reported for a wild-type yeast enzyme in BRENDA ) was set ., These criteria were adopted in order to minimally constrain the model ., Importantly , the 535 sampled enzymes and kcat values could in principle be used to impose constraints on 1 , 128 of the model’s 3 , 493 reactions ( each enzyme catalyzes two reactions on average ) , but it was found that imposing all of these constraints impedes the growth of the modeled cells to levels well below that seen experimentally ., Several enzyme turnover rates were found to have published values well below those necessary to allow realistic growth ., For example , phosphofructokinase ( PFK ) , which is made up of two subunits , had mean copy numbers measured to be 103 , 880 ( α subunit ) and 47 , 919 ( β subunit ) and a reported turnover rate of 62 s−1 ( See Table 2 ) ., This led to a maximum reaction flux of 1 . 16 mmol gDwt−1 hr−1—approximately ten-fold smaller than the experimentally measured 13C glycolytic flux 40 ., In cases like this , the kcat values , which are relatively uncertain ( reported values for phosphofructokinase , for example , range over four orders of magnitude 57 ) , were “doubled” until the mean population growth rate of 0 . 35 hr−1 was achieved ( see Methods Section Constraint relaxation for realistic growth for details ) ., Our doubling methodology involves iteratively generating small populations of modeled cells ( 400 ) and then determining which reaction most constrains cellular growth , and doubling its kcat ., This strategy revealed that certain enzymes required excessive numbers of doublings ( for example , the kcat for Glycogen Synthase was doubled 19 times in our 13C simulations ) ., These rates were investigated further , and in many cases we were able to find significantly higher kcat values in the literature than were reported in BRENDA ( See Table A3 in S1 Text ) ., Even after including kcat values from literature , some protein’s kcat needed significant doubling e . g Acetylornithine aminotransferase needed 13 doublings ., We also found kcat for NAD dependent methylenetetrahydrafolate dehydrogenase ( YKR080W ) , was wrongly listed in BRENDA as 1 . 63 s−1 and needed | Introduction, Methods, Results and discussion | Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism , Population flux balance analysis ( Population FBA ) successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium ., We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media ., We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments , and that our model largely recovers the Crabtree effect ( the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen ) , while FBA without proteomics constraints predicts respirative metabolism almost exclusively ., The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature ., The minor secretion fluxes for glycerol and acetate are underestimated by our method , which indicate a need for further refinements to the metabolic model ., For yeast cells grown in synthetic defined ( SD ) medium , the calculated broad distribution of growth rates matches experimental observations from single cell studies , and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways ., Fast growing yeast cells are predicted to perform significant amount of respiration , use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells ., We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally ., We find that a core set of 51 constraints are essential but that additional constraints are still necessary to recover the observed growth rate distribution in SD medium . | No two living cells are exactly the same ., Even cells from a clonal population with identical genomes living in the same environment will express proteins in different numbers simply due to the random nature of the chemistry involved in gene expression ., The consequences of this stochastic gene expression are complex and not well understood , especially at the level of large reaction networks like metabolism ., Here we investigate how variability in the copy numbers of metabolic enzymes affects how individual cells extract nourishment from their environment and grow ., We model 100 , 000 independent yeast cells , each with their own set of enzyme copy numbers sampled from experimental distributions , and use flux balance analysis ( FBA ) to compute the optimal way that each cell can use its metabolic pathways—an approach we dubbed Population FBA ., We find that enzyme variability gives rise to a wide distribution of growth rates , and several metabolic phenotypes—subpopulations relying on diverse metabolic pathways ., Most importantly , we compare the predicted fluxes through the different pathways to experimental values; we find that Population FBA is able to correctly predict Crabtree effect , while traditional FBA , which lacks the proteomics constraints our method imposes , differs both qualitatively and quantitatively from experiment . | cell physiology, protein metabolism, chemical compounds, aliphatic amino acids, enzymes, enzymology, carbohydrates, cell metabolism, organic compounds, glucose, threonine, fungi, model organisms, experimental organism systems, amino acids, saccharomyces, research and analysis methods, glycine, proteins, chemistry, yeast, biochemistry, eukaryota, cell biology, organic chemistry, monosaccharides, hydroxyl amino acids, biology and life sciences, yeast and fungal models, saccharomyces cerevisiae, physical sciences, metabolism, organisms | null |
journal.pcbi.1003622 | 2,014 | Slow Noise in the Period of a Biological Oscillator Underlies Gradual Trends and Abrupt Transitions in Phasic Relationships in Hybrid Neural Networks | Synchronized neural firing is a characteristic activity pattern of neural systems ., Synchronized neural activity in cortical circuits 1 is thought to underlie many aspects of cognition 2 , 3 , including recognition 4 , recall 5 , perception 6 , 7 , and attention 8 ., Phase-locked neural activity is also an essential component of central pattern generators ( CPGs ) located in the spinal cords of vertebrates and the ganglia of invertebrates 9 , 10 ., Inhibition plays a central role in oscillatory synchrony , and in this study we focus on reciprocal inhibitory coupling ., A major contribution of this paper is a distinct notion of noise in coupled oscillatory neurons , which we explore by comparing three models of noise intrinsic to the neurons ( see Methods ) ., The dominant source of noise in neurons is thought to be synaptic 11 ., This thinking is shaped by studies of cortical circuits , in which neurons in a high conductance state that receive a stochastic barrage of fast and balanced excitatory and inhibitory input show fast fluctuations in membrane potential 12 ., An early attempt to quantify the effect of noise on neural activity 13 examined the case of a perfect integrator with additive white noise ., The output of the integrator is interpreted as the membrane potential ., In the absence of noise , a baseline current produces a regular oscillator with constant angular velocity that is reset each time it reaches threshold ., The noise takes the form of Gaussian current noise added to the baseline current ., When this noise is integrated , it is analogous to a trajectory produced by Brownian motion , and produces a one-dimensional random walk in the membrane potential superimposed on the steady upward trend caused by the constant baseline current ., In this model , membrane potential is proportional to the phase of the oscillation , so a random walk in the phase occurs ., The time scale of this noise is fast , due to its theoretically flat spectrum which includes very high frequency components ., The current noise has no history-dependence since the value at each time point is random and independent of all previous values ., However , the membrane potential does have a memory , because at each time step , the value is a perturbed version of the value at the previous time step ., The second moment , or variance , of the displacement of the membrane potential from its original value is proportional to the product of the diffusion constant and the time step ., The mean squared displacement therefore grows as the square root of the size of the time step 14 ., The memory of noise on the previous cycle is wiped out when the membrane potential and the phase are reset when the spike threshold is reached ., Based on this model , a common way to add noise to phase models of neurons is simply to add Gaussian noise to the phase 15 , 16 , which is one of the noise models that we use in this study ., Real neurons have complex nonlinear intrinsic currents , and thus may not linearly integrate their extrinsic inputs ., We modeled the intrinsic period as stochastic due to random fluctuations in factors that influence the period ., If these factors have little history dependence , for example , variability in the number of ions passing through an open channel at any given time , then successive interspike intervals are uncorrelated and may appear to be drawn from a Gaussian distribution 13 , 17 , 18 ., Gaussian noise added to the period is the second model used in this study ., If the period of one cycle depends on the previous cycle because the stochastic fluctuations occur in history-dependent processes , then a different model must be used 19 ., History dependent noise may arise from slowly changing levels of stochastic fluctuations in the numbers of open channels for adaptation currents 20 or levels of second messengers , channel phosphorylation , insertion and deletion of channels into the membrane , and other unknown factors ., Instead of drawing the period from a distribution , the period itself can be made to undergo Brownian motion under the assumption that the period is equally likely to be perturbed in either direction at a given instant , and that the displacement is therefore proportional to the square root of the time step ., Finally , if we assume that the mean of the noise reverts to zero , we obtain an Ornstein-Uhlenbeck 21 process added to a constant period , which is the third and final noise model used in this study ., This latter model is novel , although it shares some elements with the model of Schwalger et al . 20 , and constitutes a different noise model that may complement the fast noise in some circumstances ., We postulate that the period of biological oscillatory neurons varies randomly but with history dependence ., The direct effect on network activity of slow stochastic dynamics that cause history dependence in the period of component oscillators has not been previously investigated ., This slow form of intrinsic noise may have implications for synchronization and phase locking in neural circuits ., In this study , we construct hybrid neural circuits consisting of one biological and one computationally modeled neuron ., These coupled pairs exhibit different patterns of activity , which we refer to as motifs , during coupling ., Understanding how and why synchronization and phase locking occur in populations of neurons is critical to understanding how neural circuits function ., Phase-locking implies a constant phase relationship between neural oscillators; synchrony is a special case of phase-locking in which spikes occur in different neurons at about the same time ., Another observed motif is phase slipping ., In this motif , the spiking activity of the faster cell “laps” the slower one and the timing relationships are different in every cycle ., Our analysis of these dynamics utilizes the phase resetting curve ( PRC ) measured from both biological and model neurons in response to the same stimulus pulses that the neurons receive in the circuit; an action potential in the presynaptic neuron triggers a predetermined conductance waveform in the postsynaptic neuron both in the hybrid network and in the protocol for measuring the PRC ., The PRC describes how a neurons period is shortened or lengthened depending upon at what point in the cycle a perturbation was received 22 , 23 ., This PRC is a useful tool for predicting synchronization and phase locking in neural systems under the assumption that the phase resetting due to an input is complete by the time the neuron receiving the input spikes next or by the time it receives another input , whichever occurs first ., The PRC for biological neurons as well as the hybrid circuit activity is measured in the presence of ubiquitous biological noise ., The impact of noise on PRC-based predictions is an open question ., The overall aim of this work was to assess why different dynamical motifs , such as phase locking and phase slipping , were observed in hybrid circuits and to explain how random transitions between these motifs occurred ., Using PRC-based maps , we were able to predict phase locking and synchronization in two-neuron networks and describe the activity motifs observed in these circuits ., By comparing the performance of three noise models in simulations of hybrid circuit activity , we were able to show that noise contributes to variability within and switching between different motifs , and that history-dependent noise in the period was necessary to mimic motif variability and transitions seen in experiments ., Aplysia californica were acquired from the Miami National Resource for Aplysia ( Miami , FL ) and kept in saltwater tanks at room temperature for 1–2 weeks until used ., Animals were anesthetized using a solution of 71 . 2 g MgCl2 in 1 L 1X artificial sea water ( 1X ASW ) ., 1X ASW was comprised of ( in mM ) 460 NaCl , 10 KCl , 11 CaCl2 , 30 MgCl2 , 25 MgSO4 , and 10 HEPES ( pH 7 . 6 ) 24 ., The abdominal ganglion was dissected out of the animal and pinned in a Sylgard-lined ( Dow Corning ) dish filled with dissection solution ( 30% 1X MgCl2 solution and 70% ASW solution ) for desheathing ., The ganglion was desheathed under a dissection microscope ., The dish solution was then replaced with a high-Mg2+ low Ca 2+ recording solution , which contained ( in mM ) 330 NaCl , 10 KCl , 90 MgCl2 , 20 MgSO4 , 2 CaCl2 , and 10 HEPES , pH 7 . 6 25 ., Electrodes consisted of pulled ( Sutter P-97 puller ) glass pipettes containing 3 M potassium acetate and silver wire chlorided in bleach ., Regularly spiking neurons in the lower left quadrant of the Aplysia were used as the biological neurons in the hybrid circuits ., An Axoclamp 2B amplifier with Clampex 8 . 2 software ( Molecular Devices ) was used to supply stimulus currents and record membrane potential ., A Digidata 1322A Digitizer ( Molecular Devices ) was used to sample electrophysiological data at 10 kHz ., Wang-Buzsaki ( WB ) model neurons were used in the hybrid circuit experiments ., The equations and parameters for the WB model neuron were the same as in 26 except that the leak reversal potential EL was set to -60 . 0 mV and the applied current Iapp was controlled to match the 1–5 Hz spiking frequency of the Aplysia spiking neuron ., This modified WB model matches both the spike dynamics and PRC shape of experimentally measured neurons 27 , 28 ., Iapp for the model neuron was chosen such that the spiking frequency was similar to that of the biological neuron ., Synaptic conductance values for the model neuron were selected to increase the likelihood of 1∶1 phase locking in hybrid circuits ., The differential equations for the state variables of the WB model and the two virtual synapses were updated in real time ., The voltage measured in the biological neuron was used to determine the time course of the conductance for the synapse onto the model neuron and the driving force for the synaptic current of the synapse onto the biological neuron ., Dynamic clamp is a real-time computational and experimental technique used to add data-driven simulated ion channel conductances to biological neurons 29–31 ., For these experiments , we used the Model Reference Current Injection ( MRCI ) 32 system to construct hybrid circuits and measure phase resetting curves ., The dynamic clamp system operated at a frequency of 10 kHz , which corresponds to a closed-loop sampling and computation period of 100 µs ., Reciprocal inhibitory synapses were used in hybrid circuits , and inhibitory perturbations were used to measure phase resetting curves ., The alpha-shaped conductance waveform was calculated using the following equations: dy/dt = −y/τ +itrig; dα/dt = −α/τ + y; Isyn =\u200agsyn α ( V- Esyn ) e/τ ., V corresponds to the membrane potential of the postsynaptic cell , Esyn was set to −70 mV , and τ and gsyn were varied as in Table 1 ., The value of itrig was zero except when an input was triggered , either because the presynaptic cell spiked in the hybrid circuit or a perturbation was needed to measure a point on the PRC , then itrig was set to amplitude 1 for 1 ms . The e/τ term normalizes the maximum amplitude of the conductance waveform to gsyn ., PRCs were measured using the dynamic clamp to apply inhibitory inputs at various times during the neurons interspike interval ( ISI ) ., Perturbations were separated by at least 10 cycles to allow ISIs to return to pre-perturbation magnitudes ., The stimulus interval ts corresponds to the time interval between the previous spike in the neuron receiving the input and the start of the applied perturbation ., This interval was normalized by unperturbed period P0 , which was the average of the five ISIs prior to the perturbation , to obtain the phase θ\u200a=\u200ats/P0 ., Phase reset ( Figure 1B ) was calculated as the perturbed period P1 minus the unperturbed period P0 , normalized by the unperturbed period P0 ( see Figure 1A ) ., Neuronal spikes were detected using a −40 mV threshold ., Biological PRCs were fit using 3rd or 4th order polynomials to minimize least squared error and promote randomly-distributed residuals ., Noiseless model neuron PRCs were spline fit ., This fit was necessary in order to use the PRCs as functions in the network simulations described below ., An alternative way to present the information from a PRC is in the stimulus interval – recovery interval ( ts-tr ) plane ( Figure 1C ) ., Stimulus interval refers to the time interval between when the neuron last spiked and when a perturbation arrived ., The recovery interval tr refers to the interval between the time of application of the perturbation and the time of the next spike in the perturbed neuron ., This description preserves time information , unlike the PRC whose quantities are unitless ., Very strong perturbations result in more pronounced curves on the ts-tr plane , whereas less strong perturbations manifest in the ts-tr plane as nearly straight lines ., As seen in Figure 1B and C , a PRC with peak magnitude of around 0 . 05 ( black curve ) , looks somewhat like a straight line on the ts-tr plane ., This apparent flattening occurs because the PRC plot is scaled to the maximum PRC amplitude , whereas the scale of the ts-tr plot is determined by the maximum period of the oscillation ., Hybrid circuits of one biological neuron and one model neuron were constructed using the dynamic clamp; 13 distinct biological neurons were used to construct the 35 hybrid circuits presented here ., No noise was added to the circuit , all noise was intrinsic to the biological neuron ., A single biological neuron was used for multiple hybrid circuits , with different conductance and time constant values , for as long as the experiment remained viable ., All synapses were inhibitory because the reversal potential for both synapses ( Esyn ) was set to −70 mV ., In nearly all cases , PRCs of the biological neurons were measured with conductance parameters gsyn and τ that were used for the coupling experiments ., In a limited number of cases , coupling experiments were performed with a weaker conductance than the one at which the corresponding PRC was measured ., In such cases , the PRC was linearly scaled to calculate the curves that describe the network interactions ., We previously showed that for conductance below a certain threshold , PRC shape is preserved and scales linearly with amplitude 33 ., Our goal was to choose coupling values that resulted in 1∶1 synchrony; however , because the PRC measured before the experiment constrains the coupling parameters used in the experiment , but the biological neuron activity can change over time , in practice a range of effective couplings were obtained ., Dynamical motifs were defined as characteristically different episodes of network activity ., Network phase φnet was defined as the position of the spike in the biological neuron within the cycle in the model neuron that contains the spike ., Network phase was calculated as tsM/ ( tsM + trM ) , where tsM is the time interval between a spike in the model neuron and the following spike in the biological neuron ( which perturbs the model neuron ) , and trM is the time interval between the spike in the biological neuron and the next spike in the model neuron ., The first 10 network phases were discarded to eliminate transient effects ., Network phase that remained within ±0 . 1 units of the network phase for 20 or more cycles was defined as phase-locked ( Figure 2A ) ., Activity in which the network phase transitioned through consecutive increasing or decreasing phases , which often resulted in one neuron spiking twice during the ISI of the other neuron , was defined as phase slipping ( Figure 2B ) ., Episodes that did not meet either criterion were categorized as other ., See Text S1 for more detailed information on the algorithm used for automated characterization ., In some cases , coupling was turned on and off during an experiment; this was done to determine the robustness of the hybrid circuit activity ., To measure the consistency of phase locking , we used circular statistics to find the R2 metric , often referred to as the vector strength , for each experiment 34 ., In circular statistics , values are represented by a unit vector and an angle ., The average vector captures the mean angle φave of all the data and the magnitude R , which is a measure of the tightness of the locking 35 ., In our case , the average network phase is φave for the phase of the firing of the model neuron within the cycle of the biological neuron , and the magnitude R corresponds to how consistent the network phase is during an experiment ., The strength of phase locking is represented by the length of the vector , R , where R2\u200a=\u200aX2+Y2 ., As in 34 , φave and R2 are calculated usingwhere atan2 is the two argument arctan function that returns a value between –π and π , Pn is the network period measured in cycle n , N is the number of network periods , and tsM , n is the nth stimulus time for neuron M , the model cell ., Note that the signs of X and Y must be considered in the two argument version of arctan to put φave in the appropriate quadrant ., In 34 , an R2 threshold of 0 . 7 is used to distinguish strongly phase-locked systems , which have R2 near 1 , from those with weaker locking ., Higher R2 magnitudes indicate that a system does not deviate much from the phase-locked angle and has a dominant phase-locked mode , while lower R2 magnitudes indicate more variability in network phase ., R2 calculations and PRC fits were performed in MATLAB ( The MathWorks ) ., Each hybrid circuit experiment was simulated using PRC-based maps ., In these simulations , the phase variable evolves at a rate determined by the intrinsic frequency , with instantaneous phase resetting applied at the time of input from the other neuron according to the measured PRCs ., A key assumption is that the shape of the PRC does not change with the relatively small changes in the period of the oscillator ., Simulated PRCs were constructed to mimic the shape and magnitude of biological and model neurons used during experiments ., Network simulations were performed in C . Conceptually , our noiseless map 36 , 37 is a modified Winfree 22 phase model in which the intrinsic phase θi ranges from 0 to 1 , and is reset from 1 to 0 when a spike occurs ( 1 ) where ηi is the angular velocity in neuron i , θj is the phase in presynaptic neuron j and fi ( θi ) is the phase resetting due to each spike in presynaptic neuron j ., We do not integrate Equation 1 , instead we assume the phase changes at a constant velocity between inputs , and jumps instantaneously when an input is received ., The result is a coupled nonlinear map , which was used to simulate both the PRC experiments and the hybrid circuit experiments and implemented as follows ., The map requires the PRC and the initial value of the intrinsic period for each neuron , and the initial values of the phase of each neuron ., The phases are only updated at the times associated with each episode of neural firing , so the first step after initialization is to determine which neuron ( s ) will fire next ., This is accomplished by finding the shortest recovery interval ( tri\u200a=\u200aPi ( 1- θi ) ) , where Pi is the current estimate of intrinsic period of the ith neuron , based on the noise models given in the main text , and θi is its phase ., At the next firing time , the phase of the firing neuron is reset to zero ., The recovery interval in the next neuron ( j ) to fire is also the stimulus interval ( tsi ) for the nonfiring neuron, ( i ) ., The phase of neuron i is calculated as θi\u200a=\u200atsi/Pi and the phase is decremented by the resetting fi ( θi ) calculated at that phase when a spike occurs in the presynaptic neuron j ., The next event is again determined by finding the shortest recovery interval ( trj\u200a=\u200aPj ( 1- θj ) ) until the next spike ., We added noise to Eq ., 1 model in three ways , which renders it a Langevin equation in phase ., To construct hybrid circuits , one biological neuron from the abdominal ganglion of Aplysia californica was reciprocally coupled to one Wang-Buzsaki ( WB ) 26 conductance-based model neuron using the dynamic clamp 29 , 30 ., The dynamic clamp measures the potential in the biological neuron , integrates the differential equations for the WB model and the two virtual synapses , and injects synaptic current into the biological neuron ., The WB model was used because it produces phase resetting curves ( PRCs ) that are comprised of only delays in response to an inhibitory input ( Figure 1B ) , and because the WB PRCs resemble those measured in Aplysia neurons 27 , 28 ., Parameters for the hybrid circuits and maximum phase resetting values for the biological and model neurons are shown in Table, 1 . Notice that the maximum phase resetting is different between the biological and model neurons; this discrepancy creates a heterogeneous system ., The average interspike interval of the biological neuron during coupling , which corresponds to the network period if the system is phase-locked , is different than the uncoupled biological neuron period; this provides evidence that the motifs observed in our hybrid networks result from mutual coupling effects , and do not reflect entrainment of the model neuron by the biological neuron ., All 35 hybrid circuits showed episodes of phase locking , phase slipping , or both ( see Figure 2 ) ., In Figure 3 , the horizontal axis represents time and each experiment is represented on one row ., The experiments are ranked vertically in order of R2 , a metric of the consistency of phase locking during coupling ., Coupled neurons with high R2 values remain phase-locked for the entire experiment duration ., As R2 decreases , more episodes of phase slipping and undefined activity occur in the hybrid circuit ., Note that an experiment with motif changes can nonetheless have a higher R2 value than one that is always phase-locked , particularly when the network phase in the first case has less variability than the network phase values in the second case ., Well-defined network motifs occurred in every experiment ., When two neurons are coupled , the dynamics of the resulting network can be predicted by plotting the PRC data of each neuron in the ts-tr plane ., As stated in the Methods , the stimulus interval ts is the interval between the previous spike and an input from the other neuron , whereas the recovery interval tr is the interval between the arrival of an input and the next spike ., We refer to these curves in the ts-tr plane as interaction curves ., In contrast to the weak coupling approach 39 , 40 using the infinitesimal PRC ( iPRC ) , we do not ignore the effects of phase resetting on the network period nor do we require the relative phase of the neurons to change slowly compared to their absolute phases , however we do require that the coupling be pulsatile , meaning that the effects of an input die out quickly , before the next event occurs ., In the coupled system , the stimulus interval for one neuron equals the recovery interval for the other neuron ( Figure 4A ) and vice versa ., In a one-to-one periodic phase-locked mode , the intervals do not change from cycle to cycle , indicated by the index ∞ in Figure 4B1 ., For each neuron , a pair of stimulus and recovery intervals correspond to each phase at which an input is received ( Figure 4A ) ., In Figure 4B2 , the stimulus interval for one neuron ( magenta , model neuron ) is plotted on the x-axis and the corresponding recovery interval is plotted on the y-axis , whereas the stimulus interval for the other neuron ( black , biological neuron ) is plotted on the y-axis and recovery interval on the x-axis ., Therefore the two pairs of stimulus and recovery intervals ( in two different neurons ) that must be equal in a phase-locked mode are plotted on the same axes ., The intersections of these curves then correspond to any possible periodic phase-locked modes of the two neuron network , as well as to fixed points of the ts-tr map in Figure 4C that is described below ., The information in the ts-tr interaction curves is not restricted to the location of the fixed points , but also provides the transient dynamics that may lead to a phase-locked mode or persist indefinitely in the absence of such a mode ., The stimulus interval in one neuron determines the recovery interval in that same neuron; this leads to a map ( Figure 4C1 ) with the following dynamics ., The index n indicates successive cycles in the model neuron ., The movement of the operating point from the black to the magenta curve is constrained to be horizontal because the recovery interval in the biological neuron determines the next stimulus interval in the model neuron ( trBn\u200a=\u200atrMn ) ., Similarly , the movement of the operating point from the magenta to the black curve is constrained to be vertical because the recovery interval in the model neuron determines the next stimulus interval in the biological neuron ( trMn\u200a=\u200atrBn+1 ) ., For a stable fixed point that attracts nearby trajectories , the magenta curve with the coordinates listed in the order ( trM , tsM ) curve must have a steeper slope ( 41 , see also derivation in Text S1 ) than the black curve in which the coordinates are listed in the opposite order ( tsB , trB ) , otherwise the point is unstable and repels trajectories ., The white circle in Figure 4C2 ( and B2 ) repels trajectories and therefore denotes an unstable fixed point , whereas the red circle in Figure 4B2 is stable because nearby trajectories would be attracted rather than repelled ., Figure 5 shows an example of stationary phase locking that occurs when there is a stable fixed point on the PRC-based map ., The ts-tr interaction curves in Figure 5A were generated with the period observed in the biological neuron just prior to coupling , and intersect at two fixed points , one unstable ( white ) and one stable ( red ) ., The latter corresponds to the phase locking observed in both experiments and simulations ., The insets reflect that a change in the intrinsic period of the biological neuron results in a shift of the ts-tr interaction curve for the biological neuron ., As the neuron period gets longer , the curve shifts upward and rightward along the x-y diagonal ( see left inset ) , and as the period gets shorter , the curve shifts inward toward the ts-tr origin ( see right inset ) ., The network phase remains relatively constant for the entire duration of coupling in this experiment ( Figure 5B1 ) , resulting in a histogram of the network phases with a distinct peak ., In simulation , we can produce a similar time series of network phases and histogram in the presence of the three types of noise ( Figure 5B2-B4 ) , although only the OU noise produces a sufficiently broad peak ., Figure 5C explains why the phase locking is robust to noise ., The red curve shows the location of the stable fixed point in terms of tsB ( and trM ) as a function of the period of the biological neurons shown on the y-axis ., The initial value of period ( used as μ in the noise models ) is shown by the lowermost dashed horizontal line labeled μ ., The initial value of tsB at the fixed point is about 600 ms as shown in Figure 5A ., If the period of the biological neuron decreases , the curves no longer intersect below a period of about 740 ms and a tsB of about 755 ms ( rightmost vertical dashed line labeled C2 ) corresponding to the situation in the inset at right ., Similarly , if the period of the biological neuron increases , the curves no longer intersect above a period of about 875 ms , and a tsB of about 430 ms ( leftmost vertical dashed line labeled C1 ) corresponding to the situation in the inset at left ., The ts-tr interaction curves are a snapshot of the constraints on the trajectories based on the current value of the period of the biological neuron ., The variability in all three models constrains the 95% confidence interval of the intrinsic period ( μ±2σeff ) to lie well within the range of periods that supports phase locking , and therefore constrains the variability in the network phase observed in Figure 5B2-4 , and presumably in Figure 5B1 as well ., The PRC-based map helps illustrate what happens during phase slipping ., Figure 6A1 shows the ts-tr interaction curves using the period observed in the biological neuron just prior to coupling , with a trajectory around the PRC-based map indicated by dashed blue lines with arrows indicating direction ., Every time one neuron spikes , a vertical or horizontal “step” is taken between the two curves ., The trajectory spends more time near the point of closest approach between the two curves because it takes smaller steps in that region ., Here , the ghost of a fixed point that exists at a slightly different set of parameters ( at which the curves do intersect ) has a significant impact on the dynamics 42 , 43 ., Figure 6A2 shows the sequence of network phases observed during the long episode of slipping in experiment 5 , and the histogram at right shows a broad peak in the network phases ., The peak and distribution of the histogram of the network phases produced by a map based on the PRC with OU noise in the period and shown in Figure 6A3 was in reasonable agreement with the experimental data in Figure 6A2 ., The peak of the histogram is due to the tendency to stick near a phase corresponding to the point of closest approach of the curves in Figure 6A1 ., Each phase slip in the network activity is associated with a trajectory that dropped down from the upper left edge of the map and was reinjected at the lower right edge ( Figure 6A1 ) ., Figure 6B displays the mechanism for dropping off the map at the upper left and returning at the lower left ., This occurs when the next recovery interval in one neuron ( the biological neuron in Figure 6B1 ) is so long that the other neuron ( the model neuron in Figure 6B2 ) spikes twice during one biological neuron period ., We defined a recovery interval tr* ( see two pulse PRC protocol in Figure 6B4 ) that gives the interval to the next spike after two inputs separated by the intrinsic frequency of the partner neuron are received ( brown curve in Figure 6B3 ) ., Therefore the trajectory is reinjected at the lower right when it falls off the upper left , and vice versa ., The recovery interval tr* was not measured , but instead was calculated from the previously collected phase resetting data by assuming that the second input was received at a phase determined not only by the elapsed time but by taking into account the phase resetting from the first pulse ., The only way to transition between the ends of the map is for one neuron to spike twice in a row , and the modified map can handle any firing pattern in which any single neuron does not spike more than twice in a row ., Across a phase slip transition , there is a change 44 in leader-follower pattern of the neurons ., It is important to note that there exists a similar analogy of ghost attractor and cycle slipping in return map of Poincare phase map of neural oscillators 45 ., Phase locking and phase slipping do not always persist throughout an experiment; as seen in Figure 3 , motifs can vary over time ., Of the 35 hybrid experiments presented here , 12 experiments represent the case where the system is phase-locked ( Figure 3 , experiment 16 and experiments 24–35 ) ., In the remaining 23 cases , the coupled neurons transition between phase locking , phase slipping , and undefined phase relationships ( Figure 3 , experiments 1–15 and experiments 17–23 ) ., It is likely these transitions are due to fluctuations in the intrinsic spiking frequency of the biological neuron ., As illustrated in Figure 5A , shifts in the ts-tr interaction curves due to drift in the period of the biological neuron can move , create , or eliminate the fixed points of the system ., Figure 7A1 shows experimentally observed phase-locked activity with a single slip ., O | Introduction, Materials and Methods, Results, Discussion | In order to study the ability of coupled neural oscillators to synchronize in the presence of intrinsic as opposed to synaptic noise , we constructed hybrid circuits consisting of one biological and one computational model neuron with reciprocal synaptic inhibition using the dynamic clamp ., Uncoupled , both neurons fired periodic trains of action potentials ., Most coupled circuits exhibited qualitative changes between one-to-one phase-locking with fairly constant phasic relationships and phase slipping with a constant progression in the phasic relationships across cycles ., The phase resetting curve ( PRC ) and intrinsic periods were measured for both neurons , and used to construct a map of the firing intervals for both the coupled and externally forced ( PRC measurement ) conditions ., For the coupled network , a stable fixed point of the map predicted phase locking , and its absence produced phase slipping ., Repetitive application of the map was used to calibrate different noise models to simultaneously fit the noise level in the measurement of the PRC and the dynamics of the hybrid circuit experiments ., Only a noise model that added history-dependent variability to the intrinsic period could fit both data sets with the same parameter values , as well as capture bifurcations in the fixed points of the map that cause switching between slipping and locking ., We conclude that the biological neurons in our study have slowly-fluctuating stochastic dynamics that confer history dependence on the period ., Theoretical results to date on the behavior of ensembles of noisy biological oscillators may require re-evaluation to account for transitions induced by slow noise dynamics . | Many biological phenomena exhibit synchronized oscillations in the presence of noise and heterogeneity ., These include brain rhythms that underlie cognition and spinal rhythms that underlie rhythmic motor activity like breathing and locomotion ., A two oscillator system was constructed in which most of the circuit was implemented in a computer model , and was therefore completely known and under the control of the investigators ., The one biological component was an oscillator in which an apparently novel manifestation of biological noise was identified , dynamical noise in the period of the oscillator itself ., This study quantifies how much noise and heterogeneity this simple two oscillator system can tolerate before desynchronizing ., More complicated systems of oscillators may follow similar principles . | biology and life sciences | null |
journal.ppat.1007151 | 2,018 | PGL I expression in live bacteria allows activation of a CD206/PPARγ cross-talk that may contribute to successful Mycobacterium leprae colonization of peripheral nerves | The most serious consequence of leprosy is the peripheral nerve damage that occurs in all clinical forms of the disease ., Nerve damage results from the capacity of M . leprae , an obligate intracellular bacillus , to infect SCs , the glial cells of the peripheral nervous system ( PNS ) ., SCs show remarkable plasticity and contribute to the regenerative capacity of the adult PNS even after severe injury has occurred ., M . leprae-nerve fiber colonization results in loss of sensation , an early symptom of the disease ., While multidrug therapy ( MDT ) treats the infection , it may be unsuccessful in either preventing or arresting the nerve damage responsible for disfigurement and disabilities 1 , 2 ., In-depth investigation of M . leprae-nerve interactions objectifying the development of new strategies for the prevention and treatment of leprosy-related nerve impairments is , therefore , of utmost importance ., M . leprae is easily seen inside vacuoles in the non-myelinating and myelinating SC cytoplasm in nerve specimens of leprosy patients 3 , 4 and , as a consequence , the three physiological functions of nerves–sensory , motor and autonomic–are affected ., However , the first symptoms of leprosy are related to loss of temperature sensation and decreased touch sensation , functions provided by non-myelinating fibers , indicating their early invasion by the leprosy bacillus during the natural course of the disease 5 ., Thus , the use of non-myelinating SCs as an in vitro model of infection is physiologically relevant and may reveal early fundamental aspects of M . leprae neuropathogenesis ., The tropism of M . leprae to the peripheral nerves has been attributed to its capacity to bind to the globular domain of the α2 chain of laminin-2 6 , a laminin isotype with restricted tissue distribution constituting a major component of the basal lamina surrounding SC-axon units 7–9 ., Two M . leprae adhesins named Hlp and PGL I have been characterized as laminin-binding molecules responsible for attachment to SC ., Shimoji et al . 10 and Marques et al . 11 have described the 21-kDa histone-like protein ( Hlp ) , a conserved molecule among species of mycobacteria , as a laminin-binding protein ( also called ML-LBP21 ) ., The other molecule , PGL I 12 is an abundant lipid composing the outermost layer of the M . leprae envelope 13 ., PGLs are present in other species of mycobacteria , but differ among themselves in their carbohydrate moieties 14–16 ( Fig 1 ) ., The PGL I trisaccharide is highly specific to M . leprae 13 , 17 , having been shown to bind exclusively to the G domain of the laminin-2 α2 chain 12 ., It has been proposed that this interaction most convincingly explains the specific neural tropism displayed by M . leprae since Hlp can also bind the α1 and β1 chains that make up other laminin isotypes 10 , 11 , 18 , 19 such as laminin-1 with a wide range of tissue distribution 7 ., It has been speculated that , due to its abundance , PGL I would then initiate the specific M . leprae-SC interaction while Hlp would increase avidity binding by enacting a secondary role 12 ., Upon infection , M . leprae seems to evoke drastic metabolic and phenotypic changes in SC , abetting infection and bacterial persistence in the host ., In previous studies , we were able to show that M . leprae induces the production of insulin-like growth factor 1 ( IGF-1 ) to advantage host cell survival 20 while triggering drastic changes in the SC lipid and glucose metabolism that promote bacterial persistence 21–23 ., Moreover , recent data have shown the capacity of M . leprae to induce SC reprogramming to a progenitor/stem-like cell stage , most probably increasing the spread of infection 24 ., However , the molecular mechanisms underlying these events and the contributions of such M . leprae constituents as PGL I continue to be poorly understood ., Multiple attempts to grow M . leprae in axenic or tissue cultures have been in vain so that infected nine-banded armadillo and athymic nude mouse still constitute the major bacterial sources for both biochemical and functional studies 2 ., Since mutated strains of M . leprae are yet unobtainable , a genetically-engineered M . bovis BCG strain producing and secreting PGL I ( BCG PGL I ) instead of its own PGL was created as an alternative tool to better decipher the role of this glycolipid in leprosy pathogenesis 15 ., Tabouret et al . 15 used the recombinant BCG strain to analyze the role of PGL I in the interaction of the leprosy bacillus with macrophages and dendritic cells ., As a result , previous data were confirmed and new insights into the molecular bases of PGL I to down modulate the innate immune response were provided ., Here , the recombinant BCG PGL I was used as an alternative tool to elucidate the role of PGL I along with the molecular mechanisms by which it participates in the M . leprae-SC interaction ., As a first step , the critical role of PGL I on M . leprae adhesion and SC internalization was addressed by comparing the capacity of the recombinants BCG PGL I , BCG PGL TB—a recombinant BCG strain that produces PGL from M . tuberculosis ( BCG PGL TB ) instead of PGL bovis 25—and wild type BCG ( BCG WT ) , their parental strain , to infect the human schwannoma cell line ST8814 ., The recombinant bacteria were labeled with the green fluorescent dye PKH67; and association ( % ) was determined after 4 h , 24 h and 48 h of incubation with SC at MOI 50:1 by flow cytometry ., Association ( adhesion + internalization ) values showed that the PGL I-expressing recombinant strain acquired a high capacity to interact with these cells as opposed to the lesser capacity of the BCG WT strain ( histogram plot-Fig 2A ) ., Interestingly , BCG PGL TB behaved similarly to the wild-type strain , indicating that the observed effect was mediated by the unique trissacharide moiety of M . leprae PGL ( Fig 2A ) ., For BCG PGL I , a time-dependent association between the bacterium and SCs was observed , with about 80% of cells evidencing associated bacilli after 48 h of incubation ( Fig 2A ) ., At this time point , most of the bacilli had been internalized , as monitored by quenching extracellular bacteria with Trypan Blue ( S1A Fig ) ., Moreover , S1B Fig delineates that , in assays performed at 4°C to inhibit internalization , PGL I was shown to mediate the adhesion step ., We also made use of GFP expressing recombinant BCG PGL I or PGL TB and the parental BCG strain to confirm their differential capacity to associate to SCs ( S1C Fig ) ., Since host cell internalization could be an active process dependent on bacterial viability , we next compared the ability of live versus lethally-irradiated bacterial cells to invade SCs ( Fig 2B ) ., For this purpose , the degree of internalization was determined by quenching the fluorescence of adhered bacteria with Trypan Blue ( Fig 2B , S2A Fig ) ., M . leprae was included in these assays with results equivalent to those obtained with BCG PGL I . Both dead BCG PGL I and dead M . leprae measured roughly 50% less internalization capacity in contrast to live BCG PGL I and live M . leprae , respectively ( dead BCG PGL I 30 . 33 ± 5 . 21% and dead M . leprae 40 . 66 ± 4 . 41% ) ., S2B Fig shows that the percentage of SCs with internalized bacteria augmented in accordance with an increasing multiplicity of infection ( MOI ) ., In comparing dead with live bacilli at a MOI of 10:1 and 50:1 , it was seen that when dead bacteria were used the SC population with internalized bacteria fell about 50% ., Interestingly , however , at a MOI of 100:1 , both live and dead bacilli were present in nearly 100% of the cell population ., Nevertheless , regardless of the MOI used , in analyzing the Median Intensity of Fluorescence ( MFI ) of the cell population , the use of live bacteria resulted in significantly higher values , indicating that the number of bacilli per cell was always higher in the ones infected with live bacilli ( S2C Fig ) ., The importance of PGL I in SC invasion was confirmed by fluorescence microscopy ( Fig 2C ) ., After 48 h of infection , the percentage of ST8814 cells with associated BCG PGL I was more elevated ( 88 . 33 ± 3 . 79% ) than that found for either BCG WT ( 10 . 17 ± 2 . 47% ) or BCG PGL TB ( 10 . 50 ± 1 . 00% ) ., M . leprae was used as a positive control , resulting in the presence of associated bacteria in 88 . 67 ± 3 . 51% of the cells ., Also , the influence of viability on SC invasion was corroborated since the association capacity of dead BCG PGL I and dead M . leprae was about 50% less in comparison to live BCG PGL I and live M . leprae , respectively ( dead BCG PGL I 41 . 00 ± 6 . 00% and dead M . leprae 47 . 33 ± 2 . 52% ) while , when compared to live mycobacteria , dead BCG WT and BCG PGL TB maintained the same levels ., Since ST8814 is a tumor cell line , similar experiments were repeated with Primary Human Schwann Cells ( PHSC ) ., As shown in Fig 2D , the critical role played by PGL I in the bacterial SC invasion was validated in that the association values attained by BCG PGL I were similar to those for M . leprae itself ., Likewise , as seen with ST8814 cells , the internalization of live bacteria was significantly higher than that found for dead bacilli in the context of both BCG PGL I and M . leprae ., Moreover , PGL I-covered beads showed a higher internalization rate than uncovered ones ( Fig 2E ) , attesting to the importance of PGL I in mycobacterium SC internalization ., Altogether , these results confirm that the unique phenolic glycolipid produced by M . leprae is a key molecule involved in bacterial adhesion and SC internalization and that bacterial viability strongly facilitates entry ., Intracellular pathogens are known to modulate host-cell phagocytic receptors as a way to facilitate their entrance into host cells ., We , therefore , turned to investigating whether M . leprae or BCG PGL I modulate the SC phagocytic phenotype ., A series of experiments using different combinations of pre- and secondary stimuli were conducted with ST8814 SC ., The first stimulus consisted of unlabeled bacteria or latex beads in a 10:1 proportion ., The second stimulus with a 50:1 proportion consisted of the addition of PKH67 ( green ) -labeled bacteria or green fluorescent latex beads 1 hour after the initial stimulus ., Cells were then incubated for 48 h and analyzed via flow cytometry or fluorescence microscopy ., Surprisingly , pre-infection with BCG PGL I or M . leprae led to a significant internalization of BCG WT ( live or dead ) in SCs ( Fig 3A and 3B ) ., Contrariwise , dead M . leprae and BCG PGL I were unable to instigate a major upgrade in SC internalization ( Fig 3C ) ., PGL I-covered latex beads were likewise unable to mimic the effect of PGL I-expressing bacteria ( Fig 3C ) ., Alternatively , even the direct addition of purified PGL I to the medium had no tangible effect on the SC phagocytic response ( S3A Fig ) ., Moreover , the expanded phagocytic capacity induced by M . leprae and BCG PGL I on SC was specific to BCG WT since the degree of internalization of non-pathogenic M . smegmatis ( Fig 3D ) and the uptake of green fluorescent latex beads remained unchanged ( S3B Fig ) ., To summarize , these results suggest that live PGL I-producing mycobacteria modulate the expression in SCs of phagocytic receptors that recognize cell wall envelope components specifically expressed by pathogenic or slow growing mycobacteria ., LAM , present in the mycobacterial cell envelope , is an abundant glycolipid shown to play a key role in mycobacteria-host cell interaction 26 , 27 ., In pathogenic mycobacteria , this molecule , denominated ManLAM 28 , is mannose capped ., It has been demonstrated that the terminal mannose residues of ManLAM are of critical importance in macrophage recognition of M . tuberculosis , M . leprae and M . bovis BCG 29 , 30 via the capacity of these residues to bind to the host cell mannose receptor ( MR/CD206 ) 31 ., In contrast , M . smegmatis produces a structurally different LAM that is capped with phosphatidyl-myo-inositol residues that do not bind to CD206 32 ., Based on this knowledge , we postulated the candidacy of the mannose receptor as possible target of upregulation by PGL I-expressing mycobacteria in infected SCs ., To validate this hypothesis , the effect of excess free mannose on BCG WT internalization in SCs pre-infected with M . leprae or BCG PGL I by flow cytometry was evaluated ., This competitive assay showed that the presence of mannose at 100 μg/mL or 1000 μg/mL significantly reduced BCG WT internalization induced by pre-infection with BCG PGL I or M . leprae ( Fig 4A ) ., Under the condition of 1000 μg/mL of mannose , the degree of BCG WT internalization decreased by about 40% ., Fluorescence microscopy also showed decrease in association degree ( S4 Fig ) , suggesting the recognition of LAM mannose caps during BCG WT internalization ., To further explore this idea , green fluorescent latex beads were covered with M . leprae-purified ManLAM and used as the second stimulus in SCs pre-infected with live BCG PGL I or M . leprae ., ManLAM-covered beads showed an increased uptake as seen for BCG WT , pointing to the upregulation of a mannose receptor in pre-infected cells ( Fig 4B ) ., Since CD206 was previously shown to be expressed by SCs 33 , 34 , we then explored the ability of M . leprae or BCG PGL I to modulate this receptor ., The mrc1 gene transcriptional levels in SCs were assayed at different time points by quantitative RT-PCR ., Fig 4C shows the relative expressions of normalized mrc1 ( delta delta Ct ) in BCG PGL I- , M . leprae- , or BCG WT-infected SCs at 4 h , 24 h and 48 h ., Results are presented in terms of a fold change after normalization with ribosomal protein L13 ( RPL13 ) mRNA ., It was found that M . leprae and BCG PGL I were capable of inducing mrc1 transcription in SCs ., It was also seen that the upregulation of mrc1 occurred at an early stage in the infection in that , a mere 4 h later , the transcription level had reached a peak ., Of note , BCG WT also induced mrc1 gene upregulation but at a 50%-or-less efficacy rate as compared to the bacilli-expressing PGL I ., It was , however , solely in the presence of M . leprae or BCG PGL I that upregulation was sustained until 24 h post-infection ( Fig 4C ) ., Accordingly , flow cytometry assays showed an increased expression of CD206 upon M . leprae or BCG PGL I but not BCG WT infection ( Fig 4D ) ., Comparable results were observed by fluorescence microscopy analysis ( Fig 4E ) , indicating that the expression of CD206 is upregulated in SCs infected with PGL I-expressing bacilli ., That M . leprae and BCG PGL I infection led to the upregulation of CD206 and its subsequent involvement in the uptake of BCG WT was confirmed by mrc1 knockdown experiments ., Treatment with mrc1 siRNA targeting the mrc1 gene coding for CD206 , led to a significant reduction in CD206 expression in BCG PGL I and M . leprae-infected SC , as compared to cells transfected with the control siRNA ( Fig 5A ) ., As monitored by flow cytometry ( Fig 5B ) and fluorescence microscopy ( Fig 5C ) , mrc1 knockdown resulted in a significant reduction in the degree of BCG WT internalization ., Of note , mrc1 knockdown also decreased M . leprae internalization ( Fig 5B and 5C ) ., Taken together , these results show that live PGL I-producing mycobacteria induce the expression of the mannose receptor CD206 in SCs , a mechanism that may promote M . leprae internalization by an alternative pathway ., Previous reports have shown a reciprocal regulation between CD206 and the transcription factor peroxisome proliferator-activated receptor gamma ( PPARγ ) 35 , 36 ., Furthermore , the induction and activation of PPARγ by pathogenic mycobacteria following macrophage infection has been linked to their capacity to persist in these cells 34 , 36 , 37 ., We thus analyzed the potential involvement of PPARγ in inducing CD206 in M . leprae and BCG PGL I-infected SCs ., Fig 6A indicates that M . leprae and BCG PGL I infection induce PPARγ expression in contrast to BCG WT , which does not ., Moreover , cells pre-treated with GW9662 , an irreversible antagonist of PPARγ , showed reduced CD206 levels in response to M . leprae and BCG PGL I monitored 24 h post-infection by both flow cytometry ( Fig 6B ) and fluorescence microscopy ( Fig 6C ) ., At this time point , the percentage of SCs with internalized bacteria remained unchanged in GW9662-treated cells as compared to untreated ones ( S5A Fig ) ., However , after 48 h of infection , GW9662 pre-treated cells demonstrated a lower number of M . leprae bacilli per cell , comparable to that observed after the mrc1 knockdown ( S5B Fig ) ., Additionally , cell pre-treatment with GW9662 combined with pre-infection with PGL I -expressing mycobacteria were unable to increase ManLAM-covered bead uptake ( Fig 6D ) , confirming the involvement of PPARγ in the upregulation of CD206 in SCs infected with mycobacteria expressing PGL I . An evaluation was then carried out to determine whether bacterial recognition via CD206 was involved in PPARγ induction and activation ., To test this hypothesis , live bacteria were incubated with cells in the presence of an excess of free mannose to block bacterial recognition by CD206 ., The induction and activation of PPARγ was monitored via immunofluorescence ., As shown in Fig 7A , significantly lower levels of PPARγ induction and activation were observed in the presence of free mannose , suggesting the involvement of bacterial recognition via CD206 in the capacity of M . leprae to activate this nuclear receptor/transcriptional factor in SCs ., Since PPARγ has been implicated in LD biogenesis induced by mycobacteria 37 , we next evaluated whether this was the case in the context of M . leprae infected SCs ., Inhibition of PPARγ with GW9662 abolished the induction of LDs ( Fig 7B ) by M . leprae ., Moreover , inhibiting signaling from MR/CD206 by infecting the cells in the presence of excess of free mannose reduced LDs levels significantly ( Fig 7C ) ., In previous reports we showed that M . leprae- induced LDs constitute sites of ( PGE2 ) synthesis 22 ., Next , we investigated the role of MR/PPARγ crosstalk in PGE2 production ., PPARγ inhibition decreased PGE2 production to baseline levels in response to M . leprae ( Fig 7D ) ., A similar effect was observed when cells were infected in the presence of excess of free mannose ( Fig 7E ) ., Experiments were then conducted to monitor the effect of mrc1 knockdown on PGE2 production in response to M . leprae ., However , the high background of PGE2 production in the uninfected cells transfected with the mrc1 siRNA prevented reaching a reliable conclusion ( S6 Fig ) ., It was then determined to examine if the recognition of M . leprae by CD206 causes an effect on subsequent bacterial intracellular survival since LDs formation was previously shown to promote M . leprae persistence in infected cells 23 ., To block recognition of M . leprae by CD206 , an excess of free mannose ( 100 μg/mL ) was added to SCs followed by the monitoring of bacterial viability ., Fig 7F shows a 27% ( *p<0 . 05 ) decrease in cellular bacterial viability upon 48 h of infection when bacterial recognition by CD206 was inhibited ., These results provide evidence that CD206-PPARγ crosstalk promotes bacterial survival in M . leprae-infected SCs ., The connection of CD206 to PPARγ and its role as an important regulator of macrophage immune response to M . tuberculosis has recently been reported 35 , 38 ., In that study , a signaling pathway involving recognition of Man-LAM by CD206 followed by PPARγ expression and activation terminating in IL-8 induction was described 35 ., In this context , our next step involved examining whether this pathway was active in mycobacterium-infected SCs ., To answer this question , the effect of mrc1 knockdown or inhibition of PPARγ activity on IL-8 was investigated ., Our results demonstrated that M . leprae infection enhanced IL-8 production and that knocking down mrc1 caused a decrease on IL-8 production ( Fig 8A ) ., Moreover , infection in the presence of an excess of free mannose evoked a similar inhibitory effect of IL-8 secretion ( Fig 8B ) ., Also , treatment with GW9662 decreased IL-8 production indicating the involvement of PPARγ ( Fig 8C ) ., Altogether , these results suggest that there is an active crosstalk between PPARγ and CD206 that links lipid metabolism with the downstream innate immune response in SCs infected with PGL I-producing mycobacteria ., The ability of M . leprae to induce the expression of CD206 in SCs cultures prompted us to further examine leprosy nerve specimens to corroborate the in vitro findings with in situ evidence ., To verify if CD206 was expressed by SCs , tissue sections of patients with leprosy and non-leprosy neuropathies were also stained with anti-S100 , a specific SC marker ( Fig 9 , S7 Fig , S8 Fig ) ., Nerve biopsies previously known to be bacilli positive ( AFB ( + ) ) were chosen for this analysis , as shown by Wades 39 staining in Fig 9A ., Fig 9B and 9C show the staining profile of S100 and the mannose receptor CD206 , respectively ., Cells with a SC morphology expressing CD206 and S100 were observed in leprosy patients ( Fig 9D ) ., CD206-expressing SCs can be better visualized in the insets ( 1 , 2 ) with a magnified view ., Nerve biopsies from other 4 patients were analyzed generating similar results ( S8 Fig ) ., Nerve biopsies obtained from patients with non-leprosy neuropathies showed no CD206/S100 colocalization ( Fig 9E–9G ( insets 3 and 4 ) , S7 Fig ) ., In order to confirm that SCs expressing CD206 were harboring M . leprae , leprosy nerve tissue sections were labeled with anti-CD206 , anti-Myelin Basic Protein ( MBP ) , a SC marker , and anti-Liporabinomannan ( LAM ) for mycobacterium staining ., Fig 10 shows most cells in the field double stained for CD206 and MBP , confirming the presence of SCs expressing CD206 in leprosy patients nerves ., Since one Schwann cell may spread over hundreds of μm long nerve , a serial slices analysis would probably be necessary to confirm the presence of bacteria in a single cell ., Nevertheless , examining a single cross-sectioned slice of the nerve biopsy we were able to detect M . leprae inside half of the SCs ., Altogether , these data strongly suggest that M . leprae induces the expression of CD206 in in vivo-infected SCs reproducing our in vitro findings ., The most severe symptoms of leprosy are caused by nerve infection ., Thus , deciphering the molecular basis of the early events of mycobacterial peripheral nerve infection is a crucial step towards acquiring a basic understanding of nerve pathogenesis with the potential to generate new tools for its prevention and treatment ., The capacity of M . leprae to bind to laminin-2 , a major component of the SC basal lamina , has been described as a fundamental feature of the bacterial predilection for the peripheral nervous system 6 ., Moreover , the PGL I and Hlp/LBP-21 molecules produced by the leprosy bacillus located on the bacterial surface have been implicated as likely adhesins involved in this interaction 12 ., In the present study , we revisited the involvement of PGL I in M . leprae-SC interactions by using a recombinant strain of M . bovis BCG engineered to express PGL I ., We demonstrate for the first time that PGL I is essential for mycobacterium adhesion and SC internalization ., We were also able to confirm that the unique trisaccharide moiety of PGL I mediates the specific M . leprae-SC interaction since other phenolic glycolipids with identical lipid moieties proved incapable of mediating bacterial internalization into these cells ., Of note , it was found that live PGL I-producing mycobacterium induces the activation of a crosstalk between the endocytic receptor MR/CD206 and the transcriptional factor PPARγ , allowing bacterial recognition and entry by way of this alternative pathway ., Bacterial sensing via mannose recognition was shown to be essential for the induction of LD formation , organelles previously shown to play a key role in M . leprae-SC interaction 22 , 23 ., Finally , the detection of strongly positive CD206 SCs in leprosy nerve sections suggests that M . leprae induces CD206 expression in in vivo-infected SCs , which may be critical in the development of bacterial pathogenesis in the nerve ., The results herein presented firmly indicate that PGL I is the key molecule responsible for capacitating M . leprae to successfully invade SCs ., In this process , PGL I imposes a secondary role on other potential adhesins such as Hlp/LBP-21 , a well-conserved , histone-like protein present on the surface of several species of mycobacteria , including BCG 19 ., Recombinant Hlp binds in vitro to the laminin-2 globular domain; and latex beads covered with the protein have been seen to display a greater capacity to adhere to and be internalized by SC 10 , 11 ., Indeed , other species of mycobacteria were shown to bind laminin-2 and adhere to SCs in the presence of soluble alpha2-laminin 40 ., However , most evidence suggesting a role for Hlp in M . leprae–SC interactions have been drawn from assays conducted with the isolated protein alone in the absence of PGL I . Moreover , previous studies on M . leprae adhesion to SC were always performed with killed bacteria that may in the end display altered cell-wall structures , and , therefore , lead to misleading conclusions regarding the relative importance of the different bacterial molecules involved in host-cell binding 12 , 31 , 41 ., The use of a PGL I-expressing BCG strain made it possible to decipher the essential role played by PGL I in this process in view of the fact that the BCG WT strain expressing Hlp , but not PGL I , was unable to invade SC ., In any case , it is deemed worthwhile to investigate the chance that , subsequent to the initial PGL-I-mediated M . leprae-SC interaction , the Hlp binding to laminin may provide the bacilli with higher avidity ., The present study likewise showed that CD206 , an important mycobacterial recognition receptor 28 , 32 , 42–45 , is significantly upregulated at both the mRNA and protein levels at early time points after SCs infection by PGL I-producing mycobacteria ., When stimulus occurred with dead bacteria or PGL I-covered latex beads , this effect was not detected , suggesting that while PGL I binding to SC receptors is necessary , it is insufficient in terms of inducing this phenotypic change in SCs ., This observation indicates that the status of bacilli ( live or dead ) is a key aspect in the interplay between M . leprae and SC and that live bacteria might modulate several pathways that go beyond the binding of PGL I to laminin 2 in these cells ., Indeed , in previous studies , we have shown that only live M . leprae was able to induce the accumulation of lipids leading up to the formation of a foamy phenotype in infected SCs 23 ., More recently , the capacity of M . leprae to modulate host-cell glucose metabolism and activate the IFN type I response in SCs has also been demonstrated to primarily depend on bacterial viability 21 , 46 ., CD206 or MR is a member of the C-type lectin family that binds mannose and fucose with the highest affinity 47 ., MR is a pattern-recognition receptor ( PRR ) performing a key role in binding to microbial pathogens and facilitating their uptake by innate immune system cells ., CD206 was shown to play a major role in the interaction of pathogenic mycobacteria with human macrophages , via recognition of Man-LAM abundantly present on their cell surface 45 , 48–50 ., Our findings suggest that the involvement of CD206 in the first wave of M . leprae entry in SCs is probably minimal , increasing in importance at later time points of infection ., The observance of a lower M . leprae-invasion rate into SCs in which mrc1 was silenced is indicative that the ManLAM-MR binding may participate in bacterial recognition and uptake after PGL I-mediated internalization ., Moreover , infection of SCs for 48h in the presence of excess of free mannose reduced M . leprae internalization by about 50% ( S9 Fig ) ., Also , the induction of CD206 only by live M . leprae may at least partially explain the higher efficiency of live bacteria in comparison to dead organisms to enter into SCs ., Although we observed a clear upregulation of CD206 in infected SCs , the induction by M . leprae of additional receptors such as Dectin-2 or the dendritic cell-specific adhesion molecule 3-grabbing nonintegrin ( DC-SIGN or CD209 ) cannot be ruled out since they also bind to ManLAM 51 , 52 ., Teles et al . 52 have furnished evidence that human SCs may express CD209 , both in vitro and in neural leprosy lesions ., Although M . leprae was also found in CD209 ( - ) SCs , it was shown to contribute to M . leprae-SC binding and be consistent with the detection of M . leprae-specific antigens in vitro and in situ in CD209 ( + ) SCs ., The capacity of M . leprae to induce DC-SIGN expression is a topic deserving of further study ., We also showed the involvement of the nuclear receptor/transcriptional factor PPARγ in the MR induction by PGL I-producing mycobacteria 36 , 53 ., Moreover , a positive loop between CD206 and PPARγ was detected since blocking M . leprae recognition with excess of free mannose inhibited PPARγ activation ., PPARγ is a master transcriptional factor regulating multiple cellular functions , including lipid metabolism and foam-cell generation 54–57 ., The role of PPARγ and subsequent lipid-droplet biogenesis in mycobacterium-infected macrophages has also been described 37 , 55 ., Since M . leprae induces LD formation 23 , 58 , an obvious subsequent investigation was the involvement of PPARγ in LD formation ., Of note , an important finding of the current study was that interference in the CD206/ PPARγ crosstalk , either by inhibiting bacterial recognition by mannose receptors or by blocking PPARγ activation completely abolished M . leprae- induced lipid droplets in SCs ., Next , we identified a dependence of PGE2 production on PPARγ activity in SCs ., This observation , in combination with the known role of this nuclear receptor on LD formation 55 , is in agreement with our previous studies indicating that PGE2 synthesis occurs in lipid droplets and its secretion is in correlation with the lipid droplet levels in M . leprae infected SCs 23 ., Actually , in that same study we had identified a link between lipid metabolism through LD formation and the innate immune response triggered by live M . leprae in SCs ., Besides PGE2 , M . leprae was able to induce IL-10 that was abolished when LD formation was inhibited ., Moreover , SCs even started producing IL-12 in the absence of LD formation ., Interestingly , the same phenotype ( decreased IL-10 and increased IL-12 ) was observed when NS-398 , a COX-2 inhibitor , was used , suggesting that PGE2 may contribute to the negative modulation of the innate immune response toward intracellular infection ., This goes in line with the immunomodulatory properties of PGE2 , which has been shown to inhibit Th1 response and the microbicidal mechanisms of macrophages 59–62 ., Furthermore , Schreiber et al . 63 found evidence that the mannose receptor biosynthesis is up-regulated by E-series prostaglandins ., The hypothesis that a PGE2 autocrine loop may participate in raising MR expression levels in M . leprae-infected SC , needs more scrutiny ., In previous studies , we detailed the capacity of M . leprae to induce lipid accumulation in both macrophages and SCs 23 , 58 , 64 ., In SCs , this effect was only observed with live bacteria , being in agreement with the findings here presented ., Moreover , lipid droplets were shown to be recruited to mycobacterium-containing phagosomes , and both blockage of this recruitment or inhibition of LD formation lead to bacterial killing 23 ., Moreover , as mentioned above , M . leprae-induced LD biogenesis and PGE2 production play a role in the genera | Introduction, Results, Discussion, Materials and methods | Mycobacterium leprae , an obligate intracellular bacillus , infects Schwann cells ( SCs ) , leading to peripheral nerve damage , the most severe leprosy symptom ., In the present study , we revisited the involvement of phenolic glycolipid I ( PGL I ) , an abundant , private , surface M . leprae molecule , in M . leprae-SC interaction by using a recombinant strain of M . bovis BCG engineered to express this glycolipid ., We demonstrate that PGL I is essential for bacterial adhesion and SC internalization ., We also show that live mycobacterium-producing PGL I induces the expression of the endocytic mannose receptor ( MR/CD206 ) in infected cells in a peroxisome proliferator-activated receptor gamma ( PPARγ ) -dependent manner ., Of note , blocking mannose recognition decreased bacterial entry and survival , pointing to a role for this alternative recognition pathway in bacterial pathogenesis in the nerve ., Moreover , an active crosstalk between CD206 and the nuclear receptor PPARγ was detected that led to the induction of lipid droplets ( LDs ) formation and prostaglandin E2 ( PGE2 ) , previously described as fundamental players in bacterial pathogenesis ., Finally , this pathway was shown to induce IL-8 secretion ., Altogether , our study provides evidence that the entry of live M . leprae through PGL I recognition modulates the SC phenotype , favoring intracellular bacterial persistence with the concomitant secretion of inflammatory mediators that may ultimately be involved in neuroinflammation . | Nerve damage is the most severe symptom of leprosy , an ancient disease that continues to be a major health problem in several countries ., Nerve damage is due to the ability of Mycobacterium leprae , the etiologic agent , to invade SCs , the glial cells of the peripheral nervous system ., Understanding the molecular basis of M . leprae–SC interaction is essential for the creation of new tools aiming to treat and , above all , prevent leprosy neuropathy ., This study demonstrates the critical role of PGL I , an M . leprae-abundant specific cell wall lipid , in establishing infection ., PGL I is not only a prerequisite in initiating bacterial adhesion to and subsequent invasion of SCs , but also for changing the repertoire of cell surface proteins to allow for the entrance of bacteria via alternative pathways ., These new invasive pathways induced by PGL I involve recognition of other bacterial cell surface glycolipids that , in turn , evoke functional changes in the infected cell , including the accumulation of host cell-derived lipids , which favor bacterial survival ., These pathways also promote the secretion of inflammatory mediators that may contribute to nerve damage ., In an era of translational-oriented research , exploring these receptors in depth could lead to the development of attractive strategies to ensure the targeted intracellular delivery of therapeutics aiming to prevent neuropathy . | flow cytometry, mycobacterium leprae, medicine and health sciences, chemical compounds, gene regulation, tropical diseases, light microscopy, carbohydrates, organic compounds, macroglial cells, organisms, bacterial diseases, microscopy, schwann cells, neglected tropical diseases, bacteria, research and analysis methods, small interfering rnas, infectious diseases, animal cells, fluorescence microscopy, gene expression, chemistry, actinobacteria, glial cells, spectrophotometry, cytophotometry, biochemistry, rna, organic chemistry, cell biology, nucleic acids, mycobacterium tuberculosis, mannose, genetics, leprosy, biology and life sciences, cellular types, physical sciences, non-coding rna, spectrum analysis techniques | null |
journal.ppat.1000570 | 2,009 | Evolutionarily Conserved Herpesviral Protein Interaction Networks | Herpesviruses are subdivided into three taxonomic subfamilies ( α , β and γ ) based on both genomic composition and biology according to a well-known phylogeny 1 , 2 , 3 ( Figure 1A ) ., While all herpesviruses are structurally similar , the different subfamilies are highly divergent in genome size , content and organization ., The genome size ranges from 120 kbp for varicella-zoster virus ( VZV ) , which belongs to the α-herpesviruses , to 240 kbp for human cytomegalovirus ( hCMV ) , a member of the β-herpesviruses 4 , 5 ., Gene-coding potential is reflected in the size of the genomes with VZV containing 70 open reading frames ( ORFs ) and hCMV containing ∼170 ORFs ., The overlap between the protein sets of the five viruses clearly supports the known phylogeny , but there are also some proteins shared among viruses not consistent with the phylogeny ( Figure 2A ) ., Although the three subfamilies are thought to have diverged from a common ancestor around 400 million years ago ( McGeoch 2006 ) , they still contain a set of 41 core orthologs present in all herpesviruses 6 , 7 ., Herpesviral core proteins are generally involved in fundamental aspects of viral morphogenesis ( e . g . DNA replication , DNA packaging , structure and egress ) , and are consequently often essential for replication in cell culture 8 , 9 , 10 ., Several genome-wide yeast-two-hybrid ( Y2H ) studies of protein-protein interactions in eukaryotes have been published over the last years , including Saccharomyces cerevisiae 11 , Caenorhabditis elegans 12 , Drosophila melanogaster 13 , Plasmodium falciparum 14 and Homo sapiens 15 , 16 ., The first complete genome-wide interaction study , however , was published for the E . coli phage T7 17 ., With their relatively small genomes and few genes , viruses seem the ideal candidates for studying protein-protein interactions on a genome-wide level and to address the generally low coverage of Y2H measurements in a systematic way ., It is therefore surprising that not more genome-wide studies of intraviral interactions have been performed to date ., With the exception of bacteriophage T7 17 and Vaccinia virus 18 , most of the studies of viral interactions have been performed on small RNA viruses 19 , 20 , 21 , 22 ., Recently more studies have been focusing on larger DNA viruses ., In addition to our previous studies of VZV and Kaposis sarcoma-associated herpesvirus ( KSHV ) 23 , a study by Calderwood and colleagues identified 43 interactions between viral proteins in Epstein-Barr virus ( EBV ) 24 ., Two Y2H studies on herpes simplex virus 1 ( HSV-1 ) and KSHV have also focused on interactions between structural components of these viruses 25 , 26 ., To add to our understanding of intraviral interactions in herpesviruses we present in this article the first interactomes for herpes simplex virus I and murine cytomegalovirus ( mCMV ) , in addition to a second and independent interactome for Epstein-Barr virus ., Based on these data we are able to compare five related interactomes , obtained using a standardized experimental setup for all five species ., From the comparison and extensive experimental testing by CoIP we conclude that, i ) genome-wide interaction studies are sufficiently sensitive for between-species comparisons to identify the basic sunflower structure of the interaction networks and their common core ,, ii ) interactions are to a large degree conserved between orthologs in herpesviruses ,, iii ) comparing interactomes from several species can improve the low coverage of individual Y2H measurements and, iv ) biologically relevant interactions which may not be apparent from the interactome of a single species , often become obvious when multiple interactomes are aligned and compared ., To study intraviral protein-protein interactions of herpesviruses we recombinatorially cloned the individual open reading frames of HSV-1 , mCMV and EBV into the yeast-two-hybrid ( Y2H ) vectors pGBKT7-DEST and pGADT7-DEST and tested all pairwise intraviral protein interactions using an array-based Y2H strategy 27 ., To address the issue of false negative interactions , viral proteins containing transmembrane domains were cloned both as full-length and as intracellular and/or extracellular domains ., From the mCMV Y2H analysis we observed that 33% of the tested preys , and 40% of the baits , gave positive interactions ., Similar results were observed with HSV-1 and EBV with ∼1/3 of the clones yielding positive interactions ( Table S1 ) ., In total , the Y2H analysis revealed 111 interactions for HSV-1 , 406 for mCMV and 218 for EBV ( Figures 1B , S1 and S2 , Tables S2 and S13 ) ., Combined with our previously published interactomes for VZV ( 173 interactions ) and KSHV ( 123 interactions ) , we obtained altogether 1 , 031 intraviral interactions in five herpesviral species ( Tables S2 and S13 ) ., To evaluate the coverage of our five interactomes we performed an extensive literature search which identified 257 previously published interactions for these herpesviruses ( including human cytomegalovirus ( hCMV ) homologues ) ., Of these 257 interactions we were able to detect 24 ( 9 . 3% ) in at least one virus ( Figures S3 and S4 and Tables S3 and S14 ) ., When comparing our EBV interactome with the recently published EBV network by Calderwood et al . , 6 out of 43 ( 13 . 9% ) interactions could be confirmed 24 ., Such low confirmation rates are common to Y2H studies , even for studies within the same species , which in general suffer from low coverage 28 , 29 , 30 ., For instance , in a previous study of human interactions only 2 . 3–8 . 4% of known interactions were identified 15 ., On the other hand , this implies that ∼3% of the interactions found in the present study have been published so far in the literature or identified in previous genome-wide screens in the case of EBV ., In the case of HSV-1 our study added 102 new interactions to the network of already known interactions ( coloured grey in Figure 1B ) ., As is typical for such interaction networks , no apparent structure can easily be recognized ., A comparison of the five herpesviral networks revealed that the degree distribution differed from cellular networks , local clustering was not as high as expected in small-world networks of this size ( Figures 1C and S5 and Table S4 ) , and attack tolerance and robustness were increased compared to cellular networks ( Figure 1D and S6 ) , probably reflecting that the viral interactome in itself only represents a minor part of the complete interactome of the infected cell ., In a previous study we observed that the topology of the KSHV and VZV networks approached that of cellular networks as the viral interactomes were connected into a human interactome 23 ., The observations presented here confirm our previous findings , and indicate that herpesviral PPI networks share an evolutionarily conserved topology ., Apart from general topological features , herpesviral interactomes were also compared on the level of individual interactions ., For this purpose , we used the orthology assignments based on sequence similarity and gene order ( Table S5 ) 31 ., Species within the same subfamily are generally characterized by higher sequence similarity between orthologous proteins ., They also share more orthologous proteins with each other than species from different subfamilies ( Figure 2A ) ., In previous inter-species comparisons 32 , very few interactions were found to be shared between different species ( yeast , worm , fly ) ., Unlike the previous comparative studies , the five different interactomes analysed in this study were obtained using exactly the same experimental protocols ., Nevertheless , we still observed little overlap between the networks of the five herpesviruses ., Of 488 ( 409 non-redundant , i . e . conserved interactions are only counted once ) interactions between proteins conserved in more than one species , 140 ( 61 non-redundant ) ( 28 . 7% or 14 . 9% non-redundant , respectively ) interactions were conserved between at least two species ., For any two herpesvirus species , we compared the number of interactions between proteins conserved in both species against the number of interactions found in both species ( Figure 2B ) ., Although the pair wise overlaps observed were small , they were nevertheless significantly higher than observed with randomized orthology assignments ( Figures 2C and S7 ) ., Randomized orthology assignments for each pair of herpesviruses were obtained by first selecting the sub-network of conserved proteins between the two species , and then randomizing the orthology assignments for these sub-networks ., A similar analysis was performed for all five networks taken together ., First , networks were divided into interactions conserved within a subfamily or between different subfamilies , and the number of interactions conserved in 2 , 3 or 4 species in each category was evaluated ., We furthermore compared the number of interactions conserved in 2 , 3 , 4 and 5 species against the results for randomized orthology assignments and found in each case a significant enrichment ( Figure 2D ) ., This shows that despite the low coverage of the Y2H system significant conservation can still be observed ., Herpesviruses share a set of 41 core orthologous proteins which are conserved throughout the three subfamilies ( Table S5 ) 31 ., These core orthologs comprise approximately half of the genome of HSV-1 , VZV , EBV and KSHV but less than 25% of mCMV ., They can be further subdivided into a group of 31 orthologs with relatively high sequence similarity ( approximately 30–60% sequence similarity ) , and a group of 10 orthologs with little similarity ( approximately 16–30% similarity ) ( Table S6 ) ., Based on this orthology assignment , we generated an overlay of all protein interactions between the core orthologs detected in any of the five herpesviruses ( Core network , Figure 3A ) ., Of a total of 283 ( 218 non-redundant ) core protein interactions detected , 113 ( 48 non-redundant , 39 . 9% ) were found in more than one species ( Table S7 ) ., For the core network , we did not observe a correlation between sequence similarity and the number of conserved interactions detected ( Figures 3B and 3C ) ., For example , the interaction between the two tegument proteins UL11 and UL16 in HSV-1 was also detected in mCMV and EBV , although sequence similarity of UL11 and its orthologs across subfamilies is quite low ( 28% ) ., This interaction was interestingly also observed for HSV-1 in a recent report by Vittone and colleagues 33 ., In addition , interactions were not preferentially conserved between closely related species ( Figures 3D and S8 ) ., Accordingly , overlaps between the interaction sets in the core network were not correlated to the true phylogeny of herpesviruses ( Figure 3E ) ., Indeed , the highest overlap was observed between HSV-1 ( α-subfamily ) and mCMV ( β ) which belong to lineages separated early in herpesvirus evolution 7 ., However , since our phylogenetic trees are based on relative overlaps between the different species , we cannot exclude that a more complete set of core interactions might have allowed for better separation of the subfamilies ., In contrast , when also including subfamily- and species specific interactions ( i . e . the complete interaction network of the five herpesviruses with the characteristic sunflower structure , see Figure 4A ) , the analysis yielded a phylogeny that was consistent with the known evolutionary relationships ( Figure 4B ) ., This indicates that the presence of conserved subfamily specific interactions provides sufficient conserved and non-conserved interactions to accurately separate the subfamilies from each other ., In the overlay of all five herpesviral networks ( Figure 4A , sunflower structure ) , the core network is indicated as a central node common to all herpesviruses ., Subfamily- and species-specific networks are attached ( as leafs ) to this core ., Only few connections exist between the subfamily-specific networks due to few shared proteins outside of the core ., Our data provides evidence that the viral core network is extremely dense while the non-core network appears relatively sparse ., However , since non-core interactions were tested in at most two species , and not in five as the core interactions , the non-core network may be equally dense ., Indeed , no consistent difference was observed between the number of intraviral core and non-core interactions when considering each network separately ( Table S8 ) ., To further evaluate whether interactions between orthologous proteins are conserved we used co-immunoprecipitation to test 92 interactions predicted from 55 interactions detected in KSHV for the corresponding orthologs in HSV-1 , mCMV and EBV ., 11/19 ( 58% ) of the predicted interactions could be confirmed by CoIP in HSV-1 , 12/18 ( 67% ) in mCMV and 36/55 ( 65% ) in EBV , in comparison to 29/55 ( 53% ) in KSHV itself ( Figures 5A and S9A and Table S9 ) ., The percentage of core-derived orthologs that were confirmed by CoIP significantly correlated with the number of species in which the interactions were detected in Y2H screens , suggesting that the accuracy increases with the number of positive assays ( Figure S9B , Table S10 ) ., As negative controls , ten interactions which were not detected in any of the Y2H screens were tested in four viruses ( 39 interactions in total , Table S10 ) ., Although the confirmation rate of these negative controls seems relatively high ( 6/39 ( 15% ) ) , it is still significantly smaller than for the predicted interactions and correlates well with the confirmation rates of interactions observed in 2 , 3 and 4 species ( Figure S9B ) ., Due to the low coverage of the Y2H system many ( particular weak ) interactions were most likely missed , and the positively tested controls may be examples of such interactions ., It also suggests that , although our core interactome is very dense , it has not yet reached full coverage ., Since the confirmation rate by CoIP for the Y2H interactions in KSHV is not higher than for the predicted interactions in HSV-1 , mCMV and EBV , we conclude that a high percentage of interactions between core orthologs are conserved despite low sequence similarity of some of the orthologs across subfamilies ., To further assess the level of completeness of our core network , we evaluated the average number of new interactions added to the core network with each new Y2H screen ( Figure 5B ) ., If core interactions indeed are conserved , as indicated by our predicted interactions , we would expect the coverage to increase with each new herpesviral interactome ., Although the number of newly discovered interactions steadily decreased with each new screen , saturation does not seem to be reached yet ., Thus , although coverage for the core network could be increased , a significant fraction of interactions might still be missing ., Finally , to determine if conserved intra-viral interactions allow viral proteins to interact across different species , we tested four interactions which were detected in at least two herpesviruses in the original screens by Y2H and LUMIER ( luminescence-based mammalian interactome mapping ) pull-down assays ( Figure S10A and B ) 34 ., While Y2H in general yielded few cross-species interactions , we detected a larger number of interactions by LUMIER ( Figure S10B ) ., The cross-species interactions between the HSV-1 UL11 and UL16 tegument and between the HSV-1 UL19 and UL35 capsid orthologs were mainly observed within a specific subfamily , in accordance with previous observations by Schnee et al . 35 ., For the two other interactions , involving orthologs with both a high and low degree of sequence similarity based on Table S6 , we saw a more promiscuous interaction pattern ., HSV1 UL14 for example was able to interact with HSV-1 UL33 and its orthologs in all five species , suggesting that sequence similarity might be a poor predictor of interspecies interactions in herpesviruses ., Additionally , we tested 4 core and 4 noncore VZV baits against prey libraries of all five viruses ., As expected , the intra-species analysis ( VZV baits against VZV preys ) yielded the highest fraction of positive interactions ( 2 . 8% ) , compared to 0 . 5% positive interactions in the cross-species screens ., Of the positive cross-species interactions we observed 4 core-core , 15 core-noncore and 2 noncore-noncore interactions ( Table S11 ) ., When the number of positive interactions was correlated to the number of interactions tested for each class , we observed a significant enrichment of positive interactions for the core-core and core-noncore classes compared to the noncore-noncore class ( Figure S11 ) ., Most core proteins are essential , and a majority can be found in herpesvirus virions composed of an icosahedral capsid of 162 capsomers , an amorphous tegument layer and a lipid bilayer membrane with embedded glycoproteins ., Using the high-coverage core network , a map of conserved protein interactions in herpesviral particles was generated ( Figure 6A and S12 ) ., One outstanding example for a highly connected protein in this virion map is the mCMV M51 ortholog ( HSV-1 UL33 , VZV Orf25 , EBV BFRF4 and KSHV Orf67 . 5 ) , which interacted with 14 tegument proteins ., Since, ( i ) 11 of the 14 interactions ( 79% ) of this protein were found in more than 1 species ,, ( ii ) most Y2H interactions were confirmed even under high concentrations of the competitive HIS3 inhibitor 3-amino-1 , 2 , 4-triazole which can be used to suppress non-specific Y2H interactions ( Figure S13A and S13B ) , and, ( iii ) a majority of interactions were confirmed by CoIP ( Table S12 ) , we considered them as high-confidence interactions ., Furthermore , 4 of the 5 interactions conserved in 4 herpesviral species are M51 interactions ., One example is the interaction of mCMV M51 orthologs ( HSV-1_UL33/VZV_25/EBV_BFRF4/KSHV_67 . 5 ) with M53 orthologs ( HSV-1_UL31/VZV_27/EBV_BFLF2/KSHV_69 ) , which also interact in 4 species with M50 orthologs ( HSV-1_UL34/VZV_24/EBV_BFRF1/KSHV_67 ) ., M50 and M53 and their orthologs are involved in the nuclear egress of viral capsids and are well-characterised in mCMV , HSV-1 , EBV and pseudorabiesvirus 36 , 37 , 38 , 39 ., Both M50 in mCMV and its ortholog UL34 in HSV-1 recruit protein kinase C to the nuclear membrane , which subsequently phosphorylates lamins to dissolve the nuclear lamina allowing the capsids to reach the inner nuclear envelope 38 , 40 ., In mCMV , we confirmed 17/22 ( >75% ) ( Table S12 ) of M51 interactions by CoIP , and showed that M51 is targeted to the nuclear membrane by M50 and co-localizes with both M50 and M53 ( Figure 6B ) ., Our results suggest that M51 and its orthologs are part of a larger protein complex and may be involved in nuclear egress ., Since most of its interaction partners are present in the virion tegument we hypothesize that it plays a role in tegument formation , and represents a possible link between DNA packaging , nuclear egress and tegumentation ., Here we present an extensive study of intraviral protein-protein interactions for the three herpesviruses HSV-1 , mCMV and EBV , using Y2H as the main experimental method ., By combining the results with our previous studies of interactions in VZV and KSHV we were able to compare the interactomes of five related herpesviral species ., Although there was little overlap between the five viral networks according to the Y2H maps , we were able to show that interactions between core orthologous proteins are to a large degree conserved between species of different subfamilies ., By generating a separate network of interactions between core proteins of five herpesviruses , we were also able to overcome the coverage problem of Y2H and to identify interactions of interest from the common network which were not apparent in each single network ., While the overlaps between the different interactomes were generally quite low , there was still a significant enrichment of conserved interactions between orthologous proteins for any pair of the five species ( Figures 2C and S7A to I ) ., The same holds true for the conservation between all five viruses ., Interactions observed in two , three or four species were all enriched significantly as compared to background expectations ( Figure 2D ) ., This argues that , although troubled with false negative and false positive interactions , Y2H as a technique is still sufficiently sensitive and specific to obtain data for a comparative analysis of related interactomes ., Similar observations were also made for the recently published Campylobacter jejuni interaction network , where highly significant overlaps were found with both the Escherichia coli and Helicobacter pylori interactomes 41 ., It is very likely that true overlaps between the herpesvirus interactomes are higher , but that due to false negative interactions we only observe modest overlaps ., There are numerous reasons why interactions may be missed in the Y2H system , including improper folding of fusion proteins and post-translational modifications ., In an attempt to address some of these issues we cloned all the viral proteins containing transmembrane domains as both full length and extra/intracellular fragments , which has been reported to increase sensitivity 42 ., Our observations indicate that intraviral interactions between core proteins are conserved , and as a result we are not able to separate the Y2H interactomes into their phylogenetic subfamilies solely based on their core interactions ., However , when interactions involving subfamily-specific proteins present in at least two of the virus species were included , we were able to generate a correct phylogenetic tree ., This implies that interactions involving subfamily-specific proteins are at least partly conserved ., Indeed , several of the interactions predicted from KSHV and confirmed in EBV by CoIP involved subfamily specific proteins ., From published literature there are several examples of core interactions being conserved between species of different herpesviral subfamilies , e . g . the interactions between HSV-1 UL31 and UL34 36 , 37 , 38 , 39 , HSV-1 UL15 and UL28 43 , 44 and the HSV-1 UL54 self-interaction 45 , 46 ., Indeed , much of what is currently known about herpesvirus biology is derived from studies of Herpes Simplex Virus and extrapolated to other species ., Our study indicates that it is effectively possible to transfer intraviral interactions between orthologous proteins from one species to another ., Thus , by generating an overlay network from several genome-wide Y2H screens in related species , the large number of false negative interactions within each individual analysis can be overcome and a more complete picture of the core interaction network obtained ., In general , interactions are transferred between different species based on the sequence similarity between the corresponding proteins ., In addition , one might expect interactions among orthologous proteins with high sequence similarity to have a higher likelihood of being conserved ., Yu and colleagues found that interactions could be confidently transferred from one species to another if the joint sequence identity of the interacting orthologs was >80% 47 ., However , since none of the herpesviral core proteins shares such a high degree of sequence similarity across subfamilies , these criteria cannot be applied to herpesviruses ., Furthermore , no correlation was observed between sequence similarity and the number of species in which an interaction was observed in the Y2H experiments ., Thus , our results show that sequence similarity alone seems to be insufficient for predicting herpesviral interactions from one species onto another ., Our analysis of cross-species interactions indicates an enrichment of interactions involving core proteins ( either core-core or core-noncore ) ., The detailed cross-species analysis of the interaction between the major capsid protein ( MCP ) and the smallest capsid protein ( SCP ) ( HSV-1 UL19-UL35 , Figure S10B ) only yielded 1 intraspecies interaction by Y2H , however 4 by LUMIER , indicating that this interaction is conserved despite being observed in only one species by Y2H ., While capsid proteins and interactions are thought to be highly conserved , most of them were indeed only observed in one species in our genome-wide Y2H screens ., However , three of the four observed capsid interactions ( HSV-1 UL19-UL35 , UL18-UL38 , UL18-18 and UL35-UL35 ) have been published previously ( Tables S3 and S14 ) , and , in addition , the LUMIER analysis resulted in an increased number of cross-species interactions ., The cross-species interactions between the two tegument proteins HSV-1 UL11 and UL16 ( and their orthologs ) , as well as between the two capsid proteins HSV-1 UL19 and UL35 ( and their orthologs ) , were mainly observed between species within the same herpesviral subfamily ., A similar observation has recently been reported by Schnee and colleagues 35 , and may indicate that some binding sites are more conserved within herpesvirus subfamilies ., The other two interactions could be detected in a larger number of cross-species interactions by both Y2H and LUMIER ., HSV-1 UL14 , for example , was observed to interact with all orthologs of HSV-1 UL33 by LUMIER ., Previous reports suggested that HSV-2 UL14 shares certain similarities with cellular chaperones which may account for its promiscuous binding pattern 48 ., In the core network derived from the overlap of all five herpesviruses , mCMV M51 , and its orthologs HSV-1 UL33 , VZV ORF 25 , EBV BFRF4 and KSHV ORF 67 . 5 , show up as intraviral hubs with a number of conserved interactions ., For instance , the interaction between M51 and M53 was observed in all species apart from HSV-1 ., Interestingly , when retesting UL33 interactions under more stringent conditions ( Figure S12 ) , the corresponding interaction between UL33 and the M53 ortholog in HSV-1 , UL31 , is clearly one of the positive interactions on both 2 . 5 and 5 mM 3AT ., These interactions were not included in the HSV interactome to prevent an overrepresentation of interactions tested more than once ., While not much is known about M51 , M53 has been extensively documented to be involved in nuclear egress through its binding to M50 35 , 38 , 49 , 50 ., The interaction between M53 and M50 was confirmed in this study in four viruses ., In addition , from our study of interactions in VZV we observed an association between the ortholog of M51 ( ORF25 ) with the M50 ortholog ( ORF24 ) 23 , and retesting of HSV-1 UL33 interactions also revealed its binding to HSV-1 UL34 lacking the transmembrane region ( Figure S12 ) ., Finally , immunofluorescense studies indicated that M51 co-localizes with both M53 and M50 when using fluorescent fusion proteins ., These results suggest a possible role for M51 in nuclear egress through its interactions with M53 and/or M50 ., As interactions between orthologs of M51 and M53 were observed in members of all three subfamilies , it is likely that this represents a conserved function of M51 ., Previous studies have indicated that the M51 ortholog in HSV-1 ( UL33 ) is involved in packaging of DNA 51 , and that it interacts with at least one of the subunits of the terminase complex ( UL28 ) 52 ., In the data presented here , UL33 was observed to interact with UL15 and UL28 in three different species ., These results suggest that UL33 represents an association between packaging and egress ., Studies done in HSV-1 have indicated that UL33 is associated with the external surface of capsids 53 , which would make such a dual role reasonable ., While it is not known exactly how UL33 associates with the capsid , the interaction observed between M51 and the smallest capsid protein ( m48 . 2 ) in mCMV and EBV suggests a possible manner of association ., In summary , this study suggests that a distinctive network topology is still present in all vertebrate herpesvirus species although herpesviruses co-evolved with their hosts for millions of years ., Moreover , it provides evidence, ( i ) that interactions and hence functions of proteins may be more conserved than their sequence and, ( ii ) that a common core of protein interactions is conserved in all herpesviruses ., We hope that the data presented will inspire future herpesvirus research and facilitate the selection of potential targets for antiviral therapy 54 ., The nucleotide sequences for all ORFs were obtained from the ncbi ( http://www . ncbi . nlm . nih . gov/ ) , and cloned into the Y2H vectors pGBKT7-DEST and pGADT7-DEST by recombinatorial cloning 55 ( Protocol S1 ) ., All clones were sequence verified ., Yeast strains AH109 and Y187 were transformed using 1 µg of prey ( pGADT7-DEST ) or bait ( pGBKT7-DEST ) plasmid DNA , respectively , and grown on SD medium lacking either leucine ( -leu ) or tryptophane ( -trp ) ., Prey- and bait-expressing yeast were arrayed in a 384-pin format using a Biomek 2000 workstation ( Beckman-Coulter ) ( 4 replicas for each interaction tested ) , and mated in an all-against-all matrix approach 27 ., Diploid colonies were grown for 2 days at 30°C on SD –leu-trp plates , and subsequently transferred to selective SD -leu-trp-his plates ., Interactions were considered positive if at least 3 out of 4 colonies grew ( Protocol S1 ) ., pGBKT7-DEST and pGADT7-DEST were co-transfected into HEK-293 cells by means of calcium phosphate , and superinfected with recombinant vaccinia virus ( vTF-7 ) expressing T7 RNA polymerase ( NIH AIDS repository ) at a MOI of 10 ., After 24 h cells were lysed , and precipitation of proteins was done using 1 µg of either anti-myc ( Santa Cruz ) or anti-HA ( Roche ) antibodies in addition to protein G Sepharose beads ., Precipitates were separated by SDS-PAGE , and western blots initially reacted with the anti-myc and anti-HA antibodies , and secondary , peroxidase-conjugated anti-mouse IgG or anti-rat IgG antibodies ( Jackson ) ., The CoIP was scored positive if a co-precipitate was detected in at least one direction ( Protocol S1 ) ., HeLa cells were grown on a cover slip until ∼50% confluence , and subsequently transfected with 1 µg of DNA for each of the fluorescent vectors analyzed , either alone or in combinations , by means of Effectene ( Qiagen ) ., Cells were incubated for 24 h , and fixed by incubating with 4% paraformaldehyde for 30 min at RT ., Coverslips with fixed cells were mounted in Vectashield Mounting Medium ( Vector Labs ) , and imaged on an OLYMPUS BX61 microscope ., Literature interactions were identified by combining automatic text mining and manual curation ., A set of ∼87000 MEDLINE abstracts on herpesviruses was screened using ProMiner 56 for occurrences of proteins of any of the five viruses considered ., Subsequently , 565 abstracts were selected containing a reference to interactions and at least two different proteins of the same virus ., Physical interactions were then extracted manually from the corresponding articles ., From the five individual networks an overlay network was created by merging orthologous proteins and interactions between orthologous proteins ., Orthology relationships were assigned based on Davison 31 ., The overlay network was then used to predict interactions between core proteins and to analyze network characteristics ( Protocol S1 ) ., For all core orthologous proteins the average pairwise global sequence similarity across all five viruses was calculated ., Global similarity was used to avoid a distortion of the results by short but high local similarities between orthologous proteins ., For an interacting pair of core proteins , the similarity was calculated as the geometric mean of the average similarities for the corresponding proteins ., The distance metric used to construct the phylogenetic tree ( Figure 3E ) for the complete and core network , respectively | Introduction, Results, Discussion, Materials and Methods | Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases ., They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames ., We previously reported the protein interaction networks of two herpesviruses , varicella-zoster virus ( VZV ) and Kaposis sarcoma-associated herpesvirus ( KSHV ) ., In this study , we systematically tested three additional herpesvirus species , herpes simplex virus 1 ( HSV-1 ) , murine cytomegalovirus and Epstein-Barr virus , for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies ., We identified 735 interactions by genome-wide yeast-two-hybrid screens ( Y2H ) , and , together with the interactomes of VZV and KSHV , included a total of 1 , 007 intraviral protein interactions in the analysis ., Whereas a large number of interactions have not been reported previously , we were able to identify a core set of highly conserved protein interactions , like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34 ., Interactions were conserved between orthologous proteins despite generally low sequence similarity , suggesting that function may be more conserved than sequence ., By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species . | Herpesvirus proteins interact with each other in a complex manner throughout the infectious cycle ., This is probably best exemplified in the process where a large number of viral proteins come together to form new viral particles which are subsequently released from the infected cell ., A more detailed understanding of how viral proteins interact with each other might assist the development of drugs which may inhibit these interactions and consequently block viral replication ., Here we present three genome-wide studies of protein-protein interactions in the herpesviruses herpes simplex virus I , murine cytomegalovirus and Epstein-Barr virus ., Altogether we identified 735 interactions in the three viruses , most of which have not previously been reported ., By combining these studies with our previously published studies for Kaposis sarcoma-associated herpesvirus and varicella-zoster virus we were able to perform a comparative analysis of interactions in five related viral species ., We observed that a high proportion of interactions were conserved between the different species , despite a low degree of sequence conservation ., This implies that by comparing interaction data , we were able to increase the coverage of our viral networks and thus obtain a better and more complete picture of interactions between herpesviral proteins . | virology/virus evolution and symbiosis, virology/virion structure, assembly, and egress, virology, molecular biology/bioinformatics, computational biology/systems biology | null |
journal.pntd.0002898 | 2,014 | Deciphering the Origin of the 2012 Cholera Epidemic in Guinea by Integrating Epidemiological and Molecular Analyses | Cholera is generally considered endemic in West Africa 1 , especially in countries such as Nigeria , Benin , Togo , Ghana , Liberia and the Republic of Guinea 2 ., In 2004–2008 , Guinea was struck by a succession of regional cholera outbreaks responsible for 17 , 638 reported cases and 786 deaths 3 ., In 2009 , the country established an early cholera alert system including cholera microbiological surveillance to quickly detect emerging epidemics 4 ., However , the following years in Guinea were marked by a lull in cholera transmission until new cases were reported between February and April 2012 in several maritime prefectures spanning 200 km 5 ., Between April and June , a reactive oral cholera vaccination campaign was implemented by the Guinean Ministry of Health and Médecins Sans Frontières ( Doctors Without Borders ) in two prefectures , Forecariah and Boffa 6 ., However , during the rainy season in July and August , the epidemic exploded in the capital Conakry and then spread to inland areas ., By the time the end of the epidemic was declared in December 2012 , 7 , 350 cases and 133 deaths had been officially reported to the World Health Organization ( WHO ) , from 11 out of 34 prefectures 7 ., To provide a scientific foundation for the control and prevention of future outbreaks , it is critical to understand the origin of cholera epidemics in coastal areas , which has remained subject to debate ., In Peru and Bangladesh , a similar near simultaneous appearance of cholera at different locales along coastal or estuarine areas has been considered a key argument in favor of the “cholera paradigm” 8 ., According to this general model for cholera transmission , coastal waters in these regions represent reservoirs of multiclonal epidemic-provoking Vibrio cholerae strains whose growth is directly associated with plankton blooms driven by climatic and environmental conditions 8–12 ., Conversely , whole-genome-based phylogenetic analyses of Peruvian and other South American isolates from the 1990s have found that the strains form a clonal and independent lineage within the seventh pandemic 13 , 14 ., Such molecular approaches have recently highlighted the function of human-to-human transmission of the disease 12 , which could be the main driver of clonal outbreak diffusion , even along coastal areas ., To assess whether the 2012 Guinean cholera epidemic was caused by local environment-to-human transmission or was rather initiated by the human-driven importation of a single toxigenic clone we used a multidisciplinary approach involving spatiotemporal analyses , field investigations and several complementary V . cholerae genotyping methods ., The Republic of Guinea spans 245 , 857 km2 and is administratively divided into 33 prefectures plus the capital Conakry ., In 2012 , the country had an estimated population of 12 million inhabitants ., At that time , the Guinean national health surveillance system prospectively reported all suspected cholera cases based on the WHO definition of the disease 15 ., Each Prefectural Health Directorate ( DPS – Direction Préfectorale de la Santé ) tallied new cases recorded at the various health structures of the prefecture on a weekly basis ., Aggregated morbidity and mortality cholera data were then transmitted to the Directorate of Prevention and Disease Control ( DPLM – Direction de la Prévention et de la Lutte contre la Maladie ) , which compiled the information in a national database of 7 , 350 cases ., DPLM also retrospectively compiled a line list of 6 , 568 patients , which included the date of consultation and geographical origin down to the village level and was anonymized prior to analysis ., To limit notification bias , both databases were subsequently compared and merged , which enabled the retrieval of 393 additional cases ., The use of these data for epidemiological , research and publication purposes was approved by the Guinean Ministry of Health ( Ministère de la Santé Publique et de lHygiène Publique ) ., Daily-accumulated rainfall data were obtained from satellite estimates ( TMPA-RT 3B42RT derived ) provided by the National Aeronautics and Space Administration ( available at: http://disc2 . nascom . nasa . gov/Giovanni/tovas/realtime . 3B42RT_daily . 2 . shtml ) ., As most cases were recorded in Maritime Guinea and , to a lesser extent , Middle Guinea , daily rainfall data were averaged on the position 9 . 00N-12 . 00N/15 . 00-11 . 75W , which excluded the eastern two-thirds of the country where precipitation levels were lower and much fewer cholera cases were reported ., Population estimates for 2012 were obtained from the Guinean Expanded Program for Immunization at both the prefectural and sub-prefectural levels ., Their estimates were based on the general population census of 1996 considering prefecture-specific annual population growth rates , which were provided by the Guinean Statistics National Institute ( INS – Institut National de la Statistique de Guinée ) and ranged from 0 . 71% to 6 . 51% ., Field investigations of index cases and local conditions that supported cholera emergence and transmission were prospectively conducted in affected areas throughout the epidemic by epidemiologists of the Guinean Health Ministry and the country team of the African Cholera Surveillance Network ( Africhol; http://www . africhol . org ) to organize the public health response ., They included basic interviews among affected communities identified by the hospital- and community-based surveillance system ( including rumors ) and followed routine procedures of the Integrated Disease Surveillance and Response System of the Guinean Ministry of Health ., Retrospective field investigations were also conducted in August and September 2012 mainly to review the register books of treatment facilities , but also to interview local health authorities and staff regarding the 2012 outbreak as well as to observe ecological , social , water and sanitation conditions in affected areas ., With the support of the Africhol Consortium and following standard procedures 16 , the reference laboratory of the Public Health National Institute ( INSP – Institut National de Santé Publique ) tested 236 clinical samples positive for V . cholerae O1 throughout the duration of the 2012 epidemic , out of which 212 isolates were prospectively stored in a biobank created for that purpose ., In September 2012 , 50 of these isolates were selected for genotyping , subcultured and then transported in glycerol tubes at room temperature to Marseille , France ., Isolates were selected in a manner in which the samples were temporally and spatially representative of outbreak diffusion during the first 8 months of the epidemic and included early and later isolates from all 7 prefectures available in the biobank ., Upon arrival in Marseille , the strains were recultivated on non-selective trypticase soy agar ( TSA ) medium ( Difco Laboratories/BD ) for 24 hours at 37°C ., Suspected V . cholerae colonies were identified via Gram-staining , oxidase reaction and agglutination assessment with V . cholerae O1 polyvalent antisera ( Bio-Rad ) ., For DNA extraction , an aliquot of cultured cells was suspended in 500 µL deionized water , incubated for 10 min at 100°C and centrifuged for 10 min at 1500× g ., The pellet was then resuspended in 250 µL deionized water and incubated for 5 min at 100°C ., The supernatant ( containing DNA ) was subsequently stored at −20°C ., DNA was directly extracted from the glycerol transport tubes for the isolates that failed to grow upon culture ., Genotyping of the V . cholerae strains was performed via MLVA ( Multiple Loci VNTR ( Variable Number Tandem Repeat ) Analysis ) of 6 VNTRs ( Table 1 ) , including 4 previously described assays 17 , 18 and 2 assays specifically designed for this study to improve the discriminating power of the analysis ., The novel VNTR assays were designed based on the reference strain El Tor N16961 ( GenBank accession numbers AE003852 . 1 and AE003853 . 1 ) using Perfect Microsatellite Repeat Finder webserver ( currently unavailable ) ., Specific primer pairs were subsequently designed using the Primer3 program ( http://simgene . com/Primer3 ) ( Table 1 ) ., Fluorescent-labeled primers were purchased from Applied Biosystems ., For each PCR assay , DNA amplification was carried out by mixing 0 . 375 µL of each primer ( 20 µM ) , 1 X LightCycler 480 Probes Master ( Roche Diagnostics ) and approximately 100 ng of template DNA in a total volume of 30 µL ., PCR was performed using a LightCycler 480 System ( Roche Diagnostics ) with the thermal cycling conditions described in Table 1 ., PCR amplicons were subsequently verified via agarose gel ( 2% ) electrophoresis ., VNTR PCR product size was determined via capillary electrophoresis ., Aliquots of the PCR products were first diluted 1∶100 in sterile water , which was further diluted 1∶100 in a solution containing 25 µL Hi-Di Formamide 3500 Dx Series ( Applied Biosystems ) and 0 . 5 µL GeneScan 500 LIZ Size Standard ( Applied Biosystems ) ., The fluorescent end-labeled amplicons were analyzed using an ABI PRISM 310 Genetic Analyzer ( Applied Biosystems ) with POP-7 Polymer ( Applied Biosystems ) ., Finally , amplicon size was determined using GeneMapper v . 3 . 0 software ( Applied Biosystems ) ., To better characterize the V . cholerae strains responsible for the epidemic , whole-genome sequencing was performed on a strain isolated at the onset of the epidemic ( strain G298_Guinea ) using a GS FLX+ System ( 454 Life Science , a Roche company ) ., The DNA sequence was assembled using Newbler , from GS De novo Assembler ( http://454 . com/products/analysis-software/index . asp ) ., To perform a phylogenetic assessment of the core V . cholerae genome based on genome-wide SNPs ( single nucleotide polymorphisms ) , strain G298_Guinea DNA was re-sequenced using a HiSeq Illumina System ( Illumina ) ., For the spatiotemporal description of the epidemic , rainfall data were aggregated weekly and graphically represented in parallel with cholera morbidity ., Cholera attack rates were calculated and mapped , by prefecture and sub-prefecture , for various time periods using shapefiles of administrative divisions obtained from the HealthMapper application ( WHO , Geneva , Switzerland ) and Quantum GIS v1 . 8 . 0 ( QGIS Geographic Information System , Open Source Geospatial Foundation Project , available at: http://qgis . osgeo . org ) ., MLVA-based genotypes were compared at each of the 6 VNTR loci ., Genetic relatedness between the strains was first assessed using eBURSTv3 ( http://eburst . mlst . net/ ) , which aims to identify the founding genotype ., A simple network of all possible links between genotypes was also assembled using Gephi . 0 . 8 . 1 beta software ( https://gephi . org/ ) ., Molecular epidemiology analyses were completed via the sequential mapping of each genotype by month at the prefecture level ., After the first whole-genome sequence was obtained with the GS FLX+ System , proteins were predicted using Prodigal software ( http://prodigal . ornl . gov/ ) ., Data was then annotated employing the GenBank database ( http://www . ncbi . nlm . nih . gov/genbank ) and the Clusters of Orthologous Groups database using BLASTP with an E-value of 10−5 ., Allelic polymorphism of the cholera toxin B subunit and other virulence factors was characterized by comparing the obtained sequence with the genome description of V . cholerae strains available in GenBank and recent literature ., For phylogenetic analyses , the paired-end read data obtained with the HiSeq Illumina System and sequence data from 198 previously sequenced strains available in the NCBI SRA database were mapped to the reference N16961 El Tor strain ( NCBI accession numbers AE003852 and AE003853 ) using SMALT software ( http://www . sanger . ac . uk/resources/software/smalt ) ., A whole-genome alignment was obtained for each strain in this analysis , and SNPs were called using the approach described by Harris et al . 19 ., The reads that did not map to the N16961 genome were filtered out during SNP calling , and any SNP with a quality score less than 30 was excluded ., A true SNP was only called if there were at least 75% of the reads at any heterogeneously mapped ambiguous sites ., High-density SNP clusters indicating possible recombination sites were excluded using the methodology previously described by Croucher et al . 20 ., Maximum Likelihood phylogenetic trees were estimated using the default settings of RAxML v0 . 7 . 4 21 based on all the SNPs called in the manner explained above ., M66 ( accession numbers CP001233 and CP001234 ) , a pre-seventh pandemic strain , was used to root the final phylogenetic tree of the seventh pandemic strains 13 ., FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) was used to visualize and order the nodes of the phylogenetic tree ., Taking into account both the national database and patient line list , this epidemic was responsible for an estimated 7 , 743 suspected cases ( global attack rate: 6 . 3 cases/10 , 000 inhabitants ) and 138 deaths ( case fatality ratio: 1 . 8% ) ., The initial case was reported on February 2 , 2012 ( epidemiological week 5 ) in the midst of the dry season ( Figure 1 ) ., The weekly number of new cases remained below 100 until July ., The epidemic then peaked in August , 5 months after the onset of the rainy season , with nearly 1 , 188 new cases recorded during week 34 ., Cholera incidence began to markedly decline in September ., The final case was recorded on December 11 , 2012 , and the Minister of Health officially declared the end of the epidemic on February 6 , 2013 ., Overall , the capital of Conakry reported 4 , 642 cases ( 25 . 9 cases/10 , 000 inhab . ) , which represented more than half of the national case total , but only 24 deaths ( case fatality ratio: 0 . 5% ) ., Moreover , 2 , 178 additional patients were located in the 5 other prefectures that border the Atlantic Ocean , with the highest attack rate observed in Coyah ( 55 . 1 cases/10 , 000 inhab . ) ( Figure 2 ) ., Twelve other prefectures were also affected , including distant inland prefectures such as Kerouane ( Figure 2 ) ., The initial cholera cases in Guinea emerged on February 2 , 2012 on Kaback Island ( Prefecture of Forecariah ) ( Figure 3 ) , which is located in a remote mangrove zone close to the border with Sierra Leone , where an epidemic of acute diarrhea and vomiting had been reported in January ., The Guinean index case was a fisherman who had just traveled by boat from Sierra Leone ( a village on Yeliboyah Island , Kambia District ) and arrived in the fishing village of Khounyi , on a land strip of the southern tip of Kaback Island ., During the first month of the epidemic , this small village , which lacked safe water and improved sanitation facilities , recorded over 100 cases and represented the most affected community in the prefecture ., The cholera epidemic then progressively diffused northwestward along the Guinean coast , striking the prefectures of Boffa on February 23 and Boke on April 22 ( Figure 3 ) ., As observed in Forecariah Prefecture , the initial cases in Boffa and Boke were also reported in fishing camps , namely Sakama and Yongosale , respectively ., At each of these lowland fishing locales , the index case was a fisherman who had recently returned from an already affected area ( i . e . , travelling from Kaback to Sakama and from Koukoude ( Boffa prefecture , Douprou sub-prefecture ) to Yongosale ) ., Concomitant with the expansion of the epidemic along the coast , cholera had also begun to spread inland ., However , the inland prefectures were not significantly affected until the onset of the rainy season ., Likewise , although Conakry is situated on a peninsula between the early affected regions of Kaback and Boffa , cholera did not strike the capital until a month after the inception of the rainy season ., The first case in Conakry was officially recorded on May 29 , who appeared to be a merchant returning from the Kaback market ., Conakry subsequently acted as an amplifier of epidemic spread , especially towards the interior portions of the country , where several identified index cases were found to be drivers , merchants or students recently returning from the capital ., Fourteen samples out of 50 were not positive by culture and 2 additional samples were heavily contaminated ., However , direct DNA extraction from transport tubes was successful for 4 culture-negative isolates ., Genotype analysis with the 6-VNTR panel was thus performed on 38 V . cholerae isolates ., All strains displayed constant results for the VC1 , VC5 and LAV8 assays , while the VC4 , VC9 and LAV6 assays revealed 4 , 3 and 6 allelic variants , respectively ., Based on the MLVA results , the strains were grouped into 12 different genotypic profiles , all of which were very closely related ( Figure 4 ) ., All strains seemed to have arisen from genotype #1 , which was identified as the founder genotype using the eBURST algorithm ., Genotype #1 represented the earliest genotype isolated during the 2012 epidemic ( on Kaback Island in February 2012 ) as well as the most frequent genotype identified ( Figure 4 ) ., Subsequent diversification of this clone occurred via 1 or 2 mutational events during its propagation across the country ( Figure 4 ) ., The genome of a genotype #1 strain isolated on February 28 , 2012 in Kaback was examined via whole-genome sequencing ., The cluster composition of the virulence genes displayed one “hybrid” CTXϕ prophage on chromosome 1 but no RS1 fragment ., Sequence results showed that this “hybrid” CTXϕ harbors a majority of El Tor allele genes ( e . g . , zot , ace and cep ) with a classical ctxB gene ( encoding the B subunit of the cholera toxin ) and a classical rstR gene ., Strain phylogeny based on genome-wide SNP analysis situated this Guinean “atypical” El Tor variant within a new clade of the third and most recent wave of the seventh pandemic ( Figure 5 ) ., This strain was thus distinct from both strains isolated in Mozambique in 2004–2005 ( second wave ) and strains isolated between 2005 and 2010 in Eastern Africa ( i . e . , the Kenyan clade within the third wave , indicated in purple on Figure 5 ) ., The Guinean 2012 strain was also found to be clearly separated from two South Asian clades ( indicated in sky blue on Figure 5 ) , which includes the Haitian clone ., The closest relative of the Guinean strain was a strain isolated in 1994 in Bangladesh ., While tracking the origin of the 2012 Guinean cholera epidemic , this multidisciplinary study demonstrates the monoclonal nature of the epidemic , as clinical V . cholerae strains exhibited a progressive genetic diversification that paralleled outbreak diffusion from Kaback Island ., Molecular results confirmed the epidemiological findings , as the single ancestral and most abundant genotype was the sole V . cholerae strain isolated during the onset of the epidemic in February , at the initial focus of Kaback ., According to field investigations , the index case was a fisherman arriving from a nearby cholera-affected district of Sierra Leone ., Cholera then bounced along the Guinean coast , likely carried by other infected fishermen , before exploding during the rainy season in the capital Conakry and subsequently spreading inland ., This clone was found to be an “atypical” El Tor variant of V . cholerae , as determined via whole-genome sequencing ., Furthermore , this Guinean strain phylogenetically grouped into a new clade of the third wave of the current pandemic , and the closest known relative was a strain isolated in Bangladesh in 1994 ., This study represents the first such molecular analysis of a cholera epidemic conducted in West Africa ., Overall , these results strongly suggest that cholera spread along the coast of Guinea due to human-driven diffusion of the bacterium ., According to the molecular analyses , this epidemic was caused by a single clone , which rapidly evolved in parallel with the spatiotemporal spread of the epidemic ., A few weeks after identification of the founder clone in Kaback , the same genotype was identified in new outbreak foci further along the coast , where it was likely transported by infected traveling fishermen ., Likewise , isolates characterized by descendant genotypes were found to have spread across the country throughout the year , with strains of the most distant genotypes primarily identified in distant prefectures , such as Kerouane , several months later ( e . g . , August and September ) ., Such genotype analysis has rarely been conducted to assess cholera epidemic diffusion from the onset ., However , similar genetic diversification from an initial V . cholerae clone has been recently observed throughout the current epidemic in Haiti 22 , where the human-associated importation of cholera is largely undoubted 23 , 24 ., Furthermore , the diffusion of cholera by traveling fishermen has already been documented in West Africa ., For example , the arrival of the seventh cholera pandemic in Ghana in 1971 was linked to the repatriation of a man who had succumbed to the disease while fishing in the waters of Togo , Liberia and Guinea 25 ., Conversely , had the 2012 cholera epidemic originated from a local aquatic reservoir of proliferating vibrios , the diversity of V . cholerae strains found in the environment would have resulted in the early identification of several distinct clones 8 , 26 ., Therefore , the emergence of a unique V . cholerae genotype in clinical samples isolated on Kaback Island in February does not correlate with environment-to-human transmission of the disease ., Furthermore , this period was not characterized by the wet and warm climatic conditions that are considered to be a favorable to V . cholerae proliferation in water bodies 8–12 ., Finally , a recent review addressing cholera epidemics in African coastal areas has indicated that no perennial environmental reservoir of toxigenic V . cholerae O1 has yet been identified in West Africa , which may be attributed to the lack of appropriate studies 27 ., The epidemiological data rather suggest that cholera was imported to Guinea from Sierra Leone ., Indeed , Kaback is situated less than 30 km away from this neighboring country ., Nearby districts of Sierra Leone , including Kambia and Port Loko , were already affected by the disease in early January 2012 28 ., Furthermore , the index case identified in Kaback was a travelling fisherman who had just arrived from a fishing village in Kambia ., Unlike Guinea , where an efficient early alert system 4 enabled the detection , report , investigation , laboratory-confirmation and official declaration of the outbreak within 8 days after the appearance of the first cholera case observed in the past 3 years , health authorities in Sierra Leone did not perform similar investigations ., Thus , the origin of this cholera epidemic in Sierra Leone remains unclear , although possible importation events by fishermen travelling from Liberia and Ghana have been reported 29 ., Finally , according to whole-genome sequence analysis , this epidemic was caused by an “atypical” El Tor variant of V . cholerae O1 , a type of strain that harbors both El Tor biotype genetic elements and the Classical biotype ctxB gene 30 ., Such “atypical” El Tor strains initially emerged in Asia in 1991 and were first detected on the African continent in 2004 31 ., This may also present major public health implications as these strains have been suggested to be associated with more severe clinical symptoms compared with conventional El Tor strains 32 , 33 ., Furthermore , genome-wide SNP-based phylogeny analysis grouped the Guinean 2012 clone into a recent clade within the third wave of the seventh pandemic ., Several studies have shown that this monophyletic radiation is largely distinct from the vast diversity of V . cholerae environmental strains 14 , 34 , which suggests that cholera epidemics are clonal and caused by a specific subset of related V . cholerae strains often spread via human-to-human transmission 14 , 35 ., Nevertheless , to confirm the origin of the V . cholerae clone responsible for this epidemic , it would have been ideal to analyze pre-epidemic environmental isolates as well as isolates from previous epidemics in Guinea , isolates from Sierra Leone and strains from other countries the region ., However , earlier Guinean isolates were not stored and we did not have access to strains from Sierra Leone ., Furthermore , no study of environmental V . cholerae strains had previously been performed in the region ., In conclusion , by tracking the origin of the 2012 cholera epidemic in the Republic of Guinea , this study identified fishermen as cholera victims and vectors during the early phase of epidemic propagation ., Improving water and sanitation infrastructures , implementing enhanced hygiene education programs and targeting oral cholera vaccination campaigns in high-risk coastal areas could thus benefit these vulnerable populations and prevent the spread of future cholera outbreaks ., The likely Sierra Leonean origin of this Guinean epidemic highlights the importance of encouraging transborder collaboration in the surveillance and control of highly mobile populations and main communication routes so as to rapidly identify emerging foci and organize coordinated targeted responses ., These results also support the implementation of biobanks dedicated to prospective clinical and environmental V . cholerae isolates , to perform molecular epidemiological analyses , which have become essential to interpret field investigation data ., Such an integrated approach would provide valuable insights concerning cholera in other African regions , where the key determinants of all too frequent epidemics still remain poorly understood and prevention or control strategies are not always accurately oriented . | Introduction, Materials and Methods, Results, Discussion | Cholera is typically considered endemic in West Africa , especially in the Republic of Guinea ., However , a three-year lull period was observed from 2009 to 2011 , before a new epidemic struck the country in 2012 , which was officially responsible for 7 , 350 suspected cases and 133 deaths ., To determine whether cholera re-emerged from the aquatic environment or was rather imported due to human migration , a comprehensive epidemiological and molecular survey was conducted ., A spatiotemporal analysis of the national case databases established Kaback Island , located off the southern coast of Guinea , as the initial focus of the epidemic in early February ., According to the field investigations , the index case was found to be a fisherman who had recently arrived from a coastal district of neighboring Sierra Leone , where a cholera outbreak had recently occurred ., MLVA-based genotype mapping of 38 clinical Vibrio cholerae O1 El Tor isolates sampled throughout the epidemic demonstrated a progressive genetic diversification of the strains from a single genotype isolated on Kaback Island in February , which correlated with spatial epidemic spread ., Whole-genome sequencing characterized this strain as an “atypical” El Tor variant ., Furthermore , genome-wide SNP-based phylogeny analysis grouped the Guinean strain into a new clade of the third wave of the seventh pandemic , distinct from previously analyzed African strains and directly related to a Bangladeshi isolate ., Overall , these results highly suggest that the Guinean 2012 epidemic was caused by a V . cholerae clone that was likely imported from Sierra Leone by an infected individual ., These results indicate the importance of promoting the cross-border identification and surveillance of mobile and vulnerable populations , including fishermen , to prevent , detect and control future epidemics in the region ., Comprehensive epidemiological investigations should be expanded to better understand cholera dynamics and improve disease control strategies throughout the African continent . | Cholera is a potentially deadly diarrheic disease caused by the toxin-secreting bacterium Vibrio cholerae ., In many poor countries , this prototypical waterborne disease is considered endemic and linked to the climate-driven proliferation of environmental reservoirs of the pathogen ., Although such a statement implies radical public health consequences , it has never been proven in Africa ., The present study aimed to elucidate the origin of the cholera epidemic that struck the Republic of Guinea in 2012 following a three-year lull period ., This investigation integrated a spatiotemporal analysis of the national case databases , field investigations and thorough genetic analyses of 38 clinical bacterial isolates sampled throughout the Guinean epidemic ., The Guinean V . cholerae DNA sequence results were aligned and compared with the sequences of nearly 200 strains isolated throughout the world over the past 60 years ., Overall , these results suggest that the 2012 cholera epidemic strain was likely imported from Sierra Leone to Guinea by traveling fishermen ., The emergence of cholera epidemics due to human-driven activity may be widespread throughout Africa ., This highlights the importance of transborder collaborative public health strategies targeting highly mobile and high-risk populations ., Similar integrated studies should be conducted in other countries impacted by the disease to better understand the spread of recent epidemics and thus better intercept future outbreaks . | medicine and health sciences, infectious disease epidemiology, population genetics, spatial epidemiology, bacterial diseases, phylogenetics, molecular biology techniques, infectious disease control, genetic epidemiology, infectious diseases, cholera, genetic polymorphism, gene mapping, molecular epidemiology, environmental epidemiology, epidemiology, evolutionary systematics, molecular biology, disease surveillance, biology and life sciences, evolutionary biology | null |
journal.pgen.1002413 | 2,011 | An Assessment of the Individual and Collective Effects of Variants on Height Using Twins and a Developmentally Informative Study Design | Adult height is a model multigenic phenotype for genetic association studies ., Twin and adoption studies suggest that height is highly heritable ( ∼80% ) 1 , 2 , 3 , but the identification of individual genetic variants that contribute large effects to normal-range adult height ( as with most complex traits ) has proven to be very difficult 4 , 5 ., Despite this , it does appear possible that common SNPs with individually small effects can account for a large proportion of phenotypic variance in adult height ( ∼45% ) 6 and may be identified with appropriately large sample sizes ., To that end , the GIANT consortium has identified 180 SNPs that collectively account for 10 . 5% of variance in adult height in a sample of 183 , 727 individuals 7 ., For any person adult height reflects roughly two decades of growth ., Change in height is relatively rapid throughout infancy , slows down in early childhood , and increases again during puberty when a notable growth spurt occurs 8 , 9 ., The heritability of growth during any particular developmental period appears to be high , and it has been shown that some genetic variants affect a substantial proportion of heights phenotypic variance throughout development ., For example , a longitudinal study of Swedish male twins found a genetic correlation of 0 . 73 between height at age 2 and at age 18 , suggesting that 53% of the genetic variance in height at these ages is shared 10 ., In contrast to this genetic consistency , the same study found that height measured during the pubertal growth spurt ( ages 11 to 17 ) to be most influenced by new genetic variation ., This differential effect of individual genetic variants on different stages of growth remains largely to be investigated , and the present study is a step in that direction ., To accomplish this , we evaluated the relative effect of the SNP variants identified by 7 as part of the GIANT consortium efforts on both prepubertal height and growth during puberty ., SNPs were genotyped on an Illumina 660quad array using DNA derived from whole blood for approximately 90% of the sample and from saliva samples for the remainder ., For quality control purposes , each 96-well plate included DNA from two members of a single CEPH family ( rotated across plates ) and one duplicate sample ., Markers were excluded if ( see ref 20 for additional details ) :, 1 ) they had been identified as a poorly genotyped marker by Illumina;, 2 ) had more than one mismatch in duplicated QC samples;, 3 ) had a call rate <99%;, 4 ) had a MAF <1%;, 5 ) had more than 2 Mendelian inconsistencies across families;, 6 ) significantly deviated from Hardy-Weinberg equilibrium at p<1e-7;, 7 ) was an autosomal marker but associated with sex at p<1e-7;, 8 ) had a significant batch effect at p<1e-7; or, 8 ) there were more than 2 heterozygous X chromosome calls for males or mitochondrial calls for anyone ., In total , 32 , 153 ( 5 . 7% ) of the 559 , 982 SNP markers were eliminated by these screens , with the majority ( 3 . 6% ) being eliminated because of low MAF ., Samples were eliminated if:, 1 ) they had >5000 no-calls;, 2 ) had a low GenCall score;, 3 ) had extreme heterozygosity or homozygosity; or, 4 ) represented a sample mix-up or we could not confirm known genetic relationships ., In total , 160 ( 2 . 2% ) of the total genotyped sample of 7438 failed one or more of these criteria , with the majority ( 1 . 7% ) failing because of low call rate ., Of the 180 SNPs described by the GIANT consortium as associated with height , 52 existed on the Illumina 660quad ., The remaining were imputed with best-guess genotypes using MaCH 21 , 22 and haplotypes from the 1000 Genomes 2010-06 reference dataset ., Of the 128 imputed SNPs , three had poor imputation quality ( rs17511102 , rs11144688 , rs473902; r2\u200a= . 08 , . 46 , and . 20 ) ., One SNP ( rs5017948 ) was not contained in the 1000 Genomes 2010-06 or 2010-08 reference datasets and so was discarded ., The average r2 of the remaining 124 imputed SNPs was . 96 ( SD\u200a= . 07 , range\u200a=\u200a . 55 , 1 . 0 ) ., In total , 176 of the 180 SNPs from the GIANT Consortium meta-analysis 7 were available in the current dataset ., A genetic score was created by summing these 176 SNPs , weighted by their individual meta-analytic regression coefficient reported in the GIANT Consortium report 7 ., All analyses accounted for the following covariates: sex , year of birth , cohort status ( younger versus older ) , and the 10 first principal components from Eigenstrat 23 based on a subsample of 10 , 000 SNPs from sample founders ( i . e . , unrelated subjects ) ., Growth models were also fit separately to the male and female subsamples ., After scaling male heights for each age of assessment to have the females mean and variance , the growth model variance component parameters were different between the sexes ( χ2\u200a=\u200a78 . 04 , df\u200a=\u200a9 , p\u200a=\u200a4e−13 ) ., While heritability of the intercept was similar ( . 84 for males versus . 82 for females ) the heritability of the slope was different ( . 93 versus . 64 , respectively ) ., Females had a larger shared environmental contribution to their slope variance ( . 01 for males and . 31 for females ) ., Males and females had similar phenotypic correlations between the intercept and slope ( − . 66 for males and − . 64 for females ) ., The genetic and environmental contributions to this correlation were different between the sexes ., The genetic correlation between intercept and slope was − . 60 for males and − . 47 for females ., The shared environment correlation was − . 04 for males and − . 14 for females ., The unshared environmental correlation was − . 02 in males and − . 03 in females ., The overall SNP association trends were similar in both sexes ( i . e . larger effects on the intercept and smaller effects on the slope ) ., All SNP and score statistics are included in Table S1 ., Notable differences included the following ., The effect of rs7759938 on pubertal growth is only significant for females ( see Table S1 ) ., This sex difference has been noted previously 26 ., Second , as can be seen in Figure S2 , the overall genetic and phenotypic variance accounted for in height by the score is larger for males than for females ., Growth model parameters did not change dramatically after correcting chronological ages at 11 and 14 for pubertal status ., The negative correlation between the intercept and slope was unchanged ( − . 62 ) with a larger genetic contribution ( − . 58 ) and smaller contributions by shared environment ( − . 02 ) and unshared environment ( − . 03 ) ., The intercept was 88% heritable with contributions of 7% and 5% from shared and unshared environment , respectively ., The slope was 84% heritable with contributions of 10% and 6% from shared and unshared environment , respectively ., SNP associations also remained largely unchanged after correcting for pubertal status ., Figure S3 gives association plots for the puberty-corrected associations ., The correlation between regression weights from corrected versus uncorrected models was very high , for associations with the intercept ( r\u200a= . 99 ) and the slope ( r\u200a= . 99 ) ., The mean regression weight onto the intercept in the uncorrected model was . 06 ( SD\u200a= . 43 ) versus . 05 ( SD\u200a= . 43 ) ., The mean weight onto the slope in the uncorrected model was − . 005 ( SD\u200a= . 06 ) versus − . 003 ( SD\u200a= . 06 ) ., Correlations between standard errors and p-values were equally similar between the corrected and uncorrected models ., The first wave of GWAS research has been successful in identifying numerous common variants associated with various adult disorders and traits 27 ., Yet virtually all disorders and traits are a consequence of a sequence of developmental processes , and we know very little about how these genetic variants play out across development ., Research on FTO , where the minor allele of rs9939609 is a well established risk-factor for adult obesity , illustrates the importance of a developmental perspective ., Specifically , the minor allele of rs9939609 is negatively associated with body mass index ( BMI ) until the age of 2 . 5 , but , because it is associated with an earlier onset of the adiposity rebound that occurs in childhood , positively associated with BMI after age 5 . 5 years 28 ., Research placing genetic association results in a developmental context will be necessary to understand how genetic variants contribute to a phenotype and , in the context of disease phenotypes and personalized medicine , to determine when and how intervention and/or prevention is possible ., The present study extended genetic analysis of developmental phenotypes by implementing a growth model to partition observed measures into two biologically meaningful constructs: pre-pubertal height and pubertal growth ., We focused here on an established literature of SNP effects on height ., This is necessary because individual genetic effects are too small to be detected at genome-wide levels by most individual studies , and combining longitudinal studies with commensurate phenotypes can be prohibitively difficult ( longitudinal data is expensive and rare , investigators gathering different data on individuals from different populations at different ages and developmental levels ) ., It may be that consortia of cross-sectional data will largely be necessary to discover replicable genetic variants while smaller , methodologically-unique individual studies will be left to understand those effects within a developmental context ., The vast majority of SNPs identified by Allen et al . 7 appear to be more strongly related to pre-pubertal height than to the pubertal growth spurt ., The sample size precludes definite conclusions without replication or meta-analysis , however ., In addition , age is only a fallible proxy for developmental stage or pubertal status ., While many boys are expected to be pre-pubertal at age 10 . 75 , this is less certain for females ., In the present study 15% of females had already experienced menarche by the time they were first assessed ., When we adjusted the ages for pubertal status , however , the results were highly similar to those using uncorrected ages ., Nonetheless , future work evaluating genetic effects on growth would clearly benefit from including younger ages of assessment and more frequent follow up ., While most SNPs were unrelated to pubertal growth , one was ., rs7759938 in LIN28B has previously been identified as relevant for adult height 7 and timing of pubertal onset 26 , 29 , 30 ., Transgenic mice in ortholog Lin28a were found to have accelerated growth during the first 60 weeks of life in addition to later onset of puberty 25 ., Our analysis also found accelerated growth related to the G allele of rs7759938 ., However , the effect was not significant for males and was confounded with pubertal onset for females , as about 15% of our 11-year-old females had already entered puberty by their age-11 assessment 31 ., The effect remained in females even after adjustment for pubertal status , suggesting the variant is associated with rate of growth during these ages ., The effect remained non-significant for males even when later growth periods were used as an attempt to better measure pubertal onset ( i . e . , investigating growth from age 14 to adulthood or age 17 to adulthood ) ., The lack of an effect for males appears to be a sex-moderated effect ( ref 26 , 30 also reported small effects for males ) ., We also evaluated the effect of a genetic score on zygosity-derived genetic variance , as opposed to phenotypic variance , using a sample of twins ., The score accounted for 14 . 3% of genetic variance in adult height , but only 9 . 2% of phenotypic variance , illustrating the possible advantages of using a twin sample ., The use of twins provides concrete advantages over analyses that estimate the fraction of heritable variance attributable to multiple loci indirectly either based on previously reported heritability estimates or genome-wide markers in unrelated individuals ., Admittedly , the advantage may not be extremely powerful in the present context , given heights high heritability , where the genetic variance is 80% or more of the total phenotypic variance ., However , for less-heritable phenotypes , or where heritability is less well known , the approach will provide improved information about the magnitude of a SNPs ( or genes , or pathways ) relationship to the phenotype ., A growth model is not necessary to evaluate genetic r2 , but so-called “genetically informative” samples such as twins or adoptive families are ., An array of statistical techniques have been developed for such samples 15 , and incorporating genetic variants like SNPs is always possible and in many cases straightforward ., In summary , genomic findings from consortia may be fruitfully characterized within a developmental framework ., Many analytic approaches exist , and the best may depend on the data structure at hand ., Genetically informative samples such as twins remain important and viable tools in investigating genomic variation , even as genotyping or sequencing becomes routine . | Introduction, Materials and Methods, Results, Discussion | In a sample of 3 , 187 twins and 3 , 294 of their parents , we sought to investigate association of both individual variants and a genotype-based height score involving 176 of the 180 common genetic variants with adult height identified recently by the GIANT consortium ., First , longitudinal observations on height spanning pre-adolescence through adulthood in the twin sample allowed us to investigate the separate effects of the previously identified SNPs on pre-pubertal height and pubertal growth spurt ., We show that the effect of SNPs identified by the GIANT consortium is primarily on prepubertal height ., Only one SNP , rs7759938 in LIN28B , approached a significant association with pubertal growth ., Second , we show how using the twin data to control statistically for environmental variance can provide insight into the ultimate magnitude of SNP effects and consequently the genetic architecture of a phenotype ., Specifically , we computed a genetic score by weighting SNPs according to their effects as assessed via meta-analysis ., This weighted score accounted for 9 . 2% of the phenotypic variance in height , but 14 . 3% of the corresponding genetic variance ., Longitudinal samples will be needed to understand the developmental context of common genetic variants identified through GWAS , while genetically informative designs will be helpful in accurately characterizing the extent to which these variants account for genetic , and not just phenotypic , variance . | We evaluated the developmental specificity of 176 SNPs known to affect adult height based on meta-analysis from the GIANT consortium ., First , longitudinal observations on height spanning pre-adolescence through adulthood in a twin sample allowed us to investigate the individual effects of the previously identified SNPs on both pre-pubertal height and pubertal growth spurt ., We show that the effect of the SNPs identified by the GIANT consortium is primarily on prepubertal height ., Only one SNP , rs7759938 in LIN28B , approached a significant association with pubertal growth ., Second , using standard twin heritability models , we investigated the extent to which the collective effect of these SNPs explained genetic variance in height—as opposed to phenotypic variance , as other studies have done ., We computed a genetic score by weighting SNPs according to their effects as assessed via meta-analysis ., We show that , while the score accounts for ∼9% of the phenotypic variance in height ( i . e . , the overall variance ) , it accounts for ∼14% of the corresponding genetic variance ., Longitudinal samples are necessary to understand the developmental context of common genetic variants identified through GWAS , while twin samples will be helpful in accurately characterizing the extent to which these variants account for genetic , and not just phenotypic , variance . | aging, genome-wide association studies, developmental biology, organism development, heredity, genetic association studies, genetics, biology, quantitative traits, genetics and genomics, complex traits, human genetics | null |
journal.ppat.1002133 | 2,011 | Cyclic di-GMP is Essential for the Survival of the Lyme Disease Spirochete in Ticks | Bis- ( 3′-5′ ) -cyclic dimeric guanosine monophosphate ( c-di-GMP ) , discovered by Benziman and colleagues in the mid-80s 1 , is now widely recognized as a ubiquitous second messenger that modulates many aspects of biological processes in bacteria ( for reviews , see 2 , 3 , 4 ) ., C-di-GMP is synthesized by diguanylate cyclases ( DGCs ) , a group of GGDEF domain-containing proteins , and is broken down by phosphodiesterases ( PDEs ) that contain a conserved EAL or HD-GYP domain 5 , 6 , 7 , 8 , 9 , 10 , 11 ., GGDEF , EAL and HD-GYP domains are among the most abundant domains encoded in bacterial genomes 5 , 12 ., Numerous studies on c-di-GMP signaling pathways in the Proteobacteria revealed that c-di-GMP controls the transition between planktonic and biofilm lifestyles by stimulating the biosynthesis of adhesins and exopolysaccharide matrix substances in biofilms while inhibiting various forms of motility 13 , 14 , 15 , 16 , 17 , 18 , 19 ., Several classes of c-di-GMP receptor/effector proteins have been identified 20 ., Despite tremendous progress , the role of c-di-GMP in bacterial pathogenesis and the mechanisms of action of c-di-GMP remain poorly understood 4 , 21 , 22 ., Further , very little is known about the function of c-di-GMP beyond Proteobacteria ., Borrelia burgdorferi is a spirochete that causes Lyme disease , the most prevalent vector-borne infection in the United States 23 ., As an obligate pathogen , B . burgdorferi has a reduced genome that contains a limited number of genes that are known to be involved in signal transduction and gene regulation 24 , 25 ., For instance , the genome only has two sets of two-component signal transduction systems: Hk1-Rrp1 ( BB0420-BB0419 ) and Hk2-Rrp2 ( BB0764-BB0763 ) , in addition to the chemotaxis CheA-CheY system ., On the other hand , the enzootic life cycle of B . burgdorferi is complex ., It involves two markedly different hosts , an arthropod vector and a small mammal ., This unique lifestyle requires B . burgdorferi to utilize its limited signaling capabilities for adapting to dramatic changes in host environments during its natural cycle ., In this regard , the Hk2-Rrp2 two-component signaling pathway has been shown to modulate differential expression of numerous surface lipoprotein genes and plays an essential role for spirochetal transmission and mammalian infection 26 , 27 , 28 , 29 , 30 ., Little is known about the function of the second two-component system present in B . burgdorferi , Hk1-Rrp1 ., The response regulator Rrp1 contains an N-terminal response regulator receiver domain and a C-terminal GGDEF domain 8 ., Ryjenkov et al . demonstrated that recombinant Rrp1 has DGC activity that strictly depends on the phosphorylation status of Rrp1 8 ., The complete enzootic cycle of B . burgdorferi and the pathogenesis of the disease can be largely reproduced in the laboratory 31 ., Rrp1 is the only GGDEF-domain protein in B . burgdorferi , making this organism attractive for uncovering the role of c-di-GMP-mediated signaling in bacterial pathogenesis 5 , 8 ., Two recent studies have shed light on the potential role that c-di-GMP plays in the life cycle of B . burgdorferi ., Rogers et al . showed that rrp1 is significantly upregulated upon tick feeding 32 ., They also generated an rrp1 mutant in the non-infectious clone B31 5A13 ., The mutant showed altered expression of more than 140 genes ( 8% of the genome ) whose functions covered almost all functional categories , including cell envelope biosynthesis , transport , metabolism , chemotaxis , and flagellar biosynthesis 32 ., The rrp1 mutant also showed reduced growth at room temperature and increased serum sensitivity 32 ., Another study focused on BB0363 , the only EAL-domain protein encoded in the B . burgdorferi genome 33 ., Recombinant BB0363 was shown to have c-di-GMP phosphodiesterase activity ., The bb0363 mutant , which likely has high intracellular levels of c-di-GMP , was found to be defective in motility in vitro 33 ., In vivo , the bb0363 mutant was able to survive in ticks but failed to establish infection in mice , suggesting that high levels of c-di-GMP are detrimental for spirochetes to replicate in a mammalian host ., However , whether c-di-GMP is required for any stage of the infectious cycle of B . burgdorferi remains undetermined ., In this study , we generated an rrp1 mutant in the infectious clone of B . burgdorferi , B31 5A4NP1 ., We show that Rrp1 is dispensable for mammalian infection but is essential for spirochetal survival in the tick vector ., We further show that the Rrp1 requirement is , in part , due to its control over the expression of glycerol transport and metabolism in B . burgdorferi ., To determine the role of c-di-GMP in B . burgdorferi pathogenesis , we constructed an rrp1 mutant in the infectious B . burgdorferi strain 5A4NP1 ( See Table 1 for a list of strains used in this study ) ., This was accomplished by replacing the wild-type chromosomal rrp1 copy with a disrupted gene via homologous recombination ( Fig . 1A ) ., A similar approach was used to repair the wild-type rrp1 gene by replacing the mutated copy with a wild-type rrp1 ( Fig . 1A ) ., The genotypes of the rrp1 mutant and the repaired strain ( rrp1com ) were confirmed by PCR ( Fig . 1B ) and by immunoblot analyses ( Fig 1C ) ., To determine the role of c-di-GMP in mammalian infection , we needle-inoculated groups of mice with various B . burgdorferi strains ( 105 spirochetes/mouse ) ., Two-weeks post inoculation , ear punch biopsies were cultured in BSKII medium for the presence of spirochetes ., Similar to wild-type spirochetes , the rrp1 mutant was readily detected in either immunocompetent ( C3H/HeN ) or immunocompromised ( C3H-SCID ) mouse strains ( Table 2 ) ., No major difference in ID50 values between wild-type and the rrp1 mutant ( Table 3 ) ., Further analysis of histopathology revealed that the rrp1 mutant elicited Lyme arthritis similar to that induced by wild-type B . burgdorferi ., ( Supplemental Fig . S1 ) ., This result indicates that abrogation of c-di-GMP synthesis does not affect the ability of B . burgdorferi to infect mice ., We conclude that c-di-GMP is dispensable for mammalian infection ., This is in contrast with an avirulent phenotype observed in B . burgdorferi lacking the c-di-GMP phosphodiesterase BB0363 33 ., To examine the rrp1 mutants phenotype in the tick cycle , groups of pathogen-free Ixodes scapularis larvae were fed on C3H/SCID mice that were needle-infected with the wild-type , rrp1mut or rrp1com strains two weeks after infection ., Engorged larvae were collected after repletion and tick contents were subjected to immunofluorescence assay ( IFA ) ., In contrast to the wild-type and rrp1com strains that were readily detectable in fed larvae , virtually no rrp1 mutant spirochetes were observed ( Fig . 2A ) ., Further quantitative PCR analysis revealed that there were significantly lower numbers of the rrp1 mutant than that of wild-type or rrp1com strains in ticks ( Fig . 2B ) ., The inability to detect the rrp1 mutant in tick midguts after feeding could be due either to a defect in tick midgut survival or a defect in migration from the mouse to the tick ., To test these two possibilities , we used microinjection to directly place spirochetes into midguts of nymphal ticks 34 , 35 ., These artificially infected ticks then fed on naïve mice ., Detached ticks were collected and subjected to IFA analysis ., As shown in Fig . 3 , the wild-type and rrp1com strains were readily detectable in ticks , whereas the rrp1 mutant remained undetected ., To confirm that the rrp1 mutant is defective in the ability to survive in ticks , engorged larvae that were fed on infected mice from the experiments described above were allowed to molt to nymphs in an environmental chamber ., Unfed nymphs were then fed on naïve mice ., Ticks that were infected with either the wild-type or rrp1com strains could readily infect naïve mice , whereas ticks infected with the rrp1 mutant could not ( Table 2 ) ., Similarly , ticks that were artificially infected with the rrp1 mutant were also unable to infect C3H/SCID mice ( Table 2 ) ., These results support the notion that the rrp1 mutant is unable to survive in the tick vector ., To investigate the molecular mechanisms underlying the requirement of c-di-GMP for spirochete survival in ticks , we sought to identify genes whose expression was affected by the deletion of rrp1 ., To do so , we performed two independent microarray analyses: one comparing transcriptional profiles of the wild-type and rrp1 mutant and the other comparing transcriptional profiles of the rrp1 mutant and the rrp1com strain ., The comparison of the transcriptomes of the wild-type and rrp1 mutant revealed 120 genes whose expressions were up- or down-regulated by Rrp1 ( cut-off >3-fold ) ( Text S1 ) ., Among these , 39 genes whose dependence on Rrp1 could be confirmed by the comparison of the transcriptomes of the rrp1 and rrp1com strains ( cut-off >3-fold ) ( Table 4 ) ., We considered these genes to be the most reliable candidates for Rrp1-dependent regulation ., Genes regulated by Rrp1 are distributed throughout the genome and extra-chromosomal segments of B . burgdorferi ( Table 4 , Locus numbers start with BB and a letter are extra-chromosomal genes 24 ) ., Among these genes , an intriguing target of Rrp1 regulation was an apparent glp operon encoding glycerol transport/metabolism genes , bb0240-bb0243 24 , 25 , 36 , 37 ., The first gene of the operon , bb0240 , encodes a putative glycerol uptake facilitator ( GlpF ) , followed by a putative glycerol kinase gene ( bb0241 , glpK ) , a small putative hypothetical gene ( bb0242 ) , and a putative glycerol-3-phosphate dehydrogenase gene ( bb0243 , glpA/glpD ) ., Glycerol can be utilized in energy production as a biosynthetic precursor to membrane lipids or lipoproteins 24 , 25 , 36 , 37 ., qRT-PCR analysis confirmed that induction of bb0240-bb0243 was indeed under the control of Rrp1 ( Fig . 4A ) ., We hypothesized that if bb0240-bb0243 is involved in glycerol transport and metabolism , the rrp1 mutant may have a defect in the utilization of glycerol as a carbon source ., To test this hypothesis , the wild-type , rrp1 and rrp1com strains were cultivated in either standard BSKII medium or in a modified BSKII medium where glucose was replaced with glycerol ( BSK-glycerol , which was prepared from glucose free CMRL ) 37 ., The rrp1 mutant was not impaired in growth in the standard BSKII medium at either 35°C ( Fig . 4B ) or 23°C ( Fig . 4D ) ., However , when grown in the BSK-glycerol medium , the rrp1 mutant failed to reach the cell density of the wild-type or rrp1com ( Fig . 4C and 4E ) ., Thus , glycerol transport and metabolism appeared to be particularly important at later time points in the growth curve ., BSK medium is a complex medium containing many undefined components including rabbit serum and BSA as well as other potential carbon source such as pyruvate ., Presence or absence of pyruvate did not significantly affect the growth of either wild-type or the rrp1 mutant in BSK-II or BSK-glycerol medium ( data not shown ) ., BSK-glycerol medium also contains 0 . 1 g/L of glucose , determined by D-Glucose Kit ( Roche Applied Science , Indianapolis , IN ) , which may contribute to the initial growth of the rrp1 mutant in BSK-glycerol medium ( the standard BSK-II medium contains 6 g/L of glucose ) ., Nevertheless , these experiments verified the involvement of Rrp1 in glycerol transport/metabolism ., We further tested the possibility that expression of rrp1 is also influenced by glycerol ., RNA was extracted from wild-type B . burgdorferi grown in either standard BSKII or BSKII-glycerol medium ., The extracted RNAs were subjected to qRT-PCR analysis ., Growth in the BSKII-glycerol medium did not significantly alter expression of Rrp2-dependent genes such as rpoS and ospC ., However , the transcript level of rrp1 as well as the glycerol metabolic genes bb0240-bb0243 were dramatically upregulated when grown in BSKII-glycerol medium ( Fig . 5A ) ., However , Rrp1 protein level is much less influenced by this growth condition ( 1 . 7 fold ) ( Fig . 5B ) ., Nevertheless , this observation suggests that glycerol may potentially enhance rrp1 expression ., Because Rrp1 was required for full induction of the glycerol operon and for maximal growth in the BSKII-glycerol medium , we hypothesized that defective glycerol metabolism by the rrp1 mutant could contribute to the mutants inability to survive in ticks ., If so , a mutant defective in glycerol metabolism would be expected to have a phenotype similar to that of the rrp1 mutant ., To test this hypothesis , we constructed a glp mutant by deleting a portion of the first gene bb0240 and its upstream promoter of the bb0240-bb0243 operon ( Fig . 6A ) ., qRT-PCR analysis confirmed that the glp mutant lacks bb0240 bb0241 , bb0242 , and bb0243 mRNA ( Fig . 6B ) ., Expression of bb0240-bb0243 was restored when the mutated bb0240 gene and the promoter region was replaced by the wild-type copy of bb0240 at the native location ( designated as glpcom . Fig . 6A and 6B ) ., We first examined the growth phenotype of the glp mutant in vitro ., The mutant had no detectable growth defect when grown in standard BSKII medium ( Fig . 6C ) ., However , similar to the rrp1 mutant , the glp mutant could not reach the same cell density as the parent wild-type strain when grown in the BSK-glycerol medium ( Fig . 6D ) ., This defect resulted from abrogation of bb0240-0243 expression , as the growth defect was readily restored upon restoration of bb0240-bb0243 expression in glpcom ( Fig . 6A & 6D ) ., This result is consistent with the prediction that the growth defect of the rrp1 mutant in the BSK-glycerol medium is due to the loss of expression of bb0240-bb0243 ., We then examined the phenotype of the glp mutant in vivo ., The wild-type , glp mutant or glpcom spirochetes ( 105 spirochetes/mouse ) , were intradermally inoculated into C3H/HeN mice ., Two weeks after inoculation , ear punch biopsies from all mice were culture-positive for spirochetes , suggesting that BB0240-BB0243 are not required for mammalian infection ( Table 5 ) ., Further determination of the ID50 values showed that the glp mutant has a slight infectivity deficit relative to wild-type B . burgdorferi , with 1-log-unit increase in the ID50 ( Table 3 ) ., To examine the role of bb0240-0243 in the tick-mouse cycle , pathogen-free unfed larvae were placed on infected mice ., Fed larvae were collected and allowed to molt to nymphs ., Unfed nymphs then fed on groups of naïve C3H/HeN mice ., Ticks at various stages were collected for IFA and/or qRT-PCR analyses ., We observed that although detectable in ticks , the glp mutant had reduced spirochetal loads compared to the wild-type or glpcom strains ( Fig . 7A & 7B , only results from nymphs were shown ) ., These data suggest that , similar to Rrp1 , the glycerol transport/metabolic pathway is required for the optimal colonization of B . burgdorferi in ticks and that the loss of bb0240-bb0243 expression in the rrp1 mutant contributes to its poor survival in ticks ., Mice two weeks post tick feeding were also examined for the presence of spirochetes in various tissue samples ( skin , heart , and joint ) ., Unlike the rrp1 mutant that failed to infect mice via tick bites , the glp mutant was capable of completing the tick-mouse cycle and subsequently infecting naïve mice upon tick feeding ( Table 5 ) , despite its reduced survival in ticks ., Note that both the glp mutant and glpcom strains showed partially reduced infectivity via tick bites , indicating that this reduction of infectivity is not due to the loss of bb0240-bb0243 ( Table 5 ) ., These results indicate that loss of bb0240-bb0243 expression of the rrp1 mutant could not fully account for the inability of the rrp1 mutant to complete its enzootic cycle and that Rrp1 controls additional factor ( s ) involved in the spirochetal life cycle in ticks ., To further investigate the role of glycerol transport and metabolism during tick infection , we constitutively expressed the bb0240-bb0243 operon in the rrp1 mutant using an independent flaB promoter ( Fig . 8A ) ., The resulting strain , designated as rrp1mut/flaBp-glp , expressed bb0240-bb0243 in an Rrp1-independent fashion ( Fig . 8B ) and fully rescued the growth defect of the rrp1 mutant in the BSK-glycerol medium ( Fig . 4B & 4C ) ., This observation provides additional genetic evidence that the growth defect of the rrp1 mutant is due to impaired glycerol transport/metabolism ., To compare the phenotypes in ticks , the wild-type , rrp1 mutant , or rrp1/flaBp-glp strains were needle-infected into naïve mice ., Unfed larvae were allowed to feed on these infected mice ., qPCR analyses on fed larvae showed that the rrp1mut/flaBp-glp spirochetes had a 4- to 5-fold increase in spirochetal load compared to the load of the rrp1 mutant ( Fig . 8C & 8D ) ., This increase suggests that restoration of expression of glycerol transport/metabolism can improve survival of the rrp1 mutant in ticks ., However , the spirochete load of rrp1/flaBp-glp was still drastically lower than that of wild-type spirochetes ( Fig . 8C & 8D ) ., To determine if the rrp1/flaBp-glp spirochetes are able to migrate to mice , fed larvae were collected and allowed to molt to nymphs ., Infected nymphs were then used to infect naïve C3H/SCID mice ., The result showed that , similar to the rrp1 mutant , the rrp1mut/flaBp-glp strain was incapable of completing the tick-mouse cycle to infect naïve mice ( Table 6 ) ., These data further support the conclusions that while glycerol transport/metabolism is important during tick residence , additional Rrp1-dependent factor ( s ) are involved in the tick-mouse cycle of B . burgdorferi ., During the transmission process between mammals and ticks , B . burgdorferi dramatically alters the expression of many genes that are essential for spirochete survival in either host ( for reviews , see 31 , 38 ) ., In the past few years , we and others have shown that one of the B . burgdorferi two-component signaling systems , Hk2-Rrp2 , functions as a key signaling pathway that governs expression of genes necessary for mammalian host infection 26 , 27 , 29 , 30 , 39 ., In this study , we provide genetic evidence that the other two-component system , Hk1-Rrp1 , is dispensable for mammalian infection , yet plays a vital role in the tick , in part , by controlling expression of the glycerol transport/metabolic pathway of B . burgdorferi ., Rrp1 is a diguanylate cyclase responsible for synthesis of the second messenger c-di-GMP 8 , 32 ., The importance of c-di-GMP to bacterial pathogenesis has been well documented 4 , 21 , 22 ., In many cases , the impact of c-di-GMP on pathogenesis is due to its effect on biofilm formation or motility 40 , 41 , 42 , 43 ., An interesting example that is related to this study involves another vector-borne pathogen , Yersinia pestis ., Similar to the phenotype of the rrp1 mutant in ticks that we have described herein , disruption of hmsT , a gene encoding diguanylate cyclase in Y . pestis , reduces the transmission of plague bacteria from fleas to mammals 44 , 45 , 46 , 47 ., However , the mechanisms of influencing transmission by c-di-GMP in these two pathogens seem to be different ., Inactivation of hmsT results in a defect in biofilm formation but not in replication of Y . pestis in fleas , which is important for the spread of Y . pestis from fleas to mammals ., Currently there is no evidence that B . burgdorferi forms biofilms ., The B . burgdorferi genome encodes a luxS gene responsible for the autoinducer AI2 synthesis , which is necessary for biofilm formation in some bacteria 48 , 49 , 50 ., However , inactivation of luxS does not affect the life cycle of B . burgdorferi in either ticks or mice 51 , 52 ., Therefore , the mechanism of action of c-di-GMP in the enzootic cycle of B . burgdorferi is different from that of Y . pestis ., In addition to affecting biofilm formation and motility , c-di-GMP modulates many other activities that may not be related to multicellular behavior such as cell division , phage resistance , heavy metal resistance , etc ., 2 , 3 , 4 ., With regards to bacterial pathogenesis , c-di-GMP has been reported to affect the processes of adhesion , invasion , and toxin production by modulating the production or activities of virulence factors 21 , 22 , 53 , 54 , 55 ., However , modulation of bacterial infection by the control of glycerol metabolism is observed here for the first time ., In this study , we provide the following lines of evidence supporting the notion that c-di-GMP controls glycerol transport and metabolism in B . burgdorferi , which in turn is important for its survival in ticks ., 1 ) Expression of bb0240-bb0243 is significantly downregulated by abrogation of Rrp1 ( Table 4 & Fig . 4A ) ., 2 ) Both the rrp1 mutant and the glp mutant show growth defects in BSK-glycerol medium ( Fig . 4C & Fig . 6D ) ., 3 ) The glp mutant has reduced survival in ticks ( Fig . 7 ) ., 4 ) Restoration of bb0240-bb0243 expression in the rrp1 mutant rescues the growth defect in vitro and enhances the survival of the rrp1 mutant in ticks ., What roles does glycerol transport/metabolism play in B . burgdorferi physiology ?, As an obligate pathogen , B . burgdorferi has a reduced genome and lacks many metabolic pathways such as the TCA cycle and those for synthesis of amino acids , nucleotides , and fatty acids 24 , 25 , 36 ., B . burgdorferi does encode proteins for the utilization of several sugars in addition to glucose 24 , 25 , 36 , 37 ., Notably , a complete pathway for transport and utilization of glycerol ( BB0240-BB0243 ) is preserved ., Bioinformatics analysis suggests that upon uptake of glycerol ( by glycerol uptake facilitator BB0240 , GlpF ) , glycerol is converted to glycerol-3-P by glycerol kinase ( BB0241 , GlpK ) 36 , 37 ., Glycerol-3-P can either feed into lipid/lipoprotein biosynthesis or enter the ATP-generating stage of glycolysis via conversion to glyceraldehyde 3-phosphate by glycerol-3-P dehydrogenase ( BB0243 , GlpA/GlpD ) and triosephosphate isomerase ( BB0561 ) 36 , 37 ., In other words , the glycerol and glucose pathways interconnect , and glycerol can be an important carbon and energy source at times when glucose becomes limited ., This notion is supported by a previous study 37 as well as the in vitro growth data from this study ( Fig . 4B & 4C ) ., Based on the observation that the glycerol pathway-defective glp mutant replicates normally in mice but has reduced growth in ticks , we postulate that B . burgdorferi utilizes different carbon/energy sources within each host environment ., During mammalian infection when glucose is readily available ( 0 . 1–0 . 2% in mouse blood ) 56 , B . burgdorferi utilizes glucose as the main carbon and energy source ., Thus , inactivation of the glp operon does not dramatically affect spirochete replication in mammals ., When spirochetes enter the tick vector , initially the glp mutant may be able to replicate with the presence of glucose from blood ., Then , glucose may become limiting , while glycerol , on the other hand , may be available in ticks ., This notion is consistent with the fact that the glp mutant remains capable of surviving in ticks but with reduced spirochetal numbers ( Fig . 7 ) ., It is noteworthy that many insects including ticks produce glycerol as an anti-freezing molecule 57 ., Therefore , activation of the glycerol transport and metabolism via Rrp1 could ensure optimal growth of B . burgdorferi in the tick vector ., Further , growth on glycerol appears to provide a positive feedback on rrp1 gene expression ( Fig . 5 ) ., How does c-di-GMP control the expression of bb0240-bb0243 ?, One of the characterized mechanisms employed by c-di-GMP to influence gene regulation is through a unique riboswitch RNA structure ., It was shown that c-di-GMP can directly bind to a riboswitch located in the 5′ UTR region of target genes and can influence gene transcription and/or translation 58 ., We did not find c-di-GMP-specific riboswitches upstream of bb0240 ., C-di-GMP can also modulate gene expression by affecting expression or activity of transcription factors 59 , 60 , 61 , 62 , 63 ., Some of these transcription factors bind c-di-GMP directly 64 , 65 ., Interestingly , c-di-GMP controls DNA binding of a subgroup of CRP ( cAMP receptor protein ) transcription factors that activate genes involved in utilization of alternative carbon and energy sources ( other than glucose ) ., For example , Clp , a CRP homolog from Xanthomonas campestris binds c-di-GMP and regulates virulence gene expression 65 , 66 , 67 ., In Vibrio cholerae , it was shown that cAMP-CRP controls expression of a DGC that , in turn , governs the production of c-di-GMP and biofilm formation 68 ., Bioinformatic analysis did not identify any CRP homologue encoded in the B . burgdorferi genome ., Recently , it was reported that another transcriptional regulator in B . burgdorferi , BosR , also affects glp expression 69 , 70 ., Thus , it is possible that c-di-GMP may influence glp via BosR ., Nevertheless , elucidating the mechanism of how Rrp1 controls expression of the glycerol pathway in B . burgdorferi will shed light on the interplay between c-di-GMP and carbon utilization networks ., Work on Rrp1 from this study and previous studies 8 , 32 strongly supports the notion that c-di-GMP is essential for spirochetal adaptation in the tick vector but is not required for mammalian infection ., In fact , c-di-GMP is not only dispensable , shutting down the synthesis of c-di-GMP is necessary for B . burgdorferi to successfully establish infection in the mammalian host ., This was recently demonstrated by Sultan et al . , when they showed that the B . burgdorferi mutant missing c-di-GMP phosphodiesterase ( BB0363 ) failed to infect mice 33 ., This is consistent with an emerging theme that uncontrolled production of c-di-GMP is detrimental to the acute phase of bacterial infection 3 , 21 , 22 ., Thus , a tight regulation of the synthesis of c-di-GMP is important for Borrelia adaptation in both the tick vector and the mammalian host ., What are the downstream effectors of c-di-GMP in B . burgdorferi ?, The bb0363 mutant showed a defect in motility , suggesting that flagellar proteins or gene transcription of B . burgdorferi may be direct targets of c-di-GMP , as shown in other bacteria 15 , 17 , 68 , 71 ., The rrp1 mutant did not have an apparent defect in motility , suggesting that c-di-GMP controls other bacterial factor ( s ) that are important to spirochetal survival in ticks ., Note that although c-di-GMP may regulate transcription of flagellar genes 71 , our microarray analysis indicates that flaB expression is not affected by rrp1 deletion and thus using the flaB as the reference gene in this study remains valid ., In addition , expression of previously identified genes important for spirochetal survival in ticks , including ospA/B , bptA , dps , bb0365 and lp6 . 6 34 , 72 , 73 , 74 , 75 , 76 , were not affected by Rrp1 ( Table 4 ) ., Although glycerol transport/metabolism is important to the optimal growth of B . burgdorferi in ticks , independent expression of the glycerol transport/metabolism genes in the rrp1 mutant does not fully rescue spirochete survival in ticks , and the rrp1/flaB-glp spirochetes remain incapable of completing its entire enzootic cycle ( Table 6 ) ., Thus , c-di-GMP likely controls yet-to-be-identified factor ( s ) that contribute to B . burgdorferi proliferation in ticks ., In this regard , relatively few c-di-GMP targets have been identified in other bacteria to date ., The best characterized c-di-GMP targets are PilZ domain-containing proteins , such as cellulose synthase subunit BcsA in Gluconacetobacter xylinus and motility regulatory protein YcgR in Escherichia coli 77 , 78 ., The B . burgdorferi genome encodes one PliZ protein , PlzA ( BB0733 ) 79 ., Interestingly , Freedman et al . showed that plzA expression is upregulated during tick feeding , suggesting a potential role of PlzA in the tick vector 79 ., Whether PlzA plays a role in the enzootic cycle of B . burgdorferi remains to be determined ., Microarray analyses from this study and previous studies by Roger et al 32 suggest that expressions of several membrane-associated proteins including Rev , Mlps , and Erps are influenced by Rrp1 ., Whether these proteins/lipoproteins contribute to B . burgdorferi survival in ticks needs to be further determined ., In addition , there are some significant differences between these two microarray results ., Roger et al . showed that Rrp1 influences expression of more than 140 genes , most of which are chromosome-encoded core genes 32 ., Our study reveals only few chromosome-encoded genes whose expression was affected by rrp1 deletion and such effect could be further restored in rrp1com ., One difference between the two studies is the strain used ., In this study , an infectious strain B31 5A4NP1 that contains all endogenous plasmids was used , whereas Rogers et al . , a non-infectious strain B31 5A13 that lost lp25 was used 32 ., In addition , differences in media used for cultivation of B . burgdorferi might also contribute to differences of the results ( we used BSK-II whereas Roger et al . used commercially purchased BSK-H complete medium 32 ) ., Another factor that may contribute to this discrepancy is that many genes revealed by WT/rrp1 microarray analysis could not be confirmed by rrp1com/rrp1 analysis ., In fact , there are only 39 genes whose dependence on Rrp1 could be confirmed by rrp1com/rrp1 microarray analysis ., We do not fully understand what might contribute to this phenomenon , but it may reflect the complexity of B . burgdorferi plasmid contents and gene regulation ., Nevertheless , since the expression of rrp1 as well as the in vitro growth defect and the tick survival defect of the rrp1 mutant were fully restored in rrp1com , the difference between the microarray results of WT/rrp1 and rrp1com/rrp1 is not due to Rrp1 and does not affect the overall conclusion of the work presented in the manuscript ., The difference of microarray results observed herein also raises caution on microarray analysis of B . burgdorferi gene expression and reinforces the importance of performing complementation experiments for identification of genes that are truly affected by inactivation of the target gene ., What signal activates Rrp1 during tick feeding ?, As a two-component response regulator , the diguanylate cyclase activity of Rrp1 is dependent on phosphorylation 8 ., The predicted cognate histidine kinase for Rrp1 is Hk1 ., Bioinformatics analysis suggests that Hk1 contains a periplasm-located sensor domain homologous to the family 3 periplasmic substrate-binding proteins ( SBP_3 ) 80 ., Proteins in this family often bind to amino acids or opine molecules 80 , suggesting that B . burgdorferi may sense such a molecule and activates the c-di-GMP signaling pathway to achieve successful adaptation of the harsh environments of feeding ticks ., In summary , the findings on Hk1-Rrp1 and Hk2-Rrp2 two-component systems suggest a seemingly simple signal transduction model in B . burgdorferi ., Through evolution , B . burgdorferi reduced its genome and only kept these two sets of two-component systems for the adaptation to each of the two hosts encountered in its entire enzootic life cycle ., When spirochetes migrate from ticks to the mammalian host , the Hk2-Rrp2 pathway is activated during tick feeding , leading to the production of OspC , DbpA/B , BBK32 BBA64 and many factors that are important for B . burgdorferi to establish infection in the mammalian host 81 , 82 , 83 , 84 , 85 , 86 ., Prior and | Introduction, Results, Discussion, Materials and Methods | Cyclic dimeric GMP ( c-di-GMP ) is a bacterial second messenger that modulates many biological processes ., Although its role in bacterial pathogenesis during mammalian infection has been documented , the role of c-di-GMP in a pathogens life cycle within a vector host is less understood ., The enzootic cycle of the Lyme disease pathogen Borrelia burgdorferi involves both a mammalian host and an Ixodes tick vector ., The B . burgdorferi genome encodes a single copy of the diguanylate cyclase gene ( rrp1 ) , which is responsible for c-di-GMP synthesis ., To determine the role of c-di-GMP in the life cycle of B . burgdorferi , an Rrp1-deficient B . burgdorferi strain was generated ., The rrp1 mutant remains infectious in the mammalian host but cannot survive in the tick vector ., Microarray analyses revealed that expression of a four-gene operon involved in glycerol transport and metabolism , bb0240-bb0243 , was significantly downregulated by abrogation of Rrp1 ., In vitro , the rrp1 mutant is impaired in growth in the media containing glycerol as the carbon source ( BSK-glycerol ) ., To determine the contribution of the glycerol metabolic pathway to the rrp1 mutant phenotype , a glp mutant , in which the entire bb0240-bb0243 operon is not expressed , was generated ., Similar to the rrp1 mutant , the glp mutant has a growth defect in BSK-glycerol medium ., In vivo , the glp mutant is also infectious in mice but has reduced survival in ticks ., Constitutive expression of the bb0240-bb0243 operon in the rrp1 mutant fully rescues the growth defect in BSK-glycerol medium and partially restores survival of the rrp1 mutant in ticks ., Thus , c-di-GMP appears to govern a catabolic switch in B . burgdorferi and plays a vital role in the tick part of the spirochetal enzootic cycle ., This work provides the first evidence that c-di-GMP is essential for a pathogens survival in its vector host . | The Lyme disease pathogen Borrelia burgdorferi has two sets of two-component systems , Hk1-Rrp1 and Hk2-Rrp2 ., The Hk2-Rrp2 signaling system has been shown to modulate differential expression of numerous surface lipoprotein genes and to play an essential role in spirochete transformation from a tick colonizer to a mammalian host-adapted state ., In this study , we show that Rrp1 , the only diguanylate cyclase in B . burgdorferi , is not required for mammalian infection but is essential for spirochete survival in the tick vector ., We identify over 39 genes whose expression is influenced by this c-di-GMP signaling system ., We further demonstrate that one set of the Rrp1-dependent genes , the glp operon for glycerol transport and metabolism , plays an important role in the spirochete adaptation to tick environment and partially accounts for the essentiality of c-di-GMP for B . burgdorferi survival in ticks . | medicine, infectious diseases, biology, microbiology | null |
journal.pcbi.1002438 | 2,012 | Dynamic Effective Connectivity of Inter-Areal Brain Circuits | In Arcimboldos ( 1527–1593 ) paintings , whimsical portraits emerge out of arrangements of flowers and vegetables ., Only directing attention to details , the illusion of seeing a face is suppressed ( Figure 1A–B ) ., Our brain is indeed hardwired to detect facial features and a complex network of brain areas is devoted to face perception 1 ., The capacity to detect faces in an Arcimboldo canvas may be lost when lesions impair the connectivity between these areas 2 ., It is not conceivable , however , that , in a healthy subject , shifts between alternate perceptions are obtained by actual “plugging and unplugging” of synapses , as in a manual telephone switchboard ., Brain functions –from vision 3 or motor preparation 4 up to memory 5 , attention 6–8 or awareness 9– as well as their complex coordination 10 require the control of inter-areal interactions on time-scales faster than synaptic changes 11 , 12 ., In particular , strength and direction of causal influences between areas , described by the so-called effective connectivity 13–15 , must be reconfigurable even when the underlying structural ( i . e . anatomic ) connectivity is fixed ., The ability to quickly reshape effective connectivity –interpreted , in the context of the present study , as “causal connectivity” 16 or “directed functional connectivity” ( see Discussion ) – is a chief requirement for performance in a changing environment ., Yet it is an open problem to understand which circuit mechanisms allow for achieving this ability ., How can manifold effective connectivities –corresponding to different patterns of inter-areal interactions , or brain states 17– result from a fixed structural connectivity ?, And how can effective connectivity be controlled without resorting to structural plasticity , leading to a flexible “on demand” selection of function ?, Several experimental and theoretical studies have suggested that multi-stability of neural circuits might underlie the switching between different perceptions or behaviors 18–22 ., In this view , transitions between many possible attractors of the neural dynamics would occur under the combined influence of structured “brain noise” 23 and of the bias exerted by sensory or cognitive driving 24–26 ., Recent reports have more specifically highlighted how dynamic multi-stability can give rise to transitions between different oscillatory states of brain dynamics 27 , 28 ., This is particularly relevant in this context , because long-range oscillatory coherence 12 , 29 –in particular in the gamma band of frequency ( 30–100 Hz ) 29–32– is believed to play a central role in inter-areal communication ., Ongoing local oscillatory activity modulates rhythmically neuronal excitability 33 ., As a consequence , according to the influential communication-through-coherence hypothesis 31 , neuronal groups oscillating in a suitable phase coherence relation –such to align their respective “communication windows”– are likely to interact more efficiently than neuronal groups which are not synchronized ., However , despite accumulating experimental evidence of communication-through-coherence mechanisms 34–38 and of their involvement in selective attention and top-down modulation 30 , 39 , 40 , a complete understanding of how inter-areal phase coherence can be flexibly regulated at the circuit level is still missing ., In this study we go beyond earlier contributions , by showing that the self-organization properties of interacting brain rhythms lead spontaneously to the emergence of mechanisms for the robust and reliable control of inter-areal phase-relations and information routing ., Through large-scale simulations of networks of spiking neurons and rigorous analysis of mean-field rate models , we model the oscillatory dynamics of generic brain circuits involving a small number of interacting areas ( structural connectivity motifs at the mesoscopic scale ) ., Following 41 , we extract then the effective connectivity associated to this simulated neural activity ., In the framework of this study , we use a data driven rather than a model driven approach to effective connectivity 16 ( see also Discussion section ) , and we quantify causal influences in an operational sense , based on a statistical analysis of multivariate time-series of synthetic “LFP” signals ., Our causality measure of choice is Transfer Entropy ( TE ) 42 , 43 ., TE is based on information theory 44 ( and therefore more general than causality measures based on regression 45 , 46 ) , is “model-agnostic” and in principle capable of capturing arbitrary linear and nonlinear inter-areal interactions ., Through our analyses , we first confirm the intuition that “causality follows dynamics” ., Indeed we show that our causal analysis based on TE is able to capture the complex multi-stable dynamics of the simulated neural activity ., As a result , different effective connectivity motifs stem out of different dynamical states of the underlying structural connectivity motif ( more specifically , different phase-locking patterns of coherent gamma oscillations ) ., Transitions between these effective connectivity motifs correspond to switchings between alternative dynamic attractors ., We show then that transitions can be reliably induced through brief transient perturbations properly timed with respect to the ongoing rhythms , due to the non-linear phase-response properties 47 of oscillating neuronal populations ., Based on dynamics , this neurally-plausible mechanism for brain-state switching is metabolically more efficient than coordinated plastic changes of a large number of synapses , and is faster than neuromodulation 48 ., Finally , we find that “information follows causality” ( and , thus , again , dynamics ) ., As a matter of fact , effective connectivity is measured in terms of time-series of “LFP-like” signals reflecting collective activity of population of neurons , while the information encoded in neuronal representations is carried by spiking activity ., Therefore an effective connectivity analysis –even when based on TE– does not provide an actual description of information transmission in the sense of neural information processing and complementary analyses are required to investigate this aspect ., Based on a general information theoretical perspective , which does not require specifying details of the used encoding 44 , we consider information encoded in spiking patterns 49–53 , rather than in modulations of the population firing rate ., As a matter of fact , the spiking of individual neurons can be very irregular even when the collective rate oscillations are regular 54–57 ., Therefore , even local rhythms in which the firing rate is modulated in a very stereotyped way , might correspond to irregular ( highly entropic ) sequences of codewords encoding information in a digital-like fashion ( e . g . by the firing –“1”– or missed firing –“0”– of specific spikes at a given cycle 58 ) ., In such a framework , oscillations would not directly represent information , but would rather act as a carrier of “data-packets” associated to spike patterns of synchronously active cell assemblies ., By quantifying through a Mutual Information ( MI ) analysis the maximum amount of information encoded potentially in the spiking activity of a local area and by evaluating how much of this information is actually transferred to distant interconnected areas , we demonstrate that different effective connectivity configurations correspond to different modalities of information routing ., Therefore , the pathways along which information propagates can be reconfigured within the time of a few reference oscillation cycles , by switching to a different effective connectivity motif ., Our results provide thus novel theoretical support to the hypothesis that dynamic effective connectivity stems from the self-organization of brain rhythmic activity ., Going beyond previous proposals , which stressed the importance of oscillations for feature binding 59 or for efficient inter-areal “communication-through-coherence” , we advance that the complex dynamics of interacting brain rhythms allow to implement reconfigurable routing of information in a self-organized manner and in a way reminiscent of a clocked device ( in which digital-like spike pattern codewords are exchanged at each cycle of an analog rate oscillation ) ., In order to model the neuronal activity of interacting areas , we use two different approaches , previously introduced in 60 ., First , each area is modeled as a large network of thousands of excitatory and inhibitory spiking neurons , driven by uncorrelated noise representing background cortical input ( network model ) ., Recurrent synaptic connections are random and sparse ., In these networks , local interactions are excitatory and inhibitory ., A scheme of the network model for a local area is depicted in Figure 2A ( left ) ., In agreement with experimental evidence that the recruitment of local interneuronal networks is necessary for obtaining coherent gamma cortical activity in vitro and in vivo 61 , 62 , the model develops synchronous oscillations ( ) when inhibition is strong , i . e . for a sufficiently large probability of inhibitory connection 54–57 , 63 ., These fast oscillations are clearly visible in the average membrane potential ( denoted in the following as “LFP” ) , an example trace of which is represented in Figure 2A ( bottom right ) ., Despite the regularity of these collective rhythms , the ongoing neural activity is only sparsely synchronized ., The spiking of individual neurons is indeed very irregular 54 , 56 and neurons do not fire an action potential at every oscillation cycle , as visible from the example spike trains represented in Figure 2A ( top right ) ., Structural network motifs involving areas are constructed by allowing excitatory neurons to establish in addition long-range connections toward excitatory or inhibitory neurons in a distant target area ( see a schematic representation of an structural connectivity motif in Figure 2C ) ., The strength of inter-areal coupling is regulated by varying the probability of establishing an excitatory connection ., In a second analytically more tractable approach , each area is described by a mean-field firing rate variable ( rate model ) ., The firing rate of a local population of neurons obeys the non-linear dynamical equation ( 4 ) ( see Methods ) ., All incorporated interactions are delayed , accounting for axonal propagation and synaptic integration ., Local interactions are dominantly inhibitory ( with coupling strength and delay ) ., Driving is provided by a constant external current ., A cartoon of the rate model for a local area is depicted in Figure 2B ( left ) ., As in the network model , the firing rates undergo fast oscillations for strong inhibition ( , 60 ) ., An example firing rate trace is shown in Figure 2B ( right ) ., In order to build structural networks involving areas , different mean-field units are coupled together reciprocally by excitatory long range interactions with strength and delay ( see a schematic representation of an structural motif in Figure 2D ) ., Remarkably , the rate model and the network model display matching dynamical states 60 ( see also later , Figures 3 , 4 and 5 ) ., More details on the network and the rate models are given in the Methods section and in the Supporting Text S1 ., For simplicity , we study fully connected structural motifs involving a few areas ( ) ., Note however that our approach might be extended to other structural motifs 64 or even to larger-scale networks with more specific topologies 41 , 65 ., In the simple structural motifs we consider , delays and strengths of local excitation and inhibition are homogeneous across different areas ., Long-range inter-areal connections are as well isotropic , i . e . strengths and delays of inter-areal interactions are the same in all directions ., Delay and strength of local and long-range connections can be changed parametrically , but only in a matching way for homologous connections , in such a way that the overall topology of the structural motif is left unchanged ., As previously shown in 60 , different dynamical states –characterized by oscillations with different phase-locking relations and degrees of periodicity– can arise from these simple structural motif topologies ., Changes in the strength of local inhibition , of long-range excitation or of delays of local and long-range connections can lead to phase transitions between qualitatively distinct dynamical states ., Interestingly , however , within broad ranges of parameters , multi-stabilities between dynamical states with different phase-locking patterns take place even for completely fixed interaction strengths and delays ., We generate multivariate time-series of simulated “LFPs” in different dynamical states of our models and we calculate TEs for all the possible directed pairwise interactions ., We show then that effective connectivities associated to different dynamical states are also different ., The resulting effective connectivities can be depicted in diagrammatic form by drawing an arrow for each statistically significant causal interaction ., The thickness of each arrow encodes the strength of the corresponding interaction ., This graphical representation makes apparent , then , that effective connectivity motifs or , more briefly , effective motifs , with many different topologies emerge from structural motifs with a same fixed topology ., Such effective motifs are organized into families ., All the motifs within a same family correspond to dynamical states which are multi-stable for a given choice of parameters , while different families of motifs are obtained for different ranges of parameters leading to different ensembles of dynamical states ., We analyze in detail , in Figures 3 , 4 and 5 , three families of motifs arising for strong intra-areal inhibition and similarly small values of delays for local and long-range connections ., We consider ( panels A and B ) and ( panels C and D ) structural motifs ., Panels A and C show TEs for different directions of interaction , together with “LFPs” and example spike trains ( from the network model ) , and rate traces ( from matching dynamical states of the rate model ) ., Panels B and D display motifs belonging to the corresponding effective motif families ., A first family of effective motifs occurs for weak inter-areal coupling ., In this case , neuronal activity oscillates in a roughly periodic fashion ( Figure 3A and C , left sub-panel ) ., When local inhibition is strong , the local oscillations generated within different areas lock in an out-of-phase fashion ., It is therefore possible to identify a leader area whose oscillations lead in phase over the oscillation of laggard areas 60 ., In this family , causal interactions are statistically significant only for pairwise interactions proceeding from a phase-leading area to a phase-lagging area , as shown by the the box-plots of Figure 3A and C ( right sub-panel , see Discussion and Methods for a discussion of the threshold used for statistical significancy ) ., As commented more in detail in the Discussion section , the anisotropy of causal influences in leader-to-laggard and laggard-to-leader directions can be understood in terms of the communication-through-coherence theory ., Indeed the longer latency from the oscillations of the laggard area to the oscillations of the leader area reduces the likelihood that rate fluctuations originated locally within a laggard area trigger correlated rate fluctuations within a leading area 35 ( see also Discussion ) ., Thus , out-of-phase lockings for weak inter-areal coupling give rise to a family of unidirectional driving effective motifs ., In the case of , causality is significant only in one of two possible directions ( Figure 3B ) , depending on which of the two areas assumes the role of leader ., In the case of , it is possible to identify a “causal source” area and a “causal sink” area ( see 66 for an analogous terminology ) , such that no direct or indirect causal interactions in a backward sense from the sink area to the source area are statistically significant ., Therefore , the unidirectional driving effective motif family for contains six motifs ( Figure 3D ) , corresponding to all the possible combinations of source and sink areas ., A second family of effective motifs occurs for intermediate inter-areal coupling ., In this case , the periodicity of the “LFP” oscillations is disrupted by the emergence of large correlated fluctuations in oscillation cycle amplitudes and durations ., As a result , the phase-locking between “LFPs” becomes only approximate , even if it continues to be out-of-phase on average ., The rhythm of the laggard area is now more irregular than the rhythm in the leader area ., Laggard oscillation amplitudes and durations in fact fluctuate chaotically ( Figure 4A and C , left sub-panel ) ., Fluctuations in cycle length do occasionally shorten the laggard-to-leader latencies , enhancing non-linearly and transiently the influence of laggard areas on the leader activity ., Correspondingly , TEs in leader-to-laggard directions continue to be larger , but TEs in laggard-to-leader directions are now also statistically significant ( Figure 4A and C , right sub-panel ) ., The associated effective motifs are no more unidirectional , but continue to display a dominant direction or sense of rotation ( Figure 4B and D ) ., We refer to this family of effective motifs as to a family of leaky driving effective motifs ( containing two motifs for and six motifs for ) ., Finally , a third family of effective motifs occurs for stronger inter-areal coupling ., In this case the rhythms of all the areas become equally irregular , characterized by an analogous level of fluctuations in cycle and duration amplitudes ., During brief transients , leader areas can still be identified , but these transients do not lead to a stable dynamic behavior and different areas in the structural motif continually exchange their leadership role ( Figure 5A and C , left sub-panel ) ., As a result of the instability of phase-leadership relations , only average TEs can be evaluated , yielding to equally large TE values for all pairwise directed interactions ( Figure 5A and C , right sub-panel ) ., This results in a family containing a single mutual driving effective motif ( Figure 5B and D ) ., Further increases of the inter-areal coupling strength do not restore stable phase-locking relations and , consequently , do not lead to additional families of effective motifs ., Note however that the effective motif families explored in Figures 3 , 4 and 5 are not the only one that can be generated by the considered fully symmetric structural motifs ., Indeed other dynamical configurations exist ., In particular , anti-phase locking ( i . e . locking with phase-shifts of for and of for ) would become stable when assuming the same interaction delays and inter-areal coupling strengths of Figures 3 , 4 and 5 , but a weaker local inhibition ., Assuming different interaction delays for local and long-range interactions , out-of-phase lockings continue to be very common , but in-phase and anti-phase locking can become stable even for strong local inhibition , within specific ranges of the ratio between local and long-range delays 60 ., For , in the case of general delays , more complex combinations can arise as well , like , for instance , states in which two areas oscillate in-phase , while a third is out-of-phase ., In-phase locking between areas gives rise to identical TEs for all possible directed interactions , resulting in effective motifs without a dominant directionality ., Anti-phase lockings for give rise to relatively large inter-areal phase-shifts and , correspondingly , to weak inter-areal influences ( at least in the case of weak inter-areal coupling ) , resulting in small TE levels which are not statistically significant ( not shown ) ., However , in the framework of this study , we focus exclusively on out-of-phase-locked dynamical states , because they are particularly relevant when trying to achieve a reconfigurable inter-areal routing of information ( see later results and Discussion section ) ., To conclude , we remark that absolute values of TE depend on specific parameter choices ( notably , on time-lag and signal quantization , see Methods ) ., However , the relative strengths of TE in different directions –and , therefore , the resulting topology of the associated effective motifs– are rather robust against changes of these parameters ., Robustness of causality estimation is analyzed more in detail in the Discussion section ., How can asymmetric causal influences emerge from a symmetric structural connectivity ?, A fundamental dynamical mechanism involved in this phenomenon is known as spontaneous symmetry breaking ., As shown in 60 , for the case of the structural motif , a phase transition occurs at a critical value of the strength of inter-areal inhibition ., When local inhibition is stronger than this critical threshold , a phase-locked configuration in which the two areas oscillate in anti-phase loses its stability in favor of a pair of out-of-phase-locking configurations , which become concomitantly stable ., The considered structural motif is symmetric , since it is left unchanged after a permutation of the two areas ., However , while the anti-phase-locking configuration , stable for weak local inhibition , share this permutation symmetry with the full system , this is no more true for the out-of-phase-locking configurations , stable for strong local inhibition ., Note , nevertheless , that the configuration in which leader and laggard area are inverted is also a stable equilibrium , i . e . the complete set of stable equilibria continue to be symmetric , even if individual stable equilibria are not ( leading thus to multi-stability ) ., In general , one speaks about spontaneous symmetry breaking whenever a system with specific symmetry properties assumes dynamic configurations whose degree of symmetry is reduced with respect to the full symmetry of the system ., The occurrence of symmetry breaking is the signature of a phase transition ( of the second order 67 ) , which leads to the stabilization of states with reduced symmetry ., The existence of a symmetry-breaking phase transition in the simple structural motifs we analyze here ( for simplicity , we consider the case ) can be proven analytically for the rate model , by deriving the function , which describes the temporal evolution of the phase-shift between two areas when they are weakly interacting 47: ( 1 ) The function for the rate model is shown in the left panel of Figure 6B ., Stable phase lockings are given by zeroes of with negative slope crossing and are surrounded by basins of attraction ( i . e . sets of configurations leading to a same equilibrium ) , whose boundaries are unstable in- and anti-phase lockings ( Figure 6A ) ., For the network model , a function with an analogous interpretation and a similar shape , shown in the right panel of Figure 6B , can be extracted from simulations , based on a phase description of “LFP” time-series ( see Methods and Supporting Figure S1A ) ., The analogous distribution of the zero-crossings of and results in equivalent phase-locking behaviors for the rate and network models ., Thus spontaneous symmetry breaking leads to multi-stability between alternative out-of-phase-lockings and to the emergence of unidirectional effective driving within a symmetric structural motif ., Because of multi-stability , transitions between effective motifs within a family can be triggered by transient perturbations , without need for structural changes ., We theoretically determine conditions for such transitions to occur ., The application of a pulse of current of small intensity advances or delays the phase of the ongoing local oscillation ( see Supporting Figure S1B ) ., This is true for rate oscillations of the mean-field rate model , but also for “LFP” oscillations reflecting rhythmic synchronization in the network model ., In the latter case , the collective dynamics is perturbed by synchronously injecting pulse currents into all of the neurons within an area ., The induced phase shift depends on the perturbation strength but also on the phase at which the perturbation is applied ., For the network model , this can be measured directly from numeric simulations of a perturbed dynamics ( see Methods and right panel of Figure 6D ) ., For the rate model , the phase shift induced by an instantaneous phased perturbation can be described analytically in terms of the Phase Response Curve ( PRC ) 47 ( see Figure 6D , left , and Supporting Text S1 ) ., After a pulse , the phase-shift between two areas is “kicked out” of the current equilibrium locking and assumes a new transient value ( solid paths in Figure 6C ) , which , for weak perturbations and inter-areal coupling , reads: ( 2 ) where the approximate equality between square brackets holds for the mean-field rate model ., If falls into the basin of attraction of a different phase-locking configuration than , the system will settle within few oscillation cycles into an effective connectivity motif with a different directionality ( dashed green path in Figure 6C ) ., Even relatively small perturbations can induce an actual transition , if applied in selected narrow phase intervals in which the induced grows to large values ., For most application phases , however , even relatively large perturbations fail to modify the effective driving direction ( dashed red path in Figure 6C ) , because the induced perturbation is vanishingly small over large phase intervals ( Figure 6D ) ., This is a robust property , shared by the two ( radically different ) models we consider here and –we hypothesize– by any local circuit generating fast oscillations through a mechanism based on delayed mutual inhibition ., As a consequence , for a given perturbation intensity , a successful switching to a different effective motif occurs only if the perturbation is applied within a specific phase interval , that can be determined analytically from the knowledge of and of for the rate model , or semi-analytically from the knowledge of and ( see Methods ) ., Figure 6E–F reports the fraction of simulated phased pulses that induced a change of effective directionality as a function of the phase of application of the perturbation ., The phase intervals for successful switching predicted by the theory are highlighted in green ., We performed simulations of the rate ( Figs . 6E–F , left column ) and of the network ( Figs . 6E–F , middle column ) models , for unidirectional ( Figs . 6E ) and leaky driving ( Figs . 6F ) effective motifs ., Although our theory assumes small inter-areal coupling and is rigorous only for the rate model , the match between simulations and predictions is very good for both models and families of motifs ., In Figs ., 6E–F , we perturb the dynamics of the laggard area , but changes in directionality can also be achieved by perturbing the leader area ( Supporting Figure S2 ) ., Note also that , in the network model , direction switchings can take place spontaneously , due to noisy background inputs ., Such noise-induced transitions , however , occur typically on time-scales of the order of seconds , i . e . slow in terms of biologic function , because the phase range for successful switching induction is narrow ., A second non-linear dynamic mechanism underlying the sequence of effective motifs of Figures 3 and 4 is effective entrainment ., In this phenomenon , the complex dynamics of neural activity seems intriguingly to be dictated by effective rather than by structural connectivity ., We consider as before a rate model of reciprocally connected areas ( Figure 2D ) ., In order to properly characterize effective entrainment , we review the concept of bifurcation diagram 68 ., As shown in 60 , when the inter-areal coupling is increased , rate oscillations become gradually more complex ( cfr . Figure 7A ) , due to the onset of deterministic chaos ( see also 69 for a similar mechanism in a more complex network model ) ., For small , oscillations are simply periodic ( e . g . ) ., Then , for intermediate ( e . g . ) , the peak amplitudes of the laggard area oscillation assume in alternation a small number of possible values ( period doubling ) ., Finally , for larger ( e . g . ) , the laggard peak amplitudes fluctuate in a random-like manner within a continuous range ., This sequence of transitions can be visualized by plotting a dot for every observed value of the peak amplitudes of oscillation cycles , at different values of ., The accumulation of these dots traces an intricate branched structure , which constitutes the bifurcation diagram ( Figure 7B ) ., Bifurcation diagrams for the leader and for the laggard area are plotted in Figure 7B ( top panel , in orange and green color , respectively ) ., We compare these bifurcation diagrams with the analogous diagrams constructed in the case of two unidirectionally coupled oscillating areas ., Qualitatively similar bifurcation sequences are associated to the dynamics of the laggard area ( bidirectional coupling ) and of the driven area ( unidirectional coupling , Figure 7B , bottom panel , green color ) , for not too strong inter-areal couplings ., In the case of unidirectional coupling , the peak amplitudes of the unperturbed driver area oscillations do not fluctuate at all ., Therefore , the corresponding bifurcation diagram is given by a constant line ( Figure 7B , bottom panel , orange color ) ., In the case of bidirectional coupling , the peak amplitudes of the leader area oscillations undergo fluctuations , but only with a tiny variance ., Thus , the corresponding bifurcation diagram has still the appearance of a line , although now “thick” and curved ( zooming would reveal bifurcating branches ) ., Note that , for unidirectional coupling , the structural connectivity is explicitly asymmetric ., The periodic forcing exerted by the driving area is then known to entrain the driven area into chaos 70 ., Such direct entrainment is the dynamical cause of chaos ., On the other hand , for bidirectional coupling , the structural connectivity is symmetric ., However , due to spontaneous symmetry breaking , the resulting effective connectivity is asymmetric and the system behaves as if the leader area was a driver area , entraining the laggard area into chaos being only negligibly affected by its back-reaction ., Such effective entrainment can be seen as an emergent dynamical cause of chaos ., Thus , the dynamics of a symmetric structural motif with asymmetric effective connectivity and of a structural motif with a matching asymmetric topology are equivalent ., For a sufficiently strong inter-areal coupling , symmetry in the dynamics of the bidirectional structural motif is suddenly restored 60 , in correspondence with a transition to the mutual driving family of effective motifs ( Figure 5 ) ., As a result , in absence of symmetry breaking , effective driving cannot anymore take place ., Thus , for a too strong inter-areal coupling , the emergent anisotropy of effective connectivity is lost , and , with it , the possibility of a dynamic control of effective connectivity ( at least via the previously discussed strategies ) ., Despite its name , Transfer Entropy is not directly related to a transfer of information in the sense of neuronal information processing ., The TE from area to area measures indeed just the degree to which the knowledge of the past “LFP” of reduces the uncertainty about the future “LFP” of 43 , 71 ., As a matter of fact , however , the information stored in neural representations must be encoded in terms of spikes , independently from the neural code used ., Therefore , it is important to understand to which extent an effective connectivity analysis based on “macroscopic” dynami | Introduction, Results, Discussion, Methods | Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity ., However , such structural connectivity does not coincide with effective connectivity ( or , more precisely , causal connectivity ) , related to the elusive question “Which areas cause the present activity of which others ? ” ., Effective connectivity is directed and depends flexibly on contexts and tasks ., Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity ., Integrating simulation and semi-analytic approaches , we study mesoscale network motifs of interacting cortical areas , modeled as large random networks of spiking neurons or as simple rate units ., Through a causal analysis of time-series of model neural activity , we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs ., Such effective motifs can display a dominant directionality , due to spontaneous symmetry breaking and effective entrainment between local brain rhythms , although all connections in the considered structural motifs are reciprocal ., We show then that transitions between effective connectivity configurations ( like , for instance , reversal in the direction of inter-areal interactions ) can be triggered reliably by brief perturbation inputs , properly timed with respect to an ongoing local oscillation , without the need for plastic synaptic changes ., Finally , we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif , demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer ., Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas ., Going beyond these early proposals , we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this “communication-through-coherence” , making thus possible a fast “on-demand” reconfiguration of global information routing modalities . | The circuits of the brain must perform a daunting amount of functions ., But how can “brain states” be flexibly controlled , given that anatomic inter-areal connections can be considered as fixed , on timescales relevant for behavior ?, We hypothesize that , thanks to the nonlinear interaction between brain rhythms , even a simple circuit involving few brain areas can originate a multitude of effective circuits , associated with alternative functions selectable “on demand” ., A distinction is usually made between structural connectivity , which describes actual synaptic connections , and effective connectivity , quantifying , beyond correlation , directed inter-areal causal influences ., In our study , we measure effective connectivity based on time-series of neural activity generated by model inter-areal circuits ., We find that “causality follows dynamics” ., We show indeed that different effective networks correspond to different dynamical states associated to a same structural network ( in particular , different phase-locking patterns between local neuronal oscillations ) ., We then find that “information follows causality” ( and thus , again , dynamics ) ., We demonstrate that different effective networks give rise to alternative modalities of information routing between brain areas wired together in a fixed structural network ., In particular , we show that the self-organization of interacting “analog” rate oscillations control the flow of “digital-like” information encoded in complex spiking patterns . | physics, medicine, condensed-matter physics, mathematics, neuroanatomy, computational neuroscience, neurology, interdisciplinary physics, neurological disorders, biology, neuroscience, nonlinear dynamics | null |
journal.pcbi.1006046 | 2,018 | Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units | Stochastic ( random ) models play a pivotal role in the description of transmission and control of infection within small closely-connected populations ., The typical example of such populations is households 2–5 , although the household methodology is generalisable to any well-connected population sub-unit such as the residential and nursing care homes ( RNCs ) we consider here ., These models are increasingly becoming useful tools for studying disease transmission and control in structured populations , which can in part be attributed to the availability of household-stratified infection data that can be used to parameterise the models 6–8 as well as affordability in the amount of computing power ., A type of household transmission model that is currently growing in popularity is a stochastic Markovian household model 2 , 9 , 10 ., In this type of model , individuals are assumed to have two levels of mixing: one representing transmission between people sharing/living in the same household and the other representing global contacts within the population ., These kind of models have the advantage that they capture the temporal behavior of the epidemic and offer a computational trade-off between simpler whole-population models 11 , 12 and more complex , computationally-intensive individual-based models 13 , 14 ., Stochastic models have traditionally been studied via simulation and estimation based upon a large number of event-driven integer-based simulations 15 , 16 ., These methods are powerful but require a large number of replicates to reduce Monte Carlo error , since it is typically unclear if a single simulation represents the average behaviour of the system or the outcome of a combination of rare events ., They therefore quickly become computationally intensive due to the combination of the sheer number of replicates required and the number of possible events that can occur in a given time step ., In this work , we have presented a method that allows for the complete range of stochastic behaviours to be captured by a large set of ordinary differential equations ( ODEs ) which we will refer to as the master equation ( also known as the forward equation ) ., The master equation is a set of linear ODEs representing the probability of being in each possible state with the dynamics driven by the rates of transition between states ., This method has previously been applied to the study of stochastic disease dynamics 9 , 17–19 ., The use of a master equation has existed for quite a long time but has not been widely used in epidemiology and this is partly due to the algorithmic difficulties involved in solving the resulting large system of linear ODEs ., There are a range of methods that can be used to solve the system and in this work we concentrate on so-called series-expansion and projection-based methods ., Both method classes are based on the efficient approximation of the action of the matrix exponential on a vector , a problem that has attracted a lot of research and for which various numerical methods have been proposed 20–22 ., Besides the existence of these numerical methods used to solve the master equation , there still lacks a body of work that computationally benchmarks the algorithms for use in mathematical epidemiology , an area where these algorithms can be of great utility ., In this work , we benchmark a number of competing algorithms and then use the best algorithm to solve a system of linear ODEs describing the transmission of scabies in care homes in the UK to enhance the efficiency and quality of an inferential and policy-driven modelling study ., We also consider how the safety and efficacy of pharmaceutical interventions can be assessed using probabilistic models ., Sarcoptes scabiei is an ectoparasite that infests human skin , where it burrows and lays eggs causing intense itching and scratching , which may in turn lead to secondary bacterial infection 23 ., Global prevalence in 2015 was estimated at 204 million 24 but varies enormously both between and within nations 25 ., All age prevalence over 30% has been reported in some countries ( Papua New Guinea , Panama , Fiji ) 26 and overcrowded settings such as slums and refugee camps can have very high prevalence 27 ., In most low and middle income settings it particularly affects children 27 ., However in developed high-resource settings , the incidence and prevalence in children and schools has declined , while outbreaks are commonly reported in residential and nursing care homes for the elderly 28 , 29 ., The mite is transmitted mainly through skin to skin contact , and also to a lesser extent through “fomites” ( e . g . , bedding , skin flakes ) 23 , 30 ., Itching begins 4–6 weeks after exposure for a first episode , but can start within 24 hours in subsequent infections 30 , 31 ., An infested person becomes infectious in most cases around 10–14 days after becoming exposed , when newly fertilised adult female mites become ready to seek new burrows in which to lay eggs 30 ., Scabies is commonly misdiagnosed since the classical physical signs ( burrows and papules ) are variably present ., This is a particular problem in RNCs since many residents have dementia and may not be able to say they are itching , while their increased agitation may be attributed to other causes ., As a consequence , recognition of cases and outbreaks in RNCs is often delayed 28 ., In the absence of interventions , scabies is generally not self-limiting 30 , 32 with a study in Bangladesh observing that children could remain infected for more than six months 33 ., In the UK , first-line therapy for scabies is topical permethrin , which is applied all over the body , left on for 8 hours before being washed off , and repeated 7 days later ., In RNC outbreaks , residents and staff need to be treated simultaneously ., This can be distressing and logistically challenging 28 , especially as some guidelines recommend prophylactic mass treatment of all residents and staff once an outbreak is declared 34 ., Oral ivermectin has been suggested as an effective alternative in healthcare settings , and is included in French national outbreak management guidelines for RNCs 34 ., However , its wider usage as a mass treatment in RNCs has been limited in part due to safety concerns raised by Barkwell and Shields 1 ., The retrospective study was carried out in a 47-bed closed unit for residents with behavioural tendencies over a period of six months between June and November 1995 ., A scabies outbreak occurred during this period and the individuals were treated with two different topical agents , lindale and permethrin , but scabies symptoms re-occured ., Consequently , the patients were treated with a single oral dose of ivermectin and all the rashes and symptoms had cleared within five days with the individuals requiring no further treatment ., However , during the following six months , the authors observed an increased pattern of excess deaths among the residents who had received ivermectin ., Barkwell and Shields , in their conclusion , subsequently advised against using ivermectin to treat scabies in the elderly and/or those with an underlying medical condition , suggesting a potential causal association with deaths in the facility ., We re-examine their statistical analysis and interpretation and provide a rigorous method for more careful analysis ., The natural history of scabies infection in the absence of interventions is highly dependent on the history of previous exposure as well as immunological competency of the individuals ., Walton 35 has reported that spontaneous recovery of scabies in healthy adults can occur only with subsequent re-infestations ., Additionally , parasite numbers can be reduced and in approximately 60% of cases re-infestation of sensitised hosts was unsuccessful ., It is still unknown how long this capacity for some level of acquired immunity persists , though 15-24 months after infestation with scabies mite extracts injected intradermally have failed to induce immediate wheal reactions in patients ., So in the elderly and especially those in care homes with high co-morbidities and compromised immunological responses , we do not include the possibility of spontaneous recovery in the absence of treatment ., This would mean that following exposure to scabies , the individuals would have a protracted infection that is not self limiting ., We therefore assume that scabies follows an SEI model framework in which individuals are initially susceptible ( S ) , then following an infection event spend some time in a latent ‘exposed’ class ( E ) ., Once a fertilised female mite is transferred to a susceptible individual , mite generation time means there is a delay , between 7 and 14 days , before the host can become infective ., However , during this period , the mite burrows can still be observed on the host’s skin 30 ., Eventually , the individuals become infectious , ( I ) , and are able to infect others ., Our starting point is therefore the stochastic SEI model in a closed population of size N . This consists of three non-independent random variables , S ( t ) , E ( t ) and I ( t ) such that S ( t ) + E ( t ) + I ( t ) = N , for all values of t , representing the number of individuals who are uninfected with scabies ( Susceptible ) , who have been infected but are not able to infect others ( Exposed ) , and who have been infected and are able to infect others ( Infectious ) respectively ., The state transitions and rates in this model are, ( S , E , I ) → ( S - 1 , E + 1 , I ) at rate λ S I , ( S , E , I ) → ( S , E - 1 , I + 1 ) at rate γ E ., ( 1 ), If we define the expectations S ¯ = E S , E ¯ = E E and I ¯ = E I then in the limit of large N the dynamics of this model will be governed by the more familiar deterministic differential equations:, d S ¯ d t = - λ S ¯ I ¯ , d E ¯ d t = λ S ¯ I ¯ - γ E ¯ , d I ¯ d t = γ E ¯ ., ( 2 ), To model the finite-population dynamics , let Ps , e , i ( t ) represent the probability that there are s , e , i numbers of susceptibles , exposed and infected respectively in the population at time t , then the complete dynamics will be modelled by considering all the possible infection configurations as shown in the system ( 3 ) :, d P s , e , i d t = γ ( - e P s , e , i + ( e + 1 ) P s , e + 1 , i - 1 ) + λ ( - s i P s , e , i + ( s + 1 ) i P s + 1 , e - 1 , i ) + τ I ( t ) ( - s P s , e , i + ( s + 1 ) P s + 1 , e - 1 , i ) ., ( 3 ) Eq ( 3 ) can be equivalently represented by counting the number of events of each type that occur rather than the number of individuals in each compartment ., This is known as the DA ( Degree of Advancement ) representation and has previously been described elsewhere 36 , 37 ., If we define Z1 and Z2 as the number of exposure ( E ) and progression to active infection ( I ) events respectively , then the state space of the process at time t can be denoted as Z ( t ) = ( Z1 , Z2 ) ., If we index the states of the system as zi = ( z1 , z2 ) with i = 1 , … , n where n = ( N + 1 ) ( N + 2 ) 2 is the size of the state space , we can then order the states of the system such that zi < zi+1 ., The within-household transmission parameter λ in the system ( 3 ) is modelled as, λ = β ( N - 1 ) α , ( 4 ), where β > 0 is an overall scaling for transmission and α represents the different ways that mixing behaviour can change with population size , N . If α = 0 then every pair of individuals in the same population makes contacts capable of spreading disease at the same rate regardless of N; and if α > 0 then larger populations reduce the rate of transmission as if each infective individual had a certain demand for contacts that are evenly spread throughout the population ., If α < 0 then larger populations enhance transmission—while this would not normally be considered in the context of households , for RNCs we consider there is the possibility that larger facilities will have more opportunities for contact due to , for example more activities in larger communal areas ., The parameter τ , represents the transmission between general members of the community i . e . between household mixing , and the proportion of the overall population that is infective is given as I ( t ) = ∑ s , e , i i P s , e , i ( t ) ∑ s , e , i ( s + e + i ) P s , e , i ( t ) ., As we are considering a small number of carehomes in a large population 28 , we assume that there is no contact between members of different carehomes and therefore no between carehome transmission ., It follows then that if carehomes are independent then τ = 0 . A more rigorous derivation of Eq 3 can be found in literature 2 , 38 ., More insight can be gained by representing the system ( 3 ) in vector notation ., Let p be the column vector of the probabilities of a household being in a certain configuration at time t ., Then ( 3 ) can be expressed more succinctly as a linear constant-coefficient initial value problem , the so-called master equation ,, d p d t = Q p , p ( 0 ) = p 0 , ( 5 ), where Q ∈ R n × n is the household transition matrix of order n ( with n being equal to the total number of states the system can occupy; for our SEI model n = ( N + 1 ) ( N + 2 ) /2 as detailed above ) and the probability column vector p 0 ∈ R n represents the initial configuration at time t = 0 . The household transition matrix has the property that its elements sum to zero column-wise ., The solution vector p ( t ) represents the transient behavior of the finite-state Markov chain and is easily shown to be a probability vector for all t ≥ 0 . The solution of the master Eq ( 5 ) is given by, p ( t ) = exp ( t Q ) p 0 , ( 6 ), where exp ( tQ ) = I + ( tQ ) + ( tQ ) 2/2 !, + ⋯ denotes the matrix exponential; see , e . g . , 39 , Chapter 10 ., In what follows we sometime drop time t for notational simplicity ., Note that the matrix Q is typically sparse because there is a limited number of transitions that a household in a certain configuration can make , i . e . , there are few epidemiological state changes compared to the size of the matrix Q . Because a household can only move to a state following the current one in the DA representation , the states can be ordered so that Q is an upper triangular matrix ., This leads to computational savings in some of the algorithms discussed later ., The matrices we consider here also have a small bandwidth because we consider epidemiological processes with a limited number of events , though this is not a general feature of epidemiological models ., Technically , we are concerned with the fast and sufficiently-accurate computation of the matrix exponential in Eq ( 6 ) ., For scalar problems ( i . e . , n = 1 ) the computation of the exponential is trivial ., However , the problem becomes challenging as n gets larger in which case the matrix Q is hopefully sparse or otherwise structured as in our case ., We make use of the fact that the full matrix exponential exp ( tQ ) is not required , but merely the vector-matrix product exp ( tQ ) p0 ., Computationally , these two are different problems and this section focuses on methods which compute this product directly without forming the matrix exponential itself ., Methods based on polynomial and rational approximants have proven to be particularly efficient for this task ., They have in common that exp ( tQ ) p0 ≈ r ( Q ) p0 where r is a well-chosen polynomial , or more generally a rational function , which depends on t and the required approximation accuracy ., In the following we review a number of methods which fit into this framework ., We refer the reader to 20 and 39 , Chapter 10 for further reading ., It is easy to ensure that all discussed methods return probability vectors by adding a procedure at each time step that zeros-out all negative numbers and renormalizes the result to have unit 1-norm ., If the computed vector is sufficiently accurate , such a normalization procedure does not affect the error significantly ., More precisely , let p = exp ( tQ ) p0 be the exact probability vector such that p ≥ 0 component-wise and ‖p‖1 = 1 . Further , let p ˜ ≈ p be a numerical approximation such that ‖ p - p ˜ ‖ 1 ≤ ε ⪡ 1 . We define P to be the operator that zeros out negative entries of a vector , i . e . ,, ( P p ˜ ) i = { p i ˜ , if p ˜ i ≥ 0 , 0 , if p i ˜ < 0 ,, where the subscript i refers to the ith component of a vector ., Then , using basic vector norm inequalities and the fact that | p i - ( P p ˜ ) i | ≤ | p i - p ˜ i | , we have, | 1 - ‖ P p ˜ ‖ 1 | = | ‖ p ‖ 1 - ‖ P p ˜ ‖ 1 | ≤ ‖ p - P p ˜ ‖ 1 ≤ ‖ p - p ˜ ‖ 1 ≤ ε ,, and hence ‖ P p ˜ ‖ 1 = 1 + δ with |δ| ≤ ε ., Now , for the the normalized vector p ^ = P p ˜ / ‖ P p ˜ ‖ 1 we have, ‖ e - p ^ ‖ 1 = 1 1 + δ ‖ ( 1 + δ ) p - P p ˜ ‖ 1 ≤ 2 ε 1 - ε ., Hence , the normalization procedure guarantees a probability vector p ^ and only increases the approximation error ‖ e - p ^ ‖ 1 by a factor ≈2 compared to the error of the non-normalized approximation p ˜ ., In general , we work in a Bayesian framework , which has the benefit of dealing with the statistical challenges of the small datasets we consider in a systematic manner ., We do this by calculating the posterior distribution , f , over parameters θ , given data D , using Bayes’ theorem:, f ( θ | D ) = L ( D | θ ) π ( θ ) ∫ L ( D | ϑ ) π ( ϑ ) d ϑ , ( 10 ), where L is the likelihood of the data given the parameters and π is the prior distribution over parameters ., If the integral in the denominator of the right-hand side of ( 10 ) above is tractable , then we can simply evaluate f directly , but if it is not then we can use Markov chain Monte Carlo ( MCMC ) methods to produce samples from the posterior distribution 51 , 52 ., We fitted the stochastic SEI model above to scabies infection data from a study that enrolled carehomes in the UK 28 ., In the study , the authors investigated a series of suspected scabies outbreaks in residential care homes , exploring barriers to early recognition and optimal management ., Seven care homes agreed to participate and questionnaires were administered requiring details about dates of onset , diagnosis and treatment , clinical features , underlying illness , pre-existing skin conditions and mobility ., An outbreak was determined if a report of two or more clinically suspected cases of scabies in a residential care home were reported to the Surrey and Sussex Health Protection Teams of the Public Health England ( PHE ) by a GP or a carehome manager ., Case definitions included suspected cases because a definite diagnosis of scabies by dermatoscopy or microscopy is rare within the carehome setting and not all symptomatic cases had been seen by a doctor ., The data we used to fit to the model are tabulated in Table 1 . These data involves scabies outbreaks in seven different care homes , for which the resident population ( N ) , days from onset to diagnosis ( T ) which is also the point at which treatment is initiated , and the number of scabies cases treated ( C ) are recorded ., Days from onset of symptoms was defined as the first reported day of itching or rash ., Frequently an exact date was not available and participants stated that symptoms began e . g . ‘over a year ago’ ., The number of cases included suspected cases and hence would include individuals who have been exposed to the mite but not yet infectious , E , and the infectious individuals , I . As a result , the predicted number of cases from the model , comprised of E and I , is then compared to the number of cases reported , C . Formally , we write the data D , as the number of scabies cases {ci} that are observed at time {ti} in a care home with population {ni} where i ∈ {1 , … , 7} , and assume the stochastic SEI model as our generative model for the data ., Our likelihood , assuming that carehomes are independent , therefore takes the form, L ( { c i } | { n i } , { t i } ; α , β , γ ) = ∏ i = 1 7 P E ( t i ) + I ( t i ) = c i | N = n i ; α , β , γ ( 11 ), where P E ( t i ) + I ( t i ) = P s , e , i ( t i ; α , β , γ ) which is obtained by solving Eq 6 ., Our MCMC procedure was Random-Walk Metropolis Hastings with hand-tuned Gaussian proposals , which was used to obtain samples from the posterior distribution of the model parameters ., We ran 16 MCMC chains in parallel each of length 2 . 5 × 104 , burn-in time for each chain was 104 and samples were thinned by a factor of 20 ., Mixing was assessed using trace plots and the total number of samples visualised is 1 . 2 × 104 ., We consider here two costs associated with a scabies outbreak with the first case at time 0 and with an intervention starting at time t ., Barkwell and Shields 1 reported 172 deaths in a population of size 210 over a 36 month period , and 15 subsequent deaths over 6 months in a sub-population of 47 who had received ivermectin treatment , as well as 10 deaths in the remaining population of 163 over that 6 month period ., They reported deaths for each month in the two sub-populations over the six months following ivermectin treatment ., Barkwell and Shields performed two statistical tests on these data: chi-squared and Fisher’s exact ., Of these , Fisher’s exact test is more accurate for small populations and answers the following question: if two groups , one of size 163 and one of size 47 , are formed by picking individuals from the total population of 210 ( with 25 deaths ) uniformly at random , then what is the probability p of the pattern of deaths observed , or one with more deaths in the population of size 47 ., This test gives p < 0 . 0001 when applied to the data ., This work received criticism from a more standard biostatistical and epidemiological perspective—in particular due to the absence of control for illnesses other than scabies—shortly after its publication 56 , 57 ., Here we do not comment on these issues , but rather focus on the extent to which more general heterogeneity between individuals can invalidate methods such as Fisher’s exact test ., Mathematically , we model heterogeneity by assuming that the mortality rates in the population are variable , and that in particular the probability of k deaths in a unit of size n over a time period t are given by a Poisson mixture, L ( k | μ , θ ; n , t ) = ∫ λ = 0 ∞ Poisson ( k | n t λ ) Gamma ( λ | ( μ / θ ) , θ ) d λ = ( n t θ ) k ( 1 + n t θ ) - k - ( μ / θ ) Γ ( k + ( μ / θ ) ) k !, Γ ( μ / θ ) ., ( 14 ), Here μ is the mean death rate in the population and θ is the variance divided by the mean , which we call the overdispersion ., When θ → 0 , we recover the situation where death rates are homogeneous , and larger values of θ imply more heterogeneity ., We will carry out two analyses of the Barkwell and Shields data ., We have implemented the exponential integrators discussed above in Matlab 58 using the codes provided by the respective authors ., All arising linear systems have been solved using Matlab’s backslash operator ( ∖ ) ., The time integrations have been performed over the same interval 0 , tmax , where tmax = 360 days ., The matrix Q corresponds to a Markovian household model with three epidemiological compartments , i . e . , S , E and I ( test results in the main paper ) , and to a more complicated model , Fig A in S1 Text , representing complex transmission dynamics of a multi-strain infection in the population ( test results in the S1 Text ) ., Our computations were carried out on a 64-bit desktop computer running Ubuntu 14 . 04 LTS with 32GB RAM and 16 processors each capable of 3 . 30GHz ., To compare the error of our algorithms’ output at the final time point with the reference solution , we have chosen to use the relative infinite norm ( ℓ∞-norm ) , i . e . , the infinite norm of the difference between the approximate and the reference solution at the final time point divided by the infinite norm of the reference solution ., The result of the reference simulation was obtained by solving the ODE system using Matlab’s function expm ( ) which gives a machine precision estimate of the matrix exponential ., We performed the benchmarking by assuming a population sub-unit with a small ( N = 10 ) , medium ( N = 30 ) and large ( N = 99 ) number of individuals with corresponding Q matrices of size 66 , 496 and 5 , 050 respectively for the SEI model ., For the more complex multi-strain model , the matrix Q is of size 120 and 11 , 440 corresponding to a household with 3 and 8 individuals respectively ., We run each algorithm with 100 replicates and report the mean for the computational time against the error , i . e . , the relative ℓ∞-norm ., The results for numerical performance for the SEI model with the small Q matrix of size 66 × 66 are presented in Fig 1 with the x-axis showing the log time in seconds and the y-axis the error as measured by log of relative ℓ∞-norm ., From the figure , we can observe that the accuracy of DA1 , DA2 and DA3 increases with an increase in the step size in what seems to be linear in log scale ., The higher the degree to which the polynomial is expanded , the greater the accuracy ., However , for comparable accuracy , DA1 takes almost 2 orders of magnitude more time than DA3 ., This is because DA1 needs to be evaluated over a much smaller step size in order to achieve the same accuracy as DA3 which consequently increases the computational time ., The RK4 approximation appears to be relatively less accurate than all of the other methods for large step sizes but the accuracy increases with a reduction in the step size ., The computational time does not appear to be greatly influenced by varying the tolerance for Chebyshev expansion although it does take more time for a lesser accuracy in the solution vector compared to DA1 , 2 , 3 , RATKRYL and RK4 ., RATKRYL has the advantage of a steep increase in the accuracy of the solution for very minimal increase in computational time ., ODE45 is the slowest of all the methods followed by EXPM2010 and EXPOKIT ., However , for a small matrix , EXPOKIT , results in the most accurate solution ., These results are however dependent on the size of the system ., For a medium sized matrix of size 496 , all the algorithms take more computational time with the accuracy of CHEBY and EXPOKIT decreasing significantly , see Fig 1 ., For a large matrix , RATKRYL takes the shortest time albeit at the expense of reduced accuracy ., However , the error can be further decreased to the desired level by reducing the relative tolerance ., Fig B and C in S1 Text ., Complex transmission dynamics model show comparable results when the integrators are applied to a more complex multi-strain model ., The outcome is consistent with that of the simple SEI model considered here and the efficiency of the methods depends on the system size in both cases ., Since the size of the care homes in our data range between 18 and 99 , leading to matrix sizes of between 190 and 4 , 371 , we opt to use the RATKRYL method in the following model fitting computations ., This method seems to strike a good trade-off between the accuracy and the computational time for both small and large system sizes ., Since it does not guarantee a probability vector , we implement the projection mapping discussed above , guaranteeing probability vectors at an almost negligible computational cost ., The error tolerance used for the model fitting is taken to be 10−3 ., Using RATKRYL method , we solve the SEI scabies model and fit it to data collected from 7 carehomes in the UK 28 ., We used MATLAB’s kdensity , to produce kernel posterior predictive densities , which gives a smooth probability density function from a finite sample of the random variables β , α and γ ., The contour plots in Fig 2 show the joint posteriors with the histograms showing the marginal posterior densities for the three parameters β , α and γ with the black solid lines showing the prior distributions ., The blue dashed lines , in the sub-plots of the first column of Figs 3 and 4 , show the mean of the posterior samples representing how well the model fits the data ( blue circles ) from the seven care homes ( row A to G ) ., The black solid lines ( mean ) and the grey dashed lines ( 95% CI ) , in the first column of Figs 3 and 4 , represent the model predictions of the number of scabies cases in the presence of treatment with permethrin and ivermectin respectively ., Treatment is implemented immediately when one or more cases have been detected in a care home ., In all care homes , we can observe that treatment with permethrin leads to a total eradication of scabies with the exception of care home D in which case a late observation of scabies cases occured ., On the other hand , treatment with a single dose of oral ivermectin , first column Fig 4 , does not lead to eradication except in carehome G but leads to a reduction in the number of cases that later rebound and saturate at long time ., In the second column sub-plots of Figs 3 and 4 treatment with permethrin and ivermectin is seen to lead to a reduction in the QALY cost , computed as the cumulative person-days of symptomatic infection during the epidemic , with permethrin leading to a greater reduction with the exception of care home D . Fig 5A shows the full posterior we obtain for μ and θ ., Given the level of uncertainty for such a small dataset , we perform an analysis for given fixed overdispersal θ ( Fig 5B , 5D and 5F ) which indicates that as θ is increased , the uncertainty in μ also increases and that the probabilities of a given number of deaths in 6 months in a population of size 47 as defined in ( 16 ) also become more spread out , which we assume is the salient fact to explain in the Barkwell and Shields data ., In particular , the probability that κ ≥ 15 is 0 . 0033 for overdispersal 0 , which is consistent with the very low p-values reported by Barkwell and Shields ., As we increase the overdispersal , we obtain probabilities that κ ≥ 15 of 0 . 20 for overdispersal θ = 0 . 01 , 0 . 31 for overdispersal θ = 0 . 02 , 0 . 52 for overdispersal θ = 0 . 1 and 0 . 58 for overdispersal θ = 1 ., Therefore , the mortality pattern reported by Barkwell and Shields is not a particularly low probability event ., These conclusions are confirmed by our analysis over the full posterior including overdispersal ( Fig 5C , 5E and 5G ) ., Expansion and projection based methods to compute the exponential of a matrix were used to compare how well they perform when applied to a Markovian SEI household model describing the transmission of scabies ., The computational accuracy and efficiency were tested by performing various numerical tests by changing the step size h and the system size ., In order to test the accuracy , the reference solution was obtained by EXPM and computing the relative ℓ∞-norm ., The methods indicate that for large system sizes , the RATKRYL method was superior both in terms of computational time and accuracy ., The DA methods would be appropriate when a fast solution is needed without the need for strict accuracy ., Otherwise , for strict accuracy , obtained by reducing the time step , there seems to be a linear increase ( in log scale ) in time ., DA1 has the inherent benefit of ensuring the solution is a probability vector negating the need for correction which might come with some computational time saving and a reduction on the error ., In instances where the solution is required at multiple time points , the expansion based methods can deal with this by applying them repeatedly using the last obtained approximation; the accuracy of which is dependent on the step size ., However , the projection based methods have the advantage that the projection space is chosen independent of t and hence no time stepping is required ., To run the Scabies model , we chose RATKRYL as it runs fast for different choices of tolerance over a wide range of matrix sizes ., The efficiency of the method allows us to perform efficient Bayesian inference on limited scabies data , allowing us to incorporate prior information and quantify remaining uncertainty in a consistent manner ., A proper choice for the best method will however depend upo | Introduction, Methods, Results, Discussion | In the context of an ageing population , understanding the transmission of infectious diseases such as scabies through well-connected sub-units of the population , such as residential care homes , is particularly important for the design of efficient interventions to mitigate against the effects of those diseases ., Here , we present a modelling methodology based on the efficient solution of a large-scale system of linear differential equations that allows statistical calibration of individual-based random models to real data on scabies in residential care homes ., In particular , we review and benchmark different numerical methods for the integration of the differential equation system , and then select the most appropriate of these methods to perform inference using Markov chain Monte Carlo ., We test the goodness-of-fit of this model using posterior predictive intervals and propagate forward the resulting parameter uncertainty in a Bayesian framework to consider the economic cost of delayed interventions against scabies , quantifying the benefits of prompt action in the event of detection ., We also revisit the previous methodology used to assess the safety of treatments in small population sub-units—in this context ivermectin—and demonstrate that even a very slight relaxation of the implicit assumption of homogeneous death rates significantly increases the plausibility of the hypothesis that ivermectin does not cause excess mortality based upon the data of Barkwell and Shields . | Our work makes five main contributions ., ( 1 ) We study a previously under-modelled scenario—transmission of scabies in residential care homes—that is of significant and growing public health importance in the context of an ageing population ., ( 2 ) We develop a Markov-chain-based modelling framework that accurately captures the disease dynamics in well-connected sub-units such as care homes , but whose use has previously been limited due to computational cost ., ( 3 ) We demonstrate that appropriate numerical methods ( in particular rational Krylov approaches ) for the solution of the mechanistic model for transmission of scabies in care homes speeds up evaluation by several orders of magnitude compared to other methods ., ( 4 ) We demonstrate a Bayesian approach in which the model is fitted to data using computationally-intensive MCMC methods , validated using posterior predictive intervals , and has its uncertainty quantified in forward predictions ., ( 5 ) We revisit the question of safety of ivermectin using appropriate methods and demonstrate that relaxation of the assumption of homogeneous death rates can make previous influential conclusions on lack of safety unsound . | death rates, invertebrates, medicine and health sciences, markov models, dose prediction methods, applied mathematics, tropical diseases, social sciences, parasitic diseases, animals, health care, simulation and modeling, algorithms, mathematics, ectoparasitic infections, pharmaceutics, sexually transmitted diseases, algebra, neglected tropical diseases, population biology, polynomials, research and analysis methods, infectious diseases, health economics, scabies, mites, economics, probability theory, arthropoda, population metrics, eukaryota, biology and life sciences, physical sciences, organisms | null |
journal.pgen.1007354 | 2,018 | Extensive reshaping of bacterial operons by programmed mRNA decay | One of the defining features of bacterial gene expression is the co-transcription of multiple genes within polycistronic mRNAs 1 ., Co-transcribed genes often encode proteins that participate in the same biological process or interact in a protein complex , allowing these genes to share the rate limiting steps of transcription ., Thus , polycistronic transcription can synchronize the expression of functionally related genes , streamlining gene regulation in the compact genomes of bacteria ., A major limitation of polycistronic transcription is that it theoretically generates equal amounts of mRNA for all genes sharing an operon ., This can be disadvantageous in the common scenario where one of the proteins encoded in the operon is needed in higher amounts than the others ., In many such cases this inconsistency is resolved by differential translation efficiencies of individual genes encoded on the same transcript 2 , 3 , or by internal transcription start sites ( iTSSs ) in the middle of the operon 4 ., However , while differential translation efficiencies and iTSSs have been identified in many transcriptional units 2 , 3 , 5 , 6 , differential operon expression patterns cannot always be explained by such mechanisms ., More than three decades ago it was shown that mRNA degradation can lead to differential mRNA stoichiometries for genes encoded on a single polycistron 7 , 8 ., This regulation by differential mRNA decay was demonstrated for the E . coli malEFG operon encoding the maltose ABC-transporter complex , where MalF and MalG form the transmembrane channel and MalE acts as the substrate-binding protein , which is needed in higher quantities 8 ., It was shown that the 3’ portion of the polycistronic mRNA ( including malFG ) is rapidly degraded by RNases whereas the 5’ portion ( malE ) is stabilized , leading to accumulation of the malE mRNA and to a substantial relative increase of the MalE protein as compared to MalF and MalG 8 ., While regulation via differential operon decay was reported in multiple bacterial operons over the last few decades 9–17 , the extent of this regulatory mechanism or its evolutionary conservation in bacteria remains unknown ., In this study , we combine a set of multi-layered high-resolution RNA-seq approaches to extensively map and characterize differentially decaying operons in E . coli ., We find that regulation by differential decay is widespread and reshapes the stoichiometries of ~12% of polycistronic mRNAs in this model organism ., This process is highly conserved also in the Salmonella and Enterobacter transcriptomes , and depends on conserved RNase-resistant RNA structures that guide the RNA decay dynamics ., Furthermore , we find that differential decay is often dependent on translation and that stabilized operon segments are characterized by relatively higher ribosome densities than non-stabilized segments , suggesting that variation in translation efficiency guides endonuclease cleavage to ribosome depleted sections ., Taken together , our data support differential operon decay as a common and frequently conserved mode of regulation in bacteria ., To examine the possible extent of differential RNA degradation in shaping operon stoichiometries , we first sought to limit our analysis to a set of well-defined E . coli operons annotated in the EcoCyc database 18 ., We discarded from the set operons that were expressed at low levels or that are likely regulated by iTSSs or by internal partial transcription termination under the conditions tested in this study ( Methods ) ., For this , we comprehensively mapped the TSSs in exponentially growing E . coli bacteria using a previously established approach 18 , 19 ( S1 Table ) , and excluded cases suspected as regulated by iTSSs ( Methods ) ., We further excluded cases where two consecutive genes within the operon were separated by an intrinsic terminator motif ( a stem-loop structure followed by a uridine tract ) or in which substantial Rho-dependent termination was previously described 20 ( Methods ) ., The final set included 390 significantly expressed multi-gene operons overall encompassing 1292 genes , representing 31% of the genes in the E . coli BW25113 genome ( S2 Table ) ., We then treated exponentially growing E . coli cultures with rifampicin , a transcription initiation inhibitor 21 , and tracked the RNA decay process by sequencing mRNAs collected at 1-minute intervals following transcription inhibition 22 , 23 ( S1 Table; Methods ) ., These data were used to calculate the half-life of individual genes ( average half-life = 1 . 5 +/- 0 . 9 minutes ) , with the results overall agreeing well with recent RNA half-life calculations for E . coli genes 22 , 23 ( S1 Fig; Pearson R = 0 . 75 , p < 10−15; Methods ) ., However , we detected 47 operons ( 12% of the 390 analyzed operons ) , encompassing 177 genes , in which two consecutive genes displayed correlated differential decay rates and differential steady-state mRNA levels ( Fig 1A–1D; S3 Table; Methods ) ., Re-analyzing RNA decay data from recent studies corroborated our results 22 ( S3 Table ) ., It is well established mathematically and experimentally that the ratio in steady-state mRNA levels of two equally transcribed genes should equal to the ratio in their half-lives ( for example , if the half-life of gene A is 2 minutes and the half-life of gene B is 1 minute , then at steady-state gene A will be 2-fold overexpressed as compared to gene B ) 24 ., Indeed , for the majority of operonic gene-pairs in which we found differential decay rates , the estimated differences in half-life closely matched the observed differences in steady-state mRNA abundance ( Fig 1D; S3 Table ) ., In about one quarter of the cases we found that the differences in decay rates did not fully match the differences observed in the mRNA level , indicating either measurement error or additional regulation by iTSSs or leaky termination in these operons ( S3 Table ) ., Among the decay-regulated operons we identified was the previously described maltose ABC transport operon , malEFG 8 ( Fig 1A ) ., We measured the half-lives of the malFG and the malE segments to be 1 and 3 . 5 minutes , respectively , consistent with the relative stabilities measured for these genes in previous reports and validating our measurements ., In addition to the malEFG operon we identified four other ABC transport systems , including those transporting polyamines , arginine , histidine and methionine ( Fig 1B and 1C; S3 Table ) ., In all these operons , we found that the stabilized gene was invariably the substrate-binding subunit , which is typically needed in higher quantities than the channel-forming units ., This effect was independent of the gene’s position within the operon ., For example , while the stabilized maltose substrate-binding subunit gene , malE , is the first gene in the operon ( Fig 1A ) , the substrate-binding subunits of the polyamine ( potD ) and arginine ( artI ) transporters occur at the end or the middle of their operons , respectively ( Fig 1B and 1C ) ., Stabilization of the mRNA of the substrate-binding subunit results in higher steady-state mRNA levels of the respective gene , suggesting that differential decay is a common mechanism for tuning differential stoichiometries in ABC transport systems ., Another transport system subject to decay-based regulation was the tatABC operon , which encodes the evolutionarily conserved twin-arginine protein export system 25 ( Fig 1D ) ., In this system the TatA protein forms homo-polymeric ring structures that contain on average 25 TatA copies for each TatBC complex 25 , 26 , and we indeed found the mRNA segment encoding the TatA subunit to be highly stabilized as compared to that encoding the TatBC subunits ( S3 Fig ) ., We also observed differential decay in the rpoZ-spoT containing operon , such that the segment encoding rpoZ is stabilized compared with that containing spoT ., The rpoZ gene encodes the RNAP omega subunit , which binds the regulatory alarmone ppGpp 27 , and SpoT is one of the major enzymes involved in the synthesis and degradation of this alarmone 28 ( S3 Table ) ., As the enzymatic activity of a single SpoT rapidly converts multiple ppGpp molecules and hence can affect multiple rpoZ gene products , it is likely that RpoZ protein expression levels need to be higher than that of SpoT ., Conceptually similar , we also identified differential decay in operons encoding 4 different two-component systems , where in each operon the transcription factor component was consistently more stable than the histidine-kinase enzyme 29 ., Finally , we identified additional differentially decaying operons involved in cellular signaling and regulation , protein translocation across the membrane , antibiotic resistance and various metabolic processes , suggesting that differential decay plays an important role in post transcriptional regulation of many physiological processes in E . coli ( S3 Table ) ., To examine whether regulation by differential decay is evolutionarily conserved we performed an identical rifampicin-based decay assay using the human pathogenic bacterium Enterobacter aerogenes grown under the exact same conditions as E . coli ( S1 Table; Methods ) ., We found that 71% ( 20/28 ) of decay-regulated orthologous operons expressed in E . aerogenes under the tested conditions show conserved differential decay patterns ( S2 Fig; S4 Table ) ., In addition , we compared the steady-state mRNA levels of E . coli decay-regulated operons with that of their orthologous operons annotated in Salmonella typhimurium , grown under the same conditions ., This analysis showed that 68% ( 26/38 ) of the decay-regulated E . coli operons shared between the two species present similar patterns of sub-operon differential expression in S . typhimurium ( S3 Fig; S1 and S5 Tables; Methods ) ., The observed conservation of differential decay patterns between different bacteria highlights the functional importance of this phenomenon in bacterial gene regulation ., Our analyses discover many new cases where the steady-state levels of individual genes within polycistronic mRNAs are controlled via selective stabilization ( differential operonic decay ) ( Fig 1; S3 Table ) ., However , why some transcript regions are protected from digestion whereas others are rapidly degraded was unclear ., In the well-studied maltose operon , it was shown that the malE gene is stabilized due to the presence of a 3’ protective RNA structure that resides in the intergenic region between malE and malF ., This structure exerts its protective effect on malE following an initial endonucleolytic cleavage event that generates a functional malE fragment still physically attached to the protective element ., This structure can then resists the 3’-5’ exonuclease processive activity typically performed in Gram negative bacteria by the Polynucleotide Phosphorylase ( PNPase ) and RNase II enzymes 30 , 31 , thus stabilizing the malE gene , which is the 5’-most gene in the operon 8 , 32 ( Fig 2A ) ., Similar stabilization via 3’ RNA structures was also described in a few additional operons 9 , 10 ., The set of differentially decaying operons we found included 34 gene-pairs ( 70% ) in which the 5’-most gene is stabilized as compared with the downstream region , akin to the case of malEFG ( Fig 1A; S3 Table ) ., In 76% of these ( 26/34 ) , the 3’ end of the stabilized mRNA portion ( as determined by the term-seq method 33 ) occurred immediately downstream to an energetically stable RNA-hairpin ( Fig 2B–2D; S6 Table; Methods ) ., These RNA structures were significantly more stable than those occurring randomly across the genome ( S4 Fig; p < 10−14 , Wilcoxon ) and were not followed by the uridine tract required for Rho-independent termination 34 ( S6 Table ) ., In addition , we could not detect significant Rho-dependent termination at these sites using RNA-seq data from bicyclomycin-treated bacteria 20 , supporting that these RNA structures have a role in 3’-5’ exonuclease resistance rather than transcription termination ., In the majority of cases ( 17/26 ) , the structural RNA elements presumed to protect the upstream gene from degradation were embedded within the protein-coding sequence of the downstream gene , positioned on average 107 +/- 56 nt into the coding sequence ( Fig 2B–2D; S6 Table ) ., The sequence at the ORF of the downstream gene therefore carries , in addition to the protein-coding information , also the information guiding the differential decay of the transcript and thus , its stoichiometry at steady state ., Examining the homologous genes in the Salmonella and Enterobacter genomes revealed that these protective RNA structures are conserved in 75% and 91% of the differential decay instances shared between E . coli and Salmonella , or E . coli and Enterobacter , respectively ( S4 and S5 Tables ) ., This correlated with an enhanced sequence conservation at the wobble codon positions that overlap protective RNA structures ( p = 0 . 0008; S5 and S6 Figs; Methods ) ., Interestingly , multiple protective structures can be observed in a single operon , generating complex decay patterns: for example , in the menBCE operon , we detected consecutive protective hairpins downstream of both the menB and menC genes , in correlation with the gradual decrease in stability and mRNA abundance detected in this operon ( Fig 2D ) ., In 30% of the decay-regulated operons we detected , the stabilized gene of the operon occurred in the middle of the transcript or was the most downstream one ( e . g . , Fig 1B and 1C; S3 Table ) ., In these cases , protection from exoribonuclease activity cannot fully explain the observed stabilization , because no 5’-3’ exoribonucleases are known to exist in E . coli 30 ., However , a similar downstream stabilization pattern was previously reported in the papBA operon of uropathogenic E . coli , encoding a transcription factor , PapB , and the major pilus protein , PapA 12 ., It was found that the RNase E endoribonuclease cleaves the papBA mRNA at the intercistronic region separating the papB and papA genes , at a site located upstream of an RNA hairpin structure 36 ., Consequentially , this processing event produces two transcript species: the unstable papB segment , which is rapidly degraded by 3’-5’ exonucleases , and the papA mRNA , which is protected by the 5’ RNA-structure from additional RNase E cleavage , as well as an RNase resistant terminator structure at its 3’ end 36 ( Fig 3A ) ., To examine whether similar 5’-protective structures also occur in the decay-regulated operons described above , we conducted experiments with WT and a temperature-sensitive RNase E mutant , which is inactivated in the non-permissive temperature of 44°C 37 , 38 ( Methods ) ., We used 5’-end RNA sequencing to compare the repertoire of exposed mono-phosphorylated mRNA 5’-ends in the WT and mutant strains following brief incubation in 44°C , and identified RNase E cleavage sites as 5’-ends that were present in the WT but were consistently and substantially depleted in the inactivated RNase E mutant across three biological replicates ( S1 Table; Methods ) ., In 67% ( 10/15 ) of the operons described above , a clear RNAse E cleavage site was detected immediately upstream of the stabilized gene ( Fig 3B–3D; S7 Table ) ., All cleavage sites occurred in unstructured mRNA sections and at sequences closely matching the RNase E RN|WUU cleavage motif , with the conserved +2 uridine present in all cases 38 ( Fig 3; S7 Table ) ., Moreover , in 7 of these 10 operons , the cleavage site occurred closely upstream of a stable RNA structure , supporting a potential papBA-like protection mechanism in these cases ( Fig 3B–3D ) ., We found that all of the detected 5’ protective structures reside within the protein-coding region of the upstream unstable gene ( Fig 3B–3D; S7 Table ) ., For 80% ( 8/10 ) of these operons , an identical or closely positioned RNase E cleavage site , as well as protective structures , could be detected in S . typhimurium , based on recently published in-vivo cleavage maps , indicating a high degree of evolutionary conservation at the mechanistic level 38 ( S7 Table ) ., While the RNA structures described above can insulate specific transcript regions during active degradation , the endonucleolytic cleavage events that initiate differential decay must first be directed to particular operon segments ., For example , in the maltose operon , RNase E must preferentially cleave the mRNA encoding malFG , but not that of malE , such that the malE mRNA will remain physically connected to its protective RNA structure 8 , 32 ( Fig 2A ) ., However , the guiding factors that direct initializing cleavage events are poorly understood , even in the well characterized example of the malEFG operon 8 , 30 , 32 ., Ribosome densities were previously found to positively correlate with mRNA stability , presumably by physically restricting access to RNase cleavage sites 39–42 ., Re-analyzing recently published ribosome profiling data 3 , we found that in almost all differentially decaying gene-pairs the stabilized gene was covered by substantially more ribosomes than the non-stabilized genes in the operon ( after normalization to transcript levels ) , with a median of 4 . 7-fold more ribosomes per transcript coating the stabilized mRNA segments ( Fig 4A; Methods ) ., Furthermore , such differential ribosome density profiles were significantly enriched in decay-regulated operons ( Fig 4A; p < 10−8 , Wilcoxon; Methods ) , implying that ribosome densities play a common role in shaping the differential decay process , possibly by decreasing endonucleolytic cleavage rates in operon regions populated by more ribosomes ., To directly examine whether differential ribosome density is involved in guiding differential decay , we analyzed recently published data in which E . coli mRNA half-lives were measured following a brief pre-exposure to kasugamycin , a translation initiation inhibitor 22 ., In this experiment , kasugamycin treatment is expected to result in polycistronic transcripts where all genes are equally devoid of ribosomes , providing an approach to study the contribution of differential translation to operon decay ( Fig 4B ) ., Notably , the short inhibition of translation initiation resulted in a substantial reduction in differential decay in the vast majority of the regulated operons in our set ( Fig 4C; S8 Table; Methods ) , providing evidence that differential decay within operons is often dependent on differences in translation efficiency ., These results suggest that differences in ribosome densities guide the endonuclease cleavage events that initiate the differential decay process within polycistronic transcripts ., Combined with the results from the above sections , we chart a general model for differential decay of polycistronic transcripts in E . coli ( Fig 5 ) ., Differential decay of polycistronic operons enables bacteria to reshape uniform transcription into differential expression ., This process has been studied for the last 3 decades in multiple different species and operons , including in malEFG 8 , lacZYA 15 , focA-pflB 43 , and iscRSUA 10 in E . coli K12; papBA 12 and cfaAB 11 in pathogenic E . coli; pldB-yigL in Salmonella 13; and pufBALMX 9 in Rhodobacter capsulatus ., In the current study , we took a transcriptome-wide approach to extensively map differentially decaying operons in E . coli ., We find that differential decay is a common mode of regulation in bacteria , involved in re-shaping the stoichiometry of at least 12% of the E . coli operons , in a manner conserved between related species ., We note that this number may actually be higher as recent studies reported the existence of condition specific differential decay 10 , 13 ., The differential decay data produced in this study were organized into an interactive online browser that is available at: http://www . weizmann . ac . il/molgen/Sorek/operon_decay/ ., We show , using a combination of 3’ and 5’ RNA-termini sequencing , that protective RNA structures occur at the boundaries of stabilized operon segments in the vast majority of cases and are generally encoded within the protein-coding regions of the flanking , unstable genes ., In addition , we find that the less stable genes in the operon are covered by fewer ribosomes per transcript and that translation plays a major role in shaping the differential decay process ., While the relation between ribosomes and mRNA decay has been well-established for monocistronic mRNAs 39–41 , 44 , our results extend this concept to multi-gene operons with differentially decaying transcript segments ., Importantly , these observations provide a potential explanation for how specific operon segments are selected for initial endonucleolytic cleavage by RNases with degenerate target motifs , a piece of the mechanism that was so far less understood ( Fig 5 ) ., Interestingly , stable structures at protein-coding mRNA regions were previously suggested to reduce translation efficiency 44–46 , implying that protective structures could actually play a dual role: first , blocking translation initiation , which reduces ribosome density on the flanking gene and exposes the region to increased RNase E dependent cleavage , and second , direct protection of the stable transcript region from the decay process ., Although our differential decay models provide a potential mechanism for most of the regulated operons in our dataset , additional factors , such as trans-acting ncRNAs , have been found to play a role in shaping operon decay patterns by modulating access to rate-limiting endonuclease cleavage sites 10 , 13 ., Notably , such ncRNAs enable condition-specific stoichiometric regulation in operons that are otherwise degraded uniformly ., Indeed , our analysis failed to detect differential decay in both the iscRSUA and the pldB-yigL operons , which were recently shown to be regulated by ncRNAs activated under conditions other than the ones employed in our study 10 , 13 ., Thus , considering the large number of trans-acting ncRNAs and antisense RNAs in bacteria , the extent of differential decay-based regulation is likely even greater than our current estimates ., Whereas our proposed models for differential decay likely hold for other organisms that share similar RNA decay machineries ( especially proteobacteria that rely on RNase E and 3’-to-5’ exonucleases similar to E . coli ) , many bacterial lineages harbor different RNase combinations and properties , for example 5’-3’ processive exonucleases in Firmicutes 30 ., Presumably , differential decay in such organisms may rely on different principles or additional molecular signals ., Escherichia coli BW25113 , Salmonella enterica subsp ., enterica serovar Typhimurium SL1344 and Enterobacter aerogenes KCTC 2190 were cultured in LB media ( 10g/L tryptone , 5g/L yeast extract 5g/L NaCl ) under aerobic conditions at 37°C with shaking to an optical density ( OD600 ) of 0 . 5 ., Prior to sample collection , 1:10 ice-cold stop solution ( 90% ethanol and 10% saturated phenol ) was added and the cultures were immediately placed on ice to stop all cellular processes 22 , 47 ., Bacterial pellets were collected by centrifugation ( 4000 rpm , 5 min , at 4°C ) ; flash frozen and stored in -80°C until RNA extraction ., For RNA isolation , frozen pellets were thoroughly resuspended and mixed in 100μl lysozyme solution ( 3mg/ml in 10mM Tris-HCl and 1mM EDTA ) pre-warmed to 37°C and then incubated at 37°C for 1min ., The cells were then lysed by immediately adding 1ml tri-reagent ( Trizol ) followed by vigorous vortexing for 10s until solution is cleared ., Following an incubation period of 5min at room temp ( RT ) , 200μl chloroform was added and the sample was vortexed for another 10s until homogeneous ., The sample was incubated for 2-5min at RT until visible phase separation was observed and then centrifuged at 12 , 000g for 10min ., The upper phase was gently collected ( about 600μl ) and mixed at a 1:1 ratio with 100% isopropanol and then mixed by vortexing for 2-3s ., The sample was incubated for 1h at -20°C and then centrifuged ( 14 , 000rpm , 30min , at 4°C ) to collect the RNA pellet ., The solution was removed without disturbing the pellet , followed by two consecutive wash rounds using 750μl 70% ethanol ., The pellets were air dried for 5min and then dissolved in nuclease free H20 and incubated for 5min at 50°C ., All RNA samples were treated with TURBO deoxyribonuclease ( DNase ) ( Life technologies , AM2238 ) ., RNA-seq , term-seq and 5’-sequencing libraries were prepared and sequenced as previously described 33 , 48 ., Sequencing was performed using the Illumina NextSeq 500 and the data was deposited in the European Nucleotide Database ( ENA ) under accession no ., PRJEB21982 ( S1 Table ) ., Sequencing reads generated for E . coli , S . typhimurium and E . aerogenes were mapped to the CP009273 , NC_016810 . 1 and NC_015663 . 1 RefSeq genomes , respectively , using NovoAlign ( Novocraft ) V3 . 02 . 02 with default parameters , discarding reads that were non-uniquely mapped as previously described 33 ., For 5′-end sequencing , the RNA was divided into a tobacco acid pyrophosphatase ( TAP ) –treated and untreated ( noTAP ) reactions to enable primary transcript detection and then sequenced using 5’-seq 33 , 49 ., TSSs were mapped as was recently described 19 ., E . coli or E . aerogenes overnight cultures were diluted 1:100 into 25ml fresh LB media and incubated at 37°C until reaching an optical density ( OD600 ) of 0 . 5 ., The culture was then placed in a preheated 37°C shallow water bath to preserve the experiment temperature and 125μl Rifampicin ( 100mg/ml , for a final concentration of 500μg/ml ) were immediately added to the culture to inhibit RNA synthesis ., Selected time points were sampled by collecting 1 . 4ml from the culture into a pre-chilled tube containing 170ul of ice-cold stop solution ( 90% ethanol and 10% saturated phenol ) to deactivate cellular processes and RNA-decay ., The sample was quickly vortexed and then placed on ice until all time points were collected ., The samples were centrifuged for 5min at 4000rpm to collect cell pellets and were flash frozen ., During RNA extraction , after tri-reagent mediated lysis , each sample was spiked with 5fmol of the ERCC RNA ( Ambion , 4456740 ) to allow normalization of RNA abundance between samples ., RNA-seq libraries were prepared and sequenced as described above ., Gene-expression was calculated as the median coverage per nucleotide ( reads/nt ) normalized by the number of reads that mapped to all ERCC spike-in RNA , an estimate which we found is more robust than the total number of reads or average coverage in cases of non-uniform decay patterns , where overlapping operon regions , or small sub-ORF regions can persist long after the full transcript has been eliminated ., Transcript half-lives were calculated by fitting the decay time-course abundance measurements per gene with a delayed exponential-decay function as previously described 22 ., The previously published half-lives for the 779 transcripts described in S1 Fig were taken from the “Fig 4 Source data 1” in 22 , which provides the estimated Decay rate ( λ ) per gene ., To extract the half-life we calculated t1/2 = ln ( 2 ) / λ ., Operon gene annotations were extracted from EcoCyc 18 ( S2 Table ) ., To identify and analyze gene-pairs found within the same transcriptional unit , consecutive gene-pairs were only considered if the following criteria were met:, i ) the intergenic region interspacing the genes was shorter than 200nt ., ii ) The downstream gene was not associated with an independent TSS under the growth conditions of this study 19 ., iii ) The upstream gene was not associated with a term-seq site displaying an intrinsic terminator signature ( i . e . , hairpin followed by a uridine stretch ) ., iv ) No substantial rho-dependent termination measured in E . coli treated with the Rho inhibitor Bicyclomycin 20 ., v ) The stable and unstable genes were covered by median greater than or equal to 10 and 1 reads/nt , respectively ., vi ) The expression difference between the genes was not greater than 10-fold , as we find such high values were sometimes indicative of incorrect operon annotation or highly active secondary promoters ., vii ) The decay rate of both genes was measured in at least one biological replicate of the experiment ., Gene-pairs in which one gene was at least 2-fold more stable and 2-fold more abundant than its consecutive neighbor gene were classified as putatively decay-regulated ( S3 Table ) ., We manually accepted 4 gene-pairs displaying borderline , yet consistent signal as well as 7 differentially expressed gene-pairs in which the decay rate was not measured in our experiment usually due to lack of expression in the conditions tested , but for which differential decay could clearly be identified in a recently published dataset 22 ( S3 Table ) ., Term-seq libraries were prepared as previously described 33 and sequenced using a paired-end sequencing approach 48 ( S1 Table ) ., The number of 3’-end reads per genomic position was calculated and for each 3′ site the average library insert length was calculated using the paired-end read mapping positions ., Sites were then associated with their respective genes , requiring that the average insert length would overlap the gene coding region 48 ., The position supported by the highest number of reads associated with a stabilized gene was selected as the representative 3’-terminus of the stabilized RNA in steady-state ( S4 and S6 Tables ) ., In a few cases , a different , slightly less covered position was selected instead if it provided a substantially better fit for the decay pattern observed ., The sequence upstream of each selected site was extracted from the genome and folded using RNAfold 35 to evaluate the predicted structure and its estimated stability ( kcal/mol ) ( S4 and S6 Tables ) ., The S . typhimurium and E . aerogenes protein-coding sequences were retrieved from NCBI and blasted against the E . coli protein database , with E-value set to 10−5 ., Gene and operon orthologues were classified as the Best Bi-directional Hits ( BBHs ) ., Gene-pairs were compared if they occurred consecutively as in E . coli and were substantially expressed , as described above ( S4 and S5 Tables ) ., Genes containing a protective structure embedded at least 50 bases into the coding region were selected ( n = 23 ) and their orthologues from up to 21 different bacterial strains belonging to the Enterobacteriaceae family ( including the E . aerogenes and S . typhimurium strains in this study ) were identified using blast ( S9 Table ) ., Gene information for each organism was downloaded from the Integrated Microbial Genomes ( IMG ) database 50 ., Gene orthologues were discarded if the gene sizes differed by more than 50 bases ., Genes were then aligned using Clustalw2 51 and the conservation at each position in the alignment was defined as the maximal base frequency detected at that position ., Conservation was calculated for each of the codon positions independently ( as shown in S5 Fig ) ., The average conservation at positions overlapping protective structures was calculated using a window size of 50 bases ., In cases where the stabilizing structure occurred at the end of the gene ( as in Fig 3 ) the window was defined as 70 bases to accommodate the longer structures found in these genes ., The conservation in control gene regions that do not contain a known protective structure was calculated using a sliding window approach ( using the same window size and sliding each window by 5 codons at a time over the entire protein coding region ) ., WT E . coli and temperature-sensitive RNase E mutants were generously provided by the McDowall lab 37 ., Triplicates of each strain were grown in the permissive temperature of 30°C overnight in LB and were diluted the next day 1:100 in fresh media ., The cultures were then grown in 3 | Introduction, Results, Discussion, Methods | Bacterial operons synchronize the expression of multiple genes by placing them under the control of a shared promoter ., It was previously shown that polycistronic transcripts can undergo differential RNA decay , leaving some genes within the polycistron more stable than others , but the extent of regulation by differential mRNA decay or its evolutionary conservation remains unknown ., Here , we find that a substantial fraction of E . coli genes display non-uniform mRNA stoichiometries despite being coded from the same operon ., We further show that these altered operon stoichiometries are shaped post-transcriptionally by differential mRNA decay , which is regulated by RNA structures that protect specific regions in the transcript from degradation ., These protective RNA structures are generally coded within the protein-coding regions of the regulated genes and are frequently evolutionarily conserved ., Furthermore , we provide evidence that differences in ribosome densities across polycistronic transcript segments , together with the conserved structural RNA elements , play a major role in the differential decay process ., Our results highlight a major role for differential mRNA decay in shaping bacterial transcriptomes . | Bacteria utilize operonic transcription to synchronize the expression of multiple consecutive genes ., However , this strategy lacks the ability to fine-tune the expression of specific operon members , which is often biologically important ., In this report , we integrate multiple transcriptome-wide RNA-sequencing methods to show that bacteria commonly employ differential mRNA decay rates for genes residing within the same operon , generating differential transcript abundances for equally transcribed operon members , at steady state ., By comparing the transcriptomes of different bacteria , we show that differential decay not only regulates the expression levels of hundreds of genes but also often evolutionarily conserved , providing support for its biological importance ., By mapping the RNA termini positions at steady-state , we show that stabilized operon segments are protected from different RNases through a combination of protective RNA structures , which surprisingly , are often encoded within protein-coding regions and are evolutionarily conserved ., In addition , we provide evidence that differential ribosome densities over the regulated operons guide the initial events in the differential decay mechanism ., Our results highlight differential mRNA decay as a major shaping force of bacterial transcriptomes and gene regulatory programs . | sequencing techniques, medicine and health sciences, pathology and laboratory medicine, nucleases, enzymes, pathogens, messenger rna, dna-binding proteins, operons, enzymology, microbiology, bacterial diseases, enterobacteriaceae, genome analysis, dna, molecular biology techniques, rna sequencing, cellular structures and organelles, bacteria, bacterial pathogens, salmonella typhimurium, research and analysis methods, infectious diseases, rna structure, genomics, proteins, medical microbiology, ribonucleases, microbial pathogens, molecular biology, salmonella, ribosomes, biochemistry, rna, hydrolases, cell biology, nucleic acids, genetics, transcriptome analysis, biology and life sciences, computational biology, organisms, macromolecular structure analysis | null |
journal.pcbi.1005686 | 2,017 | Upregulation of an inward rectifying K+ channel can rescue slow Ca2+ oscillations in K(ATP) channel deficient pancreatic islets | 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 t | Introduction, Materials and methods, Results, Discussion | 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 . | 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 . | 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 | null |
journal.pcbi.1000006 | 2,008 | Multi-Scale Simulations Provide Supporting Evidence for the Hypothesis of Intramolecular Protein Translocation in GroEL/GroES Complexes | Proteins that have not yet folded to their native state may interfere with the machinery of the cell ., For this reason , prokaryotic and eukaryotic cells have evolved special macro-molecular “chaperone” complexes that capture and refold partially folded proteins , thereby preventing them from indulging in cellular mischief 1 , 2 , 3 , ., An important class of chaperone complexes are the cage chaperones or chaperonins ., These complexes can efficiently trap partially folded proteins in a cavity that is barely larger than the target protein , and assist in the folding of an entire class of proteins with different amino acid sequences ., Hence , the chaperonin is able to distinguish partly folded states from the native state , independently of the specific amino-acid sequence ., It is important to stress that in the presence of molecular crowding ( similar to the one present in a cell ) the chaperonin complex has been demonstrated to not release the substrate protein before it reaches the native state 4 ., Below , we report a detailed numerical study of protein dynamics inside the so-called GroEL-GroES chaperone complex ., The GroEL complex consists of two barrel-shaped protein complexes joined at the bottom ( see Figure 1 ) ., Non-native proteins can be captured in an open GroEL “barrel” ., The GroES “lid” can then cap a protein-containing barrel , thereby initiating the refolding process ., After about 15 seconds and several refolding cycles , the GroES cap is released and the other barrel is capped ( if it contains a protein ) ., A single “cycle” of the GroEL-GroES chaperone hydrolyses seven ATPs 5 ., This energy is presumably used to compress the protein in a smaller , more hydrophilic GroEL cavity , thus increasing the thermodynamic driving force to expel this protein ., Recently we reported simulations of the kinetics of chaperone-induced protein refolding , using a lattice model for the GroEL-GroES complex 6 ., This study suggested that proteins may refold either inside the cavity in which it has been captured or , surprisingly , by translocating from one barrel of the GroEL dimer to the other ( see Figure 2 ) ., This second route is unexpected because it is generally believed that proteins cannot cross the equatorial plane that separates the joined GroEL barrels 7 , 8 , 9 ., In the present paper we use atomistic and mesoscopic simulations to test whether such a translocation scenario is compatible with the available structural information on the GroEL complex ., Our simulation studies focus on the equatorial regime of the GroEL complex that might be expected to act as a barrier against translocation ., Crystallographic studies indicate that most protein units in the chaperonin complex have a fairly rigid structure both in the open and closed configurations 5 ., However , low-resolution small-angle neutron scattering experiments 7 and cryo-electron microscopy 8 , 9 indicate the presence of disordered residues in a central cavity of the equatorial region ., These chains do not show up in the X-ray crystallographic structure of the GroEL complex ., The presence of disordered protein chains in the pore that joins the two GroEL chambers will certainly affect the permeability of the equatorial plane , but they need not block translocation ., There are , in fact , examples 10 where disordered protein chains near a pore act to enhance the selectivity of the translocation process ., Interestingly , the chemical composition of the disordered chains in the GroEL complex is similar to that of chains in known translocation channels in the nuclear pore complex ., We start by considering a very naive estimate that has the advantage that it is based on the fully atomistic simulations ., From these simulations , we know the density profile of Cα atoms in the trans ring ( see Figure 4 ) ., If , in the spirit of the Flory model , we assume that the density fluctuations of independent polymer Kuhn segments are Poisson distributed , we can estimate the probability P0 that a tube with the diameter of an α-helix contains no Cα atoms at all ., This would lead us to an estimate of the free energy barrier that is equal to −kTlnP0 ., Using the density profile of Figure 4 and an estimate 14 for the persistence length of a protein filament , we obtain a translocation barrier of approximately 4 kBT ., If we make the ( unrealistic ) assumption that all Cαs in a single chain are fully correlated , then we estimate the barrier height to be only 1 kBT , which should be a significant underestimate ., To see whether such a rough estimate is at all reasonable , we can repeat the same procedure for the coarse-grained model where we can also perform direct free-energy calculations ., To be consistent with the previous case , we assume that the there are only excluded-volume interactions between the ( mainly Gly ) chains and the helix residues ., In terms of the interaction matrix of 15 this is equivalent to assuming that the helix consist entirely of Thr residues ., Assuming all Kuhn segments fluctuate independently , we estimate the barrier to be 4 kBT , and the assumption of fully correlated fluctuations will again yield an estimate of order 1 kBT ., The good agreement between the fully atomistic and coarse grained estimates is , of course , somewhat fortuitous , in view of the fact that the two density distributions are not identical ., However , it suggests that the coarse-grained model may be of practical use ., Next , we compute the free energy barrier for the coarse-grained model system using the MC method described in the Methods ., First we considered the case of pure steric interactions between both the chains and the helix ., In Figure 5 we plot the free energy F ( QS ) as a function of the reaction coordinate QS that measures the number of Cαs that have entered the pore region ., The plot shows a symmetric barrier with a height of approximately 2 kBT , which is surprisingly close to the estimate obtained assuming fully correlated fluctuations of protein segments ., In other words: the chains tend to move as a whole in an out of the central area of the pore ., This picture is supported by the snapshot of the pore region ( Figure S4 ) ., The main conclusion that we can draw from the coarse-grained free-energy calculations is that the presence of seven protein chains in the central core region of the trans ring is not enough to obstruct translocation on steric grounds alone ., Of course , the interaction between a typical translocation protein segment and the ring chains is not purely steric ., To consider the effect of both attractive and repulsive interactions , we consider the two cases separately ., As the chains consist predominantly of Gly , we consider the scenarios that the interactions between the filament residues and the Cα atoms of the helix are all equal to the twice the average of all attractive ( resp . repulsive ) interaction energies of Gly in the Betancourt-Thirumalai interaction matrix 15 ( −0 . 1kBT and +0 . 1kBT , respectively ) ., The strength of attractive/repulsive interactions between the Cαs of the helix and the filament is therefore −0 . 2kBT ( resp +0 . 2kBT ) ., By taking an interaction that is double the average attractive/repulsive interaction strength , we are presumably modeling rather extreme cases that should put bounds on the actual translocation barrier ., Figure 5 shows the computed free-energy barriers for translocation in the case of attractive ( resp . repulsive ) interactions ., The translocation barrier is appreciably lower when the chains attract the α helix ( 2 kBT ) than in the opposite limit ( 4 . 5 kBT ) ., However , the most striking observation is that the barrier is quite small in either case - a barrier of 4 . 5 kBT can easily be crossed due to the action of thermal fluctuations ., In fact , in the case of attractive interactions , there is virtually no barrier for translocation ., This absence of a barrier may provide a rationale for the experimental observation that Krueger et al . observed in their SANS experiments 13 that a non-native protein ( DPJ-9 ) was partially sucked into isolated trans rings ., If proteins can indeed translocate through the GroEL equatorial plane then this may also be relevant for the mechanism by which the GroEL/GroES chaperonin can help the refolding of proteins that are too big to be encapsulated ., In such cases , portions of the protein could be attracted to the inside of the pore and perform either a complete or a partial translocation ( Figure S5 ) ., According to 6 either process can enhance the refolding efficiency ., The translocation of encapsulated non-native proteins is most likely in cases where the initial structure is far native ., The reason is two fold: first of all , for such conformation there should be a low free-energy cost associated with partial unfolding—a necessary first step in translocation ., Secondly , non-native chains that are trapped in a hydrophilic cage tend to be compressed ., They can lower their free energy by translocating out of the cage ., The simulations of 6 suggest that the driving force for such translocation can be as much as 0 . 5 kBT per amino-acid residue ., Such a free-energy gradient is enough to completely remove a small free-energy barrier that might oppose translocation ( Figure S6 ) ., In conclusion , our simulation results are not compatible with the assumption that the disordered protein chains in the cis or trans rings provide an effective barrier against translocation ., The present findings may help explain a puzzling experimental finding concerning refolding experiments in the presence of crowding agents 4 ., The experiments of 4 demonstrated that , under physiological crowding conditions , the substrate protein does not escape from the chaperonin until it has reached its native state ., This phenomenon is difficult to reconcile with the standard scenario where a protein ( folded or not ) is expelled from the cis-chamber as another non-native protein binds to the ATP-trans chamber ., However , if it is not another protein that binds to the hydrophobic rim of the trans chamber , but the original protein that has translocated from the cis-chamber ( see Figure 2 ) , then it becomes clear why non-native proteins are unlikely to escape ., We stress that the present findings do not rule out the possibility that non-native proteins fold into the native state without translocation 16—translocation is simply an added route for protein folding ., Such a route maybe very important for proteins that folds co-translationally , where confinement in a optimal size tunnel is crucial for efficiently reaching the native state 17 ., Our simulations suggest that it would be interesting to carry out refolding experiments on GroEL with mutated chains that would strongly stick to each other ( or that could be cross-linked ) ., Such mutation would impede the translocation and should thereby reduce the efficiency of the GroEL/GroES complex ., The flexible nature of this region prevented accurate X-ray determination of the chains filling the interconnecting pore ., To obtain a full-atomistic model , the program MODELLER 18 has been used to generate a starting configuration of the chains missing in the X-ray structure ( PDB code: 1AON ) of the GroEL/GroES complex loaded with ADP ., The reconstructed fragments ( sequence KNDAADLGAAGGMGGMGGMGGM ) are added at the C-term extremity of each monomeric building block of the chambers ., In order to avoid steric clashes between the chains , the procedure has taken into account of the quaternary assembly of the chains ., After the generation of the chains structures , three steepest-descent minimisations were performed , using the program GROMACS 11 ( energy minimisation tolerance: 0 . 1 , 0 . 05 and 0 . 01 kJ/mol−1nm−1 ) ., Molecular Dynamics ( MD ) simulations were subsequently performed with the GROMACS 11 package by using GROMOS96 force field with an integration time step of 2 fs ., Non-bonded interactions were accounted for by using the particle-mesh Ewald method ( grid spacing 0 . 12 nm ) 19 for the electrostatic contribution and cut-off distances of 1 . 4 nm for Van der Waals terms ., Bonds were constrained by LINCS 20 algorithm ., The system was simulated in the NPT ensemble by keeping constant the temperature ( 300 K ) and pressure ( 1 atm ) ; a weak coupling 21 to external heat and pressure baths was applied with relaxation times of 0 . 1 ps and 0 . 5 ps , respectively ., As we intended to simulate a solution at a pH-value of 7 the protonation states of pH sensitive residues were assigned as follow: Arg and Lys were positively charged , Asp and Glu were negatively charged and His was neutral ., The proteins net charge was neutralised by the addition of Cl− and Na+ ions ., It would have been prohibitively expensive to simulate the entire chaperonin plus surrounding water ., However , this was not necessary , as our aim was to study the structure and dynamics of the strongly fluctuating the equatorial rings , rather than the relatively rigid remainder of the GroEL “chamber” ., We therefore immobilised the chamber atoms that are not directly connected to the pore chains ., Of course , the equatorial chains were free to move and relax in the pore ., In order to further reduce the number of degrees of freedom treated , we only considered water molecules ( SPCE 22 ) inside the GroEL chamber ., We achieved this by imposing a strong repulsive external potential outside the GroEL chamber ., Ignoring the water outside the cage is not an unreasonable simplification , as we found that the disordered chains were completely solvated by water molecules and never moved outside the atoms of the internal surface of the chamber ., We assumed periodic boundary conditions only along the symmetry axis of the GroEL complex ( “z-axis” ) ., The Caterpillar model is a modification of the tube model of Maritan and co-workers 14 , 23 , 24 ., The main differences are that we treat the structure of the backbone in more detail and that our scheme to account for self avoidance by means of bulky side groups is computationally cheaper than the approach of Maritan et al . who introduced a three-body interaction to achieve the same ., The interaction between amino acids with different side chain ECA is given by the following expression ( 1 ) where is the distance between nonadjacent Cα atoms in the protein and rmax is the distance at which the potential has reaches half ε ., For ε we use the 20×20 matrix derived with the method of Betancourt and Thirumalai 15 ., Although these interaction energies are strictly speaking neither energies nor free energies , they do provide a reasonable representation of the heterogeneity in the interactions between different amino acids ., We modeled the hydrogen bonds between the hydrogen and the oxygen of the backbone with a 10-12 Lennard-Jones potential: ( 2 ) where the minimum is at σ\u200a=\u200a2 . 0 Å and ELJ\u200a=\u200a3 . 1 kBT ., The directionality of the hydrogen bond was taken into account by multiplying the Lennard-Jones potential by a pre-factor ( 3 ) where θ1 and θ2 are the angles between the atoms COH and OHN respectively ., The large hard spheres centered on the Cα atoms ensure that the orientation factor is maximum only for angles close to π ., Apart from rotations around the dihedral angles φ1 and φ2 ( Figure S3 ) , the backbone is rigid ., We have verified that this model can indeed reproduce typical protein motifs such as alpha helices and beta sheets , depending on the amino-acid sequence ., To sample the conformations of the protein chains anchored on the trans ring , we use two basic Monte-Carlo moves: branch rotation and an improved version of the biased Gaussian step 25 , while for the translocating alpha helix we allow only translation moves and rotation around the center of mass . | Introduction, Results, Discussion, Materials and Methods | The biological function of chaperone complexes is to assist the folding of non-native proteins ., The widely studied GroEL chaperonin is a double-barreled complex that can trap non-native proteins in one of its two barrels ., The ATP-driven binding of a GroES cap then results in a major structural change of the chamber where the substrate is trapped and initiates a refolding attempt ., The two barrels operate anti-synchronously ., The central region between the two barrels contains a high concentration of disordered protein chains , the role of which was thus far unclear ., In this work we report a combination of atomistic and coarse-grained simulations that probe the structure and dynamics of the equatorial region of the GroEL/GroES chaperonin complex ., Surprisingly , our simulations show that the equatorial region provides a translocation channel that will block the passage of folded proteins but allows the passage of secondary units with the diameter of an alpha-helix ., We compute the free-energy barrier that has to be overcome during translocation and find that it can easily be crossed under the influence of thermal fluctuations ., Hence , strongly non-native proteins can be squeezed like toothpaste from one barrel to the next where they will refold ., Proteins that are already fairly close to the native state will not translocate but can refold in the chamber where they were trapped ., Several experimental results are compatible with this scenario , and in the case of the experiments of Martin and Hartl , intra chaperonin translocation could explain why under physiological crowding conditions the chaperonin does not release the substrate protein . | Chaperonin complexes capture proteins that have not yet reached their functional ( “native” ) state ., Non-native proteins cannot perform their function correctly and threaten the survival of the cell ., The chaperonins help these proteins to reach their native state ., The prokaryotic GroEL-GroES chaperonin is an ellipsoidal protein complex that is approximately 16 nm long ., It consists of two chambers that are joined at the bottom ., Interestingly , protein repair by this chaperonin is not a one-step process ., Typically , several capture and release steps are needed before the target protein reaches its native state ., It is commonly assumed that substrate proteins cannot translocate , i . e . , move inside the complex from one chamber to the other ., In the absence of translocation , proteins that have not yet reached their functional conformation have to be released into the cytosol before being recaptured by a chaperonin ., We present multi-scale simulations that show that it is , in fact , surprisingly easy for substrate proteins to translocate between the two chambers via an axial pore that is filled with disordered protein filaments ., This finding suggests that non-native proteins can be squeezed like toothpaste from one chamber to the other: the incorrect structure of the protein is broken up during translocation and the protein has an increased probability to find its native state when it reaches the other chamber ., The possibility for intra-chaperonin translocation obviates the need for a potentially dangerous release of non-native proteins . | biophysics/macromolecular assemblies and machines, biophysics/theory and simulation, biophysics/protein folding | null |
journal.pntd.0001948 | 2,012 | Diagnosis of Brugian Filariasis by Loop-Mediated Isothermal Amplification | Lymphatic filariasis is one of the worlds most debilitating infectious diseases ., According to the World Health Organization ( WHO ) ( http://www . who . int/mediacentre/factsheets/fs102/en/ ) , over 120 million people are currently infected in more than 80 countries ., Approximately 40 million individuals are disfigured and incapacitated by the disease , including 15 million who have lymphoedema ( elephantiasis ) and 25 million men who have urogenital swelling , principally scrotal hydrocele ., WHO estimates the loss of 5 . 1 million disability-adjusted life years ( DALYs ) as a result of infection by one of three filarial species ( Brugia malayi , Brugia timori and Wuchereria bancrofti ) 1 ., Male and female parasites form “nests” in the lymphatic system , and after mating females produce large numbers of microfilariae that predominantly circulate in the blood at night ., Microfilariae are ingested by a mosquito during a blood meal and develop to infective stage larvae that are subsequently transmitted to a new host ., In recent years there has been significant progress in the control of these diseases by the Global Programme to Eliminate Lymphatic Filariasis ( GPELF ) in which whole populations are treated by repeated , yearly cycles of mass drug administration ( MDA ) with antifilarial drugs 2 , 3 ., Over 2 . 6 billion treatments have been administered in 48 countries in the first 8 years 3 , and this campaign continues to grow as new regions are included ., Mapping of infected human and vector populations is required to identify areas in need of MDA ., Following implementation , monitoring is necessary to determine the endpoint of treatment , with continued surveillance being required to identify areas of ongoing transmission or recrudescence ., These activities and overall management of MDA programs are performed most efficiently with accurate diagnostic tools suitable for field use ., Point-of-care diagnosis of lymphatic filariasis is largely based on microscopic examination of night blood , and morphological assessment of stained microfilariae ., A more accurate , rapid-format , immunochromatography card test ( ICT ) which detects circulating antigen is available for bancroftian filariasis 4 , 5 but not for other filarial infections ., Detection of microfilariae in conjunction with antibody testing , mainly in clinical settings , is being used as an interim measure for brugian filariasis 5–7 ., However , the antibody tests indicate exposure rather than active infection 8 , 9 and do not distinguish between bancroftian and brugian filariasis 10 , thereby limiting their use for surveillance in areas where these infections are co-endemic ., Molecular-based diagnostic tools are considered more accurate since they detect active infection and have been used in laboratories for reliable differential identification of filarial parasites ., Several polymerase chain reaction ( PCR ) based methods have been used to amplify DNA in blood from B . malayi and B . timori 11–16 and W . bancrofti 17–22 ., Molecular monitoring of insect vectors by PCR is also the preferred method for xenodiagnosis and has been used extensively for W . bancrofti 20–29 and to a lesser extent for B . malayi 27 , 30–32 ., PCR however , requires highly skilled personnel and expensive equipment ., An alternative to PCR , is a technique termed loop-mediated isothermal amplification ( LAMP ) which amplifies DNA with high specificity , sensitivity and rapidity under isothermal conditions using a polymerase with strand displacement activity ., The enzyme generates a mixture of stem-loops containing alternately inverted repeats of the target sequence and cauliflower-like structures resulting in exponential amplification of the target sequence ( >10 µg , >50× PCR yield ) 33–35 ., The LAMP reaction uses two sets of primers , outer primers ( F3 and B3 ) and inner primers ( FIP and BIP ) that hybridize to six sites on the target DNA ., Specially designed FIP and BIP primers each consisting of two distinct sequences correspond to sense and antisense sites on the target DNA ., The addition of a third set of primers , known as loop primers , has been shown to accelerate the reaction 34 ., Using three primer sets recognizing eight sites in the target DNA lends LAMP the specificity to discriminate between genomic DNA at both genus- and species-specific levels 36 , 37 ., In recent years this technology has been explored for the diagnosis of certain parasitic 38–45 , bacterial 46 , fungal 47 and viral 48 , 49 infections ., Because of its simplicity , rapidity , and versatility in readout options , LAMP offers a distinct advantage over other molecular diagnostic methods for use in the field ., LAMP test kits are now commercially available or in development for the detection of Mycobacterium tuberculosis complex 50 , and human African trypanosomiasis 52 for use in resource-limited settings ., In the present study we report on the development of a simple LAMP test that amplifies the Brugia-specific Hha I repeat for the rapid detection of B . malayi or B . timori DNA ., We evaluated the efficacy of several thermophilic DNA polymerases using real-time LAMP , and also compared read-out options ., Our results demonstrate that the Hha I LAMP test is sensitive and specific with the potential to be developed further as a field tool for diagnosis and mapping of brugian filariasis ., DNA samples were generously donated by the following: B . malayi and B . timori , L . A . McReynolds ( New England Biolabs ) ; Onchocerca volvulus and Homo sapiens , F . Perler ( New England Biolabs ) ; Dirofilaria immitis , C . Maina ( New England Biolabs ) ; and Aedes albopictus , Z . Li ( New England Biolabs ) ., Whole genome amplified Wuchereria bancrofti DNA , heparinized B . malayi infected feline blood and uninfected dog blood were obtained from the NIH/NIAID Filariasis Research Reagent Resource Center ( http://www . filariasiscenter . org ) ., The purity and quantity of DNA in samples was determined using a Nano Drop Spectrophotometer , ND-1000 ( Nano Drop Technologies ) ., A two-fold dilution series of B . malayi microfilaraemic feline blood was diluted using uninfected dog blood ., Forty µl aliquots of each dilution were used for quantifying microfilarial titers and the same volume used for DNA extraction ., Microfilarial counts were determined using a membrane concentration technique 53 , 54 ., Briefly , 40 µl aliquots of each dilution were mixed with 160 µl PBS then filtered through a 5 . 0 µm pore polycarbonate membrane ( Nucleopore , Whatman ) ., Filters were placed , microfilariae side up , on a microscope slide and stained 55 , 56 ., Parasites were counted using an Axio Scope A1 ( Zeiss ) at 40× magnification ., DNA was extracted from 40 µl of each dilution using a QIAamp DNA Mini Kit ( Qiagen ) after digesting the samples with proteinase K in the supplied AL Buffer for 2 hrs at 56°C ., Purified DNA was eluted in a 200 µl volume; one µl of which was used in LAMP assays ., To generate primers for LAMP , multiple B . malayi Hha I repeat sequences were aligned using ClustalW 57 ., Accession number M12691 58 was used to query the B . malayi whole-genome shotgun ( WGS ) database at GenBank using the blastn program ( http://blast . ncbi . nlm . nih . gov ) ., Full-length repeats were selected from accession numbers: M12691 , AAQA01025653 , AAQA01026145 , AAQA01018878 , AAQA01011954 , AAQA01021048 , AAQA01005386 , AAQA01005790 , AAQA01007277 , AAQA01004714 , AAQA01005124 , and used to generate a consensus sequence ( Figure 1A and Figure S1 ) ., LAMP primers ( Figure 1B ) were designed manually using “A guide to LAMP primer design” available from the Eiken Chemical Co . ( http://primerexplorer . jp/e/ ) ., To facilitate the design of PCR primers for amplification of actin , sequences from B . malayi ( NW_001892317 . 1 , region: 12826–14482; NW_001893014 . 1 , region: complement 253210–256438 ) , O . volvulus ( M84915 , M84916 ) , W . bancrofti ( AF184961 ) , Aedes aegypti ( NW_001810656 , region: complement ( 1446599–1449390 ) and Homo sapiens ( NC_000001 , region: complement 229569843–229566992 ) were downloaded from GenBank and aligned using ClustalW 57 ., The region corresponding to exon 2 in the W . bancrofti and O . volvulus actin genes 59 , 60 , that also exhibited high identity among all members in the alignment , was used to design degenerate PCR primers ., The forward and reverse primer sequences are ( 5′ GCTCAGTCBAAGAGAGGTAT 3′ ) and ( 5′ACAGCYTGGATDGCAACGTACA 3′ ) , respectively , where B\u200a=\u200aC , G or T; Y\u200a=\u200aC or T , and D\u200a=\u200aA , G or T . PCR and LAMP primers were synthesized by Integrated DNA Technologies ( Coralville , Iowa ) ., LAMP reactions with Bst DNA polymerase , large fragment ( LF , New England Biolabs ) contained 1 . 6 µM each of FIP and BIP , 0 . 2 µM each of F3 and B3 , 1 . 4 mM of each dNTP , 20 mM Tris-HCl ( pH 8 . 8 ) , 10 mM KCl , 10 mM ( NH4 ) 2SO4 , 8 mM MgSO4 , 0 . 1% Tween-20 and 8 U of enzyme mixed with 1 µl of various genomic DNAs in a total volume of 25 µl ., For the evaluation of the Hha I LAMP primer set with either Bst 2 . 0 DNA polymerase or Bst 2 . 0 WarmStart DNA polymerase ( New England Biolabs ) , reactions were set up and performed as described above , except 50 mM KCl was used ., Loop Forward ( LF ) and Loop Back ( LB ) primers were added to some reactions ( 0 . 4 µM ) to assess their ability to decrease the threshold time under various conditions ., Reactions were incubated at 63°C for 60–90 minutes in a Loopamp Realtime Turbidimeter ( LA-320c , Eiken Chemical Co . ) ., The instrument measures the change in turbidity at 650 nm caused by the precipitation of magnesium pyrophosphate with time ., Turbidity data were analyzed using the LA-320c software package that reports when the change in turbidity over time ( dT/dt ) reaches a value of 0 . 1 , which we then assigned to be the threshold time ( Tt ) ., When amplification was evaluated using the calcein-based Fluorescent Detection Reagent ( 61 and the Eiken chemical Co . ) rather than turbidity , reactions were heat killed for 20 min at 80°C then visualized within 60 minutes with UV light at 365 nm as recommended by the manufacturer ., As a positive control for the presence of intact DNA , a 244 bp actin fragment was PCR amplified from various genomic DNAs using 1 . 25 U of Taq DNA polymerase in 1× standard buffer ( New England Biolabs ) containing 3 . 5 mM MgCl2 , 0 . 2 mM each dNTP , and 0 . 2 µM each of the forward and reverse actin primers in a 50 µl reaction ., One ng of genomic DNA was used as template , except for B . timori ( 5 ng ) and human ( 10 ng ) ., Reactions containing human DNA , contained 4 mM MgCl2 ., All reactions was denatured once at 95°C for 30 sec then cycled 30 times at 95°C for 30 sec , 55°C for 30 sec and 68°C for 30 sec , except 35 cycles were used for B . timori and non-template controls ., After cycling , reactions were incubated for 5 min at 68°C then the reaction products were analyzed by electrophoresis using 1 . 2% agarose gels equilibrated with TBE buffer ., To maximize assay sensitivity , the B . malayi Hha I repeat was selected as a target for amplification because of its abundance in the genome 58 ., A B . malayi Hha I consensus sequence derived by aligning 34 repeats was used to design a primer set for LAMP ( Figure 1 and Figure S1 ) ., Primers were designed manually as the AT richness ( 79% ) of the consensus sequence precluded use of the Primer Explorer software ( http://primerexplorer . jp/e/ ) for LAMP primer design ., The sensitivity of the Hha I primer set was evaluated by real time turbidity using three thermophilic DNA polymerases , Bst DNA polymerase , LF , Bst 2 . 0 DNA polymerase or Bst 2 . 0 WarmStart DNA polymerase ., Ten-fold serial dilutions of genomic B . malayi DNA ranging from 0 . 1–0 . 001 ng were amplified using both the Hha I primer set alone ( Figure 2A ) and in the presence of loop primers ( Figure 2B ) ., At the highest concentration of template DNA ( 0 . 1 ng ) , reactions reached a turbidity threshold of 0 . 1 in approximately 30 minutes regardless of the polymerase employed ( Figure 2A ) ., As the concentration of template DNA decreased , there was a corresponding increase in the amount of time required to reach the threshold value of 0 . 1 ., Reactions using Bst 2 . 0 DNA polymerase improved the most with reliable detection of 0 . 001 ng parasite DNA , corresponding to 1/100th of a microfilariae , within 45 minutes compared to ∼70 minutes without loop primers ( Figure 2A and 2B ) ., In the absence of template or primers , no turbidity was observed ., Likewise at concentrations of template ≤0 . 0001 ng , only one or none of the triplicate samples amplified ( data not shown ) ., Reaction times were slightly slower using Bst 2 . 0 WarmStart DNA polymerase regardless of the presence of loop primers ( Figure 2A and 2B ) ., Similar sensitivity was obtained within the same time frame when the calcein-based Fluorescent Detection Reagent rather than turbidity was used as the output ., Positive reactions turned green while no color change was apparent in the absence of amplification or when no target DNA ( or ≤0 . 0001 ng ) was present ( Figure 2C ) ., To mimic a clinical situation , the assay was performed using Bst 2 . 0 DNA polymerase on DNA extracted from a two-fold dilution series equivalent to 25–9000 mf/ml blood ., Three experiments were performed using a different but overlapping range of DNA dilutions equivalent to 1/200th-2 microfilariae per LAMP reaction ., Good concordance was observed between samples containing equivalent amounts of template DNA ., A turbidity threshold of 0 . 1 was reached in 25–30 minutes with slightly more time required ( <5 minutes ) as the concentration of template DNA decreased ( Figure 3 ) ., No turbidity was detected when uninfected blood was processed in the same manner ( data not shown ) ., We evaluated the performance of LAMP for the differential detection of the Hha I repeat in genomic DNA samples isolated from the closely related parasites B . timori , W . bancrofti , D . immitis , and O . volvulus ., DNA from human and mosquito ( Aedes albopictus ) samples and a non-template control were also included for comparison ., As observed in previous experiments , turbidity reached a threshold value of 0 . 1 in approximately 30 minutes when 0 . 1 ng of B . malayi or B . timori DNA was added to the reaction , whereas no turbidity was observed when DNA from the other filarial parasites , human or mosquito was used ( Figure 4A ) ., The integrity of these various DNAs was confirmed in PCR experiments using primers designed to amplify an actin gene ., A single amplification product of 244 bp , the expected fragment size was obtained ( Figure 4B ) ., The Brugia Hha I repeat was selected as the biomarker for a LAMP-based diagnostic test for brugian filariasis ., The repeats are non-protein coding , approximately 322 bp in length , and arranged in direct tandem arrays ., They comprise between 1–12% of the B . malayi genome 58 , 62 and are highly conserved , with the nucleotide identity of the repeats used in this study varying from 82–98% ., In order to target the greatest number of repeats and maximize assay sensitivity , primers were designed based on the most highly conserved nucleotide blocks in a consensus sequence ., The Hha I PCR amplification system has been shown to be extremely sensitive in detecting Brugia DNA 11 , 63 , exceeding the theoretical limit of detection of one microfilaria per ml using conventional microscopy and concentration techniques 64 ., There are several important advantages offered by LAMP over PCR ., Its operational simplicity and isothermal nature make it ideally suited for use in the field ., In PCR , thermal cycling is required to denature the template , anneal primers and extend the amplicon ., LAMP employs Bst DNA polymerase , LF which provides both strand displacement and target amplification at a single temperature in a simple heat block or water bath at 60–65°C 33 ., High levels of sensitivity and specificity can be achieved in LAMP because the amplification reaction involves four specific oligonucleotide primers that anneal to six distinct regions within the target sequence 33 ., Also , the addition of loop primers may further improve performance 34 ., Levels of sensitivity comparable to the Hha I PCR amplification system were obtained in the Hha I LAMP test using either DNA isolated from worms or from blood containing microfilariae ., It is estimated that a single microfilaria contains approximately 100 pg of DNA 11 , 14 , 21 and using the LAMP Hha I test it is possible to easily detect as little as 1 pg of total genomic DNA purified from B . malayi worms which is equivalent to 1/100th of a microfilaria ., In mock experiments using DNA prepared from a dilution series of microfilaremic blood , we detected the equivalent of 1/200th of a microfilaria in approximately 30 minutes ., This was the most dilute DNA sample tested and is equivalent to one mf in 40 µl of whole blood ., It is possible that free DNA contributed to the output signal when using blood since it has been suggested that 130 fg of repeat can be released by a single dead microfilaria 63 ., In a Hha I based PCR-ELISA , free DNA of nocturnally periodic B . malayi was detected in 200 µl of day blood , achieving a sensitivity comparable with filtration of 1 ml of night blood 63 ., In the present study , LAMP reaction times were fastest at lowest DNA concentrations of template using a new isothermal strand-displacing polymerase Bst 2 . 0 DNA polymerase in the presence of loop primers ., Bst 2 . 0 DNA polymerase is an in silico designed homologue of Bst DNA polymerase , LF engineered for improved amplification speed , yield , salt tolerance and thermostability 65 ., Its warmstart version ( Bst 2 . 0 WarmStart DNA polymerase ) possesses a reversibly-bound aptamer which inhibits polymerase activity at temperatures below 45°C ., This circumvents a common problem that can occur in nucleic acid amplification namely the undesired activity from DNA polymerases during room temperature reaction set-up 65–67 ., In our experiments , reaction times were slightly slower using Bst 2 . 0 WarmStart DNA polymerase due to the presence of the aptamer ., In assays designed to mimic field conditions , Bst 2 . 0 WarmStart DNA polymerase enables amplification of the Hha I repeat without generating a signal in the non-template controls when incubated at 35°C for intervals up to 2 hrs before initiating amplification , in contrast to Bst DNA polymerase , LF and Bst 2 . 0 DNA polymerase ( data not shown ) ., The ability to allow LAMP reactions to be assembled and stored at room temperature for hours with no change in the final readout can offer a distinct advantage in resource-limited settings ., In addition to sensitivity , the Hha I LAMP test offers the high level of specificity required for diagnosis and mapping ., The Hha I LAMP primer amplified B . malayi and B . timori DNA but not DNA isolated from the closely related filarial parasites W . bancrofti , D . immitis , or O . volvulus , or from human or mosquito ., Previous studies have shown that the Hha I repeat family in B . timori is highly homologous to the B . malayi Hha I repeat family 15 , 68 ., Therefore the test may be useful for diagnosing infection in patient samples and monitoring transmission of B . malayi or B . timori in mosquito vectors ., Recently species-specific primers have also been used in LAMP to detect DNA from D . immitis 69 , 70 and W . bancrofti 43 in blood and mosquito samples ., Rapidity and versatility in readout options also make LAMP a particularly appealing technology ., Positive results can be visualized by turbidity caused by precipitation of magnesium pyrophosphate , a by-product of the reaction that can be seen with the naked eye 71–73 within 15–60 minutes ., The reaction product can also be detected under UV light with the addition of fluorescent dyes 72 , 74–79 or colorimetrically using hydroxy naphthol 80 ., In the present study , real-time turbidity was used for assay design and optimization ., The time at which the reaction reaches a threshold of 0 . 1 turbidity was used to precisely evaluate various parameters ., Similar results were obtained when calcein 61 was added to reactions ., In addition , recent estimates suggest that diagnostic LAMP tests are significantly cheaper than PCR ., The estimated cost of a W . bancrofti LAMP test is $0 . 82 compared with more than $2 . 20 for PCR 43 ., The operational simplicity of the LAMP technique makes it particularly appealing for neglected tropical diseases , as evidenced by the rate of adoption of this diagnostic DNA technology by laboratories in developing countries ., Since many of these diseases are co-endemic , it is desirable to leverage resources and integrate diagnostic platforms wherever possible ., Multiplexing of the LAMP reaction has been demonstrated for the detection of Babesia parasites in cattle 37 malaria and heartworm in mosquitoes 69 and human arboviruses 81 ., More recently a real-time , multiplex LAMP technique was described that enables detection of up to 4 distinct LAMP targets in a single reaction 65 ., In summary , we describe a promising Hha I-based LAMP diagnostic assay for brugian filariasis using Bst 2 . 0 DNA polymerase and loop primers that generates a robust read-out within 60 minutes ., The assay warrants further testing with endemic samples as the next stage in development towards its use as a field tool for implementation and management of MDA programs . | Introduction, Materials and Methods, Results, Discussion | In this study we developed and evaluated a Brugia Hha I repeat loop-mediated isothermal amplification ( LAMP ) assay for the rapid detection of Brugia genomic DNA ., Amplification was detected using turbidity or fluorescence as readouts ., Reactions generated a turbidity threshold value or a clear visual positive within 30 minutes using purified genomic DNA equivalent to one microfilaria ., Similar results were obtained using DNA isolated from blood samples containing B . malayi microfilariae ., Amplification was specific to B . malayi and B . timori , as no turbidity was observed using DNA from the related filarial parasites Wuchereria bancrofti , Onchocerca volvulus or Dirofilaria immitis , or from human or mosquito ., Furthermore , the assay was most robust using a new strand-displacing DNA polymerase termed Bst 2 . 0 compared to wild-type Bst DNA polymerase , large fragment ., The results indicate that the Brugia Hha I repeat LAMP assay is rapid , sensitive and Brugia-specific with the potential to be developed further as a field tool for diagnosis and mapping of brugian filariasis . | Brugian filariasis is a debilitating neglected tropical disease caused by infection with the filarial parasites Brugia malayi or Brugia timori ., Adult worms live in the lymphatic system and produce large numbers of microfilariae that predominantly circulate in the blood at night ., Bloodsucking mosquitoes spread the disease by ingesting microfilariae that develop into infective stage larvae in the insect ., In rural areas , diagnosis still relies largely on microscopic examination of night blood and morphological assessment of stained microfilariae ., Loop-mediated isothermal amplification ( LAMP ) is a technique that can amplify DNA with high specificity , sensitivity and rapidity under isothermal conditions ., The operational simplicity , versatility and low-cost of the technique make it particularly appealing for use in diagnosis and geographical mapping of neglected tropical diseases ., In the present study , we have developed and evaluated a Brugia Hha I repeat LAMP assay for the rapid detection of B . malayi and B . timori genomic DNA ., The results indicate that the Brugia Hha I repeat LAMP diagnostic assay is sensitive and rapid , detecting a single microfilariae in blood within 30 minutes , and Brugia-specific ., The test has the potential to be developed further as a field tool for use in the implementation and management of mass drug administration programs for brugian filariasis . | medicine, infectious diseases, diagnostic medicine, neglected tropical diseases, parasitic diseases | null |
journal.pcbi.1005076 | 2,016 | Stimulation-Based Control of Dynamic Brain Networks | Brain stimulation is increasingly used to diagnose 1 , monitor 2 , and treat neurological 3 and psychiatric 4 disorders ., Non-invasive stimulation , such as transcranial magnetic stimulation ( TMS ) or transcranial direct current stimulation ( tDCS ) , is used , for example , in epilepsy 5 , 6 , stroke 7 , attention deficit hyperactivity disorder 2 , tinnitus 8 , headache 9 , aphasia 10 , traumatic brain injury 11 , schizophrenia 12 , Huntington’s disease 13 , and pain 14 , while invasive deep brain stimulation ( DBS ) is approved for essential tremor and Parkinson’s disease and is being tested in multiple Phase III clinical trials in major depressive disorder , Tourette’s syndrome , dystonia , epilepsy , and obsessive-compulsive disorder 15 ., In addition to its clinical utility , emerging evidence suggests that stimulation can also be used to optimize human performance in healthy individuals 16 , 17 , potentially by altering cortical plasticity 17 ., Despite its broad utility , many engineering challenges remain 18 , from the optimization of stimulation parameters to the identification of target areas that maximize clinical utility 19 ., Critically , an understanding of the local effects of stimulation on neurophysiological processes—and the downstream effects of stimulation on distributed cortical and subcortical networks—remains elusive 20 ., This gap has motivated the combination of stimulation techniques with various recording devices ( PET 21 , MEG 22 , fast optical imaging 23 , EEG 17 , and fMRI 24 , 25 ) to monitor the effects of stimulation on cortical activity 26–28 ., In this context , it has become apparent that there is a critical need for biologically informed computational models and theory for predicting the impact of focal neurostimulation on distributed brain networks , thereby enabling the generalization of these effects across clinical cohorts as well as the optimization of stimulation protocols 29 , including refinements for individualized treatment and personalized medicine ., To meet this fast-growing need , we utilize network control theory 30 to understand and predict the effects of stimulation on brain networks ., The advent of network theory as a ubiquitous paradigm to study complex engineering systems as well as social and biological models has changed the face of the field of automatic control and redefined its classic application domain ., Control of a network refers to the possibility of manipulating local interactions of dynamic components to steer the global system along a chosen trajectory ., Network control theory offers a mechanistic explanation for how specific regions within well-studied cognitive systems may enable task-relevant neural computations: for example , activity in the primary visual cortex can initiate a trajectory of processing along the extended visual pathway ( V2 , V3 , etc . ) , driving visual perception and scene understanding ., More broadly , network control theory also offers a mechanistic framework in which to understand clinical interventions such as brain stimulation , which elicits a strategic functional effect within the network for synchronized neural processing ., In both cases ( regional activity and stimulation ) , it is critically important to understand how underlying structural connectivity can constrain or modulate the functional effect of regional alterations in activity ., Yet , the application of control-theoretic techniques to brain networks is underexplored , even though the questions posed by the neuroscientific community are uniquely suited to the application of these tools 30 ., In a novel approach to link network control theory and brain dynamics , we examine the effects of regional stimulation on brain states using a nonlinear meso-scale computational model , built on data-driven structural brain networks ., We demonstrate that the dynamics of our model are highly variable across subjects , but highly reproducible across multiple scans of the same subject ., We confirm the pragmatic utility of network control theory for nonlinear systems , extending previous work on linear approaches 30 , and show that general control diagnostics ( average and modal controllability ) are strongly correlated with the density of structural connections linking brain regions ., Finally , we investigate the interplay between functional and structural effects of stimulation by examining how the global functional activity across brain regions is modulated by region-specific stimulation ( a region’s functional effect ) and whether the region’s structural connectivity accounts for its influence on the larger brain network ( structural effect ) ., Results show that the default mode system and subcortical regions produce the strongest functional effects; the subcortical structures display weak structural effects , being diversely connected across many cognitive systems ., Collectively , our results indicate the value of data-driven , biologically motivated brain models to understand how individual variability in brain networks influences the functional effects of region-specific stimulation for clinical intervention or cognitive enhancement ., Our broad goal is to understand the role of regional stimulation to differentially control brain dynamics ., Theoretical predictions of regional controllability can be developed based on the underlying structural connectivity of the computational model 30 ., Given our subject-specific data-driven approach , it is therefore important to understand how variability between structural brain networks affects the dynamics of our model ., Wilson-Cowan oscillators are a biologically driven mathematical model of the mean-field dynamics of a spatially localized population of neurons 32 , 33 , modeled through equations governing the firing rate of coupled excitatory ( E ) and inhibitory ( I ) neuronal populations ( Methods ) ., Here , we measure a single brain region’s dynamics by the firing rate of the excitatory population ., An important feature of these Wilson-Cowan oscillators is that an uncoupled oscillator can exhibit one of three states , depending upon the amount of external current applied to the system ( Fig 2a and 2b ) ., When no external current is applied ( P = 0 ) , the system relaxes to a low fixed point ( Fig 2a and 2b ) ., For moderate amounts of applied current , the oscillator is pushed into an oscillatory limit cycle , and if sufficiently high amounts of current are applied , the system settles at a high fixed point ., For this system of coupled oscillators , brain regions ( oscillators ) can receive current from an external input ( stimulation , i . e . , P > 0 ) or from the activity of other brain regions to which they are connected ., The global coupling parameter , c5 ( see Methods ) , therefore serves to govern the global state of the system by regulating the overall amplitude of current transmitted between brain regions ., For low values of c5 , the system will fluctuate around the low fixed point , whereas for high values of c5 , the system will transition into the oscillatory ( limit cycle ) regime ., For a given structural connectivity , we can systematically increase the global coupling parameter and record the value at which the system transitions from the low fixed point to the oscillatory regime ., Individual differences in structural connectivity can cause this transition to occur at different points in the parameter space ( see S2 Fig ) , and we therefore use this point of transition to assess the sensitivity of our model to inter subject differences in the structural connectivity ., By measuring the point of oscillatory transition using structural connectivity matrices obtained from each of three scans for eight subjects , we see in Fig 2c that model dynamics are highly reproducible within scans of a single subject , but show variability across subjects ., We quantify the within versus between subject reproducibility using the intraclass correlation coefficient ( ICC ) ., We see high reproducibility within scans of a single subject ( Fig 2d , ICC = 0 . 826 ) and low reproducibility between different subjects ( ICC = −0 . 006 ) , which also corresponds to a low within subject variance ( V = 0 . 0019 ) and high between subject variance ( V = 0 . 0143 ) ., Due to the high within subject reproducibility across the three scans of each subject , the remaining findings are presented at the subject level by averaging the results over simulations derived from each of the three single subject scans ., In order to elucidate the role of regional stimulation to differentially control brain dynamics , we first turn to predictions made using linear network control theory 30 ., Linear network control theory assumes a simplified linear model of network dynamics and computes controllability measures based upon the topological features of the structural network architecture ., Here , we assess two different types of regional controllability derived from our structural brain networks: average controllability and modal controllability ., Regions with high average controllability are capable of moving the system into many easy to reach states with a low energy input , whereas regions with high modal controllability can move the system into difficult to reach states but require a high energy input ( see Methods for detailed descriptions of controllability measures and S1 Fig and S1 Text for their relationship to the steady state network response from regional stimulation with a constant current input ) ., As previously described 30 , we observe a strong correlation between regional degree and average controllability and a strong inverse correlation between regional degree and modal controllability that is robust across structural networks derived from all subjects ( Fig 3 ) ., We predict that brain regions with a high average controllability have the ability to impart large changes in network dynamics , easily moving the system into many nearby states ., However , these predictions are made under the assumption of linear dynamics , and we know that the brain is in fact a highly nonlinear system ., Our modeling approach allows us to directly test the validity of these linear controllability predictions in a nonlinear setting by systematically studying the effects of focal stimulation to brain regions and studying how the system moves ., Using our computational model , we select the value of global coupling that places our brain model just before the transition to the oscillatory regime such that all brain regions are fluctuating near the low fixed point ., We then select a single brain region and add an external stimulating current that brings the selected region into its oscillatory state ( Fig 4a ) ., We can quantify the changes in brain state due to this stimulation by computing the functional matrix of pairwise correlations between brain region dynamics during a period before stimulation occurs ., We compare this pre-stimulation matrix to the functional matrix obtained during the stimulation period ., By taking the difference between the functional brain state before and during regional stimulation , we measure the distance that the system moves ., As expected , stimulation of low controllability regions produces smaller changes in the functional brain state than stimulation of high controllability regions ( Fig 4b and 4c ) ., The results of systematically stimulating each brain region are shown in Fig 5 and S3 Fig . We quantify the overall change in brain state configuration by measuring the functional effect of regional stimulation: the absolute value of the pairwise change in functional connectivity , averaged over all brain region pairs ., We observe that stimulation of regions with a high average controllability produce a large functional effect , while stimulation of regions with a high modal controllability result in a low functional effect ( Fig 5a and 5b ) ., We next ask how the underlying structural connectivity differentially constrains the functional effect of the stimulation for different brain regions ., We quantify this structural constraint by calculating the structural effect on network dynamics , which measures the change in spatial correlation between the structural connectivity matrix and the functional brain state matrix before and during stimulation ( Methods ) ., Brain regions with a higher structural effect show a greater increase in similarity between structural and functional matrices when stimulated than regions with a lower structural effect ., Thus , stimulation of these regions is more constrained by the underlying structural connectivity ., As seen in Fig 5, ( c ) and 5, ( d ) , the relationship between the structural effect and regional controllability is opposite that of the functional effect and controllability ., Stimulation of regions with a high average controllability results in a smaller structural effect , while stimulation of regions with a high modal controllability results in a higher structural effect ., The distributions of brain regions as a function of degree , average controllability , modal controllability , functional effect , and structural effect are shown in S4 Fig . In order to better understand why stimulation of regions with a high average controllability easily moved the system ( high functional effect ) and were less constrained by the underlying structure ( low structural effect ) , we quantified the spread of activation from the regional stimulation ., Specifically , we asked if the stimulation of a given region induced focal or global changes in the brain state by calculating the fractional activation of the functional connectivity matrix ., The fractional activation is given by the fraction of pairwise regions that experience a change in their functional connectivity value that is above a given threshold ( yellow pixels in Fig 6a and 6b ) ., If the stimulation of a brain region results in large changes that occur globally throughout the brain , the fractional activation will be high , but if the stimulation has only a focal effect , the fractional activation will be low ., As one might expect , we observe a strong positive correlation between the functional effect and the fractional activation ( Fig 6c , Spearman’s ρ = . 992 , p ≪ . 001 ) ., Specifically , a large functional effect is due to a global effect of regional stimulation that pushes the system into a nearby state , while a small functional effect is due to a focal effect of regional stimulation that moves the system toward a more distant state ., However , the relationship between the structural effect and fractional activation is more complex as seen in the crescent shaped curve of Fig 6d ., A maximum structural effect occurs at the curve of the crescent ( arrow in Fig 6d ) , where the effects of stimulation are neither focal or global ., This result indicates that the underlying structural connections constrain the effects of stimulation the most in situations when regional stimulation impacts a moderately sized portion of the brain ., We next investigated the interplay between the structural and functional effects by examining the structure-function landscape ( Fig 7 ) ., Stimulation of individual brain regions revealed a range of tradeoffs between structural and functional effect values , with some regions displaying a high functional effect but low structural effect and others displaying a high structural effect but moderate or low functional effect ., We therefore asked if there was a relationship between the cognitive function associated with a brain region and its location in the structure-function landscape ., Brain regions were assigned to one of nine cognitive systems ( Fig 7 and Table A in S1 Text ) , eight of which were based on a data-driven clustering of functional brain networks 34 that group regions that perform similar roles across a diverse set of tasks , as well as a group of subcortical regions ., Although in reality no region has a singular function , we use these system assignments as a pragmatic means to assess whether controllability diagnostics are differentially identified in distributed brain networks ., In Fig 7a , we can see that some cognitive systems cover a wide range of the structure-function landscape , whereas other systems tend to be more localized ., Although the subcortical regions are well connected structurally , stimulation to any single subcortical region produces a functional effect that is less constrained by the underlying structural connections ( low structural effect ) ., In contrast , regions in other systems remain similarly constrained by the underlying structural connections , but vary in their ability to impart a functional effect on the system ., Clusters of these nine cognitive systems emerge , suggesting that the parcellation scheme may be too fine-grained to understand the general organizing principles across the structure-function landscape; for example , there may be general network principles for sensory processing that are independent of modality ( sight , sound , touch ) ., Consequently , we created a more coarse-grained grouping of four cognitive systems ( three functional and one structural ) to examine broad-stroke differences among well-studied cognitive systems: sensory and association cortex , higher order cognitive , medial default mode network , and subcortical regions ., As seen in Fig 7b , regions in the sensory and association cortex and higher order cognitive regions show a wide variation in their ability to impart a large functional effect when stimulated ., Interestingly , although regions in the default mode network are similarly constrained by structure when compared to the sensory and association and higher order cognitive regions , regions within the default mode network consistently impart a large functional effect on the system ., Stimulation of subcortical regions also results in a large functional effect , but these regions are less constrained by structure than those in the default mode and therefore these two systems occupy different spaces in the structure-function landscape ( Fig 7b; two sample , two-dimensional Kolmogorov-Smirnov test , p = 0 . 0003 ) ., This separation in the structure-function landscape could reflect the fact that the default mode network represents a functionally defined system , whereas subcortical regions are functionally diverse despite being well connected structurally ., In our final analysis , we examined why the default mode and subcortical regions had a stronger functional effect , and interestingly , why the subcortical structures showed a lower structural effect ., We hypothesized that differential properties of these four systems can be better understood by investigating the density of connections between brain regions within a system versus the density of connections between systems ., Both the sensory and association cortex and the higher order regions are defined functionally and are composed of multiple distributed structural systems ., They therefore , as a whole , have a low density of structural connections both within the system and between the system and the rest of the brain ( Fig 7c ) ., In contrast , subcortical regions form a highly connected structural subnetwork while remaining well connected to the rest of the brain ., Thus , stimulation to these regions is globally distributed , resulting in a high functional effect ., The medial default mode network also forms a well-connected subnetwork , but is less connected to the sensory and association cortex and higher order cognitive regions than the subcortical regions ( see Table 1 ) ., While stimulation to regions in the default mode network results in a large functional effect , regions in this subnetwork remain more constrained by the underlying structure ., This latter result is consistent with previous work showing that brain regions in the default mode network display the highest correlation between structural and functional connectivity 35 ., These results capture a mechanism for the subcortical regions to strongly influence regions across cerebral cortex where the default mode has a strong but targeted functional effect within its network , which enables these regions to quickly adapt from rest to a wide variety of task states 30 ., As neuromodulation is increasingly used to treat neurological disorders , it is essential to develop an understanding of the network-wide effects of focal stimulation ., Such knowledge would directly inform the development of targeted protocols that effectively and efficiently maximize therapeutic benefits while minimizing the potential for adverse effects on brain dynamics and cognition ., An initial step towards achieving this goal lies in the examination of neuroimaging data through the lens of linear network control theory , a mathematical framework that predicts highly controllable brain regions from the pattern of underlying structural connectivity ., However , these techniques rely on the assumption that brain dynamics are linear , when in reality they are highly nonlinear ., Here , we developed a computational modeling approach to investigate whether the predictions of a region’s controllability drawn from a linear model could be validated in a nonlinear model ., Specifically , we built data-derived structural connectivity matrices to computationally explore the effects of regional stimulation on network dynamics and functional states ., Using this model , we confirmed predictions from linear control theory , showing that stimulation of high average controllability regions resulted in global activation that produced little change in the topology of the functional connectivity , underscoring their role in moving the brain to nearby states ., Furthermore , we investigated the interplay between functional and structural effects of stimulation by examining how the global functional activity across brain regions was modulated by region-specific stimulation ( a region’s functional effect ) and whether the region’s structural connectivity accounted for its influence on the larger brain network ( structural effect ) ., We observed that the underlying network connectivity differentially constrained the effects of stimulation: regions of high average controllability ( strongly connected hubs , most often associated with regions within the default mode network , see 30 for more discussion ) displayed a high functional effect—meaning that they greatly increased the magnitude of functional connectivity—while regions of low average controllability ( weakly connected areas 30 ) did not ., Yet , stimulation that led to larger changes in functional connectivity magnitude ( functional effect ) induced global changes in functional connectivity topology ( fractional activation ) , moving the system towards easily reachable states as opposed to the more distant states accessed through focal activation due to stimulation of low controllability regions ., Interestingly , when we parsed brain regions into cognitive systems , we found that stimulation of the medial default mode network showed both high structural and functional effects , differentiating it from other subnetworks by its ability to move the system while remaining influenced by the underlying network connectivity ., Perhaps one of the most striking observations from these data lies in the tradeoff between two competing consequences of stimulation: the magnitude of changes in functional connectivity , and the spatial specificity of changes in functional connectivity ., We observed that stimulation to network hubs , predominantly located in default mode and subcortical structures 30 , 31 , 37 , induces widespread increases in the magnitude of functional connectivity between brain regions ., Yet , this broad impact is affected only at the expense of spatial specificity ., In contrast , stimulation targeted to weakly connected areas ( low average controllability ) predominantly located in fronto-parietal regions 30 , induces focal changes in functional connectivity ., The differential impact of stimulation to these two strongly versus weakly connected regions suggests the possibility of two different classes of therapeautic interventions:, ( i ) a broad reset , in which brain dynamics are globally altered , and, ( ii ) a focal change , in which brain dynamics of a few regions are altered ., While global alterations could be obtained through pharmacological interventions , the ability of focal regional stimulation to impart both global and focal alterations may offer mechanistic insights into the role of stimulation in distinguishing fine-scale differences between the concepts 38versus broadly altering general cognitive processes 39 , 40 or brain states 41 ., The differences in the impact of stimulation to hubs versus non-hubs further supports the predictions of linear network control theory 42 and their recent applications to neuroimaging data 30 ., In this prior work , theoretical insights from structural controllability 43 were used to make the prediction that network hubs—particularly in the default mode system—facilitate the movement of the brain to many easily reachable states ., In contrast , weakly connected nodes of the network—particularly in cognitive control systems—were predicted to facilitate the movement of the brain to difficult-to-reach states ., Our results confirm these predictions and offer further insights into the mechanisms of these control strategies ., Specifically , easily reachable states are those that display patterns of functional connectivity that are very similar to those observed in the initial state , while difficult-to-reach states are those that display patterns of functional connectivity that are very different from those observed in the initial state ., In addition to these large-scale observations of network state change , we also probed the degree to which the pattern of functional connectivity ( whether focal or global ) was correlated with the pattern of structural connectivity ., Prior work has focused largely on the relationship between resting state functional connectivity and structural connectivity 44–46 , under the assumption that the brain’s resting baseline might be highly constrained by anatomy ., However , structural connections likely constrain functional connectivity present in all brain states , irrespective of the cognitive process at play 30 ., Indeed , a few recent studies have demonstrated the non-trivial relationships between individual differences in the pattern of structural connections and the observed functional connectivity across multiple cognitive states 47 , 48 ., Here we observe that the similarity between structural connectivity and observed functional connectivity depends significantly on the brain region that was stimulated ., Critically , this relationship was not driven by the average controllability of the region ., Along with prior links between average controllability and degree 30 , these results suggest that structural constraints on stimulation-elicited functional connectivity can not easily be predicted by whether a region is a hub or a non-hub ., The relative independence of the functional and structural effects is particularly evident across large-scale cognitive systems ., Indeed , we observe an inverted U-shaped curve between these two variables: systems that display a middling change in the magnitude of functional connectivity with stimulation tend to display functional connectivity patterns that are most reminiscent of the underlying structural connectivity ., In contrast , systems that display a very large or very small change in the magnitude of functional connectivity with stimulation tend to display functional connectivity patterns that are very different from the structural connectivity ., Intuitively , stimulation to hubs or non-hubs produces brain states that are far from those simply predicted by structural connectivity , and potentially thus far from normative 48 ., It will be interesting in future to determine which brain states elicited by stimulation are consistent versus inconsistent with states observed in normative brain dynamics ., Such a question is reminiscent of similar work in control theory identifying so-called allowable transitions 49 , 50 ., More broadly , an understanding of potential brain states elicited by stimulation is key to deploying stimulation in such a way as to maximize clinical benefit while minimizing pathological configurations of the network ., There are several important methodological considerations pertinent to this work ., While this work represents an important step in characterizing the effects of stimulation and control in nonlinear brain networks , it should be noted that the nonlinear model of brain dynamics employed here , while biologically inspired , is a simplified mean-field model of neuronal dynamics ., The sigmoidal transfer function introduced by Wilson and Cowan 32 simplifies the detailed spatial and temporal dynamics that underlie brain connectivity , and the fact that we have chosen to couple brain regions only through the excitatory populations reflects an assumption that most long range connections will be excitatory ., Additionally , although noise is added to our model equations , we assume that all model parameters are identical between regions ., While these assumptions impart limitations on the ability of the model to fully reproduce brain dynamics , our model represents a first step in understanding the role of brain topology in driving function , and we are encouraged by other studies showing that measures of network topology and similar modeling approaches depict a correlation between structural connectivity , simulated functional connectivity , and resting state fMRI functional connectivity 44 , 51 ., Hopefully , as experimental advances provide more insight into the nuances of brain connectivity , these features can be incorporated into future work , providing more insights into brain structure-function coupling and the role of stimulation and control ., Additionally , we have performed only a rough partitioning of the brain into regions and finer scales of regional partitioning could lead to greater distinction between regional roles as more subtle patterns of brain connectivity are revealed ., While future work is necessary to confirm the effects of spatial resolution on models of targeted stimulation , in S5 Fig , we present the results of the relationships between controllability and the functional and structural effect for a single scan that has been parcellated into 234 regions ., This figure shows that we see the same overall trends when parcellating the brain at a finer scale , indicating that the overall findings presented here still hold when partitioning the brain with greater spatial resolution ., Finally , an important finding of this study was that while we observed a high level of reproducibility in simulations run using connectivities derived from separate scans within a single subject , we observed variance across subjects in the range of coupling corresponding to the fixed point and oscillatory regimes of the model ., While measures of controllability , functional , and structural effects collapsed to the same curves across subjects ( Fig 5 ) , the shifting of the oscillatory regime in the coupling parameter space ( Fig 2c ) indicates that the model is sensitive to variances in structural connectivities between individuals ., Although the set of 8 subjects studied here is underpowered to study individual variation in connectivity and its impact on model performance , this encouraging result suggests the utility of such approaches to understand individual variability in structural connections and how it may change the functional effect of stimulation ., This modeling approach provides a means to individualize stimulation protocols for personalized medical treatments or performance enhancements ., All participants volunteered with informed consent in writing in accordance with the Institutional Review Board/Human Subjects Committee , University of California , Santa Barbara ., | Introduction, Results, Discussion, Methods | The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance ., Despite the growing interest in brain stimulation as a form of neuromodulation , much remains unknown about the network-level impact of these focal perturbations ., To study the system wide impact of regional stimulation , we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation ., Validating predictions from network control theory , we uncover the relationship between regional controllability and the focal versus global impact of stimulation , and we relate these findings to differences in the underlying network architecture ., Finally , by mapping brain regions to cognitive systems , we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity ., This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement . | Brain stimulation is increasingly used in clinical settings to treat neurological disorders , but much remains unknown about how stimulation to a single brain region impacts large-scale , brain network activity ., Using structural neuroimaging scans , we create computational models of brain dynamics for eight participants to explore how structure-function relationships constrain the effect of stimulation to a single region on the brain as a whole ., Our results show that network control theory can be used to predict if the effects of stimulation remain focal or spread globally , and structural connectivity differentially constrains the effects of regional stimulation ., Additionally , we study how stimulation of different cognitive systems spreads throughout the brain and find that stimulation of regions within the default mode network provide a mechanism to impart large change in overall brain dynamics through a densely connected structural network ., By revealing how the stimulation of different brain regions and cognitive systems spreads differently through the brain , we provide a modeling framework to develop stimulation protocols to personalize medical treatments , enable performance enhancements , and facilitate cortical plasticity . | control theory, medicine and health sciences, diagnostic radiology, neural networks, engineering and technology, brain, neuroscience, magnetic resonance imaging, control engineering, brain morphometry, systems science, mathematics, cognition, network analysis, brain mapping, neuroimaging, research and analysis methods, computer and information sciences, imaging techniques, diffusion spectrum imaging, nonlinear dynamics, radiology and imaging, diagnostic medicine, anatomy, diffusion weighted imaging, biology and life sciences, physical sciences, cognitive science | null |
journal.pcbi.1006557 | 2,018 | Representations of regular and irregular shapes by deep Convolutional Neural Networks, monkey inferotemporal neurons and human judgments | Recently , several studies compared the representations of visual images in deep Convolutional Neural Networks ( CNN ) with those of biological systems , such as the primate ventral visual stream 1–4 ., These studies showed that the representation of visual objects in macaque inferior temporal ( IT ) cortex corresponds better with the representations of these images in deep CNN layers than with representations of older computational models such as HMAX 5 ., Similar findings were obtained with human fMRI data 6–10 ., The images used in these studies were those of real objects in cluttered scenes , which are the same class of images as those employed to train the deep CNNs for classification ., Other single unit studies of IT neurons employed two-dimensional ( 2D ) shapes and observed highly selective responses to such stimuli ( for review see 11 ) ., If deep CNNs provide a realistic model of IT responses , then the CNNs should capture also the selectivity observed for such two-dimensional shapes in IT ., To our knowledge , thus far there has been no comparison between the 2D-shape representation of IT neurons , measured with such reduced stimuli , and that of deep CNN models ., It is impossible to predict from existing studies that compared deep CNN activations and neurophysiology whether the deep CNNs , which are trained with natural images , can faithfully model the selectivity of IT neurons for two-dimensional abstract shapes ., Nonetheless , such correspondence between CNN models and single unit selectivity for abstract shapes is critical for assessing the generalizability of CNN models to stimuli that differ markedly from those of the trained task but have been shown to drive selectively IT neurons ., Previously , we showed that a linear combination of units of deep convolutional layers of CNNs trained with natural images could predict reasonably well the shape selectivity of single neurons recorded from an fMRI-defined body patch 4 ., However , in that study , we adapted for each single unit the shapes to the shape preference of that neuron , precluding a comparison between the shape representation of the population of IT neurons and deep CNNs ., To perform such a comparison , one should measure the responses of IT neurons to the same set of shapes ., Furthermore , the shape set should include variations in shape properties IT neurons were shown to be sensitive to ., Also , the IT response selectivities for such shapes should not trivially be explainable by physical image similarities , such as pixel-based differences in graylevels ., Kayaert et al . 12 measured the responses of single IT neurons to a set of shapes that varied in regularity and the presence of curved versus straight boundaries ( Fig 1 ) ., The first group of stimuli of 12 was composed of regular geometric shapes ( shown in the first two rows of Fig 1 and denoted as Regular ( R ) ) that all have at least one axis of symmetry ., These shapes are simple , i . e . , have low medial axis complexity 13 ., The stimulus pairs in each column of these two rows ( denoted by a and b ) differed in a non-accidental property ( NAP ) ., NAPs are stimulus properties that are relatively invariant with orientation in depth , such as whether a contour is straight or curved or whether a pair of edges is parallel or not ., These properties can allow efficient object recognition at different orientations in depth not previously experienced 14–16 ., NAPs can be contrasted with metric properties ( MPs ) , which vary with orientation in depth , such as aspect ratio or the degree of curvature ., The three other groups are all ‘Irregular’ ., They differed from the Regular shapes in that they do not have a single axis of symmetry ., The two shapes in each row of the three Irregular groups differed in the configuration of their concavities and convexities or corners ., The shapes in the Irregular Simple Curved ( ISC ) set all had curved contours ., The Irregular Simple Straight ( ISS ) shapes were derived from the ISC shapes by replacing the curved contours with straight lines ., Thus , the corresponding stimuli in the ISS and ISC shapes differed in a NAP ., Last , the Irregular Complex ( IC ) group was more complex in that the shapes in that group had a greater number of contours ., Kayaert et al . 12 found that anterior IT neurons distinguished the four groups of shapes ., Importantly , the differences in IT responses amongst the shapes could not be explained by pixel-based gray level differences , nor by HMAX C2 unit differences ., In fact , none of the tested quantitative models of object processing could explain the IT response modulations ., Furthermore , the IT response modulations were greater for the Regular shapes and when comparing the curved and straight Irregular Simple shapes than within the 3 Irregular shape groups , suggesting a greater sensitivity for NAPs than for MPs ( see also 17 , 18 ) ., We reasoned that this shape set and corresponding IT responses was useful to examine to what degree different layers of deep CNNs and IT neurons represent abstract shapes similarly ., We employed deep CNNs that were pretrained to classify ImageNet data 19 , consisting of images of natural objects in scenes ., Hence , the CNNs were not exposed during training to silhouette shapes shown to the IT neurons ., Deep CNNs have a particular architecture with early units having small receptive fields , nonlinear pooling of units of the previous layer , etc ., Such a serial , hierarchical network architecture with increasing receptive field size across layers may result in itself , i . e . without training , in changes in the representational similarity across layers ., To assess whether potential correlations between IT and CNN layer response modulations resulted from classification training or from the CNN architecture per se , we also compared the activations of untrained CNNs with the IT response modulations ., Kayaert et al . 12 had also human subjects sort the same shapes based on similarity and found that human subjects had a pronounced higher sensitivity to the difference between the curved and straight simple irregular shapes ( relative to the regular shapes ) than the IT neurons ., We examined whether a similar difference in response pattern between macaque IT neurons and human similarity judgements would emerge in the deep CNNs ., We expected that deeper layers would resemble the human response patterns while the IT response pattern would peak at less deep layers ., Kayaert et al 12 recorded the responses of 119 IT neurons to the 64 shapes shown in Fig 1 ., The 64 shapes are divided in four groups based on their regularity , complexity and whether they differed in NAPs ., We presented the same shapes to 3 deep CNNs: Alexnet 20 , VGG-16 , VGG-19 21 and measured the activations of the units in each layer of the deep nets ., These deep nets differ in their number of layers , the number of units in each layer and the presence of a normalization stage , but each have rectifying non-linearity ( RELU ) and max pooling stages ( Fig 2 ) ., We employed deep nets that were pre-trained in classification of a database of natural images , which were very different in nature from the abstract shape stimuli that we employ here to test the models and neurons ., The aim was to compare the representations of the shapes between IT neurons and each layer of the deep nets ., To do this , we employed representational similarity analyses 22 , 23 , following the logic of second order isomorphism 24 , 25 , and examined the correlation between the neural IT-based similarities and CNN-based similarities in responses to shapes ., We are not trying to reconstruct the shapes based on IT neuron or CNN unit outputs but we are examining whether shapes that are represented close to each other in the neural IT space are also represented close to each other in the CNN layer space ., In a first analysis , we computed the pairwise dissimilarity between all 64 stimuli using the responses of the IT neurons and the activations in each of the CNN layers ., We employed two dissimilarity metrics: Euclidean distance and 1 –Spearman rank correlation ρ ., The dissimilarity matrices computed with the Euclidean distance metric for the IT neurons and for 5 layers of the trained CNNs are illustrated in Fig 3B and 3C , respectively ., In this and the next figures , we will show only the data for Alexnet and VGG-19 , since VGG-16 and VGG19 produced similar results ., In addition , Fig 3A shows the pixel-based dissimilarities for all image pairs ., Visual inspection of the dissimilarity matrices suggests that ( 1 ) the pattern of dissimilarities changes from the superficial to deep layers in a relatively similar way in the CNNs , ( 2 ) the dissimilarity matrix of the first layer ( e . g . conv1 . 1 ) resembles the pixel-based similarities ( Fig 3A ) and ( 3 ) the deeper layers resemble more the IT neural data ( Fig 3B ) ., We quantified the similarity between the IT shape representation and that of each layer by computing the Spearman Rank correlation between the corresponding pairwise dissimilarities of IT and each layer ., Thus , we could assess to what degree stimuli that produce a very different ( similar ) pattern of responses in IT also show a different ( similar ) pattern of activations in a CNN layer ., We found that for both dissimilarity metrics the similarity between IT neuronal responses and trained CNN layer activations increased significantly with the depth of the layer ., This is shown using the Euclidean distance metric for Alexnet and VGG-19 in Fig 4 ( see S1 Fig for the data of both distance metrics and the 3 networks ) ., In the VGG nets , the similarity peaked at the deepest convolutional layers ( Fig, 4 ) and then decreased for the deepest layers ., In fact , the Spearman correlations for the last two fully connected layers did not differ significantly from that of the first convolutional layer in each CNN ( Fig 4 ) ., The decrease in similarity for the deepest layers was weaker in Alexnet ., The peak similarity was similar amongst the 3 nets , with ρ hovering around 0 . 60 , and were larger for the correlation ( mean peak ρ = 0 . 64 ) compared with Euclidean distance metric ( mean peak ρ = 0 . 58 ) ., To assess the degree to which the models explained the neural data , we computed the reliability of the neural-based distances giving the finite sampling of the IT neuron population ., This noise ceiling was computed by randomly splitting the neurons into two groups , computing the dissimilarities for each group , followed by computation of the Spearman rank correlation between the dissimilarities of the two groups ., This split-half reliability computation was performed for 10000 random splits ., Fig 4 shows the 2 . 5 , 50 ( median ) and 97 . 5 percentiles of the Spearman-Brown corrected correlations between the two groups ., The correlations between ( some ) CNN layers and neural responses were close but still below the estimated noise of the neural dissimilarities ., In order to assess to what degree the similarity between neural data and the CNN layers reflects the architecture of the CNNs versus image classification training , we computed also the similarity for untrained networks with random weights ., Fig 3C illustrates dissimilarity matrices computed using Euclidean distances for 5 untrained layers of two CNNs ., Visual inspection suggests little change in the dissimilarity matrices of the different layers of the CNNs , except for fc8 ., Furthermore , the pattern of dissimilarities resembled the pixel-based dissimilarities shown in Fig 3A ., Both observations were confirmed by the quantitative analysis ., The Spearman correlations of the neural data and untrained CNNs increased only weakly with depth , except for a marked decrease in correlation for the last two fully connected layers ., Except for the deep convolutional and the last two layers , the trained and untrained networks showed similar Spearman correlations of the neural and CNN distances ( Fig 4 ) ., This suggests that overall the similarity between the IT data and the shallow CNN layers are unrelated to classification training but reflect merely the CNN architecture ., Significant differences between trained and untrained CNNs were observed for the deeper convolutional layers ( Fig 4 ) , suggesting that the similarity between IT and the deep convolutional layers depends on classification training ., The similarities for the first fully connected layer ( fc6 and relu6 in Fig, 4 ) did not differ significantly between the trained and untrained layers ( except for the correlation metric in AlexNet ( S1 Fig ) ., The deepest two ( fully connected ) layers showed again a significantly greater similarity for the trained compared with the untrained networks ., However , this can be the result of the sharp drop in correlations for these layers in the untrained network ., Overall , these data suggest that the shape representations of the trained deep convolutional layers , but not of the deepest layers , shows the highest similarity with shape representations in macaque IT ., Receptive field ( RF ) size increases along the layers of the CNNs , allowing deeper layer units to integrate information from larger spatial regions ., The difference in IT-CNN similarity between untrained and trained layers shows that the increase in RF size cannot by itself explain the increased IT-CNN similarity in deeper layers , since untrained CNN also increase their RFs along the layer hierarchy ., Also , the decrease in similarity between IT responses and the fully connected layers argues against RF size being the mere factor ., Nonetheless , although not the only contributing factor , RF size is expected to matter since arguably small RFs cannot capture overall shape when the shape is relatively large ., Hence , it is possible that the degree of IT-CNN similarity for different layers depends on shape size , with smaller shapes showing a greater IT-CNN similarity at earlier layers ., We tested this by computing the activations to shapes that were reduced in size by a factor of two in all layers of each of the 3 trained CNNs ., Fig 5 compares the correlations between dissimilarities of the trained Alexnet and VGG-19 networks and IT dissimilarities for the original and reduced sizes , with dissimilarities computed using Euclidean distances ., The stars indicate significant differences between the similarities for the two sizes ( tested with a FDR corrected randomization test; same procedure as in Fig 4 when comparing trained and untrained correlations ) ., In each of the CNNs ( S2 Fig ) , the IT-CNN similarity increased at more superficial layers for the smaller shape ., The overall peak IT-CNN similarity was highly similar for the two sizes in the VGG networks and occurred at the deep convolutional layers ., For Alexnet , the overall similarity was significantly higher for the smaller shapes in the deep layers ., This analysis indicates that shape size is a contributing factor that determines at which layer the IT-CNN similarity increases , but that for the VGG networks , peak similarity in the deep layers does not depend on size ( at least not for the twofold variation in size employed here ) ., Note that also for the smaller size the IT-CNN similarity drops markedly for the fully connected layers in the VGG networks ., Thus , the overall trends are independent of a twofold change in shape size ., In the preceding analyses , we included all units of each CNN layer ., To examine whether the similarity between the CNN layers and the IT responses depends on a relatively small number of CNN units or is distributed amongst many units , we reran the representational similarity analysis of deep CNN layers and IT neurons for the whole shape set for smaller samples of CNN units ., We took for each network the layer showing the peak IT-CNN similarity and for that layer sampled 10000 times at random a fixed percentage of units ., We restricted the population of units to those that showed a differential activation ( standard deviation of activation across stimuli greater than 0 ) since only those can contribute to the Euclidean distance ., Fig 6A plots the median and 95% range of Spearman rank correlation coefficients between IT and CNN layer dissimilarities for the whole shape set as a function of the percent of sampled units for two CNNs ., We found that the IT-CNN similarity was quite robust to the number of sampled units ., For instance , for Alexnet , the IT-CNN similarity for the original and the 95% range of the 10% samples overlap , indicating that 315 Alexnet units can produce the same IT-CNN similarity as the full population of units ., Note also that the lower bound of the 95% range is still above the IT-CNN similarities observed for the untrained network ( median Spearman rho about 0 . 40; see Fig 4 ) ., This indicates that the IT-CNN similarity does not depend on a small subset of units , since otherwise the range of similarities ( Spearman rho correlations ) for the 10% samples would be much greater ., The same holds for the other CNNs ( S3 Fig ) , except that these tolerated even smaller percent sample size ( for VGG19 even 0 . 1% , which corresponds to 100 units ) ., The above analysis appears to suggest that the activations of the CNN units to the shapes are highly correlated with each other ., To address this directly , we performed Principal Component Analysis ( PCA ) of unit activations of the same peak CNN layers as in Fig 6 and computed Euclidean distance based dissimilarities between all stimulus pairs for the first , first two , etc . principal components ( PCs ) , followed by correlation with the neural dissimilarities as done before for the distances computed across all units of a CNN layer ., For both the Alexnet and VGG-19 layer , the first 10 PCs explained about 70% of the variance in CNN unit activations to the 64 stimuli ( Fig 7B ) ., Only the first 3 ( Alexnet ) or 5 ( VGG-19 ) PCs were required to obtain a similar correlation between the model and neural distances as observed when using all model units of the layer ( Fig 7A; about 7 PCs were required for VGG-16; see S4 Fig ) ., This analysis shows that the neural distances between the abstract shapes relate to a relatively low dimensional shape representation in the CNN layer , with a high redundancy between the CNN units ., In the above analyses , we compared the overall similarity of the shape representations in IT and CNN layers ., However , a more stringent comparison between the shape representations in IT and the CNNs involves response modulations for the shape pairs for which Kayaert et al 12 observed striking differences between predictions of pixel-based models or computational models like HMAX and the neural responses ., The average response modulations ( quantified by pairwise Euclidean distances ) for the different group pairs comparisons are shown in Fig 8 for the IT neural data , the HMAX C2 layer and the pixel differences ., Kayaert et al 12 showed that the mean response modulation in IT ( Fig 8A ) was significantly greater for the regular shape pairs ( 1–8 in Fig, 1 ) than for the 3 irregular shape group pairs , despite the pixel differences between members of a pair being , on average , lower or similar for the regular group than for the 3 irregular groups ( Fig 8D ) ., In addition , the response modulation to ISC vs . ISS was significantly greater than the modulations within IC , ISC and ISS , although the average pixel-difference within the ISC vs . ISS-pairs was much lower than the pixel-differences within the other pairs ., This differential neural response modulation to ISC vs ISS was present for both members of the ISC and ISS pairs ( a and b members: “ISCa vs ISSa” and “ISCb vs ISSb” ) and thus was highly reliable ., Note that the difference between ISC vs . ISS and the IC and ISS shape groups that are present in the neural data is not present for the HMAX C2 distances ( Fig 8C ) ., Kayaert et al . 12 reported also a relatively higher sensitivity to the straight vs . curved contrast of the ISC vs . ISS comparison compared with the regular shapes in human similarity ratings ( Fig 8B ) , compared with the IT neural data ., In other words , human subjects appear to be more sensitive to the curved versus straight NAP difference than macaque IT neurons ., In a second analysis , we determined whether the marked differences in IT response modulations and human judgements shown in Fig 8 are present in the dissimilarities for the different layers of the deep CNNs ., Fig 9 illustrates the results for 8 layers of VGG-19 ., The left column of the figure plots the distances for the trained network ., The dissimilarities for the first convolutional layer fits the pixel-based distances amongst the shape pairs ( Fig 8D; Pearson correlation between pixel-based distances and first layer distances = 0 . 966 ) , but differ from those observed in IT and for human judgements ., Similar trends are present until the very deep convolutional layers where the dissimilarities became strikingly similar to those observed in macaque IT ( e . g . compare trained conv5 . 4 or pool5 of Fig 9 with Fig 8A ) ., The dissimilarities for the last two layers ( e . g . trained relu7 and fc8 in Fig, 9 ) are strikingly similar to those observed for the human judgements ( Fig 8B ) , and differ from the pattern seen in macaque IT neurons ., Indeed , as noted above , the human judgements differ from the IT responses in their sensitivity for the ISC vs ISS comparison relative to that for the regular shape pairs: for the human judgement distances , the ISC vs ISS distances are greater than for the regular shape distances while for the neural distances both are statistically indistinguishable ( Kayaert et al . 12 ) ., Therefore , we tested statistically for which CNN layer the ISC vs ISS distances were significantly greater than the regular shape distances ( Wilcoxon test ) , thus mimicking the human distances ., We found a significant difference for the very deep VGG19 layer fc8 ( p = 0 . 039 ) and VGG16 layers fc7 ( p = 0 . 039 ) , relu7 ( p = 0 . 023 ) , and fc8 ( p = 0 . 023 ) ., Although the deepest Alexnet ( fully connected ) layers showed the same trend , this failed to reach significance ., These tests showed that only the very deep CNN layers mimicked the human judgements ., None of the untrained CNN layers showed a dissimilarity profile similar to that observed in monkey IT or in human judgements ( Fig 9 , right column ) ., In fact , the untrained data resembled more the pixel-based distances ( see Fig 8D ) ., Indeed , the Pearson correlation between the pixel-based distances and the conv1 . 1 distances was 0 . 999 for the untrained VGG-19 ., We quantified the correspondence between the neural response dissimilarities of Fig 8A and the CNN layer dissimilarities ( as in Fig, 9 ) by computing the Pearson correlation coefficient between the dissimilarity profiles ( n = 6 dissimilarity pairs ) ., The same quantification was performed for the human judgements ( Fig 8B ) and the CNN dissimilarities ( n = 5 pairs ) ., These correlations are plotted in Fig 10A and 10B as a function of layer for two CNNs , trained and untrained ., For the neural data , the correlations are negative for the shallow layers and highly similar for the trained and untrained CNNs ., The negative correlations are a result of the nearly inverse relationship between neural and low-level ( pixel ) differences between the shapes ( Fig 8D ) ., This was not accidental , but by design: when creating the stimuli , Kayaert et al 12 ensured that the NAP differences ( e . g . between ISC and ISS ) were smaller than MP differences ., For both VGG networks ( S5 Fig; Fig 10B ) , there was a sharp increase in correlations at the trained deep 5 . 1 convolutional layer , followed by a decrease of the correlations for the fully connected layers ., This trend was similar , although more abrupt , to that observed for the global dissimilarities of Fig 4 ., For Alexnet , the increase of the correlations with increasing depth of the trained convolutional layers was more gradual , but like the VGG networks , high correlations were observed for the deeper trained convolutional layers ., For the human judgement data , the correlations were already higher for the trained compared with the untrained CNNs at the shallow layers , although still negative ., Like the neural data , there was a marked increase in correlation at the very deep trained layers ., Contrary to the neural data , the correlations for the human judgements continued to increase along the trained fully connected layers , approaching a correlation of 1 at the last layer ., These data show that the average response modulations of IT neurons for the shape groups of Fig 1 correspond nearly perfectly with those of the deeper layers of CNNs , while the differences in human similarity judgements between the groups are captured by the later fully connected layers of the CNNs ., This holds for Alexnet and VGG nets ., Note that the deep CNN layers performed better at predicting the neural IT and human perceptual dissimilarities than the HMAX C2 layer output ( Fig 10C ) ., As for the representational similarity analysis for all shapes ( Fig 6A ) , we computed also the Pearson correlation coefficients between the dissimilarity profiles ( n = 6 dissimilarity pairs ) of the same peak CNN layers and the IT distances for the 6 shape groups ( as in Fig, 10 ) for smaller samples of units ., As shown in Fig 6B , we observed similar IT-CNN correlations for the within-group distances up to the 1% and 0 . 1% samples compared with the full population of units for Alexnet and VGG , respectively ., Again , this suggests that IT-CNN similarity does not depend on a small number of outlier CNN units ., The greater tolerance for percent sample size for the VGG units is because the VGG layers consisted of a larger number of units per se ( total number of units are indicated in the legend of Fig 6 ) ., In addition , we computed the mean distances for the same layers and their correlation with the mean neural modulations as a function of retained PCs ( Fig 7B ) ., Up to 30 PCs were required to obtain a similar correlation between neural and CNN layer distances for the six groups of shapes as when including all units of the layer ( Fig 7B ) ., This suggests that the close to perfect modeling of the mean response modulations across the 6 shape groups required a relatively high dimensional representation of the shapes within the CNN layer ., The particular set of shapes that we employed in the present study was designed originally to test the idea that the shape selectivity of IT neurons reflects the computational challenges posed when differentiating objects at different orientations in depth 12 , 14 ., Here , we show that deep CNNs that were trained to classify a large set of natural images show response modulations to these shapes that are similar to those observed in macaque IT neurons ., We show that untrained CNNs with the same architecture than the trained CNNs , but with random weights , demonstrate a poorer IT-CNN similarity than the CNNs trained in classification ., The difference between the trained and untrained CNNs emerged at the deep convolutional layers , where the similarity between the shape-related response modulations of IT neurons and the trained CNNs was high ., Unlike macaque IT neurons , human similarity judgements of the same shapes correlated best with the deepest layers of the trained CNNs ., Early and many later studies of IT neurons employed shapes as stimuli ( e . g . 26–31 , 22 , 32–37 ) , in keeping with shape being an essential object property for identification and categorization ., Deep CNNs are trained with natural images of objects in cluttered scenes ., If deep CNNs are useful models of biological object recognition 38 , their shape representations should mimic those of the biological system , although the CNNs were not trained with such isolated shapes ., We show here that indeed the representation of the response modulations by rather abstract , unnatural shapes is highly similar for deep CNN layers and macaque IT neurons ., Note that the parameters of these CNN models are set via supervised machine learning methods to do a task ( i . e . classify objects ) rather than to replicate the properties of the neural responses , as done in classic computational modeling of neural selectivities ., Thus , the same CNN model that fits neural responses to natural images 1–4 also simulates the selectivity of IT neurons for abstract shapes , demonstrating that these models show generalization across highly different stimulus families ., Of course , the high similarity between deep CNN layers and IT neurons activation patterns we show here may not generalize for ( perhaps less fundamental ) shape properties that we did not vary in our study ., Kubilius et al . 39 showed that deep nets captured shape sensitivities of human observers ., They showed that deep Nets , in particular their deeper layers , show a NAP advantage for objects ( “geons” ) , as does human perception ( and macaque IT 18 ) ., Although we also manipulated NAPs , our shapes differed in addition in other properties such as regularity and complexity ., Furthermore , our shapes are unlike real objects and more abstract than the shaded 3D objects employed by Kubilius et al . 39 when manipulating NAPs ., In both the representational similarity analysis and the response modulations comparisons amongst shape groups , we found that the correspondence between IT and deep CNN layers peaked at the deep convolutional layers and then decreased for the deeper layers ., Recently , we observed a similar pattern when using deep CNN activations of individual layers to model the shape selectivity of single neurons of the middle Superior Temporal Sulcus body patch 4 , a fMRI-defined region of IT that is located posterior with respect to the present recordings ., The increase with deeper layers of the fit between CNN activations and neural responses has also been observed when predicting with CNN layers macaque IT multi-unit selectivity 40 , voxel activations in human LO 9 and the representational similarity of macaque and human ( putative ) IT 8 , 10 using natural images ., However , the decrease in correlation between CNNs and neural data that we observed for the deepest layers was not found in fMRI studies that examined human putative IT 8 , 10 , although such a trend was present in 6 when predicting CNN features from fMRI activations ., The deepest layers are close to or at the categorization stage of the CNN and hence strongly dependent on the classifications the network was trained on ., The relatively poor performance of the last layers is in line with previous findings that IT neurons show little invariance across exemplars of the same semantic category 41 , 42 , unlike the deepest CNN units 43 ., The question of what the different layers in the various CNN models with different depths represent neurally remains basically unanswered ., Shallow CNN layers can be related to early visual areas ( e . g . V1; V4 ) and deeper layers to late areas ( e . g . IT ) ., However , different laminae within the same visual area ( e . g . input and output layers ) may also correspond to different layers of CNNs ., Furthermore , units of a single CNN layer may not correspond to a single area , but the mapping might be more mixed with some units of different CNN layers being mapped to area 1 , while other units of partially overlapping CNN layers to area 2 , etc ., Finally , different CNN layers may represent different temporal processing stages within an area , although this may map partially to the different laminae within an area ., Further research in which recordings in different laminae of several areas will be obtained for the same stimulus sets , followed by mapping these to units of different layers in various CNNs , may start to answer this complex issue ., In contrast with IT neurons , human similarity judgements of our shapes | Introduction, Results, Discussion, Materials and methods | Recent studies suggest that deep Convolutional Neural Network ( CNN ) models show higher representational similarity , compared to any other existing object recognition models , with macaque inferior temporal ( IT ) cortical responses , human ventral stream fMRI activations and human object recognition ., These studies employed natural images of objects ., A long research tradition employed abstract shapes to probe the selectivity of IT neurons ., If CNN models provide a realistic model of IT responses , then they should capture the IT selectivity for such shapes ., Here , we compare the activations of CNN units to a stimulus set of 2D regular and irregular shapes with the response selectivity of macaque IT neurons and with human similarity judgements ., The shape set consisted of regular shapes that differed in nonaccidental properties , and irregular , asymmetrical shapes with curved or straight boundaries ., We found that deep CNNs ( Alexnet , VGG-16 and VGG-19 ) that were trained to classify natural images show response modulations to these shapes that were similar to those of IT neurons ., Untrained CNNs with the same architecture than trained CNNs , but with random weights , demonstrated a poorer similarity than CNNs trained in classification ., The difference between the trained and untrained CNNs emerged at the deep convolutional layers , where the similarity between the shape-related response modulations of IT neurons and the trained CNNs was high ., Unlike IT neurons , human similarity judgements of the same shapes correlated best with the last layers of the trained CNNs ., In particular , these deepest layers showed an enhanced sensitivity for straight versus curved irregular shapes , similar to that shown in human shape judgments ., In conclusion , the representations of abstract shape similarity are highly comparable between macaque IT neurons and deep convolutional layers of CNNs that were trained to classify natural images , while human shape similarity judgments correlate better with the deepest layers . | The primate inferior temporal ( IT ) cortex is considered to be the final stage of visual processing that allows for object recognition , identification and categorization of objects ., Electrophysiology studies suggest that an object’s shape is a strong determinant of the neuronal response patterns in IT ., Here we examine whether deep Convolutional Neural Networks ( CNNs ) , that were trained to classify natural images of objects , show response modulations for abstract shapes similar to those of macaque IT neurons ., For trained and untrained versions of three state-of-the-art CNNs , we assessed the response modulations for a set of 2D shapes at each of their stages and compared these to those of a population of macaque IT neurons and human shape similarity judgements ., We show that an IT-like representation of similarity amongst 2D abstract shapes develops in the deep convolutional CNN layers when these are trained to classify natural images ., Our results reveal a high correspondence between the representation of shape similarity of deep trained CNN stages and macaque IT neurons and an analogous correspondence of the last trained CNN stages with shape similarity as judged by humans . | medicine and health sciences, diagnostic radiology, functional magnetic resonance imaging, neural networks, visual object recognition, vertebrates, social sciences, neuroscience, animals, mammals, learning and memory, magnetic resonance imaging, primates, perception, cognitive psychology, cognition, brain mapping, memory, vision, neuroimaging, old world monkeys, research and analysis methods, computer and information sciences, imaging techniques, monkeys, animal cells, macaque, cellular neuroscience, psychology, eukaryota, diagnostic medicine, radiology and imaging, cell biology, neurons, biology and life sciences, cellular types, sensory perception, cognitive science, amniotes, organisms | null |
journal.pcbi.1004199 | 2,015 | Modelling Circulating Tumour Cells for Personalised Survival Prediction in Metastatic Breast Cancer | Breast cancer is characterised by multi-year survival from the first diagnosis of bone metastases ., It is a leading cause of cancer death among women , and if detected at an early stage , its prognosis is favourable , with 5-year survival—for death from the cancer—in more than 90% of the patients ., However , when initial diagnosis is of advanced metastatic disease , the 5-year survivals decrease to around 30% ., The survival and prognosis of cancer patients with metastatic skeletal disease vary widely and depend on many factors including features of the primary tumour ( histological type and grade ) , presence of extraosseus metastatic disease , patient’s characteristic ( performance status and age ) , level of tumour markers and extension of skeletal disease ., In fact , every cancer is different; as cancer grows , a mixture of cells builds up over time and becomes more and more complex ., Cancer cells often detach from the primary tumour , become circulating tumour cells ( CTC ) and invade blood vessels ., Once in the bloodstream , they reach the skeleton and adhering to the endosteal surface , they colonize the bone , subverting the cellular processes of normal remodelling and causing bone pathology 1 ., Cancer phenotypic heterogeneity may be due to progressive , but asynchronous changes in tumour–bone interactions ( i . e . progressive accumulation of driver and non driver mutations ) ., In particular , the Transforming Growth Factor-β ( TGF-β ) pathway mutations are determinant in generating cancer heterogeneity and in the formation of CTCs causing bone metastasis ., TGF-β is among the most abundant growth factors in bone , and its role in skeletal metastases is established ., It is deposited in the bone matrix by osteoblasts , released and activated during osteoclastic resorption , and it regulates bone development and remodelling 2 ., Advanced cancers frequently escape growth inhibition by TGF-β , which also activates epithelial-mesenchymal transition ( EMT ) and invasion , promoting metastases ., TGF-β also increases angiogenesis and suppresses immune surveillance ., It specifically stimulates bone metastases by inducing pro-osteolytic gene expression in cancer cells , such as parathyroid hormone related protein ( PTHrP ) 3 ., Moreover , therapies acting on the TGF-β pathway seem effective at all levels and compartments where TGF-β is involved , generating a retroaction effects on the primary tumour , the circulatory system and the bone 4 ., Recently , Baccelli et al . 5 have identified a set of genetic markers in CTCs which are key players in establishing bone metastasis ( metastasis-initiating cells ) and largely influencing outcomes and patient’s survival ., The overexpression of EPCAM , CD44 , CD47 and MET cell proteins in a subset of CTCs correlates with lower overall survival ., These four markers are known to be involved in tumourigenesis 6–8 and are co-regulated with the TGF-β signalling pathway 9 ., Because of the complexly structured and heterogeneous process as well as the paucity of experimental data , it is important to model how the dynamics of TGF-β-driven CTCs couples with the primary ( mammary duct ) and secondary ( bone niche ) cancers ., Indeed , cancer mathematical models play an important role in assisting biologists in the interpretation of results and in experimental design ( see Maini 10 , Bellomo 11 and Chaplain 12 for breast cancer and bone cancer modelling , among others ) with a growing interest in combining epidemiological ( e . g . survival information ) , clinical and molecular data ., In a recent work 2 , we have modeled how TGF-β drives the formation of early neoplastic signature in breast and perturbs the bone remodelling process ., Here , we present a multi-compartment mathematical model that aims at elucidating the effects of the TGF-β and the concomitant therapies in the three microenvironments ( mammary duct , circulatory system and bone niche ) ., Fig 1 summarises the structure of the model ., Starting from statistical data ( including molecular and clinical data ) , we develop a model able to predict the survival probability by using the gene expression profile of CTCs ., We aim at a quantitative understanding of the relationship between gene expression levels in breast cancer and formation of bone metastasis with respect to the survival statistics ., Indeed , we propose a mathematical model linking the amount of CTCs to the survival times , in order to predict the patient-specific survival ., By using a branching process technique 13 , we compute the probability of developing EPCAM+ CD44+ MET+ CD47+ CTCs ., Through the model it is also possible to predict bisphosphonates-therapy outcomes based on the patient’s specific markers ., Bisphosphonates are drugs commonly used as treatment for several bone diseases in order to reduce osteoporosis and recent works have shown the anti-tumour effectiveness of bisphosphonates administered in a biological window therapy in naive bone-only metastatic and locally advanced breast cancer 14 ( see also 15 ) ., This work is organised in the following way: in the first two subsections of the Results section , we discuss the roles of TGF-β and CTCs in metastatic breast cancer ., In the third subsection , we present the system of ODEs for each compartment ( mammary duct and circulatory system ) ., The equations including the treatment are reported in forth subsection ., The fifth subsection shows how we used the model to simulate the disease evolution so to produce survival curves ., In the sixth subsection , we present the results obtained by numerical simulations and we discuss the cases of higher number of driver mutations and the case of immune response delay ., Information relative to the analysis of gene expression data is given in Methods section ., Finally , the conclusions give a brief summary and critique of the findings ., The earliest stage of breast cancer is revealed by abnormal cells inside a ductal lobular unit in the breast ., In these cells , the TGF-β is highly expressed and induces cells to undergo apoptosis ., Recent studies about TGF-β activation have highlighted the important role of integrins , an adhesion molecule that mediates the attachment between a cell and its surroundings 16 ., In particular , the binding of integrins to the latent TGF-β promotes the production of active TGF-β ., The invasive ductal carcinoma is characterised by the loss of epithelial cadherin ( E-cad ) function via epigenetic silencing , or via genetic inactivation by TGF-β 17 ., E-cad is a hallmark of well differentiated epithelium , and it maintains the junction between cells preventing the cancer cell proliferation and migration ., Indeed , the E-cad downregulation by TGF-β is proved to prevent mammary cell differentiation and produces more spherical cells which promote metastatic growth 18 ., Cells with driver mutations causing TGF-β resistance , when located close to sites of elevated activation of the transforming growth factor , diminish their E-cad induced adhesion and reduce the probability of incurring death 18 ., Amongst all the mutated cells , only those freed from the cell-to-cell junctions have a higher possibility of migrating through the TGF-β altered tissue and reaching the near blood capillaries ., Therefore , CTCs are cells originated from the primary tumour site ( and/or secondary metastatic sites ) and sharing the same characteristics and the same phenotype heterogeneity of the primary tumour cells ., In breast cancer , the epithelial cells in the mammary ducts affected by the tumour represent the main sources of CTCs 19 ., Moreover , the size of the tumour is a sort of power source factor that contributes to the quantity of CTCs in the blood stream , so that bigger tumour sizes correspond to higher numbers of CTCs ., Different types of breast cancer affect the power source as a result of their different velocity in evolving and growing throughout the tissue ., On the other hand , we assume that in tumours of the same size and with the same rate of E-cad unbinding , CTC sources behave equally ( i . e . they release the same amount of CTCs per unit of time ) ., The explanation is given by the fact that only when a cell is completely separated from the surrounding neighbour cells and the extracellular matrix , it can be part of the amount of CTCs ., CTCs is associated with large quantities of TGF-β as well as their progenitors 20 ., The synthesised TGF-β , which depends on the cell phenotypes , might not suffice the cancer cells’ need because of the more dispersive space and less cramped geometry which facilitate the dispersion of TGF-β ., Nevertheless , TGF-β production serves also as an alerting inflammatory signal helping the immune system to detect and attack the CTCs ., In part for their instabilities caused by mutations and in part for the immune system response , generally CTCs do not survive long in the blood stream ., This is true especially when the concentration of CTCs is low while , at higher concentration , CTCs cluttering and overwhelming of the immune system might extend the life of the same cancer cells 21 ., Furthermore , CTCs seem to have very low proliferation rate when flowing in the blood stream ., Using different technological platforms , clusters of CTCs has been detected within the circulating system of patients with cancers of different origin 21 ., While most clusters are relatively small , ranging from 2 to 50 cancer cells , they have from 23- to 50-fold increased metastatic potential ., This property of CTC clusters , together with the adverse prognosis of breast cancer patients with abundant CTCs clusters , support an important role for these cells in the blood-bone spread of cancer ., It has been experimentally shown that CTCs overexpressing EPCAM , CD47 , CD44 and MET have a high probability of succeeding in generating bone metastasis 5 ., Overexpression of EPCAM is a phenotypical characteristic inherited since the tumour cell was in the lobular duct and remained present during the epithelial-mesenchymal transition ( EMT ) process ., In the extravasation process , EPCAM helps cancer cells in exiting the circulatory system , by inducing the anchorage between CTCs and the vascular endothelium 7 , 8 , 22 ., CD47 is a protein expressed on all the cell membranes , and it interacts with integrins and immunogenic complexes on the cells ., It is involved in several processes , including the spreading and aggregation of platelets 23 , and modulation of T-cell activation 24 , 25 ., CD47 operates as a “self” marker on red blood cells in order to prevent their clearance by macrophages 26 ., Elevated expression of CD47 helps CTCs to evade the immune system ., CD44 is a receptor principally present on lymphocytes ., This protein is implicated in a variety of immunological functions , such as vascular extravasation and T-cell co-stimulation 27 ., CD44 is prevalently upregulated at various stages of the cancer evolution , and the protein also mediates adhesion between stroma cells and bone marrow progenitor cells ., Promotion of CTCs extravasation across endothelial vessels and homing into peripheral organs makes CD44 responsible for metastasis formation in the bone tissue 28–31 ., MET is a receptor involved in embryonic development and organ regeneration ., It contributes to establish the normal tissue patterning by orchestrating cell proliferation , disrupting the cell-to-cell junctions , facilitating the migration through the extracellular matrix and inhibiting apoptosis 6 ., MET deregulation induces cancer cells to leave the primary tumour , move towards different organs and give rise to metastasis 32 ., In the long run , the heterogeneity of CTCs will increase reflecting the phenotypic cellular diversity in the primary tumour source ., At the same time , a small component of the whole CTC population capable of evading the immune system ( EPCAM+ CD47+ CD44+ MET+ CTCs ) , extravasating and seeding in the bone will branch from the rest of the CTCs and initiate the process of development of metastasis ., CTCs and bone metastasis formation ., The CTC populations have different phenotypes reflecting the heterogeneity of the primary tumour source ., Among different cancer cells , those with a phenotype more sensitive to the TGF-β chemoattraction reach the bone niche ., Furthermore , during the bone remodelling process , TGF-β and cytokines attract the near blood vessels toward the portion of lesioned bone matrix ., The reduced distance between the peripheral blood stream and the source of TGF-β increases the probability of few CTCs exiting the capillary and entering the bone tissue ., CTCs in the fractured bone rarely begin a fast invasion of the tissue , on the other hand , they change the remodelling process of the bone by strongly interfering with the quantity of TGF-β involved in the differentiation and maturation of the osteoblasts and cause a prolonged osteolytic activity ., Cancer cells provoke a reduction of bone re-mineralization which results in a weaker bone , hence higher probability of re-occurrence of new fracture-remodelling cycles ., Meanwhile , the number of CTCs slowly increases taking advantage of the extra TGF-β released and extra space left in the bone multicellular unit ( BMU ) at each cycle ., In order to describe the early stages of breast cancer and the formation of metastasis , we develop a model that includes three compartments representing three distinct body systems and involves different regions of the body: 1 ) the epithelial tissue in the mammary duct , 2 ) the circulatory system and 3 ) the bone ., Our approach , even though it encompases extense and distinct body parts , allows us to semiquantitatively reproduce the progression of the disease ., The first and the third compartments are geometrically connected through the circulatory system which plays a fundamental role in the migration of cancer cells from the primary tumour site in the mammary duct to the secondary sites in the bone tissue ., Hence , the three compartments are kinematically and dynamically interconnected ., The model shows the evolution of the tumour in terms of invasion of the three compartments by cancer cells ., The different fitness landscapes of cancer cells surviving in each compartment ( cancer cells intravasation , extravasation and metastasis formation ) represent the interactions between the cancer cells and the environment ., We constrained ourselves to the early stages of breast cancer for the sake of simplicity ., During the early stages , the concentration of cancer cells is limited , and tissue irregularities are negligible as well as the volumes of tissues interested by the disease; therefore , hypoxia effect can be disregarded , and cancer cell dynamics can be considered as a perturbative effect on the normal dynamics or on the homeostasis of the compartments ., Under these constraints , also a self-seeding phenomenon causing a feedback signal from the bone niche toward the breast lobular duct is negligible ., In our model , we also address the case of cancer progression when medical treatments are provided ., More precisely , we focus our attention on the effects of bisphosphonates on cancer cells ., Drugs represent a further form of coupling between the model compartments affecting the dynamics of the microenvironment and the cancer cells fitness landscapes ( see 33 , 34 for a description of fitness landscapes ) ., Under the assumption that the mean field approximation holds true ( i . e . average over all the cell populations ) , the system dynamics is described in terms of ordinary differential equations ( ODE ) for molecule concentrations and cell population densities ., Below , we present and discuss the system of ODEs for each compartment , we show the results obtained by numerical simulations and how we used the model in order to simulate different trajectories representing the disease evolution so to produce the respective survival probability curves ., Branching processes and heterogeneity ., The possibility that cancer cells , developed in the breast , form metastasis in the bone tissue is due to the occurrence of driver mutations causing overexpression of specific proteins which help the cells to accomplish such process ., The numerosity of these populations with improved pro-metastatic behaviour depends on their capability of surviving in a given environment ., Indeed , the development of a mutation occurs during asymmetric proliferation , a rare process in which a cell divides in two daughter cells where one of the two is equal to the parent , while the other presents a mutation ., In our model , we focus on the number of cells that develop one given mutation or present simultaneously all the pro-metastatic mutations ., Such multi-mutation path can be obtained through several paths , where a path is a sequence of mutations leading from the initial profile j = ( 0 , 0 , … , 0 ) ( no mutation ) to the final profile j′ = ( 1 , 1 , … , 1 ) ( all the genes in the path are mutated ) ., When the considered system is characterized by various cell groups , each of which is different from another due to specific properties , the branching process is the process used to dynamically link these cells together , as well as , to describe the relation between groups in terms of parents and offspring ., As a consequence , in the present case , the acquiring of new genetic mutations by cancer cells can be described in terms of a branching process 13 ., Let xj ( t ) be the expected number of cells at time t with j = ( j1 , j2 , … , jm ) driver mutations ., Each component ji of j , corresponding to the specific driver mutation i , can have value 1 if the mutation occurred , or zero otherwise ., The length m of j is the minimum number of driver mutations necessary to perform an action ( i . e . forming metastasis in the bone ) ., We assume that each driver mutation with integer index i ∈ 1 , m can be caused by the variation of the state of a single gene ., A mutational path , 𝓟 = j → j′ , corresponds to an ordered set of driver mutations s = ( i1 , … , ik , … , im ) ., We can choose another path by rearranging the driver mutations in a different order ., The path 𝓟 describes the passage from the cell population xj , where ji = 0 for all the elements i ∈ s , to the population xj′ , where ji = 1 for all the elements i ∈ s , through the sequence of acquired mutational steps: i1 , … , ik , … im ., If we consider only the cases in which each driver mutation gives the cells a single specific pro-metastatic capability ( i . e . overexpression of a protein ) , and we also neglect the possibility for a cell and its progenies to loose such a capability due to future random mutations , then , for a specific path 𝓟 , the evolution of the sub-population xk ( t ) of CTCs being at the k-th mutational step is given by:, ∂ t x k ( t ) = r b ( 1 − u 0 ) x k ( t ) ︷ symmetric proliferation + ( r b u 0 C k x k − 1 ( t ) ︷ k − th mutation − r b u 0 C k + 1 x k ( t ) ︷ ( k + 1 ) − th mutation ) − r d x k ( t ) ︷ apoptosis , ( 1 ), where rb is the cell proliferation rate , rd is the cell death rate , u0 is the punctual probability of mutation per unit of RNA expression level , Ck is the k-th gene expression level and u0 = um+1 = 0 ., In Eq ( 1 ) , the integer index k ∈ 0 , m , and the initial conditions are x0 ( 0 ) = 1 and xl ( 0 ) = 0 for any l ≠ 0 ., In the RHS of Eq ( 1 ) , the first term takes into account the proliferation of cells when none of the m driver mutations occur ., The second term in the parenthesis describes the asymmetric proliferation of both the sub-populations xk−1 and xk involved in k-th and ( k+1 ) -th driver mutation of the path 𝓟 , respectively ., The last term represents the apoptotic process ., It is important to notice that on the one hand , when a cell mutates , it branches , and on the other hand , heterogeneity in the gene expression of a cell sub-population influences the probability of branching , or more precisely , it affects the time rate at which similar cells mutate ., In order to take into account the genetic cell heterogeneity , we could have introduced a second index in the cell sub-population so to discriminate them in sub-populations of sub-populations ., Nevertheless , due to the lack of specific datasets ( at least to our knowledge ) for gene expression on single CTCs derived from breast cancer , it would be difficult to determine the corresponding probabilities of mutation ., In this work , we focus on the tumour cells characterised by CD44 , CD47 and MET mutations 5 ., Hence , we apply Eq ( 1 ) to describe all the possible paths leading from the initial profile ( 0 , 0 , 0 ) to the final profile ( 1 , 1 , 1 ) with all the three genes mutated ., By solving the corresponding system of equations , the number of cells with a profile j⋆ corresponding to a single mutation at the ji-th position is given by:, x j ⋆ ( t ) = x 0 ( 0 ) e r t ( 1 − e − r b u 0 C i t ) ,, where r = rb−rd ., It is very convenient to rewrite the solutions independently from the specific traversed path and order of mutations in terms of sub-populations x 𝓓 ¯ ( t ) = ∑ { j ∣ j k = 1 ∀ k ∈ 𝓓 } x j ( t ) of cells which have acquired at least a specific sub-group of pro-metastatic behaviours 𝓓 ., Rearranging the solution of Eq ( 1 ) , we have:, x 𝓓 ¯ ( t ) = x 0 ¯ ( t ) ∏ k ∈ 𝓓 1 − e − r b u 0 C k t , ( 2 ), where x 0 ¯ ( t ) = x 0 ( t ) = x 0 ( 0 ) e r t is the sum of all the sub-populations ( see Supplementary Information S1 Text for the mathematical derivation ) ., From Eq ( 2 ) , the ratio x 𝓓 ¯ ( t ) x 0 ¯ ( t ) is a number in 0 , 1 representing the portion of cells with 𝓓 mutations ., Identifying this ratio with the joint probability of a cell having those pro-metastatic properties derived by the 𝓓 mutations and under the condition that each driver-mutation occurs independently from the others , it follows that x 𝓓 ¯ ( t ) x 0 ¯ ( t ) = ∏ k ∈ 𝓓 Γ ( C k ) , where each Γ ( Ck ) corresponds to the probability of acquiring the pro-metastatic behaviour k ., In order to describe the effects of EPCAM on CTCs , we consider a first part of the branching process , strictly related to EPCAM , which occurs on breast cancer cells and identifies the cells that can intravasate ., Considering all the tumour cells that are about to enter the near blood vessels , they will have a small probability of proliferating as CTCs; hence , all their pro-metastatic behaviours are due to previous cell divisions and mutations ., Consequently , the second part of the branching process ( related to CD47 , CD44 and MET ) occurs while tumour cells are still in the mammary duct ., In the blood vessels , cells with low values of CD47 are attacked by the immune system and eliminated; therefore , only cells with sufficiently high CD47 proteins on their surfaces can evade the immune system ., More precisely , the higher the concentration of CD47 , the longer the survival probability of CTCs is ., The proteins CD44 and MET are involved in the extravasation process of circulating cells ., Hence , their absence contributes to the permanence of the tumour cells in the circulatory system , and their presence contributes to characterise the component of CTCs population able to reach the bone and seed ., The branching process strictly divides the CTCs population in sub-populations of circulating cells labelled by specific driver mutations which follow specific cell behaviours ., Nevertheless , in the blood stream CTCs follow trajectories which are much less distinct ., The causes are due to the interactions with the microenvironment which are responsible for the selection on the basis of the four proteins concentrations and give rise to variability and further heterogeneity ., Based on the results in 5 , we consider only the four proteins overexpressed in CTCs with a high potential of generating bone metastasis: EPCAM , CD44 , CD47 and MET ., Nevertheless , the method can be extended , or modified to include other proteins for other type of cancers ., In Simulations subsection , we discuss what happens increasing or decreasing the minimum number of proteins necessary for creating metastasis ., Mammary duct compartment ., In order to describe the tissue dynamics as populations of healthy and mutated cells , we introduce a branching process based on the tissue scale model proposed in 2 where the cell populations are ρ ( ϕ , t ) and the index ϕ ∈ 0 , Φ represents the cell state which is identified with the cell phenotype ., We perform an order parameters reduction of that model neglecting the intra/extra-cell scale equations since the reactions involved are much faster than those at the tissue level ., Hence , the TGF-β synthesised , activated and bounded with the receptors on the cells membrane R ec ⋆ ( ϕ ) , which are internalised so to generate the signalling inside the epithelial cells of the mammary duct , can be considered constant without significantly affecting the dynamics at larger scales ., The TGF-β values are set equal to those at the equilibrium reached during the dynamical evolution of the tissue sub-system ., We neglect also asymmetric proliferation and we constrain the cells to change their phenotype only in sequential steps ., Using the same terminology in 2 , healthy cells have phenotype ϕ = 0 , pre-neoplastic cells are indexed as ϕ = 1 , tumoural cells correspond to ϕ = 2 and cells with aggressive tumoural behaviour and strong resistance to TGF-β inhibiting signalling have phenotype ϕ = Φ = 3 ., We associate the cell phenotype to the TGF-β which is one of the proteins involved in the reduction of cell-to-cell E-cad connection ., Hence , the activated TGF-β , when internalized , induces morphological changes on the cells which become more round and unconstrained ., The TGF-β synthesised by cancer cells with index ϕ > 0 are more elevated than the quantity produced by healthy cells; therefore , the higher is the index ϕ , the higher is the chance it moves and/or positions itself unrespective of the morphological structure of the tissue ., It is important to remark that the index ϕ is not related to the expression of proteins involved in the metastasis formation processes ., The equation governing the cell sub-populations density ρ of the mammary duct epithelium tissue in a unit volume containing a cell and its nearest neighbour cells at time t and having phenotype ϕ is:, ∂ t ρ ( ϕ , t ) = r p ( 1 − δ ϕ , 0 ρ 0 ˜ C ϕ − ∑ 0 ≤ η ≤ Φ η ≠ ϕ ρ ( η , t ) C η ) ( 1 − ∑ 0 ≤ η ≤ Φ ρ ( η , t ) C η ) ρ ( ϕ , t ) R ec ⋆ ( ϕ ) g ( ϕ ) ︷ symmetric cell proliferation + − ∑ ϕ = 0 ϕ ρ ( ϕ , t ) C ϕ r a R ec ⋆ ( ϕ ) g ( ϕ ) ρ ( ϕ , t ) ︷ TGF − β induced apoptosis + ∑ ϕ = 0 ϕ ρ ( ϕ , t ) C ϕ r m ( 1 − δ ϕ , 0 ) ρ ( ϕ − 1 , t ) − ( 1 − δ ϕ , Φ ) ρ ( ϕ , t ) ︷ cell mutation ∑ 2 1 2 + − ∑ ϕ = 1 ϕ − 1 ρ ( ϕ , t ) C ϕ r i n t ( 1 − δ ϕ , 0 ) ρ ( ϕ , t ) Γ ( C E P C ) ︷ cells entering the blood stream ∑ 2 1 2 ., ( 3 ) The first term on the RHS of Eq ( 3 ) represents the proliferation process of cells ., The factors in the parentheses take into account the maximum volumetric capacity Cϕ and the minimum capacity ρ 0 ˜ left to healthy cells by cell populations with ϕ > 0 , respectively ., Cell proliferation is regulated by the TGF-β entering the cell ( R ec ⋆ ) , and the effect of this protein depends on the phenotype sensing exponent g ( ϕ ) ., For non-tumoral cells ( ϕ < 2 ) , the capacity Cϕ expresses the average maximum number which can lay on the surface of the mammary duct , and for tumoral cells ( ϕ ≥ 2 ) , it represents the average maximum number of cells which can be hosted above , below and on the surface of the mammary duct ., The second term describes the apoptosis induced by the TGF-β and depends on the phenotype sensing exponent g ( ϕ ) ., For sub-population with phenotypes ϕ < Φ , the exponent g ( ϕ ) are non-negative and decreasing with ϕ; consequently , higher quantity of TGF-β inhibits proliferation and increases the apopotosis rate of these sub-populations ., On the contrary , g is negative when ϕ = Φ ., Therefore , TGF-β enhances the proliferation and reduces the apoptosis of the aggressive population highlighting the anti-oncogenic and pro-oncogenic role of TGF-β on different cell populations ., The third terms expresses the mutation transition of a cell from a state ϕ to the state ϕ+1 , and the delta of Kronecker δα , β , which is 1 for α = β and 0 otherwise , implies that there are no cell which mutate to healthy cells and no further mutation occurs on cells in the state Φ ., The last term on the RHS of Eq ( 3 ) is the first step of the branching process relative to the expression of cell membrane proteins favouring the formation of metastasis , and it describes the intravasation of cancer cells in the nearest blood vessels occurring at rate rint with probability Γ ( CEPC ) , where CEPC indicates the EPCAM gene expression level ( see Methods section and Branching process and survival probability prediction subsection ) ., Overexpression of EPCAM increases the probability that a cell per unit of time passes through the nearest cells and reaches the circulatory system ., Hence , because of driver mutations and over-production of TGF-β , cancer cells with EPCAM overexpression will easily unbind from the neighbour cells increasing their chance of reaching the local blood vessels and becoming CTCs; therefore , only cells with ϕ > 0 contributes in generating CTCs ., It is worthy to notice that there is an obvious relationship between the cell density phenotypes ρϕ and the frequencies of the branching process populations xk ., The index ϕ refers to mutations inducing TGF-β resistance , and the index k refers to mutations affecting the expression of the three specific markers on the membrane of bone metastasising cancer cells ., The former mutations are related to the behaviours of the source of the CTCs ( the epithelial cells in the breast ) , while the latter are related to the behaviours of the CTCs ., Hence , mutations altering the normal TGF-β signalling will propagate their effects on the concentration of the populations xk ., Furthermore , in a complex biological process as the breast cancer cells metastasising in the bone , the order and the times at which all these mutations occur might play a relevant role ., Nevertheless , for the sake of simplicity , we divided the two type of mutations ( indexed ϕ and k , respectively ) into two independent groups ., Therefore , the two types of mutations occur in parallel introducing only a partial complexity in the system and disregarding further time interdependent causalities ., CTCs in the bloodstream compartment ., After the first step of the branching process depending on the expression of EPCAM , the remaning branching of cancer cells discriminates groups of CTC sub-populations with different genetic characteristics ., All the sub-populations of cancer cells with overexpressed EPCAM , by definition , will intravasate , but not all of them will survive to the immune system control and not all of them will be able to extravasate and seed ., The outcomes and the time of permanence of CTCs in the blood system depends on the properties of the CTCs themselves ., Indeed , only a small component of all the CTCs with sufficient high pro-metastatic behaviours have high chances in forming metastasis ., For example , CTCs with low CD47 are eliminated by the immune system control at rate rimm , and even though they might have high CD44 or MET , they will have a small chance of surviving and a short lapse of time to attempt extravasation ., Similarly , cells with high CD47 will have a high chance of surviving the immune system attacks , but if they express low quantity of CD44 or MET proteins , they do not have a high probability of forming metastases ., Nevertheless , since these CTCs remain longer in the circulatory system , they can attempt to extravasate more times with rate rext ., The time evolution of the CTCs population is described by the following equation:, ∂ t C T C ( t ) = r i n t V R O I ∑ ϕ = 1 Φ ρ ( ϕ , t ) Γ ( C E P C ) ︷ intravasating CTCs | Introduction, Results, Discussion, Methods | Ductal carcinoma is one of the most common cancers among women , and the main cause of death is the formation of metastases ., The development of metastases is caused by cancer cells that migrate from the primary tumour site ( the mammary duct ) through the blood vessels and extravasating they initiate metastasis ., Here , we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct , in the circulatory system and in the bone ., Through a branching process model , we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer ( EPCAM , CD47 , CD44 and MET ) ., In particular , the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability ., We also include the administration of drugs as bisphosphonates , which reduce the formation of circulating tumour cells and their survival in the blood vessels , in order to analyse the dynamic changes induced by the therapy ., We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue ., Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics . | Breast cancer is caused by genetic mutations leading to uncontrollable cell reproduction ., During successive proliferations , the progenies of tumour cells acquire further mutations increasing their heterogeneity ., Among the tumoural mutated cells , there are some which present specific markers of increased aggressiveness and resistance ., Sufficiently skilled cancer cells detach from the mammary epithelial cells , enter the blood vessels becoming circulating tumour cells , and reach the bone tissue where they seed ., Breast cancer survival probability is the statistical representation of clinical data describing the times patients will survive after the diagnosis of the disease ., Breast cancer survival is strongly correlated to genetic markers which increase the resistance and the invading skills of cancer cells but , it is poorly correlated to the amount of circulating tumour cells ., To improve the understanding of the dynamic progression of the disease and assisting biologists in the interpretation of results and in experimental design , we developed a mathematical model encompassing the evolution of cancer cells originated in the breast , passing through the circulatory system , and invading the bone tissue based on survival probabilities of patients with different genetic expressions ., The model allows us to strongly correlate the gene expression data of cancer cells with the survival probability by identifying the circulating tumour cells responsible for the formation of metastasis ., Survival probabilities generated with the model are a useful tool to identify the presence of hidden markers not yet taken into consideration and study the effects of drugs’ administration . | null | null |
journal.pntd.0006996 | 2,018 | Exploration of a simplified clinical examination for scabies to support public health decision-making | Scabies , a skin condition due to the microscopic mite Sarcoptes scabiei 1 , is a major public health problem worldwide , particularly in low and middle income tropical settings , and has recently been adopted as a neglected tropical disease by the World Health Organization ( WHO ) 2 ., This designation arose because of the increasing recognition of the importance of scabies , as well as the emerging evidence that effective control can be achieved by the strategy of mass drug administration ( MDA ) ., Evidence from studies using permethrin and ivermectin have demonstrated that MDA has a substantial impact on the prevalence of both scabies and secondary bacterial skin infections ( impetigo ) at the community level 3–6 ., The scabies mite can only be directly visualised with a microscope , and there is currently no laboratory test for infestation ., Therefore , the diagnosis of scabies is most often reliant on detection of characteristic signs on clinical examination , with only a limited role for direct visualisation in high resource settings ., Scaling up MDA for scabies control will require a substantial effort in disease mapping to define populations and communities likely to benefit from intervention and then determining the impact of interventions ., Development and validation of a simplified approach to diagnosis of scabies would facilitate these efforts ., In the clinical setting , the purpose of scabies diagnosis is to support optimal decision-making about individual patient management ., As such , a thorough examination , that generally covers the entire skin surface , is required to minimise errors in diagnosis ., However , for public health decisions , such as whether or not to initiate MDA , the aim is to assess the community prevalence of scabies , so a simplified examination might be appropriate , as has been used for other NTDs 7 ., Such a protocol , if valid at the community level , could speed up data collection and reduce the imposition on survey participants that comes with full body examination which can be a barrier to accepting examination in some settings ., Therefore , we aimed to evaluate the accuracy of a simplified examination for the diagnosis of scabies that could be used to guide public health decision making ., We utilised data from three recent , large population-based surveys of scabies , two conducted in the Solomon Islands , in Western and Choiseul provinces respectively , and one in Fiji ., 6 , 8 ., These studies were all population-based prevalence surveys using similar diagnostic and data collection tools ., The surveys in Choiseul and Fiji were conducted as baseline for intervention trials ., In all three studies examination involved examination of arms , legs , face and torso ( excluding the breasts in women ) ., Patients were asked if they had itch in the groin , buttocks or breasts and , if so , these areas were also examined ., The whole body was examined in children aged under 1 year ., Examination for all surveys was performed by individuals with experience in the diagnosis of scabies in low-resource settings ., From each survey’s primary database , we extracted patient-level data , including demographic characteristics , the presence or absence of both scabies and impetigo ( bacterial infection ) lesions and , if present , their number and distribution ., Each study had used similar diagnostic criteria for scabies based on the finding of typical lesions ( burrows , papules , nodules , vesicles ) in a classical distribution9 ., In both the original studies and the combined analysis presented here , scabies severity was defined by the number of lesion detected as mild ( ≤10 lesions over all areas examined ) , moderate ( 11–49 lesions ) or severe ( ≥ 50 lesions or crusted scabies ) 9 ., In each study , lesions which were moist , purulent or crusted were considered to indicate the presence of impetigo ., In each of the original studies , examination findings were recorded for each of nine body regions ( Fig 1 ) ., To obtain a reference diagnosis , we classified each individual as having scabies or not , according to whether scabies had been detected at any body site , using the standard examination conducted in the original studies ., We further classified those with scabies , based on the presence or absence of scabies at each of the nine regions ., We defined body regions as “exposed” if they could be routinely examined without removing clothes ., The face , the upper arm ( including the elbow ) and lower arm ( including the wrist ) , hands and the lower leg ( including the ankles ) and feet met this definition ., The torso , upper legs ( including the knee ) , buttocks and groin were the “unexposed” regions ., We then evaluated simplified diagnostic algorithms based on body region-specific findings: A person would be classified as having scabies if it was detected at a particular region or grouping of regions ., For these algorithms , we calculated the sensitivity compared to the reference standard based on whole-body examination ., We then identified groupings which provided greater than 90% sensitivity in comparison to the reference standard ., We assessed sensitivity across subgroups defined by gender , age group , severity of scabies and the presence or absence of impetigo ., We calculated the prevalence of scabies that would have been measured in each of the three original studies using optimal combinations based on simplified examination ., We used a one-sided test to compare the proportion of individuals diagnosed with scabies based on the standard examination with the proportion diagnosed based on an examination of ‘exposed’ body regions ., We considered a p-value of <0 . 05 to be consistent with a statistically significant difference ., Statistical analysis was performed in R 3 . 4 . 3 ( The R Foundation for Statistical Computing ) ., The combined sample size of the three study datasets was 5 , 358 , with similar numbers contributed from each of the three surveys ( 1908 , 1399 and 2051 from Western and Choiseul provinces of the Solomon Islands , and Fiji ) ., Overall 2 , 801 ( 52 . 3% ) of study participants were female and the median age was 14 years ( IQR 7–36 years ) ( Table 1 ) ., In the original studies 1 , 373 individuals ( 25 . 6% ) were diagnosed with scabies ( 18 . 1% , 18 . 7% and 36 . 4% across the surveys ) at any body location ., Scabies was present in a median of 2 body regions ( IQR 1–3 ) ., Of the 1373 cases of scabies , the disease was classified as mild in 684 ( 49 . 7% ) participants , moderate in 513 ( 37 . 5% ) and severe in 176 ( 12 . 8% ) ., Data on scabies severity was missing for two participants , so they were excluded from subgroup analyses ., Overall the proportion of individuals with impetigo in the original studies was 26 . 6% ( n = 1 , 425 ) and was significantly higher among individuals with scabies ( 45 . 1% vs 20 . 2% , OR 3 . 24 , p <0 . 001 ) ., The highest diagnostic yield was through examination of the hands ( sensitivity compared to whole body examination 51 . 2% ) , feet ( 49 . 7% ) , and lower legs ( 48 . 3% ) ., As shown in Table 2 , examination of the whole of the upper limb ( upper arm , lower arm and hand ) had a sensitivity of 67 . 4% ( 95% CI 64 . 8–69 . 8% ) compared to the reference standard examination ., Examination of the exposed part of the lower limbs ( lower leg and feet ) had a sensitivity of 55 . 8% ( 95% CI 53 . 2–68 . 5% ) compared to the reference standard examination ., Examination of the exposed components of both limbs had sensitivity of 93 . 2% ( 95% CI 91 . 2–94 . 4% ) ., The sensitivity of the algorithm based on exposed regions was above 90% across all subgroups defined by sex and age group except people over 50 years , in whom it was 88 . 0% ( 95% CI 81 . 3–92 . 7 ) ., It was greater than 90% in mild , moderate and severe scabies and individuals with or without impetigo ( Table 3 ) ., Excluding the upper arms from the examination significantly reduced the sensitivity in a number of subgroups while examining the remaining exposed site , the face , did not significantly increase sensitivity ( Table 3 ) ., The prevalence estimates derived from simplified examination did not differ significantly from those obtained in any of the original surveys ., Excluding the upper arm resulted in a statistically significant difference in the prevalence estimate in a single survey ( Table 4 ) ., Based on analysis of primary data from three large , population-based surveys of scabies prevalence , we found that restriction to particular body regions defined as exposed had close to 90% sensitivity for detecting scabies , compared to a whole-body examination ., Use of a restricted examination would have generated prevalence estimates very similar to those obtained from full body examination ., Importantly , this finding was not dependent on severity of scabies or the presence or absence of impetigo ., With scabies newly recognised as a neglected tropical disease by WHO , efforts are underway to identify and implement intervention strategies , potentially including MDA ., In order to scale up interventions , it will be necessary to have standardised means of classifying geographic areas in regard to scabies prevalence ., Best practice methods of assessment reported from recent prevalence surveys and trials have generally depended on whole body examination by experts in dermatology ., This method has a number of limitations , including the need for private examination rooms , the time required , and participant sensitivities about examination ., Defining and validating a simplified form of examination will facilitate mapping , especially in resource-limited settings ., The approach of seeking simplifications in diagnostic processes has been used in the context of other NTDs such as the WHO grading criteria for trachoma 7 , and allowed the large-scale mapping of disease prevalence 10 ., Even more limited examination , such as the hands alone , had a sensitivity of only 51 . 2% ., It might be argued that for public health decision-making , it is more important to provide a broad ranking of prevalence than to accurately estimate the absolute level , but the markedly reduced sensitivity of examining the hands alone would substantially increase the likelihood of making the wrong decision ., More feasibly , an examination of both arms and both lower legs had greater than 90% sensitivity , providing an option that balances accuracy for public health purposes , while being practical in the field ., In all three studies that were the source of data for the analyses presented here , the diagnosis of scabies was made by an individual experienced in the diagnosis of scabies ., We used current best available diagnostic criteria which have previously been validated in both the Pacific and Africa 9 , 11 ., Potentially , the sensitivity of a more limited examination might be reduced if conducted by a person with less training or experience ., It will be important to conduct further validation of simplified examination performed by those with less experience ., Validation of the simplified criteria could be conducted alongside prospective validation of recently published consensus diagnostic criteria for scabies 12 ., A crucial step in preparing for such validations will be the development of standardised training materials so that a much larger number of assessors can be engaged in evaluations of scabies prevalence , as has been done for trachoma grading 10 ., A limitation of the data sources analysed is that the breasts and groin were only examined in the underlying surveys if participants reported itch ., It is therefore possible that some people had scabies in these regions but were classified as not having scabies ., The consequence of this would be an over-estimate of the sensitivity of a more limited examination ., However , these differences would be unlikely to alter the prevalence estimates sufficiently to be of public health importance ., Our data are derived entirely from studies conducted in the Pacific region ., Evaluation of the proposed simplified diagnostic approach will need to be conducted in a wider range of demographic and geographic settings to ensure the findings are broadly applicable ., The extent to which areas of the skin are exposed and may be examined is to a large extent culturally dependent , and will therefore vary by region ., For example , in some regions lifting up a sleeve to examine the lower portion of the upper arm may therefore be considered unobtrusive but lifting up a shirt to expose the abdomen less so ., Further studies are necessary to evaluate our proposed simplified algorithm in a variety of epidemiologic settings , using prospective methodology ., Further validation of the simplified assessment will need to consider both the accuracy , and acceptability of different levels of examination ., Other issues , such as the gender of the examiner and setting of the examination , will also be relevant in ensuring that culturally appropriate methods of prevalence assessment are widely available in scabies-endemic areas ., The adoption of scabies as a neglected tropical disease by the WHO has provided fresh impetus to the development of tools to control scabies as a public health problem ., Our study adds valuable data to the development of a simplified diagnostic process for scabies that may be applied to guide decisions about future public health interventions . | Introduction, Methods, Results, Discussion | In most settings , the diagnosis of scabies is reliant on time-consuming and potentially intrusive clinical examination of all accesible regions of skin ., With the recent recognition of scabies as a neglected tropical disease by the World Health Organization there is a need for standardised approaches to disease mapping to define populations likely to benefit from intervention , and to measure the impact of interventions ., Development and validation of simplified approaches to diagnose scabies would facilitate these efforts ., We utilised data from three population-based surveys of scabies ., We classified each individual as having scabies absent or present overall , based on whole body assessment , and in each of 9 regions of the body ., We calculated the sensitivity of diagnosing the presence of scabies based on each individual body region compared to the reference standard based on whole body examination and identified combinations of regions which provided greater than 90% sensitivity ., We assessed the sensitivity according to gender , age group , severity of scabies and the presence or absence of impetigo ., We included 1 , 373 individuals with scabies ., The body regions with highest yield were the hands ( sensitivity compared to whole body examination 51 . 2% ) , feet ( 49 . 7% ) , and lower legs ( 48 . 3% ) ., Examination of the exposed components of both limbs provided a sensitivity of 93 . 2% ( 95% CI 91 . 2–94 . 4% ) ., The sensitivity of this more limited examination was greater than 90% regardless of scabies severity or the presence or absence of secondary impetigo ., We found that examination limited to hands , feet and lower legs was close to 90% for detecting scabies compared to a full body examination ., A simplified and less intrusive diagnostic process for scabies will allow expansion of mapping and improved decision-making about public health interventions ., Further studies in other settings are needed to prospectively validate this simplified approach . | Scabies , caused by infestation with the microscopic mite Sarcoptes scabiei , is a major public health problem worldwide , particularly in low- and middle-income tropical settings ., The diagnosis of scabies is reliant on detection of characteristic signs on clinical examination ., Examination of the whole body is time-consuming and intrusive , whereas a more limited examination might be sufficient to guide public health decisions ., We analysed data from several large scabies prevalence surveys to see if a more limited examination of the body provided acceptable sensitivity ., We found that limiting examination to the exposed components of both limbs had high sensitivity compared to full body examination ., Further studies are needed to prospectively validate simplified diagnostic approaches and aid scale up of scabies control programmes . | medicine and health sciences, legs, feet, decision making, tropical diseases, social sciences, parasitic diseases, neuroscience, cognitive psychology, ectoparasitic infections, sexually transmitted diseases, cognition, neglected tropical diseases, body limbs, public and occupational health, infectious diseases, musculoskeletal system, hands, scabies, arms, psychology, anatomy, biology and life sciences, cognitive science | null |
journal.pcbi.1002625 | 2,012 | Unifying Time to Contact Estimation and Collision Avoidance across Species | Monocular presentation of a looming object elicits escape or avoidance reactions in many species , including humans 1–4 ., When a planar object travels perpendicular to a surface toward an observer ( i . e . the object approaches the observer on a direct collision course ) , it projects a symmetrically expanding image on the retina ., Notice that in the present paper we only focus on a subset of approaches where the approaching object eventually collides with the observer ., We assume that collision happens at time ( time to contact , “ttc” ) ., At time before , the image subtends an angle , and its outer contours expand with angular velocity ., Both angular variables grow nearly exponentially with decreasing distance between object and eye ( assuming a constant velocity ) ., With knowledge of a predators or objects typical size 5 , it is therefore possible to trigger a behavioral response as soon as or , respectively , crosses a threshold 1 , 6 , 7 ., The visual systems of various species are also known to “compute” functions of and ( see e . g . 8 for a recent review ) ., The Tau-function ( “” ) is defined by ., Under the assumption that the object is a rigid sphere that approaches with , has several interesting properties 9 , 10: First , provides a running estimation of ttc during the approach ., Second , the ttc estimation is largely independent of physical object size , provided that and are noise-free ., Third , decreases approximately linearly with time with a constant slope of , but eventually linearity is compromised , as has a minimum shortly before ttc ., It therefore would be comparatively easy to track the remaining time until impact , and to precisely time avoidance reactions , for example as soon as is below a certain threshold value ., These three properties , however , are valid only for “sufficiently small” angular sizes ., Any quantitative criterion for “sufficiently small” implicates an error threshold for the deviation of from linearity , that is ., For example , according to Text S6 a corresponding threshold for the visual angle can be defined as with some constant ., Notice that the -criterion is independent from stimulus parameters such as object diameter or approach velocity ., Because is well suited for the estimation of , it could in principal serve as a universal mechanism for guiding motor actions during object approaches or during self-motion towards static objects ., Indeed , several studies related to behavioral responses in this context , thus asserting that many organisms , including humans , rely on for their timing of motor actions ( e . g . 10–12 ) ., But a critical re-evaluation of the -hypothesis arrived at the conclusion that does not necessarily play a unique role for ttc estimation 13 , 14 ., For example , humans also rely on the rate of change of relative disparity , particularly in the late phase of an approach , for small object sizes 15–18 , for low speeds 19 , 20 , or if knowledge of object size is available 7 ., In addition , the task at hand ( e . g . catching a ball or eluding a meteorite ) seems to dictate the information that will eventually be used for action timing 14 , 18 , 21 ., Further inconsistencies with respect to were reported with psychophysical results , where tended to be underestimated 16 ., In addition , ttc -estimation reveals a certain dependence on object size 22 , which is also not predicted by at “sufficiently small” angular sizes ., The Tau-function is often studied in the context of ttc -estimation ., It appears , however , that in order to describe the responses of collision-sensitive neurons in certain species is inadequate ., For example , the Lobula Giant Movement Detector ( LGMD ) neuron in locusts responds with increasing activity to a stimulation with a symmetrically expanding image , if the expansion pattern is consistent with an approaching object 23 , 24 ., The response curve of the LGMD neuron gradually increases to a maximum and then abruptly ceases ( often to a nonzero baseline response ) ., Because does not have a maximum , a different function has been proposed for modeling LGMD responses: The Eta-function ( “” ) ., It is defined as , with a constant 25 ., Theoretically , the time when the activity peak occurs depends linearly on the ratio of object half-size to object velocity ., The peak will shift closer to for smaller or faster objects , and always occurs at angular size , independent of 26 ., The LGMD activity peak could in principle signal a critical angular size for escaping ., Indeed , a recent study with freely behaving locust suggests that the time of peak firing rate of the Descending Contralateral Movement Detector ( DCMD ) predicts that of jump 27 ( each LGMD spike triggers a spike in the postsynaptic DCMD as well , because the LGMD is strongly coupled to the DCMD by a combined electrical and chemical synapse 28 , 29 ) ., It has nevertheless been argued that – in some ecologically meaningful situations ( small ) – there is no guarantee for the peak to occur before 2 , 5 ., This statement may be true to the extent that in freely behaving locusts , a reliable escape jump is triggered before collision only in the range of to 30 ., For , the jump would occur after projected collision , and this value thus may reflect the typical sizes and speeds of predators ., Apart from the locust , other species have collision-sensitive neurons with -like properties , for instance fruitflies 31 and bullfrogs 32 ., In pigeons , the response properties of one of three classes of neurons in the dorsal posterior zone of the nucleus rotundus also seems to be compatible with the -function 1 ., ( The two remaining classes seem to compute and , respectively ) ., In the goldfish , responses of the M-cell to looming stimuli also appear to follow a version of the -function , in which replaces , such that the new function does only depend on 33 ., The Tau-function and the Eta-function are the two prevailing models for studying ttc -perception and ( interceptive ) action timing on the one hand , and escape behavior and collision avoidance on the other ., In other words , we have two different models for two seemingly separated contexts ., Each model brings about some hitherto unresolved issues , which are subsequently described ., From a computational point of view , is numerically unstable: In the presence of noise , we have to reckon with the fact that can get very small – or even reach zero – at certain instants during the initial phase of the approach ( cf . 17 ) ., As a consequence , fluctuations of with large amplitudes may occur ., If , however , noise levels are constant in time , and noise is not multiplicative , the signal to noise ratio continuously improves as is approached ., It is furthermore not entirely clear how could be biophysically implemented in a neuron ., As for the -function , the LGMD neuron seems to bypass a direct multiplication or division by computing with subsequent exponentiation of the result 34 ., From a mathematical viewpoint , however , taking the logarithm introduces an instability for , although neuronal circuits with divisive inhibition can be adjusted such that no stability problems occur 35 ., Moreover , Gabbiani et al . 34 found that a third-order power law fitted the mean instantaneous firing rate of the LGMD better than an exponential or a linear function ( see also reference 36 ) ., Our original motivation was to improve the stability of with a simple modification ., This modification led us to the modified Tau function ., Similar to the -function , the -function also reveals a maximum before ttc ., We were able to fit the response curves of -type neurons with ( Text S4 ) ., Our -function represents the equilibrium solution of an equation for describing neuronal firing rate ., Because of this , is based on a biophysically plausible mechanism ., But comes with a disadvantage: Unlike , it no longer provides a running value of ttc ., In order to recover the ttc prediction , we needed to add a correction term to ., This so-defined corrected modified Tau function ( ) recovers the ttc prediction of the original -function , but suppresses noise better than ., Most importantly , the corrected m-Tau function predicts the results of a psychophysical experiment , requiring subjects to estimate ttc ., Theoretically , we therefore can explain -type and -type responses within the framework , which contains ( but also ! ) as a special case ., Until now , and did not have any obvious relationship with each other ( although we show in Text S6 how could formally be related to ) ., The -function could thus serve to explain why -type and -type neurons could be found alongside each other in the pigeon brain 1 ., Behavioral and neural responses to optical variables ( e . g . , , , , ) in the initial part of a trajectory are very noisy signals ., Signal fluctuations may occur as a consequence of the discrete structure of the retinal photoreceptor array and its limited spatial resolution ., The signal-to-noise ratio continuously improves as ttc is approached ( Text S3 ) ., Our first step adds computational stability to the model ., Let be a constant ( in units of ) ., The modified Tau model is defined as: ( 1 ) Biophysically , can be interpreted as leakage conductance ( equation S2 in Text S1 ) ., According to equation ( 1 ) , can formally be expressed in terms of multiplied with a gain control factor , which depends only on angular velocity ., Notice , however , that the multiplicative version “” would again compromise stability , because appears as one of the factors in the product ., Figure 1 a juxtaposes and the factors and , respectively ., Let the initial distance between the eye and a circular object ( diameter ) be denoted by ., Then , choosing will create a maximum of at time ( i . e . , a maximum before ) : ( 2 ) ( the previous equation is derived in the Methods Section ) ., The time when assumes its maximum can thus be controlled by specifying , where bigger values will place the maximum closer to ., The maximum depends as follows on approach velocity and object diameter , respectively ., Assume fixed values for and ., Then , will have an activity maximum at ( default case ) ., Now increase approach velocity and initial distance , such that remains constant ., As a consequence , the peak will shift closer to with respect to the default case ( triangle symbols in Figure 1a; further figures in Text S2 ) ., This is the velocity effect ., Now increase the object diameter ., The maximum of will then occur earlier compared to the default case ( circle symbols in Figure 1 ) ., This is the size effect ., Assuming that the peak signals an imminent collision , this shifting behavior is consistent with larger objects being perceived to have an earlier ttc than smaller ones 22 ., Note that the original -function ( i . e . and noise-free angular variables ) does not show a strong dependence on object size where holds ( but see Text S6 ) ., The -function is the prevailing model for describing responses from collision sensitive neurons to object approaches with constant velocity ., Its characteristic feature is its maximum ., Because also has a maximum , we fit previously published neuronal response curves with the -function and ( Text S4 ) ., Figure 2 summarizes these fits by comparing the response maxima of the experimental curves ( “” ) with the maxima predicted by the best fits achieved with the two functions ( “” ) ., Predictions of are slightly better with -fits , both in terms of mean and median of absolute differences ( ) ., With respect to goodness of fit measures ( root-mean-square-errors , , F-statistics ) , both functions perform again on par with each other ., Therefore , both and the -function describe neuronal responses of object approaches with constant velocity ., The experimental maxima at time depend linearly on 26 ., The -function predicts this linear relationship ( equation S5 in Text S2 ) , where slope is identified by , and intercept by a temporal delay of corresponding line fits ( Figure 3a ) ., The maximum of the -function , however , depends in a nonlinear way on ( equation 2 & equation S6 in Text S2; illustration: Figure 4 ) ., ( Nonlinearity means that the slope depends on , and linearity means that it does not ) ., Linearity is approached with increasing values of , eventually reaching a slope of one for ( equation S9 in Text S2 ) ., This is nevertheless inconsistent with experimental evidence , as the experimental values for are underestimated ( typically ) ., We thus explored a different possibility: Can the nonlinear function be hidden by noise ?, Figure 3b suggests that it nearly can , as seen when fitting a line to a version of with additive Gaussian noise ., Noise levels were set as reported in 26 ., This hide-and-seek works quite well , and the fitting statistics ( , KS-test on residuals , F-statistics ) are consistent with linearity in many random trials ( detailed analysis: Text S2 ) ., Figure 4 suggests a correlation between intercept and slope of line fits for different values of ., We thus fit lines to the noisified version of for various values of ., As before , noise levels were set as reported , and we again identified intercept and slope of the line fits to with and , respectively ., The result of this procedure is shown in Figure 5 , and agrees well with Figure 4 in 26 ., Thus , consistently predicts a good correlation between intercepts and slopes both in the presence and in the absence of noise ., Maximum detection of in the initial phase of an object approach ( i . e . , for small values of ) is problematic , due to the signals poor signal-to-noise ratio and the rather “shallow” curvature around the maximum ., The situation gets progressively better if we place the maximum closer to , that is for bigger values of : The signal-to-noise ratio is better , and curvature is higher ., With , however , we fell short of explaining the results of our psychophysical experiment ( which is below described further ) ., This led us to modify as follows ., Observe that for all , and thus ( 3 ) is a positive correction factor to , such that ., As with , the correction factor per se is again susceptible to fluctuations in the angular variable , and we would have gained no improvement by simply adding it to ., Now , the crucial idea is to render insensitive to such fluctuations ., This is achieved with a first order low-pass filter ( a short introduction is given in Text S8 ) ., Low-pass filtering of and transforms into a slowly varying signal , which is eventually added to : ( 4 ) and are low-pass filtered visual angle and angular velocity , respectively , and is the systems integration time constant ., In order to avoid initial filter transients , the filter variables were initialized with and , respectively ., The are filter memory coefficients with for ., No filtering would take place for ( no memory ) , and the filters would never change their initial state for ( infinite memory ) ., The corrected , modified model ( “corrected m-Tau” ) is then defined as: ( 5 ) where is a small constant , such that possible division-by-zero errors are avoided in the simulation ., Nevertheless , in the presence of noise , division-by-zero errors do not typically represent a problem during an approach with , because if the following two conditions hold:, ( i ) appropriate initialization of , and, ( ii ) “sufficiently strong” lowpass filtering ., The offset is included for the sake of completeness ., It was only considered for simulating our psychophysical experiment ( described below ) , where it turned out to be negligibly small ., In general , therefore , it is safe to assume ., Similar to , the new corrected m-Tau-model also computes an estimation of ttc for “sufficiently small” angular sizes ., But the principal advantage of over is that it is less sensitive to noise ., The noise suppression of the corrected m-Tau-model is constrained by the noise suppression performance of two “limit functions” , which are approached dependent on the values of , , and ( Figure 6 ) ., For the derivation of these limit functions , assume ( to simplify matters ) that in equation ( 5 ) with ( and ) ., Then , as we will show subsequently , the constraining functions are the ordinary function for , on the one hand ( equation 6 ) , and for a version of with lowpass-filtered angular variables , on the other ( equation 8 ) ., Thus , , where , provided that we exclude the case , , which would imply that is unbounded ., Details on our psychophysical experiment are spelled out in the Methods Section ., In a nutshell , subjects viewed approaching balls on a monitor ., The balls had two different sizes ( big & small , corresponding to object diameters & , respectively ) , and disappeared after ( presentation time ) until ., A beep sounded always at the same time , , in order to indicate a reference time to the subjects ., Approaches with different values of were presented , where could occur before or after ., Subjects were asked to judge whether they were hit by the ball before or after ., Responses were pooled , and the “proportion of later responses” for each presentation time ( corresponding to “ball hit me after ” ) was computed as a function of ttc ., Figure 7a shows the corresponding data points for , along with the best matching Gaussian cumulative density function ( “GCDF”-fit ) for each object diameter ., The GCDF-fits represent an estimate of the underlying psychometric curves or psychometric functions , respectively ., Figure 7b suggests that subjects did not respond to the average of the stimulus set , because the mean of the distribution ( point of subjective simultaneity ) shifted with presentation time ., In addition , the variance of the distribution decreased with increasing presentation time ., The small object diameter is furthermore associated with a higher variance than the big one ., The full set of data points is shown in Figure 8 , where each figure panel corresponds to a different presentation time ( small object size: circles; big: triangles ) ., The curves shown in Figure 8 do not represent GCDF-fits ( as in Figure 7a ) , but rather display simulation results from the -model ., For short presentation times , subjects show near-random performance across ttc ( Figure 8a , b ) , thereby revealing a bias towards later responses ( i . e . “ball hit me after ” ) ., The GCDF-fits reveal a higher bias for the small object diameter ( Figure 7b ) ., The corresponding psychometric functions ( not shown ) and -predictions for the shortest presentation time ( ; Figure 8a ) are thus rather flat and noisy ., This bias is progressively reduced with increasing , indicating improving performance: For , the point of subjective simultaneity approaches for both object diameters , and psychometric functions get closer to a step-wise increase at ( Figure 7a ) ., We already mentioned that we simulated the psychometric functions with the corrected m-Tau -model ( equation 5 ) , at which we added noise to angular size and angular velocity ( equation 9 ) ., By assuming a constant approach velocity , one could compute an estimation of ttc with equation ( 12 ) ., Note that this estimation should be constant throughout the approach in a noise-free situation and for “sufficiently small” angular sizes ., As a consequence of having noise , however , the ttc estimation fluctuates ., We therefore computed an average estimation with equation ( 14 ) , by taking the mean value across a time interval ( the interval contained the last estimates ) ., The average ttc estimation was evaluated at presentation time , and compared with the reference ., With a total number of such trials , we then counted occurrences where the average estimate occurred after ., The simulated proportion of later responses is then obtained by dividing by ( equation 13 ) ., In order to find the appropriate -parameters for predicting psychophysical performance , the error between -predictions and psychophysical data points was minimized ., We refer to this procedure as optimization ., Optimization was carried out separately for object diameters big and small ., The first step of the optimization procedure consisted in parsing the parameter space , and recording the error associated with each set of -parameters ., The error was determined with two measures ( “score measures” ) : The root mean square error ( ) , and an outlier-insensitive robust error ( ) ., In the second step , the parameter sets were sorted in ascending order with respect to their associated score measure ., Sorting took place separately for and , leading to corresponding tables where the best set of parameters was assigned rank one ( 1st table row ) , the second best rank two ( 2nd table row ) , and so on ( Tables S1 & S2 in Text S5 ) ., A third table of -parameters was then computed which was optimal for both object diameters simultaneously ( combined; Table S3 in Text S5 ) ., This could be done in a straightforward way , simply by averaging the score measures of big and small of corresponding parameter sets , and subsequently sorting the averaged errors ( more details on finding parameters are given in Text S5 ) ., For the computation of and , all psychophysical data points that represent the proportion of later responses entered equivalently , in the sense that no weighting coefficients were used to bias the optimization process toward longer presentation times ( as GCDF-fits at longer presentation times have a smaller variance , see Figure 7b ) ., Notice that parameter optimization for the combined diameter naturally implicates a trade off – the errors with respect to big and small will be bigger compared to individual parameter optimization ., Figure 8 shows that the corrected m-Tau -model adjusts fairly well to the psychophysical data of both object diameters ., Nevertheless , the -predictions for are somewhat worse with the combined parameter optimization ( Figure 8e ) when compared to a separate optimization for big and small ( corresponding figures in Text S7 ) ., The most likely explanation for this discrepancy ( individual versus combined parametrizations ) is that each object size is associated with a different noise level ( noise levels are represented by the -parameters with ; see equation 9 ) ., We investigated this hypothesis by comparing the corresponding values of for big and small , as a function of their rank ., Figure 9 shows that the for small are consistently higher than for big ., Therefore , the corrected m-Tau -model generally supports the notion that smaller object diameters imply higher noise levels in angular size and angular velocity , respectively ., We also studied two models with less degrees of freedom than corrected m-Tau : The first was , and the second was with for ( ) ., The best ( i . e . smallest ) score measures achieved with these reduced models were consistently higher than the best values achieved by the corrected m-Tau -model ( Text S5 ) , and their best-ranked parameter sets resulted in psychometric curve predictions that were also inferior by visual inspection ( not shown ) ., With the corrected m-Tau -model equation ( 5 ) , we proposed a general framework that comprises the -function and several properties of the -function ., By means of adjusting only a single parameter ( ) , the corrected m-Tau -model can approximate and , respectively ., Moreover , the -approximation is less sensitive to noise than the original -function , and accounts well for the performance of the psychophysical experiment that we carried out ., In the experiment , subjects had to decide whether a ( displayed ) ball reached them before or after a reference signal at time ., However , balls were only presented until , and disappeared afterwards ., In other words , subjects had to estimate ( could occur before or after ) ., With respect to our experiment , the corrected m-Tau -model suggests the following conclusions: The modified -model ( “” ) constitutes a special case of ., It is obtained from equation ( 5 ) for ( by default ) ., Its distinguishing feature is a maximum before , which can be shifted via ( equation 2 ) ., The -maximum decreases as it is positioned closer to , because this implies bigger values of ., The time of the -maximum depends furthermore on size and velocity ( Figure 1 ) ., The curve shape of is reminiscent of the -function , since both functions have a maximum ., We thus decided to fit previously published response curves from collision sensitive neurons to both functions , and observed that both functions fit the neural curves well in terms of goodness-of-fit criteria ( Text S4 ) ., We must not forget , however , two important differences between and ., First , since reveals a minimum shortly before ( Text S6 ) and derives from , the -response is more precisely biphasic ., The biphasic structure gets pronounced in some of the curve fits , especially when is close to ( see corresponding figures in Text S4 ) ., Then , the amplitude of the -maximum is small , and consequently the fitting algorithm has to scale it to the maximum of the neuronal recording data ., In this way , the minimum is also scaled ., Second , depends in a nonlinear way on the size-to-velocity ratio ( see Figure 4 for an illustration ) ., This is contradictory to several studies that found a linear dependence ., A linear dependence is also predicted by the -function ( equation S5 in Text S2 ) ., The contradiction can be alleviated by adding noise to relative time of the -maximum ( ; equation S10 in Text S2 ) , with noise amplitudes as reported in 26 ., As a consequence of noise , the nonlinear relationship can be literally hidden ( Figure 3 ) , such that statistical tests would affirm an underlying linear process ( Text S2 ) ., Masking by noise is more effective for bigger values of , because the noise level is proportional to ., The -function in its original form cannot explain the neuronal response curves for an approach with ( “linear approach” ) 25: Rather than predicting a decreasing response with time , the -function would linearly increase ., In contrast , the -function makes correct predictions ., Correct predictions with can nevertheless be made by including an additional inhibitory process in the firing rate equation of ( equation S3 in Text S1 , where a full proof of concept is described ) ., Important , this extension of, ( i ) is based on a power function with an exponent between and , but not on an exponential function as with , and in this regard it may hence be considered as being biophysically more plausible than ( see also reference 36 ) ;, ( ii ) does not interfere with the “normal” behavior ( i . e . normal object approaches are not affected ) ; and, ( iii ) tolerates high noise levels ( i . e . , the mechanism is robust ) ., What about alternative models which also have a response peak ?, In Text S6 we studied two such functions , namely “inverse ” ( ) , and angular acceleration ( ) ., Both of them reveal a linear dependence of on ( equations S24 & S26 , respectively , in Text S6 ) ., The maximum of always precedes that of ., However , does not make correct predictions for the “linear approach” , as we would obtain ab initio for ( although a dynamical version may predict the decreasing LGMD-activity on the basis of temporal filtering effects ) ., In contrast , would make consistent predictions in that case ., Without further modifications , though , neither nor seems to be adequate for fitting the response curves of collision sensitive neurons , because there is no free model parameter to shift their respective maximum ., Although the occurrence of their maxima could principally be controlled by a global shift of the time scale , the corresponding values ( obtained by fitting the neuronal response curves ) would overestimate experimental values ( Text S6 ) ., Similarly , when “fitting” the -function to and the so obtained values of would underestimate experimental values: The -maximum would coincide with the maximum of for , and with the maximum of for ., In conclusion , is no replacement for the -function , at least for describing neuronal responses of collision sensitive neurons in insects ., However , in the nucleus rotundus of pigeons three classes of neurons were reported 1 , 38 ., They conform to -like , -like , and -like responses ., The fact that is just a special case of could possibly explain why neurons with -like and -like properties can be found in a single brain ., Within the -framework , the function corresponds to , and is obtained for choosing ., Thus , the adjustment of only a single weight ( ) is necessary to go from one function to the other ., The corrected m-Tau -framework could thus offer a parsimonious yet full-fledged explanation of the implementation of -like and -like neurons at the circuit level ., We simulated our psychophysical experiment with the corrected m-Tau -model ( equation 3 ) , where we plugged in noisified versions of the optical variables ( i . e . ) , ( 9 ) with noise probabilities ( ) , and with the dot denoting the time derivative ., The are random variables , which at each instant return a value that is drawn from a centered normal distribution ., In the last equations , we used the explicit expression for angular size , ( 10 ) and angular velocity ( rate of expansion ) ( 11 ) with and ., The values of and are the psychophysical stimulus parameters ., Simulations were carried out with a temporal resolution of ., The corrected m-Tau -model is constrained by two limit functions: Ordinary on the one hand ( equation 6 ) , and on the other ( equation 8 ) ., Both limit functions decrease approximately as ( illustration: Figure 6 ) ., Thus , a ttc estimation at time can be computed as ( 12 ) ( Nomenclature: is the model prediction for ttc at time , and is the experimentally set parameter ) ., In the psychophysical study , subjects were asked to estimate whether they were hit by the approaching object before or after ., We accordingly define their proportion of later responses as the number of trials ( where subjects responded with being struck after ) divided by the total number of trials : ( 13 ) is represented by circle and triangle symbols in Figure 7 and 8 ., The corresponding predictions from the model are denoted by ., Specifically , with and , and analogous for ., Computation of is required for , which we did with equation ( 12 ) as per ( 14 ) Notice that , due to noise ( equation 9 ) , will be subjected to random jitter with each trial ., Therefore , in order to obtain a more robust estimate of ttc , we do not use only : The integral in the last equation computes – in the discrete case – the mean value across the last time steps until ( typically , what amounts to a time interval for averaging of , cf . first figure in Text S7 ) ., In order to illustrate the noise level at each , we also computed the standard deviation of the last values of ., The shaded areas in the figures which visualize & correspond to ., Predictions of the corrected m-Tau -model are shown as curves in Figure 8 , as well as in Text S7 ., The corrected m-Tau -model has eight free parameters: , , , , , , , ., The parameter space was parsed with constant step widths ., For each set of parameter values , -predictions for the proportion-of-later-response curves were computed according to the procedure described in the previous section ., The corresponding “goodness of prediction” ( or “prediction performance” ) was evaluated with the root mean square error ( rmse , ) , and the outlier insensitive , robust error ( robe , ) , see equation S18 in Text S5 ., The “goodness of prediction” measures are referred to as score-measures ( rmse-scores & robe-scores , respectively ) ., Parameter values were sorted according to their scores ., In this way we ended up with several score tables , which list the best set of parameters , according to object size: Table S1 in Text S5 for small object d | Introduction, Results, Discussion, Methods | The -function and the -function are phenomenological models that are widely used in the context of timing interceptive actions and collision avoidance , respectively ., Both models were previously considered to be unrelated to each other: is a decreasing function that provides an estimation of time-to-contact ( ttc ) in the early phase of an object approach; in contrast , has a maximum before ttc ., Furthermore , it is not clear how both functions could be implemented at the neuronal level in a biophysically plausible fashion ., Here we propose a new framework – the corrected modified Tau function – capable of predicting both -type ( “” ) and -type ( “” ) responses ., The outstanding property of our new framework is its resilience to noise ., We show that can be derived from a firing rate equation , and , as , serves to describe the response curves of collision sensitive neurons ., Furthermore , we show that predicts the psychophysical performance of subjects determining ttc ., Our new framework is thus validated successfully against published and novel experimental data ., Within the framework , links between -type and -type neurons are established ., Therefore , it could possibly serve as a model for explaining the co-occurrence of such neurons in the brain . | In 1957 , Sir Fred Hoyle published a science fiction novel in which he described humanitys encounter with an extraterrestrial life form ., It came in the shape of a huge black cloud which approached the earth ., Hoyle proposed a formula ( “” ) for computing the remaining time until contact ( “ttc” ) of the cloud with the earth ., Nowadays in real science , serves as a model for ttc -perception for animals and humans , although it is not entirely undisputed ., For instance , seems to be incompatible with a collision-sensitive neuron in locusts ( the Lobula Giant Movement Detector or LGMD neuron ) ., LGMD neurons are instead better described by the -function , which differs from ., Here we propose a generic model ( “” ) that contains and as special cases ., The validity of the model was confirmed with a psychophysical experiment ., Also , we fitted many published response curves of LGMD neurons with our new model and with the -function ., Both models fit these response curves well , and we thus can conclude that and possibly result from a generic neuronal circuit template such as it is described by . | signal processing, neuroscience, signal filtering, cognitive neuroscience, behavioral neuroscience, mathematics, computational neuroscience, circuit models, biology, differential equations, visual system, calculus, psychophysics, sensory systems, sensory perception, motor reactions, engineering | null |
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